🟢 Starting Calibration Pipeline

=== Processing MDC030_W032_2026-03-24 ===

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                        Step 1: Digitization                       
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[🔄] 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 508 stationary frames, 342 remain


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→ 500 (342 images) Phase 1:  94%|█████████▎| 320/342 [00:04<00:00, 86.07it/s]

→ 500 (342 images) Phase 1:  97%|█████████▋| 331/342 [00:05<00:00, 72.01it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/342 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:  74%|███████▎  | 252/342 [00:01<00:00, 142.82it/s]

→ 500 Phase 2: corners:  78%|███████▊  | 267/342 [00:02<00:00, 141.61it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 282/342 [00:02<00:00, 142.63it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 299/342 [00:02<00:00, 148.85it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 316/342 [00:02<00:00, 154.12it/s]

                                                                          [PASS2] Frame 0000426: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000427: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000428: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000429: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000433: fixed corners {0, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000434: fixed corners {0, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000435: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000436: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000437: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000441: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000442: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000443: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000454: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000456: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000458: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000459: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000460: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000461: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000534: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000537: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000631: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000647: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000648: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000650: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000651: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000652: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000653: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000654: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000655: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000656: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000657: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000658: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000659: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000660: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000661: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000662: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000663: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000664: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000665: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000667: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000718: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000719: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000720: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000721: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000722: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000723: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000724: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000725: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000726: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000727: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000731: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000732: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000736: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000737: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000738: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000739: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000760: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000761: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000762: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000763: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000764: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000766: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000767: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000768: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000769: fixed corners {1, 2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000827: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000828: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000829: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000830: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000854: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000859: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000860: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000861: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000863: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000865: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000866: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000867: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000868: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000871: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000901: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000902: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000903: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000904: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000905: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000907: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000908: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000909: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000910: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000911: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000912: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000913: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000914: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000915: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000916: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000917: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000918: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000919: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000920: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000921: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000923: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000924: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000925: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000926: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000930: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000931: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000932: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000934: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000940: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000941: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000942: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000943: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000978: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000981: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000982: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000984: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000985: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000988: fixed corners {0, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000990: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000428: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000435: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000436: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000443: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000444: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000445: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000446: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000447: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000448: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000449: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000450: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000455: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000459: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000460: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000461: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000534: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000537: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000653: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000665: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000726: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000730: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000731: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000732: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000738: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000739: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000762: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000763: fixed corners {1, 2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000764: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000765: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000766: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000767: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000768: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000829: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000865: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000866: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000867: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000902: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000904: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000909: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000911: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000912: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000913: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000914: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000917: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000918: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000919: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000923: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000924: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000925: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000931: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000933: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000981: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000982: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000983: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000987: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000989: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000455: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000458: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000459: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000460: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000534: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000537: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000665: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000723: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000730: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000731: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000732: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000738: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000739: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000865: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000866: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000867: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000912: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000913: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000914: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000916: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000917: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000918: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000923: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000924: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000930: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000981: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000982: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000986: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000987: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Summary: 88 frames fixed, 110 untrusted, 342 total


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→ 500 Phase 3: finalize:   6%|▌         | 19/342 [00:00<00:07, 41.66it/s]

→ 500 Phase 3: finalize:   7%|▋         | 24/342 [00:00<00:07, 42.24it/s]

→ 500 Phase 3: finalize:   8%|▊         | 29/342 [00:00<00:07, 43.00it/s]

→ 500 Phase 3: finalize:  10%|▉         | 34/342 [00:00<00:07, 43.46it/s]

→ 500 Phase 3: finalize:  11%|█▏        | 39/342 [00:00<00:06, 44.34it/s]

→ 500 Phase 3: finalize:  13%|█▎        | 44/342 [00:01<00:06, 44.96it/s]

→ 500 Phase 3: finalize:  14%|█▍        | 49/342 [00:01<00:06, 45.34it/s]

→ 500 Phase 3: finalize:  16%|█▌        | 54/342 [00:01<00:06, 45.45it/s]

→ 500 Phase 3: finalize:  17%|█▋        | 59/342 [00:01<00:06, 45.50it/s]

→ 500 Phase 3: finalize:  19%|█▊        | 64/342 [00:01<00:06, 45.47it/s]

→ 500 Phase 3: finalize:  20%|██        | 69/342 [00:01<00:06, 45.16it/s]

→ 500 Phase 3: finalize:  22%|██▏       | 74/342 [00:01<00:05, 44.84it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 79/342 [00:01<00:05, 45.05it/s]

→ 500 Phase 3: finalize:  25%|██▍       | 84/342 [00:01<00:05, 45.01it/s]

→ 500 Phase 3: finalize:  26%|██▌       | 89/342 [00:02<00:05, 45.57it/s]

→ 500 Phase 3: finalize:  27%|██▋       | 94/342 [00:02<00:05, 45.34it/s]

→ 500 Phase 3: finalize:  29%|██▉       | 99/342 [00:02<00:05, 45.03it/s]

→ 500 Phase 3: finalize:  30%|███       | 104/342 [00:02<00:05, 44.77it/s]

→ 500 Phase 3: finalize:  32%|███▏      | 110/342 [00:02<00:04, 46.98it/s]

→ 500 Phase 3: finalize:  34%|███▍      | 116/342 [00:02<00:04, 48.13it/s]

→ 500 Phase 3: finalize:  35%|███▌      | 121/342 [00:02<00:04, 47.88it/s]

→ 500 Phase 3: finalize:  37%|███▋      | 126/342 [00:02<00:04, 47.20it/s]

→ 500 Phase 3: finalize:  39%|███▊      | 132/342 [00:02<00:04, 48.01it/s]

→ 500 Phase 3: finalize:  40%|████      | 137/342 [00:03<00:04, 48.38it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 143/342 [00:03<00:04, 48.95it/s]

→ 500 Phase 3: finalize:  43%|████▎     | 148/342 [00:03<00:03, 49.08it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 153/342 [00:03<00:03, 48.93it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 158/342 [00:03<00:03, 48.00it/s]

→ 500 Phase 3: finalize:  48%|████▊     | 163/342 [00:03<00:03, 47.10it/s]

→ 500 Phase 3: finalize:  49%|████▉     | 168/342 [00:03<00:03, 45.98it/s]

→ 500 Phase 3: finalize:  51%|█████     | 173/342 [00:03<00:03, 45.29it/s]

→ 500 Phase 3: finalize:  52%|█████▏    | 178/342 [00:03<00:03, 44.64it/s]

→ 500 Phase 3: finalize:  54%|█████▎    | 183/342 [00:04<00:03, 44.23it/s]

→ 500 Phase 3: finalize:  55%|█████▍    | 188/342 [00:04<00:03, 43.86it/s]

→ 500 Phase 3: finalize:  56%|█████▋    | 193/342 [00:04<00:03, 43.78it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 198/342 [00:04<00:03, 43.60it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 203/342 [00:04<00:03, 43.37it/s]

→ 500 Phase 3: finalize:  61%|██████    | 208/342 [00:04<00:03, 43.06it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 213/342 [00:04<00:03, 42.70it/s]

→ 500 Phase 3: finalize:  64%|██████▎   | 218/342 [00:04<00:02, 42.46it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 223/342 [00:04<00:02, 42.31it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 228/342 [00:05<00:02, 42.16it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 233/342 [00:05<00:02, 42.05it/s]

→ 500 Phase 3: finalize:  70%|██████▉   | 238/342 [00:05<00:02, 41.83it/s]

→ 500 Phase 3: finalize:  71%|███████   | 243/342 [00:05<00:02, 41.91it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 248/342 [00:05<00:02, 42.15it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 253/342 [00:05<00:02, 42.43it/s]

→ 500 Phase 3: finalize:  75%|███████▌  | 258/342 [00:05<00:01, 43.16it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 263/342 [00:05<00:01, 43.70it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 268/342 [00:06<00:01, 43.42it/s]

→ 500 Phase 3: finalize:  80%|███████▉  | 273/342 [00:06<00:01, 43.33it/s]

→ 500 Phase 3: finalize:  81%|████████▏ | 278/342 [00:06<00:01, 43.29it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 283/342 [00:06<00:01, 43.00it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 288/342 [00:06<00:01, 42.99it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 293/342 [00:06<00:01, 42.74it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 298/342 [00:06<00:01, 42.83it/s]

→ 500 Phase 3: finalize:  89%|████████▊ | 303/342 [00:06<00:00, 43.14it/s]

→ 500 Phase 3: finalize:  90%|█████████ | 308/342 [00:06<00:00, 43.54it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 313/342 [00:07<00:00, 43.75it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 318/342 [00:07<00:00, 44.31it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 323/342 [00:07<00:00, 44.33it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 328/342 [00:07<00:00, 43.83it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 333/342 [00:07<00:00, 43.33it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 338/342 [00:07<00:00, 42.98it/s]

                                                                          
[TIMING] 500 — 342 frames
  Phase 1  radial detect   (parallel)  :   5.33s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.49s  (7.3 ms/frame)
  Phase 2b corner fix      (sequential):   3.16s
  Phase 3  finalize        (sequential):   7.73s  (22.6 ms/frame)
    decode + undistort                 :   4.44s  (13.0 ms/frame)
    corner apply + refinement          :   1.30s  (3.8 ms/frame)
    segment                            :   1.26s  (3.7 ms/frame)
    smooth                             :   0.62s  (1.8 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  18.70s


Processing Rigs:  50%|█████     | 1/2 [00:23<00:23, 23.70s/it][INFO] Filtered 507 stationary frames, 457 remain


→ 1000 (457 images) Phase 1:   0%|          | 0/457 [00:00<?, ?it/s]

→ 1000 (457 images) Phase 1:   0%|          | 1/457 [00:00<06:06,  1.24it/s]

→ 1000 (457 images) Phase 1:   7%|▋         | 33/457 [00:01<00:12, 33.07it/s]

→ 1000 (457 images) Phase 1:  14%|█▍        | 65/457 [00:01<00:08, 47.94it/s]

→ 1000 (457 images) Phase 1:  21%|██        | 97/457 [00:02<00:06, 56.31it/s]

→ 1000 (457 images) Phase 1:  28%|██▊       | 127/457 [00:02<00:04, 79.72it/s]

→ 1000 (457 images) Phase 1:  31%|███       | 141/457 [00:02<00:04, 64.36it/s]

→ 1000 (457 images) Phase 1:  35%|███▌      | 161/457 [00:03<00:04, 59.80it/s]

→ 1000 (457 images) Phase 1:  41%|████▏     | 189/457 [00:03<00:03, 83.65it/s]

→ 1000 (457 images) Phase 1:  45%|████▍     | 204/457 [00:03<00:03, 66.41it/s]

→ 1000 (457 images) Phase 1:  49%|████▉     | 225/457 [00:03<00:03, 63.69it/s]

→ 1000 (457 images) Phase 1:  52%|█████▏    | 238/457 [00:03<00:03, 70.93it/s]

→ 1000 (457 images) Phase 1:  56%|█████▌    | 257/457 [00:04<00:03, 64.99it/s]

→ 1000 (457 images) Phase 1:  59%|█████▉    | 271/457 [00:04<00:02, 73.93it/s]

→ 1000 (457 images) Phase 1:  63%|██████▎   | 289/457 [00:04<00:02, 65.53it/s]

→ 1000 (457 images) Phase 1:  67%|██████▋   | 304/457 [00:04<00:02, 76.22it/s]

→ 1000 (457 images) Phase 1:  70%|███████   | 321/457 [00:05<00:02, 65.93it/s]

→ 1000 (457 images) Phase 1:  74%|███████▎  | 336/457 [00:05<00:01, 77.60it/s]

→ 1000 (457 images) Phase 1:  77%|███████▋  | 353/457 [00:05<00:01, 65.83it/s]

→ 1000 (457 images) Phase 1:  80%|████████  | 367/457 [00:05<00:01, 76.52it/s]

→ 1000 (457 images) Phase 1:  84%|████████▍ | 385/457 [00:06<00:01, 66.25it/s]

→ 1000 (457 images) Phase 1:  88%|████████▊ | 400/457 [00:06<00:00, 77.93it/s]

→ 1000 (457 images) Phase 1:  91%|█████████ | 417/457 [00:06<00:00, 65.92it/s]

→ 1000 (457 images) Phase 1:  95%|█████████▍| 432/457 [00:06<00:00, 78.22it/s]

→ 1000 (457 images) Phase 1:  98%|█████████▊| 449/457 [00:06<00:00, 78.74it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/457 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/457 [00:00<02:40,  2.84it/s]

→ 1000 Phase 2: corners:   3%|▎         | 12/457 [00:00<00:13, 33.36it/s]

→ 1000 Phase 2: corners:   6%|▌         | 26/457 [00:00<00:06, 64.09it/s]

→ 1000 Phase 2: corners:   8%|▊         | 36/457 [00:00<00:05, 73.44it/s]

→ 1000 Phase 2: corners:  11%|█         | 50/457 [00:00<00:04, 90.81it/s]

→ 1000 Phase 2: corners:  14%|█▍        | 63/457 [00:00<00:03, 100.27it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 78/457 [00:00<00:03, 112.48it/s]

→ 1000 Phase 2: corners:  20%|██        | 92/457 [00:01<00:03, 118.81it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 105/457 [00:01<00:02, 121.34it/s]

→ 1000 Phase 2: corners:  26%|██▌       | 118/457 [00:01<00:02, 121.24it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 133/457 [00:01<00:02, 128.24it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 148/457 [00:01<00:02, 128.21it/s]

→ 1000 Phase 2: corners:  35%|███▌      | 162/457 [00:01<00:02, 131.44it/s]

→ 1000 Phase 2: corners:  39%|███▊      | 176/457 [00:01<00:02, 125.75it/s]

→ 1000 Phase 2: corners:  42%|████▏     | 191/457 [00:01<00:02, 130.85it/s]

→ 1000 Phase 2: corners:  45%|████▌     | 207/457 [00:01<00:01, 136.43it/s]

→ 1000 Phase 2: corners:  49%|████▊     | 222/457 [00:02<00:01, 138.91it/s]

→ 1000 Phase 2: corners:  52%|█████▏    | 237/457 [00:02<00:01, 140.75it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 254/457 [00:02<00:01, 146.73it/s]

→ 1000 Phase 2: corners:  59%|█████▉    | 270/457 [00:02<00:01, 148.85it/s]

→ 1000 Phase 2: corners:  63%|██████▎   | 286/457 [00:02<00:01, 151.65it/s]

→ 1000 Phase 2: corners:  66%|██████▋   | 303/457 [00:02<00:00, 155.12it/s]

→ 1000 Phase 2: corners:  70%|██████▉   | 319/457 [00:02<00:00, 153.24it/s]

→ 1000 Phase 2: corners:  74%|███████▍  | 338/457 [00:02<00:00, 160.41it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 355/457 [00:02<00:00, 162.96it/s]

→ 1000 Phase 2: corners:  81%|████████▏ | 372/457 [00:03<00:00, 159.57it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 389/457 [00:03<00:00, 161.97it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 406/457 [00:03<00:00, 160.72it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 423/457 [00:03<00:00, 149.26it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 448/457 [00:03<00:00, 176.80it/s]

                                                                           [PASS2] Frame 0000092: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000250: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000251: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000252: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000253: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000469: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000470: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000474: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000539: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000540: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000541: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000542: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000543: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000547: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000548: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000549: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000550: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000557: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000558: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000559: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000560: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000564: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000565: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000566: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000567: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000573: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000574: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000578: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000579: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000580: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000589: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000590: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000591: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000592: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000594: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000597: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000598: fixed corners {2} (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 0000615: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000616: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000617: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000618: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000678: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000679: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000681: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000709: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000710: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000711: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000712: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000745: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000746: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000747: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000748: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000252: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000469: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000540: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000542: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000549: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000559: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000566: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000575: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000576: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000579: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000591: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000597: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000617: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000711: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000747: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 38 frames fixed, 38 untrusted, 457 total


→ 1000 Phase 3: finalize:   0%|          | 0/457 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   1%|          | 5/457 [00:00<00:09, 46.36it/s]

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→ 1000 Phase 3: finalize:   3%|▎         | 15/457 [00:00<00:09, 47.34it/s]

→ 1000 Phase 3: finalize:   4%|▍         | 20/457 [00:00<00:09, 46.79it/s]

→ 1000 Phase 3: finalize:   5%|▌         | 25/457 [00:00<00:09, 46.82it/s]

→ 1000 Phase 3: finalize:   7%|▋         | 30/457 [00:00<00:09, 46.71it/s]

→ 1000 Phase 3: finalize:   8%|▊         | 35/457 [00:00<00:09, 46.42it/s]

→ 1000 Phase 3: finalize:   9%|▉         | 40/457 [00:00<00:08, 46.46it/s]

→ 1000 Phase 3: finalize:  10%|▉         | 45/457 [00:00<00:08, 46.62it/s]

→ 1000 Phase 3: finalize:  11%|█         | 50/457 [00:01<00:08, 46.68it/s]

→ 1000 Phase 3: finalize:  12%|█▏        | 55/457 [00:01<00:08, 46.56it/s]

→ 1000 Phase 3: finalize:  13%|█▎        | 60/457 [00:01<00:08, 46.30it/s]

→ 1000 Phase 3: finalize:  14%|█▍        | 65/457 [00:01<00:08, 46.15it/s]

→ 1000 Phase 3: finalize:  15%|█▌        | 70/457 [00:01<00:08, 45.83it/s]

→ 1000 Phase 3: finalize:  16%|█▋        | 75/457 [00:01<00:08, 45.70it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 80/457 [00:01<00:08, 44.82it/s]

→ 1000 Phase 3: finalize:  19%|█▊        | 85/457 [00:01<00:08, 43.82it/s]

→ 1000 Phase 3: finalize:  20%|█▉        | 90/457 [00:01<00:08, 43.12it/s]

→ 1000 Phase 3: finalize:  21%|██        | 95/457 [00:02<00:08, 42.67it/s]

→ 1000 Phase 3: finalize:  22%|██▏       | 100/457 [00:02<00:08, 42.39it/s]

→ 1000 Phase 3: finalize:  23%|██▎       | 105/457 [00:02<00:08, 42.10it/s]

→ 1000 Phase 3: finalize:  24%|██▍       | 110/457 [00:02<00:08, 41.97it/s]

→ 1000 Phase 3: finalize:  25%|██▌       | 115/457 [00:02<00:08, 41.85it/s]

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→ 1000 Phase 3: finalize:  27%|██▋       | 125/457 [00:02<00:07, 41.91it/s]

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→ 1000 Phase 3: finalize:  94%|█████████▍| 430/457 [00:09<00:00, 42.28it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 435/457 [00:10<00:00, 42.47it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 440/457 [00:10<00:00, 42.71it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 445/457 [00:10<00:00, 42.93it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 450/457 [00:10<00:00, 43.22it/s]

→ 1000 Phase 3: finalize: 100%|█████████▉| 455/457 [00:10<00:00, 43.48it/s]

                                                                           
[TIMING] 1000 — 457 frames
  Phase 1  radial detect   (parallel)  :   7.02s  (15.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.54s  (7.7 ms/frame)
  Phase 2b corner fix      (sequential):   1.59s
  Phase 3  finalize        (sequential):  10.55s  (23.1 ms/frame)
    decode + undistort                 :   5.84s  (12.8 ms/frame)
    corner apply + refinement          :   2.30s  (5.0 ms/frame)
    segment                            :   1.83s  (4.0 ms/frame)
    smooth                             :   0.42s  (0.9 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.69s


