2026-05-06T17:57:18 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC030_W032_2026-03-24 ===
2026-05-06T17:57:18 - INFO: Camera ID: 15091876
2026-05-06T17:57:18 - INFO: Wand ID: 
2026-05-06T17:57:18 - INFO: Recorded At: 2026-03-23T22:57:39Z

-------------------------------------------------------------------
                        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 507 stationary frames, 457 remain


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                                                                           [PASS2] Frame 0000092: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000250: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000251: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000252: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000253: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000469: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000470: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000474: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000539: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000540: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000541: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000542: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000543: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000547: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000548: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000549: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000550: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000557: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000558: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000559: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000560: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000564: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000565: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000566: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000567: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000573: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000574: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000578: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000579: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000580: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000589: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000590: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000591: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000592: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000594: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000597: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000598: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000610: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000611: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000613: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000615: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000616: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000617: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000618: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000678: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000679: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000681: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000709: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000710: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000711: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000712: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000745: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000746: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000747: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000748: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000252: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000469: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000540: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000542: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000549: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000559: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000566: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000575: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000576: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000579: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000591: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000597: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000617: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000711: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000747: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 38 frames fixed, 38 untrusted, 457 total


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→ 1000 Phase 3: finalize:  74%|███████▎  | 336/457 [00:07<00:02, 42.22it/s]

→ 1000 Phase 3: finalize:  75%|███████▍  | 341/457 [00:07<00:02, 42.09it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 346/457 [00:07<00:02, 41.98it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 351/457 [00:07<00:02, 41.90it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 356/457 [00:08<00:02, 41.75it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 361/457 [00:08<00:02, 41.76it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 366/457 [00:08<00:02, 41.79it/s]

→ 1000 Phase 3: finalize:  81%|████████  | 371/457 [00:08<00:02, 41.87it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 376/457 [00:08<00:01, 41.78it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 381/457 [00:08<00:01, 41.79it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 386/457 [00:08<00:01, 41.78it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 391/457 [00:08<00:01, 41.71it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 396/457 [00:09<00:01, 41.72it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 401/457 [00:09<00:01, 41.74it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 406/457 [00:09<00:01, 41.82it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 411/457 [00:09<00:01, 41.85it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 416/457 [00:09<00:00, 41.95it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 421/457 [00:09<00:00, 42.27it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 426/457 [00:09<00:00, 42.53it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 431/457 [00:09<00:00, 42.88it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 436/457 [00:09<00:00, 43.21it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 441/457 [00:10<00:00, 43.50it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 446/457 [00:10<00:00, 43.77it/s]

→ 1000 Phase 3: finalize:  99%|█████████▊| 451/457 [00:10<00:00, 44.11it/s]

→ 1000 Phase 3: finalize: 100%|█████████▉| 456/457 [00:10<00:00, 44.36it/s]

                                                                           
[TIMING] 1000 — 457 frames
  Phase 1  radial detect   (parallel)  :   7.12s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.35s  (7.3 ms/frame)
  Phase 2b corner fix      (sequential):   1.80s
  Phase 3  finalize        (sequential):  10.44s  (22.8 ms/frame)
    decode + undistort                 :   5.79s  (12.7 ms/frame)
    corner apply + refinement          :   2.19s  (4.8 ms/frame)
    segment                            :   1.87s  (4.1 ms/frame)
    smooth                             :   0.42s  (0.9 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.70s


Processing Rigs:  50%|█████     | 1/2 [00:28<00:28, 28.53s/it][INFO] Filtered 508 stationary frames, 342 remain


→ 500 (342 images) Phase 1:   0%|          | 0/342 [00:00<?, ?it/s]

→ 500 (342 images) Phase 1:   0%|          | 1/342 [00:00<03:43,  1.52it/s]

→ 500 (342 images) Phase 1:   5%|▍         | 17/342 [00:00<00:10, 29.71it/s]

→ 500 (342 images) Phase 1:   9%|▉         | 30/342 [00:00<00:06, 49.53it/s]

→ 500 (342 images) Phase 1:  12%|█▏        | 41/342 [00:01<00:07, 41.43it/s]

→ 500 (342 images) Phase 1:  18%|█▊        | 62/342 [00:01<00:04, 67.76it/s]

→ 500 (342 images) Phase 1:  22%|██▏       | 74/342 [00:01<00:05, 53.21it/s]

→ 500 (342 images) Phase 1:  27%|██▋       | 94/342 [00:01<00:03, 75.16it/s]

→ 500 (342 images) Phase 1:  31%|███       | 106/342 [00:02<00:04, 58.23it/s]

→ 500 (342 images) Phase 1:  37%|███▋      | 125/342 [00:02<00:02, 78.39it/s]

→ 500 (342 images) Phase 1:  40%|████      | 138/342 [00:02<00:03, 60.57it/s]

→ 500 (342 images) Phase 1:  46%|████▌     | 157/342 [00:02<00:02, 78.14it/s]

→ 500 (342 images) Phase 1:  49%|████▉     | 169/342 [00:03<00:02, 60.71it/s]

→ 500 (342 images) Phase 1:  55%|█████▍    | 188/342 [00:03<00:01, 79.30it/s]

→ 500 (342 images) Phase 1:  58%|█████▊    | 200/342 [00:03<00:02, 60.75it/s]

→ 500 (342 images) Phase 1:  64%|██████▍   | 220/342 [00:03<00:01, 79.88it/s]

→ 500 (342 images) Phase 1:  68%|██████▊   | 232/342 [00:03<00:01, 61.29it/s]

→ 500 (342 images) Phase 1:  74%|███████▎  | 252/342 [00:04<00:01, 81.61it/s]

→ 500 (342 images) Phase 1:  77%|███████▋  | 265/342 [00:04<00:01, 62.48it/s]

→ 500 (342 images) Phase 1:  84%|████████▎ | 286/342 [00:04<00:00, 84.23it/s]

→ 500 (342 images) Phase 1:  88%|████████▊ | 300/342 [00:04<00:00, 63.97it/s]

→ 500 (342 images) Phase 1:  94%|█████████▍| 321/342 [00:05<00:00, 72.61it/s]

→ 500 (342 images) Phase 1:  97%|█████████▋| 332/342 [00:05<00:00, 75.86it/s]

                                                                             

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

→ 500 Phase 2: corners:   0%|          | 1/342 [00:00<01:42,  3.31it/s]

→ 500 Phase 2: corners:   5%|▍         | 16/342 [00:00<00:06, 49.65it/s]

→ 500 Phase 2: corners:   8%|▊         | 28/342 [00:00<00:04, 70.54it/s]

→ 500 Phase 2: corners:  12%|█▏        | 40/342 [00:00<00:03, 85.36it/s]

→ 500 Phase 2: corners:  17%|█▋        | 58/342 [00:00<00:02, 111.62it/s]

→ 500 Phase 2: corners:  21%|██        | 71/342 [00:00<00:02, 108.92it/s]

→ 500 Phase 2: corners:  25%|██▍       | 84/342 [00:00<00:02, 105.74it/s]

→ 500 Phase 2: corners:  29%|██▊       | 98/342 [00:01<00:02, 113.39it/s]

→ 500 Phase 2: corners:  35%|███▍      | 119/342 [00:01<00:01, 138.48it/s]

→ 500 Phase 2: corners:  40%|████      | 137/342 [00:01<00:01, 149.89it/s]

→ 500 Phase 2: corners:  45%|████▍     | 153/342 [00:01<00:01, 147.51it/s]

→ 500 Phase 2: corners:  50%|█████     | 171/342 [00:01<00:01, 154.15it/s]

→ 500 Phase 2: corners:  55%|█████▍    | 187/342 [00:01<00:01, 151.34it/s]

→ 500 Phase 2: corners:  61%|██████    | 207/342 [00:01<00:00, 160.44it/s]

→ 500 Phase 2: corners:  65%|██████▌   | 224/342 [00:01<00:00, 153.83it/s]

→ 500 Phase 2: corners:  70%|███████   | 241/342 [00:01<00:00, 153.99it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 257/342 [00:02<00:00, 145.74it/s]

→ 500 Phase 2: corners:  80%|███████▉  | 272/342 [00:02<00:00, 145.20it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 288/342 [00:02<00:00, 148.88it/s]

→ 500 Phase 2: corners:  90%|█████████ | 308/342 [00:02<00:00, 160.08it/s]

→ 500 Phase 2: corners:  95%|█████████▌| 325/342 [00:02<00:00, 146.60it/s]

→ 500 Phase 2: corners: 100%|█████████▉| 341/342 [00:02<00:00, 150.05it/s]

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


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→ 500 Phase 3: finalize:  99%|█████████▊| 337/342 [00:07<00:00, 43.84it/s]

→ 500 Phase 3: finalize: 100%|██████████| 342/342 [00:07<00:00, 43.55it/s]

                                                                          
[TIMING] 500 — 342 frames
  Phase 1  radial detect   (parallel)  :   5.34s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.71s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   3.29s
  Phase 3  finalize        (sequential):   7.44s  (21.8 ms/frame)
    decode + undistort                 :   4.24s  (12.4 ms/frame)
    corner apply + refinement          :   1.19s  (3.5 ms/frame)
    segment                            :   1.28s  (3.7 ms/frame)
    smooth                             :   0.61s  (1.8 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  18.77s


Processing Rigs: 100%|██████████| 2/2 [00:52<00:00, 25.75s/it]
Processing Rigs: 100%|██████████| 2/2 [00:52<00:00, 26.17s/it]
[💾] Saved RigFrameDataset to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 799 frames from 2 rigs to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 457
[📋] 500: 100 frames selected from 342
[💾] Saved RigFrameDataset to /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC030_W032_2026-03-24/calibration/debug/1_digitized/rig_frames.json

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

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

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
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Computing max image from bright frames:  10%|█         | 4/40 [00:13<02:01,  3.37s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:57,  3.35s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:20<01:53,  3.35s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:23<01:50,  3.35s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:26<01:47,  3.35s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:30<01:44,  3.36s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:33<01:40,  3.36s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:36<01:37,  3.37s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:40<01:34,  3.36s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:43<01:30,  3.36s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:47<01:27,  3.36s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:50<01:23,  3.35s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:53<01:20,  3.35s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:57<01:17,  3.36s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [01:00<01:13,  3.36s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:03<01:10,  3.36s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:07<01:07,  3.36s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:10<01:04,  3.38s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:14<01:00,  3.39s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:17<00:57,  3.38s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:20<00:54,  3.40s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:24<00:51,  3.40s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:27<00:47,  3.40s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:31<00:44,  3.40s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:34<00:40,  3.39s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:37<00:37,  3.39s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:41<00:33,  3.39s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:44<00:30,  3.42s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:48<00:27,  3.43s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:51<00:23,  3.41s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:54<00:20,  3.40s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:58<00:17,  3.41s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [02:01<00:13,  3.42s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [02:05<00:10,  3.41s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:08<00:06,  3.40s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:11<00:03,  3.41s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:15<00:00,  3.41s/it]
                                                                                       
[✅] Casing detection complete: cx=1002.0, cy=766.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
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Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:06, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:06, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:06, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:06, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:06, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:05, 15.82it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  24%|██▍       | 24/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  25%|██▌       | 25/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 519.192} (0.030):  26%|██▌       | 26/100 [00:00<00:01, 71.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 519.192} (0.030):  27%|██▋       | 27/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 519.192} (0.030):  27%|██▋       | 27/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.008 | Best: {'fx': 519.192} (0.030):  28%|██▊       | 28/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 519.192} (0.030):  29%|██▉       | 29/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 519.192} (0.030):  30%|███       | 30/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.020 | Best: {'fx': 519.192} (0.030):  31%|███       | 31/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.049 | Best: {'fx': 526.263} (0.049):  32%|███▏      | 32/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.065 | Best: {'fx': 533.333} (0.065):  33%|███▎      | 33/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.043 | Best: {'fx': 533.333} (0.065):  34%|███▍      | 34/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.031 | Best: {'fx': 533.333} (0.065):  35%|███▌      | 35/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.065):  36%|███▌      | 36/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.065):  37%|███▋      | 37/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.065):  38%|███▊      | 38/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.065):  39%|███▉      | 39/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.065):  40%|████      | 40/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.065):  41%|████      | 41/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.065):  42%|████▏     | 42/100 [00:00<00:00, 84.85it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.065):  43%|████▎     | 43/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  43%|████▎     | 43/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  44%|████▍     | 44/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  45%|████▌     | 45/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  46%|████▌     | 46/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  47%|████▋     | 47/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  48%|████▊     | 48/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  49%|████▉     | 49/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  50%|█████     | 50/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  51%|█████     | 51/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  52%|█████▏    | 52/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  53%|█████▎    | 53/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  54%|█████▍    | 54/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  55%|█████▌    | 55/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  56%|█████▌    | 56/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  57%|█████▋    | 57/100 [00:00<00:00, 107.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  58%|█████▊    | 58/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  58%|█████▊    | 58/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  59%|█████▉    | 59/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  60%|██████    | 60/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  61%|██████    | 61/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  62%|██████▏   | 62/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  63%|██████▎   | 63/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  64%|██████▍   | 64/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  65%|██████▌   | 65/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  66%|██████▌   | 66/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  67%|██████▋   | 67/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  68%|██████▊   | 68/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  69%|██████▉   | 69/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  70%|███████   | 70/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  71%|███████   | 71/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  72%|███████▏  | 72/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  73%|███████▎  | 73/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  74%|███████▍  | 74/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  75%|███████▌  | 75/100 [00:00<00:00, 118.32it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  76%|███████▌  | 76/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  76%|███████▌  | 76/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  77%|███████▋  | 77/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  78%|███████▊  | 78/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  79%|███████▉  | 79/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  80%|████████  | 80/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  81%|████████  | 81/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  82%|████████▏ | 82/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  83%|████████▎ | 83/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  84%|████████▍ | 84/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  85%|████████▌ | 85/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  86%|████████▌ | 86/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  87%|████████▋ | 87/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  88%|████████▊ | 88/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  89%|████████▉ | 89/100 [00:00<00:00, 133.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  90%|█████████ | 90/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  90%|█████████ | 90/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  91%|█████████ | 91/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  92%|█████████▏| 92/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  93%|█████████▎| 93/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  94%|█████████▍| 94/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  95%|█████████▌| 95/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  96%|█████████▌| 96/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  97%|█████████▋| 97/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  98%|█████████▊| 98/100 [00:00<00:00, 134.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  99%|█████████▉| 99/100 [00:00<00:00, 134.88it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.019 | Best: {'fx': 511.395} (0.019):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.019 | Best: {'fx': 511.395} (0.019):   2%|▏         | 1/50 [00:00<00:05,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.014 | Best: {'fx': 511.395} (0.019):   2%|▏         | 1/50 [00:00<00:05,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.015 | Best: {'fx': 511.395} (0.019):   4%|▍         | 2/50 [00:00<00:05,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.018 | Best: {'fx': 511.395} (0.019):   6%|▌         | 3/50 [00:00<00:05,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.021 | Best: {'fx': 513.435} (0.021):   8%|▊         | 4/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.027 | Best: {'fx': 517.517} (0.027):  10%|█         | 5/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.022 | Best: {'fx': 517.517} (0.027):  12%|█▏        | 6/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.020 | Best: {'fx': 517.517} (0.027):  14%|█▍        | 7/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 517.517} (0.027):  16%|█▌        | 8/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 517.517} (0.027):  18%|█▊        | 9/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.031 | Best: {'fx': 519.558} (0.031):  20%|██        | 10/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 519.558} (0.031):  22%|██▏       | 11/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.033 | Best: {'fx': 520.578} (0.033):  24%|██▍       | 12/50 [00:00<00:04,  9.28it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.033 | Best: {'fx': 520.578} (0.033):  26%|██▌       | 13/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.037 | Best: {'fx': 521.599} (0.037):  26%|██▌       | 13/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.039 | Best: {'fx': 522.619} (0.039):  28%|██▊       | 14/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.042 | Best: {'fx': 523.639} (0.042):  30%|███       | 15/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.043 | Best: {'fx': 524.66} (0.043):  32%|███▏      | 16/50 [00:00<00:00, 61.64it/s] 
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.049 | Best: {'fx': 525.68} (0.049):  34%|███▍      | 17/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.051 | Best: {'fx': 527.721} (0.051):  36%|███▌      | 18/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.057 | Best: {'fx': 528.741} (0.057):  38%|███▊      | 19/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.050 | Best: {'fx': 528.741} (0.057):  40%|████      | 20/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.068 | Best: {'fx': 531.803} (0.068):  42%|████▏     | 21/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.058 | Best: {'fx': 531.803} (0.068):  44%|████▍     | 22/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.064 | Best: {'fx': 531.803} (0.068):  46%|████▌     | 23/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.059 | Best: {'fx': 531.803} (0.068):  48%|████▊     | 24/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.064 | Best: {'fx': 531.803} (0.068):  50%|█████     | 25/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.064 | Best: {'fx': 531.803} (0.068):  52%|█████▏    | 26/50 [00:00<00:00, 61.64it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.064 | Best: {'fx': 531.803} (0.068):  54%|█████▍    | 27/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.062 | Best: {'fx': 531.803} (0.068):  54%|█████▍    | 27/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.054 | Best: {'fx': 531.803} (0.068):  56%|█████▌    | 28/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.050 | Best: {'fx': 531.803} (0.068):  58%|█████▊    | 29/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.036 | Best: {'fx': 531.803} (0.068):  60%|██████    | 30/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.038 | Best: {'fx': 531.803} (0.068):  62%|██████▏   | 31/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.046 | Best: {'fx': 531.803} (0.068):  64%|██████▍   | 32/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.044 | Best: {'fx': 531.803} (0.068):  66%|██████▌   | 33/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.042 | Best: {'fx': 531.803} (0.068):  68%|██████▊   | 34/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.039 | Best: {'fx': 531.803} (0.068):  70%|███████   | 35/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.032 | Best: {'fx': 531.803} (0.068):  72%|███████▏  | 36/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.034 | Best: {'fx': 531.803} (0.068):  74%|███████▍  | 37/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 531.803} (0.068):  76%|███████▌  | 38/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.033 | Best: {'fx': 531.803} (0.068):  78%|███████▊  | 39/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.031 | Best: {'fx': 531.803} (0.068):  80%|████████  | 40/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 531.803} (0.068):  82%|████████▏ | 41/50 [00:00<00:00, 93.06it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 531.803} (0.068):  84%|████████▍ | 42/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.026 | Best: {'fx': 531.803} (0.068):  84%|████████▍ | 42/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.024 | Best: {'fx': 531.803} (0.068):  86%|████████▌ | 43/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.027 | Best: {'fx': 531.803} (0.068):  88%|████████▊ | 44/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 531.803} (0.068):  90%|█████████ | 45/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.028 | Best: {'fx': 531.803} (0.068):  92%|█████████▏| 46/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 531.803} (0.068):  94%|█████████▍| 47/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.028 | Best: {'fx': 531.803} (0.068):  96%|█████████▌| 48/50 [00:00<00:00, 113.65it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 531.803} (0.068):  98%|█████████▊| 49/50 [00:00<00:00, 113.65it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1002.0
    py: 766.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


