2026-07-16T01:08:43 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC013_W008_2026-07-02 ===
2026-07-16T01:08:43 - INFO: Camera ID: 14302213
2026-07-16T01:08:43 - INFO: Wand ID: 
2026-07-16T01:08:43 - INFO: Recorded At: 2026-07-02T14:50:25Z

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

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

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 582 stationary frames, 430 remain


→ 1000 (430 images) Phase 1:   0%|          | 0/430 [00:00<?, ?it/s]

→ 1000 (430 images) Phase 1:   0%|          | 1/430 [00:00<03:46,  1.90it/s]

→ 1000 (430 images) Phase 1:   3%|▎         | 14/430 [00:00<00:15, 27.66it/s]

→ 1000 (430 images) Phase 1:   8%|▊         | 33/430 [00:00<00:09, 43.01it/s]

→ 1000 (430 images) Phase 1:   9%|▉         | 40/430 [00:01<00:08, 45.80it/s]

→ 1000 (430 images) Phase 1:  12%|█▏        | 53/430 [00:01<00:06, 61.08it/s]

→ 1000 (430 images) Phase 1:  15%|█▌        | 65/430 [00:01<00:06, 60.40it/s]

→ 1000 (430 images) Phase 1:  17%|█▋        | 73/430 [00:01<00:06, 55.70it/s]

→ 1000 (430 images) Phase 1:  22%|██▏       | 96/430 [00:01<00:03, 89.12it/s]

→ 1000 (430 images) Phase 1:  25%|██▌       | 108/430 [00:02<00:05, 61.90it/s]

→ 1000 (430 images) Phase 1:  30%|███       | 129/430 [00:02<00:04, 70.58it/s]

→ 1000 (430 images) Phase 1:  32%|███▏      | 138/430 [00:02<00:04, 63.91it/s]

→ 1000 (430 images) Phase 1:  37%|███▋      | 160/430 [00:02<00:02, 90.13it/s]

→ 1000 (430 images) Phase 1:  40%|████      | 172/430 [00:02<00:04, 64.21it/s]

→ 1000 (430 images) Phase 1:  45%|████▍     | 193/430 [00:03<00:03, 72.86it/s]

→ 1000 (430 images) Phase 1:  47%|████▋     | 203/430 [00:03<00:03, 65.14it/s]

→ 1000 (430 images) Phase 1:  52%|█████▏    | 225/430 [00:03<00:02, 75.32it/s]

→ 1000 (430 images) Phase 1:  54%|█████▍    | 234/430 [00:03<00:02, 67.14it/s]

→ 1000 (430 images) Phase 1:  59%|█████▉    | 254/430 [00:03<00:01, 89.09it/s]

→ 1000 (430 images) Phase 1:  62%|██████▏   | 266/430 [00:04<00:02, 65.69it/s]

→ 1000 (430 images) Phase 1:  66%|██████▌   | 283/430 [00:04<00:01, 81.06it/s]

→ 1000 (430 images) Phase 1:  68%|██████▊   | 294/430 [00:04<00:02, 65.38it/s]

→ 1000 (430 images) Phase 1:  70%|███████   | 303/430 [00:04<00:01, 67.73it/s]

→ 1000 (430 images) Phase 1:  75%|███████▍  | 321/430 [00:04<00:01, 78.72it/s]

→ 1000 (430 images) Phase 1:  77%|███████▋  | 331/430 [00:05<00:01, 66.03it/s]

→ 1000 (430 images) Phase 1:  82%|████████▏ | 351/430 [00:05<00:00, 88.30it/s]

→ 1000 (430 images) Phase 1:  84%|████████▍ | 362/430 [00:05<00:01, 64.97it/s]

→ 1000 (430 images) Phase 1:  89%|████████▊ | 381/430 [00:05<00:00, 85.79it/s]

→ 1000 (430 images) Phase 1:  91%|█████████▏| 393/430 [00:05<00:00, 64.01it/s]

→ 1000 (430 images) Phase 1:  96%|█████████▌| 411/430 [00:06<00:00, 81.99it/s]

→ 1000 (430 images) Phase 1:  98%|█████████▊| 423/430 [00:06<00:00, 78.75it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/430 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/430 [00:00<01:18,  5.45it/s]

→ 1000 Phase 2: corners:   4%|▍         | 18/430 [00:00<00:05, 74.83it/s]

→ 1000 Phase 2: corners:   7%|▋         | 32/430 [00:00<00:04, 93.72it/s]

→ 1000 Phase 2: corners:  10%|█         | 45/430 [00:00<00:03, 105.22it/s]

→ 1000 Phase 2: corners:  14%|█▎        | 59/430 [00:00<00:03, 113.41it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 73/430 [00:00<00:03, 118.54it/s]

→ 1000 Phase 2: corners:  20%|██        | 87/430 [00:00<00:02, 122.32it/s]

→ 1000 Phase 2: corners:  25%|██▍       | 107/430 [00:00<00:02, 138.48it/s]

→ 1000 Phase 2: corners:  29%|██▊       | 123/430 [00:01<00:02, 141.55it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 138/430 [00:01<00:02, 131.03it/s]

→ 1000 Phase 2: corners:  35%|███▌      | 152/430 [00:01<00:02, 128.11it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 165/430 [00:01<00:02, 127.37it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 183/430 [00:01<00:01, 138.68it/s]

→ 1000 Phase 2: corners:  46%|████▌     | 197/430 [00:01<00:01, 135.47it/s]

→ 1000 Phase 2: corners:  50%|█████     | 215/430 [00:01<00:01, 147.86it/s]

→ 1000 Phase 2: corners:  54%|█████▍    | 232/430 [00:01<00:01, 153.54it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 248/430 [00:01<00:01, 148.51it/s]

→ 1000 Phase 2: corners:  62%|██████▏   | 268/430 [00:02<00:01, 158.88it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 284/430 [00:02<00:00, 154.63it/s]

→ 1000 Phase 2: corners:  70%|███████   | 302/430 [00:02<00:00, 158.69it/s]

→ 1000 Phase 2: corners:  74%|███████▍  | 318/430 [00:02<00:00, 155.87it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 334/430 [00:02<00:00, 142.25it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 352/430 [00:02<00:00, 150.73it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 370/430 [00:02<00:00, 156.69it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 391/430 [00:02<00:00, 168.93it/s]

                                                                           [PASS2] Frame 0000269: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000270: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000271: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000272: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000273: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000274: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000275: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000283: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000284: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000285: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000286: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000287: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000288: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000290: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000291: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000292: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000293: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000294: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000295: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000296: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000297: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000298: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000300: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000302: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000303: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000305: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000306: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000307: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000382: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000383: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000384: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000387: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000388: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000390: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000391: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000392: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000393: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000406: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000407: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000408: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000409: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000410: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000411: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000413: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000414: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000415: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000416: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000417: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000418: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000421: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000422: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000423: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000424: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000425: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000426: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000428: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000429: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000430: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000435: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000499: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000500: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000501: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000502: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000503: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000506: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000512: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000513: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000515: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000516: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000517: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000518: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000519: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000520: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000528: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000529: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000530: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000531: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000544: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000545: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000546: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000547: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000633: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000634: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000635: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000636: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000817: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000818: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000819: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000820: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000825: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000826: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000827: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000828: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000829: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000835: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000836: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000837: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000838: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000839: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000840: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000841: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000842: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000843: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000844: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000846: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000883: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000885: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000886: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000887: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000888: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000889: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000890: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000891: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000892: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000893: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000894: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000895: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000896: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000897: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000904: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000905: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000906: fixed corners {3} (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 0000911: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000912: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000913: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000917: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000918: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000919: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000921: 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=True)
[PASS2] Frame 0000933: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000934: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000935: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000936: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000937: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000940: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000987: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000988: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000989: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000990: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000991: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000993: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000995: fixed corners {1, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000997: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001003: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001004: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001005: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001006: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001007: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001008: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001013: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001014: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001015: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001016: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001018: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001019: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001020: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001021: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000271: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000274: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000285: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000289: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000293: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000294: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000301: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000302: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000307: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000386: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000387: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000390: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000391: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000392: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000408: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000410: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000415: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000418: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000423: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000425: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000430: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000435: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000501: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000512: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000517: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000519: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000520: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000530: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000546: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000635: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000819: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000828: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000837: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000839: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000840: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000843: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000884: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000894: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000895: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000905: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000909: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000910: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000912: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000920: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000921: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000935: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000936: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000937: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000989: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000994: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000996: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001005: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001007: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001015: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001020: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000299: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000301: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000384: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000386: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000390: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000391: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000519: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000520: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000884: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000891: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000920: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000933: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000934: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000935: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000993: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000994: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 108 frames fixed, 111 untrusted, 430 total


→ 1000 Phase 3: finalize:   0%|          | 0/430 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   1%|          | 5/430 [00:00<00:08, 47.88it/s]

→ 1000 Phase 3: finalize:   3%|▎         | 11/430 [00:00<00:08, 50.04it/s]

→ 1000 Phase 3: finalize:   4%|▍         | 17/430 [00:00<00:08, 50.81it/s]

→ 1000 Phase 3: finalize:   5%|▌         | 23/430 [00:00<00:07, 51.94it/s]

→ 1000 Phase 3: finalize:   7%|▋         | 29/430 [00:00<00:07, 52.70it/s]

→ 1000 Phase 3: finalize:   8%|▊         | 35/430 [00:00<00:07, 53.16it/s]

→ 1000 Phase 3: finalize:  10%|▉         | 41/430 [00:00<00:07, 52.67it/s]

→ 1000 Phase 3: finalize:  11%|█         | 47/430 [00:00<00:07, 52.87it/s]

→ 1000 Phase 3: finalize:  12%|█▏        | 53/430 [00:01<00:07, 53.72it/s]

→ 1000 Phase 3: finalize:  14%|█▎        | 59/430 [00:01<00:06, 53.96it/s]

→ 1000 Phase 3: finalize:  15%|█▌        | 65/430 [00:01<00:06, 54.16it/s]

→ 1000 Phase 3: finalize:  17%|█▋        | 71/430 [00:01<00:06, 54.21it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 77/430 [00:01<00:06, 53.96it/s]

→ 1000 Phase 3: finalize:  19%|█▉        | 83/430 [00:01<00:06, 54.14it/s]

→ 1000 Phase 3: finalize:  21%|██        | 89/430 [00:01<00:06, 53.98it/s]

→ 1000 Phase 3: finalize:  22%|██▏       | 95/430 [00:01<00:06, 54.33it/s]

→ 1000 Phase 3: finalize:  23%|██▎       | 101/430 [00:01<00:05, 55.13it/s]

→ 1000 Phase 3: finalize:  25%|██▌       | 108/430 [00:01<00:05, 58.07it/s]

→ 1000 Phase 3: finalize:  27%|██▋       | 114/430 [00:02<00:05, 56.55it/s]

→ 1000 Phase 3: finalize:  28%|██▊       | 121/430 [00:02<00:05, 58.90it/s]

→ 1000 Phase 3: finalize:  30%|██▉       | 127/430 [00:02<00:05, 59.02it/s]

→ 1000 Phase 3: finalize:  31%|███       | 133/430 [00:02<00:05, 56.98it/s]

→ 1000 Phase 3: finalize:  32%|███▏      | 139/430 [00:02<00:05, 55.08it/s]

→ 1000 Phase 3: finalize:  34%|███▎      | 145/430 [00:02<00:05, 53.51it/s]

→ 1000 Phase 3: finalize:  35%|███▌      | 151/430 [00:02<00:05, 52.27it/s]

→ 1000 Phase 3: finalize:  37%|███▋      | 157/430 [00:02<00:05, 51.34it/s]

→ 1000 Phase 3: finalize:  38%|███▊      | 163/430 [00:03<00:05, 50.78it/s]

→ 1000 Phase 3: finalize:  39%|███▉      | 169/430 [00:03<00:05, 50.09it/s]

→ 1000 Phase 3: finalize:  41%|████      | 175/430 [00:03<00:05, 49.62it/s]

→ 1000 Phase 3: finalize:  42%|████▏     | 180/430 [00:03<00:05, 49.30it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 185/430 [00:03<00:04, 49.02it/s]

→ 1000 Phase 3: finalize:  44%|████▍     | 190/430 [00:03<00:04, 48.85it/s]

→ 1000 Phase 3: finalize:  45%|████▌     | 195/430 [00:03<00:04, 48.69it/s]

→ 1000 Phase 3: finalize:  47%|████▋     | 200/430 [00:03<00:04, 48.61it/s]

→ 1000 Phase 3: finalize:  48%|████▊     | 205/430 [00:03<00:04, 48.70it/s]

→ 1000 Phase 3: finalize:  49%|████▉     | 210/430 [00:04<00:04, 48.66it/s]

→ 1000 Phase 3: finalize:  50%|█████     | 215/430 [00:04<00:04, 48.71it/s]

→ 1000 Phase 3: finalize:  51%|█████     | 220/430 [00:04<00:04, 48.64it/s]

→ 1000 Phase 3: finalize:  52%|█████▏    | 225/430 [00:04<00:04, 48.69it/s]

→ 1000 Phase 3: finalize:  53%|█████▎    | 230/430 [00:04<00:04, 48.76it/s]

→ 1000 Phase 3: finalize:  55%|█████▍    | 235/430 [00:04<00:04, 48.68it/s]

→ 1000 Phase 3: finalize:  56%|█████▌    | 240/430 [00:04<00:03, 48.77it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 245/430 [00:04<00:03, 48.98it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 250/430 [00:04<00:03, 49.00it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 256/430 [00:04<00:03, 49.53it/s]

→ 1000 Phase 3: finalize:  61%|██████    | 261/430 [00:05<00:03, 49.37it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 266/430 [00:05<00:03, 49.41it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 271/430 [00:05<00:03, 49.10it/s]

→ 1000 Phase 3: finalize:  64%|██████▍   | 276/430 [00:05<00:03, 49.08it/s]

→ 1000 Phase 3: finalize:  65%|██████▌   | 281/430 [00:05<00:03, 48.70it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 286/430 [00:05<00:02, 48.61it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 291/430 [00:05<00:02, 48.68it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 296/430 [00:05<00:02, 48.93it/s]

→ 1000 Phase 3: finalize:  70%|███████   | 302/430 [00:05<00:02, 49.50it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 308/430 [00:05<00:02, 49.99it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 314/430 [00:06<00:02, 50.74it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 320/430 [00:06<00:02, 51.43it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 326/430 [00:06<00:02, 51.89it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 332/430 [00:06<00:01, 52.31it/s]

→ 1000 Phase 3: finalize:  79%|███████▊  | 338/430 [00:06<00:01, 52.71it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 344/430 [00:06<00:01, 53.24it/s]

→ 1000 Phase 3: finalize:  81%|████████▏ | 350/430 [00:06<00:01, 53.34it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 356/430 [00:06<00:01, 52.41it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 362/430 [00:07<00:01, 51.02it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 368/430 [00:07<00:01, 52.03it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 374/430 [00:07<00:01, 52.71it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 380/430 [00:07<00:00, 53.16it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 386/430 [00:07<00:00, 53.64it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 392/430 [00:07<00:00, 54.24it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 398/430 [00:07<00:00, 54.60it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 404/430 [00:07<00:00, 54.61it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 410/430 [00:07<00:00, 54.35it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 416/430 [00:08<00:00, 54.24it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 422/430 [00:08<00:00, 54.09it/s]

→ 1000 Phase 3: finalize: 100%|█████████▉| 428/430 [00:08<00:00, 53.97it/s]

                                                                           
[TIMING] 1000 — 430 frames
  Phase 1  radial detect   (parallel)  :   6.42s  (14.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.96s  (6.9 ms/frame)
  Phase 2b corner fix      (sequential):   4.04s
  Phase 3  finalize        (sequential):   8.28s  (19.2 ms/frame)
    decode + undistort                 :   4.26s  (9.9 ms/frame)
    corner apply + refinement          :   1.86s  (4.3 ms/frame)
    segment                            :   1.68s  (3.9 ms/frame)
    smooth                             :   0.34s  (0.8 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  21.70s


Processing Rigs:  50%|█████     | 1/2 [00:26<00:26, 26.30s/it][INFO] Filtered 389 stationary frames, 385 remain


→ 500 (385 images) Phase 1:   0%|          | 0/385 [00:00<?, ?it/s]

→ 500 (385 images) Phase 1:   0%|          | 1/385 [00:00<03:33,  1.80it/s]

→ 500 (385 images) Phase 1:   1%|          | 4/385 [00:00<00:52,  7.21it/s]

→ 500 (385 images) Phase 1:   6%|▌         | 24/385 [00:00<00:07, 48.33it/s]

→ 500 (385 images) Phase 1:   9%|▉         | 34/385 [00:01<00:07, 47.62it/s]

→ 500 (385 images) Phase 1:  11%|█         | 42/385 [00:01<00:06, 50.89it/s]

→ 500 (385 images) Phase 1:  15%|█▌        | 58/385 [00:01<00:04, 74.39it/s]

→ 500 (385 images) Phase 1:  18%|█▊        | 68/385 [00:01<00:05, 56.05it/s]

→ 500 (385 images) Phase 1:  22%|██▏       | 85/385 [00:01<00:03, 75.36it/s]

→ 500 (385 images) Phase 1:  25%|██▍       | 95/385 [00:01<00:03, 78.67it/s]

→ 500 (385 images) Phase 1:  27%|██▋       | 105/385 [00:02<00:04, 61.89it/s]

→ 500 (385 images) Phase 1:  31%|███       | 120/385 [00:02<00:03, 78.66it/s]

→ 500 (385 images) Phase 1:  34%|███▍      | 130/385 [00:02<00:04, 63.50it/s]

→ 500 (385 images) Phase 1:  36%|███▌      | 139/385 [00:02<00:03, 68.24it/s]

→ 500 (385 images) Phase 1:  40%|███▉      | 153/385 [00:02<00:02, 82.90it/s]

→ 500 (385 images) Phase 1:  42%|████▏     | 163/385 [00:02<00:03, 62.82it/s]

→ 500 (385 images) Phase 1:  47%|████▋     | 180/385 [00:02<00:02, 83.10it/s]

→ 500 (385 images) Phase 1:  50%|████▉     | 191/385 [00:03<00:02, 79.42it/s]

→ 500 (385 images) Phase 1:  52%|█████▏    | 201/385 [00:03<00:02, 63.63it/s]

→ 500 (385 images) Phase 1:  56%|█████▋    | 217/385 [00:03<00:02, 80.18it/s]

→ 500 (385 images) Phase 1:  59%|█████▉    | 227/385 [00:03<00:02, 63.49it/s]

→ 500 (385 images) Phase 1:  63%|██████▎   | 242/385 [00:03<00:01, 79.29it/s]

→ 500 (385 images) Phase 1:  66%|██████▌   | 253/385 [00:03<00:01, 81.21it/s]

→ 500 (385 images) Phase 1:  68%|██████▊   | 263/385 [00:04<00:01, 62.21it/s]

→ 500 (385 images) Phase 1:  72%|███████▏  | 278/385 [00:04<00:01, 75.31it/s]

→ 500 (385 images) Phase 1:  75%|███████▍  | 288/385 [00:04<00:01, 78.59it/s]

→ 500 (385 images) Phase 1:  77%|███████▋  | 298/385 [00:04<00:01, 65.18it/s]

→ 500 (385 images) Phase 1:  82%|████████▏ | 314/385 [00:04<00:00, 83.84it/s]

→ 500 (385 images) Phase 1:  84%|████████▍ | 325/385 [00:05<00:00, 63.67it/s]

→ 500 (385 images) Phase 1:  89%|████████▊ | 341/385 [00:05<00:00, 80.69it/s]

→ 500 (385 images) Phase 1:  91%|█████████▏| 352/385 [00:05<00:00, 76.58it/s]

→ 500 (385 images) Phase 1:  94%|█████████▍| 362/385 [00:05<00:00, 68.46it/s]

→ 500 (385 images) Phase 1:  99%|█████████▉| 381/385 [00:05<00:00, 91.73it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/385 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/385 [00:00<02:01,  3.15it/s]

→ 500 Phase 2: corners:   4%|▍         | 16/385 [00:00<00:07, 48.07it/s]

→ 500 Phase 2: corners:   8%|▊         | 30/385 [00:00<00:04, 73.88it/s]

→ 500 Phase 2: corners:  12%|█▏        | 47/385 [00:00<00:03, 98.84it/s]

→ 500 Phase 2: corners:  16%|█▌        | 62/385 [00:00<00:02, 111.79it/s]

→ 500 Phase 2: corners:  19%|█▉        | 75/385 [00:00<00:02, 116.51it/s]

→ 500 Phase 2: corners:  23%|██▎       | 88/385 [00:00<00:02, 120.06it/s]

→ 500 Phase 2: corners:  26%|██▋       | 102/385 [00:01<00:02, 124.78it/s]

→ 500 Phase 2: corners:  30%|███       | 117/385 [00:01<00:02, 132.11it/s]

→ 500 Phase 2: corners:  34%|███▍      | 132/385 [00:01<00:01, 135.05it/s]

→ 500 Phase 2: corners:  38%|███▊      | 147/385 [00:01<00:01, 134.95it/s]

