
🟢 Starting Calibration Pipeline

=== Processing MDC028_W009(6)_2024-07-23 ===

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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 278 stationary frames, 517 remain

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                                                                           [A[PASS2] Frame 0000443: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000444: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000445: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000446: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000447: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000448: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000449: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000450: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000451: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000453: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000455: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000456: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000457: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000458: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000463: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000469: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000470: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000471: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000472: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000473: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000474: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000475: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000479: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000480: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000481: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000482: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000484: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000485: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000486: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000491: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000492: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000496: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000497: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000498: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000499: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000500: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000501: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000502: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000503: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000506: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000507: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000508: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000509: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000510: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000511: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000512: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000513: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000514: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000515: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000516: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000517: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000520: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000522: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000523: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000524: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000525: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000526: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000527: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000528: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000529: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000530: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000531: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000532: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000533: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000534: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000535: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000537: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000538: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000539: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000445: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000447: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000449: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000452: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000457: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000466: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000470: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000472: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000474: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000481: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000486: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000488: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000491: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000498: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000500: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000503: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000507: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000508: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000509: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000513: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000516: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000522: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000526: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000528: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000532: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000535: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000536: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000537: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000538: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000506: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000507: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000508: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000537: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000538: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 58 frames fixed, 43 untrusted, 517 total

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                                                                            [AProcessing Rigs:  25%|██▌       | 1/4 [10:09<30:27, 609.20s/it][INFO] Filtered 193 stationary frames, 404 remain

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                                                                          [A[PASS2] Frame 0000084: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000085: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000086: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000087: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000585: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000586: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000587: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000086: fixed corners {3} (was_fixed=True, trusted=True)

[PASS2] Frame 0000587: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 5 untrusted, 404 total
