🟢 Starting Calibration Pipeline

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

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 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:  81%|████████  | 369/457 [00:08<00:02, 41.29it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 374/457 [00:08<00:02, 41.42it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 379/457 [00:08<00:01, 41.39it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 384/457 [00:09<00:01, 41.39it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 389/457 [00:09<00:01, 41.40it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 394/457 [00:09<00:01, 41.47it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 399/457 [00:09<00:01, 41.54it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 404/457 [00:09<00:01, 41.59it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 409/457 [00:09<00:01, 41.66it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 414/457 [00:09<00:01, 41.69it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 419/457 [00:09<00:00, 41.73it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 424/457 [00:09<00:00, 41.82it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 429/457 [00:10<00:00, 41.96it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 434/457 [00:10<00:00, 42.14it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 439/457 [00:10<00:00, 42.40it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 444/457 [00:10<00:00, 42.68it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 449/457 [00:10<00:00, 42.91it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 454/457 [00:10<00:00, 42.48it/s]

                                                                           
[TIMING] 1000 — 457 frames
  Phase 1  radial detect   (parallel)  :   7.08s  (15.5 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.41s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   1.61s
  Phase 3  finalize        (sequential):  10.74s  (23.5 ms/frame)
    decode + undistort                 :   5.99s  (13.1 ms/frame)
    corner apply + refinement          :   2.33s  (5.1 ms/frame)
    segment                            :   1.83s  (4.0 ms/frame)
    smooth                             :   0.42s  (0.9 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.83s


Processing Rigs:  50%|█████     | 1/2 [00:28<00:28, 28.67s/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<04:21,  1.30it/s]

→ 500 (342 images) Phase 1:  10%|▉         | 33/342 [00:01<00:09, 33.81it/s]

→ 500 (342 images) Phase 1:  19%|█▉        | 65/342 [00:01<00:05, 48.85it/s]

→ 500 (342 images) Phase 1:  28%|██▊       | 95/342 [00:01<00:03, 76.86it/s]

→ 500 (342 images) Phase 1:  32%|███▏      | 110/342 [00:02<00:03, 61.37it/s]

→ 500 (342 images) Phase 1:  38%|███▊      | 129/342 [00:02<00:03, 57.18it/s]

→ 500 (342 images) Phase 1:  45%|████▌     | 154/342 [00:02<00:02, 79.19it/s]

→ 500 (342 images) Phase 1:  49%|████▉     | 169/342 [00:03<00:02, 62.41it/s]

→ 500 (342 images) Phase 1:  56%|█████▋    | 193/342 [00:03<00:02, 62.25it/s]

→ 500 (342 images) Phase 1:  62%|██████▏   | 213/342 [00:03<00:01, 77.71it/s]

→ 500 (342 images) Phase 1:  66%|██████▌   | 226/342 [00:03<00:01, 62.50it/s]

→ 500 (342 images) Phase 1:  73%|███████▎  | 249/342 [00:03<00:01, 83.81it/s]

→ 500 (342 images) Phase 1:  77%|███████▋  | 263/342 [00:04<00:01, 64.19it/s]

→ 500 (342 images) Phase 1:  85%|████████▍ | 289/342 [00:04<00:00, 65.13it/s]

→ 500 (342 images) Phase 1:  89%|████████▉ | 306/342 [00:04<00:00, 77.73it/s]

→ 500 (342 images) Phase 1:  94%|█████████▍| 321/342 [00:05<00:00, 74.33it/s]

→ 500 (342 images) Phase 1:  97%|█████████▋| 332/342 [00:05<00:00, 78.97it/s]

                                                                             

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

→ 500 Phase 2: corners:   0%|          | 1/342 [00:00<02:04,  2.74it/s]

→ 500 Phase 2: corners:   4%|▎         | 12/342 [00:00<00:10, 32.58it/s]

→ 500 Phase 2: corners:   7%|▋         | 25/342 [00:00<00:05, 59.26it/s]

→ 500 Phase 2: corners:  12%|█▏        | 40/342 [00:00<00:03, 85.11it/s]

→ 500 Phase 2: corners:  15%|█▌        | 52/342 [00:00<00:03, 94.60it/s]

→ 500 Phase 2: corners:  19%|█▉        | 66/342 [00:00<00:02, 107.62it/s]

→ 500 Phase 2: corners:  23%|██▎       | 80/342 [00:00<00:02, 115.99it/s]

→ 500 Phase 2: corners:  27%|██▋       | 93/342 [00:01<00:02, 108.42it/s]

→ 500 Phase 2: corners:  32%|███▏      | 110/342 [00:01<00:01, 124.08it/s]

→ 500 Phase 2: corners:  38%|███▊      | 129/342 [00:01<00:01, 142.40it/s]

→ 500 Phase 2: corners:  43%|████▎     | 148/342 [00:01<00:01, 155.71it/s]

→ 500 Phase 2: corners:  48%|████▊     | 165/342 [00:01<00:01, 154.43it/s]

→ 500 Phase 2: corners:  53%|█████▎    | 181/342 [00:01<00:01, 151.57it/s]

→ 500 Phase 2: corners:  58%|█████▊    | 197/342 [00:01<00:00, 150.59it/s]

→ 500 Phase 2: corners:  62%|██████▏   | 213/342 [00:01<00:00, 149.49it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 229/342 [00:01<00:00, 146.43it/s]

→ 500 Phase 2: corners:  72%|███████▏  | 246/342 [00:02<00:00, 152.13it/s]

→ 500 Phase 2: corners:  77%|███████▋  | 265/342 [00:02<00:00, 160.85it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 282/342 [00:02<00:00, 157.48it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 299/342 [00:02<00:00, 160.25it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 316/342 [00:02<00:00, 155.31it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 335/342 [00:02<00:00, 164.83it/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:  95%|█████████▍| 324/342 [00:07<00:00, 44.94it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 329/342 [00:07<00:00, 44.67it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 334/342 [00:07<00:00, 44.04it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 339/342 [00:07<00:00, 43.71it/s]

                                                                          
[TIMING] 500 — 342 frames
  Phase 1  radial detect   (parallel)  :   5.33s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.72s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   3.16s
  Phase 3  finalize        (sequential):   7.46s  (21.8 ms/frame)
    decode + undistort                 :   4.29s  (12.5 ms/frame)
    corner apply + refinement          :   1.19s  (3.5 ms/frame)
    segment                            :   1.25s  (3.7 ms/frame)
    smooth                             :   0.62s  (1.8 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  18.67s


Processing Rigs: 100%|██████████| 2/2 [00:52<00:00, 25.75s/it]
Processing Rigs: 100%|██████████| 2/2 [00:52<00:00, 26.19s/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]
Computing max image from bright frames:   2%|▎         | 1/40 [00:03<02:04,  3.20s/it]
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Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:56,  3.15s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:53,  3.15s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:50,  3.14s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:46,  3.14s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:43,  3.13s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:40,  3.13s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:37,  3.13s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:34,  3.14s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:34<01:31,  3.15s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:37<01:27,  3.13s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:40<01:24,  3.13s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:43<01:21,  3.13s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:47<01:18,  3.13s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:50<01:15,  3.13s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:53<01:12,  3.14s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:56<01:09,  3.14s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [00:59<01:06,  3.15s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:02<01:02,  3.15s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:05<00:59,  3.16s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:09<00:56,  3.16s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:12<00:53,  3.16s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:15<00:50,  3.18s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:18<00:47,  3.18s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:21<00:44,  3.18s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:25<00:41,  3.18s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:28<00:38,  3.18s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:31<00:35,  3.19s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:34<00:31,  3.18s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:37<00:28,  3.21s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:41<00:25,  3.21s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:44<00:22,  3.19s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:47<00:19,  3.18s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:50<00:15,  3.19s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:53<00:12,  3.20s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:57<00:09,  3.20s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:00<00:06,  3.18s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:03<00:03,  3.19s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:06<00:00,  3.19s/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]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:11,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.44it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 68.76it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 490.909} (0.001):  24%|██▍       | 24/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 490.909} (0.001):  25%|██▌       | 25/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 490.909} (0.001):  26%|██▌       | 26/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 497.98} (0.003):  27%|██▋       | 27/100 [00:00<00:00, 84.73it/s] 
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.007 | Best: {'fx': 505.051} (0.007):  28%|██▊       | 28/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 505.051} (0.007):  29%|██▉       | 29/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.020 | Best: {'fx': 512.121} (0.020):  30%|███       | 30/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.032 | Best: {'fx': 547.475} (0.032):  31%|███       | 31/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.067 | Best: {'fx': 533.333} (0.067):  32%|███▏      | 32/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 533.333} (0.067):  33%|███▎      | 33/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.049 | Best: {'fx': 533.333} (0.067):  34%|███▍      | 34/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.067):  35%|███▌      | 35/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.043 | Best: {'fx': 533.333} (0.067):  36%|███▌      | 36/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.067):  37%|███▋      | 37/100 [00:00<00:00, 84.73it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.067):  38%|███▊      | 38/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.067):  38%|███▊      | 38/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.067):  39%|███▉      | 39/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.067):  40%|████      | 40/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.067):  41%|████      | 41/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.067):  42%|████▏     | 42/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.067):  43%|████▎     | 43/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  44%|████▍     | 44/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  45%|████▌     | 45/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.067):  46%|████▌     | 46/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  47%|████▋     | 47/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  48%|████▊     | 48/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  49%|████▉     | 49/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  50%|█████     | 50/100 [00:00<00:00, 100.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  51%|█████     | 51/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  51%|█████     | 51/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  52%|█████▏    | 52/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  53%|█████▎    | 53/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  54%|█████▍    | 54/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  55%|█████▌    | 55/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  56%|█████▌    | 56/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  57%|█████▋    | 57/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  58%|█████▊    | 58/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  59%|█████▉    | 59/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  60%|██████    | 60/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  61%|██████    | 61/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  62%|██████▏   | 62/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  63%|██████▎   | 63/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  64%|██████▍   | 64/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  65%|██████▌   | 65/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  66%|██████▌   | 66/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  67%|██████▋   | 67/100 [00:00<00:00, 109.35it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  68%|██████▊   | 68/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  68%|██████▊   | 68/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  69%|██████▉   | 69/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  70%|███████   | 70/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  71%|███████   | 71/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  72%|███████▏  | 72/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  73%|███████▎  | 73/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  74%|███████▍  | 74/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  75%|███████▌  | 75/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  76%|███████▌  | 76/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  77%|███████▋  | 77/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  78%|███████▊  | 78/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  79%|███████▉  | 79/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  80%|████████  | 80/100 [00:00<00:00, 127.83it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  81%|████████  | 81/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  81%|████████  | 81/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  82%|████████▏ | 82/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  83%|████████▎ | 83/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  84%|████████▍ | 84/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  85%|████████▌ | 85/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  86%|████████▌ | 86/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  87%|████████▋ | 87/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  88%|████████▊ | 88/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  89%|████████▉ | 89/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  90%|█████████ | 90/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  91%|█████████ | 91/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  92%|█████████▏| 92/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  93%|█████████▎| 93/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  94%|█████████▍| 94/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  95%|█████████▌| 95/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  96%|█████████▌| 96/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  97%|█████████▋| 97/100 [00:00<00:00, 125.77it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  98%|█████████▊| 98/100 [00:00<00:00, 133.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  98%|█████████▊| 98/100 [00:00<00:00, 133.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  99%|█████████▉| 99/100 [00:00<00:00, 133.09it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.014 | Best: {'fx': 508.333} (0.014):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.014 | Best: {'fx': 508.333} (0.014):   2%|▏         | 1/50 [00:00<00:05,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.016 | Best: {'fx': 509.354} (0.016):   2%|▏         | 1/50 [00:00<00:05,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.023 | Best: {'fx': 515.476} (0.023):   4%|▍         | 2/50 [00:00<00:05,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.019 | Best: {'fx': 515.476} (0.023):   6%|▌         | 3/50 [00:00<00:05,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.022 | Best: {'fx': 515.476} (0.023):   8%|▊         | 4/50 [00:00<00:05,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.020 | Best: {'fx': 515.476} (0.023):  10%|█         | 5/50 [00:00<00:05,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.019 | Best: {'fx': 515.476} (0.023):  12%|█▏        | 6/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.025 | Best: {'fx': 516.497} (0.025):  14%|█▍        | 7/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.021 | Best: {'fx': 516.497} (0.025):  16%|█▌        | 8/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.027 | Best: {'fx': 517.517} (0.027):  18%|█▊        | 9/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.029 | Best: {'fx': 518.537} (0.029):  20%|██        | 10/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.030 | Best: {'fx': 519.558} (0.030):  22%|██▏       | 11/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.033 | Best: {'fx': 520.578} (0.033):  24%|██▍       | 12/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.036 | Best: {'fx': 521.599} (0.036):  26%|██▌       | 13/50 [00:00<00:04,  8.84it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.036 | Best: {'fx': 521.599} (0.036):  28%|██▊       | 14/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 522.619} (0.038):  28%|██▊       | 14/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.043 | Best: {'fx': 523.639} (0.043):  30%|███       | 15/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.045 | Best: {'fx': 524.66} (0.045):  32%|███▏      | 16/50 [00:00<00:00, 75.99it/s] 
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.051 | Best: {'fx': 527.721} (0.051):  34%|███▍      | 17/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.047 | Best: {'fx': 527.721} (0.051):  36%|███▌      | 18/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.049 | Best: {'fx': 527.721} (0.051):  38%|███▊      | 19/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.068 | Best: {'fx': 531.803} (0.068):  40%|████      | 20/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.062 | Best: {'fx': 531.803} (0.068):  42%|████▏     | 21/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.055 | Best: {'fx': 531.803} (0.068):  44%|████▍     | 22/50 [00:00<00:00, 75.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.055 | Best: {'fx': 531.803} (0.068):  46%|████▌     | 23/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.067 | Best: {'fx': 531.803} (0.068):  46%|████▌     | 23/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.057 | Best: {'fx': 531.803} (0.068):  48%|████▊     | 24/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.065 | Best: {'fx': 531.803} (0.068):  50%|█████     | 25/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.062 | Best: {'fx': 531.803} (0.068):  52%|█████▏    | 26/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.054 | Best: {'fx': 531.803} (0.068):  54%|█████▍    | 27/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.059 | Best: {'fx': 531.803} (0.068):  56%|█████▌    | 28/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.052 | Best: {'fx': 531.803} (0.068):  58%|█████▊    | 29/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.046 | Best: {'fx': 531.803} (0.068):  60%|██████    | 30/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.043 | Best: {'fx': 531.803} (0.068):  62%|██████▏   | 31/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.042 | Best: {'fx': 531.803} (0.068):  64%|██████▍   | 32/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.040 | Best: {'fx': 531.803} (0.068):  66%|██████▌   | 33/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 531.803} (0.068):  68%|██████▊   | 34/50 [00:00<00:00, 82.16it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 531.803} (0.068):  70%|███████   | 35/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.036 | Best: {'fx': 531.803} (0.068):  70%|███████   | 35/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.032 | Best: {'fx': 531.803} (0.068):  72%|███████▏  | 36/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 531.803} (0.068):  74%|███████▍  | 37/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.035 | Best: {'fx': 531.803} (0.068):  76%|███████▌  | 38/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.031 | Best: {'fx': 531.803} (0.068):  78%|███████▊  | 39/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.030 | Best: {'fx': 531.803} (0.068):  80%|████████  | 40/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.029 | Best: {'fx': 531.803} (0.068):  82%|████████▏ | 41/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.024 | Best: {'fx': 531.803} (0.068):  84%|████████▍ | 42/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.023 | Best: {'fx': 531.803} (0.068):  86%|████████▌ | 43/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.028 | Best: {'fx': 531.803} (0.068):  88%|████████▊ | 44/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.023 | Best: {'fx': 531.803} (0.068):  90%|█████████ | 45/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.025 | Best: {'fx': 531.803} (0.068):  92%|█████████▏| 46/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.027 | Best: {'fx': 531.803} (0.068):  94%|█████████▍| 47/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.026 | Best: {'fx': 531.803} (0.068):  96%|█████████▌| 48/50 [00:00<00:00, 93.11it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.025 | Best: {'fx': 531.803} (0.068):  98%|█████████▊| 49/50 [00:00<00:00, 93.11it/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: 37.8162: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 37.8162, picklable_objective(x0) = 37.8666