→ 500 Phase 2: corners:  42%|████▏     | 161/385 [00:01<00:01, 132.17it/s]

→ 500 Phase 2: corners:  46%|████▌     | 176/385 [00:01<00:01, 136.22it/s]

→ 500 Phase 2: corners:  49%|████▉     | 190/385 [00:01<00:01, 134.83it/s]

→ 500 Phase 2: corners:  53%|█████▎    | 204/385 [00:01<00:01, 130.75it/s]

→ 500 Phase 2: corners:  57%|█████▋    | 219/385 [00:01<00:01, 132.97it/s]

→ 500 Phase 2: corners:  61%|██████    | 233/385 [00:02<00:01, 133.42it/s]

→ 500 Phase 2: corners:  64%|██████▍   | 248/385 [00:02<00:01, 134.21it/s]

→ 500 Phase 2: corners:  69%|██████▊   | 264/385 [00:02<00:00, 141.01it/s]

→ 500 Phase 2: corners:  74%|███████▎  | 283/385 [00:02<00:00, 154.90it/s]

→ 500 Phase 2: corners:  79%|███████▊  | 303/385 [00:02<00:00, 163.29it/s]

→ 500 Phase 2: corners:  83%|████████▎ | 320/385 [00:02<00:00, 161.73it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 338/385 [00:02<00:00, 164.40it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 372/385 [00:02<00:00, 214.61it/s]

                                                                          [PASS2] Frame 0000212: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000213: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000214: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000215: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000223: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000280: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000281: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000282: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000481: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000482: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000483: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000484: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000530: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000544: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000547: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000577: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000583: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000584: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000590: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000591: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000592: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000593: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000595: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000599: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000613: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000614: fixed corners {0, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000615: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000616: fixed corners {0, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000620: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000622: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000623: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000624: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000625: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000631: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000655: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000724: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000725: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000726: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000727: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000812: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000813: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000814: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000815: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000214: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000281: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000483: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000583: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000591: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000592: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000593: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000595: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000614: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000622: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000624: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000726: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000814: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000591: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000593: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000614: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 29 frames fixed, 21 untrusted, 385 total


→ 500 Phase 3: finalize:   0%|          | 0/385 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   2%|▏         | 6/385 [00:00<00:06, 55.70it/s]

→ 500 Phase 3: finalize:   3%|▎         | 12/385 [00:00<00:06, 57.32it/s]

→ 500 Phase 3: finalize:   5%|▍         | 18/385 [00:00<00:06, 57.21it/s]

→ 500 Phase 3: finalize:   6%|▌         | 24/385 [00:00<00:06, 56.97it/s]

→ 500 Phase 3: finalize:   8%|▊         | 30/385 [00:00<00:06, 57.37it/s]

→ 500 Phase 3: finalize:   9%|▉         | 36/385 [00:00<00:06, 58.13it/s]

→ 500 Phase 3: finalize:  11%|█         | 42/385 [00:00<00:05, 58.36it/s]

→ 500 Phase 3: finalize:  12%|█▏        | 48/385 [00:00<00:05, 58.04it/s]

→ 500 Phase 3: finalize:  14%|█▍        | 54/385 [00:00<00:05, 56.71it/s]

→ 500 Phase 3: finalize:  16%|█▌        | 60/385 [00:01<00:05, 55.95it/s]

→ 500 Phase 3: finalize:  17%|█▋        | 66/385 [00:01<00:05, 55.48it/s]

→ 500 Phase 3: finalize:  19%|█▊        | 72/385 [00:01<00:05, 55.14it/s]

→ 500 Phase 3: finalize:  20%|██        | 78/385 [00:01<00:05, 54.64it/s]

→ 500 Phase 3: finalize:  22%|██▏       | 84/385 [00:01<00:05, 54.12it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 90/385 [00:01<00:05, 53.76it/s]

→ 500 Phase 3: finalize:  25%|██▍       | 96/385 [00:01<00:05, 53.54it/s]

→ 500 Phase 3: finalize:  26%|██▋       | 102/385 [00:01<00:05, 53.44it/s]

→ 500 Phase 3: finalize:  28%|██▊       | 108/385 [00:01<00:05, 53.51it/s]

→ 500 Phase 3: finalize:  30%|██▉       | 114/385 [00:02<00:05, 53.29it/s]

→ 500 Phase 3: finalize:  31%|███       | 120/385 [00:02<00:04, 53.24it/s]

→ 500 Phase 3: finalize:  33%|███▎      | 126/385 [00:02<00:04, 53.10it/s]

→ 500 Phase 3: finalize:  34%|███▍      | 132/385 [00:02<00:04, 52.99it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 138/385 [00:02<00:04, 53.01it/s]

→ 500 Phase 3: finalize:  37%|███▋      | 144/385 [00:02<00:04, 53.03it/s]

→ 500 Phase 3: finalize:  39%|███▉      | 150/385 [00:02<00:04, 53.00it/s]

→ 500 Phase 3: finalize:  41%|████      | 156/385 [00:02<00:04, 53.10it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 162/385 [00:02<00:04, 53.13it/s]

→ 500 Phase 3: finalize:  44%|████▎     | 168/385 [00:03<00:04, 53.11it/s]

→ 500 Phase 3: finalize:  45%|████▌     | 174/385 [00:03<00:03, 53.06it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 180/385 [00:03<00:03, 53.06it/s]

→ 500 Phase 3: finalize:  48%|████▊     | 186/385 [00:03<00:03, 53.04it/s]

→ 500 Phase 3: finalize:  50%|████▉     | 192/385 [00:03<00:03, 52.70it/s]

→ 500 Phase 3: finalize:  51%|█████▏    | 198/385 [00:03<00:03, 52.28it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 204/385 [00:03<00:03, 52.08it/s]

→ 500 Phase 3: finalize:  55%|█████▍    | 210/385 [00:03<00:03, 51.93it/s]

→ 500 Phase 3: finalize:  56%|█████▌    | 216/385 [00:04<00:03, 51.94it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 222/385 [00:04<00:03, 51.89it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 228/385 [00:04<00:03, 51.75it/s]

→ 500 Phase 3: finalize:  61%|██████    | 234/385 [00:04<00:02, 51.62it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 240/385 [00:04<00:02, 51.64it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 246/385 [00:04<00:02, 51.58it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 252/385 [00:04<00:02, 51.64it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 258/385 [00:04<00:02, 51.75it/s]

→ 500 Phase 3: finalize:  69%|██████▊   | 264/385 [00:04<00:02, 51.79it/s]

→ 500 Phase 3: finalize:  70%|███████   | 270/385 [00:05<00:02, 51.69it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 276/385 [00:05<00:02, 51.72it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 282/385 [00:05<00:01, 51.84it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 288/385 [00:05<00:01, 52.06it/s]

→ 500 Phase 3: finalize:  76%|███████▋  | 294/385 [00:05<00:01, 52.24it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 300/385 [00:05<00:01, 52.34it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 306/385 [00:05<00:01, 52.30it/s]

→ 500 Phase 3: finalize:  81%|████████  | 312/385 [00:05<00:01, 52.36it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 318/385 [00:05<00:01, 52.36it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 324/385 [00:06<00:01, 52.45it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 330/385 [00:06<00:01, 52.82it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 336/385 [00:06<00:00, 53.25it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 342/385 [00:06<00:00, 53.21it/s]

→ 500 Phase 3: finalize:  90%|█████████ | 348/385 [00:06<00:00, 53.36it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 354/385 [00:06<00:00, 53.33it/s]

→ 500 Phase 3: finalize:  94%|█████████▎| 360/385 [00:06<00:00, 53.29it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 366/385 [00:06<00:00, 53.44it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 372/385 [00:06<00:00, 53.59it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 378/385 [00:07<00:00, 53.62it/s]

→ 500 Phase 3: finalize: 100%|█████████▉| 384/385 [00:07<00:00, 53.57it/s]

                                                                          
[TIMING] 500 — 385 frames
  Phase 1  radial detect   (parallel)  :   5.76s  (15.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.84s  (7.4 ms/frame)
  Phase 2b corner fix      (sequential):   1.11s
  Phase 3  finalize        (sequential):   7.22s  (18.7 ms/frame)
    decode + undistort                 :   3.73s  (9.7 ms/frame)
    corner apply + refinement          :   1.44s  (3.7 ms/frame)
    segment                            :   1.53s  (4.0 ms/frame)
    smooth                             :   0.40s  (1.0 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  16.92s


Processing Rigs: 100%|██████████| 2/2 [00:45<00:00, 22.32s/it]
Processing Rigs: 100%|██████████| 2/2 [00:45<00:00, 22.91s/it]
[💾] Saved RigFrameDataset to /working/MDC013_W008_2026-07-02/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 815 frames from 2 rigs to /working/MDC013_W008_2026-07-02/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC013_W008_2026-07-02/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 430
[📋] 500: 100 frames selected from 385
[💾] Saved RigFrameDataset to /working/MDC013_W008_2026-07-02/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC013_W008_2026-07-02/calibration/debug/1_digitized/rig_frames.json

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

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

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:04<03:06,  4.78s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:04<01:18,  2.07s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:05<00:44,  1.20s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:19,  1.75it/s]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:05<00:11,  2.87it/s]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:05<00:07,  4.10it/s]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:05<00:05,  5.38it/s]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:05,  5.13it/s]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:06<00:05,  4.90it/s]
Computing max image from bright frames:  40%|████      | 16/40 [00:06<00:04,  5.95it/s]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:06<00:02,  7.68it/s]
Computing max image from bright frames:  52%|█████▎    | 21/40 [00:06<00:01, 10.90it/s]
Computing max image from bright frames:  57%|█████▊    | 23/40 [00:07<00:01, 10.70it/s]
Computing max image from bright frames:  68%|██████▊   | 27/40 [00:07<00:01, 12.31it/s]
Computing max image from bright frames:  75%|███████▌  | 30/40 [00:07<00:00, 13.78it/s]
Computing max image from bright frames:  80%|████████  | 32/40 [00:07<00:00, 14.27it/s]
Computing max image from bright frames:  85%|████████▌ | 34/40 [00:08<00:01,  4.99it/s]
Computing max image from bright frames:  90%|█████████ | 36/40 [00:09<00:00,  5.54it/s]
Computing max image from bright frames:  98%|█████████▊| 39/40 [00:09<00:00,  7.24it/s]
                                                                                       
[✅] Casing detection complete: cx=985.0, cy=746.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:09,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:09,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:09,  8.73it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.002 | Best: {'fx': 490.909} (0.002):  27%|██▋       | 27/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  28%|██▊       | 28/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.006 | Best: {'fx': 519.192} (0.033):  29%|██▉       | 29/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.053 | Best: {'fx': 526.263} (0.053):  30%|███       | 30/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.043 | Best: {'fx': 526.263} (0.053):  31%|███       | 31/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.003 | Best: {'fx': 526.263} (0.053):  32%|███▏      | 32/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.021 | Best: {'fx': 526.263} (0.053):  33%|███▎      | 33/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.066 | Best: {'fx': 533.333} (0.066):  34%|███▍      | 34/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.031 | Best: {'fx': 533.333} (0.066):  35%|███▌      | 35/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.024 | Best: {'fx': 533.333} (0.066):  36%|███▌      | 36/100 [00:00<00:00, 86.77it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.024 | Best: {'fx': 533.333} (0.066):  37%|███▋      | 37/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.066):  37%|███▋      | 37/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.021 | Best: {'fx': 533.333} (0.066):  38%|███▊      | 38/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.019 | Best: {'fx': 533.333} (0.066):  39%|███▉      | 39/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.017 | Best: {'fx': 533.333} (0.066):  40%|████      | 40/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.014 | Best: {'fx': 533.333} (0.066):  41%|████      | 41/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.016 | Best: {'fx': 533.333} (0.066):  42%|████▏     | 42/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.015 | Best: {'fx': 533.333} (0.066):  43%|████▎     | 43/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.066):  44%|████▍     | 44/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.066):  45%|████▌     | 45/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.066):  46%|████▌     | 46/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.066):  47%|████▋     | 47/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  48%|████▊     | 48/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.066):  49%|████▉     | 49/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.066):  50%|█████     | 50/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.066):  51%|█████     | 51/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.066):  52%|█████▏    | 52/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  53%|█████▎    | 53/100 [00:00<00:00, 98.05it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  54%|█████▍    | 54/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  54%|█████▍    | 54/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  55%|█████▌    | 55/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  56%|█████▌    | 56/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  57%|█████▋    | 57/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  58%|█████▊    | 58/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  59%|█████▉    | 59/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  60%|██████    | 60/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.066):  61%|██████    | 61/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  62%|██████▏   | 62/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  63%|██████▎   | 63/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  64%|██████▍   | 64/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  65%|██████▌   | 65/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  66%|██████▌   | 66/100 [00:00<00:00, 121.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  67%|██████▋   | 67/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  67%|██████▋   | 67/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  68%|██████▊   | 68/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  69%|██████▉   | 69/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  70%|███████   | 70/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  71%|███████   | 71/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  72%|███████▏  | 72/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  73%|███████▎  | 73/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  74%|███████▍  | 74/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  75%|███████▌  | 75/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  76%|███████▌  | 76/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  77%|███████▋  | 77/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  78%|███████▊  | 78/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  79%|███████▉  | 79/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  80%|████████  | 80/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  81%|████████  | 81/100 [00:00<00:00, 124.06it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  82%|████████▏ | 82/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  82%|████████▏ | 82/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  83%|████████▎ | 83/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  84%|████████▍ | 84/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  85%|████████▌ | 85/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  86%|████████▌ | 86/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  87%|████████▋ | 87/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  88%|████████▊ | 88/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  89%|████████▉ | 89/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  90%|█████████ | 90/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  91%|█████████ | 91/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  92%|█████████▏| 92/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  93%|█████████▎| 93/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  94%|█████████▍| 94/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  95%|█████████▌| 95/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  96%|█████████▌| 96/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  97%|█████████▋| 97/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  98%|█████████▊| 98/100 [00:00<00:00, 124.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.066):  99%|█████████▉| 99/100 [00:00<00:00, 124.71it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.011 | Best: {'fx': 508.333} (0.011):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.011 | Best: {'fx': 508.333} (0.011):   2%|▏         | 1/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.011 | Best: {'fx': 509.354} (0.011):   2%|▏         | 1/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   4%|▍         | 2/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.018 | Best: {'fx': 511.395} (0.018):   6%|▌         | 3/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.027 | Best: {'fx': 515.476} (0.027):   8%|▊         | 4/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.021 | Best: {'fx': 515.476} (0.027):  10%|█         | 5/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.023 | Best: {'fx': 515.476} (0.027):  12%|█▏        | 6/50 [00:00<00:05,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.029 | Best: {'fx': 516.497} (0.029):  14%|█▍        | 7/50 [00:00<00:04,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.026 | Best: {'fx': 516.497} (0.029):  16%|█▌        | 8/50 [00:00<00:04,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:04,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.032 | Best: {'fx': 518.537} (0.032):  20%|██        | 10/50 [00:00<00:04,  8.74it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.032 | Best: {'fx': 518.537} (0.032):  22%|██▏       | 11/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.044 | Best: {'fx': 524.66} (0.044):  22%|██▏       | 11/50 [00:00<00:00, 59.00it/s] 
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.034 | Best: {'fx': 524.66} (0.044):  24%|██▍       | 12/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.036 | Best: {'fx': 524.66} (0.044):  26%|██▌       | 13/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.037 | Best: {'fx': 524.66} (0.044):  28%|██▊       | 14/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.040 | Best: {'fx': 524.66} (0.044):  30%|███       | 15/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.042 | Best: {'fx': 524.66} (0.044):  32%|███▏      | 16/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.060 | Best: {'fx': 527.721} (0.060):  34%|███▍      | 17/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.050 | Best: {'fx': 527.721} (0.060):  36%|███▌      | 18/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.054 | Best: {'fx': 527.721} (0.060):  38%|███▊      | 19/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.072 | Best: {'fx': 532.823} (0.072):  40%|████      | 20/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.069 | Best: {'fx': 532.823} (0.072):  42%|████▏     | 21/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.062 | Best: {'fx': 532.823} (0.072):  44%|████▍     | 22/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.076 | Best: {'fx': 530.782} (0.076):  46%|████▌     | 23/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.072 | Best: {'fx': 530.782} (0.076):  48%|████▊     | 24/50 [00:00<00:00, 59.00it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.072 | Best: {'fx': 530.782} (0.076):  50%|█████     | 25/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.072 | Best: {'fx': 530.782} (0.076):  50%|█████     | 25/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.063 | Best: {'fx': 530.782} (0.076):  52%|█████▏    | 26/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.039 | Best: {'fx': 530.782} (0.076):  54%|█████▍    | 27/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.058 | Best: {'fx': 530.782} (0.076):  56%|█████▌    | 28/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.042 | Best: {'fx': 530.782} (0.076):  58%|█████▊    | 29/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.037 | Best: {'fx': 530.782} (0.076):  60%|██████    | 30/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.053 | Best: {'fx': 530.782} (0.076):  62%|██████▏   | 31/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.049 | Best: {'fx': 530.782} (0.076):  64%|██████▍   | 32/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.047 | Best: {'fx': 530.782} (0.076):  66%|██████▌   | 33/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.040 | Best: {'fx': 530.782} (0.076):  68%|██████▊   | 34/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.035 | Best: {'fx': 530.782} (0.076):  70%|███████   | 35/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.034 | Best: {'fx': 530.782} (0.076):  72%|███████▏  | 36/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.026 | Best: {'fx': 530.782} (0.076):  74%|███████▍  | 37/50 [00:00<00:00, 94.55it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.026 | Best: {'fx': 530.782} (0.076):  76%|███████▌  | 38/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.032 | Best: {'fx': 530.782} (0.076):  76%|███████▌  | 38/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.025 | Best: {'fx': 530.782} (0.076):  78%|███████▊  | 39/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.031 | Best: {'fx': 530.782} (0.076):  80%|████████  | 40/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.030 | Best: {'fx': 530.782} (0.076):  82%|████████▏ | 41/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.029 | Best: {'fx': 530.782} (0.076):  84%|████████▍ | 42/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.028 | Best: {'fx': 530.782} (0.076):  86%|████████▌ | 43/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.024 | Best: {'fx': 530.782} (0.076):  88%|████████▊ | 44/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.023 | Best: {'fx': 530.782} (0.076):  90%|█████████ | 45/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.027 | Best: {'fx': 530.782} (0.076):  92%|█████████▏| 46/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.024 | Best: {'fx': 530.782} (0.076):  94%|█████████▍| 47/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.023 | Best: {'fx': 530.782} (0.076):  96%|█████████▌| 48/50 [00:00<00:00, 107.06it/s]
Brute | Start: {'fx': 533.333} (0.066) | Current: 0.025 | Best: {'fx': 530.782} (0.076):  98%|█████████▊| 49/50 [00:00<00:00, 107.06it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 985.0
    py: 746.0
    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: 30.4683: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 30.4683, picklable_objective(x0) = 30.4369

