
🟢 Starting Calibration Pipeline

=== Processing MDC028_W009(6)_2025-03-21 ===

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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 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 358 stationary frames, 371 remain

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                                                                          [A[PASS2] Frame 0000677: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000678: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000679: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000681: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000682: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000683: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000892: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000893: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000894: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000895: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000897: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000898: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000899: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000900: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000901: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000902: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000903: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000679: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000682: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000894: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000899: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000902: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 10 frames fixed, 10 untrusted, 371 total

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                                                                           [AProcessing Rigs:  25%|██▌       | 1/4 [07:24<22:13, 444.63s/it][INFO] Filtered 647 stationary frames, 580 remain

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                                                                           [A[PASS2] Frame 0000546: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000547: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000548: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000549: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000550: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000557: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000558: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000559: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000560: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000563: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000564: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000565: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000566: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000570: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000573: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000574: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000583: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000584: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000585: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000586: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000587: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000616: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000655: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000657: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000658: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000660: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000661: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000662: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000663: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000665: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000666: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000667: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000668: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000669: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000677: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000678: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000679: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000681: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000682: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000683: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000684: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000685: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000686: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000696: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000697: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000698: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000699: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000701: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000702: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000703: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000704: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000706: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000707: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000708: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000709: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000710: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000711: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000712: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000713: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000714: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000715: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000716: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000717: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000548: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000549: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000559: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000565: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000585: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000655: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000657: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000662: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000667: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000668: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000685: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000698: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000703: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000708: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000709: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000712: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000716: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000680: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Summary: 40 frames fixed, 38 untrusted, 580 total

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                                                                            [AProcessing Rigs:  50%|█████     | 2/4 [18:12<18:47, 563.93s/it][INFO] Filtered 488 stationary frames, 587 remain

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                                                                           [A[PASS2] Frame 0000736: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000737: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000741: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000742: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000753: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000754: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000755: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000757: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000758: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000759: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000762: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000763: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000764: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000765: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000767: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000768: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000769: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000779: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000780: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000781: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000782: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000785: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000786: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000787: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000788: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000789: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000790: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000791: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000792: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000793: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000794: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000795: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000796: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000808: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000809: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000810: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000811: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000812: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000813: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000814: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000815: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000816: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000817: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000818: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000819: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000820: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000851: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000852: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000853: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000854: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000859: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000860: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000861: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000862: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000871: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000872: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000873: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000874: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000876: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000877: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000878: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000879: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000881: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000882: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000883: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000884: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000885: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000886: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000887: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000888: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000741: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000755: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000756: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000758: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000764: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000765: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000766: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000768: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000781: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000787: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000791: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000793: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000810: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000811: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000812: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000816: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000817: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000818: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000819: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000853: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000861: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000873: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000878: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000883: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000886: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000887: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000808: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000809: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000810: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000817: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000818: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000819: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 42 frames fixed, 37 untrusted, 587 total

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                                                                            [AProcessing Rigs:  75%|███████▌  | 3/4 [28:49<09:57, 597.65s/it][INFO] Filtered 254 stationary frames, 230 remain

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                                                                          [A[PASS2] Frame 0000205: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 0 untrusted, 230 total

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                                                                           [AProcessing Rigs: 100%|██████████| 4/4 [34:17<00:00, 491.19s/it]Processing Rigs: 100%|██████████| 4/4 [34:17<00:00, 514.46s/it]
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2025-03-21/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1768 frames from 4 rigs to /working/MDC028_W009(6)_2025-03-21/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2025-03-21/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 371
[📋] 2000R: 100 frames selected from 580
[📋] 2000L: 100 frames selected from 587
[📋] 1000: 100 frames selected from 230
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2025-03-21/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC028_W009(6)_2025-03-21/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                        Step 2: Calibration                        
-------------------------------------------------------------------

