
🟢 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 [05:31<16:33, 331.26s/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 [14:30<15:07, 453.65s/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:  75%|███████▌  | 3/4 [24:51<08:50, 530.03s/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: 100%|██████████| 4/4 [35:05<00:00, 563.29s/it]Processing Rigs: 100%|██████████| 4/4 [35:05<00:00, 526.45s/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
[📋] 2000L: 100 frames selected from 589
[📋] 2000R: 100 frames selected from 569
[💾] 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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1.79s/it]Computing max image from bright frames:  71%|███████▏  | 57/80 [02:28<00:49,  2.14s/it]Computing max image from bright frames:  72%|███████▎  | 58/80 [02:31<00:52,  2.38s/it]Computing max image from bright frames:  74%|███████▍  | 59/80 [02:34<00:53,  2.57s/it]Computing max image from bright frames:  75%|███████▌  | 60/80 [02:37<00:53,  2.68s/it]Computing max image from bright frames:  76%|███████▋  | 61/80 [02:40<00:52,  2.76s/it]Computing max image from bright frames:  78%|███████▊  | 62/80 [02:43<00:50,  2.82s/it]Computing max image from bright frames:  79%|███████▉  | 63/80 [02:46<00:48,  2.84s/it]Computing max image from bright frames:  80%|████████  | 64/80 [02:49<00:45,  2.86s/it]Computing max image from bright frames:  81%|████████▏ | 65/80 [02:52<00:43,  2.88s/it]Computing max image from bright frames:  82%|████████▎ | 66/80 [02:55<00:40,  2.91s/it]Computing max image from bright frames:  84%|████████▍ | 67/80 [02:58<00:37,  2.92s/it]Computing max image from bright frames:  85%|████████▌ | 68/80 [03:01<00:35,  2.93s/it]Computing max image from bright frames:  86%|████████▋ | 69/80 [03:03<00:32,  2.93s/it]Computing max image from bright frames:  88%|████████▊ | 70/80 [03:06<00:29,  2.93s/it]Computing max image from bright frames:  89%|████████▉ | 71/80 [03:09<00:26,  2.93s/it]Computing max image from bright frames:  90%|█████████ | 72/80 [03:12<00:22,  2.78s/it]Computing max image from bright frames:  91%|█████████▏| 73/80 [03:12<00:13,  2.00s/it]Computing max image from bright frames:  92%|█████████▎| 74/80 [03:12<00:08,  1.45s/it]Computing max image from bright frames:  94%|█████████▍| 75/80 [03:15<00:08,  1.77s/it]Computing max image from bright frames:  95%|█████████▌| 76/80 [03:18<00:08,  2.12s/it]Computing max image from bright frames:  96%|█████████▋| 77/80 [03:20<00:07,  2.37s/it]Computing max image from bright frames:  98%|█████████▊| 78/80 [03:23<00:05,  2.56s/it]Computing max image from bright frames:  99%|█████████▉| 79/80 [03:26<00:02,  2.68s/it]Computing max image from bright frames: 100%|██████████| 80/80 [03:29<00:00,  2.77s/it]                                                                                         0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:28,  3.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.42it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:09, 10.20it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:09, 10.20it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:09, 10.20it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:09, 10.20it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   7%|▋         | 7/100 [00:00<00:06, 15.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:06, 15.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:06, 15.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   9%|▉         | 9/100 [00:00<00:05, 16.28it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:05, 16.28it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:05, 16.28it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  11%|█         | 11/100 [00:00<00:05, 15.53it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  11%|█         | 11/100 [00:00<00:05, 15.53it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:05, 15.53it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  13%|█▎        | 13/100 [00:00<00:05, 16.28it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:05, 16.28it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:05, 16.28it/s] Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  15%|█▌        | 15/100 [00:01<00:05, 16.28it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  16%|█▌        | 16/100 [00:01<00:04, 17.05it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 17.05it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:01<00:04, 17.05it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  18%|█▊        | 18/100 [00:01<00:04, 17.68it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:01<00:04, 17.68it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:04, 17.68it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:04, 17.68it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  21%|██        | 21/100 [00:01<00:04, 18.46it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:04, 18.46it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.011 | Best: {'fx': 455.556} (0.011):  22%|██▏       | 22/100 [00:01<00:04, 18.46it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.011 | Best: {'fx': 455.556} (0.011):  23%|██▎       | 23/100 [00:01<00:04, 18.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:04, 18.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:04, 18.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 476.768} (0.019):  25%|██▌       | 25/100 [00:01<00:04, 18.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 476.768} (0.019):  26%|██▌       | 26/100 [00:01<00:03, 20.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 483.838} (0.023):  26%|██▌       | 26/100 [00:01<00:03, 20.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 490.909} (0.027):  27%|██▋       | 27/100 [00:01<00:03, 20.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.035 | Best: {'fx': 497.98} (0.035):  28%|██▊       | 28/100 [00:01<00:03, 20.64it/s] Brute | Start: {'fx': 500.0} (0.038) | Current: 0.035 | Best: {'fx': 497.98} (0.035):  29%|██▉       | 29/100 [00:01<00:03, 18.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.042 | Best: {'fx': 505.051} (0.042):  29%|██▉       | 29/100 [00:01<00:03, 18.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.050 | Best: {'fx': 512.121} (0.050):  30%|███       | 30/100 [00:01<00:03, 18.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.059 | Best: {'fx': 519.192} (0.059):  31%|███       | 31/100 [00:02<00:03, 18.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.059 | Best: {'fx': 519.192} (0.059):  32%|███▏      | 32/100 [00:02<00:04, 14.16it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.071 | Best: {'fx': 526.263} (0.071):  32%|███▏      | 32/100 [00:02<00:04, 14.16it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.070 | Best: {'fx': 526.263} (0.071):  33%|███▎      | 33/100 [00:02<00:04, 14.16it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.070 | Best: {'fx': 526.263} (0.071):  34%|███▍      | 34/100 [00:02<00:04, 14.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.062 | Best: {'fx': 526.263} (0.071):  34%|███▍      | 34/100 [00:02<00:04, 14.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.056 | Best: {'fx': 526.263} (0.071):  35%|███▌      | 35/100 [00:02<00:04, 14.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.049 | Best: {'fx': 526.263} (0.071):  36%|███▌      | 36/100 [00:02<00:04, 14.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.049 | Best: {'fx': 526.263} (0.071):  37%|███▋      | 37/100 [00:02<00:03, 16.80it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.044 | Best: {'fx': 526.263} (0.071):  37%|███▋      | 37/100 [00:02<00:03, 16.80it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.040 | Best: {'fx': 526.263} (0.071):  38%|███▊      | 38/100 [00:02<00:03, 16.80it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.040 | Best: {'fx': 526.263} (0.071):  39%|███▉      | 39/100 [00:02<00:03, 16.94it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.034 | Best: {'fx': 526.263} (0.071):  39%|███▉      | 39/100 [00:02<00:03, 16.94it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.037 | Best: {'fx': 526.263} (0.071):  40%|████      | 40/100 [00:02<00:03, 16.94it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.033 | Best: {'fx': 526.263} (0.071):  41%|████      | 41/100 [00:02<00:03, 16.94it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.033 | Best: {'fx': 526.263} (0.071):  42%|████▏     | 42/100 [00:02<00:03, 18.70it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.031 | Best: {'fx': 526.263} (0.071):  42%|████▏     | 42/100 [00:02<00:03, 18.70it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.030 | Best: {'fx': 526.263} (0.071):  43%|████▎     | 43/100 [00:02<00:03, 18.70it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.029 | Best: {'fx': 526.263} (0.071):  44%|████▍     | 44/100 [00:02<00:02, 18.70it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 526.263} (0.071):  45%|████▌     | 45/100 [00:02<00:02, 18.70it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 526.263} (0.071):  46%|████▌     | 46/100 [00:02<00:02, 20.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.026 | Best: {'fx': 526.263} (0.071):  46%|████▌     | 46/100 [00:02<00:02, 20.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.025 | Best: {'fx': 526.263} (0.071):  47%|████▋     | 47/100 [00:02<00:02, 20.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.028 | Best: {'fx': 526.263} (0.071):  48%|████▊     | 48/100 [00:02<00:02, 20.