Best score: 30.4369: : 0iter [00:01, ?iter/s] 
Best score: 30.4369: : 1iter [00:01,  1.95s/iter]
Best score: 30.4369: : 1iter [00:01,  1.95s/iter]
Best score: 30.4369: : 2iter [00:02,  1.95s/iter]
Best score: 30.4369: : 3iter [00:02,  1.95s/iter]
Best score: 30.4369: : 4iter [00:02,  1.95s/iter]
Best score: 30.4369: : 5iter [00:02,  3.21iter/s]
Best score: 30.4369: : 5iter [00:02,  3.21iter/s]
Best score: 30.4369: : 6iter [00:02,  3.21iter/s]
Best score: 30.4369: : 7iter [00:02,  3.21iter/s]
Best score: 30.4369: : 8iter [00:02,  3.21iter/s]
Best score: 30.4369: : 9iter [00:02,  6.47iter/s]
Best score: 30.4369: : 9iter [00:02,  6.47iter/s]
Best score: 30.4369: : 10iter [00:02,  6.47iter/s]
Best score: 30.4369: : 11iter [00:02,  6.47iter/s]
Best score: 30.4369: : 12iter [00:02,  6.47iter/s]
Best score: 30.4369: : 13iter [00:02, 10.21iter/s]
Best score: 30.4369: : 13iter [00:02, 10.21iter/s]
Best score: 30.4369: : 14iter [00:02, 10.21iter/s]
Best score: 30.4369: : 15iter [00:02, 10.21iter/s]
Best score: 30.4369: : 16iter [00:02, 10.21iter/s]
Best score: 30.4369: : 17iter [00:02, 10.21iter/s]
Best score: 30.4369: : 18iter [00:02, 15.19iter/s]
Best score: 30.4369: : 18iter [00:02, 15.19iter/s]
Best score: 30.4369: : 19iter [00:02, 15.19iter/s]
Best score: 30.4369: : 20iter [00:02, 15.19iter/s]
Best score: 30.4369: : 21iter [00:02, 15.19iter/s]
Best score: 30.4369: : 22iter [00:02, 19.10iter/s]
Best score: 30.4369: : 22iter [00:02, 19.10iter/s]
Best score: 30.4369: : 23iter [00:02, 19.10iter/s]
Best score: 30.4369: : 24iter [00:02, 19.10iter/s]
Best score: 30.4369: : 25iter [00:02, 19.10iter/s]
Best score: 30.4369: : 26iter [00:02, 19.10iter/s]
Best score: 30.4369: : 27iter [00:02, 23.78iter/s]
Best score: 30.4369: : 27iter [00:02, 23.78iter/s]
Best score: 30.4369: : 28iter [00:02, 23.78iter/s]
Best score: 30.4369: : 29iter [00:02, 23.78iter/s]
Best score: 30.4369: : 30iter [00:02, 23.78iter/s]
Best score: 30.4369: : 31iter [00:02, 26.84iter/s]
Best score: 30.4369: : 31iter [00:02, 26.84iter/s]
Best score: 30.4369: : 32iter [00:02, 26.84iter/s]
Best score: 30.4369: : 33iter [00:02, 26.84iter/s]
Best score: 30.4369: : 34iter [00:02, 26.84iter/s]
Best score: 30.4369: : 35iter [00:02, 26.84iter/s]
Best score: 30.4369: : 36iter [00:02, 30.35iter/s]
Best score: 30.4369: : 36iter [00:02, 30.35iter/s]
Best score: 30.4369: : 37iter [00:02, 30.35iter/s]
Best score: 30.4369: : 38iter [00:02, 30.35iter/s]
Best score: 30.4369: : 39iter [00:02, 30.35iter/s]
Best score: 30.4369: : 40iter [00:02, 32.41iter/s]
Best score: 30.4369: : 40iter [00:02, 32.41iter/s]
Best score: 30.4369: : 41iter [00:02, 32.41iter/s]
Best score: 30.4369: : 42iter [00:03, 32.41iter/s]
Best score: 30.4369: : 43iter [00:03, 32.41iter/s]
Best score: 30.4369: : 44iter [00:03, 32.41iter/s]
Best score: 30.4369: : 45iter [00:03, 34.69iter/s]
Best score: 30.4369: : 45iter [00:03, 34.69iter/s]
Best score: 30.4369: : 46iter [00:03, 34.69iter/s]
Best score: 30.4369: : 47iter [00:03, 34.69iter/s]
Best score: 30.4369: : 48iter [00:03, 34.69iter/s]
[ITER 50] Best: 30.4369
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 985.0000
  py: 746.0000
  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: 30.4369: : 49iter [00:03, 34.69iter/s]
Best score: 30.4369: : 50iter [00:03, 36.12iter/s]
Best score: 30.4369: : 50iter [00:03, 36.12iter/s]
Best score: 30.4369: : 51iter [00:03, 36.12iter/s]
Best score: 30.4369: : 52iter [00:03, 36.12iter/s]
Best score: 30.4369: : 53iter [00:03, 36.12iter/s]
Best score: 30.4369: : 54iter [00:03, 36.12iter/s]
Best score: 30.4369: : 55iter [00:03, 37.31iter/s]
Best score: 30.4369: : 55iter [00:03, 37.31iter/s]
Best score: 30.4369: : 56iter [00:03, 37.31iter/s]
Best score: 30.4369: : 57iter [00:03, 37.31iter/s]
Best score: 30.4369: : 58iter [00:03, 37.31iter/s]
Best score: 30.4369: : 59iter [00:03, 37.31iter/s]
Best score: 30.4369: : 60iter [00:03, 38.10iter/s]
Best score: 30.4369: : 60iter [00:03, 38.10iter/s]
Best score: 30.4369: : 61iter [00:03, 38.10iter/s]
Best score: 30.4369: : 62iter [00:03, 38.10iter/s]
Best score: 30.4369: : 63iter [00:03, 38.10iter/s]
Best score: 30.4369: : 64iter [00:03, 38.10iter/s]
Best score: 30.4369: : 65iter [00:03, 38.75iter/s]
Best score: 30.4369: : 65iter [00:03, 38.75iter/s]
Best score: 30.4369: : 66iter [00:03, 38.75iter/s]
Best score: 30.4369: : 67iter [00:03, 38.75iter/s]
Best score: 30.4369: : 68iter [00:03, 38.75iter/s]
Best score: 30.4369: : 69iter [00:03, 38.75iter/s]
Best score: 30.4369: : 70iter [00:03, 39.07iter/s]
Best score: 30.4369: : 70iter [00:03, 39.07iter/s]
Best score: 30.4369: : 71iter [00:03, 39.07iter/s]
Best score: 30.4369: : 72iter [00:03, 39.07iter/s]
Best score: 30.4369: : 73iter [00:03, 39.07iter/s]
Best score: 30.4369: : 74iter [00:03, 39.07iter/s]
Best score: 30.4369: : 75iter [00:03, 39.40iter/s]
Best score: 30.4369: : 75iter [00:03, 39.40iter/s]
Best score: 30.4369: : 76iter [00:03, 39.40iter/s]
Best score: 30.4369: : 77iter [00:03, 39.40iter/s]
Best score: 30.4369: : 78iter [00:03, 39.40iter/s]
Best score: 30.4369: : 79iter [00:03, 39.40iter/s]
Best score: 30.4369: : 80iter [00:03, 39.54iter/s]
Best score: 30.4369: : 80iter [00:03, 39.54iter/s]
Best score: 30.4369: : 81iter [00:03, 39.54iter/s]
Best score: 30.4369: : 82iter [00:04, 39.54iter/s]
Best score: 30.4369: : 83iter [00:04, 39.54iter/s]
Best score: 30.4369: : 84iter [00:04, 39.63iter/s]
Best score: 30.4369: : 84iter [00:04, 39.63iter/s]
Best score: 30.4369: : 85iter [00:04, 39.63iter/s]
Best score: 30.4369: : 86iter [00:04, 39.63iter/s]
Best score: 30.4369: : 87iter [00:04, 39.63iter/s]
Best score: 30.4369: : 88iter [00:04, 39.63iter/s]
Best score: 30.4369: : 89iter [00:04, 39.78iter/s]
Best score: 30.4369: : 89iter [00:04, 39.78iter/s]
Best score: 30.4369: : 90iter [00:04, 39.78iter/s]
Best score: 30.4369: : 91iter [00:04, 39.78iter/s]
Best score: 30.4369: : 92iter [00:04, 39.78iter/s]
Best score: 30.4369: : 93iter [00:04, 39.68iter/s]
Best score: 30.4369: : 93iter [00:04, 39.68iter/s]
Best score: 30.4369: : 94iter [00:04, 39.68iter/s]
Best score: 30.4369: : 95iter [00:04, 39.68iter/s]
Best score: 30.4369: : 96iter [00:04, 39.68iter/s]
Best score: 30.4369: : 97iter [00:04, 39.68iter/s]
Best score: 30.4369: : 98iter [00:04, 39.85iter/s]
Best score: 30.4369: : 98iter [00:04, 39.85iter/s]
[ITER 100] Best: 30.4369
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 985.0000
  py: 746.0000
  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: 30.4369: : 99iter [00:04, 39.85iter/s]
Best score: 30.4369: : 100iter [00:04, 39.85iter/s]
Best score: 30.4369: : 101iter [00:04, 39.85iter/s]
Best score: 30.4369: : 102iter [00:04, 37.22iter/s]
Best score: 30.4369: : 102iter [00:04, 37.22iter/s]
Best score: 30.4369: : 103iter [00:04, 37.22iter/s]
Best score: 30.4369: : 104iter [00:04, 37.22iter/s]
Best score: 30.4369: : 105iter [00:04, 37.22iter/s]
Best score: 30.4369: : 106iter [00:04, 37.22iter/s]
Best score: 30.4369: : 107iter [00:04, 38.11iter/s]
Best score: 30.4369: : 107iter [00:04, 38.11iter/s]
Best score: 30.4369: : 108iter [00:04, 38.11iter/s]
Best score: 30.4369: : 109iter [00:04, 38.11iter/s]
Best score: 30.4369: : 110iter [00:04, 38.11iter/s]
Best score: 30.4369: : 111iter [00:04, 38.36iter/s]
Best score: 30.4369: : 111iter [00:04, 38.36iter/s]
Best score: 30.4369: : 112iter [00:04, 38.36iter/s]
Best score: 30.4369: : 113iter [00:04, 38.36iter/s]
Best score: 30.4369: : 114iter [00:04, 38.36iter/s]
Best score: 30.4369: : 115iter [00:04, 38.36iter/s]
Best score: 30.4369: : 116iter [00:04, 39.06iter/s]
Best score: 30.4369: : 116iter [00:04, 39.06iter/s]
Best score: 30.4369: : 117iter [00:04, 39.06iter/s]
Best score: 30.4369: : 118iter [00:04, 39.06iter/s]
Best score: 30.4369: : 119iter [00:04, 39.06iter/s]
Best score: 30.4369: : 120iter [00:04, 39.08iter/s]
Best score: 30.4369: : 120iter [00:04, 39.08iter/s]
Best score: 30.4369: : 121iter [00:05, 39.08iter/s]
Best score: 30.4369: : 122iter [00:05, 39.08iter/s]
Best score: 30.4369: : 123iter [00:05, 39.08iter/s]
Best score: 30.4369: : 124iter [00:05, 39.30iter/s]
Best score: 30.4369: : 124iter [00:05, 39.30iter/s]
Best score: 30.4369: : 125iter [00:05, 39.30iter/s]
Best score: 30.4369: : 126iter [00:05, 39.30iter/s]
Best score: 30.4369: : 127iter [00:05, 39.30iter/s]
Best score: 30.4369: : 128iter [00:05, 39.30iter/s]
Best score: 30.4369: : 129iter [00:05, 39.57iter/s]
Best score: 30.4369: : 129iter [00:05, 39.57iter/s]
Best score: 30.4369: : 130iter [00:05, 39.57iter/s]
Best score: 30.4369: : 131iter [00:05, 39.57iter/s]
Best score: 30.4369: : 132iter [00:05, 39.57iter/s]
Best score: 30.4369: : 133iter [00:05, 39.52iter/s]
Best score: 30.4369: : 133iter [00:05, 39.52iter/s]
Best score: 30.4369: : 134iter [00:05, 39.52iter/s]
Best score: 30.4369: : 135iter [00:05, 39.52iter/s]
Best score: 30.4369: : 136iter [00:05, 39.52iter/s]
Best score: 30.4369: : 137iter [00:05, 39.52iter/s]
Best score: 30.4369: : 138iter [00:05, 39.91iter/s]
Best score: 30.4369: : 138iter [00:05, 39.91iter/s]
Best score: 30.4369: : 139iter [00:05, 39.91iter/s]
Best score: 30.4369: : 140iter [00:05, 39.91iter/s]
Best score: 30.4369: : 141iter [00:05, 39.91iter/s]
Best score: 30.4369: : 142iter [00:05, 39.73iter/s]
Best score: 30.4369: : 142iter [00:05, 39.73iter/s]
Best score: 30.4369: : 143iter [00:05, 39.73iter/s]
Best score: 30.4369: : 144iter [00:05, 39.73iter/s]
Best score: 30.4369: : 145iter [00:05, 39.73iter/s]
Best score: 30.4369: : 146iter [00:05, 39.73iter/s]
Best score: 30.4369: : 147iter [00:05, 39.99iter/s]
Best score: 30.4369: : 147iter [00:05, 39.99iter/s]
Best score: 30.4369: : 148iter [00:05, 39.99iter/s]
[ITER 150] Best: 30.4369
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 985.0000
  py: 746.0000
  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: 30.4369: : 149iter [00:05, 39.99iter/s]
Best score: 30.4369: : 150iter [00:05, 39.99iter/s]
Best score: 30.4369: : 151iter [00:05, 33.10iter/s]
Best score: 30.4369: : 151iter [00:05, 33.10iter/s]
Best score: 30.4369: : 152iter [00:05, 33.10iter/s]
Best score: 30.4369: : 153iter [00:05, 33.10iter/s]
Best score: 30.4369: : 154iter [00:05, 33.10iter/s]
Best score: 30.4369: : 155iter [00:05, 33.10iter/s]
Best score: 30.4369: : 156iter [00:05, 35.19iter/s]
Best score: 30.4369: : 156iter [00:05, 35.19iter/s]
Best score: 30.4369: : 157iter [00:05, 35.19iter/s]
Best score: 30.4369: : 158iter [00:06, 35.19iter/s]
Best score: 30.4369: : 159iter [00:06, 35.19iter/s]
Best score: 30.4369: : 160iter [00:06, 36.34iter/s]
Best score: 30.4369: : 160iter [00:06, 36.34iter/s]
Best score: 30.4369: : 161iter [00:06, 36.34iter/s]
Best score: 30.4369: : 162iter [00:06, 36.34iter/s]
Best score: 30.4369: : 163iter [00:06, 36.34iter/s]
Best score: 30.4369: : 164iter [00:06, 36.34iter/s]
Best score: 30.4369: : 165iter [00:06, 37.55iter/s]
Best score: 30.4369: : 165iter [00:06, 37.55iter/s]
Best score: 30.4369: : 166iter [00:06, 37.55iter/s]
Best score: 30.4369: : 167iter [00:06, 37.55iter/s]
Best score: 30.4369: : 168iter [00:06, 37.55iter/s]
Best score: 30.4369: : 169iter [00:06, 37.55iter/s]
Best score: 30.4369: : 170iter [00:06, 38.18iter/s]
Best score: 30.4369: : 170iter [00:06, 38.18iter/s]
Best score: 30.4369: : 171iter [00:06, 38.18iter/s]
Best score: 30.4369: : 172iter [00:06, 38.18iter/s]
Best score: 30.4369: : 173iter [00:06, 38.18iter/s]
Best score: 30.4369: : 174iter [00:06, 38.18iter/s]
Best score: 30.4369: : 175iter [00:06, 38.93iter/s]
Best score: 30.4369: : 175iter [00:06, 38.93iter/s]
Best score: 30.4369: : 176iter [00:06, 38.93iter/s]
Best score: 30.4369: : 177iter [00:06, 38.93iter/s]
Best score: 30.4369: : 178iter [00:06, 38.93iter/s]
Best score: 30.4369: : 179iter [00:06, 38.93iter/s]
Best score: 30.4369: : 180iter [00:06, 39.19iter/s]
Best score: 30.4369: : 180iter [00:06, 39.19iter/s]
Best score: 30.4369: : 181iter [00:06, 39.19iter/s]
Best score: 30.4369: : 182iter [00:06, 39.19iter/s]
Best score: 30.4369: : 183iter [00:06, 39.19iter/s]
Best score: 30.4369: : 184iter [00:06, 39.31iter/s]
Best score: 30.4369: : 184iter [00:06, 39.31iter/s]
Best score: 30.4369: : 185iter [00:06, 39.31iter/s]
Best score: 30.4369: : 186iter [00:06, 39.31iter/s]
Best score: 30.4369: : 187iter [00:06, 39.31iter/s]
Best score: 30.4369: : 188iter [00:06, 39.31iter/s]
Best score: 30.4369: : 189iter [00:06, 39.73iter/s]
Best score: 30.4369: : 189iter [00:06, 39.73iter/s]
Best score: 30.4369: : 190iter [00:06, 39.73iter/s]
Best score: 30.4369: : 191iter [00:06, 39.73iter/s]
Best score: 30.4369: : 192iter [00:06, 39.73iter/s]
Best score: 30.4369: : 193iter [00:06, 39.73iter/s]
Best score: 30.4369: : 193iter [00:06, 39.73iter/s]
Best score: 30.4369: : 194iter [00:06, 39.73iter/s]
Best score: 30.4369: : 195iter [00:06, 39.73iter/s]
Best score: 30.4369: : 196iter [00:06, 39.73iter/s]
Best score: 30.4369: : 197iter [00:06, 39.73iter/s]
Best score: 30.4369: : 198iter [00:06, 40.00iter/s]
Best score: 30.4369: : 198iter [00:07, 40.00iter/s]
[ITER 200] Best: 22.9474
[PARAMS] Current best parameters:
  fx: 534.4853
  fy: 532.9848
  px: 980.9746
  py: 743.3303
  k1: -0.00348169
  k2: -0.00277180
  k3: 0.00672416
  k4: -0.00287594
  p1: 0.00506456
  p2: -0.00065821
  p3: 0.00051066
  p4: -0.00208965
  s1: -0.0010
  s2: -0.0046
  s3: -0.0065
  s4: -0.0010
  tilt_x: -0.0366
  tilt_y: 0.0216
  scale: 2.9703
  rotation: -0.7057

Best score: 22.9474: : 199iter [00:07, 40.00iter/s]
Best score: 22.9474: : 200iter [00:07, 40.00iter/s]
Best score: 22.9474: : 201iter [00:07, 40.00iter/s]
Best score: 22.9474: : 202iter [00:07, 40.00iter/s]
Best score: 22.9474: : 203iter [00:07, 33.50iter/s]
Best score: 22.9474: : 203iter [00:07, 33.50iter/s]
Best score: 22.9474: : 204iter [00:07, 33.50iter/s]
Best score: 22.9474: : 205iter [00:07, 33.50iter/s]
Best score: 22.9474: : 206iter [00:07, 33.50iter/s]
Best score: 22.9474: : 207iter [00:07, 33.50iter/s]
Best score: 22.9474: : 208iter [00:07, 35.49iter/s]
Best score: 22.9474: : 208iter [00:07, 35.49iter/s]
Best score: 22.9474: : 209iter [00:07, 35.49iter/s]
Best score: 22.9474: : 210iter [00:07, 35.49iter/s]
Best score: 22.9474: : 211iter [00:07, 35.49iter/s]
Best score: 22.9474: : 212iter [00:07, 36.52iter/s]
Best score: 22.9474: : 212iter [00:07, 36.52iter/s]
Best score: 22.9474: : 213iter [00:07, 36.52iter/s]
Best score: 22.9474: : 214iter [00:07, 36.52iter/s]
Best score: 22.9474: : 215iter [00:07, 36.52iter/s]
Best score: 22.9474: : 216iter [00:07, 36.52iter/s]
Best score: 22.9474: : 217iter [00:07, 37.78iter/s]
Best score: 22.9474: : 217iter [00:07, 37.78iter/s]
Best score: 22.9474: : 218iter [00:07, 37.78iter/s]
Best score: 19.3846: : 219iter [00:07, 37.78iter/s]
Best score: 19.3846: : 220iter [00:07, 37.78iter/s]
Best score: 19.3846: : 221iter [00:07, 37.78iter/s]
Best score: 19.3846: : 222iter [00:07, 38.51iter/s]
Best score: 19.3846: : 222iter [00:07, 38.51iter/s]
Best score: 19.3846: : 223iter [00:07, 38.51iter/s]
Best score: 19.3846: : 224iter [00:07, 38.51iter/s]
Best score: 19.3846: : 225iter [00:07, 38.51iter/s]
Best score: 19.3846: : 226iter [00:07, 31.04iter/s]
Best score: 19.3846: : 226iter [00:07, 31.04iter/s]
Best score: 19.3846: : 227iter [00:07, 31.04iter/s]
Best score: 19.3846: : 228iter [00:07, 31.04iter/s]
Best score: 18.7163: : 229iter [00:07, 31.04iter/s]
Best score: 18.7163: : 230iter [00:07, 33.01iter/s]
Best score: 18.7163: : 230iter [00:07, 33.01iter/s]
Best score: 18.7163: : 231iter [00:08, 33.01iter/s]
Best score: 18.7163: : 232iter [00:08, 33.01iter/s]
Best score: 18.7163: : 233iter [00:08, 33.01iter/s]
Best score: 18.7163: : 234iter [00:08, 33.01iter/s]
Best score: 18.7163: : 235iter [00:08, 35.33iter/s]
Best score: 18.7163: : 235iter [00:08, 35.33iter/s]
Best score: 18.7163: : 236iter [00:08, 35.33iter/s]
Best score: 18.7163: : 237iter [00:08, 35.33iter/s]
Best score: 18.7163: : 238iter [00:08, 35.33iter/s]
Best score: 4.5328: : 239iter [00:08, 35.33iter/s] 
Best score: 4.5328: : 240iter [00:08, 36.65iter/s]
Best score: 4.5328: : 240iter [00:08, 36.65iter/s]
Best score: 4.5328: : 241iter [00:08, 36.65iter/s]
Best score: 4.5328: : 242iter [00:08, 36.65iter/s]
Best score: 4.5328: : 243iter [00:08, 36.65iter/s]
Best score: 4.5328: : 244iter [00:08, 36.65iter/s]
Best score: 4.5328: : 245iter [00:08, 37.86iter/s]
Best score: 4.5328: : 245iter [00:08, 37.86iter/s]
Best score: 4.5328: : 246iter [00:08, 37.86iter/s]
Best score: 4.5328: : 247iter [00:08, 37.86iter/s]
Best score: 4.5328: : 248iter [00:08, 37.86iter/s]
[ITER 250] Best: 4.5328
[PARAMS] Current best parameters:
  fx: 556.8148
  fy: 556.1004
  px: 980.7393
  py: 751.4771
  k1: 0.02087734
  k2: -0.07243598
  k3: 0.04508377
  k4: -0.00994837
  p1: -0.00351808
  p2: -0.02108927
  p3: -0.00407737
  p4: 0.01123596
  s1: 0.0160
  s2: -0.0096
  s3: 0.0081
  s4: -0.0137
  tilt_x: 0.0224
  tilt_y: -0.0002
  scale: 2.9241
  rotation: 0.0409

