
🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-012_W039(6)_2026-03-04 ===

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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 617 stationary frames, 331 remain

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                                                                          [A[PASS2] Frame 0000283: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000366: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000368: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000369: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000377: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000378: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000380: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000636: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000637: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000638: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000652: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000717: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000790: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000794: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000795: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000796: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000797: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000798: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000799: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000800: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000801: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000894: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000895: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000896: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000897: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001161: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001162: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001163: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001164: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001165: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000368: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000635: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000636: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000651: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000652: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000800: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000895: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000896: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001163: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001164: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000635: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000636: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000651: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000652: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Summary: 21 frames fixed, 17 untrusted, 330 total

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                                                                           [AProcessing Rigs:  25%|██▌       | 1/4 [05:23<16:10, 323.57s/it][INFO] Filtered 633 stationary frames, 377 remain

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                                                                           [A[PASS2] Frame 0001200: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001223: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001467: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001468: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001469: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001470: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001481: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001482: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001483: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001484: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001200: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001469: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001483: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001200: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 6 frames fixed, 6 untrusted, 376 total

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                                                                            [AProcessing Rigs:  50%|█████     | 2/4 [11:07<11:11, 335.64s/it][INFO] Filtered 544 stationary frames, 455 remain

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                                                                          [A[PASS2] Frame 0000457: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000458: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000459: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000460: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000549: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000550: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000551: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000553: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000555: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000557: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000558: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000559: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000560: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000561: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000562: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000563: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000564: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000565: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000566: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000567: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000574: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000578: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000579: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000580: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000582: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000589: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000856: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000935: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001036: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001196: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001198: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001199: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001200: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001201: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001265: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001266: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001267: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001268: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001269: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000459: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000551: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000552: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000553: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000554: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000563: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000564: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000566: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001198: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001267: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001268: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001198: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 31 frames fixed, 25 untrusted, 454 total

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                                                                           [AProcessing Rigs:  75%|███████▌  | 3/4 [18:15<06:17, 377.87s/it][INFO] Filtered 449 stationary frames, 421 remain

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                                                                           [A[PASS2] Frame 0000550: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000551: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000552: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000553: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000554: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000555: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000560: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000561: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000562: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000563: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000570: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000596: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000597: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000598: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000599: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000615: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000552: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000554: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 9 frames fixed, 13 untrusted, 420 total

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                                                                            [AProcessing Rigs: 100%|██████████| 4/4 [24:54<00:00, 386.26s/it]Processing Rigs: 100%|██████████| 4/4 [24:54<00:00, 373.73s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1580 frames from 4 rigs to /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 330
[📋] 2000R: 100 frames selected from 376
[📋] 1500: 100 frames selected from 454
[📋] 2000L: 100 frames selected from 420
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:  11%|█▏        | 9/80 [00:00<00:00, 83.45it/s]Computing max image from bright frames:  22%|██▎       | 18/80 [00:00<00:00, 83.99it/s]Computing max image from bright frames:  34%|███▍      | 27/80 [00:00<00:00, 86.04it/s]Computing max image from bright frames:  45%|████▌     | 36/80 [00:00<00:00, 85.48it/s]Computing max image from bright frames:  57%|█████▊    | 46/80 [00:00<00:00, 87.30it/s]Computing max image from bright frames:  69%|██████▉   | 55/80 [00:00<00:00, 85.74it/s]Computing max image from bright frames:  80%|████████  | 64/80 [00:00<00:00, 87.06it/s]Computing max image from bright frames:  91%|█████████▏| 73/80 [00:00<00:00, 85.81it/s]                                                                                       [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 27, black threshold: 81
[INFO] Found 10890 casing boundary candidate points