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:  10%|█         | 8/80 [00:00<00:00, 78.78it/s]Computing max image from bright frames:  21%|██▏       | 17/80 [00:00<00:00, 79.72it/s]Computing max image from bright frames:  32%|███▎      | 26/80 [00:00<00:00, 80.59it/s]Computing max image from bright frames:  44%|████▍     | 35/80 [00:00<00:00, 80.91it/s]Computing max image from bright frames:  55%|█████▌    | 44/80 [00:00<00:00, 81.12it/s]Computing max image from bright frames:  66%|██████▋   | 53/80 [00:00<00:00, 80.48it/s]Computing max image from bright frames:  78%|███████▊  | 62/80 [00:00<00:00, 81.05it/s]Computing max image from bright frames:  89%|████████▉ | 71/80 [00:00<00:00, 81.26it/s]Computing max image from bright frames: 100%|██████████| 80/80 [00:00<00:00, 81.24it/s]                                                                                       [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 22, black threshold: 66
[INFO] Found 4910 casing boundary candidate points
[RESULT] Detected casing: center=(1019, 766), radius=837, inliers=3246/4910
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:27,  3.64it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:27,  3.64it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:26,  3.64it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.09it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.09it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.09it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.09it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   6%|▌         | 6/100 [00:00<00:06, 14.72it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:06, 14.72it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   7%|▋         | 7/100 [00:00<00:06, 14.72it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:06, 14.72it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   9%|▉         | 9/100 [00:00<00:05, 17.40it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:05, 17.40it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:05, 17.40it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  11%|█         | 11/100 [00:00<00:05, 17.40it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  12%|█▏        | 12/100 [00:00<00:04, 18.73it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:04, 18.73it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 18.73it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 18.73it/s] Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  15%|█▌        | 15/100 [00:00<00:04, 19.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:00<00:04, 19.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 19.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:01<00:04, 19.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  18%|█▊        | 18/100 [00:01<00:04, 20.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:01<00:04, 20.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:03, 20.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:03, 20.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  21%|██        | 21/100 [00:01<00:03, 20.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:03, 20.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.012 | Best: {'fx': 455.556} (0.012):  22%|██▏       | 22/100 [00:01<00:03, 20.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:03, 20.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  24%|██▍       | 24/100 [00:01<00:04, 18.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:04, 18.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 476.768} (0.019):  25%|██▌       | 25/100 [00:01<00:04, 18.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 483.838} (0.023):  26%|██▌       | 26/100 [00:01<00:04, 18.26it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 483.838} (0.023):  27%|██▋       | 27/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.029 | Best: {'fx': 490.909} (0.029):  27%|██▋       | 27/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.039 | Best: {'fx': 497.98} (0.039):  28%|██▊       | 28/100 [00:01<00:03, 19.52it/s] Brute | Start: {'fx': 500.0} (0.041) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  29%|██▉       | 29/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  30%|███       | 30/100 [00:01<00:03, 19.34it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.056 | Best: {'fx': 512.121} (0.056):  30%|███       | 30/100 [00:01<00:03, 19.34it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.073 | Best: {'fx': 519.192} (0.073):  31%|███       | 31/100 [00:01<00:03, 19.34it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.073 | Best: {'fx': 519.192} (0.073):  32%|███▏      | 32/100 [00:01<00:04, 13.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.093 | Best: {'fx': 526.263} (0.093):  32%|███▏      | 32/100 [00:02<00:04, 13.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.100 | Best: {'fx': 533.333} (0.100):  33%|███▎      | 33/100 [00:02<00:04, 13.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.082 | Best: {'fx': 533.333} (0.100):  34%|███▍      | 34/100 [00:02<00:04, 13.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.064 | Best: {'fx': 533.333} (0.100):  35%|███▌      | 35/100 [00:02<00:04, 13.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.048 | Best: {'fx': 533.333} (0.100):  36%|███▌      | 36/100 [00:02<00:04, 13.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.048 | Best: {'fx': 533.333} (0.100):  37%|███▋      | 37/100 [00:02<00:03, 18.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.055 | Best: {'fx': 533.333} (0.100):  37%|███▋      | 37/100 [00:02<00:03, 18.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.038 | Best: {'fx': 533.333} (0.100):  38%|███▊      | 38/100 [00:02<00:03, 18.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.044 | Best: {'fx': 533.333} (0.100):  39%|███▉      | 39/100 [00:02<00:03, 18.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.044 | Best: {'fx': 533.333} (0.100):  40%|████      | 40/100 [00:02<00:03, 19.22it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.032 | Best: {'fx': 533.333} (0.100):  40%|████      | 40/100 [00:02<00:03, 19.22it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.036 | Best: {'fx': 533.333} (0.100):  41%|████      | 41/100 [00:02<00:03, 19.22it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.031 | Best: {'fx': 533.333} (0.100):  42%|████▏     | 42/100 [00:02<00:03, 19.22it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.041 | Best: {'fx': 533.333} (0.100):  43%|████▎     | 43/100 [00:02<00:02, 19.22it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.041 | Best: {'fx': 533.333} (0.100):  44%|████▍     | 44/100 [00:02<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.030 | Best: {'fx': 533.333} (0.100):  44%|████▍     | 44/100 [00:02<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.028 | Best: {'fx': 533.333} (0.100):  45%|████▌     | 45/100 [00:02<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.034 | Best: {'fx': 533.333} (0.100):  46%|████▌     | 46/100 [00:02<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.028 | Best: {'fx': 533.333} (0.100):  47%|████▋     | 47/100 [00:02<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.028 | Best: {'fx': 533.333} (0.100):  48%|████▊     | 48/100 [00:02<00:02, 24.17it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.100):  48%|████▊     | 48/100 [00:02<00:02, 24.17it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 533.333} (0.100):  49%|████▉     | 49/100 [00:02<00:02, 24.17it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 533.333} (0.100):  50%|█████     | 50/100 [00:02<00:02, 24.17it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.100):  51%|█████     | 51/100 [00:02<00:02, 24.17it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.100):  52%|█████▏    | 52/100 [00:02<00:01, 26.83it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.100):  52%|█████▏    | 52/100 [00:02<00:01, 26.83it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.100):  53%|█████▎    | 53/100 [00:02<00:01, 26.83it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.100):  54%|█████▍    | 54/100 [00:02<00:01, 26.83it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.100):  55%|█████▌    | 55/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.100):  55%|█████▌    | 55/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.100):  56%|█████▌    | 56/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.100):  57%|█████▋    | 57/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.100):  58%|█████▊    | 58/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.100):  59%|█████▉    | 59/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.100):  60%|██████    | 60/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  61%|██████    | 61/100 [00:02<00:01, 24.50it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  62%|██████▏   | 62/100 [00:02<00:01, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.100):  62%|██████▏   | 62/100 [00:02<00:01, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.100):  63%|██████▎   | 63/100 [00:02<00:01, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.100):  64%|██████▍   | 64/100 [00:02<00:01, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.100):  65%|██████▌   | 65/100 [00:03<00:01, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  66%|██████▌   | 66/100 [00:03<00:01, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.100):  67%|██████▋   | 67/100 [00:03<00:00, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  68%|██████▊   | 68/100 [00:03<00:00, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  69%|██████▉   | 69/100 [00:03<00:00, 33.15it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  70%|███████   | 70/100 [00:03<00:00, 37.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  70%|███████   | 70/100 [00:03<00:00, 37.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  71%|███████   | 71/100 [00:03<00:00, 37.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.100):  72%|███████▏  | 72/100 [00:03<00:00, 37.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  73%|███████▎  | 73/100 [00:03<00:00, 37.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  74%|███████▍  | 74/100 [00:03<00:00, 37.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  75%|███████▌  | 75/100 [00:03<00:00, 38.56it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  75%|███████▌  | 75/100 [00:03<00:00, 38.56it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  76%|███████▌  | 76/100 [00:03<00:00, 38.56it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  77%|███████▋  | 77/100 [00:03<00:00, 38.56it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  78%|███████▊  | 78/100 [00:03<00:00, 38.56it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  79%|███████▉  | 79/100 [00:03<00:00, 38.56it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  80%|████████  | 80/100 [00:03<00:00, 39.41it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  80%|████████  | 80/100 [00:03<00:00, 39.41it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  81%|████████  | 81/100 [00:03<00:00, 39.41it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  82%|████████▏ | 82/100 [00:03<00:00, 39.41it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  83%|████████▎ | 83/100 [00:03<00:00, 39.41it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  84%|████████▍ | 84/100 [00:03<00:00, 39.41it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  85%|████████▌ | 85/100 [00:03<00:00, 32.