Best score: 4.5328: : 249iter [00:08, 37.86iter/s]
Best score: 4.5328: : 250iter [00:08, 38.49iter/s]
Best score: 4.5328: : 250iter [00:08, 38.49iter/s]
Best score: 4.5328: : 251iter [00:08, 38.49iter/s]
Best score: 4.5328: : 252iter [00:08, 38.49iter/s]
Best score: 4.5328: : 253iter [00:08, 38.49iter/s]
Best score: 4.5328: : 254iter [00:08, 32.74iter/s]
Best score: 4.5328: : 254iter [00:08, 32.74iter/s]
Best score: 4.5328: : 255iter [00:08, 32.74iter/s]
Best score: 4.5328: : 256iter [00:08, 32.74iter/s]
Best score: 4.5328: : 257iter [00:08, 32.74iter/s]
Best score: 4.5328: : 258iter [00:08, 32.74iter/s]
Best score: 4.5328: : 259iter [00:08, 34.99iter/s]
Best score: 2.5235: : 259iter [00:08, 34.99iter/s]
Best score: 2.5235: : 260iter [00:08, 34.99iter/s]
Best score: 2.5235: : 261iter [00:08, 34.99iter/s]
Best score: 2.5235: : 262iter [00:08, 34.99iter/s]
Best score: 2.5235: : 263iter [00:08, 36.12iter/s]
Best score: 2.5235: : 263iter [00:08, 36.12iter/s]
Best score: 2.5235: : 264iter [00:08, 36.12iter/s]
Best score: 2.5235: : 265iter [00:08, 36.12iter/s]
Best score: 2.5235: : 266iter [00:08, 36.12iter/s]
Best score: 2.5235: : 267iter [00:08, 36.12iter/s]
Best score: 2.5235: : 268iter [00:08, 37.59iter/s]
Best score: 2.5235: : 268iter [00:08, 37.59iter/s]
Best score: 2.5235: : 269iter [00:09, 37.59iter/s]
Best score: 2.5235: : 270iter [00:09, 37.59iter/s]
Best score: 2.5235: : 271iter [00:09, 37.59iter/s]
Best score: 2.5235: : 272iter [00:09, 37.59iter/s]
Best score: 2.5235: : 273iter [00:09, 38.42iter/s]
Best score: 2.5235: : 273iter [00:09, 38.42iter/s]
Best score: 2.5235: : 274iter [00:09, 38.42iter/s]
Best score: 2.5235: : 275iter [00:09, 38.42iter/s]
Best score: 2.5235: : 276iter [00:09, 38.42iter/s]
Best score: 2.5235: : 277iter [00:09, 38.42iter/s]
Best score: 2.5235: : 278iter [00:09, 39.28iter/s]
Best score: 2.5235: : 278iter [00:09, 39.28iter/s]
Best score: 1.9682: : 279iter [00:09, 39.28iter/s]
Best score: 1.9682: : 280iter [00:09, 39.28iter/s]
Best score: 1.9682: : 281iter [00:09, 39.28iter/s]
Best score: 1.9682: : 282iter [00:09, 39.28iter/s]
Best score: 1.9682: : 283iter [00:09, 39.61iter/s]
Best score: 1.9682: : 283iter [00:09, 39.61iter/s]
Best score: 1.9682: : 284iter [00:09, 39.61iter/s]
Best score: 1.9682: : 285iter [00:09, 39.61iter/s]
Best score: 1.9682: : 286iter [00:09, 39.61iter/s]
Best score: 1.9682: : 287iter [00:09, 39.61iter/s]
Best score: 1.9682: : 288iter [00:09, 40.05iter/s]
Best score: 1.9682: : 288iter [00:09, 40.05iter/s]
Best score: 1.9682: : 289iter [00:09, 40.05iter/s]
Best score: 1.9682: : 290iter [00:09, 40.05iter/s]
Best score: 1.9682: : 291iter [00:09, 40.05iter/s]
Best score: 1.9682: : 292iter [00:09, 40.05iter/s]
Best score: 1.9682: : 293iter [00:09, 40.05iter/s]
Best score: 1.9682: : 293iter [00:09, 40.05iter/s]
Best score: 1.9682: : 294iter [00:09, 40.05iter/s]
Best score: 1.9682: : 295iter [00:09, 40.05iter/s]
Best score: 1.9682: : 296iter [00:09, 40.05iter/s]
Best score: 1.9682: : 297iter [00:09, 40.05iter/s]
Best score: 1.9682: : 298iter [00:09, 40.09iter/s]
Best score: 1.9682: : 298iter [00:09, 40.09iter/s]
[ITER 300] Best: 1.9351
[PARAMS] Current best parameters:
  fx: 558.6939
  fy: 557.9334
  px: 993.3851
  py: 743.5849
  k1: 0.01804004
  k2: -0.06328205
  k3: 0.03681289
  k4: -0.00788776
  p1: -0.01303381
  p2: -0.00008129
  p3: -0.00047436
  p4: 0.00025076
  s1: 0.0204
  s2: -0.0065
  s3: 0.0108
  s4: 0.0004
  tilt_x: -0.0024
  tilt_y: -0.0149
  scale: 2.9138
  rotation: -0.3088

Best score: 1.9351: : 299iter [00:09, 40.09iter/s]
Best score: 1.9351: : 300iter [00:09, 40.09iter/s]
Best score: 1.9351: : 301iter [00:09, 40.09iter/s]
Best score: 1.9351: : 302iter [00:09, 40.09iter/s]
Best score: 1.9351: : 303iter [00:09, 33.74iter/s]
Best score: 1.9351: : 303iter [00:09, 33.74iter/s]
Best score: 1.9351: : 304iter [00:09, 33.74iter/s]
Best score: 1.9351: : 305iter [00:09, 33.74iter/s]
Best score: 1.9351: : 306iter [00:10, 33.74iter/s]
Best score: 1.9351: : 307iter [00:10, 33.74iter/s]
Best score: 1.9351: : 308iter [00:10, 35.64iter/s]
Best score: 1.9351: : 308iter [00:10, 35.64iter/s]
Best score: 1.9351: : 309iter [00:10, 35.64iter/s]
Best score: 1.9351: : 310iter [00:10, 35.64iter/s]
Best score: 1.9351: : 311iter [00:10, 35.64iter/s]
Best score: 1.9351: : 312iter [00:10, 36.59iter/s]
Best score: 1.9351: : 312iter [00:10, 36.59iter/s]
Best score: 1.9351: : 313iter [00:10, 36.59iter/s]
Best score: 1.9351: : 314iter [00:10, 36.59iter/s]
Best score: 1.9351: : 315iter [00:10, 36.59iter/s]
Best score: 1.9351: : 316iter [00:10, 36.59iter/s]
Best score: 1.9351: : 317iter [00:10, 37.94iter/s]
Best score: 1.9351: : 317iter [00:10, 37.94iter/s]
Best score: 1.9351: : 318iter [00:10, 37.94iter/s]
Best score: 1.8777: : 319iter [00:10, 37.94iter/s]
Best score: 1.8777: : 320iter [00:10, 37.94iter/s]
Best score: 1.8777: : 321iter [00:10, 38.44iter/s]
Best score: 1.8777: : 321iter [00:10, 38.44iter/s]
Best score: 1.8777: : 322iter [00:10, 38.44iter/s]
Best score: 1.8777: : 323iter [00:10, 38.44iter/s]
Best score: 1.8777: : 324iter [00:10, 38.44iter/s]
Best score: 1.8777: : 325iter [00:10, 38.44iter/s]
Best score: 1.8777: : 326iter [00:10, 39.17iter/s]
Best score: 1.8777: : 326iter [00:10, 39.17iter/s]
Best score: 1.8777: : 327iter [00:10, 39.17iter/s]
Best score: 1.8777: : 328iter [00:10, 39.17iter/s]
Best score: 1.8777: : 329iter [00:10, 39.17iter/s]
Best score: 1.8777: : 330iter [00:10, 39.31iter/s]
Best score: 1.8777: : 330iter [00:10, 39.31iter/s]
Best score: 1.8777: : 331iter [00:10, 39.31iter/s]
Best score: 1.8777: : 332iter [00:10, 39.31iter/s]
Best score: 1.8777: : 333iter [00:10, 39.31iter/s]
Best score: 1.8777: : 334iter [00:10, 39.31iter/s]
Best score: 1.8777: : 335iter [00:10, 39.73iter/s]
Best score: 1.8777: : 335iter [00:10, 39.73iter/s]
Best score: 1.8777: : 336iter [00:10, 39.73iter/s]
Best score: 1.8777: : 337iter [00:10, 39.73iter/s]
Best score: 1.8777: : 338iter [00:10, 39.73iter/s]
Best score: 1.4869: : 339iter [00:10, 39.73iter/s]
Best score: 1.4869: : 340iter [00:10, 39.89iter/s]
Best score: 1.4869: : 340iter [00:10, 39.89iter/s]
Best score: 1.4869: : 341iter [00:10, 39.89iter/s]
Best score: 1.4869: : 342iter [00:10, 39.89iter/s]
Best score: 1.4869: : 343iter [00:10, 39.89iter/s]
Best score: 1.4869: : 344iter [00:10, 39.89iter/s]
Best score: 1.4869: : 345iter [00:10, 40.29iter/s]
Best score: 1.4869: : 345iter [00:10, 40.29iter/s]
Best score: 1.4869: : 346iter [00:10, 40.29iter/s]
Best score: 1.4869: : 347iter [00:11, 40.29iter/s]
Best score: 1.4869: : 348iter [00:11, 40.29iter/s]
[ITER 350] Best: 1.3282
[PARAMS] Current best parameters:
  fx: 563.6540
  fy: 564.1109
  px: 986.8420
  py: 742.6387
  k1: 0.04080956
  k2: -0.08793805
  k3: 0.04063799
  k4: -0.00684482
  p1: -0.00954391
  p2: -0.01977759
  p3: -0.00536507
  p4: 0.00703063
  s1: 0.0315
  s2: -0.0073
  s3: 0.0023
  s4: -0.0036
  tilt_x: -0.0097
  tilt_y: -0.0245
  scale: 2.8620
  rotation: -0.2955

Best score: 1.3282: : 349iter [00:11, 40.29iter/s]
Best score: 1.3282: : 350iter [00:11, 40.07iter/s]
Best score: 1.3282: : 350iter [00:11, 40.07iter/s]
Best score: 1.3282: : 351iter [00:11, 40.07iter/s]
Best score: 1.3282: : 352iter [00:11, 40.07iter/s]
Best score: 1.3282: : 353iter [00:11, 40.07iter/s]
Best score: 1.3282: : 354iter [00:11, 40.07iter/s]
Best score: 1.3282: : 355iter [00:11, 33.67iter/s]
Best score: 1.3282: : 355iter [00:11, 33.67iter/s]
Best score: 1.3282: : 356iter [00:11, 33.67iter/s]
Best score: 1.3282: : 357iter [00:11, 33.67iter/s]
Best score: 1.3282: : 358iter [00:11, 33.67iter/s]
Best score: 1.3282: : 359iter [00:11, 33.67iter/s]
Best score: 1.3282: : 360iter [00:11, 35.44iter/s]
Best score: 1.3282: : 360iter [00:11, 35.44iter/s]
Best score: 1.3282: : 361iter [00:11, 35.44iter/s]
Best score: 1.3282: : 362iter [00:11, 35.44iter/s]
Best score: 1.3282: : 363iter [00:11, 35.44iter/s]
Best score: 1.3282: : 364iter [00:11, 35.44iter/s]
Best score: 1.3282: : 365iter [00:11, 36.90iter/s]
Best score: 1.3282: : 365iter [00:11, 36.90iter/s]
Best score: 1.3282: : 366iter [00:11, 36.90iter/s]
Best score: 1.3282: : 367iter [00:11, 36.90iter/s]
Best score: 1.3282: : 368iter [00:11, 36.90iter/s]
Best score: 1.2476: : 369iter [00:11, 36.90iter/s]
Best score: 1.2476: : 370iter [00:11, 37.76iter/s]
Best score: 1.2476: : 370iter [00:11, 37.76iter/s]
Best score: 1.2476: : 371iter [00:11, 37.76iter/s]
Best score: 1.2476: : 372iter [00:11, 37.76iter/s]
Best score: 1.2476: : 373iter [00:11, 37.76iter/s]
Best score: 1.2476: : 374iter [00:11, 37.76iter/s]
Best score: 1.2476: : 375iter [00:11, 38.76iter/s]
Best score: 1.2476: : 375iter [00:11, 38.76iter/s]
Best score: 1.2476: : 376iter [00:11, 38.76iter/s]
Best score: 1.2476: : 377iter [00:11, 38.76iter/s]
Best score: 1.2476: : 378iter [00:11, 38.76iter/s]
Best score: 1.1198: : 379iter [00:11, 38.76iter/s]
Best score: 1.1198: : 380iter [00:11, 39.23iter/s]
Best score: 1.1198: : 380iter [00:11, 39.23iter/s]
Best score: 1.1198: : 381iter [00:11, 39.23iter/s]
Best score: 1.1198: : 382iter [00:11, 39.23iter/s]
Best score: 1.1198: : 383iter [00:11, 39.23iter/s]
Best score: 1.1198: : 384iter [00:12, 39.23iter/s]
Best score: 1.1198: : 385iter [00:12, 39.64iter/s]
Best score: 1.1198: : 385iter [00:12, 39.64iter/s]
Best score: 1.1198: : 386iter [00:12, 39.64iter/s]
Best score: 1.1198: : 387iter [00:12, 39.64iter/s]
Best score: 1.1198: : 388iter [00:12, 39.64iter/s]
Best score: 1.0160: : 389iter [00:12, 39.64iter/s]
Best score: 1.0160: : 390iter [00:12, 39.82iter/s]
Best score: 1.0160: : 390iter [00:12, 39.82iter/s]
Best score: 1.0160: : 391iter [00:12, 39.82iter/s]
Best score: 1.0160: : 392iter [00:12, 39.82iter/s]
Best score: 1.0160: : 393iter [00:12, 39.82iter/s]
Best score: 1.0160: : 394iter [00:12, 39.82iter/s]
Best score: 1.0160: : 395iter [00:12, 40.14iter/s]
Best score: 1.0160: : 395iter [00:12, 40.14iter/s]
Best score: 1.0160: : 396iter [00:12, 40.14iter/s]
Best score: 1.0160: : 397iter [00:12, 40.14iter/s]
Best score: 1.0160: : 398iter [00:12, 40.14iter/s]
[ITER 400] Best: 0.8160
[PARAMS] Current best parameters:
  fx: 559.0028
  fy: 559.1226
  px: 988.2533
  py: 744.9819
  k1: 0.02693040
  k2: -0.07121055
  k3: 0.03592133
  k4: -0.00654630
  p1: -0.00901105
  p2: -0.01480044
  p3: -0.00234750
  p4: 0.00711768
  s1: 0.0205
  s2: -0.0066
  s3: 0.0110
  s4: -0.0077
  tilt_x: -0.0018
  tilt_y: -0.0225
  scale: 2.9126
  rotation: -0.2954

Best score: 0.8160: : 399iter [00:12, 40.14iter/s]
Best score: 0.8160: : 400iter [00:12, 39.97iter/s]
Best score: 0.8160: : 400iter [00:12, 39.97iter/s]
Best score: 0.8160: : 401iter [00:12, 39.97iter/s]
Best score: 0.8160: : 402iter [00:12, 39.97iter/s]
Best score: 0.8160: : 403iter [00:12, 39.97iter/s]
Best score: 0.8160: : 404iter [00:12, 39.97iter/s]
Best score: 0.8160: : 405iter [00:12, 33.97iter/s]
Best score: 0.8160: : 405iter [00:12, 33.97iter/s]
Best score: 0.8160: : 406iter [00:12, 33.97iter/s]
Best score: 0.8160: : 407iter [00:12, 33.97iter/s]
Best score: 0.8160: : 408iter [00:12, 33.97iter/s]
Best score: 0.8160: : 409iter [00:12, 33.97iter/s]
Best score: 0.8160: : 410iter [00:12, 35.59iter/s]
Best score: 0.8160: : 410iter [00:12, 35.59iter/s]
Best score: 0.8160: : 411iter [00:12, 35.59iter/s]
Best score: 0.8160: : 412iter [00:12, 35.59iter/s]
Best score: 0.8160: : 413iter [00:12, 35.59iter/s]
Best score: 0.8160: : 414iter [00:12, 35.59iter/s]
Best score: 0.8160: : 415iter [00:12, 36.99iter/s]
Best score: 0.8160: : 415iter [00:12, 36.99iter/s]
Best score: 0.8160: : 416iter [00:12, 36.99iter/s]
Best score: 0.8160: : 417iter [00:12, 36.99iter/s]
Best score: 0.8160: : 418iter [00:12, 36.99iter/s]
Best score: 0.8160: : 419iter [00:12, 36.99iter/s]
Best score: 0.8160: : 420iter [00:12, 37.84iter/s]
Best score: 0.8160: : 420iter [00:12, 37.84iter/s]
Best score: 0.8160: : 421iter [00:13, 37.84iter/s]
Best score: 0.8160: : 422iter [00:13, 37.84iter/s]
Best score: 0.8160: : 423iter [00:13, 37.84iter/s]
Best score: 0.8160: : 424iter [00:13, 37.84iter/s]
Best score: 0.8160: : 425iter [00:13, 38.73iter/s]
Best score: 0.8160: : 425iter [00:13, 38.73iter/s]
Best score: 0.8160: : 426iter [00:13, 38.73iter/s]
Best score: 0.8160: : 427iter [00:13, 38.73iter/s]
Best score: 0.8160: : 428iter [00:13, 38.73iter/s]
Best score: 0.8160: : 429iter [00:13, 38.73iter/s]
Best score: 0.8160: : 430iter [00:13, 39.16iter/s]
Best score: 0.8160: : 430iter [00:13, 39.16iter/s]
Best score: 0.8160: : 431iter [00:13, 39.16iter/s]
Best score: 0.8160: : 432iter [00:13, 39.16iter/s]
Best score: 0.8160: : 433iter [00:13, 39.16iter/s]
Best score: 0.8160: : 434iter [00:13, 39.16iter/s]
Best score: 0.8160: : 435iter [00:13, 39.60iter/s]
Best score: 0.8160: : 435iter [00:13, 39.60iter/s]
Best score: 0.8160: : 436iter [00:13, 39.60iter/s]
Best score: 0.8160: : 437iter [00:13, 39.60iter/s]
Best score: 0.8160: : 438iter [00:13, 39.60iter/s]
Best score: 0.8160: : 439iter [00:13, 39.60iter/s]
Best score: 0.8160: : 440iter [00:13, 39.74iter/s]
Best score: 0.8160: : 440iter [00:13, 39.74iter/s]
Best score: 0.8160: : 441iter [00:13, 39.74iter/s]
Best score: 0.8160: : 442iter [00:13, 39.74iter/s]
Best score: 0.8160: : 443iter [00:13, 39.74iter/s]
Best score: 0.8160: : 444iter [00:13, 39.74iter/s]
Best score: 0.8160: : 445iter [00:13, 39.92iter/s]
Best score: 0.8160: : 445iter [00:13, 39.92iter/s]
Best score: 0.8160: : 446iter [00:13, 39.92iter/s]
Best score: 0.8160: : 447iter [00:13, 39.92iter/s]
Best score: 0.8160: : 448iter [00:13, 39.92iter/s]
[ITER 450] Best: 0.8143
[PARAMS] Current best parameters:
  fx: 554.9725
  fy: 554.8683
  px: 991.3067
  py: 741.9671
  k1: 0.01601059
  k2: -0.05847788
  k3: 0.03297736
  k4: -0.00658413
  p1: -0.01207606
  p2: -0.01225738
  p3: -0.00115805
  p4: 0.00755242
  s1: 0.0205
  s2: -0.0080
  s3: 0.0074
  s4: -0.0068
  tilt_x: -0.0106
  tilt_y: -0.0333
  scale: 2.9550
  rotation: -0.2982