Processing Rigs: 100%|██████████| 2/2 [00:52<00:00, 26.55s/it]
Processing Rigs: 100%|██████████| 2/2 [00:52<00:00, 26.12s/it]
[💾] Saved RigFrameDataset to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 799 frames from 2 rigs to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 342
[📋] 1000: 100 frames selected from 457
[💾] Saved RigFrameDataset to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC030_W032_2026-03-24/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 [00:03<02:06,  3.24s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:02,  3.23s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:58,  3.21s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:55,  3.22s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:52,  3.22s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:19<01:49,  3.21s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:46,  3.22s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:43,  3.22s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:39,  3.22s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:32<01:36,  3.21s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:35<01:33,  3.23s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:38<01:30,  3.23s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:41<01:26,  3.21s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:45<01:23,  3.20s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:48<01:20,  3.21s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:51<01:17,  3.22s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:54<01:14,  3.22s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:57<01:10,  3.22s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:01<01:07,  3.22s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:04<01:04,  3.22s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:07<01:00,  3.21s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:10<00:57,  3.19s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:13<00:54,  3.18s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:17<00:50,  3.18s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:20<00:47,  3.17s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:23<00:44,  3.17s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:26<00:41,  3.17s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:29<00:38,  3.17s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:32<00:34,  3.17s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:36<00:31,  3.18s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:39<00:28,  3.18s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:42<00:25,  3.17s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:45<00:22,  3.17s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:48<00:19,  3.17s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:51<00:15,  3.17s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:55<00:12,  3.17s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:58<00:09,  3.17s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:01<00:06,  3.18s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:04<00:03,  3.18s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:07<00:00,  3.18s/it]
                                                                                       
[✅] Casing detection complete: cx=1002.0, cy=766.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   1%|          | 1/100 [00:00<00:10,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   1%|          | 1/100 [00:00<00:10,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   2%|▏         | 2/100 [00:00<00:10,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   5%|▌         | 5/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   6%|▌         | 6/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   7%|▋         | 7/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   8%|▊         | 8/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  11%|█         | 11/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  12%|█▏        | 12/100 [00:00<00:09,  9.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  20%|██        | 20/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  21%|██        | 21/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  22%|██▏       | 22/100 [00:00<00:01, 72.07it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  23%|██▎       | 23/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 490.909} (0.001):  23%|██▎       | 23/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 490.909} (0.001):  24%|██▍       | 24/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 490.909} (0.001):  25%|██▌       | 25/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 497.98} (0.003):  26%|██▌       | 26/100 [00:00<00:00, 84.28it/s] 
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 497.98} (0.003):  27%|██▋       | 27/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 497.98} (0.003):  28%|██▊       | 28/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.008 | Best: {'fx': 505.051} (0.008):  29%|██▉       | 29/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 519.192} (0.030):  30%|███       | 30/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 519.192} (0.030):  31%|███       | 31/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.031 | Best: {'fx': 547.475} (0.031):  32%|███▏      | 32/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.048 | Best: {'fx': 526.263} (0.048):  33%|███▎      | 33/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.068 | Best: {'fx': 533.333} (0.068):  34%|███▍      | 34/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.043 | Best: {'fx': 533.333} (0.068):  35%|███▌      | 35/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.018 | Best: {'fx': 533.333} (0.068):  36%|███▌      | 36/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.068):  37%|███▋      | 37/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.068):  38%|███▊      | 38/100 [00:00<00:00, 84.28it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.068):  39%|███▉      | 39/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.068):  39%|███▉      | 39/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.068):  40%|████      | 40/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.068):  41%|████      | 41/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.068):  42%|████▏     | 42/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.068):  43%|████▎     | 43/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.068):  44%|████▍     | 44/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.068):  45%|████▌     | 45/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.068):  46%|████▌     | 46/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.068):  47%|████▋     | 47/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.068):  48%|████▊     | 48/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.068):  49%|████▉     | 49/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  50%|█████     | 50/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  51%|█████     | 51/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  52%|█████▏    | 52/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.068):  53%|█████▎    | 53/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  54%|█████▍    | 54/100 [00:00<00:00, 109.87it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  55%|█████▌    | 55/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  55%|█████▌    | 55/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  56%|█████▌    | 56/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  57%|█████▋    | 57/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  58%|█████▊    | 58/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  59%|█████▉    | 59/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  60%|██████    | 60/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  61%|██████    | 61/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.068):  62%|██████▏   | 62/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  63%|██████▎   | 63/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  64%|██████▍   | 64/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  65%|██████▌   | 65/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  66%|██████▌   | 66/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  67%|██████▋   | 67/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  68%|██████▊   | 68/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  69%|██████▉   | 69/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  70%|███████   | 70/100 [00:00<00:00, 126.75it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  71%|███████   | 71/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  71%|███████   | 71/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  72%|███████▏  | 72/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  73%|███████▎  | 73/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  74%|███████▍  | 74/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  75%|███████▌  | 75/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  76%|███████▌  | 76/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  77%|███████▋  | 77/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  78%|███████▊  | 78/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  79%|███████▉  | 79/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  80%|████████  | 80/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  81%|████████  | 81/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  82%|████████▏ | 82/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  83%|████████▎ | 83/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  84%|████████▍ | 84/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  85%|████████▌ | 85/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  86%|████████▌ | 86/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  87%|████████▋ | 87/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  88%|████████▊ | 88/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  89%|████████▉ | 89/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  90%|█████████ | 90/100 [00:00<00:00, 137.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  91%|█████████ | 91/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  91%|█████████ | 91/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  92%|█████████▏| 92/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  93%|█████████▎| 93/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  94%|█████████▍| 94/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  95%|█████████▌| 95/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  96%|█████████▌| 96/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  97%|█████████▋| 97/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  98%|█████████▊| 98/100 [00:00<00:00, 150.65it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.068):  99%|█████████▉| 99/100 [00:00<00:00, 150.65it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.014 | Best: {'fx': 508.333} (0.014):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.014 | Best: {'fx': 508.333} (0.014):   2%|▏         | 1/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.016 | Best: {'fx': 509.354} (0.016):   2%|▏         | 1/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.018 | Best: {'fx': 510.374} (0.018):   4%|▍         | 2/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.019 | Best: {'fx': 511.395} (0.019):   6%|▌         | 3/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.020 | Best: {'fx': 512.415} (0.020):   8%|▊         | 4/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.026 | Best: {'fx': 517.517} (0.026):  10%|█         | 5/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.021 | Best: {'fx': 517.517} (0.026):  12%|█▏        | 6/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.022 | Best: {'fx': 517.517} (0.026):  14%|█▍        | 7/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.029 | Best: {'fx': 518.537} (0.029):  16%|█▌        | 8/50 [00:00<00:05,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.023 | Best: {'fx': 518.537} (0.029):  18%|█▊        | 9/50 [00:00<00:04,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.025 | Best: {'fx': 518.537} (0.029):  20%|██        | 10/50 [00:00<00:04,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.033 | Best: {'fx': 520.578} (0.033):  22%|██▏       | 11/50 [00:00<00:04,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.031 | Best: {'fx': 520.578} (0.033):  24%|██▍       | 12/50 [00:00<00:04,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.038 | Best: {'fx': 522.619} (0.038):  26%|██▌       | 13/50 [00:00<00:04,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.037 | Best: {'fx': 522.619} (0.038):  28%|██▊       | 14/50 [00:00<00:04,  8.38it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.044 | Best: {'fx': 524.66} (0.044):  30%|███       | 15/50 [00:00<00:04,  8.38it/s] 
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.044 | Best: {'fx': 524.66} (0.044):  32%|███▏      | 16/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.054 | Best: {'fx': 527.721} (0.054):  32%|███▏      | 16/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.042 | Best: {'fx': 527.721} (0.054):  34%|███▍      | 17/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.061 | Best: {'fx': 529.762} (0.061):  36%|███▌      | 18/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.048 | Best: {'fx': 529.762} (0.061):  38%|███▊      | 19/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.055 | Best: {'fx': 529.762} (0.061):  40%|████      | 20/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.050 | Best: {'fx': 529.762} (0.061):  42%|████▏     | 21/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.065 | Best: {'fx': 530.782} (0.065):  44%|████▍     | 22/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.065 | Best: {'fx': 533.844} (0.065):  46%|████▌     | 23/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.068 | Best: {'fx': 531.803} (0.068):  48%|████▊     | 24/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.066 | Best: {'fx': 531.803} (0.068):  50%|█████     | 25/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.063 | Best: {'fx': 531.803} (0.068):  52%|█████▏    | 26/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.059 | Best: {'fx': 531.803} (0.068):  54%|█████▍    | 27/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.055 | Best: {'fx': 531.803} (0.068):  56%|█████▌    | 28/50 [00:00<00:00, 75.61it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.055 | Best: {'fx': 531.803} (0.068):  58%|█████▊    | 29/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.050 | Best: {'fx': 531.803} (0.068):  58%|█████▊    | 29/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.046 | Best: {'fx': 531.803} (0.068):  60%|██████    | 30/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.044 | Best: {'fx': 531.803} (0.068):  62%|██████▏   | 31/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.031 | Best: {'fx': 531.803} (0.068):  64%|██████▍   | 32/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.041 | Best: {'fx': 531.803} (0.068):  66%|██████▌   | 33/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.036 | Best: {'fx': 531.803} (0.068):  68%|██████▊   | 34/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.039 | Best: {'fx': 531.803} (0.068):  70%|███████   | 35/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.034 | Best: {'fx': 531.803} (0.068):  72%|███████▏  | 36/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.038 | Best: {'fx': 531.803} (0.068):  74%|███████▍  | 37/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.034 | Best: {'fx': 531.803} (0.068):  76%|███████▌  | 38/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.031 | Best: {'fx': 531.803} (0.068):  78%|███████▊  | 39/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.024 | Best: {'fx': 531.803} (0.068):  80%|████████  | 40/50 [00:00<00:00, 97.53it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.024 | Best: {'fx': 531.803} (0.068):  82%|████████▏ | 41/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.026 | Best: {'fx': 531.803} (0.068):  82%|████████▏ | 41/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.029 | Best: {'fx': 531.803} (0.068):  84%|████████▍ | 42/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.027 | Best: {'fx': 531.803} (0.068):  86%|████████▌ | 43/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.028 | Best: {'fx': 531.803} (0.068):  88%|████████▊ | 44/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.023 | Best: {'fx': 531.803} (0.068):  90%|█████████ | 45/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.026 | Best: {'fx': 531.803} (0.068):  92%|█████████▏| 46/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.025 | Best: {'fx': 531.803} (0.068):  94%|█████████▍| 47/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.024 | Best: {'fx': 531.803} (0.068):  96%|█████████▌| 48/50 [00:00<00:00, 105.63it/s]
Brute | Start: {'fx': 533.333} (0.068) | Current: 0.023 | Best: {'fx': 531.803} (0.068):  98%|█████████▊| 49/50 [00:00<00:00, 105.63it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.3333333333334
    px: 1002.0
    py: 766.0
    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: 40.2841: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 40.2841, picklable_objective(x0) = 40.2403