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

Initial score: 37.3468: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 37.3468, picklable_objective(x0) = 37.3546

Best score: 37.3546: : 0iter [00:01, ?iter/s] 
Best score: 37.3546: : 1iter [00:01,  1.95s/iter]
Best score: 37.3546: : 1iter [00:01,  1.95s/iter]
Best score: 37.3546: : 2iter [00:02,  1.95s/iter]
Best score: 37.3546: : 3iter [00:02,  1.95s/iter]
Best score: 37.3546: : 4iter [00:02,  1.95s/iter]
Best score: 37.3546: : 5iter [00:02,  3.20iter/s]
Best score: 37.3546: : 5iter [00:02,  3.20iter/s]
Best score: 37.3546: : 6iter [00:02,  3.20iter/s]
Best score: 37.3546: : 7iter [00:02,  3.20iter/s]
Best score: 37.3546: : 8iter [00:02,  3.20iter/s]
Best score: 37.3546: : 9iter [00:02,  6.46iter/s]
Best score: 37.3546: : 9iter [00:02,  6.46iter/s]
Best score: 37.3546: : 10iter [00:02,  6.46iter/s]
Best score: 37.3546: : 11iter [00:02,  6.46iter/s]
Best score: 37.3546: : 12iter [00:02,  6.46iter/s]
Best score: 37.3546: : 13iter [00:02, 10.20iter/s]
Best score: 37.3546: : 13iter [00:02, 10.20iter/s]
Best score: 37.3546: : 14iter [00:02, 10.20iter/s]
Best score: 37.3546: : 15iter [00:02, 10.20iter/s]
Best score: 37.3546: : 16iter [00:02, 10.20iter/s]
Best score: 37.3546: : 17iter [00:02, 14.28iter/s]
Best score: 37.3546: : 17iter [00:02, 14.28iter/s]
Best score: 37.3546: : 18iter [00:02, 14.28iter/s]
Best score: 37.3546: : 19iter [00:02, 14.28iter/s]
Best score: 37.3546: : 20iter [00:02, 14.28iter/s]
Best score: 37.3546: : 21iter [00:02, 18.44iter/s]
Best score: 37.3546: : 21iter [00:02, 18.44iter/s]
Best score: 37.3546: : 22iter [00:02, 18.44iter/s]
Best score: 37.3546: : 23iter [00:02, 18.44iter/s]
Best score: 37.3546: : 24iter [00:02, 18.44iter/s]
Best score: 37.3546: : 25iter [00:02, 22.50iter/s]
Best score: 37.3546: : 25iter [00:02, 22.50iter/s]
Best score: 37.3546: : 26iter [00:02, 22.50iter/s]
Best score: 37.3546: : 27iter [00:02, 22.50iter/s]
Best score: 37.3546: : 28iter [00:02, 22.50iter/s]
Best score: 37.3546: : 29iter [00:02, 26.19iter/s]
Best score: 37.3546: : 29iter [00:02, 26.19iter/s]
Best score: 37.3546: : 30iter [00:02, 26.19iter/s]
Best score: 37.3546: : 31iter [00:02, 26.19iter/s]
Best score: 37.3546: : 32iter [00:02, 26.19iter/s]
Best score: 37.3546: : 33iter [00:02, 29.16iter/s]
Best score: 37.3546: : 33iter [00:02, 29.16iter/s]
Best score: 37.3546: : 34iter [00:02, 29.16iter/s]
Best score: 37.3546: : 35iter [00:02, 29.16iter/s]
Best score: 37.3546: : 36iter [00:02, 29.16iter/s]
Best score: 37.3546: : 37iter [00:02, 31.59iter/s]
Best score: 37.3546: : 37iter [00:02, 31.59iter/s]
Best score: 37.3546: : 38iter [00:02, 31.59iter/s]
Best score: 37.3546: : 39iter [00:02, 31.59iter/s]
Best score: 37.3546: : 40iter [00:02, 31.59iter/s]
Best score: 37.3546: : 41iter [00:02, 33.45iter/s]
Best score: 37.3546: : 41iter [00:02, 33.45iter/s]
Best score: 37.3546: : 42iter [00:03, 33.45iter/s]
Best score: 37.3546: : 43iter [00:03, 33.45iter/s]
Best score: 37.3546: : 44iter [00:03, 33.45iter/s]
Best score: 37.3546: : 45iter [00:03, 35.05iter/s]
Best score: 37.3546: : 45iter [00:03, 35.05iter/s]
Best score: 37.3546: : 46iter [00:03, 35.05iter/s]
Best score: 37.3546: : 47iter [00:03, 35.05iter/s]
Best score: 37.3546: : 48iter [00:03, 35.05iter/s]
Best score: 37.3546: : 49iter [00:03, 36.35iter/s]
[ITER 50] Best: 37.3546
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1002.0000
  py: 766.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 37.3546: : 49iter [00:03, 36.35iter/s]
Best score: 37.3546: : 50iter [00:03, 36.35iter/s]
Best score: 37.3546: : 51iter [00:03, 36.35iter/s]
Best score: 37.3546: : 52iter [00:03, 36.35iter/s]
Best score: 37.3546: : 53iter [00:03, 37.17iter/s]
Best score: 37.3546: : 53iter [00:03, 37.17iter/s]
Best score: 37.3546: : 54iter [00:03, 37.17iter/s]
Best score: 37.3546: : 55iter [00:03, 37.17iter/s]
Best score: 37.3546: : 56iter [00:03, 37.17iter/s]
Best score: 37.3546: : 57iter [00:03, 37.84iter/s]
Best score: 37.3546: : 57iter [00:03, 37.84iter/s]
Best score: 37.3546: : 58iter [00:03, 37.84iter/s]
Best score: 37.3546: : 59iter [00:03, 37.84iter/s]
Best score: 37.3546: : 60iter [00:03, 37.84iter/s]
Best score: 37.3546: : 61iter [00:03, 38.16iter/s]
Best score: 37.3546: : 61iter [00:03, 38.16iter/s]
Best score: 37.3546: : 62iter [00:03, 38.16iter/s]
Best score: 37.3546: : 63iter [00:03, 38.16iter/s]
Best score: 37.3546: : 64iter [00:03, 38.16iter/s]
Best score: 37.3546: : 65iter [00:03, 38.57iter/s]
Best score: 37.3546: : 65iter [00:03, 38.57iter/s]
Best score: 37.3546: : 66iter [00:03, 38.57iter/s]
Best score: 37.3546: : 67iter [00:03, 38.57iter/s]
Best score: 37.3546: : 68iter [00:03, 38.57iter/s]
Best score: 37.3546: : 69iter [00:03, 38.57iter/s]
Best score: 37.3546: : 70iter [00:03, 38.82iter/s]
Best score: 37.3546: : 70iter [00:03, 38.82iter/s]
Best score: 37.3546: : 71iter [00:03, 38.82iter/s]
Best score: 37.3546: : 72iter [00:03, 38.82iter/s]
Best score: 37.3546: : 73iter [00:03, 38.82iter/s]
Best score: 37.3546: : 74iter [00:03, 38.82iter/s]
Best score: 37.3546: : 75iter [00:03, 39.30iter/s]
Best score: 37.3546: : 75iter [00:03, 39.30iter/s]
Best score: 37.3546: : 76iter [00:03, 39.30iter/s]
Best score: 37.3546: : 77iter [00:03, 39.30iter/s]
Best score: 37.3546: : 78iter [00:03, 39.30iter/s]
Best score: 37.3546: : 79iter [00:03, 39.30iter/s]
Best score: 37.3546: : 80iter [00:03, 39.34iter/s]
Best score: 37.3546: : 80iter [00:03, 39.34iter/s]
Best score: 37.3546: : 81iter [00:04, 39.34iter/s]
Best score: 37.3546: : 82iter [00:04, 39.34iter/s]
Best score: 37.3546: : 83iter [00:04, 39.34iter/s]
Best score: 37.3546: : 84iter [00:04, 39.48iter/s]
Best score: 37.3546: : 84iter [00:04, 39.48iter/s]
Best score: 37.3546: : 85iter [00:04, 39.48iter/s]
Best score: 37.3546: : 86iter [00:04, 39.48iter/s]
Best score: 37.3546: : 87iter [00:04, 39.48iter/s]
Best score: 37.3546: : 88iter [00:04, 39.48iter/s]
Best score: 37.3546: : 89iter [00:04, 39.77iter/s]
Best score: 37.3546: : 89iter [00:04, 39.77iter/s]
Best score: 37.3546: : 90iter [00:04, 39.77iter/s]
Best score: 37.3546: : 91iter [00:04, 39.77iter/s]
Best score: 37.3546: : 92iter [00:04, 39.77iter/s]
Best score: 37.3546: : 93iter [00:04, 39.52iter/s]
Best score: 37.3546: : 93iter [00:04, 39.52iter/s]
Best score: 37.3546: : 94iter [00:04, 39.52iter/s]
Best score: 37.3546: : 95iter [00:04, 39.52iter/s]
Best score: 37.3546: : 96iter [00:04, 39.52iter/s]
Best score: 37.3546: : 97iter [00:04, 39.52iter/s]
Best score: 37.3546: : 98iter [00:04, 39.73iter/s]
Best score: 37.3546: : 98iter [00:04, 39.73iter/s]
[ITER 100] Best: 18.1125
[PARAMS] Current best parameters:
  fx: 549.4226
  fy: 548.8184
  px: 1000.5021
  py: 766.9058
  k1: 0.04407711
  k2: -0.03393770
  k3: -0.00288284
  k4: 0.00232088
  p1: -0.00738507
  p2: 0.01198345
  p3: -0.01605413
  p4: -0.00211411
  s1: 0.0122
  s2: 0.0021
  s3: -0.0102
  s4: 0.0179
  tilt_x: 0.0331
  tilt_y: 0.0367
  scale: 2.7985
  rotation: 1.0228

Best score: 18.1125: : 99iter [00:04, 39.73iter/s]
Best score: 18.1125: : 100iter [00:04, 39.73iter/s]
Best score: 18.1125: : 101iter [00:04, 39.73iter/s]
Best score: 18.1125: : 102iter [00:04, 36.98iter/s]
Best score: 18.1125: : 102iter [00:04, 36.98iter/s]
Best score: 18.1125: : 103iter [00:04, 36.98iter/s]
Best score: 18.1125: : 104iter [00:04, 36.98iter/s]
Best score: 18.1125: : 105iter [00:04, 36.98iter/s]
Best score: 18.1125: : 106iter [00:04, 37.60iter/s]
Best score: 18.1125: : 106iter [00:04, 37.60iter/s]
Best score: 18.1125: : 107iter [00:04, 37.60iter/s]
Best score: 18.1125: : 108iter [00:04, 37.60iter/s]
Best score: 18.1125: : 109iter [00:04, 37.60iter/s]
Best score: 18.1125: : 110iter [00:04, 37.90iter/s]
Best score: 18.1125: : 110iter [00:04, 37.90iter/s]
Best score: 18.1125: : 111iter [00:04, 37.90iter/s]
Best score: 18.1125: : 112iter [00:04, 37.90iter/s]
Best score: 18.1125: : 113iter [00:04, 37.90iter/s]
Best score: 18.1125: : 114iter [00:04, 38.35iter/s]
Best score: 18.1125: : 114iter [00:04, 38.35iter/s]
Best score: 18.1125: : 115iter [00:04, 38.35iter/s]
Best score: 18.1125: : 116iter [00:04, 38.35iter/s]
Best score: 18.1125: : 117iter [00:04, 38.35iter/s]
Best score: 18.1125: : 118iter [00:04, 38.66iter/s]
Best score: 18.1125: : 118iter [00:04, 38.66iter/s]
Best score: 18.1125: : 119iter [00:05, 38.66iter/s]
Best score: 18.1125: : 120iter [00:05, 38.66iter/s]
Best score: 18.1125: : 121iter [00:05, 38.66iter/s]
Best score: 18.1125: : 122iter [00:05, 38.64iter/s]
Best score: 18.1125: : 122iter [00:05, 38.64iter/s]
Best score: 18.1125: : 123iter [00:05, 38.64iter/s]
Best score: 18.1125: : 124iter [00:05, 38.64iter/s]
Best score: 18.1125: : 125iter [00:05, 38.64iter/s]
Best score: 18.1125: : 126iter [00:05, 38.94iter/s]
Best score: 18.1125: : 126iter [00:05, 38.94iter/s]
Best score: 18.1125: : 127iter [00:05, 38.94iter/s]
Best score: 18.1125: : 128iter [00:05, 38.94iter/s]
Best score: 18.1125: : 129iter [00:05, 38.94iter/s]
Best score: 18.1125: : 130iter [00:05, 38.80iter/s]
Best score: 18.1125: : 130iter [00:05, 38.80iter/s]
Best score: 18.1125: : 131iter [00:05, 38.80iter/s]
Best score: 18.1125: : 132iter [00:05, 38.80iter/s]
Best score: 18.1125: : 133iter [00:05, 38.80iter/s]
Best score: 18.1125: : 134iter [00:05, 39.06iter/s]
Best score: 18.1125: : 134iter [00:05, 39.06iter/s]
Best score: 18.1125: : 135iter [00:05, 39.06iter/s]
Best score: 18.1125: : 136iter [00:05, 39.06iter/s]
Best score: 18.1125: : 137iter [00:05, 39.06iter/s]
Best score: 18.1125: : 138iter [00:05, 39.19iter/s]
Best score: 18.1125: : 138iter [00:05, 39.19iter/s]
Best score: 18.1125: : 139iter [00:05, 39.19iter/s]
Best score: 18.1125: : 140iter [00:05, 39.19iter/s]
Best score: 18.1125: : 141iter [00:05, 39.19iter/s]
Best score: 18.1125: : 142iter [00:05, 38.96iter/s]
Best score: 18.1125: : 142iter [00:05, 38.96iter/s]
Best score: 18.1125: : 143iter [00:05, 38.96iter/s]
Best score: 18.1125: : 144iter [00:05, 38.96iter/s]
Best score: 18.1125: : 145iter [00:05, 38.96iter/s]
Best score: 18.1125: : 146iter [00:05, 39.16iter/s]
Best score: 18.1125: : 146iter [00:05, 39.16iter/s]
Best score: 18.1125: : 147iter [00:05, 39.16iter/s]
Best score: 18.1125: : 148iter [00:05, 39.16iter/s]
[ITER 150] Best: 18.1125
[PARAMS] Current best parameters:
  fx: 549.4226
  fy: 548.8184
  px: 1000.5021
  py: 766.9058
  k1: 0.04407711
  k2: -0.03393770
  k3: -0.00288284
  k4: 0.00232088
  p1: -0.00738507
  p2: 0.01198345
  p3: -0.01605413
  p4: -0.00211411
  s1: 0.0122
  s2: 0.0021
  s3: -0.0102
  s4: 0.0179
  tilt_x: 0.0331
  tilt_y: 0.0367
  scale: 2.7985
  rotation: 1.0228

Best score: 18.1125: : 149iter [00:05, 39.16iter/s]
Best score: 18.1125: : 150iter [00:05, 39.09iter/s]
Best score: 18.1125: : 150iter [00:05, 39.09iter/s]
Best score: 18.1125: : 151iter [00:05, 39.09iter/s]
Best score: 18.1125: : 152iter [00:05, 39.09iter/s]
Best score: 18.1125: : 153iter [00:05, 39.09iter/s]
Best score: 18.1125: : 154iter [00:05, 31.96iter/s]
Best score: 18.1125: : 154iter [00:05, 31.96iter/s]
Best score: 18.1125: : 155iter [00:05, 31.96iter/s]
Best score: 18.1125: : 156iter [00:06, 31.96iter/s]
Best score: 18.1125: : 157iter [00:06, 31.96iter/s]
Best score: 18.1125: : 158iter [00:06, 33.99iter/s]
Best score: 18.1125: : 158iter [00:06, 33.99iter/s]
Best score: 18.1125: : 159iter [00:06, 33.99iter/s]
Best score: 18.1125: : 160iter [00:06, 33.99iter/s]
Best score: 18.1125: : 161iter [00:06, 33.99iter/s]
Best score: 18.1125: : 162iter [00:06, 35.30iter/s]
Best score: 18.1125: : 162iter [00:06, 35.30iter/s]
Best score: 18.1125: : 163iter [00:06, 35.30iter/s]
Best score: 18.1125: : 164iter [00:06, 35.30iter/s]
Best score: 18.1125: : 165iter [00:06, 35.30iter/s]
Best score: 18.1125: : 166iter [00:06, 36.56iter/s]
Best score: 18.1125: : 166iter [00:06, 36.56iter/s]
Best score: 18.1125: : 167iter [00:06, 36.56iter/s]
Best score: 18.1125: : 168iter [00:06, 36.56iter/s]
Best score: 18.1125: : 169iter [00:06, 36.56iter/s]
Best score: 18.1125: : 170iter [00:06, 37.27iter/s]
Best score: 18.1125: : 170iter [00:06, 37.27iter/s]
Best score: 18.1125: : 171iter [00:06, 37.27iter/s]
Best score: 18.1125: : 172iter [00:06, 37.27iter/s]
Best score: 18.1125: : 173iter [00:06, 37.27iter/s]
Best score: 18.1125: : 174iter [00:06, 37.98iter/s]
Best score: 18.1125: : 174iter [00:06, 37.98iter/s]
Best score: 18.1125: : 175iter [00:06, 37.98iter/s]
Best score: 18.1125: : 176iter [00:06, 37.98iter/s]
Best score: 18.1125: : 177iter [00:06, 37.98iter/s]
Best score: 18.1125: : 178iter [00:06, 38.39iter/s]
Best score: 18.1125: : 178iter [00:06, 38.39iter/s]
Best score: 18.1125: : 179iter [00:06, 38.39iter/s]
Best score: 18.1125: : 180iter [00:06, 38.39iter/s]
Best score: 18.1125: : 181iter [00:06, 38.39iter/s]
Best score: 18.1125: : 182iter [00:06, 38.58iter/s]
Best score: 18.1125: : 182iter [00:06, 38.58iter/s]
Best score: 18.1125: : 183iter [00:06, 38.58iter/s]
Best score: 18.1125: : 184iter [00:06, 38.58iter/s]
Best score: 18.1125: : 185iter [00:06, 38.58iter/s]
Best score: 18.1125: : 186iter [00:06, 38.58iter/s]
Best score: 18.1125: : 187iter [00:06, 39.07iter/s]
Best score: 18.1125: : 187iter [00:06, 39.07iter/s]
Best score: 18.1125: : 188iter [00:06, 39.07iter/s]
Best score: 18.1125: : 189iter [00:06, 39.07iter/s]
Best score: 18.1125: : 190iter [00:06, 39.07iter/s]
Best score: 18.1125: : 191iter [00:06, 39.14iter/s]
Best score: 18.1125: : 191iter [00:06, 39.14iter/s]
Best score: 18.1125: : 192iter [00:06, 39.14iter/s]
Best score: 18.1125: : 193iter [00:06, 39.14iter/s]
Best score: 18.1125: : 194iter [00:06, 39.14iter/s]
Best score: 18.1125: : 195iter [00:06, 39.36iter/s]
Best score: 18.1125: : 195iter [00:07, 39.36iter/s]
Best score: 18.1125: : 196iter [00:07, 39.36iter/s]
Best score: 18.1125: : 197iter [00:07, 39.36iter/s]
Best score: 18.1125: : 198iter [00:07, 39.36iter/s]
[ITER 200] Best: 18.1125
[PARAMS] Current best parameters:
  fx: 549.4226
  fy: 548.8184
  px: 1000.5021
  py: 766.9058
  k1: 0.04407711
  k2: -0.03393770
  k3: -0.00288284
  k4: 0.00232088
  p1: -0.00738507
  p2: 0.01198345
  p3: -0.01605413
  p4: -0.00211411
  s1: 0.0122
  s2: 0.0021
  s3: -0.0102
  s4: 0.0179
  tilt_x: 0.0331
  tilt_y: 0.0367
  scale: 2.7985
  rotation: 1.0228