Best score: 37.8666: : 0iter [00:01, ?iter/s] 
Best score: 37.8666: : 1iter [00:01,  1.95s/iter]
Best score: 37.8666: : 1iter [00:01,  1.95s/iter]
Best score: 37.8666: : 2iter [00:02,  1.95s/iter]
Best score: 37.8666: : 3iter [00:02,  1.95s/iter]
Best score: 37.8666: : 4iter [00:02,  1.95s/iter]
Best score: 37.8666: : 5iter [00:02,  3.20iter/s]
Best score: 37.8666: : 5iter [00:02,  3.20iter/s]
Best score: 37.8666: : 6iter [00:02,  3.20iter/s]
Best score: 37.8666: : 7iter [00:02,  3.20iter/s]
Best score: 37.8666: : 8iter [00:02,  3.20iter/s]
Best score: 37.8666: : 9iter [00:02,  6.45iter/s]
Best score: 37.8666: : 9iter [00:02,  6.45iter/s]
Best score: 37.8666: : 10iter [00:02,  6.45iter/s]
Best score: 37.8666: : 11iter [00:02,  6.45iter/s]
Best score: 37.8666: : 12iter [00:02,  6.45iter/s]
Best score: 37.8666: : 13iter [00:02, 10.18iter/s]
Best score: 37.8666: : 13iter [00:02, 10.18iter/s]
Best score: 37.8666: : 14iter [00:02, 10.18iter/s]
Best score: 37.8666: : 15iter [00:02, 10.18iter/s]
Best score: 37.8666: : 16iter [00:02, 10.18iter/s]
Best score: 37.8666: : 17iter [00:02, 14.24iter/s]
Best score: 37.8666: : 17iter [00:02, 14.24iter/s]
Best score: 37.8666: : 18iter [00:02, 14.24iter/s]
Best score: 37.8666: : 19iter [00:02, 14.24iter/s]
Best score: 37.8666: : 20iter [00:02, 14.24iter/s]
Best score: 37.8666: : 21iter [00:02, 18.34iter/s]
Best score: 37.8666: : 21iter [00:02, 18.34iter/s]
Best score: 37.8666: : 22iter [00:02, 18.34iter/s]
Best score: 37.8666: : 23iter [00:02, 18.34iter/s]
Best score: 37.8666: : 24iter [00:02, 18.34iter/s]
Best score: 37.8666: : 25iter [00:02, 22.37iter/s]
Best score: 37.8666: : 25iter [00:02, 22.37iter/s]
Best score: 37.8666: : 26iter [00:02, 22.37iter/s]
Best score: 37.8666: : 27iter [00:02, 22.37iter/s]
Best score: 37.8666: : 28iter [00:02, 22.37iter/s]
Best score: 37.8666: : 29iter [00:02, 26.00iter/s]
Best score: 37.8666: : 29iter [00:02, 26.00iter/s]
Best score: 37.8666: : 30iter [00:02, 26.00iter/s]
Best score: 37.8666: : 31iter [00:02, 26.00iter/s]
Best score: 37.8666: : 32iter [00:02, 26.00iter/s]
Best score: 37.8666: : 33iter [00:02, 29.06iter/s]
Best score: 37.8666: : 33iter [00:02, 29.06iter/s]
Best score: 37.8666: : 34iter [00:02, 29.06iter/s]
Best score: 37.8666: : 35iter [00:02, 29.06iter/s]
Best score: 37.8666: : 36iter [00:02, 29.06iter/s]
Best score: 37.8666: : 37iter [00:02, 31.66iter/s]
Best score: 37.8666: : 37iter [00:02, 31.66iter/s]
Best score: 37.8666: : 38iter [00:02, 31.66iter/s]
Best score: 37.8666: : 39iter [00:02, 31.66iter/s]
Best score: 37.8666: : 40iter [00:02, 31.66iter/s]
Best score: 37.8666: : 41iter [00:02, 33.50iter/s]
Best score: 37.8666: : 41iter [00:03, 33.50iter/s]
Best score: 37.8666: : 42iter [00:03, 33.50iter/s]
Best score: 37.8666: : 43iter [00:03, 33.50iter/s]
Best score: 37.8666: : 44iter [00:03, 33.50iter/s]
Best score: 37.8666: : 45iter [00:03, 35.21iter/s]
Best score: 37.8666: : 45iter [00:03, 35.21iter/s]
Best score: 37.8666: : 46iter [00:03, 35.21iter/s]
Best score: 37.8666: : 47iter [00:03, 35.21iter/s]
Best score: 37.8666: : 48iter [00:03, 35.21iter/s]
Best score: 37.8666: : 49iter [00:03, 36.40iter/s]
[ITER 50] Best: 37.8666
[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: 37.8666: : 49iter [00:03, 36.40iter/s]
Best score: 37.8666: : 50iter [00:03, 36.40iter/s]
Best score: 37.8666: : 51iter [00:03, 36.40iter/s]
Best score: 37.8666: : 52iter [00:03, 36.40iter/s]
Best score: 37.8666: : 53iter [00:03, 37.01iter/s]
Best score: 37.8666: : 53iter [00:03, 37.01iter/s]
Best score: 37.8666: : 54iter [00:03, 37.01iter/s]
Best score: 37.8666: : 55iter [00:03, 37.01iter/s]
Best score: 37.8666: : 56iter [00:03, 37.01iter/s]
Best score: 37.8666: : 57iter [00:03, 37.44iter/s]
Best score: 37.8666: : 57iter [00:03, 37.44iter/s]
Best score: 37.8666: : 58iter [00:03, 37.44iter/s]
Best score: 37.8666: : 59iter [00:03, 37.44iter/s]
Best score: 37.8666: : 60iter [00:03, 37.44iter/s]
Best score: 37.8666: : 61iter [00:03, 37.68iter/s]
Best score: 37.8666: : 61iter [00:03, 37.68iter/s]
Best score: 37.8666: : 62iter [00:03, 37.68iter/s]
Best score: 37.8666: : 63iter [00:03, 37.68iter/s]
Best score: 37.8666: : 64iter [00:03, 37.68iter/s]
Best score: 37.8666: : 65iter [00:03, 38.15iter/s]
Best score: 37.8666: : 65iter [00:03, 38.15iter/s]
Best score: 37.8666: : 66iter [00:03, 38.15iter/s]
Best score: 37.8666: : 67iter [00:03, 38.15iter/s]
Best score: 37.8666: : 68iter [00:03, 38.15iter/s]
Best score: 37.8666: : 69iter [00:03, 38.46iter/s]
Best score: 37.8666: : 69iter [00:03, 38.46iter/s]
Best score: 37.8666: : 70iter [00:03, 38.46iter/s]
Best score: 37.8666: : 71iter [00:03, 38.46iter/s]
Best score: 37.8666: : 72iter [00:03, 38.46iter/s]
Best score: 37.8666: : 73iter [00:03, 38.55iter/s]
Best score: 37.8666: : 73iter [00:03, 38.55iter/s]
Best score: 37.8666: : 74iter [00:03, 38.55iter/s]
Best score: 37.8666: : 75iter [00:03, 38.55iter/s]
Best score: 37.8666: : 76iter [00:03, 38.55iter/s]
Best score: 37.8666: : 77iter [00:03, 38.78iter/s]
Best score: 37.8666: : 77iter [00:03, 38.78iter/s]
Best score: 37.8666: : 78iter [00:03, 38.78iter/s]
Best score: 37.8666: : 79iter [00:03, 38.78iter/s]
Best score: 37.8666: : 80iter [00:04, 38.78iter/s]
Best score: 37.8666: : 81iter [00:04, 38.78iter/s]
Best score: 37.8666: : 81iter [00:04, 38.78iter/s]
Best score: 37.8666: : 82iter [00:04, 38.78iter/s]
Best score: 37.8666: : 83iter [00:04, 38.78iter/s]
Best score: 37.8666: : 84iter [00:04, 38.78iter/s]
Best score: 37.8666: : 85iter [00:04, 39.01iter/s]
Best score: 37.8666: : 85iter [00:04, 39.01iter/s]
Best score: 37.8666: : 86iter [00:04, 39.01iter/s]
Best score: 37.8666: : 87iter [00:04, 39.01iter/s]
Best score: 37.8666: : 88iter [00:04, 39.01iter/s]
Best score: 37.8666: : 89iter [00:04, 39.16iter/s]
Best score: 37.8666: : 89iter [00:04, 39.16iter/s]
Best score: 37.8666: : 90iter [00:04, 39.16iter/s]
Best score: 37.8666: : 91iter [00:04, 39.16iter/s]
Best score: 37.8666: : 92iter [00:04, 39.16iter/s]
Best score: 37.8666: : 93iter [00:04, 39.04iter/s]
Best score: 37.8666: : 93iter [00:04, 39.04iter/s]
Best score: 37.8666: : 94iter [00:04, 39.04iter/s]
Best score: 37.8666: : 95iter [00:04, 39.04iter/s]
Best score: 37.8666: : 96iter [00:04, 39.04iter/s]
Best score: 37.8666: : 97iter [00:04, 39.04iter/s]
Best score: 37.8666: : 98iter [00:04, 39.35iter/s]
Best score: 37.8666: : 98iter [00:04, 39.35iter/s]
[ITER 100] Best: 37.8666
[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: 37.8666: : 99iter [00:04, 39.35iter/s]
Best score: 37.8666: : 100iter [00:04, 39.35iter/s]
Best score: 37.8666: : 101iter [00:04, 39.35iter/s]
Best score: 37.8666: : 102iter [00:04, 36.54iter/s]
Best score: 37.8666: : 102iter [00:04, 36.54iter/s]
Best score: 37.8666: : 103iter [00:04, 36.54iter/s]
Best score: 37.8666: : 104iter [00:04, 36.54iter/s]
Best score: 37.8666: : 105iter [00:04, 36.54iter/s]
Best score: 37.8666: : 106iter [00:04, 37.25iter/s]
Best score: 37.8666: : 106iter [00:04, 37.25iter/s]
Best score: 37.8666: : 107iter [00:04, 37.25iter/s]
Best score: 37.8666: : 108iter [00:04, 37.25iter/s]
Best score: 37.8666: : 109iter [00:04, 37.25iter/s]
Best score: 37.8666: : 110iter [00:04, 37.60iter/s]
Best score: 37.8666: : 110iter [00:04, 37.60iter/s]
Best score: 37.8666: : 111iter [00:04, 37.60iter/s]
Best score: 37.8666: : 112iter [00:04, 37.60iter/s]
Best score: 37.8666: : 113iter [00:04, 37.60iter/s]
Best score: 37.8666: : 114iter [00:04, 38.15iter/s]
Best score: 37.8666: : 114iter [00:04, 38.15iter/s]
Best score: 37.8666: : 115iter [00:04, 38.15iter/s]
Best score: 37.8666: : 116iter [00:04, 38.15iter/s]
Best score: 37.8666: : 117iter [00:04, 38.15iter/s]
Best score: 37.8666: : 118iter [00:04, 38.46iter/s]
Best score: 37.8666: : 118iter [00:04, 38.46iter/s]
Best score: 37.8666: : 119iter [00:05, 38.46iter/s]
Best score: 37.8666: : 120iter [00:05, 38.46iter/s]
Best score: 37.8666: : 121iter [00:05, 38.46iter/s]
Best score: 37.8666: : 122iter [00:05, 38.47iter/s]
Best score: 37.8666: : 122iter [00:05, 38.47iter/s]
Best score: 37.8666: : 123iter [00:05, 38.47iter/s]
Best score: 37.8666: : 124iter [00:05, 38.47iter/s]
Best score: 37.8666: : 125iter [00:05, 38.47iter/s]
Best score: 37.8666: : 126iter [00:05, 38.67iter/s]
Best score: 37.8666: : 126iter [00:05, 38.67iter/s]
Best score: 37.8666: : 127iter [00:05, 38.67iter/s]
Best score: 37.8666: : 128iter [00:05, 38.67iter/s]
Best score: 37.8666: : 129iter [00:05, 38.67iter/s]
Best score: 37.8666: : 130iter [00:05, 38.56iter/s]
Best score: 37.8666: : 130iter [00:05, 38.56iter/s]
Best score: 37.8666: : 131iter [00:05, 38.56iter/s]
Best score: 37.8666: : 132iter [00:05, 38.56iter/s]
Best score: 37.8666: : 133iter [00:05, 38.56iter/s]
Best score: 37.8666: : 134iter [00:05, 38.60iter/s]
Best score: 37.8666: : 134iter [00:05, 38.60iter/s]
Best score: 37.8666: : 135iter [00:05, 38.60iter/s]
Best score: 37.8666: : 136iter [00:05, 38.60iter/s]
Best score: 37.8666: : 137iter [00:05, 38.60iter/s]
Best score: 37.8666: : 138iter [00:05, 38.62iter/s]
Best score: 37.8666: : 138iter [00:05, 38.62iter/s]
Best score: 37.8666: : 139iter [00:05, 38.62iter/s]
Best score: 37.8666: : 140iter [00:05, 38.62iter/s]
Best score: 37.8666: : 141iter [00:05, 38.62iter/s]
Best score: 37.8666: : 142iter [00:05, 38.70iter/s]
Best score: 37.8666: : 142iter [00:05, 38.70iter/s]
Best score: 37.8666: : 143iter [00:05, 38.70iter/s]
Best score: 37.8666: : 144iter [00:05, 38.70iter/s]
Best score: 37.8666: : 145iter [00:05, 38.70iter/s]
Best score: 37.8666: : 146iter [00:05, 38.85iter/s]
Best score: 37.8666: : 146iter [00:05, 38.85iter/s]
Best score: 37.8666: : 147iter [00:05, 38.85iter/s]
Best score: 37.8666: : 148iter [00:05, 38.85iter/s]
[ITER 150] Best: 37.8666
[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: 37.8666: : 149iter [00:05, 38.85iter/s]
Best score: 37.8666: : 150iter [00:05, 38.73iter/s]
Best score: 37.8666: : 150iter [00:05, 38.73iter/s]
Best score: 37.8666: : 151iter [00:05, 38.73iter/s]
Best score: 37.8666: : 152iter [00:05, 38.73iter/s]
Best score: 37.8666: : 153iter [00:05, 38.73iter/s]
Best score: 37.8666: : 154iter [00:05, 31.68iter/s]
Best score: 37.8666: : 154iter [00:06, 31.68iter/s]
Best score: 37.8666: : 155iter [00:06, 31.68iter/s]
Best score: 37.8666: : 156iter [00:06, 31.68iter/s]
Best score: 37.8666: : 157iter [00:06, 31.68iter/s]
Best score: 37.8666: : 158iter [00:06, 33.62iter/s]
Best score: 37.8666: : 158iter [00:06, 33.62iter/s]
Best score: 37.8666: : 159iter [00:06, 33.62iter/s]
Best score: 37.8666: : 160iter [00:06, 33.62iter/s]
Best score: 37.8666: : 161iter [00:06, 33.62iter/s]
Best score: 37.8666: : 162iter [00:06, 35.01iter/s]
Best score: 37.8666: : 162iter [00:06, 35.01iter/s]
Best score: 37.8666: : 163iter [00:06, 35.01iter/s]
Best score: 37.8666: : 164iter [00:06, 35.01iter/s]
Best score: 37.8666: : 165iter [00:06, 35.01iter/s]
Best score: 37.8666: : 166iter [00:06, 36.21iter/s]
Best score: 37.8666: : 166iter [00:06, 36.21iter/s]
Best score: 37.8666: : 167iter [00:06, 36.21iter/s]
Best score: 37.8666: : 168iter [00:06, 36.21iter/s]
Best score: 37.8666: : 169iter [00:06, 36.21iter/s]
Best score: 37.8666: : 170iter [00:06, 36.92iter/s]
Best score: 37.8666: : 170iter [00:06, 36.92iter/s]
Best score: 37.8666: : 171iter [00:06, 36.92iter/s]
Best score: 37.8666: : 172iter [00:06, 36.92iter/s]
Best score: 37.8666: : 173iter [00:06, 36.92iter/s]
Best score: 37.8666: : 174iter [00:06, 37.47iter/s]
Best score: 37.8666: : 174iter [00:06, 37.47iter/s]
Best score: 37.8666: : 175iter [00:06, 37.47iter/s]
Best score: 37.8666: : 176iter [00:06, 37.47iter/s]
Best score: 37.8666: : 177iter [00:06, 37.47iter/s]
Best score: 37.8666: : 178iter [00:06, 37.96iter/s]
Best score: 37.8666: : 178iter [00:06, 37.96iter/s]
Best score: 37.8666: : 179iter [00:06, 37.96iter/s]
Best score: 37.8666: : 180iter [00:06, 37.96iter/s]
Best score: 37.8666: : 181iter [00:06, 37.96iter/s]
Best score: 37.8666: : 182iter [00:06, 38.11iter/s]
Best score: 37.8666: : 182iter [00:06, 38.11iter/s]
Best score: 37.8666: : 183iter [00:06, 38.11iter/s]
Best score: 37.8666: : 184iter [00:06, 38.11iter/s]
Best score: 37.8666: : 185iter [00:06, 38.11iter/s]
Best score: 37.8666: : 186iter [00:06, 38.42iter/s]
Best score: 37.8666: : 186iter [00:06, 38.42iter/s]
Best score: 37.8666: : 187iter [00:06, 38.42iter/s]
Best score: 37.8666: : 188iter [00:06, 38.42iter/s]
Best score: 37.8666: : 189iter [00:06, 38.42iter/s]
Best score: 37.8666: : 190iter [00:06, 38.45iter/s]
Best score: 37.8666: : 190iter [00:06, 38.45iter/s]
Best score: 37.8666: : 191iter [00:06, 38.45iter/s]
Best score: 37.8666: : 192iter [00:06, 38.45iter/s]
Best score: 37.8666: : 193iter [00:07, 38.45iter/s]
Best score: 37.8666: : 194iter [00:07, 38.78iter/s]
Best score: 37.8666: : 194iter [00:07, 38.78iter/s]
Best score: 37.8666: : 195iter [00:07, 38.78iter/s]
Best score: 37.8666: : 196iter [00:07, 38.78iter/s]
Best score: 37.8666: : 197iter [00:07, 38.78iter/s]
Best score: 37.8666: : 198iter [00:07, 38.95iter/s]
Best score: 37.8666: : 198iter [00:07, 38.95iter/s]
[ITER 200] Best: 37.8666
[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: 37.8666: : 199iter [00:07, 38.95iter/s]
Best score: 37.8666: : 200iter [00:07, 38.95iter/s]
Best score: 37.8666: : 201iter [00:07, 38.95iter/s]
Best score: 37.8666: : 202iter [00:07, 31.66iter/s]
Best score: 37.8666: : 202iter [00:07, 31.66iter/s]
Best score: 37.8666: : 203iter [00:07, 31.66iter/s]
Best score: 37.8666: : 204iter [00:07, 31.66iter/s]
Best score: 37.8666: : 205iter [00:07, 31.66iter/s]
Best score: 37.8666: : 206iter [00:07, 33.61iter/s]
Best score: 37.8666: : 206iter [00:07, 33.61iter/s]
Best score: 37.8666: : 207iter [00:07, 33.61iter/s]
Best score: 37.8666: : 208iter [00:07, 33.61iter/s]
Best score: 37.8666: : 209iter [00:07, 33.61iter/s]
Best score: 37.8666: : 210iter [00:07, 35.01iter/s]
Best score: 37.8666: : 210iter [00:07, 35.01iter/s]
Best score: 37.8666: : 211iter [00:07, 35.01iter/s]
Best score: 37.8666: : 212iter [00:07, 35.01iter/s]
Best score: 37.8666: : 213iter [00:07, 35.01iter/s]
Best score: 37.8666: : 214iter [00:07, 36.33iter/s]
Best score: 37.8666: : 214iter [00:07, 36.33iter/s]
Best score: 37.8666: : 215iter [00:07, 36.33iter/s]
Best score: 37.8666: : 216iter [00:07, 36.33iter/s]
Best score: 37.8666: : 217iter [00:07, 36.33iter/s]
Best score: 37.8666: : 218iter [00:07, 36.33iter/s]
Best score: 37.8666: : 219iter [00:07, 37.55iter/s]
Best score: 37.8666: : 219iter [00:07, 37.55iter/s]
Best score: 37.8666: : 220iter [00:07, 37.55iter/s]
Best score: 37.8666: : 221iter [00:07, 37.55iter/s]
Best score: 37.8666: : 222iter [00:07, 37.55iter/s]
Best score: 37.8666: : 223iter [00:07, 38.01iter/s]
Best score: 37.8666: : 223iter [00:07, 38.01iter/s]
Best score: 37.8666: : 224iter [00:07, 38.01iter/s]
Best score: 37.8666: : 225iter [00:07, 38.01iter/s]
Best score: 37.8666: : 226iter [00:07, 38.01iter/s]
Best score: 37.8666: : 227iter [00:07, 38.56iter/s]
Best score: 37.8666: : 227iter [00:07, 38.56iter/s]
Best score: 37.8666: : 228iter [00:07, 38.56iter/s]
Best score: 37.8666: : 229iter [00:07, 38.56iter/s]
Best score: 37.8666: : 230iter [00:08, 38.56iter/s]
Best score: 37.8666: : 231iter [00:08, 38.74iter/s]
Best score: 37.8666: : 231iter [00:08, 38.74iter/s]
Best score: 37.8666: : 232iter [00:08, 38.74iter/s]
Best score: 37.8666: : 233iter [00:08, 38.74iter/s]
Best score: 37.8666: : 234iter [00:08, 38.74iter/s]
Best score: 37.8666: : 235iter [00:08, 38.98iter/s]
Best score: 37.8666: : 235iter [00:08, 38.98iter/s]
Best score: 37.8666: : 236iter [00:08, 38.98iter/s]
Best score: 37.8666: : 237iter [00:08, 38.98iter/s]
Best score: 37.8666: : 238iter [00:08, 38.98iter/s]
Best score: 37.8666: : 239iter [00:08, 38.98iter/s]
Best score: 37.8666: : 240iter [00:08, 39.06iter/s]
Best score: 37.8666: : 240iter [00:08, 39.06iter/s]
Best score: 37.8666: : 241iter [00:08, 39.06iter/s]
Best score: 37.8666: : 242iter [00:08, 39.06iter/s]
Best score: 37.8666: : 243iter [00:08, 39.06iter/s]
Best score: 37.8666: : 244iter [00:08, 39.23iter/s]
Best score: 37.8666: : 244iter [00:08, 39.23iter/s]
Best score: 37.8666: : 245iter [00:08, 39.23iter/s]
Best score: 37.8666: : 246iter [00:08, 39.23iter/s]
Best score: 37.8666: : 247iter [00:08, 39.23iter/s]
Best score: 37.8666: : 248iter [00:08, 39.23iter/s]
Best score: 37.8666: : 249iter [00:08, 39.52iter/s]
[ITER 250] Best: 37.8666
[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: 37.8666: : 249iter [00:08, 39.52iter/s]
Best score: 37.8666: : 250iter [00:08, 39.52iter/s]
Best score: 37.8666: : 251iter [00:08, 39.52iter/s]
Best score: 37.8666: : 252iter [00:08, 39.52iter/s]
Best score: 37.8666: : 253iter [00:08, 32.42iter/s]
Best score: 37.8666: : 253iter [00:08, 32.42iter/s]
Best score: 37.8666: : 254iter [00:08, 32.42iter/s]
Best score: 37.8666: : 255iter [00:08, 32.42iter/s]
Best score: 37.8666: : 256iter [00:08, 32.42iter/s]
Best score: 37.8666: : 257iter [00:08, 32.42iter/s]
Best score: 37.8666: : 258iter [00:08, 34.60iter/s]
Best score: 37.8666: : 258iter [00:08, 34.60iter/s]
Best score: 20.4505: : 259iter [00:08, 34.60iter/s]
Best score: 20.4505: : 260iter [00:08, 34.60iter/s]
Best score: 20.4505: : 261iter [00:08, 34.60iter/s]
Best score: 20.4505: : 262iter [00:08, 35.65iter/s]
Best score: 20.4505: : 262iter [00:08, 35.65iter/s]
Best score: 20.4505: : 263iter [00:08, 35.65iter/s]
Best score: 20.4505: : 264iter [00:08, 35.65iter/s]
Best score: 20.4505: : 265iter [00:08, 35.65iter/s]
Best score: 20.4505: : 266iter [00:08, 36.77iter/s]
Best score: 20.4505: : 266iter [00:09, 36.77iter/s]
Best score: 20.4505: : 267iter [00:09, 36.77iter/s]
Best score: 20.4505: : 268iter [00:09, 36.77iter/s]
Best score: 20.4505: : 269iter [00:09, 36.77iter/s]
Best score: 20.4505: : 270iter [00:09, 37.33iter/s]
Best score: 20.4505: : 270iter [00:09, 37.33iter/s]
Best score: 20.4505: : 271iter [00:09, 37.33iter/s]
Best score: 20.4505: : 272iter [00:09, 37.33iter/s]
Best score: 20.4505: : 273iter [00:09, 37.33iter/s]
Best score: 20.4505: : 274iter [00:09, 37.86iter/s]
Best score: 20.4505: : 274iter [00:09, 37.86iter/s]
Best score: 20.4505: : 275iter [00:09, 37.86iter/s]
Best score: 20.4505: : 276iter [00:09, 37.86iter/s]
Best score: 20.4505: : 277iter [00:09, 37.86iter/s]
Best score: 20.4505: : 278iter [00:09, 38.46iter/s]
Best score: 20.4505: : 278iter [00:09, 38.46iter/s]
Best score: 13.3992: : 279iter [00:09, 38.46iter/s]
Best score: 13.3992: : 280iter [00:09, 38.46iter/s]
Best score: 13.3992: : 281iter [00:09, 38.46iter/s]
Best score: 13.3992: : 282iter [00:09, 38.66iter/s]
Best score: 13.3992: : 282iter [00:09, 38.66iter/s]
Best score: 13.3992: : 283iter [00:09, 38.66iter/s]
Best score: 13.3992: : 284iter [00:09, 38.66iter/s]
Best score: 13.3992: : 285iter [00:09, 38.66iter/s]
Best score: 13.3992: : 286iter [00:09, 38.99iter/s]
Best score: 13.3992: : 286iter [00:09, 38.99iter/s]
Best score: 13.3992: : 287iter [00:09, 38.99iter/s]
Best score: 13.3992: : 288iter [00:09, 38.99iter/s]
Best score: 8.7394: : 289iter [00:09, 38.99iter/s] 
Best score: 8.7394: : 290iter [00:09, 39.00iter/s]
Best score: 8.7394: : 290iter [00:09, 39.00iter/s]
Best score: 8.7394: : 291iter [00:09, 39.00iter/s]
Best score: 8.7394: : 292iter [00:09, 39.00iter/s]
Best score: 8.7394: : 293iter [00:09, 39.00iter/s]
Best score: 8.7394: : 294iter [00:09, 39.00iter/s]
Best score: 8.7394: : 295iter [00:09, 30.82iter/s]
Best score: 8.7394: : 295iter [00:09, 30.82iter/s]
Best score: 8.7394: : 296iter [00:09, 30.82iter/s]
Best score: 8.7394: : 297iter [00:09, 30.82iter/s]
Best score: 8.7394: : 298iter [00:09, 30.82iter/s]
Best score: 8.7394: : 299iter [00:09, 32.92iter/s]
[ITER 300] Best: 8.7394
[PARAMS] Current best parameters:
  fx: 549.9611
  fy: 551.5597
  px: 990.3589
  py: 782.4841
  k1: -0.03034531
  k2: 0.01859443
  k3: -0.00886628
  k4: 0.00131673
  p1: 0.01616694
  p2: -0.00917051
  p3: 0.01916824
  p4: -0.00877751
  s1: 0.0070
  s2: -0.0072
  s3: -0.0142
  s4: 0.0075
  tilt_x: 0.0612
  tilt_y: 0.0308
  scale: 3.0115
  rotation: 0.6304