Best score: 40.2403: : 0iter [00:01, ?iter/s] 
Best score: 40.2403: : 1iter [00:01,  1.97s/iter]
Best score: 40.2403: : 1iter [00:01,  1.97s/iter]
Best score: 40.2403: : 2iter [00:02,  1.97s/iter]
Best score: 40.2403: : 3iter [00:02,  1.97s/iter]
Best score: 40.2403: : 4iter [00:02,  1.97s/iter]
Best score: 40.2403: : 5iter [00:02,  3.18iter/s]
Best score: 40.2403: : 5iter [00:02,  3.18iter/s]
Best score: 40.2403: : 6iter [00:02,  3.18iter/s]
Best score: 40.2403: : 7iter [00:02,  3.18iter/s]
Best score: 40.2403: : 8iter [00:02,  3.18iter/s]
Best score: 40.2403: : 9iter [00:02,  6.41iter/s]
Best score: 40.2403: : 9iter [00:02,  6.41iter/s]
Best score: 40.2403: : 10iter [00:02,  6.41iter/s]
Best score: 40.2403: : 11iter [00:02,  6.41iter/s]
Best score: 40.2403: : 12iter [00:02,  6.41iter/s]
Best score: 40.2403: : 13iter [00:02, 10.11iter/s]
Best score: 40.2403: : 13iter [00:02, 10.11iter/s]
Best score: 40.2403: : 14iter [00:02, 10.11iter/s]
Best score: 40.2403: : 15iter [00:02, 10.11iter/s]
Best score: 40.2403: : 16iter [00:02, 10.11iter/s]
Best score: 40.2403: : 17iter [00:02, 14.18iter/s]
Best score: 40.2403: : 17iter [00:02, 14.18iter/s]
Best score: 40.2403: : 18iter [00:02, 14.18iter/s]
Best score: 40.2403: : 19iter [00:02, 14.18iter/s]
Best score: 40.2403: : 20iter [00:02, 14.18iter/s]
Best score: 40.2403: : 21iter [00:02, 18.28iter/s]
Best score: 40.2403: : 21iter [00:02, 18.28iter/s]
Best score: 40.2403: : 22iter [00:02, 18.28iter/s]
Best score: 40.2403: : 23iter [00:02, 18.28iter/s]
Best score: 40.2403: : 24iter [00:02, 18.28iter/s]
Best score: 40.2403: : 25iter [00:02, 22.33iter/s]
Best score: 40.2403: : 25iter [00:02, 22.33iter/s]
Best score: 40.2403: : 26iter [00:02, 22.33iter/s]
Best score: 40.2403: : 27iter [00:02, 22.33iter/s]
Best score: 40.2403: : 28iter [00:02, 22.33iter/s]
Best score: 40.2403: : 29iter [00:02, 26.01iter/s]
Best score: 40.2403: : 29iter [00:02, 26.01iter/s]
Best score: 40.2403: : 30iter [00:02, 26.01iter/s]
Best score: 40.2403: : 31iter [00:02, 26.01iter/s]
Best score: 40.2403: : 32iter [00:02, 26.01iter/s]
Best score: 40.2403: : 33iter [00:02, 29.07iter/s]
Best score: 40.2403: : 33iter [00:02, 29.07iter/s]
Best score: 40.2403: : 34iter [00:02, 29.07iter/s]
Best score: 40.2403: : 35iter [00:02, 29.07iter/s]
Best score: 40.2403: : 36iter [00:02, 29.07iter/s]
Best score: 40.2403: : 37iter [00:02, 31.77iter/s]
Best score: 40.2403: : 37iter [00:02, 31.77iter/s]
Best score: 40.2403: : 38iter [00:02, 31.77iter/s]
Best score: 40.2403: : 39iter [00:02, 31.77iter/s]
Best score: 40.2403: : 40iter [00:02, 31.77iter/s]
Best score: 40.2403: : 41iter [00:02, 33.50iter/s]
Best score: 40.2403: : 41iter [00:03, 33.50iter/s]
Best score: 40.2403: : 42iter [00:03, 33.50iter/s]
Best score: 40.2403: : 43iter [00:03, 33.50iter/s]
Best score: 40.2403: : 44iter [00:03, 33.50iter/s]
Best score: 40.2403: : 45iter [00:03, 35.15iter/s]
Best score: 40.2403: : 45iter [00:03, 35.15iter/s]
Best score: 40.2403: : 46iter [00:03, 35.15iter/s]
Best score: 40.2403: : 47iter [00:03, 35.15iter/s]
Best score: 40.2403: : 48iter [00:03, 35.15iter/s]
Best score: 40.2403: : 49iter [00:03, 36.43iter/s]
[ITER 50] Best: 40.2403
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 1002.0000
  py: 766.0000
  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: 40.2403: : 49iter [00:03, 36.43iter/s]
Best score: 40.2403: : 50iter [00:03, 36.43iter/s]
Best score: 40.2403: : 51iter [00:03, 36.43iter/s]
Best score: 40.2403: : 52iter [00:03, 36.43iter/s]
Best score: 40.2403: : 53iter [00:03, 37.12iter/s]
Best score: 40.2403: : 53iter [00:03, 37.12iter/s]
Best score: 40.2403: : 54iter [00:03, 37.12iter/s]
Best score: 40.2403: : 55iter [00:03, 37.12iter/s]
Best score: 40.2403: : 56iter [00:03, 37.12iter/s]
Best score: 40.2403: : 57iter [00:03, 37.80iter/s]
Best score: 40.2403: : 57iter [00:03, 37.80iter/s]
Best score: 40.2403: : 58iter [00:03, 37.80iter/s]
Best score: 40.2403: : 59iter [00:03, 37.80iter/s]
Best score: 40.2403: : 60iter [00:03, 37.80iter/s]
Best score: 40.2403: : 61iter [00:03, 38.13iter/s]
Best score: 40.2403: : 61iter [00:03, 38.13iter/s]
Best score: 40.2403: : 62iter [00:03, 38.13iter/s]
Best score: 40.2403: : 63iter [00:03, 38.13iter/s]
Best score: 40.2403: : 64iter [00:03, 38.13iter/s]
Best score: 40.2403: : 65iter [00:03, 38.56iter/s]
Best score: 40.2403: : 65iter [00:03, 38.56iter/s]
Best score: 40.2403: : 66iter [00:03, 38.56iter/s]
Best score: 40.2403: : 67iter [00:03, 38.56iter/s]
Best score: 40.2403: : 68iter [00:03, 38.56iter/s]
Best score: 40.2403: : 69iter [00:03, 38.90iter/s]
Best score: 40.2403: : 69iter [00:03, 38.90iter/s]
Best score: 40.2403: : 70iter [00:03, 38.90iter/s]
Best score: 40.2403: : 71iter [00:03, 38.90iter/s]
Best score: 40.2403: : 72iter [00:03, 38.90iter/s]
Best score: 40.2403: : 73iter [00:03, 38.86iter/s]
Best score: 40.2403: : 73iter [00:03, 38.86iter/s]
Best score: 40.2403: : 74iter [00:03, 38.86iter/s]
Best score: 40.2403: : 75iter [00:03, 38.86iter/s]
Best score: 40.2403: : 76iter [00:03, 38.86iter/s]
Best score: 40.2403: : 77iter [00:03, 39.17iter/s]
Best score: 40.2403: : 77iter [00:03, 39.17iter/s]
Best score: 40.2403: : 78iter [00:03, 39.17iter/s]
Best score: 40.2403: : 79iter [00:03, 39.17iter/s]
Best score: 40.2403: : 80iter [00:04, 39.17iter/s]
Best score: 40.2403: : 81iter [00:04, 38.99iter/s]
Best score: 40.2403: : 81iter [00:04, 38.99iter/s]
Best score: 40.2403: : 82iter [00:04, 38.99iter/s]
Best score: 40.2403: : 83iter [00:04, 38.99iter/s]
Best score: 40.2403: : 84iter [00:04, 38.99iter/s]
Best score: 40.2403: : 85iter [00:04, 39.17iter/s]
Best score: 40.2403: : 85iter [00:04, 39.17iter/s]
Best score: 40.2403: : 86iter [00:04, 39.17iter/s]
Best score: 40.2403: : 87iter [00:04, 39.17iter/s]
Best score: 40.2403: : 88iter [00:04, 39.17iter/s]
Best score: 40.2403: : 89iter [00:04, 39.32iter/s]
Best score: 40.2403: : 89iter [00:04, 39.32iter/s]
Best score: 40.2403: : 90iter [00:04, 39.32iter/s]
Best score: 40.2403: : 91iter [00:04, 39.32iter/s]
Best score: 40.2403: : 92iter [00:04, 39.32iter/s]
Best score: 40.2403: : 93iter [00:04, 39.22iter/s]
Best score: 40.2403: : 93iter [00:04, 39.22iter/s]
Best score: 40.2403: : 94iter [00:04, 39.22iter/s]
Best score: 40.2403: : 95iter [00:04, 39.22iter/s]
Best score: 40.2403: : 96iter [00:04, 39.22iter/s]
Best score: 40.2403: : 97iter [00:04, 39.22iter/s]
Best score: 40.2403: : 98iter [00:04, 39.47iter/s]
Best score: 40.2403: : 98iter [00:04, 39.47iter/s]
[ITER 100] Best: 40.2403
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 1002.0000
  py: 766.0000
  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: 40.2403: : 99iter [00:04, 39.47iter/s]
Best score: 40.2403: : 100iter [00:04, 39.47iter/s]
Best score: 40.2403: : 101iter [00:04, 39.47iter/s]
Best score: 40.2403: : 102iter [00:04, 36.80iter/s]
Best score: 40.2403: : 102iter [00:04, 36.80iter/s]
Best score: 40.2403: : 103iter [00:04, 36.80iter/s]
Best score: 40.2403: : 104iter [00:04, 36.80iter/s]
Best score: 40.2403: : 105iter [00:04, 36.80iter/s]
Best score: 40.2403: : 106iter [00:04, 37.42iter/s]
Best score: 40.2403: : 106iter [00:04, 37.42iter/s]
Best score: 40.2403: : 107iter [00:04, 37.42iter/s]
Best score: 40.2403: : 108iter [00:04, 37.42iter/s]
Best score: 40.2403: : 109iter [00:04, 37.42iter/s]
Best score: 40.2403: : 110iter [00:04, 37.84iter/s]
Best score: 40.2403: : 110iter [00:04, 37.84iter/s]
Best score: 40.2403: : 111iter [00:04, 37.84iter/s]
Best score: 40.2403: : 112iter [00:04, 37.84iter/s]
Best score: 40.2403: : 113iter [00:04, 37.84iter/s]
Best score: 40.2403: : 114iter [00:04, 38.32iter/s]
Best score: 40.2403: : 114iter [00:04, 38.32iter/s]
Best score: 40.2403: : 115iter [00:04, 38.32iter/s]
Best score: 40.2403: : 116iter [00:04, 38.32iter/s]
Best score: 40.2403: : 117iter [00:04, 38.32iter/s]
Best score: 40.2403: : 118iter [00:04, 38.56iter/s]
Best score: 40.2403: : 118iter [00:04, 38.56iter/s]
Best score: 40.2403: : 119iter [00:05, 38.56iter/s]
Best score: 40.2403: : 120iter [00:05, 38.56iter/s]
Best score: 40.2403: : 121iter [00:05, 38.56iter/s]
Best score: 40.2403: : 122iter [00:05, 38.56iter/s]
Best score: 40.2403: : 122iter [00:05, 38.56iter/s]
Best score: 40.2403: : 123iter [00:05, 38.56iter/s]
Best score: 40.2403: : 124iter [00:05, 38.56iter/s]
Best score: 40.2403: : 125iter [00:05, 38.56iter/s]
Best score: 40.2403: : 126iter [00:05, 38.74iter/s]
Best score: 40.2403: : 126iter [00:05, 38.74iter/s]
Best score: 40.2403: : 127iter [00:05, 38.74iter/s]
Best score: 40.2403: : 128iter [00:05, 38.74iter/s]
Best score: 40.2403: : 129iter [00:05, 38.74iter/s]
Best score: 40.2403: : 130iter [00:05, 38.59iter/s]
Best score: 40.2403: : 130iter [00:05, 38.59iter/s]
Best score: 40.2403: : 131iter [00:05, 38.59iter/s]
Best score: 40.2403: : 132iter [00:05, 38.59iter/s]
Best score: 40.2403: : 133iter [00:05, 38.59iter/s]
Best score: 40.2403: : 134iter [00:05, 38.87iter/s]
Best score: 40.2403: : 134iter [00:05, 38.87iter/s]
Best score: 40.2403: : 135iter [00:05, 38.87iter/s]
Best score: 40.2403: : 136iter [00:05, 38.87iter/s]
Best score: 40.2403: : 137iter [00:05, 38.87iter/s]
Best score: 40.2403: : 138iter [00:05, 39.01iter/s]
Best score: 40.2403: : 138iter [00:05, 39.01iter/s]
Best score: 40.2403: : 139iter [00:05, 39.01iter/s]
Best score: 40.2403: : 140iter [00:05, 39.01iter/s]
Best score: 40.2403: : 141iter [00:05, 39.01iter/s]
Best score: 40.2403: : 142iter [00:05, 38.89iter/s]
Best score: 40.2403: : 142iter [00:05, 38.89iter/s]
Best score: 40.2403: : 143iter [00:05, 38.89iter/s]
Best score: 40.2403: : 144iter [00:05, 38.89iter/s]
Best score: 40.2403: : 145iter [00:05, 38.89iter/s]
Best score: 40.2403: : 146iter [00:05, 39.10iter/s]
Best score: 40.2403: : 146iter [00:05, 39.10iter/s]
Best score: 40.2403: : 147iter [00:05, 39.10iter/s]
Best score: 40.2403: : 148iter [00:05, 39.10iter/s]
[ITER 150] Best: 40.2403
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 1002.0000
  py: 766.0000
  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: 40.2403: : 149iter [00:05, 39.10iter/s]
Best score: 40.2403: : 150iter [00:05, 38.78iter/s]
Best score: 40.2403: : 150iter [00:05, 38.78iter/s]
Best score: 40.2403: : 151iter [00:05, 38.78iter/s]
Best score: 40.2403: : 152iter [00:05, 38.78iter/s]
Best score: 40.2403: : 153iter [00:05, 38.78iter/s]
Best score: 40.2403: : 154iter [00:05, 31.60iter/s]
Best score: 40.2403: : 154iter [00:06, 31.60iter/s]
Best score: 40.2403: : 155iter [00:06, 31.60iter/s]
Best score: 40.2403: : 156iter [00:06, 31.60iter/s]
Best score: 40.2403: : 157iter [00:06, 31.60iter/s]
Best score: 40.2403: : 158iter [00:06, 31.60iter/s]
Best score: 40.2403: : 159iter [00:06, 34.06iter/s]
Best score: 40.2403: : 159iter [00:06, 34.06iter/s]
Best score: 40.2403: : 160iter [00:06, 34.06iter/s]
Best score: 40.2403: : 161iter [00:06, 34.06iter/s]
Best score: 40.2403: : 162iter [00:06, 34.06iter/s]
Best score: 40.2403: : 163iter [00:06, 35.24iter/s]
Best score: 40.2403: : 163iter [00:06, 35.24iter/s]
Best score: 40.2403: : 164iter [00:06, 35.24iter/s]
Best score: 40.2403: : 165iter [00:06, 35.24iter/s]
Best score: 40.2403: : 166iter [00:06, 35.24iter/s]
Best score: 40.2403: : 167iter [00:06, 36.49iter/s]
Best score: 40.2403: : 167iter [00:06, 36.49iter/s]
Best score: 40.2403: : 168iter [00:06, 36.49iter/s]
Best score: 40.2403: : 169iter [00:06, 36.49iter/s]
Best score: 40.2403: : 170iter [00:06, 36.49iter/s]
Best score: 40.2403: : 171iter [00:06, 37.11iter/s]
Best score: 40.2403: : 171iter [00:06, 37.11iter/s]
Best score: 40.2403: : 172iter [00:06, 37.11iter/s]
Best score: 40.2403: : 173iter [00:06, 37.11iter/s]
Best score: 40.2403: : 174iter [00:06, 37.11iter/s]
Best score: 40.2403: : 175iter [00:06, 37.76iter/s]
Best score: 40.2403: : 175iter [00:06, 37.76iter/s]
Best score: 40.2403: : 176iter [00:06, 37.76iter/s]
Best score: 40.2403: : 177iter [00:06, 37.76iter/s]
Best score: 40.2403: : 178iter [00:06, 37.76iter/s]
Best score: 40.2403: : 179iter [00:06, 38.27iter/s]
Best score: 40.2403: : 179iter [00:06, 38.27iter/s]
Best score: 40.2403: : 180iter [00:06, 38.27iter/s]
Best score: 40.2403: : 181iter [00:06, 38.27iter/s]
Best score: 40.2403: : 182iter [00:06, 38.27iter/s]
Best score: 40.2403: : 183iter [00:06, 38.47iter/s]
Best score: 40.2403: : 183iter [00:06, 38.47iter/s]
Best score: 40.2403: : 184iter [00:06, 38.47iter/s]
Best score: 40.2403: : 185iter [00:06, 38.47iter/s]
Best score: 40.2403: : 186iter [00:06, 38.47iter/s]
Best score: 40.2403: : 187iter [00:06, 38.79iter/s]
Best score: 40.2403: : 187iter [00:06, 38.79iter/s]
Best score: 40.2403: : 188iter [00:06, 38.79iter/s]
Best score: 40.2403: : 189iter [00:06, 38.79iter/s]
Best score: 40.2403: : 190iter [00:06, 38.79iter/s]
Best score: 40.2403: : 191iter [00:06, 38.74iter/s]
Best score: 40.2403: : 191iter [00:06, 38.74iter/s]
Best score: 40.2403: : 192iter [00:06, 38.74iter/s]
Best score: 40.2403: : 193iter [00:06, 38.74iter/s]
Best score: 40.2403: : 194iter [00:07, 38.74iter/s]
Best score: 40.2403: : 195iter [00:07, 38.72iter/s]
Best score: 40.2403: : 195iter [00:07, 38.72iter/s]
Best score: 40.2403: : 196iter [00:07, 38.72iter/s]
Best score: 40.2403: : 197iter [00:07, 38.72iter/s]
Best score: 40.2403: : 198iter [00:07, 38.72iter/s]
Best score: 40.2403: : 199iter [00:07, 38.97iter/s]
[ITER 200] Best: 40.2403
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 1002.0000
  py: 766.0000
  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: 40.2403: : 199iter [00:07, 38.97iter/s]
Best score: 40.2403: : 200iter [00:07, 38.97iter/s]
Best score: 40.2403: : 201iter [00:07, 38.97iter/s]
Best score: 40.2403: : 202iter [00:07, 38.97iter/s]
Best score: 40.2403: : 203iter [00:07, 31.78iter/s]
Best score: 40.2403: : 203iter [00:07, 31.78iter/s]
Best score: 40.2403: : 204iter [00:07, 31.78iter/s]
Best score: 40.2403: : 205iter [00:07, 31.78iter/s]
Best score: 40.2403: : 206iter [00:07, 31.78iter/s]
Best score: 40.2403: : 207iter [00:07, 33.84iter/s]
Best score: 40.2403: : 207iter [00:07, 33.84iter/s]
Best score: 40.2403: : 208iter [00:07, 33.84iter/s]
Best score: 40.2403: : 209iter [00:07, 33.84iter/s]
Best score: 40.2403: : 210iter [00:07, 33.84iter/s]
Best score: 40.2403: : 211iter [00:07, 35.14iter/s]
Best score: 40.2403: : 211iter [00:07, 35.14iter/s]
Best score: 40.2403: : 212iter [00:07, 35.14iter/s]
Best score: 40.2403: : 213iter [00:07, 35.14iter/s]
Best score: 40.2403: : 214iter [00:07, 35.14iter/s]
Best score: 40.2403: : 215iter [00:07, 36.43iter/s]
Best score: 40.2403: : 215iter [00:07, 36.43iter/s]
Best score: 40.2403: : 216iter [00:07, 36.43iter/s]
Best score: 40.2403: : 217iter [00:07, 36.43iter/s]
Best score: 40.2403: : 218iter [00:07, 36.43iter/s]
Best score: 40.2403: : 219iter [00:07, 37.41iter/s]
Best score: 39.2241: : 219iter [00:07, 37.41iter/s]
Best score: 39.2241: : 220iter [00:07, 37.41iter/s]
Best score: 39.2241: : 221iter [00:07, 37.41iter/s]
Best score: 39.2241: : 222iter [00:07, 37.41iter/s]
Best score: 39.2241: : 223iter [00:07, 37.91iter/s]
Best score: 39.2241: : 223iter [00:07, 37.91iter/s]
Best score: 39.2241: : 224iter [00:07, 37.91iter/s]
Best score: 39.2241: : 225iter [00:07, 37.91iter/s]
Best score: 39.2241: : 226iter [00:07, 37.91iter/s]
Best score: 39.2241: : 227iter [00:07, 38.51iter/s]
Best score: 39.2241: : 227iter [00:07, 38.51iter/s]
Best score: 39.2241: : 228iter [00:07, 38.51iter/s]
Best score: 39.2241: : 229iter [00:07, 38.51iter/s]
Best score: 39.2241: : 230iter [00:08, 38.51iter/s]
Best score: 39.2241: : 231iter [00:08, 38.63iter/s]
Best score: 39.2241: : 231iter [00:08, 38.63iter/s]
Best score: 39.2241: : 232iter [00:08, 38.63iter/s]
Best score: 39.2241: : 233iter [00:08, 38.63iter/s]
Best score: 39.2241: : 234iter [00:08, 38.63iter/s]
Best score: 39.2241: : 235iter [00:08, 38.63iter/s]
Best score: 39.2241: : 236iter [00:08, 39.09iter/s]
Best score: 39.2241: : 236iter [00:08, 39.09iter/s]
Best score: 39.2241: : 237iter [00:08, 39.09iter/s]
Best score: 39.2241: : 238iter [00:08, 39.09iter/s]
Best score: 39.2241: : 239iter [00:08, 39.09iter/s]
Best score: 39.2241: : 240iter [00:08, 39.07iter/s]
Best score: 39.2241: : 240iter [00:08, 39.07iter/s]
Best score: 39.2241: : 241iter [00:08, 39.07iter/s]
Best score: 39.2241: : 242iter [00:08, 39.07iter/s]
Best score: 39.2241: : 243iter [00:08, 39.07iter/s]
Best score: 39.2241: : 244iter [00:08, 39.07iter/s]
Best score: 39.2241: : 245iter [00:08, 39.43iter/s]
Best score: 39.2241: : 245iter [00:08, 39.43iter/s]
Best score: 39.2241: : 246iter [00:08, 39.43iter/s]
Best score: 39.2241: : 247iter [00:08, 39.43iter/s]
Best score: 39.2241: : 248iter [00:08, 39.43iter/s]
[ITER 250] Best: 39.2241
[PARAMS] Current best parameters:
  fx: 518.1773
  fy: 513.3289
  px: 994.3115
  py: 757.0831
  k1: -0.01251308
  k2: 0.06546499
  k3: -0.06512816
  k4: 0.01949838
  p1: -0.02866387
  p2: -0.03616026
  p3: 0.02215119
  p4: 0.01701911
  s1: 0.0147
  s2: -0.0142
  s3: 0.0053
  s4: -0.0199
  tilt_x: 0.0232
  tilt_y: -0.0549
  scale: 3.1726
  rotation: 1.4928

Best score: 39.2241: : 249iter [00:08, 39.43iter/s]
Best score: 39.2241: : 250iter [00:08, 39.36iter/s]
Best score: 39.2241: : 250iter [00:08, 39.36iter/s]
Best score: 39.2241: : 251iter [00:08, 39.36iter/s]
Best score: 39.2241: : 252iter [00:08, 39.36iter/s]
Best score: 39.2241: : 253iter [00:08, 39.36iter/s]
Best score: 39.2241: : 254iter [00:08, 32.79iter/s]
Best score: 39.2241: : 254iter [00:08, 32.79iter/s]
Best score: 39.2241: : 255iter [00:08, 32.79iter/s]
Best score: 39.2241: : 256iter [00:08, 32.79iter/s]
Best score: 39.2241: : 257iter [00:08, 32.79iter/s]
Best score: 39.2241: : 258iter [00:08, 34.50iter/s]
Best score: 39.2241: : 258iter [00:08, 34.50iter/s]
Best score: 39.2241: : 259iter [00:08, 34.50iter/s]
Best score: 39.2241: : 260iter [00:08, 34.50iter/s]
Best score: 39.2241: : 261iter [00:08, 34.50iter/s]
Best score: 39.2241: : 262iter [00:08, 35.61iter/s]
Best score: 39.2241: : 262iter [00:08, 35.61iter/s]
Best score: 39.2241: : 263iter [00:08, 35.61iter/s]
Best score: 39.2241: : 264iter [00:08, 35.61iter/s]
Best score: 39.2241: : 265iter [00:08, 35.61iter/s]
Best score: 39.2241: : 266iter [00:08, 35.61iter/s]
Best score: 39.2241: : 267iter [00:08, 37.05iter/s]
Best score: 39.2241: : 267iter [00:09, 37.05iter/s]
Best score: 39.2241: : 268iter [00:09, 37.05iter/s]
Best score: 35.9112: : 269iter [00:09, 37.05iter/s]
Best score: 35.9112: : 270iter [00:09, 37.05iter/s]
Best score: 35.9112: : 271iter [00:09, 37.62iter/s]
Best score: 35.9112: : 271iter [00:09, 37.62iter/s]
Best score: 35.9112: : 272iter [00:09, 37.62iter/s]
Best score: 35.9112: : 273iter [00:09, 37.62iter/s]
Best score: 35.9112: : 274iter [00:09, 37.62iter/s]
Best score: 35.9112: : 275iter [00:09, 37.62iter/s]
Best score: 35.9112: : 276iter [00:09, 38.35iter/s]
Best score: 35.9112: : 276iter [00:09, 38.35iter/s]
Best score: 35.9112: : 277iter [00:09, 38.35iter/s]
Best score: 35.9112: : 278iter [00:09, 38.35iter/s]
Best score: 35.9112: : 279iter [00:09, 38.35iter/s]
Best score: 35.9112: : 280iter [00:09, 38.47iter/s]
Best score: 35.9112: : 280iter [00:09, 38.47iter/s]
Best score: 35.9112: : 281iter [00:09, 38.47iter/s]
Best score: 35.9112: : 282iter [00:09, 38.47iter/s]
Best score: 35.9112: : 283iter [00:09, 38.47iter/s]
Best score: 35.9112: : 284iter [00:09, 38.47iter/s]
Best score: 35.9112: : 285iter [00:09, 38.99iter/s]
Best score: 35.9112: : 285iter [00:09, 38.99iter/s]
Best score: 35.9112: : 286iter [00:09, 38.99iter/s]
Best score: 35.9112: : 287iter [00:09, 38.99iter/s]
Best score: 35.9112: : 288iter [00:09, 38.99iter/s]
Best score: 18.8067: : 289iter [00:09, 38.99iter/s]
Best score: 18.8067: : 290iter [00:09, 39.22iter/s]
Best score: 18.8067: : 290iter [00:09, 39.22iter/s]
Best score: 18.8067: : 291iter [00:09, 39.22iter/s]
Best score: 18.8067: : 292iter [00:09, 39.22iter/s]
Best score: 18.8067: : 293iter [00:09, 39.22iter/s]
Best score: 18.8067: : 294iter [00:09, 39.22iter/s]
Best score: 18.8067: : 295iter [00:09, 31.47iter/s]
Best score: 18.8067: : 295iter [00:09, 31.47iter/s]
Best score: 18.8067: : 296iter [00:09, 31.47iter/s]
Best score: 18.8067: : 297iter [00:09, 31.47iter/s]
Best score: 18.8067: : 298iter [00:09, 31.47iter/s]
[ITER 300] Best: 16.0506
[PARAMS] Current best parameters:
  fx: 557.8289
  fy: 556.0063
  px: 999.8524
  py: 745.2594
  k1: -0.03173549
  k2: 0.04662845
  k3: -0.03709272
  k4: 0.00780173
  p1: -0.04211613
  p2: -0.01955080
  p3: 0.01865845
  p4: -0.00489124
  s1: -0.0007
  s2: 0.0045
  s3: 0.0290
  s4: -0.0018
  tilt_x: -0.0072
  tilt_y: -0.0310
  scale: 2.9259
  rotation: 1.2219

