
🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-010_W046(6)_2026-04-09 ===

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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 336 stationary frames, 283 remain

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                                                                          [A[PASS2] Frame 0000343: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000580: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000618: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000619: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000618: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 3 frames fixed, 24 untrusted, 282 total

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                                                                           [AProcessing Rigs:  25%|██▌       | 1/4 [05:32<16:38, 332.72s/it][INFO] Filtered 384 stationary frames, 491 remain

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                                                                          [A[PASS2] Frame 0000445: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000930: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000932: fixed corners {1, 2, 3} (was_fixed=True, trusted=False)
[PASS2] Summary: 2 frames fixed, 36 untrusted, 490 total

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                                                                           [AProcessing Rigs:  50%|█████     | 2/4 [14:34<15:11, 455.70s/it][INFO] Filtered 410 stationary frames, 570 remain

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                                                                           [A[PASS2] Frame 0000246: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000593: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000605: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000641: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000642: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000643: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000644: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000645: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000656: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000657: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000658: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000659: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000693: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000694: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000695: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000696: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000697: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000698: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000700: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000701: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000702: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000703: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000704: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000705: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000706: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000707: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000708: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000712: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000713: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000714: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001076: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0001077: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0001078: fixed corners {0, 1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001182: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000246: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000644: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000658: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000695: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000696: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000698: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000699: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000701: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000704: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000705: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000713: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001182: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000246: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0001182: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Summary: 21 frames fixed, 39 untrusted, 569 total

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                                                                            [AProcessing Rigs:  75%|███████▌  | 3/4 [24:50<08:48, 528.72s/it][INFO] Filtered 437 stationary frames, 590 remain

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                                                                           [A[PASS2] Frame 0000233: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000337: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000338: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000339: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000340: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000554: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000686: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000687: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000691: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000692: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000693: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000694: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000695: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000696: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000697: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000699: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000700: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000701: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000736: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000737: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000738: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000740: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000742: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000743: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000744: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000745: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000749: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001145: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001149: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000698: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000700: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000738: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000739: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000741: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000742: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000743: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000746: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001145: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001149: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000698: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000741: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000746: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001145: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001149: fixed corners {1, 2} (was_fixed=True, trusted=False)

[PASS2] Summary: 18 frames fixed, 51 untrusted, 589 total
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                                                                            [AProcessing Rigs: 100%|██████████| 4/4 [38:44<00:00, 649.32s/it]Processing Rigs: 100%|██████████| 4/4 [38:44<00:00, 581.08s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1930 frames from 4 rigs to /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 282
[📋] 1500: 100 frames selected from 490
[📋] 2000R: 100 frames selected from 569
[📋] 2000L: 100 frames selected from 589
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
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99%|█████████▉| 79/80 [06:03<00:04,  4.65s/it]Computing max image from bright frames: 100%|██████████| 80/80 [06:08<00:00,  4.82s/it]                                                                                         0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:43,  2.27it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:43,  2.27it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:43,  2.27it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:42,  2.27it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.04it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.04it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:11,  8.04it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   6%|▌         | 6/100 [00:00<00:08, 10.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:08, 10.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:08, 10.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:08, 10.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   9%|▉         | 9/100 [00:00<00:06, 13.94it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:06, 13.94it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:06, 13.94it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  11%|█         | 11/100 [00:00<00:05, 15.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  11%|█         | 11/100 [00:01<00:05, 15.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:01<00:05, 15.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:01<00:05, 15.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  14%|█▍        | 14/100 [00:01<00:04, 18.93it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:01<00:04, 18.93it/s] Brute | Start: {'fx': 500.0} (0.037) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  15%|█▌        | 15/100 [00:01<00:04, 18.93it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 18.93it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:01<00:04, 16.84it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  17%|█▋        | 17/100 [00:01<00:04, 16.84it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  18%|█▊        | 18/100 [00:01<00:04, 16.84it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:05, 16.15it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  19%|█▉        | 19/100 [00:01<00:05, 16.15it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:04, 16.15it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:04, 16.15it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  22%|██▏       | 22/100 [00:01<00:04, 19.06it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.012 | Best: {'fx': 455.556} (0.012):  22%|██▏       | 22/100 [00:01<00:04, 19.06it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:04, 19.06it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.016 | Best: {'fx': 469.697} (0.016):  24%|██▍       | 24/100 [00:01<00:03, 19.06it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.016 | Best: {'fx': 469.697} (0.016):  25%|██▌       | 25/100 [00:01<00:03, 19.99it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 476.768} (0.020):  25%|██▌       | 25/100 [00:01<00:03, 19.99it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.024 | Best: {'fx': 483.838} (0.024):  26%|██▌       | 26/100 [00:01<00:03, 19.99it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.028 | Best: {'fx': 490.909} (0.028):  27%|██▋       | 27/100 [00:01<00:03, 19.99it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.028 | Best: {'fx': 490.909} (0.028):  28%|██▊       | 28/100 [00:01<00:03, 19.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.035 | Best: {'fx': 497.98} (0.035):  28%|██▊       | 28/100 [00:01<00:03, 19.23it/s] Brute | Start: {'fx': 500.0} (0.037) | Current: 0.041 | Best: {'fx': 505.051} (0.041):  29%|██▉       | 29/100 [00:01<00:03, 19.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.050 | Best: {'fx': 512.121} (0.050):  30%|███       | 30/100 [00:01<00:03, 19.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.050 | Best: {'fx': 512.121} (0.050):  31%|███       | 31/100 [00:01<00:03, 19.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.044 | Best: {'fx': 512.121} (0.050):  31%|███       | 31/100 [00:02<00:03, 19.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.059 | Best: {'fx': 519.192} (0.059):  32%|███▏      | 32/100 [00:02<00:03, 19.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.069 | Best: {'fx': 533.333} (0.069):  33%|███▎      | 33/100 [00:02<00:03, 19.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.069 | Best: {'fx': 533.333} (0.069):  34%|███▍      | 34/100 [00:02<00:05, 12.25it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.070 | Best: {'fx': 526.263} (0.070):  34%|███▍      | 34/100 [00:02<00:05, 12.25it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.062 | Best: {'fx': 526.263} (0.070):  35%|███▌      | 35/100 [00:02<00:05, 12.25it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.057 | Best: {'fx': 526.263} (0.070):  36%|███▌      | 36/100 [00:02<00:05, 12.25it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.057 | Best: {'fx': 526.263} (0.070):  37%|███▋      | 37/100 [00:02<00:04, 14.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.050 | Best: {'fx': 526.263} (0.070):  37%|███▋      | 37/100 [00:02<00:04, 14.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.040 | Best: {'fx': 526.263} (0.070):  38%|███▊      | 38/100 [00:02<00:04, 14.74it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.040 | Best: {'fx': 526.263} (0.070):  39%|███▉      | 39/100 [00:02<00:04, 14.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.037 | Best: {'fx': 526.263} (0.070):  39%|███▉      | 39/100 [00:02<00:04, 14.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.035 | Best: {'fx': 526.263} (0.070):  40%|████      | 40/100 [00:02<00:04, 14.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.033 | Best: {'fx': 526.263} (0.070):  41%|████      | 41/100 [00:02<00:04, 14.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.033 | Best: {'fx': 526.263} (0.070):  42%|████▏     | 42/100 [00:02<00:03, 16.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.031 | Best: {'fx': 526.263} (0.070):  42%|████▏     | 42/100 [00:02<00:03, 16.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.030 | Best: {'fx': 526.263} (0.070):  43%|████▎     | 43/100 [00:02<00:03, 16.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.024 | Best: {'fx': 526.263} (0.070):  44%|████▍     | 44/100 [00:02<00:03, 16.