[RESULT] Detected casing: center=(910, 913), radius=894, inliers=2202/10890
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.48it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.48it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.48it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:27,  3.48it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:10,  9.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   7%|▋         | 7/100 [00:00<00:10,  9.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   8%|▊         | 8/100 [00:00<00:05, 15.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:05, 15.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:05, 15.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:05, 15.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  11%|█         | 11/100 [00:00<00:05, 17.41it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:05, 17.41it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:05, 17.41it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 17.98it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 17.98it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.005 | Best: {'fx': 398.99} (0.005):  14%|█▍        | 14/100 [00:00<00:04, 17.98it/s] Brute | Start: {'fx': 500.0} (0.045) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:01<00:04, 17.98it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 19.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 19.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:01<00:04, 19.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.007 | Best: {'fx': 427.273} (0.007):  18%|█▊        | 18/100 [00:01<00:04, 19.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.007 | Best: {'fx': 427.273} (0.007):  19%|█▉        | 19/100 [00:01<00:04, 19.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:04, 19.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  20%|██        | 20/100 [00:01<00:04, 19.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:01<00:04, 19.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  22%|██▏       | 22/100 [00:01<00:03, 19.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.014 | Best: {'fx': 455.556} (0.014):  22%|██▏       | 22/100 [00:01<00:03, 19.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.016 | Best: {'fx': 462.626} (0.016):  23%|██▎       | 23/100 [00:01<00:03, 19.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 469.697} (0.019):  24%|██▍       | 24/100 [00:01<00:03, 19.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 469.697} (0.019):  25%|██▌       | 25/100 [00:01<00:03, 20.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 476.768} (0.021):  25%|██▌       | 25/100 [00:01<00:03, 20.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.026 | Best: {'fx': 483.838} (0.026):  26%|██▌       | 26/100 [00:01<00:03, 20.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.033 | Best: {'fx': 490.909} (0.033):  27%|██▋       | 27/100 [00:01<00:03, 20.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.033 | Best: {'fx': 490.909} (0.033):  28%|██▊       | 28/100 [00:01<00:03, 19.42it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.043 | Best: {'fx': 497.98} (0.043):  28%|██▊       | 28/100 [00:01<00:03, 19.42it/s] Brute | Start: {'fx': 500.0} (0.045) | Current: 0.052 | Best: {'fx': 505.051} (0.052):  29%|██▉       | 29/100 [00:01<00:03, 19.42it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.063 | Best: {'fx': 512.121} (0.063):  30%|███       | 30/100 [00:01<00:03, 19.42it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.063 | Best: {'fx': 512.121} (0.063):  31%|███       | 31/100 [00:01<00:03, 21.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.074 | Best: {'fx': 519.192} (0.074):  31%|███       | 31/100 [00:02<00:03, 21.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.085 | Best: {'fx': 533.333} (0.085):  32%|███▏      | 32/100 [00:02<00:03, 21.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.084 | Best: {'fx': 533.333} (0.085):  33%|███▎      | 33/100 [00:02<00:03, 21.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.084 | Best: {'fx': 533.333} (0.085):  34%|███▍      | 34/100 [00:02<00:04, 15.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.077 | Best: {'fx': 533.333} (0.085):  34%|███▍      | 34/100 [00:02<00:04, 15.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.068 | Best: {'fx': 533.333} (0.085):  35%|███▌      | 35/100 [00:02<00:04, 15.24it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.068 | Best: {'fx': 533.333} (0.085):  36%|███▌      | 36/100 [00:02<00:04, 15.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.058 | Best: {'fx': 533.333} (0.085):  36%|███▌      | 36/100 [00:02<00:04, 15.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.051 | Best: {'fx': 533.333} (0.085):  37%|███▋      | 37/100 [00:02<00:04, 15.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.047 | Best: {'fx': 533.333} (0.085):  38%|███▊      | 38/100 [00:02<00:04, 15.08it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.047 | Best: {'fx': 533.333} (0.085):  39%|███▉      | 39/100 [00:02<00:03, 17.02it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.042 | Best: {'fx': 533.333} (0.085):  39%|███▉      | 39/100 [00:02<00:03, 17.02it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.039 | Best: {'fx': 533.333} (0.085):  40%|████      | 40/100 [00:02<00:03, 17.02it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.035 | Best: {'fx': 533.333} (0.085):  41%|████      | 41/100 [00:02<00:03, 17.02it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.035 | Best: {'fx': 533.333} (0.085):  42%|████▏     | 42/100 [00:02<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.037 | Best: {'fx': 533.333} (0.085):  42%|████▏     | 42/100 [00:02<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.034 | Best: {'fx': 533.333} (0.085):  43%|████▎     | 43/100 [00:02<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.032 | Best: {'fx': 533.333} (0.085):  44%|████▍     | 44/100 [00:02<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.032 | Best: {'fx': 533.333} (0.085):  45%|████▌     | 45/100 [00:02<00:02, 20.03it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.031 | Best: {'fx': 533.333} (0.085):  45%|████▌     | 45/100 [00:02<00:02, 20.03it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.030 | Best: {'fx': 533.333} (0.085):  46%|████▌     | 46/100 [00:02<00:02, 20.03it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.029 | Best: {'fx': 533.333} (0.085):  47%|████▋     | 47/100 [00:02<00:02, 20.03it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.028 | Best: {'fx': 533.333} (0.085):  48%|████▊     | 48/100 [00:02<00:02, 20.03it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.028 | Best: {'fx': 533.333} (0.085):  49%|████▉     | 49/100 [00:02<00:02, 23.32it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.028 | Best: {'fx': 533.333} (0.085):  49%|████▉     | 49/100 [00:02<00:02, 23.32it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.026 | Best: {'fx': 533.333} (0.085):  50%|█████     | 50/100 [00:02<00:02, 23.32it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.026 | Best: {'fx': 533.333} (0.085):  51%|█████     | 51/100 [00:02<00:02, 23.32it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.026 | Best: {'fx': 533.333} (0.085):  52%|█████▏    | 52/100 [00:02<00:02, 21.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.027 | Best: {'fx': 533.333} (0.085):  52%|█████▏    | 52/100 [00:02<00:02, 21.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.026 | Best: {'fx': 533.333} (0.085):  53%|█████▎    | 53/100 [00:03<00:02, 21.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.025 | Best: {'fx': 533.333} (0.085):  54%|█████▍    | 54/100 [00:03<00:02, 21.52it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.025 | Best: {'fx': 533.333} (0.085):  55%|█████▌    | 55/100 [00:03<00:02, 21.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  55%|█████▌    | 55/100 [00:03<00:02, 21.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.025 | Best: {'fx': 533.333} (0.085):  56%|█████▌    | 56/100 [00:03<00:02, 21.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.023 | Best: {'fx': 533.333} (0.085):  57%|█████▋    | 57/100 [00:03<00:01, 21.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  58%|█████▊    | 58/100 [00:03<00:01, 21.58it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  59%|█████▉    | 59/100 [00:03<00:01, 25.12it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  59%|█████▉    | 59/100 [00:03<00:01, 25.12it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  60%|██████    | 60/100 [00:03<00:01, 25.12it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  61%|██████    | 61/100 [00:03<00:01, 25.12it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  62%|██████▏   | 62/100 [00:03<00:01, 25.12it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  63%|██████▎   | 63/100 [00:03<00:01, 25.12it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  64%|██████▍   | 64/100 [00:03<00:01, 28.91it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.023 | Best: {'fx': 533.333} (0.085):  64%|██████▍   | 64/100 [00:03<00:01, 28.91it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.023 | Best: {'fx': 533.333} (0.085):  65%|██████▌   | 65/100 [00:03<00:01, 28.91it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  66%|██████▌   | 66/100 [00:03<00:01, 28.91it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  67%|██████▋   | 67/100 [00:03<00:01, 28.91it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  68%|██████▊   | 68/100 [00:03<00:01, 28.91it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  69%|██████▉   | 69/100 [00:03<00:00, 31.50it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  69%|██████▉   | 69/100 [00:03<00:00, 31.50it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  70%|███████   | 70/100 [00:03<00:00, 31.50it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  71%|███████   | 71/100 [00:03<00:00, 31.50it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  72%|███████▏  | 72/100 [00:03<00:00, 31.50it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  73%|███████▎  | 73/100 [00:03<00:00, 31.50it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  74%|███████▍  | 74/100 [00:03<00:00, 35.36it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  74%|███████▍  | 74/100 [00:03<00:00, 35.36it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  75%|███████▌  | 75/100 [00:03<00:00, 35.36it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  76%|███████▌  | 76/100 [00:03<00:00, 35.36it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  77%|███████▋  | 77/100 [00:03<00:00, 35.36it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  78%|███████▊  | 78/100 [00:03<00:00, 