Best score: 16.0506: : 299iter [00:09, 31.47iter/s]
Best score: 16.0506: : 300iter [00:09, 33.57iter/s]
Best score: 16.0506: : 300iter [00:10, 33.57iter/s]
Best score: 16.0506: : 301iter [00:10, 33.57iter/s]
Best score: 16.0506: : 302iter [00:10, 33.57iter/s]
Best score: 16.0506: : 303iter [00:10, 33.57iter/s]
Best score: 16.0506: : 304iter [00:10, 29.68iter/s]
Best score: 16.0506: : 304iter [00:10, 29.68iter/s]
Best score: 16.0506: : 305iter [00:10, 29.68iter/s]
Best score: 16.0506: : 306iter [00:10, 29.68iter/s]
Best score: 16.0506: : 307iter [00:10, 29.68iter/s]
Best score: 16.0506: : 308iter [00:10, 29.68iter/s]
Best score: 16.0506: : 309iter [00:10, 32.41iter/s]
Best score: 16.0506: : 309iter [00:10, 32.41iter/s]
Best score: 16.0506: : 310iter [00:10, 32.41iter/s]
Best score: 16.0506: : 311iter [00:10, 32.41iter/s]
Best score: 16.0506: : 312iter [00:10, 32.41iter/s]
Best score: 16.0506: : 313iter [00:10, 33.95iter/s]
Best score: 16.0506: : 313iter [00:10, 33.95iter/s]
Best score: 16.0506: : 314iter [00:10, 33.95iter/s]
Best score: 16.0506: : 315iter [00:10, 33.95iter/s]
Best score: 16.0506: : 316iter [00:10, 33.95iter/s]
Best score: 16.0506: : 317iter [00:10, 33.95iter/s]
Best score: 16.0506: : 318iter [00:10, 35.83iter/s]
Best score: 16.0506: : 318iter [00:10, 35.83iter/s]
Best score: 14.2942: : 319iter [00:10, 35.83iter/s]
Best score: 14.2942: : 320iter [00:10, 35.83iter/s]
Best score: 14.2942: : 321iter [00:10, 35.83iter/s]
Best score: 14.2942: : 322iter [00:10, 36.64iter/s]
Best score: 14.2942: : 322iter [00:10, 36.64iter/s]
Best score: 14.2942: : 323iter [00:10, 36.64iter/s]
Best score: 14.2942: : 324iter [00:10, 36.64iter/s]
Best score: 14.2942: : 325iter [00:10, 36.64iter/s]
Best score: 14.2942: : 326iter [00:10, 36.64iter/s]
Best score: 14.2942: : 327iter [00:10, 37.84iter/s]
Best score: 14.2942: : 327iter [00:10, 37.84iter/s]
Best score: 14.2942: : 328iter [00:10, 37.84iter/s]
Best score: 14.2942: : 329iter [00:10, 37.84iter/s]
Best score: 14.2942: : 330iter [00:10, 37.84iter/s]
Best score: 14.2942: : 331iter [00:10, 38.29iter/s]
Best score: 14.2942: : 331iter [00:10, 38.29iter/s]
Best score: 14.2942: : 332iter [00:10, 38.29iter/s]
Best score: 14.2942: : 333iter [00:10, 38.29iter/s]
Best score: 14.2942: : 334iter [00:10, 38.29iter/s]
Best score: 14.2942: : 335iter [00:10, 38.29iter/s]
Best score: 14.2942: : 336iter [00:10, 38.84iter/s]
Best score: 14.2942: : 336iter [00:10, 38.84iter/s]
Best score: 14.2942: : 337iter [00:10, 38.84iter/s]
Best score: 14.2942: : 338iter [00:10, 38.84iter/s]
Best score: 14.2942: : 339iter [00:11, 38.84iter/s]
Best score: 14.2942: : 340iter [00:11, 38.91iter/s]
Best score: 14.2942: : 340iter [00:11, 38.91iter/s]
Best score: 14.2942: : 341iter [00:11, 38.91iter/s]
Best score: 14.2942: : 342iter [00:11, 38.91iter/s]
Best score: 14.2942: : 343iter [00:11, 38.91iter/s]
Best score: 14.2942: : 344iter [00:11, 38.91iter/s]
Best score: 14.2942: : 345iter [00:11, 39.24iter/s]
Best score: 14.2942: : 345iter [00:11, 39.24iter/s]
Best score: 14.2942: : 346iter [00:11, 39.24iter/s]
Best score: 14.2942: : 347iter [00:11, 39.24iter/s]
Best score: 14.2942: : 348iter [00:11, 39.24iter/s]
Best score: 14.2942: : 349iter [00:11, 39.44iter/s]
[ITER 350] Best: 4.5547
[PARAMS] Current best parameters:
  fx: 554.6308
  fy: 553.7440
  px: 1003.9145
  py: 762.4292
  k1: -0.02681114
  k2: 0.02496537
  k3: -0.01848153
  k4: 0.00379542
  p1: -0.03016813
  p2: -0.00945875
  p3: 0.01303984
  p4: -0.00818532
  s1: 0.0050
  s2: 0.0039
  s3: 0.0440
  s4: -0.0144
  tilt_x: 0.0630
  tilt_y: -0.0431
  scale: 2.9766
  rotation: 1.0426

Best score: 4.5547: : 349iter [00:11, 39.44iter/s] 
Best score: 4.5547: : 350iter [00:11, 39.44iter/s]
Best score: 4.5547: : 351iter [00:11, 39.44iter/s]
Best score: 4.5547: : 352iter [00:11, 39.44iter/s]
Best score: 4.5547: : 353iter [00:11, 32.48iter/s]
Best score: 4.5547: : 353iter [00:11, 32.48iter/s]
Best score: 4.5547: : 354iter [00:11, 32.48iter/s]
Best score: 4.5547: : 355iter [00:11, 32.48iter/s]
Best score: 4.5547: : 356iter [00:11, 32.48iter/s]
Best score: 4.5547: : 357iter [00:11, 34.32iter/s]
Best score: 4.5547: : 357iter [00:11, 34.32iter/s]
Best score: 4.5547: : 358iter [00:11, 34.32iter/s]
Best score: 4.5547: : 359iter [00:11, 34.32iter/s]
Best score: 4.5547: : 360iter [00:11, 34.32iter/s]
Best score: 4.5547: : 361iter [00:11, 35.68iter/s]
Best score: 4.5547: : 361iter [00:11, 35.68iter/s]
Best score: 4.5547: : 362iter [00:11, 35.68iter/s]
Best score: 4.5547: : 363iter [00:11, 35.68iter/s]
Best score: 4.5547: : 364iter [00:11, 35.68iter/s]
Best score: 4.5547: : 365iter [00:11, 36.71iter/s]
Best score: 4.5547: : 365iter [00:11, 36.71iter/s]
Best score: 4.5547: : 366iter [00:11, 36.71iter/s]
Best score: 4.5547: : 367iter [00:11, 36.71iter/s]
Best score: 4.5547: : 368iter [00:11, 36.71iter/s]
Best score: 4.3529: : 369iter [00:11, 36.71iter/s]
Best score: 4.3529: : 370iter [00:11, 37.60iter/s]
Best score: 4.3529: : 370iter [00:11, 37.60iter/s]
Best score: 4.3529: : 371iter [00:11, 37.60iter/s]
Best score: 4.3529: : 372iter [00:11, 37.60iter/s]
Best score: 4.3529: : 373iter [00:11, 37.60iter/s]
Best score: 4.3529: : 374iter [00:11, 37.60iter/s]
Best score: 4.3529: : 375iter [00:11, 38.48iter/s]
Best score: 4.3529: : 375iter [00:11, 38.48iter/s]
Best score: 4.3529: : 376iter [00:12, 38.48iter/s]
Best score: 4.3529: : 377iter [00:12, 38.48iter/s]
Best score: 4.3529: : 378iter [00:12, 38.48iter/s]
Best score: 4.3529: : 379iter [00:12, 38.48iter/s]
Best score: 4.3529: : 380iter [00:12, 38.77iter/s]
Best score: 4.3529: : 380iter [00:12, 38.77iter/s]
Best score: 4.3529: : 381iter [00:12, 38.77iter/s]
Best score: 4.3529: : 382iter [00:12, 38.77iter/s]
Best score: 4.3529: : 383iter [00:12, 38.77iter/s]
Best score: 4.3529: : 384iter [00:12, 38.77iter/s]
Best score: 4.3529: : 385iter [00:12, 39.19iter/s]
Best score: 4.3529: : 385iter [00:12, 39.19iter/s]
Best score: 4.3529: : 386iter [00:12, 39.19iter/s]
Best score: 4.3529: : 387iter [00:12, 39.19iter/s]
Best score: 4.3529: : 388iter [00:12, 39.19iter/s]
Best score: 4.3529: : 389iter [00:12, 39.38iter/s]
Best score: 3.5856: : 389iter [00:12, 39.38iter/s]
Best score: 3.5856: : 390iter [00:12, 39.38iter/s]
Best score: 3.5856: : 391iter [00:12, 39.38iter/s]
Best score: 3.5856: : 392iter [00:12, 39.38iter/s]
Best score: 3.5856: : 393iter [00:12, 39.36iter/s]
Best score: 3.5856: : 393iter [00:12, 39.36iter/s]
Best score: 3.5856: : 394iter [00:12, 39.36iter/s]
Best score: 3.5856: : 395iter [00:12, 39.36iter/s]
Best score: 3.5856: : 396iter [00:12, 39.36iter/s]
Best score: 3.5856: : 397iter [00:12, 39.36iter/s]
Best score: 3.5856: : 398iter [00:12, 39.87iter/s]
Best score: 3.5856: : 398iter [00:12, 39.87iter/s]
[ITER 400] Best: 3.5354
[PARAMS] Current best parameters:
  fx: 550.5688
  fy: 549.0437
  px: 1003.3955
  py: 754.1745
  k1: -0.02853280
  k2: 0.03128435
  k3: -0.01534695
  k4: 0.00110993
  p1: -0.03436972
  p2: -0.00579514
  p3: 0.01323300
  p4: -0.00456943
  s1: -0.0004
  s2: 0.0067
  s3: 0.0087
  s4: 0.0046
  tilt_x: 0.0282
  tilt_y: -0.0251
  scale: 2.9366
  rotation: 1.0037

Best score: 3.5354: : 399iter [00:12, 39.87iter/s]
Best score: 3.5354: : 400iter [00:12, 39.87iter/s]
Best score: 3.5354: : 401iter [00:12, 39.87iter/s]
Best score: 3.5354: : 402iter [00:12, 32.69iter/s]
Best score: 3.5354: : 402iter [00:12, 32.69iter/s]
Best score: 3.5354: : 403iter [00:12, 32.69iter/s]
Best score: 3.5354: : 404iter [00:12, 32.69iter/s]
Best score: 3.5354: : 405iter [00:12, 32.69iter/s]
Best score: 3.5354: : 406iter [00:12, 32.69iter/s]
Best score: 3.5354: : 407iter [00:12, 34.91iter/s]
Best score: 3.5354: : 407iter [00:12, 34.91iter/s]
Best score: 3.5354: : 408iter [00:12, 34.91iter/s]
Best score: 2.8038: : 409iter [00:12, 34.91iter/s]
Best score: 2.8038: : 410iter [00:12, 34.91iter/s]
Best score: 2.8038: : 411iter [00:12, 36.06iter/s]
Best score: 2.8038: : 411iter [00:12, 36.06iter/s]
Best score: 2.8038: : 412iter [00:13, 36.06iter/s]
Best score: 2.8038: : 413iter [00:13, 36.06iter/s]
Best score: 2.8038: : 414iter [00:13, 36.06iter/s]
Best score: 2.8038: : 415iter [00:13, 36.06iter/s]
Best score: 2.8038: : 416iter [00:13, 37.48iter/s]
Best score: 2.8038: : 416iter [00:13, 37.48iter/s]
Best score: 2.8038: : 417iter [00:13, 37.48iter/s]
Best score: 2.8038: : 418iter [00:13, 37.48iter/s]
Best score: 2.0441: : 419iter [00:13, 37.48iter/s]
Best score: 2.0441: : 420iter [00:13, 38.05iter/s]
Best score: 2.0441: : 420iter [00:13, 38.05iter/s]
Best score: 2.0441: : 421iter [00:13, 38.05iter/s]
Best score: 2.0441: : 422iter [00:13, 38.05iter/s]
Best score: 2.0441: : 423iter [00:13, 38.05iter/s]
Best score: 2.0441: : 424iter [00:13, 38.05iter/s]
Best score: 2.0441: : 425iter [00:13, 38.77iter/s]
Best score: 2.0441: : 425iter [00:13, 38.77iter/s]
Best score: 2.0441: : 426iter [00:13, 38.77iter/s]
Best score: 2.0441: : 427iter [00:13, 38.77iter/s]
Best score: 2.0441: : 428iter [00:13, 38.77iter/s]
Best score: 1.9001: : 429iter [00:13, 38.77iter/s]
Best score: 1.9001: : 430iter [00:13, 38.96iter/s]
Best score: 1.9001: : 430iter [00:13, 38.96iter/s]
Best score: 1.9001: : 431iter [00:13, 38.96iter/s]
Best score: 1.9001: : 432iter [00:13, 38.96iter/s]
Best score: 1.9001: : 433iter [00:13, 38.96iter/s]
Best score: 1.9001: : 434iter [00:13, 38.96iter/s]
Best score: 1.9001: : 435iter [00:13, 39.34iter/s]
Best score: 1.9001: : 435iter [00:13, 39.34iter/s]
Best score: 1.9001: : 436iter [00:13, 39.34iter/s]
Best score: 1.9001: : 437iter [00:13, 39.34iter/s]
Best score: 1.9001: : 438iter [00:13, 39.34iter/s]
Best score: 1.3779: : 439iter [00:13, 39.34iter/s]
Best score: 1.3779: : 440iter [00:13, 39.31iter/s]
Best score: 1.3779: : 440iter [00:13, 39.31iter/s]
Best score: 1.3779: : 441iter [00:13, 39.31iter/s]
Best score: 1.3779: : 442iter [00:13, 39.31iter/s]
Best score: 1.3779: : 443iter [00:13, 39.31iter/s]
Best score: 1.3779: : 444iter [00:13, 39.31iter/s]
Best score: 1.3779: : 445iter [00:13, 39.65iter/s]
Best score: 1.3779: : 445iter [00:13, 39.65iter/s]
Best score: 1.3779: : 446iter [00:13, 39.65iter/s]
Best score: 1.3779: : 447iter [00:13, 39.65iter/s]
Best score: 1.3779: : 448iter [00:13, 39.65iter/s]
[ITER 450] Best: 1.3057
[PARAMS] Current best parameters:
  fx: 545.8400
  fy: 545.2814
  px: 1002.3079
  py: 760.5465
  k1: 0.00249871
  k2: -0.00739972
  k3: -0.00278857
  k4: 0.00141923
  p1: -0.02827788
  p2: -0.01967691
  p3: 0.02783145
  p4: 0.00069110
  s1: -0.0131
  s2: 0.0046
  s3: -0.0004
  s4: -0.0007
  tilt_x: 0.0449
  tilt_y: -0.0534
  scale: 2.9645
  rotation: 0.7256

Best score: 1.3057: : 449iter [00:13, 39.65iter/s]
Best score: 1.3057: : 450iter [00:13, 39.59iter/s]
Best score: 1.3057: : 450iter [00:14, 39.59iter/s]
Best score: 1.3057: : 451iter [00:14, 39.59iter/s]
Best score: 1.3057: : 452iter [00:14, 39.59iter/s]
Best score: 1.3057: : 453iter [00:14, 39.59iter/s]
Best score: 1.3057: : 454iter [00:14, 33.11iter/s]
Best score: 1.3057: : 454iter [00:14, 33.11iter/s]
Best score: 1.3057: : 455iter [00:14, 33.11iter/s]
Best score: 1.3057: : 456iter [00:14, 33.11iter/s]
Best score: 1.3057: : 457iter [00:14, 33.11iter/s]
Best score: 1.3057: : 458iter [00:14, 33.11iter/s]
Best score: 1.3057: : 459iter [00:14, 35.12iter/s]
Best score: 0.8729: : 459iter [00:14, 35.12iter/s]
Best score: 0.8729: : 460iter [00:14, 35.12iter/s]
Best score: 0.8729: : 461iter [00:14, 35.12iter/s]
Best score: 0.8729: : 462iter [00:14, 35.12iter/s]
Best score: 0.8729: : 463iter [00:14, 36.20iter/s]
Best score: 0.8729: : 463iter [00:14, 36.20iter/s]
Best score: 0.8729: : 464iter [00:14, 36.20iter/s]
Best score: 0.8729: : 465iter [00:14, 36.20iter/s]
Best score: 0.8729: : 466iter [00:14, 36.20iter/s]
Best score: 0.8729: : 467iter [00:14, 36.20iter/s]
Best score: 0.8729: : 468iter [00:14, 37.37iter/s]
Best score: 0.8729: : 468iter [00:14, 37.37iter/s]
Best score: 0.8729: : 469iter [00:14, 37.37iter/s]
Best score: 0.8729: : 470iter [00:14, 37.37iter/s]
Best score: 0.8729: : 471iter [00:14, 37.37iter/s]
Best score: 0.8729: : 472iter [00:14, 37.84iter/s]
Best score: 0.8729: : 472iter [00:14, 37.84iter/s]
Best score: 0.8729: : 473iter [00:14, 37.84iter/s]
Best score: 0.8729: : 474iter [00:14, 37.84iter/s]
Best score: 0.8729: : 475iter [00:14, 37.84iter/s]
Best score: 0.8729: : 476iter [00:14, 37.84iter/s]
Best score: 0.8729: : 477iter [00:14, 38.52iter/s]
Best score: 0.8729: : 477iter [00:14, 38.52iter/s]
Best score: 0.8729: : 478iter [00:14, 38.52iter/s]
Best score: 0.8714: : 479iter [00:14, 38.52iter/s]
Best score: 0.8714: : 480iter [00:14, 38.52iter/s]
Best score: 0.8714: : 481iter [00:14, 38.80iter/s]
Best score: 0.8714: : 481iter [00:14, 38.80iter/s]
Best score: 0.8714: : 482iter [00:14, 38.80iter/s]
Best score: 0.8714: : 483iter [00:14, 38.80iter/s]
Best score: 0.8714: : 484iter [00:14, 38.80iter/s]
Best score: 0.8714: : 485iter [00:14, 38.80iter/s]
Best score: 0.8714: : 486iter [00:14, 39.38iter/s]
Best score: 0.8714: : 486iter [00:14, 39.38iter/s]
Best score: 0.8714: : 487iter [00:14, 39.38iter/s]
Best score: 0.8714: : 488iter [00:14, 39.38iter/s]
Best score: 0.8010: : 489iter [00:15, 39.38iter/s]
Best score: 0.8010: : 490iter [00:15, 39.23iter/s]
Best score: 0.8010: : 490iter [00:15, 39.23iter/s]
Best score: 0.8010: : 491iter [00:15, 39.23iter/s]
Best score: 0.8010: : 492iter [00:15, 39.23iter/s]
Best score: 0.8010: : 493iter [00:15, 39.23iter/s]
Best score: 0.8010: : 494iter [00:15, 39.23iter/s]
Best score: 0.8010: : 495iter [00:15, 39.60iter/s]
Best score: 0.8010: : 495iter [00:15, 39.60iter/s]
Best score: 0.8010: : 496iter [00:15, 39.60iter/s]
Best score: 0.8010: : 497iter [00:15, 39.60iter/s]
Best score: 0.8010: : 498iter [00:15, 39.60iter/s]
[ITER 500] Best: 0.8010
[PARAMS] Current best parameters:
  fx: 542.2231
  fy: 541.4729
  px: 1003.4498
  py: 756.1263
  k1: 0.00897738
  k2: -0.01297877
  k3: 0.00015524
  k4: 0.00075525
  p1: -0.02690678
  p2: -0.00783692
  p3: 0.03300281
  p4: -0.00105247
  s1: -0.0201
  s2: -0.0009
  s3: -0.0149
  s4: 0.0024
  tilt_x: 0.0191
  tilt_y: -0.0351
  scale: 2.9761
  rotation: 0.8660