Best score: 8.7394: : 299iter [00:09, 32.92iter/s]
Best score: 8.7394: : 300iter [00:10, 32.92iter/s]
Best score: 8.7394: : 301iter [00:10, 32.92iter/s]
Best score: 8.7394: : 302iter [00:10, 32.92iter/s]
Best score: 8.7394: : 303iter [00:10, 28.95iter/s]
Best score: 8.7394: : 303iter [00:10, 28.95iter/s]
Best score: 8.7394: : 304iter [00:10, 28.95iter/s]
Best score: 8.7394: : 305iter [00:10, 28.95iter/s]
Best score: 8.7394: : 306iter [00:10, 28.95iter/s]
Best score: 8.7394: : 307iter [00:10, 28.95iter/s]
Best score: 8.7394: : 308iter [00:10, 31.99iter/s]
Best score: 8.7394: : 308iter [00:10, 31.99iter/s]
Best score: 8.7394: : 309iter [00:10, 31.99iter/s]
Best score: 8.7394: : 310iter [00:10, 31.99iter/s]
Best score: 8.7394: : 311iter [00:10, 31.99iter/s]
Best score: 8.7394: : 312iter [00:10, 33.73iter/s]
Best score: 8.7394: : 312iter [00:10, 33.73iter/s]
Best score: 8.7394: : 313iter [00:10, 33.73iter/s]
Best score: 8.7394: : 314iter [00:10, 33.73iter/s]
Best score: 8.7394: : 315iter [00:10, 33.73iter/s]
Best score: 8.7394: : 316iter [00:10, 35.28iter/s]
Best score: 8.7394: : 316iter [00:10, 35.28iter/s]
Best score: 8.7394: : 317iter [00:10, 35.28iter/s]
Best score: 8.7394: : 318iter [00:10, 35.28iter/s]
Best score: 4.8744: : 319iter [00:10, 35.28iter/s]
Best score: 4.8744: : 320iter [00:10, 36.20iter/s]
Best score: 4.8744: : 320iter [00:10, 36.20iter/s]
Best score: 4.8744: : 321iter [00:10, 36.20iter/s]
Best score: 4.8744: : 322iter [00:10, 36.20iter/s]
Best score: 4.8744: : 323iter [00:10, 36.20iter/s]
Best score: 4.8744: : 324iter [00:10, 37.20iter/s]
Best score: 4.8744: : 324iter [00:10, 37.20iter/s]
Best score: 4.8744: : 325iter [00:10, 37.20iter/s]
Best score: 4.8744: : 326iter [00:10, 37.20iter/s]
Best score: 4.8744: : 327iter [00:10, 37.20iter/s]
Best score: 4.8744: : 328iter [00:10, 37.20iter/s]
Best score: 4.8744: : 329iter [00:10, 38.26iter/s]
Best score: 3.6205: : 329iter [00:10, 38.26iter/s]
Best score: 3.6205: : 330iter [00:10, 38.26iter/s]
Best score: 3.6205: : 331iter [00:10, 38.26iter/s]
Best score: 3.6205: : 332iter [00:10, 38.26iter/s]
Best score: 3.6205: : 333iter [00:10, 38.48iter/s]
Best score: 3.6205: : 333iter [00:10, 38.48iter/s]
Best score: 3.6205: : 334iter [00:10, 38.48iter/s]
Best score: 3.6205: : 335iter [00:10, 38.48iter/s]
Best score: 3.6205: : 336iter [00:10, 38.48iter/s]
Best score: 3.6205: : 337iter [00:10, 38.48iter/s]
Best score: 3.6205: : 338iter [00:10, 39.05iter/s]
Best score: 3.6205: : 338iter [00:11, 39.05iter/s]
Best score: 3.4644: : 339iter [00:11, 39.05iter/s]
Best score: 3.4644: : 340iter [00:11, 39.05iter/s]
Best score: 3.4644: : 341iter [00:11, 39.05iter/s]
Best score: 3.4644: : 342iter [00:11, 39.17iter/s]
Best score: 3.4644: : 342iter [00:11, 39.17iter/s]
Best score: 3.4644: : 343iter [00:11, 39.17iter/s]
Best score: 3.4644: : 344iter [00:11, 39.17iter/s]
Best score: 3.4644: : 345iter [00:11, 39.17iter/s]
Best score: 3.4644: : 346iter [00:11, 39.37iter/s]
Best score: 3.4644: : 346iter [00:11, 39.37iter/s]
Best score: 3.4644: : 347iter [00:11, 39.37iter/s]
Best score: 3.4644: : 348iter [00:11, 39.37iter/s]
[ITER 350] Best: 3.0524
[PARAMS] Current best parameters:
  fx: 536.1928
  fy: 537.3078
  px: 1009.7937
  py: 773.0371
  k1: -0.01330558
  k2: 0.01063804
  k3: -0.00298738
  k4: -0.00038662
  p1: 0.00011117
  p2: 0.01204372
  p3: 0.01665468
  p4: -0.00656602
  s1: -0.0023
  s2: -0.0032
  s3: -0.0190
  s4: 0.0092
  tilt_x: 0.0533
  tilt_y: -0.0288
  scale: 2.9997
  rotation: 0.6722

Best score: 3.0524: : 349iter [00:11, 39.37iter/s]
Best score: 3.0524: : 350iter [00:11, 39.26iter/s]
Best score: 3.0524: : 350iter [00:11, 39.26iter/s]
Best score: 3.0524: : 351iter [00:11, 39.26iter/s]
Best score: 3.0524: : 352iter [00:11, 39.26iter/s]
Best score: 3.0524: : 353iter [00:11, 39.26iter/s]
Best score: 3.0524: : 354iter [00:11, 32.28iter/s]
Best score: 3.0524: : 354iter [00:11, 32.28iter/s]
Best score: 3.0524: : 355iter [00:11, 32.28iter/s]
Best score: 3.0524: : 356iter [00:11, 32.28iter/s]
Best score: 3.0524: : 357iter [00:11, 32.28iter/s]
Best score: 3.0524: : 358iter [00:11, 32.28iter/s]
Best score: 3.0524: : 359iter [00:11, 34.62iter/s]
Best score: 2.7981: : 359iter [00:11, 34.62iter/s]
Best score: 2.7981: : 360iter [00:11, 34.62iter/s]
Best score: 2.7981: : 361iter [00:11, 34.62iter/s]
Best score: 2.7981: : 362iter [00:11, 34.62iter/s]
Best score: 2.7981: : 363iter [00:11, 35.67iter/s]
Best score: 2.7981: : 363iter [00:11, 35.67iter/s]
Best score: 2.7981: : 364iter [00:11, 35.67iter/s]
Best score: 2.7981: : 365iter [00:11, 35.67iter/s]
Best score: 2.7981: : 366iter [00:11, 35.67iter/s]
Best score: 2.7981: : 367iter [00:11, 35.67iter/s]
Best score: 2.7981: : 368iter [00:11, 37.00iter/s]
Best score: 2.7981: : 368iter [00:11, 37.00iter/s]
Best score: 1.7348: : 369iter [00:11, 37.00iter/s]
Best score: 1.7348: : 370iter [00:11, 37.00iter/s]
Best score: 1.7348: : 371iter [00:11, 37.00iter/s]
Best score: 1.7348: : 372iter [00:11, 37.67iter/s]
Best score: 1.7348: : 372iter [00:11, 37.67iter/s]
Best score: 1.7348: : 373iter [00:11, 37.67iter/s]
Best score: 1.7348: : 374iter [00:11, 37.67iter/s]
Best score: 1.7348: : 375iter [00:12, 37.67iter/s]
Best score: 1.7348: : 376iter [00:12, 38.30iter/s]
Best score: 1.7348: : 376iter [00:12, 38.30iter/s]
Best score: 1.7348: : 377iter [00:12, 38.30iter/s]
Best score: 1.7348: : 378iter [00:12, 38.30iter/s]
Best score: 1.4989: : 379iter [00:12, 38.30iter/s]
Best score: 1.4989: : 380iter [00:12, 38.35iter/s]
Best score: 1.4989: : 380iter [00:12, 38.35iter/s]
Best score: 1.4989: : 381iter [00:12, 38.35iter/s]
Best score: 1.4989: : 382iter [00:12, 38.35iter/s]
Best score: 1.4989: : 383iter [00:12, 38.35iter/s]
Best score: 1.4989: : 384iter [00:12, 38.78iter/s]
Best score: 1.4989: : 384iter [00:12, 38.78iter/s]
Best score: 1.4989: : 385iter [00:12, 38.78iter/s]
Best score: 1.4989: : 386iter [00:12, 38.78iter/s]
Best score: 1.4989: : 387iter [00:12, 38.78iter/s]
Best score: 1.4989: : 388iter [00:12, 38.78iter/s]
Best score: 1.4989: : 389iter [00:12, 39.25iter/s]
Best score: 1.1809: : 389iter [00:12, 39.25iter/s]
Best score: 1.1809: : 390iter [00:12, 39.25iter/s]
Best score: 1.1809: : 391iter [00:12, 39.25iter/s]
Best score: 1.1809: : 392iter [00:12, 39.25iter/s]
Best score: 1.1809: : 393iter [00:12, 39.17iter/s]
Best score: 1.1809: : 393iter [00:12, 39.17iter/s]
Best score: 1.1809: : 394iter [00:12, 39.17iter/s]
Best score: 1.1809: : 395iter [00:12, 39.17iter/s]
Best score: 1.1809: : 396iter [00:12, 39.17iter/s]
Best score: 1.1809: : 397iter [00:12, 39.17iter/s]
Best score: 1.1809: : 398iter [00:12, 39.49iter/s]
Best score: 1.1809: : 398iter [00:12, 39.49iter/s]
[ITER 400] Best: 1.1809
[PARAMS] Current best parameters:
  fx: 556.4432
  fy: 556.8402
  px: 992.1488
  py: 767.2797
  k1: -0.03873631
  k2: 0.01661079
  k3: 0.00057216
  k4: -0.00279989
  p1: -0.01530227
  p2: -0.00369253
  p3: 0.02979236
  p4: -0.01242799
  s1: -0.0236
  s2: 0.0072
  s3: -0.0168
  s4: 0.0157
  tilt_x: 0.0536
  tilt_y: 0.0295
  scale: 2.9898
  rotation: 0.9535