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  85%|████████▌ | 85/100 [00:03<00:00, 32.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  86%|████████▌ | 86/100 [00:03<00:00, 32.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.100):  87%|████████▋ | 87/100 [00:03<00:00, 32.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  88%|████████▊ | 88/100 [00:03<00:00, 32.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  89%|████████▉ | 89/100 [00:03<00:00, 29.93it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  89%|████████▉ | 89/100 [00:03<00:00, 29.93it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  90%|█████████ | 90/100 [00:03<00:00, 29.93it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  91%|█████████ | 91/100 [00:03<00:00, 29.93it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  92%|█████████▏| 92/100 [00:03<00:00, 29.93it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  93%|█████████▎| 93/100 [00:03<00:00, 29.93it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  94%|█████████▍| 94/100 [00:03<00:00, 32.47it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  94%|█████████▍| 94/100 [00:03<00:00, 32.47it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  95%|█████████▌| 95/100 [00:03<00:00, 32.47it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  96%|█████████▌| 96/100 [00:03<00:00, 32.47it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  97%|█████████▋| 97/100 [00:03<00:00, 32.47it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  98%|█████████▊| 98/100 [00:03<00:00, 33.07it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  98%|█████████▊| 98/100 [00:04<00:00, 33.07it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.100):  99%|█████████▉| 99/100 [00:04<00:00, 33.07it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.050 | Best: {'fx': 508.333} (0.050):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.050 | Best: {'fx': 508.333} (0.050):   2%|▏         | 1/50 [00:00<00:27,  1.78it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.051 | Best: {'fx': 509.354} (0.051):   2%|▏         | 1/50 [00:00<00:27,  1.78it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.053 | Best: {'fx': 510.374} (0.053):   4%|▍         | 2/50 [00:00<00:26,  1.78it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.053 | Best: {'fx': 510.374} (0.053):   6%|▌         | 3/50 [00:00<00:08,  5.26it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.055 | Best: {'fx': 511.395} (0.055):   6%|▌         | 3/50 [00:00<00:08,  5.26it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.057 | Best: {'fx': 512.415} (0.057):   8%|▊         | 4/50 [00:00<00:08,  5.26it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.059 | Best: {'fx': 513.435} (0.059):  10%|█         | 5/50 [00:00<00:08,  5.26it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.059 | Best: {'fx': 513.435} (0.059):  12%|█▏        | 6/50 [00:00<00:04,  9.45it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.060 | Best: {'fx': 514.456} (0.060):  12%|█▏        | 6/50 [00:00<00:04,  9.45it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.062 | Best: {'fx': 515.476} (0.062):  14%|█▍        | 7/50 [00:00<00:04,  9.45it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.062 | Best: {'fx': 515.476} (0.062):  16%|█▌        | 8/50 [00:00<00:03, 11.51it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.066 | Best: {'fx': 516.497} (0.066):  16%|█▌        | 8/50 [00:01<00:03, 11.51it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.068 | Best: {'fx': 517.517} (0.068):  18%|█▊        | 9/50 [00:01<00:03, 11.51it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.068 | Best: {'fx': 517.517} (0.068):  20%|██        | 10/50 [00:01<00:03, 12.81it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.071 | Best: {'fx': 518.537} (0.071):  20%|██        | 10/50 [00:01<00:03, 12.81it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.074 | Best: {'fx': 519.558} (0.074):  22%|██▏       | 11/50 [00:01<00:03, 12.81it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.074 | Best: {'fx': 519.558} (0.074):  24%|██▍       | 12/50 [00:01<00:02, 14.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.078 | Best: {'fx': 520.578} (0.078):  24%|██▍       | 12/50 [00:01<00:02, 14.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.081 | Best: {'fx': 521.599} (0.081):  26%|██▌       | 13/50 [00:01<00:02, 14.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.081 | Best: {'fx': 521.599} (0.081):  28%|██▊       | 14/50 [00:01<00:02, 15.41it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.086 | Best: {'fx': 522.619} (0.086):  28%|██▊       | 14/50 [00:01<00:02, 15.41it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.089 | Best: {'fx': 523.639} (0.089):  30%|███       | 15/50 [00:01<00:02, 15.41it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.089 | Best: {'fx': 523.639} (0.089):  32%|███▏      | 16/50 [00:01<00:02, 15.65it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.091 | Best: {'fx': 524.66} (0.091):  32%|███▏      | 16/50 [00:01<00:02, 15.65it/s] Brute | Start: {'fx': 533.333} (0.100) | Current: 0.092 | Best: {'fx': 525.68} (0.092):  34%|███▍      | 17/50 [00:01<00:02, 15.65it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.092 | Best: {'fx': 525.68} (0.092):  36%|███▌      | 18/50 [00:01<00:01, 16.77it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.094 | Best: {'fx': 526.701} (0.094):  36%|███▌      | 18/50 [00:01<00:01, 16.77it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.096 | Best: {'fx': 527.721} (0.096):  38%|███▊      | 19/50 [00:01<00:01, 16.77it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.096 | Best: {'fx': 527.721} (0.096):  40%|████      | 20/50 [00:01<00:01, 17.61it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.098 | Best: {'fx': 528.741} (0.098):  40%|████      | 20/50 [00:01<00:01, 17.61it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.101 | Best: {'fx': 529.762} (0.101):  42%|████▏     | 21/50 [00:01<00:01, 17.61it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.101 | Best: {'fx': 529.762} (0.101):  44%|████▍     | 22/50 [00:01<00:01, 15.34it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.100 | Best: {'fx': 529.762} (0.101):  44%|████▍     | 22/50 [00:01<00:01, 15.34it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.101 | Best: {'fx': 531.803} (0.101):  46%|████▌     | 23/50 [00:01<00:01, 15.34it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.102 | Best: {'fx': 532.823} (0.102):  48%|████▊     | 24/50 [00:01<00:01, 15.34it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.102 | Best: {'fx': 532.823} (0.102):  50%|█████     | 25/50 [00:01<00:01, 17.55it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.098 | Best: {'fx': 532.823} (0.102):  50%|█████     | 25/50 [00:01<00:01, 17.55it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.082 | Best: {'fx': 532.823} (0.102):  52%|█████▏    | 26/50 [00:02<00:01, 17.55it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.082 | Best: {'fx': 532.823} (0.102):  54%|█████▍    | 27/50 [00:02<00:01, 17.68it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.080 | Best: {'fx': 532.823} (0.102):  54%|█████▍    | 27/50 [00:02<00:01, 17.68it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.077 | Best: {'fx': 532.823} (0.102):  56%|█████▌    | 28/50 [00:02<00:01, 17.68it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.092 | Best: {'fx': 532.823} (0.102):  58%|█████▊    | 29/50 [00:02<00:01, 17.68it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.090 | Best: {'fx': 532.823} (0.102):  60%|██████    | 30/50 [00:02<00:01, 17.68it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.097 | Best: {'fx': 532.823} (0.102):  62%|██████▏   | 31/50 [00:02<00:01, 17.68it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.097 | Best: {'fx': 532.823} (0.102):  64%|██████▍   | 32/50 [00:02<00:00, 24.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.074 | Best: {'fx': 532.823} (0.102):  64%|██████▍   | 32/50 [00:02<00:00, 24.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.069 | Best: {'fx': 532.823} (0.102):  66%|██████▌   | 33/50 [00:02<00:00, 24.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.086 | Best: {'fx': 532.823} (0.102):  68%|██████▊   | 34/50 [00:02<00:00, 24.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.072 | Best: {'fx': 532.823} (0.102):  70%|███████   | 35/50 [00:02<00:00, 24.35it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.072 | Best: {'fx': 532.823} (0.102):  72%|███████▏  | 36/50 [00:02<00:00, 27.56it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.066 | Best: {'fx': 532.823} (0.102):  72%|███████▏  | 36/50 [00:02<00:00, 27.56it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.085 | Best: {'fx': 532.823} (0.102):  74%|███████▍  | 37/50 [00:02<00:00, 27.56it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.064 | Best: {'fx': 532.823} (0.102):  76%|███████▌  | 38/50 [00:02<00:00, 27.56it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.064 | Best: {'fx': 532.823} (0.102):  78%|███████▊  | 39/50 [00:02<00:00, 27.28it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.063 | Best: {'fx': 532.823} (0.102):  78%|███████▊  | 39/50 [00:02<00:00, 27.28it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.061 | Best: {'fx': 532.823} (0.102):  80%|████████  | 40/50 [00:02<00:00, 27.28it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.059 | Best: {'fx': 532.823} (0.102):  82%|████████▏ | 41/50 [00:02<00:00, 27.28it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.059 | Best: {'fx': 532.823} (0.102):  84%|████████▍ | 42/50 [00:02<00:00, 27.28it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.056 | Best: {'fx': 532.823} (0.102):  86%|████████▌ | 43/50 [00:02<00:00, 27.28it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.056 | Best: {'fx': 532.823} (0.102):  88%|████████▊ | 44/50 [00:02<00:00, 30.59it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.057 | Best: {'fx': 532.823} (0.102):  88%|████████▊ | 44/50 [00:02<00:00, 30.59it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.055 | Best: {'fx': 532.823} (0.102):  90%|█████████ | 45/50 [00:02<00:00, 30.59it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.054 | Best: {'fx': 532.823} (0.102):  92%|█████████▏| 46/50 [00:02<00:00, 30.59it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.052 | Best: {'fx': 532.823} (0.102):  94%|█████████▍| 47/50 [00:02<00:00, 30.59it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.052 | Best: {'fx': 532.823} (0.102):  96%|█████████▌| 48/50 [00:02<00:00, 31.71it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.051 | Best: {'fx': 532.823} (0.102):  96%|█████████▌| 48/50 [00:02<00:00, 31.71it/s]Brute | Start: {'fx': 533.333} (0.100) | Current: 0.051 | Best: {'fx': 532.823} (0.102):  98%|█████████▊| 49/50 [00:02<00:00, 31.71it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 532.8231292517007
    px: 1019.0