Best score: 18.1125: : 199iter [00:07, 39.36iter/s]
Best score: 18.1125: : 200iter [00:07, 39.41iter/s]
Best score: 18.1125: : 200iter [00:07, 39.41iter/s]
Best score: 18.1125: : 201iter [00:07, 39.41iter/s]
Best score: 18.1125: : 202iter [00:07, 39.41iter/s]
Best score: 18.1125: : 203iter [00:07, 39.41iter/s]
Best score: 18.1125: : 204iter [00:07, 32.67iter/s]
Best score: 18.1125: : 204iter [00:07, 32.67iter/s]
Best score: 18.1125: : 205iter [00:07, 32.67iter/s]
Best score: 18.1125: : 206iter [00:07, 32.67iter/s]
Best score: 18.1125: : 207iter [00:07, 32.67iter/s]
Best score: 18.1125: : 208iter [00:07, 34.41iter/s]
Best score: 18.1125: : 208iter [00:07, 34.41iter/s]
Best score: 18.1125: : 209iter [00:07, 34.41iter/s]
Best score: 18.1125: : 210iter [00:07, 34.41iter/s]
Best score: 18.1125: : 211iter [00:07, 34.41iter/s]
Best score: 18.1125: : 212iter [00:07, 35.68iter/s]
Best score: 18.1125: : 212iter [00:07, 35.68iter/s]
Best score: 18.1125: : 213iter [00:07, 35.68iter/s]
Best score: 18.1125: : 214iter [00:07, 35.68iter/s]
Best score: 18.1125: : 215iter [00:07, 35.68iter/s]
Best score: 18.1125: : 216iter [00:07, 35.68iter/s]
Best score: 18.1125: : 217iter [00:07, 37.21iter/s]
Best score: 18.1125: : 217iter [00:07, 37.21iter/s]
Best score: 18.1125: : 218iter [00:07, 37.21iter/s]
Best score: 18.1125: : 219iter [00:07, 37.21iter/s]
Best score: 18.1125: : 220iter [00:07, 37.21iter/s]
Best score: 18.1125: : 221iter [00:07, 37.65iter/s]
Best score: 18.1125: : 221iter [00:07, 37.65iter/s]
Best score: 18.1125: : 222iter [00:07, 37.65iter/s]
Best score: 18.1125: : 223iter [00:07, 37.65iter/s]
Best score: 18.1125: : 224iter [00:07, 37.65iter/s]
Best score: 18.1125: : 225iter [00:07, 37.65iter/s]
Best score: 18.1125: : 226iter [00:07, 38.56iter/s]
Best score: 18.1125: : 226iter [00:07, 38.56iter/s]
Best score: 18.1125: : 227iter [00:07, 38.56iter/s]
Best score: 18.1125: : 228iter [00:07, 38.56iter/s]
Best score: 18.1125: : 229iter [00:07, 38.56iter/s]
Best score: 18.1125: : 230iter [00:07, 38.79iter/s]
Best score: 18.1125: : 230iter [00:07, 38.79iter/s]
Best score: 18.1125: : 231iter [00:07, 38.79iter/s]
Best score: 18.1125: : 232iter [00:08, 38.79iter/s]
Best score: 18.1125: : 233iter [00:08, 38.79iter/s]
Best score: 18.1125: : 234iter [00:08, 38.79iter/s]
Best score: 18.1125: : 235iter [00:08, 39.32iter/s]
Best score: 18.1125: : 235iter [00:08, 39.32iter/s]
Best score: 18.1125: : 236iter [00:08, 39.32iter/s]
Best score: 18.1125: : 237iter [00:08, 39.32iter/s]
Best score: 18.1125: : 238iter [00:08, 39.32iter/s]
Best score: 8.4068: : 239iter [00:08, 39.32iter/s] 
Best score: 8.4068: : 240iter [00:08, 39.33iter/s]
Best score: 8.4068: : 240iter [00:08, 39.33iter/s]
Best score: 8.4068: : 241iter [00:08, 39.33iter/s]
Best score: 8.4068: : 242iter [00:08, 39.33iter/s]
Best score: 8.4068: : 243iter [00:08, 39.33iter/s]
Best score: 8.4068: : 244iter [00:08, 39.33iter/s]
Best score: 8.4068: : 245iter [00:08, 39.58iter/s]
Best score: 8.4068: : 245iter [00:08, 39.58iter/s]
Best score: 8.4068: : 246iter [00:08, 39.58iter/s]
Best score: 8.4068: : 247iter [00:08, 39.58iter/s]
Best score: 8.4068: : 248iter [00:08, 39.58iter/s]
Best score: 8.4068: : 249iter [00:08, 39.57iter/s]
[ITER 250] Best: 6.3924
[PARAMS] Current best parameters:
  fx: 568.5469
  fy: 568.4071
  px: 995.8834
  py: 763.7767
  k1: 0.05830985
  k2: -0.05755100
  k3: -0.00745203
  k4: 0.00812661
  p1: -0.00768036
  p2: -0.00411253
  p3: -0.01777239
  p4: 0.00098447
  s1: 0.0312
  s2: -0.0067
  s3: 0.0152
  s4: 0.0025
  tilt_x: 0.0269
  tilt_y: 0.0093
  scale: 2.7035
  rotation: 1.0034

Best score: 6.3924: : 249iter [00:08, 39.57iter/s]
Best score: 6.3924: : 250iter [00:08, 39.57iter/s]
Best score: 6.3924: : 251iter [00:08, 39.57iter/s]
Best score: 6.3924: : 252iter [00:08, 39.57iter/s]
Best score: 6.3924: : 253iter [00:08, 32.44iter/s]
Best score: 6.3924: : 253iter [00:08, 32.44iter/s]
Best score: 6.3924: : 254iter [00:08, 32.44iter/s]
Best score: 6.3924: : 255iter [00:08, 32.44iter/s]
Best score: 6.3924: : 256iter [00:08, 32.44iter/s]
Best score: 6.3924: : 257iter [00:08, 32.44iter/s]
Best score: 6.3924: : 258iter [00:08, 34.73iter/s]
Best score: 6.3924: : 258iter [00:08, 34.73iter/s]
Best score: 6.3924: : 259iter [00:08, 34.73iter/s]
Best score: 6.3924: : 260iter [00:08, 34.73iter/s]
Best score: 6.3924: : 261iter [00:08, 34.73iter/s]
Best score: 6.3924: : 262iter [00:08, 35.84iter/s]
Best score: 6.3924: : 262iter [00:08, 35.84iter/s]
Best score: 6.3924: : 263iter [00:08, 35.84iter/s]
Best score: 6.3924: : 264iter [00:08, 35.84iter/s]
Best score: 6.3924: : 265iter [00:08, 35.84iter/s]
Best score: 6.3924: : 266iter [00:08, 35.84iter/s]
Best score: 6.3924: : 267iter [00:08, 37.11iter/s]
Best score: 6.3924: : 267iter [00:08, 37.11iter/s]
Best score: 6.3924: : 268iter [00:08, 37.11iter/s]
Best score: 6.3924: : 269iter [00:09, 37.11iter/s]
Best score: 6.3924: : 270iter [00:09, 37.11iter/s]
Best score: 6.3924: : 271iter [00:09, 37.65iter/s]
Best score: 6.3924: : 271iter [00:09, 37.65iter/s]
Best score: 6.3924: : 272iter [00:09, 37.65iter/s]
Best score: 6.3924: : 273iter [00:09, 37.65iter/s]
Best score: 6.3924: : 274iter [00:09, 37.65iter/s]
Best score: 6.3924: : 275iter [00:09, 37.65iter/s]
Best score: 6.3924: : 276iter [00:09, 38.42iter/s]
Best score: 6.3924: : 276iter [00:09, 38.42iter/s]
Best score: 6.3924: : 277iter [00:09, 38.42iter/s]
Best score: 6.3924: : 278iter [00:09, 38.42iter/s]
Best score: 5.1987: : 279iter [00:09, 38.42iter/s]
Best score: 5.1987: : 280iter [00:09, 38.54iter/s]
Best score: 5.1987: : 280iter [00:09, 38.54iter/s]
Best score: 5.1987: : 281iter [00:09, 38.54iter/s]
Best score: 5.1987: : 282iter [00:09, 38.54iter/s]
Best score: 5.1987: : 283iter [00:09, 38.54iter/s]
Best score: 5.1987: : 284iter [00:09, 38.91iter/s]
Best score: 5.1987: : 284iter [00:09, 38.91iter/s]
Best score: 5.1987: : 285iter [00:09, 38.91iter/s]
Best score: 5.1987: : 286iter [00:09, 38.91iter/s]
Best score: 5.1987: : 287iter [00:09, 38.91iter/s]
Best score: 5.1987: : 288iter [00:09, 38.91iter/s]
Best score: 5.1987: : 289iter [00:09, 39.35iter/s]
Best score: 4.8596: : 289iter [00:09, 39.35iter/s]
Best score: 4.8596: : 290iter [00:09, 39.35iter/s]
Best score: 4.8596: : 291iter [00:09, 39.35iter/s]
Best score: 4.8596: : 292iter [00:09, 39.35iter/s]
Best score: 4.8596: : 293iter [00:09, 39.33iter/s]
Best score: 4.8596: : 293iter [00:09, 39.33iter/s]
Best score: 4.8596: : 294iter [00:09, 39.33iter/s]
Best score: 4.8596: : 295iter [00:09, 39.33iter/s]
Best score: 4.8596: : 296iter [00:09, 39.33iter/s]
Best score: 4.8596: : 297iter [00:09, 39.33iter/s]
Best score: 4.8596: : 298iter [00:09, 39.71iter/s]
Best score: 4.8596: : 298iter [00:09, 39.71iter/s]
[ITER 300] Best: 4.6314
[PARAMS] Current best parameters:
  fx: 573.9330
  fy: 573.2936
  px: 996.4208
  py: 767.3526
  k1: 0.02272885
  k2: -0.02716506
  k3: -0.01972524
  k4: 0.01057764
  p1: -0.00999906
  p2: -0.00656865
  p3: -0.01578996
  p4: 0.01484035
  s1: 0.0060
  s2: -0.0063
  s3: 0.0120
  s4: 0.0017
  tilt_x: 0.0382
  tilt_y: 0.0030
  scale: 2.7858
  rotation: 0.8494

Best score: 4.6314: : 299iter [00:09, 39.71iter/s]
Best score: 4.6314: : 300iter [00:09, 39.71iter/s]
Best score: 4.6314: : 301iter [00:10, 39.71iter/s]
Best score: 4.6314: : 302iter [00:10, 26.51iter/s]
Best score: 4.6314: : 302iter [00:10, 26.51iter/s]
Best score: 4.6314: : 303iter [00:10, 26.51iter/s]
Best score: 4.6314: : 304iter [00:10, 26.51iter/s]
Best score: 4.6314: : 305iter [00:10, 26.51iter/s]
Best score: 4.6314: : 306iter [00:10, 26.51iter/s]
Best score: 4.6314: : 307iter [00:10, 29.88iter/s]
Best score: 4.6314: : 307iter [00:10, 29.88iter/s]
Best score: 4.6314: : 308iter [00:10, 29.88iter/s]
Best score: 3.9071: : 309iter [00:10, 29.88iter/s]
Best score: 3.9071: : 310iter [00:10, 29.88iter/s]
Best score: 3.9071: : 311iter [00:10, 31.95iter/s]
Best score: 3.9071: : 311iter [00:10, 31.95iter/s]
Best score: 3.9071: : 312iter [00:10, 31.95iter/s]
Best score: 3.9071: : 313iter [00:10, 31.95iter/s]
Best score: 3.9071: : 314iter [00:10, 31.95iter/s]
Best score: 3.9071: : 315iter [00:10, 31.95iter/s]
Best score: 3.9071: : 316iter [00:10, 34.27iter/s]
Best score: 3.9071: : 316iter [00:10, 34.27iter/s]
Best score: 3.9071: : 317iter [00:10, 34.27iter/s]
Best score: 3.9071: : 318iter [00:10, 34.27iter/s]
Best score: 2.3659: : 319iter [00:10, 34.27iter/s]
Best score: 2.3659: : 320iter [00:10, 35.54iter/s]
Best score: 2.3659: : 320iter [00:10, 35.54iter/s]
Best score: 2.3659: : 321iter [00:10, 35.54iter/s]
Best score: 2.3659: : 322iter [00:10, 35.54iter/s]
Best score: 2.3659: : 323iter [00:10, 35.54iter/s]
Best score: 2.3659: : 324iter [00:10, 35.54iter/s]
Best score: 2.3659: : 325iter [00:10, 37.07iter/s]
Best score: 2.3659: : 325iter [00:10, 37.07iter/s]
Best score: 2.3659: : 326iter [00:10, 37.07iter/s]
Best score: 2.3659: : 327iter [00:10, 37.07iter/s]
Best score: 2.3659: : 328iter [00:10, 37.07iter/s]
Best score: 2.3659: : 329iter [00:10, 37.07iter/s]
Best score: 2.3659: : 330iter [00:10, 37.82iter/s]
Best score: 2.3659: : 330iter [00:10, 37.82iter/s]
Best score: 2.3659: : 331iter [00:10, 37.82iter/s]
Best score: 2.3659: : 332iter [00:10, 37.82iter/s]
Best score: 2.3659: : 333iter [00:10, 37.82iter/s]
Best score: 2.3659: : 334iter [00:10, 37.82iter/s]
Best score: 2.3659: : 335iter [00:10, 38.60iter/s]
Best score: 2.3659: : 335iter [00:10, 38.60iter/s]
Best score: 2.3659: : 336iter [00:10, 38.60iter/s]
Best score: 2.3659: : 337iter [00:10, 38.60iter/s]
Best score: 2.3659: : 338iter [00:10, 38.60iter/s]
Best score: 1.8413: : 339iter [00:10, 38.60iter/s]
Best score: 1.8413: : 340iter [00:10, 38.88iter/s]
Best score: 1.8413: : 340iter [00:10, 38.88iter/s]
Best score: 1.8413: : 341iter [00:11, 38.88iter/s]
Best score: 1.8413: : 342iter [00:11, 38.88iter/s]
Best score: 1.8413: : 343iter [00:11, 38.88iter/s]
Best score: 1.8413: : 344iter [00:11, 38.88iter/s]
Best score: 1.8413: : 345iter [00:11, 39.37iter/s]
Best score: 1.8413: : 345iter [00:11, 39.37iter/s]
Best score: 1.8413: : 346iter [00:11, 39.37iter/s]
Best score: 1.8413: : 347iter [00:11, 39.37iter/s]
Best score: 1.8413: : 348iter [00:11, 39.37iter/s]
[ITER 350] Best: 1.2943
[PARAMS] Current best parameters:
  fx: 564.8077
  fy: 565.2664
  px: 996.4190
  py: 764.6283
  k1: -0.01001235
  k2: 0.00556983
  k3: -0.01982191
  k4: 0.00650245
  p1: 0.00532107
  p2: 0.00305015
  p3: -0.00250818
  p4: 0.00811839
  s1: -0.0123
  s2: -0.0035
  s3: -0.0015
  s4: -0.0103
  tilt_x: 0.0019
  tilt_y: 0.0204
  scale: 2.8838
  rotation: 0.8675