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                                                                           [AProcessing Rigs:  50%|█████     | 2/4 [18:39<18:22, 551.24s/it][INFO] Filtered 477 stationary frames, 612 remain

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                                                                           [A[PASS2] Frame 0000543: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000544: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000545: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000546: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000547: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000548: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000549: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000550: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000557: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000558: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000559: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000560: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000569: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000570: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000572: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000573: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000574: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000575: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000578: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000579: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000586: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000587: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000589: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000590: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000591: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000592: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000595: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000545: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000549: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000559: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000574: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000580: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000591: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 17 frames fixed, 22 untrusted, 612 total

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                                                                            [AProcessing Rigs:  75%|███████▌  | 3/4 [30:49<10:32, 632.79s/it][INFO] Filtered 295 stationary frames, 239 remain

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                                                                          [A[PASS2] Frame 0000181: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000182: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000183: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000184: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000402: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000513: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000514: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000515: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000516: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000532: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000183: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000515: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 7 frames fixed, 4 untrusted, 239 total

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                                                                           [AProcessing Rigs: 100%|██████████| 4/4 [36:41<00:00, 521.90s/it]Processing Rigs: 100%|██████████| 4/4 [36:41<00:00, 550.40s/it]
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1772 frames from 4 rigs to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 2000L: 100 frames selected from 517
[📋] 1500: 100 frames selected from 404
[📋] 2000R: 100 frames selected from 612
[📋] 1000: 100 frames selected from 239
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:  10%|█         | 8/80 [00:00<00:00, 75.94it/s]Computing max image from bright frames:  21%|██▏       | 17/80 [00:00<00:00, 79.21it/s]Computing max image from bright frames:  32%|███▎      | 26/80 [00:00<00:00, 80.41it/s]Computing max image from bright frames:  44%|████▍     | 35/80 [00:00<00:00, 81.00it/s]Computing max image from bright frames:  55%|█████▌    | 44/80 [00:00<00:00, 81.29it/s]Computing max image from bright frames:  66%|██████▋   | 53/80 [00:00<00:00, 81.52it/s]Computing max image from bright frames:  78%|███████▊  | 62/80 [00:00<00:00, 81.60it/s]Computing max image from bright frames:  89%|████████▉ | 71/80 [00:00<00:00, 81.91it/s]Computing max image from bright frames: 100%|██████████| 80/80 [00:00<00:00, 81.97it/s]                                                                                       [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 22, black threshold: 66
[INFO] Found 5472 casing boundary candidate points
[RESULT] Detected casing: center=(1019, 766), radius=836, inliers=3182/5472
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.50it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.50it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.50it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   3%|▎         | 3/100 [00:00<00:10,  8.86it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:10,  8.86it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:10,  8.86it/s]Brute | Start: {'fx': 500.0} 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(0.001):  10%|█         | 10/100 [00:00<00:05, 15.19it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:05, 15.19it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  11%|█         | 11/100 [00:00<00:05, 15.19it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:05, 15.19it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:05, 15.19it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 19.53it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 19.53it/s] Brute | Start: {'fx': 500.0} (0.040) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:01<00:04, 19.53it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 19.53it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  17%|█▋        | 17/100 [00:01<00:04, 19.21it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:01<00:04, 19.21it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:01<00:04, 19.21it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  19%|█▉        | 19/100 [00:01<00:04, 18.37it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:04, 18.37it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:04, 18.37it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:04, 18.37it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  22%|██▏       | 22/100 [00:01<00:04, 18.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.012 | Best: {'fx': 455.556} (0.012):  22%|██▏       | 22/100 [00:01<00:04, 18.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:04, 18.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  24%|██▍       | 24/100 [00:01<00:04, 18.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.016 | Best: {'fx': 469.697} (0.016):  24%|██▍       | 24/100 [00:01<00:04, 18.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 476.768} (0.019):  25%|██▌       | 25/100 [00:01<00:04, 18.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 483.838} (0.023):  26%|██▌       | 26/100 [00:01<00:04, 18.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 483.838} (0.023):  27%|██▋       | 27/100 [00:01<00:03, 20.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.031 | Best: {'fx': 490.909} (0.031):  27%|██▋       | 27/100 [00:01<00:03, 20.