Best score: 0.8010: : 499iter [00:15, 39.60iter/s]
Best score: 0.8010: : 500iter [00:15, 39.53iter/s]
Best score: 0.8010: : 500iter [00:15, 39.53iter/s]
Best score: 0.8010: : 501iter [00:15, 39.53iter/s]
Best score: 0.8010: : 502iter [00:15, 39.53iter/s]
Best score: 0.8010: : 503iter [00:15, 39.53iter/s]
Best score: 0.8010: : 504iter [00:15, 33.11iter/s]
Best score: 0.8010: : 504iter [00:15, 33.11iter/s]
Best score: 0.8010: : 505iter [00:15, 33.11iter/s]
Best score: 0.8010: : 506iter [00:15, 33.11iter/s]
Best score: 0.8010: : 507iter [00:15, 33.11iter/s]
Best score: 0.8010: : 508iter [00:15, 33.11iter/s]
Best score: 0.8010: : 509iter [00:15, 35.20iter/s]
Best score: 0.6716: : 509iter [00:15, 35.20iter/s]
Best score: 0.6716: : 510iter [00:15, 35.20iter/s]
Best score: 0.6716: : 511iter [00:15, 35.20iter/s]
Best score: 0.6716: : 512iter [00:15, 35.20iter/s]
Best score: 0.6716: : 513iter [00:15, 36.25iter/s]
Best score: 0.6716: : 513iter [00:15, 36.25iter/s]
Best score: 0.6716: : 514iter [00:15, 36.25iter/s]
Best score: 0.6716: : 515iter [00:15, 36.25iter/s]
Best score: 0.6716: : 516iter [00:15, 36.25iter/s]
Best score: 0.6716: : 517iter [00:15, 37.16iter/s]
Best score: 0.6716: : 517iter [00:15, 37.16iter/s]
Best score: 0.6716: : 518iter [00:15, 37.16iter/s]
Best score: 0.6716: : 519iter [00:15, 37.16iter/s]
Best score: 0.6716: : 520iter [00:15, 37.16iter/s]
Best score: 0.6716: : 521iter [00:15, 37.64iter/s]
Best score: 0.6716: : 521iter [00:15, 37.64iter/s]
Best score: 0.6716: : 522iter [00:15, 37.64iter/s]
Best score: 0.6716: : 523iter [00:15, 37.64iter/s]
Best score: 0.6716: : 524iter [00:15, 37.64iter/s]
Best score: 0.6716: : 525iter [00:15, 37.64iter/s]
Best score: 0.6716: : 526iter [00:15, 38.44iter/s]
Best score: 0.6716: : 526iter [00:16, 38.44iter/s]
Best score: 0.6716: : 527iter [00:16, 38.44iter/s]
Best score: 0.6716: : 528iter [00:16, 38.44iter/s]
Best score: 0.6488: : 529iter [00:16, 38.44iter/s]
Best score: 0.6488: : 530iter [00:16, 38.73iter/s]
Best score: 0.6488: : 530iter [00:16, 38.73iter/s]
Best score: 0.6488: : 531iter [00:16, 38.73iter/s]
Best score: 0.6488: : 532iter [00:16, 38.73iter/s]
Best score: 0.6488: : 533iter [00:16, 38.73iter/s]
Best score: 0.6488: : 534iter [00:16, 38.73iter/s]
Best score: 0.6488: : 535iter [00:16, 39.28iter/s]
Best score: 0.6488: : 535iter [00:16, 39.28iter/s]
Best score: 0.6488: : 536iter [00:16, 39.28iter/s]
Best score: 0.6488: : 537iter [00:16, 39.28iter/s]
Best score: 0.6488: : 538iter [00:16, 39.28iter/s]
Best score: 0.6181: : 539iter [00:16, 39.28iter/s]
Best score: 0.6181: : 540iter [00:16, 39.40iter/s]
Best score: 0.6181: : 540iter [00:16, 39.40iter/s]
Best score: 0.6181: : 541iter [00:16, 39.40iter/s]
Best score: 0.6181: : 542iter [00:16, 39.40iter/s]
Best score: 0.6181: : 543iter [00:16, 39.40iter/s]
Best score: 0.6181: : 544iter [00:16, 39.40iter/s]
Best score: 0.6181: : 545iter [00:16, 39.70iter/s]
Best score: 0.6181: : 545iter [00:16, 39.70iter/s]
Best score: 0.6181: : 546iter [00:16, 39.70iter/s]
Best score: 0.6181: : 547iter [00:16, 39.70iter/s]
Best score: 0.6181: : 548iter [00:16, 39.70iter/s]
[ITER 550] Best: 0.5993
[PARAMS] Current best parameters:
  fx: 551.7375
  fy: 551.4074
  px: 1000.4926
  py: 757.5586
  k1: -0.00536465
  k2: -0.00893456
  k3: 0.00045424
  k4: 0.00039399
  p1: -0.02188265
  p2: -0.00604417
  p3: 0.00860546
  p4: -0.00123625
  s1: -0.0026
  s2: 0.0021
  s3: 0.0043
  s4: 0.0022
  tilt_x: 0.0182
  tilt_y: -0.0178
  scale: 2.9730
  rotation: 0.8763

Best score: 0.5993: : 549iter [00:16, 39.70iter/s]
Best score: 0.5993: : 550iter [00:16, 39.52iter/s]
Best score: 0.5993: : 550iter [00:16, 39.52iter/s]
Best score: 0.5993: : 551iter [00:16, 39.52iter/s]
Best score: 0.5993: : 552iter [00:16, 39.52iter/s]
Best score: 0.5993: : 553iter [00:16, 39.52iter/s]
Best score: 0.5993: : 554iter [00:16, 32.95iter/s]
Best score: 0.5993: : 554iter [00:16, 32.95iter/s]
Best score: 0.5993: : 555iter [00:16, 32.95iter/s]
Best score: 0.5993: : 556iter [00:16, 32.95iter/s]
Best score: 0.5993: : 557iter [00:16, 32.95iter/s]
Best score: 0.5993: : 558iter [00:16, 32.95iter/s]
Best score: 0.5993: : 559iter [00:16, 34.94iter/s]
Best score: 0.4943: : 559iter [00:16, 34.94iter/s]
Best score: 0.4943: : 560iter [00:16, 34.94iter/s]
Best score: 0.4943: : 561iter [00:16, 34.94iter/s]
Best score: 0.4943: : 562iter [00:16, 34.94iter/s]
Best score: 0.4943: : 563iter [00:16, 35.89iter/s]
Best score: 0.4943: : 563iter [00:17, 35.89iter/s]
Best score: 0.4943: : 564iter [00:17, 35.89iter/s]
Best score: 0.4943: : 565iter [00:17, 35.89iter/s]
Best score: 0.4943: : 566iter [00:17, 35.89iter/s]
Best score: 0.4943: : 567iter [00:17, 36.73iter/s]
Best score: 0.4943: : 567iter [00:17, 36.73iter/s]
Best score: 0.4943: : 568iter [00:17, 36.73iter/s]
Best score: 0.4611: : 569iter [00:17, 36.73iter/s]
Best score: 0.4611: : 570iter [00:17, 36.73iter/s]
Best score: 0.4611: : 571iter [00:17, 37.32iter/s]
Best score: 0.4611: : 571iter [00:17, 37.32iter/s]
Best score: 0.4611: : 572iter [00:17, 37.32iter/s]
Best score: 0.4611: : 573iter [00:17, 37.32iter/s]
Best score: 0.4611: : 574iter [00:17, 37.32iter/s]
Best score: 0.4611: : 575iter [00:17, 37.32iter/s]
Best score: 0.4611: : 576iter [00:17, 38.20iter/s]
Best score: 0.4611: : 576iter [00:17, 38.20iter/s]
Best score: 0.4611: : 577iter [00:17, 38.20iter/s]
Best score: 0.4611: : 578iter [00:17, 38.20iter/s]
Best score: 0.4535: : 579iter [00:17, 38.20iter/s]
Best score: 0.4535: : 580iter [00:17, 38.44iter/s]
Best score: 0.4535: : 580iter [00:17, 38.44iter/s]
Best score: 0.4535: : 581iter [00:17, 38.44iter/s]
Best score: 0.4535: : 582iter [00:17, 38.44iter/s]
Best score: 0.4535: : 583iter [00:17, 38.44iter/s]
Best score: 0.4535: : 584iter [00:17, 38.44iter/s]
Best score: 0.4535: : 585iter [00:17, 39.13iter/s]
Best score: 0.4535: : 585iter [00:17, 39.13iter/s]
Best score: 0.4535: : 586iter [00:17, 39.13iter/s]
Best score: 0.4535: : 587iter [00:17, 39.13iter/s]
Best score: 0.4535: : 588iter [00:17, 39.13iter/s]
Best score: 0.4535: : 589iter [00:17, 39.13iter/s]
Best score: 0.4535: : 590iter [00:17, 39.22iter/s]
Best score: 0.4535: : 590iter [00:17, 39.22iter/s]
Best score: 0.4535: : 591iter [00:17, 39.22iter/s]
Best score: 0.4535: : 592iter [00:17, 39.22iter/s]
Best score: 0.4535: : 593iter [00:17, 39.22iter/s]
Best score: 0.4535: : 594iter [00:17, 39.22iter/s]
Best score: 0.4535: : 595iter [00:17, 39.62iter/s]
Best score: 0.4535: : 595iter [00:17, 39.62iter/s]
Best score: 0.4535: : 596iter [00:17, 39.62iter/s]
Best score: 0.4535: : 597iter [00:17, 39.62iter/s]
Best score: 0.4535: : 598iter [00:17, 39.62iter/s]
[ITER 600] Best: 0.4463
[PARAMS] Current best parameters:
  fx: 548.9757
  fy: 548.5013
  px: 997.3903
  py: 757.7605
  k1: -0.00371846
  k2: -0.01223714
  k3: 0.00480178
  k4: -0.00093227
  p1: -0.02309409
  p2: -0.01172412
  p3: 0.01055337
  p4: 0.00363605
  s1: -0.0050
  s2: -0.0020
  s3: 0.0009
  s4: -0.0007
  tilt_x: 0.0213
  tilt_y: -0.0139
  scale: 2.9856
  rotation: 0.8246

Best score: 0.4463: : 599iter [00:17, 39.62iter/s]
Best score: 0.4463: : 600iter [00:17, 39.52iter/s]
Best score: 0.4463: : 600iter [00:18, 39.52iter/s]
Best score: 0.4463: : 601iter [00:18, 39.52iter/s]
Best score: 0.4463: : 602iter [00:18, 39.52iter/s]
Best score: 0.4463: : 603iter [00:18, 39.52iter/s]
Best score: 0.4463: : 604iter [00:18, 27.59iter/s]
Best score: 0.4463: : 604iter [00:18, 27.59iter/s]
Best score: 0.4463: : 605iter [00:18, 27.59iter/s]
Best score: 0.4463: : 606iter [00:18, 27.59iter/s]
Best score: 0.4463: : 607iter [00:18, 27.59iter/s]
Best score: 0.4463: : 608iter [00:18, 27.59iter/s]
Best score: 0.4463: : 609iter [00:18, 30.76iter/s]
Best score: 0.3825: : 609iter [00:18, 30.76iter/s]
Best score: 0.3825: : 610iter [00:18, 30.76iter/s]
Best score: 0.3825: : 611iter [00:18, 30.76iter/s]
Best score: 0.3825: : 612iter [00:18, 30.76iter/s]
Best score: 0.3825: : 613iter [00:18, 32.68iter/s]
Best score: 0.3825: : 613iter [00:18, 32.68iter/s]
Best score: 0.3825: : 614iter [00:18, 32.68iter/s]
Best score: 0.3825: : 615iter [00:18, 32.68iter/s]
Best score: 0.3825: : 616iter [00:18, 32.68iter/s]
Best score: 0.3825: : 617iter [00:18, 32.68iter/s]
Best score: 0.3825: : 618iter [00:18, 34.90iter/s]
Best score: 0.3825: : 618iter [00:18, 34.90iter/s]
Best score: 0.3825: : 619iter [00:18, 34.90iter/s]
Best score: 0.3825: : 620iter [00:18, 34.90iter/s]
Best score: 0.3825: : 621iter [00:18, 34.90iter/s]
Best score: 0.3825: : 622iter [00:18, 35.99iter/s]
Best score: 0.3825: : 622iter [00:18, 35.99iter/s]
Best score: 0.3825: : 623iter [00:18, 35.99iter/s]
Best score: 0.3825: : 624iter [00:18, 35.99iter/s]
Best score: 0.3825: : 625iter [00:18, 35.99iter/s]
Best score: 0.3825: : 626iter [00:18, 35.99iter/s]
Best score: 0.3825: : 627iter [00:18, 37.33iter/s]
Best score: 0.3825: : 627iter [00:18, 37.33iter/s]
Best score: 0.3825: : 628iter [00:18, 37.33iter/s]
Best score: 0.3655: : 629iter [00:18, 37.33iter/s]
Best score: 0.3655: : 630iter [00:18, 37.33iter/s]
Best score: 0.3655: : 631iter [00:18, 37.88iter/s]
Best score: 0.3655: : 631iter [00:18, 37.88iter/s]
Best score: 0.3655: : 632iter [00:18, 37.88iter/s]
Best score: 0.3655: : 633iter [00:18, 37.88iter/s]
Best score: 0.3655: : 634iter [00:18, 37.88iter/s]
Best score: 0.3655: : 635iter [00:18, 38.41iter/s]
Best score: 0.3655: : 635iter [00:18, 38.41iter/s]
Best score: 0.3655: : 636iter [00:19, 38.41iter/s]
Best score: 0.3655: : 637iter [00:19, 38.41iter/s]
Best score: 0.3655: : 638iter [00:19, 38.41iter/s]
Best score: 0.3655: : 639iter [00:19, 38.41iter/s]
Best score: 0.3655: : 640iter [00:19, 38.75iter/s]
Best score: 0.3655: : 640iter [00:19, 38.75iter/s]
Best score: 0.3655: : 641iter [00:19, 38.75iter/s]
Best score: 0.3655: : 642iter [00:19, 38.75iter/s]
Best score: 0.3655: : 643iter [00:19, 38.75iter/s]
Best score: 0.3655: : 644iter [00:19, 39.08iter/s]
Best score: 0.3655: : 644iter [00:19, 39.08iter/s]
Best score: 0.3655: : 645iter [00:19, 39.08iter/s]
Best score: 0.3655: : 646iter [00:19, 39.08iter/s]
Best score: 0.3655: : 647iter [00:19, 39.08iter/s]
Best score: 0.3655: : 648iter [00:19, 39.08iter/s]
Best score: 0.3655: : 649iter [00:19, 39.41iter/s]
[ITER 650] Best: 0.3655
[PARAMS] Current best parameters:
  fx: 553.2525
  fy: 553.1037
  px: 997.0833
  py: 759.8331
  k1: 0.00884070
  k2: -0.03191338
  k3: 0.01362689
  k4: -0.00237420
  p1: -0.01665975
  p2: -0.01147767
  p3: 0.01279138
  p4: 0.00083649
  s1: -0.0047
  s2: -0.0001
  s3: -0.0016
  s4: -0.0002
  tilt_x: 0.0184
  tilt_y: -0.0108
  scale: 2.9511
  rotation: 0.8489

Best score: 0.3655: : 649iter [00:19, 39.41iter/s]
Best score: 0.3655: : 650iter [00:19, 39.41iter/s]
Best score: 0.3655: : 651iter [00:19, 39.41iter/s]
Best score: 0.3655: : 652iter [00:19, 39.41iter/s]
Best score: 0.3655: : 653iter [00:19, 32.40iter/s]
Best score: 0.3655: : 653iter [00:19, 32.40iter/s]
Best score: 0.3655: : 654iter [00:19, 32.40iter/s]
Best score: 0.3655: : 655iter [00:19, 32.40iter/s]
Best score: 0.3655: : 656iter [00:19, 32.40iter/s]
Best score: 0.3655: : 657iter [00:19, 32.40iter/s]
Best score: 0.3655: : 658iter [00:19, 34.68iter/s]
Best score: 0.3655: : 658iter [00:19, 34.68iter/s]
Best score: 0.3655: : 659iter [00:19, 34.68iter/s]
Best score: 0.3655: : 660iter [00:19, 34.68iter/s]
Best score: 0.3655: : 661iter [00:19, 34.68iter/s]
Best score: 0.3655: : 662iter [00:19, 35.74iter/s]
Best score: 0.3655: : 662iter [00:19, 35.74iter/s]
Best score: 0.3655: : 663iter [00:19, 35.74iter/s]
Best score: 0.3655: : 664iter [00:19, 35.74iter/s]
Best score: 0.3655: : 665iter [00:19, 35.74iter/s]
Best score: 0.3655: : 666iter [00:19, 36.83iter/s]
Best score: 0.3655: : 666iter [00:19, 36.83iter/s]
Best score: 0.3655: : 667iter [00:19, 36.83iter/s]
Best score: 0.3655: : 668iter [00:19, 36.83iter/s]
Best score: 0.3584: : 669iter [00:19, 36.83iter/s]
Best score: 0.3584: : 670iter [00:19, 37.27iter/s]
Best score: 0.3584: : 670iter [00:19, 37.27iter/s]
Best score: 0.3584: : 671iter [00:19, 37.27iter/s]
Best score: 0.3584: : 672iter [00:20, 37.27iter/s]
Best score: 0.3584: : 673iter [00:20, 37.27iter/s]
Best score: 0.3584: : 674iter [00:20, 37.90iter/s]
Best score: 0.3584: : 674iter [00:20, 37.90iter/s]
Best score: 0.3584: : 675iter [00:20, 37.90iter/s]
Best score: 0.3584: : 676iter [00:20, 37.90iter/s]
Best score: 0.3584: : 677iter [00:20, 37.90iter/s]
Best score: 0.3584: : 678iter [00:20, 37.90iter/s]
Best score: 0.3584: : 679iter [00:20, 38.59iter/s]
Best score: 0.3561: : 679iter [00:20, 38.59iter/s]
Best score: 0.3561: : 680iter [00:20, 38.59iter/s]
Best score: 0.3561: : 681iter [00:20, 38.59iter/s]
Best score: 0.3561: : 682iter [00:20, 38.59iter/s]
Best score: 0.3561: : 683iter [00:20, 38.59iter/s]
Best score: 0.3561: : 683iter [00:20, 38.59iter/s]
Best score: 0.3561: : 684iter [00:20, 38.59iter/s]
Best score: 0.3561: : 685iter [00:20, 38.59iter/s]
Best score: 0.3561: : 686iter [00:20, 38.59iter/s]
Best score: 0.3561: : 687iter [00:20, 38.59iter/s]
Best score: 0.3561: : 688iter [00:20, 39.11iter/s]
Best score: 0.3561: : 688iter [00:20, 39.11iter/s]
Best score: 0.3479: : 689iter [00:20, 39.11iter/s]
Best score: 0.3479: : 690iter [00:20, 39.11iter/s]
Best score: 0.3479: : 691iter [00:20, 39.11iter/s]
Best score: 0.3479: : 692iter [00:20, 39.15iter/s]
Best score: 0.3479: : 692iter [00:20, 39.15iter/s]
Best score: 0.3479: : 693iter [00:20, 39.15iter/s]
Best score: 0.3479: : 694iter [00:20, 39.15iter/s]
Best score: 0.3479: : 695iter [00:20, 39.15iter/s]
Best score: 0.3479: : 696iter [00:20, 39.15iter/s]
Best score: 0.3479: : 697iter [00:20, 39.61iter/s]
Best score: 0.3479: : 697iter [00:20, 39.61iter/s]
Best score: 0.3479: : 698iter [00:20, 39.61iter/s]
[ITER 700] Best: 0.3479
[PARAMS] Current best parameters:
  fx: 551.1081
  fy: 550.9227
  px: 996.1486
  py: 759.1341
  k1: 0.00437726
  k2: -0.02850318
  k3: 0.01532066
  k4: -0.00338564
  p1: -0.01830664
  p2: -0.01131895
  p3: 0.01349884
  p4: 0.00172946
  s1: -0.0061
  s2: -0.0014
  s3: -0.0008
  s4: -0.0012
  tilt_x: 0.0179
  tilt_y: -0.0055
  scale: 2.9669
  rotation: 0.8384