29.67it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  78%|███████▊  | 78/100 [00:03<00:00, 29.67it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  79%|███████▉  | 79/100 [00:03<00:00, 29.67it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  80%|████████  | 80/100 [00:03<00:00, 29.67it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  81%|████████  | 81/100 [00:03<00:00, 29.67it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  82%|████████▏ | 82/100 [00:03<00:00, 30.83it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  82%|████████▏ | 82/100 [00:03<00:00, 30.83it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  83%|████████▎ | 83/100 [00:03<00:00, 30.83it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  84%|████████▍ | 84/100 [00:03<00:00, 30.83it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  85%|████████▌ | 85/100 [00:03<00:00, 30.83it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  86%|████████▌ | 86/100 [00:03<00:00, 27.21it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  86%|████████▌ | 86/100 [00:04<00:00, 27.21it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  87%|████████▋ | 87/100 [00:04<00:00, 27.21it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  88%|████████▊ | 88/100 [00:04<00:00, 27.21it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  89%|████████▉ | 89/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  89%|████████▉ | 89/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  90%|█████████ | 90/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  91%|█████████ | 91/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  92%|█████████▏| 92/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  93%|█████████▎| 93/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  94%|█████████▍| 94/100 [00:04<00:00, 20.76it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  95%|█████████▌| 95/100 [00:04<00:00, 27.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  95%|█████████▌| 95/100 [00:04<00:00, 27.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  96%|█████████▌| 96/100 [00:04<00:00, 27.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  97%|█████████▋| 97/100 [00:04<00:00, 27.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  98%|█████████▊| 98/100 [00:04<00:00, 27.73it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  99%|█████████▉| 99/100 [00:04<00:00, 22.29it/s]Brute | Start: {'fx': 500.0} (0.045) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  99%|█████████▉| 99/100 [00:04<00:00, 22.29it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.056 | Best: {'fx': 508.333} (0.056):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.056 | Best: {'fx': 508.333} (0.056):   2%|▏         | 1/50 [00:00<00:23,  2.07it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 509.354} (0.058):   2%|▏         | 1/50 [00:00<00:23,  2.07it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 509.354} (0.058):   4%|▍         | 2/50 [00:00<00:12,  3.79it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.059 | Best: {'fx': 510.374} (0.059):   4%|▍         | 2/50 [00:00<00:12,  3.79it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.061 | Best: {'fx': 511.395} (0.061):   6%|▌         | 3/50 [00:00<00:12,  3.79it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.064 | Best: {'fx': 512.415} (0.064):   8%|▊         | 4/50 [00:00<00:12,  3.79it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.064 | Best: {'fx': 512.415} (0.064):  10%|█         | 5/50 [00:00<00:04,  9.11it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.065 | Best: {'fx': 513.435} (0.065):  10%|█         | 5/50 [00:00<00:04,  9.11it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.067 | Best: {'fx': 514.456} (0.067):  12%|█▏        | 6/50 [00:00<00:04,  9.11it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.068 | Best: {'fx': 515.476} (0.068):  14%|█▍        | 7/50 [00:00<00:04,  9.11it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.068 | Best: {'fx': 515.476} (0.068):  16%|█▌        | 8/50 [00:00<00:03, 12.35it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.070 | Best: {'fx': 516.497} (0.070):  16%|█▌        | 8/50 [00:01<00:03, 12.35it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.072 | Best: {'fx': 517.517} (0.072):  18%|█▊        | 9/50 [00:01<00:03, 12.35it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.072 | Best: {'fx': 517.517} (0.072):  20%|██        | 10/50 [00:01<00:02, 13.97it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.074 | Best: {'fx': 518.537} (0.074):  20%|██        | 10/50 [00:01<00:02, 13.97it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.075 | Best: {'fx': 519.558} (0.075):  22%|██▏       | 11/50 [00:01<00:02, 13.97it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.077 | Best: {'fx': 520.578} (0.077):  24%|██▍       | 12/50 [00:01<00:02, 13.97it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.077 | Best: {'fx': 520.578} (0.077):  26%|██▌       | 13/50 [00:01<00:02, 17.08it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.078 | Best: {'fx': 521.599} (0.078):  26%|██▌       | 13/50 [00:01<00:02, 17.08it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.081 | Best: {'fx': 522.619} (0.081):  28%|██▊       | 14/50 [00:01<00:02, 17.08it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.081 | Best: {'fx': 522.619} (0.081):  30%|███       | 15/50 [00:01<00:02, 16.28it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 523.639} (0.082):  30%|███       | 15/50 [00:01<00:02, 16.28it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 525.68} (0.084):  32%|███▏      | 16/50 [00:01<00:02, 16.28it/s] Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 525.68} (0.084):  34%|███▍      | 17/50 [00:01<00:01, 16.96it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 525.68} (0.084):  34%|███▍      | 17/50 [00:01<00:01, 16.96it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.086 | Best: {'fx': 527.721} (0.086):  36%|███▌      | 18/50 [00:01<00:01, 16.96it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.086 | Best: {'fx': 527.721} (0.086):  38%|███▊      | 19/50 [00:01<00:01, 17.10it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.085 | Best: {'fx': 527.721} (0.086):  38%|███▊      | 19/50 [00:01<00:01, 17.10it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.087 | Best: {'fx': 528.741} (0.087):  40%|████      | 20/50 [00:01<00:01, 17.10it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.088 | Best: {'fx': 529.762} (0.088):  42%|████▏     | 21/50 [00:01<00:01, 17.10it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.088 | Best: {'fx': 529.762} (0.088):  44%|████▍     | 22/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.087 | Best: {'fx': 529.762} (0.088):  44%|████▍     | 22/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.087 | Best: {'fx': 529.762} (0.088):  46%|████▌     | 23/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.086 | Best: {'fx': 529.762} (0.088):  48%|████▊     | 24/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.086 | Best: {'fx': 529.762} (0.088):  50%|█████     | 25/50 [00:01<00:01, 16.74it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.083 | Best: {'fx': 529.762} (0.088):  50%|█████     | 25/50 [00:01<00:01, 16.74it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.077 | Best: {'fx': 529.762} (0.088):  52%|█████▏    | 26/50 [00:01<00:01, 16.74it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 529.762} (0.088):  54%|█████▍    | 27/50 [00:01<00:01, 16.74it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.077 | Best: {'fx': 529.762} (0.088):  56%|█████▌    | 28/50 [00:01<00:01, 16.74it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.077 | Best: {'fx': 529.762} (0.088):  58%|█████▊    | 29/50 [00:01<00:01, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.085 | Best: {'fx': 529.762} (0.088):  58%|█████▊    | 29/50 [00:01<00:01, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.075 | Best: {'fx': 529.762} (0.088):  60%|██████    | 30/50 [00:02<00:00, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.081 | Best: {'fx': 529.762} (0.088):  62%|██████▏   | 31/50 [00:02<00:00, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.080 | Best: {'fx': 529.762} (0.088):  64%|██████▍   | 32/50 [00:02<00:00, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.074 | Best: {'fx': 529.762} (0.088):  66%|██████▌   | 33/50 [00:02<00:00, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.073 | Best: {'fx': 529.762} (0.088):  68%|██████▊   | 34/50 [00:02<00:00, 20.33it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.073 | Best: {'fx': 529.762} (0.088):  70%|███████   | 35/50 [00:02<00:00, 26.57it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.078 | Best: {'fx': 529.762} (0.088):  70%|███████   | 35/50 [00:02<00:00, 26.57it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.072 | Best: {'fx': 529.762} (0.088):  72%|███████▏  | 36/50 [00:02<00:00, 26.57it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.067 | Best: {'fx': 529.762} (0.088):  74%|███████▍  | 37/50 [00:02<00:00, 26.57it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.070 | Best: {'fx': 529.762} (0.088):  76%|███████▌  | 38/50 [00:02<00:00, 26.57it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.070 | Best: {'fx': 529.762} (0.088):  78%|███████▊  | 39/50 [00:02<00:00, 28.20it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.069 | Best: {'fx': 529.762} (0.088):  78%|███████▊  | 39/50 [00:02<00:00, 28.20it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.062 | Best: {'fx': 529.762} (0.088):  80%|████████  | 40/50 [00:02<00:00, 28.20it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.064 | Best: {'fx': 529.762} (0.088):  82%|████████▏ | 41/50 [00:02<00:00, 28.20it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.061 | Best: {'fx': 529.762} (0.088):  84%|████████▍ | 42/50 [00:02<00:00, 28.20it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.061 | Best: {'fx': 529.762} (0.088):  86%|████████▌ | 43/50 [00:02<00:00, 30.40it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.060 | Best: {'fx': 529.762} (0.088):  86%|████████▌ | 43/50 [00:02<00:00, 30.40it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.059 | Best: {'fx': 529.762} (0.088):  88%|████████▊ | 44/50 [00:02<00:00, 30.40it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.059 | Best: {'fx': 529.762} (0.088):  90%|█████████ | 45/50 [00:02<00:00, 30.40it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 529.762} (0.088):  92%|█████████▏| 46/50 [00:02<00:00, 30.40it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 529.762} (0.088):  94%|█████████▍| 47/50 [00:02<00:00, 29.70it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.057 | Best: {'fx': 529.762} (0.088):  94%|█████████▍| 47/50 [00:02<00:00, 29.70it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.055 | Best: {'fx': 529.762} (0.088):  96%|█████████▌| 48/50 [00:02<00:00, 29.70it/s]Brute | Start: {'fx': 533.333} (0.085) | Current: 0.054 | Best: {'fx': 529.762} (0.088):  98%|█████████▊| 49/50 [00:02<00:00, 29.70it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.7619047619048