Best score: 1.1809: : 399iter [00:12, 39.49iter/s]
Best score: 1.1809: : 400iter [00:12, 39.49iter/s]
Best score: 1.1809: : 401iter [00:12, 39.49iter/s]
Best score: 1.1809: : 402iter [00:12, 32.32iter/s]
Best score: 1.1809: : 402iter [00:12, 32.32iter/s]
Best score: 1.1809: : 403iter [00:12, 32.32iter/s]
Best score: 1.1809: : 404iter [00:12, 32.32iter/s]
Best score: 1.1809: : 405iter [00:12, 32.32iter/s]
Best score: 1.1809: : 406iter [00:12, 32.32iter/s]
Best score: 1.1809: : 407iter [00:12, 34.59iter/s]
Best score: 1.1809: : 407iter [00:12, 34.59iter/s]
Best score: 1.1809: : 408iter [00:12, 34.59iter/s]
Best score: 1.1809: : 409iter [00:12, 34.59iter/s]
Best score: 1.1809: : 410iter [00:12, 34.59iter/s]
Best score: 1.1809: : 411iter [00:12, 35.72iter/s]
Best score: 1.1809: : 411iter [00:13, 35.72iter/s]
Best score: 1.1809: : 412iter [00:13, 35.72iter/s]
Best score: 1.1809: : 413iter [00:13, 35.72iter/s]
Best score: 1.1809: : 414iter [00:13, 35.72iter/s]
Best score: 1.1809: : 415iter [00:13, 35.72iter/s]
Best score: 1.1809: : 416iter [00:13, 37.03iter/s]
Best score: 1.1809: : 416iter [00:13, 37.03iter/s]
Best score: 1.1809: : 417iter [00:13, 37.03iter/s]
Best score: 1.1809: : 418iter [00:13, 37.03iter/s]
Best score: 0.9020: : 419iter [00:13, 37.03iter/s]
Best score: 0.9020: : 420iter [00:13, 37.53iter/s]
Best score: 0.9020: : 420iter [00:13, 37.53iter/s]
Best score: 0.9020: : 421iter [00:13, 37.53iter/s]
Best score: 0.9020: : 422iter [00:13, 37.53iter/s]
Best score: 0.9020: : 423iter [00:13, 37.53iter/s]
Best score: 0.9020: : 424iter [00:13, 37.53iter/s]
Best score: 0.9020: : 425iter [00:13, 38.33iter/s]
Best score: 0.9020: : 425iter [00:13, 38.33iter/s]
Best score: 0.9020: : 426iter [00:13, 38.33iter/s]
Best score: 0.9020: : 427iter [00:13, 38.33iter/s]
Best score: 0.9020: : 428iter [00:13, 38.33iter/s]
Best score: 0.9020: : 429iter [00:13, 38.33iter/s]
Best score: 0.9020: : 430iter [00:13, 38.70iter/s]
Best score: 0.9020: : 430iter [00:13, 38.70iter/s]
Best score: 0.9020: : 431iter [00:13, 38.70iter/s]
Best score: 0.9020: : 432iter [00:13, 38.70iter/s]
Best score: 0.9020: : 433iter [00:13, 38.70iter/s]
Best score: 0.9020: : 434iter [00:13, 38.70iter/s]
Best score: 0.9020: : 435iter [00:13, 39.19iter/s]
Best score: 0.9020: : 435iter [00:13, 39.19iter/s]
Best score: 0.9020: : 436iter [00:13, 39.19iter/s]
Best score: 0.9020: : 437iter [00:13, 39.19iter/s]
Best score: 0.9020: : 438iter [00:13, 39.19iter/s]
Best score: 0.9020: : 439iter [00:13, 39.19iter/s]
Best score: 0.9020: : 440iter [00:13, 39.33iter/s]
Best score: 0.9020: : 440iter [00:13, 39.33iter/s]
Best score: 0.9020: : 441iter [00:13, 39.33iter/s]
Best score: 0.9020: : 442iter [00:13, 39.33iter/s]
Best score: 0.9020: : 443iter [00:13, 39.33iter/s]
Best score: 0.9020: : 444iter [00:13, 39.33iter/s]
Best score: 0.9020: : 445iter [00:13, 39.65iter/s]
Best score: 0.9020: : 445iter [00:13, 39.65iter/s]
Best score: 0.9020: : 446iter [00:13, 39.65iter/s]
Best score: 0.9020: : 447iter [00:13, 39.65iter/s]
Best score: 0.9020: : 448iter [00:13, 39.65iter/s]
[ITER 450] Best: 0.8086
[PARAMS] Current best parameters:
  fx: 556.2558
  fy: 555.9776
  px: 989.4484
  py: 762.9812
  k1: -0.04876633
  k2: 0.03764431
  k3: -0.01444744
  k4: 0.00084735
  p1: -0.01523378
  p2: -0.00908896
  p3: 0.02874411
  p4: -0.01021665
  s1: -0.0227
  s2: 0.0064
  s3: -0.0233
  s4: 0.0147
  tilt_x: 0.0321
  tilt_y: 0.0351
  scale: 2.9926
  rotation: 0.9533

Best score: 0.8086: : 449iter [00:13, 39.65iter/s]
Best score: 0.8086: : 450iter [00:13, 39.62iter/s]
Best score: 0.8086: : 450iter [00:14, 39.62iter/s]
Best score: 0.8086: : 451iter [00:14, 39.62iter/s]
Best score: 0.8086: : 452iter [00:14, 39.62iter/s]
Best score: 0.8086: : 453iter [00:14, 39.62iter/s]
Best score: 0.8086: : 454iter [00:14, 33.27iter/s]
Best score: 0.8086: : 454iter [00:14, 33.27iter/s]
Best score: 0.8086: : 455iter [00:14, 33.27iter/s]
Best score: 0.8086: : 456iter [00:14, 33.27iter/s]
Best score: 0.8086: : 457iter [00:14, 33.27iter/s]
Best score: 0.8086: : 458iter [00:14, 33.27iter/s]
Best score: 0.8086: : 459iter [00:14, 35.25iter/s]
Best score: 0.7280: : 459iter [00:14, 35.25iter/s]
Best score: 0.7280: : 460iter [00:14, 35.25iter/s]
Best score: 0.7280: : 461iter [00:14, 35.25iter/s]
Best score: 0.7280: : 462iter [00:14, 35.25iter/s]
Best score: 0.7280: : 463iter [00:14, 36.14iter/s]
Best score: 0.7280: : 463iter [00:14, 36.14iter/s]
Best score: 0.7280: : 464iter [00:14, 36.14iter/s]
Best score: 0.7280: : 465iter [00:14, 36.14iter/s]
Best score: 0.7280: : 466iter [00:14, 36.14iter/s]
Best score: 0.7280: : 467iter [00:14, 37.09iter/s]
Best score: 0.7280: : 467iter [00:14, 37.09iter/s]
Best score: 0.7280: : 468iter [00:14, 37.09iter/s]
Best score: 0.7280: : 469iter [00:14, 37.09iter/s]
Best score: 0.7280: : 470iter [00:14, 37.09iter/s]
Best score: 0.7280: : 471iter [00:14, 37.58iter/s]
Best score: 0.7280: : 471iter [00:14, 37.58iter/s]
Best score: 0.7280: : 472iter [00:14, 37.58iter/s]
Best score: 0.7280: : 473iter [00:14, 37.58iter/s]
Best score: 0.7280: : 474iter [00:14, 37.58iter/s]
Best score: 0.7280: : 475iter [00:14, 38.23iter/s]
Best score: 0.7280: : 475iter [00:14, 38.23iter/s]
Best score: 0.7280: : 476iter [00:14, 38.23iter/s]
Best score: 0.7280: : 477iter [00:14, 38.23iter/s]
Best score: 0.7280: : 478iter [00:14, 38.23iter/s]
Best score: 0.7280: : 479iter [00:14, 38.71iter/s]
Best score: 0.6775: : 479iter [00:14, 38.71iter/s]
Best score: 0.6775: : 480iter [00:14, 38.71iter/s]
Best score: 0.6775: : 481iter [00:14, 38.71iter/s]
Best score: 0.6775: : 482iter [00:14, 38.71iter/s]
Best score: 0.6775: : 483iter [00:14, 38.75iter/s]
Best score: 0.6775: : 483iter [00:14, 38.75iter/s]
Best score: 0.6775: : 484iter [00:14, 38.75iter/s]
Best score: 0.6775: : 485iter [00:14, 38.75iter/s]
Best score: 0.6775: : 486iter [00:14, 38.75iter/s]
Best score: 0.6775: : 487iter [00:14, 38.89iter/s]
Best score: 0.6775: : 487iter [00:14, 38.89iter/s]
Best score: 0.6775: : 488iter [00:15, 38.89iter/s]
Best score: 0.5622: : 489iter [00:15, 38.89iter/s]
Best score: 0.5622: : 490iter [00:15, 38.89iter/s]
Best score: 0.5622: : 491iter [00:15, 38.91iter/s]
Best score: 0.5622: : 491iter [00:15, 38.91iter/s]
Best score: 0.5622: : 492iter [00:15, 38.91iter/s]
Best score: 0.5622: : 493iter [00:15, 38.91iter/s]
Best score: 0.5622: : 494iter [00:15, 38.91iter/s]
Best score: 0.5622: : 495iter [00:15, 38.91iter/s]
Best score: 0.5622: : 496iter [00:15, 39.35iter/s]
Best score: 0.5622: : 496iter [00:15, 39.35iter/s]
Best score: 0.5622: : 497iter [00:15, 39.35iter/s]
Best score: 0.5622: : 498iter [00:15, 39.35iter/s]
[ITER 500] Best: 0.5345
[PARAMS] Current best parameters:
  fx: 564.4710
  fy: 564.1572
  px: 995.6541
  py: 762.0993
  k1: -0.02597432
  k2: -0.00148699
  k3: 0.00121047
  k4: -0.00053334
  p1: -0.01313275
  p2: -0.00029222
  p3: 0.03185005
  p4: -0.01129829
  s1: -0.0341
  s2: 0.0105
  s3: -0.0180
  s4: 0.0090
  tilt_x: 0.0241
  tilt_y: 0.0184
  scale: 2.9512
  rotation: 0.8537

Best score: 0.5345: : 499iter [00:15, 39.35iter/s]
Best score: 0.5345: : 500iter [00:15, 39.27iter/s]
Best score: 0.5345: : 500iter [00:15, 39.27iter/s]
Best score: 0.5345: : 501iter [00:15, 39.27iter/s]
Best score: 0.5345: : 502iter [00:15, 39.27iter/s]
Best score: 0.5345: : 503iter [00:15, 39.27iter/s]
Best score: 0.5345: : 504iter [00:15, 32.14iter/s]
Best score: 0.5345: : 504iter [00:15, 32.14iter/s]
Best score: 0.5345: : 505iter [00:15, 32.14iter/s]
Best score: 0.5345: : 506iter [00:15, 32.14iter/s]
Best score: 0.5345: : 507iter [00:15, 32.14iter/s]
Best score: 0.5345: : 508iter [00:15, 34.03iter/s]
Best score: 0.5345: : 508iter [00:15, 34.03iter/s]
Best score: 0.4731: : 509iter [00:15, 34.03iter/s]
Best score: 0.4731: : 510iter [00:15, 34.03iter/s]
Best score: 0.4731: : 511iter [00:15, 34.03iter/s]
Best score: 0.4731: : 512iter [00:15, 35.30iter/s]
Best score: 0.4731: : 512iter [00:15, 35.30iter/s]
Best score: 0.4731: : 513iter [00:15, 35.30iter/s]
Best score: 0.4731: : 514iter [00:15, 35.30iter/s]
Best score: 0.4731: : 515iter [00:15, 35.30iter/s]
Best score: 0.4731: : 516iter [00:15, 36.56iter/s]
Best score: 0.4731: : 516iter [00:15, 36.56iter/s]
Best score: 0.4731: : 517iter [00:15, 36.56iter/s]
Best score: 0.4731: : 518iter [00:15, 36.56iter/s]
Best score: 0.4222: : 519iter [00:15, 36.56iter/s]
Best score: 0.4222: : 520iter [00:15, 37.19iter/s]
Best score: 0.4222: : 520iter [00:15, 37.19iter/s]
Best score: 0.4222: : 521iter [00:15, 37.19iter/s]
Best score: 0.4222: : 522iter [00:15, 37.19iter/s]
Best score: 0.4222: : 523iter [00:15, 37.19iter/s]
Best score: 0.4222: : 524iter [00:16, 37.19iter/s]
Best score: 0.4222: : 525iter [00:16, 38.18iter/s]
Best score: 0.4222: : 525iter [00:16, 38.18iter/s]
Best score: 0.4222: : 526iter [00:16, 38.18iter/s]
Best score: 0.4222: : 527iter [00:16, 38.18iter/s]
Best score: 0.4222: : 528iter [00:16, 38.18iter/s]
Best score: 0.4222: : 529iter [00:16, 38.58iter/s]
Best score: 0.4222: : 529iter [00:16, 38.58iter/s]
Best score: 0.4222: : 530iter [00:16, 38.58iter/s]
Best score: 0.4222: : 531iter [00:16, 38.58iter/s]
Best score: 0.4222: : 532iter [00:16, 38.58iter/s]
Best score: 0.4222: : 533iter [00:16, 38.75iter/s]
Best score: 0.4222: : 533iter [00:16, 38.75iter/s]
Best score: 0.4222: : 534iter [00:16, 38.75iter/s]
Best score: 0.4222: : 535iter [00:16, 38.75iter/s]
Best score: 0.4222: : 536iter [00:16, 38.75iter/s]
Best score: 0.4222: : 537iter [00:16, 38.75iter/s]
Best score: 0.4222: : 538iter [00:16, 39.20iter/s]
Best score: 0.4222: : 538iter [00:16, 39.20iter/s]
Best score: 0.4181: : 539iter [00:16, 39.20iter/s]
Best score: 0.4181: : 540iter [00:16, 39.20iter/s]
Best score: 0.4181: : 541iter [00:16, 39.20iter/s]
Best score: 0.4181: : 542iter [00:16, 39.00iter/s]
Best score: 0.4181: : 542iter [00:16, 39.00iter/s]
Best score: 0.4181: : 543iter [00:16, 39.00iter/s]
Best score: 0.4181: : 544iter [00:16, 39.00iter/s]
Best score: 0.4181: : 545iter [00:16, 39.00iter/s]
Best score: 0.4181: : 546iter [00:16, 39.26iter/s]
Best score: 0.4181: : 546iter [00:16, 39.26iter/s]
Best score: 0.4181: : 547iter [00:16, 39.26iter/s]
Best score: 0.4181: : 548iter [00:16, 39.26iter/s]
[ITER 550] Best: 0.4181
[PARAMS] Current best parameters:
  fx: 560.5686
  fy: 560.4118
  px: 995.7240
  py: 763.1370
  k1: -0.03203958
  k2: 0.01056464
  k3: -0.00372908
  k4: -0.00007680
  p1: -0.01089378
  p2: -0.00431188
  p3: 0.02925181
  p4: -0.01240396
  s1: -0.0279
  s2: 0.0119
  s3: -0.0200
  s4: 0.0130
  tilt_x: 0.0234
  tilt_y: 0.0104
  scale: 2.9702
  rotation: 0.8597