Best score: 0.8143: : 449iter [00:13, 39.92iter/s]
Best score: 0.8143: : 450iter [00:13, 39.96iter/s]
Best score: 0.8143: : 450iter [00:13, 39.96iter/s]
Best score: 0.8143: : 451iter [00:13, 39.96iter/s]
Best score: 0.8143: : 452iter [00:13, 39.96iter/s]
Best score: 0.8143: : 453iter [00:13, 39.96iter/s]
Best score: 0.8143: : 454iter [00:13, 39.96iter/s]
Best score: 0.8143: : 455iter [00:13, 33.87iter/s]
Best score: 0.8143: : 455iter [00:13, 33.87iter/s]
Best score: 0.8143: : 456iter [00:13, 33.87iter/s]
Best score: 0.8143: : 457iter [00:13, 33.87iter/s]
Best score: 0.8143: : 458iter [00:14, 33.87iter/s]
Best score: 0.7468: : 459iter [00:14, 33.87iter/s]
Best score: 0.7468: : 460iter [00:14, 35.55iter/s]
Best score: 0.7468: : 460iter [00:14, 35.55iter/s]
Best score: 0.7468: : 461iter [00:14, 35.55iter/s]
Best score: 0.7468: : 462iter [00:14, 35.55iter/s]
Best score: 0.7468: : 463iter [00:14, 35.55iter/s]
Best score: 0.7468: : 464iter [00:14, 35.55iter/s]
Best score: 0.7468: : 465iter [00:14, 36.93iter/s]
Best score: 0.7468: : 465iter [00:14, 36.93iter/s]
Best score: 0.7468: : 466iter [00:14, 36.93iter/s]
Best score: 0.7468: : 467iter [00:14, 36.93iter/s]
Best score: 0.7468: : 468iter [00:14, 36.93iter/s]
Best score: 0.7439: : 469iter [00:14, 36.93iter/s]
Best score: 0.7439: : 470iter [00:14, 37.80iter/s]
Best score: 0.7439: : 470iter [00:14, 37.80iter/s]
Best score: 0.7439: : 471iter [00:14, 37.80iter/s]
Best score: 0.7439: : 472iter [00:14, 37.80iter/s]
Best score: 0.7439: : 473iter [00:14, 37.80iter/s]
Best score: 0.7439: : 474iter [00:14, 37.80iter/s]
Best score: 0.7439: : 475iter [00:14, 38.76iter/s]
Best score: 0.7439: : 475iter [00:14, 38.76iter/s]
Best score: 0.7439: : 476iter [00:14, 38.76iter/s]
Best score: 0.7439: : 477iter [00:14, 38.76iter/s]
Best score: 0.7439: : 478iter [00:14, 38.76iter/s]
Best score: 0.7154: : 479iter [00:14, 38.76iter/s]
Best score: 0.7154: : 480iter [00:14, 39.13iter/s]
Best score: 0.7154: : 480iter [00:14, 39.13iter/s]
Best score: 0.7154: : 481iter [00:14, 39.13iter/s]
Best score: 0.7154: : 482iter [00:14, 39.13iter/s]
Best score: 0.7154: : 483iter [00:14, 39.13iter/s]
Best score: 0.7154: : 484iter [00:14, 39.13iter/s]
Best score: 0.7154: : 485iter [00:14, 39.73iter/s]
Best score: 0.7154: : 485iter [00:14, 39.73iter/s]
Best score: 0.7154: : 486iter [00:14, 39.73iter/s]
Best score: 0.7154: : 487iter [00:14, 39.73iter/s]
Best score: 0.7154: : 488iter [00:14, 39.73iter/s]
Best score: 0.7154: : 489iter [00:14, 39.73iter/s]
Best score: 0.7154: : 490iter [00:14, 39.89iter/s]
Best score: 0.7154: : 490iter [00:14, 39.89iter/s]
Best score: 0.7154: : 491iter [00:14, 39.89iter/s]
Best score: 0.7154: : 492iter [00:14, 39.89iter/s]
Best score: 0.7154: : 493iter [00:14, 39.89iter/s]
Best score: 0.7154: : 494iter [00:14, 39.89iter/s]
Best score: 0.7154: : 495iter [00:14, 40.19iter/s]
Best score: 0.7154: : 495iter [00:14, 40.19iter/s]
Best score: 0.7154: : 496iter [00:14, 40.19iter/s]
Best score: 0.7154: : 497iter [00:14, 40.19iter/s]
Best score: 0.7154: : 498iter [00:14, 40.19iter/s]
[ITER 500] Best: 0.6891
[PARAMS] Current best parameters:
  fx: 564.3229
  fy: 564.3651
  px: 988.0762
  py: 748.3638
  k1: 0.01296907
  k2: -0.07321313
  k3: 0.04612880
  k4: -0.01007363
  p1: -0.00626816
  p2: -0.01143191
  p3: -0.00297728
  p4: 0.00495436
  s1: 0.0209
  s2: -0.0064
  s3: 0.0141
  s4: -0.0077
  tilt_x: 0.0118
  tilt_y: -0.0146
  scale: 2.9371
  rotation: -0.3009

Best score: 0.6891: : 499iter [00:15, 40.19iter/s]
Best score: 0.6891: : 500iter [00:15, 40.30iter/s]
Best score: 0.6891: : 500iter [00:15, 40.30iter/s]
Best score: 0.6891: : 501iter [00:15, 40.30iter/s]
Best score: 0.6891: : 502iter [00:15, 40.30iter/s]
Best score: 0.6891: : 503iter [00:15, 40.30iter/s]
Best score: 0.6891: : 504iter [00:15, 40.30iter/s]
Best score: 0.6891: : 505iter [00:15, 34.05iter/s]
Best score: 0.6891: : 505iter [00:15, 34.05iter/s]
Best score: 0.6891: : 506iter [00:15, 34.05iter/s]
Best score: 0.6891: : 507iter [00:15, 34.05iter/s]
Best score: 0.6891: : 508iter [00:15, 34.05iter/s]
Best score: 0.6811: : 509iter [00:15, 34.05iter/s]
Best score: 0.6811: : 510iter [00:15, 35.72iter/s]
Best score: 0.6811: : 510iter [00:15, 35.72iter/s]
Best score: 0.6811: : 511iter [00:15, 35.72iter/s]
Best score: 0.6811: : 512iter [00:15, 35.72iter/s]
Best score: 0.6811: : 513iter [00:15, 35.72iter/s]
Best score: 0.6811: : 514iter [00:15, 35.72iter/s]
Best score: 0.6811: : 515iter [00:15, 37.11iter/s]
Best score: 0.6811: : 515iter [00:15, 37.11iter/s]
Best score: 0.6811: : 516iter [00:15, 37.11iter/s]
Best score: 0.6811: : 517iter [00:15, 37.11iter/s]
Best score: 0.6811: : 518iter [00:15, 37.11iter/s]
Best score: 0.6811: : 519iter [00:15, 37.11iter/s]
Best score: 0.6811: : 520iter [00:15, 37.93iter/s]
Best score: 0.6811: : 520iter [00:15, 37.93iter/s]
Best score: 0.6811: : 521iter [00:15, 37.93iter/s]
Best score: 0.6811: : 522iter [00:15, 37.93iter/s]
Best score: 0.6811: : 523iter [00:15, 37.93iter/s]
Best score: 0.6811: : 524iter [00:15, 37.93iter/s]
Best score: 0.6811: : 525iter [00:15, 38.71iter/s]
Best score: 0.6811: : 525iter [00:15, 38.71iter/s]
Best score: 0.6811: : 526iter [00:15, 38.71iter/s]
Best score: 0.6811: : 527iter [00:15, 38.71iter/s]
Best score: 0.6811: : 528iter [00:15, 38.71iter/s]
Best score: 0.6811: : 529iter [00:15, 38.71iter/s]
Best score: 0.6811: : 530iter [00:15, 39.06iter/s]
Best score: 0.6811: : 530iter [00:15, 39.06iter/s]
Best score: 0.6811: : 531iter [00:15, 39.06iter/s]
Best score: 0.6811: : 532iter [00:15, 39.06iter/s]
Best score: 0.6811: : 533iter [00:15, 39.06iter/s]
Best score: 0.6811: : 534iter [00:15, 39.06iter/s]
Best score: 0.6811: : 535iter [00:15, 39.54iter/s]
Best score: 0.6811: : 535iter [00:15, 39.54iter/s]
Best score: 0.6811: : 536iter [00:16, 39.54iter/s]
Best score: 0.6811: : 537iter [00:16, 39.54iter/s]
Best score: 0.6811: : 538iter [00:16, 39.54iter/s]
Best score: 0.6811: : 539iter [00:16, 32.23iter/s]
Best score: 0.6750: : 539iter [00:16, 32.23iter/s]
Best score: 0.6750: : 540iter [00:16, 32.23iter/s]
Best score: 0.6750: : 541iter [00:16, 32.23iter/s]
Best score: 0.6750: : 542iter [00:16, 32.23iter/s]
Best score: 0.6750: : 543iter [00:16, 33.91iter/s]
Best score: 0.6750: : 543iter [00:16, 33.91iter/s]
Best score: 0.6750: : 544iter [00:16, 33.91iter/s]
Best score: 0.6750: : 545iter [00:16, 33.91iter/s]
Best score: 0.6750: : 546iter [00:16, 33.91iter/s]
Best score: 0.6750: : 547iter [00:16, 33.91iter/s]
Best score: 0.6750: : 548iter [00:16, 35.93iter/s]
Best score: 0.6750: : 548iter [00:16, 35.93iter/s]
[ITER 550] Best: 0.6669
[PARAMS] Current best parameters:
  fx: 567.6511
  fy: 567.6914
  px: 989.5013
  py: 747.0997
  k1: 0.01353357
  k2: -0.07682470
  k3: 0.04694725
  k4: -0.00996037
  p1: -0.00672574
  p2: -0.00804740
  p3: -0.00353421
  p4: 0.00217653
  s1: 0.0204
  s2: -0.0044
  s3: 0.0134
  s4: -0.0045
  tilt_x: 0.0054
  tilt_y: -0.0153
  scale: 2.9313
  rotation: -0.3068

Best score: 0.6669: : 549iter [00:16, 35.93iter/s]
Best score: 0.6669: : 550iter [00:16, 35.93iter/s]
Best score: 0.6669: : 551iter [00:16, 35.93iter/s]
Best score: 0.6669: : 552iter [00:16, 31.03iter/s]
Best score: 0.6669: : 552iter [00:16, 31.03iter/s]
Best score: 0.6669: : 553iter [00:16, 31.03iter/s]
Best score: 0.6669: : 554iter [00:16, 31.03iter/s]
Best score: 0.6669: : 555iter [00:16, 31.03iter/s]
Best score: 0.6669: : 556iter [00:16, 31.03iter/s]
Best score: 0.6669: : 557iter [00:16, 33.61iter/s]
Best score: 0.6669: : 557iter [00:16, 33.61iter/s]
Best score: 0.6669: : 558iter [00:16, 33.61iter/s]
Best score: 0.6669: : 559iter [00:16, 33.61iter/s]
Best score: 0.6669: : 560iter [00:16, 33.61iter/s]
Best score: 0.6669: : 561iter [00:16, 35.12iter/s]
Best score: 0.6669: : 561iter [00:16, 35.12iter/s]
Best score: 0.6669: : 562iter [00:16, 35.12iter/s]
Best score: 0.6669: : 563iter [00:16, 35.12iter/s]
Best score: 0.6669: : 564iter [00:16, 35.12iter/s]
Best score: 0.6669: : 565iter [00:16, 35.12iter/s]
Best score: 0.6669: : 566iter [00:16, 36.90iter/s]
Best score: 0.6669: : 566iter [00:16, 36.90iter/s]
Best score: 0.6669: : 567iter [00:16, 36.90iter/s]
Best score: 0.6669: : 568iter [00:16, 36.90iter/s]
Best score: 0.6669: : 569iter [00:16, 36.90iter/s]
Best score: 0.6669: : 570iter [00:16, 37.66iter/s]
Best score: 0.6669: : 570iter [00:17, 37.66iter/s]
Best score: 0.6669: : 571iter [00:17, 37.66iter/s]
Best score: 0.6669: : 572iter [00:17, 37.66iter/s]
Best score: 0.6669: : 573iter [00:17, 37.66iter/s]
Best score: 0.6669: : 574iter [00:17, 37.66iter/s]
Best score: 0.6669: : 575iter [00:17, 38.67iter/s]
Best score: 0.6669: : 575iter [00:17, 38.67iter/s]
Best score: 0.6669: : 576iter [00:17, 38.67iter/s]
Best score: 0.6669: : 577iter [00:17, 38.67iter/s]
Best score: 0.6669: : 578iter [00:17, 38.67iter/s]
Best score: 0.6669: : 579iter [00:17, 38.67iter/s]
Best score: 0.6669: : 580iter [00:17, 38.99iter/s]
Best score: 0.6669: : 580iter [00:17, 38.99iter/s]
Best score: 0.6669: : 581iter [00:17, 38.99iter/s]
Best score: 0.6669: : 582iter [00:17, 38.99iter/s]
Best score: 0.6669: : 583iter [00:17, 38.99iter/s]
Best score: 0.6669: : 584iter [00:17, 38.99iter/s]
Best score: 0.6669: : 585iter [00:17, 39.59iter/s]
Best score: 0.6669: : 585iter [00:17, 39.59iter/s]
Best score: 0.6669: : 586iter [00:17, 39.59iter/s]
Best score: 0.6669: : 587iter [00:17, 39.59iter/s]
Best score: 0.6669: : 588iter [00:17, 39.59iter/s]
Best score: 0.6669: : 589iter [00:17, 39.59iter/s]
Best score: 0.6669: : 590iter [00:17, 39.82iter/s]
Best score: 0.6669: : 590iter [00:17, 39.82iter/s]
Best score: 0.6669: : 591iter [00:17, 39.82iter/s]
Best score: 0.6669: : 592iter [00:17, 39.82iter/s]
Best score: 0.6669: : 593iter [00:17, 39.82iter/s]
Best score: 0.6669: : 594iter [00:17, 39.82iter/s]
Best score: 0.6669: : 595iter [00:17, 40.20iter/s]
Best score: 0.6669: : 595iter [00:17, 40.20iter/s]
Best score: 0.6669: : 596iter [00:17, 40.20iter/s]
Best score: 0.6669: : 597iter [00:17, 40.20iter/s]
Best score: 0.6669: : 598iter [00:17, 40.20iter/s]
[ITER 600] Best: 0.6669
[PARAMS] Current best parameters:
  fx: 567.6511
  fy: 567.6914
  px: 989.5013
  py: 747.0997
  k1: 0.01353357
  k2: -0.07682470
  k3: 0.04694725
  k4: -0.00996037
  p1: -0.00672574
  p2: -0.00804740
  p3: -0.00353421
  p4: 0.00217653
  s1: 0.0204
  s2: -0.0044
  s3: 0.0134
  s4: -0.0045
  tilt_x: 0.0054
  tilt_y: -0.0153
  scale: 2.9313
  rotation: -0.3068

Best score: 0.6669: : 599iter [00:17, 40.20iter/s]
Best score: 0.6669: : 600iter [00:17, 39.97iter/s]
Best score: 0.6669: : 600iter [00:17, 39.97iter/s]
Best score: 0.6669: : 601iter [00:17, 39.97iter/s]
Best score: 0.6669: : 602iter [00:17, 39.97iter/s]
Best score: 0.6669: : 603iter [00:17, 39.97iter/s]
Best score: 0.6669: : 604iter [00:17, 39.97iter/s]
Best score: 0.6669: : 605iter [00:17, 33.90iter/s]
Best score: 0.6669: : 605iter [00:17, 33.90iter/s]
Best score: 0.6669: : 606iter [00:17, 33.90iter/s]
Best score: 0.6669: : 607iter [00:18, 33.90iter/s]
Best score: 0.6669: : 608iter [00:18, 33.90iter/s]
Best score: 0.6643: : 609iter [00:18, 33.90iter/s]
Best score: 0.6643: : 610iter [00:18, 35.65iter/s]
Best score: 0.6643: : 610iter [00:18, 35.65iter/s]
Best score: 0.6643: : 611iter [00:18, 35.65iter/s]
Best score: 0.6643: : 612iter [00:18, 35.65iter/s]
Best score: 0.6643: : 613iter [00:18, 35.65iter/s]
Best score: 0.6643: : 614iter [00:18, 35.65iter/s]
Best score: 0.6643: : 615iter [00:18, 37.16iter/s]
Best score: 0.6643: : 615iter [00:18, 37.16iter/s]
Best score: 0.6643: : 616iter [00:18, 37.16iter/s]
Best score: 0.6643: : 617iter [00:18, 37.16iter/s]
Best score: 0.6643: : 618iter [00:18, 37.16iter/s]
Best score: 0.6622: : 619iter [00:18, 37.16iter/s]
Best score: 0.6622: : 620iter [00:18, 38.04iter/s]
Best score: 0.6622: : 620iter [00:18, 38.04iter/s]
Best score: 0.6622: : 621iter [00:18, 38.04iter/s]
Best score: 0.6622: : 622iter [00:18, 38.04iter/s]
Best score: 0.6622: : 623iter [00:18, 38.04iter/s]
Best score: 0.6622: : 624iter [00:18, 38.04iter/s]
Best score: 0.6622: : 625iter [00:18, 38.97iter/s]
Best score: 0.6622: : 625iter [00:18, 38.97iter/s]
Best score: 0.6622: : 626iter [00:18, 38.97iter/s]
Best score: 0.6622: : 627iter [00:18, 38.97iter/s]
Best score: 0.6622: : 628iter [00:18, 38.97iter/s]
Best score: 0.6622: : 629iter [00:18, 38.97iter/s]
Best score: 0.6622: : 630iter [00:18, 39.23iter/s]
Best score: 0.6622: : 630iter [00:18, 39.23iter/s]
Best score: 0.6622: : 631iter [00:18, 39.23iter/s]
Best score: 0.6622: : 632iter [00:18, 39.23iter/s]
Best score: 0.6622: : 633iter [00:18, 39.23iter/s]
Best score: 0.6622: : 634iter [00:18, 39.23iter/s]
Best score: 0.6622: : 635iter [00:18, 39.66iter/s]
Best score: 0.6622: : 635iter [00:18, 39.66iter/s]
Best score: 0.6622: : 636iter [00:18, 39.66iter/s]
Best score: 0.6622: : 637iter [00:18, 39.66iter/s]
Best score: 0.6622: : 638iter [00:18, 39.66iter/s]
Best score: 0.6587: : 639iter [00:18, 39.66iter/s]
Best score: 0.6587: : 640iter [00:18, 39.70iter/s]
Best score: 0.6587: : 640iter [00:18, 39.70iter/s]
Best score: 0.6587: : 641iter [00:18, 39.70iter/s]
Best score: 0.6587: : 642iter [00:18, 39.70iter/s]
Best score: 0.6587: : 643iter [00:18, 39.70iter/s]
Best score: 0.6587: : 644iter [00:18, 39.70iter/s]
Best score: 0.6587: : 645iter [00:18, 40.00iter/s]
Best score: 0.6587: : 645iter [00:18, 40.00iter/s]
Best score: 0.6587: : 646iter [00:18, 40.00iter/s]
Best score: 0.6587: : 647iter [00:18, 40.00iter/s]
Best score: 0.6587: : 648iter [00:19, 40.00iter/s]
[ITER 650] Best: 0.6562
[PARAMS] Current best parameters:
  fx: 567.2523
  fy: 567.3864
  px: 987.9026
  py: 747.3385
  k1: 0.01173304
  k2: -0.07526139
  k3: 0.04740682
  k4: -0.01036544
  p1: -0.00446644
  p2: -0.01200936
  p3: -0.00126555
  p4: 0.00149575
  s1: 0.0210
  s2: -0.0037
  s3: 0.0080
  s4: -0.0033
  tilt_x: 0.0022
  tilt_y: -0.0143
  scale: 2.9334
  rotation: -0.2927