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.028 | Best: {'fx': 526.263} (0.071):  49%|████▉     | 49/100 [00:02<00:02, 21.90it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 526.263} (0.071):  49%|████▉     | 49/100 [00:02<00:02, 21.90it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.025 | Best: {'fx': 526.263} (0.071):  50%|█████     | 50/100 [00:02<00:02, 21.90it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 526.263} (0.071):  51%|█████     | 51/100 [00:02<00:02, 21.90it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 526.263} (0.071):  52%|█████▏    | 52/100 [00:02<00:02, 23.10it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 526.263} (0.071):  52%|█████▏    | 52/100 [00:03<00:02, 23.10it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.071):  53%|█████▎    | 53/100 [00:03<00:02, 23.10it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  54%|█████▍    | 54/100 [00:03<00:01, 23.10it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  55%|█████▌    | 55/100 [00:03<00:02, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 526.263} (0.071):  55%|█████▌    | 55/100 [00:03<00:02, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  56%|█████▌    | 56/100 [00:03<00:01, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.071):  57%|█████▋    | 57/100 [00:03<00:01, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 526.263} (0.071):  58%|█████▊    | 58/100 [00:03<00:01, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.071):  59%|█████▉    | 59/100 [00:03<00:01, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  60%|██████    | 60/100 [00:03<00:01, 22.26it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  61%|██████    | 61/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  61%|██████    | 61/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.071):  62%|██████▏   | 62/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  63%|██████▎   | 63/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  64%|██████▍   | 64/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 526.263} (0.071):  65%|██████▌   | 65/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  66%|██████▌   | 66/100 [00:03<00:01, 29.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  67%|██████▋   | 67/100 [00:03<00:00, 34.54it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  67%|██████▋   | 67/100 [00:03<00:00, 34.54it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  68%|██████▊   | 68/100 [00:03<00:00, 34.54it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 526.263} (0.071):  69%|██████▉   | 69/100 [00:03<00:00, 34.54it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  70%|███████   | 70/100 [00:03<00:00, 34.54it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  71%|███████   | 71/100 [00:03<00:00, 34.54it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  72%|███████▏  | 72/100 [00:03<00:00, 37.19it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 526.263} (0.071):  72%|███████▏  | 72/100 [00:03<00:00, 37.19it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  73%|███████▎  | 73/100 [00:03<00:00, 37.19it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  74%|███████▍  | 74/100 [00:03<00:00, 37.19it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  75%|███████▌  | 75/100 [00:03<00:00, 37.19it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  76%|███████▌  | 76/100 [00:03<00:00, 32.92it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  76%|███████▌  | 76/100 [00:03<00:00, 32.92it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  77%|███████▋  | 77/100 [00:03<00:00, 32.92it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  78%|███████▊  | 78/100 [00:03<00:00, 32.92it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  79%|███████▉  | 79/100 [00:03<00:00, 32.92it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  80%|████████  | 80/100 [00:03<00:00, 30.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  80%|████████  | 80/100 [00:03<00:00, 30.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  81%|████████  | 81/100 [00:03<00:00, 30.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  82%|████████▏ | 82/100 [00:03<00:00, 30.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  83%|████████▎ | 83/100 [00:03<00:00, 30.49it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  84%|████████▍ | 84/100 [00:03<00:00, 29.32it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 526.263} (0.071):  84%|████████▍ | 84/100 [00:03<00:00, 29.32it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  85%|████████▌ | 85/100 [00:04<00:00, 29.32it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  86%|████████▌ | 86/100 [00:04<00:00, 29.32it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  87%|████████▋ | 87/100 [00:04<00:00, 29.32it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  88%|████████▊ | 88/100 [00:04<00:00, 24.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  88%|████████▊ | 88/100 [00:04<00:00, 24.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  89%|████████▉ | 89/100 [00:04<00:00, 24.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  90%|█████████ | 90/100 [00:04<00:00, 24.43it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  91%|█████████ | 91/100 [00:04<00:00, 24.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  91%|█████████ | 91/100 [00:04<00:00, 24.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  92%|█████████▏| 92/100 [00:04<00:00, 24.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  93%|█████████▎| 93/100 [00:04<00:00, 24.95it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  94%|█████████▍| 94/100 [00:04<00:00, 24.14it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  94%|█████████▍| 94/100 [00:04<00:00, 24.14it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  95%|█████████▌| 95/100 [00:04<00:00, 24.14it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  96%|█████████▌| 96/100 [00:04<00:00, 24.14it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  97%|█████████▋| 97/100 [00:04<00:00, 22.91it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  97%|█████████▋| 97/100 [00:04<00:00, 22.91it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  98%|█████████▊| 98/100 [00:04<00:00, 22.91it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071):  99%|█████████▉| 99/100 [00:04<00:00, 22.91it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 526.263} (0.071): 100%|██████████| 100/100 [00:04<00:00, 22.79it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.038 | Best: {'fx': 501.263} (0.038):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.038 | Best: {'fx': 501.263} (0.038):   2%|▏         | 1/50 [00:00<00:27,  1.80it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.039 | Best: {'fx': 502.283} (0.039):   2%|▏         | 1/50 [00:00<00:27,  1.80it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.040 | Best: {'fx': 503.303} (0.040):   4%|▍         | 2/50 [00:00<00:26,  1.80it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.041 | Best: {'fx': 504.324} (0.041):   6%|▌         | 3/50 [00:00<00:26,  1.80it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.041 | Best: {'fx': 504.324} (0.041):   8%|▊         | 4/50 [00:00<00:06,  6.84it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.042 | Best: {'fx': 505.344} (0.042):   8%|▊         | 4/50 [00:00<00:06,  6.84it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.043 | Best: {'fx': 506.365} (0.043):  10%|█         | 5/50 [00:00<00:06,  6.84it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.043 | Best: {'fx': 506.365} (0.043):  12%|█▏        | 6/50 [00:00<00:04,  9.23it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.044 | Best: {'fx': 507.385} (0.044):  12%|█▏        | 6/50 [00:00<00:04,  9.23it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.045 | Best: {'fx': 508.405} (0.045):  14%|█▍        | 7/50 [00:00<00:04,  9.23it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.045 | Best: {'fx': 508.405} (0.045):  16%|█▌        | 8/50 [00:00<00:03, 11.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.046 | Best: {'fx': 509.426} (0.046):  16%|█▌        | 8/50 [00:00<00:03, 11.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.048 | Best: {'fx': 510.446} (0.048):  18%|█▊        | 9/50 [00:01<00:03, 11.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.048 | Best: {'fx': 510.446} (0.048):  20%|██        | 10/50 [00:01<00:03, 13.12it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.049 | Best: {'fx': 511.467} (0.049):  20%|██        | 10/50 [00:01<00:03, 13.12it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.051 | Best: {'fx': 512.487} (0.051):  22%|██▏       | 11/50 [00:01<00:02, 13.12it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.051 | Best: {'fx': 512.487} (0.051):  24%|██▍       | 12/50 [00:01<00:02, 14.25it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.052 | Best: {'fx': 513.508} (0.052):  24%|██▍       | 12/50 [00:01<00:02, 14.25it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.053 | Best: {'fx': 514.528} (0.053):  26%|██▌       | 13/50 [00:01<00:02, 14.25it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.054 | Best: {'fx': 515.548} (0.054):  28%|██▊       | 14/50 [00:01<00:02, 14.25it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.054 | Best: {'fx': 515.548} (0.054):  30%|███       | 15/50 [00:01<00:01, 17.81it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.055 | Best: {'fx': 516.569} (0.055):  30%|███       | 15/50 [00:01<00:01, 17.81it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.058 | Best: {'fx': 517.589} (0.058):  32%|███▏      | 16/50 [00:01<00:01, 17.81it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.060 | Best: {'fx': 518.61} (0.060):  34%|███▍      | 17/50 [00:01<00:01, 17.81it/s] Brute | Start: {'fx': 526.263} (0.071) | Current: 0.060 | Best: {'fx': 518.61} (0.060):  36%|███▌      | 18/50 [00:01<00:02, 15.96it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.060 | Best: {'fx': 518.61} (0.060):  36%|███▌      | 18/50 [00:01<00:02, 15.96it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.061 | Best: {'fx': 520.65} (0.061):  38%|███▊      | 19/50 [00:01<00:01, 15.96it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.061 | Best: {'fx': 520.65} (0.061):  40%|████      | 20/50 [00:01<00:01, 15.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.062 | Best: {'fx': 521.671} (0.062):  40%|████      | 20/50 [00:01<00:01, 15.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.064 | Best: {'fx': 522.691} (0.064):  42%|████▏     | 21/50 [00:01<00:01, 15.