Best score: 0.4181: : 549iter [00:16, 39.26iter/s]
Best score: 0.4181: : 550iter [00:16, 39.13iter/s]
Best score: 0.4181: : 550iter [00:16, 39.13iter/s]
Best score: 0.4181: : 551iter [00:16, 39.13iter/s]
Best score: 0.4181: : 552iter [00:16, 39.13iter/s]
Best score: 0.4181: : 553iter [00:16, 39.13iter/s]
Best score: 0.4181: : 554iter [00:16, 32.18iter/s]
Best score: 0.4181: : 554iter [00:16, 32.18iter/s]
Best score: 0.4181: : 555iter [00:16, 32.18iter/s]
Best score: 0.4181: : 556iter [00:16, 32.18iter/s]
Best score: 0.4181: : 557iter [00:16, 32.18iter/s]
Best score: 0.4181: : 558iter [00:16, 34.06iter/s]
Best score: 0.4181: : 558iter [00:16, 34.06iter/s]
Best score: 0.4181: : 559iter [00:16, 34.06iter/s]
Best score: 0.4181: : 560iter [00:17, 34.06iter/s]
Best score: 0.4181: : 561iter [00:17, 34.06iter/s]
Best score: 0.4181: : 562iter [00:17, 35.36iter/s]
Best score: 0.4181: : 562iter [00:17, 35.36iter/s]
Best score: 0.4181: : 563iter [00:17, 35.36iter/s]
Best score: 0.4181: : 564iter [00:17, 35.36iter/s]
Best score: 0.4181: : 565iter [00:17, 35.36iter/s]
Best score: 0.4181: : 566iter [00:17, 36.34iter/s]
Best score: 0.4181: : 566iter [00:17, 36.34iter/s]
Best score: 0.4181: : 567iter [00:17, 36.34iter/s]
Best score: 0.4181: : 568iter [00:17, 36.34iter/s]
Best score: 0.4181: : 569iter [00:17, 36.34iter/s]
Best score: 0.4181: : 570iter [00:17, 37.02iter/s]
Best score: 0.4181: : 570iter [00:17, 37.02iter/s]
Best score: 0.4181: : 571iter [00:17, 37.02iter/s]
Best score: 0.4181: : 572iter [00:17, 37.02iter/s]
Best score: 0.4181: : 573iter [00:17, 37.02iter/s]
Best score: 0.4181: : 574iter [00:17, 37.82iter/s]
Best score: 0.4181: : 574iter [00:17, 37.82iter/s]
Best score: 0.4181: : 575iter [00:17, 37.82iter/s]
Best score: 0.4181: : 576iter [00:17, 37.82iter/s]
Best score: 0.4181: : 577iter [00:17, 37.82iter/s]
Best score: 0.4181: : 578iter [00:17, 38.28iter/s]
Best score: 0.4181: : 578iter [00:17, 38.28iter/s]
Best score: 0.3946: : 579iter [00:17, 38.28iter/s]
Best score: 0.3946: : 580iter [00:17, 38.28iter/s]
Best score: 0.3946: : 581iter [00:17, 38.28iter/s]
Best score: 0.3946: : 582iter [00:17, 38.41iter/s]
Best score: 0.3946: : 582iter [00:17, 38.41iter/s]
Best score: 0.3946: : 583iter [00:17, 38.41iter/s]
Best score: 0.3946: : 584iter [00:17, 38.41iter/s]
Best score: 0.3946: : 585iter [00:17, 38.41iter/s]
Best score: 0.3946: : 586iter [00:17, 38.77iter/s]
Best score: 0.3946: : 586iter [00:17, 38.77iter/s]
Best score: 0.3946: : 587iter [00:17, 38.77iter/s]
Best score: 0.3946: : 588iter [00:17, 38.77iter/s]
Best score: 0.3946: : 589iter [00:17, 38.77iter/s]
Best score: 0.3946: : 590iter [00:17, 38.81iter/s]
Best score: 0.3946: : 590iter [00:17, 38.81iter/s]
Best score: 0.3946: : 591iter [00:17, 38.81iter/s]
Best score: 0.3946: : 592iter [00:17, 38.81iter/s]
Best score: 0.3946: : 593iter [00:17, 38.81iter/s]
Best score: 0.3946: : 594iter [00:17, 39.09iter/s]
Best score: 0.3946: : 594iter [00:17, 39.09iter/s]
Best score: 0.3946: : 595iter [00:17, 39.09iter/s]
Best score: 0.3946: : 596iter [00:17, 39.09iter/s]
Best score: 0.3946: : 597iter [00:17, 39.09iter/s]
Best score: 0.3946: : 598iter [00:17, 39.09iter/s]
Best score: 0.3946: : 599iter [00:17, 39.38iter/s]
[ITER 600] Best: 0.3833
[PARAMS] Current best parameters:
  fx: 563.0202
  fy: 562.9424
  px: 993.9520
  py: 762.3753
  k1: -0.03400600
  k2: 0.01186055
  k3: -0.00602740
  k4: 0.00082042
  p1: -0.00885352
  p2: -0.00591060
  p3: 0.02799158
  p4: -0.01119383
  s1: -0.0282
  s2: 0.0117
  s3: -0.0217
  s4: 0.0117
  tilt_x: 0.0159
  tilt_y: 0.0170
  scale: 2.9688
  rotation: 0.8832

Best score: 0.3833: : 599iter [00:18, 39.38iter/s]
Best score: 0.3833: : 600iter [00:18, 39.38iter/s]
Best score: 0.3833: : 601iter [00:18, 39.38iter/s]
Best score: 0.3833: : 602iter [00:18, 39.38iter/s]
Best score: 0.3833: : 603iter [00:18, 26.64iter/s]
Best score: 0.3833: : 603iter [00:18, 26.64iter/s]
Best score: 0.3833: : 604iter [00:18, 26.64iter/s]
Best score: 0.3833: : 605iter [00:18, 26.64iter/s]
Best score: 0.3833: : 606iter [00:18, 26.64iter/s]
Best score: 0.3833: : 607iter [00:18, 29.24iter/s]
Best score: 0.3833: : 607iter [00:18, 29.24iter/s]
Best score: 0.3833: : 608iter [00:18, 29.24iter/s]
Best score: 0.3825: : 609iter [00:18, 29.24iter/s]
Best score: 0.3825: : 610iter [00:18, 29.24iter/s]
Best score: 0.3825: : 611iter [00:18, 31.46iter/s]
Best score: 0.3825: : 611iter [00:18, 31.46iter/s]
Best score: 0.3825: : 612iter [00:18, 31.46iter/s]
Best score: 0.3825: : 613iter [00:18, 31.46iter/s]
Best score: 0.3825: : 614iter [00:18, 31.46iter/s]
Best score: 0.3825: : 615iter [00:18, 33.54iter/s]
Best score: 0.3825: : 615iter [00:18, 33.54iter/s]
Best score: 0.3825: : 616iter [00:18, 33.54iter/s]
Best score: 0.3825: : 617iter [00:18, 33.54iter/s]
Best score: 0.3825: : 618iter [00:18, 33.54iter/s]
Best score: 0.3774: : 619iter [00:18, 33.54iter/s]
Best score: 0.3774: : 620iter [00:18, 35.33iter/s]
Best score: 0.3774: : 620iter [00:18, 35.33iter/s]
Best score: 0.3774: : 621iter [00:18, 35.33iter/s]
Best score: 0.3774: : 622iter [00:18, 35.33iter/s]
Best score: 0.3774: : 623iter [00:18, 35.33iter/s]
Best score: 0.3774: : 624iter [00:18, 36.48iter/s]
Best score: 0.3774: : 624iter [00:18, 36.48iter/s]
Best score: 0.3774: : 625iter [00:18, 36.48iter/s]
Best score: 0.3774: : 626iter [00:18, 36.48iter/s]
Best score: 0.3774: : 627iter [00:18, 36.48iter/s]
Best score: 0.3774: : 628iter [00:18, 37.22iter/s]
Best score: 0.3774: : 628iter [00:18, 37.22iter/s]
Best score: 0.3754: : 629iter [00:18, 37.22iter/s]
Best score: 0.3754: : 630iter [00:18, 37.22iter/s]
Best score: 0.3754: : 631iter [00:18, 37.22iter/s]
Best score: 0.3754: : 632iter [00:18, 37.61iter/s]
Best score: 0.3754: : 632iter [00:19, 37.61iter/s]
Best score: 0.3754: : 633iter [00:19, 37.61iter/s]
Best score: 0.3754: : 634iter [00:19, 37.61iter/s]
Best score: 0.3754: : 635iter [00:19, 37.61iter/s]
Best score: 0.3754: : 636iter [00:19, 38.23iter/s]
Best score: 0.3754: : 636iter [00:19, 38.23iter/s]
Best score: 0.3754: : 637iter [00:19, 38.23iter/s]
Best score: 0.3754: : 638iter [00:19, 38.23iter/s]
Best score: 0.3754: : 639iter [00:19, 38.23iter/s]
Best score: 0.3754: : 640iter [00:19, 38.37iter/s]
Best score: 0.3754: : 640iter [00:19, 38.37iter/s]
Best score: 0.3754: : 641iter [00:19, 38.37iter/s]
Best score: 0.3754: : 642iter [00:19, 38.37iter/s]
Best score: 0.3754: : 643iter [00:19, 38.37iter/s]
Best score: 0.3754: : 644iter [00:19, 38.83iter/s]
Best score: 0.3754: : 644iter [00:19, 38.83iter/s]
Best score: 0.3754: : 645iter [00:19, 38.83iter/s]
Best score: 0.3754: : 646iter [00:19, 38.83iter/s]
Best score: 0.3754: : 647iter [00:19, 38.83iter/s]
Best score: 0.3754: : 648iter [00:19, 39.15iter/s]
Best score: 0.3754: : 648iter [00:19, 39.15iter/s]
[ITER 650] Best: 0.3684
[PARAMS] Current best parameters:
  fx: 562.5280
  fy: 562.3544
  px: 994.2586
  py: 759.8395
  k1: -0.04182685
  k2: 0.02457493
  k3: -0.01313322
  k4: 0.00222183
  p1: -0.01026214
  p2: -0.00641116
  p3: 0.02842576
  p4: -0.01120729
  s1: -0.0278
  s2: 0.0119
  s3: -0.0239
  s4: 0.0122
  tilt_x: 0.0058
  tilt_y: 0.0147
  scale: 2.9778
  rotation: 0.8666

Best score: 0.3684: : 649iter [00:19, 39.15iter/s]
Best score: 0.3684: : 650iter [00:19, 39.15iter/s]
Best score: 0.3684: : 651iter [00:19, 39.15iter/s]
Best score: 0.3684: : 652iter [00:19, 31.54iter/s]
Best score: 0.3684: : 652iter [00:19, 31.54iter/s]
Best score: 0.3684: : 653iter [00:19, 31.54iter/s]
Best score: 0.3684: : 654iter [00:19, 31.54iter/s]
Best score: 0.3684: : 655iter [00:19, 31.54iter/s]
Best score: 0.3684: : 656iter [00:19, 33.61iter/s]
Best score: 0.3684: : 656iter [00:19, 33.61iter/s]
Best score: 0.3684: : 657iter [00:19, 33.61iter/s]
Best score: 0.3684: : 658iter [00:19, 33.61iter/s]
Best score: 0.3684: : 659iter [00:19, 33.61iter/s]
Best score: 0.3684: : 660iter [00:19, 34.98iter/s]
Best score: 0.3684: : 660iter [00:19, 34.98iter/s]
Best score: 0.3684: : 661iter [00:19, 34.98iter/s]
Best score: 0.3684: : 662iter [00:19, 34.98iter/s]
Best score: 0.3684: : 663iter [00:19, 34.98iter/s]
Best score: 0.3684: : 664iter [00:19, 34.98iter/s]
Best score: 0.3684: : 665iter [00:19, 36.63iter/s]
Best score: 0.3684: : 665iter [00:19, 36.63iter/s]
Best score: 0.3684: : 666iter [00:19, 36.63iter/s]
Best score: 0.3684: : 667iter [00:19, 36.63iter/s]
Best score: 0.3684: : 668iter [00:20, 36.63iter/s]
Best score: 0.3684: : 669iter [00:20, 37.44iter/s]
Best score: 0.3684: : 669iter [00:20, 37.44iter/s]
Best score: 0.3684: : 670iter [00:20, 37.44iter/s]
Best score: 0.3684: : 671iter [00:20, 37.44iter/s]
Best score: 0.3684: : 672iter [00:20, 37.44iter/s]
Best score: 0.3684: : 673iter [00:20, 37.79iter/s]
Best score: 0.3684: : 673iter [00:20, 37.79iter/s]
Best score: 0.3684: : 674iter [00:20, 37.79iter/s]
Best score: 0.3684: : 675iter [00:20, 37.79iter/s]
Best score: 0.3684: : 676iter [00:20, 37.79iter/s]
Best score: 0.3684: : 677iter [00:20, 38.35iter/s]
Best score: 0.3684: : 677iter [00:20, 38.35iter/s]
Best score: 0.3684: : 678iter [00:20, 38.35iter/s]
Best score: 0.3667: : 679iter [00:20, 38.35iter/s]
Best score: 0.3667: : 680iter [00:20, 38.35iter/s]
Best score: 0.3667: : 681iter [00:20, 38.51iter/s]
Best score: 0.3667: : 681iter [00:20, 38.51iter/s]
Best score: 0.3667: : 682iter [00:20, 38.51iter/s]
Best score: 0.3667: : 683iter [00:20, 38.51iter/s]
Best score: 0.3667: : 684iter [00:20, 38.51iter/s]
Best score: 0.3667: : 685iter [00:20, 38.51iter/s]
Best score: 0.3667: : 686iter [00:20, 39.08iter/s]
Best score: 0.3667: : 686iter [00:20, 39.08iter/s]
Best score: 0.3667: : 687iter [00:20, 39.08iter/s]
Best score: 0.3667: : 688iter [00:20, 39.08iter/s]
Best score: 0.3661: : 689iter [00:20, 39.08iter/s]
Best score: 0.3661: : 690iter [00:20, 39.10iter/s]
Best score: 0.3661: : 690iter [00:20, 39.10iter/s]
Best score: 0.3661: : 691iter [00:20, 39.10iter/s]
Best score: 0.3661: : 692iter [00:20, 39.10iter/s]
Best score: 0.3661: : 693iter [00:20, 39.10iter/s]
Best score: 0.3661: : 694iter [00:20, 39.34iter/s]
Best score: 0.3661: : 694iter [00:20, 39.34iter/s]
Best score: 0.3661: : 695iter [00:20, 39.34iter/s]
Best score: 0.3661: : 696iter [00:20, 39.34iter/s]
Best score: 0.3661: : 697iter [00:20, 39.34iter/s]
Best score: 0.3661: : 698iter [00:20, 39.51iter/s]
Best score: 0.3661: : 698iter [00:20, 39.51iter/s]
[ITER 700] Best: 0.3661
[PARAMS] Current best parameters:
  fx: 561.7666
  fy: 561.6315
  px: 993.6399
  py: 759.2983
  k1: -0.04420001
  k2: 0.02891923
  k3: -0.01562944
  k4: 0.00276328
  p1: -0.00995791
  p2: -0.00672564
  p3: 0.02715477
  p4: -0.01010181
  s1: -0.0270
  s2: 0.0110
  s3: -0.0248
  s4: 0.0117
  tilt_x: 0.0025
  tilt_y: 0.0173
  scale: 2.9832
  rotation: 0.8659

Best score: 0.3661: : 699iter [00:20, 39.51iter/s]
Best score: 0.3661: : 700iter [00:20, 39.51iter/s]
Best score: 0.3661: : 701iter [00:20, 39.51iter/s]
Best score: 0.3661: : 702iter [00:20, 32.25iter/s]
Best score: 0.3661: : 702iter [00:20, 32.25iter/s]
Best score: 0.3661: : 703iter [00:20, 32.25iter/s]
Best score: 0.3661: : 704iter [00:20, 32.25iter/s]
Best score: 0.3661: : 705iter [00:21, 32.25iter/s]
Best score: 0.3661: : 706iter [00:21, 34.09iter/s]
Best score: 0.3661: : 706iter [00:21, 34.09iter/s]
Best score: 0.3661: : 707iter [00:21, 34.09iter/s]
Best score: 0.3661: : 708iter [00:21, 34.09iter/s]
Best score: 0.3661: : 709iter [00:21, 34.09iter/s]
Best score: 0.3661: : 710iter [00:21, 35.45iter/s]
Best score: 0.3661: : 710iter [00:21, 35.45iter/s]
Best score: 0.3661: : 711iter [00:21, 35.45iter/s]
Best score: 0.3661: : 712iter [00:21, 35.45iter/s]
Best score: 0.3661: : 713iter [00:21, 35.45iter/s]
Best score: 0.3661: : 714iter [00:21, 36.65iter/s]
Best score: 0.3661: : 714iter [00:21, 36.65iter/s]
Best score: 0.3661: : 715iter [00:21, 36.65iter/s]
Best score: 0.3661: : 716iter [00:21, 36.65iter/s]
Best score: 0.3661: : 717iter [00:21, 36.65iter/s]
Best score: 0.3661: : 718iter [00:21, 36.65iter/s]
Best score: 0.3661: : 719iter [00:21, 37.78iter/s]
Best score: 0.3661: : 719iter [00:21, 37.78iter/s]
Best score: 0.3661: : 720iter [00:21, 37.78iter/s]
Best score: 0.3661: : 721iter [00:21, 37.78iter/s]
Best score: 0.3661: : 722iter [00:21, 37.78iter/s]
Best score: 0.3661: : 723iter [00:21, 37.98iter/s]
Best score: 0.3661: : 723iter [00:21, 37.98iter/s]
Best score: 0.3661: : 724iter [00:21, 37.98iter/s]
Best score: 0.3661: : 725iter [00:21, 37.98iter/s]
Best score: 0.3661: : 726iter [00:21, 37.98iter/s]
Best score: 0.3661: : 727iter [00:21, 38.31iter/s]
Best score: 0.3661: : 727iter [00:21, 38.31iter/s]
Best score: 0.3661: : 728iter [00:21, 38.31iter/s]
Best score: 0.3654: : 729iter [00:21, 38.31iter/s]
Best score: 0.3654: : 730iter [00:21, 38.31iter/s]
Best score: 0.3654: : 731iter [00:21, 38.32iter/s]
Best score: 0.3654: : 731iter [00:21, 38.32iter/s]
Best score: 0.3654: : 732iter [00:21, 38.32iter/s]
Best score: 0.3654: : 733iter [00:21, 38.32iter/s]
Best score: 0.3654: : 734iter [00:21, 38.32iter/s]
Best score: 0.3654: : 735iter [00:21, 38.66iter/s]
Best score: 0.3654: : 735iter [00:21, 38.66iter/s]
Best score: 0.3654: : 736iter [00:21, 38.66iter/s]
Best score: 0.3654: : 737iter [00:21, 38.66iter/s]
Best score: 0.3654: : 738iter [00:21, 38.66iter/s]
Best score: 0.3654: : 739iter [00:21, 38.89iter/s]
Best score: 0.3654: : 739iter [00:21, 38.89iter/s]
Best score: 0.3654: : 740iter [00:21, 38.89iter/s]
Best score: 0.3654: : 741iter [00:21, 38.89iter/s]
Best score: 0.3654: : 742iter [00:21, 38.89iter/s]
Best score: 0.3654: : 743iter [00:21, 38.95iter/s]
Best score: 0.3654: : 743iter [00:21, 38.95iter/s]
Best score: 0.3654: : 744iter [00:22, 38.95iter/s]
Best score: 0.3654: : 745iter [00:22, 38.95iter/s]
Best score: 0.3654: : 746iter [00:22, 38.95iter/s]
Best score: 0.3654: : 747iter [00:22, 39.22iter/s]
Best score: 0.3654: : 747iter [00:22, 39.22iter/s]
Best score: 0.3654: : 748iter [00:22, 39.22iter/s]
[ITER 750] Best: 0.3654
[PARAMS] Current best parameters:
  fx: 561.8942
  fy: 561.7921
  px: 993.7441
  py: 759.3466
  k1: -0.04298972
  k2: 0.02675210
  k3: -0.01434781
  k4: 0.00249548
  p1: -0.00982466
  p2: -0.00670544
  p3: 0.02717148
  p4: -0.01028598
  s1: -0.0270
  s2: 0.0112
  s3: -0.0248
  s4: 0.0119
  tilt_x: 0.0025
  tilt_y: 0.0168
  scale: 2.9820
  rotation: 0.8629