    px: 910.0
    py: 913.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 80 frames will be used for optimization
  Rigs: 1000(1000mm):20/20, 2000R(2000mm):20/20, 1500(1500mm):20/20, 2000L(2000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 37.1037: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 37.1037, picklable_objective(x0) = 37.1217
Best score: 37.1217: : 0iter [00:02, ?iter/s] Best score: 37.1217: : 1iter [00:02,  2.03s/iter]Best score: 37.1217: : 1iter [00:02,  2.03s/iter]Best score: 37.1217: : 2iter [00:02,  1.07iter/s]Best score: 37.1217: : 2iter [00:02,  1.07iter/s]Best score: 37.1217: : 3iter [00:02,  1.73iter/s]Best score: 37.1217: : 3iter [00:02,  1.73iter/s]Best score: 37.1217: : 4iter [00:02,  2.42iter/s]Best score: 37.1217: : 4iter [00:02,  2.42iter/s]Best score: 37.1217: : 5iter [00:02,  3.11iter/s]Best score: 37.1217: : 5iter [00:02,  3.11iter/s]Best score: 37.1217: : 6iter [00:02,  3.74iter/s]Best score: 37.1217: : 6iter [00:02,  3.74iter/s]Best score: 37.1217: : 7iter [00:02,  4.30iter/s]Best score: 37.1217: : 7iter [00:03,  4.30iter/s]Best score: 37.1217: : 8iter [00:03,  4.77iter/s]Best score: 37.1217: : 8iter [00:03,  4.77iter/s]Best score: 37.1217: : 9iter [00:03,  5.14iter/s]Best score: 37.1217: : 9iter [00:03,  5.14iter/s]Best score: 37.1217: : 10iter [00:03,  5.31iter/s]Best score: 37.1217: : 10iter [00:03,  5.31iter/s]Best score: 37.1217: : 11iter [00:03,  5.55iter/s]Best score: 37.1217: : 11iter [00:03,  5.55iter/s]Best score: 37.1217: : 12iter [00:03,  5.74iter/s]Best score: 37.1217: : 12iter [00:03,  5.74iter/s]Best score: 37.1217: : 13iter [00:03,  5.86iter/s]Best score: 37.1217: : 13iter [00:04,  5.86iter/s]Best score: 37.1217: : 14iter [00:04,  5.98iter/s]Best score: 37.1217: : 14iter [00:04,  5.98iter/s]Best score: 37.1217: : 15iter [00:04,  6.06iter/s]Best score: 37.1217: : 15iter [00:04,  6.06iter/s]Best score: 37.1217: : 16iter [00:04,  6.10iter/s]Best score: 37.1217: : 16iter [00:04,  6.10iter/s]Best score: 37.1217: : 17iter [00:04,  6.14iter/s]Best score: 37.1217: : 17iter [00:04,  6.14iter/s]Best score: 37.1217: : 18iter [00:04,  6.17iter/s]Best score: 37.1217: : 18iter [00:04,  6.17iter/s]Best score: 37.1217: : 19iter [00:04,  6.20iter/s]Best score: 37.1217: : 19iter [00:05,  6.20iter/s]Best score: 37.1217: : 20iter [00:05,  6.03iter/s]Best score: 37.1217: : 20iter [00:05,  6.03iter/s]Best score: 37.1217: : 21iter [00:05,  6.07iter/s]Best score: 37.1217: : 21iter [00:05,  6.07iter/s]Best score: 37.1217: : 22iter [00:05,  6.12iter/s]Best score: 37.1217: : 22iter [00:05,  6.12iter/s]Best score: 37.1217: : 23iter [00:05,  6.17iter/s]Best score: 37.1217: : 23iter [00:05,  6.17iter/s]Best score: 37.1217: : 24iter [00:05,  6.20iter/s]Best score: 37.1217: : 24iter [00:05,  6.20iter/s]Best score: 37.1217: : 25iter [00:05,  6.20iter/s]Best score: 37.1217: : 25iter [00:06,  6.20iter/s]Best score: 37.1217: : 26iter [00:06,  6.22iter/s]Best score: 37.1217: : 26iter [00:06,  6.22iter/s]Best score: 37.1217: : 27iter [00:06,  6.24iter/s]Best score: 37.1217: : 27iter [00:06,  6.24iter/s]Best score: 37.1217: : 28iter [00:06,  6.25iter/s]Best score: 37.1217: : 28iter [00:06,  6.25iter/s]Best score: 37.1217: : 29iter [00:06,  6.23iter/s]Best score: 18.4727: : 29iter [00:06,  6.23iter/s]Best score: 18.4727: : 30iter [00:06,  6.06iter/s]Best score: 18.4727: : 30iter [00:06,  6.06iter/s]Best score: 18.4727: : 31iter [00:06,  6.14iter/s]Best score: 18.4727: : 31iter [00:07,  6.14iter/s]Best score: 18.4727: : 32iter [00:07,  6.18iter/s]Best score: 18.4727: : 32iter [00:07,  6.18iter/s]Best score: 18.4727: : 33iter [00:07,  6.18iter/s]Best score: 18.4727: : 33iter [00:07,  6.18iter/s]Best score: 18.4727: : 34iter [00:07,  6.21iter/s]Best score: 18.4727: : 34iter [00:07,  6.21iter/s]Best score: 18.4727: : 35iter [00:07,  6.23iter/s]Best score: 18.4727: : 35iter [00:07,  6.23iter/s]Best score: 18.4727: : 36iter [00:07,  6.23iter/s]Best score: 18.4727: : 36iter [00:07,  6.23iter/s]Best score: 18.4727: : 37iter [00:07,  6.25iter/s]Best score: 18.4727: : 37iter [00:08,  6.25iter/s]Best score: 18.4727: : 38iter [00:08,  6.25iter/s]Best score: 18.4727: : 38iter [00:08,  6.25iter/s]Best score: 18.4727: : 39iter [00:08,  6.26iter/s]Best score: 12.0904: : 39iter [00:08,  6.26iter/s]Best score: 12.0904: : 40iter [00:08,  6.09iter/s]Best score: 12.0904: : 40iter [00:08,  6.09iter/s]Best score: 12.0904: : 41iter [00:08,  6.14iter/s]Best score: 12.0904: : 41iter [00:08,  6.14iter/s]Best score: 12.0904: : 42iter [00:08,  6.18iter/s]Best score: 12.0904: : 42iter [00:08,  6.18iter/s]Best score: 12.0904: : 43iter [00:08,  6.22iter/s]Best score: 12.0904: : 43iter [00:08,  6.22iter/s]Best score: 12.0904: : 44iter [00:08,  6.26iter/s]Best score: 12.0904: : 44iter [00:09,  6.26iter/s]Best score: 12.0904: : 45iter [00:09,  6.25iter/s]Best score: 12.0904: : 45iter [00:09,  6.25iter/s]Best score: 12.0904: : 46iter [00:09,  6.25iter/s]Best score: 12.0904: : 46iter [00:09,  6.25iter/s]Best score: 12.0904: : 47iter [00:09,  6.25iter/s]Best score: 12.0904: : 47iter [00:09,  6.25iter/s]Best score: 12.0904: : 48iter [00:09,  6.26iter/s]Best score: 12.0904: : 48iter [00:09,  6.26iter/s]Best score: 12.0904: : 49iter [00:09,  6.29iter/s]
[ITER 50] Best: 3.7381
[PARAMS] Current best parameters:
  fx: 508.3798
  fy: 507.9044
  px: 902.7479
  py: 922.8645
  k1: 0.03679315
  k2: -0.01364111
  k3: -0.00313488
  k4: 0.00277565
  p1: 0.00321277
  p2: -0.00768947
  p3: -0.00287119
  p4: -0.00561088
  scale: 3.0047
  rotation: -0.1790
Best score: 3.7381: : 49iter [00:09,  6.29iter/s] Best score: 3.7381: : 50iter [00:09,  6.10iter/s]Best score: 3.7381: : 50iter [00:10,  6.10iter/s]Best score: 3.7381: : 51iter [00:10,  6.14iter/s]Best score: 3.7381: : 51iter [00:10,  6.14iter/s]Best score: 3.7381: : 52iter [00:10,  6.18iter/s]Best score: 3.7381: : 52iter [00:10,  6.18iter/s]Best score: 3.7381: : 53iter [00:10,  6.20iter/s]Best score: 3.7381: : 53iter [00:10,  6.20iter/s]Best score: 3.7381: : 54iter [00:10,  6.21iter/s]Best score: 3.7381: : 54iter [00:10,  6.21iter/s]Best score: 3.7381: : 55iter [00:10,  6.23iter/s]Best score: 3.7381: : 55iter [00:10,  6.23iter/s]Best score: 3.7381: : 56iter [00:10,  6.24iter/s]Best score: 3.7381: : 56iter [00:11,  6.24iter/s]Best score: 3.7381: : 57iter [00:11,  6.26iter/s]Best score: 3.7381: : 57iter [00:11,  6.26iter/s]Best score: 3.7381: : 58iter [00:11,  6.28iter/s]Best score: 3.7381: : 58iter [00:11,  6.28iter/s]Best score: 3.7381: : 59iter [00:11,  6.30iter/s]Best score: 1.2873: : 59iter [00:11,  6.30iter/s]Best score: 1.2873: : 60iter [00:11,  6.10iter/s]Best score: 1.2873: : 60iter [00:11,  6.10iter/s]Best score: 1.2873: : 61iter [00:11,  6.14iter/s]Best score: 1.2873: : 61iter [00:11,  6.14iter/s]Best score: 1.2873: : 62iter [00:11,  6.16iter/s]Best score: 1.2873: : 62iter [00:12,  6.16iter/s]Best score: 1.2873: : 63iter [00:12,  6.19iter/s]Best score: 1.2873: : 63iter [00:12,  6.19iter/s]Best score: 1.2873: : 64iter [00:12,  6.18iter/s]Best score: 1.2873: : 64iter [00:12,  6.18iter/s]Best score: 1.2873: : 65iter [00:12,  6.19iter/s]Best score: 1.2873: : 65iter [00:12,  6.19iter/s]Best score: 1.2873: : 66iter [00:12,  6.22iter/s]Best score: 1.2873: : 66iter [00:12,  6.22iter/s]Best score: 1.2873: : 67iter [00:12,  6.22iter/s]Best score: 1.2873: : 67iter [00:12,  6.22iter/s]Best score: 1.2873: : 68iter [00:12,  6.21iter/s]Best score: 1.2873: : 68iter [00:13,  6.21iter/s]Best score: 1.2873: : 69iter [00:13,  6.22iter/s]Best score: 1.2873: : 69iter [00:13,  6.22iter/s]Best score: 1.2873: : 70iter [00:13,  6.04iter/s]Best score: 1.2873: : 70iter [00:13,  6.04iter/s]Best score: 1.2873: : 71iter [00:13,  6.10iter/s]Best score: 1.2873: : 71iter [00:13,  6.10iter/s]Best score: 1.2873: : 72iter [00:13,  6.15iter/s]Best score: 1.2873: : 72iter [00:13,  6.15iter/s]Best score: 1.2873: : 73iter [00:13,  6.19iter/s]Best score: 1.2873: : 73iter [00:13,  6.19iter/s]Best score: 1.2873: : 74iter [00:13,  6.21iter/s]Best score: 1.2873: : 74iter [00:13,  6.21iter/s]Best score: 1.2873: : 75iter [00:13,  6.22iter/s]Best score: 1.2873: : 75iter [00:14,  6.22iter/s]Best score: 1.2873: : 76iter [00:14,  6.24iter/s]Best score: 1.2873: : 76iter [00:14,  6.24iter/s]Best score: 1.2873: : 77iter [00:14,  6.28iter/s]Best score: 1.2873: : 77iter [00:14,  6.28iter/s]Best score: 1.2873: : 78iter [00:14,  6.28iter/s]Best score: 1.2873: : 78iter [00:14,  6.28iter/s]Best score: 1.2873: : 79iter [00:14,  6.29iter/s]Best score: 0.6453: : 79iter [00:14,  6.29iter/s]Best score: 0.6453: : 80iter [00:14,  6.11iter/s]Best score: 0.6453: : 80iter [00:14,  6.11iter/s]Best score: 0.6453: : 81iter [00:14,  6.18iter/s]Best score: 0.6453: : 81iter [00:15,  6.18iter/s]Best score: 0.6453: : 82iter [00:15,  6.21iter/s]Best score: 0.6453: : 82iter [00:15,  6.21iter/s]Best score: 0.6453: : 83iter [00:15,  6.25iter/s]Best score: 0.6453: : 83iter [00:15,  6.25iter/s]Best score: 0.6453: : 84iter [00:15,  6.27iter/s]Best score: 0.6453: : 84iter [00:15,  6.27iter/s]Best score: 0.6453: : 85iter [00:15,  6.27iter/s]Best score: 0.6453: : 85iter [00:15,  6.27iter/s]Best score: 0.6453: : 86iter [00:15,  6.30iter/s]Best score: 0.6453: : 86iter [00:15,  6.30iter/s]Best score: 0.6453: : 87iter [00:15,  6.28iter/s]Best score: 0.6453: : 87iter [00:16,  6.28iter/s]Best score: 0.6453: : 88iter [00:16,  6.30iter/s]Best score: 0.6453: : 88iter [00:16,  6.30iter/s]Best score: 0.6453: : 89iter [00:16,  6.30iter/s]Best score: 0.4758: : 89iter [00:16,  6.30iter/s]Best score: 0.4758: : 90iter [00:16,  6.10iter/s]Best score: 0.4758: : 90iter [00:16,  6.10iter/s]Best score: 0.4758: : 91iter [00:16,  6.17iter/s]Best score: 0.4758: : 91iter [00:16,  6.17iter/s]Best score: 0.4758: : 92iter [00:16,  6.19iter/s]Best score: 0.4758: : 92iter [00:16,  6.19iter/s]Best score: 0.4758: : 93iter [00:16,  6.22iter/s]Best score: 0.4758: : 93iter [00:17,  6.22iter/s]Best score: 0.4758: : 94iter [00:17,  6.25iter/s]Best score: 0.4758: : 94iter [00:17,  6.25iter/s]Best score: 0.4758: : 95iter [00:17,  6.27iter/s]Best score: 0.4758: : 95iter [00:17,  6.27iter/s]Best score: 0.4758: : 96iter [00:17,  6.29iter/s]Best score: 0.4758: : 96iter [00:17,  6.29iter/s]Best score: 0.4758: : 97iter [00:17,  6.27iter/s]Best score: 0.4758: : 97iter [00:17,  6.27iter/s]Best score: 0.4758: : 98iter [00:17,  6.28iter/s]Best score: 0.4758: : 98iter [00:17,  6.28iter/s]Best score: 0.4758: : 99iter [00:17,  6.28iter/s]