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.065 | Best: {'fx': 523.712} (0.065):  44%|████▍     | 22/50 [00:01<00:01, 15.19it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.065 | Best: {'fx': 523.712} (0.065):  46%|████▌     | 23/50 [00:01<00:01, 17.29it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.068 | Best: {'fx': 524.732} (0.068):  46%|████▌     | 23/50 [00:01<00:01, 17.29it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.070 | Best: {'fx': 525.752} (0.070):  48%|████▊     | 24/50 [00:01<00:01, 17.29it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.070 | Best: {'fx': 525.752} (0.070):  50%|█████     | 25/50 [00:01<00:01, 17.27it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.073 | Best: {'fx': 527.793} (0.073):  50%|█████     | 25/50 [00:01<00:01, 17.27it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.072 | Best: {'fx': 527.793} (0.073):  52%|█████▏    | 26/50 [00:01<00:01, 17.27it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.071 | Best: {'fx': 527.793} (0.073):  54%|█████▍    | 27/50 [00:01<00:01, 17.27it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.072 | Best: {'fx': 527.793} (0.073):  56%|█████▌    | 28/50 [00:02<00:01, 17.27it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.072 | Best: {'fx': 527.793} (0.073):  58%|█████▊    | 29/50 [00:02<00:01, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.072 | Best: {'fx': 527.793} (0.073):  58%|█████▊    | 29/50 [00:02<00:01, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.067 | Best: {'fx': 527.793} (0.073):  60%|██████    | 30/50 [00:02<00:00, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.067 | Best: {'fx': 527.793} (0.073):  62%|██████▏   | 31/50 [00:02<00:00, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.072 | Best: {'fx': 527.793} (0.073):  64%|██████▍   | 32/50 [00:02<00:00, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.065 | Best: {'fx': 527.793} (0.073):  66%|██████▌   | 33/50 [00:02<00:00, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.069 | Best: {'fx': 527.793} (0.073):  68%|██████▊   | 34/50 [00:02<00:00, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.064 | Best: {'fx': 527.793} (0.073):  70%|███████   | 35/50 [00:02<00:00, 20.13it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.064 | Best: {'fx': 527.793} (0.073):  72%|███████▏  | 36/50 [00:02<00:00, 28.60it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.072 | Best: {'fx': 527.793} (0.073):  72%|███████▏  | 36/50 [00:02<00:00, 28.60it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.063 | Best: {'fx': 527.793} (0.073):  74%|███████▍  | 37/50 [00:02<00:00, 28.60it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.061 | Best: {'fx': 527.793} (0.073):  76%|███████▌  | 38/50 [00:02<00:00, 28.60it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.062 | Best: {'fx': 527.793} (0.073):  78%|███████▊  | 39/50 [00:02<00:00, 28.60it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.060 | Best: {'fx': 527.793} (0.073):  80%|████████  | 40/50 [00:02<00:00, 28.60it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.060 | Best: {'fx': 527.793} (0.073):  82%|████████▏ | 41/50 [00:02<00:00, 32.85it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.059 | Best: {'fx': 527.793} (0.073):  82%|████████▏ | 41/50 [00:02<00:00, 32.85it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.059 | Best: {'fx': 527.793} (0.073):  84%|████████▍ | 42/50 [00:02<00:00, 32.85it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.058 | Best: {'fx': 527.793} (0.073):  86%|████████▌ | 43/50 [00:02<00:00, 32.85it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.057 | Best: {'fx': 527.793} (0.073):  88%|████████▊ | 44/50 [00:02<00:00, 32.85it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.057 | Best: {'fx': 527.793} (0.073):  90%|█████████ | 45/50 [00:02<00:00, 31.90it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.056 | Best: {'fx': 527.793} (0.073):  90%|█████████ | 45/50 [00:02<00:00, 31.90it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.054 | Best: {'fx': 527.793} (0.073):  92%|█████████▏| 46/50 [00:02<00:00, 31.90it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.055 | Best: {'fx': 527.793} (0.073):  94%|█████████▍| 47/50 [00:02<00:00, 31.90it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.053 | Best: {'fx': 527.793} (0.073):  96%|█████████▌| 48/50 [00:02<00:00, 31.90it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.053 | Best: {'fx': 527.793} (0.073):  98%|█████████▊| 49/50 [00:02<00:00, 30.68it/s]Brute | Start: {'fx': 526.263} (0.071) | Current: 0.052 | Best: {'fx': 527.793} (0.073):  98%|█████████▊| 49/50 [00:02<00:00, 30.68it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 527.7932385075243
    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=511.2 px

[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 5 incomplete frames): 74 frames will be used for optimization
  Rigs: 1000(1000mm):19/19, 1500(1500mm):19/20, 2000L(2000mm):18/20, 2000R(2000mm):18/20
  Total: 74/79 frames used for optimization
Initial score: 80.5612: 0iter [00:00, ?iter/s]  [Ring mask] 89,088/94,984 points within ring radius 511.2 px
[DEBUG] objective(x0) = 80.5612, picklable_objective(x0) = 79.3053
Best score: 79.3053: : 0iter [00:02, ?iter/s] Best score: 79.3053: : 1iter [00:02,  2.13s/iter]Best score: 79.3053: : 1iter [00:02,  2.13s/iter]Best score: 79.3053: : 2iter [00:02,  1.04iter/s]Best score: 79.3053: : 2iter [00:02,  1.04iter/s]Best score: 79.3053: : 3iter [00:02,  1.68iter/s]Best score: 79.3053: : 3iter [00:02,  1.68iter/s]Best score: 79.3053: : 4iter [00:02,  1.94iter/s]Best score: 79.3053: : 4iter [00:02,  1.94iter/s]Best score: 79.3053: : 5iter [00:02,  2.60iter/s]Best score: 79.3053: : 5iter [00:03,  2.60iter/s]Best score: 79.3053: : 6iter [00:03,  3.27iter/s]Best score: 79.3053: : 6iter [00:03,  3.27iter/s]Best score: 79.3053: : 7iter [00:03,  3.91iter/s]Best score: 79.3053: : 7iter [00:03,  3.91iter/s]Best score: 79.3053: : 8iter [00:03,  4.47iter/s]Best score: 79.3053: : 8iter [00:03,  4.47iter/s]Best score: 79.3053: : 9iter [00:03,  4.97iter/s]Best score: 77.8892: : 9iter [00:03,  4.97iter/s]Best score: 77.8892: : 10iter [00:03,  5.23iter/s]Best score: 77.8892: : 10iter [00:03,  5.23iter/s]Best score: 77.8892: : 11iter [00:03,  5.58iter/s]Best score: 77.8892: : 11iter [00:04,  5.58iter/s]Best score: 77.8892: : 12iter [00:04,  5.85iter/s]Best score: 77.8892: : 12iter [00:04,  5.85iter/s]Best score: 77.8892: : 13iter [00:04,  6.04iter/s]Best score: 77.8892: : 13iter [00:04,  6.04iter/s]Best score: 77.8892: : 14iter [00:04,  6.18iter/s]Best score: 77.8892: : 14iter [00:04,  6.18iter/s]Best score: 77.8892: : 15iter [00:04,  6.28iter/s]Best score: 77.8892: : 15iter [00:04,  6.28iter/s]Best score: 77.8892: : 16iter [00:04,  6.36iter/s]Best score: 77.8892: : 16iter [00:04,  6.36iter/s]Best score: 77.8892: : 17iter [00:04,  6.43iter/s]Best score: 77.8892: : 17iter [00:04,  6.43iter/s]Best score: 77.8892: : 18iter [00:04,  6.45iter/s]Best score: 77.8892: : 18iter [00:05,  6.45iter/s]Best score: 77.8892: : 19iter [00:05,  6.47iter/s]Best score: 35.0742: : 19iter [00:05,  6.47iter/s]Best score: 35.0742: : 20iter [00:05,  6.30iter/s]Best score: 35.0742: : 20iter [00:05,  6.30iter/s]Best score: 35.0742: : 21iter [00:05,  6.37iter/s]Best score: 35.0742: : 21iter [00:05,  6.37iter/s]Best score: 35.0742: : 22iter [00:05,  6.42iter/s]Best score: 35.0742: : 22iter [00:05,  6.42iter/s]Best score: 35.0742: : 23iter [00:05,  6.46iter/s]Best score: 35.0742: : 23iter [00:05,  6.46iter/s]Best score: 35.0742: : 24iter [00:05,  6.48iter/s]Best score: 35.0742: : 24iter [00:06,  6.48iter/s]Best score: 35.0742: : 25iter [00:06,  6.50iter/s]Best score: 35.0742: : 25iter [00:06,  6.50iter/s]Best score: 35.0742: : 26iter [00:06,  6.52iter/s]Best score: 35.0742: : 26iter [00:06,  6.52iter/s]Best score: 35.0742: : 27iter [00:06,  6.53iter/s]Best score: 35.0742: : 27iter [00:06,  6.53iter/s]Best score: 35.0742: : 28iter [00:06,  6.52iter/s]Best score: 35.0742: : 28iter [00:06,  6.52iter/s]Best score: 35.0742: : 29iter [00:06,  6.53iter/s]Best score: 35.0742: : 29iter [00:06,  6.53iter/s]Best score: 35.0742: : 30iter [00:06,  6.33iter/s]Best score: 35.0742: : 30iter [00:07,  6.33iter/s]Best score: 35.0742: : 31iter [00:07,  6.39iter/s]Best score: 35.0742: : 31iter [00:07,  6.39iter/s]Best score: 35.0742: : 32iter [00:07,  6.44iter/s]Best score: 35.0742: : 32iter [00:07,  6.44iter/s]Best score: 35.0742: : 33iter [00:07,  6.48iter/s]Best score: 35.0742: : 33iter [00:07,  6.48iter/s]Best score: 35.0742: : 34iter [00:07,  6.49iter/s]Best score: 35.0742: : 34iter [00:07,  6.49iter/s]Best score: 35.0742: : 35iter [00:07,  6.53iter/s]Best score: 35.0742: : 35iter [00:07,  6.53iter/s]Best score: 35.0742: : 36iter [00:07,  6.55iter/s]Best score: 35.0742: : 36iter [00:07,  6.55iter/s]Best score: 35.0742: : 37iter [00:07,  6.54iter/s]Best score: 35.0742: : 37iter [00:08,  6.54iter/s]Best score: 35.0742: : 38iter [00:08,  6.55iter/s]Best score: 35.0742: : 38iter [00:08,  6.55iter/s]Best score: 35.0742: : 39iter [00:08,  6.55iter/s]Best score: 35.0742: : 39iter [00:08,  6.55iter/s]Best score: 35.0742: : 40iter [00:08,  6.35iter/s]Best score: 35.0742: : 40iter [00:08,  6.35iter/s]Best score: 35.0742: : 41iter [00:08,  6.42iter/s]Best score: 35.0742: : 41iter [00:08,  6.42iter/s]Best score: 35.0742: : 42iter [00:08,  6.45iter/s]Best score: 35.0742: : 42iter [00:08,  6.45iter/s]Best score: 35.0742: : 43iter [00:08,  6.48iter/s]Best score: 35.0742: : 43iter [00:08,  6.48iter/s]Best score: 35.0742: : 44iter [00:08,  6.50iter/s]Best score: 35.0742: : 44iter [00:09,  6.50iter/s]Best score: 35.0742: : 45iter [00:09,  6.53iter/s]Best score: 35.0742: : 45iter [00:09,  6.53iter/s]Best score: 35.0742: : 46iter [00:09,  6.58iter/s]Best score: 35.0742: : 46iter [00:09,  6.58iter/s]Best score: 35.0742: : 47iter [00:09,  6.58iter/s]Best score: 35.0742: : 47iter [00:09,  6.58iter/s]Best score: 35.0742: : 48iter [00:09,  6.59iter/s]Best score: 35.0742: : 48iter [00:09,  6.59iter/s]Best score: 35.0742: : 49iter [00:09,  6.57iter/s]
[ITER 50] Best: 27.4693