Best score: 0.3654: : 749iter [00:22, 39.22iter/s]
Best score: 0.3654: : 750iter [00:22, 39.22iter/s]
Best score: 0.3654: : 751iter [00:22, 31.75iter/s]
Best score: 0.3654: : 751iter [00:22, 31.75iter/s]
Best score: 0.3654: : 752iter [00:22, 31.75iter/s]
Best score: 0.3654: : 753iter [00:22, 31.75iter/s]
Best score: 0.3654: : 754iter [00:22, 31.75iter/s]
Best score: 0.3654: : 755iter [00:22, 33.75iter/s]
Best score: 0.3654: : 755iter [00:22, 33.75iter/s]
Best score: 0.3654: : 756iter [00:22, 33.75iter/s]
Best score: 0.3654: : 757iter [00:22, 33.75iter/s]
Best score: 0.3654: : 758iter [00:22, 33.75iter/s]
Best score: 0.3654: : 759iter [00:22, 35.37iter/s]
Best score: 0.3652: : 759iter [00:22, 35.37iter/s]
Best score: 0.3652: : 760iter [00:22, 35.37iter/s]
Best score: 0.3652: : 761iter [00:22, 35.37iter/s]
Best score: 0.3652: : 762iter [00:22, 35.37iter/s]
Best score: 0.3652: : 763iter [00:22, 36.22iter/s]
Best score: 0.3652: : 763iter [00:22, 36.22iter/s]
Best score: 0.3652: : 764iter [00:22, 36.22iter/s]
Best score: 0.3652: : 765iter [00:22, 36.22iter/s]
Best score: 0.3652: : 766iter [00:22, 36.22iter/s]
Best score: 0.3652: : 767iter [00:22, 37.21iter/s]
Best score: 0.3652: : 767iter [00:22, 37.21iter/s]
Best score: 0.3652: : 768iter [00:22, 37.21iter/s]
Best score: 0.3640: : 769iter [00:22, 37.21iter/s]
Best score: 0.3640: : 770iter [00:22, 37.21iter/s]
Best score: 0.3640: : 771iter [00:22, 37.87iter/s]
Best score: 0.3640: : 771iter [00:22, 37.87iter/s]
Best score: 0.3640: : 772iter [00:22, 37.87iter/s]
Best score: 0.3640: : 773iter [00:22, 37.87iter/s]
Best score: 0.3640: : 774iter [00:22, 37.87iter/s]
Best score: 0.3640: : 775iter [00:22, 38.44iter/s]
Best score: 0.3640: : 775iter [00:22, 38.44iter/s]
Best score: 0.3640: : 776iter [00:22, 38.44iter/s]
Best score: 0.3640: : 777iter [00:22, 38.44iter/s]
Best score: 0.3640: : 778iter [00:22, 38.44iter/s]
Best score: 0.3640: : 779iter [00:22, 38.78iter/s]
Best score: 0.3633: : 779iter [00:22, 38.78iter/s]
Best score: 0.3633: : 780iter [00:23, 38.78iter/s]
Best score: 0.3633: : 781iter [00:23, 38.78iter/s]
Best score: 0.3633: : 782iter [00:23, 38.78iter/s]
Best score: 0.3633: : 783iter [00:23, 38.75iter/s]
Best score: 0.3633: : 783iter [00:23, 38.75iter/s]
Best score: 0.3633: : 784iter [00:23, 38.75iter/s]
Best score: 0.3633: : 785iter [00:23, 38.75iter/s]
Best score: 0.3633: : 786iter [00:23, 38.75iter/s]
Best score: 0.3633: : 787iter [00:23, 38.75iter/s]
Best score: 0.3633: : 788iter [00:23, 39.17iter/s]
Best score: 0.3633: : 788iter [00:23, 39.17iter/s]
Best score: 0.3633: : 789iter [00:23, 39.17iter/s]
Best score: 0.3633: : 790iter [00:23, 39.17iter/s]
Best score: 0.3633: : 791iter [00:23, 39.17iter/s]
Best score: 0.3633: : 792iter [00:23, 39.07iter/s]
Best score: 0.3633: : 792iter [00:23, 39.07iter/s]
Best score: 0.3633: : 793iter [00:23, 39.07iter/s]
Best score: 0.3633: : 794iter [00:23, 39.07iter/s]
Best score: 0.3633: : 795iter [00:23, 39.07iter/s]
Best score: 0.3633: : 796iter [00:23, 39.22iter/s]
Best score: 0.3633: : 796iter [00:23, 39.22iter/s]
Best score: 0.3633: : 797iter [00:23, 39.22iter/s]
Best score: 0.3633: : 798iter [00:23, 39.22iter/s]
[ITER 800] Best: 0.3632
[PARAMS] Current best parameters:
  fx: 562.5662
  fy: 562.4887
  px: 993.4964
  py: 759.7941
  k1: -0.04583533
  k2: 0.02771079
  k3: -0.01350735
  k4: 0.00209630
  p1: -0.00940317
  p2: -0.00705897
  p3: 0.02804303
  p4: -0.01031700
  s1: -0.0276
  s2: 0.0111
  s3: -0.0247
  s4: 0.0115
  tilt_x: 0.0038
  tilt_y: 0.0173
  scale: 2.9874
  rotation: 0.8589

Best score: 0.3632: : 799iter [00:23, 39.22iter/s]
Best score: 0.3632: : 800iter [00:23, 39.04iter/s]
Best score: 0.3632: : 800iter [00:23, 39.04iter/s]
Best score: 0.3632: : 801iter [00:23, 39.04iter/s]
Best score: 0.3632: : 802iter [00:23, 39.04iter/s]
Best score: 0.3632: : 803iter [00:23, 39.04iter/s]
Best score: 0.3632: : 804iter [00:23, 32.01iter/s]
Best score: 0.3632: : 804iter [00:23, 32.01iter/s]
Best score: 0.3632: : 805iter [00:23, 32.01iter/s]
Best score: 0.3632: : 806iter [00:23, 32.01iter/s]
Best score: 0.3632: : 807iter [00:23, 32.01iter/s]
Best score: 0.3632: : 808iter [00:23, 33.93iter/s]
Best score: 0.3632: : 808iter [00:23, 33.93iter/s]
Best score: 0.3614: : 809iter [00:23, 33.93iter/s]
Best score: 0.3614: : 810iter [00:23, 33.93iter/s]
Best score: 0.3614: : 811iter [00:23, 33.93iter/s]
Best score: 0.3614: : 812iter [00:23, 35.24iter/s]
Best score: 0.3614: : 812iter [00:23, 35.24iter/s]
Best score: 0.3614: : 813iter [00:23, 35.24iter/s]
Best score: 0.3614: : 814iter [00:23, 35.24iter/s]
Best score: 0.3614: : 815iter [00:23, 35.24iter/s]
Best score: 0.3614: : 816iter [00:23, 35.24iter/s]
Best score: 0.3614: : 817iter [00:23, 36.84iter/s]
Best score: 0.3614: : 817iter [00:24, 36.84iter/s]
Best score: 0.3614: : 818iter [00:24, 36.84iter/s]
Best score: 0.3606: : 819iter [00:24, 36.84iter/s]
Best score: 0.3606: : 820iter [00:24, 36.84iter/s]
Best score: 0.3606: : 821iter [00:24, 37.35iter/s]
Best score: 0.3606: : 821iter [00:24, 37.35iter/s]
Best score: 0.3606: : 822iter [00:24, 37.35iter/s]
Best score: 0.3606: : 823iter [00:24, 37.35iter/s]
Best score: 0.3606: : 824iter [00:24, 37.35iter/s]
Best score: 0.3606: : 825iter [00:24, 38.03iter/s]
Best score: 0.3606: : 825iter [00:24, 38.03iter/s]
Best score: 0.3606: : 826iter [00:24, 38.03iter/s]
Best score: 0.3606: : 827iter [00:24, 38.03iter/s]
Best score: 0.3606: : 828iter [00:24, 38.03iter/s]
Best score: 0.3606: : 829iter [00:24, 38.57iter/s]
Best score: 0.3606: : 829iter [00:24, 38.57iter/s]
Best score: 0.3606: : 830iter [00:24, 38.57iter/s]
Best score: 0.3606: : 831iter [00:24, 38.57iter/s]
Best score: 0.3606: : 832iter [00:24, 38.57iter/s]
Best score: 0.3606: : 833iter [00:24, 38.62iter/s]
Best score: 0.3606: : 833iter [00:24, 38.62iter/s]
Best score: 0.3606: : 834iter [00:24, 38.62iter/s]
Best score: 0.3606: : 835iter [00:24, 38.62iter/s]
Best score: 0.3606: : 836iter [00:24, 38.62iter/s]
Best score: 0.3606: : 837iter [00:24, 38.88iter/s]
Best score: 0.3606: : 837iter [00:24, 38.88iter/s]
Best score: 0.3606: : 838iter [00:24, 38.88iter/s]
Best score: 0.3606: : 839iter [00:24, 38.88iter/s]
Best score: 0.3606: : 840iter [00:24, 38.88iter/s]
Best score: 0.3606: : 841iter [00:24, 39.06iter/s]
Best score: 0.3606: : 841iter [00:24, 39.06iter/s]
Best score: 0.3606: : 842iter [00:24, 39.06iter/s]
Best score: 0.3606: : 843iter [00:24, 39.06iter/s]
Best score: 0.3606: : 844iter [00:24, 39.06iter/s]
Best score: 0.3606: : 845iter [00:24, 39.06iter/s]
Best score: 0.3606: : 846iter [00:24, 39.48iter/s]
Best score: 0.3606: : 846iter [00:24, 39.48iter/s]
Best score: 0.3606: : 847iter [00:24, 39.48iter/s]
Best score: 0.3606: : 848iter [00:24, 39.48iter/s]
[ITER 850] Best: 0.3602
[PARAMS] Current best parameters:
  fx: 561.3433
  fy: 561.1799
  px: 993.3214
  py: 758.9550
  k1: -0.05039450
  k2: 0.03659242
  k3: -0.01854997
  k4: 0.00309091
  p1: -0.01061622
  p2: -0.00616880
  p3: 0.02791531
  p4: -0.00931913
  s1: -0.0290
  s2: 0.0104
  s3: -0.0242
  s4: 0.0105
  tilt_x: 0.0020
  tilt_y: 0.0200
  scale: 2.9935
  rotation: 0.8590

Best score: 0.3602: : 849iter [00:24, 39.48iter/s]
Best score: 0.3602: : 850iter [00:24, 39.30iter/s]
Best score: 0.3602: : 850iter [00:24, 39.30iter/s]
Best score: 0.3602: : 851iter [00:24, 39.30iter/s]
Best score: 0.3602: : 852iter [00:24, 39.30iter/s]
Best score: 0.3602: : 853iter [00:25, 39.30iter/s]
Best score: 0.3602: : 854iter [00:25, 32.21iter/s]
Best score: 0.3602: : 854iter [00:25, 32.21iter/s]
Best score: 0.3602: : 855iter [00:25, 32.21iter/s]
Best score: 0.3602: : 856iter [00:25, 32.21iter/s]
Best score: 0.3602: : 857iter [00:25, 32.21iter/s]
Best score: 0.3602: : 858iter [00:25, 34.12iter/s]
Best score: 0.3602: : 858iter [00:25, 34.12iter/s]
Best score: 0.3600: : 859iter [00:25, 34.12iter/s]
Best score: 0.3600: : 860iter [00:25, 34.12iter/s]
Best score: 0.3600: : 861iter [00:25, 34.12iter/s]
Best score: 0.3600: : 862iter [00:25, 35.36iter/s]
Best score: 0.3600: : 862iter [00:25, 35.36iter/s]
Best score: 0.3600: : 863iter [00:25, 35.36iter/s]
Best score: 0.3600: : 864iter [00:25, 35.36iter/s]
Best score: 0.3600: : 865iter [00:25, 35.36iter/s]
Best score: 0.3600: : 866iter [00:25, 36.59iter/s]
Best score: 0.3600: : 866iter [00:25, 36.59iter/s]
Best score: 0.3600: : 867iter [00:25, 36.59iter/s]
Best score: 0.3600: : 868iter [00:25, 36.59iter/s]
Best score: 0.3595: : 869iter [00:25, 36.59iter/s]
Best score: 0.3595: : 870iter [00:25, 37.11iter/s]
Best score: 0.3595: : 870iter [00:25, 37.11iter/s]
Best score: 0.3595: : 871iter [00:25, 37.11iter/s]
Best score: 0.3595: : 872iter [00:25, 37.11iter/s]
Best score: 0.3595: : 873iter [00:25, 37.11iter/s]
Best score: 0.3595: : 874iter [00:25, 37.79iter/s]
Best score: 0.3595: : 874iter [00:25, 37.79iter/s]
Best score: 0.3595: : 875iter [00:25, 37.79iter/s]
Best score: 0.3595: : 876iter [00:25, 37.79iter/s]
Best score: 0.3595: : 877iter [00:25, 37.79iter/s]
Best score: 0.3595: : 878iter [00:25, 37.79iter/s]
Best score: 0.3595: : 879iter [00:25, 38.54iter/s]
Best score: 0.3595: : 879iter [00:25, 38.54iter/s]
Best score: 0.3595: : 880iter [00:25, 38.54iter/s]
Best score: 0.3595: : 881iter [00:25, 38.54iter/s]
Best score: 0.3595: : 882iter [00:25, 38.54iter/s]
Best score: 0.3595: : 883iter [00:25, 38.58iter/s]
Best score: 0.3595: : 883iter [00:25, 38.58iter/s]
Best score: 0.3595: : 884iter [00:25, 38.58iter/s]
Best score: 0.3595: : 885iter [00:25, 38.58iter/s]
Best score: 0.3595: : 886iter [00:25, 38.58iter/s]
Best score: 0.3595: : 887iter [00:25, 38.58iter/s]
Best score: 0.3595: : 888iter [00:25, 39.09iter/s]
Best score: 0.3595: : 888iter [00:25, 39.09iter/s]
Best score: 0.3591: : 889iter [00:25, 39.09iter/s]
Best score: 0.3591: : 890iter [00:25, 39.09iter/s]
Best score: 0.3591: : 891iter [00:25, 39.09iter/s]
Best score: 0.3591: : 892iter [00:25, 39.07iter/s]
Best score: 0.3591: : 892iter [00:26, 39.07iter/s]
Best score: 0.3591: : 893iter [00:26, 39.07iter/s]
Best score: 0.3591: : 894iter [00:26, 39.07iter/s]
Best score: 0.3591: : 895iter [00:26, 39.07iter/s]
Best score: 0.3591: : 896iter [00:26, 39.07iter/s]
Best score: 0.3591: : 897iter [00:26, 39.44iter/s]
Best score: 0.3591: : 897iter [00:26, 39.44iter/s]
Best score: 0.3591: : 898iter [00:26, 39.44iter/s]
[ITER 900] Best: 0.3591
[PARAMS] Current best parameters:
  fx: 558.8734
  fy: 558.7201
  px: 993.3780
  py: 758.6906
  k1: -0.05104922
  k2: 0.04116573
  k3: -0.02119998
  k4: 0.00357796
  p1: -0.01020892
  p2: -0.00676463
  p3: 0.02534385
  p4: -0.00848215
  s1: -0.0256
  s2: 0.0094
  s3: -0.0223
  s4: 0.0096
  tilt_x: -0.0001
  tilt_y: 0.0184
  scale: 2.9985
  rotation: 0.8640