[ITER 100] Best: 0.4758
[PARAMS] Current best parameters:
  fx: 510.6497
  fy: 510.9639
  px: 904.2359
  py: 922.7323
  k1: 0.02710015
  k2: -0.00341282
  k3: -0.00275503
  k4: 0.00059887
  p1: 0.00074787
  p2: -0.00689931
  p3: -0.00085820
  p4: -0.00218805
  scale: 2.9726
  rotation: -0.4290
Best score: 0.4758: : 99iter [00:17,  6.28iter/s]Best score: 0.4758: : 100iter [00:17,  6.10iter/s]Best score: 0.4758: : 100iter [00:18,  6.10iter/s]Best score: 0.4758: : 101iter [00:18,  6.16iter/s]Best score: 0.4758: : 101iter [00:18,  6.16iter/s]Best score: 0.4758: : 102iter [00:18,  6.20iter/s]Best score: 0.4758: : 102iter [00:18,  6.20iter/s]Best score: 0.4758: : 103iter [00:18,  6.23iter/s]Best score: 0.4758: : 103iter [00:18,  6.23iter/s]Best score: 0.4758: : 104iter [00:18,  6.23iter/s]Best score: 0.4758: : 104iter [00:18,  6.23iter/s]Best score: 0.4758: : 105iter [00:18,  6.25iter/s]Best score: 0.4758: : 105iter [00:18,  6.25iter/s]Best score: 0.4758: : 106iter [00:18,  6.26iter/s]Best score: 0.4758: : 106iter [00:19,  6.26iter/s]Best score: 0.4758: : 107iter [00:19,  6.27iter/s]Best score: 0.4758: : 107iter [00:19,  6.27iter/s]Best score: 0.4758: : 108iter [00:19,  6.28iter/s]Best score: 0.4758: : 108iter [00:19,  6.28iter/s]Best score: 0.4758: : 109iter [00:19,  6.29iter/s]Best score: 0.3245: : 109iter [00:19,  6.29iter/s]Best score: 0.3245: : 110iter [00:19,  6.09iter/s]Best score: 0.3245: : 110iter [00:19,  6.09iter/s]Best score: 0.3245: : 111iter [00:19,  6.15iter/s]Best score: 0.3245: : 111iter [00:19,  6.15iter/s]Best score: 0.3245: : 112iter [00:19,  6.19iter/s]Best score: 0.3245: : 112iter [00:20,  6.19iter/s]Best score: 0.3245: : 113iter [00:20,  6.22iter/s]Best score: 0.3245: : 113iter [00:20,  6.22iter/s]Best score: 0.3245: : 114iter [00:20,  6.24iter/s]Best score: 0.3245: : 114iter [00:20,  6.24iter/s]Best score: 0.3245: : 115iter [00:20,  6.26iter/s]Best score: 0.3245: : 115iter [00:20,  6.26iter/s]Best score: 0.3245: : 116iter [00:20,  6.27iter/s]Best score: 0.3245: : 116iter [00:20,  6.27iter/s]Best score: 0.3245: : 117iter [00:20,  6.29iter/s]Best score: 0.3245: : 117iter [00:20,  6.29iter/s]Best score: 0.3245: : 118iter [00:20,  6.30iter/s]Best score: 0.3245: : 118iter [00:21,  6.30iter/s]Best score: 0.3245: : 119iter [00:21,  6.31iter/s]Best score: 0.3245: : 119iter [00:21,  6.31iter/s]Best score: 0.3245: : 120iter [00:21,  6.13iter/s]Best score: 0.3245: : 120iter [00:21,  6.13iter/s]Best score: 0.3245: : 121iter [00:21,  6.18iter/s]Best score: 0.3245: : 121iter [00:21,  6.18iter/s]Best score: 0.3245: : 122iter [00:21,  6.23iter/s]Best score: 0.3245: : 122iter [00:21,  6.23iter/s]Best score: 0.3245: : 123iter [00:21,  6.24iter/s]Best score: 0.3245: : 123iter [00:21,  6.24iter/s]Best score: 0.3245: : 124iter [00:21,  6.26iter/s]Best score: 0.3245: : 124iter [00:21,  6.26iter/s]Best score: 0.3245: : 125iter [00:21,  6.26iter/s]Best score: 0.3245: : 125iter [00:22,  6.26iter/s]Best score: 0.3245: : 126iter [00:22,  6.27iter/s]Best score: 0.3245: : 126iter [00:22,  6.27iter/s]Best score: 0.3245: : 127iter [00:22,  6.29iter/s]Best score: 0.3245: : 127iter [00:22,  6.29iter/s]Best score: 0.3245: : 128iter [00:22,  6.29iter/s]Best score: 0.3245: : 128iter [00:22,  6.29iter/s]Best score: 0.3245: : 129iter [00:22,  6.28iter/s]Best score: 0.2799: : 129iter [00:22,  6.28iter/s]Best score: 0.2799: : 130iter [00:22,  6.11iter/s]Best score: 0.2799: : 130iter [00:22,  6.11iter/s]Best score: 0.2799: : 131iter [00:22,  6.16iter/s]Best score: 0.2799: : 131iter [00:23,  6.16iter/s]Best score: 0.2799: : 132iter [00:23,  6.21iter/s]Best score: 0.2799: : 132iter [00:23,  6.21iter/s]Best score: 0.2799: : 133iter [00:23,  6.25iter/s]Best score: 0.2799: : 133iter [00:23,  6.25iter/s]Best score: 0.2799: : 134iter [00:23,  6.27iter/s]Best score: 0.2799: : 134iter [00:23,  6.27iter/s]Best score: 0.2799: : 135iter [00:23,  6.28iter/s]Best score: 0.2799: : 135iter [00:23,  6.28iter/s]Best score: 0.2799: : 136iter [00:23,  6.29iter/s]Best score: 0.2799: : 136iter [00:23,  6.29iter/s]Best score: 0.2799: : 137iter [00:23,  6.30iter/s]Best score: 0.2799: : 137iter [00:24,  6.30iter/s]Best score: 0.2799: : 138iter [00:24,  6.31iter/s]Best score: 0.2799: : 138iter [00:24,  6.31iter/s]Best score: 0.2799: : 139iter [00:24,  6.30iter/s]Best score: 0.2799: : 139iter [00:24,  6.30iter/s]Best score: 0.2799: : 140iter [00:24,  6.12iter/s]Best score: 0.2799: : 140iter [00:24,  6.12iter/s]Best score: 0.2799: : 141iter [00:24,  6.19iter/s]Best score: 0.2799: : 141iter [00:24,  6.19iter/s]Best score: 0.2799: : 142iter [00:24,  6.22iter/s]Best score: 0.2799: : 142iter [00:24,  6.22iter/s]Best score: 0.2799: : 143iter [00:24,  6.25iter/s]Best score: 0.2799: : 143iter [00:25,  6.25iter/s]Best score: 0.2799: : 144iter [00:25,  6.27iter/s]Best score: 0.2799: : 144iter [00:25,  6.27iter/s]Best score: 0.2799: : 145iter [00:25,  6.29iter/s]Best score: 0.2799: : 145iter [00:25,  6.29iter/s]Best score: 0.2799: : 146iter [00:25,  6.29iter/s]Best score: 0.2799: : 146iter [00:25,  6.29iter/s]Best score: 0.2799: : 147iter [00:25,  6.31iter/s]Best score: 0.2799: : 147iter [00:25,  6.31iter/s]Best score: 0.2799: : 148iter [00:25,  6.31iter/s]Best score: 0.2799: : 148iter [00:25,  6.31iter/s]Best score: 0.2799: : 149iter [00:25,  6.32iter/s]
[ITER 150] Best: 0.2346
[PARAMS] Current best parameters:
  fx: 513.5706
  fy: 513.8166
  px: 905.2003
  py: 919.9790
  k1: 0.01840375
  k2: 0.00766815
  k3: -0.00970661
  k4: 0.00205944
  p1: -0.00491480
  p2: -0.00504704
  p3: 0.00122809
  p4: -0.00175096
  scale: 2.9718
  rotation: -0.5078
Best score: 0.2346: : 149iter [00:26,  6.32iter/s]Best score: 0.2346: : 150iter [00:26,  6.13iter/s]Best score: 0.2346: : 150iter [00:26,  6.13iter/s]Best score: 0.2346: : 151iter [00:26,  6.17iter/s]Best score: 0.2346: : 151iter [00:26,  6.17iter/s]Best score: 0.2346: : 152iter [00:26,  6.21iter/s]Best score: 0.2346: : 152iter [00:26,  6.21iter/s]Best score: 0.2346: : 153iter [00:26,  6.23iter/s]Best score: 0.2346: : 153iter [00:26,  6.23iter/s]Best score: 0.2346: : 154iter [00:26,  6.26iter/s]Best score: 0.2346: : 154iter [00:26,  6.26iter/s]Best score: 0.2346: : 155iter [00:26,  6.28iter/s]Best score: 0.2346: : 155iter [00:26,  6.28iter/s]Best score: 0.2346: : 156iter [00:26,  6.29iter/s]Best score: 0.2346: : 156iter [00:27,  6.29iter/s]Best score: 0.2346: : 157iter [00:27,  6.30iter/s]Best score: 0.2346: : 157iter [00:27,  6.30iter/s]Best score: 0.2346: : 158iter [00:27,  6.29iter/s]Best score: 0.2346: : 158iter [00:27,  6.29iter/s]Best score: 0.2346: : 159iter [00:27,  6.29iter/s]Best score: 0.2237: : 159iter [00:27,  6.29iter/s]Best score: 0.2237: : 160iter [00:27,  6.08iter/s]Best score: 0.2237: : 160iter [00:27,  6.08iter/s]Best score: 0.2237: : 161iter [00:27,  6.13iter/s]Best score: 0.2237: : 161iter [00:27,  6.13iter/s]Best score: 0.2237: : 162iter [00:27,  6.19iter/s]Best score: 0.2237: : 162iter [00:28,  6.19iter/s]Best score: 0.2237: : 163iter [00:28,  6.22iter/s]Best score: 0.2237: : 163iter [00:28,  6.22iter/s]Best score: 0.2237: : 164iter [00:28,  6.24iter/s]Best score: 0.2237: : 164iter [00:28,  6.24iter/s]Best score: 0.2237: : 165iter [00:28,  6.25iter/s]Best score: 0.2237: : 165iter [00:28,  6.25iter/s]Best score: 0.2237: : 166iter [00:28,  6.25iter/s]Best score: 0.2237: : 166iter [00:28,  6.25iter/s]Best score: 0.2237: : 167iter [00:28,  6.26iter/s]Best score: 0.2237: : 167iter [00:28,  6.26iter/s]Best score: 0.2237: : 168iter [00:28,  6.28iter/s]Best score: 0.2237: : 168iter [00:29,  6.28iter/s]Best score: 0.2237: : 169iter [00:29,  6.30iter/s]Best score: 0.2219: : 169iter [00:29,  6.30iter/s]Best score: 0.2219: : 170iter [00:29,  6.11iter/s]Best score: 0.2219: : 170iter [00:29,  6.11iter/s]Best score: 0.2219: : 171iter [00:29,  6.17iter/s]Best score: 0.2219: : 171iter [00:29,  6.17iter/s]Best score: 0.2219: : 172iter [00:29,  6.21iter/s]Best score: 0.2219: : 172iter [00:29,  6.21iter/s]Best score: 0.2219: : 173iter [00:29,  6.25iter/s]Best score: 0.2219: : 173iter [00:29,  6.25iter/s]Best score: 0.2219: : 174iter [00:29,  6.26iter/s]Best score: 0.2219: : 174iter [00:30,  6.26iter/s]Best score: 0.2219: : 175iter [00:30,  6.26iter/s]Best score: 0.2219: : 175iter [00:30,  6.26iter/s]Best score: 0.2219: : 176iter [00:30,  6.28iter/s]Best score: 0.2219: : 176iter [00:30,  6.28iter/s]Best score: 0.2219: : 177iter [00:30,  6.29iter/s]Best score: 0.2219: : 177iter [00:30,  6.29iter/s]Best score: 0.2219: : 178iter [00:30,  6.30iter/s]Best score: 0.2219: : 178iter [00:30,  6.30iter/s]Best score: 0.2219: : 179iter [00:30,  6.30iter/s]Best score: 0.2219: : 179iter [00:30,  6.30iter/s]Best score: 0.2219: : 180iter [00:30,  6.09iter/s]Best score: 0.2219: : 180iter [00:30,  6.09iter/s]Best score: 0.2219: : 181iter [00:30,  6.15iter/s]Best score: 0.2219: : 181iter [00:31,  6.15iter/s]Best score: 0.2219: : 182iter [00:31,  6.19iter/s]Best score: 0.2219: : 182iter [00:31,  6.19iter/s]Best score: 0.2219: : 183iter [00:31,  6.22iter/s]Best score: 0.2219: : 183iter [00:31,  6.22iter/s]Best score: 0.2219: : 184iter [00:31,  6.24iter/s]Best score: 0.2219: : 184iter [00:31,  6.24iter/s]Best score: 0.2219: : 185iter [00:31,  6.25iter/s]Best score: 0.2219: : 185iter [00:31,  6.25iter/s]Best score: 0.2219: : 186iter [00:31,  6.25iter/s]Best score: 0.2219: : 186iter [00:31,  6.25iter/s]Best score: 0.2219: : 187iter [00:31,  6.27iter/s]Best score: 0.2219: : 187iter [00:32,  6.27iter/s]Best score: 0.2219: : 188iter [00:32,  6.29iter/s]Best score: 0.2219: : 188iter [00:32,  6.29iter/s]Best score: 0.2219: : 189iter [00:32,  6.30iter/s]Best score: 0.2191: : 189iter [00:32,  6.30iter/s]Best score: 0.2191: : 190iter [00:32,  6.11iter/s]Best score: 0.2191: : 190iter [00:32,  6.11iter/s]Best score: 0.2191: : 191iter [00:32,  6.18iter/s]Best score: 0.2191: : 191iter [00:32,  6.18iter/s]Best score: 0.2191: : 192iter [00:32,  6.22iter/s]Best score: 0.2191: : 192iter [00:32,  6.22iter/s]Best score: 0.2191: : 193iter [00:32,  6.24iter/s]Best score: 0.2191: : 193iter [00:33,  6.24iter/s]Best score: 0.2191: : 194iter [00:33,  6.26iter/s]Best score: 0.2191: : 194iter [00:33,  6.26iter/s]Best score: 0.2191: : 195iter [00:33,  6.28iter/s]Best score: 0.2191: : 195iter [00:33,  6.28iter/s]Best score: 0.2191: : 196iter [00:33,  6.29iter/s]Best score: 0.2191: : 196iter [00:33,  6.29iter/s]Best score: 0.2191: : 197iter [00:33,  6.30iter/s]Best score: 0.2191: : 197iter [00:33,  6.30iter/s]Best score: 0.2191: : 198iter [00:33,  6.31iter/s]Best score: 0.2191: : 198iter [00:33,  6.31iter/s]Best score: 0.2191: : 199iter [00:33,  6.31iter/s]
[ITER 200] Best: 0.2020
[PARAMS] Current best parameters:
  fx: 513.0786
  fy: 513.2218
  px: 906.0290
  py: 919.7035
  k1: 0.01782763
  k2: 0.00969727
  k3: -0.01153031
  k4: 0.00266602
  p1: -0.00479793