[PARAMS] Current best parameters:
  fx: 526.3872
  fy: 524.1368
  px: 848.2756
  py: 1002.1339
  k1: 0.00133443
  k2: -0.00177471
  k3: -0.00510888
  k4: 0.00433121
  p1: -0.00414418
  p2: -0.00434640
  p3: -0.00136049
  p4: -0.00782757
  scale: 3.0163
  rotation: -0.6566
Best score: 27.4693: : 49iter [00:09,  6.57iter/s]Best score: 27.4693: : 50iter [00:09,  6.35iter/s]Best score: 27.4693: : 50iter [00:10,  6.35iter/s]Best score: 27.4693: : 51iter [00:10,  6.42iter/s]Best score: 27.4693: : 51iter [00:10,  6.42iter/s]Best score: 27.4693: : 52iter [00:10,  6.46iter/s]Best score: 27.4693: : 52iter [00:10,  6.46iter/s]Best score: 27.4693: : 53iter [00:10,  6.48iter/s]Best score: 27.4693: : 53iter [00:10,  6.48iter/s]Best score: 27.4693: : 54iter [00:10,  6.48iter/s]Best score: 27.4693: : 54iter [00:10,  6.48iter/s]Best score: 27.4693: : 55iter [00:10,  6.49iter/s]Best score: 27.4693: : 55iter [00:10,  6.49iter/s]Best score: 27.4693: : 56iter [00:10,  6.51iter/s]Best score: 27.4693: : 56iter [00:10,  6.51iter/s]Best score: 27.4693: : 57iter [00:10,  6.54iter/s]Best score: 27.4693: : 57iter [00:11,  6.54iter/s]Best score: 27.4693: : 58iter [00:11,  6.53iter/s]Best score: 27.4693: : 58iter [00:11,  6.53iter/s]Best score: 27.4693: : 59iter [00:11,  6.52iter/s]Best score: 20.3592: : 59iter [00:11,  6.52iter/s]Best score: 20.3592: : 60iter [00:11,  6.33iter/s]Best score: 20.3592: : 60iter [00:11,  6.33iter/s]Best score: 20.3592: : 61iter [00:11,  6.39iter/s]Best score: 20.3592: : 61iter [00:11,  6.39iter/s]Best score: 20.3592: : 62iter [00:11,  6.44iter/s]Best score: 20.3592: : 62iter [00:11,  6.44iter/s]Best score: 20.3592: : 63iter [00:11,  6.48iter/s]Best score: 20.3592: : 63iter [00:12,  6.48iter/s]Best score: 20.3592: : 64iter [00:12,  6.48iter/s]Best score: 20.3592: : 64iter [00:12,  6.48iter/s]Best score: 20.3592: : 65iter [00:12,  6.50iter/s]Best score: 20.3592: : 65iter [00:12,  6.50iter/s]Best score: 20.3592: : 66iter [00:12,  6.51iter/s]Best score: 20.3592: : 66iter [00:12,  6.51iter/s]Best score: 20.3592: : 67iter [00:12,  6.50iter/s]Best score: 20.3592: : 67iter [00:12,  6.50iter/s]Best score: 20.3592: : 68iter [00:12,  6.51iter/s]Best score: 20.3592: : 68iter [00:12,  6.51iter/s]Best score: 20.3592: : 69iter [00:12,  6.52iter/s]Best score: 20.3592: : 69iter [00:13,  6.52iter/s]Best score: 20.3592: : 70iter [00:13,  6.31iter/s]Best score: 20.3592: : 70iter [00:13,  6.31iter/s]Best score: 20.3592: : 71iter [00:13,  6.37iter/s]Best score: 20.3592: : 71iter [00:13,  6.37iter/s]Best score: 20.3592: : 72iter [00:13,  6.42iter/s]Best score: 20.3592: : 72iter [00:13,  6.42iter/s]Best score: 20.3592: : 73iter [00:13,  6.46iter/s]Best score: 20.3592: : 73iter [00:13,  6.46iter/s]Best score: 20.3592: : 74iter [00:13,  6.48iter/s]Best score: 20.3592: : 74iter [00:13,  6.48iter/s]Best score: 20.3592: : 75iter [00:13,  6.51iter/s]Best score: 20.3592: : 75iter [00:13,  6.51iter/s]Best score: 20.3592: : 76iter [00:13,  6.52iter/s]Best score: 20.3592: : 76iter [00:14,  6.52iter/s]Best score: 20.3592: : 77iter [00:14,  6.56iter/s]Best score: 20.3592: : 77iter [00:14,  6.56iter/s]Best score: 20.3592: : 78iter [00:14,  6.54iter/s]Best score: 20.3592: : 78iter [00:14,  6.54iter/s]Best score: 20.3592: : 79iter [00:14,  6.54iter/s]Best score: 16.9235: : 79iter [00:14,  6.54iter/s]Best score: 16.9235: : 80iter [00:14,  6.33iter/s]Best score: 16.9235: : 80iter [00:14,  6.33iter/s]Best score: 16.9235: : 81iter [00:14,  6.39iter/s]Best score: 16.9235: : 81iter [00:14,  6.39iter/s]Best score: 16.9235: : 82iter [00:14,  6.45iter/s]Best score: 16.9235: : 82iter [00:15,  6.45iter/s]Best score: 16.9235: : 83iter [00:15,  6.50iter/s]Best score: 16.9235: : 83iter [00:15,  6.50iter/s]Best score: 16.9235: : 84iter [00:15,  6.53iter/s]Best score: 16.9235: : 84iter [00:15,  6.53iter/s]Best score: 16.9235: : 85iter [00:15,  6.55iter/s]Best score: 16.9235: : 85iter [00:15,  6.55iter/s]Best score: 16.9235: : 86iter [00:15,  6.55iter/s]Best score: 16.9235: : 86iter [00:15,  6.55iter/s]Best score: 16.9235: : 87iter [00:15,  6.57iter/s]Best score: 16.9235: : 87iter [00:15,  6.57iter/s]Best score: 16.9235: : 88iter [00:15,  6.57iter/s]Best score: 16.9235: : 88iter [00:15,  6.57iter/s]Best score: 16.9235: : 89iter [00:15,  6.58iter/s]Best score: 16.8673: : 89iter [00:16,  6.58iter/s]Best score: 16.8673: : 90iter [00:16,  6.39iter/s]Best score: 16.8673: : 90iter [00:16,  6.39iter/s]Best score: 16.8673: : 91iter [00:16,  6.45iter/s]Best score: 16.8673: : 91iter [00:16,  6.45iter/s]Best score: 16.8673: : 92iter [00:16,  6.49iter/s]Best score: 16.8673: : 92iter [00:16,  6.49iter/s]Best score: 16.8673: : 93iter [00:16,  6.52iter/s]Best score: 16.8673: : 93iter [00:16,  6.52iter/s]Best score: 16.8673: : 94iter [00:16,  6.52iter/s]Best score: 16.8673: : 94iter [00:16,  6.52iter/s]Best score: 16.8673: : 95iter [00:16,  6.54iter/s]Best score: 16.8673: : 95iter [00:17,  6.54iter/s]Best score: 16.8673: : 96iter [00:17,  6.55iter/s]Best score: 16.8673: : 96iter [00:17,  6.55iter/s]Best score: 16.8673: : 97iter [00:17,  6.57iter/s]Best score: 16.8673: : 97iter [00:17,  6.57iter/s]Best score: 16.8673: : 98iter [00:17,  6.58iter/s]Best score: 16.8673: : 98iter [00:17,  6.58iter/s]Best score: 16.8673: : 99iter [00:17,  6.60iter/s]
[ITER 100] Best: 16.8673
[PARAMS] Current best parameters:
  fx: 511.8990
  fy: 513.0837
  px: 846.4438
  py: 1003.2905
  k1: 0.01693647
  k2: -0.00296535
  k3: 0.00237939
  k4: -0.00083172
  p1: 0.00255460
  p2: -0.00973274
  p3: 0.00370547
  p4: -0.00388762
  scale: 3.0195
  rotation: -0.4031
Best score: 16.8673: : 99iter [00:17,  6.60iter/s]Best score: 16.8673: : 100iter [00:17,  6.39iter/s]Best score: 16.8673: : 100iter [00:17,  6.39iter/s]Best score: 16.8673: : 101iter [00:17,  6.46iter/s]Best score: 16.8673: : 101iter [00:17,  6.46iter/s]Best score: 16.8673: : 102iter [00:17,  6.50iter/s]Best score: 16.8673: : 102iter [00:18,  6.50iter/s]Best score: 16.8673: : 103iter [00:18,  6.53iter/s]Best score: 16.8673: : 103iter [00:18,  6.53iter/s]Best score: 16.8673: : 104iter [00:18,  6.54iter/s]Best score: 16.8673: : 104iter [00:18,  6.54iter/s]Best score: 16.8673: : 105iter [00:18,  6.55iter/s]Best score: 16.8673: : 105iter [00:18,  6.55iter/s]Best score: 16.8673: : 106iter [00:18,  6.57iter/s]Best score: 16.8673: : 106iter [00:18,  6.57iter/s]Best score: 16.8673: : 107iter [00:18,  6.57iter/s]Best score: 16.8673: : 107iter [00:18,  6.57iter/s]Best score: 16.8673: : 108iter [00:18,  6.57iter/s]Best score: 16.8673: : 108iter [00:18,  6.57iter/s]Best score: 16.8673: : 109iter [00:18,  6.57iter/s]Best score: 16.4730: : 109iter [00:19,  6.57iter/s]Best score: 16.4730: : 110iter [00:19,  6.37iter/s]Best score: 16.4730: : 110iter [00:19,  6.37iter/s]Best score: 16.4730: : 111iter [00:19,  6.44iter/s]Best score: 16.4730: : 111iter [00:19,  6.44iter/s]Best score: 16.4730: : 112iter [00:19,  6.48iter/s]Best score: 16.4730: : 112iter [00:19,  6.48iter/s]Best score: 16.4730: : 113iter [00:19,  6.51iter/s]Best score: 16.4730: : 113iter [00:19,  6.51iter/s]Best score: 16.4730: : 114iter [00:19,  6.54iter/s]Best score: 16.4730: : 114iter [00:19,  6.54iter/s]Best score: 16.4730: : 115iter [00:19,  6.55iter/s]Best score: 16.4730: : 115iter [00:20,  6.55iter/s]Best score: 16.4730: : 116iter [00:20,  6.54iter/s]Best score: 16.4730: : 116iter [00:20,  6.54iter/s]Best score: 16.4730: : 117iter [00:20,  6.57iter/s]Best score: 16.4730: : 117iter [00:20,  6.57iter/s]Best score: 16.4730: : 118iter [00:20,  6.55iter/s]Best score: 16.4730: : 118iter [00:20,  6.55iter/s]Best score: 16.4730: : 119iter [00:20,  6.56iter/s]Best score: 16.4550: : 119iter [00:20,  6.56iter/s]Best score: 16.4550: : 120iter [00:20,  6.38iter/s]Best score: 16.4550: : 120iter [00:20,  6.38iter/s]Best score: 16.4550: : 121iter [00:20,  6.42iter/s]Best score: 16.4550: : 121iter [00:21,  6.42iter/s]Best score: 16.4550: : 122iter [00:21,  6.47iter/s]Best score: 16.4550: : 122iter [00:21,  6.47iter/s]Best score: 16.4550: : 123iter [00:21,  6.50iter/s]Best score: 16.4550: : 123iter [00:21,  6.50iter/s]Best score: 16.4550: : 124iter [00:21,  6.53iter/s]Best score: 16.4550: : 124iter [00:21,  6.53iter/s]Best score: 16.4550: : 125iter [00:21,  6.54iter/s]Best score: 16.4550: : 125iter [00:21,  6.54iter/s]Best score: 16.4550: : 126iter [00:21,  6.56iter/s]Best score: 16.4550: : 126iter [00:21,  6.56iter/s]Best score: 16.4550: : 127iter [00:21,  6.58iter/s]Best score: 16.4550: : 127iter [00:21,  6.58iter/s]Best score: 16.4550: : 128iter [00:21,  6.59iter/s]Best score: 16.4550: : 128iter [00:22,  6.59iter/s]Best score: 16.4550: : 129iter [00:22,  6.60iter/s]Best score: 16.4116: : 129iter [00:22,  6.60iter/s]Best score: 16.4116: : 130iter [00:22,  6.40iter/s]Best score: 16.4116: : 130iter [00:22,  6.40iter/s]Best score: 16.4116: : 131iter [00:22,  6.46iter/s]Best score: 16.4116: : 131iter [00:22,  6.46iter/s]Best score: 16.4116: : 132iter [00:22,  6.51iter/s]Best score: 16.4116: : 132iter [00:22,  6.51iter/s]Best score: 16.4116: : 133iter [00:22,  6.54iter/s]Best score: 16.4116: : 133iter [00:22,  6.54iter/s]Best score: 16.4116: : 134iter [00:22,  6.56iter/s]Best score: 16.4116: : 134iter [00:22,  6.56iter/s]Best score: 16.4116: : 135iter [00:22,  6.54iter/s]Best score: 16.4116: : 135iter [00:23,  6.54iter/s]Best score: 16.4116: : 136iter [00:23,  6.52iter/s]Best score: 16.4116: : 136iter [00:23,  6.52iter/s]Best score: 16.4116: : 137iter [00:23,  6.54iter/s]Best score: 16.4116: : 137iter [00:23,  6.54iter/s]Best score: 16.4116: : 138iter [00:23,  6.55iter/s]Best score: 16.4116: : 138iter [00:23,  6.55iter/s]Best score: 16.4116: : 139iter [00:23,  6.56iter/s]Best score: 16.4116: : 139iter [00:23,  6.56iter/s]Best score: 16.4116: : 140iter [00:23,  6.38iter/s]Best score: 16.4116: : 140iter [00:23,  6.38iter/s]Best score: 16.4116: : 141iter [00:23,  6.45iter/s]Best score: 16.4116: : 141iter [00:24,  6.45iter/s]Best score: 16.4116: : 142iter [00:24,  6.50iter/s]Best score: 16.4116: : 142iter [00:24,  6.50iter/s]Best score: 16.4116: : 143iter [00:24,  6.52iter/s]Best score: 16.4116: : 143iter [00:24,  6.52iter/s]Best score: 16.4116: : 144iter [00:24,  6.54iter/s]Best score: 16.4116: : 144iter [00:24,  6.54iter/s]Best score: 16.4116: : 145iter [00:24,  6.54iter/s]Best score: 16.4116: : 145iter [00:24,  6.54iter/s]Best score: 16.4116: : 146iter [00:24,  6.55iter/s]Best score: 16.4116: : 146iter [00:24,  6.55iter/s]Best score: 16.4116: : 147iter [00:24,  6.56iter/s]Best score: 16.4116: : 147iter [00:24,  6.56iter/s]Best score: 16.4116: : 148iter [00:24,  6.57iter/s]Best score: 16.4116: : 148iter [00:25,  6.57iter/s]Best score: 16.4116: : 149iter [00:25,  6.57iter/s]