Best score: 1.2943: : 349iter [00:11, 39.37iter/s]
Best score: 1.2943: : 350iter [00:11, 39.37iter/s]
Best score: 1.2943: : 350iter [00:11, 39.37iter/s]
Best score: 1.2943: : 351iter [00:11, 39.37iter/s]
Best score: 1.2943: : 352iter [00:11, 39.37iter/s]
Best score: 1.2943: : 353iter [00:11, 39.37iter/s]
Best score: 1.2943: : 354iter [00:11, 33.05iter/s]
Best score: 1.2943: : 354iter [00:11, 33.05iter/s]
Best score: 1.2943: : 355iter [00:11, 33.05iter/s]
Best score: 1.2943: : 356iter [00:11, 33.05iter/s]
Best score: 1.2943: : 357iter [00:11, 33.05iter/s]
Best score: 1.2943: : 358iter [00:11, 34.67iter/s]
Best score: 1.2943: : 358iter [00:11, 34.67iter/s]
Best score: 1.2943: : 359iter [00:11, 34.67iter/s]
Best score: 1.2943: : 360iter [00:11, 34.67iter/s]
Best score: 1.2943: : 361iter [00:11, 34.67iter/s]
Best score: 1.2943: : 362iter [00:11, 35.83iter/s]
Best score: 1.2943: : 362iter [00:11, 35.83iter/s]
Best score: 1.2943: : 363iter [00:11, 35.83iter/s]
Best score: 1.2943: : 364iter [00:11, 35.83iter/s]
Best score: 1.2943: : 365iter [00:11, 35.83iter/s]
Best score: 1.2943: : 366iter [00:11, 35.83iter/s]
Best score: 1.2943: : 367iter [00:11, 37.22iter/s]
Best score: 1.2943: : 367iter [00:11, 37.22iter/s]
Best score: 1.2943: : 368iter [00:11, 37.22iter/s]
Best score: 1.2943: : 369iter [00:11, 37.22iter/s]
Best score: 1.2943: : 370iter [00:11, 37.22iter/s]
Best score: 1.2943: : 371iter [00:11, 37.81iter/s]
Best score: 1.2943: : 371iter [00:11, 37.81iter/s]
Best score: 1.2943: : 372iter [00:11, 37.81iter/s]
Best score: 1.2943: : 373iter [00:11, 37.81iter/s]
Best score: 1.2943: : 374iter [00:11, 37.81iter/s]
Best score: 1.2943: : 375iter [00:11, 37.75iter/s]
Best score: 1.2943: : 375iter [00:11, 37.75iter/s]
Best score: 1.2943: : 376iter [00:11, 37.75iter/s]
Best score: 1.2943: : 377iter [00:11, 37.75iter/s]
Best score: 1.2943: : 378iter [00:12, 37.75iter/s]
Best score: 1.2943: : 379iter [00:12, 37.75iter/s]
Best score: 1.2943: : 380iter [00:12, 38.26iter/s]
Best score: 1.2943: : 380iter [00:12, 38.26iter/s]
Best score: 1.2943: : 381iter [00:12, 38.26iter/s]
Best score: 1.2943: : 382iter [00:12, 38.26iter/s]
Best score: 1.2943: : 383iter [00:12, 38.26iter/s]
Best score: 1.2943: : 384iter [00:12, 38.26iter/s]
Best score: 1.2943: : 385iter [00:12, 38.95iter/s]
Best score: 1.2943: : 385iter [00:12, 38.95iter/s]
Best score: 1.2943: : 386iter [00:12, 38.95iter/s]
Best score: 1.2943: : 387iter [00:12, 38.95iter/s]
Best score: 1.2943: : 388iter [00:12, 38.95iter/s]
Best score: 0.9361: : 389iter [00:12, 38.95iter/s]
Best score: 0.9361: : 390iter [00:12, 39.16iter/s]
Best score: 0.9361: : 390iter [00:12, 39.16iter/s]
Best score: 0.9361: : 391iter [00:12, 39.16iter/s]
Best score: 0.9361: : 392iter [00:12, 39.16iter/s]
Best score: 0.9361: : 393iter [00:12, 39.16iter/s]
Best score: 0.9361: : 394iter [00:12, 39.34iter/s]
Best score: 0.9361: : 394iter [00:12, 39.34iter/s]
Best score: 0.9361: : 395iter [00:12, 39.34iter/s]
Best score: 0.9361: : 396iter [00:12, 39.34iter/s]
Best score: 0.9361: : 397iter [00:12, 39.34iter/s]
Best score: 0.9361: : 398iter [00:12, 39.34iter/s]
Best score: 0.9361: : 399iter [00:12, 39.58iter/s]
[ITER 400] Best: 0.9361
[PARAMS] Current best parameters:
  fx: 555.6750
  fy: 555.5679
  px: 997.1383
  py: 765.0860
  k1: 0.01095145
  k2: -0.01215694
  k3: -0.01303976
  k4: 0.00590731
  p1: -0.00071839
  p2: -0.00196514
  p3: -0.00988326
  p4: 0.00820690
  s1: -0.0027
  s2: -0.0016
  s3: 0.0107
  s4: -0.0086
  tilt_x: 0.0138
  tilt_y: 0.0049
  scale: 2.8948
  rotation: 0.8405

Best score: 0.9361: : 399iter [00:12, 39.58iter/s]
Best score: 0.9361: : 400iter [00:12, 39.58iter/s]
Best score: 0.9361: : 401iter [00:12, 39.58iter/s]
Best score: 0.9361: : 402iter [00:12, 39.58iter/s]
Best score: 0.9361: : 403iter [00:12, 32.64iter/s]
Best score: 0.9361: : 403iter [00:12, 32.64iter/s]
Best score: 0.9361: : 404iter [00:12, 32.64iter/s]
Best score: 0.9361: : 405iter [00:12, 32.64iter/s]
Best score: 0.9361: : 406iter [00:12, 32.64iter/s]
Best score: 0.9361: : 407iter [00:12, 32.64iter/s]
Best score: 0.9361: : 408iter [00:12, 34.79iter/s]
Best score: 0.9361: : 408iter [00:12, 34.79iter/s]
Best score: 0.7443: : 409iter [00:12, 34.79iter/s]
Best score: 0.7443: : 410iter [00:12, 34.79iter/s]
Best score: 0.7443: : 411iter [00:12, 34.79iter/s]
Best score: 0.7443: : 412iter [00:12, 35.79iter/s]
Best score: 0.7443: : 412iter [00:12, 35.79iter/s]
Best score: 0.7443: : 413iter [00:12, 35.79iter/s]
Best score: 0.7443: : 414iter [00:13, 35.79iter/s]
Best score: 0.7443: : 415iter [00:13, 35.79iter/s]
Best score: 0.7443: : 416iter [00:13, 35.79iter/s]
Best score: 0.7443: : 417iter [00:13, 37.13iter/s]
Best score: 0.7443: : 417iter [00:13, 37.13iter/s]
Best score: 0.7443: : 418iter [00:13, 37.13iter/s]
Best score: 0.7443: : 419iter [00:13, 37.13iter/s]
Best score: 0.7443: : 420iter [00:13, 37.13iter/s]
Best score: 0.7443: : 421iter [00:13, 37.67iter/s]
Best score: 0.7443: : 421iter [00:13, 37.67iter/s]
Best score: 0.7443: : 422iter [00:13, 37.67iter/s]
Best score: 0.7443: : 423iter [00:13, 37.67iter/s]
Best score: 0.7443: : 424iter [00:13, 37.67iter/s]
Best score: 0.7443: : 425iter [00:13, 37.67iter/s]
Best score: 0.7443: : 426iter [00:13, 38.62iter/s]
Best score: 0.7443: : 426iter [00:13, 38.62iter/s]
Best score: 0.7443: : 427iter [00:13, 38.62iter/s]
Best score: 0.7443: : 428iter [00:13, 38.62iter/s]
Best score: 0.7443: : 429iter [00:13, 38.62iter/s]
Best score: 0.7443: : 430iter [00:13, 38.80iter/s]
Best score: 0.7443: : 430iter [00:13, 38.80iter/s]
Best score: 0.7443: : 431iter [00:13, 38.80iter/s]
Best score: 0.7443: : 432iter [00:13, 38.80iter/s]
Best score: 0.7443: : 433iter [00:13, 38.80iter/s]
Best score: 0.7443: : 434iter [00:13, 38.80iter/s]
Best score: 0.7443: : 435iter [00:13, 39.32iter/s]
Best score: 0.7443: : 435iter [00:13, 39.32iter/s]
Best score: 0.7443: : 436iter [00:13, 39.32iter/s]
Best score: 0.7443: : 437iter [00:13, 39.32iter/s]
Best score: 0.7443: : 438iter [00:13, 39.32iter/s]
Best score: 0.7443: : 439iter [00:13, 39.43iter/s]
Best score: 0.6655: : 439iter [00:13, 39.43iter/s]
Best score: 0.6655: : 440iter [00:13, 39.43iter/s]
Best score: 0.6655: : 441iter [00:13, 39.43iter/s]
Best score: 0.6655: : 442iter [00:13, 39.43iter/s]
Best score: 0.6655: : 443iter [00:13, 39.32iter/s]
Best score: 0.6655: : 443iter [00:13, 39.32iter/s]
Best score: 0.6655: : 444iter [00:13, 39.32iter/s]
Best score: 0.6655: : 445iter [00:13, 39.32iter/s]
Best score: 0.6655: : 446iter [00:13, 39.32iter/s]
Best score: 0.6655: : 447iter [00:13, 39.32iter/s]
Best score: 0.6655: : 448iter [00:13, 39.76iter/s]
Best score: 0.6655: : 448iter [00:13, 39.76iter/s]
[ITER 450] Best: 0.6546
[PARAMS] Current best parameters:
  fx: 549.5596
  fy: 549.3925
  px: 994.6096
  py: 764.4540
  k1: 0.01435222
  k2: -0.02207562
  k3: -0.00073805
  k4: 0.00225291
  p1: 0.00021185
  p2: -0.00400684
  p3: -0.00984059
  p4: 0.00630268
  s1: 0.0060
  s2: -0.0038
  s3: 0.0016
  s4: -0.0012
  tilt_x: 0.0090
  tilt_y: 0.0167
  scale: 2.9250
  rotation: 0.8842

Best score: 0.6546: : 449iter [00:13, 39.76iter/s]
Best score: 0.6546: : 450iter [00:13, 39.76iter/s]
Best score: 0.6546: : 451iter [00:14, 39.76iter/s]
Best score: 0.6546: : 452iter [00:14, 32.62iter/s]
Best score: 0.6546: : 452iter [00:14, 32.62iter/s]
Best score: 0.6546: : 453iter [00:14, 32.62iter/s]
Best score: 0.6546: : 454iter [00:14, 32.62iter/s]
Best score: 0.6546: : 455iter [00:14, 32.62iter/s]
Best score: 0.6546: : 456iter [00:14, 32.62iter/s]
Best score: 0.6546: : 457iter [00:14, 34.83iter/s]
Best score: 0.6546: : 457iter [00:14, 34.83iter/s]
Best score: 0.6546: : 458iter [00:14, 34.83iter/s]
Best score: 0.5639: : 459iter [00:14, 34.83iter/s]
Best score: 0.5639: : 460iter [00:14, 34.83iter/s]
Best score: 0.5639: : 461iter [00:14, 35.88iter/s]
Best score: 0.5639: : 461iter [00:14, 35.88iter/s]
Best score: 0.5639: : 462iter [00:14, 35.88iter/s]
Best score: 0.5639: : 463iter [00:14, 35.88iter/s]
Best score: 0.5639: : 464iter [00:14, 35.88iter/s]
Best score: 0.5639: : 465iter [00:14, 35.88iter/s]
Best score: 0.5639: : 466iter [00:14, 37.23iter/s]
Best score: 0.5639: : 466iter [00:14, 37.23iter/s]
Best score: 0.5639: : 467iter [00:14, 37.23iter/s]
Best score: 0.5639: : 468iter [00:14, 37.23iter/s]
Best score: 0.5028: : 469iter [00:14, 37.23iter/s]
Best score: 0.5028: : 470iter [00:14, 37.79iter/s]
Best score: 0.5028: : 470iter [00:14, 37.79iter/s]
Best score: 0.5028: : 471iter [00:14, 37.79iter/s]
Best score: 0.5028: : 472iter [00:14, 37.79iter/s]
Best score: 0.5028: : 473iter [00:14, 37.79iter/s]
Best score: 0.5028: : 474iter [00:14, 38.30iter/s]
Best score: 0.5028: : 474iter [00:14, 38.30iter/s]
Best score: 0.5028: : 475iter [00:14, 38.30iter/s]
Best score: 0.5028: : 476iter [00:14, 38.30iter/s]
Best score: 0.5028: : 477iter [00:14, 38.30iter/s]
Best score: 0.5028: : 478iter [00:14, 38.30iter/s]
Best score: 0.5028: : 479iter [00:14, 38.92iter/s]
Best score: 0.5028: : 479iter [00:14, 38.92iter/s]
Best score: 0.5028: : 480iter [00:14, 38.92iter/s]
Best score: 0.5028: : 481iter [00:14, 38.92iter/s]
Best score: 0.5028: : 482iter [00:14, 38.92iter/s]
Best score: 0.5028: : 483iter [00:14, 39.01iter/s]
Best score: 0.5028: : 483iter [00:14, 39.01iter/s]
Best score: 0.5028: : 484iter [00:14, 39.01iter/s]
Best score: 0.5028: : 485iter [00:14, 39.01iter/s]
Best score: 0.5028: : 486iter [00:14, 39.01iter/s]
Best score: 0.5028: : 487iter [00:14, 39.01iter/s]
Best score: 0.5028: : 488iter [00:14, 39.35iter/s]
Best score: 0.5028: : 488iter [00:14, 39.35iter/s]
Best score: 0.4708: : 489iter [00:14, 39.35iter/s]
Best score: 0.4708: : 490iter [00:14, 39.35iter/s]
Best score: 0.4708: : 491iter [00:15, 39.35iter/s]
Best score: 0.4708: : 492iter [00:15, 39.25iter/s]
Best score: 0.4708: : 492iter [00:15, 39.25iter/s]
Best score: 0.4708: : 493iter [00:15, 39.25iter/s]
Best score: 0.4708: : 494iter [00:15, 39.25iter/s]
Best score: 0.4708: : 495iter [00:15, 39.25iter/s]
Best score: 0.4708: : 496iter [00:15, 39.43iter/s]
Best score: 0.4708: : 496iter [00:15, 39.43iter/s]
Best score: 0.4708: : 497iter [00:15, 39.43iter/s]
Best score: 0.4708: : 498iter [00:15, 39.43iter/s]
[ITER 500] Best: 0.4708
[PARAMS] Current best parameters:
  fx: 555.7569
  fy: 555.6972
  px: 993.1317
  py: 763.4869
  k1: -0.02293207
  k2: 0.00646078
  k3: -0.00389362
  k4: 0.00040512
  p1: 0.00613160
  p2: 0.00290112
  p3: -0.00490221
  p4: 0.00505544
  s1: -0.0080
  s2: -0.0025
  s3: -0.0030
  s4: -0.0046
  tilt_x: -0.0080
  tilt_y: 0.0387
  scale: 2.9717
  rotation: 0.8854

Best score: 0.4708: : 499iter [00:15, 39.43iter/s]
Best score: 0.4708: : 500iter [00:15, 39.35iter/s]
Best score: 0.4708: : 500iter [00:15, 39.35iter/s]
Best score: 0.4708: : 501iter [00:15, 39.35iter/s]
Best score: 0.4708: : 502iter [00:15, 39.35iter/s]
Best score: 0.4708: : 503iter [00:15, 39.35iter/s]
Best score: 0.4708: : 504iter [00:15, 31.96iter/s]
Best score: 0.4708: : 504iter [00:15, 31.96iter/s]
Best score: 0.4708: : 505iter [00:15, 31.96iter/s]
Best score: 0.4708: : 506iter [00:15, 31.96iter/s]
Best score: 0.4708: : 507iter [00:15, 31.96iter/s]
Best score: 0.4708: : 508iter [00:15, 33.94iter/s]
Best score: 0.4708: : 508iter [00:15, 33.94iter/s]
Best score: 0.4708: : 509iter [00:15, 33.94iter/s]
Best score: 0.4708: : 510iter [00:15, 33.94iter/s]
Best score: 0.4708: : 511iter [00:15, 33.94iter/s]
Best score: 0.4708: : 512iter [00:15, 35.30iter/s]
Best score: 0.4708: : 512iter [00:15, 35.30iter/s]
Best score: 0.4708: : 513iter [00:15, 35.30iter/s]
Best score: 0.4708: : 514iter [00:15, 35.30iter/s]
Best score: 0.4708: : 515iter [00:15, 35.30iter/s]
Best score: 0.4708: : 516iter [00:15, 35.30iter/s]
Best score: 0.4708: : 517iter [00:15, 36.81iter/s]
Best score: 0.4708: : 517iter [00:15, 36.81iter/s]
Best score: 0.4708: : 518iter [00:15, 36.81iter/s]
Best score: 0.4393: : 519iter [00:15, 36.81iter/s]
Best score: 0.4393: : 520iter [00:15, 36.81iter/s]
Best score: 0.4393: : 521iter [00:15, 37.55iter/s]
Best score: 0.4393: : 521iter [00:15, 37.55iter/s]
Best score: 0.4393: : 522iter [00:15, 37.55iter/s]
Best score: 0.4393: : 523iter [00:15, 37.55iter/s]
Best score: 0.4393: : 524iter [00:15, 37.55iter/s]
Best score: 0.4393: : 525iter [00:15, 37.55iter/s]
Best score: 0.4393: : 526iter [00:15, 38.50iter/s]
Best score: 0.4393: : 526iter [00:15, 38.50iter/s]
Best score: 0.4393: : 527iter [00:16, 38.50iter/s]
Best score: 0.4393: : 528iter [00:16, 38.50iter/s]
Best score: 0.4393: : 529iter [00:16, 38.50iter/s]
Best score: 0.4393: : 530iter [00:16, 38.78iter/s]
Best score: 0.4393: : 530iter [00:16, 38.78iter/s]
Best score: 0.4393: : 531iter [00:16, 38.78iter/s]
Best score: 0.4393: : 532iter [00:16, 38.78iter/s]
Best score: 0.4393: : 533iter [00:16, 38.78iter/s]
Best score: 0.4393: : 534iter [00:16, 38.78iter/s]
Best score: 0.4393: : 535iter [00:16, 39.27iter/s]
Best score: 0.4393: : 535iter [00:16, 39.27iter/s]
Best score: 0.4393: : 536iter [00:16, 39.27iter/s]
Best score: 0.4393: : 537iter [00:16, 39.27iter/s]
Best score: 0.4393: : 538iter [00:16, 39.27iter/s]
Best score: 0.4393: : 539iter [00:16, 39.27iter/s]
Best score: 0.4393: : 540iter [00:16, 39.24iter/s]
Best score: 0.4393: : 540iter [00:16, 39.24iter/s]
Best score: 0.4393: : 541iter [00:16, 39.24iter/s]
Best score: 0.4393: : 542iter [00:16, 39.24iter/s]
Best score: 0.4393: : 543iter [00:16, 39.24iter/s]
Best score: 0.4393: : 544iter [00:16, 39.27iter/s]
Best score: 0.4393: : 544iter [00:16, 39.27iter/s]
Best score: 0.4393: : 545iter [00:16, 39.27iter/s]
Best score: 0.4393: : 546iter [00:16, 39.27iter/s]
Best score: 0.4393: : 547iter [00:16, 39.27iter/s]
Best score: 0.4393: : 548iter [00:16, 39.27iter/s]
Best score: 0.4393: : 549iter [00:16, 39.55iter/s]
[ITER 550] Best: 0.4242
[PARAMS] Current best parameters:
  fx: 556.0668
  fy: 555.8950
  px: 993.8146
  py: 764.3494
  k1: -0.01661700
  k2: -0.00328102
  k3: 0.00147741
  k4: -0.00065701
  p1: 0.00571971
  p2: 0.00383403
  p3: -0.00524950
  p4: 0.00621140
  s1: -0.0070
  s2: -0.0044
  s3: -0.0032
  s4: -0.0051
  tilt_x: -0.0026
  tilt_y: 0.0366
  scale: 2.9632
  rotation: 0.8747