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.024 | Best: {'fx': 526.263} (0.070):  45%|████▌     | 45/100 [00:02<00:02, 18.55it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.029 | Best: {'fx': 526.263} (0.070):  45%|████▌     | 45/100 [00:02<00:02, 18.55it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.028 | Best: {'fx': 526.263} (0.070):  46%|████▌     | 46/100 [00:02<00:02, 18.55it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.026 | Best: {'fx': 526.263} (0.070):  47%|████▋     | 47/100 [00:03<00:02, 18.55it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.026 | Best: {'fx': 526.263} (0.070):  48%|████▊     | 48/100 [00:03<00:02, 19.01it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.027 | Best: {'fx': 526.263} (0.070):  48%|████▊     | 48/100 [00:03<00:02, 19.01it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.025 | Best: {'fx': 526.263} (0.070):  49%|████▉     | 49/100 [00:03<00:02, 19.01it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.025 | Best: {'fx': 526.263} (0.070):  50%|█████     | 50/100 [00:03<00:02, 19.01it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.025 | Best: {'fx': 526.263} (0.070):  51%|█████     | 51/100 [00:03<00:02, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.024 | Best: {'fx': 526.263} (0.070):  51%|█████     | 51/100 [00:03<00:02, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.023 | Best: {'fx': 526.263} (0.070):  52%|█████▏    | 52/100 [00:03<00:02, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.070):  53%|█████▎    | 53/100 [00:03<00:02, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.070):  54%|█████▍    | 54/100 [00:03<00:02, 18.11it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.023 | Best: {'fx': 526.263} (0.070):  54%|█████▍    | 54/100 [00:03<00:02, 18.11it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.070):  55%|█████▌    | 55/100 [00:03<00:02, 18.11it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.070):  56%|█████▌    | 56/100 [00:03<00:02, 18.11it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.070):  57%|█████▋    | 57/100 [00:03<00:02, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.070):  57%|█████▋    | 57/100 [00:03<00:02, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.070):  58%|█████▊    | 58/100 [00:03<00:02, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.070):  59%|█████▉    | 59/100 [00:03<00:02, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.070):  60%|██████    | 60/100 [00:03<00:02, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.070):  61%|██████    | 61/100 [00:03<00:01, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.070):  62%|██████▏   | 62/100 [00:03<00:01, 19.67it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.070):  63%|██████▎   | 63/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  63%|██████▎   | 63/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.070):  64%|██████▍   | 64/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  65%|██████▌   | 65/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  66%|██████▌   | 66/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.070):  67%|██████▋   | 67/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  68%|██████▊   | 68/100 [00:03<00:01, 26.89it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  69%|██████▉   | 69/100 [00:03<00:00, 32.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.070):  69%|██████▉   | 69/100 [00:03<00:00, 32.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  70%|███████   | 70/100 [00:03<00:00, 32.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  71%|███████   | 71/100 [00:03<00:00, 32.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  72%|███████▏  | 72/100 [00:03<00:00, 32.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  73%|███████▎  | 73/100 [00:03<00:00, 32.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.070):  74%|███████▍  | 74/100 [00:03<00:00, 33.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  74%|███████▍  | 74/100 [00:03<00:00, 33.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  75%|███████▌  | 75/100 [00:04<00:00, 33.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  76%|███████▌  | 76/100 [00:04<00:00, 33.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  77%|███████▋  | 77/100 [00:04<00:00, 33.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  78%|███████▊  | 78/100 [00:04<00:00, 26.24it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  78%|███████▊  | 78/100 [00:04<00:00, 26.24it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  79%|███████▉  | 79/100 [00:04<00:00, 26.24it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  80%|████████  | 80/100 [00:04<00:00, 26.24it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  81%|████████  | 81/100 [00:04<00:00, 26.63it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  81%|████████  | 81/100 [00:04<00:00, 26.63it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  82%|████████▏ | 82/100 [00:04<00:00, 26.63it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  83%|████████▎ | 83/100 [00:04<00:00, 26.63it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  84%|████████▍ | 84/100 [00:04<00:00, 26.63it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  85%|████████▌ | 85/100 [00:04<00:00, 26.63it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  86%|████████▌ | 86/100 [00:04<00:00, 30.08it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  86%|████████▌ | 86/100 [00:04<00:00, 30.08it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  87%|████████▋ | 87/100 [00:04<00:00, 30.08it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  88%|████████▊ | 88/100 [00:04<00:00, 30.08it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  89%|████████▉ | 89/100 [00:04<00:00, 30.08it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  90%|█████████ | 90/100 [00:04<00:00, 24.26it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.070):  90%|█████████ | 90/100 [00:04<00:00, 24.26it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  91%|█████████ | 91/100 [00:04<00:00, 24.26it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  92%|█████████▏| 92/100 [00:04<00:00, 24.26it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  93%|█████████▎| 93/100 [00:04<00:00, 25.33it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  93%|█████████▎| 93/100 [00:04<00:00, 25.33it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  94%|█████████▍| 94/100 [00:04<00:00, 25.33it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  95%|█████████▌| 95/100 [00:04<00:00, 25.33it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  96%|█████████▌| 96/100 [00:04<00:00, 23.98it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  96%|█████████▌| 96/100 [00:04<00:00, 23.98it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  97%|█████████▋| 97/100 [00:05<00:00, 23.98it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  98%|█████████▊| 98/100 [00:05<00:00, 23.98it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  99%|█████████▉| 99/100 [00:05<00:00, 19.26it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.070):  99%|█████████▉| 99/100 [00:05<00:00, 19.26it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.038 | Best: {'fx': 501.263} (0.038):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.038 | Best: {'fx': 501.263} (0.038):   2%|▏         | 1/50 [00:00<00:43,  1.14it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.039 | Best: {'fx': 502.283} (0.039):   2%|▏         | 1/50 [00:00<00:43,  1.14it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.040 | Best: {'fx': 503.303} (0.040):   4%|▍         | 2/50 [00:00<00:42,  1.14it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.040 | Best: {'fx': 503.303} (0.040):   6%|▌         | 3/50 [00:00<00:12,  3.72it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.041 | Best: {'fx': 504.324} (0.041):   6%|▌         | 3/50 [00:00<00:12,  3.72it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.041 | Best: {'fx': 505.344} (0.041):   8%|▊         | 4/50 [00:01<00:12,  3.72it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.041 | Best: {'fx': 505.344} (0.041):  10%|█         | 5/50 [00:01<00:07,  6.28it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.043 | Best: {'fx': 506.365} (0.043):  10%|█         | 5/50 [00:01<00:07,  6.28it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.044 | Best: {'fx': 507.385} (0.044):  12%|█▏        | 6/50 [00:01<00:07,  6.28it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.045 | Best: {'fx': 508.405} (0.045):  14%|█▍        | 7/50 [00:01<00:06,  6.28it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.045 | Best: {'fx': 508.405} (0.045):  16%|█▌        | 8/50 [00:01<00:04, 10.24it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.046 | Best: {'fx': 509.426} (0.046):  16%|█▌        | 8/50 [00:01<00:04, 10.24it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.048 | Best: {'fx': 510.446} (0.048):  18%|█▊        | 9/50 [00:01<00:04, 10.24it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.049 | Best: {'fx': 511.467} (0.049):  20%|██        | 10/50 [00:01<00:03, 10.24it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.049 | Best: {'fx': 511.467} (0.049):  22%|██▏       | 11/50 [00:01<00:02, 13.39it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.052 | Best: {'fx': 513.508} (0.052):  22%|██▏       | 11/50 [00:01<00:02, 13.39it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.051 | Best: {'fx': 513.508} (0.052):  24%|██▍       | 12/50 [00:01<00:02, 13.39it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.051 | Best: {'fx': 513.508} (0.052):  26%|██▌       | 13/50 [00:01<00:02, 13.48it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.053 | Best: {'fx': 514.528} (0.053):  26%|██▌       | 13/50 [00:01<00:02, 13.48it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.054 | Best: {'fx': 515.548} (0.054):  28%|██▊       | 14/50 [00:01<00:02, 13.48it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.055 | Best: {'fx': 516.569} (0.055):  30%|███       | 15/50 [00:01<00:02, 13.48it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.055 | Best: {'fx': 516.569} (0.055):  32%|███▏      | 16/50 [00:01<00:02, 14.50it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.058 | Best: {'fx': 517.589} (0.058):  32%|███▏      | 16/50 [00:01<00:02, 14.50it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.060 | Best: {'fx': 518.61} (0.060):  34%|███▍      | 17/50 [00:01<00:02, 14.50it/s] Brute | Start: {'fx': 526.263} (0.070) | Current: 0.071 | Best: {'fx': 526.773} (0.071):  36%|███▌      | 18/50 [00:01<00:02, 14.50it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.071 | Best: {'fx': 526.773} (0.071):  38%|███▊      | 19/50 [00:01<00:01, 16.47it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.060 | Best: {'fx': 526.773} (0.071):  38%|███▊      | 19/50 [00:01<00:01, 16.47it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.062 | Best: {'fx': 526.773} (0.071):  40%|████      | 20/50 [00:01<00:01, 16.47it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.063 | Best: {'fx': 526.773} (0.071):  42%|████▏     | 21/50 [00:01<00:01, 16.47it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.063 | Best: {'fx': 526.773} (0.071):  44%|████▍     | 22/50 [00:01<00:01, 18.67it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.065 | Best: {'fx': 526.773} (0.071):  44%|████▍     | 22/50 [00:01<00:01, 18.67it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.066 | Best: {'fx': 526.773} (0.071):  46%|████▌     | 23/50 [00:02<00:01, 18.67it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.068 | Best: {'fx': 526.773} (0.071):  48%|████▊     | 24/50 [00:02<00:01, 18.67it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.068 | Best: {'fx': 526.773} (0.071):  50%|█████     | 25/50 [00:02<00:01, 19.40it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.070 | Best: {'fx': 526.773} (0.071):  50%|█████     | 25/50 [00:02<00:01, 19.40it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.070 | Best: {'fx': 526.773} (0.071):  52%|█████▏    | 26/50 [00:02<00:01, 19.40it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.069 | Best: {'fx': 526.773} (0.071):  54%|█████▍    | 27/50 [00:02<00:01, 19.40it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.069 | Best: {'fx': 526.773} (0.071):  56%|█████▌    | 28/50 [00:02<00:01, 20.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.068 | Best: {'fx': 526.773} (0.071):  56%|█████▌    | 28/50 [00:02<00:01, 20.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.073 | Best: {'fx': 527.793} (0.073):  58%|█████▊    | 29/50 [00:02<00:01, 20.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.068 | Best: {'fx': 527.793} (0.073):  60%|██████    | 30/50 [00:02<00:00, 20.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.073 | Best: {'fx': 527.793} (0.073):  62%|██████▏   | 31/50 [00:02<00:00, 20.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.067 | Best: {'fx': 527.793} (0.073):  64%|██████▍   | 32/50 [00:02<00:00, 20.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.067 | Best: {'fx': 527.793} (0.073):  66%|██████▌   | 33/50 [00:02<00:00, 25.94it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.065 | Best: {'fx': 527.793} (0.073):  66%|██████▌   | 33/50 [00:02<00:00, 25.94it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.073 | Best: {'fx': 529.834} (0.073):  68%|██████▊   | 34/50 [00:02<00:00, 25.94it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.063 | Best: {'fx': 529.834} (0.073):  70%|███████   | 35/50 [00:02<00:00, 25.94it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.073 | Best: {'fx': 530.854} (0.073):  72%|███████▏  | 36/50 [00:02<00:00, 25.94it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.073 | Best: {'fx': 530.854} (0.073):  74%|███████▍  | 37/50 [00:02<00:00, 28.13it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.062 | Best: {'fx': 530.854} (0.073):  74%|███████▍  | 37/50 [00:02<00:00, 28.13it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.072 | Best: {'fx': 530.854} (0.073):  76%|███████▌  | 38/50 [00:02<00:00, 28.13it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.061 | Best: {'fx': 530.854} (0.073):  78%|███████▊  | 39/50 [00:02<00:00, 28.13it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.060 | Best: {'fx': 530.854} (0.073):  80%|████████  | 40/50 [00:02<00:00, 28.13it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.059 | Best: {'fx': 530.854} (0.073):  82%|████████▏ | 41/50 [00:02<00:00, 28.13it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.059 | Best: {'fx': 530.854} (0.073):  84%|████████▍ | 42/50 [00:02<00:00, 29.03it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.058 | Best: {'fx': 530.854} (0.073):  84%|████████▍ | 42/50 [00:02<00:00, 29.03it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.057 | Best: {'fx': 530.854} (0.073):  86%|████████▌ | 43/50 [00:02<00:00, 29.03it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.057 | Best: {'fx': 530.854} (0.073):  88%|████████▊ | 44/50 [00:02<00:00, 29.03it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.057 | Best: {'fx': 530.854} (0.073):  90%|█████████ | 45/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.056 | Best: {'fx': 530.854} (0.073):  90%|█████████ | 45/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.055 | Best: {'fx': 530.854} (0.073):  92%|█████████▏| 46/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.054 | Best: {'fx': 530.854} (0.073):  94%|█████████▍| 47/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.060 | Best: {'fx': 530.854} (0.073):  96%|█████████▌| 48/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.060 | Best: {'fx': 530.854} (0.073):  98%|█████████▊| 49/50 [00:02<00:00, 25.02it/s]Brute | Start: {'fx': 526.263} (0.070) | Current: 0.053 | Best: {'fx': 530.854} (0.073):  98%|█████████▊| 49/50 [00:03<00:00, 25.02it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.8544629973202
    px: 859.0
    py: 990.0
    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