[ITER 150] Best: 16.3405
[PARAMS] Current best parameters:
  fx: 490.2030
  fy: 490.4944
  px: 848.1421
  py: 1004.3888
  k1: 0.04907525
  k2: 0.01216848
  k3: -0.02083437
  k4: 0.00557788
  p1: 0.00381906
  p2: -0.00886628
  p3: 0.00705376
  p4: -0.00308277
  scale: 2.9823
  rotation: -0.3366
Best score: 16.3405: : 149iter [00:25,  6.57iter/s]Best score: 16.3405: : 150iter [00:25,  6.37iter/s]Best score: 16.3405: : 150iter [00:25,  6.37iter/s]Best score: 16.3405: : 151iter [00:25,  6.42iter/s]Best score: 16.3405: : 151iter [00:25,  6.42iter/s]Best score: 16.3405: : 152iter [00:25,  6.46iter/s]Best score: 16.3405: : 152iter [00:25,  6.46iter/s]Best score: 16.3405: : 153iter [00:25,  6.50iter/s]Best score: 16.3405: : 153iter [00:25,  6.50iter/s]Best score: 16.3405: : 154iter [00:25,  6.52iter/s]Best score: 16.3405: : 154iter [00:26,  6.52iter/s]Best score: 16.3405: : 155iter [00:26,  6.54iter/s]Best score: 16.3405: : 155iter [00:26,  6.54iter/s]Best score: 16.3405: : 156iter [00:26,  6.56iter/s]Best score: 16.3405: : 156iter [00:26,  6.56iter/s]Best score: 16.3405: : 157iter [00:26,  6.58iter/s]Best score: 16.3405: : 157iter [00:26,  6.58iter/s]Best score: 16.3405: : 158iter [00:26,  6.59iter/s]Best score: 16.3405: : 158iter [00:26,  6.59iter/s]Best score: 16.3405: : 159iter [00:26,  6.59iter/s]Best score: 16.3093: : 159iter [00:26,  6.59iter/s]Best score: 16.3093: : 160iter [00:26,  6.39iter/s]Best score: 16.3093: : 160iter [00:26,  6.39iter/s]Best score: 16.3093: : 161iter [00:26,  6.46iter/s]Best score: 16.3093: : 161iter [00:27,  6.46iter/s]Best score: 16.3093: : 162iter [00:27,  6.51iter/s]Best score: 16.3093: : 162iter [00:27,  6.51iter/s]Best score: 16.3093: : 163iter [00:27,  6.54iter/s]Best score: 16.3093: : 163iter [00:27,  6.54iter/s]Best score: 16.3093: : 164iter [00:27,  6.55iter/s]Best score: 16.3093: : 164iter [00:27,  6.55iter/s]Best score: 16.3093: : 165iter [00:27,  6.57iter/s]Best score: 16.3093: : 165iter [00:27,  6.57iter/s]Best score: 16.3093: : 166iter [00:27,  6.57iter/s]Best score: 16.3093: : 166iter [00:27,  6.57iter/s]Best score: 16.3093: : 167iter [00:27,  6.59iter/s]Best score: 16.3093: : 167iter [00:28,  6.59iter/s]Best score: 16.3093: : 168iter [00:28,  6.60iter/s]Best score: 16.3093: : 168iter [00:28,  6.60iter/s]Best score: 16.3093: : 169iter [00:28,  6.58iter/s]Best score: 16.2758: : 169iter [00:28,  6.58iter/s]Best score: 16.2758: : 170iter [00:28,  6.39iter/s]Best score: 16.2758: : 170iter [00:28,  6.39iter/s]Best score: 16.2758: : 171iter [00:28,  6.46iter/s]Best score: 16.2758: : 171iter [00:28,  6.46iter/s]Best score: 16.2758: : 172iter [00:28,  6.50iter/s]Best score: 16.2758: : 172iter [00:28,  6.50iter/s]Best score: 16.2758: : 173iter [00:28,  6.51iter/s]Best score: 16.2758: : 173iter [00:28,  6.51iter/s]Best score: 16.2758: : 174iter [00:28,  6.55iter/s]Best score: 16.2758: : 174iter [00:29,  6.55iter/s]Best score: 16.2758: : 175iter [00:29,  6.58iter/s]Best score: 16.2758: : 175iter [00:29,  6.58iter/s]Best score: 16.2758: : 176iter [00:29,  6.58iter/s]Best score: 16.2758: : 176iter [00:29,  6.58iter/s]Best score: 16.2758: : 177iter [00:29,  6.61iter/s]Best score: 16.2758: : 177iter [00:29,  6.61iter/s]Best score: 16.2758: : 178iter [00:29,  6.61iter/s]Best score: 16.2758: : 178iter [00:29,  6.61iter/s]Best score: 16.2758: : 179iter [00:29,  6.60iter/s]Best score: 16.2428: : 179iter [00:29,  6.60iter/s]Best score: 16.2428: : 180iter [00:29,  6.40iter/s]Best score: 16.2428: : 180iter [00:30,  6.40iter/s]Best score: 16.2428: : 181iter [00:30,  6.47iter/s]Best score: 16.2428: : 181iter [00:30,  6.47iter/s]Best score: 16.2428: : 182iter [00:30,  6.52iter/s]Best score: 16.2428: : 182iter [00:30,  6.52iter/s]Best score: 16.2428: : 183iter [00:30,  6.55iter/s]Best score: 16.2428: : 183iter [00:30,  6.55iter/s]Best score: 16.2428: : 184iter [00:30,  6.57iter/s]Best score: 16.2428: : 184iter [00:30,  6.57iter/s]Best score: 16.2428: : 185iter [00:30,  6.59iter/s]Best score: 16.2428: : 185iter [00:30,  6.59iter/s]Best score: 16.2428: : 186iter [00:30,  6.61iter/s]Best score: 16.2428: : 186iter [00:30,  6.61iter/s]Best score: 16.2428: : 187iter [00:30,  6.61iter/s]Best score: 16.2428: : 187iter [00:31,  6.61iter/s]Best score: 16.2428: : 188iter [00:31,  6.61iter/s]Best score: 16.2428: : 188iter [00:31,  6.61iter/s]Best score: 16.2428: : 189iter [00:31,  6.61iter/s]Best score: 16.2428: : 189iter [00:31,  6.61iter/s]Best score: 16.2428: : 190iter [00:31,  6.42iter/s]Best score: 16.2428: : 190iter [00:31,  6.42iter/s]Best score: 16.2428: : 191iter [00:31,  6.48iter/s]Best score: 16.2428: : 191iter [00:31,  6.48iter/s]Best score: 16.2428: : 192iter [00:31,  6.52iter/s]Best score: 16.2428: : 192iter [00:31,  6.52iter/s]Best score: 16.2428: : 193iter [00:31,  6.55iter/s]Best score: 16.2428: : 193iter [00:32,  6.55iter/s]Best score: 16.2428: : 194iter [00:32,  6.57iter/s]Best score: 16.2428: : 194iter [00:32,  6.57iter/s]Best score: 16.2428: : 195iter [00:32,  6.58iter/s]Best score: 16.2428: : 195iter [00:32,  6.58iter/s]Best score: 16.2428: : 196iter [00:32,  6.58iter/s]Best score: 16.2428: : 196iter [00:32,  6.58iter/s]Best score: 16.2428: : 197iter [00:32,  6.60iter/s]Best score: 16.2428: : 197iter [00:32,  6.60iter/s]Best score: 16.2428: : 198iter [00:32,  6.61iter/s]Best score: 16.2428: : 198iter [00:32,  6.61iter/s]Best score: 16.2428: : 199iter [00:32,  6.61iter/s]
[ITER 200] Best: 16.2381
[PARAMS] Current best parameters:
  fx: 484.1814
  fy: 484.8352
  px: 847.2904
  py: 1003.8114
  k1: 0.04762225
  k2: 0.01521316
  k3: -0.01833905
  k4: 0.00440333
  p1: 0.00374892
  p2: -0.00924918
  p3: 0.00541917
  p4: -0.00360010
  scale: 3.0003
  rotation: -0.3878
Best score: 16.2381: : 199iter [00:33,  6.61iter/s]Best score: 16.2381: : 200iter [00:33,  4.89iter/s]Best score: 16.2381: : 200iter [00:33,  4.89iter/s]Best score: 16.2381: : 201iter [00:33,  5.31iter/s]Best score: 16.2381: : 201iter [00:33,  5.31iter/s]Best score: 16.2381: : 202iter [00:33,  5.64iter/s]Best score: 16.2381: : 202iter [00:33,  5.64iter/s]Best score: 16.2381: : 203iter [00:33,  5.91iter/s]Best score: 16.2381: : 203iter [00:33,  5.91iter/s]Best score: 16.2381: : 204iter [00:33,  6.10iter/s]Best score: 16.2381: : 204iter [00:33,  6.10iter/s]Best score: 16.2381: : 205iter [00:33,  6.24iter/s]Best score: 16.2381: : 205iter [00:34,  6.24iter/s]Best score: 16.2381: : 206iter [00:34,  6.32iter/s]Best score: 16.2381: : 206iter [00:34,  6.32iter/s]Best score: 16.2381: : 207iter [00:34,  6.40iter/s]Best score: 16.2381: : 207iter [00:34,  6.40iter/s]Best score: 16.2381: : 208iter [00:34,  6.46iter/s]Best score: 16.2381: : 208iter [00:34,  6.46iter/s]Best score: 16.2381: : 209iter [00:34,  6.51iter/s]Best score: 16.2353: : 209iter [00:34,  6.51iter/s]Best score: 16.2353: : 210iter [00:34,  6.34iter/s]Best score: 16.2353: : 210iter [00:34,  6.34iter/s]Best score: 16.2353: : 211iter [00:34,  6.42iter/s]Best score: 16.2353: : 211iter [00:34,  6.42iter/s]Best score: 16.2353: : 212iter [00:34,  6.45iter/s]Best score: 16.2353: : 212iter [00:35,  6.45iter/s]Best score: 16.2353: : 213iter [00:35,  6.50iter/s]Best score: 16.2353: : 213iter [00:35,  6.50iter/s]Best score: 16.2353: : 214iter [00:35,  6.55iter/s]Best score: 16.2353: : 214iter [00:35,  6.55iter/s]Best score: 16.2353: : 215iter [00:35,  6.56iter/s]Best score: 16.2353: : 215iter [00:35,  6.56iter/s]Best score: 16.2353: : 216iter [00:35,  6.59iter/s]Best score: 16.2353: : 216iter [00:35,  6.59iter/s]Best score: 16.2353: : 217iter [00:35,  6.60iter/s]Best score: 16.2353: : 217iter [00:35,  6.60iter/s]Best score: 16.2353: : 218iter [00:35,  6.60iter/s]Best score: 16.2353: : 218iter [00:35,  6.60iter/s]Best score: 16.2353: : 219iter [00:35,  6.62iter/s]Best score: 16.2353: : 219iter [00:36,  6.62iter/s]Best score: 16.2353: : 220iter [00:36,  6.43iter/s]Best score: 16.2353: : 220iter [00:36,  6.43iter/s]Best score: 16.2353: : 221iter [00:36,  6.49iter/s]Best score: 16.2353: : 221iter [00:36,  6.49iter/s]Best score: 16.2353: : 222iter [00:36,  6.53iter/s]Best score: 16.2353: : 222iter [00:36,  6.53iter/s]Best score: 16.2353: : 223iter [00:36,  6.56iter/s]Best score: 16.2353: : 223iter [00:36,  6.56iter/s]Best score: 16.2353: : 224iter [00:36,  6.57iter/s]Best score: 16.2353: : 224iter [00:36,  6.57iter/s]Best score: 16.2353: : 225iter [00:36,  6.58iter/s]Best score: 16.2353: : 225iter [00:37,  6.58iter/s]Best score: 16.2353: : 226iter [00:37,  6.59iter/s]Best score: 16.2353: : 226iter [00:37,  6.59iter/s]Best score: 16.2353: : 227iter [00:37,  6.60iter/s]Best score: 16.2353: : 227iter [00:37,  6.60iter/s]Best score: 16.2353: : 228iter [00:37,  6.62iter/s]Best score: 16.2353: : 228iter [00:37,  6.62iter/s]Best score: 16.2353: : 229iter [00:37,  6.62iter/s]Best score: 16.2301: : 229iter [00:37,  6.62iter/s]Best score: 16.2301: : 230iter [00:37,  6.42iter/s]Best score: 16.2301: : 230iter [00:37,  6.42iter/s]Best score: 16.2301: : 231iter [00:37,  6.50iter/s]Best score: 16.2301: : 231iter [00:37,  6.50iter/s]Best score: 16.2301: : 232iter [00:37,  6.55iter/s]Best score: 16.2301: : 232iter [00:38,  6.55iter/s]Best score: 16.2301: : 233iter [00:38,  6.57iter/s]Best score: 16.2301: : 233iter [00:38,  6.57iter/s]Best score: 16.2301: : 234iter [00:38,  6.58iter/s]Best score: 16.2301: : 234iter [00:38,  6.58iter/s]Best score: 16.2301: : 235iter [00:38,  6.60iter/s]Best score: 16.2301: : 235iter [00:38,  6.60iter/s]Best score: 16.2301: : 236iter [00:38,  6.61iter/s]Best score: 16.2301: : 236iter [00:38,  6.61iter/s]Best score: 16.2301: : 237iter [00:38,  6.62iter/s]Best score: 16.2301: : 237iter [00:38,  6.62iter/s]Best score: 16.2301: : 238iter [00:38,  6.63iter/s]Best score: 16.2301: : 238iter [00:39,  6.63iter/s]Best score: 16.2301: : 239iter [00:39,  6.63iter/s]Best score: 16.2298: : 239iter [00:39,  6.63iter/s]Best score: 16.2298: : 240iter [00:39,  6.44iter/s]Best score: 16.2298: : 240iter [00:39,  6.44iter/s]Best score: 16.2298: : 241iter [00:39,  6.50iter/s]Best score: 16.2298: : 241iter [00:39,  6.50iter/s]Best score: 16.2298: : 242iter [00:39,  6.54iter/s]Best score: 16.2298: : 242iter [00:39,  6.54iter/s]Best score: 16.2298: : 243iter [00:39,  6.57iter/s]Best score: 16.2298: : 243iter [00:39,  6.57iter/s]Best score: 16.2298: : 244iter [00:39,  6.58iter/s]Best score: 16.2298: : 244iter [00:39,  6.58iter/s]Best score: 16.2298: : 245iter [00:39,  6.59iter/s]Best score: 16.2298: : 245iter [00:40,  6.59iter/s]Best score: 16.2298: : 246iter [00:40,  6.58iter/s]Best score: 16.2298: : 246iter [00:40,  6.58iter/s]Best score: 16.2298: : 247iter [00:40,  6.58iter/s]Best score: 16.2298: : 247iter [00:40,  6.58iter/s]Best score: 16.2298: : 248iter [00:40,  6.60iter/s]Best score: 16.2298: : 248iter [00:40,  6.60iter/s]Best score: 16.2298: : 249iter [00:40,  6.60iter/s]