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.038 | Best: {'fx': 497.98} (0.038):  28%|██▊       | 28/100 [00:01<00:03, 20.00it/s] Brute | Start: {'fx': 500.0} (0.040) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  29%|██▉       | 29/100 [00:01<00:03, 20.00it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  30%|███       | 30/100 [00:01<00:03, 20.02it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.056 | Best: {'fx': 512.121} (0.056):  30%|███       | 30/100 [00:01<00:03, 20.02it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.076 | Best: {'fx': 519.192} (0.076):  31%|███       | 31/100 [00:02<00:03, 20.02it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.107 | Best: {'fx': 526.263} (0.107):  32%|███▏      | 32/100 [00:02<00:03, 20.02it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.107 | Best: {'fx': 526.263} (0.107):  33%|███▎      | 33/100 [00:02<00:04, 13.80it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.099 | Best: {'fx': 526.263} (0.107):  33%|███▎      | 33/100 [00:02<00:04, 13.80it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.082 | Best: {'fx': 526.263} (0.107):  34%|███▍      | 34/100 [00:02<00:04, 13.80it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.065 | Best: {'fx': 526.263} (0.107):  35%|███▌      | 35/100 [00:02<00:04, 13.80it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.056 | Best: {'fx': 526.263} (0.107):  36%|███▌      | 36/100 [00:02<00:04, 13.80it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.056 | Best: {'fx': 526.263} (0.107):  37%|███▋      | 37/100 [00:02<00:03, 16.90it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.050 | Best: {'fx': 526.263} (0.107):  37%|███▋      | 37/100 [00:02<00:03, 16.90it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.041 | Best: {'fx': 526.263} (0.107):  38%|███▊      | 38/100 [00:02<00:03, 16.90it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.038 | Best: {'fx': 526.263} (0.107):  39%|███▉      | 39/100 [00:02<00:03, 16.90it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.038 | Best: {'fx': 526.263} (0.107):  40%|████      | 40/100 [00:02<00:03, 19.34it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.045 | Best: {'fx': 526.263} (0.107):  40%|████      | 40/100 [00:02<00:03, 19.34it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.035 | Best: {'fx': 526.263} (0.107):  41%|████      | 41/100 [00:02<00:03, 19.34it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.033 | Best: {'fx': 526.263} (0.107):  42%|████▏     | 42/100 [00:02<00:02, 19.34it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.033 | Best: {'fx': 526.263} (0.107):  43%|████▎     | 43/100 [00:02<00:02, 20.26it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.029 | Best: {'fx': 526.263} (0.107):  43%|████▎     | 43/100 [00:02<00:02, 20.26it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.030 | Best: {'fx': 526.263} (0.107):  44%|████▍     | 44/100 [00:02<00:02, 20.26it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.032 | Best: {'fx': 526.263} (0.107):  45%|████▌     | 45/100 [00:02<00:02, 20.26it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.032 | Best: {'fx': 526.263} (0.107):  46%|████▌     | 46/100 [00:02<00:02, 20.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.028 | Best: {'fx': 526.263} (0.107):  46%|████▌     | 46/100 [00:02<00:02, 20.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.027 | Best: {'fx': 526.263} (0.107):  47%|████▋     | 47/100 [00:02<00:02, 20.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.028 | Best: {'fx': 526.263} (0.107):  48%|████▊     | 48/100 [00:02<00:02, 20.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.028 | Best: {'fx': 526.263} (0.107):  49%|████▉     | 49/100 [00:02<00:02, 19.52it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.025 | Best: {'fx': 526.263} (0.107):  49%|████▉     | 49/100 [00:02<00:02, 19.52it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.026 | Best: {'fx': 526.263} (0.107):  50%|█████     | 50/100 [00:02<00:02, 19.52it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.025 | Best: {'fx': 526.263} (0.107):  51%|█████     | 51/100 [00:02<00:02, 19.52it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.025 | Best: {'fx': 526.263} (0.107):  52%|█████▏    | 52/100 [00:02<00:02, 21.73it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.024 | Best: {'fx': 526.263} (0.107):  52%|█████▏    | 52/100 [00:02<00:02, 21.73it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 526.263} (0.107):  53%|█████▎    | 53/100 [00:03<00:02, 21.73it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 526.263} (0.107):  54%|█████▍    | 54/100 [00:03<00:02, 21.73it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 526.263} (0.107):  55%|█████▌    | 55/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 526.263} (0.107):  55%|█████▌    | 55/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 526.263} (0.107):  56%|█████▌    | 56/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 526.263} (0.107):  57%|█████▋    | 57/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 526.263} (0.107):  58%|█████▊    | 58/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 526.263} (0.107):  59%|█████▉    | 59/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.024 | Best: {'fx': 526.263} (0.107):  60%|██████    | 60/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 526.263} (0.107):  61%|██████    | 61/100 [00:03<00:02, 18.48it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 526.263} (0.107):  62%|██████▏   | 62/100 [00:03<00:01, 27.81it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 526.263} (0.107):  62%|██████▏   | 62/100 [00:03<00:01, 27.81it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 526.263} (0.107):  63%|██████▎   | 63/100 [00:03<00:01, 27.81it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  64%|██████▍   | 64/100 [00:03<00:01, 27.81it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  65%|██████▌   | 65/100 [00:03<00:01, 27.81it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  66%|██████▌   | 66/100 [00:03<00:01, 28.83it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  66%|██████▌   | 66/100 [00:03<00:01, 28.83it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  67%|██████▋   | 67/100 [00:03<00:01, 28.83it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 526.263} (0.107):  68%|██████▊   | 68/100 [00:03<00:01, 28.83it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 526.263} (0.107):  69%|██████▉   | 69/100 [00:03<00:01, 28.83it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  70%|███████   | 70/100 [00:03<00:01, 28.83it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  71%|███████   | 71/100 [00:03<00:00, 32.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 526.263} (0.107):  71%|███████   | 71/100 [00:03<00:00, 32.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  72%|███████▏  | 72/100 [00:03<00:00, 32.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  73%|███████▎  | 73/100 [00:03<00:00, 32.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  74%|███████▍  | 74/100 [00:03<00:00, 32.07it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  75%|███████▌  | 75/100 [00:03<00:00, 31.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  75%|███████▌  | 75/100 [00:03<00:00, 31.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  76%|███████▌  | 76/100 [00:03<00:00, 31.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  77%|███████▋  | 77/100 [00:03<00:00, 31.