Best score: 0.3591: : 899iter [00:26, 39.44iter/s]
Best score: 0.3591: : 900iter [00:26, 39.44iter/s]
Best score: 0.3591: : 901iter [00:26, 32.26iter/s]
Best score: 0.3591: : 901iter [00:26, 32.26iter/s]
Best score: 0.3591: : 902iter [00:26, 32.26iter/s]
Best score: 0.3591: : 903iter [00:26, 32.26iter/s]
Best score: 0.3591: : 904iter [00:26, 32.26iter/s]
Best score: 0.3591: : 905iter [00:26, 27.98iter/s]
Best score: 0.3591: : 905iter [00:26, 27.98iter/s]
Best score: 0.3591: : 906iter [00:26, 27.98iter/s]
Best score: 0.3591: : 907iter [00:26, 27.98iter/s]
Best score: 0.3591: : 908iter [00:26, 27.98iter/s]
Best score: 0.3591: : 909iter [00:26, 30.52iter/s]
Best score: 0.3589: : 909iter [00:26, 30.52iter/s]
Best score: 0.3589: : 910iter [00:26, 30.52iter/s]
Best score: 0.3589: : 911iter [00:26, 30.52iter/s]
Best score: 0.3589: : 912iter [00:26, 30.52iter/s]
Best score: 0.3589: : 913iter [00:26, 32.59iter/s]
Best score: 0.3589: : 913iter [00:26, 32.59iter/s]
Best score: 0.3589: : 914iter [00:26, 32.59iter/s]
Best score: 0.3589: : 915iter [00:26, 32.59iter/s]
Best score: 0.3589: : 916iter [00:26, 32.59iter/s]
Best score: 0.3589: : 917iter [00:26, 32.59iter/s]
Best score: 0.3589: : 918iter [00:26, 34.81iter/s]
Best score: 0.3589: : 918iter [00:26, 34.81iter/s]
Best score: 0.3587: : 919iter [00:26, 34.81iter/s]
Best score: 0.3587: : 920iter [00:26, 34.81iter/s]
Best score: 0.3587: : 921iter [00:26, 34.81iter/s]
Best score: 0.3587: : 922iter [00:26, 35.79iter/s]
Best score: 0.3587: : 922iter [00:26, 35.79iter/s]
Best score: 0.3587: : 923iter [00:26, 35.79iter/s]
Best score: 0.3587: : 924iter [00:26, 35.79iter/s]
Best score: 0.3587: : 925iter [00:27, 35.79iter/s]
Best score: 0.3587: : 926iter [00:27, 36.76iter/s]
Best score: 0.3587: : 926iter [00:27, 36.76iter/s]
Best score: 0.3587: : 927iter [00:27, 36.76iter/s]
Best score: 0.3587: : 928iter [00:27, 36.76iter/s]
Best score: 0.3584: : 929iter [00:27, 36.76iter/s]
Best score: 0.3584: : 930iter [00:27, 37.34iter/s]
Best score: 0.3584: : 930iter [00:27, 37.34iter/s]
Best score: 0.3584: : 931iter [00:27, 37.34iter/s]
Best score: 0.3584: : 932iter [00:27, 37.34iter/s]
Best score: 0.3584: : 933iter [00:27, 37.34iter/s]
Best score: 0.3584: : 934iter [00:27, 37.90iter/s]
Best score: 0.3584: : 934iter [00:27, 37.90iter/s]
Best score: 0.3584: : 935iter [00:27, 37.90iter/s]
Best score: 0.3584: : 936iter [00:27, 37.90iter/s]
Best score: 0.3584: : 937iter [00:27, 37.90iter/s]
Best score: 0.3584: : 938iter [00:27, 38.26iter/s]
Best score: 0.3584: : 938iter [00:27, 38.26iter/s]
Best score: 0.3584: : 939iter [00:27, 38.26iter/s]
Best score: 0.3584: : 940iter [00:27, 38.26iter/s]
Best score: 0.3584: : 941iter [00:27, 38.26iter/s]
Best score: 0.3584: : 942iter [00:27, 38.31iter/s]
Best score: 0.3584: : 942iter [00:27, 38.31iter/s]
Best score: 0.3584: : 943iter [00:27, 38.31iter/s]
Best score: 0.3584: : 944iter [00:27, 38.31iter/s]
Best score: 0.3584: : 945iter [00:27, 38.31iter/s]
Best score: 0.3584: : 946iter [00:27, 38.60iter/s]
Best score: 0.3584: : 946iter [00:27, 38.60iter/s]
Best score: 0.3584: : 947iter [00:27, 38.60iter/s]
Best score: 0.3584: : 948iter [00:27, 38.60iter/s]
[ITER 950] Best: 0.3583
[PARAMS] Current best parameters:
  fx: 557.7564
  fy: 557.6015
  px: 993.3157
  py: 758.5078
  k1: -0.05240324
  k2: 0.04576768
  k3: -0.02432186
  k4: 0.00427414
  p1: -0.01069517
  p2: -0.00686124
  p3: 0.02476965
  p4: -0.00777883
  s1: -0.0251
  s2: 0.0088
  s3: -0.0213
  s4: 0.0089
  tilt_x: -0.0001
  tilt_y: 0.0184
  scale: 3.0003
  rotation: 0.8614

Best score: 0.3583: : 949iter [00:27, 38.60iter/s]
Best score: 0.3583: : 950iter [00:27, 38.66iter/s]
Best score: 0.3583: : 950iter [00:27, 38.66iter/s]
Best score: 0.3583: : 951iter [00:27, 38.66iter/s]
Best score: 0.3583: : 952iter [00:27, 38.66iter/s]
Best score: 0.3583: : 953iter [00:27, 38.66iter/s]
Best score: 0.3583: : 954iter [00:27, 31.39iter/s]
Best score: 0.3583: : 954iter [00:27, 31.39iter/s]
Best score: 0.3583: : 955iter [00:27, 31.39iter/s]
Best score: 0.3583: : 956iter [00:27, 31.39iter/s]
Best score: 0.3583: : 957iter [00:27, 31.39iter/s]
Best score: 0.3583: : 958iter [00:27, 33.46iter/s]
Best score: 0.3583: : 958iter [00:27, 33.46iter/s]
Best score: 0.3580: : 959iter [00:27, 33.46iter/s]
Best score: 0.3580: : 960iter [00:27, 33.46iter/s]
Best score: 0.3580: : 961iter [00:28, 33.46iter/s]
Best score: 0.3580: : 962iter [00:28, 34.87iter/s]
Best score: 0.3580: : 962iter [00:28, 34.87iter/s]
Best score: 0.3580: : 963iter [00:28, 34.87iter/s]
Best score: 0.3580: : 964iter [00:28, 34.87iter/s]
Best score: 0.3580: : 965iter [00:28, 34.87iter/s]
Best score: 0.3580: : 966iter [00:28, 34.87iter/s]
Best score: 0.3580: : 967iter [00:28, 36.47iter/s]
Best score: 0.3580: : 967iter [00:28, 36.47iter/s]
Best score: 0.3580: : 968iter [00:28, 36.47iter/s]
Best score: 0.3580: : 969iter [00:28, 36.47iter/s]
Best score: 0.3580: : 970iter [00:28, 36.47iter/s]
Best score: 0.3580: : 971iter [00:28, 37.03iter/s]
Best score: 0.3580: : 971iter [00:28, 37.03iter/s]
Best score: 0.3580: : 972iter [00:28, 37.03iter/s]
Best score: 0.3580: : 973iter [00:28, 37.03iter/s]
Best score: 0.3580: : 974iter [00:28, 37.03iter/s]
Best score: 0.3580: : 975iter [00:28, 37.62iter/s]
Best score: 0.3580: : 975iter [00:28, 37.62iter/s]
Best score: 0.3580: : 976iter [00:28, 37.62iter/s]
Best score: 0.3580: : 977iter [00:28, 37.62iter/s]
Best score: 0.3580: : 978iter [00:28, 37.62iter/s]
Best score: 0.3580: : 979iter [00:28, 37.91iter/s]
Best score: 0.3579: : 979iter [00:28, 37.91iter/s]
Best score: 0.3579: : 980iter [00:28, 37.91iter/s]
Best score: 0.3579: : 981iter [00:28, 37.91iter/s]
Best score: 0.3579: : 982iter [00:28, 37.91iter/s]
Best score: 0.3579: : 983iter [00:28, 38.06iter/s]
Best score: 0.3579: : 983iter [00:28, 38.06iter/s]
Best score: 0.3579: : 984iter [00:28, 38.06iter/s]
Best score: 0.3579: : 985iter [00:28, 38.06iter/s]
Best score: 0.3579: : 986iter [00:28, 38.06iter/s]
Best score: 0.3579: : 987iter [00:28, 38.54iter/s]
Best score: 0.3579: : 987iter [00:28, 38.54iter/s]
Best score: 0.3579: : 988iter [00:28, 38.54iter/s]
Best score: 0.3574: : 989iter [00:28, 38.54iter/s]
Best score: 0.3574: : 990iter [00:28, 38.54iter/s]
Best score: 0.3574: : 991iter [00:28, 38.70iter/s]
Best score: 0.3574: : 991iter [00:28, 38.70iter/s]
Best score: 0.3574: : 992iter [00:28, 38.70iter/s]
Best score: 0.3574: : 993iter [00:28, 38.70iter/s]
Best score: 0.3574: : 994iter [00:28, 38.70iter/s]
Best score: 0.3574: : 995iter [00:28, 39.00iter/s]
Best score: 0.3574: : 995iter [00:28, 39.00iter/s]
Best score: 0.3574: : 996iter [00:28, 39.00iter/s]
Best score: 0.3574: : 997iter [00:28, 39.00iter/s]
Best score: 0.3574: : 998iter [00:28, 39.00iter/s]
Best score: 0.3574: : 999iter [00:28, 39.16iter/s]
[ITER 1000] Best: 0.3570
[PARAMS] Current best parameters:
  fx: 555.6122
  fy: 555.4556
  px: 993.5558
  py: 758.3508
  k1: -0.05187785
  k2: 0.04886702
  k3: -0.02661883
  k4: 0.00479398
  p1: -0.01163433
  p2: -0.00699877
  p3: 0.02412248
  p4: -0.00675125
  s1: -0.0239
  s2: 0.0076
  s3: -0.0198
  s4: 0.0079
  tilt_x: 0.0009
  tilt_y: 0.0168
  scale: 3.0022
  rotation: 0.8603

Best score: 0.3570: : 999iter [00:28, 39.16iter/s]
Best score: 0.3570: : 1000iter [00:29, 39.16iter/s]
Best score: 0.3570: : 1001iter [00:29, 39.16iter/s]
Best score: 0.3570: : 1002iter [00:29, 39.16iter/s]
Best score: 0.3570: : 1003iter [00:29, 31.53iter/s]
Best score: 0.3570: : 1003iter [00:29, 31.53iter/s]
Best score: 0.3570: : 1004iter [00:29, 31.53iter/s]
Best score: 0.3570: : 1005iter [00:29, 31.53iter/s]
Best score: 0.3570: : 1006iter [00:29, 31.53iter/s]
Best score: 0.3570: : 1007iter [00:29, 33.59iter/s]
Best score: 0.3570: : 1007iter [00:29, 33.59iter/s]
Best score: 0.3570: : 1008iter [00:29, 33.59iter/s]
Best score: 0.3570: : 1009iter [00:29, 33.59iter/s]
Best score: 0.3570: : 1010iter [00:29, 33.59iter/s]
Best score: 0.3570: : 1011iter [00:29, 35.02iter/s]
Best score: 0.3570: : 1011iter [00:29, 35.02iter/s]
Best score: 0.3570: : 1012iter [00:29, 35.02iter/s]
Best score: 0.3570: : 1013iter [00:29, 35.02iter/s]
Best score: 0.3570: : 1014iter [00:29, 35.02iter/s]
Best score: 0.3570: : 1015iter [00:29, 36.37iter/s]
Best score: 0.3570: : 1015iter [00:29, 36.37iter/s]
Best score: 0.3570: : 1016iter [00:29, 36.37iter/s]
Best score: 0.3570: : 1017iter [00:29, 36.37iter/s]
Best score: 0.3570: : 1018iter [00:29, 36.37iter/s]
Best score: 0.3570: : 1019iter [00:29, 37.33iter/s]
Best score: 0.3570: : 1019iter [00:29, 37.33iter/s]
Best score: 0.3570: : 1020iter [00:29, 37.33iter/s]
Best score: 0.3570: : 1021iter [00:29, 37.33iter/s]
Best score: 0.3570: : 1022iter [00:29, 37.33iter/s]
Best score: 0.3570: : 1023iter [00:29, 37.76iter/s]
Best score: 0.3570: : 1023iter [00:29, 37.76iter/s]
Best score: 0.3570: : 1024iter [00:29, 37.76iter/s]
Best score: 0.3570: : 1025iter [00:29, 37.76iter/s]
Best score: 0.3570: : 1026iter [00:29, 37.76iter/s]
Best score: 0.3570: : 1027iter [00:29, 38.30iter/s]
Best score: 0.3570: : 1027iter [00:29, 38.30iter/s]
Best score: 0.3570: : 1028iter [00:29, 38.30iter/s]
Best score: 0.3570: : 1029iter [00:29, 38.30iter/s]
Best score: 0.3570: : 1030iter [00:29, 38.30iter/s]
Best score: 0.3570: : 1031iter [00:29, 38.37iter/s]
Best score: 0.3570: : 1031iter [00:29, 38.37iter/s]
Best score: 0.3570: : 1032iter [00:29, 38.37iter/s]
Best score: 0.3570: : 1033iter [00:29, 38.37iter/s]
Best score: 0.3570: : 1034iter [00:29, 38.37iter/s]
Best score: 0.3570: : 1035iter [00:29, 38.74iter/s]
Best score: 0.3570: : 1035iter [00:29, 38.74iter/s]
Best score: 0.3570: : 1036iter [00:30, 38.74iter/s]
Best score: 0.3570: : 1037iter [00:30, 38.74iter/s]
Best score: 0.3570: : 1038iter [00:30, 38.74iter/s]
Best score: 0.3570: : 1039iter [00:30, 39.04iter/s]
Best score: 0.3570: : 1039iter [00:30, 39.04iter/s]
Best score: 0.3570: : 1040iter [00:30, 39.04iter/s]
Best score: 0.3570: : 1041iter [00:30, 39.04iter/s]
Best score: 0.3570: : 1042iter [00:30, 39.04iter/s]
Best score: 0.3570: : 1043iter [00:30, 39.02iter/s]
Best score: 0.3570: : 1043iter [00:30, 39.02iter/s]
Best score: 0.3570: : 1044iter [00:30, 39.02iter/s]
Best score: 0.3570: : 1045iter [00:30, 39.02iter/s]
Best score: 0.3570: : 1046iter [00:30, 39.02iter/s]
Best score: 0.3570: : 1047iter [00:30, 39.23iter/s]
Best score: 0.3570: : 1047iter [00:30, 39.23iter/s]
Best score: 0.3570: : 1048iter [00:30, 39.23iter/s]
[ITER 1050] Best: 0.3570
[PARAMS] Current best parameters:
  fx: 555.1564
  fy: 554.9959
  px: 993.5248
  py: 758.1030
  k1: -0.05268145
  k2: 0.05097522
  k3: -0.02795303
  k4: 0.00508050
  p1: -0.01240928
  p2: -0.00699149
  p3: 0.02450136
  p4: -0.00629812
  s1: -0.0243
  s2: 0.0071
  s3: -0.0196
  s4: 0.0075
  tilt_x: 0.0012
  tilt_y: 0.0169
  scale: 3.0035
  rotation: 0.8604

Best score: 0.3570: : 1049iter [00:30, 39.23iter/s]
Best score: 0.3570: : 1050iter [00:30, 39.23iter/s]
Best score: 0.3570: : 1051iter [00:30, 31.69iter/s]
Best score: 0.3570: : 1051iter [00:30, 31.69iter/s]
Best score: 0.3570: : 1052iter [00:30, 31.69iter/s]
Best score: 0.3570: : 1053iter [00:30, 31.69iter/s]
Best score: 0.3570: : 1054iter [00:30, 31.69iter/s]
Best score: 0.3570: : 1055iter [00:30, 33.79iter/s]
Best score: 0.3570: : 1055iter [00:30, 33.79iter/s]
Best score: 0.3570: : 1056iter [00:30, 33.79iter/s]
Best score: 0.3570: : 1057iter [00:30, 33.79iter/s]
Best score: 0.3570: : 1058iter [00:30, 33.79iter/s]
Best score: 0.3567: : 1059iter [00:30, 33.79iter/s]
Best score: 0.3567: : 1060iter [00:30, 35.50iter/s]
Best score: 0.3567: : 1060iter [00:30, 35.50iter/s]
Best score: 0.3567: : 1061iter [00:30, 35.50iter/s]
Best score: 0.3567: : 1062iter [00:30, 35.50iter/s]
Best score: 0.3567: : 1063iter [00:30, 35.50iter/s]
Best score: 0.3567: : 1064iter [00:30, 36.60iter/s]
Best score: 0.3567: : 1064iter [00:30, 36.60iter/s]
Best score: 0.3567: : 1065iter [00:30, 36.60iter/s]
Best score: 0.3567: : 1066iter [00:30, 36.60iter/s]
Best score: 0.3567: : 1067iter [00:30, 36.60iter/s]
Best score: 0.3567: : 1068iter [00:30, 36.60iter/s]
Best score: 0.3567: : 1069iter [00:30, 37.69iter/s]
Best score: 0.3556: : 1069iter [00:30, 37.69iter/s]
Best score: 0.3556: : 1070iter [00:30, 37.69iter/s]
Best score: 0.3556: : 1071iter [00:30, 37.69iter/s]
Best score: 0.3556: : 1072iter [00:30, 37.69iter/s]
Best score: 0.3556: : 1073iter [00:30, 38.09iter/s]
Best score: 0.3556: : 1073iter [00:31, 38.09iter/s]
Best score: 0.3556: : 1074iter [00:31, 38.09iter/s]
Best score: 0.3556: : 1075iter [00:31, 38.09iter/s]
Best score: 0.3556: : 1076iter [00:31, 38.09iter/s]
Best score: 0.3556: : 1077iter [00:31, 38.09iter/s]
Best score: 0.3556: : 1078iter [00:31, 38.67iter/s]
Best score: 0.3556: : 1078iter [00:31, 38.67iter/s]
Best score: 0.3551: : 1079iter [00:31, 38.67iter/s]
Best score: 0.3551: : 1080iter [00:31, 38.67iter/s]
Best score: 0.3551: : 1081iter [00:31, 38.67iter/s]
Best score: 0.3551: : 1082iter [00:31, 38.69iter/s]
Best score: 0.3551: : 1082iter [00:31, 38.69iter/s]
Best score: 0.3551: : 1083iter [00:31, 38.69iter/s]
Best score: 0.3551: : 1084iter [00:31, 38.69iter/s]
Best score: 0.3551: : 1085iter [00:31, 38.69iter/s]
Best score: 0.3551: : 1086iter [00:31, 39.03iter/s]
Best score: 0.3551: : 1086iter [00:31, 39.03iter/s]
Best score: 0.3551: : 1087iter [00:31, 39.03iter/s]
Best score: 0.3551: : 1088iter [00:31, 39.03iter/s]
Best score: 0.3544: : 1089iter [00:31, 39.03iter/s]
Best score: 0.3544: : 1090iter [00:31, 38.97iter/s]
Best score: 0.3544: : 1090iter [00:31, 38.97iter/s]
Best score: 0.3544: : 1091iter [00:31, 38.97iter/s]
Best score: 0.3544: : 1092iter [00:31, 38.97iter/s]
Best score: 0.3544: : 1093iter [00:31, 38.97iter/s]
Best score: 0.3544: : 1094iter [00:31, 39.18iter/s]
Best score: 0.3544: : 1094iter [00:31, 39.18iter/s]
Best score: 0.3544: : 1095iter [00:31, 39.18iter/s]
Best score: 0.3544: : 1096iter [00:31, 39.18iter/s]
Best score: 0.3544: : 1097iter [00:31, 39.18iter/s]
Best score: 0.3544: : 1098iter [00:31, 39.18iter/s]
Best score: 0.3544: : 1099iter [00:31, 39.43iter/s]
[ITER 1100] Best: 0.3534
[PARAMS] Current best parameters:
  fx: 552.6815
  fy: 552.6331
  px: 994.7670
  py: 757.7636
  k1: -0.04951933
  k2: 0.04864103
  k3: -0.02621129
  k4: 0.00462179
  p1: -0.01046171
  p2: -0.01062402
  p3: 0.02338626
  p4: -0.00467173
  s1: -0.0179
  s2: 0.0052
  s3: -0.0208
  s4: 0.0058
  tilt_x: -0.0044
  tilt_y: 0.0033
  scale: 3.0050
  rotation: 0.8643