  [Ring preference] diameter=3500 mm  →  radius=507.1 px

[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 6 incomplete frames): 73 frames will be used for optimization
  Rigs: 1000(1000mm):19/19, 1500(1500mm):18/20, 2000R(2000mm):18/20, 2000L(2000mm):18/20
  Total: 73/79 frames used for optimization
Initial score: 73.4616: 0iter [00:00, ?iter/s]  [Ring mask] 87,892/93,604 points within ring radius 507.1 px
[DEBUG] objective(x0) = 73.4616, picklable_objective(x0) = 71.0955
Best score: 71.0955: : 0iter [00:03, ?iter/s] Best score: 71.0955: : 1iter [00:03,  3.30s/iter]Best score: 71.0955: : 1iter [00:03,  3.30s/iter]Best score: 71.0955: : 2iter [00:03,  1.47s/iter]Best score: 71.0955: : 2iter [00:03,  1.47s/iter]Best score: 71.0955: : 3iter [00:03,  1.11iter/s]Best score: 71.0955: : 3iter [00:03,  1.11iter/s]Best score: 71.0955: : 4iter [00:03,  1.59iter/s]Best score: 71.0955: : 4iter [00:04,  1.59iter/s]Best score: 71.0955: : 5iter [00:04,  2.11iter/s]Best score: 71.0955: : 5iter [00:04,  2.11iter/s]Best score: 71.0955: : 6iter [00:04,  2.62iter/s]Best score: 71.0955: : 6iter [00:04,  2.62iter/s]Best score: 71.0955: : 7iter [00:04,  3.11iter/s]Best score: 71.0955: : 7iter [00:04,  3.11iter/s]Best score: 71.0955: : 8iter [00:04,  3.53iter/s]Best score: 71.0955: : 8iter [00:04,  3.53iter/s]Best score: 71.0955: : 9iter [00:04,  3.89iter/s]Best score: 71.0955: : 9iter [00:05,  3.89iter/s]Best score: 71.0955: : 10iter [00:05,  4.03iter/s]Best score: 71.0955: : 10iter [00:05,  4.03iter/s]Best score: 71.0955: : 11iter [00:05,  4.30iter/s]Best score: 71.0955: : 11iter [00:05,  4.30iter/s]Best score: 71.0955: : 12iter [00:05,  4.50iter/s]Best score: 71.0955: : 12iter [00:05,  4.50iter/s]Best score: 71.0955: : 13iter [00:05,  4.61iter/s]Best score: 71.0955: : 13iter [00:05,  4.61iter/s]Best score: 71.0955: : 14iter [00:05,  4.63iter/s]Best score: 71.0955: : 14iter [00:06,  4.63iter/s]Best score: 71.0955: : 15iter [00:06,  4.70iter/s]Best score: 71.0955: : 15iter [00:06,  4.70iter/s]Best score: 71.0955: : 16iter [00:06,  4.72iter/s]Best score: 71.0955: : 16iter [00:06,  4.72iter/s]Best score: 71.0955: : 17iter [00:06,  4.71iter/s]Best score: 71.0955: : 17iter [00:06,  4.71iter/s]Best score: 71.0955: : 18iter [00:06,  4.76iter/s]Best score: 71.0955: : 18iter [00:07,  4.76iter/s]Best score: 71.0955: : 19iter [00:07,  4.78iter/s]Best score: 38.1744: : 19iter [00:07,  4.78iter/s]Best score: 38.1744: : 20iter [00:07,  3.32iter/s]Best score: 38.1744: : 20iter [00:07,  3.32iter/s]Best score: 38.1744: : 21iter [00:07,  3.69iter/s]Best score: 38.1744: : 21iter [00:07,  3.69iter/s]Best score: 38.1744: : 22iter [00:07,  4.03iter/s]Best score: 38.1744: : 22iter [00:08,  4.03iter/s]Best score: 38.1744: : 23iter [00:08,  4.28iter/s]Best score: 38.1744: : 23iter [00:08,  4.28iter/s]Best score: 38.1744: : 24iter [00:08,  4.40iter/s]Best score: 38.1744: : 24iter [00:08,  4.40iter/s]Best score: 38.1744: : 25iter [00:08,  4.59iter/s]Best score: 38.1744: : 25iter [00:08,  4.59iter/s]Best score: 38.1744: : 26iter [00:08,  4.67iter/s]Best score: 38.1744: : 26iter [00:08,  4.67iter/s]Best score: 38.1744: : 27iter [00:08,  4.75iter/s]Best score: 38.1744: : 27iter [00:09,  4.75iter/s]Best score: 38.1744: : 28iter [00:09,  4.82iter/s]Best score: 38.1744: : 28iter [00:09,  4.82iter/s]Best score: 38.1744: : 29iter [00:09,  4.84iter/s]Best score: 38.1744: : 29iter [00:09,  4.84iter/s]Best score: 38.1744: : 30iter [00:09,  4.70iter/s]Best score: 38.1744: : 30iter [00:09,  4.70iter/s]Best score: 38.1744: : 31iter [00:09,  4.78iter/s]Best score: 38.1744: : 31iter [00:09,  4.78iter/s]Best score: 38.1744: : 32iter [00:09,  4.87iter/s]Best score: 38.1744: : 32iter [00:10,  4.87iter/s]Best score: 38.1744: : 33iter [00:10,  4.89iter/s]Best score: 38.1744: : 33iter [00:10,  4.89iter/s]Best score: 38.1744: : 34iter [00:10,  4.88iter/s]Best score: 38.1744: : 34iter [00:10,  4.88iter/s]Best score: 38.1744: : 35iter [00:10,  4.87iter/s]Best score: 38.1744: : 35iter [00:10,  4.87iter/s]Best score: 38.1744: : 36iter [00:10,  4.83iter/s]Best score: 38.1744: : 36iter [00:10,  4.83iter/s]Best score: 38.1744: : 37iter [00:10,  4.84iter/s]Best score: 38.1744: : 37iter [00:11,  4.84iter/s]Best score: 38.1744: : 38iter [00:11,  4.96iter/s]Best score: 38.1744: : 38iter [00:11,  4.96iter/s]Best score: 38.1744: : 39iter [00:11,  5.07iter/s]Best score: 38.1744: : 39iter [00:11,  5.07iter/s]Best score: 38.1744: : 40iter [00:11,  4.86iter/s]Best score: 38.1744: : 40iter [00:11,  4.86iter/s]Best score: 38.1744: : 41iter [00:11,  4.82iter/s]Best score: 38.1744: : 41iter [00:12,  4.82iter/s]Best score: 38.1744: : 42iter [00:12,  4.80iter/s]Best score: 38.1744: : 42iter [00:12,  4.80iter/s]Best score: 38.1744: : 43iter [00:12,  4.88iter/s]Best score: 38.1744: : 43iter [00:12,  4.88iter/s]Best score: 38.1744: : 44iter [00:12,  4.89iter/s]Best score: 38.1744: : 44iter [00:12,  4.89iter/s]Best score: 38.1744: : 45iter [00:12,  4.89iter/s]Best score: 38.1744: : 45iter [00:12,  4.89iter/s]Best score: 38.1744: : 46iter [00:12,  4.87iter/s]Best score: 38.1744: : 46iter [00:13,  4.87iter/s]Best score: 38.1744: : 47iter [00:13,  4.89iter/s]Best score: 38.1744: : 47iter [00:13,  4.89iter/s]Best score: 38.1744: : 48iter [00:13,  4.87iter/s]Best score: 38.1744: : 48iter [00:13,  4.87iter/s]Best score: 38.1744: : 49iter [00:13,  4.88iter/s]Best score: 27.6782: : 49iter [00:13,  4.88iter/s]Best score: 27.6782: : 50iter [00:13,  4.84iter/s]
[ITER 50] Best: 27.6782
[PARAMS] Current best parameters:
  fx: 524.1844
  fy: 521.7964
  px: 850.3022
  py: 1004.1363
  k1: 0.01041349
  k2: -0.01788577
  k3: 0.00181877
  k4: 0.00305199
  p1: 0.00080820
  p2: 0.00039443
  p3: -0.00016000
  p4: 0.00613352
  scale: 3.1250
  rotation: -0.8681
Best score: 27.6782: : 50iter [00:13,  4.84iter/s]Best score: 27.6782: : 51iter [00:13,  4.97iter/s]Best score: 27.6782: : 51iter [00:14,  4.97iter/s]Best score: 27.6782: : 52iter [00:14,  4.99iter/s]Best score: 27.6782: : 52iter [00:14,  4.99iter/s]Best score: 27.6782: : 53iter [00:14,  4.94iter/s]Best score: 27.6782: : 53iter [00:14,  4.94iter/s]Best score: 27.6782: : 54iter [00:14,  4.98iter/s]Best score: 27.6782: : 54iter [00:14,  4.98iter/s]Best score: 27.6782: : 55iter [00:14,  5.02iter/s]Best score: 27.6782: : 55iter [00:14,  5.02iter/s]Best score: 27.6782: : 56iter [00:14,  4.99iter/s]Best score: 27.6782: : 56iter [00:15,  4.99iter/s]Best score: 27.6782: : 57iter [00:15,  4.98iter/s]Best score: 27.6782: : 57iter [00:15,  4.98iter/s]Best score: 27.6782: : 58iter [00:15,  4.97iter/s]Best score: 27.6782: : 58iter [00:15,  4.97iter/s]Best score: 27.6782: : 59iter [00:15,  4.95iter/s]Best score: 18.9705: : 59iter [00:15,  4.95iter/s]Best score: 18.9705: : 60iter [00:15,  4.84iter/s]Best score: 18.9705: : 60iter [00:15,  4.84iter/s]Best score: 18.9705: : 61iter [00:15,  4.89iter/s]Best score: 18.9705: : 61iter [00:16,  4.89iter/s]Best score: 18.9705: : 62iter [00:16,  4.96iter/s]Best score: 18.9705: : 62iter [00:16,  4.96iter/s]Best score: 18.9705: : 63iter [00:16,  4.95iter/s]Best score: 18.9705: : 63iter [00:16,  4.95iter/s]Best score: 18.9705: : 64iter [00:16,  4.92iter/s]Best score: 18.9705: : 64iter [00:16,  4.92iter/s]Best score: 18.9705: : 65iter [00:16,  4.94iter/s]Best score: 18.9705: : 65iter [00:16,  4.94iter/s]Best score: 18.9705: : 66iter [00:16,  4.96iter/s]Best score: 18.9705: : 66iter [00:17,  4.96iter/s]Best score: 18.9705: : 67iter [00:17,  4.97iter/s]Best score: 18.9705: : 67iter [00:17,  4.97iter/s]Best score: 18.9705: : 68iter [00:17,  4.90iter/s]Best score: 18.9705: : 68iter [00:17,  4.90iter/s]Best score: 18.9705: : 69iter [00:17,  4.95iter/s]Best score: 17.9079: : 69iter [00:17,  4.95iter/s]Best score: 17.9079: : 70iter [00:17,  4.82iter/s]Best score: 17.9079: : 70iter [00:17,  4.82iter/s]Best score: 17.9079: : 71iter [00:17,  4.81iter/s]Best score: 17.9079: : 71iter [00:18,  4.81iter/s]Best score: 17.9079: : 72iter [00:18,  4.88iter/s]Best score: 17.9079: : 72iter [00:18,  4.88iter/s]Best score: 17.9079: : 73iter [00:18,  4.94iter/s]Best score: 17.9079: : 73iter [00:18,  4.94iter/s]Best score: 17.9079: : 74iter [00:18,  4.93iter/s]Best score: 17.9079: : 74iter [00:18,  4.93iter/s]Best score: 17.9079: : 75iter [00:18,  4.98iter/s]Best score: 17.9079: : 75iter [00:18,  4.98iter/s]Best score: 17.9079: : 76iter [00:18,  5.02iter/s]Best score: 17.9079: : 76iter [00:19,  5.02iter/s]Best score: 17.9079: : 77iter [00:19,  5.03iter/s]Best score: 17.9079: : 77iter [00:19,  5.03iter/s]Best score: 17.9079: : 78iter [00:19,  5.08iter/s]Best score: 17.9079: : 78iter [00:19,  5.08iter/s]Best score: 17.9079: : 79iter [00:19,  4.98iter/s]Best score: 16.6609: : 79iter [00:19,  4.98iter/s]Best score: 16.6609: : 80iter [00:19,  4.81iter/s]Best score: 16.6609: : 80iter [00:19,  4.81iter/s]Best score: 16.6609: : 81iter [00:19,  4.87iter/s]Best score: 16.6609: : 81iter [00:20,  4.87iter/s]Best score: 16.6609: : 82iter [00:20,  4.88iter/s]Best score: 16.6609: : 82iter [00:20,  4.88iter/s]Best score: 16.6609: : 83iter [00:20,  4.97iter/s]Best score: 16.6609: : 83iter [00:20,  4.97iter/s]Best score: 16.6609: : 84iter [00:20,  5.00iter/s]Best score: 16.6609: : 84iter [00:20,  5.00iter/s]Best score: 16.6609: : 85iter [00:20,  4.98iter/s]Best score: 16.6609: : 85iter [00:20,  4.98iter/s]Best score: 16.6609: : 86iter [00:20,  4.97iter/s]Best score: 16.6609: : 86iter [00:21,  4.97iter/s]Best score: 16.6609: : 87iter [00:21,  4.94iter/s]Best score: 16.6609: : 87iter [00:21,  4.94iter/s]Best score: 16.6609: : 88iter [00:21,  5.00iter/s]Best score: 16.6609: : 88iter [00:21,  5.00iter/s]Best score: 16.6609: : 89iter [00:21,  4.97iter/s]Best score: 16.6609: : 89iter [00:21,  4.97iter/s]Best score: 16.6609: : 90iter [00:21,  4.90iter/s]Best score: 16.6609: : 90iter [00:21,  4.90iter/s]Best score: 16.6609: : 91iter [00:21,  4.94iter/s]Best score: 16.6609: : 91iter [00:22,  4.94iter/s]Best score: 16.6609: : 92iter [00:22,  4.94iter/s]Best score: 16.6609: : 92iter [00:22,  4.94iter/s]Best score: 16.6609: : 93iter [00:22,  5.00iter/s]Best score: 16.6609: : 93iter [00:22,  5.00iter/s]Best score: 16.6609: : 94iter [00:22,  5.03iter/s]Best score: 16.6609: : 94iter [00:22,  5.03iter/s]Best score: 16.6609: : 95iter [00:22,  4.99iter/s]Best score: 16.6609: : 95iter [00:22,  4.99iter/s]Best score: 16.6609: : 96iter [00:22,  5.05iter/s]Best score: 16.6609: : 96iter [00:23,  5.05iter/s]Best score: 16.6609: : 97iter [00:23,  4.99iter/s]Best score: 16.6609: : 97iter [00:23,  4.99iter/s]Best score: 16.6609: : 98iter [00:23,  5.01iter/s]Best score: 16.6609: : 98iter [00:23,  5.01iter/s]Best score: 16.6609: : 99iter [00:23,  5.04iter/s]
[ITER 100] Best: 16.6609
[PARAMS] Current best parameters:
  fx: 561.6348
  fy: 561.6192
  px: 848.7107
  py: 1003.8586
  k1: -0.02297780
  k2: -0.01601133
  k3: 0.01178574
  k4: -0.00265258
  p1: 0.00506227
  p2: -0.00817638
  p3: -0.00557363
  p4: 0.00703206
  scale: 3.0224
  rotation: -0.4414
Best score: 16.6609: : 99iter [00:23,  5.04iter/s]Best score: 16.6609: : 100iter [00:23,  4.87iter/s]Best score: 16.6609: : 100iter [00:23,  4.87iter/s]Best score: 16.6609: : 101iter [00:23,  4.87iter/s]Best score: 16.6609: : 101iter [00:24,  4.87iter/s]Best score: 16.6609: : 102iter [00:24,  4.92iter/s]Best score: 16.6609: : 102iter [00:24,  4.92iter/s]Best score: 16.6609: : 103iter [00:24,  4.98iter/s]Best score: 16.6609: : 103iter [00:24,  4.98iter/s]Best score: 16.6609: : 104iter [00:24,  5.02iter/s]Best score: 16.6609: : 104iter [00:24,  5.02iter/s]Best score: 16.6609: : 105iter [00:24,  5.02iter/s]Best score: 16.6609: : 105iter [00:24,  5.02iter/s]Best score: 16.6609: : 106iter [00:24,  5.11iter/s]Best score: 16.6609: : 106iter [00:25,  5.11iter/s]Best score: 16.6609: : 107iter [00:25,  5.08iter/s]Best score: 16.6609: : 107iter [00:25,  5.08iter/s]Best score: 16.6609: : 108iter [00:25,  5.08iter/s]Best score: 16.6609: : 108iter [00:25,  5.08iter/s]Best score: 16.6609: : 109iter [00:25,  5.03iter/s]Best score: 16.4584: : 109iter [00:25,  5.03iter/s]Best score: 16.4584: : 110iter [00:25,  4.87iter/s]Best score: 16.4584: : 110iter [00:25,  4.87iter/s]Best score: 16.4584: : 111iter [00:25,  4.95iter/s]Best score: 16.4584: : 111iter [00:26,  4.95iter/s]Best score: 16.4584: : 112iter [00:26,  4.96iter/s]Best score: 16.4584: : 112iter [00:26,  4.96iter/s]Best score: 16.4584: : 113iter [00:26,  4.91iter/s]Best score: 16.4584: : 113iter [00:26,  4.91iter/s]Best score: 16.4584: : 114iter [00:26,  4.96iter/s]Best score: 16.4584: : 114iter [00:26,  4.96iter/s]Best score: 16.4584: : 115iter [00:26,  4.96iter/s]Best score: 16.4584: : 115iter [00:26,  4.96iter/s]Best score: 16.4584: : 116iter [00:26,  4.96iter/s]Best score: 16.4584: : 116iter [00:27,  4.96iter/s]Best score: 16.4584: : 117iter [00:27,  5.03iter/s]Best score: 16.4584: : 117iter [00:27,  5.03iter/s]Best score: 16.4584: : 118iter [00:27,  4.99iter/s]Best score: 16.4584: : 118iter [00:27,  4.99iter/s]Best score: 16.4584: : 119iter [00:27,  5.00iter/s]Best score: 16.2840: : 119iter [00:27,  5.00iter/s]Best score: 16.2840: : 120iter [00:27,  4.84iter/s]Best score: 16.2840: : 120iter [00:27,  4.84iter/s]Best score: 16.2840: : 121iter [00:27,  4.93iter/s]Best score: 16.2840: : 121iter [00:28,  4.93iter/s]Best score: 16.2840: : 122iter [00:28,  4.93iter/s]Best score: 16.2840: : 122iter [00:28,  4.93iter/s]Best score: 16.2840: : 123iter [00:28,  4.96iter/s]Best score: 16.2840: : 123iter [00:28,  4.96iter/s]Best score: 16.2840: : 124iter [00:28,  4.98iter/s]Best score: 16.2840: : 124iter [00:28,  4.98iter/s]Best score: 16.2840: : 125iter [00:28,  5.02iter/s]Best score: 16.2840: : 125iter [00:28,  5.02iter/s]Best score: 16.2840: : 126iter [00:28,  5.06iter/s]Best score: 16.2840: : 126iter [00:29,  5.06iter/s]Best score: 16.2840: : 127iter [00:29,  5.01iter/s]Best score: 16.2840: : 127iter [00:29,  5.01iter/s]Best score: 16.2840: : 128iter [00:29,  5.12iter/s]Best score: 16.2840: : 128iter [00:29,  5.12iter/s]Best score: 16.2840: : 129iter [00:29,  5.06iter/s]Best score: 16.2836: : 129iter [00:29,  5.06iter/s]Best score: 16.2836: : 130iter [00:29,  4.95iter/s]Best score: 16.2836: : 130iter [00:29,  4.95iter/s]Best score: 16.2836: : 131iter [00:29,  4.96iter/s]Best score: 16.2836: : 131iter [00:30,  4.96iter/s]Best score: 16.2836: : 132iter [00:30,  4.96iter/s]Best score: 16.2836: : 132iter [00:30,  4.96iter/s]Best score: 16.2836: : 133iter [00:30,  4.95iter/s]Best score: 16.2836: : 133iter [00:30,  4.95iter/s]Best score: 16.2836: : 134iter [00:30,  4.98iter/s]Best score: 16.2836: : 134iter [00:30,  4.98iter/s]Best score: 16.2836: : 135iter [00:30,  4.91iter/s]Best score: 16.2836: : 135iter [00:30,  4.91iter/s]Best score: 16.2836: : 136iter [00:30,  4.92iter/s]Best score: 16.2836: : 136iter [00:31,  4.92iter/s]Best score: 16.2836: : 137iter [00:31,  4.96iter/s]Best score: 16.2836: : 137iter [00:31,  4.96iter/s]Best score: 16.2836: : 138iter [00:31,  4.96iter/s]Best score: 16.2836: : 138iter [00:31,  4.96iter/s]Best score: 16.2836: : 139iter [00:31,  4.93iter/s]Best score: 16.2642: : 139iter [00:31,  4.93iter/s]Best score: 16.2642: : 140iter [00:31,  4.82iter/s]Best score: 16.2642: : 140iter [00:32,  4.82iter/s]Best score: 16.2642: : 141iter [00:32,  4.85iter/s]Best score: 16.2642: : 141iter [00:32,  4.85iter/s]Best score: 16.2642: : 142iter [00:32,  4.90iter/s]Best score: 16.2642: : 142iter [00:32,  4.90iter/s]Best score: 16.2642: : 143iter [00:32,  4.99iter/s]Best score: 16.2642: : 143iter [00:32,  4.99iter/s]Best score: 16.2642: : 144iter [00:32,  4.98iter/s]Best score: 16.2642: : 144iter [00:32,  4.98iter/s]Best score: 16.2642: : 145iter [00:32,  5.02iter/s]Best score: 16.2642: : 145iter [00:32,  5.02iter/s]Best score: 16.2642: : 146iter [00:32,  5.00iter/s]Best score: 16.2642: : 146iter [00:33,  5.00iter/s]Best score: 16.2642: : 147iter [00:33,  5.04iter/s]Best score: 16.2642: : 147iter [00:33,  5.04iter/s]Best score: 16.2642: : 148iter [00:33,  5.01iter/s]Best score: 16.2642: : 148iter [00:33,  5.01iter/s]Best score: 16.2642: : 149iter [00:33,  5.01iter/s]
[ITER 150] Best: 16.2642
[PARAMS] Current best parameters:
  fx: 562.7714
  fy: 563.1609
  px: 849.0729
  py: 1003.8614
  k1: -0.01619207
  k2: -0.01812451
  k3: 0.00532457
  k4: 0.00073604
  p1: 0.00492456
  p2: -0.00730330
  p3: 0.00255091
  p4: -0.00235315
  scale: 2.9922
  rotation: -0.3341
Best score: 16.2642: : 149iter [00:33,  5.01iter/s]Best score: 16.2642: : 150iter [00:33,  4.88iter/s]Best score: 16.2642: : 150iter [00:34,  4.88iter/s]Best score: 16.2642: : 151iter [00:34,  4.91iter/s]Best score: 16.2642: : 151iter [00:34,  4.91iter/s]Best score: 16.2642: : 152iter [00:34,  4.94iter/s]Best score: 16.2642: : 152iter [00:34,  4.94iter/s]Best score: 16.2642: : 153iter [00:34,  4.98iter/s]Best score: 16.2642: : 153iter [00:34,  4.98iter/s]Best score: 16.2642: : 154iter [00:34,  4.94iter/s]Best score: 16.2642: : 154iter [00:34,  4.94iter/s]Best score: 16.2642: : 155iter [00:34,  4.91iter/s]Best score: 16.2642: : 155iter [00:35,  4.91iter/s]Best score: 16.2642: : 156iter [00:35,  4.98iter/s]Best score: 16.2642: : 156iter [00:35,  4.98iter/s]Best score: 16.2642: : 157iter [00:35,  4.97iter/s]Best score: 16.2642: : 157iter [00:35,  4.97iter/s]Best score: 16.2642: : 158iter [00:35,  5.01iter/s]Best score: 16.2642: : 158iter [00:35,  5.01iter/s]Best score: 16.2642: : 159iter [00:35,  5.05iter/s]Best score: 16.2523: : 159iter [00:35,  5.05iter/s]Best score: 16.2523: : 160iter [00:35,  4.87iter/s]Best score: 16.2523: : 160iter [00:36,  4.87iter/s]Best score: 16.2523: : 161iter [00:36,  4.97iter/s]Best score: 16.2523: : 161iter [00:36,  4.97iter/s]Best score: 16.2523: : 162iter [00:36,  5.04iter/s]Best score: 16.2523: : 162iter [00:36,  5.04iter/s]Best score: 16.2523: : 163iter [00:36,  4.99iter/s]Best score: 16.2523: : 163iter [00:36,  4.99iter/s]Best score: 16.2523: : 164iter [00:36,  4.95iter/s]Best score: 16.2523: : 164iter [00:36,  4.95iter/s]Best score: 16.2523: : 165iter [00:36,  4.98iter/s]Best score: 16.2523: : 165iter [00:37,  4.98iter/s]Best score: 16.2523: : 166iter [00:37,  4.97iter/s]Best score: 16.2523: : 166iter [00:37,  4.97iter/s]Best score: 16.2523: : 167iter [00:37,  4.96iter/s]Best score: 16.2523: : 167iter [00:37,  4.96iter/s]Best score: 16.2523: : 168iter [00:37,  4.95iter/s]Best score: 16.2523: : 168iter [00:37,  4.95iter/s]Best score: 16.2523: : 169iter [00:37,  4.95iter/s]Best score: 16.2054: : 169iter [00:37,  4.95iter/s]Best