    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): 80 frames will be used for optimization
  Rigs: 1500(1500mm):20/20, 2000R(2000mm):20/20, 2000L(2000mm):20/20, 1000(1000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 8.4444: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 8.4444, picklable_objective(x0) = 8.4481
Best score: 8.4481: : 0iter [00:02, ?iter/s] Best score: 8.4481: : 1iter [00:02,  2.02s/iter]Best score: 8.4481: : 1iter [00:02,  2.02s/iter]Best score: 8.4481: : 2iter [00:02,  1.08iter/s]Best score: 8.4481: : 2iter [00:02,  1.08iter/s]Best score: 8.4481: : 3iter [00:02,  1.73iter/s]Best score: 8.4481: : 3iter [00:02,  1.73iter/s]Best score: 8.4481: : 4iter [00:02,  2.42iter/s]Best score: 8.4481: : 4iter [00:02,  2.42iter/s]Best score: 8.4481: : 5iter [00:02,  3.10iter/s]Best score: 8.4481: : 5iter [00:02,  3.10iter/s]Best score: 8.4481: : 6iter [00:02,  3.74iter/s]Best score: 8.4481: : 6iter [00:02,  3.74iter/s]Best score: 8.4481: : 7iter [00:02,  4.29iter/s]Best score: 8.4481: : 7iter [00:03,  4.29iter/s]Best score: 8.4481: : 8iter [00:03,  4.76iter/s]Best score: 8.4481: : 8iter [00:03,  4.76iter/s]Best score: 8.4481: : 9iter [00:03,  5.13iter/s]Best score: 8.4481: : 9iter [00:03,  5.13iter/s]Best score: 8.4481: : 10iter [00:03,  5.29iter/s]Best score: 8.4481: : 10iter [00:03,  5.29iter/s]Best score: 8.4481: : 11iter [00:03,  5.53iter/s]Best score: 8.4481: : 11iter [00:03,  5.53iter/s]Best score: 8.4481: : 12iter [00:03,  5.72iter/s]Best score: 8.4481: : 12iter [00:03,  5.72iter/s]Best score: 8.4481: : 13iter [00:03,  5.85iter/s]Best score: 8.4481: : 13iter [00:04,  5.85iter/s]Best score: 8.4481: : 14iter [00:04,  5.95iter/s]Best score: 8.4481: : 14iter [00:04,  5.95iter/s]Best score: 8.4481: : 15iter [00:04,  6.02iter/s]Best score: 8.4481: : 15iter [00:04,  6.02iter/s]Best score: 8.4481: : 16iter [00:04,  6.07iter/s]Best score: 8.4481: : 16iter [00:04,  6.07iter/s]Best score: 8.4481: : 17iter [00:04,  6.10iter/s]Best score: 8.4481: : 17iter [00:04,  6.10iter/s]Best score: 8.4481: : 18iter [00:04,  6.13iter/s]Best score: 8.4481: : 18iter [00:04,  6.13iter/s]Best score: 8.4481: : 19iter [00:04,  6.15iter/s]Best score: 8.4481: : 19iter [00:05,  6.15iter/s]Best score: 8.4481: : 20iter [00:05,  5.99iter/s]Best score: 8.4481: : 20iter [00:05,  5.99iter/s]Best score: 8.4481: : 21iter [00:05,  6.06iter/s]Best score: 8.4481: : 21iter [00:05,  6.06iter/s]Best score: 8.4481: : 22iter [00:05,  6.10iter/s]Best score: 8.4481: : 22iter [00:05,  6.10iter/s]Best score: 8.4481: : 23iter [00:05,  6.11iter/s]Best score: 8.4481: : 23iter [00:05,  6.11iter/s]Best score: 8.4481: : 24iter [00:05,  6.15iter/s]Best score: 8.4481: : 24iter [00:05,  6.15iter/s]Best score: 8.4481: : 25iter [00:05,  6.17iter/s]Best score: 8.4481: : 25iter [00:06,  6.17iter/s]Best score: 8.4481: : 26iter [00:06,  6.17iter/s]Best score: 8.4481: : 26iter [00:06,  6.17iter/s]Best score: 8.4481: : 27iter [00:06,  6.19iter/s]Best score: 8.4481: : 27iter [00:06,  6.19iter/s]Best score: 8.4481: : 28iter [00:06,  6.19iter/s]Best score: 8.4481: : 28iter [00:06,  6.19iter/s]Best score: 8.4481: : 29iter [00:06,  6.20iter/s]Best score: 8.4481: : 29iter [00:06,  6.20iter/s]Best score: 8.4481: : 30iter [00:06,  6.04iter/s]Best score: 8.4481: : 30iter [00:06,  6.04iter/s]Best score: 8.4481: : 31iter [00:06,  6.10iter/s]Best score: 8.4481: : 31iter [00:07,  6.10iter/s]Best score: 8.4481: : 32iter [00:07,  6.12iter/s]Best score: 8.4481: : 32iter [00:07,  6.12iter/s]Best score: 8.4481: : 33iter [00:07,  6.16iter/s]Best score: 8.4481: : 33iter [00:07,  6.16iter/s]Best score: 8.4481: : 34iter [00:07,  6.18iter/s]Best score: 8.4481: : 34iter [00:07,  6.18iter/s]Best score: 8.4481: : 35iter [00:07,  6.19iter/s]Best score: 8.4481: : 35iter [00:07,  6.19iter/s]Best score: 8.4481: : 36iter [00:07,  6.20iter/s]Best score: 8.4481: : 36iter [00:07,  6.20iter/s]Best score: 8.4481: : 37iter [00:07,  6.20iter/s]Best score: 8.4481: : 37iter [00:08,  6.20iter/s]Best score: 8.4481: : 38iter [00:08,  6.21iter/s]Best score: 8.4481: : 38iter [00:08,  6.21iter/s]Best score: 8.4481: : 39iter [00:08,  6.21iter/s]Best score: 4.3173: : 39iter [00:08,  6.21iter/s]Best score: 4.3173: : 40iter [00:08,  6.03iter/s]Best score: 4.3173: : 40iter [00:08,  6.03iter/s]Best score: 4.3173: : 41iter [00:08,  6.08iter/s]Best score: 4.3173: : 41iter [00:08,  6.08iter/s]Best score: 4.3173: : 42iter [00:08,  6.10iter/s]Best score: 4.3173: : 42iter [00:08,  6.10iter/s]Best score: 4.3173: : 43iter [00:08,  6.14iter/s]Best score: 4.3173: : 43iter [00:09,  6.14iter/s]Best score: 4.3173: : 44iter [00:09,  6.16iter/s]Best score: 4.3173: : 44iter [00:09,  6.16iter/s]Best score: 4.3173: : 45iter [00:09,  6.19iter/s]Best score: 4.3173: : 45iter [00:09,  6.19iter/s]Best score: 4.3173: : 46iter [00:09,  6.22iter/s]Best score: 4.3173: : 46iter [00:09,  6.22iter/s]Best score: 4.3173: : 47iter [00:09,  6.21iter/s]Best score: 4.3173: : 47iter [00:09,  6.21iter/s]Best score: 4.3173: : 48iter [00:09,  6.20iter/s]Best score: 4.3173: : 48iter [00:09,  6.20iter/s]Best score: 4.3173: : 49iter [00:09,  6.19iter/s]
[ITER 50] Best: 4.1288
[PARAMS] Current best parameters:
  fx: 519.9293
  fy: 518.4983
  px: 1023.3341
  py: 771.0846
  k1: -0.03871515
  k2: 0.04191121
  k3: -0.01473951
  k4: 0.00260798
  p1: 0.00121451
  p2: 0.00868430
  p3: 0.00335201
  p4: -0.00634703
  scale: 3.1720
  rotation: 0.6975
Best score: 4.1288: : 49iter [00:09,  6.19iter/s]Best score: 4.1288: : 50iter [00:09,  6.01iter/s]Best score: 4.1288: : 50iter [00:10,  6.01iter/s]Best score: 4.1288: : 51iter [00:10,  6.07iter/s]Best score: 4.1288: : 51iter [00:10,  6.07iter/s]Best score: 4.1288: : 52iter [00:10,  6.11iter/s]Best score: 4.1288: : 52iter [00:10,  6.11iter/s]Best score: 4.1288: : 53iter [00:10,  6.12iter/s]Best score: 4.1288: : 53iter [00:10,  6.12iter/s]Best score: 4.1288: : 54iter [00:10,  6.14iter/s]Best score: 4.1288: : 54iter [00:10,  6.14iter/s]Best score: 4.1288: : 55iter [00:10,  6.16iter/s]Best score: 4.1288: : 55iter [00:10,  6.16iter/s]Best score: 4.1288: : 56iter [00:10,  6.18iter/s]Best score: 4.1288: : 56iter [00:11,  6.18iter/s]Best score: 4.1288: : 57iter [00:11,  6.18iter/s]Best score: 4.1288: : 57iter [00:11,  6.18iter/s]Best score: 4.1288: : 58iter [00:11,  6.18iter/s]Best score: 4.1288: : 58iter [00:11,  6.18iter/s]Best score: 4.1288: : 59iter [00:11,  6.20iter/s]Best score: 3.5061: : 59iter [00:11,  6.20iter/s]Best score: 3.5061: : 60iter [00:11,  6.02iter/s]Best score: 3.5061: : 60iter [00:11,  6.02iter/s]Best score: 3.5061: : 61iter [00:11,  6.09iter/s]Best score: 3.5061: : 61iter [00:11,  6.09iter/s]Best score: 3.5061: : 62iter [00:11,  6.13iter/s]Best score: 3.5061: : 62iter [00:12,  6.13iter/s]Best score: 3.5061: : 63iter [00:12,  6.16iter/s]Best score: 3.5061: : 63iter [00:12,  6.16iter/s]Best score: 3.5061: : 64iter [00:12,  6.18iter/s]Best score: 3.5061: : 64iter [00:12,  6.18iter/s]Best score: 3.5061: : 65iter [00:12,  6.18iter/s]Best score: 3.5061: : 65iter [00:12,  6.18iter/s]Best score: 3.5061: : 66iter [00:12,  6.19iter/s]Best score: 3.5061: : 66iter [00:12,  6.19iter/s]Best score: 3.5061: : 67iter [00:12,  6.22iter/s]Best score: 3.5061: : 67iter [00:12,  6.22iter/s]Best score: 3.5061: : 68iter [00:12,  6.22iter/s]Best score: 3.5061: : 68iter [00:13,  6.22iter/s]Best score: 3.5061: : 69iter [00:13,  6.23iter/s]Best score: 1.6748: : 69iter [00:13,  6.23iter/s]Best score: 1.6748: : 70iter [00:13,  6.04iter/s]Best score: 1.6748: : 70iter [00:13,  6.04iter/s]Best score: 1.6748: : 71iter [00:13,  6.07iter/s]Best score: 1.6748: : 71iter [00:13,  6.07iter/s]Best score: 1.6748: : 72iter [00:13,  6.12iter/s]Best score: 1.6748: : 72iter [00:13,  6.12iter/s]Best score: 1.6748: : 73iter [00:13,  6.16iter/s]Best score: 1.6748: : 73iter [00:13,  6.16iter/s]Best score: 1.6748: : 74iter [00:13,  6.17iter/s]Best score: 1.6748: : 74iter [00:14,  6.17iter/s]Best score: 1.6748: : 75iter [00:14,  6.19iter/s]Best score: 1.6748: : 75iter [00:14,  6.19iter/s]Best score: 1.6748: : 76iter [00:14,  6.21iter/s]Best score: 1.6748: : 76iter [00:14,  6.21iter/s]Best score: 1.6748: : 77iter [00:14,  6.21iter/s]Best score: 1.6748: : 77iter [00:14,  6.21iter/s]Best score: 1.6748: : 78iter [00:14,  6.21iter/s]Best score: 1.6748: : 78iter [00:14,  6.21iter/s]Best score: 1.6748: : 79iter [00:14,  6.19iter/s]Best score: 1.6748: : 79iter [00:14,  6.19iter/s]Best score: 1.6748: : 80iter [00:14,  6.00iter/s]Best score: 1.6748: : 80iter [00:15,  6.00iter/s]Best score: 1.6748: : 81iter [00:15,  6.07iter/s]Best score: 1.6748: : 81iter [00:15,  6.07iter/s]Best score: 1.6748: : 82iter [00:15,  6.12iter/s]Best score: 1.6748: : 82iter [00:15,  6.12iter/s]Best score: 1.6748: : 83iter [00:15,  6.15iter/s]Best score: 1.6748: : 83iter [00:15,  6.15iter/s]Best score: 1.6748: : 84iter [00:15,  6.16iter/s]Best score: 1.6748: : 84iter [00:15,  6.16iter/s]Best score: 1.6748: : 85iter [00:15,  6.20iter/s]Best score: 1.6748: : 85iter [00:15,  6.20iter/s]Best score: 1.6748: : 86iter [00:15,  6.20iter/s]Best score: 1.6748: : 86iter [00:15,  6.20iter/s]Best score: 1.6748: : 87iter [00:15,  6.21iter/s]Best score: 1.6748: : 87iter [00:16,  6.21iter/s]Best score: 1.6748: : 88iter [00:16,  6.22iter/s]Best score: 1.6748: : 88iter [00:16,  6.22iter/s]Best score: 1.6748: : 89iter [00:16,  6.23iter/s]Best score: 0.6693: : 89iter [00:16,  6.23iter/s]Best score: 0.6693: : 90iter [00:16,  6.02iter/s]Best score: 0.6693: : 90iter [00:16,  6.02iter/s]Best score: 0.6693: : 91iter [00:16,  6.07iter/s]Best score: 0.6693: : 91iter [00:16,  6.07iter/s]Best score: 0.6693: : 92iter [00:16,  6.12iter/s]Best score: 0.6693: : 92iter [00:16,  6.12iter/s]Best score: 0.6693: : 93iter [00:16,  6.15iter/s]Best score: 0.6693: : 93iter [00:17,  6.15iter/s]Best score: 0.6693: : 94iter [00:17,  6.17iter/s]Best score: 0.6693: : 94iter [00:17,  6.17iter/s]Best score: 0.6693: : 95iter [00:17,  6.19iter/s]Best score: 0.6693: : 95iter [00:17,  6.19iter/s]Best score: 0.6693: : 96iter [00:17,  6.20iter/s]Best score: 0.6693: : 96iter [00:17,  6.20iter/s]Best score: 0.6693: : 97iter [00:17,  6.21iter/s]Best score: 0.6693: : 97iter [00:17,  6.21iter/s]Best score: 0.6693: : 98iter [00:17,  6.21iter/s]Best score: 0.6693: : 98iter [00:17,  6.21iter/s]Best score: 0.6693: : 99iter [00:17,  6.22iter/s]
[ITER 100] Best: 0.4506
[PARAMS] Current best parameters:
  fx: 521.8712
  fy: 521.3335
  px: 1024.0498
  py: 768.9368
  k1: -0.02666666
  k2: 0.05089445
  k3: -0.02419244
  k4: 0.00291039
  p1: -0.00109653
  p2: 0.00959152
  p3: 0.00248519
  p4: -0.00226872
  scale: 3.0592
  rotation: 0.5253