  p2: -0.00304694
  p3: -0.00036496
  p4: -0.00126156
  scale: 2.9737
  rotation: -0.4874
Best score: 0.2020: : 199iter [00:34,  6.31iter/s]Best score: 0.2020: : 200iter [00:34,  6.12iter/s]Best score: 0.2020: : 200iter [00:34,  6.12iter/s]Best score: 0.2020: : 201iter [00:34,  6.18iter/s]Best score: 0.2020: : 201iter [00:34,  6.18iter/s]Best score: 0.2020: : 202iter [00:34,  6.23iter/s]Best score: 0.2020: : 202iter [00:34,  6.23iter/s]Best score: 0.2020: : 203iter [00:34,  6.26iter/s]Best score: 0.2020: : 203iter [00:34,  6.26iter/s]Best score: 0.2020: : 204iter [00:34,  6.29iter/s]Best score: 0.2020: : 204iter [00:34,  6.29iter/s]Best score: 0.2020: : 205iter [00:34,  6.30iter/s]Best score: 0.2020: : 205iter [00:34,  6.30iter/s]Best score: 0.2020: : 206iter [00:34,  6.31iter/s]Best score: 0.2020: : 206iter [00:35,  6.31iter/s]Best score: 0.2020: : 207iter [00:35,  6.31iter/s]Best score: 0.2020: : 207iter [00:35,  6.31iter/s]Best score: 0.2020: : 208iter [00:35,  6.32iter/s]Best score: 0.2020: : 208iter [00:35,  6.32iter/s]Best score: 0.2020: : 209iter [00:35,  6.31iter/s]Best score: 0.2004: : 209iter [00:35,  6.31iter/s]Best score: 0.2004: : 210iter [00:35,  6.12iter/s]Best score: 0.2004: : 210iter [00:35,  6.12iter/s]Best score: 0.2004: : 211iter [00:35,  6.18iter/s]Best score: 0.2004: : 211iter [00:35,  6.18iter/s]Best score: 0.2004: : 212iter [00:35,  6.22iter/s]Best score: 0.2004: : 212iter [00:36,  6.22iter/s]Best score: 0.2004: : 213iter [00:36,  6.25iter/s]Best score: 0.2004: : 213iter [00:36,  6.25iter/s]Best score: 0.2004: : 214iter [00:36,  6.29iter/s]Best score: 0.2004: : 214iter [00:36,  6.29iter/s]Best score: 0.2004: : 215iter [00:36,  6.29iter/s]Best score: 0.2004: : 215iter [00:36,  6.29iter/s]Best score: 0.2004: : 216iter [00:36,  6.30iter/s]Best score: 0.2004: : 216iter [00:36,  6.30iter/s]Best score: 0.2004: : 217iter [00:36,  6.31iter/s]Best score: 0.2004: : 217iter [00:36,  6.31iter/s]Best score: 0.2004: : 218iter [00:36,  6.31iter/s]Best score: 0.2004: : 218iter [00:37,  6.31iter/s]Best score: 0.2004: : 219iter [00:37,  6.31iter/s]Best score: 0.1979: : 219iter [00:37,  6.31iter/s]Best score: 0.1979: : 220iter [00:37,  6.11iter/s]Best score: 0.1979: : 220iter [00:37,  6.11iter/s]Best score: 0.1979: : 221iter [00:37,  6.16iter/s]Best score: 0.1979: : 221iter [00:37,  6.16iter/s]Best score: 0.1979: : 222iter [00:37,  6.21iter/s]Best score: 0.1979: : 222iter [00:37,  6.21iter/s]Best score: 0.1979: : 223iter [00:37,  6.24iter/s]Best score: 0.1979: : 223iter [00:37,  6.24iter/s]Best score: 0.1979: : 224iter [00:37,  6.26iter/s]Best score: 0.1979: : 224iter [00:38,  6.26iter/s]Best score: 0.1979: : 225iter [00:38,  6.28iter/s]Best score: 0.1979: : 225iter [00:38,  6.28iter/s]Best score: 0.1979: : 226iter [00:38,  6.28iter/s]Best score: 0.1979: : 226iter [00:38,  6.28iter/s]Best score: 0.1979: : 227iter [00:38,  6.30iter/s]Best score: 0.1979: : 227iter [00:38,  6.30iter/s]Best score: 0.1979: : 228iter [00:38,  6.29iter/s]Best score: 0.1979: : 228iter [00:38,  6.29iter/s]Best score: 0.1979: : 229iter [00:38,  6.30iter/s]Best score: 0.1949: : 229iter [00:38,  6.30iter/s]Best score: 0.1949: : 230iter [00:38,  6.10iter/s]Best score: 0.1949: : 230iter [00:38,  6.10iter/s]Best score: 0.1949: : 231iter [00:38,  6.16iter/s]Best score: 0.1949: : 231iter [00:39,  6.16iter/s]Best score: 0.1949: : 232iter [00:39,  6.19iter/s]Best score: 0.1949: : 232iter [00:39,  6.19iter/s]Best score: 0.1949: : 233iter [00:39,  6.23iter/s]Best score: 0.1949: : 233iter [00:39,  6.23iter/s]Best score: 0.1949: : 234iter [00:39,  6.26iter/s]Best score: 0.1949: : 234iter [00:39,  6.26iter/s]Best score: 0.1949: : 235iter [00:39,  6.28iter/s]Best score: 0.1949: : 235iter [00:39,  6.28iter/s]Best score: 0.1949: : 236iter [00:39,  6.29iter/s]Best score: 0.1949: : 236iter [00:39,  6.29iter/s]Best score: 0.1949: : 237iter [00:39,  6.30iter/s]Best score: 0.1949: : 237iter [00:40,  6.30iter/s]Best score: 0.1949: : 238iter [00:40,  6.31iter/s]Best score: 0.1949: : 238iter [00:40,  6.31iter/s]Best score: 0.1949: : 239iter [00:40,  6.32iter/s]Best score: 0.1949: : 239iter [00:40,  6.32iter/s]Best score: 0.1949: : 240iter [00:40,  6.13iter/s]Best score: 0.1949: : 240iter [00:40,  6.13iter/s]Best score: 0.1949: : 241iter [00:40,  6.18iter/s]Best score: 0.1949: : 241iter [00:40,  6.18iter/s]Best score: 0.1949: : 242iter [00:40,  6.21iter/s]Best score: 0.1949: : 242iter [00:40,  6.21iter/s]Best score: 0.1949: : 243iter [00:40,  6.25iter/s]Best score: 0.1949: : 243iter [00:41,  6.25iter/s]Best score: 0.1949: : 244iter [00:41,  6.27iter/s]Best score: 0.1949: : 244iter [00:41,  6.27iter/s]Best score: 0.1949: : 245iter [00:41,  6.29iter/s]Best score: 0.1949: : 245iter [00:41,  6.29iter/s]Best score: 0.1949: : 246iter [00:41,  6.31iter/s]Best score: 0.1949: : 246iter [00:41,  6.31iter/s]Best score: 0.1949: : 247iter [00:41,  6.32iter/s]Best score: 0.1949: : 247iter [00:41,  6.32iter/s]Best score: 0.1949: : 248iter [00:41,  6.31iter/s]Best score: 0.1949: : 248iter [00:41,  6.31iter/s]Best score: 0.1949: : 249iter [00:41,  6.32iter/s]
[ITER 250] Best: 0.1922
[PARAMS] Current best parameters:
  fx: 506.2213
  fy: 506.3572
  px: 905.5443
  py: 918.3013
  k1: 0.01897180
  k2: 0.01763713
  k3: -0.01494365
  k4: 0.00295327
  p1: -0.00769985
  p2: -0.00432956
  p3: -0.00077860
  p4: 0.00010875
  scale: 2.9802
  rotation: -0.4854
Best score: 0.1922: : 249iter [00:42,  6.32iter/s]Best score: 0.1922: : 250iter [00:42,  6.13iter/s]Best score: 0.1922: : 250iter [00:42,  6.13iter/s]Best score: 0.1922: : 251iter [00:42,  6.19iter/s]Best score: 0.1922: : 251iter [00:42,  6.19iter/s]Best score: 0.1922: : 252iter [00:42,  6.23iter/s]Best score: 0.1922: : 252iter [00:42,  6.23iter/s]Best score: 0.1922: : 253iter [00:42,  6.26iter/s]Best score: 0.1922: : 253iter [00:42,  6.26iter/s]Best score: 0.1922: : 254iter [00:42,  6.28iter/s]Best score: 0.1922: : 254iter [00:42,  6.28iter/s]Best score: 0.1922: : 255iter [00:42,  6.28iter/s]Best score: 0.1922: : 255iter [00:42,  6.28iter/s]Best score: 0.1922: : 256iter [00:42,  6.28iter/s]Best score: 0.1922: : 256iter [00:43,  6.28iter/s]Best score: 0.1922: : 257iter [00:43,  6.30iter/s]Best score: 0.1922: : 257iter [00:43,  6.30iter/s]Best score: 0.1922: : 258iter [00:43,  6.31iter/s]Best score: 0.1922: : 258iter [00:43,  6.31iter/s]Best score: 0.1922: : 259iter [00:43,  6.32iter/s]Best score: 0.1922: : 259iter [00:43,  6.32iter/s]Best score: 0.1922: : 260iter [00:43,  6.12iter/s]Best score: 0.1922: : 260iter [00:43,  6.12iter/s]Best score: 0.1922: : 261iter [00:43,  6.18iter/s]Best score: 0.1922: : 261iter [00:43,  6.18iter/s]Best score: 0.1922: : 262iter [00:43,  6.23iter/s]Best score: 0.1922: : 262iter [00:44,  6.23iter/s]Best score: 0.1922: : 263iter [00:44,  6.25iter/s]Best score: 0.1922: : 263iter [00:44,  6.25iter/s]Best score: 0.1922: : 264iter [00:44,  6.27iter/s]Best score: 0.1922: : 264iter [00:44,  6.27iter/s]Best score: 0.1922: : 265iter [00:44,  6.29iter/s]Best score: 0.1922: : 265iter [00:44,  6.29iter/s]Best score: 0.1922: : 266iter [00:44,  6.30iter/s]Best score: 0.1922: : 266iter [00:44,  6.30iter/s]Best score: 0.1922: : 267iter [00:44,  6.30iter/s]Best score: 0.1922: : 267iter [00:44,  6.30iter/s]Best score: 0.1922: : 268iter [00:44,  6.31iter/s]Best score: 0.1922: : 268iter [00:45,  6.31iter/s]Best score: 0.1922: : 269iter [00:45,  6.32iter/s]Best score: 0.1922: : 269iter [00:45,  6.32iter/s]Best score: 0.1922: : 270iter [00:45,  6.12iter/s]Best score: 0.1922: : 270iter [00:45,  6.12iter/s]Best score: 0.1922: : 271iter [00:45,  6.18iter/s]Best score: 0.1922: : 271iter [00:45,  6.18iter/s]Best score: 0.1922: : 272iter [00:45,  6.22iter/s]Best score: 0.1922: : 272iter [00:45,  6.22iter/s]Best score: 0.1922: : 273iter [00:45,  6.25iter/s]Best score: 0.1922: : 273iter [00:45,  6.25iter/s]Best score: 0.1922: : 274iter [00:45,  6.27iter/s]Best score: 0.1922: : 274iter [00:46,  6.27iter/s]Best score: 0.1922: : 275iter [00:46,  6.29iter/s]Best score: 0.1922: : 275iter [00:46,  6.29iter/s]Best score: 0.1922: : 276iter [00:46,  6.29iter/s]Best score: 0.1922: : 276iter [00:46,  6.29iter/s]Best score: 0.1922: : 277iter [00:46,  6.30iter/s]Best score: 0.1922: : 277iter [00:46,  6.30iter/s]Best score: 0.1922: : 278iter [00:46,  6.32iter/s]Best score: 0.1922: : 278iter [00:46,  6.32iter/s]Best score: 0.1922: : 279iter [00:46,  6.33iter/s]Best score: 0.1907: : 279iter [00:46,  6.33iter/s]Best score: 0.1907: : 280iter [00:46,  6.13iter/s]Best score: 0.1907: : 280iter [00:46,  6.13iter/s]Best score: 0.1907: : 281iter [00:46,  6.19iter/s]Best score: 0.1907: : 281iter [00:47,  6.19iter/s]Best score: 0.1907: : 282iter [00:47,  6.23iter/s]Best score: 0.1907: : 282iter [00:47,  6.23iter/s]Best score: 0.1907: : 283iter [00:47,  6.26iter/s]Best score: 0.1907: : 283iter [00:47,  6.26iter/s]Best score: 0.1907: : 284iter [00:47,  6.27iter/s]Best score: 0.1907: : 284iter [00:47,  6.27iter/s]Best score: 0.1907: : 285iter [00:47,  6.29iter/s]Best score: 0.1907: : 285iter [00:47,  6.29iter/s]Best score: 0.1907: : 286iter [00:47,  6.30iter/s]Best score: 0.1907: : 286iter [00:47,  6.30iter/s]Best score: 0.1907: : 287iter [00:47,  6.31iter/s]Best score: 0.1907: : 287iter [00:48,  6.31iter/s]Best score: 0.1907: : 288iter [00:48,  6.32iter/s]Best score: 0.1907: : 288iter [00:48,  6.32iter/s]Best score: 0.1907: : 289iter [00:48,  6.32iter/s]Best score: 0.1897: : 289iter [00:48,  6.32iter/s]Best score: 0.1897: : 290iter [00:48,  6.14iter/s]Best score: 0.1897: : 290iter [00:48,  6.14iter/s]Best score: 0.1897: : 291iter [00:48,  6.19iter/s]Best score: 0.1897: : 291iter [00:48,  6.19iter/s]Best score: 0.1897: : 292iter [00:48,  6.23iter/s]Best score: 0.1897: : 292iter [00:48,  6.23iter/s]Best score: 0.1897: : 293iter [00:48,  6.27iter/s]Best score: 0.1897: : 293iter [00:49,  6.27iter/s]Best score: 0.1897: : 294iter [00:49,  6.29iter/s]Best score: 0.1897: : 294iter [00:49,  6.29iter/s]Best score: 0.1897: : 295iter [00:49,  6.30iter/s]Best score: 0.1897: : 295iter [00:49,  6.30iter/s]Best score: 0.1897: : 296iter [00:49,  6.30iter/s]Best score: 0.1897: : 296iter [00:49,  6.30iter/s]Best score: 0.1897: : 297iter [00:49,  6.32iter/s]Best score: 0.1897: : 297iter [00:49,  6.32iter/s]Best score: 0.1897: : 298iter [00:49,  6.34iter/s]Best score: 0.1897: : 298iter [00:49,  6.34iter/s]Best score: 0.1897: : 299iter [00:49,  6.33iter/s]
[ITER 300] Best: 0.1893
[PARAMS] Current best parameters:
  fx: 508.3161
  fy: 508.4729
  px: 904.2240
  py: 917.6636
  k1: 0.01176044
  k2: 0.02403869
  k3: -0.01801391
  k4: 0.00354701
  p1: -0.00923604
  p2: -0.00719913
  p3: 0.00066185
  p4: -0.00000095
  scale: 2.9865
  rotation: -0.4913