Best score: 0.6562: : 649iter [00:19, 40.00iter/s]
Best score: 0.6562: : 650iter [00:19, 40.02iter/s]
Best score: 0.6562: : 650iter [00:19, 40.02iter/s]
Best score: 0.6562: : 651iter [00:19, 40.02iter/s]
Best score: 0.6562: : 652iter [00:19, 40.02iter/s]
Best score: 0.6562: : 653iter [00:19, 40.02iter/s]
Best score: 0.6562: : 654iter [00:19, 40.02iter/s]
Best score: 0.6562: : 655iter [00:19, 33.77iter/s]
Best score: 0.6562: : 655iter [00:19, 33.77iter/s]
Best score: 0.6562: : 656iter [00:19, 33.77iter/s]
Best score: 0.6562: : 657iter [00:19, 33.77iter/s]
Best score: 0.6562: : 658iter [00:19, 33.77iter/s]
Best score: 0.6554: : 659iter [00:19, 33.77iter/s]
Best score: 0.6554: : 660iter [00:19, 35.37iter/s]
Best score: 0.6554: : 660iter [00:19, 35.37iter/s]
Best score: 0.6554: : 661iter [00:19, 35.37iter/s]
Best score: 0.6554: : 662iter [00:19, 35.37iter/s]
Best score: 0.6554: : 663iter [00:19, 35.37iter/s]
Best score: 0.6554: : 664iter [00:19, 35.37iter/s]
Best score: 0.6554: : 665iter [00:19, 36.94iter/s]
Best score: 0.6554: : 665iter [00:19, 36.94iter/s]
Best score: 0.6554: : 666iter [00:19, 36.94iter/s]
Best score: 0.6554: : 667iter [00:19, 36.94iter/s]
Best score: 0.6554: : 668iter [00:19, 36.94iter/s]
Best score: 0.6508: : 669iter [00:19, 36.94iter/s]
Best score: 0.6508: : 670iter [00:19, 37.80iter/s]
Best score: 0.6508: : 670iter [00:19, 37.80iter/s]
Best score: 0.6508: : 671iter [00:19, 37.80iter/s]
Best score: 0.6508: : 672iter [00:19, 37.80iter/s]
Best score: 0.6508: : 673iter [00:19, 37.80iter/s]
Best score: 0.6508: : 674iter [00:19, 37.80iter/s]
Best score: 0.6508: : 675iter [00:19, 38.66iter/s]
Best score: 0.6508: : 675iter [00:19, 38.66iter/s]
Best score: 0.6508: : 676iter [00:19, 38.66iter/s]
Best score: 0.6508: : 677iter [00:19, 38.66iter/s]
Best score: 0.6508: : 678iter [00:19, 38.66iter/s]
Best score: 0.6508: : 679iter [00:19, 38.96iter/s]
Best score: 0.6501: : 679iter [00:19, 38.96iter/s]
Best score: 0.6501: : 680iter [00:19, 38.96iter/s]
Best score: 0.6501: : 681iter [00:19, 38.96iter/s]
Best score: 0.6501: : 682iter [00:19, 38.96iter/s]
Best score: 0.6501: : 683iter [00:19, 39.13iter/s]
Best score: 0.6501: : 683iter [00:19, 39.13iter/s]
Best score: 0.6501: : 684iter [00:19, 39.13iter/s]
Best score: 0.6501: : 685iter [00:20, 39.13iter/s]
Best score: 0.6501: : 686iter [00:20, 39.13iter/s]
Best score: 0.6501: : 687iter [00:20, 39.13iter/s]
Best score: 0.6501: : 688iter [00:20, 39.68iter/s]
Best score: 0.6501: : 688iter [00:20, 39.68iter/s]
Best score: 0.6489: : 689iter [00:20, 39.68iter/s]
Best score: 0.6489: : 690iter [00:20, 39.68iter/s]
Best score: 0.6489: : 691iter [00:20, 39.68iter/s]
Best score: 0.6489: : 692iter [00:20, 39.68iter/s]
Best score: 0.6489: : 693iter [00:20, 39.89iter/s]
Best score: 0.6489: : 693iter [00:20, 39.89iter/s]
Best score: 0.6489: : 694iter [00:20, 39.89iter/s]
Best score: 0.6489: : 695iter [00:20, 39.89iter/s]
Best score: 0.6489: : 696iter [00:20, 39.89iter/s]
Best score: 0.6489: : 697iter [00:20, 39.89iter/s]
Best score: 0.6489: : 698iter [00:20, 40.05iter/s]
Best score: 0.6489: : 698iter [00:20, 40.05iter/s]
[ITER 700] Best: 0.6480
[PARAMS] Current best parameters:
  fx: 567.9523
  fy: 568.1820
  px: 987.2611
  py: 746.4891
  k1: 0.01018648
  k2: -0.07382482
  k3: 0.04659974
  k4: -0.01020987
  p1: -0.00264889
  p2: -0.01182617
  p3: -0.00022169
  p4: -0.00095618
  s1: 0.0204
  s2: -0.0020
  s3: 0.0044
  s4: -0.0010
  tilt_x: -0.0058
  tilt_y: -0.0106
  scale: 2.9339
  rotation: -0.3034

Best score: 0.6480: : 699iter [00:20, 40.05iter/s]
Best score: 0.6480: : 700iter [00:20, 40.05iter/s]
Best score: 0.6480: : 701iter [00:20, 40.05iter/s]
Best score: 0.6480: : 702iter [00:20, 40.05iter/s]
Best score: 0.6480: : 703iter [00:20, 33.54iter/s]
Best score: 0.6480: : 703iter [00:20, 33.54iter/s]
Best score: 0.6480: : 704iter [00:20, 33.54iter/s]
Best score: 0.6480: : 705iter [00:20, 33.54iter/s]
Best score: 0.6480: : 706iter [00:20, 33.54iter/s]
Best score: 0.6480: : 707iter [00:20, 33.54iter/s]
Best score: 0.6480: : 708iter [00:20, 35.53iter/s]
Best score: 0.6480: : 708iter [00:20, 35.53iter/s]
Best score: 0.6480: : 709iter [00:20, 35.53iter/s]
Best score: 0.6480: : 710iter [00:20, 35.53iter/s]
Best score: 0.6480: : 711iter [00:20, 35.53iter/s]
Best score: 0.6480: : 712iter [00:20, 36.48iter/s]
Best score: 0.6480: : 712iter [00:20, 36.48iter/s]
Best score: 0.6480: : 713iter [00:20, 36.48iter/s]
Best score: 0.6480: : 714iter [00:20, 36.48iter/s]
Best score: 0.6480: : 715iter [00:20, 36.48iter/s]
Best score: 0.6480: : 716iter [00:20, 36.48iter/s]
Best score: 0.6480: : 717iter [00:20, 37.75iter/s]
Best score: 0.6480: : 717iter [00:20, 37.75iter/s]
Best score: 0.6480: : 718iter [00:20, 37.75iter/s]
Best score: 0.6457: : 719iter [00:20, 37.75iter/s]
Best score: 0.6457: : 720iter [00:20, 37.75iter/s]
Best score: 0.6457: : 721iter [00:20, 38.20iter/s]
Best score: 0.6457: : 721iter [00:20, 38.20iter/s]
Best score: 0.6457: : 722iter [00:21, 38.20iter/s]
Best score: 0.6457: : 723iter [00:21, 38.20iter/s]
Best score: 0.6457: : 724iter [00:21, 38.20iter/s]
Best score: 0.6457: : 725iter [00:21, 38.20iter/s]
Best score: 0.6457: : 726iter [00:21, 38.94iter/s]
Best score: 0.6457: : 726iter [00:21, 38.94iter/s]
Best score: 0.6457: : 727iter [00:21, 38.94iter/s]
Best score: 0.6457: : 728iter [00:21, 38.94iter/s]
Best score: 0.6421: : 729iter [00:21, 38.94iter/s]
Best score: 0.6421: : 730iter [00:21, 38.94iter/s]
Best score: 0.6421: : 731iter [00:21, 39.35iter/s]
Best score: 0.6421: : 731iter [00:21, 39.35iter/s]
Best score: 0.6421: : 732iter [00:21, 39.35iter/s]
Best score: 0.6421: : 733iter [00:21, 39.35iter/s]
Best score: 0.6421: : 734iter [00:21, 39.35iter/s]
Best score: 0.6421: : 735iter [00:21, 39.35iter/s]
Best score: 0.6421: : 736iter [00:21, 39.79iter/s]
Best score: 0.6421: : 736iter [00:21, 39.79iter/s]
Best score: 0.6421: : 737iter [00:21, 39.79iter/s]
Best score: 0.6421: : 738iter [00:21, 39.79iter/s]
Best score: 0.6421: : 739iter [00:21, 39.79iter/s]
Best score: 0.6421: : 740iter [00:21, 39.79iter/s]
Best score: 0.6421: : 741iter [00:21, 39.91iter/s]
Best score: 0.6421: : 741iter [00:21, 39.91iter/s]
Best score: 0.6421: : 742iter [00:21, 39.91iter/s]
Best score: 0.6421: : 743iter [00:21, 39.91iter/s]
Best score: 0.6421: : 744iter [00:21, 39.91iter/s]
Best score: 0.6421: : 745iter [00:21, 39.91iter/s]
Best score: 0.6421: : 746iter [00:21, 40.05iter/s]
Best score: 0.6421: : 746iter [00:21, 40.05iter/s]
Best score: 0.6421: : 747iter [00:21, 40.05iter/s]
Best score: 0.6421: : 748iter [00:21, 40.05iter/s]
[ITER 750] Best: 0.6419
[PARAMS] Current best parameters:
  fx: 567.3077
  fy: 567.5948
  px: 987.5174
  py: 746.0261
  k1: 0.01421937
  k2: -0.08055743
  k3: 0.05099096
  k4: -0.01118153
  p1: -0.00060352
  p2: -0.00956392
  p3: -0.00382339
  p4: -0.00187148
  s1: 0.0219
  s2: -0.0010
  s3: 0.0070
  s4: -0.0009
  tilt_x: -0.0123
  tilt_y: -0.0075
  scale: 2.9336
  rotation: -0.3073

Best score: 0.6419: : 749iter [00:21, 40.05iter/s]
Best score: 0.6419: : 750iter [00:21, 40.05iter/s]
Best score: 0.6419: : 751iter [00:21, 33.65iter/s]
Best score: 0.6419: : 751iter [00:21, 33.65iter/s]
Best score: 0.6419: : 752iter [00:21, 33.65iter/s]
Best score: 0.6419: : 753iter [00:21, 33.65iter/s]
Best score: 0.6419: : 754iter [00:21, 33.65iter/s]
Best score: 0.6419: : 755iter [00:21, 33.65iter/s]
Best score: 0.6419: : 756iter [00:21, 35.59iter/s]
Best score: 0.6419: : 756iter [00:21, 35.59iter/s]
Best score: 0.6419: : 757iter [00:21, 35.59iter/s]
Best score: 0.6419: : 758iter [00:21, 35.59iter/s]
Best score: 0.6419: : 759iter [00:22, 35.59iter/s]
Best score: 0.6419: : 760iter [00:22, 36.53iter/s]
Best score: 0.6419: : 760iter [00:22, 36.53iter/s]
Best score: 0.6419: : 761iter [00:22, 36.53iter/s]
Best score: 0.6419: : 762iter [00:22, 36.53iter/s]
Best score: 0.6419: : 763iter [00:22, 36.53iter/s]
Best score: 0.6419: : 764iter [00:22, 36.53iter/s]
Best score: 0.6419: : 765iter [00:22, 37.78iter/s]
Best score: 0.6419: : 765iter [00:22, 37.78iter/s]
Best score: 0.6419: : 766iter [00:22, 37.78iter/s]
Best score: 0.6419: : 767iter [00:22, 37.78iter/s]
Best score: 0.6419: : 768iter [00:22, 37.78iter/s]
Best score: 0.6419: : 769iter [00:22, 37.78iter/s]
Best score: 0.6419: : 770iter [00:22, 38.53iter/s]
Best score: 0.6419: : 770iter [00:22, 38.53iter/s]
Best score: 0.6419: : 771iter [00:22, 38.53iter/s]
Best score: 0.6419: : 772iter [00:22, 38.53iter/s]
Best score: 0.6419: : 773iter [00:22, 38.53iter/s]
Best score: 0.6419: : 774iter [00:22, 38.53iter/s]
Best score: 0.6419: : 775iter [00:22, 39.25iter/s]
Best score: 0.6419: : 775iter [00:22, 39.25iter/s]
Best score: 0.6419: : 776iter [00:22, 39.25iter/s]
Best score: 0.6419: : 777iter [00:22, 39.25iter/s]
Best score: 0.6419: : 778iter [00:22, 39.25iter/s]
Best score: 0.6419: : 779iter [00:22, 39.25iter/s]
Best score: 0.6419: : 780iter [00:22, 39.36iter/s]
Best score: 0.6419: : 780iter [00:22, 39.36iter/s]
Best score: 0.6419: : 781iter [00:22, 39.36iter/s]
Best score: 0.6419: : 782iter [00:22, 39.36iter/s]
Best score: 0.6419: : 783iter [00:22, 39.36iter/s]
Best score: 0.6419: : 784iter [00:22, 39.36iter/s]
Best score: 0.6419: : 785iter [00:22, 39.82iter/s]
Best score: 0.6419: : 785iter [00:22, 39.82iter/s]
Best score: 0.6419: : 786iter [00:22, 39.82iter/s]
Best score: 0.6419: : 787iter [00:22, 39.82iter/s]
Best score: 0.6419: : 788iter [00:22, 39.82iter/s]
Best score: 0.6358: : 789iter [00:22, 39.82iter/s]
Best score: 0.6358: : 790iter [00:22, 39.81iter/s]
Best score: 0.6358: : 790iter [00:22, 39.81iter/s]
Best score: 0.6358: : 791iter [00:22, 39.81iter/s]
Best score: 0.6358: : 792iter [00:22, 39.81iter/s]
Best score: 0.6358: : 793iter [00:22, 39.81iter/s]
Best score: 0.6358: : 794iter [00:22, 39.81iter/s]
Best score: 0.6358: : 795iter [00:22, 40.05iter/s]
Best score: 0.6358: : 795iter [00:22, 40.05iter/s]
Best score: 0.6358: : 796iter [00:22, 40.05iter/s]
Best score: 0.6358: : 797iter [00:22, 40.05iter/s]
Best score: 0.6358: : 798iter [00:22, 40.05iter/s]
[ITER 800] Best: 0.6342
[PARAMS] Current best parameters:
  fx: 567.3069
  fy: 567.6000
  px: 988.2593
  py: 746.2907
  k1: 0.00807152
  k2: -0.07072487
  k3: 0.04539155
  k4: -0.01007737
  p1: 0.00007948
  p2: -0.00980359
  p3: 0.00080097
  p4: -0.00318606
  s1: 0.0176
  s2: 0.0006
  s3: 0.0014
  s4: 0.0008
  tilt_x: -0.0124
  tilt_y: -0.0120
  scale: 2.9397
  rotation: -0.3048

Best score: 0.6342: : 799iter [00:23, 40.05iter/s]
Best score: 0.6342: : 800iter [00:23, 39.98iter/s]
Best score: 0.6342: : 800iter [00:23, 39.98iter/s]
Best score: 0.6342: : 801iter [00:23, 39.98iter/s]
Best score: 0.6342: : 802iter [00:23, 39.98iter/s]
Best score: 0.6342: : 803iter [00:23, 39.98iter/s]
Best score: 0.6342: : 804iter [00:23, 39.98iter/s]
Best score: 0.6342: : 805iter [00:23, 33.79iter/s]
Best score: 0.6342: : 805iter [00:23, 33.79iter/s]
Best score: 0.6342: : 806iter [00:23, 33.79iter/s]
Best score: 0.6342: : 807iter [00:23, 33.79iter/s]
Best score: 0.6342: : 808iter [00:23, 33.79iter/s]
Best score: 0.6318: : 809iter [00:23, 33.79iter/s]
Best score: 0.6318: : 810iter [00:23, 35.38iter/s]
Best score: 0.6318: : 810iter [00:23, 35.38iter/s]
Best score: 0.6318: : 811iter [00:23, 35.38iter/s]
Best score: 0.6318: : 812iter [00:23, 35.38iter/s]
Best score: 0.6318: : 813iter [00:23, 35.38iter/s]
Best score: 0.6318: : 814iter [00:23, 35.38iter/s]
Best score: 0.6318: : 815iter [00:23, 36.94iter/s]
Best score: 0.6318: : 815iter [00:23, 36.94iter/s]
Best score: 0.6318: : 816iter [00:23, 36.94iter/s]
Best score: 0.6318: : 817iter [00:23, 36.94iter/s]
Best score: 0.6318: : 818iter [00:23, 36.94iter/s]
Best score: 0.6318: : 819iter [00:23, 36.94iter/s]
Best score: 0.6318: : 820iter [00:23, 37.82iter/s]
Best score: 0.6318: : 820iter [00:23, 37.82iter/s]
Best score: 0.6318: : 821iter [00:23, 37.82iter/s]
Best score: 0.6318: : 822iter [00:23, 37.82iter/s]
Best score: 0.6318: : 823iter [00:23, 37.82iter/s]
Best score: 0.6318: : 824iter [00:23, 37.82iter/s]
Best score: 0.6318: : 825iter [00:23, 38.66iter/s]
Best score: 0.6318: : 825iter [00:23, 38.66iter/s]
Best score: 0.6318: : 826iter [00:23, 38.66iter/s]
Best score: 0.6318: : 827iter [00:23, 38.66iter/s]
Best score: 0.6318: : 828iter [00:23, 38.66iter/s]
Best score: 0.6315: : 829iter [00:23, 38.66iter/s]
Best score: 0.6315: : 830iter [00:23, 39.06iter/s]
Best score: 0.6315: : 830iter [00:23, 39.06iter/s]
Best score: 0.6315: : 831iter [00:23, 39.06iter/s]
Best score: 0.6315: : 832iter [00:23, 39.06iter/s]
Best score: 0.6315: : 833iter [00:23, 39.06iter/s]
Best score: 0.6315: : 834iter [00:23, 39.06iter/s]
Best score: 0.6315: : 835iter [00:23, 39.51iter/s]
Best score: 0.6315: : 835iter [00:23, 39.51iter/s]
Best score: 0.6315: : 836iter [00:23, 39.51iter/s]
Best score: 0.6315: : 837iter [00:24, 39.51iter/s]
Best score: 0.6315: : 838iter [00:24, 39.51iter/s]
Best score: 0.6310: : 839iter [00:24, 39.51iter/s]
Best score: 0.6310: : 840iter [00:24, 39.63iter/s]
Best score: 0.6310: : 840iter [00:24, 39.63iter/s]
Best score: 0.6310: : 841iter [00:24, 39.63iter/s]
Best score: 0.6310: : 842iter [00:24, 39.63iter/s]
Best score: 0.6310: : 843iter [00:24, 39.63iter/s]
Best score: 0.6310: : 844iter [00:24, 39.63iter/s]
Best score: 0.6310: : 845iter [00:24, 40.08iter/s]
Best score: 0.6310: : 845iter [00:24, 40.08iter/s]
Best score: 0.6310: : 846iter [00:24, 40.08iter/s]
Best score: 0.6310: : 847iter [00:24, 40.08iter/s]
Best score: 0.6310: : 848iter [00:24, 40.08iter/s]
[ITER 850] Best: 0.6299
[PARAMS] Current best parameters:
  fx: 566.8407
  fy: 567.0934
  px: 988.7667
  py: 745.3586
  k1: 0.00654647
  k2: -0.06655768
  k3: 0.04249648
  k4: -0.00943324
  p1: 0.00145828
  p2: -0.00933634
  p3: 0.00250805
  p4: -0.00501307
  s1: 0.0155
  s2: 0.0025
  s3: -0.0023
  s4: 0.0025
  tilt_x: -0.0203
  tilt_y: -0.0141
  scale: 2.9411
  rotation: -0.2931

Best score: 0.6299: : 849iter [00:24, 40.08iter/s]
Best score: 0.6299: : 850iter [00:24, 40.14iter/s]
Best score: 0.6299: : 850iter [00:24, 40.14iter/s]
Best score: 0.6299: : 851iter [00:24, 40.14iter/s]
Best score: 0.6299: : 852iter [00:24, 40.14iter/s]
Best score: 0.6299: : 853iter [00:24, 40.14iter/s]
Best score: 0.6299: : 854iter [00:24, 40.14iter/s]
Best score: 0.6299: : 855iter [00:24, 33.88iter/s]
Best score: 0.6299: : 855iter [00:24, 33.88iter/s]
Best score: 0.6299: : 856iter [00:24, 33.88iter/s]
Best score: 0.6299: : 857iter [00:24, 33.88iter/s]
Best score: 0.6299: : 858iter [00:24, 33.88iter/s]
Best score: 0.6299: : 859iter [00:24, 29.44iter/s]
Best score: 0.6286: : 859iter [00:24, 29.44iter/s]
Best score: 0.6286: : 860iter [00:24, 29.44iter/s]
Best score: 0.6286: : 861iter [00:24, 29.44iter/s]
Best score: 0.6286: : 862iter [00:24, 29.44iter/s]
Best score: 0.6286: : 863iter [00:24, 29.44iter/s]
Best score: 0.6286: : 864iter [00:24, 32.17iter/s]
Best score: 0.6286: : 864iter [00:24, 32.17iter/s]
Best score: 0.6286: : 865iter [00:24, 32.17iter/s]
Best score: 0.6286: : 866iter [00:24, 32.17iter/s]
Best score: 0.6286: : 867iter [00:24, 32.17iter/s]
Best score: 0.6286: : 868iter [00:24, 32.17iter/s]
Best score: 0.6286: : 869iter [00:24, 34.45iter/s]
Best score: 0.6256: : 869iter [00:24, 34.45iter/s]
Best score: 0.6256: : 870iter [00:25, 34.45iter/s]
Best score: 0.6256: : 871iter [00:25, 34.45iter/s]
Best score: 0.6256: : 872iter [00:25, 34.45iter/s]
Best score: 0.6256: : 873iter [00:25, 35.63iter/s]
Best score: 0.6256: : 873iter [00:25, 35.63iter/s]
Best score: 0.6256: : 874iter [00:25, 35.63iter/s]
Best score: 0.6256: : 875iter [00:25, 35.63iter/s]
Best score: 0.6256: : 876iter [00:25, 35.63iter/s]
Best score: 0.6256: : 877iter [00:25, 35.63iter/s]
Best score: 0.6256: : 878iter [00:25, 37.11iter/s]
Best score: 0.6256: : 878iter [00:25, 37.11iter/s]
Best score: 0.6256: : 879iter [00:25, 37.11iter/s]
Best score: 0.6256: : 880iter [00:25, 37.11iter/s]
Best score: 0.6256: : 881iter [00:25, 37.11iter/s]
Best score: 0.6256: : 882iter [00:25, 37.80iter/s]
Best score: 0.6256: : 882iter [00:25, 37.80iter/s]
Best score: 0.6256: : 883iter [00:25, 37.80iter/s]
Best score: 0.6256: : 884iter [00:25, 37.80iter/s]
Best score: 0.6256: : 885iter [00:25, 37.80iter/s]
Best score: 0.6256: : 886iter [00:25, 37.80iter/s]
Best score: 0.6256: : 887iter [00:25, 38.66iter/s]
Best score: 0.6256: : 887iter [00:25, 38.66iter/s]
Best score: 0.6256: : 888iter [00:25, 38.66iter/s]
Best score: 0.6256: : 889iter [00:25, 38.66iter/s]
Best score: 0.6256: : 890iter [00:25, 38.66iter/s]
Best score: 0.6256: : 891iter [00:25, 38.66iter/s]
Best score: 0.6256: : 892iter [00:25, 39.16iter/s]
Best score: 0.6256: : 892iter [00:25, 39.16iter/s]
Best score: 0.6256: : 893iter [00:25, 39.16iter/s]
Best score: 0.6256: : 894iter [00:25, 39.16iter/s]
Best score: 0.6256: : 895iter [00:25, 39.16iter/s]
Best score: 0.6256: : 896iter [00:25, 39.16iter/s]
Best score: 0.6256: : 897iter [00:25, 39.78iter/s]
Best score: 0.6256: : 897iter [00:25, 39.78iter/s]
Best score: 0.6256: : 898iter [00:25, 39.78iter/s]
[ITER 900] Best: 0.6256
[PARAMS] Current best parameters:
  fx: 564.4458
  fy: 564.6699
  px: 988.8752
  py: 746.1625
  k1: 0.00823264
  k2: -0.06392779
  k3: 0.04008468
  k4: -0.00884505
  p1: 0.00396714
  p2: -0.00541693
  p3: 0.00067952
  p4: -0.00631251
  s1: 0.0136
  s2: 0.0038
  s3: -0.0026
  s4: 0.0039
  tilt_x: -0.0207
  tilt_y: -0.0067
  scale: 2.9394
  rotation: -0.3109