Best score: 0.4506: : 99iter [00:18,  6.22iter/s]Best score: 0.4506: : 100iter [00:18,  6.03iter/s]Best score: 0.4506: : 100iter [00:18,  6.03iter/s]Best score: 0.4506: : 101iter [00:18,  6.09iter/s]Best score: 0.4506: : 101iter [00:18,  6.09iter/s]Best score: 0.4506: : 102iter [00:18,  6.13iter/s]Best score: 0.4506: : 102iter [00:18,  6.13iter/s]Best score: 0.4506: : 103iter [00:18,  6.17iter/s]Best score: 0.4506: : 103iter [00:18,  6.17iter/s]Best score: 0.4506: : 104iter [00:18,  6.19iter/s]Best score: 0.4506: : 104iter [00:18,  6.19iter/s]Best score: 0.4506: : 105iter [00:18,  6.20iter/s]Best score: 0.4506: : 105iter [00:19,  6.20iter/s]Best score: 0.4506: : 106iter [00:19,  6.21iter/s]Best score: 0.4506: : 106iter [00:19,  6.21iter/s]Best score: 0.4506: : 107iter [00:19,  6.22iter/s]Best score: 0.4506: : 107iter [00:19,  6.22iter/s]Best score: 0.4506: : 108iter [00:19,  6.22iter/s]Best score: 0.4506: : 108iter [00:19,  6.22iter/s]Best score: 0.4506: : 109iter [00:19,  6.22iter/s]Best score: 0.4506: : 109iter [00:19,  6.22iter/s]Best score: 0.4506: : 110iter [00:19,  6.03iter/s]Best score: 0.4506: : 110iter [00:19,  6.03iter/s]Best score: 0.4506: : 111iter [00:19,  6.09iter/s]Best score: 0.4506: : 111iter [00:20,  6.09iter/s]Best score: 0.4506: : 112iter [00:20,  6.12iter/s]Best score: 0.4506: : 112iter [00:20,  6.12iter/s]Best score: 0.4506: : 113iter [00:20,  6.15iter/s]Best score: 0.4506: : 113iter [00:20,  6.15iter/s]Best score: 0.4506: : 114iter [00:20,  6.18iter/s]Best score: 0.4506: : 114iter [00:20,  6.18iter/s]Best score: 0.4506: : 115iter [00:20,  6.21iter/s]Best score: 0.4506: : 115iter [00:20,  6.21iter/s]Best score: 0.4506: : 116iter [00:20,  6.22iter/s]Best score: 0.4506: : 116iter [00:20,  6.22iter/s]Best score: 0.4506: : 117iter [00:20,  6.22iter/s]Best score: 0.4506: : 117iter [00:21,  6.22iter/s]Best score: 0.4506: : 118iter [00:21,  6.22iter/s]Best score: 0.4506: : 118iter [00:21,  6.22iter/s]Best score: 0.4506: : 119iter [00:21,  6.20iter/s]Best score: 0.4207: : 119iter [00:21,  6.20iter/s]Best score: 0.4207: : 120iter [00:21,  6.02iter/s]Best score: 0.4207: : 120iter [00:21,  6.02iter/s]Best score: 0.4207: : 121iter [00:21,  6.08iter/s]Best score: 0.4207: : 121iter [00:21,  6.08iter/s]Best score: 0.4207: : 122iter [00:21,  6.11iter/s]Best score: 0.4207: : 122iter [00:21,  6.11iter/s]Best score: 0.4207: : 123iter [00:21,  6.15iter/s]Best score: 0.4207: : 123iter [00:22,  6.15iter/s]Best score: 0.4207: : 124iter [00:22,  6.18iter/s]Best score: 0.4207: : 124iter [00:22,  6.18iter/s]Best score: 0.4207: : 125iter [00:22,  6.20iter/s]Best score: 0.4207: : 125iter [00:22,  6.20iter/s]Best score: 0.4207: : 126iter [00:22,  6.21iter/s]Best score: 0.4207: : 126iter [00:22,  6.21iter/s]Best score: 0.4207: : 127iter [00:22,  6.22iter/s]Best score: 0.4207: : 127iter [00:22,  6.22iter/s]Best score: 0.4207: : 128iter [00:22,  6.23iter/s]Best score: 0.4207: : 128iter [00:22,  6.23iter/s]Best score: 0.4207: : 129iter [00:22,  6.23iter/s]Best score: 0.4074: : 129iter [00:22,  6.23iter/s]Best score: 0.4074: : 130iter [00:22,  6.05iter/s]Best score: 0.4074: : 130iter [00:23,  6.05iter/s]Best score: 0.4074: : 131iter [00:23,  6.10iter/s]Best score: 0.4074: : 131iter [00:23,  6.10iter/s]Best score: 0.4074: : 132iter [00:23,  6.13iter/s]Best score: 0.4074: : 132iter [00:23,  6.13iter/s]Best score: 0.4074: : 133iter [00:23,  6.13iter/s]Best score: 0.4074: : 133iter [00:23,  6.13iter/s]Best score: 0.4074: : 134iter [00:23,  6.15iter/s]Best score: 0.4074: : 134iter [00:23,  6.15iter/s]Best score: 0.4074: : 135iter [00:23,  6.16iter/s]Best score: 0.4074: : 135iter [00:23,  6.16iter/s]Best score: 0.4074: : 136iter [00:23,  6.17iter/s]Best score: 0.4074: : 136iter [00:24,  6.17iter/s]Best score: 0.4074: : 137iter [00:24,  6.18iter/s]Best score: 0.4074: : 137iter [00:24,  6.18iter/s]Best score: 0.4074: : 138iter [00:24,  6.18iter/s]Best score: 0.4074: : 138iter [00:24,  6.18iter/s]Best score: 0.4074: : 139iter [00:24,  6.19iter/s]Best score: 0.3504: : 139iter [00:24,  6.19iter/s]Best score: 0.3504: : 140iter [00:24,  6.00iter/s]Best score: 0.3504: : 140iter [00:24,  6.00iter/s]Best score: 0.3504: : 141iter [00:24,  6.06iter/s]Best score: 0.3504: : 141iter [00:24,  6.06iter/s]Best score: 0.3504: : 142iter [00:24,  6.12iter/s]Best score: 0.3504: : 142iter [00:25,  6.12iter/s]Best score: 0.3504: : 143iter [00:25,  6.14iter/s]Best score: 0.3504: : 143iter [00:25,  6.14iter/s]Best score: 0.3504: : 144iter [00:25,  6.16iter/s]Best score: 0.3504: : 144iter [00:25,  6.16iter/s]Best score: 0.3504: : 145iter [00:25,  6.18iter/s]Best score: 0.3504: : 145iter [00:25,  6.18iter/s]Best score: 0.3504: : 146iter [00:25,  6.19iter/s]Best score: 0.3504: : 146iter [00:25,  6.19iter/s]Best score: 0.3504: : 147iter [00:25,  6.19iter/s]Best score: 0.3504: : 147iter [00:25,  6.19iter/s]Best score: 0.3504: : 148iter [00:25,  6.21iter/s]Best score: 0.3504: : 148iter [00:26,  6.21iter/s]Best score: 0.3504: : 149iter [00:26,  6.22iter/s]
[ITER 150] Best: 0.3347
[PARAMS] Current best parameters:
  fx: 537.5631
  fy: 536.7235
  px: 1022.4922
  py: 769.7624
  k1: -0.03644976
  k2: 0.05512000
  k3: -0.03575301
  k4: 0.00766220
  p1: 0.00024433
  p2: 0.00682392
  p3: 0.00106687
  p4: 0.00014190
  scale: 3.0407
  rotation: 0.4868
Best score: 0.3347: : 149iter [00:26,  6.22iter/s]Best score: 0.3347: : 150iter [00:26,  6.02iter/s]Best score: 0.3347: : 150iter [00:26,  6.02iter/s]Best score: 0.3347: : 151iter [00:26,  6.09iter/s]Best score: 0.3347: : 151iter [00:26,  6.09iter/s]Best score: 0.3347: : 152iter [00:26,  6.14iter/s]Best score: 0.3347: : 152iter [00:26,  6.14iter/s]Best score: 0.3347: : 153iter [00:26,  6.16iter/s]Best score: 0.3347: : 153iter [00:26,  6.16iter/s]Best score: 0.3347: : 154iter [00:26,  6.19iter/s]Best score: 0.3347: : 154iter [00:27,  6.19iter/s]Best score: 0.3347: : 155iter [00:27,  6.21iter/s]Best score: 0.3347: : 155iter [00:27,  6.21iter/s]Best score: 0.3347: : 156iter [00:27,  6.22iter/s]Best score: 0.3347: : 156iter [00:27,  6.22iter/s]Best score: 0.3347: : 157iter [00:27,  6.22iter/s]Best score: 0.3347: : 157iter [00:27,  6.22iter/s]Best score: 0.3347: : 158iter [00:27,  6.22iter/s]Best score: 0.3347: : 158iter [00:27,  6.22iter/s]Best score: 0.3347: : 159iter [00:27,  6.22iter/s]Best score: 0.2987: : 159iter [00:27,  6.22iter/s]Best score: 0.2987: : 160iter [00:27,  6.01iter/s]Best score: 0.2987: : 160iter [00:28,  6.01iter/s]Best score: 0.2987: : 161iter [00:28,  6.07iter/s]Best score: 0.2987: : 161iter [00:28,  6.07iter/s]Best score: 0.2987: : 162iter [00:28,  6.12iter/s]Best score: 0.2987: : 162iter [00:28,  6.12iter/s]Best score: 0.2987: : 163iter [00:28,  6.15iter/s]Best score: 0.2987: : 163iter [00:28,  6.15iter/s]Best score: 0.2987: : 164iter [00:28,  6.18iter/s]Best score: 0.2987: : 164iter [00:28,  6.18iter/s]Best score: 0.2987: : 165iter [00:28,  6.18iter/s]Best score: 0.2987: : 165iter [00:28,  6.18iter/s]Best score: 0.2987: : 166iter [00:28,  6.18iter/s]Best score: 0.2987: : 166iter [00:28,  6.18iter/s]Best score: 0.2987: : 167iter [00:28,  6.18iter/s]Best score: 0.2987: : 167iter [00:29,  6.18iter/s]Best score: 0.2987: : 168iter [00:29,  6.20iter/s]Best score: 0.2987: : 168iter [00:29,  6.20iter/s]Best score: 0.2987: : 169iter [00:29,  6.20iter/s]Best score: 0.2775: : 169iter [00:29,  6.20iter/s]Best score: 0.2775: : 170iter [00:29,  6.01iter/s]Best score: 0.2775: : 170iter [00:29,  6.01iter/s]Best score: 0.2775: : 171iter [00:29,  6.04iter/s]Best score: 0.2775: : 171iter [00:29,  6.04iter/s]Best score: 0.2775: : 172iter [00:29,  6.07iter/s]Best score: 0.2775: : 172iter [00:29,  6.07iter/s]Best score: 0.2775: : 173iter [00:29,  6.09iter/s]Best score: 0.2775: : 173iter [00:30,  6.09iter/s]Best score: 0.2775: : 174iter [00:30,  6.12iter/s]Best score: 0.2775: : 174iter [00:30,  6.12iter/s]Best score: 0.2775: : 175iter [00:30,  6.13iter/s]Best score: 0.2775: : 175iter [00:30,  6.13iter/s]Best score: 0.2775: : 176iter [00:30,  6.15iter/s]Best score: 0.2775: : 176iter [00:30,  6.15iter/s]Best score: 0.2775: : 177iter [00:30,  6.16iter/s]Best score: 0.2775: : 177iter [00:30,  6.16iter/s]Best score: 0.2775: : 178iter [00:30,  6.16iter/s]Best score: 0.2775: : 178iter [00:30,  6.16iter/s]Best score: 0.2775: : 179iter [00:30,  6.17iter/s]Best score: 0.2577: : 179iter [00:31,  6.17iter/s]Best score: 0.2577: : 180iter [00:31,  6.00iter/s]Best score: 0.2577: : 180iter [00:31,  6.00iter/s]Best score: 0.2577: : 181iter [00:31,  6.06iter/s]Best score: 0.2577: : 181iter [00:31,  6.06iter/s]Best score: 0.2577: : 182iter [00:31,  6.10iter/s]Best score: 0.2577: : 182iter [00:31,  6.10iter/s]Best score: 0.2577: : 183iter [00:31,  6.14iter/s]Best score: 0.2577: : 183iter [00:31,  6.14iter/s]Best score: 0.2577: : 184iter [00:31,  6.16iter/s]Best score: 0.2577: : 184iter [00:31,  6.16iter/s]Best score: 0.2577: : 185iter [00:31,  6.17iter/s]Best score: 0.2577: : 185iter [00:32,  6.17iter/s]Best score: 0.2577: : 186iter [00:32,  6.19iter/s]Best score: 0.2577: : 186iter [00:32,  6.19iter/s]Best score: 0.2577: : 187iter [00:32,  6.19iter/s]Best score: 0.2577: : 187iter [00:32,  6.19iter/s]Best score: 0.2577: : 188iter [00:32,  6.21iter/s]Best score: 0.2577: : 188iter [00:32,  6.21iter/s]Best score: 0.2577: : 189iter [00:32,  6.21iter/s]Best score: 0.2536: : 189iter [00:32,  6.21iter/s]Best score: 0.2536: : 190iter [00:32,  6.02iter/s]Best score: 0.2536: : 190iter [00:32,  6.02iter/s]Best score: 0.2536: : 191iter [00:32,  6.07iter/s]Best score: 0.2536: : 191iter [00:33,  6.07iter/s]Best score: 0.2536: : 192iter [00:33,  6.12iter/s]Best score: 0.2536: : 192iter [00:33,  6.12iter/s]Best score: 0.2536: : 193iter [00:33,  6.15iter/s]Best score: 0.2536: : 193iter [00:33,  6.15iter/s]Best score: 0.2536: : 194iter [00:33,  6.16iter/s]Best score: 0.2536: : 194iter [00:33,  6.16iter/s]Best score: 0.2536: : 195iter [00:33,  6.18iter/s]Best score: 0.2536: : 195iter [00:33,  6.18iter/s]Best score: 0.2536: : 196iter [00:33,  6.19iter/s]Best score: 0.2536: : 196iter [00:33,  6.19iter/s]Best score: 0.2536: : 197iter [00:33,  6.20iter/s]Best score: 0.2536: : 197iter [00:34,  6.20iter/s]Best score: 0.2536: : 198iter [00:34,  6.20iter/s]Best score: 0.2536: : 198iter [00:34,  6.20iter/s]Best score: 0.2536: : 199iter [00:34,  6.19iter/s]
[ITER 200] Best: 0.2419
[PARAMS] Current best parameters:
  fx: 537.0900
  fy: 536.5718
  px: 1021.0360
  py: 772.4460
  k1: -0.01309313
  k2: 0.00704841
  k3: -0.00088640
  k4: -0.00089793
  p1: 0.00589866
  p2: 0.00412485
  p3: 0.00082555
  p4: -0.00050442
  scale: 3.0322
  rotation: 0.4938