score: 16.2054: : 170iter [00:37,  4.79iter/s]Best score: 16.2054: : 170iter [00:38,  4.79iter/s]Best score: 16.2054: : 171iter [00:38,  4.85iter/s]Best score: 16.2054: : 171iter [00:38,  4.85iter/s]Best score: 16.2054: : 172iter [00:38,  4.97iter/s]Best score: 16.2054: : 172iter [00:38,  4.97iter/s]Best score: 16.2054: : 173iter [00:38,  4.96iter/s]Best score: 16.2054: : 173iter [00:38,  4.96iter/s]Best score: 16.2054: : 174iter [00:38,  4.99iter/s]Best score: 16.2054: : 174iter [00:38,  4.99iter/s]Best score: 16.2054: : 175iter [00:38,  5.08iter/s]Best score: 16.2054: : 175iter [00:39,  5.08iter/s]Best score: 16.2054: : 176iter [00:39,  5.04iter/s]Best score: 16.2054: : 176iter [00:39,  5.04iter/s]Best score: 16.2054: : 177iter [00:39,  5.00iter/s]Best score: 16.2054: : 177iter [00:39,  5.00iter/s]Best score: 16.2054: : 178iter [00:39,  4.99iter/s]Best score: 16.2054: : 178iter [00:39,  4.99iter/s]Best score: 16.2054: : 179iter [00:39,  5.06iter/s]Best score: 16.2054: : 179iter [00:39,  5.06iter/s]Best score: 16.2054: : 180iter [00:39,  4.94iter/s]Best score: 16.2054: : 180iter [00:40,  4.94iter/s]Best score: 16.2054: : 181iter [00:40,  5.00iter/s]Best score: 16.2054: : 181iter [00:40,  5.00iter/s]Best score: 16.2054: : 182iter [00:40,  5.02iter/s]Best score: 16.2054: : 182iter [00:40,  5.02iter/s]Best score: 16.2054: : 183iter [00:40,  5.05iter/s]Best score: 16.2054: : 183iter [00:40,  5.05iter/s]Best score: 16.2054: : 184iter [00:40,  5.11iter/s]Best score: 16.2054: : 184iter [00:40,  5.11iter/s]Best score: 16.2054: : 185iter [00:40,  5.08iter/s]Best score: 16.2054: : 185iter [00:41,  5.08iter/s]Best score: 16.2054: : 186iter [00:41,  5.05iter/s]Best score: 16.2054: : 186iter [00:41,  5.05iter/s]Best score: 16.2054: : 187iter [00:41,  5.06iter/s]Best score: 16.2054: : 187iter [00:41,  5.06iter/s]Best score: 16.2054: : 188iter [00:41,  5.08iter/s]Best score: 16.2054: : 188iter [00:41,  5.08iter/s]Best score: 16.2054: : 189iter [00:41,  5.09iter/s]Best score: 16.1934: : 189iter [00:41,  5.09iter/s]Best score: 16.1934: : 190iter [00:41,  4.90iter/s]Best score: 16.1934: : 190iter [00:42,  4.90iter/s]Best score: 16.1934: : 191iter [00:42,  4.94iter/s]Best score: 16.1934: : 191iter [00:42,  4.94iter/s]Best score: 16.1934: : 192iter [00:42,  4.99iter/s]Best score: 16.1934: : 192iter [00:42,  4.99iter/s]Best score: 16.1934: : 193iter [00:42,  4.99iter/s]Best score: 16.1934: : 193iter [00:42,  4.99iter/s]Best score: 16.1934: : 194iter [00:42,  4.99iter/s]Best score: 16.1934: : 194iter [00:42,  4.99iter/s]Best score: 16.1934: : 195iter [00:42,  5.00iter/s]Best score: 16.1934: : 195iter [00:43,  5.00iter/s]Best score: 16.1934: : 196iter [00:43,  4.94iter/s]Best score: 16.1934: : 196iter [00:43,  4.94iter/s]Best score: 16.1934: : 197iter [00:43,  5.02iter/s]Best score: 16.1934: : 197iter [00:43,  5.02iter/s]Best score: 16.1934: : 198iter [00:43,  5.00iter/s]Best score: 16.1934: : 198iter [00:43,  5.00iter/s]Best score: 16.1934: : 199iter [00:43,  4.92iter/s]
[ITER 200] Best: 16.1934
[PARAMS] Current best parameters:
  fx: 548.6035
  fy: 548.9371
  px: 849.1121
  py: 1004.9316
  k1: -0.00581009
  k2: -0.01435878
  k3: 0.00488600
  k4: -0.00023843
  p1: 0.00763798
  p2: -0.00628124
  p3: 0.00341330
  p4: -0.00468524
  scale: 3.0045
  rotation: -0.4108
Best score: 16.1934: : 199iter [00:43,  4.92iter/s]Best score: 16.1934: : 200iter [00:43,  4.74iter/s]Best score: 16.1934: : 200iter [00:44,  4.74iter/s]Best score: 16.1934: : 201iter [00:44,  4.82iter/s]Best score: 16.1934: : 201iter [00:44,  4.82iter/s]Best score: 16.1934: : 202iter [00:44,  4.89iter/s]Best score: 16.1934: : 202iter [00:44,  4.89iter/s]Best score: 16.1934: : 203iter [00:44,  4.94iter/s]Best score: 16.1934: : 203iter [00:44,  4.94iter/s]Best score: 16.1934: : 204iter [00:44,  5.00iter/s]Best score: 16.1934: : 204iter [00:44,  5.00iter/s]Best score: 16.1934: : 205iter [00:44,  5.02iter/s]Best score: 16.1934: : 205iter [00:45,  5.02iter/s]Best score: 16.1934: : 206iter [00:45,  5.00iter/s]Best score: 16.1934: : 206iter [00:45,  5.00iter/s]Best score: 16.1934: : 207iter [00:45,  5.06iter/s]Best score: 16.1934: : 207iter [00:45,  5.06iter/s]Best score: 16.1934: : 208iter [00:45,  4.94iter/s]Best score: 16.1934: : 208iter [00:45,  4.94iter/s]Best score: 16.1934: : 209iter [00:45,  4.92iter/s]Best score: 16.1934: : 209iter [00:45,  4.92iter/s]Best score: 16.1934: : 210iter [00:45,  4.77iter/s]Best score: 16.1934: : 210iter [00:46,  4.77iter/s]Best score: 16.1934: : 211iter [00:46,  4.82iter/s]Best score: 16.1934: : 211iter [00:46,  4.82iter/s]Best score: 16.1934: : 212iter [00:46,  4.88iter/s]Best score: 16.1934: : 212iter [00:46,  4.88iter/s]Best score: 16.1934: : 213iter [00:46,  4.95iter/s]Best score: 16.1934: : 213iter [00:46,  4.95iter/s]Best score: 16.1934: : 214iter [00:46,  4.94iter/s]Best score: 16.1934: : 214iter [00:46,  4.94iter/s]Best score: 16.1934: : 215iter [00:46,  4.94iter/s]Best score: 16.1934: : 215iter [00:47,  4.94iter/s]Best score: 16.1934: : 216iter [00:47,  3.61iter/s]Best score: 16.1934: : 216iter [00:47,  3.61iter/s]Best score: 16.1934: : 217iter [00:47,  3.95iter/s]Best score: 16.1934: : 217iter [00:47,  3.95iter/s]Best score: 16.1934: : 218iter [00:47,  4.19iter/s]Best score: 16.1934: : 218iter [00:47,  4.19iter/s]Best score: 16.1934: : 219iter [00:47,  4.44iter/s]Best score: 16.1861: : 219iter [00:48,  4.44iter/s]Best score: 16.1861: : 220iter [00:48,  4.47iter/s]Best score: 16.1861: : 220iter [00:48,  4.47iter/s]Best score: 16.1861: : 221iter [00:48,  4.65iter/s]Best score: 16.1861: : 221iter [00:48,  4.65iter/s]Best score: 16.1861: : 222iter [00:48,  4.75iter/s]Best score: 16.1861: : 222iter [00:48,  4.75iter/s]Best score: 16.1861: : 223iter [00:48,  4.79iter/s]Best score: 16.1861: : 223iter [00:48,  4.79iter/s]Best score: 16.1861: : 224iter [00:48,  4.90iter/s]Best score: 16.1861: : 224iter [00:49,  4.90iter/s]Best score: 16.1861: : 225iter [00:49,  4.99iter/s]Best score: 16.1861: : 225iter [00:49,  4.99iter/s]Best score: 16.1861: : 226iter [00:49,  4.97iter/s]Best score: 16.1861: : 226iter [00:49,  4.97iter/s]Best score: 16.1861: : 227iter [00:49,  4.90iter/s]Best score: 16.1861: : 227iter [00:49,  4.90iter/s]Best score: 16.1861: : 228iter [00:49,  4.96iter/s]Best score: 16.1861: : 228iter [00:49,  4.96iter/s]Best score: 16.1861: : 229iter [00:49,  4.98iter/s]Best score: 16.1812: : 229iter [00:50,  4.98iter/s]Best score: 16.1812: : 230iter [00:50,  4.78iter/s]Best score: 16.1812: : 230iter [00:50,  4.78iter/s]Best score: 16.1812: : 231iter [00:50,  4.84iter/s]Best score: 16.1812: : 231iter [00:50,  4.84iter/s]Best score: 16.1812: : 232iter [00:50,  4.90iter/s]Best score: 16.1812: : 232iter [00:50,  4.90iter/s]Best score: 16.1812: : 233iter [00:50,  4.95iter/s]Best score: 16.1812: : 233iter [00:50,  4.95iter/s]Best score: 16.1812: : 234iter [00:50,  4.95iter/s]Best score: 16.1812: : 234iter [00:51,  4.95iter/s]Best score: 16.1812: : 235iter [00:51,  4.88iter/s]Best score: 16.1812: : 235iter [00:51,  4.88iter/s]Best score: 16.1812: : 236iter [00:51,  4.94iter/s]Best score: 16.1812: : 236iter [00:51,  4.94iter/s]Best score: 16.1812: : 237iter [00:51,  4.99iter/s]Best score: 16.1812: : 237iter [00:51,  4.99iter/s]Best score: 16.1812: : 238iter [00:51,  5.05iter/s]Best score: 16.1812: : 238iter [00:51,  5.05iter/s]Best score: 16.1812: : 239iter [00:51,  4.96iter/s]Best score: 16.1802: : 239iter [00:52,  4.96iter/s]Best score: 16.1802: : 240iter [00:52,  4.80iter/s]Best score: 16.1802: : 240iter [00:52,  4.80iter/s]Best score: 16.1802: : 241iter [00:52,  4.91iter/s]Best score: 16.1802: : 241iter [00:52,  4.91iter/s]Best score: 16.1802: : 242iter [00:52,  4.97iter/s]Best score: 16.1802: : 242iter [00:52,  4.97iter/s]Best score: 16.1802: : 243iter [00:52,  4.96iter/s]Best score: 16.1802: : 243iter [00:53,  4.96iter/s]Best score: 16.1802: : 244iter [00:53,  4.90iter/s]Best score: 16.1802: : 244iter [00:53,  4.90iter/s]Best score: 16.1802: : 245iter [00:53,  4.97iter/s]Best score: 16.1802: : 245iter [00:53,  4.97iter/s]Best score: 16.1802: : 246iter [00:53,  4.98iter/s]Best score: 16.1802: : 246iter [00:53,  4.98iter/s]Best score: 16.1802: : 247iter [00:53,  4.98iter/s]Best score: 16.1802: : 247iter [00:53,  4.98iter/s]Best score: 16.1802: : 248iter [00:53,  4.96iter/s]Best score: 16.1802: : 248iter [00:54,  4.96iter/s]Best score: 16.1802: : 249iter [00:54,  4.95iter/s]
[ITER 250] Best: 16.1756
[PARAMS] Current best parameters:
  fx: 548.2958
  fy: 548.7557
  px: 849.0071
  py: 1005.2084
  k1: -0.00757885
  k2: -0.01315280
  k3: 0.00519689
  k4: -0.00043781
  p1: 0.00799521
  p2: -0.00667256
  p3: 0.00174848
  p4: -0.00302070
  scale: 3.0082
  rotation: -0.4019