[ITER 250] Best: 16.2292
[PARAMS] Current best parameters:
  fx: 481.1548
  fy: 481.7192
  px: 847.1207
  py: 1003.5347
  k1: 0.04949401
  k2: 0.02133538
  k3: -0.02420078
  k4: 0.00601775
  p1: 0.00341107
  p2: -0.00953269
  p3: 0.00456787
  p4: -0.00321379
  scale: 2.9988
  rotation: -0.3855
Best score: 16.2292: : 249iter [00:40,  6.60iter/s]Best score: 16.2292: : 250iter [00:40,  6.41iter/s]Best score: 16.2292: : 250iter [00:40,  6.41iter/s]Best score: 16.2292: : 251iter [00:40,  6.46iter/s]Best score: 16.2292: : 251iter [00:41,  6.46iter/s]Best score: 16.2292: : 252iter [00:41,  6.51iter/s]Best score: 16.2292: : 252iter [00:41,  6.51iter/s]Best score: 16.2292: : 253iter [00:41,  6.54iter/s]Best score: 16.2292: : 253iter [00:41,  6.54iter/s]Best score: 16.2292: : 254iter [00:41,  6.55iter/s]Best score: 16.2292: : 254iter [00:41,  6.55iter/s]Best score: 16.2292: : 255iter [00:41,  6.56iter/s]Best score: 16.2292: : 255iter [00:41,  6.56iter/s]Best score: 16.2292: : 256iter [00:41,  6.57iter/s]Best score: 16.2292: : 256iter [00:41,  6.57iter/s]Best score: 16.2292: : 257iter [00:41,  6.57iter/s]Best score: 16.2292: : 257iter [00:41,  6.57iter/s]Best score: 16.2292: : 258iter [00:41,  6.58iter/s]Best score: 16.2292: : 258iter [00:42,  6.58iter/s]Best score: 16.2292: : 259iter [00:42,  6.56iter/s]Best score: 16.2277: : 259iter [00:42,  6.56iter/s]Best score: 16.2277: : 260iter [00:42,  6.37iter/s]Best score: 16.2277: : 260iter [00:42,  6.37iter/s]Best score: 16.2277: : 261iter [00:42,  6.44iter/s]Best score: 16.2277: : 261iter [00:42,  6.44iter/s]Best score: 16.2277: : 262iter [00:42,  6.49iter/s]Best score: 16.2277: : 262iter [00:42,  6.49iter/s]Best score: 16.2277: : 263iter [00:42,  6.52iter/s]Best score: 16.2277: : 263iter [00:42,  6.52iter/s]Best score: 16.2277: : 264iter [00:42,  6.55iter/s]Best score: 16.2277: : 264iter [00:43,  6.55iter/s]Best score: 16.2277: : 265iter [00:43,  6.56iter/s]Best score: 16.2277: : 265iter [00:43,  6.56iter/s]Best score: 16.2277: : 266iter [00:43,  6.58iter/s]Best score: 16.2277: : 266iter [00:43,  6.58iter/s]Best score: 16.2277: : 267iter [00:43,  6.58iter/s]Best score: 16.2277: : 267iter [00:43,  6.58iter/s]Best score: 16.2277: : 268iter [00:43,  6.59iter/s]Best score: 16.2277: : 268iter [00:43,  6.59iter/s]Best score: 16.2277: : 269iter [00:43,  6.59iter/s]Best score: 16.2277: : 269iter [00:43,  6.59iter/s]Best score: 16.2277: : 270iter [00:43,  6.39iter/s]Best score: 16.2277: : 270iter [00:43,  6.39iter/s]Best score: 16.2277: : 271iter [00:43,  6.46iter/s]Best score: 16.2277: : 271iter [00:44,  6.46iter/s]Best score: 16.2277: : 272iter [00:44,  6.51iter/s]Best score: 16.2277: : 272iter [00:44,  6.51iter/s]Best score: 16.2277: : 273iter [00:44,  6.53iter/s]Best score: 16.2277: : 273iter [00:44,  6.53iter/s]Best score: 16.2277: : 274iter [00:44,  6.56iter/s]Best score: 16.2277: : 274iter [00:44,  6.56iter/s]Best score: 16.2277: : 275iter [00:44,  6.57iter/s]Best score: 16.2277: : 275iter [00:44,  6.57iter/s]Best score: 16.2277: : 276iter [00:44,  6.57iter/s]Best score: 16.2277: : 276iter [00:44,  6.57iter/s]Best score: 16.2277: : 277iter [00:44,  6.59iter/s]Best score: 16.2277: : 277iter [00:45,  6.59iter/s]Best score: 16.2277: : 278iter [00:45,  6.60iter/s]Best score: 16.2277: : 278iter [00:45,  6.60iter/s]Best score: 16.2277: : 279iter [00:45,  6.61iter/s]Best score: 16.2273: : 279iter [00:45,  6.61iter/s]Best score: 16.2273: : 280iter [00:45,  6.41iter/s]Best score: 16.2273: : 280iter [00:45,  6.41iter/s]Best score: 16.2273: : 281iter [00:45,  6.48iter/s]Best score: 16.2273: : 281iter [00:45,  6.48iter/s]Best score: 16.2273: : 282iter [00:45,  6.52iter/s]Best score: 16.2273: : 282iter [00:45,  6.52iter/s]Best score: 16.2273: : 283iter [00:45,  6.55iter/s]Best score: 16.2273: : 283iter [00:45,  6.55iter/s]Best score: 16.2273: : 284iter [00:45,  6.58iter/s]Best score: 16.2273: : 284iter [00:46,  6.58iter/s]Best score: 16.2273: : 285iter [00:46,  6.59iter/s]Best score: 16.2273: : 285iter [00:46,  6.59iter/s]Best score: 16.2273: : 286iter [00:46,  6.61iter/s]Best score: 16.2273: : 286iter [00:46,  6.61iter/s]Best score: 16.2273: : 287iter [00:46,  6.62iter/s]Best score: 16.2273: : 287iter [00:46,  6.62iter/s]Best score: 16.2273: : 288iter [00:46,  6.62iter/s]Best score: 16.2273: : 288iter [00:46,  6.62iter/s]Best score: 16.2273: : 289iter [00:46,  6.62iter/s]Best score: 16.2260: : 289iter [00:46,  6.62iter/s]Best score: 16.2260: : 290iter [00:46,  6.42iter/s]Best score: 16.2260: : 290iter [00:47,  6.42iter/s]Best score: 16.2260: : 291iter [00:47,  6.48iter/s]Best score: 16.2260: : 291iter [00:47,  6.48iter/s]Best score: 16.2260: : 292iter [00:47,  6.52iter/s]Best score: 16.2260: : 292iter [00:47,  6.52iter/s]Best score: 16.2260: : 293iter [00:47,  6.56iter/s]Best score: 16.2260: : 293iter [00:47,  6.56iter/s]Best score: 16.2260: : 294iter [00:47,  6.60iter/s]Best score: 16.2260: : 294iter [00:47,  6.60iter/s]Best score: 16.2260: : 295iter [00:47,  6.60iter/s]Best score: 16.2260: : 295iter [00:47,  6.60iter/s]Best score: 16.2260: : 296iter [00:47,  6.61iter/s]Best score: 16.2260: : 296iter [00:47,  6.61iter/s]Best score: 16.2260: : 297iter [00:47,  6.62iter/s]Best score: 16.2260: : 297iter [00:48,  6.62iter/s]Best score: 16.2260: : 298iter [00:48,  6.62iter/s]Best score: 16.2260: : 298iter [00:48,  6.62iter/s]Best score: 16.2260: : 299iter [00:48,  6.62iter/s]
[ITER 300] Best: 16.2248
[PARAMS] Current best parameters:
  fx: 475.9939
  fy: 476.4875
  px: 847.3218
  py: 1004.5095
  k1: 0.04741039
  k2: 0.03394520
  k3: -0.03132866
  k4: 0.00731394
  p1: 0.00517406
  p2: -0.00890346
  p3: 0.00387452
  p4: -0.00296654
  scale: 3.0047
  rotation: -0.3941
Best score: 16.2248: : 299iter [00:48,  6.62iter/s]Best score: 16.2248: : 300iter [00:48,  6.42iter/s]Best score: 16.2248: : 300iter [00:48,  6.42iter/s]Best score: 16.2248: : 301iter [00:48,  6.48iter/s]Best score: 16.2248: : 301iter [00:48,  6.48iter/s]Best score: 16.2248: : 302iter [00:48,  6.53iter/s]Best score: 16.2248: : 302iter [00:48,  6.53iter/s]Best score: 16.2248: : 303iter [00:48,  6.56iter/s]Best score: 16.2248: : 303iter [00:48,  6.56iter/s]Best score: 16.2248: : 304iter [00:48,  6.58iter/s]Best score: 16.2248: : 304iter [00:49,  6.58iter/s]Best score: 16.2248: : 305iter [00:49,  6.59iter/s]Best score: 16.2248: : 305iter [00:49,  6.59iter/s]Best score: 16.2248: : 306iter [00:49,  6.59iter/s]Best score: 16.2248: : 306iter [00:49,  6.59iter/s]Best score: 16.2248: : 307iter [00:49,  6.61iter/s]Best score: 16.2248: : 307iter [00:49,  6.61iter/s]Best score: 16.2248: : 308iter [00:49,  6.62iter/s]Best score: 16.2248: : 308iter [00:49,  6.62iter/s]Best score: 16.2248: : 309iter [00:49,  6.61iter/s]Best score: 16.2227: : 309iter [00:49,  6.61iter/s]Best score: 16.2227: : 310iter [00:49,  6.40iter/s]Best score: 16.2227: : 310iter [00:50,  6.40iter/s]Best score: 16.2227: : 311iter [00:50,  6.47iter/s]Best score: 16.2227: : 311iter [00:50,  6.47iter/s]Best score: 16.2227: : 312iter [00:50,  6.51iter/s]Best score: 16.2227: : 312iter [00:50,  6.51iter/s]Best score: 16.2227: : 313iter [00:50,  6.54iter/s]Best score: 16.2227: : 313iter [00:50,  6.54iter/s]Best score: 16.2227: : 314iter [00:50,  6.54iter/s]Best score: 16.2227: : 314iter [00:50,  6.54iter/s]Best score: 16.2227: : 315iter [00:50,  6.56iter/s]Best score: 16.2227: : 315iter [00:50,  6.56iter/s]Best score: 16.2227: : 316iter [00:50,  6.56iter/s]Best score: 16.2227: : 316iter [00:50,  6.56iter/s]Best score: 16.2227: : 317iter [00:50,  6.59iter/s]Best score: 16.2227: : 317iter [00:51,  6.59iter/s]Best score: 16.2227: : 318iter [00:51,  6.59iter/s]Best score: 16.2227: : 318iter [00:51,  6.59iter/s]Best score: 16.2227: : 319iter [00:51,  6.60iter/s]Best score: 16.2210: : 319iter [00:51,  6.60iter/s]Best score: 16.2210: : 320iter [00:51,  6.40iter/s]Best score: 16.2210: : 320iter [00:51,  6.40iter/s]Best score: 16.2210: : 321iter [00:51,  6.47iter/s]Best score: 16.2210: : 321iter [00:51,  6.47iter/s]Best score: 16.2210: : 322iter [00:51,  6.52iter/s]Best score: 16.2210: : 322iter [00:51,  6.52iter/s]Best score: 16.2210: : 323iter [00:51,  6.56iter/s]Best score: 16.2210: : 323iter [00:52,  6.56iter/s]Best score: 16.2210: : 324iter [00:52,  6.58iter/s]Best score: 16.2210: : 324iter [00:52,  6.58iter/s]Best score: 16.2210: : 325iter [00:52,  6.60iter/s]Best score: 16.2210: : 325iter [00:52,  6.60iter/s]Best score: 16.2210: : 326iter [00:52,  6.61iter/s]Best score: 16.2210: : 326iter [00:52,  6.61iter/s]Best score: 16.2210: : 327iter [00:52,  6.60iter/s]Best score: 16.2210: : 327iter [00:52,  6.60iter/s]Best score: 16.2210: : 328iter [00:52,  6.60iter/s]Best score: 16.2210: : 328iter [00:52,  6.60iter/s]Best score: 16.2210: : 329iter [00:52,  6.60iter/s]Best score: 16.2136: : 329iter [00:52,  