Best score: 0.2419: : 199iter [00:34,  6.19iter/s]Best score: 0.2419: : 200iter [00:34,  6.01iter/s]Best score: 0.2419: : 200iter [00:34,  6.01iter/s]Best score: 0.2419: : 201iter [00:34,  6.08iter/s]Best score: 0.2419: : 201iter [00:34,  6.08iter/s]Best score: 0.2419: : 202iter [00:34,  6.12iter/s]Best score: 0.2419: : 202iter [00:34,  6.12iter/s]Best score: 0.2419: : 203iter [00:34,  6.15iter/s]Best score: 0.2419: : 203iter [00:35,  6.15iter/s]Best score: 0.2419: : 204iter [00:35,  6.17iter/s]Best score: 0.2419: : 204iter [00:35,  6.17iter/s]Best score: 0.2419: : 205iter [00:35,  6.19iter/s]Best score: 0.2419: : 205iter [00:35,  6.19iter/s]Best score: 0.2419: : 206iter [00:35,  6.21iter/s]Best score: 0.2419: : 206iter [00:35,  6.21iter/s]Best score: 0.2419: : 207iter [00:35,  6.21iter/s]Best score: 0.2419: : 207iter [00:35,  6.21iter/s]Best score: 0.2419: : 208iter [00:35,  6.20iter/s]Best score: 0.2419: : 208iter [00:35,  6.20iter/s]Best score: 0.2419: : 209iter [00:35,  6.20iter/s]Best score: 0.2419: : 209iter [00:36,  6.20iter/s]Best score: 0.2419: : 210iter [00:36,  6.01iter/s]Best score: 0.2419: : 210iter [00:36,  6.01iter/s]Best score: 0.2419: : 211iter [00:36,  6.08iter/s]Best score: 0.2419: : 211iter [00:36,  6.08iter/s]Best score: 0.2419: : 212iter [00:36,  6.12iter/s]Best score: 0.2419: : 212iter [00:36,  6.12iter/s]Best score: 0.2419: : 213iter [00:36,  6.15iter/s]Best score: 0.2419: : 213iter [00:36,  6.15iter/s]Best score: 0.2419: : 214iter [00:36,  6.19iter/s]Best score: 0.2419: : 214iter [00:36,  6.19iter/s]Best score: 0.2419: : 215iter [00:36,  6.19iter/s]Best score: 0.2419: : 215iter [00:36,  6.19iter/s]Best score: 0.2419: : 216iter [00:36,  6.20iter/s]Best score: 0.2419: : 216iter [00:37,  6.20iter/s]Best score: 0.2419: : 217iter [00:37,  6.22iter/s]Best score: 0.2419: : 217iter [00:37,  6.22iter/s]Best score: 0.2419: : 218iter [00:37,  6.23iter/s]Best score: 0.2419: : 218iter [00:37,  6.23iter/s]Best score: 0.2419: : 219iter [00:37,  6.23iter/s]Best score: 0.2417: : 219iter [00:37,  6.23iter/s]Best score: 0.2417: : 220iter [00:37,  6.02iter/s]Best score: 0.2417: : 220iter [00:37,  6.02iter/s]Best score: 0.2417: : 221iter [00:37,  6.08iter/s]Best score: 0.2417: : 221iter [00:37,  6.08iter/s]Best score: 0.2417: : 222iter [00:37,  6.12iter/s]Best score: 0.2417: : 222iter [00:38,  6.12iter/s]Best score: 0.2417: : 223iter [00:38,  6.15iter/s]Best score: 0.2417: : 223iter [00:38,  6.15iter/s]Best score: 0.2417: : 224iter [00:38,  6.17iter/s]Best score: 0.2417: : 224iter [00:38,  6.17iter/s]Best score: 0.2417: : 225iter [00:38,  6.19iter/s]Best score: 0.2417: : 225iter [00:38,  6.19iter/s]Best score: 0.2417: : 226iter [00:38,  6.20iter/s]Best score: 0.2417: : 226iter [00:38,  6.20iter/s]Best score: 0.2417: : 227iter [00:38,  6.20iter/s]Best score: 0.2417: : 227iter [00:38,  6.20iter/s]Best score: 0.2417: : 228iter [00:38,  6.21iter/s]Best score: 0.2417: : 228iter [00:39,  6.21iter/s]Best score: 0.2417: : 229iter [00:39,  6.20iter/s]Best score: 0.2363: : 229iter [00:39,  6.20iter/s]Best score: 0.2363: : 230iter [00:39,  6.02iter/s]Best score: 0.2363: : 230iter [00:39,  6.02iter/s]Best score: 0.2363: : 231iter [00:39,  6.06iter/s]Best score: 0.2363: : 231iter [00:39,  6.06iter/s]Best score: 0.2363: : 232iter [00:39,  6.10iter/s]Best score: 0.2363: : 232iter [00:39,  6.10iter/s]Best score: 0.2363: : 233iter [00:39,  6.13iter/s]Best score: 0.2363: : 233iter [00:39,  6.13iter/s]Best score: 0.2363: : 234iter [00:39,  6.16iter/s]Best score: 0.2363: : 234iter [00:40,  6.16iter/s]Best score: 0.2363: : 235iter [00:40,  6.18iter/s]Best score: 0.2363: : 235iter [00:40,  6.18iter/s]Best score: 0.2363: : 236iter [00:40,  6.20iter/s]Best score: 0.2363: : 236iter [00:40,  6.20iter/s]Best score: 0.2363: : 237iter [00:40,  6.21iter/s]Best score: 0.2363: : 237iter [00:40,  6.21iter/s]Best score: 0.2363: : 238iter [00:40,  6.20iter/s]Best score: 0.2363: : 238iter [00:40,  6.20iter/s]Best score: 0.2363: : 239iter [00:40,  6.19iter/s]Best score: 0.2314: : 239iter [00:40,  6.19iter/s]Best score: 0.2314: : 240iter [00:40,  6.00iter/s]Best score: 0.2314: : 240iter [00:41,  6.00iter/s]Best score: 0.2314: : 241iter [00:41,  6.06iter/s]Best score: 0.2314: : 241iter [00:41,  6.06iter/s]Best score: 0.2314: : 242iter [00:41,  6.11iter/s]Best score: 0.2314: : 242iter [00:41,  6.11iter/s]Best score: 0.2314: : 243iter [00:41,  6.13iter/s]Best score: 0.2314: : 243iter [00:41,  6.13iter/s]Best score: 0.2314: : 244iter [00:41,  6.16iter/s]Best score: 0.2314: : 244iter [00:41,  6.16iter/s]Best score: 0.2314: : 245iter [00:41,  6.18iter/s]Best score: 0.2314: : 245iter [00:41,  6.18iter/s]Best score: 0.2314: : 246iter [00:41,  6.20iter/s]Best score: 0.2314: : 246iter [00:42,  6.20iter/s]Best score: 0.2314: : 247iter [00:42,  6.21iter/s]Best score: 0.2314: : 247iter [00:42,  6.21iter/s]Best score: 0.2314: : 248iter [00:42,  6.22iter/s]Best score: 0.2314: : 248iter [00:42,  6.22iter/s]Best score: 0.2314: : 249iter [00:42,  6.21iter/s]
[ITER 250] Best: 0.2267
[PARAMS] Current best parameters:
  fx: 551.2251
  fy: 550.8000
  px: 1017.9165
  py: 769.1032
  k1: -0.01754061
  k2: -0.01196688
  k3: 0.01386687
  k4: -0.00453207
  p1: -0.00230152
  p2: -0.00369649
  p3: 0.00616178
  p4: -0.00095828
  scale: 3.0190
  rotation: 0.4956
Best score: 0.2267: : 249iter [00:42,  6.21iter/s]Best score: 0.2267: : 250iter [00:42,  6.02iter/s]Best score: 0.2267: : 250iter [00:42,  6.02iter/s]Best score: 0.2267: : 251iter [00:42,  6.07iter/s]Best score: 0.2267: : 251iter [00:42,  6.07iter/s]Best score: 0.2267: : 252iter [00:42,  6.12iter/s]Best score: 0.2267: : 252iter [00:42,  6.12iter/s]Best score: 0.2267: : 253iter [00:42,  6.15iter/s]Best score: 0.2267: : 253iter [00:43,  6.15iter/s]Best score: 0.2267: : 254iter [00:43,  6.17iter/s]Best score: 0.2267: : 254iter [00:43,  6.17iter/s]Best score: 0.2267: : 255iter [00:43,  6.18iter/s]Best score: 0.2267: : 255iter [00:43,  6.18iter/s]Best score: 0.2267: : 256iter [00:43,  6.18iter/s]Best score: 0.2267: : 256iter [00:43,  6.18iter/s]Best score: 0.2267: : 257iter [00:43,  6.18iter/s]Best score: 0.2267: : 257iter [00:43,  6.18iter/s]Best score: 0.2267: : 258iter [00:43,  6.20iter/s]Best score: 0.2267: : 258iter [00:43,  6.20iter/s]Best score: 0.2267: : 259iter [00:43,  6.19iter/s]Best score: 0.2253: : 259iter [00:44,  6.19iter/s]Best score: 0.2253: : 260iter [00:44,  6.00iter/s]Best score: 0.2253: : 260iter [00:44,  6.00iter/s]Best score: 0.2253: : 261iter [00:44,  6.06iter/s]Best score: 0.2253: : 261iter [00:44,  6.06iter/s]Best score: 0.2253: : 262iter [00:44,  6.10iter/s]Best score: 0.2253: : 262iter [00:44,  6.10iter/s]Best score: 0.2253: : 263iter [00:44,  6.12iter/s]Best score: 0.2253: : 263iter [00:44,  6.12iter/s]Best score: 0.2253: : 264iter [00:44,  6.14iter/s]Best score: 0.2253: : 264iter [00:44,  6.14iter/s]Best score: 0.2253: : 265iter [00:44,  6.17iter/s]Best score: 0.2253: : 265iter [00:45,  6.17iter/s]Best score: 0.2253: : 266iter [00:45,  6.18iter/s]Best score: 0.2253: : 266iter [00:45,  6.18iter/s]Best score: 0.2253: : 267iter [00:45,  6.19iter/s]Best score: 0.2253: : 267iter [00:45,  6.19iter/s]Best score: 0.2253: : 268iter [00:45,  6.19iter/s]Best score: 0.2253: : 268iter [00:45,  6.19iter/s]Best score: 0.2253: : 269iter [00:45,  6.20iter/s]Best score: 0.2211: : 269iter [00:45,  6.20iter/s]Best score: 0.2211: : 270iter [00:45,  6.01iter/s]Best score: 0.2211: : 270iter [00:45,  6.01iter/s]Best score: 0.2211: : 271iter [00:45,  6.07iter/s]Best score: 0.2211: : 271iter [00:46,  6.07iter/s]Best score: 0.2211: : 272iter [00:46,  6.11iter/s]Best score: 0.2211: : 272iter [00:46,  6.11iter/s]Best score: 0.2211: : 273iter [00:46,  6.14iter/s]Best score: 0.2211: : 273iter [00:46,  6.14iter/s]Best score: 0.2211: : 274iter [00:46,  6.15iter/s]Best score: 0.2211: : 274iter [00:46,  6.15iter/s]Best score: 0.2211: : 275iter [00:46,  6.18iter/s]Best score: 0.2211: : 275iter [00:46,  6.18iter/s]Best score: 0.2211: : 276iter [00:46,  6.20iter/s]Best score: 0.2211: : 276iter [00:46,  6.20iter/s]Best score: 0.2211: : 277iter [00:46,  6.21iter/s]Best score: 0.2211: : 277iter [00:47,  6.21iter/s]Best score: 0.2211: : 278iter [00:47,  6.23iter/s]Best score: 0.2211: : 278iter [00:47,  6.23iter/s]Best score: 0.2211: : 279iter [00:47,  6.24iter/s]Best score: 0.2160: : 279iter [00:47,  6.24iter/s]Best score: 0.2160: : 280iter [00:47,  6.06iter/s]Best score: 0.2160: : 280iter [00:47,  6.06iter/s]Best score: 0.2160: : 281iter [00:47,  6.11iter/s]Best score: 0.2160: : 281iter [00:47,  6.11iter/s]Best score: 0.2160: : 282iter [00:47,  6.15iter/s]Best score: 0.2160: : 282iter [00:47,  6.15iter/s]Best score: 0.2160: : 283iter [00:47,  6.18iter/s]Best score: 0.2160: : 283iter [00:48,  6.18iter/s]Best score: 0.2160: : 284iter [00:48,  6.20iter/s]Best score: 0.2160: : 284iter [00:48,  6.20iter/s]Best score: 0.2160: : 285iter [00:48,  6.22iter/s]Best score: 0.2160: : 285iter [00:48,  6.22iter/s]Best score: 0.2160: : 286iter [00:48,  6.22iter/s]Best score: 0.2160: : 286iter [00:48,  6.22iter/s]Best score: 0.2160: : 287iter [00:48,  6.22iter/s]Best score: 0.2160: : 287iter [00:48,  6.22iter/s]Best score: 0.2160: : 288iter [00:48,  6.23iter/s]Best score: 0.2160: : 288iter [00:48,  6.23iter/s]Best score: 0.2160: : 289iter [00:48,  6.24iter/s]Best score: 0.2121: : 289iter [00:49,  6.24iter/s]Best score: 0.2121: : 290iter [00:49,  6.04iter/s]Best score: 0.2121: : 290iter [00:49,  6.04iter/s]Best score: 0.2121: : 291iter [00:49,  6.10iter/s]Best score: 0.2121: : 291iter [00:49,  6.10iter/s]Best score: 0.2121: : 292iter [00:49,  6.14iter/s]Best score: 0.2121: : 292iter [00:49,  6.14iter/s]Best score: 0.2121: : 293iter [00:49,  6.18iter/s]Best score: 0.2121: : 293iter [00:49,  6.18iter/s]Best score: 0.2121: : 294iter [00:49,  6.19iter/s]Best score: 0.2121: : 294iter [00:49,  6.19iter/s]Best score: 0.2121: : 295iter [00:49,  6.19iter/s]Best score: 0.2121: : 295iter [00:49,  6.19iter/s]Best score: 0.2121: : 296iter [00:49,  6.20iter/s]Best score: 0.2121: : 296iter [00:50,  6.20iter/s]Best score: 0.2121: : 297iter [00:50,  6.21iter/s]Best score: 0.2121: : 297iter [00:50,  6.21iter/s]Best score: 0.2121: : 298iter [00:50,  6.22iter/s]Best score: 0.2121: : 298iter [00:50,  6.22iter/s]Best score: 0.2121: : 299iter [00:50,  6.21iter/s]
[ITER 300] Best: 0.2114
[PARAMS] Current best parameters:
  fx: 551.0264
  fy: 550.6970
  px: 1015.5660
  py: 766.9909
  k1: -0.02890231
  k2: 0.00933905
  k3: -0.00066674
  k4: -0.00105397
  p1: -0.00767279
  p2: -0.00972415
  p3: 0.00861398
  p4: -0.00002758
  scale: 3.0278
  rotation: 0.4976
Best score: 0.2114: : 299iter [00:50,  6.21iter/s]Best score: 0.2114: : 300iter [00:50,  6.02iter/s]Best score: 0.2114: : 300iter [00:50,  6.02iter/s]Best score: 0.2114: : 301iter [00:50,  6.08iter/s]Best score: 0.2114: : 301iter [00:50,  6.08iter/s]Best score: 0.2114: : 302iter [00:50,  6.13iter/s]Best score: 0.2114: : 302iter [00:51,  6.13iter/s]Best score: 0.2114: : 303iter [00:51,  6.17iter/s]Best score: 0.2114: : 303iter [00:51,  6.17iter/s]Best score: 0.2114: : 304iter [00:51,  6.19iter/s]Best score: 0.2114: : 304iter [00:51,  6.19iter/s]Best score: 0.2114: : 305iter [00:51,  6.19iter/s]Best score: 0.2114: : 305iter [00:51,  6.19iter/s]Best score: 0.2114: : 306iter [00:51,  6.19iter/s]Best score: 0.2114: : 306iter [00:51,  6.19iter/s]Best score: 0.2114: : 307iter [00:51,  6.19iter/s]Best score: 0.2114: : 307iter [00:51,  6.19iter/s]Best score: 0.2114: : 308iter [00:51,  6.20iter/s]Best score: 0.2114: : 308iter [00:52,  6.20iter/s]Best score: 0.2114: : 309iter [00:52,  6.21iter/s]Best score: 0.2100: : 309iter [00:52,  6.21iter/s]Best score: 0.2100: : 310iter [00:52,  6.02iter/s]Best score: 0.2100: : 310iter [00:52,  6.02iter/s]Best score: 0.2100: : 311iter [00:52,  6.08iter/s]Best score: 0.2100: : 311iter [00:52,  6.08iter/s]Best score: 0.2100: : 312iter [00:52,  6.12iter/s]Best score: 0.2100: : 312iter [00:52,  6.12iter/s]Best score: 0.2100: : 313iter [00:52,  6.14iter/s]Best score: 0.2100: : 313iter [00:52,  6.14iter/s]Best score: 0.2100: : 314iter [00:52,  6.15iter/s]Best score: 0.2100: : 314iter [00:53,  6.15iter/s]Best score: 0.2100: : 315iter [00:53,  6.18iter/s]Best score: 0.2100: : 315iter [00:53,  6.18iter/s]Best score: 0.2100: : 316iter [00:53,  6.19iter/s]Best score: 0.2100: : 316iter [00:53,  6.19iter/s]Best score: 0.2100: : 317iter [00:53,  6.20iter/s]Best score: 0.2100: : 317iter [00:53,  6.20iter/s]Best score: 0.2100: : 318iter [00:53,  6.22iter/s]Best score: 0.2100: : 318iter [00:53,  6.22iter/s]Best score: 0.2100: : 319iter [00:53,  6.22iter/s]Best score: 0.2097: : 319iter [00:53,  6.22iter/s]Best score: 0.2097: : 320iter [00:53,  