Best score: 16.1756: : 249iter [00:54,  4.95iter/s]Best score: 16.1756: : 250iter [00:54,  4.83iter/s]Best score: 16.1756: : 250iter [00:54,  4.83iter/s]Best score: 16.1756: : 251iter [00:54,  4.86iter/s]Best score: 16.1756: : 251iter [00:54,  4.86iter/s]Best score: 16.1756: : 252iter [00:54,  4.93iter/s]Best score: 16.1756: : 252iter [00:54,  4.93iter/s]Best score: 16.1756: : 253iter [00:54,  4.91iter/s]Best score: 16.1756: : 253iter [00:55,  4.91iter/s]Best score: 16.1756: : 254iter [00:55,  4.97iter/s]Best score: 16.1756: : 254iter [00:55,  4.97iter/s]Best score: 16.1756: : 255iter [00:55,  4.99iter/s]Best score: 16.1756: : 255iter [00:55,  4.99iter/s]Best score: 16.1756: : 256iter [00:55,  4.99iter/s]Best score: 16.1756: : 256iter [00:55,  4.99iter/s]Best score: 16.1756: : 257iter [00:55,  4.98iter/s]Best score: 16.1756: : 257iter [00:55,  4.98iter/s]Best score: 16.1756: : 258iter [00:55,  5.01iter/s]Best score: 16.1756: : 258iter [00:56,  5.01iter/s]Best score: 16.1756: : 259iter [00:56,  5.06iter/s]Best score: 16.1756: : 259iter [00:56,  5.06iter/s]Best score: 16.1756: : 260iter [00:56,  4.93iter/s]Best score: 16.1756: : 260iter [00:56,  4.93iter/s]Best score: 16.1756: : 261iter [00:56,  4.97iter/s]Best score: 16.1756: : 261iter [00:56,  4.97iter/s]Best score: 16.1756: : 262iter [00:56,  4.96iter/s]Best score: 16.1756: : 262iter [00:56,  4.96iter/s]Best score: 16.1756: : 263iter [00:56,  4.93iter/s]Best score: 16.1756: : 263iter [00:57,  4.93iter/s]Best score: 16.1756: : 264iter [00:57,  4.99iter/s]Best score: 16.1756: : 264iter [00:57,  4.99iter/s]Best score: 16.1756: : 265iter [00:57,  5.00iter/s]Best score: 16.1756: : 265iter [00:57,  5.00iter/s]Best score: 16.1756: : 266iter [00:57,  4.93iter/s]Best score: 16.1756: : 266iter [00:57,  4.93iter/s]Best score: 16.1756: : 267iter [00:57,  4.94iter/s]Best score: 16.1756: : 267iter [00:57,  4.94iter/s]Best score: 16.1756: : 268iter [00:57,  5.01iter/s]Best score: 16.1756: : 268iter [00:58,  5.01iter/s]Best score: 16.1756: : 269iter [00:58,  5.03iter/s]Best score: 16.1721: : 269iter [00:58,  5.03iter/s]Best score: 16.1721: : 270iter [00:58,  4.90iter/s]Best score: 16.1721: : 270iter [00:58,  4.90iter/s]Best score: 16.1721: : 271iter [00:58,  4.93iter/s]Best score: 16.1721: : 271iter [00:58,  4.93iter/s]Best score: 16.1721: : 272iter [00:58,  4.94iter/s]Best score: 16.1721: : 272iter [00:58,  4.94iter/s]Best score: 16.1721: : 273iter [00:58,  4.97iter/s]Best score: 16.1721: : 273iter [00:59,  4.97iter/s]Best score: 16.1721: : 274iter [00:59,  4.93iter/s]Best score: 16.1721: : 274iter [00:59,  4.93iter/s]Best score: 16.1721: : 275iter [00:59,  4.91iter/s]Best score: 16.1721: : 275iter [00:59,  4.91iter/s]Best score: 16.1721: : 276iter [00:59,  5.00iter/s]Best score: 16.1721: : 276iter [00:59,  5.00iter/s]Best score: 16.1721: : 277iter [00:59,  4.94iter/s]Best score: 16.1721: : 277iter [00:59,  4.94iter/s]Best score: 16.1721: : 278iter [00:59,  4.97iter/s]Best score: 16.1721: : 278iter [01:00,  4.97iter/s]Best score: 16.1721: : 279iter [01:00,  5.03iter/s]Best score: 16.1616: : 279iter [01:00,  5.03iter/s]Best score: 16.1616: : 280iter [01:00,  4.84iter/s]Best score: 16.1616: : 280iter [01:00,  4.84iter/s]Best score: 16.1616: : 281iter [01:00,  4.86iter/s]Best score: 16.1616: : 281iter [01:00,  4.86iter/s]Best score: 16.1616: : 282iter [01:00,  4.87iter/s]Best score: 16.1616: : 282iter [01:00,  4.87iter/s]Best score: 16.1616: : 283iter [01:00,  4.85iter/s]Best score: 16.1616: : 283iter [01:01,  4.85iter/s]Best score: 16.1616: : 284iter [01:01,  4.85iter/s]Best score: 16.1616: : 284iter [01:01,  4.85iter/s]Best score: 16.1616: : 285iter [01:01,  4.99iter/s]Best score: 16.1616: : 285iter [01:01,  4.99iter/s]Best score: 16.1616: : 286iter [01:01,  4.95iter/s]Best score: 16.1616: : 286iter [01:01,  4.95iter/s]Best score: 16.1616: : 287iter [01:01,  4.95iter/s]Best score: 16.1616: : 287iter [01:01,  4.95iter/s]Best score: 16.1616: : 288iter [01:01,  4.95iter/s]Best score: 16.1616: : 288iter [01:02,  4.95iter/s]Best score: 16.1616: : 289iter [01:02,  4.91iter/s]Best score: 16.1439: : 289iter [01:02,  4.91iter/s]Best score: 16.1439: : 290iter [01:02,  4.80iter/s]Best score: 16.1439: : 290iter [01:02,  4.80iter/s]Best score: 16.1439: : 291iter [01:02,  4.85iter/s]Best score: 16.1439: : 291iter [01:02,  4.85iter/s]Best score: 16.1439: : 292iter [01:02,  4.88iter/s]Best score: 16.1439: : 292iter [01:02,  4.88iter/s]Best score: 16.1439: : 293iter [01:02,  4.87iter/s]Best score: 16.1439: : 293iter [01:03,  4.87iter/s]Best score: 16.1439: : 294iter [01:03,  4.93iter/s]Best score: 16.1439: : 294iter [01:03,  4.93iter/s]Best score: 16.1439: : 295iter [01:03,  4.96iter/s]Best score: 16.1439: : 295iter [01:03,  4.96iter/s]Best score: 16.1439: : 296iter [01:03,  4.97iter/s]Best score: 16.1439: : 296iter [01:03,  4.97iter/s]Best score: 16.1439: : 297iter [01:03,  4.99iter/s]Best score: 16.1439: : 297iter [01:03,  4.99iter/s]Best score: 16.1439: : 298iter [01:03,  5.03iter/s]Best score: 16.1439: : 298iter [01:04,  5.03iter/s]Best score: 16.1439: : 299iter [01:04,  5.06iter/s]
[ITER 300] Best: 16.1418
[PARAMS] Current best parameters:
  fx: 534.1748
  fy: 534.5905
  px: 848.1935
  py: 1004.9435
  k1: -0.02314231
  k2: 0.04343495
  k3: -0.03372755
  k4: 0.00839495
  p1: 0.00717526
  p2: -0.00799653
  p3: 0.00206050
  p4: -0.00293845
  scale: 3.0314
  rotation: -0.3959
Best score: 16.1418: : 299iter [01:04,  5.06iter/s]Best score: 16.1418: : 300iter [01:04,  4.88iter/s]Best score: 16.1418: : 300iter [01:04,  4.88iter/s]Best score: 16.1418: : 301iter [01:04,  4.94iter/s]Best score: 16.1418: : 301iter [01:04,  4.94iter/s]Best score: 16.1418: : 302iter [01:04,  4.89iter/s]Best score: 16.1418: : 302iter [01:04,  4.89iter/s]Best score: 16.1418: : 303iter [01:04,  4.91iter/s]Best score: 16.1418: : 303iter [01:05,  4.91iter/s]Best score: 16.1418: : 304iter [01:05,  4.93iter/s]Best score: 16.1418: : 304iter [01:05,  4.93iter/s]Best score: 16.1418: : 305iter [01:05,  4.95iter/s]Best score: 16.1418: : 305iter [01:05,  4.95iter/s]Best score: 16.1418: : 306iter [01:05,  5.00iter/s]Best score: 16.1418: : 306iter [01:05,  5.00iter/s]Best score: 16.1418: : 307iter [01:05,  5.02iter/s]Best score: 16.1418: : 307iter [01:05,  5.02iter/s]Best score: 16.1418: : 308iter [01:05,  4.89iter/s]Best score: 16.1418: : 308iter [01:06,  4.89iter/s]Best score: 16.1418: : 309iter [01:06,  4.92iter/s]Best score: 16.1402: : 309iter [01:06,  4.92iter/s]Best score: 16.1402: : 310iter [01:06,  4.80iter/s]Best score: 16.1402: : 310iter [01:06,  4.80iter/s]Best score: 16.1402: : 311iter [01:06,  4.82iter/s]Best score: 16.1402: : 311iter [01:06,  4.82iter/s]Best score: 16.1402: : 312iter [01:06,  4.89iter/s]Best score: 16.1402: : 312iter [01:06,  4.89iter/s]Best score: 16.1402: : 313iter [01:06,  4.95iter/s]Best score: 16.1402: : 313iter [01:07,  4.95iter/s]Best score: 16.1402: : 314iter [01:07,  4.92iter/s]Best score: 16.1402: : 314iter [01:07,  4.92iter/s]Best score: 16.1402: : 315iter [01:07,  4.96iter/s]Best score: 16.1402: : 315iter [01:07,  4.96iter/s]Best score: 16.1402: : 316iter [01:07,  5.01iter/s]Best score: 16.1402: : 316iter [01:07,  5.01iter/s]Best score: 16.1402: : 317iter [01:07,  4.94iter/s]Best score: 16.1402: : 317iter [01:07,  4.94iter/s]Best score: 16.1402: : 318iter [01:07,  4.98iter/s]Best score: 16.1402: : 318iter [01:08,  4.98iter/s]Best score: 16.1402: : 319iter [01:08,  5.07iter/s]Best score: 16.1330: : 319iter [01:08,  5.07iter/s]Best score: 16.1330: : 320iter [01:08,  4.89iter/s]Best score: 16.1330: : 320iter [01:08,  4.89iter/s]Best score: 16.1330: : 321iter [01:08,  4.91iter/s]Best score: 16.1330: : 321iter [01:08,  4.91iter/s]Best score: 16.1330: : 322iter [01:08,  4.84iter/s]Best score: 16.1330: : 322iter [01:09,  4.84iter/s]Best score: 16.1330: : 323iter [01:09,  4.87iter/s]Best score: 16.1330: : 323iter [01:09,  4.87iter/s]Best score: 16.1330: : 324iter [01:09,  4.89iter/s]Best score: 16.1330: : 324iter [01:09,  4.89iter/s]Best score: 16.1330: : 325iter [01:09,  4.91iter/s]Best score: 16.1330: : 325iter [01:09,  4.91iter/s]Best score: 16.1330: : 326iter [01:09,  4.95iter/s]Best score: 16.1330: : 326iter [01:09,  4.95iter/s]Best score: 16.1330: : 327iter [01:09,  5.00iter/s]Best score: 16.1330: : 327iter [01:09,  5.00iter/s]Best score: 16.1330: : 328iter [01:09,  5.09iter/s]Best score: 16.1330: : 328iter [01:10,  5.09iter/s]Best score: 16.1330: : 329iter [01:10,  5.07iter/s]Best score: 16.1330: : 329iter [01:10,  5.07iter/s]Best score: 16.1330: : 330iter [01:10,  4.94iter/s]Best score: 16.1330: : 330iter [01:10,  4.94iter/s]Best score: 16.1330: : 331iter [01:10,  4.95iter/s]Best score: 16.1330: : 331iter [01:10,  4.95iter/s]Best score: 16.1330: : 332iter [01:10,  5.02iter/s]Best score: 16.1330: : 332iter [01:10,  5.02iter/s]Best score: 16.1330: : 333iter [01:10,  5.04iter/s]Best score: 16.1330: : 333iter [01:11,  5.04iter/s]Best score: 16.1330: : 334iter [01:11,  5.01iter/s]Best score: 16.1330: : 334iter [01:11,  5.01iter/s]Best score: 16.1330: : 335iter [01:11,  4.95iter/s]Best score: 16.1330: : 335iter [01:11,  4.95iter/s]Best score: 16.1330: : 336iter [01:11,  4.90iter/s]Best score: 16.1330: : 336iter [01:11,  4.90iter/s]Best score: 16.1330: : 337iter [01:11,  4.92iter/s]Best score: 16.1330: : 337iter [01:12,  4.92iter/s]Best score: 16.1330: : 338iter [01:12,  4.97iter/s]Best score: 16.1330: : 338iter [01:12,  4.97iter/s]Best score: 16.1330: : 339iter [01:12,  4.98iter/s]Best score: 16.1330: : 339iter [01:12,  4.98iter/s]Best score: 16.1330: : 340iter [01:12,  4.87iter/s]Best score: 16.1330: : 340iter [01:12,  4.87iter/s]Best score: 16.1330: : 341iter [01:12,  4.94iter/s]Best score: 16.1330: : 341iter [01:12,  4.94iter/s]Best score: 16.1330: : 342iter [01:12,  4.93iter/s]Best score: 16.1330: : 342iter [01:13,  4.93iter/s]Best score: 16.1330: : 343iter [01:13,  4.93iter/s]Best score: 16.1330: : 343iter [01:13,  4.93iter/s]Best score: 16.1330: : 344iter [01:13,  4.97iter/s]Best score: 16.1330: : 344iter [01:13,  4.97iter/s]Best score: 16.1330: : 345iter [01:13,  4.93iter/s]Best score: 16.1330: : 345iter [01:13,  4.93iter/s]Best score: 16.1330: : 346iter [01:13,  4.93iter/s]Best score: 16.1330: : 346iter [01:13,  4.93iter/s]Best score: 16.1330: : 347iter [01:13,  5.02iter/s]Best score: 16.1330: : 347iter [01:14,  5.02iter/s]Best score: 16.1330: : 348iter [01:14,  5.07iter/s]Best score: 16.1330: : 348iter [01:14,  5.07iter/s]Best score: 16.1330: : 349iter [01:14,  5.06iter/s]