Best score: 0.4242: : 549iter [00:16, 39.55iter/s]
Best score: 0.4242: : 550iter [00:16, 39.55iter/s]
Best score: 0.4242: : 551iter [00:16, 39.55iter/s]
Best score: 0.4242: : 552iter [00:16, 39.55iter/s]
Best score: 0.4242: : 553iter [00:16, 32.63iter/s]
Best score: 0.4242: : 553iter [00:16, 32.63iter/s]
Best score: 0.4242: : 554iter [00:16, 32.63iter/s]
Best score: 0.4242: : 555iter [00:16, 32.63iter/s]
Best score: 0.4242: : 556iter [00:16, 32.63iter/s]
Best score: 0.4242: : 557iter [00:16, 32.63iter/s]
Best score: 0.4242: : 558iter [00:16, 34.77iter/s]
Best score: 0.4242: : 558iter [00:16, 34.77iter/s]
Best score: 0.4242: : 559iter [00:16, 34.77iter/s]
Best score: 0.4242: : 560iter [00:16, 34.77iter/s]
Best score: 0.4242: : 561iter [00:16, 34.77iter/s]
Best score: 0.4242: : 562iter [00:16, 35.78iter/s]
Best score: 0.4242: : 562iter [00:16, 35.78iter/s]
Best score: 0.4242: : 563iter [00:16, 35.78iter/s]
Best score: 0.4242: : 564iter [00:17, 35.78iter/s]
Best score: 0.4242: : 565iter [00:17, 35.78iter/s]
Best score: 0.4242: : 566iter [00:17, 35.78iter/s]
Best score: 0.4242: : 567iter [00:17, 37.17iter/s]
Best score: 0.4242: : 567iter [00:17, 37.17iter/s]
Best score: 0.4242: : 568iter [00:17, 37.17iter/s]
Best score: 0.4242: : 569iter [00:17, 37.17iter/s]
Best score: 0.4242: : 570iter [00:17, 37.17iter/s]
Best score: 0.4242: : 571iter [00:17, 37.81iter/s]
Best score: 0.4242: : 571iter [00:17, 37.81iter/s]
Best score: 0.4242: : 572iter [00:17, 37.81iter/s]
Best score: 0.4242: : 573iter [00:17, 37.81iter/s]
Best score: 0.4242: : 574iter [00:17, 37.81iter/s]
Best score: 0.4242: : 575iter [00:17, 37.81iter/s]
Best score: 0.4242: : 576iter [00:17, 38.68iter/s]
Best score: 0.4242: : 576iter [00:17, 38.68iter/s]
Best score: 0.4242: : 577iter [00:17, 38.68iter/s]
Best score: 0.4242: : 578iter [00:17, 38.68iter/s]
Best score: 0.4228: : 579iter [00:17, 38.68iter/s]
Best score: 0.4228: : 580iter [00:17, 38.75iter/s]
Best score: 0.4228: : 580iter [00:17, 38.75iter/s]
Best score: 0.4228: : 581iter [00:17, 38.75iter/s]
Best score: 0.4228: : 582iter [00:17, 38.75iter/s]
Best score: 0.4228: : 583iter [00:17, 38.75iter/s]
Best score: 0.4228: : 584iter [00:17, 38.75iter/s]
Best score: 0.4228: : 585iter [00:17, 39.10iter/s]
Best score: 0.4228: : 585iter [00:17, 39.10iter/s]
Best score: 0.4228: : 586iter [00:17, 39.10iter/s]
Best score: 0.4228: : 587iter [00:17, 39.10iter/s]
Best score: 0.4228: : 588iter [00:17, 39.10iter/s]
Best score: 0.4228: : 589iter [00:17, 39.31iter/s]
Best score: 0.4191: : 589iter [00:17, 39.31iter/s]
Best score: 0.4191: : 590iter [00:17, 39.31iter/s]
Best score: 0.4191: : 591iter [00:17, 39.31iter/s]
Best score: 0.4191: : 592iter [00:17, 39.31iter/s]
Best score: 0.4191: : 593iter [00:17, 39.21iter/s]
Best score: 0.4191: : 593iter [00:17, 39.21iter/s]
Best score: 0.4191: : 594iter [00:17, 39.21iter/s]
Best score: 0.4191: : 595iter [00:17, 39.21iter/s]
Best score: 0.4191: : 596iter [00:17, 39.21iter/s]
Best score: 0.4191: : 597iter [00:17, 39.43iter/s]
Best score: 0.4191: : 597iter [00:17, 39.43iter/s]
Best score: 0.4191: : 598iter [00:17, 39.43iter/s]
[ITER 600] Best: 0.4067
[PARAMS] Current best parameters:
  fx: 556.2822
  fy: 556.1341
  px: 993.7115
  py: 762.6479
  k1: -0.02400223
  k2: 0.01075676
  k3: -0.00784941
  k4: 0.00145362
  p1: 0.00229787
  p2: 0.00272815
  p3: -0.00304746
  p4: 0.00605301
  s1: -0.0069
  s2: -0.0046
  s3: -0.0026
  s4: -0.0048
  tilt_x: -0.0051
  tilt_y: 0.0353
  scale: 2.9640
  rotation: 0.8652

Best score: 0.4067: : 599iter [00:17, 39.43iter/s]
Best score: 0.4067: : 600iter [00:18, 39.43iter/s]
Best score: 0.4067: : 601iter [00:18, 32.10iter/s]
Best score: 0.4067: : 601iter [00:18, 32.10iter/s]
Best score: 0.4067: : 602iter [00:18, 32.10iter/s]
Best score: 0.4067: : 603iter [00:18, 32.10iter/s]
Best score: 0.4067: : 604iter [00:18, 32.10iter/s]
Best score: 0.4067: : 605iter [00:18, 32.10iter/s]
Best score: 0.4067: : 606iter [00:18, 34.43iter/s]
Best score: 0.4067: : 606iter [00:18, 34.43iter/s]
Best score: 0.4067: : 607iter [00:18, 34.43iter/s]
Best score: 0.4067: : 608iter [00:18, 34.43iter/s]
Best score: 0.4059: : 609iter [00:18, 34.43iter/s]
Best score: 0.4059: : 610iter [00:18, 28.69iter/s]
Best score: 0.4059: : 610iter [00:18, 28.69iter/s]
Best score: 0.4059: : 611iter [00:18, 28.69iter/s]
Best score: 0.4059: : 612iter [00:18, 28.69iter/s]
Best score: 0.4059: : 613iter [00:18, 28.69iter/s]
Best score: 0.4059: : 614iter [00:18, 31.13iter/s]
Best score: 0.4059: : 614iter [00:18, 31.13iter/s]
Best score: 0.4059: : 615iter [00:18, 31.13iter/s]
Best score: 0.4059: : 616iter [00:18, 31.13iter/s]
Best score: 0.4059: : 617iter [00:18, 31.13iter/s]
Best score: 0.4059: : 618iter [00:18, 31.13iter/s]
Best score: 0.4059: : 619iter [00:18, 33.70iter/s]
Best score: 0.4059: : 619iter [00:18, 33.70iter/s]
Best score: 0.4059: : 620iter [00:18, 33.70iter/s]
Best score: 0.4059: : 621iter [00:18, 33.70iter/s]
Best score: 0.4059: : 622iter [00:18, 33.70iter/s]
Best score: 0.4059: : 623iter [00:18, 35.05iter/s]
Best score: 0.4059: : 623iter [00:18, 35.05iter/s]
Best score: 0.4059: : 624iter [00:18, 35.05iter/s]
Best score: 0.4059: : 625iter [00:18, 35.05iter/s]
Best score: 0.4059: : 626iter [00:18, 35.05iter/s]
Best score: 0.4059: : 627iter [00:18, 35.05iter/s]
Best score: 0.4059: : 628iter [00:18, 36.70iter/s]
Best score: 0.4059: : 628iter [00:18, 36.70iter/s]
Best score: 0.4054: : 629iter [00:18, 36.70iter/s]
Best score: 0.4054: : 630iter [00:18, 36.70iter/s]
Best score: 0.4054: : 631iter [00:18, 36.70iter/s]
Best score: 0.4054: : 632iter [00:18, 37.35iter/s]
Best score: 0.4054: : 632iter [00:18, 37.35iter/s]
Best score: 0.4054: : 633iter [00:18, 37.35iter/s]
Best score: 0.4054: : 634iter [00:18, 37.35iter/s]
Best score: 0.4054: : 635iter [00:18, 37.35iter/s]
Best score: 0.4054: : 636iter [00:19, 37.35iter/s]
Best score: 0.4054: : 637iter [00:19, 38.24iter/s]
Best score: 0.4054: : 637iter [00:19, 38.24iter/s]
Best score: 0.4054: : 638iter [00:19, 38.24iter/s]
Best score: 0.3952: : 639iter [00:19, 38.24iter/s]
Best score: 0.3952: : 640iter [00:19, 38.24iter/s]
Best score: 0.3952: : 641iter [00:19, 38.36iter/s]
Best score: 0.3952: : 641iter [00:19, 38.36iter/s]
Best score: 0.3952: : 642iter [00:19, 38.36iter/s]
Best score: 0.3952: : 643iter [00:19, 38.36iter/s]
Best score: 0.3952: : 644iter [00:19, 38.36iter/s]
Best score: 0.3952: : 645iter [00:19, 38.72iter/s]
Best score: 0.3952: : 645iter [00:19, 38.72iter/s]
Best score: 0.3952: : 646iter [00:19, 38.72iter/s]
Best score: 0.3952: : 647iter [00:19, 38.72iter/s]
Best score: 0.3952: : 648iter [00:19, 38.72iter/s]
[ITER 650] Best: 0.3952
[PARAMS] Current best parameters:
  fx: 555.0037
  fy: 554.7231
  px: 993.3345
  py: 762.4716
  k1: -0.02358676
  k2: 0.00913774
  k3: -0.00551259
  k4: 0.00073478
  p1: 0.00340805
  p2: 0.00265076
  p3: -0.00492539
  p4: 0.00564412
  s1: -0.0057
  s2: -0.0043
  s3: -0.0011
  s4: -0.0052
  tilt_x: -0.0080
  tilt_y: 0.0367
  scale: 2.9711
  rotation: 0.8596

Best score: 0.3952: : 649iter [00:19, 38.72iter/s]
Best score: 0.3952: : 650iter [00:19, 38.86iter/s]
Best score: 0.3952: : 650iter [00:19, 38.86iter/s]
Best score: 0.3952: : 651iter [00:19, 38.86iter/s]
Best score: 0.3952: : 652iter [00:19, 38.86iter/s]
Best score: 0.3952: : 653iter [00:19, 38.86iter/s]
Best score: 0.3952: : 654iter [00:19, 32.08iter/s]
Best score: 0.3952: : 654iter [00:19, 32.08iter/s]
Best score: 0.3952: : 655iter [00:19, 32.08iter/s]
Best score: 0.3952: : 656iter [00:19, 32.08iter/s]
Best score: 0.3952: : 657iter [00:19, 32.08iter/s]
Best score: 0.3952: : 658iter [00:19, 33.95iter/s]
Best score: 0.3952: : 658iter [00:19, 33.95iter/s]
Best score: 0.3952: : 659iter [00:19, 33.95iter/s]
Best score: 0.3952: : 660iter [00:19, 33.95iter/s]
Best score: 0.3952: : 661iter [00:19, 33.95iter/s]
Best score: 0.3952: : 662iter [00:19, 35.23iter/s]
Best score: 0.3952: : 662iter [00:19, 35.23iter/s]
Best score: 0.3952: : 663iter [00:19, 35.23iter/s]
Best score: 0.3952: : 664iter [00:19, 35.23iter/s]
Best score: 0.3952: : 665iter [00:19, 35.23iter/s]
Best score: 0.3952: : 666iter [00:19, 35.23iter/s]
Best score: 0.3952: : 667iter [00:19, 36.83iter/s]
Best score: 0.3952: : 667iter [00:19, 36.83iter/s]
Best score: 0.3952: : 668iter [00:19, 36.83iter/s]
Best score: 0.3952: : 669iter [00:19, 36.83iter/s]
Best score: 0.3952: : 670iter [00:19, 36.83iter/s]
Best score: 0.3952: : 671iter [00:19, 37.36iter/s]
Best score: 0.3952: : 671iter [00:19, 37.36iter/s]
Best score: 0.3952: : 672iter [00:20, 37.36iter/s]
Best score: 0.3952: : 673iter [00:20, 37.36iter/s]
Best score: 0.3952: : 674iter [00:20, 37.36iter/s]
Best score: 0.3952: : 675iter [00:20, 37.36iter/s]
Best score: 0.3952: : 676iter [00:20, 38.18iter/s]
Best score: 0.3952: : 676iter [00:20, 38.18iter/s]
Best score: 0.3952: : 677iter [00:20, 38.18iter/s]
Best score: 0.3952: : 678iter [00:20, 38.18iter/s]
Best score: 0.3939: : 679iter [00:20, 38.18iter/s]
Best score: 0.3939: : 680iter [00:20, 38.42iter/s]
Best score: 0.3939: : 680iter [00:20, 38.42iter/s]
Best score: 0.3939: : 681iter [00:20, 38.42iter/s]
Best score: 0.3939: : 682iter [00:20, 38.42iter/s]
Best score: 0.3939: : 683iter [00:20, 38.42iter/s]
Best score: 0.3939: : 684iter [00:20, 38.42iter/s]
Best score: 0.3939: : 685iter [00:20, 38.90iter/s]
Best score: 0.3939: : 685iter [00:20, 38.90iter/s]
Best score: 0.3939: : 686iter [00:20, 38.90iter/s]
Best score: 0.3939: : 687iter [00:20, 38.90iter/s]
Best score: 0.3939: : 688iter [00:20, 38.90iter/s]
Best score: 0.3939: : 689iter [00:20, 39.11iter/s]
Best score: 0.3939: : 689iter [00:20, 39.11iter/s]
Best score: 0.3939: : 690iter [00:20, 39.11iter/s]
Best score: 0.3939: : 691iter [00:20, 39.11iter/s]
Best score: 0.3939: : 692iter [00:20, 39.11iter/s]
Best score: 0.3939: : 693iter [00:20, 39.13iter/s]
Best score: 0.3939: : 693iter [00:20, 39.13iter/s]
Best score: 0.3939: : 694iter [00:20, 39.13iter/s]
Best score: 0.3939: : 695iter [00:20, 39.13iter/s]
Best score: 0.3939: : 696iter [00:20, 39.13iter/s]
Best score: 0.3939: : 697iter [00:20, 39.32iter/s]
Best score: 0.3939: : 697iter [00:20, 39.32iter/s]
Best score: 0.3939: : 698iter [00:20, 39.32iter/s]
[ITER 700] Best: 0.3939
[PARAMS] Current best parameters:
  fx: 554.9220
  fy: 554.6734
  px: 993.1872
  py: 761.4379
  k1: -0.02731481
  k2: 0.01451805
  k3: -0.00828276
  k4: 0.00124196
  p1: 0.00249552
  p2: 0.00156071
  p3: -0.00345123
  p4: 0.00616565
  s1: -0.0064
  s2: -0.0047
  s3: -0.0024
  s4: -0.0059
  tilt_x: -0.0116
  tilt_y: 0.0354
  scale: 2.9755
  rotation: 0.8529