Best score: 0.6256: : 899iter [00:25, 39.78iter/s]
Best score: 0.6256: : 900iter [00:25, 39.78iter/s]
Best score: 0.6256: : 901iter [00:25, 39.78iter/s]
Best score: 0.6256: : 902iter [00:25, 33.35iter/s]
Best score: 0.6256: : 902iter [00:25, 33.35iter/s]
Best score: 0.6256: : 903iter [00:25, 33.35iter/s]
Best score: 0.6256: : 904iter [00:25, 33.35iter/s]
Best score: 0.6256: : 905iter [00:25, 33.35iter/s]
Best score: 0.6256: : 906iter [00:25, 33.35iter/s]
Best score: 0.6256: : 907iter [00:25, 35.25iter/s]
Best score: 0.6256: : 907iter [00:25, 35.25iter/s]
Best score: 0.6256: : 908iter [00:26, 35.25iter/s]
Best score: 0.6248: : 909iter [00:26, 35.25iter/s]
Best score: 0.6248: : 910iter [00:26, 35.25iter/s]
Best score: 0.6248: : 911iter [00:26, 36.24iter/s]
Best score: 0.6248: : 911iter [00:26, 36.24iter/s]
Best score: 0.6248: : 912iter [00:26, 36.24iter/s]
Best score: 0.6248: : 913iter [00:26, 36.24iter/s]
Best score: 0.6248: : 914iter [00:26, 36.24iter/s]
Best score: 0.6248: : 915iter [00:26, 36.24iter/s]
Best score: 0.6248: : 916iter [00:26, 37.58iter/s]
Best score: 0.6248: : 916iter [00:26, 37.58iter/s]
Best score: 0.6248: : 917iter [00:26, 37.58iter/s]
Best score: 0.6248: : 918iter [00:26, 37.58iter/s]
Best score: 0.6248: : 919iter [00:26, 37.58iter/s]
Best score: 0.6248: : 920iter [00:26, 38.16iter/s]
Best score: 0.6248: : 920iter [00:26, 38.16iter/s]
Best score: 0.6248: : 921iter [00:26, 38.16iter/s]
Best score: 0.6248: : 922iter [00:26, 38.16iter/s]
Best score: 0.6248: : 923iter [00:26, 38.16iter/s]
Best score: 0.6248: : 924iter [00:26, 38.16iter/s]
Best score: 0.6248: : 925iter [00:26, 39.02iter/s]
Best score: 0.6248: : 925iter [00:26, 39.02iter/s]
Best score: 0.6248: : 926iter [00:26, 39.02iter/s]
Best score: 0.6248: : 927iter [00:26, 39.02iter/s]
Best score: 0.6248: : 928iter [00:26, 39.02iter/s]
Best score: 0.6248: : 929iter [00:26, 39.02iter/s]
Best score: 0.6248: : 930iter [00:26, 39.32iter/s]
Best score: 0.6248: : 930iter [00:26, 39.32iter/s]
Best score: 0.6248: : 931iter [00:26, 39.32iter/s]
Best score: 0.6248: : 932iter [00:26, 39.32iter/s]
Best score: 0.6248: : 933iter [00:26, 39.32iter/s]
Best score: 0.6248: : 934iter [00:26, 39.32iter/s]
Best score: 0.6248: : 935iter [00:26, 39.81iter/s]
Best score: 0.6248: : 935iter [00:26, 39.81iter/s]
Best score: 0.6248: : 936iter [00:26, 39.81iter/s]
Best score: 0.6248: : 937iter [00:26, 39.81iter/s]
Best score: 0.6248: : 938iter [00:26, 39.81iter/s]
Best score: 0.6236: : 939iter [00:26, 39.81iter/s]
Best score: 0.6236: : 940iter [00:26, 39.99iter/s]
Best score: 0.6236: : 940iter [00:26, 39.99iter/s]
Best score: 0.6236: : 941iter [00:26, 39.99iter/s]
Best score: 0.6236: : 942iter [00:26, 39.99iter/s]
Best score: 0.6236: : 943iter [00:26, 39.99iter/s]
Best score: 0.6236: : 944iter [00:26, 39.99iter/s]
Best score: 0.6236: : 945iter [00:26, 40.36iter/s]
Best score: 0.6236: : 945iter [00:26, 40.36iter/s]
Best score: 0.6236: : 946iter [00:26, 40.36iter/s]
Best score: 0.6236: : 947iter [00:26, 40.36iter/s]
Best score: 0.6236: : 948iter [00:27, 40.36iter/s]
[ITER 950] Best: 0.6236
[PARAMS] Current best parameters:
  fx: 562.5908
  fy: 562.8129
  px: 989.4566
  py: 745.2219
  k1: 0.00876897
  k2: -0.06282771
  k3: 0.03971563
  k4: -0.00883745
  p1: 0.00411365
  p2: -0.00466174
  p3: 0.00015698
  p4: -0.00615004
  s1: 0.0137
  s2: 0.0038
  s3: -0.0032
  s4: 0.0039
  tilt_x: -0.0259
  tilt_y: -0.0082
  scale: 2.9446
  rotation: -0.3069

Best score: 0.6236: : 949iter [00:27, 40.36iter/s]
Best score: 0.6236: : 950iter [00:27, 40.27iter/s]
Best score: 0.6236: : 950iter [00:27, 40.27iter/s]
Best score: 0.6236: : 951iter [00:27, 40.27iter/s]
Best score: 0.6236: : 952iter [00:27, 40.27iter/s]
Best score: 0.6236: : 953iter [00:27, 40.27iter/s]
Best score: 0.6236: : 954iter [00:27, 40.27iter/s]
Best score: 0.6236: : 955iter [00:27, 33.98iter/s]
Best score: 0.6236: : 955iter [00:27, 33.98iter/s]
Best score: 0.6236: : 956iter [00:27, 33.98iter/s]
Best score: 0.6236: : 957iter [00:27, 33.98iter/s]
Best score: 0.6236: : 958iter [00:27, 33.98iter/s]
Best score: 0.6234: : 959iter [00:27, 33.98iter/s]
Best score: 0.6234: : 960iter [00:27, 35.60iter/s]
Best score: 0.6234: : 960iter [00:27, 35.60iter/s]
Best score: 0.6234: : 961iter [00:27, 35.60iter/s]
Best score: 0.6234: : 962iter [00:27, 35.60iter/s]
Best score: 0.6234: : 963iter [00:27, 35.60iter/s]
Best score: 0.6234: : 964iter [00:27, 35.60iter/s]
Best score: 0.6234: : 965iter [00:27, 37.05iter/s]
Best score: 0.6234: : 965iter [00:27, 37.05iter/s]
Best score: 0.6234: : 966iter [00:27, 37.05iter/s]
Best score: 0.6234: : 967iter [00:27, 37.05iter/s]
Best score: 0.6234: : 968iter [00:27, 37.05iter/s]
Best score: 0.6230: : 969iter [00:27, 37.05iter/s]
Best score: 0.6230: : 970iter [00:27, 37.91iter/s]
Best score: 0.6230: : 970iter [00:27, 37.91iter/s]
Best score: 0.6230: : 971iter [00:27, 37.91iter/s]
Best score: 0.6230: : 972iter [00:27, 37.91iter/s]
Best score: 0.6230: : 973iter [00:27, 37.91iter/s]
Best score: 0.6230: : 974iter [00:27, 37.91iter/s]
Best score: 0.6230: : 975iter [00:27, 38.71iter/s]
Best score: 0.6230: : 975iter [00:27, 38.71iter/s]
Best score: 0.6230: : 976iter [00:27, 38.71iter/s]
Best score: 0.6230: : 977iter [00:27, 38.71iter/s]
Best score: 0.6230: : 978iter [00:27, 38.71iter/s]
Best score: 0.6230: : 979iter [00:27, 38.71iter/s]
Best score: 0.6230: : 980iter [00:27, 39.05iter/s]
Best score: 0.6230: : 980iter [00:27, 39.05iter/s]
Best score: 0.6230: : 981iter [00:27, 39.05iter/s]
Best score: 0.6230: : 982iter [00:27, 39.05iter/s]
Best score: 0.6230: : 983iter [00:27, 39.05iter/s]
Best score: 0.6230: : 984iter [00:27, 39.05iter/s]
Best score: 0.6230: : 985iter [00:27, 39.48iter/s]
Best score: 0.6230: : 985iter [00:28, 39.48iter/s]
Best score: 0.6230: : 986iter [00:28, 39.48iter/s]
Best score: 0.6230: : 987iter [00:28, 39.48iter/s]
Best score: 0.6230: : 988iter [00:28, 39.48iter/s]
Best score: 0.6222: : 989iter [00:28, 39.48iter/s]
Best score: 0.6222: : 990iter [00:28, 39.70iter/s]
Best score: 0.6222: : 990iter [00:28, 39.70iter/s]
Best score: 0.6222: : 991iter [00:28, 39.70iter/s]
Best score: 0.6222: : 992iter [00:28, 39.70iter/s]
Best score: 0.6222: : 993iter [00:28, 39.70iter/s]
Best score: 0.6222: : 994iter [00:28, 39.70iter/s]
Best score: 0.6222: : 995iter [00:28, 40.16iter/s]
Best score: 0.6222: : 995iter [00:28, 40.16iter/s]
Best score: 0.6222: : 996iter [00:28, 40.16iter/s]
Best score: 0.6222: : 997iter [00:28, 40.16iter/s]
Best score: 0.6222: : 998iter [00:28, 40.16iter/s]
[ITER 1000] Best: 0.6221
[PARAMS] Current best parameters:
  fx: 561.9201
  fy: 562.1936
  px: 989.6567
  py: 744.8137
  k1: 0.01483907
  k2: -0.07097192
  k3: 0.04388255
  k4: -0.00957366
  p1: 0.00434711
  p2: -0.00402234
  p3: -0.00339421
  p4: -0.00575672
  s1: 0.0167
  s2: 0.0034
  s3: -0.0000
  s4: 0.0034
  tilt_x: -0.0285
  tilt_y: -0.0082
  scale: 2.9407
  rotation: -0.3107

Best score: 0.6221: : 999iter [00:28, 40.16iter/s]
Best score: 0.6221: : 1000iter [00:28, 40.06iter/s]
Best score: 0.6221: : 1000iter [00:28, 40.06iter/s]
Best score: 0.6221: : 1001iter [00:28, 40.06iter/s]
Best score: 0.6221: : 1002iter [00:28, 40.06iter/s]
Best score: 0.6221: : 1003iter [00:28, 40.06iter/s]
Best score: 0.6221: : 1004iter [00:28, 40.06iter/s]
Best score: 0.6221: : 1005iter [00:28, 33.92iter/s]
Best score: 0.6221: : 1005iter [00:28, 33.92iter/s]
Best score: 0.6221: : 1006iter [00:28, 33.92iter/s]
Best score: 0.6221: : 1007iter [00:28, 33.92iter/s]
Best score: 0.6221: : 1008iter [00:28, 33.92iter/s]
Best score: 0.6217: : 1009iter [00:28, 33.92iter/s]
Best score: 0.6217: : 1010iter [00:28, 35.47iter/s]
Best score: 0.6217: : 1010iter [00:28, 35.47iter/s]
Best score: 0.6217: : 1011iter [00:28, 35.47iter/s]
Best score: 0.6217: : 1012iter [00:28, 35.47iter/s]
Best score: 0.6217: : 1013iter [00:28, 35.47iter/s]
Best score: 0.6217: : 1014iter [00:28, 35.47iter/s]
Best score: 0.6217: : 1015iter [00:28, 36.99iter/s]
Best score: 0.6217: : 1015iter [00:28, 36.99iter/s]
Best score: 0.6217: : 1016iter [00:28, 36.99iter/s]
Best score: 0.6217: : 1017iter [00:28, 36.99iter/s]
Best score: 0.6217: : 1018iter [00:28, 36.99iter/s]
Best score: 0.6217: : 1019iter [00:28, 36.99iter/s]
Best score: 0.6217: : 1020iter [00:28, 38.00iter/s]
Best score: 0.6217: : 1020iter [00:28, 38.00iter/s]
Best score: 0.6217: : 1021iter [00:28, 38.00iter/s]
Best score: 0.6217: : 1022iter [00:28, 38.00iter/s]
Best score: 0.6217: : 1023iter [00:29, 38.00iter/s]
Best score: 0.6217: : 1024iter [00:29, 38.00iter/s]
Best score: 0.6217: : 1025iter [00:29, 38.92iter/s]
Best score: 0.6217: : 1025iter [00:29, 38.92iter/s]
Best score: 0.6217: : 1026iter [00:29, 38.92iter/s]
Best score: 0.6217: : 1027iter [00:29, 38.92iter/s]
Best score: 0.6217: : 1028iter [00:29, 38.92iter/s]
Best score: 0.6213: : 1029iter [00:29, 38.92iter/s]
Best score: 0.6213: : 1030iter [00:29, 39.03iter/s]
Best score: 0.6213: : 1030iter [00:29, 39.03iter/s]
Best score: 0.6213: : 1031iter [00:29, 39.03iter/s]
Best score: 0.6213: : 1032iter [00:29, 39.03iter/s]
Best score: 0.6213: : 1033iter [00:29, 39.03iter/s]
Best score: 0.6213: : 1034iter [00:29, 39.00iter/s]
Best score: 0.6213: : 1034iter [00:29, 39.00iter/s]
Best score: 0.6213: : 1035iter [00:29, 39.00iter/s]
Best score: 0.6213: : 1036iter [00:29, 39.00iter/s]
Best score: 0.6213: : 1037iter [00:29, 39.00iter/s]
Best score: 0.6213: : 1038iter [00:29, 39.00iter/s]
Best score: 0.6213: : 1039iter [00:29, 39.49iter/s]
Best score: 0.6213: : 1039iter [00:29, 39.49iter/s]
Best score: 0.6213: : 1040iter [00:29, 39.49iter/s]
Best score: 0.6213: : 1041iter [00:29, 39.49iter/s]
Best score: 0.6213: : 1042iter [00:29, 39.49iter/s]
Best score: 0.6213: : 1043iter [00:29, 39.49iter/s]
Best score: 0.6213: : 1044iter [00:29, 39.74iter/s]
Best score: 0.6213: : 1044iter [00:29, 39.74iter/s]
Best score: 0.6213: : 1045iter [00:29, 39.74iter/s]
Best score: 0.6213: : 1046iter [00:29, 39.74iter/s]
Best score: 0.6213: : 1047iter [00:29, 39.74iter/s]
Best score: 0.6213: : 1048iter [00:29, 39.74iter/s]
Best score: 0.6213: : 1049iter [00:29, 40.13iter/s]
[ITER 1050] Best: 0.6212
[PARAMS] Current best parameters:
  fx: 561.0400
  fy: 561.2567
  px: 990.3280
  py: 745.7198
  k1: 0.00995431
  k2: -0.06225126
  k3: 0.03913087
  k4: -0.00868475
  p1: 0.00568305
  p2: -0.00180676
  p3: -0.00151037
  p4: -0.00728467
  s1: 0.0130
  s2: 0.0050
  s3: -0.0027
  s4: 0.0051
  tilt_x: -0.0263
  tilt_y: -0.0074
  scale: 2.9470
  rotation: -0.3166

Best score: 0.6212: : 1049iter [00:29, 40.13iter/s]
Best score: 0.6212: : 1050iter [00:29, 40.13iter/s]
Best score: 0.6212: : 1051iter [00:29, 40.13iter/s]
Best score: 0.6212: : 1052iter [00:29, 40.13iter/s]
Best score: 0.6212: : 1053iter [00:29, 40.13iter/s]
Best score: 0.6212: : 1054iter [00:29, 33.77iter/s]
Best score: 0.6212: : 1054iter [00:29, 33.77iter/s]
Best score: 0.6212: : 1055iter [00:29, 33.77iter/s]
Best score: 0.6212: : 1056iter [00:29, 33.77iter/s]
Best score: 0.6212: : 1057iter [00:29, 33.77iter/s]
Best score: 0.6212: : 1058iter [00:29, 33.77iter/s]
Best score: 0.6212: : 1059iter [00:29, 35.69iter/s]
Best score: 0.6212: : 1059iter [00:29, 35.69iter/s]
Best score: 0.6212: : 1060iter [00:30, 35.69iter/s]
Best score: 0.6212: : 1061iter [00:30, 35.69iter/s]
Best score: 0.6212: : 1062iter [00:30, 35.69iter/s]
Best score: 0.6212: : 1063iter [00:30, 36.54iter/s]
Best score: 0.6212: : 1063iter [00:30, 36.54iter/s]
Best score: 0.6212: : 1064iter [00:30, 36.54iter/s]
Best score: 0.6212: : 1065iter [00:30, 36.54iter/s]
Best score: 0.6212: : 1066iter [00:30, 36.54iter/s]
Best score: 0.6212: : 1067iter [00:30, 36.54iter/s]
Best score: 0.6212: : 1068iter [00:30, 37.70iter/s]
Best score: 0.6212: : 1068iter [00:30, 37.70iter/s]
Best score: 0.6204: : 1069iter [00:30, 37.70iter/s]
Best score: 0.6204: : 1070iter [00:30, 37.70iter/s]
Best score: 0.6204: : 1071iter [00:30, 37.70iter/s]
Best score: 0.6204: : 1072iter [00:30, 38.16iter/s]
Best score: 0.6204: : 1072iter [00:30, 38.16iter/s]
Best score: 0.6204: : 1073iter [00:30, 38.16iter/s]
Best score: 0.6204: : 1074iter [00:30, 38.16iter/s]
Best score: 0.6204: : 1075iter [00:30, 38.16iter/s]
Best score: 0.6204: : 1076iter [00:30, 38.16iter/s]
Best score: 0.6204: : 1077iter [00:30, 39.08iter/s]
Best score: 0.6204: : 1077iter [00:30, 39.08iter/s]
Best score: 0.6204: : 1078iter [00:30, 39.08iter/s]
Best score: 0.6204: : 1079iter [00:30, 39.08iter/s]
Best score: 0.6204: : 1080iter [00:30, 39.08iter/s]
Best score: 0.6204: : 1081iter [00:30, 39.30iter/s]
Best score: 0.6204: : 1081iter [00:30, 39.30iter/s]
Best score: 0.6204: : 1082iter [00:30, 39.30iter/s]
Best score: 0.6204: : 1083iter [00:30, 39.30iter/s]
Best score: 0.6204: : 1084iter [00:30, 39.30iter/s]
Best score: 0.6204: : 1085iter [00:30, 39.30iter/s]
Best score: 0.6204: : 1086iter [00:30, 39.77iter/s]
Best score: 0.6204: : 1086iter [00:30, 39.77iter/s]
Best score: 0.6204: : 1087iter [00:30, 39.77iter/s]
Best score: 0.6204: : 1088iter [00:30, 39.77iter/s]
Best score: 0.6203: : 1089iter [00:30, 39.77iter/s]
Best score: 0.6203: : 1090iter [00:30, 39.77iter/s]
Best score: 0.6203: : 1091iter [00:30, 39.72iter/s]
Best score: 0.6203: : 1091iter [00:30, 39.72iter/s]
Best score: 0.6203: : 1092iter [00:30, 39.72iter/s]
Best score: 0.6203: : 1093iter [00:30, 39.72iter/s]
Best score: 0.6203: : 1094iter [00:30, 39.72iter/s]
Best score: 0.6203: : 1095iter [00:30, 39.72iter/s]
Best score: 0.6203: : 1096iter [00:30, 40.03iter/s]
Best score: 0.6203: : 1096iter [00:30, 40.03iter/s]
Best score: 0.6203: : 1097iter [00:30, 40.03iter/s]
Best score: 0.6203: : 1098iter [00:30, 40.03iter/s]
[ITER 1100] Best: 0.6203
[PARAMS] Current best parameters:
  fx: 561.0558
  fy: 561.2920
  px: 990.4168
  py: 745.8698
  k1: 0.01413378
  k2: -0.06869879
  k3: 0.04272583
  k4: -0.00939086
  p1: 0.00629005
  p2: -0.00168566
  p3: -0.00381252
  p4: -0.00662407
  s1: 0.0156
  s2: 0.0041
  s3: -0.0009
  s4: 0.0045
  tilt_x: -0.0268
  tilt_y: -0.0076
  scale: 2.9421
  rotation: -0.3181