6.60iter/s]Best score: 16.2136: : 330iter [00:52,  6.39iter/s]Best score: 16.2136: : 330iter [00:53,  6.39iter/s]Best score: 16.2136: : 331iter [00:53,  6.44iter/s]Best score: 16.2136: : 331iter [00:53,  6.44iter/s]Best score: 16.2136: : 332iter [00:53,  6.49iter/s]Best score: 16.2136: : 332iter [00:53,  6.49iter/s]Best score: 16.2136: : 333iter [00:53,  6.52iter/s]Best score: 16.2136: : 333iter [00:53,  6.52iter/s]Best score: 16.2136: : 334iter [00:53,  6.54iter/s]Best score: 16.2136: : 334iter [00:53,  6.54iter/s]Best score: 16.2136: : 335iter [00:53,  6.55iter/s]Best score: 16.2136: : 335iter [00:53,  6.55iter/s]Best score: 16.2136: : 336iter [00:53,  6.56iter/s]Best score: 16.2136: : 336iter [00:54,  6.56iter/s]Best score: 16.2136: : 337iter [00:54,  6.59iter/s]Best score: 16.2136: : 337iter [00:54,  6.59iter/s]Best score: 16.2136: : 338iter [00:54,  6.58iter/s]Best score: 16.2136: : 338iter [00:54,  6.58iter/s]Best score: 16.2136: : 339iter [00:54,  6.60iter/s]Best score: 16.2136: : 339iter [00:54,  6.60iter/s]Best score: 16.2136: : 340iter [00:54,  6.40iter/s]Best score: 16.2136: : 340iter [00:54,  6.40iter/s]Best score: 16.2136: : 341iter [00:54,  6.45iter/s]Best score: 16.2136: : 341iter [00:54,  6.45iter/s]Best score: 16.2136: : 342iter [00:54,  6.50iter/s]Best score: 16.2136: : 342iter [00:54,  6.50iter/s]Best score: 16.2136: : 343iter [00:54,  6.53iter/s]Best score: 16.2136: : 343iter [00:55,  6.53iter/s]Best score: 16.2136: : 344iter [00:55,  6.56iter/s]Best score: 16.2136: : 344iter [00:55,  6.56iter/s]Best score: 16.2136: : 345iter [00:55,  6.56iter/s]Best score: 16.2136: : 345iter [00:55,  6.56iter/s]Best score: 16.2136: : 346iter [00:55,  6.58iter/s]Best score: 16.2136: : 346iter [00:55,  6.58iter/s]Best score: 16.2136: : 347iter [00:55,  6.59iter/s]Best score: 16.2136: : 347iter [00:55,  6.59iter/s]Best score: 16.2136: : 348iter [00:55,  6.59iter/s]Best score: 16.2136: : 348iter [00:55,  6.59iter/s]Best score: 16.2136: : 349iter [00:55,  6.59iter/s]
[ITER 350] Best: 16.2126
[PARAMS] Current best parameters:
  fx: 475.0273
  fy: 475.5930
  px: 847.2019
  py: 1003.6116
  k1: 0.03071495
  k2: 0.06236289
  k3: -0.04799832
  k4: 0.01074154
  p1: 0.00337522
  p2: -0.00930404
  p3: 0.00472519
  p4: -0.00300631
  scale: 3.0198
  rotation: -0.3886
Best score: 16.2126: : 349iter [00:56,  6.59iter/s]Best score: 16.2126: : 350iter [00:56,  6.39iter/s]Best score: 16.2126: : 350iter [00:56,  6.39iter/s]Best score: 16.2126: : 351iter [00:56,  6.44iter/s]Best score: 16.2126: : 351iter [00:56,  6.44iter/s]Best score: 16.2126: : 352iter [00:56,  6.49iter/s]Best score: 16.2126: : 352iter [00:56,  6.49iter/s]Best score: 16.2126: : 353iter [00:56,  6.52iter/s]Best score: 16.2126: : 353iter [00:56,  6.52iter/s]Best score: 16.2126: : 354iter [00:56,  6.54iter/s]Best score: 16.2126: : 354iter [00:56,  6.54iter/s]Best score: 16.2126: : 355iter [00:56,  6.55iter/s]Best score: 16.2126: : 355iter [00:56,  6.55iter/s]Best score: 16.2126: : 356iter [00:56,  6.57iter/s]Best score: 16.2126: : 356iter [00:57,  6.57iter/s]Best score: 16.2126: : 357iter [00:57,  6.58iter/s]Best score: 16.2126: : 357iter [00:57,  6.58iter/s]Best score: 16.2126: : 358iter [00:57,  6.59iter/s]Best score: 16.2126: : 358iter [00:57,  6.59iter/s]Best score: 16.2126: : 359iter [00:57,  6.60iter/s]Best score: 16.2126: : 359iter [00:57,  6.60iter/s]Best score: 16.2126: : 360iter [00:57,  6.39iter/s]Best score: 16.2126: : 360iter [00:57,  6.39iter/s]Best score: 16.2126: : 361iter [00:57,  6.46iter/s]Best score: 16.2126: : 361iter [00:57,  6.46iter/s]Best score: 16.2126: : 362iter [00:57,  6.49iter/s]Best score: 16.2126: : 362iter [00:58,  6.49iter/s]Best score: 16.2126: : 363iter [00:58,  6.54iter/s]Best score: 16.2126: : 363iter [00:58,  6.54iter/s]Best score: 16.2126: : 364iter [00:58,  6.56iter/s]Best score: 16.2126: : 364iter [00:58,  6.56iter/s]Best score: 16.2126: : 365iter [00:58,  6.57iter/s]Best score: 16.2126: : 365iter [00:58,  6.57iter/s]Best score: 16.2126: : 366iter [00:58,  6.58iter/s]Best score: 16.2126: : 366iter [00:58,  6.58iter/s]Best score: 16.2126: : 367iter [00:58,  6.59iter/s]Best score: 16.2126: : 367iter [00:58,  6.59iter/s]Best score: 16.2126: : 368iter [00:58,  6.59iter/s]Best score: 16.2126: : 368iter [00:58,  6.59iter/s]Best score: 16.2126: : 369iter [00:58,  6.59iter/s]Best score: 16.2120: : 369iter [00:59,  6.59iter/s]Best score: 16.2120: : 370iter [00:59,  6.38iter/s]Best score: 16.2120: : 370iter [00:59,  6.38iter/s]Best score: 16.2120: : 371iter [00:59,  6.44iter/s]Best score: 16.2120: : 371iter [00:59,  6.44iter/s]Best score: 16.2120: : 372iter [00:59,  6.49iter/s]Best score: 16.2120: : 372iter [00:59,  6.49iter/s]Best score: 16.2120: : 373iter [00:59,  6.51iter/s]Best score: 16.2120: : 373iter [00:59,  6.51iter/s]Best score: 16.2120: : 374iter [00:59,  6.53iter/s]Best score: 16.2120: : 374iter [00:59,  6.53iter/s]Best score: 16.2120: : 375iter [00:59,  6.55iter/s]Best score: 16.2120: : 375iter [00:59,  6.55iter/s]Best score: 16.2120: : 376iter [00:59,  6.57iter/s]Best score: 16.2120: : 376iter [01:00,  6.57iter/s]Best score: 16.2120: : 377iter [01:00,  6.57iter/s]Best score: 16.2120: : 377iter [01:00,  6.57iter/s]Best score: 16.2120: : 378iter [01:00,  6.59iter/s]Best score: 16.2120: : 378iter [01:00,  6.59iter/s]Best score: 16.2120: : 379iter [01:00,  6.61iter/s]Best score: 16.2113: : 379iter [01:00,  6.61iter/s]Best score: 16.2113: : 380iter [01:00,  6.40iter/s]Best score: 16.2113: : 380iter [01:00,  6.40iter/s]Best score: 16.2113: : 381iter [01:00,  6.45iter/s]Best score: 16.2113: : 381iter [01:00,  6.45iter/s]Best score: 16.2113: : 382iter [01:00,  6.50iter/s]Best score: 16.2113: : 382iter [01:01,  6.50iter/s]Best score: 16.2113: : 383iter [01:01,  6.53iter/s]Best score: 16.2113: : 383iter [01:01,  6.53iter/s]Best score: 16.2113: : 384iter [01:01,  6.55iter/s]Best score: 16.2113: : 384iter [01:01,  6.55iter/s]Best score: 16.2113: : 385iter [01:01,  6.57iter/s]Best score: 16.2113: : 385iter [01:01,  6.57iter/s]Best score: 16.2113: : 386iter [01:01,  6.57iter/s]Best score: 16.2113: : 386iter [01:01,  6.57iter/s]Best score: 16.2113: : 387iter [01:01,  6.58iter/s]Best score: 16.2113: : 387iter [01:01,  6.58iter/s]Best score: 16.2113: : 388iter [01:01,  6.59iter/s]Best score: 16.2113: : 388iter [01:01,  6.59iter/s]Best score: 16.2113: : 389iter [01:01,  6.60iter/s]Best score: 16.2103: : 389iter [01:02,  6.60iter/s]Best score: 16.2103: : 390iter [01:02,  6.40iter/s]Best score: 16.2103: : 390iter [01:02,  6.40iter/s]Best score: 16.2103: : 391iter [01:02,  6.46iter/s]Best score: 16.2103: : 391iter [01:02,  6.46iter/s]Best score: 16.2103: : 392iter [01:02,  6.49iter/s]Best score: 16.2103: : 392iter [01:02,  6.49iter/s]Best score: 16.2103: : 393iter [01:02,  6.52iter/s]Best score: 16.2103: : 393iter [01:02,  6.52iter/s]Best score: 16.2103: : 394iter [01:02,  6.54iter/s]Best score: 16.2103: : 394iter [01:02,  6.54iter/s]Best score: 16.2103: : 395iter [01:02,  6.56iter/s]Best score: 16.2103: : 395iter [01:03,  6.56iter/s]Best score: 16.2103: : 396iter [01:03,  6.57iter/s]Best score: 16.2103: : 396iter [01:03,  6.57iter/s]Best score: 16.2103: : 397iter [01:03,  6.58iter/s]Best score: 16.2103: : 397iter [01:03,  6.58iter/s]Best score: 16.2103: : 398iter [01:03,  6.58iter/s]Best score: 16.2103: : 398iter [01:03,  6.58iter/s]Best score: 16.2103: : 399iter [01:03,  6.57iter/s]
[ITER 400] Best: 16.2103
[PARAMS] Current best parameters:
  fx: 475.4062
  fy: 475.9481
  px: 847.1087
  py: 1003.4711
  k1: 0.02490133
  k2: 0.06811992
  k3: -0.04977184
  k4: 0.01082236
  p1: 0.00311946
  p2: -0.00949835
  p3: 0.00482502
  p4: -0.00315981
  scale: 3.0267
  rotation: -0.3938
Best score: 16.2103: : 399iter [01:03,  6.57iter/s]Best score: 16.2103: : 400iter [01:03,  6.36iter/s]Best score: 16.2103: : 400iter [01:03,  6.36iter/s]Best score: 16.2103: : 401iter [01:03,  6.43iter/s]Best score: 16.2103: : 401iter [01:03,  6.43iter/s]Best score: 16.2103: : 402iter [01:03,  6.48iter/s]Best score: 16.2103: : 402iter [01:04,  6.48iter/s]Best score: 16.2103: : 403iter [01:04,  6.53iter/s]Best score: 16.2103: : 403iter [01:04,  6.53iter/s]Best score: 16.2103: : 404iter [01:04,  6.55iter/s]Best score: 16.2103: : 404iter [01:04,  6.55iter/s]Best score: 16.2103: : 405iter [01:04,  6.57iter/s]Best score: 16.2103: : 405iter [01:04,  6.57iter/s]Best score: 16.2103: : 406iter [01:04,  6.58iter/s]Best score: 16.2103: : 406iter [01:04,  6.58iter/s]Best score: 16.2103: : 407iter [01:04,  6.59iter/s]Best score: 16.2103: : 407iter [01:04,  6.59iter/s]Best score: 16.2103: : 408iter [01:04,  6.57iter/s]Best score: 16.2103: : 408iter [01:05,  6.57iter/s]Best score: 16.2103: : 409iter [01:05,  6.59iter/s]Best score: 16.2096: : 409iter [01:05,  6.59iter/s]Best score: 16.2096: : 410iter [01:05,  6.39iter/s]Best score: 16.2096: : 410iter [01:05,  6.39iter/s]Best score: 16.2096: : 411iter [01:05,  6.46iter/s]Best score: 16.2096: : 411iter [01:05,  6.46iter/s]Best score: 16.2096: : 412iter [01:05,  6.50iter/s]Best score: 16.2096: : 412iter [01:05,  6.50iter/s]Best score: 16.2096: : 413iter [01:05,  6.53iter/s]Best score: 16.2096: : 413iter [01:05,  6.53iter/s]Best score: 16.2096: : 414iter [01:05,  6.54iter/s]Best score: 16.2096: : 414iter [01:05,  6.54iter/s]Best score: 16.2096: : 415iter [01:05,  6.57iter/s]Best score: 16.2096: : 415iter [01:06,  6.57iter/s]Best score: 16.2096: : 416iter [01:06,  6.58iter/s]Best score: 16.2096: : 416iter [01:06,  6.58iter/s]Best score: 16.2096: : 417iter [01:06,  6.59iter/s]Best score: 16.2096: : 417iter [01:06,  6.59iter/s]Best score: 16.2096: : 418iter [01:06,  6.60iter/s]Best score: 16.2096: : 418iter [01:06,  6.60iter/s]Best score: 16.2096: : 419iter [01:06,  6.60iter/s]Best score: 16.2096: : 419iter [01:06,  6.60iter/s]Best score: 16.2096: : 420iter [01:06,  6.39iter/s]Best score: 16.2096: : 420iter [01:06,  6.39iter/s]Best score: 16.2096: : 421iter [01:06,  6.46iter/s]Best score: 16.2096: : 421iter [01:07,  6.46iter/s]Best score: 16.2096: : 422iter [01:07,  6.50iter/s]Best score: 16.2096: : 422iter [01:07,  6.50iter/s]Best