Best score: 0.3939: : 699iter [00:20, 39.32iter/s]
Best score: 0.3939: : 700iter [00:20, 39.32iter/s]
Best score: 0.3939: : 701iter [00:20, 31.93iter/s]
Best score: 0.3939: : 701iter [00:20, 31.93iter/s]
Best score: 0.3939: : 702iter [00:20, 31.93iter/s]
Best score: 0.3939: : 703iter [00:20, 31.93iter/s]
Best score: 0.3939: : 704iter [00:20, 31.93iter/s]
Best score: 0.3939: : 705iter [00:20, 33.83iter/s]
Best score: 0.3939: : 705iter [00:20, 33.83iter/s]
Best score: 0.3939: : 706iter [00:20, 33.83iter/s]
Best score: 0.3939: : 707iter [00:20, 33.83iter/s]
Best score: 0.3939: : 708iter [00:20, 33.83iter/s]
Best score: 0.3939: : 709iter [00:20, 35.42iter/s]
Best score: 0.3900: : 709iter [00:21, 35.42iter/s]
Best score: 0.3900: : 710iter [00:21, 35.42iter/s]
Best score: 0.3900: : 711iter [00:21, 35.42iter/s]
Best score: 0.3900: : 712iter [00:21, 35.42iter/s]
Best score: 0.3900: : 713iter [00:21, 36.45iter/s]
Best score: 0.3900: : 713iter [00:21, 36.45iter/s]
Best score: 0.3900: : 714iter [00:21, 36.45iter/s]
Best score: 0.3900: : 715iter [00:21, 36.45iter/s]
Best score: 0.3900: : 716iter [00:21, 36.45iter/s]
Best score: 0.3900: : 717iter [00:21, 36.45iter/s]
Best score: 0.3900: : 718iter [00:21, 37.70iter/s]
Best score: 0.3900: : 718iter [00:21, 37.70iter/s]
Best score: 0.3895: : 719iter [00:21, 37.70iter/s]
Best score: 0.3895: : 720iter [00:21, 37.70iter/s]
Best score: 0.3895: : 721iter [00:21, 37.70iter/s]
Best score: 0.3895: : 722iter [00:21, 37.92iter/s]
Best score: 0.3895: : 722iter [00:21, 37.92iter/s]
Best score: 0.3895: : 723iter [00:21, 37.92iter/s]
Best score: 0.3895: : 724iter [00:21, 37.92iter/s]
Best score: 0.3895: : 725iter [00:21, 37.92iter/s]
Best score: 0.3895: : 726iter [00:21, 38.32iter/s]
Best score: 0.3895: : 726iter [00:21, 38.32iter/s]
Best score: 0.3895: : 727iter [00:21, 38.32iter/s]
Best score: 0.3895: : 728iter [00:21, 38.32iter/s]
Best score: 0.3862: : 729iter [00:21, 38.32iter/s]
Best score: 0.3862: : 730iter [00:21, 38.31iter/s]
Best score: 0.3862: : 730iter [00:21, 38.31iter/s]
Best score: 0.3862: : 731iter [00:21, 38.31iter/s]
Best score: 0.3862: : 732iter [00:21, 38.31iter/s]
Best score: 0.3862: : 733iter [00:21, 38.31iter/s]
Best score: 0.3862: : 734iter [00:21, 38.31iter/s]
Best score: 0.3862: : 735iter [00:21, 38.92iter/s]
Best score: 0.3862: : 735iter [00:21, 38.92iter/s]
Best score: 0.3862: : 736iter [00:21, 38.92iter/s]
Best score: 0.3862: : 737iter [00:21, 38.92iter/s]
Best score: 0.3862: : 738iter [00:21, 38.92iter/s]
Best score: 0.3857: : 739iter [00:21, 38.92iter/s]
Best score: 0.3857: : 740iter [00:21, 39.08iter/s]
Best score: 0.3857: : 740iter [00:21, 39.08iter/s]
Best score: 0.3857: : 741iter [00:21, 39.08iter/s]
Best score: 0.3857: : 742iter [00:21, 39.08iter/s]
Best score: 0.3857: : 743iter [00:21, 39.08iter/s]
Best score: 0.3857: : 744iter [00:21, 39.04iter/s]
Best score: 0.3857: : 744iter [00:21, 39.04iter/s]
Best score: 0.3857: : 745iter [00:21, 39.04iter/s]
Best score: 0.3857: : 746iter [00:21, 39.04iter/s]
Best score: 0.3857: : 747iter [00:21, 39.04iter/s]
Best score: 0.3857: : 748iter [00:21, 39.10iter/s]
Best score: 0.3857: : 748iter [00:22, 39.10iter/s]
[ITER 750] Best: 0.3832
[PARAMS] Current best parameters:
  fx: 552.3524
  fy: 552.2586
  px: 994.6729
  py: 759.2257
  k1: -0.05035251
  k2: 0.04987810
  k3: -0.02627841
  k4: 0.00446732
  p1: -0.00070757
  p2: -0.00059306
  p3: 0.00773922
  p4: 0.00449573
  s1: -0.0131
  s2: -0.0035
  s3: -0.0132
  s4: -0.0037
  tilt_x: -0.0167
  tilt_y: 0.0237
  scale: 3.0078
  rotation: 0.8540

Best score: 0.3832: : 749iter [00:22, 39.10iter/s]
Best score: 0.3832: : 750iter [00:22, 39.10iter/s]
Best score: 0.3832: : 751iter [00:22, 39.10iter/s]
Best score: 0.3832: : 752iter [00:22, 31.98iter/s]
Best score: 0.3832: : 752iter [00:22, 31.98iter/s]
Best score: 0.3832: : 753iter [00:22, 31.98iter/s]
Best score: 0.3832: : 754iter [00:22, 31.98iter/s]
Best score: 0.3832: : 755iter [00:22, 31.98iter/s]
Best score: 0.3832: : 756iter [00:22, 31.98iter/s]
Best score: 0.3832: : 757iter [00:22, 34.33iter/s]
Best score: 0.3832: : 757iter [00:22, 34.33iter/s]
Best score: 0.3832: : 758iter [00:22, 34.33iter/s]
Best score: 0.3775: : 759iter [00:22, 34.33iter/s]
Best score: 0.3775: : 760iter [00:22, 34.33iter/s]
Best score: 0.3775: : 761iter [00:22, 35.47iter/s]
Best score: 0.3775: : 761iter [00:22, 35.47iter/s]
Best score: 0.3775: : 762iter [00:22, 35.47iter/s]
Best score: 0.3775: : 763iter [00:22, 35.47iter/s]
Best score: 0.3775: : 764iter [00:22, 35.47iter/s]
Best score: 0.3775: : 765iter [00:22, 35.47iter/s]
Best score: 0.3775: : 766iter [00:22, 36.88iter/s]
Best score: 0.3775: : 766iter [00:22, 36.88iter/s]
Best score: 0.3775: : 767iter [00:22, 36.88iter/s]
Best score: 0.3775: : 768iter [00:22, 36.88iter/s]
Best score: 0.3763: : 769iter [00:22, 36.88iter/s]
Best score: 0.3763: : 770iter [00:22, 37.42iter/s]
Best score: 0.3763: : 770iter [00:22, 37.42iter/s]
Best score: 0.3763: : 771iter [00:22, 37.42iter/s]
Best score: 0.3763: : 772iter [00:22, 37.42iter/s]
Best score: 0.3763: : 773iter [00:22, 37.42iter/s]
Best score: 0.3763: : 774iter [00:22, 38.10iter/s]
Best score: 0.3763: : 774iter [00:22, 38.10iter/s]
Best score: 0.3763: : 775iter [00:22, 38.10iter/s]
Best score: 0.3763: : 776iter [00:22, 38.10iter/s]
Best score: 0.3763: : 777iter [00:22, 38.10iter/s]
Best score: 0.3763: : 778iter [00:22, 38.10iter/s]
Best score: 0.3763: : 779iter [00:22, 38.75iter/s]
Best score: 0.3751: : 779iter [00:22, 38.75iter/s]
Best score: 0.3751: : 780iter [00:22, 38.75iter/s]
Best score: 0.3751: : 781iter [00:22, 38.75iter/s]
Best score: 0.3751: : 782iter [00:22, 38.75iter/s]
Best score: 0.3751: : 783iter [00:22, 38.75iter/s]
Best score: 0.3751: : 783iter [00:22, 38.75iter/s]
Best score: 0.3751: : 784iter [00:22, 38.75iter/s]
Best score: 0.3751: : 785iter [00:23, 38.75iter/s]
Best score: 0.3751: : 786iter [00:23, 38.75iter/s]
Best score: 0.3751: : 787iter [00:23, 38.75iter/s]
Best score: 0.3751: : 788iter [00:23, 39.26iter/s]
Best score: 0.3751: : 788iter [00:23, 39.26iter/s]
Best score: 0.3751: : 789iter [00:23, 39.26iter/s]
Best score: 0.3751: : 790iter [00:23, 39.26iter/s]
Best score: 0.3751: : 791iter [00:23, 39.26iter/s]
Best score: 0.3751: : 792iter [00:23, 39.17iter/s]
Best score: 0.3751: : 792iter [00:23, 39.17iter/s]
Best score: 0.3751: : 793iter [00:23, 39.17iter/s]
Best score: 0.3751: : 794iter [00:23, 39.17iter/s]
Best score: 0.3751: : 795iter [00:23, 39.17iter/s]
Best score: 0.3751: : 796iter [00:23, 39.17iter/s]
Best score: 0.3751: : 797iter [00:23, 39.46iter/s]
Best score: 0.3751: : 797iter [00:23, 39.46iter/s]
Best score: 0.3751: : 798iter [00:23, 39.46iter/s]
[ITER 800] Best: 0.3744
[PARAMS] Current best parameters:
  fx: 553.7055
  fy: 553.5700
  px: 994.1520
  py: 758.3133
  k1: -0.08711808
  k2: 0.10294573
  k3: -0.05530699
  k4: 0.01016259
  p1: -0.00306099
  p2: -0.00837285
  p3: 0.02454085
  p4: 0.00043485
  s1: -0.0214
  s2: -0.0000
  s3: -0.0287
  s4: 0.0007
  tilt_x: -0.0166
  tilt_y: 0.0111
  scale: 3.0454
  rotation: 0.8828

Best score: 0.3744: : 799iter [00:23, 39.46iter/s]
Best score: 0.3744: : 800iter [00:23, 39.46iter/s]
Best score: 0.3744: : 801iter [00:23, 32.45iter/s]
Best score: 0.3744: : 801iter [00:23, 32.45iter/s]
Best score: 0.3744: : 802iter [00:23, 32.45iter/s]
Best score: 0.3744: : 803iter [00:23, 32.45iter/s]
Best score: 0.3744: : 804iter [00:23, 32.45iter/s]
Best score: 0.3744: : 805iter [00:23, 32.45iter/s]
Best score: 0.3744: : 806iter [00:23, 34.62iter/s]
Best score: 0.3744: : 806iter [00:23, 34.62iter/s]
Best score: 0.3744: : 807iter [00:23, 34.62iter/s]
Best score: 0.3744: : 808iter [00:23, 34.62iter/s]
Best score: 0.3717: : 809iter [00:23, 34.62iter/s]
Best score: 0.3717: : 810iter [00:23, 35.83iter/s]
Best score: 0.3717: : 810iter [00:23, 35.83iter/s]
Best score: 0.3717: : 811iter [00:23, 35.83iter/s]
Best score: 0.3717: : 812iter [00:23, 35.83iter/s]
Best score: 0.3717: : 813iter [00:23, 35.83iter/s]
Best score: 0.3717: : 814iter [00:23, 35.83iter/s]
Best score: 0.3717: : 815iter [00:23, 37.24iter/s]
Best score: 0.3717: : 815iter [00:23, 37.24iter/s]
Best score: 0.3717: : 816iter [00:23, 37.24iter/s]
Best score: 0.3717: : 817iter [00:23, 37.24iter/s]
Best score: 0.3717: : 818iter [00:23, 37.24iter/s]
Best score: 0.3710: : 819iter [00:23, 37.24iter/s]
Best score: 0.3710: : 820iter [00:23, 37.89iter/s]
Best score: 0.3710: : 820iter [00:23, 37.89iter/s]
Best score: 0.3710: : 821iter [00:24, 37.89iter/s]
Best score: 0.3710: : 822iter [00:24, 37.89iter/s]
Best score: 0.3710: : 823iter [00:24, 37.89iter/s]
Best score: 0.3710: : 824iter [00:24, 37.89iter/s]
Best score: 0.3710: : 825iter [00:24, 38.56iter/s]
Best score: 0.3710: : 825iter [00:24, 38.56iter/s]
Best score: 0.3710: : 826iter [00:24, 38.56iter/s]
Best score: 0.3710: : 827iter [00:24, 38.56iter/s]
Best score: 0.3710: : 828iter [00:24, 38.56iter/s]
Best score: 0.3710: : 829iter [00:24, 38.56iter/s]
Best score: 0.3710: : 830iter [00:24, 38.78iter/s]
Best score: 0.3710: : 830iter [00:24, 38.78iter/s]
Best score: 0.3710: : 831iter [00:24, 38.78iter/s]
Best score: 0.3710: : 832iter [00:24, 38.78iter/s]
Best score: 0.3710: : 833iter [00:24, 38.78iter/s]
Best score: 0.3710: : 834iter [00:24, 38.59iter/s]
Best score: 0.3710: : 834iter [00:24, 38.59iter/s]
Best score: 0.3710: : 835iter [00:24, 38.59iter/s]
Best score: 0.3710: : 836iter [00:24, 38.59iter/s]
Best score: 0.3710: : 837iter [00:24, 38.59iter/s]
Best score: 0.3710: : 838iter [00:24, 38.88iter/s]
Best score: 0.3710: : 838iter [00:24, 38.88iter/s]
Best score: 0.3699: : 839iter [00:24, 38.88iter/s]
Best score: 0.3699: : 840iter [00:24, 38.88iter/s]
Best score: 0.3699: : 841iter [00:24, 38.88iter/s]
Best score: 0.3699: : 842iter [00:24, 38.85iter/s]
Best score: 0.3699: : 842iter [00:24, 38.85iter/s]
Best score: 0.3699: : 843iter [00:24, 38.85iter/s]
Best score: 0.3699: : 844iter [00:24, 38.85iter/s]
Best score: 0.3699: : 845iter [00:24, 38.85iter/s]
Best score: 0.3699: : 846iter [00:24, 39.13iter/s]
Best score: 0.3699: : 846iter [00:24, 39.13iter/s]
Best score: 0.3699: : 847iter [00:24, 39.13iter/s]
Best score: 0.3699: : 848iter [00:24, 39.13iter/s]
[ITER 850] Best: 0.3696
[PARAMS] Current best parameters:
  fx: 553.6580
  fy: 553.5287
  px: 994.2259
  py: 757.7329
  k1: -0.09755508
  k2: 0.11875536
  k3: -0.06408162
  k4: 0.01189897
  p1: -0.00587825
  p2: -0.01066907
  p3: 0.02874700
  p4: -0.00027000
  s1: -0.0239
  s2: 0.0009
  s3: -0.0308
  s4: 0.0014
  tilt_x: -0.0137
  tilt_y: 0.0059
  scale: 3.0572
  rotation: 0.8814

Best score: 0.3696: : 849iter [00:24, 39.13iter/s]
Best score: 0.3696: : 850iter [00:24, 39.10iter/s]
Best score: 0.3696: : 850iter [00:24, 39.10iter/s]
Best score: 0.3696: : 851iter [00:24, 39.10iter/s]
Best score: 0.3696: : 852iter [00:24, 39.10iter/s]
Best score: 0.3696: : 853iter [00:24, 39.10iter/s]
Best score: 0.3696: : 854iter [00:24, 31.94iter/s]
Best score: 0.3696: : 854iter [00:24, 31.94iter/s]
Best score: 0.3696: : 855iter [00:24, 31.94iter/s]
Best score: 0.3696: : 856iter [00:24, 31.94iter/s]
Best score: 0.3696: : 857iter [00:25, 31.94iter/s]
Best score: 0.3696: : 858iter [00:25, 33.89iter/s]
Best score: 0.3696: : 858iter [00:25, 33.89iter/s]
Best score: 0.3685: : 859iter [00:25, 33.89iter/s]
Best score: 0.3685: : 860iter [00:25, 33.89iter/s]
Best score: 0.3685: : 861iter [00:25, 33.89iter/s]
Best score: 0.3685: : 862iter [00:25, 35.31iter/s]
Best score: 0.3685: : 862iter [00:25, 35.31iter/s]
Best score: 0.3685: : 863iter [00:25, 35.31iter/s]
Best score: 0.3685: : 864iter [00:25, 35.31iter/s]
Best score: 0.3685: : 865iter [00:25, 35.31iter/s]
Best score: 0.3685: : 866iter [00:25, 36.56iter/s]
Best score: 0.3685: : 866iter [00:25, 36.56iter/s]
Best score: 0.3685: : 867iter [00:25, 36.56iter/s]
Best score: 0.3685: : 868iter [00:25, 36.56iter/s]
Best score: 0.3685: : 869iter [00:25, 36.56iter/s]
Best score: 0.3685: : 870iter [00:25, 37.32iter/s]
Best score: 0.3685: : 870iter [00:25, 37.32iter/s]
Best score: 0.3685: : 871iter [00:25, 37.32iter/s]
Best score: 0.3685: : 872iter [00:25, 37.32iter/s]
Best score: 0.3685: : 873iter [00:25, 37.32iter/s]
Best score: 0.3685: : 874iter [00:25, 38.07iter/s]
Best score: 0.3685: : 874iter [00:25, 38.07iter/s]
Best score: 0.3685: : 875iter [00:25, 38.07iter/s]
Best score: 0.3685: : 876iter [00:25, 38.07iter/s]
Best score: 0.3685: : 877iter [00:25, 38.07iter/s]
Best score: 0.3685: : 878iter [00:25, 38.07iter/s]
Best score: 0.3685: : 879iter [00:25, 38.73iter/s]
Best score: 0.3684: : 879iter [00:25, 38.73iter/s]
Best score: 0.3684: : 880iter [00:25, 38.73iter/s]
Best score: 0.3684: : 881iter [00:25, 38.73iter/s]
Best score: 0.3684: : 882iter [00:25, 38.73iter/s]
Best score: 0.3684: : 883iter [00:25, 38.77iter/s]
Best score: 0.3684: : 883iter [00:25, 38.77iter/s]
Best score: 0.3684: : 884iter [00:25, 38.77iter/s]
Best score: 0.3684: : 885iter [00:25, 38.77iter/s]
Best score: 0.3684: : 886iter [00:25, 38.77iter/s]
Best score: 0.3684: : 887iter [00:25, 39.03iter/s]
Best score: 0.3684: : 887iter [00:25, 39.03iter/s]
Best score: 0.3684: : 888iter [00:25, 39.03iter/s]
Best score: 0.3684: : 889iter [00:25, 39.03iter/s]
Best score: 0.3684: : 890iter [00:25, 39.03iter/s]
Best score: 0.3684: : 891iter [00:25, 38.87iter/s]
Best score: 0.3684: : 891iter [00:25, 38.87iter/s]
Best score: 0.3684: : 892iter [00:25, 38.87iter/s]
Best score: 0.3684: : 893iter [00:25, 38.87iter/s]
Best score: 0.3684: : 894iter [00:25, 38.87iter/s]
Best score: 0.3684: : 895iter [00:25, 39.10iter/s]
Best score: 0.3684: : 895iter [00:25, 39.10iter/s]
Best score: 0.3684: : 896iter [00:25, 39.10iter/s]
Best score: 0.3684: : 897iter [00:26, 39.10iter/s]
Best score: 0.3684: : 898iter [00:26, 39.10iter/s]
Best score: 0.3684: : 899iter [00:26, 39.28iter/s]
[ITER 900] Best: 0.3684
[PARAMS] Current best parameters:
  fx: 553.3031
  fy: 553.1533
  px: 994.6624
  py: 757.8334
  k1: -0.10841446
  k2: 0.13414784
  k3: -0.07180667
  k4: 0.01328852
  p1: -0.00601133
  p2: -0.01020953
  p3: 0.03220809
  p4: -0.00230737
  s1: -0.0272
  s2: 0.0028
  s3: -0.0337
  s4: 0.0033
  tilt_x: -0.0130
  tilt_y: 0.0045
  scale: 3.0716
  rotation: 0.8757