[ITER 350] Best: 16.1320
[PARAMS] Current best parameters:
  fx: 529.7108
  fy: 530.2480
  px: 847.8774
  py: 1005.3098
  k1: -0.02801609
  k2: 0.06303394
  k3: -0.04832434
  k4: 0.01193809
  p1: 0.00770963
  p2: -0.00873158
  p3: 0.00271388
  p4: -0.00329915
  scale: 3.0381
  rotation: -0.4026
Best score: 16.1320: : 349iter [01:14,  5.06iter/s]Best score: 16.1320: : 350iter [01:14,  4.93iter/s]Best score: 16.1320: : 350iter [01:14,  4.93iter/s]Best score: 16.1320: : 351iter [01:14,  4.98iter/s]Best score: 16.1320: : 351iter [01:14,  4.98iter/s]Best score: 16.1320: : 352iter [01:14,  4.99iter/s]Best score: 16.1320: : 352iter [01:15,  4.99iter/s]Best score: 16.1320: : 353iter [01:15,  4.93iter/s]Best score: 16.1320: : 353iter [01:15,  4.93iter/s]Best score: 16.1320: : 354iter [01:15,  4.95iter/s]Best score: 16.1320: : 354iter [01:15,  4.95iter/s]Best score: 16.1320: : 355iter [01:15,  5.02iter/s]Best score: 16.1320: : 355iter [01:15,  5.02iter/s]Best score: 16.1320: : 356iter [01:15,  5.03iter/s]Best score: 16.1320: : 356iter [01:15,  5.03iter/s]Best score: 16.1320: : 357iter [01:15,  5.04iter/s]Best score: 16.1320: : 357iter [01:16,  5.04iter/s]Best score: 16.1320: : 358iter [01:16,  5.05iter/s]Best score: 16.1320: : 358iter [01:16,  5.05iter/s]Best score: 16.1320: : 359iter [01:16,  5.02iter/s]Best score: 16.1320: : 359iter [01:16,  5.02iter/s]Best score: 16.1320: : 360iter [01:16,  4.85iter/s]Best score: 16.1320: : 360iter [01:16,  4.85iter/s]Best score: 16.1320: : 361iter [01:16,  4.79iter/s]Best score: 16.1320: : 361iter [01:16,  4.79iter/s]Best score: 16.1320: : 362iter [01:16,  4.87iter/s]Best score: 16.1320: : 362iter [01:17,  4.87iter/s]Best score: 16.1320: : 363iter [01:17,  4.88iter/s]Best score: 16.1320: : 363iter [01:17,  4.88iter/s]Best score: 16.1320: : 364iter [01:17,  4.93iter/s]Best score: 16.1320: : 364iter [01:17,  4.93iter/s]Best score: 16.1320: : 365iter [01:17,  4.94iter/s]Best score: 16.1320: : 365iter [01:17,  4.94iter/s]Best score: 16.1320: : 366iter [01:17,  4.99iter/s]Best score: 16.1320: : 366iter [01:17,  4.99iter/s]Best score: 16.1320: : 367iter [01:17,  4.93iter/s]Best score: 16.1320: : 367iter [01:18,  4.93iter/s]Best score: 16.1320: : 368iter [01:18,  4.95iter/s]Best score: 16.1320: : 368iter [01:18,  4.95iter/s]Best score: 16.1320: : 369iter [01:18,  5.01iter/s]Best score: 16.1310: : 369iter [01:18,  5.01iter/s]Best score: 16.1310: : 370iter [01:18,  4.84iter/s]Best score: 16.1310: : 370iter [01:18,  4.84iter/s]Best score: 16.1310: : 371iter [01:18,  4.84iter/s]Best score: 16.1310: : 371iter [01:18,  4.84iter/s]Best score: 16.1310: : 372iter [01:18,  4.84iter/s]Best score: 16.1310: : 372iter [01:19,  4.84iter/s]Best score: 16.1310: : 373iter [01:19,  4.87iter/s]Best score: 16.1310: : 373iter [01:19,  4.87iter/s]Best score: 16.1310: : 374iter [01:19,  4.92iter/s]Best score: 16.1310: : 374iter [01:19,  4.92iter/s]Best score: 16.1310: : 375iter [01:19,  5.02iter/s]Best score: 16.1310: : 375iter [01:19,  5.02iter/s]Best score: 16.1310: : 376iter [01:19,  5.06iter/s]Best score: 16.1310: : 376iter [01:19,  5.06iter/s]Best score: 16.1310: : 377iter [01:19,  5.12iter/s]Best score: 16.1310: : 377iter [01:20,  5.12iter/s]Best score: 16.1310: : 378iter [01:20,  5.19iter/s]Best score: 16.1310: : 378iter [01:20,  5.19iter/s]Best score: 16.1310: : 379iter [01:20,  5.19iter/s]Best score: 16.1301: : 379iter [01:20,  5.19iter/s]Best score: 16.1301: : 380iter [01:20,  5.03iter/s]Best score: 16.1301: : 380iter [01:20,  5.03iter/s]Best score: 16.1301: : 381iter [01:20,  5.02iter/s]Best score: 16.1301: : 381iter [01:20,  5.02iter/s]Best score: 16.1301: : 382iter [01:20,  5.03iter/s]Best score: 16.1301: : 382iter [01:21,  5.03iter/s]Best score: 16.1301: : 383iter [01:21,  5.03iter/s]Best score: 16.1301: : 383iter [01:21,  5.03iter/s]Best score: 16.1301: : 384iter [01:21,  5.04iter/s]Best score: 16.1301: : 384iter [01:21,  5.04iter/s]Best score: 16.1301: : 385iter [01:21,  5.02iter/s]Best score: 16.1301: : 385iter [01:21,  5.02iter/s]Best score: 16.1301: : 386iter [01:21,  4.95iter/s]Best score: 16.1301: : 386iter [01:21,  4.95iter/s]Best score: 16.1301: : 387iter [01:21,  4.99iter/s]Best score: 16.1301: : 387iter [01:22,  4.99iter/s]Best score: 16.1301: : 388iter [01:22,  4.95iter/s]Best score: 16.1301: : 388iter [01:22,  4.95iter/s]Best score: 16.1301: : 389iter [01:22,  4.98iter/s]Best score: 16.1298: : 389iter [01:22,  4.98iter/s]Best score: 16.1298: : 390iter [01:22,  4.78iter/s]Best score: 16.1298: : 390iter [01:22,  4.78iter/s]Best score: 16.1298: : 391iter [01:22,  4.83iter/s]Best score: 16.1298: : 391iter [01:22,  4.83iter/s]Best score: 16.1298: : 392iter [01:22,  4.85iter/s]Best score: 16.1298: : 392iter [01:23,  4.85iter/s]Best score: 16.1298: : 393iter [01:23,  4.83iter/s]Best score: 16.1298: : 393iter [01:23,  4.83iter/s]Best score: 16.1298: : 394iter [01:23,  4.86iter/s]Best score: 16.1298: : 394iter [01:23,  4.86iter/s]Best score: 16.1298: : 395iter [01:23,  4.99iter/s]Best score: 16.1298: : 395iter [01:23,  4.99iter/s]Best score: 16.1298: : 396iter [01:23,  4.96iter/s]Best score: 16.1298: : 396iter [01:23,  4.96iter/s]Best score: 16.1298: : 397iter [01:23,  4.95iter/s]Best score: 16.1298: : 397iter [01:24,  4.95iter/s]Best score: 16.1298: : 398iter [01:24,  4.95iter/s]Best score: 16.1298: : 398iter [01:24,  4.95iter/s]Best score: 16.1298: : 399iter [01:24,  4.97iter/s]
[ITER 400] Best: 16.1287
[PARAMS] Current best parameters:
  fx: 528.6682
  fy: 529.2361
  px: 847.4001
  py: 1004.5233
  k1: -0.02831280
  k2: 0.06515386
  k3: -0.04979427
  k4: 0.01232464
  p1: 0.00602883
  p2: -0.00984561
  p3: 0.00439036
  p4: -0.00437497
  scale: 3.0397
  rotation: -0.4061
Best score: 16.1287: : 399iter [01:24,  4.97iter/s]Best score: 16.1287: : 400iter [01:24,  4.89iter/s]Best score: 16.1287: : 400iter [01:24,  4.89iter/s]Best score: 16.1287: : 401iter [01:24,  4.83iter/s]Best score: 16.1287: : 401iter [01:24,  4.83iter/s]Best score: 16.1287: : 402iter [01:24,  4.89iter/s]Best score: 16.1287: : 402iter [01:25,  4.89iter/s]Best score: 16.1287: : 403iter [01:25,  4.91iter/s]Best score: 16.1287: : 403iter [01:25,  4.91iter/s]Best score: 16.1287: : 404iter [01:25,  4.90iter/s]Best score: 16.1287: : 404iter [01:25,  4.90iter/s]Best score: 16.1287: : 405iter [01:25,  4.98iter/s]Best score: 16.1287: : 405iter [01:25,  4.98iter/s]Best score: 16.1287: : 406iter [01:25,  4.98iter/s]Best score: 16.1287: : 406iter [01:25,  4.98iter/s]Best score: 16.1287: : 407iter [01:25,  5.03iter/s]Best score: 16.1287: : 407iter [01:26,  5.03iter/s]Best score: 16.1287: : 408iter [01:26,  5.03iter/s]Best score: 16.1287: : 408iter [01:26,  5.03iter/s]Best score: 16.1287: : 409iter [01:26,  5.00iter/s]Best score: 16.1284: : 409iter [01:26,  5.00iter/s]Best score: 16.1284: : 410iter [01:26,  4.83iter/s]Best score: 16.1284: : 410iter [01:26,  4.83iter/s]Best score: 16.1284: : 411iter [01:26,  4.87iter/s]Best score: 16.1284: : 411iter [01:26,  4.87iter/s]Best score: 16.1284: : 412iter [01:26,  4.91iter/s]Best score: 16.1284: : 412iter [01:27,  4.91iter/s]Best score: 16.1284: : 413iter [01:27,  4.93iter/s]Best score: 16.1284: : 413iter [01:27,  4.93iter/s]Best score: 16.1284: : 414iter [01:27,  4.89iter/s]Best score: 16.1284: : 414iter [01:27,  4.89iter/s]Best score: 16.1284: : 415iter [01:27,  4.93iter/s]Best score: 16.1284: : 415iter [01:27,  4.93iter/s]Best score: 16.1284: : 416iter [01:27,  4.91iter/s]Best score: 16.1284: : 416iter [01:27,  4.91iter/s]Best score: 16.1284: : 417iter [01:27,  4.93iter/s]Best score: 16.1284: : 417iter [01:28,  4.93iter/s]Best score: 16.1284: : 418iter [01:28,  4.94iter/s]Best score: 16.1284: : 418iter [01:28,  4.94iter/s]Best score: 16.1284: : 419iter [01:28,  4.90iter/s]Best score: 16.1284: : 419iter [01:28,  4.90iter/s]Best score: 16.1284: : 420iter [01:28,  4.80iter/s]Best score: 16.1284: : 420iter [01:28,  4.80iter/s]Best score: 16.1284: : 421iter [01:28,  4.87iter/s]Best score: 16.1284: : 421iter [01:28,  4.87iter/s]Best score: 16.1284: : 422iter [01:28,  4.93iter/s]Best score: 16.1284: : 422iter [01:29,  4.93iter/s]Best score: 16.1284: : 423iter [01:29,  4.91iter/s]Best score: 16.1284: : 423iter [01:29,  4.91iter/s]Best score: 16.1284: : 424iter [01:29,  4.93iter/s]Best score: 16.1284: : 424iter [01:29,  4.93iter/s]Best score: 16.1284: : 425iter [01:29,  4.95iter/s]Best score: 16.1284: : 425iter [01:29,  4.95iter/s]Best score: 16.1284: : 426iter [01:29,  4.98iter/s]Best score: 16.1284: : 