Best score: 0.3534: : 1099iter [00:31, 39.43iter/s]
Best score: 0.3534: : 1100iter [00:31, 34.63iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.353375 -> 0.352892

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 26,012 → 26,012 within 3500mm ring
  Ring polish improved score: 0.352892 -> 0.352339
  Success: False
  Tangential distortion: p1=-0.010439, p2=-0.010616, p3=0.023373, p4=-0.004683
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3523
[✅] 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: 552.6815424008252
    fy: 552.6330999668161
    px: 994.7670266542975
    py: 757.7635580907058
    k1: -0.04952127893443386
    k2: 0.04864484156529631
    k3: -0.026204307011296734
    k4: 0.004616413536105815
    p1: -0.010438594780618616
    p2: -0.010615689781876056
    p3: 0.023373298193494162
    p4: -0.0046828406334565
    scale: 3.0050059452589637
    rotation: 0.864314417681415
    mmpp: 1.2786022280486653
    nmpp: 1278602.2280486652

[🔄] 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, 168.33frame/s]

   🔄 1000:   8%|▊         | 35/457 [00:00<00:02, 169.69frame/s]

   🔄 1000:  12%|█▏        | 53/457 [00:00<00:02, 170.13frame/s]

   🔄 1000:  16%|█▌        | 71/457 [00:00<00:02, 170.69frame/s]

   🔄 1000:  19%|█▉        | 89/457 [00:00<00:02, 171.95frame/s]

   🔄 1000:  23%|██▎       | 107/457 [00:00<00:02, 172.61frame/s]

   🔄 1000:  27%|██▋       | 125/457 [00:00<00:01, 172.99frame/s]

   🔄 1000:  31%|███▏      | 143/457 [00:00<00:01, 173.85frame/s]

   🔄 1000:  35%|███▌      | 161/457 [00:00<00:01, 173.91frame/s]

   🔄 1000:  39%|███▉      | 179/457 [00:01<00:01, 174.99frame/s]

   🔄 1000:  43%|████▎     | 197/457 [00:01<00:01, 175.70frame/s]

   🔄 1000:  47%|████▋     | 215/457 [00:01<00:01, 176.70frame/s]

   🔄 1000:  51%|█████     | 233/457 [00:01<00:01, 177.02frame/s]

   🔄 1000:  55%|█████▍    | 251/457 [00:01<00:01, 177.26frame/s]

   🔄 1000:  59%|█████▉    | 269/457 [00:01<00:01, 177.36frame/s]

   🔄 1000:  63%|██████▎   | 287/457 [00:01<00:00, 177.30frame/s]

   🔄 1000:  67%|██████▋   | 305/457 [00:01<00:00, 177.40frame/s]

   🔄 1000:  71%|███████   | 323/457 [00:01<00:00, 176.76frame/s]

   🔄 1000:  75%|███████▍  | 341/457 [00:01<00:00, 175.75frame/s]

   🔄 1000:  79%|███████▊  | 359/457 [00:02<00:00, 175.14frame/s]

   🔄 1000:  82%|████████▏ | 377/457 [00:02<00:00, 174.80frame/s]

   🔄 1000:  86%|████████▋ | 395/457 [00:02<00:00, 174.38frame/s]

   🔄 1000:  90%|█████████ | 413/457 [00:02<00:00, 173.97frame/s]

   🔄 1000:  94%|█████████▍| 431/457 [00:02<00:00, 173.56frame/s]

   🔄 1000:  98%|█████████▊| 449/457 [00:02<00:00, 173.54frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:04<00:04,  4.88s/rig]

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

   🔄 500:   5%|▌         | 18/342 [00:00<00:01, 178.53frame/s]

   🔄 500:  11%|█         | 38/342 [00:00<00:01, 186.26frame/s]

   🔄 500:  17%|█▋        | 59/342 [00:00<00:01, 194.74frame/s]

   🔄 500:  23%|██▎       | 79/342 [00:00<00:01, 194.26frame/s]

   🔄 500:  29%|██▉       | 99/342 [00:00<00:01, 190.01frame/s]

   🔄 500:  35%|███▌      | 121/342 [00:00<00:01, 196.68frame/s]

   🔄 500:  42%|████▏     | 144/342 [00:00<00:00, 205.75frame/s]

   🔄 500:  48%|████▊     | 165/342 [00:00<00:00, 205.00frame/s]

   🔄 500:  54%|█████▍    | 186/342 [00:00<00:00, 196.81frame/s]

   🔄 500:  60%|██████    | 206/342 [00:01<00:00, 191.95frame/s]

   🔄 500:  66%|██████▌   | 226/342 [00:01<00:00, 186.77frame/s]

   🔄 500:  72%|███████▏  | 245/342 [00:01<00:00, 184.12frame/s]

   🔄 500:  77%|███████▋  | 265/342 [00:01<00:00, 186.78frame/s]

   🔄 500:  83%|████████▎ | 284/342 [00:01<00:00, 185.35frame/s]

   🔄 500:  89%|████████▊ | 303/342 [00:01<00:00, 185.95frame/s]

   🔄 500:  94%|█████████▍| 322/342 [00:01<00:00, 186.92frame/s]

   🔄 500: 100%|█████████▉| 341/342 [00:01<00:00, 184.98frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  3.86s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.01s/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, 166.65frame/s]

   🔄 1000:  35%|███▌      | 35/100 [00:00<00:00, 169.38frame/s]

   🔄 1000:  53%|█████▎    | 53/100 [00:00<00:00, 172.98frame/s]

   🔄 1000:  71%|███████   | 71/100 [00:00<00:00, 173.87frame/s]

   🔄 1000:  89%|████████▉ | 89/100 [00:00<00:00, 173.31frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.04s/rig]

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

   🔄 500:  19%|█▉        | 19/100 [00:00<00:00, 188.23frame/s]

   🔄 500:  40%|████      | 40/100 [00:00<00:00, 196.18frame/s]

   🔄 500:  60%|██████    | 60/100 [00:00<00:00, 193.80frame/s]

   🔄 500:  80%|████████  | 80/100 [00:00<00:00, 189.15frame/s]

   🔄 500:  99%|█████████▉| 99/100 [00:00<00:00, 187.65frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.00s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
[✅] Saved PTDs: /working/MDC030_W032_2026-03-24/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 78391/3080448 [00:00<00:03, 783907.01px/s]
📤 Writing LUT:   5%|▌         | 162008/3080448 [00:00<00:03, 814646.93px/s]
📤 Writing LUT:   8%|▊         | 247014/3080448 [00:00<00:03, 830811.26px/s]
📤 Writing LUT:  11%|█         | 331717/3080448 [00:00<00:03, 837210.58px/s]
📤 Writing LUT:  14%|█▎        | 415925/3080448 [00:00<00:03, 838965.36px/s]
📤 Writing LUT:  16%|█▋        | 501494/3080448 [00:00<00:03, 844646.52px/s]
📤 Writing LUT:  19%|█▉        | 588040/3080448 [00:00<00:02, 851448.49px/s]
📤 Writing LUT:  22%|██▏       | 673185/3080448 [00:00<00:02, 851229.56px/s]
📤 Writing LUT:  25%|██▍       | 759384/3080448 [00:00<00:02, 854592.44px/s]
📤 Writing LUT:  27%|██▋       | 844844/3080448 [00:01<00:02, 850722.64px/s]
📤 Writing LUT:  30%|███       | 929921/3080448 [00:01<00:02, 843445.17px/s]
📤 Writing LUT:  33%|███▎      | 1014280/3080448 [00:01<00:02, 839919.62px/s]
📤 Writing LUT:  36%|███▌      | 1098283/3080448 [00:01<00:02, 839689.69px/s]
📤 Writing LUT:  38%|███▊      | 1184644/3080448 [00:01<00:02, 846868.90px/s]
📤 Writing LUT:  41%|████▏     | 1271767/3080448 [00:01<00:02, 854178.12px/s]
📤 Writing LUT:  44%|████▍     | 1358123/3080448 [00:01<00:02, 856992.37px/s]
📤 Writing LUT:  47%|████▋     | 1443831/3080448 [00:01<00:01, 853682.64px/s]
📤 Writing LUT:  50%|████▉     | 1529207/3080448 [00:01<00:01, 849392.91px/s]
📤 Writing LUT:  52%|█████▏    | 1615433/3080448 [00:01<00:01, 853231.95px/s]
📤 Writing LUT:  55%|█████▌    | 1702245/3080448 [00:02<00:01, 857679.35px/s]
📤 Writing LUT:  58%|█████▊    | 1788021/3080448 [00:02<00:01, 854215.69px/s]
📤 Writing LUT:  61%|██████    | 1873451/3080448 [00:02<00:01, 850952.27px/s]
📤 Writing LUT:  64%|██████▎   | 1958553/3080448 [00:02<00:01, 850207.45px/s]
📤 Writing LUT:  66%|██████▋   | 2044746/3080448 [00:02<00:01, 853706.88px/s]
📤 Writing LUT:  69%|██████▉   | 2131350/3080448 [00:02<00:01, 857392.10px/s]
📤 Writing LUT:  72%|███████▏  | 2217094/3080448 [00:02<00:01, 856502.16px/s]
📤 Writing LUT:  75%|███████▍  | 2302748/3080448 [00:02<00:00, 855246.84px/s]
📤 Writing LUT:  78%|███████▊  | 2388276/3080448 [00:02<00:00, 854291.75px/s]
📤 Writing LUT:  80%|████████  | 2476011/3080448 [00:02<00:00, 861189.62px/s]
📤 Writing LUT:  83%|████████▎ | 2563136/3080448 [00:03<00:00, 864200.44px/s]
📤 Writing LUT:  86%|████████▌ | 2649559/3080448 [00:03<00:00, 863397.30px/s]
📤 Writing LUT:  89%|████████▉ | 2735901/3080448 [00:03<00:00, 858577.79px/s]
📤 Writing LUT:  92%|█████████▏| 2821766/3080448 [00:03<00:00, 856026.32px/s]
📤 Writing LUT:  94%|█████████▍| 2908565/3080448 [00:03<00:00, 859594.28px/s]
📤 Writing LUT:  97%|█████████▋| 2994531/3080448 [00:03<00:00, 858716.07px/s]
📤 Writing LUT: 100%|█████████▉| 3080407/3080448 [00:03<00:00, 855937.74px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 851445.94px/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%|▏         | 9/457 [00:00<00:05, 81.76frame/s]

   🔥 1000:   4%|▍         | 18/457 [00:00<00:05, 85.19frame/s]

   🔥 1000:   6%|▌         | 27/457 [00:00<00:05, 85.61frame/s]

   🔥 1000:   8%|▊         | 36/457 [00:00<00:04, 86.41frame/s]

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   🔥 1000:  12%|█▏        | 54/457 [00:00<00:04, 85.40frame/s]

   🔥 1000:  14%|█▍        | 63/457 [00:00<00:04, 84.20frame/s]

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   🔥 1000:  22%|██▏       | 99/457 [00:01<00:04, 82.04frame/s]

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   🔥 1000:  26%|██▌       | 117/457 [00:01<00:04, 81.34frame/s]

   🔥 1000:  28%|██▊       | 126/457 [00:01<00:04, 80.83frame/s]

   🔥 1000:  30%|██▉       | 135/457 [00:01<00:03, 80.68frame/s]

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

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   🔥 1000:  41%|████▏     | 189/457 [00:02<00:03, 81.58frame/s]

   🔥 1000:  43%|████▎     | 198/457 [00:02<00:03, 82.51frame/s]

   🔥 1000:  45%|████▌     | 207/457 [00:02<00:03, 82.71frame/s]

   🔥 1000:  47%|████▋     | 216/457 [00:02<00:02, 83.92frame/s]

   🔥 1000:  49%|████▉     | 226/457 [00:02<00:02, 86.08frame/s]

   🔥 1000:  51%|█████▏    | 235/457 [00:02<00:02, 86.46frame/s]

   🔥 1000:  53%|█████▎    | 244/457 [00:02<00:02, 85.83frame/s]

   🔥 1000:  55%|█████▌    | 253/457 [00:03<00:02, 84.39frame/s]

   🔥 1000:  57%|█████▋    | 262/457 [00:03<00:02, 83.02frame/s]

   🔥 1000:  59%|█████▉    | 271/457 [00:03<00:02, 82.25frame/s]

   🔥 1000:  61%|██████▏   | 280/457 [00:03<00:02, 82.11frame/s]

   🔥 1000:  63%|██████▎   | 289/457 [00:03<00:02, 82.10frame/s]

   🔥 1000:  65%|██████▌   | 298/457 [00:03<00:01, 81.90frame/s]

   🔥 1000:  67%|██████▋   | 307/457 [00:03<00:01, 81.68frame/s]

   🔥 1000:  69%|██████▉   | 316/457 [00:03<00:01, 82.17frame/s]

   🔥 1000:  71%|███████   | 325/457 [00:03<00:01, 82.13frame/s]

   🔥 1000:  73%|███████▎  | 334/457 [00:04<00:01, 82.33frame/s]

   🔥 1000:  75%|███████▌  | 343/457 [00:04<00:01, 81.58frame/s]

   🔥 1000:  77%|███████▋  | 352/457 [00:04<00:01, 82.11frame/s]

   🔥 1000:  79%|███████▉  | 361/457 [00:04<00:01, 81.54frame/s]

   🔥 1000:  81%|████████  | 370/457 [00:04<00:01, 80.86frame/s]

   🔥 1000:  83%|████████▎ | 379/457 [00:04<00:00, 80.69frame/s]

   🔥 1000:  85%|████████▍ | 388/457 [00:04<00:00, 81.21frame/s]

   🔥 1000:  87%|████████▋ | 397/457 [00:04<00:00, 82.20frame/s]

   🔥 1000:  89%|████████▉ | 406/457 [00:04<00:00, 83.63frame/s]

   🔥 1000:  91%|█████████ | 415/457 [00:05<00:00, 85.35frame/s]

   🔥 1000:  93%|█████████▎| 424/457 [00:05<00:00, 84.12frame/s]

   🔥 1000:  95%|█████████▍| 433/457 [00:05<00:00, 83.55frame/s]

   🔥 1000:  97%|█████████▋| 442/457 [00:05<00:00, 82.81frame/s]

   🔥 1000:  99%|█████████▊| 451/457 [00:05<00:00, 83.67frame/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,  8.44s/rig]

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

   🔥 500:   2%|▏         | 8/342 [00:00<00:04, 77.31frame/s]

   🔥 500:   5%|▍         | 17/342 [00:00<00:03, 82.29frame/s]

   🔥 500:   8%|▊         | 26/342 [00:00<00:03, 85.24frame/s]

   🔥 500:  10%|█         | 35/342 [00:00<00:03, 87.05frame/s]

   🔥 500:  13%|█▎        | 44/342 [00:00<00:03, 87.59frame/s]

   🔥 500:  16%|█▌        | 54/342 [00:00<00:03, 88.80frame/s]

   🔥 500:  18%|█▊        | 63/342 [00:00<00:03, 87.71frame/s]

   🔥 500:  21%|██        | 72/342 [00:00<00:03, 88.33frame/s]

   🔥 500:  24%|██▎       | 81/342 [00:00<00:02, 87.64frame/s]

   🔥 500:  26%|██▋       | 90/342 [00:01<00:02, 87.19frame/s]

   🔥 500:  29%|██▉       | 100/342 [00:01<00:02, 88.97frame/s]

   🔥 500:  32%|███▏      | 110/342 [00:01<00:02, 92.06frame/s]

   🔥 500:  35%|███▌      | 121/342 [00:01<00:02, 95.52frame/s]

   🔥 500:  38%|███▊      | 131/342 [00:01<00:02, 96.74frame/s]

   🔥 500:  42%|████▏     | 142/342 [00:01<00:02, 99.10frame/s]

   🔥 500:  45%|████▍     | 153/342 [00:01<00:01, 99.32frame/s]

   🔥 500:  48%|████▊     | 163/342 [00:01<00:01, 94.67frame/s]

   🔥 500:  51%|█████     | 173/342 [00:01<00:01, 89.39frame/s]

   🔥 500:  54%|█████▎    | 183/342 [00:02<00:01, 86.32frame/s]

   🔥 500:  56%|█████▌    | 192/342 [00:02<00:01, 84.77frame/s]

   🔥 500:  59%|█████▉    | 201/342 [00:02<00:01, 83.18frame/s]

   🔥 500:  61%|██████▏   | 210/342 [00:02<00:01, 82.35frame/s]

   🔥 500:  64%|██████▍   | 219/342 [00:02<00:01, 81.37frame/s]

   🔥 500:  67%|██████▋   | 228/342 [00:02<00:01, 80.65frame/s]

   🔥 500:  69%|██████▉   | 237/342 [00:02<00:01, 80.05frame/s]

   🔥 500:  72%|███████▏  | 246/342 [00:02<00:01, 80.35frame/s]

   🔥 500:  75%|███████▍  | 255/342 [00:02<00:01, 80.98frame/s]

   🔥 500:  77%|███████▋  | 264/342 [00:03<00:00, 82.11frame/s]

   🔥 500:  80%|███████▉  | 273/342 [00:03<00:00, 81.41frame/s]

   🔥 500:  82%|████████▏ | 282/342 [00:03<00:00, 79.34frame/s]

   🔥 500:  85%|████████▍ | 290/342 [00:03<00:00, 79.18frame/s]

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   🔥 500:  90%|█████████ | 308/342 [00:03<00:00, 81.07frame/s]

   🔥 500:  93%|█████████▎| 318/342 [00:03<00:00, 83.24frame/s]

   🔥 500:  96%|█████████▌| 328/342 [00:03<00:00, 85.42frame/s]

   🔥 500:  99%|█████████▊| 337/342 [00:03<00:00, 85.95frame/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,  6.49s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  6.79s/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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