Best score: 0.3684: : 899iter [00:26, 39.28iter/s]
Best score: 0.3684: : 900iter [00:26, 39.28iter/s]
Best score: 0.3684: : 901iter [00:26, 39.28iter/s]
Best score: 0.3684: : 902iter [00:26, 39.28iter/s]
Best score: 0.3684: : 903iter [00:26, 31.88iter/s]
Best score: 0.3684: : 903iter [00:26, 31.88iter/s]
Best score: 0.3684: : 904iter [00:26, 31.88iter/s]
Best score: 0.3684: : 905iter [00:26, 31.88iter/s]
Best score: 0.3684: : 906iter [00:26, 31.88iter/s]
Best score: 0.3684: : 907iter [00:26, 33.85iter/s]
Best score: 0.3684: : 907iter [00:26, 33.85iter/s]
Best score: 0.3684: : 908iter [00:26, 33.85iter/s]
Best score: 0.3684: : 909iter [00:26, 33.85iter/s]
Best score: 0.3684: : 910iter [00:26, 33.85iter/s]
Best score: 0.3684: : 911iter [00:26, 35.16iter/s]
Best score: 0.3684: : 911iter [00:26, 35.16iter/s]
Best score: 0.3684: : 912iter [00:26, 35.16iter/s]
Best score: 0.3684: : 913iter [00:26, 35.16iter/s]
Best score: 0.3684: : 914iter [00:26, 35.16iter/s]
Best score: 0.3684: : 915iter [00:26, 35.16iter/s]
Best score: 0.3684: : 916iter [00:26, 29.30iter/s]
Best score: 0.3684: : 916iter [00:26, 29.30iter/s]
Best score: 0.3684: : 917iter [00:26, 29.30iter/s]
Best score: 0.3684: : 918iter [00:26, 29.30iter/s]
Best score: 0.3683: : 919iter [00:26, 29.30iter/s]
Best score: 0.3683: : 920iter [00:26, 31.53iter/s]
Best score: 0.3683: : 920iter [00:26, 31.53iter/s]
Best score: 0.3683: : 921iter [00:26, 31.53iter/s]
Best score: 0.3683: : 922iter [00:26, 31.53iter/s]
Best score: 0.3683: : 923iter [00:26, 31.53iter/s]
Best score: 0.3683: : 924iter [00:26, 33.58iter/s]
Best score: 0.3683: : 924iter [00:26, 33.58iter/s]
Best score: 0.3683: : 925iter [00:26, 33.58iter/s]
Best score: 0.3683: : 926iter [00:26, 33.58iter/s]
Best score: 0.3683: : 927iter [00:26, 33.58iter/s]
Best score: 0.3683: : 928iter [00:26, 35.14iter/s]
Best score: 0.3683: : 928iter [00:26, 35.14iter/s]
Best score: 0.3683: : 929iter [00:27, 35.14iter/s]
Best score: 0.3683: : 930iter [00:27, 35.14iter/s]
Best score: 0.3683: : 931iter [00:27, 35.14iter/s]
Best score: 0.3683: : 932iter [00:27, 36.11iter/s]
Best score: 0.3683: : 932iter [00:27, 36.11iter/s]
Best score: 0.3683: : 933iter [00:27, 36.11iter/s]
Best score: 0.3683: : 934iter [00:27, 36.11iter/s]
Best score: 0.3683: : 935iter [00:27, 36.11iter/s]
Best score: 0.3683: : 936iter [00:27, 37.11iter/s]
Best score: 0.3683: : 936iter [00:27, 37.11iter/s]
Best score: 0.3683: : 937iter [00:27, 37.11iter/s]
Best score: 0.3683: : 938iter [00:27, 37.11iter/s]
Best score: 0.3681: : 939iter [00:27, 37.11iter/s]
Best score: 0.3681: : 940iter [00:27, 37.60iter/s]
Best score: 0.3681: : 940iter [00:27, 37.60iter/s]
Best score: 0.3681: : 941iter [00:27, 37.60iter/s]
Best score: 0.3681: : 942iter [00:27, 37.60iter/s]
Best score: 0.3681: : 943iter [00:27, 37.60iter/s]
Best score: 0.3681: : 944iter [00:27, 38.27iter/s]
Best score: 0.3681: : 944iter [00:27, 38.27iter/s]
Best score: 0.3681: : 945iter [00:27, 38.27iter/s]
Best score: 0.3681: : 946iter [00:27, 38.27iter/s]
Best score: 0.3681: : 947iter [00:27, 38.27iter/s]
Best score: 0.3681: : 948iter [00:27, 38.27iter/s]
Best score: 0.3681: : 949iter [00:27, 38.91iter/s]
[ITER 950] Best: 0.3679
[PARAMS] Current best parameters:
  fx: 553.8863
  fy: 553.7620
  px: 994.7986
  py: 758.2529
  k1: -0.10517383
  k2: 0.12814685
  k3: -0.06831840
  k4: 0.01258115
  p1: -0.00590449
  p2: -0.00935287
  p3: 0.03089954
  p4: -0.00192252
  s1: -0.0267
  s2: 0.0025
  s3: -0.0320
  s4: 0.0028
  tilt_x: -0.0111
  tilt_y: 0.0055
  scale: 3.0675
  rotation: 0.8724

Best score: 0.3679: : 949iter [00:27, 38.91iter/s]
Best score: 0.3679: : 950iter [00:27, 38.91iter/s]
Best score: 0.3679: : 951iter [00:27, 38.91iter/s]
Best score: 0.3679: : 952iter [00:27, 38.91iter/s]
Best score: 0.3679: : 953iter [00:27, 31.68iter/s]
Best score: 0.3679: : 953iter [00:27, 31.68iter/s]
Best score: 0.3679: : 954iter [00:27, 31.68iter/s]
Best score: 0.3679: : 955iter [00:27, 31.68iter/s]
Best score: 0.3679: : 956iter [00:27, 31.68iter/s]
Best score: 0.3679: : 957iter [00:27, 33.64iter/s]
Best score: 0.3679: : 957iter [00:27, 33.64iter/s]
Best score: 0.3679: : 958iter [00:27, 33.64iter/s]
Best score: 0.3679: : 959iter [00:27, 33.64iter/s]
Best score: 0.3679: : 960iter [00:27, 33.64iter/s]
Best score: 0.3679: : 961iter [00:27, 34.98iter/s]
Best score: 0.3679: : 961iter [00:27, 34.98iter/s]
Best score: 0.3679: : 962iter [00:27, 34.98iter/s]
Best score: 0.3679: : 963iter [00:27, 34.98iter/s]
Best score: 0.3679: : 964iter [00:27, 34.98iter/s]
Best score: 0.3679: : 965iter [00:27, 36.32iter/s]
Best score: 0.3679: : 965iter [00:28, 36.32iter/s]
Best score: 0.3679: : 966iter [00:28, 36.32iter/s]
Best score: 0.3679: : 967iter [00:28, 36.32iter/s]
Best score: 0.3679: : 968iter [00:28, 36.32iter/s]
Best score: 0.3679: : 969iter [00:28, 36.32iter/s]
Best score: 0.3679: : 970iter [00:28, 37.33iter/s]
Best score: 0.3679: : 970iter [00:28, 37.33iter/s]
Best score: 0.3679: : 971iter [00:28, 37.33iter/s]
Best score: 0.3679: : 972iter [00:28, 37.33iter/s]
Best score: 0.3679: : 973iter [00:28, 37.33iter/s]
Best score: 0.3679: : 974iter [00:28, 37.33iter/s]
Best score: 0.3679: : 975iter [00:28, 38.25iter/s]
Best score: 0.3679: : 975iter [00:28, 38.25iter/s]
Best score: 0.3679: : 976iter [00:28, 38.25iter/s]
Best score: 0.3679: : 977iter [00:28, 38.25iter/s]
Best score: 0.3679: : 978iter [00:28, 38.25iter/s]
Best score: 0.3679: : 979iter [00:28, 38.25iter/s]
Best score: 0.3679: : 980iter [00:28, 38.67iter/s]
Best score: 0.3679: : 980iter [00:28, 38.67iter/s]
Best score: 0.3679: : 981iter [00:28, 38.67iter/s]
Best score: 0.3679: : 982iter [00:28, 38.67iter/s]
Best score: 0.3679: : 983iter [00:28, 38.67iter/s]
Best score: 0.3679: : 984iter [00:28, 38.96iter/s]
Best score: 0.3679: : 984iter [00:28, 38.96iter/s]
Best score: 0.3679: : 985iter [00:28, 38.96iter/s]
Best score: 0.3679: : 986iter [00:28, 38.96iter/s]
Best score: 0.3679: : 987iter [00:28, 38.96iter/s]
Best score: 0.3679: : 988iter [00:28, 38.96iter/s]
Best score: 0.3679: : 989iter [00:28, 39.36iter/s]
Best score: 0.3678: : 989iter [00:28, 39.36iter/s]
Best score: 0.3678: : 990iter [00:28, 39.36iter/s]
Best score: 0.3678: : 991iter [00:28, 39.36iter/s]
Best score: 0.3678: : 992iter [00:28, 39.36iter/s]
Best score: 0.3678: : 993iter [00:28, 39.28iter/s]
Best score: 0.3678: : 993iter [00:28, 39.28iter/s]
Best score: 0.3678: : 994iter [00:28, 39.28iter/s]
Best score: 0.3678: : 995iter [00:28, 39.28iter/s]
Best score: 0.3678: : 996iter [00:28, 39.28iter/s]
Best score: 0.3678: : 997iter [00:28, 39.28iter/s]
Best score: 0.3678: : 998iter [00:28, 39.69iter/s]
Best score: 0.3678: : 998iter [00:28, 39.69iter/s]
[ITER 1000] Best: 0.3675
[PARAMS] Current best parameters:
  fx: 552.7816
  fy: 552.6631
  px: 994.8599
  py: 757.9221
  k1: -0.10406205
  k2: 0.12795425
  k3: -0.06822964
  k4: 0.01255014
  p1: -0.00630673
  p2: -0.01003405
  p3: 0.03089909
  p4: -0.00179096
  s1: -0.0260
  s2: 0.0024
  s3: -0.0322
  s4: 0.0028
  tilt_x: -0.0119
  tilt_y: 0.0039
  scale: 3.0684
  rotation: 0.8750

Best score: 0.3675: : 999iter [00:28, 39.69iter/s]
Best score: 0.3675: : 1000iter [00:28, 39.69iter/s]
Best score: 0.3675: : 1001iter [00:28, 39.69iter/s]
Best score: 0.3675: : 1002iter [00:28, 32.49iter/s]
Best score: 0.3675: : 1002iter [00:29, 32.49iter/s]
Best score: 0.3675: : 1003iter [00:29, 32.49iter/s]
Best score: 0.3675: : 1004iter [00:29, 32.49iter/s]
Best score: 0.3675: : 1005iter [00:29, 32.49iter/s]
Best score: 0.3675: : 1006iter [00:29, 32.49iter/s]
Best score: 0.3675: : 1007iter [00:29, 34.63iter/s]
Best score: 0.3675: : 1007iter [00:29, 34.63iter/s]
Best score: 0.3675: : 1008iter [00:29, 34.63iter/s]
Best score: 0.3675: : 1009iter [00:29, 34.63iter/s]
Best score: 0.3675: : 1010iter [00:29, 34.63iter/s]
Best score: 0.3675: : 1011iter [00:29, 35.78iter/s]
Best score: 0.3675: : 1011iter [00:29, 35.78iter/s]
Best score: 0.3675: : 1012iter [00:29, 35.78iter/s]
Best score: 0.3675: : 1013iter [00:29, 35.78iter/s]
Best score: 0.3675: : 1014iter [00:29, 35.78iter/s]
Best score: 0.3675: : 1015iter [00:29, 35.78iter/s]
Best score: 0.3675: : 1016iter [00:29, 37.10iter/s]
Best score: 0.3675: : 1016iter [00:29, 37.10iter/s]
Best score: 0.3675: : 1017iter [00:29, 37.10iter/s]
Best score: 0.3675: : 1018iter [00:29, 37.10iter/s]
Best score: 0.3674: : 1019iter [00:29, 37.10iter/s]
Best score: 0.3674: : 1020iter [00:29, 37.60iter/s]
Best score: 0.3674: : 1020iter [00:29, 37.60iter/s]
Best score: 0.3674: : 1021iter [00:29, 37.60iter/s]
Best score: 0.3674: : 1022iter [00:29, 37.60iter/s]
Best score: 0.3674: : 1023iter [00:29, 37.60iter/s]
Best score: 0.3674: : 1024iter [00:29, 37.60iter/s]
Best score: 0.3674: : 1025iter [00:29, 38.39iter/s]
Best score: 0.3674: : 1025iter [00:29, 38.39iter/s]
Best score: 0.3674: : 1026iter [00:29, 38.39iter/s]
Best score: 0.3674: : 1027iter [00:29, 38.39iter/s]
Best score: 0.3674: : 1028iter [00:29, 38.39iter/s]
Best score: 0.3673: : 1029iter [00:29, 38.39iter/s]
Best score: 0.3673: : 1030iter [00:29, 38.67iter/s]
Best score: 0.3673: : 1030iter [00:29, 38.67iter/s]
Best score: 0.3673: : 1031iter [00:29, 38.67iter/s]
Best score: 0.3673: : 1032iter [00:29, 38.67iter/s]
Best score: 0.3673: : 1033iter [00:29, 38.67iter/s]
Best score: 0.3673: : 1034iter [00:29, 38.67iter/s]
Best score: 0.3673: : 1035iter [00:29, 39.15iter/s]
Best score: 0.3673: : 1035iter [00:29, 39.15iter/s]
Best score: 0.3673: : 1036iter [00:29, 39.15iter/s]
Best score: 0.3673: : 1037iter [00:29, 39.15iter/s]
Best score: 0.3673: : 1038iter [00:29, 39.15iter/s]
Best score: 0.3671: : 1039iter [00:29, 39.15iter/s]
Best score: 0.3671: : 1040iter [00:29, 39.23iter/s]
Best score: 0.3671: : 1040iter [00:29, 39.23iter/s]
Best score: 0.3671: : 1041iter [00:29, 39.23iter/s]
Best score: 0.3671: : 1042iter [00:30, 39.23iter/s]
Best score: 0.3671: : 1043iter [00:30, 39.23iter/s]
Best score: 0.3671: : 1044iter [00:30, 39.29iter/s]
Best score: 0.3671: : 1044iter [00:30, 39.29iter/s]
Best score: 0.3671: : 1045iter [00:30, 39.29iter/s]
Best score: 0.3671: : 1046iter [00:30, 39.29iter/s]
Best score: 0.3671: : 1047iter [00:30, 39.29iter/s]
Best score: 0.3671: : 1048iter [00:30, 39.40iter/s]
Best score: 0.3671: : 1048iter [00:30, 39.40iter/s]
[ITER 1050] Best: 0.3671
[PARAMS] Current best parameters:
  fx: 552.4278
  fy: 552.3109
  px: 994.7902
  py: 757.9069
  k1: -0.10210009
  k2: 0.12571316
  k3: -0.06711994
  k4: 0.01234866
  p1: -0.00648952
  p2: -0.01052533
  p3: 0.03033089
  p4: -0.00158305
  s1: -0.0251
  s2: 0.0022
  s3: -0.0314
  s4: 0.0026
  tilt_x: -0.0116
  tilt_y: 0.0032
  scale: 3.0664
  rotation: 0.8737

Best score: 0.3671: : 1049iter [00:30, 39.40iter/s]
Best score: 0.3671: : 1050iter [00:30, 39.40iter/s]
Best score: 0.3671: : 1051iter [00:30, 39.40iter/s]
Best score: 0.3671: : 1052iter [00:30, 32.26iter/s]
Best score: 0.3671: : 1052iter [00:30, 32.26iter/s]
Best score: 0.3671: : 1053iter [00:30, 32.26iter/s]
Best score: 0.3671: : 1054iter [00:30, 32.26iter/s]
Best score: 0.3671: : 1055iter [00:30, 32.26iter/s]
Best score: 0.3671: : 1056iter [00:30, 34.08iter/s]
Best score: 0.3671: : 1056iter [00:30, 34.08iter/s]
Best score: 0.3671: : 1057iter [00:30, 34.08iter/s]
Best score: 0.3671: : 1058iter [00:30, 34.08iter/s]
Best score: 0.3671: : 1059iter [00:30, 34.08iter/s]
Best score: 0.3671: : 1060iter [00:30, 35.29iter/s]
Best score: 0.3671: : 1060iter [00:30, 35.29iter/s]
Best score: 0.3671: : 1061iter [00:30, 35.29iter/s]
Best score: 0.3671: : 1062iter [00:30, 35.29iter/s]
Best score: 0.3671: : 1063iter [00:30, 35.29iter/s]
Best score: 0.3671: : 1064iter [00:30, 35.29iter/s]
Best score: 0.3671: : 1065iter [00:30, 36.80iter/s]
Best score: 0.3671: : 1065iter [00:30, 36.80iter/s]
Best score: 0.3671: : 1066iter [00:30, 36.80iter/s]
Best score: 0.3671: : 1067iter [00:30, 36.80iter/s]
Best score: 0.3671: : 1068iter [00:30, 36.80iter/s]
Best score: 0.3664: : 1069iter [00:30, 36.80iter/s]
Best score: 0.3664: : 1070iter [00:30, 37.63iter/s]
Best score: 0.3664: : 1070iter [00:30, 37.63iter/s]
Best score: 0.3664: : 1071iter [00:30, 37.63iter/s]
Best score: 0.3664: : 1072iter [00:30, 37.63iter/s]
Best score: 0.3664: : 1073iter [00:30, 37.63iter/s]
Best score: 0.3664: : 1074iter [00:30, 37.63iter/s]
Best score: 0.3664: : 1075iter [00:30, 38.40iter/s]
Best score: 0.3664: : 1075iter [00:30, 38.40iter/s]
Best score: 0.3664: : 1076iter [00:30, 38.40iter/s]
Best score: 0.3664: : 1077iter [00:30, 38.40iter/s]
Best score: 0.3664: : 1078iter [00:31, 38.40iter/s]
Best score: 0.3655: : 1079iter [00:31, 38.40iter/s]
Best score: 0.3655: : 1080iter [00:31, 38.68iter/s]
Best score: 0.3655: : 1080iter [00:31, 38.68iter/s]
Best score: 0.3655: : 1081iter [00:31, 38.68iter/s]
Best score: 0.3655: : 1082iter [00:31, 38.68iter/s]
Best score: 0.3655: : 1083iter [00:31, 38.68iter/s]
Best score: 0.3655: : 1084iter [00:31, 38.68iter/s]
Best score: 0.3655: : 1085iter [00:31, 39.09iter/s]
Best score: 0.3655: : 1085iter [00:31, 39.09iter/s]
Best score: 0.3655: : 1086iter [00:31, 39.09iter/s]
Best score: 0.3655: : 1087iter [00:31, 39.09iter/s]
Best score: 0.3655: : 1088iter [00:31, 39.09iter/s]
Best score: 0.3655: : 1089iter [00:31, 39.30iter/s]
Best score: 0.3638: : 1089iter [00:31, 39.30iter/s]
Best score: 0.3638: : 1090iter [00:31, 39.30iter/s]
Best score: 0.3638: : 1091iter [00:31, 39.30iter/s]
Best score: 0.3638: : 1092iter [00:31, 39.30iter/s]
Best score: 0.3638: : 1093iter [00:31, 39.07iter/s]
Best score: 0.3638: : 1093iter [00:31, 39.07iter/s]
Best score: 0.3638: : 1094iter [00:31, 39.07iter/s]
Best score: 0.3638: : 1095iter [00:31, 39.07iter/s]
Best score: 0.3638: : 1096iter [00:31, 39.07iter/s]
Best score: 0.3638: : 1097iter [00:31, 39.07iter/s]
Best score: 0.3638: : 1098iter [00:31, 39.42iter/s]
Best score: 0.3638: : 1098iter [00:31, 39.42iter/s]
[ITER 1100] Best: 0.3635
[PARAMS] Current best parameters:
  fx: 546.9996
  fy: 546.8959
  px: 994.6889
  py: 757.2232
  k1: -0.08757010
  k2: 0.11163546
  k3: -0.05963073
  k4: 0.01083662
  p1: -0.01007139
  p2: -0.01726185
  p3: 0.03025605
  p4: -0.00064333
  s1: -0.0184
  s2: 0.0014
  s3: -0.0279
  s4: 0.0015
  tilt_x: -0.0077
  tilt_y: -0.0099
  scale: 3.0583
  rotation: 0.8924