426iter [01:30,  4.98iter/s]Best score: 16.1284: : 427iter [01:30,  4.98iter/s]Best score: 16.1284: : 427iter [01:30,  4.98iter/s]Best score: 16.1284: : 428iter [01:30,  5.01iter/s]Best score: 16.1284: : 428iter [01:30,  5.01iter/s]Best score: 16.1284: : 429iter [01:30,  5.00iter/s]Best score: 16.1281: : 429iter [01:30,  5.00iter/s]Best score: 16.1281: : 430iter [01:30,  4.89iter/s]Best score: 16.1281: : 430iter [01:30,  4.89iter/s]Best score: 16.1281: : 431iter [01:30,  4.91iter/s]Best score: 16.1281: : 431iter [01:31,  4.91iter/s]Best score: 16.1281: : 432iter [01:31,  4.93iter/s]Best score: 16.1281: : 432iter [01:31,  4.93iter/s]Best score: 16.1281: : 433iter [01:31,  4.99iter/s]Best score: 16.1281: : 433iter [01:31,  4.99iter/s]Best score: 16.1281: : 434iter [01:31,  5.05iter/s]Best score: 16.1281: : 434iter [01:31,  5.05iter/s]Best score: 16.1281: : 435iter [01:31,  4.99iter/s]Best score: 16.1281: : 435iter [01:31,  4.99iter/s]Best score: 16.1281: : 436iter [01:31,  4.97iter/s]Best score: 16.1281: : 436iter [01:32,  4.97iter/s]Best score: 16.1281: : 437iter [01:32,  5.00iter/s]Best score: 16.1281: : 437iter [01:32,  5.00iter/s]Best score: 16.1281: : 438iter [01:32,  4.99iter/s]Best score: 16.1281: : 438iter [01:32,  4.99iter/s]Best score: 16.1281: : 439iter [01:32,  5.04iter/s]Best score: 16.1279: : 439iter [01:32,  5.04iter/s]Best score: 16.1279: : 440iter [01:32,  4.83iter/s]Best score: 16.1279: : 440iter [01:32,  4.83iter/s]Best score: 16.1279: : 441iter [01:32,  4.87iter/s]Best score: 16.1279: : 441iter [01:33,  4.87iter/s]Best score: 16.1279: : 442iter [01:33,  4.89iter/s]Best score: 16.1279: : 442iter [01:33,  4.89iter/s]Best score: 16.1279: : 443iter [01:33,  5.02iter/s]Best score: 16.1279: : 443iter [01:33,  5.02iter/s]Best score: 16.1279: : 444iter [01:33,  5.16iter/s]Best score: 16.1279: : 444iter [01:33,  5.16iter/s]Best score: 16.1279: : 445iter [01:33,  5.28iter/s]Best score: 16.1279: : 445iter [01:33,  5.28iter/s]Best score: 16.1279: : 446iter [01:33,  5.23iter/s]Best score: 16.1279: : 446iter [01:33,  5.23iter/s]Best score: 16.1279: : 447iter [01:33,  5.21iter/s]Best score: 16.1279: : 447iter [01:34,  5.21iter/s]Best score: 16.1279: : 448iter [01:34,  5.14iter/s]Best score: 16.1279: : 448iter [01:34,  5.14iter/s]Best score: 16.1279: : 449iter [01:34,  5.15iter/s]
[ITER 450] Best: 16.1278
[PARAMS] Current best parameters:
  fx: 527.4843
  fy: 528.0043
  px: 847.4165
  py: 1004.0921
  k1: -0.02729776
  k2: 0.06548917
  k3: -0.04998036
  k4: 0.01232712
  p1: 0.00507778
  p2: -0.00991743
  p3: 0.00427205
Best score: 16.1278: : 449iter [01:34,  5.15iter/s]Best score: 16.1278: : 450iter [01:34,  4.97iter/s]  p4: -0.00384431
  scale: 3.0400
  rotation: -0.3967
Best score: 16.1278: : 450iter [01:34,  4.97iter/s]Best score: 16.1278: : 451iter [01:34,  5.03iter/s]Best score: 16.1278: : 451iter [01:34,  5.03iter/s]Best score: 16.1278: : 452iter [01:34,  5.06iter/s]Best score: 16.1278: : 452iter [01:35,  5.06iter/s]Best score: 16.1278: : 453iter [01:35,  5.07iter/s]Best score: 16.1278: : 453iter [01:35,  5.07iter/s]Best score: 16.1278: : 454iter [01:35,  5.10iter/s]Best score: 16.1278: : 454iter [01:35,  5.10iter/s]Best score: 16.1278: : 455iter [01:35,  5.09iter/s]Best score: 16.1278: : 455iter [01:35,  5.09iter/s]Best score: 16.1278: : 456iter [01:35,  5.17iter/s]Best score: 16.1278: : 456iter [01:35,  5.17iter/s]Best score: 16.1278: : 457iter [01:35,  5.14iter/s]Best score: 16.1278: : 457iter [01:36,  5.14iter/s]Best score: 16.1278: : 458iter [01:36,  5.17iter/s]Best score: 16.1278: : 458iter [01:36,  5.17iter/s]Best score: 16.1278: : 459iter [01:36,  5.10iter/s]Best score: 16.1277: : 459iter [01:36,  5.10iter/s]Best score: 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469iter [01:38,  4.97iter/s]Best score: 16.1275: : 470iter [01:38,  4.87iter/s]Best score: 16.1275: : 470iter [01:38,  4.87iter/s]Best score: 16.1275: : 471iter [01:38,  4.96iter/s]Best score: 16.1275: : 471iter [01:38,  4.96iter/s]Best score: 16.1275: : 472iter [01:38,  4.99iter/s]Best score: 16.1275: : 472iter [01:39,  4.99iter/s]Best score: 16.1275: : 473iter [01:39,  5.03iter/s]Best score: 16.1275: : 473iter [01:39,  5.03iter/s]Best score: 16.1275: : 474iter [01:39,  5.04iter/s]Best score: 16.1275: : 474iter [01:39,  5.04iter/s]Best score: 16.1275: : 475iter [01:39,  5.04iter/s]Best score: 16.1275: : 475iter [01:39,  5.04iter/s]Best score: 16.1275: : 476iter [01:39,  5.02iter/s]Best score: 16.1275: : 476iter [01:39,  5.02iter/s]Best score: 16.1275: : 477iter [01:39,  5.09iter/s]Best score: 16.1275: : 477iter [01:40,  5.09iter/s]Best score: 16.1275: : 478iter [01:40,  5.06iter/s]Best score: 16.1275: : 478iter [01:40,  5.06iter/s]Best score: 16.1275: : 479iter [01:40,  5.03iter/s]Best score: 16.1275: : 479iter [01:40,  5.03iter/s]Best score: 16.1275: : 480iter [01:40,  4.87iter/s]Best score: 16.1275: : 480iter [01:40,  4.87iter/s]Best score: 16.1275: : 481iter [01:40,  4.87iter/s]Best score: 16.1275: : 481iter [01:40,  4.87iter/s]Best score: 16.1275: : 482iter [01:40,  5.03iter/s]Best score: 16.1275: : 482iter [01:41,  5.03iter/s]Best score: 16.1275: : 483iter [01:41,  5.00iter/s]Best score: 16.1275: : 483iter [01:41,  5.00iter/s]Best score: 16.1275: : 484iter [01:41,  4.95iter/s]Best score: 16.1275: : 484iter [01:41,  4.95iter/s]Best score: 16.1275: : 485iter [01:41,  4.96iter/s]Best score: 16.1275: : 485iter [01:41,  4.96iter/s]Best score: 16.1275: : 486iter [01:41,  4.90iter/s]Best score: 16.1275: : 486iter [01:41,  4.90iter/s]Best score: 16.1275: : 487iter [01:41,  4.95iter/s]Best score: 16.1275: : 487iter [01:42,  4.95iter/s]Best score: 16.1275: : 488iter [01:42,  4.91iter/s]Best score: 16.1275: : 488iter [01:42,  4.91iter/s]Best score: 16.1275: : 489iter [01:42,  4.96iter/s]Best score: 16.1275: : 489iter [01:42,  4.96iter/s]Best score: 16.1275: : 490iter [01:42,  4.88iter/s]Best score: 16.1275: : 490iter [01:42,  4.88iter/s]Best score: 16.1275: : 491iter [01:42,  4.93iter/s]Best score: 16.1275: : 491iter [01:42,  4.93iter/s]Best score: 16.1275: : 492iter [01:42,  4.97iter/s]Best score: 16.1275: : 492iter [01:43,  4.97iter/s]Best score: 16.1275: : 493iter [01:43,  4.94iter/s]Best score: 16.1275: : 493iter [01:43,  4.94iter/s]Best score: 16.1275: : 494iter [01:43,  4.94iter/s]Best score: 16.1275: : 494iter [01:43,  4.94iter/s]Best score: 16.1275: : 495iter [01:43,  5.00iter/s]Best score: 16.1275: : 495iter [01:43,  5.00iter/s]Best score: 16.1275: : 496iter [01:43,  5.03iter/s]Best score: 16.1275: : 496iter [01:43,  5.03iter/s]Best score: 16.1275: : 497iter [01:43,  4.99iter/s]Best score: 16.1275: : 497iter [01:44,  4.99iter/s]Best score: 16.1275: : 498iter [01:44,  4.97iter/s]Best score: 16.1275: : 498iter [01:44,  4.97iter/s]Best score: 16.1275: : 499iter [01:44,  5.06iter/s]Best score: 16.1274: : 499iter [01:44,  5.06iter/s]Best score: 16.1274: : 500iter [01:44,  4.82iter/s]
[ITER 500] Best: 16.1274
[PARAMS] Current best parameters:
  fx: 527.6554
  fy: 528.1759
  px: 847.4204
  py: 1003.9348
  k1: -0.02712638
  k2: 0.06515387
  k3: -0.04991462
  k4: 0.01234111
  p1: 0.00471978
  p2: -0.00995306
  p3: 0.00422922
  p4: -0.00376707
  scale: 3.0396
  rotation: -0.3962
Best score: 16.1274: : 500iter [01:44,  4.78iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 16.127357 -> 16.127263
  Success: True
  Tangential distortion: p1=0.004722, p2=-0.009954, p3=0.004232, p4=-0.003764
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 16.1273
[✅] Parameters copied to: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 527.6553714518715
    fy: 528.175856783617
    px: 847.4204421739233
    py: 1003.9347899042922
    k1: -0.027133001412043854
    k2: 0.06515101429272194
    k3: -0.04991875020195369
    k4: 0.012346941060228088
    p1: 0.004721951486117916
    p2: -0.00995446751477947
    p3: 0.004231908551700886
    p4: -0.0037640992812758302
    scale: 3.0396280133198377
    rotation: -0.3961475141921006
    mmpp: 3.450975829506847
    nmpp: 3450975.829506847

[🔄] Converting 1930 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDCP3D-SL-010_W046(6)_2026-04-09/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/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-SL-010_W046(6)_2026-04-09' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/MDCP3D-SL-010_W046(6)_2026-04-09_unknown.lut

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

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                                                                [A[✅] 1500: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap/1500_heatmap.png
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                                                                 [A[✅] 2000R: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap/2000R_heatmap.png
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                                                                 [A[✅] 2000L: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap/2000L_heatmap.png
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[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap
[🏁] Finished processing MDCP3D-SL-010_W046(6)_2026-04-09


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