Best score: 0.3479: : 699iter [00:20, 39.61iter/s]
Best score: 0.3479: : 700iter [00:20, 39.61iter/s]
Best score: 0.3479: : 701iter [00:20, 32.44iter/s]
Best score: 0.3479: : 701iter [00:20, 32.44iter/s]
Best score: 0.3479: : 702iter [00:20, 32.44iter/s]
Best score: 0.3479: : 703iter [00:20, 32.44iter/s]
Best score: 0.3479: : 704iter [00:20, 32.44iter/s]
Best score: 0.3479: : 705iter [00:20, 34.25iter/s]
Best score: 0.3479: : 705iter [00:20, 34.25iter/s]
Best score: 0.3479: : 706iter [00:20, 34.25iter/s]
Best score: 0.3479: : 707iter [00:20, 34.25iter/s]
Best score: 0.3479: : 708iter [00:20, 34.25iter/s]
Best score: 0.3479: : 709iter [00:21, 34.25iter/s]
Best score: 0.3479: : 710iter [00:21, 35.82iter/s]
Best score: 0.3479: : 710iter [00:21, 35.82iter/s]
Best score: 0.3479: : 711iter [00:21, 35.82iter/s]
Best score: 0.3479: : 712iter [00:21, 35.82iter/s]
Best score: 0.3479: : 713iter [00:21, 35.82iter/s]
Best score: 0.3479: : 714iter [00:21, 36.87iter/s]
Best score: 0.3479: : 714iter [00:21, 36.87iter/s]
Best score: 0.3479: : 715iter [00:21, 36.87iter/s]
Best score: 0.3479: : 716iter [00:21, 36.87iter/s]
Best score: 0.3479: : 717iter [00:21, 36.87iter/s]
Best score: 0.3479: : 718iter [00:21, 36.87iter/s]
Best score: 0.3479: : 719iter [00:21, 37.87iter/s]
Best score: 0.3479: : 719iter [00:21, 37.87iter/s]
Best score: 0.3479: : 720iter [00:21, 37.87iter/s]
Best score: 0.3479: : 721iter [00:21, 37.87iter/s]
Best score: 0.3479: : 722iter [00:21, 37.87iter/s]
Best score: 0.3479: : 723iter [00:21, 38.19iter/s]
Best score: 0.3479: : 723iter [00:21, 38.19iter/s]
Best score: 0.3479: : 724iter [00:21, 38.19iter/s]
Best score: 0.3479: : 725iter [00:21, 38.19iter/s]
Best score: 0.3479: : 726iter [00:21, 38.19iter/s]
Best score: 0.3479: : 727iter [00:21, 38.19iter/s]
Best score: 0.3479: : 728iter [00:21, 38.90iter/s]
Best score: 0.3479: : 728iter [00:21, 38.90iter/s]
Best score: 0.3479: : 729iter [00:21, 38.90iter/s]
Best score: 0.3479: : 730iter [00:21, 38.90iter/s]
Best score: 0.3479: : 731iter [00:21, 38.90iter/s]
Best score: 0.3479: : 732iter [00:21, 38.91iter/s]
Best score: 0.3479: : 732iter [00:21, 38.91iter/s]
Best score: 0.3479: : 733iter [00:21, 38.91iter/s]
Best score: 0.3479: : 734iter [00:21, 38.91iter/s]
Best score: 0.3479: : 735iter [00:21, 38.91iter/s]
Best score: 0.3479: : 736iter [00:21, 38.91iter/s]
Best score: 0.3479: : 737iter [00:21, 39.33iter/s]
Best score: 0.3479: : 737iter [00:21, 39.33iter/s]
Best score: 0.3479: : 738iter [00:21, 39.33iter/s]
Best score: 0.3478: : 739iter [00:21, 39.33iter/s]
Best score: 0.3478: : 740iter [00:21, 39.33iter/s]
Best score: 0.3478: : 741iter [00:21, 39.21iter/s]
Best score: 0.3478: : 741iter [00:21, 39.21iter/s]
Best score: 0.3478: : 742iter [00:21, 39.21iter/s]
Best score: 0.3478: : 743iter [00:21, 39.21iter/s]
Best score: 0.3478: : 744iter [00:21, 39.21iter/s]
Best score: 0.3478: : 745iter [00:21, 39.21iter/s]
Best score: 0.3478: : 746iter [00:21, 39.70iter/s]
Best score: 0.3478: : 746iter [00:21, 39.70iter/s]
Best score: 0.3478: : 747iter [00:21, 39.70iter/s]
Best score: 0.3478: : 748iter [00:22, 39.70iter/s]
[ITER 750] Best: 0.3464
[PARAMS] Current best parameters:
  fx: 552.5238
  fy: 552.2966
  px: 996.1279
  py: 758.3573
  k1: 0.00477965
  k2: -0.02792750
  k3: 0.01305398
  k4: -0.00254456
  p1: -0.01736773
  p2: -0.00974521
  p3: 0.00839492
  p4: 0.00221801
  s1: -0.0022
  s2: -0.0020
  s3: 0.0018
  s4: -0.0013
  tilt_x: 0.0121
  tilt_y: -0.0021
  scale: 2.9610
  rotation: 0.8475

Best score: 0.3464: : 749iter [00:22, 39.70iter/s]
Best score: 0.3464: : 750iter [00:22, 39.58iter/s]
Best score: 0.3464: : 750iter [00:22, 39.58iter/s]
Best score: 0.3464: : 751iter [00:22, 39.58iter/s]
Best score: 0.3464: : 752iter [00:22, 39.58iter/s]
Best score: 0.3464: : 753iter [00:22, 39.58iter/s]
Best score: 0.3464: : 754iter [00:22, 32.57iter/s]
Best score: 0.3464: : 754iter [00:22, 32.57iter/s]
Best score: 0.3464: : 755iter [00:22, 32.57iter/s]
Best score: 0.3464: : 756iter [00:22, 32.57iter/s]
Best score: 0.3464: : 757iter [00:22, 32.57iter/s]
Best score: 0.3464: : 758iter [00:22, 34.29iter/s]
Best score: 0.3464: : 758iter [00:22, 34.29iter/s]
Best score: 0.3452: : 759iter [00:22, 34.29iter/s]
Best score: 0.3452: : 760iter [00:22, 34.29iter/s]
Best score: 0.3452: : 761iter [00:22, 34.29iter/s]
Best score: 0.3452: : 762iter [00:22, 35.59iter/s]
Best score: 0.3452: : 762iter [00:22, 35.59iter/s]
Best score: 0.3452: : 763iter [00:22, 35.59iter/s]
Best score: 0.3452: : 764iter [00:22, 35.59iter/s]
Best score: 0.3452: : 765iter [00:22, 35.59iter/s]
Best score: 0.3452: : 766iter [00:22, 35.59iter/s]
Best score: 0.3452: : 767iter [00:22, 37.03iter/s]
Best score: 0.3452: : 767iter [00:22, 37.03iter/s]
Best score: 0.3452: : 768iter [00:22, 37.03iter/s]
Best score: 0.3452: : 769iter [00:22, 37.03iter/s]
Best score: 0.3452: : 770iter [00:22, 37.03iter/s]
Best score: 0.3452: : 771iter [00:22, 37.65iter/s]
Best score: 0.3452: : 771iter [00:22, 37.65iter/s]
Best score: 0.3452: : 772iter [00:22, 37.65iter/s]
Best score: 0.3452: : 773iter [00:22, 37.65iter/s]
Best score: 0.3452: : 774iter [00:22, 37.65iter/s]
Best score: 0.3452: : 775iter [00:22, 38.27iter/s]
Best score: 0.3452: : 775iter [00:22, 38.27iter/s]
Best score: 0.3452: : 776iter [00:22, 38.27iter/s]
Best score: 0.3452: : 777iter [00:22, 38.27iter/s]
Best score: 0.3452: : 778iter [00:22, 38.27iter/s]
Best score: 0.3452: : 779iter [00:22, 38.27iter/s]
Best score: 0.3452: : 780iter [00:22, 38.61iter/s]
Best score: 0.3452: : 780iter [00:22, 38.61iter/s]
Best score: 0.3452: : 781iter [00:22, 38.61iter/s]
Best score: 0.3452: : 782iter [00:22, 38.61iter/s]
Best score: 0.3452: : 783iter [00:22, 38.61iter/s]
Best score: 0.3452: : 784iter [00:22, 38.98iter/s]
Best score: 0.3452: : 784iter [00:22, 38.98iter/s]
Best score: 0.3452: : 785iter [00:23, 38.98iter/s]
Best score: 0.3452: : 786iter [00:23, 38.98iter/s]
Best score: 0.3452: : 787iter [00:23, 38.98iter/s]
Best score: 0.3452: : 788iter [00:23, 38.98iter/s]
Best score: 0.3452: : 789iter [00:23, 39.50iter/s]
Best score: 0.3441: : 789iter [00:23, 39.50iter/s]
Best score: 0.3441: : 790iter [00:23, 39.50iter/s]
Best score: 0.3441: : 791iter [00:23, 39.50iter/s]
Best score: 0.3441: : 792iter [00:23, 39.50iter/s]
Best score: 0.3441: : 793iter [00:23, 39.54iter/s]
Best score: 0.3441: : 793iter [00:23, 39.54iter/s]
Best score: 0.3441: : 794iter [00:23, 39.54iter/s]
Best score: 0.3441: : 795iter [00:23, 39.54iter/s]
Best score: 0.3441: : 796iter [00:23, 39.54iter/s]
Best score: 0.3441: : 797iter [00:23, 39.54iter/s]
Best score: 0.3441: : 798iter [00:23, 39.78iter/s]
Best score: 0.3441: : 798iter [00:23, 39.78iter/s]
[ITER 800] Best: 0.3441
[PARAMS] Current best parameters:
  fx: 552.7425
  fy: 552.5870
  px: 995.6519
  py: 759.0357
  k1: 0.00756125
  k2: -0.03239843
  k3: 0.01546165
  k4: -0.00298798
  p1: -0.01646810
  p2: -0.01045842
  p3: 0.00849330
  p4: 0.00256366
  s1: -0.0024
  s2: -0.0022
  s3: 0.0007
  s4: -0.0011
  tilt_x: 0.0138
  tilt_y: -0.0011
  scale: 2.9573
  rotation: 0.8538

Best score: 0.3441: : 799iter [00:23, 39.78iter/s]
Best score: 0.3441: : 800iter [00:23, 39.78iter/s]
Best score: 0.3441: : 801iter [00:23, 39.78iter/s]
Best score: 0.3441: : 802iter [00:23, 32.73iter/s]
Best score: 0.3441: : 802iter [00:23, 32.73iter/s]
Best score: 0.3441: : 803iter [00:23, 32.73iter/s]
Best score: 0.3441: : 804iter [00:23, 32.73iter/s]
Best score: 0.3441: : 805iter [00:23, 32.73iter/s]
Best score: 0.3441: : 806iter [00:23, 34.45iter/s]
Best score: 0.3441: : 806iter [00:23, 34.45iter/s]
Best score: 0.3441: : 807iter [00:23, 34.45iter/s]
Best score: 0.3441: : 808iter [00:23, 34.45iter/s]
Best score: 0.3437: : 809iter [00:23, 34.45iter/s]
Best score: 0.3437: : 810iter [00:23, 35.64iter/s]
Best score: 0.3437: : 810iter [00:23, 35.64iter/s]
Best score: 0.3437: : 811iter [00:23, 35.64iter/s]
Best score: 0.3437: : 812iter [00:23, 35.64iter/s]
Best score: 0.3437: : 813iter [00:23, 35.64iter/s]
Best score: 0.3437: : 814iter [00:23, 36.79iter/s]
Best score: 0.3437: : 814iter [00:23, 36.79iter/s]
Best score: 0.3437: : 815iter [00:23, 36.79iter/s]
Best score: 0.3437: : 816iter [00:23, 36.79iter/s]
Best score: 0.3437: : 817iter [00:23, 36.79iter/s]
Best score: 0.3437: : 818iter [00:23, 36.79iter/s]
Best score: 0.3437: : 819iter [00:23, 37.98iter/s]
Best score: 0.3420: : 819iter [00:23, 37.98iter/s]
Best score: 0.3420: : 820iter [00:23, 37.98iter/s]
Best score: 0.3420: : 821iter [00:23, 37.98iter/s]
Best score: 0.3420: : 822iter [00:24, 37.98iter/s]
Best score: 0.3420: : 823iter [00:24, 38.31iter/s]
Best score: 0.3420: : 823iter [00:24, 38.31iter/s]
Best score: 0.3420: : 824iter [00:24, 38.31iter/s]
Best score: 0.3420: : 825iter [00:24, 38.31iter/s]
Best score: 0.3420: : 826iter [00:24, 38.31iter/s]
Best score: 0.3420: : 827iter [00:24, 38.31iter/s]
Best score: 0.3420: : 828iter [00:24, 39.03iter/s]
Best score: 0.3420: : 828iter [00:24, 39.03iter/s]
Best score: 0.3420: : 829iter [00:24, 39.03iter/s]
Best score: 0.3420: : 830iter [00:24, 39.03iter/s]
Best score: 0.3420: : 831iter [00:24, 39.03iter/s]
Best score: 0.3420: : 832iter [00:24, 39.16iter/s]
Best score: 0.3420: : 832iter [00:24, 39.16iter/s]
Best score: 0.3420: : 833iter [00:24, 39.16iter/s]
Best score: 0.3420: : 834iter [00:24, 39.16iter/s]
Best score: 0.3420: : 835iter [00:24, 39.16iter/s]
Best score: 0.3420: : 836iter [00:24, 39.16iter/s]
Best score: 0.3420: : 837iter [00:24, 39.58iter/s]
Best score: 0.3420: : 837iter [00:24, 39.58iter/s]
Best score: 0.3420: : 838iter [00:24, 39.58iter/s]
Best score: 0.3419: : 839iter [00:24, 39.58iter/s]
Best score: 0.3419: : 840iter [00:24, 39.58iter/s]
Best score: 0.3419: : 841iter [00:24, 39.47iter/s]
Best score: 0.3419: : 841iter [00:24, 39.47iter/s]
Best score: 0.3419: : 842iter [00:24, 39.47iter/s]
Best score: 0.3419: : 843iter [00:24, 39.47iter/s]
Best score: 0.3419: : 844iter [00:24, 39.47iter/s]
Best score: 0.3419: : 845iter [00:24, 39.47iter/s]
Best score: 0.3419: : 846iter [00:24, 39.71iter/s]
Best score: 0.3419: : 846iter [00:24, 39.71iter/s]
Best score: 0.3419: : 847iter [00:24, 39.71iter/s]
Best score: 0.3419: : 848iter [00:24, 39.71iter/s]
[ITER 850] Best: 0.3408
[PARAMS] Current best parameters:
  fx: 551.4541
  fy: 551.3162
  px: 995.5724
  py: 758.2644
  k1: 0.00885436
  k2: -0.03362982
  k3: 0.01675086
  k4: -0.00338040
  p1: -0.01586036
  p2: -0.01158354
  p3: 0.01086790
  p4: 0.00234241
  s1: -0.0038
  s2: -0.0018
  s3: -0.0025
  s4: -0.0012
  tilt_x: 0.0086
  tilt_y: -0.0030
  scale: 2.9598
  rotation: 0.8523

Best score: 0.3408: : 849iter [00:24, 39.71iter/s]
Best score: 0.3408: : 850iter [00:24, 39.37iter/s]
Best score: 0.3408: : 850iter [00:24, 39.37iter/s]
Best score: 0.3408: : 851iter [00:24, 39.37iter/s]
Best score: 0.3408: : 852iter [00:24, 39.37iter/s]
Best score: 0.3408: : 853iter [00:24, 39.37iter/s]
Best score: 0.3408: : 854iter [00:24, 32.51iter/s]
Best score: 0.3408: : 854iter [00:24, 32.51iter/s]
Best score: 0.3408: : 855iter [00:24, 32.51iter/s]
Best score: 0.3408: : 856iter [00:24, 32.51iter/s]
Best score: 0.3408: : 857iter [00:24, 32.51iter/s]
Best score: 0.3408: : 858iter [00:24, 34.33iter/s]
Best score: 0.3408: : 858iter [00:25, 34.33iter/s]
Best score: 0.3406: : 859iter [00:25, 34.33iter/s]
Best score: 0.3406: : 860iter [00:25, 34.33iter/s]
Best score: 0.3406: : 861iter [00:25, 34.33iter/s]
Best score: 0.3406: : 862iter [00:25, 35.56iter/s]
Best score: 0.3406: : 862iter [00:25, 35.56iter/s]
Best score: 0.3406: : 863iter [00:25, 35.56iter/s]
Best score: 0.3406: : 864iter [00:25, 35.56iter/s]
Best score: 0.3406: : 865iter [00:25, 35.56iter/s]
Best score: 0.3406: : 866iter [00:25, 35.56iter/s]
Best score: 0.3406: : 867iter [00:25, 37.01iter/s]
Best score: 0.3406: : 867iter [00:25, 37.01iter/s]
Best score: 0.3406: : 868iter [00:25, 37.01iter/s]
Best score: 0.3399: : 869iter [00:25, 37.01iter/s]
Best score: 0.3399: : 870iter [00:25, 37.01iter/s]
Best score: 0.3399: : 871iter [00:25, 37.62iter/s]
Best score: 0.3399: : 871iter [00:25, 37.62iter/s]
Best score: 0.3399: : 872iter [00:25, 37.62iter/s]
Best score: 0.3399: : 873iter [00:25, 37.62iter/s]
Best score: 0.3399: : 874iter [00:25, 37.62iter/s]
Best score: 0.3399: : 875iter [00:25, 38.16iter/s]
Best score: 0.3399: : 875iter [00:25, 38.16iter/s]
Best score: 0.3399: : 876iter [00:25, 38.16iter/s]
Best score: 0.3399: : 877iter [00:25, 38.16iter/s]
Best score: 0.3399: : 878iter [00:25, 38.16iter/s]
Best score: 0.3399: : 879iter [00:25, 38.27iter/s]
Best score: 0.3381: : 879iter [00:25, 38.27iter/s]
Best score: 0.3381: : 880iter [00:25, 38.27iter/s]
Best score: 0.3381: : 881iter [00:25, 38.27iter/s]
Best score: 0.3381: : 882iter [00:25, 38.27iter/s]
Best score: 0.3381: : 883iter [00:25, 38.66iter/s]
Best score: 0.3381: : 883iter [00:25, 38.66iter/s]
Best score: 0.3381: : 884iter [00:25, 38.66iter/s]
Best score: 0.3381: : 885iter [00:25, 38.66iter/s]
Best score: 0.3381: : 886iter [00:25, 38.66iter/s]
Best score: 0.3381: : 887iter [00:25, 38.66iter/s]
Best score: 0.3381: : 888iter [00:25, 39.26iter/s]
Best score: 0.3381: : 888iter [00:25, 39.26iter/s]
Best score: 0.3381: : 889iter [00:25, 39.26iter/s]
Best score: 0.3381: : 890iter [00:25, 39.26iter/s]
Best score: 0.3381: : 891iter [00:25, 39.26iter/s]
Best score: 0.3381: : 892iter [00:25, 39.34iter/s]
Best score: 0.3381: : 892iter [00:25, 39.34iter/s]
Best score: 0.3381: : 893iter [00:25, 39.34iter/s]
Best score: 0.3381: : 894iter [00:25, 39.34iter/s]
Best score: 0.3381: : 895iter [00:25, 39.34iter/s]
Best score: 0.3381: : 896iter [00:25, 39.51iter/s]
Best score: 0.3381: : 896iter [00:25, 39.51iter/s]
Best score: 0.3381: : 897iter [00:25, 39.51iter/s]
Best score: 0.3381: : 898iter [00:26, 39.51iter/s]
[ITER 900] Best: 0.3381
[PARAMS] Current best parameters:
  fx: 549.7270
  fy: 549.6721
  px: 994.7162
  py: 757.9167
  k1: 0.01002181
  k2: -0.03252067
  k3: 0.01572387
  k4: -0.00310994
  p1: -0.01481404
  p2: -0.01303708
  p3: 0.01048058
  p4: 0.00316856
  s1: -0.0027
  s2: -0.0026
  s3: -0.0033
  s4: -0.0021
  tilt_x: 0.0049
  tilt_y: -0.0013
  scale: 2.9611
  rotation: 0.8580