Best score: 0.1893: : 299iter [00:50,  6.33iter/s]Best score: 0.1893: : 300iter [00:50,  6.13iter/s]Best score: 0.1893: : 300iter [00:50,  6.13iter/s]Best score: 0.1893: : 301iter [00:50,  6.18iter/s]Best score: 0.1893: : 301iter [00:50,  6.18iter/s]Best score: 0.1893: : 302iter [00:50,  6.22iter/s]Best score: 0.1893: : 302iter [00:50,  6.22iter/s]Best score: 0.1893: : 303iter [00:50,  6.25iter/s]Best score: 0.1893: : 303iter [00:50,  6.25iter/s]Best score: 0.1893: : 304iter [00:50,  6.28iter/s]Best score: 0.1893: : 304iter [00:50,  6.28iter/s]Best score: 0.1893: : 305iter [00:50,  6.30iter/s]Best score: 0.1893: : 305iter [00:50,  6.30iter/s]Best score: 0.1893: : 306iter [00:50,  6.31iter/s]Best score: 0.1893: : 306iter [00:51,  6.31iter/s]Best score: 0.1893: : 307iter [00:51,  6.32iter/s]Best score: 0.1893: : 307iter [00:51,  6.32iter/s]Best score: 0.1893: : 308iter [00:51,  6.31iter/s]Best score: 0.1893: : 308iter [00:51,  6.31iter/s]Best score: 0.1893: : 309iter [00:51,  6.31iter/s]Best score: 0.1893: : 309iter [00:51,  6.31iter/s]Best score: 0.1893: : 310iter [00:51,  6.12iter/s]Best score: 0.1893: : 310iter [00:51,  6.12iter/s]Best score: 0.1893: : 311iter [00:51,  6.17iter/s]Best score: 0.1893: : 311iter [00:51,  6.17iter/s]Best score: 0.1893: : 312iter [00:51,  6.23iter/s]Best score: 0.1893: : 312iter [00:52,  6.23iter/s]Best score: 0.1893: : 313iter [00:52,  6.26iter/s]Best score: 0.1893: : 313iter [00:52,  6.26iter/s]Best score: 0.1893: : 314iter [00:52,  6.28iter/s]Best score: 0.1893: : 314iter [00:52,  6.28iter/s]Best score: 0.1893: : 315iter [00:52,  6.28iter/s]Best score: 0.1893: : 315iter [00:52,  6.28iter/s]Best score: 0.1893: : 316iter [00:52,  6.30iter/s]Best score: 0.1893: : 316iter [00:52,  6.30iter/s]Best score: 0.1893: : 317iter [00:52,  6.29iter/s]Best score: 0.1893: : 317iter [00:52,  6.29iter/s]Best score: 0.1893: : 318iter [00:52,  6.30iter/s]Best score: 0.1893: : 318iter [00:53,  6.30iter/s]Best score: 0.1893: : 319iter [00:53,  6.30iter/s]Best score: 0.1886: : 319iter [00:53,  6.30iter/s]Best score: 0.1886: : 320iter [00:53,  6.12iter/s]Best score: 0.1886: : 320iter [00:53,  6.12iter/s]Best score: 0.1886: : 321iter [00:53,  6.18iter/s]Best score: 0.1886: : 321iter [00:53,  6.18iter/s]Best score: 0.1886: : 322iter [00:53,  6.22iter/s]Best score: 0.1886: : 322iter [00:53,  6.22iter/s]Best score: 0.1886: : 323iter [00:53,  6.22iter/s]Best score: 0.1886: : 323iter [00:53,  6.22iter/s]Best score: 0.1886: : 324iter [00:53,  6.24iter/s]Best score: 0.1886: : 324iter [00:53,  6.24iter/s]Best score: 0.1886: : 325iter [00:53,  6.26iter/s]Best score: 0.1886: : 325iter [00:54,  6.26iter/s]Best score: 0.1886: : 326iter [00:54,  6.27iter/s]Best score: 0.1886: : 326iter [00:54,  6.27iter/s]Best score: 0.1886: : 327iter [00:54,  6.29iter/s]Best score: 0.1886: : 327iter [00:54,  6.29iter/s]Best score: 0.1886: : 328iter [00:54,  6.31iter/s]Best score: 0.1886: : 328iter [00:54,  6.31iter/s]Best score: 0.1886: : 329iter [00:54,  6.32iter/s]Best score: 0.1886: : 329iter [00:54,  6.32iter/s]Best score: 0.1886: : 330iter [00:54,  4.67iter/s]Best score: 0.1886: : 330iter [00:55,  4.67iter/s]Best score: 0.1886: : 331iter [00:55,  5.07iter/s]Best score: 0.1886: : 331iter [00:55,  5.07iter/s]Best score: 0.1886: : 332iter [00:55,  5.39iter/s]Best score: 0.1886: : 332iter [00:55,  5.39iter/s]Best score: 0.1886: : 333iter [00:55,  5.63iter/s]Best score: 0.1886: : 333iter [00:55,  5.63iter/s]Best score: 0.1886: : 334iter [00:55,  5.82iter/s]Best score: 0.1886: : 334iter [00:55,  5.82iter/s]Best score: 0.1886: : 335iter [00:55,  5.95iter/s]Best score: 0.1886: : 335iter [00:55,  5.95iter/s]Best score: 0.1886: : 336iter [00:55,  6.06iter/s]Best score: 0.1886: : 336iter [00:56,  6.06iter/s]Best score: 0.1886: : 337iter [00:56,  6.14iter/s]Best score: 0.1886: : 337iter [00:56,  6.14iter/s]Best score: 0.1886: : 338iter [00:56,  6.19iter/s]Best score: 0.1886: : 338iter [00:56,  6.19iter/s]Best score: 0.1886: : 339iter [00:56,  6.22iter/s]Best score: 0.1880: : 339iter [00:56,  6.22iter/s]Best score: 0.1880: : 340iter [00:56,  6.06iter/s]Best score: 0.1880: : 340iter [00:56,  6.06iter/s]Best score: 0.1880: : 341iter [00:56,  6.14iter/s]Best score: 0.1880: : 341iter [00:56,  6.14iter/s]Best score: 0.1880: : 342iter [00:56,  6.20iter/s]Best score: 0.1880: : 342iter [00:57,  6.20iter/s]Best score: 0.1880: : 343iter [00:57,  6.24iter/s]Best score: 0.1880: : 343iter [00:57,  6.24iter/s]Best score: 0.1880: : 344iter [00:57,  6.26iter/s]Best score: 0.1880: : 344iter [00:57,  6.26iter/s]Best score: 0.1880: : 345iter [00:57,  6.28iter/s]Best score: 0.1880: : 345iter [00:57,  6.28iter/s]Best score: 0.1880: : 346iter [00:57,  6.30iter/s]Best score: 0.1880: : 346iter [00:57,  6.30iter/s]Best score: 0.1880: : 347iter [00:57,  6.32iter/s]Best score: 0.1880: : 347iter [00:57,  6.32iter/s]Best score: 0.1880: : 348iter [00:57,  6.31iter/s]Best score: 0.1880: : 348iter [00:57,  6.31iter/s]Best score: 0.1880: : 349iter [00:57,  6.32iter/s]
[ITER 350] Best: 0.1878
[PARAMS] Current best parameters:
  fx: 509.8767
  fy: 509.9811
  px: 904.0800
  py: 917.3243
  k1: 0.01400629
  k2: 0.01931140
  k3: -0.01620793
  k4: 0.00340694
  p1: -0.00995684
  p2: -0.00753080
  p3: 0.00097764
  p4: -0.00001263
  scale: 2.9818
  rotation: -0.4958
Best score: 0.1878: : 349iter [00:58,  6.32iter/s]Best score: 0.1878: : 350iter [00:58,  6.13iter/s]Best score: 0.1878: : 350iter [00:58,  6.13iter/s]Best score: 0.1878: : 351iter [00:58,  6.19iter/s]Best score: 0.1878: : 351iter [00:58,  6.19iter/s]Best score: 0.1878: : 352iter [00:58,  6.24iter/s]Best score: 0.1878: : 352iter [00:58,  6.24iter/s]Best score: 0.1878: : 353iter [00:58,  6.27iter/s]Best score: 0.1878: : 353iter [00:58,  6.27iter/s]Best score: 0.1878: : 354iter [00:58,  6.29iter/s]Best score: 0.1878: : 354iter [00:58,  6.29iter/s]Best score: 0.1878: : 355iter [00:58,  6.30iter/s]Best score: 0.1878: : 355iter [00:59,  6.30iter/s]Best score: 0.1878: : 356iter [00:59,  6.31iter/s]Best score: 0.1878: : 356iter [00:59,  6.31iter/s]Best score: 0.1878: : 357iter [00:59,  6.31iter/s]Best score: 0.1878: : 357iter [00:59,  6.31iter/s]Best score: 0.1878: : 358iter [00:59,  6.31iter/s]Best score: 0.1878: : 358iter [00:59,  6.31iter/s]Best score: 0.1878: : 359iter [00:59,  6.31iter/s]Best score: 0.1875: : 359iter [00:59,  6.31iter/s]Best score: 0.1875: : 360iter [00:59,  6.13iter/s]Best score: 0.1875: : 360iter [00:59,  6.13iter/s]Best score: 0.1875: : 361iter [00:59,  6.20iter/s]Best score: 0.1875: : 361iter [01:00,  6.20iter/s]Best score: 0.1875: : 362iter [01:00,  6.25iter/s]Best score: 0.1875: : 362iter [01:00,  6.25iter/s]Best score: 0.1875: : 363iter [01:00,  6.28iter/s]Best score: 0.1875: : 363iter [01:00,  6.28iter/s]Best score: 0.1875: : 364iter [01:00,  6.29iter/s]Best score: 0.1875: : 364iter [01:00,  6.29iter/s]Best score: 0.1875: : 365iter [01:00,  6.29iter/s]Best score: 0.1875: : 365iter [01:00,  6.29iter/s]Best score: 0.1875: : 366iter [01:00,  6.31iter/s]Best score: 0.1875: : 366iter [01:00,  6.31iter/s]Best score: 0.1875: : 367iter [01:00,  6.31iter/s]Best score: 0.1875: : 367iter [01:01,  6.31iter/s]Best score: 0.1875: : 368iter [01:01,  6.31iter/s]Best score: 0.1875: : 368iter [01:01,  6.31iter/s]Best score: 0.1875: : 369iter [01:01,  6.29iter/s]Best score: 0.1874: : 369iter [01:01,  6.29iter/s]Best score: 0.1874: : 370iter [01:01,  6.11iter/s]Best score: 0.1874: : 370iter [01:01,  6.11iter/s]Best score: 0.1874: : 371iter [01:01,  6.16iter/s]Best score: 0.1874: : 371iter [01:01,  6.16iter/s]Best score: 0.1874: : 372iter [01:01,  6.20iter/s]Best score: 0.1874: : 372iter [01:01,  6.20iter/s]Best score: 0.1874: : 373iter [01:01,  6.23iter/s]Best score: 0.1874: : 373iter [01:01,  6.23iter/s]Best score: 0.1874: : 374iter [01:01,  6.25iter/s]Best score: 0.1874: : 374iter [01:02,  6.25iter/s]Best score: 0.1874: : 375iter [01:02,  6.27iter/s]Best score: 0.1874: : 375iter [01:02,  6.27iter/s]Best score: 0.1874: : 376iter [01:02,  6.29iter/s]Best score: 0.1874: : 376iter [01:02,  6.29iter/s]Best score: 0.1874: : 377iter [01:02,  6.31iter/s]Best score: 0.1874: : 377iter [01:02,  6.31iter/s]Best score: 0.1874: : 378iter [01:02,  6.32iter/s]Best score: 0.1874: : 378iter [01:02,  6.32iter/s]Best score: 0.1874: : 379iter [01:02,  6.32iter/s]Best score: 0.1874: : 379iter [01:02,  6.32iter/s]Best score: 0.1874: : 380iter [01:02,  6.12iter/s]Best score: 0.1874: : 380iter [01:03,  6.12iter/s]Best score: 0.1874: : 381iter [01:03,  6.19iter/s]Best score: 0.1874: : 381iter [01:03,  6.19iter/s]Best score: 0.1874: : 382iter [01:03,  6.22iter/s]Best score: 0.1874: : 382iter [01:03,  6.22iter/s]Best score: 0.1874: : 383iter [01:03,  6.24iter/s]Best score: 0.1874: : 383iter [01:03,  6.24iter/s]Best score: 0.1874: : 384iter [01:03,  6.27iter/s]Best score: 0.1874: : 384iter [01:03,  6.27iter/s]Best score: 0.1874: : 385iter [01:03,  6.28iter/s]Best score: 0.1874: : 385iter [01:03,  6.28iter/s]Best score: 0.1874: : 386iter [01:03,  6.28iter/s]Best score: 0.1874: : 386iter [01:04,  6.28iter/s]Best score: 0.1874: : 387iter [01:04,  6.28iter/s]Best score: 0.1874: : 387iter [01:04,  6.28iter/s]Best score: 0.1874: : 388iter [01:04,  6.29iter/s]Best score: 0.1874: : 388iter [01:04,  6.29iter/s]Best score: 0.1874: : 389iter [01:04,  6.29iter/s]Best score: 0.1872: : 389iter [01:04,  6.29iter/s]Best score: 0.1872: : 390iter [01:04,  6.09iter/s]Best score: 0.1872: : 390iter [01:04,  6.09iter/s]Best score: 0.1872: : 391iter [01:04,  6.16iter/s]Best score: 0.1872: : 391iter [01:04,  6.16iter/s]Best score: 0.1872: : 392iter [01:04,  6.22iter/s]Best score: 0.1872: : 392iter [01:05,  6.22iter/s]Best score: 0.1872: : 393iter [01:05,  6.25iter/s]Best score: 0.1872: : 393iter [01:05,  6.25iter/s]Best score: 0.1872: : 394iter [01:05,  6.26iter/s]Best score: 0.1872: : 394iter [01:05,  6.26iter/s]Best score: 0.1872: : 395iter [01:05,  6.27iter/s]Best score: 0.1872: : 395iter [01:05,  6.27iter/s]Best score: 0.1872: : 396iter [01:05,  6.27iter/s]Best score: 0.1872: : 396iter [01:05,  6.27iter/s]Best score: 0.1872: : 397iter [01:05,  6.29iter/s]Best score: 0.1872: : 397iter [01:05,  6.29iter/s]Best score: 0.1872: : 398iter [01:05,  6.30iter/s]Best score: 0.1872: : 398iter [01:05,  6.30iter/s]Best score: 0.1872: : 399iter [01:05,  6.31iter/s]
[ITER 400] Best: 0.1870
[PARAMS] Current best parameters:
  fx: 508.6912
  fy: 508.8272
  px: 904.1743
  py: 917.3680
  k1: 0.01735322
  k2: 0.01564994
  k3: -0.01383475
  k4: 0.00283233
  p1: -0.00987477
  p2: -0.00732681
  p3: 0.00096658
  p4: -0.00002058
  scale: 2.9803
  rotation: -0.4998