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  78%|███████▊  | 78/100 [00:03<00:00, 31.97it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  79%|███████▉  | 79/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  79%|███████▉  | 79/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  80%|████████  | 80/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  81%|████████  | 81/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  82%|████████▏ | 82/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  83%|████████▎ | 83/100 [00:03<00:00, 28.35it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 526.263} (0.107):  83%|████████▎ | 83/100 [00:03<00:00, 28.35it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  84%|████████▍ | 84/100 [00:04<00:00, 28.35it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  85%|████████▌ | 85/100 [00:04<00:00, 28.35it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  86%|████████▌ | 86/100 [00:04<00:00, 25.95it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  86%|████████▌ | 86/100 [00:04<00:00, 25.95it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  87%|████████▋ | 87/100 [00:04<00:00, 25.95it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  88%|████████▊ | 88/100 [00:04<00:00, 25.95it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  89%|████████▉ | 89/100 [00:04<00:00, 23.39it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  89%|████████▉ | 89/100 [00:04<00:00, 23.39it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  90%|█████████ | 90/100 [00:04<00:00, 23.39it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  91%|█████████ | 91/100 [00:04<00:00, 23.39it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  92%|█████████▏| 92/100 [00:04<00:00, 23.39it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  93%|█████████▎| 93/100 [00:04<00:00, 23.39it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  94%|█████████▍| 94/100 [00:04<00:00, 27.08it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  94%|█████████▍| 94/100 [00:04<00:00, 27.08it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  95%|█████████▌| 95/100 [00:04<00:00, 27.08it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  96%|█████████▌| 96/100 [00:04<00:00, 27.08it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  97%|█████████▋| 97/100 [00:04<00:00, 18.86it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  97%|█████████▋| 97/100 [00:04<00:00, 18.86it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  98%|█████████▊| 98/100 [00:04<00:00, 18.86it/s]Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 526.263} (0.107):  99%|█████████▉| 99/100 [00:04<00:00, 18.86it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.041 | Best: {'fx': 501.263} (0.041):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.041 | Best: {'fx': 501.263} (0.041):   2%|▏         | 1/50 [00:00<00:28,  1.73it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.043 | Best: {'fx': 502.283} (0.043):   2%|▏         | 1/50 [00:00<00:28,  1.73it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.044 | Best: {'fx': 503.303} (0.044):   4%|▍         | 2/50 [00:00<00:27,  1.73it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.044 | Best: {'fx': 503.303} (0.044):   6%|▌         | 3/50 [00:00<00:08,  5.31it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.045 | Best: {'fx': 504.324} (0.045):   6%|▌         | 3/50 [00:00<00:08,  5.31it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.046 | Best: {'fx': 505.344} (0.046):   8%|▊         | 4/50 [00:00<00:08,  5.31it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.046 | Best: {'fx': 505.344} (0.046):  10%|█         | 5/50 [00:00<00:05,  8.42it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.047 | Best: {'fx': 506.365} (0.047):  10%|█         | 5/50 [00:00<00:05,  8.42it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.049 | Best: {'fx': 507.385} (0.049):  12%|█▏        | 6/50 [00:00<00:05,  8.42it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.049 | Best: {'fx': 507.385} (0.049):  14%|█▍        | 7/50 [00:00<00:04, 10.73it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.051 | Best: {'fx': 508.405} (0.051):  14%|█▍        | 7/50 [00:00<00:04, 10.73it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.051 | Best: {'fx': 509.426} (0.051):  16%|█▌        | 8/50 [00:01<00:03, 10.73it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.051 | Best: {'fx': 509.426} (0.051):  18%|█▊        | 9/50 [00:01<00:03, 12.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.054 | Best: {'fx': 510.446} (0.054):  18%|█▊        | 9/50 [00:01<00:03, 12.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.055 | Best: {'fx': 511.467} (0.055):  20%|██        | 10/50 [00:01<00:03, 12.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.057 | Best: {'fx': 512.487} (0.057):  22%|██▏       | 11/50 [00:01<00:03, 12.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.057 | Best: {'fx': 512.487} (0.057):  24%|██▍       | 12/50 [00:01<00:02, 16.64it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.062 | Best: {'fx': 514.528} (0.062):  24%|██▍       | 12/50 [00:01<00:02, 16.64it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.059 | Best: {'fx': 514.528} (0.062):  26%|██▌       | 13/50 [00:01<00:02, 16.64it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.071 | Best: {'fx': 517.589} (0.071):  28%|██▊       | 14/50 [00:01<00:02, 16.64it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.071 | Best: {'fx': 517.589} (0.071):  30%|███       | 15/50 [00:01<00:02, 13.99it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.065 | Best: {'fx': 517.589} (0.071):  30%|███       | 15/50 [00:01<00:02, 13.99it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.067 | Best: {'fx': 517.589} (0.071):  32%|███▏      | 16/50 [00:01<00:02, 13.99it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.074 | Best: {'fx': 518.61} (0.074):  34%|███▍      | 17/50 [00:01<00:02, 13.99it/s] Brute | Start: {'fx': 526.263} (0.107) | Current: 0.074 | Best: {'fx': 518.61} (0.074):  36%|███▌      | 18/50 [00:01<00:01, 16.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.080 | Best: {'fx': 520.65} (0.080):  36%|███▌      | 18/50 [00:01<00:01, 16.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.079 | Best: {'fx': 520.65} (0.080):  38%|███▊      | 19/50 [00:01<00:01, 16.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.086 | Best: {'fx': 521.671} (0.086):  40%|████      | 20/50 [00:01<00:01, 16.76it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.086 | Best: {'fx': 521.671} (0.086):  42%|████▏     | 21/50 [00:01<00:01, 19.61it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.091 | Best: {'fx': 522.691} (0.091):  42%|████▏     | 21/50 [00:01<00:01, 19.61it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.096 | Best: {'fx': 523.712} (0.096):  44%|████▍     | 22/50 [00:01<00:01, 19.61it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.106 | Best: {'fx': 525.752} (0.106):  46%|████▌     | 23/50 [00:01<00:01, 19.61it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.106 | Best: {'fx': 525.752} (0.106):  48%|████▊     | 24/50 [00:01<00:01, 17.70it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.101 | Best: {'fx': 525.752} (0.106):  48%|████▊     | 24/50 [00:01<00:01, 17.70it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.107 | Best: {'fx': 526.773} (0.107):  50%|█████     | 25/50 [00:01<00:01, 17.70it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.106 | Best: {'fx': 526.773} (0.107):  52%|█████▏    | 26/50 [00:01<00:01, 17.70it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.106 | Best: {'fx': 526.773} (0.107):  54%|█████▍    | 27/50 [00:01<00:01, 19.35it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.097 | Best: {'fx': 526.773} (0.107):  54%|█████▍    | 27/50 [00:02<00:01, 19.35it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.100 | Best: {'fx': 526.773} (0.107):  56%|█████▌    | 28/50 [00:02<00:01, 19.35it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.110 | Best: {'fx': 529.834} (0.110):  58%|█████▊    | 29/50 [00:02<00:01, 19.35it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.110 | Best: {'fx': 529.834} (0.110):  60%|██████    | 30/50 [00:02<00:00, 20.79it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.096 | Best: {'fx': 529.834} (0.110):  60%|██████    | 30/50 [00:02<00:00, 20.79it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.093 | Best: {'fx': 529.834} (0.110):  62%|██████▏   | 31/50 [00:02<00:00, 20.79it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.106 | Best: {'fx': 529.834} (0.110):  64%|██████▍   | 32/50 [00:02<00:00, 20.79it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.108 | Best: {'fx': 529.834} (0.110):  66%|██████▌   | 33/50 [00:02<00:00, 20.79it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.108 | Best: {'fx': 529.834} (0.110):  68%|██████▊   | 34/50 [00:02<00:00, 24.08it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.108 | Best: {'fx': 529.834} (0.110):  68%|██████▊   | 34/50 [00:02<00:00, 24.08it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.082 | Best: {'fx': 529.834} (0.110):  70%|███████   | 35/50 [00:02<00:00, 24.08it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.091 | Best: {'fx': 529.834} (0.110):  72%|███████▏  | 36/50 [00:02<00:00, 24.08it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.090 | Best: {'fx': 529.834} (0.110):  74%|███████▍  | 37/50 [00:02<00:00, 24.08it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.086 | Best: {'fx': 529.834} (0.110):  76%|███████▌  | 38/50 [00:02<00:00, 24.08it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.086 | Best: {'fx': 529.834} (0.110):  78%|███████▊  | 39/50 [00:02<00:00, 28.94it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.079 | Best: {'fx': 529.834} (0.110):  78%|███████▊  | 39/50 [00:02<00:00, 28.94it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.076 | Best: {'fx': 529.834} (0.110):  80%|████████  | 40/50 [00:02<00:00, 28.94it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.077 | Best: {'fx': 529.834} (0.110):  82%|████████▏ | 41/50 [00:02<00:00, 28.94it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.073 | Best: {'fx': 529.834} (0.110):  84%|████████▍ | 42/50 [00:02<00:00, 28.94it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.073 | Best: {'fx': 529.834} (0.110):  86%|████████▌ | 43/50 [00:02<00:00, 30.60it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.071 | Best: {'fx': 529.834} (0.110):  86%|████████▌ | 43/50 [00:02<00:00, 30.60it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.069 | Best: {'fx': 529.834} (0.110):  88%|████████▊ | 44/50 [00:02<00:00, 30.60it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.066 | Best: {'fx': 529.834} (0.110):  90%|█████████ | 45/50 [00:02<00:00, 30.60it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.063 | Best: {'fx': 529.834} (0.110):  92%|█████████▏| 46/50 [00:02<00:00, 30.60it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.063 | Best: {'fx': 529.834} (0.110):  94%|█████████▍| 47/50 [00:02<00:00, 26.99it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.062 | Best: {'fx': 529.834} (0.110):  94%|█████████▍| 47/50 [00:02<00:00, 26.99it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.065 | Best: {'fx': 529.834} (0.110):  96%|█████████▌| 48/50 [00:02<00:00, 26.99it/s]Brute | Start: {'fx': 526.263} (0.107) | Current: 0.060 | Best: {'fx': 529.834} (0.110):  98%|█████████▊| 49/50 [00:02<00:00, 26.99it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.8340548340549
    px: 1019.0
    py: 766.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 80 frames will be used for optimization
  Rigs: 2000L(2000mm):20/20, 1500(1500mm):20/20, 2000R(2000mm):20/20, 1000(1000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 10.7522: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 10.7522, picklable_objective(x0) = 10.7415
Best score: 10.7415: : 0iter [00:02, ?iter/s] Best score: 10.7415: : 1iter [00:02,  2.08s/iter]Best score: 10.7415: : 1iter [00:02,  2.08s/iter]Best score: 10.7415: : 2iter [00:02,  1.05iter/s]Best score: 10.7415: : 2iter [00:02,  1.05iter/s]Best score: 10.7415: : 3iter [00:02,  1.68iter/s]Best score: 10.7415: : 3iter [00:02,  1.68iter/s]Best score: 10.7415: : 4iter [00:02,  2.35iter/s]Best score: 10.7415: : 4iter [00:02,  2.35iter/s]Best score: 10.7415: : 5iter [00:02,  3.01iter/s]Best score: 10.7415: : 5iter [00:02,  3.01iter/s]Best score: 10.7415: : 6iter [00:02,  3.63iter/s]Best score: 10.7415: : 6iter [00:03,  3.63iter/s]Best score: 10.7415: : 7iter [00:03,  4.19iter/s]Best score: 10.7415: : 7iter [00:03,  4.19iter/s]Best score: 10.7415: : 8iter [00:03,  4.65iter/s]Best score: 10.7415: : 8iter [00:03,  4.65iter/s]Best score: 10.7415: : 9iter [00:03,  5.03iter/s]Best score: 10.7415: : 9iter [00:03,  5.03iter/s]Best score: 10.7415: : 10iter [00:03,  5.19iter/s]Best score: 10.7415: : 10iter [00:03,  5.19iter/s]Best score: 10.7415: : 11iter [00:03,  5.43iter/s]Best score: 10.7415: : 11iter [00:03,  5.43iter/s]Best score: 10.7415: : 12iter [00:03,  5.61iter/s]Best score: 10.7415: : 12iter [00:04,  5.61iter/s]Best score: 10.7415: : 13iter [00:04,  5.75iter/s]Best score: 10.7415: : 13iter [00:04,  5.75iter/s]Best score: 10.7415: : 14iter [00:04,  5.85iter/s]Best score: 10.7415: : 14iter [00:04,  5.85iter/s]Best score: 10.7415: : 15iter [00:04,  5.93iter/s]Best score: 10.7415: : 15iter [00:04,  5.93iter/s]Best score: 10.7415: : 16iter [00:04,  5.98iter/s]Best score: 10.7415: : 16iter [00:04,  5.98iter/s]Best score: 10.7415: : 17iter [00:04,  6.01iter/s]Best score: 10.7415: : 17iter [00:04,  6.01iter/s]Best score: 10.7415: : 18iter [00:04,  6.03iter/s]Best score: 10.7415: : 18iter [00:05,  6.03iter/s]Best score: 10.7415: : 19iter [00:05,  6.06iter/s]Best score: 10.7415: : 19iter [00:05,  6.06iter/s]Best score: 10.7415: : 20iter [00:05,  5.91iter/s]Best score: 10.7415: : 20iter [00:05,  5.91iter/s]Best score: 10.7415: : 21iter [00:05,  5.96iter/s]Best score: 10.7415: : 21iter [00:05,  5.96iter/s]Best score: 10.7415: : 22iter [00:05,  6.00iter/s]Best score: 10.7415: : 22iter [00:05,  6.00iter/s]Best score: 10.7415: : 23iter [00:05,  6.02iter/s]Best score: 10.7415: : 23iter [00:05,  6.02iter/s]Best score: 10.7415: : 24iter [00:05,  6.04iter/s]Best score: 10.7415: : 24iter [00:06,  6.04iter/s]Best score: 10.7415: : 25iter [00:06,  6.05iter/s]Best score: 10.7415: : 25iter [00:06,  6.05iter/s]Best score: 10.7415: : 26iter [00:06,  6.05iter/s]Best score: 10.7415: : 26iter [00:06,  6.05iter/s]Best score: 10.7415: : 27iter [00:06,  6.06iter/s]Best score: 10.7415: : 27iter [00:06,  6.06iter/s]Best score: 10.7415: : 28iter [00:06,  6.07iter/s]Best score: 10.7415: : 28iter [00:06,  6.07iter/s]Best score: 10.7415: : 29iter [00:06,  6.08iter/s]Best score: 10.7415: : 29iter [00:06,  6.08iter/s]Best score: 10.7415: : 30iter [00:06,  5.93iter/s]Best score: 10.7415: : 30iter [00:07,  5.93iter/s]Best score: 10.7415: : 31iter [00:07,  5.97iter/s]Best score: 10.7415: : 31iter [00:07,  5.97iter/s]Best score: 10.7415: : 32iter [00:07,  6.02iter/s]Best score: 10.7415: : 32iter [00:07,  6.02iter/s]Best score: 10.7415: : 33iter [00:07,  6.04iter/s]Best score: 10.7415: : 33iter [00:07,  6.04iter/s]Best score: 10.7415: : 34iter [00:07,  6.06iter/s]Best score: 10.7415: : 34iter [00:07,  6.06iter/s]Best score: 10.7415: : 35iter [00:07,  6.08iter/s]Best score: 10.7415: : 