Best score: 0.3381: : 899iter [00:26, 39.51iter/s]
Best score: 0.3381: : 900iter [00:26, 39.14iter/s]
Best score: 0.3381: : 900iter [00:26, 39.14iter/s]
Best score: 0.3381: : 901iter [00:26, 39.14iter/s]
Best score: 0.3381: : 902iter [00:26, 39.14iter/s]
Best score: 0.3381: : 903iter [00:26, 39.14iter/s]
Best score: 0.3381: : 904iter [00:26, 32.13iter/s]
Best score: 0.3381: : 904iter [00:26, 32.13iter/s]
Best score: 0.3381: : 905iter [00:26, 32.13iter/s]
Best score: 0.3381: : 906iter [00:26, 32.13iter/s]
Best score: 0.3381: : 907iter [00:26, 32.13iter/s]
Best score: 0.3381: : 908iter [00:26, 34.07iter/s]
Best score: 0.3381: : 908iter [00:26, 34.07iter/s]
Best score: 0.3381: : 909iter [00:26, 34.07iter/s]
Best score: 0.3381: : 910iter [00:26, 34.07iter/s]
Best score: 0.3381: : 911iter [00:26, 34.07iter/s]
Best score: 0.3381: : 912iter [00:26, 28.41iter/s]
Best score: 0.3381: : 912iter [00:26, 28.41iter/s]
Best score: 0.3381: : 913iter [00:26, 28.41iter/s]
Best score: 0.3381: : 914iter [00:26, 28.41iter/s]
Best score: 0.3381: : 915iter [00:26, 28.41iter/s]
Best score: 0.3381: : 916iter [00:26, 28.41iter/s]
Best score: 0.3381: : 917iter [00:26, 31.55iter/s]
Best score: 0.3381: : 917iter [00:26, 31.55iter/s]
Best score: 0.3381: : 918iter [00:26, 31.55iter/s]
Best score: 0.3378: : 919iter [00:26, 31.55iter/s]
Best score: 0.3378: : 920iter [00:26, 31.55iter/s]
Best score: 0.3378: : 921iter [00:26, 33.39iter/s]
Best score: 0.3378: : 921iter [00:26, 33.39iter/s]
Best score: 0.3378: : 922iter [00:26, 33.39iter/s]
Best score: 0.3378: : 923iter [00:26, 33.39iter/s]
Best score: 0.3378: : 924iter [00:26, 33.39iter/s]
Best score: 0.3378: : 925iter [00:26, 33.39iter/s]
Best score: 0.3378: : 926iter [00:26, 35.38iter/s]
Best score: 0.3378: : 926iter [00:26, 35.38iter/s]
Best score: 0.3378: : 927iter [00:26, 35.38iter/s]
Best score: 0.3378: : 928iter [00:26, 35.38iter/s]
Best score: 0.3368: : 929iter [00:26, 35.38iter/s]
Best score: 0.3368: : 930iter [00:26, 36.33iter/s]
Best score: 0.3368: : 930iter [00:27, 36.33iter/s]
Best score: 0.3368: : 931iter [00:27, 36.33iter/s]
Best score: 0.3368: : 932iter [00:27, 36.33iter/s]
Best score: 0.3368: : 933iter [00:27, 36.33iter/s]
Best score: 0.3368: : 934iter [00:27, 37.23iter/s]
Best score: 0.3368: : 934iter [00:27, 37.23iter/s]
Best score: 0.3368: : 935iter [00:27, 37.23iter/s]
Best score: 0.3368: : 936iter [00:27, 37.23iter/s]
Best score: 0.3368: : 937iter [00:27, 37.23iter/s]
Best score: 0.3368: : 938iter [00:27, 37.23iter/s]
Best score: 0.3368: : 939iter [00:27, 38.20iter/s]
Best score: 0.3368: : 939iter [00:27, 38.20iter/s]
Best score: 0.3368: : 940iter [00:27, 38.20iter/s]
Best score: 0.3368: : 941iter [00:27, 38.20iter/s]
Best score: 0.3368: : 942iter [00:27, 38.20iter/s]
Best score: 0.3368: : 943iter [00:27, 38.47iter/s]
Best score: 0.3368: : 943iter [00:27, 38.47iter/s]
Best score: 0.3368: : 944iter [00:27, 38.47iter/s]
Best score: 0.3368: : 945iter [00:27, 38.47iter/s]
Best score: 0.3368: : 946iter [00:27, 38.47iter/s]
Best score: 0.3368: : 947iter [00:27, 38.85iter/s]
Best score: 0.3368: : 947iter [00:27, 38.85iter/s]
Best score: 0.3368: : 948iter [00:27, 38.85iter/s]
[ITER 950] Best: 0.3368
[PARAMS] Current best parameters:
  fx: 548.2819
  fy: 548.2553
  px: 994.1484
  py: 757.8432
  k1: 0.00998293
  k2: -0.02936970
  k3: 0.01340502
  k4: -0.00258301
  p1: -0.01322818
  p2: -0.01337848
  p3: 0.00709408
  p4: 0.00430967
  s1: 0.0008
  s2: -0.0040
  s3: -0.0017
  s4: -0.0033
  tilt_x: 0.0015
  tilt_y: 0.0008
  scale: 2.9620
  rotation: 0.8626

Best score: 0.3368: : 949iter [00:27, 38.85iter/s]
Best score: 0.3368: : 950iter [00:27, 38.85iter/s]
Best score: 0.3368: : 951iter [00:27, 31.88iter/s]
Best score: 0.3368: : 951iter [00:27, 31.88iter/s]
Best score: 0.3368: : 952iter [00:27, 31.88iter/s]
Best score: 0.3368: : 953iter [00:27, 31.88iter/s]
Best score: 0.3368: : 954iter [00:27, 31.88iter/s]
Best score: 0.3368: : 955iter [00:27, 31.88iter/s]
Best score: 0.3368: : 956iter [00:27, 34.36iter/s]
Best score: 0.3368: : 956iter [00:27, 34.36iter/s]
Best score: 0.3368: : 957iter [00:27, 34.36iter/s]
Best score: 0.3368: : 958iter [00:27, 34.36iter/s]
Best score: 0.3365: : 959iter [00:27, 34.36iter/s]
Best score: 0.3365: : 960iter [00:27, 35.56iter/s]
Best score: 0.3365: : 960iter [00:27, 35.56iter/s]
Best score: 0.3365: : 961iter [00:27, 35.56iter/s]
Best score: 0.3365: : 962iter [00:27, 35.56iter/s]
Best score: 0.3365: : 963iter [00:27, 35.56iter/s]
Best score: 0.3365: : 964iter [00:27, 35.56iter/s]
Best score: 0.3365: : 965iter [00:27, 36.93iter/s]
Best score: 0.3365: : 965iter [00:27, 36.93iter/s]
Best score: 0.3365: : 966iter [00:27, 36.93iter/s]
Best score: 0.3365: : 967iter [00:28, 36.93iter/s]
Best score: 0.3365: : 968iter [00:28, 36.93iter/s]
Best score: 0.3365: : 969iter [00:28, 37.66iter/s]
Best score: 0.3360: : 969iter [00:28, 37.66iter/s]
Best score: 0.3360: : 970iter [00:28, 37.66iter/s]
Best score: 0.3360: : 971iter [00:28, 37.66iter/s]
Best score: 0.3360: : 972iter [00:28, 37.66iter/s]
Best score: 0.3360: : 973iter [00:28, 38.08iter/s]
Best score: 0.3360: : 973iter [00:28, 38.08iter/s]
Best score: 0.3360: : 974iter [00:28, 38.08iter/s]
Best score: 0.3360: : 975iter [00:28, 38.08iter/s]
Best score: 0.3360: : 976iter [00:28, 38.08iter/s]
Best score: 0.3360: : 977iter [00:28, 38.53iter/s]
Best score: 0.3360: : 977iter [00:28, 38.53iter/s]
Best score: 0.3360: : 978iter [00:28, 38.53iter/s]
Best score: 0.3354: : 979iter [00:28, 38.53iter/s]
Best score: 0.3354: : 980iter [00:28, 38.53iter/s]
Best score: 0.3354: : 981iter [00:28, 38.64iter/s]
Best score: 0.3354: : 981iter [00:28, 38.64iter/s]
Best score: 0.3354: : 982iter [00:28, 38.64iter/s]
Best score: 0.3354: : 983iter [00:28, 38.64iter/s]
Best score: 0.3354: : 984iter [00:28, 38.64iter/s]
Best score: 0.3354: : 985iter [00:28, 38.64iter/s]
Best score: 0.3354: : 986iter [00:28, 39.27iter/s]
Best score: 0.3354: : 986iter [00:28, 39.27iter/s]
Best score: 0.3354: : 987iter [00:28, 39.27iter/s]
Best score: 0.3354: : 988iter [00:28, 39.27iter/s]
Best score: 0.3349: : 989iter [00:28, 39.27iter/s]
Best score: 0.3349: : 990iter [00:28, 39.29iter/s]
Best score: 0.3349: : 990iter [00:28, 39.29iter/s]
Best score: 0.3349: : 991iter [00:28, 39.29iter/s]
Best score: 0.3349: : 992iter [00:28, 39.29iter/s]
Best score: 0.3349: : 993iter [00:28, 39.29iter/s]
Best score: 0.3349: : 994iter [00:28, 39.29iter/s]
Best score: 0.3349: : 995iter [00:28, 39.56iter/s]
Best score: 0.3349: : 995iter [00:28, 39.56iter/s]
Best score: 0.3349: : 996iter [00:28, 39.56iter/s]
Best score: 0.3349: : 997iter [00:28, 39.56iter/s]
Best score: 0.3349: : 998iter [00:28, 39.56iter/s]
[ITER 1000] Best: 0.3345
[PARAMS] Current best parameters:
  fx: 547.9035
  fy: 547.9085
  px: 994.8629
  py: 757.6190
  k1: 0.01387235
  k2: -0.03324293
  k3: 0.01466613
  k4: -0.00267415
  p1: -0.01406388
  p2: -0.01630292
  p3: 0.01319698
  p4: 0.00528117
  s1: -0.0023
  s2: -0.0046
  s3: -0.0075
  s4: -0.0041
  tilt_x: 0.0021
  tilt_y: -0.0088
  scale: 2.9571
  rotation: 0.8755

Best score: 0.3345: : 999iter [00:28, 39.56iter/s]
Best score: 0.3345: : 1000iter [00:28, 39.56iter/s]
Best score: 0.3345: : 1000iter [00:28, 39.56iter/s]
Best score: 0.3345: : 1001iter [00:28, 39.56iter/s]
Best score: 0.3345: : 1002iter [00:28, 39.56iter/s]
Best score: 0.3345: : 1003iter [00:28, 39.56iter/s]
Best score: 0.3345: : 1004iter [00:28, 32.78iter/s]
Best score: 0.3345: : 1004iter [00:29, 32.78iter/s]
Best score: 0.3345: : 1005iter [00:29, 32.78iter/s]
Best score: 0.3345: : 1006iter [00:29, 32.78iter/s]
Best score: 0.3345: : 1007iter [00:29, 32.78iter/s]
Best score: 0.3345: : 1008iter [00:29, 32.78iter/s]
Best score: 0.3345: : 1009iter [00:29, 34.88iter/s]
Best score: 0.3342: : 1009iter [00:29, 34.88iter/s]
Best score: 0.3342: : 1010iter [00:29, 34.88iter/s]
Best score: 0.3342: : 1011iter [00:29, 34.88iter/s]
Best score: 0.3342: : 1012iter [00:29, 34.88iter/s]
Best score: 0.3342: : 1013iter [00:29, 35.53iter/s]
Best score: 0.3342: : 1013iter [00:29, 35.53iter/s]
Best score: 0.3342: : 1014iter [00:29, 35.53iter/s]
Best score: 0.3342: : 1015iter [00:29, 35.53iter/s]
Best score: 0.3342: : 1016iter [00:29, 35.53iter/s]
Best score: 0.3342: : 1017iter [00:29, 35.53iter/s]
Best score: 0.3342: : 1018iter [00:29, 36.97iter/s]
Best score: 0.3342: : 1018iter [00:29, 36.97iter/s]
Best score: 0.3336: : 1019iter [00:29, 36.97iter/s]
Best score: 0.3336: : 1020iter [00:29, 36.97iter/s]
Best score: 0.3336: : 1021iter [00:29, 36.97iter/s]
Best score: 0.3336: : 1022iter [00:29, 37.55iter/s]
Best score: 0.3336: : 1022iter [00:29, 37.55iter/s]
Best score: 0.3336: : 1023iter [00:29, 37.55iter/s]
Best score: 0.3336: : 1024iter [00:29, 37.55iter/s]
Best score: 0.3336: : 1025iter [00:29, 37.55iter/s]
Best score: 0.3336: : 1026iter [00:29, 37.55iter/s]
Best score: 0.3336: : 1027iter [00:29, 38.31iter/s]
Best score: 0.3336: : 1027iter [00:29, 38.31iter/s]
Best score: 0.3336: : 1028iter [00:29, 38.31iter/s]
Best score: 0.3336: : 1029iter [00:29, 38.31iter/s]
Best score: 0.3336: : 1030iter [00:29, 38.31iter/s]
Best score: 0.3336: : 1031iter [00:29, 38.51iter/s]
Best score: 0.3336: : 1031iter [00:29, 38.51iter/s]
Best score: 0.3336: : 1032iter [00:29, 38.51iter/s]
Best score: 0.3336: : 1033iter [00:29, 38.51iter/s]
Best score: 0.3336: : 1034iter [00:29, 38.51iter/s]
Best score: 0.3336: : 1035iter [00:29, 38.78iter/s]
Best score: 0.3336: : 1035iter [00:29, 38.78iter/s]
Best score: 0.3336: : 1036iter [00:29, 38.78iter/s]
Best score: 0.3336: : 1037iter [00:29, 38.78iter/s]
Best score: 0.3336: : 1038iter [00:29, 38.78iter/s]
Best score: 0.3333: : 1039iter [00:29, 38.78iter/s]
Best score: 0.3333: : 1040iter [00:29, 38.97iter/s]
Best score: 0.3333: : 1040iter [00:29, 38.97iter/s]
Best score: 0.3333: : 1041iter [00:29, 38.97iter/s]
Best score: 0.3333: : 1042iter [00:29, 38.97iter/s]
Best score: 0.3333: : 1043iter [00:30, 38.97iter/s]
Best score: 0.3333: : 1044iter [00:30, 38.99iter/s]
Best score: 0.3333: : 1044iter [00:30, 38.99iter/s]
Best score: 0.3333: : 1045iter [00:30, 38.99iter/s]
Best score: 0.3333: : 1046iter [00:30, 38.99iter/s]
Best score: 0.3333: : 1047iter [00:30, 38.99iter/s]
Best score: 0.3333: : 1048iter [00:30, 39.21iter/s]
Best score: 0.3333: : 1048iter [00:30, 39.21iter/s]
[ITER 1050] Best: 0.3328
[PARAMS] Current best parameters:
  fx: 546.0936
  fy: 546.0841
  px: 994.9010
  py: 757.5778
  k1: 0.01226629
  k2: -0.02818057
  k3: 0.01179674
  k4: -0.00212392
  p1: -0.01433194
  p2: -0.01729511
  p3: 0.01390667
  p4: 0.00667155
  s1: -0.0018
  s2: -0.0061
  s3: -0.0074
  s4: -0.0057
  tilt_x: 0.0024
  tilt_y: -0.0110
  scale: 2.9619
  rotation: 0.8772

Best score: 0.3328: : 1049iter [00:30, 39.21iter/s]
Best score: 0.3328: : 1050iter [00:30, 39.21iter/s]
Best score: 0.3328: : 1051iter [00:30, 39.21iter/s]
Best score: 0.3328: : 1052iter [00:30, 32.07iter/s]
Best score: 0.3328: : 1052iter [00:30, 32.07iter/s]
Best score: 0.3328: : 1053iter [00:30, 32.07iter/s]
Best score: 0.3328: : 1054iter [00:30, 32.07iter/s]
Best score: 0.3328: : 1055iter [00:30, 32.07iter/s]
Best score: 0.3328: : 1056iter [00:30, 33.93iter/s]
Best score: 0.3328: : 1056iter [00:30, 33.93iter/s]
Best score: 0.3328: : 1057iter [00:30, 33.93iter/s]
Best score: 0.3328: : 1058iter [00:30, 33.93iter/s]
Best score: 0.3325: : 1059iter [00:30, 33.93iter/s]
Best score: 0.3325: : 1060iter [00:30, 35.26iter/s]
Best score: 0.3325: : 1060iter [00:30, 35.26iter/s]
Best score: 0.3325: : 1061iter [00:30, 35.26iter/s]
Best score: 0.3325: : 1062iter [00:30, 35.26iter/s]
Best score: 0.3325: : 1063iter [00:30, 35.26iter/s]
Best score: 0.3325: : 1064iter [00:30, 36.53iter/s]
Best score: 0.3325: : 1064iter [00:30, 36.53iter/s]
Best score: 0.3325: : 1065iter [00:30, 36.53iter/s]
Best score: 0.3325: : 1066iter [00:30, 36.53iter/s]
Best score: 0.3325: : 1067iter [00:30, 36.53iter/s]
Best score: 0.3325: : 1068iter [00:30, 36.53iter/s]
Best score: 0.3325: : 1069iter [00:30, 37.85iter/s]
Best score: 0.3324: : 1069iter [00:30, 37.85iter/s]
Best score: 0.3324: : 1070iter [00:30, 37.85iter/s]
Best score: 0.3324: : 1071iter [00:30, 37.85iter/s]
Best score: 0.3324: : 1072iter [00:30, 37.85iter/s]
Best score: 0.3324: : 1073iter [00:30, 38.14iter/s]
Best score: 0.3324: : 1073iter [00:30, 38.14iter/s]
Best score: 0.3324: : 1074iter [00:30, 38.14iter/s]
Best score: 0.3324: : 1075iter [00:30, 38.14iter/s]
Best score: 0.3324: : 1076iter [00:30, 38.14iter/s]
Best score: 0.3324: : 1077iter [00:30, 38.14iter/s]
Best score: 0.3324: : 1078iter [00:30, 38.77iter/s]
Best score: 0.3324: : 1078iter [00:30, 38.77iter/s]
Best score: 0.3321: : 1079iter [00:30, 38.77iter/s]
Best score: 0.3321: : 1080iter [00:31, 38.77iter/s]
Best score: 0.3321: : 1081iter [00:31, 38.77iter/s]
Best score: 0.3321: : 1082iter [00:31, 38.90iter/s]
Best score: 0.3321: : 1082iter [00:31, 38.90iter/s]
Best score: 0.3321: : 1083iter [00:31, 38.90iter/s]
Best score: 0.3321: : 1084iter [00:31, 38.90iter/s]
Best score: 0.3321: : 1085iter [00:31, 38.90iter/s]
Best score: 0.3321: : 1086iter [00:31, 38.90iter/s]
Best score: 0.3321: : 1087iter [00:31, 39.31iter/s]
Best score: 0.3321: : 1087iter [00:31, 39.31iter/s]
Best score: 0.3321: : 1088iter [00:31, 39.31iter/s]
Best score: 0.3321: : 1089iter [00:31, 39.31iter/s]
Best score: 0.3321: : 1090iter [00:31, 39.31iter/s]
Best score: 0.3321: : 1091iter [00:31, 39.30iter/s]
Best score: 0.3321: : 1091iter [00:31, 39.30iter/s]
Best score: 0.3321: : 1092iter [00:31, 39.30iter/s]
Best score: 0.3321: : 1093iter [00:31, 39.30iter/s]
Best score: 0.3321: : 1094iter [00:31, 39.30iter/s]
Best score: 0.3321: : 1095iter [00:31, 39.48iter/s]
Best score: 0.3321: : 1095iter [00:31, 39.48iter/s]
Best score: 0.3321: : 1096iter [00:31, 39.48iter/s]
Best score: 0.3321: : 1097iter [00:31, 39.48iter/s]
Best score: 0.3321: : 1098iter [00:31, 39.48iter/s]
[ITER 1100] Best: 0.3310
[PARAMS] Current best parameters:
  fx: 543.6843
  fy: 543.6842
  px: 995.7331
  py: 757.5900
  k1: 0.00496360
  k2: -0.01454576
  k3: 0.00478108
  k4: -0.00089265
  p1: -0.01360612
  p2: -0.01588916
  p3: 0.00796810
  p4: 0.00615710
  s1: 0.0040
  s2: -0.0058
  s3: -0.0019
  s4: -0.0053
  tilt_x: 0.0009
  tilt_y: -0.0124
  scale: 2.9745
  rotation: 0.8679