score: 16.2096: : 423iter [01:07,  6.53iter/s]Best score: 16.2096: : 423iter [01:07,  6.53iter/s]Best score: 16.2096: : 424iter [01:07,  6.55iter/s]Best score: 16.2096: : 424iter [01:07,  6.55iter/s]Best score: 16.2096: : 425iter [01:07,  6.57iter/s]Best score: 16.2096: : 425iter [01:07,  6.57iter/s]Best score: 16.2096: : 426iter [01:07,  6.58iter/s]Best score: 16.2096: : 426iter [01:07,  6.58iter/s]Best score: 16.2096: : 427iter [01:07,  6.58iter/s]Best score: 16.2096: : 427iter [01:07,  6.58iter/s]Best score: 16.2096: : 428iter [01:07,  6.59iter/s]Best score: 16.2096: : 428iter [01:08,  6.59iter/s]Best score: 16.2096: : 429iter [01:08,  6.59iter/s]Best score: 16.2096: : 429iter [01:08,  6.59iter/s]Best score: 16.2096: : 430iter [01:08,  6.39iter/s]Best score: 16.2096: : 430iter [01:08,  6.39iter/s]Best score: 16.2096: : 431iter [01:08,  6.46iter/s]Best score: 16.2096: : 431iter [01:08,  6.46iter/s]Best score: 16.2096: : 432iter [01:08,  6.50iter/s]Best score: 16.2096: : 432iter [01:08,  6.50iter/s]Best score: 16.2096: : 433iter [01:08,  6.54iter/s]Best score: 16.2096: : 433iter [01:08,  6.54iter/s]Best score: 16.2096: : 434iter [01:08,  6.56iter/s]Best score: 16.2096: : 434iter [01:09,  6.56iter/s]Best score: 16.2096: : 435iter [01:09,  6.58iter/s]Best score: 16.2096: : 435iter [01:09,  6.58iter/s]Best score: 16.2096: : 436iter [01:09,  6.59iter/s]Best score: 16.2096: : 436iter [01:09,  6.59iter/s]Best score: 16.2096: : 437iter [01:09,  6.59iter/s]Best score: 16.2096: : 437iter [01:09,  6.59iter/s]Best score: 16.2096: : 438iter [01:09,  6.61iter/s]Best score: 16.2096: : 438iter [01:09,  6.61iter/s]Best score: 16.2096: : 439iter [01:09,  6.60iter/s]Best score: 16.2096: : 439iter [01:09,  6.60iter/s]Best score: 16.2096: : 440iter [01:09,  6.40iter/s]Best score: 16.2096: : 440iter [01:09,  6.40iter/s]Best score: 16.2096: : 441iter [01:09,  6.46iter/s]Best score: 16.2096: : 441iter [01:10,  6.46iter/s]Best score: 16.2096: : 442iter [01:10,  6.51iter/s]Best score: 16.2096: : 442iter [01:10,  6.51iter/s]Best score: 16.2096: : 443iter [01:10,  6.54iter/s]Best score: 16.2096: : 443iter [01:10,  6.54iter/s]Best score: 16.2096: : 444iter [01:10,  6.56iter/s]Best score: 16.2096: : 444iter [01:10,  6.56iter/s]Best score: 16.2096: : 445iter [01:10,  6.59iter/s]Best score: 16.2096: : 445iter [01:10,  6.59iter/s]Best score: 16.2096: : 446iter [01:10,  6.59iter/s]Best score: 16.2096: : 446iter [01:10,  6.59iter/s]Best score: 16.2096: : 447iter [01:10,  6.60iter/s]Best score: 16.2096: : 447iter [01:11,  6.60iter/s]Best score: 16.2096: : 448iter [01:11,  6.59iter/s]Best score: 16.2096: : 448iter [01:11,  6.59iter/s]Best score: 16.2096: : 449iter [01:11,  6.60iter/s]
[ITER 450] Best: 16.2094
[PARAMS] Current best parameters:
  fx: 475.9429
  fy: 476.5814
  px: 846.8887
  py: 1003.5802
  k1: 0.02602738
  k2: 0.06656082
  k3: -0.04964408
  k4: 0.01096327
  p1: 0.00329001
  p2: -0.00997866
  p3: 0.00498198
  p4: -0.00315887
  scale: 3.0240
  rotation: -0.3933
Best score: 16.2094: : 449iter [01:11,  6.60iter/s]Best score: 16.2094: : 450iter [01:11,  6.39iter/s]Best score: 16.2094: : 450iter [01:11,  6.39iter/s]Best score: 16.2094: : 451iter [01:11,  6.45iter/s]Best score: 16.2094: : 451iter [01:11,  6.45iter/s]Best score: 16.2094: : 452iter [01:11,  6.50iter/s]Best score: 16.2094: : 452iter [01:11,  6.50iter/s]Best score: 16.2094: : 453iter [01:11,  6.53iter/s]Best score: 16.2094: : 453iter [01:11,  6.53iter/s]Best score: 16.2094: : 454iter [01:11,  6.56iter/s]Best score: 16.2094: : 454iter [01:12,  6.56iter/s]Best score: 16.2094: : 455iter [01:12,  6.57iter/s]Best score: 16.2094: : 455iter [01:12,  6.57iter/s]Best score: 16.2094: : 456iter [01:12,  6.58iter/s]Best score: 16.2094: : 456iter [01:12,  6.58iter/s]Best score: 16.2094: : 457iter [01:12,  6.59iter/s]Best score: 16.2094: : 457iter [01:12,  6.59iter/s]Best score: 16.2094: : 458iter [01:12,  6.60iter/s]Best score: 16.2094: : 458iter [01:12,  6.60iter/s]Best score: 16.2094: : 459iter [01:12,  6.60iter/s]Best score: 16.2094: : 459iter [01:12,  6.60iter/s]Best score: 16.2094: : 460iter [01:12,  6.40iter/s]Best score: 16.2094: : 460iter [01:13,  6.40iter/s]Best score: 16.2094: : 461iter [01:13,  6.47iter/s]Best score: 16.2094: : 461iter [01:13,  6.47iter/s]Best score: 16.2094: : 462iter [01:13,  6.52iter/s]Best score: 16.2094: : 462iter [01:13,  6.52iter/s]Best score: 16.2094: : 463iter [01:13,  6.55iter/s]Best score: 16.2094: : 463iter [01:13,  6.55iter/s]Best score: 16.2094: : 464iter [01:13,  6.58iter/s]Best score: 16.2094: : 464iter [01:13,  6.58iter/s]Best score: 16.2094: : 465iter [01:13,  6.60iter/s]Best score: 16.2094: : 465iter [01:13,  6.60iter/s]Best score: 16.2094: : 466iter [01:13,  6.62iter/s]Best score: 16.2094: : 466iter [01:13,  6.62iter/s]Best score: 16.2094: : 467iter [01:13,  6.64iter/s]Best score: 16.2094: : 467iter [01:14,  6.64iter/s]Best score: 16.2094: : 468iter [01:14,  6.64iter/s]Best score: 16.2094: : 468iter [01:14,  6.64iter/s]Best score: 16.2094: : 469iter [01:14,  6.64iter/s]Best score: 16.2093: : 469iter [01:14,  6.64iter/s]Best score: 16.2093: : 470iter [01:14,  6.43iter/s]Best score: 16.2093: : 470iter [01:14,  6.43iter/s]Best score: 16.2093: : 471iter [01:14,  6.49iter/s]Best score: 16.2093: : 471iter [01:14,  6.49iter/s]Best score: 16.2093: : 472iter [01:14,  6.54iter/s]Best score: 16.2093: : 472iter [01:14,  6.54iter/s]Best score: 16.2093: : 473iter [01:14,  6.57iter/s]Best score: 16.2093: : 473iter [01:14,  6.57iter/s]Best score: 16.2093: : 474iter [01:14,  6.59iter/s]Best score: 16.2093: : 474iter [01:15,  6.59iter/s]Best score: 16.2093: : 475iter [01:15,  6.61iter/s]Best score: 16.2093: : 475iter [01:15,  6.61iter/s]Best score: 16.2093: : 476iter [01:15,  6.62iter/s]Best score: 16.2093: : 476iter [01:15,  6.62iter/s]Best score: 16.2093: : 477iter [01:15,  6.63iter/s]Best score: 16.2093: : 477iter [01:15,  6.63iter/s]Best score: 16.2093: : 478iter [01:15,  6.64iter/s]Best score: 16.2093: : 478iter [01:15,  6.64iter/s]Best score: 16.2093: : 479iter [01:15,  6.63iter/s]Best score: 16.2093: : 479iter [01:15,  6.63iter/s]Best score: 16.2093: : 480iter [01:15,  6.43iter/s]Best score: 16.2093: : 480iter [01:16,  6.43iter/s]Best score: 16.2093: : 481iter [01:16,  6.49iter/s]Best score: 16.2093: : 481iter [01:16,  6.49iter/s]Best score: 16.2093: : 482iter [01:16,  6.52iter/s]Best score: 16.2093: : 482iter [01:16,  6.52iter/s]Best score: 16.2093: : 483iter [01:16,  6.54iter/s]Best score: 16.2093: : 483iter [01:16,  6.54iter/s]Best score: 16.2093: : 484iter [01:16,  6.55iter/s]Best score: 16.2093: : 484iter [01:16,  6.55iter/s]Best score: 16.2093: : 485iter [01:16,  6.55iter/s]Best score: 16.2093: : 485iter [01:16,  6.55iter/s]Best score: 16.2093: : 486iter [01:16,  6.56iter/s]Best score: 16.2093: : 486iter [01:16,  6.56iter/s]Best score: 16.2093: : 487iter [01:16,  6.58iter/s]Best score: 16.2093: : 487iter [01:17,  6.58iter/s]Best score: 16.2093: : 488iter [01:17,  6.59iter/s]Best score: 16.2093: : 488iter [01:17,  6.59iter/s]Best score: 16.2093: : 489iter [01:17,  6.61iter/s]Best score: 16.2090: : 489iter [01:17,  6.61iter/s]Best score: 16.2090: : 490iter [01:17,  6.41iter/s]Best score: 16.2090: : 490iter [01:17,  6.41iter/s]Best score: 16.2090: : 491iter [01:17,  6.48iter/s]Best score: 16.2090: : 491iter [01:17,  6.48iter/s]Best score: 16.2090: : 492iter [01:17,  6.52iter/s]Best score: 16.2090: : 492iter [01:17,  6.52iter/s]Best score: 16.2090: : 493iter [01:17,  6.55iter/s]Best score: 16.2090: : 493iter [01:18,  6.55iter/s]Best score: 16.2090: : 494iter [01:18,  6.58iter/s]Best score: 16.2090: : 494iter [01:18,  6.58iter/s]Best score: 16.2090: : 495iter [01:18,  6.60iter/s]Best score: 16.2090: : 495iter [01:18,  6.60iter/s]Best score: 16.2090: : 496iter [01:18,  6.61iter/s]Best score: 16.2090: : 496iter [01:18,  6.61iter/s]Best score: 16.2090: : 497iter [01:18,  6.63iter/s]Best score: 16.2090: : 497iter [01:18,  6.63iter/s]Best score: 16.2090: : 498iter [01:18,  6.64iter/s]Best score: 16.2090: : 498iter [01:18,  6.64iter/s]Best score: 16.2090: : 499iter [01:18,  6.64iter/s]
[ITER 500] Best: 16.2089
[PARAMS] Current best parameters:
  fx: 475.3959
  fy: 476.0083
  px: 846.8546
  py: 1003.6156
  k1: 0.02564981
  k2: 0.06772332
  k3: -0.04990412
  k4: 0.01089722
  p1: 0.00331191
  p2: -0.00999579
  p3: 0.00482542
  p4: -0.00301070
  scale: 3.0249
  rotation: -0.3925
Best score: 16.2089: : 499iter [01:18,  6.64iter/s]Best score: 16.2089: : 500iter [01:18,  6.43iter/s]Best score: 16.2089: : 500iter [01:18,  6.33iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 16.208905 -> 16.208862
  Success: True
  Tangential distortion: p1=0.003313, p2=-0.009995, p3=0.004824, p4=-0.003014
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 16.2089
[✅] 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: 475.39590953252747
    fy: 476.00825031527427
    px: 846.8545662649124
    py: 1003.6155579656926
    k1: 0.025654455498047242
    k2: 0.06772442546987467
    k3: -0.04990831357084384
    k4: 0.010896995613887614
    p1: 0.0033131179313533204
    p2: -0.009995311385670953
    p3: 0.004824041229640473
    p4: -0.003013770660911622
    scale: 3.024887408542647
    rotation: -0.39246032383432883
    mmpp: 3.42316898552678
    nmpp: 3423168.98552678

[🔄] 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[✅] 1000: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap/1000_heatmap.png
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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[✅] 2000L: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap/2000L_heatmap.png
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                                                                  [A[✅] 2000R: /working/MDCP3D-SL-010_W046(6)_2026-04-09/calibration/debug/4_heatmap/2000R_heatmap.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:17<00:00,  4.67s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:17<00:00,  4.35s/rig]
[✅] 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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