35iter [00:07,  6.08iter/s]Best score: 10.7415: : 36iter [00:07,  6.09iter/s]Best score: 10.7415: : 36iter [00:08,  6.09iter/s]Best score: 10.7415: : 37iter [00:08,  6.06iter/s]Best score: 10.7415: : 37iter [00:08,  6.06iter/s]Best score: 10.7415: : 38iter [00:08,  6.09iter/s]Best score: 10.7415: : 38iter [00:08,  6.09iter/s]Best score: 10.7415: : 39iter [00:08,  6.09iter/s]Best score: 10.7415: : 39iter [00:08,  6.09iter/s]Best score: 10.7415: : 40iter [00:08,  5.88iter/s]Best score: 10.7415: : 40iter [00:08,  5.88iter/s]Best score: 10.7415: : 41iter [00:08,  5.93iter/s]Best score: 10.7415: : 41iter [00:08,  5.93iter/s]Best score: 10.7415: : 42iter [00:08,  6.00iter/s]Best score: 10.7415: : 42iter [00:09,  6.00iter/s]Best score: 10.7415: : 43iter [00:09,  6.04iter/s]Best score: 10.7415: : 43iter [00:09,  6.04iter/s]Best score: 10.7415: : 44iter [00:09,  6.06iter/s]Best score: 10.7415: : 44iter [00:09,  6.06iter/s]Best score: 10.7415: : 45iter [00:09,  6.10iter/s]Best score: 10.7415: : 45iter [00:09,  6.10iter/s]Best score: 10.7415: : 46iter [00:09,  6.10iter/s]Best score: 10.7415: : 46iter [00:09,  6.10iter/s]Best score: 10.7415: : 47iter [00:09,  6.12iter/s]Best score: 10.7415: : 47iter [00:09,  6.12iter/s]Best score: 10.7415: : 48iter [00:09,  6.10iter/s]Best score: 10.7415: : 48iter [00:10,  6.10iter/s]Best score: 10.7415: : 49iter [00:10,  6.09iter/s]
[ITER 50] Best: 10.7415
[PARAMS] Current best parameters:
  fx: 529.8341
  fy: 529.8341
  px: 1019.0000
  py: 766.0000
  k1: 0.00000000
  k2: 0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  scale: 3.0000
  rotation: 0.0000
Best score: 10.7415: : 49iter [00:10,  6.09iter/s]Best score: 10.7415: : 50iter [00:10,  5.94iter/s]Best score: 10.7415: : 50iter [00:10,  5.94iter/s]Best score: 10.7415: : 51iter [00:10,  5.99iter/s]Best score: 10.7415: : 51iter [00:10,  5.99iter/s]Best score: 10.7415: : 52iter [00:10,  6.02iter/s]Best score: 10.7415: : 52iter [00:10,  6.02iter/s]Best score: 10.7415: : 53iter [00:10,  6.05iter/s]Best score: 10.7415: : 53iter [00:10,  6.05iter/s]Best score: 10.7415: : 54iter [00:10,  6.08iter/s]Best score: 10.7415: : 54iter [00:11,  6.08iter/s]Best score: 10.7415: : 55iter [00:11,  6.00iter/s]Best score: 10.7415: : 55iter [00:11,  6.00iter/s]Best score: 10.7415: : 56iter [00:11,  5.59iter/s]Best score: 10.7415: : 56iter [00:11,  5.59iter/s]Best score: 10.7415: : 57iter [00:11,  5.27iter/s]Best score: 10.7415: : 57iter [00:11,  5.27iter/s]Best score: 10.7415: : 58iter [00:11,  5.10iter/s]Best score: 10.7415: : 58iter [00:11,  5.10iter/s]Best score: 10.7415: : 59iter [00:11,  5.00iter/s]Best score: 4.7787: : 59iter [00:12,  5.00iter/s] Best score: 4.7787: : 60iter [00:12,  4.76iter/s]Best score: 4.7787: : 60iter [00:12,  4.76iter/s]Best score: 4.7787: : 61iter [00:12,  4.73iter/s]Best score: 4.7787: : 61iter [00:12,  4.73iter/s]Best score: 4.7787: : 62iter [00:12,  4.70iter/s]Best score: 4.7787: : 62iter [00:12,  4.70iter/s]Best score: 4.7787: : 63iter [00:12,  4.66iter/s]Best score: 4.7787: : 63iter [00:12,  4.66iter/s]Best score: 4.7787: : 64iter [00:12,  4.66iter/s]Best score: 4.7787: : 64iter [00:13,  4.66iter/s]Best score: 4.7787: : 65iter [00:13,  4.62iter/s]Best score: 4.7787: : 65iter [00:13,  4.62iter/s]Best score: 4.7787: : 66iter [00:13,  4.60iter/s]Best score: 4.7787: : 66iter [00:13,  4.60iter/s]Best score: 4.7787: : 67iter [00:13,  4.60iter/s]Best score: 4.7787: : 67iter [00:13,  4.60iter/s]Best score: 4.7787: : 68iter [00:13,  4.60iter/s]Best score: 4.7787: : 68iter [00:14,  4.60iter/s]Best score: 4.7787: : 69iter [00:14,  4.56iter/s]Best score: 2.9413: : 69iter [00:14,  4.56iter/s]Best score: 2.9413: : 70iter [00:14,  4.46iter/s]Best score: 2.9413: : 70iter [00:14,  4.46iter/s]Best score: 2.9413: : 71iter [00:14,  4.52iter/s]Best score: 2.9413: : 71iter [00:14,  4.52iter/s]Best score: 2.9413: : 72iter [00:14,  4.58iter/s]Best score: 2.9413: : 72iter [00:14,  4.58iter/s]Best score: 2.9413: : 73iter [00:14,  4.61iter/s]Best score: 2.9413: : 73iter [00:15,  4.61iter/s]Best score: 2.9413: : 74iter [00:15,  4.58iter/s]Best score: 2.9413: : 74iter [00:15,  4.58iter/s]Best score: 2.9413: : 75iter [00:15,  4.61iter/s]Best score: 2.9413: : 75iter [00:15,  4.61iter/s]Best score: 2.9413: : 76iter [00:15,  4.63iter/s]Best score: 2.9413: : 76iter [00:15,  4.63iter/s]Best score: 2.9413: : 77iter [00:15,  4.60iter/s]Best score: 2.9413: : 77iter [00:16,  4.60iter/s]Best score: 2.9413: : 78iter [00:16,  4.61iter/s]Best score: 2.9413: : 78iter [00:16,  4.61iter/s]Best score: 2.9413: : 79iter [00:16,  4.60iter/s]Best score: 2.9413: : 79iter [00:16,  4.60iter/s]Best score: 2.9413: : 80iter [00:16,  4.51iter/s]Best score: 2.9413: : 80iter [00:16,  4.51iter/s]Best score: 2.9413: : 81iter [00:16,  4.60iter/s]Best score: 2.9413: : 81iter [00:16,  4.60iter/s]Best score: 2.9413: : 82iter [00:16,  4.62iter/s]Best score: 2.9413: : 82iter [00:17,  4.62iter/s]Best score: 2.9413: : 83iter [00:17,  4.66iter/s]Best score: 2.9413: : 83iter [00:17,  4.66iter/s]Best score: 2.9413: : 84iter [00:17,  4.65iter/s]Best score: 2.9413: : 84iter [00:17,  4.65iter/s]Best score: 2.9413: : 85iter [00:17,  4.63iter/s]Best score: 2.9413: : 85iter [00:17,  4.63iter/s]Best score: 2.9413: : 86iter [00:17,  4.65iter/s]Best score: 2.9413: : 86iter [00:17,  4.65iter/s]Best score: 2.9413: : 87iter [00:17,  4.68iter/s]Best score: 2.9413: : 87iter [00:18,  4.68iter/s]Best score: 2.9413: : 88iter [00:18,  4.66iter/s]Best score: 2.9413: : 88iter [00:18,  4.66iter/s]Best score: 2.9413: : 89iter [00:18,  4.68iter/s]Best score: 1.5168: : 89iter [00:18,  4.68iter/s]Best score: 1.5168: : 90iter [00:18,  4.54iter/s]Best score: 1.5168: : 90iter [00:18,  4.54iter/s]Best score: 1.5168: : 91iter [00:18,  4.61iter/s]Best score: 1.5168: : 91iter [00:19,  4.61iter/s]Best score: 1.5168: : 92iter [00:19,  4.61iter/s]Best score: 1.5168: : 92iter [00:19,  4.61iter/s]Best score: 1.5168: : 93iter [00:19,  4.63iter/s]Best score: 1.5168: : 93iter [00:19,  4.63iter/s]Best score: 1.5168: : 94iter [00:19,  4.59iter/s]Best score: 1.5168: : 94iter [00:19,  4.59iter/s]Best score: 1.5168: : 95iter [00:19,  4.61iter/s]Best score: 1.5168: : 95iter [00:19,  4.61iter/s]Best score: 1.5168: : 96iter [00:19,  4.61iter/s]Best score: 1.5168: : 96iter [00:20,  4.61iter/s]Best score: 1.5168: : 97iter [00:20,  4.64iter/s]Best score: 1.5168: : 97iter [00:20,  4.64iter/s]Best score: 1.5168: : 98iter [00:20,  4.61iter/s]Best score: 1.5168: : 98iter [00:20,  4.61iter/s]Best score: 1.5168: : 99iter [00:20,  4.63iter/s]
[ITER 100] Best: 1.5168
[PARAMS] Current best parameters:
  fx: 552.5841
  fy: 552.3652
  px: 1018.9961
  py: 763.3678
  k1: -0.01186292
  k2: 0.00864143
  k3: -0.02457359
  k4: 0.00999443
  p1: -0.00876393
  p2: -0.00243204
  p3: 0.00009640
  p4: 0.00412420
  scale: 2.9695
  rotation: -1.3012