6.03iter/s]Best score: 0.2097: : 320iter [00:54,  6.03iter/s]Best score: 0.2097: : 321iter [00:54,  6.10iter/s]Best score: 0.2097: : 321iter [00:54,  6.10iter/s]Best score: 0.2097: : 322iter [00:54,  6.13iter/s]Best score: 0.2097: : 322iter [00:54,  6.13iter/s]Best score: 0.2097: : 323iter [00:54,  6.15iter/s]Best score: 0.2097: : 323iter [00:54,  6.15iter/s]Best score: 0.2097: : 324iter [00:54,  6.16iter/s]Best score: 0.2097: : 324iter [00:54,  6.16iter/s]Best score: 0.2097: : 325iter [00:54,  6.18iter/s]Best score: 0.2097: : 325iter [00:54,  6.18iter/s]Best score: 0.2097: : 326iter [00:54,  6.20iter/s]Best score: 0.2097: : 326iter [00:55,  6.20iter/s]Best score: 0.2097: : 327iter [00:55,  6.21iter/s]Best score: 0.2097: : 327iter [00:55,  6.21iter/s]Best score: 0.2097: : 328iter [00:55,  6.21iter/s]Best score: 0.2097: : 328iter [00:55,  6.21iter/s]Best score: 0.2097: : 329iter [00:55,  6.22iter/s]Best score: 0.2097: : 329iter [00:55,  6.22iter/s]Best score: 0.2097: : 330iter [00:55,  6.02iter/s]Best score: 0.2097: : 330iter [00:55,  6.02iter/s]Best score: 0.2097: : 331iter [00:55,  6.08iter/s]Best score: 0.2097: : 331iter [00:55,  6.08iter/s]Best score: 0.2097: : 332iter [00:55,  6.11iter/s]Best score: 0.2097: : 332iter [00:56,  6.11iter/s]Best score: 0.2097: : 333iter [00:56,  4.60iter/s]Best score: 0.2097: : 333iter [00:56,  4.60iter/s]Best score: 0.2097: : 334iter [00:56,  4.99iter/s]Best score: 0.2097: : 334iter [00:56,  4.99iter/s]Best score: 0.2097: : 335iter [00:56,  5.31iter/s]Best score: 0.2097: : 335iter [00:56,  5.31iter/s]Best score: 0.2097: : 336iter [00:56,  5.56iter/s]Best score: 0.2097: : 336iter [00:56,  5.56iter/s]Best score: 0.2097: : 337iter [00:56,  5.74iter/s]Best score: 0.2097: : 337iter [00:56,  5.74iter/s]Best score: 0.2097: : 338iter [00:56,  5.89iter/s]Best score: 0.2097: : 338iter [00:57,  5.89iter/s]Best score: 0.2097: : 339iter [00:57,  5.99iter/s]Best score: 0.2091: : 339iter [00:57,  5.99iter/s]Best score: 0.2091: : 340iter [00:57,  5.87iter/s]Best score: 0.2091: : 340iter [00:57,  5.87iter/s]Best score: 0.2091: : 341iter [00:57,  5.98iter/s]Best score: 0.2091: : 341iter [00:57,  5.98iter/s]Best score: 0.2091: : 342iter [00:57,  6.05iter/s]Best score: 0.2091: : 342iter [00:57,  6.05iter/s]Best score: 0.2091: : 343iter [00:57,  6.09iter/s]Best score: 0.2091: : 343iter [00:57,  6.09iter/s]Best score: 0.2091: : 344iter [00:57,  6.13iter/s]Best score: 0.2091: : 344iter [00:58,  6.13iter/s]Best score: 0.2091: : 345iter [00:58,  6.14iter/s]Best score: 0.2091: : 345iter [00:58,  6.14iter/s]Best score: 0.2091: : 346iter [00:58,  6.16iter/s]Best score: 0.2091: : 346iter [00:58,  6.16iter/s]Best score: 0.2091: : 347iter [00:58,  6.18iter/s]Best score: 0.2091: : 347iter [00:58,  6.18iter/s]Best score: 0.2091: : 348iter [00:58,  6.18iter/s]Best score: 0.2091: : 348iter [00:58,  6.18iter/s]Best score: 0.2091: : 349iter [00:58,  6.20iter/s]
[ITER 350] Best: 0.2089
[PARAMS] Current best parameters:
  fx: 555.7797
  fy: 555.3476
  px: 1015.5423
  py: 766.9496
  k1: -0.02888549
  k2: 0.00070024
  k3: 0.00543597
  k4: -0.00241757
  p1: -0.00778789
  p2: -0.00984192
  p3: 0.00872023
  p4: -0.00009434
  scale: 3.0217
  rotation: 0.4832
Best score: 0.2089: : 349iter [00:58,  6.20iter/s]Best score: 0.2089: : 350iter [00:58,  6.02iter/s]Best score: 0.2089: : 350iter [00:59,  6.02iter/s]Best score: 0.2089: : 351iter [00:59,  6.09iter/s]Best score: 0.2089: : 351iter [00:59,  6.09iter/s]Best score: 0.2089: : 352iter [00:59,  6.12iter/s]Best score: 0.2089: : 352iter [00:59,  6.12iter/s]Best score: 0.2089: : 353iter [00:59,  6.16iter/s]Best score: 0.2089: : 353iter [00:59,  6.16iter/s]Best score: 0.2089: : 354iter [00:59,  6.17iter/s]Best score: 0.2089: : 354iter [00:59,  6.17iter/s]Best score: 0.2089: : 355iter [00:59,  6.19iter/s]Best score: 0.2089: : 355iter [00:59,  6.19iter/s]Best score: 0.2089: : 356iter [00:59,  6.20iter/s]Best score: 0.2089: : 356iter [01:00,  6.20iter/s]Best score: 0.2089: : 357iter [01:00,  6.19iter/s]Best score: 0.2089: : 357iter [01:00,  6.19iter/s]Best score: 0.2089: : 358iter [01:00,  6.20iter/s]Best score: 0.2089: : 358iter [01:00,  6.20iter/s]Best score: 0.2089: : 359iter [01:00,  6.20iter/s]Best score: 0.2084: : 359iter [01:00,  6.20iter/s]Best score: 0.2084: : 360iter [01:00,  5.99iter/s]Best score: 0.2084: : 360iter [01:00,  5.99iter/s]Best score: 0.2084: : 361iter [01:00,  6.06iter/s]Best score: 0.2084: : 361iter [01:00,  6.06iter/s]Best score: 0.2084: : 362iter [01:00,  6.11iter/s]Best score: 0.2084: : 362iter [01:01,  6.11iter/s]Best score: 0.2084: : 363iter [01:01,  6.15iter/s]Best score: 0.2084: : 363iter [01:01,  6.15iter/s]Best score: 0.2084: : 364iter [01:01,  6.18iter/s]Best score: 0.2084: : 364iter [01:01,  6.18iter/s]Best score: 0.2084: : 365iter [01:01,  6.20iter/s]Best score: 0.2084: : 365iter [01:01,  6.20iter/s]Best score: 0.2084: : 366iter [01:01,  6.20iter/s]Best score: 0.2084: : 366iter [01:01,  6.20iter/s]Best score: 0.2084: : 367iter [01:01,  6.22iter/s]Best score: 0.2084: : 367iter [01:01,  6.22iter/s]Best score: 0.2084: : 368iter [01:01,  6.22iter/s]Best score: 0.2084: : 368iter [01:02,  6.22iter/s]Best score: 0.2084: : 369iter [01:02,  6.22iter/s]Best score: 0.2083: : 369iter [01:02,  6.22iter/s]Best score: 0.2083: : 370iter [01:02,  6.02iter/s]Best score: 0.2083: : 370iter [01:02,  6.02iter/s]Best score: 0.2083: : 371iter [01:02,  6.08iter/s]Best score: 0.2083: : 371iter [01:02,  6.08iter/s]Best score: 0.2083: : 372iter [01:02,  6.12iter/s]Best score: 0.2083: : 372iter [01:02,  6.12iter/s]Best score: 0.2083: : 373iter [01:02,  6.16iter/s]Best score: 0.2083: : 373iter [01:02,  6.16iter/s]Best score: 0.2083: : 374iter [01:02,  6.18iter/s]Best score: 0.2083: : 374iter [01:02,  6.18iter/s]Best score: 0.2083: : 375iter [01:02,  6.19iter/s]Best score: 0.2083: : 375iter [01:03,  6.19iter/s]Best score: 0.2083: : 376iter [01:03,  6.21iter/s]Best score: 0.2083: : 376iter [01:03,  6.21iter/s]Best score: 0.2083: : 377iter [01:03,  6.21iter/s]Best score: 0.2083: : 377iter [01:03,  6.21iter/s]Best score: 0.2083: : 378iter [01:03,  6.21iter/s]Best score: 0.2083: : 378iter [01:03,  6.21iter/s]Best score: 0.2083: : 379iter [01:03,  6.21iter/s]Best score: 0.2083: : 379iter [01:03,  6.21iter/s]Best score: 0.2083: : 380iter [01:03,  6.03iter/s]Best score: 0.2083: : 380iter [01:03,  6.03iter/s]Best score: 0.2083: : 381iter [01:03,  6.09iter/s]Best score: 0.2083: : 381iter [01:04,  6.09iter/s]Best score: 0.2083: : 382iter [01:04,  6.13iter/s]Best score: 0.2083: : 382iter [01:04,  6.13iter/s]Best score: 0.2083: : 383iter [01:04,  6.17iter/s]Best score: 0.2083: : 383iter [01:04,  6.17iter/s]Best score: 0.2083: : 384iter [01:04,  6.17iter/s]Best score: 0.2083: : 384iter [01:04,  6.17iter/s]Best score: 0.2083: : 385iter [01:04,  6.18iter/s]Best score: 0.2083: : 385iter [01:04,  6.18iter/s]Best score: 0.2083: : 386iter [01:04,  6.18iter/s]Best score: 0.2083: : 386iter [01:04,  6.18iter/s]Best score: 0.2083: : 387iter [01:04,  6.19iter/s]Best score: 0.2083: : 387iter [01:05,  6.19iter/s]Best score: 0.2083: : 388iter [01:05,  6.19iter/s]Best score: 0.2083: : 388iter [01:05,  6.19iter/s]Best score: 0.2083: : 389iter [01:05,  6.20iter/s]Best score: 0.2081: : 389iter [01:05,  6.20iter/s]Best score: 0.2081: : 390iter [01:05,  6.00iter/s]Best score: 0.2081: : 390iter [01:05,  6.00iter/s]Best score: 0.2081: : 391iter [01:05,  6.06iter/s]Best score: 0.2081: : 391iter [01:05,  6.06iter/s]Best score: 0.2081: : 392iter [01:05,  6.11iter/s]Best score: 0.2081: : 392iter [01:05,  6.11iter/s]Best score: 0.2081: : 393iter [01:05,  6.15iter/s]Best score: 0.2081: : 393iter [01:06,  6.15iter/s]Best score: 0.2081: : 394iter [01:06,  6.18iter/s]Best score: 0.2081: : 394iter [01:06,  6.18iter/s]Best score: 0.2081: : 395iter [01:06,  6.18iter/s]Best score: 0.2081: : 395iter [01:06,  6.18iter/s]Best score: 0.2081: : 396iter [01:06,  6.18iter/s]Best score: 0.2081: : 396iter [01:06,  6.18iter/s]Best score: 0.2081: : 397iter [01:06,  6.18iter/s]Best score: 0.2081: : 397iter [01:06,  6.18iter/s]Best score: 0.2081: : 398iter [01:06,  6.18iter/s]Best score: 0.2081: : 398iter [01:06,  6.18iter/s]Best score: 0.2081: : 399iter [01:06,  6.19iter/s]
[ITER 400] Best: 0.2081
[PARAMS] Current best parameters:
  fx: 557.1535
  fy: 556.7221
  px: 1015.5250
  py: 766.8041
  k1: -0.03217762
  k2: 0.00409060
  k3: 0.00326063
  k4: -0.00187942
  p1: -0.00815988
  p2: -0.00997486
  p3: 0.00883548
  p4: 0.00003413
  scale: 3.0232
  rotation: 0.4940
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[ITER 450] Best: 0.2079
[PARAMS] Current best parameters:
  fx: 556.3903
  fy: 555.9337
  px: 1015.5009
  py: 766.7678
  k1: -0.03005044
  k2: 0.00199419
  k3: 0.00436895
  k4: -0.00210106
  p1: -0.00820808
  p2: -0.00999322
  p3: 0.00879727
  p4: 0.00003730
  scale: 3.0221
  rotation: 0.4924
Best score: 0.2079: : 449iter [01:15,  6.18iter/s]Best score: 0.2079: : 450iter [01:15,  6.00iter/s]Best score: 0.2079: : 450iter [01:15,  6.00iter/s]Best score: 0.2079: : 451iter [01:15,  6.06iter/s]Best score: 0.2079: : 451iter [01:15,  6.06iter/s]Best score: 0.2079: : 452iter [01:15,  6.11iter/s]Best score: 0.2079: : 452iter [01:15,  6.11iter/s]Best score: 0.2079: : 453iter [01:15,  6.13iter/s]Best score: 0.2079: : 453iter [01:15,  6.13iter/s]Best score: 0.2079: : 454iter [01:15,  6.17iter/s]Best score: 0.2079: : 454iter [01:16,  6.17iter/s]Best score: 0.2079: : 455iter [01:16,  6.18iter/s]Best score: 0.2079: : 455iter [01:16,  6.18iter/s]Best score: 0.2079: : 456iter [01:16,  6.20iter/s]Best score: 0.2079: : 456iter [01:16,  6.20iter/s]Best score: 0.2079: : 457iter [01:16,  6.21iter/s]Best score: 0.2079: : 457iter [01:16,  6.21iter/s]Best score: 0.2079: : 458iter [01:16,  6.21iter/s]Best score: 0.2079: : 458iter [01:16,  6.21iter/s]Best score: 0.2079: : 459iter 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[01:22,  6.18iter/s]Best score: 0.2078: : 498iter [01:23,  6.18iter/s]Best score: 0.2078: : 499iter [01:23,  6.17iter/s]
[ITER 500] Best: 0.2078
[PARAMS] Current best parameters:
  fx: 552.7415
  fy: 552.2888
  px: 1015.4894
  py: 766.7463
  k1: -0.02445795
  k2: -0.00088420
  k3: 0.00563072
  k4: -0.00235911
  p1: -0.00823716
  p2: -0.00998497
  p3: 0.00889456
  p4: 0.00000322
  scale: 3.0214
  rotation: 0.4902
Best score: 0.2078: : 499iter [01:23,  6.17iter/s]Best score: 0.2078: : 500iter [01:23,  5.98iter/s]Best score: 0.2078: : 500iter [01:23,  6.00iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.207785 -> 0.207762
  Success: True
  Tangential distortion: p1=-0.008236, p2=-0.010000, p3=0.008895, p4=0.000010
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2078
[✅] Parameters copied to: /working/MDC028_W009(6)_2025-03-21/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 3: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 552.741467122887
    fy: 552.2887542393769
    px: 1015.4894092486749
    py: 766.7463006819919
    k1: -0.02445519894499029
    k2: -0.0008824929151272563
    k3: 0.005629738910722541
    k4: -0.0023596746927236497
    p1: -0.00823635742590684
    p2: -0.009999996793107969
    p3: 0.00889504107459414
    p4: 9.753659223963735e-06
    scale: 3.021423541866492
    rotation: 0.49019660343450544
    mmpp: 3.46420532579135
    nmpp: 3464205.32579135