Best score: 0.1870: : 399iter [01:06,  6.31iter/s]Best score: 0.1870: : 400iter [01:06,  6.11iter/s]Best score: 0.1870: : 400iter [01:06,  6.11iter/s]Best score: 0.1870: : 401iter [01:06,  6.17iter/s]Best score: 0.1870: : 401iter [01:06,  6.17iter/s]Best score: 0.1870: : 402iter [01:06,  6.20iter/s]Best score: 0.1870: : 402iter [01:06,  6.20iter/s]Best score: 0.1870: : 403iter [01:06,  6.24iter/s]Best score: 0.1870: : 403iter [01:06,  6.24iter/s]Best score: 0.1870: : 404iter [01:06,  6.27iter/s]Best score: 0.1870: : 404iter [01:06,  6.27iter/s]Best score: 0.1870: : 405iter [01:06,  6.28iter/s]Best score: 0.1870: : 405iter [01:07,  6.28iter/s]Best score: 0.1870: : 406iter [01:07,  6.29iter/s]Best score: 0.1870: : 406iter [01:07,  6.29iter/s]Best score: 0.1870: : 407iter [01:07,  6.29iter/s]Best score: 0.1870: : 407iter [01:07,  6.29iter/s]Best score: 0.1870: : 408iter [01:07,  6.29iter/s]Best score: 0.1870: : 408iter [01:07,  6.29iter/s]Best score: 0.1870: : 409iter [01:07,  6.30iter/s]Best score: 0.1870: : 409iter [01:07,  6.30iter/s]Best score: 0.1870: : 410iter [01:07,  6.12iter/s]Best score: 0.1870: : 410iter [01:07,  6.12iter/s]Best score: 0.1870: : 411iter [01:07,  6.18iter/s]Best score: 0.1870: : 411iter [01:08,  6.18iter/s]Best score: 0.1870: : 412iter [01:08,  6.21iter/s]Best score: 0.1870: : 412iter [01:08,  6.21iter/s]Best score: 0.1870: : 413iter [01:08,  6.23iter/s]Best score: 0.1870: : 413iter [01:08,  6.23iter/s]Best score: 0.1870: : 414iter [01:08,  6.26iter/s]Best score: 0.1870: : 414iter [01:08,  6.26iter/s]Best score: 0.1870: : 415iter [01:08,  6.28iter/s]Best score: 0.1870: : 415iter [01:08,  6.28iter/s]Best score: 0.1870: : 416iter [01:08,  6.30iter/s]Best score: 0.1870: : 416iter [01:08,  6.30iter/s]Best score: 0.1870: : 417iter [01:08,  6.29iter/s]Best score: 0.1870: : 417iter [01:09,  6.29iter/s]Best score: 0.1870: : 418iter [01:09,  6.29iter/s]Best score: 0.1870: : 418iter [01:09,  6.29iter/s]Best score: 0.1870: : 419iter [01:09,  6.30iter/s]Best score: 0.1870: : 419iter [01:09,  6.30iter/s]Best score: 0.1870: : 420iter [01:09,  6.10iter/s]Best score: 0.1870: : 420iter [01:09,  6.10iter/s]Best score: 0.1870: : 421iter [01:09,  6.16iter/s]Best score: 0.1870: : 421iter [01:09,  6.16iter/s]Best score: 0.1870: : 422iter [01:09,  6.21iter/s]Best score: 0.1870: : 422iter [01:09,  6.21iter/s]Best score: 0.1870: : 423iter [01:09,  6.23iter/s]Best score: 0.1870: : 423iter [01:10,  6.23iter/s]Best score: 0.1870: : 424iter [01:10,  6.25iter/s]Best score: 0.1870: : 424iter [01:10,  6.25iter/s]Best score: 0.1870: : 425iter [01:10,  6.28iter/s]Best score: 0.1870: : 425iter [01:10,  6.28iter/s]Best score: 0.1870: : 426iter [01:10,  6.30iter/s]Best score: 0.1870: : 426iter [01:10,  6.30iter/s]Best score: 0.1870: : 427iter [01:10,  6.29iter/s]Best score: 0.1870: : 427iter [01:10,  6.29iter/s]Best score: 0.1870: : 428iter [01:10,  6.30iter/s]Best score: 0.1870: : 428iter [01:10,  6.30iter/s]Best score: 0.1870: : 429iter [01:10,  6.32iter/s]Best score: 0.1869: : 429iter [01:10,  6.32iter/s]Best score: 0.1869: : 430iter [01:10,  6.13iter/s]Best score: 0.1869: : 430iter [01:11,  6.13iter/s]Best score: 0.1869: : 431iter [01:11,  6.18iter/s]Best score: 0.1869: : 431iter [01:11,  6.18iter/s]Best score: 0.1869: : 432iter [01:11,  6.22iter/s]Best score: 0.1869: : 432iter [01:11,  6.22iter/s]Best score: 0.1869: : 433iter [01:11,  6.24iter/s]Best score: 0.1869: : 433iter [01:11,  6.24iter/s]Best score: 0.1869: : 434iter [01:11,  6.26iter/s]Best score: 0.1869: : 434iter [01:11,  6.26iter/s]Best score: 0.1869: : 435iter [01:11,  6.28iter/s]Best score: 0.1869: : 435iter [01:11,  6.28iter/s]Best score: 0.1869: : 436iter [01:11,  6.30iter/s]Best score: 0.1869: : 436iter [01:12,  6.30iter/s]Best score: 0.1869: : 437iter [01:12,  6.31iter/s]Best score: 0.1869: : 437iter [01:12,  6.31iter/s]Best score: 0.1869: : 438iter [01:12,  6.30iter/s]Best score: 0.1869: : 438iter [01:12,  6.30iter/s]Best score: 0.1869: : 439iter [01:12,  6.29iter/s]Best score: 0.1868: : 439iter [01:12,  6.29iter/s]Best score: 0.1868: : 440iter [01:12,  6.10iter/s]Best score: 0.1868: : 440iter [01:12,  6.10iter/s]Best score: 0.1868: : 441iter [01:12,  6.16iter/s]Best score: 0.1868: : 441iter [01:12,  6.16iter/s]Best score: 0.1868: : 442iter [01:12,  6.19iter/s]Best score: 0.1868: : 442iter [01:13,  6.19iter/s]Best score: 0.1868: : 443iter [01:13,  6.23iter/s]Best score: 0.1868: : 443iter [01:13,  6.23iter/s]Best score: 0.1868: : 444iter [01:13,  6.24iter/s]Best score: 0.1868: : 444iter [01:13,  6.24iter/s]Best score: 0.1868: : 445iter [01:13,  6.27iter/s]Best score: 0.1868: : 445iter [01:13,  6.27iter/s]Best score: 0.1868: : 446iter [01:13,  6.27iter/s]Best score: 0.1868: : 446iter [01:13,  6.27iter/s]Best score: 0.1868: : 447iter [01:13,  6.28iter/s]Best score: 0.1868: : 447iter [01:13,  6.28iter/s]Best score: 0.1868: : 448iter [01:13,  6.28iter/s]Best score: 0.1868: : 448iter [01:14,  6.28iter/s]Best score: 0.1868: : 449iter [01:14,  6.29iter/s]
[ITER 450] Best: 0.1868
[PARAMS] Current best parameters:
  fx: 506.5863
  fy: 506.7128
  px: 904.1525
  py: 917.3443
  k1: 0.02403950
  k2: 0.00795364
  k3: -0.00907927
  k4: 0.00177055
  p1: -0.00988955
  p2: -0.00736796
  p3: 0.00095333
  p4: 0.00002970
  scale: 2.9777
  rotation: -0.5001
Best score: 0.1868: : 449iter [01:14,  6.29iter/s]Best score: 0.1868: : 450iter [01:14,  6.10iter/s]Best score: 0.1868: : 450iter [01:14,  6.10iter/s]Best score: 0.1868: : 451iter [01:14,  6.16iter/s]Best score: 0.1868: : 451iter [01:14,  6.16iter/s]Best score: 0.1868: : 452iter [01:14,  6.20iter/s]Best score: 0.1868: : 452iter [01:14,  6.20iter/s]Best score: 0.1868: : 453iter [01:14,  6.23iter/s]Best score: 0.1868: : 453iter [01:14,  6.23iter/s]Best score: 0.1868: : 454iter [01:14,  6.25iter/s]Best score: 0.1868: : 454iter [01:14,  6.25iter/s]Best score: 0.1868: : 455iter [01:14,  6.24iter/s]Best score: 0.1868: : 455iter [01:15,  6.24iter/s]Best score: 0.1868: : 456iter [01:15,  6.27iter/s]Best score: 0.1868: : 456iter [01:15,  6.27iter/s]Best score: 0.1868: : 457iter [01:15,  6.29iter/s]Best score: 0.1868: : 457iter [01:15,  6.29iter/s]Best score: 0.1868: : 458iter [01:15,  6.29iter/s]Best score: 0.1868: : 458iter [01:15,  6.29iter/s]Best score: 0.1868: : 459iter 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[01:21,  6.29iter/s]Best score: 0.1867: : 498iter [01:22,  6.29iter/s]Best score: 0.1867: : 499iter [01:22,  6.29iter/s]
[ITER 500] Best: 0.1867
[PARAMS] Current best parameters:
  fx: 505.3059
  fy: 505.4213
  px: 904.1864
  py: 917.3072
  k1: 0.02703612
  k2: 0.00548964
  k3: -0.00770465
  k4: 0.00146760
  p1: -0.00993655
  p2: -0.00727259
  p3: 0.00098704
  p4: 0.00000352
  scale: 2.9762
  rotation: -0.5013
Best score: 0.1867: : 499iter [01:22,  6.29iter/s]Best score: 0.1867: : 500iter [01:22,  6.10iter/s]Best score: 0.1867: : 500iter [01:22,  6.08iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.186699 -> 0.186687
  Success: True
  Tangential distortion: p1=-0.009937, p2=-0.007273, p3=0.000987, p4=0.000003
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1867
[✅] Parameters copied to: /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 505.3058624496961
    fy: 505.42128414693116
    px: 904.1863529598505
    py: 917.3072197548896
    k1: 0.027036251860596803
    k2: 0.005490115146901914
    k3: -0.00770426201006324
    k4: 0.001467759502447246
    p1: -0.009936559909079379
    p2: -0.007272616461993544
    p3: 0.0009869631869044975
    p4: 3.439957511939326e-06
    scale: 2.9762194034872853
    rotation: -0.501323907358852
    mmpp: 3.4044547559378597
    nmpp: 3404454.75593786

[🔄] Converting 1580 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/3_undistorted/output/ptd
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                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.00rig/s]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.00rig/s]
[✅] Saved PTDs: /working/MDCP3D-SL-012_W039(6)_2026-03-04/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.
📤 Writing LUT:   0%|          | 0/3240000 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 86030/3240000 [00:00<00:03, 860294.67px/s]📤 Writing LUT:   6%|▌         | 181013/3240000 [00:00<00:03, 912960.33px/s]📤 Writing LUT:   8%|▊         | 274865/3240000 [00:00<00:03, 924626.75px/s]📤 Writing LUT:  11%|█▏        | 367594/3240000 [00:00<00:03, 925675.90px/s]📤 Writing LUT:  14%|█▍        | 460290/3240000 [00:00<00:03, 926138.48px/s]📤 Writing LUT:  17%|█▋        | 553502/3240000 [00:00<00:02, 928169.48px/s]📤 Writing LUT:  20%|██        | 648586/3240000 [00:00<00:02, 935577.62px/s]📤 Writing LUT:  23%|██▎       | 743401/3240000 [00:00<00:02, 939577.48px/s]📤 Writing LUT:  26%|██▌       | 837359/3240000 [00:00<00:02, 936259.71px/s]📤 Writing LUT:  29%|██▉       | 931880/3240000 [00:01<00:02, 939014.29px/s]📤 Writing LUT:  32%|███▏      | 1025784/3240000 [00:01<00:02, 936767.83px/s]📤 Writing LUT:  35%|███▍      | 1121966/3240000 [00:01<00:02, 944365.43px/s]📤 Writing LUT:  38%|███▊      | 1217915/3240000 [00:01<00:02, 948937.65px/s]📤 Writing LUT:  41%|████      | 1314150/3240000 [00:01<00:02, 952981.41px/s]📤 Writing LUT:  44%|████▎     | 1410242/3240000 [00:01<00:01, 955371.61px/s]📤 Writing LUT:  46%|████▋     | 1505782/3240000 [00:01<00:01, 954624.61px/s]📤 Writing LUT:  49%|████▉     | 1601247/3240000 [00:01<00:01, 949446.98px/s]📤 Writing LUT:  52%|█████▏    | 1697044/3240000 [00:01<00:01, 951995.38px/s]📤 Writing LUT:  55%|█████▌    | 1792250/3240000 [00:01<00:01, 950132.98px/s]📤 Writing LUT:  58%|█████▊    | 1889054/3240000 [00:02<00:01, 955488.91px/s]📤 Writing LUT:  61%|██████▏   | 1985750/3240000 [00:02<00:01, 958920.32px/s]📤 Writing LUT:  64%|██████▍   | 2081647/3240000 [00:02<00:01, 958339.27px/s]📤 Writing LUT:  67%|██████▋   | 2178545/3240000 [00:02<00:01, 961525.23px/s]📤 Writing LUT:  70%|███████   | 2275069/3240000 [00:02<00:01, 962636.09px/s]📤 Writing LUT:  73%|███████▎  | 2371335/3240000 [00:02<00:00, 954954.43px/s]📤 Writing LUT:  76%|███████▌  | 2466846/3240000 [00:02<00:00, 951499.92px/s]📤 Writing LUT:  79%|███████▉  | 2562817/3240000 [00:02<00:00, 953945.03px/s]📤 Writing LUT:  82%|████████▏ | 2658579/3240000 [00:02<00:00, 955041.15px/s]📤 Writing LUT:  85%|████████▌ | 2754091/3240000 [00:02<00:00, 947502.22px/s]📤 Writing LUT:  88%|████████▊ | 2849797/3240000 [00:03<00:00, 950345.81px/s]📤 Writing LUT:  91%|█████████ | 2944846/3240000 [00:03<00:00, 948302.82px/s]📤 Writing LUT:  94%|█████████▍| 3039862/3240000 [00:03<00:00, 948854.52px/s]📤 Writing LUT:  97%|█████████▋| 3135093/3240000 [00:03<00:00, 949886.81px/s]📤 Writing LUT: 100%|█████████▉| 3231453/3240000 [00:03<00:00, 953989.39px/s]📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 947044.10px/s]
[✅] LUT written to: /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-SL-012_W039(6)_2026-03-04' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-SL-012_W039(6)_2026-03-04/calibration/MDCP3D-SL-012_W039(6)_2026-03-04_unknown.lut
[🏁] Finished processing MDCP3D-SL-012_W039(6)_2026-03-04


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