Best score: 1.5168: : 99iter [00:20,  4.63iter/s]Best score: 1.5168: : 100iter [00:20,  4.52iter/s]Best score: 1.5168: : 100iter [00:21,  4.52iter/s]Best score: 1.5168: : 101iter [00:21,  4.56iter/s]Best score: 1.5168: : 101iter [00:21,  4.56iter/s]Best score: 1.5168: : 102iter [00:21,  4.58iter/s]Best score: 1.5168: : 102iter [00:21,  4.58iter/s]Best score: 1.5168: : 103iter [00:21,  4.61iter/s]Best score: 1.5168: : 103iter [00:21,  4.61iter/s]Best score: 1.5168: : 104iter [00:21,  4.66iter/s]Best score: 1.5168: : 104iter [00:21,  4.66iter/s]Best score: 1.5168: : 105iter [00:21,  4.66iter/s]Best score: 1.5168: : 105iter [00:22,  4.66iter/s]Best score: 1.5168: : 106iter [00:22,  4.65iter/s]Best score: 1.5168: : 106iter [00:22,  4.65iter/s]Best score: 1.5168: : 107iter [00:22,  4.62iter/s]Best score: 1.5168: : 107iter [00:22,  4.62iter/s]Best score: 1.5168: : 108iter [00:22,  4.61iter/s]Best score: 1.5168: : 108iter [00:22,  4.61iter/s]Best score: 1.5168: : 109iter [00:22,  4.61iter/s]Best score: 0.9987: : 109iter [00:22,  4.61iter/s]Best score: 0.9987: : 110iter [00:22,  4.53iter/s]Best score: 0.9987: : 110iter [00:23,  4.53iter/s]Best score: 0.9987: : 111iter [00:23,  4.61iter/s]Best score: 0.9987: : 111iter [00:23,  4.61iter/s]Best score: 0.9987: : 112iter [00:23,  4.62iter/s]Best score: 0.9987: : 112iter [00:23,  4.62iter/s]Best score: 0.9987: : 113iter [00:23,  4.62iter/s]Best score: 0.9987: : 113iter [00:23,  4.62iter/s]Best score: 0.9987: : 114iter [00:23,  4.63iter/s]Best score: 0.9987: : 114iter [00:24,  4.63iter/s]Best score: 0.9987: : 115iter [00:24,  4.61iter/s]Best score: 0.9987: : 115iter [00:24,  4.61iter/s]Best score: 0.9987: : 116iter [00:24,  4.61iter/s]Best score: 0.9987: : 116iter [00:24,  4.61iter/s]Best score: 0.9987: : 117iter [00:24,  4.60iter/s]Best score: 0.9987: : 117iter [00:24,  4.60iter/s]Best score: 0.9987: : 118iter [00:24,  4.63iter/s]Best score: 0.9987: : 118iter [00:24,  4.63iter/s]Best score: 0.9987: : 119iter [00:24,  4.62iter/s]Best score: 0.9987: : 119iter [00:25,  4.62iter/s]Best score: 0.9987: : 120iter [00:25,  4.52iter/s]Best score: 0.9987: : 120iter [00:25,  4.52iter/s]Best score: 0.9987: : 121iter [00:25,  4.53iter/s]Best score: 0.9987: : 121iter [00:25,  4.53iter/s]Best score: 0.9987: : 122iter [00:25,  4.56iter/s]Best score: 0.9987: : 122iter [00:25,  4.56iter/s]Best score: 0.9987: : 123iter [00:25,  4.57iter/s]Best score: 0.9987: : 123iter [00:26,  4.57iter/s]Best score: 0.9987: : 124iter [00:26,  4.55iter/s]Best score: 0.9987: : 124iter [00:26,  4.55iter/s]Best score: 0.9987: : 125iter [00:26,  4.58iter/s]Best score: 0.9987: : 125iter [00:26,  4.58iter/s]Best score: 0.9987: : 126iter [00:26,  4.59iter/s]Best score: 0.9987: : 126iter [00:26,  4.59iter/s]Best score: 0.9987: : 127iter [00:26,  4.59iter/s]Best score: 0.9987: : 127iter [00:26,  4.59iter/s]Best score: 0.9987: : 128iter [00:26,  4.59iter/s]Best score: 0.9987: : 128iter [00:27,  4.59iter/s]Best score: 0.9987: : 129iter [00:27,  4.58iter/s]Best score: 0.7779: : 129iter [00:27,  4.58iter/s]Best score: 0.7779: : 130iter [00:27,  4.48iter/s]Best score: 0.7779: : 130iter [00:27,  4.48iter/s]Best score: 0.7779: : 131iter [00:27,  4.51iter/s]Best score: 0.7779: : 131iter [00:27,  4.51iter/s]Best score: 0.7779: : 132iter [00:27,  4.55iter/s]Best score: 0.7779: : 132iter [00:27,  4.55iter/s]Best score: 0.7779: : 133iter [00:27,  4.56iter/s]Best score: 0.7779: : 133iter [00:28,  4.56iter/s]Best score: 0.7779: : 134iter [00:28,  4.51iter/s]Best score: 0.7779: : 134iter [00:28,  4.51iter/s]Best score: 0.7779: : 135iter [00:28,  4.51iter/s]Best score: 0.7779: : 135iter [00:28,  4.51iter/s]Best score: 0.7779: : 136iter [00:28,  4.50iter/s]Best score: 0.7779: : 136iter [00:28,  4.50iter/s]Best score: 0.7779: : 137iter [00:28,  4.51iter/s]Best score: 0.7779: : 137iter [00:29,  4.51iter/s]Best score: 0.7779: : 138iter [00:29,  4.55iter/s]Best score: 0.7779: : 138iter [00:29,  4.55iter/s]Best score: 0.7779: : 139iter [00:29,  4.56iter/s]Best score: 0.6536: : 139iter [00:29,  4.56iter/s]Best score: 0.6536: : 140iter [00:29,  4.47iter/s]Best score: 0.6536: : 140iter [00:29,  4.47iter/s]Best score: 0.6536: : 141iter [00:29,  4.52iter/s]Best score: 0.6536: : 141iter [00:29,  4.52iter/s]Best score: 0.6536: : 142iter [00:29,  4.56iter/s]Best score: 0.6536: : 142iter [00:30,  4.56iter/s]Best score: 0.6536: : 143iter [00:30,  4.56iter/s]Best score: 0.6536: : 143iter [00:30,  4.56iter/s]Best score: 0.6536: : 144iter [00:30,  4.55iter/s]Best score: 0.6536: : 144iter [00:30,  4.55iter/s]Best score: 0.6536: : 145iter [00:30,  4.58iter/s]Best score: 0.6536: : 145iter [00:30,  4.58iter/s]Best score: 0.6536: : 146iter [00:30,  4.56iter/s]Best score: 0.6536: : 146iter [00:31,  4.56iter/s]Best score: 0.6536: : 147iter [00:31,  4.54iter/s]Best score: 0.6536: : 147iter [00:31,  4.54iter/s]Best score: 0.6536: : 148iter [00:31,  4.55iter/s]Best score: 0.6536: : 148iter [00:31,  4.55iter/s]Best score: 0.6536: : 149iter [00:31,  4.58iter/s]
[ITER 150] Best: 0.6090
[PARAMS] Current best parameters:
  fx: 539.7363
  fy: 539.2847
  px: 1018.5035
  py: 763.5250
  k1: 0.00091467
  k2: -0.01506514
  k3: 0.00902696
  k4: -0.00228942
  p1: -0.00978290
  p2: -0.00582832
  p3: 0.00696496
  p4: -0.00201234
  scale: 2.9824
  rotation: -1.1392
Best score: 0.6090: : 149iter [00:31,  4.58iter/s]Best score: 0.6090: : 150iter [00:31,  4.49iter/s]Best score: 0.6090: : 150iter [00:31,  4.49iter/s]Best score: 0.6090: : 151iter [00:31,  4.57iter/s]Best score: 0.6090: : 151iter [00:32,  4.57iter/s]Best score: 0.6090: : 152iter [00:32,  4.60iter/s]Best score: 0.6090: : 152iter [00:32,  4.60iter/s]Best score: 0.6090: : 153iter [00:32,  4.61iter/s]Best score: 0.6090: : 153iter [00:32,  4.61iter/s]Best score: 0.6090: : 154iter [00:32,  4.61iter/s]Best score: 0.6090: : 154iter [00:32,  4.61iter/s]Best score: 0.6090: : 155iter [00:32,  4.61iter/s]Best score: 0.6090: : 155iter [00:33,  4.61iter/s]Best score: 0.6090: : 156iter [00:33,  4.60iter/s]Best score: 0.6090: : 156iter [00:33,  4.60iter/s]Best score: 0.6090: : 157iter [00:33,  4.62iter/s]Best score: 0.6090: : 157iter [00:33,  4.62iter/s]Best score: 0.6090: : 158iter [00:33,  4.61iter/s]Best score: 0.6090: : 158iter [00:33,  4.61iter/s]Best score: 0.6090: : 159iter [00:33,  4.58iter/s]Best score: 0.6090: : 159iter [00:33,  4.58iter/s]Best score: 0.6090: : 160iter [00:33,  4.51iter/s]Best score: 0.6090: : 160iter [00:34,  4.51iter/s]Best score: 0.6090: : 161iter [00:34,  4.57iter/s]Best score: 0.6090: : 161iter [00:34,  4.57iter/s]Best score: 0.6090: : 162iter [00:34,  4.58iter/s]Best score: 0.6090: : 162iter [00:34,  4.58iter/s]Best score: 0.6090: : 163iter [00:34,  4.61iter/s]Best score: 0.6090: : 163iter [00:34,  4.61iter/s]Best score: 0.6090: : 164iter [00:34,  4.61iter/s]Best score: 0.6090: : 164iter [00:34,  4.61iter/s]Best score: 0.6090: : 165iter [00:34,  4.64iter/s]Best score: 0.6090: : 165iter [00:35,  4.64iter/s]Best score: 0.6090: : 166iter [00:35,  4.65iter/s]Best score: 0.6090: : 166iter [00:35,  4.65iter/s]Best score: 0.6090: : 167iter [00:35,  4.65iter/s]Best score: 0.6090: : 167iter [00:35,  4.65iter/s]Best score: 0.6090: : 168iter [00:35,  4.62iter/s]Best score: 0.6090: : 168iter [00:35,  4.62iter/s]Best score: 0.6090: : 169iter [00:35,  4.62iter/s]Best score: 0.6090: : 169iter [00:36,  4.62iter/s]Best score: 0.6090: : 170iter [00:36,  4.51iter/s]Best score: 0.6090: : 170iter [00:36,  4.51iter/s]Best score: 0.6090: : 171iter [00:36,  4.57iter/s]Best score: 0.6090: : 171iter [00:36,  4.57iter/s]Best score: 0.6090: : 172iter [00:36,  4.59iter/s]Best score: 0.6090: : 172iter [00:36,  4.59iter/s]Best score: 0.6090: : 173iter [00:36,  4.63iter/s]Best score: 0.6090: : 173iter [00:36,  4.63iter/s]Best score: 0.6090: : 174iter [00:36,  4.60iter/s]Best score: 0.6090: : 174iter [00:37,  4.60iter/s]Best score: 0.6090: : 175iter [00:37,  4.59iter/s]Best score: 0.6090: : 175iter [00:37,  4.59iter/s]Best score: 0.6090: : 176iter [00:37,  4.56iter/s]Best score: 0.6090: : 176iter [00:37,  4.56iter/s]Best score: 0.6090: : 177iter [00:37,  4.55iter/s]Best score: 0.6090: : 177iter [00:37,  4.55iter/s]Best score: 0.6090: : 178iter [00:37,  4.59iter/s]Best score: 0.6090: : 178iter [00:38,  4.59iter/s]Best score: 0.6090: : 179iter [00:38,  4.63iter/s]Best score: 0.6090: : 179iter [00:38,  4.63iter/s]Best score: 0.6090: : 180iter [00:38,  4.49iter/s]Best score: 0.6090: : 180iter [00:38,  4.49iter/s]Best score: 0.6090: : 181iter [00:38,  4.53iter/s]Best score: 