[🔄] Converting 1768 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDC028_W009(6)_2025-03-21/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC028_W009(6)_2025-03-21/calibration/debug/3_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.
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[✅] LUT written to: /working/MDC028_W009(6)_2025-03-21/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC028_W009(6)_2025-03-21' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC028_W009(6)_2025-03-21/calibration/MDC028_W009(6)_2025-03-21_unknown.lut

-------------------------------------------------------------------
                          Step 4: Heatmap                          
-------------------------------------------------------------------

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                                                                 [A[✅] 1500: /working/MDC028_W009(6)_2025-03-21/calibration/debug/4_heatmap/1500_heatmap.png
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                                                                  [A[✅] 2000R: /working/MDC028_W009(6)_2025-03-21/calibration/debug/4_heatmap/2000R_heatmap.png
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                                                                  [A[✅] 2000L: /working/MDC028_W009(6)_2025-03-21/calibration/debug/4_heatmap/2000L_heatmap.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:12<00:04,  4.23s/rig]
   🔥 1000:   0%|          | 0/230 [00:00<?, ?frame/s][A
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                                                                 [A[✅] 1000: /working/MDC028_W009(6)_2025-03-21/calibration/debug/4_heatmap/1000_heatmap.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:13<00:00,  3.22s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:13<00:00,  3.44s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC028_W009(6)_2025-03-21/calibration/debug/4_heatmap
[🏁] Finished processing MDC028_W009(6)_2025-03-21


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