Best score: 0.6203: : 1099iter [00:30, 40.03iter/s]
Best score: 0.6203: : 1100iter [00:31, 35.41iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.620319 -> 0.619976

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,676 → 25,676 within 3500mm ring
  Ring polish did not improve score: 0.619976
  Success: True
  Tangential distortion: p1=0.006286, p2=-0.001675, p3=-0.003814, p4=-0.006630
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.6200
[✅] Parameters copied to: /working/MDC013_W008_2026-07-02/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 561.0558226772432
    fy: 561.2920487779983
    px: 990.416749816575
    py: 745.8698357419744
    k1: 0.014139313205729497
    k2: -0.06869199558550271
    k3: 0.04272523145702195
    k4: -0.009394639009527044
    p1: 0.006286006867333118
    p2: -0.0016754595559157475
    p3: -0.0038136898543856164
    p4: -0.006630444582944254
    scale: 2.9421560797133686
    rotation: -0.3180592013816075
    mmpp: 1.269483224093775

[🔄] Converting 815 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/430 [00:00<?, ?frame/s]

   🔄 1000:   4%|▍         | 18/430 [00:00<00:02, 172.81frame/s]

   🔄 1000:   8%|▊         | 36/430 [00:00<00:02, 174.34frame/s]

   🔄 1000:  13%|█▎        | 54/430 [00:00<00:02, 176.61frame/s]

   🔄 1000:  17%|█▋        | 72/430 [00:00<00:02, 177.48frame/s]

   🔄 1000:  21%|██        | 90/430 [00:00<00:01, 178.17frame/s]

   🔄 1000:  26%|██▌       | 111/430 [00:00<00:01, 187.40frame/s]

   🔄 1000:  30%|███       | 131/430 [00:00<00:01, 188.53frame/s]

   🔄 1000:  35%|███▍      | 150/430 [00:00<00:01, 184.67frame/s]

   🔄 1000:  39%|███▉      | 169/430 [00:00<00:01, 181.41frame/s]

   🔄 1000:  44%|████▎     | 188/430 [00:01<00:01, 179.39frame/s]

   🔄 1000:  48%|████▊     | 206/430 [00:01<00:01, 178.08frame/s]

   🔄 1000:  52%|█████▏    | 224/430 [00:01<00:01, 138.31frame/s]

   🔄 1000:  56%|█████▋    | 242/430 [00:01<00:01, 148.39frame/s]

   🔄 1000:  61%|██████    | 261/430 [00:01<00:01, 158.07frame/s]

   🔄 1000:  65%|██████▌   | 280/430 [00:01<00:00, 164.33frame/s]

   🔄 1000:  69%|██████▉   | 298/430 [00:01<00:00, 167.26frame/s]

   🔄 1000:  73%|███████▎  | 316/430 [00:01<00:00, 169.89frame/s]

   🔄 1000:  78%|███████▊  | 334/430 [00:01<00:00, 171.95frame/s]

   🔄 1000:  82%|████████▏ | 352/430 [00:02<00:00, 173.58frame/s]

   🔄 1000:  86%|████████▋ | 371/430 [00:02<00:00, 176.70frame/s]

   🔄 1000:  90%|█████████ | 389/430 [00:02<00:00, 176.90frame/s]

   🔄 1000:  95%|█████████▍| 407/430 [00:02<00:00, 176.68frame/s]

   🔄 1000:  99%|█████████▉| 425/430 [00:02<00:00, 176.24frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:04<00:04,  4.43s/rig]

   🔄 500:   0%|          | 0/385 [00:00<?, ?frame/s]

   🔄 500:   5%|▍         | 18/385 [00:00<00:02, 176.16frame/s]

   🔄 500:   9%|▉         | 36/385 [00:00<00:01, 177.62frame/s]

   🔄 500:  14%|█▍        | 54/385 [00:00<00:01, 178.39frame/s]

   🔄 500:  19%|█▊        | 72/385 [00:00<00:01, 178.83frame/s]

   🔄 500:  23%|██▎       | 90/385 [00:00<00:01, 178.55frame/s]

   🔄 500:  28%|██▊       | 108/385 [00:00<00:01, 177.52frame/s]

   🔄 500:  33%|███▎      | 126/385 [00:00<00:01, 176.61frame/s]

   🔄 500:  37%|███▋      | 144/385 [00:00<00:01, 176.31frame/s]

   🔄 500:  42%|████▏     | 162/385 [00:00<00:01, 176.56frame/s]

   🔄 500:  47%|████▋     | 180/385 [00:01<00:01, 176.81frame/s]

   🔄 500:  51%|█████▏    | 198/385 [00:01<00:01, 176.66frame/s]

   🔄 500:  56%|█████▌    | 216/385 [00:01<00:00, 176.98frame/s]

   🔄 500:  61%|██████    | 234/385 [00:01<00:00, 176.54frame/s]

   🔄 500:  65%|██████▌   | 252/385 [00:01<00:00, 175.40frame/s]

   🔄 500:  70%|███████   | 270/385 [00:01<00:00, 175.17frame/s]

   🔄 500:  75%|███████▍  | 288/385 [00:01<00:00, 174.85frame/s]

   🔄 500:  79%|███████▉  | 306/385 [00:01<00:00, 175.97frame/s]

   🔄 500:  84%|████████▍ | 324/385 [00:01<00:00, 177.05frame/s]

   🔄 500:  89%|████████▉ | 342/385 [00:01<00:00, 177.65frame/s]

   🔄 500:  94%|█████████▎| 360/385 [00:02<00:00, 177.89frame/s]

   🔄 500:  98%|█████████▊| 378/385 [00:02<00:00, 177.36frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.09s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.15s/rig]
[✅] Saved PTDs: /working/MDC013_W008_2026-07-02/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 176.13frame/s]

   🔄 1000:  37%|███▋      | 37/100 [00:00<00:00, 179.59frame/s]

   🔄 1000:  55%|█████▌    | 55/100 [00:00<00:00, 176.94frame/s]

   🔄 1000:  74%|███████▍  | 74/100 [00:00<00:00, 179.30frame/s]

   🔄 1000:  92%|█████████▏| 92/100 [00:00<00:00, 178.86frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.12rig/s]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 177.24frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 175.79frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 176.16frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 175.93frame/s]

   🔄 500:  91%|█████████ | 91/100 [00:00<00:00, 177.57frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.03rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.04rig/s]
[✅] Saved PTDs: /working/MDC013_W008_2026-07-02/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%|▎         | 79202/3080448 [00:00<00:03, 792011.31px/s]
📤 Writing LUT:   5%|▌         | 163266/3080448 [00:00<00:03, 820611.00px/s]
📤 Writing LUT:   8%|▊         | 247789/3080448 [00:00<00:03, 831851.94px/s]
📤 Writing LUT:  11%|█         | 332926/3080448 [00:00<00:03, 839556.95px/s]
📤 Writing LUT:  14%|█▎        | 419341/3080448 [00:00<00:03, 848394.28px/s]
📤 Writing LUT:  17%|█▋        | 510591/3080448 [00:00<00:02, 870187.17px/s]
📤 Writing LUT:  19%|█▉        | 599960/3080448 [00:00<00:02, 877870.24px/s]
📤 Writing LUT:  22%|██▏       | 689279/3080448 [00:00<00:02, 882746.85px/s]
📤 Writing LUT:  25%|██▌       | 778696/3080448 [00:00<00:02, 886316.81px/s]
📤 Writing LUT:  28%|██▊       | 867489/3080448 [00:01<00:02, 886811.83px/s]
📤 Writing LUT:  31%|███       | 956171/3080448 [00:01<00:02, 882037.12px/s]
📤 Writing LUT:  34%|███▍      | 1044381/3080448 [00:01<00:02, 878576.29px/s]
📤 Writing LUT:  37%|███▋      | 1132286/3080448 [00:01<00:02, 878717.38px/s]
📤 Writing LUT:  40%|███▉      | 1220528/3080448 [00:01<00:02, 879828.64px/s]
📤 Writing LUT:  42%|████▏     | 1308610/3080448 [00:01<00:02, 880125.04px/s]
📤 Writing LUT:  45%|████▌     | 1400540/3080448 [00:01<00:01, 891894.62px/s]
📤 Writing LUT:  48%|████▊     | 1489734/3080448 [00:01<00:01, 891135.82px/s]
📤 Writing LUT:  51%|█████▏    | 1580469/3080448 [00:01<00:01, 895999.00px/s]
📤 Writing LUT:  54%|█████▍    | 1670128/3080448 [00:01<00:01, 896175.29px/s]
📤 Writing LUT:  57%|█████▋    | 1759748/3080448 [00:02<00:01, 893804.63px/s]
📤 Writing LUT:  60%|██████    | 1849132/3080448 [00:02<00:01, 890249.14px/s]
📤 Writing LUT:  63%|██████▎   | 1938529/3080448 [00:02<00:01, 891359.84px/s]
📤 Writing LUT:  66%|██████▌   | 2027669/3080448 [00:02<00:01, 889069.69px/s]
📤 Writing LUT:  69%|██████▊   | 2116580/3080448 [00:02<00:01, 887592.60px/s]
📤 Writing LUT:  72%|███████▏  | 2205342/3080448 [00:02<00:00, 885325.73px/s]
📤 Writing LUT:  75%|███████▍  | 2297210/3080448 [00:02<00:00, 895284.22px/s]
📤 Writing LUT:  78%|███████▊  | 2389187/3080448 [00:02<00:00, 902605.83px/s]
📤 Writing LUT:  81%|████████  | 2480934/3080448 [00:02<00:00, 907051.63px/s]
📤 Writing LUT:  83%|████████▎ | 2571645/3080448 [00:02<00:00, 903056.05px/s]
📤 Writing LUT:  86%|████████▋ | 2661958/3080448 [00:03<00:00, 902352.78px/s]
📤 Writing LUT:  89%|████████▉ | 2754883/3080448 [00:03<00:00, 910392.72px/s]
📤 Writing LUT:  92%|█████████▏| 2846680/3080448 [00:03<00:00, 912657.35px/s]
📤 Writing LUT:  95%|█████████▌| 2938422/3080448 [00:03<00:00, 914081.99px/s]
📤 Writing LUT:  98%|█████████▊| 3030783/3080448 [00:03<00:00, 916936.56px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 889359.00px/s]
[✅] LUT written to: /working/MDC013_W008_2026-07-02/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC013_W008_2026-07-02' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC013_W008_2026-07-02/calibration/MDC013_W008_2026-07-02_unknown.lut

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

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

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

   🔥 1000:   0%|          | 0/430 [00:00<?, ?frame/s]

   🔥 1000:   2%|▏         | 8/430 [00:00<00:05, 78.18frame/s]

   🔥 1000:   4%|▍         | 17/430 [00:00<00:04, 83.68frame/s]

   🔥 1000:   6%|▌         | 26/430 [00:00<00:04, 85.18frame/s]

   🔥 1000:   8%|▊         | 35/430 [00:00<00:04, 86.40frame/s]

   🔥 1000:  10%|█         | 44/430 [00:00<00:04, 87.26frame/s]

   🔥 1000:  12%|█▏        | 53/430 [00:00<00:04, 86.12frame/s]

   🔥 1000:  14%|█▍        | 62/430 [00:00<00:04, 85.51frame/s]

   🔥 1000:  17%|█▋        | 71/430 [00:00<00:04, 85.01frame/s]

   🔥 1000:  19%|█▊        | 80/430 [00:00<00:04, 84.29frame/s]

   🔥 1000:  21%|██        | 89/430 [00:01<00:04, 83.28frame/s]

   🔥 1000:  23%|██▎       | 98/430 [00:01<00:04, 82.64frame/s]

   🔥 1000:  25%|██▌       | 108/430 [00:01<00:03, 87.01frame/s]

   🔥 1000:  27%|██▋       | 117/430 [00:01<00:03, 85.40frame/s]

   🔥 1000:  29%|██▉       | 126/430 [00:01<00:03, 85.62frame/s]

   🔥 1000:  31%|███▏      | 135/430 [00:01<00:03, 83.90frame/s]

   🔥 1000:  33%|███▎      | 144/430 [00:01<00:03, 83.69frame/s]

   🔥 1000:  36%|███▌      | 153/430 [00:01<00:03, 83.39frame/s]

   🔥 1000:  38%|███▊      | 162/430 [00:01<00:03, 84.14frame/s]

   🔥 1000:  40%|███▉      | 171/430 [00:02<00:03, 84.90frame/s]

   🔥 1000:  42%|████▏     | 180/430 [00:02<00:02, 84.81frame/s]

   🔥 1000:  44%|████▍     | 189/430 [00:02<00:02, 85.61frame/s]

   🔥 1000:  46%|████▋     | 199/430 [00:02<00:02, 86.95frame/s]

   🔥 1000:  48%|████▊     | 208/430 [00:02<00:02, 86.22frame/s]

   🔥 1000:  50%|█████     | 217/430 [00:02<00:02, 86.19frame/s]

   🔥 1000:  53%|█████▎    | 226/430 [00:02<00:02, 85.08frame/s]

   🔥 1000:  55%|█████▍    | 235/430 [00:02<00:02, 84.58frame/s]

   🔥 1000:  57%|█████▋    | 244/430 [00:02<00:02, 83.86frame/s]

   🔥 1000:  59%|█████▉    | 253/430 [00:02<00:02, 83.80frame/s]

   🔥 1000:  61%|██████    | 262/430 [00:03<00:01, 84.12frame/s]

   🔥 1000:  63%|██████▎   | 271/430 [00:03<00:01, 83.42frame/s]

   🔥 1000:  65%|██████▌   | 280/430 [00:03<00:01, 82.92frame/s]

   🔥 1000:  67%|██████▋   | 289/430 [00:03<00:01, 81.51frame/s]

   🔥 1000:  69%|██████▉   | 298/430 [00:03<00:01, 81.36frame/s]

   🔥 1000:  71%|███████▏  | 307/430 [00:03<00:01, 81.36frame/s]

   🔥 1000:  73%|███████▎  | 316/430 [00:03<00:01, 82.89frame/s]

   🔥 1000:  76%|███████▌  | 326/430 [00:03<00:01, 85.45frame/s]

   🔥 1000:  78%|███████▊  | 335/430 [00:03<00:01, 85.63frame/s]

   🔥 1000:  80%|████████  | 344/430 [00:04<00:00, 86.60frame/s]

   🔥 1000:  82%|████████▏ | 353/430 [00:04<00:00, 86.51frame/s]

   🔥 1000:  84%|████████▍ | 363/430 [00:04<00:00, 88.03frame/s]

   🔥 1000:  87%|████████▋ | 372/430 [00:04<00:00, 87.63frame/s]

   🔥 1000:  89%|████████▊ | 381/430 [00:04<00:00, 86.97frame/s]

   🔥 1000:  91%|█████████ | 390/430 [00:04<00:00, 85.79frame/s]

   🔥 1000:  93%|█████████▎| 399/430 [00:04<00:00, 85.00frame/s]

   🔥 1000:  95%|█████████▍| 408/430 [00:04<00:00, 86.38frame/s]

   🔥 1000:  97%|█████████▋| 417/430 [00:04<00:00, 87.17frame/s]

   🔥 1000:  99%|█████████▉| 426/430 [00:05<00:00, 87.05frame/s]

                                                                [✅] 1000: /working/MDC013_W008_2026-07-02/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC013_W008_2026-07-02/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:08<00:08,  8.18s/rig]

   🔥 500:   0%|          | 0/385 [00:00<?, ?frame/s]

   🔥 500:   2%|▏         | 8/385 [00:00<00:05, 74.18frame/s]

   🔥 500:   4%|▍         | 16/385 [00:00<00:04, 76.71frame/s]

   🔥 500:   6%|▌         | 24/385 [00:00<00:04, 77.67frame/s]

   🔥 500:   8%|▊         | 32/385 [00:00<00:04, 77.65frame/s]

   🔥 500:  10%|█         | 40/385 [00:00<00:04, 77.43frame/s]

   🔥 500:  12%|█▏        | 48/385 [00:00<00:04, 77.52frame/s]

   🔥 500:  15%|█▍        | 56/385 [00:00<00:04, 78.31frame/s]

   🔥 500:  17%|█▋        | 65/385 [00:00<00:04, 79.54frame/s]

   🔥 500:  19%|█▉        | 74/385 [00:00<00:03, 80.80frame/s]

   🔥 500:  22%|██▏       | 83/385 [00:01<00:03, 80.95frame/s]

   🔥 500:  24%|██▍       | 92/385 [00:01<00:03, 80.20frame/s]

   🔥 500:  26%|██▌       | 101/385 [00:01<00:03, 79.73frame/s]

   🔥 500:  28%|██▊       | 109/385 [00:01<00:03, 78.86frame/s]

   🔥 500:  30%|███       | 117/385 [00:01<00:03, 78.54frame/s]

   🔥 500:  32%|███▏      | 125/385 [00:01<00:03, 78.14frame/s]

   🔥 500:  35%|███▍      | 133/385 [00:01<00:03, 77.97frame/s]

   🔥 500:  37%|███▋      | 141/385 [00:01<00:03, 78.43frame/s]

   🔥 500:  39%|███▊      | 149/385 [00:01<00:03, 78.56frame/s]

   🔥 500:  41%|████      | 158/385 [00:02<00:02, 79.23frame/s]

   🔥 500:  43%|████▎     | 167/385 [00:02<00:02, 79.63frame/s]

   🔥 500:  45%|████▌     | 175/385 [00:02<00:02, 79.59frame/s]

   🔥 500:  48%|████▊     | 183/385 [00:02<00:02, 78.73frame/s]

   🔥 500:  50%|████▉     | 191/385 [00:02<00:02, 77.67frame/s]

   🔥 500:  52%|█████▏    | 199/385 [00:02<00:02, 77.42frame/s]

   🔥 500:  54%|█████▍    | 207/385 [00:02<00:02, 77.25frame/s]

   🔥 500:  56%|█████▌    | 215/385 [00:02<00:02, 76.96frame/s]

   🔥 500:  58%|█████▊    | 223/385 [00:02<00:02, 76.33frame/s]

   🔥 500:  60%|██████    | 231/385 [00:02<00:02, 75.48frame/s]

   🔥 500:  62%|██████▏   | 239/385 [00:03<00:01, 75.52frame/s]

   🔥 500:  64%|██████▍   | 247/385 [00:03<00:01, 76.11frame/s]

   🔥 500:  66%|██████▌   | 255/385 [00:03<00:01, 76.55frame/s]

   🔥 500:  68%|██████▊   | 263/385 [00:03<00:01, 76.77frame/s]

   🔥 500:  70%|███████   | 271/385 [00:03<00:01, 76.14frame/s]

   🔥 500:  72%|███████▏  | 279/385 [00:03<00:01, 76.24frame/s]

   🔥 500:  75%|███████▍  | 287/385 [00:03<00:01, 76.32frame/s]

   🔥 500:  77%|███████▋  | 295/385 [00:03<00:01, 76.68frame/s]

   🔥 500:  79%|███████▊  | 303/385 [00:03<00:01, 76.62frame/s]

   🔥 500:  81%|████████  | 311/385 [00:03<00:00, 76.96frame/s]

   🔥 500:  83%|████████▎ | 319/385 [00:04<00:00, 76.89frame/s]

   🔥 500:  85%|████████▍ | 327/385 [00:04<00:00, 76.99frame/s]

   🔥 500:  87%|████████▋ | 335/385 [00:04<00:00, 76.99frame/s]

   🔥 500:  89%|████████▉ | 343/385 [00:04<00:00, 77.41frame/s]

   🔥 500:  91%|█████████ | 351/385 [00:04<00:00, 77.87frame/s]

   🔥 500:  93%|█████████▎| 359/385 [00:04<00:00, 77.27frame/s]

   🔥 500:  95%|█████████▌| 367/385 [00:04<00:00, 77.16frame/s]

   🔥 500:  97%|█████████▋| 375/385 [00:04<00:00, 77.66frame/s]

   🔥 500:  99%|█████████▉| 383/385 [00:04<00:00, 77.88frame/s]

                                                               [✅] 500: /working/MDC013_W008_2026-07-02/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC013_W008_2026-07-02/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:14<00:00,  6.99s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:14<00:00,  7.17s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC013_W008_2026-07-02/calibration/debug/5_heatmap
[🏁] Finished processing MDC013_W008_2026-07-02


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