Best score: 0.3310: : 1099iter [00:31, 39.48iter/s]
Best score: 0.3310: : 1100iter [00:31, 39.48iter/s]
Best score: 0.3310: : 1100iter [00:31, 34.83iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.331043 -> 0.330539

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 26,010 → 26,010 within 3500mm ring
  Ring polish did not improve score: 0.330539
  Success: True
  Tangential distortion: p1=-0.013595, p2=-0.015890, p3=0.007964, p4=0.006156
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3305
[✅] Parameters copied to: /working/MDC030_W032_2026-03-24/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 543.684344411078
    fy: 543.684201810452
    px: 995.7330671476748
    py: 757.5900192713391
    k1: 0.004949637435678542
    k2: -0.014543480039036284
    k3: 0.0047821502043177205
    k4: -0.0008909074452649121
    p1: -0.013595393246396902
    p2: -0.015889552154067735
    p3: 0.007963973798141333
    p4: 0.006156429623584254
    scale: 2.9743726244588804
    rotation: 0.8678737638959904
    mmpp: 1.2878340049251948
    nmpp: 1287834.004925195

[🔄] Converting 799 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 500:   0%|          | 0/342 [00:00<?, ?frame/s]

   🔄 500:   5%|▌         | 18/342 [00:00<00:01, 175.87frame/s]

   🔄 500:  11%|█         | 37/342 [00:00<00:01, 182.46frame/s]

   🔄 500:  17%|█▋        | 58/342 [00:00<00:01, 192.97frame/s]

   🔄 500:  23%|██▎       | 78/342 [00:00<00:01, 193.49frame/s]

   🔄 500:  29%|██▊       | 98/342 [00:00<00:01, 190.10frame/s]

   🔄 500:  35%|███▌      | 120/342 [00:00<00:01, 197.24frame/s]

   🔄 500:  42%|████▏     | 143/342 [00:00<00:00, 205.36frame/s]

   🔄 500:  48%|████▊     | 164/342 [00:00<00:00, 205.28frame/s]

   🔄 500:  54%|█████▍    | 185/342 [00:00<00:00, 196.43frame/s]

   🔄 500:  60%|█████▉    | 205/342 [00:01<00:00, 191.41frame/s]

   🔄 500:  66%|██████▌   | 225/342 [00:01<00:00, 187.59frame/s]

   🔄 500:  71%|███████▏  | 244/342 [00:01<00:00, 184.93frame/s]

   🔄 500:  77%|███████▋  | 264/342 [00:01<00:00, 188.10frame/s]

   🔄 500:  83%|████████▎ | 283/342 [00:01<00:00, 187.00frame/s]

   🔄 500:  88%|████████▊ | 302/342 [00:01<00:00, 187.19frame/s]

   🔄 500:  94%|█████████▍| 321/342 [00:01<00:00, 188.00frame/s]

   🔄 500:  99%|█████████▉| 340/342 [00:01<00:00, 185.83frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:03<00:03,  3.34s/rig]

   🔄 1000:   0%|          | 0/457 [00:00<?, ?frame/s]

   🔄 1000:   4%|▍         | 18/457 [00:00<00:02, 172.82frame/s]

   🔄 1000:   8%|▊         | 36/457 [00:00<00:02, 173.48frame/s]

   🔄 1000:  12%|█▏        | 54/457 [00:00<00:02, 173.79frame/s]

   🔄 1000:  16%|█▌        | 72/457 [00:00<00:02, 174.20frame/s]

   🔄 1000:  20%|█▉        | 90/457 [00:00<00:02, 174.64frame/s]

   🔄 1000:  24%|██▎       | 108/457 [00:00<00:01, 174.87frame/s]

   🔄 1000:  28%|██▊       | 126/457 [00:00<00:01, 174.88frame/s]

   🔄 1000:  32%|███▏      | 144/457 [00:00<00:01, 175.51frame/s]

   🔄 1000:  35%|███▌      | 162/457 [00:00<00:01, 176.58frame/s]

   🔄 1000:  39%|███▉      | 180/457 [00:01<00:01, 177.15frame/s]

   🔄 1000:  43%|████▎     | 198/457 [00:01<00:01, 177.23frame/s]

   🔄 1000:  47%|████▋     | 216/457 [00:01<00:01, 177.20frame/s]

   🔄 1000:  51%|█████     | 234/457 [00:01<00:01, 177.20frame/s]

   🔄 1000:  55%|█████▌    | 252/457 [00:01<00:01, 177.14frame/s]

   🔄 1000:  59%|█████▉    | 270/457 [00:01<00:01, 176.71frame/s]

   🔄 1000:  63%|██████▎   | 288/457 [00:01<00:00, 176.75frame/s]

   🔄 1000:  67%|██████▋   | 306/457 [00:01<00:00, 176.54frame/s]

   🔄 1000:  71%|███████   | 324/457 [00:01<00:00, 175.84frame/s]

   🔄 1000:  75%|███████▍  | 342/457 [00:01<00:00, 174.67frame/s]

   🔄 1000:  79%|███████▉  | 360/457 [00:02<00:00, 173.93frame/s]

   🔄 1000:  83%|████████▎ | 378/457 [00:02<00:00, 173.17frame/s]

   🔄 1000:  87%|████████▋ | 396/457 [00:02<00:00, 172.70frame/s]

   🔄 1000:  91%|█████████ | 414/457 [00:02<00:00, 172.30frame/s]

   🔄 1000:  95%|█████████▍| 432/457 [00:02<00:00, 172.10frame/s]

   🔄 1000:  98%|█████████▊| 450/457 [00:02<00:00, 172.06frame/s]

                                                                 
📁 Rigs: 100%|██████████| 2/2 [00:07<00:00,  4.11s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:07<00:00,  4.00s/rig]
[✅] Saved PTDs: /working/MDC030_W032_2026-03-24/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 500:  19%|█▉        | 19/100 [00:00<00:00, 186.69frame/s]

   🔄 500:  39%|███▉      | 39/100 [00:00<00:00, 192.23frame/s]

   🔄 500:  59%|█████▉    | 59/100 [00:00<00:00, 192.05frame/s]

   🔄 500:  79%|███████▉  | 79/100 [00:00<00:00, 187.99frame/s]

   🔄 500:  98%|█████████▊| 98/100 [00:00<00:00, 187.82frame/s]

                                                               
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.05rig/s]

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 172.30frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 173.61frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 175.35frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 175.65frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 174.42frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.02s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
[✅] Saved PTDs: /working/MDC030_W032_2026-03-24/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%|▎         | 79572/3080448 [00:00<00:03, 795716.96px/s]
📤 Writing LUT:   6%|▌         | 169774/3080448 [00:00<00:03, 858246.14px/s]
📤 Writing LUT:   8%|▊         | 257073/3080448 [00:00<00:03, 864976.03px/s]
📤 Writing LUT:  11%|█         | 344691/3080448 [00:00<00:03, 869397.91px/s]
📤 Writing LUT:  14%|█▍        | 432021/3080448 [00:00<00:03, 870803.83px/s]
📤 Writing LUT:  17%|█▋        | 520983/3080448 [00:00<00:02, 877199.48px/s]
📤 Writing LUT:  20%|█▉        | 609765/3080448 [00:00<00:02, 880670.47px/s]
📤 Writing LUT:  23%|██▎       | 697833/3080448 [00:00<00:02, 879831.67px/s]
📤 Writing LUT:  26%|██▌       | 787368/3080448 [00:00<00:02, 884678.68px/s]
📤 Writing LUT:  28%|██▊       | 877709/3080448 [00:01<00:02, 890455.77px/s]
📤 Writing LUT:  31%|███▏      | 966756/3080448 [00:01<00:02, 888537.34px/s]
📤 Writing LUT:  34%|███▍      | 1055611/3080448 [00:01<00:02, 887783.59px/s]
📤 Writing LUT:  37%|███▋      | 1144391/3080448 [00:01<00:02, 887071.88px/s]
📤 Writing LUT:  40%|████      | 1235764/3080448 [00:01<00:02, 895106.97px/s]
📤 Writing LUT:  43%|████▎     | 1328038/3080448 [00:01<00:01, 903422.84px/s]
📤 Writing LUT:  46%|████▌     | 1418799/3080448 [00:01<00:01, 904681.01px/s]
📤 Writing LUT:  49%|████▉     | 1509269/3080448 [00:01<00:01, 903094.60px/s]
📤 Writing LUT:  52%|█████▏    | 1599581/3080448 [00:01<00:01, 901969.66px/s]
📤 Writing LUT:  55%|█████▍    | 1689780/3080448 [00:01<00:01, 899664.12px/s]
📤 Writing LUT:  58%|█████▊    | 1779999/3080448 [00:02<00:01, 900419.02px/s]
📤 Writing LUT:  61%|██████    | 1870043/3080448 [00:02<00:01, 899307.59px/s]
📤 Writing LUT:  64%|██████▎   | 1960526/3080448 [00:02<00:01, 900959.23px/s]
📤 Writing LUT:  67%|██████▋   | 2050624/3080448 [00:02<00:01, 897117.41px/s]
📤 Writing LUT:  70%|██████▉   | 2141259/3080448 [00:02<00:01, 899874.43px/s]
📤 Writing LUT:  72%|███████▏  | 2231251/3080448 [00:02<00:00, 898614.14px/s]
📤 Writing LUT:  75%|███████▌  | 2321325/3080448 [00:02<00:00, 899247.71px/s]
📤 Writing LUT:  78%|███████▊  | 2411252/3080448 [00:02<00:00, 894781.39px/s]
📤 Writing LUT:  81%|████████  | 2502240/3080448 [00:02<00:00, 899288.23px/s]
📤 Writing LUT:  84%|████████▍ | 2592176/3080448 [00:02<00:00, 896025.90px/s]
📤 Writing LUT:  87%|████████▋ | 2681785/3080448 [00:03<00:00, 895255.23px/s]
📤 Writing LUT:  90%|█████████ | 2772514/3080448 [00:03<00:00, 898849.80px/s]
📤 Writing LUT:  93%|█████████▎| 2863742/3080448 [00:03<00:00, 902866.88px/s]
📤 Writing LUT:  96%|█████████▌| 2954632/3080448 [00:03<00:00, 904672.77px/s]
📤 Writing LUT:  99%|█████████▉| 3045103/3080448 [00:03<00:00, 904010.73px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 893643.26px/s]
[✅] LUT written to: /working/MDC030_W032_2026-03-24/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC030_W032_2026-03-24' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC030_W032_2026-03-24/calibration/MDC030_W032_2026-03-24_unknown.lut

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

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

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

   🔥 500:   0%|          | 0/342 [00:00<?, ?frame/s]

   🔥 500:   2%|▏         | 8/342 [00:00<00:04, 72.52frame/s]

   🔥 500:   5%|▍         | 17/342 [00:00<00:04, 78.48frame/s]

   🔥 500:   8%|▊         | 26/342 [00:00<00:03, 82.30frame/s]

   🔥 500:  11%|█         | 36/342 [00:00<00:03, 85.77frame/s]

   🔥 500:  13%|█▎        | 45/342 [00:00<00:03, 86.17frame/s]

   🔥 500:  16%|█▌        | 55/342 [00:00<00:03, 87.71frame/s]

   🔥 500:  19%|█▊        | 64/342 [00:00<00:03, 88.41frame/s]

   🔥 500:  22%|██▏       | 74/342 [00:00<00:02, 90.81frame/s]

   🔥 500:  25%|██▍       | 84/342 [00:00<00:02, 88.29frame/s]

   🔥 500:  27%|██▋       | 94/342 [00:01<00:02, 90.27frame/s]

   🔥 500:  30%|███       | 104/342 [00:01<00:02, 91.20frame/s]

   🔥 500:  34%|███▎      | 115/342 [00:01<00:02, 95.24frame/s]

   🔥 500:  37%|███▋      | 125/342 [00:01<00:02, 94.84frame/s]

   🔥 500:  40%|███▉      | 136/342 [00:01<00:02, 96.59frame/s]

   🔥 500:  43%|████▎     | 147/342 [00:01<00:01, 98.22frame/s]

   🔥 500:  46%|████▌     | 157/342 [00:01<00:01, 96.14frame/s]

   🔥 500:  49%|████▉     | 167/342 [00:01<00:01, 90.67frame/s]

   🔥 500:  52%|█████▏    | 177/342 [00:01<00:01, 86.21frame/s]

   🔥 500:  54%|█████▍    | 186/342 [00:02<00:01, 84.43frame/s]

   🔥 500:  57%|█████▋    | 195/342 [00:02<00:01, 83.49frame/s]

   🔥 500:  60%|█████▉    | 204/342 [00:02<00:01, 81.23frame/s]

   🔥 500:  62%|██████▏   | 213/342 [00:02<00:01, 80.69frame/s]

   🔥 500:  65%|██████▍   | 222/342 [00:02<00:01, 80.12frame/s]

   🔥 500:  68%|██████▊   | 231/342 [00:02<00:01, 79.83frame/s]

   🔥 500:  70%|██████▉   | 239/342 [00:02<00:01, 79.01frame/s]

   🔥 500:  72%|███████▏  | 247/342 [00:02<00:01, 78.96frame/s]

   🔥 500:  75%|███████▍  | 256/342 [00:02<00:01, 80.23frame/s]

   🔥 500:  77%|███████▋  | 265/342 [00:03<00:00, 80.72frame/s]

   🔥 500:  80%|████████  | 274/342 [00:03<00:00, 79.50frame/s]

   🔥 500:  82%|████████▏ | 282/342 [00:03<00:00, 78.85frame/s]

   🔥 500:  85%|████████▍ | 290/342 [00:03<00:00, 77.91frame/s]

   🔥 500:  87%|████████▋ | 299/342 [00:03<00:00, 79.83frame/s]

   🔥 500:  90%|█████████ | 308/342 [00:03<00:00, 80.87frame/s]

   🔥 500:  93%|█████████▎| 317/342 [00:03<00:00, 82.34frame/s]

   🔥 500:  95%|█████████▌| 326/342 [00:03<00:00, 83.92frame/s]

   🔥 500:  98%|█████████▊| 335/342 [00:03<00:00, 84.68frame/s]

                                                               [✅] 500: /working/MDC030_W032_2026-03-24/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC030_W032_2026-03-24/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.15s/rig]

   🔥 1000:   0%|          | 0/457 [00:00<?, ?frame/s]

   🔥 1000:   2%|▏         | 9/457 [00:00<00:05, 85.64frame/s]

   🔥 1000:   4%|▍         | 18/457 [00:00<00:05, 87.21frame/s]

   🔥 1000:   6%|▌         | 27/457 [00:00<00:04, 86.75frame/s]

   🔥 1000:   8%|▊         | 36/457 [00:00<00:04, 86.02frame/s]

   🔥 1000:  10%|▉         | 45/457 [00:00<00:04, 85.74frame/s]

   🔥 1000:  12%|█▏        | 54/457 [00:00<00:04, 84.44frame/s]

   🔥 1000:  14%|█▍        | 63/457 [00:00<00:04, 83.76frame/s]

   🔥 1000:  16%|█▌        | 72/457 [00:00<00:04, 82.88frame/s]

   🔥 1000:  18%|█▊        | 81/457 [00:00<00:04, 81.78frame/s]

   🔥 1000:  20%|█▉        | 90/457 [00:01<00:04, 81.37frame/s]

   🔥 1000:  22%|██▏       | 99/457 [00:01<00:04, 81.13frame/s]

   🔥 1000:  24%|██▎       | 108/457 [00:01<00:04, 81.10frame/s]

   🔥 1000:  26%|██▌       | 117/457 [00:01<00:04, 81.38frame/s]

   🔥 1000:  28%|██▊       | 126/457 [00:01<00:04, 81.06frame/s]

   🔥 1000:  30%|██▉       | 135/457 [00:01<00:04, 79.99frame/s]

   🔥 1000:  32%|███▏      | 144/457 [00:01<00:03, 80.24frame/s]

   🔥 1000:  33%|███▎      | 153/457 [00:01<00:03, 80.87frame/s]

   🔥 1000:  35%|███▌      | 162/457 [00:01<00:03, 81.60frame/s]

   🔥 1000:  37%|███▋      | 171/457 [00:02<00:03, 81.86frame/s]

   🔥 1000:  39%|███▉      | 180/457 [00:02<00:03, 81.87frame/s]

   🔥 1000:  41%|████▏     | 189/457 [00:02<00:03, 81.74frame/s]

   🔥 1000:  43%|████▎     | 198/457 [00:02<00:03, 82.59frame/s]

   🔥 1000:  45%|████▌     | 207/457 [00:02<00:03, 83.01frame/s]

   🔥 1000:  47%|████▋     | 216/457 [00:02<00:02, 84.11frame/s]

   🔥 1000:  49%|████▉     | 226/457 [00:02<00:02, 85.98frame/s]

   🔥 1000:  51%|█████▏    | 235/457 [00:02<00:02, 86.82frame/s]

   🔥 1000:  53%|█████▎    | 244/457 [00:02<00:02, 86.09frame/s]

   🔥 1000:  55%|█████▌    | 253/457 [00:03<00:02, 85.25frame/s]

   🔥 1000:  57%|█████▋    | 262/457 [00:03<00:02, 83.75frame/s]

   🔥 1000:  59%|█████▉    | 271/457 [00:03<00:02, 82.73frame/s]

   🔥 1000:  61%|██████▏   | 280/457 [00:03<00:02, 82.48frame/s]

   🔥 1000:  63%|██████▎   | 289/457 [00:03<00:02, 82.35frame/s]

   🔥 1000:  65%|██████▌   | 298/457 [00:03<00:01, 82.17frame/s]

   🔥 1000:  67%|██████▋   | 307/457 [00:03<00:01, 82.20frame/s]

   🔥 1000:  69%|██████▉   | 316/457 [00:03<00:01, 82.05frame/s]

   🔥 1000:  71%|███████   | 325/457 [00:03<00:01, 82.18frame/s]

   🔥 1000:  73%|███████▎  | 334/457 [00:04<00:01, 82.50frame/s]

   🔥 1000:  75%|███████▌  | 343/457 [00:04<00:01, 82.79frame/s]

   🔥 1000:  77%|███████▋  | 352/457 [00:04<00:01, 82.57frame/s]

   🔥 1000:  79%|███████▉  | 361/457 [00:04<00:01, 82.11frame/s]

   🔥 1000:  81%|████████  | 370/457 [00:04<00:01, 81.50frame/s]

   🔥 1000:  83%|████████▎ | 379/457 [00:04<00:00, 81.34frame/s]

   🔥 1000:  85%|████████▍ | 388/457 [00:04<00:00, 81.99frame/s]

   🔥 1000:  87%|████████▋ | 397/457 [00:04<00:00, 82.53frame/s]

   🔥 1000:  89%|████████▉ | 406/457 [00:04<00:00, 84.30frame/s]

   🔥 1000:  91%|█████████ | 416/457 [00:05<00:00, 85.72frame/s]

   🔥 1000:  93%|█████████▎| 425/457 [00:05<00:00, 84.42frame/s]

   🔥 1000:  95%|█████████▍| 434/457 [00:05<00:00, 82.93frame/s]

   🔥 1000:  97%|█████████▋| 443/457 [00:05<00:00, 83.11frame/s]

   🔥 1000:  99%|█████████▉| 452/457 [00:05<00:00, 84.19frame/s]

                                                                [✅] 1000: /working/MDC030_W032_2026-03-24/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC030_W032_2026-03-24/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  7.01s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  6.73s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC030_W032_2026-03-24/calibration/debug/5_heatmap
[🏁] Finished processing MDC030_W032_2026-03-24


================================================================================================================================
================================================================================================================================