Best score: 0.3635: : 1099iter [00:31, 39.42iter/s]
Best score: 0.3635: : 1100iter [00:31, 34.79iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.363505 -> 0.362195

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 26,022 → 26,022 within 3500mm ring
  Ring polish improved score: 0.362195 -> 0.362167
  Success: True
  Tangential distortion: p1=-0.010108, p2=-0.017259, p3=0.030229, p4=-0.000656
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3622
[✅] Parameters copied to: /working/MDC030_W032_2026-03-24/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 546.9995873386206
    fy: 546.8959383149001
    px: 994.6889286125314
    py: 757.2232248218306
    k1: -0.08756530998426484
    k2: 0.1116536956622337
    k3: -0.05960979787181363
    k4: 0.010817689896105168
    p1: -0.010107754498136699
    p2: -0.017258774175487445
    p3: 0.030228664196961987
    p4: -0.0006558177445084421
    scale: 3.0582801085962337
    rotation: 0.8923982217285866
    mmpp: 1.2789374827194344
    nmpp: 1278937.4827194344

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

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

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

   🔄 1000:   4%|▎         | 17/457 [00:00<00:02, 166.03frame/s]

   🔄 1000:   7%|▋         | 34/457 [00:00<00:02, 166.73frame/s]

   🔄 1000:  11%|█         | 51/457 [00:00<00:02, 167.85frame/s]

   🔄 1000:  15%|█▍        | 68/457 [00:00<00:02, 168.63frame/s]

   🔄 1000:  19%|█▉        | 86/457 [00:00<00:02, 169.50frame/s]

   🔄 1000:  23%|██▎       | 104/457 [00:00<00:02, 170.07frame/s]

   🔄 1000:  27%|██▋       | 122/457 [00:00<00:01, 170.41frame/s]

   🔄 1000:  31%|███       | 140/457 [00:00<00:01, 170.96frame/s]

   🔄 1000:  35%|███▍      | 158/457 [00:00<00:01, 171.97frame/s]

   🔄 1000:  39%|███▊      | 176/457 [00:01<00:01, 172.77frame/s]

   🔄 1000:  42%|████▏     | 194/457 [00:01<00:01, 173.53frame/s]

   🔄 1000:  46%|████▋     | 212/457 [00:01<00:01, 173.94frame/s]

   🔄 1000:  50%|█████     | 230/457 [00:01<00:01, 135.25frame/s]

   🔄 1000:  54%|█████▍    | 248/457 [00:01<00:01, 145.31frame/s]

   🔄 1000:  58%|█████▊    | 266/457 [00:01<00:01, 153.09frame/s]

   🔄 1000:  62%|██████▏   | 284/457 [00:01<00:01, 159.12frame/s]

   🔄 1000:  66%|██████▌   | 302/457 [00:01<00:00, 163.26frame/s]

   🔄 1000:  70%|███████   | 320/457 [00:01<00:00, 165.90frame/s]

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   🔄 1000:  78%|███████▊  | 356/457 [00:02<00:00, 168.34frame/s]

   🔄 1000:  82%|████████▏ | 374/457 [00:02<00:00, 168.71frame/s]

   🔄 1000:  86%|████████▌ | 391/457 [00:02<00:00, 169.06frame/s]

   🔄 1000:  89%|████████▉ | 408/457 [00:02<00:00, 169.10frame/s]

   🔄 1000:  93%|█████████▎| 426/457 [00:02<00:00, 169.53frame/s]

   🔄 1000:  97%|█████████▋| 444/457 [00:02<00:00, 169.69frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:04<00:04,  4.83s/rig]

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

   🔄 500:   5%|▌         | 18/342 [00:00<00:01, 179.55frame/s]

   🔄 500:  11%|█         | 37/342 [00:00<00:01, 182.45frame/s]

   🔄 500:  17%|█▋        | 58/342 [00:00<00:01, 191.75frame/s]

   🔄 500:  23%|██▎       | 78/342 [00:00<00:01, 191.39frame/s]

   🔄 500:  29%|██▊       | 98/342 [00:00<00:01, 188.92frame/s]

   🔄 500:  35%|███▍      | 119/342 [00:00<00:01, 195.40frame/s]

   🔄 500:  41%|████      | 141/342 [00:00<00:00, 203.21frame/s]

   🔄 500:  47%|████▋     | 162/342 [00:00<00:00, 204.13frame/s]

   🔄 500:  54%|█████▎    | 183/342 [00:00<00:00, 195.39frame/s]

   🔄 500:  59%|█████▉    | 203/342 [00:01<00:00, 190.20frame/s]

   🔄 500:  65%|██████▌   | 223/342 [00:01<00:00, 185.41frame/s]

   🔄 500:  71%|███████   | 242/342 [00:01<00:00, 182.29frame/s]

   🔄 500:  76%|███████▋  | 261/342 [00:01<00:00, 184.44frame/s]

   🔄 500:  82%|████████▏ | 280/342 [00:01<00:00, 183.62frame/s]

   🔄 500:  87%|████████▋ | 299/342 [00:01<00:00, 183.74frame/s]

   🔄 500:  93%|█████████▎| 318/342 [00:01<00:00, 184.53frame/s]

   🔄 500:  99%|█████████▊| 337/342 [00:01<00:00, 182.85frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  3.92s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.06s/rig]
[✅] Saved PTDs: /working/MDC030_W032_2026-03-24/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  17%|█▋        | 17/100 [00:00<00:00, 169.67frame/s]

   🔄 1000:  35%|███▌      | 35/100 [00:00<00:00, 171.05frame/s]

   🔄 1000:  53%|█████▎    | 53/100 [00:00<00:00, 173.22frame/s]

   🔄 1000:  71%|███████   | 71/100 [00:00<00:00, 173.82frame/s]

   🔄 1000:  89%|████████▉ | 89/100 [00:00<00:00, 172.43frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.08rig/s]

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

   🔄 500:  20%|██        | 20/100 [00:00<00:00, 192.88frame/s]

   🔄 500:  40%|████      | 40/100 [00:00<00:00, 195.75frame/s]

   🔄 500:  60%|██████    | 60/100 [00:00<00:00, 192.12frame/s]

   🔄 500:  80%|████████  | 80/100 [00:00<00:00, 188.29frame/s]

   🔄 500:  99%|█████████▉| 99/100 [00:00<00:00, 187.48frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.13rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.12rig/s]
[✅] Saved PTDs: /working/MDC030_W032_2026-03-24/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 82128/3080448 [00:00<00:03, 821274.91px/s]
📤 Writing LUT:   6%|▌         | 173187/3080448 [00:00<00:03, 873796.40px/s]
📤 Writing LUT:   8%|▊         | 261751/3080448 [00:00<00:03, 879202.84px/s]
📤 Writing LUT:  12%|█▏        | 354824/3080448 [00:00<00:03, 899541.97px/s]
📤 Writing LUT:  14%|█▍        | 445287/3080448 [00:00<00:02, 901373.95px/s]
📤 Writing LUT:  17%|█▋        | 537428/3080448 [00:00<00:02, 908182.79px/s]
📤 Writing LUT:  20%|██        | 628247/3080448 [00:00<00:02, 901313.37px/s]
📤 Writing LUT:  23%|██▎       | 718389/3080448 [00:00<00:02, 894695.08px/s]
📤 Writing LUT:  26%|██▋       | 808786/3080448 [00:00<00:02, 897566.46px/s]
📤 Writing LUT:  29%|██▉       | 898555/3080448 [00:01<00:02, 896881.51px/s]
📤 Writing LUT:  32%|███▏      | 988252/3080448 [00:01<00:02, 896776.52px/s]
📤 Writing LUT:  35%|███▍      | 1078144/3080448 [00:01<00:02, 897424.69px/s]
📤 Writing LUT:  38%|███▊      | 1170487/3080448 [00:01<00:02, 905281.61px/s]
📤 Writing LUT:  41%|████      | 1262128/3080448 [00:01<00:02, 908635.00px/s]
📤 Writing LUT:  44%|████▍     | 1352996/3080448 [00:01<00:01, 908344.96px/s]
📤 Writing LUT:  47%|████▋     | 1443834/3080448 [00:01<00:01, 904282.01px/s]
📤 Writing LUT:  50%|████▉     | 1534268/3080448 [00:01<00:01, 899680.02px/s]
📤 Writing LUT:  53%|█████▎    | 1625516/3080448 [00:01<00:01, 903498.51px/s]
📤 Writing LUT:  56%|█████▌    | 1716159/3080448 [00:01<00:01, 904374.05px/s]
📤 Writing LUT:  59%|█████▊    | 1807042/3080448 [00:02<00:01, 905706.09px/s]
📤 Writing LUT:  62%|██████▏   | 1897618/3080448 [00:02<00:01, 903149.81px/s]
📤 Writing LUT:  65%|██████▍   | 1987938/3080448 [00:02<00:01, 901106.46px/s]
📤 Writing LUT:  67%|██████▋   | 2078053/3080448 [00:02<00:01, 899445.05px/s]
📤 Writing LUT:  70%|███████   | 2168693/3080448 [00:02<00:01, 901522.27px/s]
📤 Writing LUT:  73%|███████▎  | 2259156/3080448 [00:02<00:00, 902449.69px/s]
📤 Writing LUT:  76%|███████▋  | 2349404/3080448 [00:02<00:00, 900361.12px/s]
📤 Writing LUT:  79%|███████▉  | 2439443/3080448 [00:02<00:00, 899749.56px/s]
📤 Writing LUT:  82%|████████▏ | 2529420/3080448 [00:02<00:00, 899538.38px/s]
📤 Writing LUT:  85%|████████▌ | 2620055/3080448 [00:02<00:00, 901576.81px/s]
📤 Writing LUT:  88%|████████▊ | 2713038/3080448 [00:03<00:00, 910040.01px/s]
📤 Writing LUT:  91%|█████████ | 2805142/3080448 [00:03<00:00, 913335.12px/s]
📤 Writing LUT:  94%|█████████▍| 2896768/3080448 [00:03<00:00, 914209.63px/s]
📤 Writing LUT:  97%|█████████▋| 2989805/3080448 [00:03<00:00, 919055.03px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 902401.92px/s]
[✅] LUT written to: /working/MDC030_W032_2026-03-24/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC030_W032_2026-03-24' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC030_W032_2026-03-24/calibration/MDC030_W032_2026-03-24_unknown.lut

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

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

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

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

   🔥 1000:   2%|▏         | 8/457 [00:00<00:05, 77.17frame/s]

   🔥 1000:   4%|▎         | 17/457 [00:00<00:05, 82.25frame/s]

   🔥 1000:   6%|▌         | 26/457 [00:00<00:05, 82.81frame/s]

   🔥 1000:   8%|▊         | 35/457 [00:00<00:05, 83.49frame/s]

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   🔥 1000:  12%|█▏        | 53/457 [00:00<00:04, 83.41frame/s]

   🔥 1000:  14%|█▎        | 62/457 [00:00<00:04, 81.26frame/s]

   🔥 1000:  16%|█▌        | 71/457 [00:00<00:04, 81.17frame/s]

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   🔥 1000:  21%|██        | 96/457 [00:01<00:04, 78.06frame/s]

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   🔥 1000:  26%|██▋       | 120/457 [00:01<00:04, 77.71frame/s]

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   🔥 1000:  32%|███▏      | 146/457 [00:01<00:03, 79.40frame/s]

   🔥 1000:  34%|███▍      | 155/457 [00:01<00:03, 79.97frame/s]

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   🔥 1000:  50%|████▉     | 228/457 [00:02<00:02, 87.23frame/s]

   🔥 1000:  52%|█████▏    | 237/457 [00:02<00:02, 87.49frame/s]

   🔥 1000:  54%|█████▍    | 246/457 [00:03<00:02, 86.04frame/s]

   🔥 1000:  56%|█████▌    | 255/457 [00:03<00:02, 83.94frame/s]

   🔥 1000:  58%|█████▊    | 264/457 [00:03<00:02, 82.51frame/s]

   🔥 1000:  60%|█████▉    | 273/457 [00:03<00:02, 81.77frame/s]

   🔥 1000:  62%|██████▏   | 282/457 [00:03<00:02, 80.76frame/s]

   🔥 1000:  64%|██████▎   | 291/457 [00:03<00:02, 80.67frame/s]

   🔥 1000:  66%|██████▌   | 300/457 [00:03<00:01, 80.86frame/s]

   🔥 1000:  68%|██████▊   | 309/457 [00:03<00:01, 81.12frame/s]

   🔥 1000:  70%|██████▉   | 318/457 [00:03<00:01, 81.61frame/s]

   🔥 1000:  72%|███████▏  | 327/457 [00:04<00:01, 81.70frame/s]

   🔥 1000:  74%|███████▎  | 336/457 [00:04<00:01, 82.69frame/s]

   🔥 1000:  75%|███████▌  | 345/457 [00:04<00:01, 82.04frame/s]

   🔥 1000:  77%|███████▋  | 354/457 [00:04<00:01, 81.96frame/s]

   🔥 1000:  79%|███████▉  | 363/457 [00:04<00:01, 81.18frame/s]

   🔥 1000:  81%|████████▏ | 372/457 [00:04<00:01, 80.57frame/s]

   🔥 1000:  83%|████████▎ | 381/457 [00:04<00:00, 80.42frame/s]

   🔥 1000:  85%|████████▌ | 390/457 [00:04<00:00, 80.79frame/s]

   🔥 1000:  87%|████████▋ | 399/457 [00:04<00:00, 81.26frame/s]

   🔥 1000:  89%|████████▉ | 408/457 [00:05<00:00, 82.74frame/s]

   🔥 1000:  91%|█████████ | 417/457 [00:05<00:00, 84.36frame/s]

   🔥 1000:  93%|█████████▎| 426/457 [00:05<00:00, 84.05frame/s]

   🔥 1000:  95%|█████████▌| 435/457 [00:05<00:00, 84.33frame/s]

   🔥 1000:  97%|█████████▋| 444/457 [00:05<00:00, 84.59frame/s]

   🔥 1000:  99%|█████████▉| 453/457 [00:05<00:00, 85.48frame/s]

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

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:08<00:08,  8.52s/rig]

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

   🔥 500:   2%|▏         | 8/342 [00:00<00:04, 78.25frame/s]

   🔥 500:   5%|▍         | 17/342 [00:00<00:04, 81.07frame/s]

   🔥 500:   8%|▊         | 26/342 [00:00<00:03, 82.57frame/s]

   🔥 500:  10%|█         | 35/342 [00:00<00:03, 83.58frame/s]

   🔥 500:  13%|█▎        | 44/342 [00:00<00:03, 83.47frame/s]

   🔥 500:  15%|█▌        | 53/342 [00:00<00:03, 84.92frame/s]

   🔥 500:  18%|█▊        | 62/342 [00:00<00:03, 84.69frame/s]

   🔥 500:  21%|██        | 71/342 [00:00<00:03, 84.27frame/s]

   🔥 500:  23%|██▎       | 80/342 [00:00<00:03, 84.62frame/s]

   🔥 500:  26%|██▌       | 89/342 [00:01<00:02, 85.29frame/s]

   🔥 500:  29%|██▊       | 98/342 [00:01<00:02, 86.63frame/s]

   🔥 500:  32%|███▏      | 108/342 [00:01<00:02, 89.92frame/s]

   🔥 500:  35%|███▍      | 119/342 [00:01<00:02, 94.01frame/s]

   🔥 500:  38%|███▊      | 130/342 [00:01<00:02, 96.36frame/s]

   🔥 500:  41%|████      | 141/342 [00:01<00:02, 98.00frame/s]

   🔥 500:  44%|████▍     | 152/342 [00:01<00:01, 98.22frame/s]

   🔥 500:  47%|████▋     | 162/342 [00:01<00:01, 93.49frame/s]

   🔥 500:  50%|█████     | 172/342 [00:01<00:01, 87.64frame/s]

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   🔥 500:  61%|██████    | 208/342 [00:02<00:01, 79.90frame/s]

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   🔥 500:  68%|██████▊   | 233/342 [00:02<00:01, 76.43frame/s]

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   🔥 500:  86%|████████▌ | 294/342 [00:03<00:00, 79.81frame/s]

   🔥 500:  88%|████████▊ | 302/342 [00:03<00:00, 79.80frame/s]

   🔥 500:  91%|█████████ | 311/342 [00:03<00:00, 80.46frame/s]

   🔥 500:  94%|█████████▎| 320/342 [00:03<00:00, 82.11frame/s]

   🔥 500:  96%|█████████▌| 329/342 [00:03<00:00, 83.38frame/s]

   🔥 500:  99%|█████████▉| 338/342 [00:04<00:00, 84.81frame/s]

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

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  6.58s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  6.87s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC030_W032_2026-03-24/calibration/debug/5_heatmap
[🏁] Finished processing MDC030_W032_2026-03-24


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