0.6090: : 181iter [00:38,  4.53iter/s]Best score: 0.6090: : 182iter [00:38,  4.57iter/s]Best score: 0.6090: : 182iter [00:38,  4.57iter/s]Best score: 0.6090: : 183iter [00:38,  4.56iter/s]Best score: 0.6090: : 183iter [00:39,  4.56iter/s]Best score: 0.6090: : 184iter [00:39,  4.56iter/s]Best score: 0.6090: : 184iter [00:39,  4.56iter/s]Best score: 0.6090: : 185iter [00:39,  4.57iter/s]Best score: 0.6090: : 185iter [00:39,  4.57iter/s]Best score: 0.6090: : 186iter [00:39,  4.54iter/s]Best score: 0.6090: : 186iter [00:39,  4.54iter/s]Best score: 0.6090: : 187iter [00:39,  4.55iter/s]Best score: 0.6090: : 187iter [00:40,  4.55iter/s]Best score: 0.6090: : 188iter [00:40,  4.56iter/s]Best score: 0.6090: : 188iter [00:40,  4.56iter/s]Best score: 0.6090: : 189iter [00:40,  4.57iter/s]Best score: 0.5726: : 189iter [00:40,  4.57iter/s]Best score: 0.5726: : 190iter [00:40,  4.47iter/s]Best score: 0.5726: : 190iter [00:40,  4.47iter/s]Best score: 0.5726: : 191iter [00:40,  4.50iter/s]Best score: 0.5726: : 191iter [00:40,  4.50iter/s]Best score: 0.5726: : 192iter [00:40,  4.51iter/s]Best score: 0.5726: : 192iter [00:41,  4.51iter/s]Best score: 0.5726: : 193iter [00:41,  4.56iter/s]Best score: 0.5726: : 193iter [00:41,  4.56iter/s]Best score: 0.5726: : 194iter [00:41,  4.57iter/s]Best score: 0.5726: : 194iter [00:41,  4.57iter/s]Best score: 0.5726: : 195iter [00:41,  4.56iter/s]Best score: 0.5726: : 195iter [00:41,  4.56iter/s]Best score: 0.5726: : 196iter [00:41,  4.58iter/s]Best score: 0.5726: : 196iter [00:42,  4.58iter/s]Best score: 0.5726: : 197iter [00:42,  4.58iter/s]Best score: 0.5726: : 197iter [00:42,  4.58iter/s]Best score: 0.5726: : 198iter [00:42,  4.56iter/s]Best score: 0.5726: : 198iter [00:42,  4.56iter/s]Best score: 0.5726: : 199iter [00:42,  4.56iter/s]Best score: 0.5248: : 199iter [00:42,  4.56iter/s]Best score: 0.5248: : 200iter [00:42,  4.47iter/s]
[ITER 200] Best: 0.5248
[PARAMS] Current best parameters:
  fx: 542.2883
  fy: 541.4451
  px: 1017.4933
  py: 764.0480
  k1: -0.01299898
  k2: -0.00476865
  k3: 0.00780815
  k4: -0.00282776
  p1: -0.00901990
  p2: -0.00807914
  p3: 0.00635215
  p4: 0.00020636
  scale: 3.0010
  rotation: -1.1671
Best score: 0.5248: : 200iter [00:42,  4.47iter/s]Best score: 0.5248: : 201iter [00:42,  4.53iter/s]Best score: 0.5248: : 201iter [00:43,  4.53iter/s]Best score: 0.5248: : 202iter [00:43,  4.57iter/s]Best score: 0.5248: : 202iter [00:43,  4.57iter/s]Best score: 0.5248: : 203iter [00:43,  4.58iter/s]Best score: 0.5248: : 203iter [00:43,  4.58iter/s]Best score: 0.5248: : 204iter [00:43,  4.56iter/s]Best score: 0.5248: : 204iter [00:43,  4.56iter/s]Best score: 0.5248: : 205iter [00:43,  4.55iter/s]Best score: 0.5248: : 205iter [00:43,  4.55iter/s]Best score: 0.5248: : 206iter [00:43,  4.52iter/s]Best score: 0.5248: : 206iter [00:44,  4.52iter/s]Best score: 0.5248: : 207iter [00:44,  4.52iter/s]Best score: 0.5248: : 207iter [00:44,  4.52iter/s]Best score: 0.5248: : 208iter [00:44,  4.52iter/s]Best score: 0.5248: : 208iter [00:44,  4.52iter/s]Best score: 0.5248: : 209iter [00:44,  4.50iter/s]Best score: 0.5248: : 209iter [00:44,  4.50iter/s]Best score: 0.5248: : 210iter [00:44,  4.40iter/s]Best score: 0.5248: : 210iter [00:45,  4.40iter/s]Best score: 0.5248: : 211iter [00:45,  4.47iter/s]Best score: 0.5248: : 211iter [00:45,  4.47iter/s]Best score: 0.5248: : 212iter [00:45,  4.48iter/s]Best score: 0.5248: : 212iter [00:45,  4.48iter/s]Best score: 0.5248: : 213iter [00:45,  4.51iter/s]Best score: 0.5248: : 213iter [00:45,  4.51iter/s]Best score: 0.5248: : 214iter [00:45,  4.53iter/s]Best score: 0.5248: : 214iter [00:45,  4.53iter/s]Best score: 0.5248: : 215iter [00:45,  4.55iter/s]Best score: 0.5248: : 215iter [00:46,  4.55iter/s]Best score: 0.5248: : 216iter [00:46,  4.54iter/s]Best score: 0.5248: : 216iter [00:46,  4.54iter/s]Best score: 0.5248: : 217iter [00:46,  4.60iter/s]Best score: 0.5248: : 217iter [00:46,  4.60iter/s]Best score: 0.5248: : 218iter [00:46,  4.61iter/s]Best score: 0.5248: : 218iter [00:46,  4.61iter/s]Best score: 0.5248: : 219iter [00:46,  4.60iter/s]Best score: 0.5198: : 219iter [00:47,  4.60iter/s]Best score: 0.5198: : 220iter [00:47,  4.48iter/s]Best score: 0.5198: : 220iter [00:47,  4.48iter/s]Best score: 0.5198: : 221iter [00:47,  4.53iter/s]Best score: 0.5198: : 221iter [00:47,  4.53iter/s]Best score: 0.5198: : 222iter [00:47,  4.53iter/s]Best score: 0.5198: : 222iter [00:47,  4.53iter/s]Best score: 0.5198: : 223iter [00:47,  4.57iter/s]Best score: 0.5198: : 223iter [00:47,  4.57iter/s]Best score: 0.5198: : 224iter [00:47,  4.57iter/s]Best score: 0.5198: : 224iter [00:48,  4.57iter/s]Best score: 0.5198: : 225iter [00:48,  4.60iter/s]Best score: 0.5198: : 225iter [00:48,  4.60iter/s]Best score: 0.5198: : 226iter [00:48,  4.60iter/s]Best score: 0.5198: : 226iter [00:48,  4.60iter/s]Best score: 0.5198: : 227iter [00:48,  4.59iter/s]Best score: 0.5198: : 227iter [00:48,  4.59iter/s]Best score: 0.5198: : 228iter [00:48,  4.59iter/s]Best score: 0.5198: : 228iter [00:49,  4.59iter/s]Best score: 0.5198: : 229iter [00:49,  4.59iter/s]Best score: 0.5155: : 229iter [00:49,  4.59iter/s]Best score: 0.5155: : 230iter [00:49,  4.48iter/s]Best score: 0.5155: : 230iter [00:49,  4.48iter/s]Best score: 0.5155: : 231iter [00:49,  4.53iter/s]Best score: 0.5155: : 231iter [00:49,  4.53iter/s]Best score: 0.5155: : 232iter [00:49,  4.53iter/s]Best score: 0.5155: : 232iter [00:49,  4.53iter/s]Best score: 0.5155: : 233iter [00:49,  4.54iter/s]Best score: 0.5155: : 233iter [00:50,  4.54iter/s]Best score: 0.5155: : 234iter [00:50,  4.54iter/s]Best score: 0.5155: : 234iter [00:50,  4.54iter/s]Best score: 0.5155: : 235iter [00:50,  4.57iter/s]Best score: 0.5155: : 235iter [00:50,  4.57iter/s]Best score: 0.5155: : 236iter [00:50,  4.57iter/s]Best score: 0.5155: : 236iter [00:50,  4.57iter/s]Best score: 0.5155: : 237iter [00:50,  4.58iter/s]Best score: 0.5155: : 237iter [00:51,  4.58iter/s]Best score: 0.5155: : 238iter [00:51,  4.59iter/s]Best score: 0.5155: : 238iter [00:51,  4.59iter/s]Best score: 0.5155: : 239iter [00:51,  4.57iter/s]Best score: 0.5127: : 239iter [00:51,  4.57iter/s]Best score: 0.5127: : 240iter [00:51,  4.45iter/s]Best score: 0.5127: : 240iter [00:51,  4.45iter/s]Best score: 0.5127: : 241iter [00:51,  4.52iter/s]Best score: 0.5127: : 241iter [00:51,  4.52iter/s]Best score: 0.5127: : 242iter [00:51,  4.55iter/s]Best score: 0.5127: : 242iter [00:52,  4.55iter/s]Best score: 0.5127: : 243iter [00:52,  4.57iter/s]Best score: 0.5127: : 243iter [00:52,  4.57iter/s]Best score: 0.5127: : 244iter [00:52,  4.61iter/s]Best score: 0.5127: : 244iter [00:52,  4.61iter/s]Best score: 0.5127: : 245iter [00:52,  4.59iter/s]Best score: 0.5127: : 245iter [00:52,  4.59iter/s]Best score: 0.5127: : 246iter [00:52,  4.58iter/s]Best score: 0.5127: : 246iter [00:52,  4.58iter/s]Best score: 0.5127: : 247iter [00:52,  4.70iter/s]Best score: 0.5127: : 247iter [00:53,  4.70iter/s]Best score: 0.5127: : 248iter [00:53,  4.88iter/s]Best score: 0.5127: : 248iter [00:53,  4.88iter/s]Best score: 0.5127: : 249iter [00:53,  4.99iter/s]
[ITER 250] Best: 0.5127
[PARAMS] Current best parameters:
  fx: 548.7785
  fy: 548.0955
  px: 1017.6211
  py: 764.0613
  k1: -0.01423207
  k2: -0.01318173
  k3: 0.01355668
  k4: -0.00415564
  p1: -0.00941525
  p2: -0.00814957
  p3: 0.00719252
  p4: 0.00016078
  scale: 2.9915
  rotation: -1.1839
Best score: 0.5127: : 249iter [00:53,  4.99iter/s]Best score: 0.5127: : 250iter [00:53,  4.90iter/s]Best score: 0.5127: : 250iter [00:53,  4.90iter/s]Best score: 0.5127: : 251iter [00:53,  4.93iter/s]Best score: 0.5127: : 251iter [00:53,  4.93iter/s]Best score: 0.5127: : 252iter [00:53,  5.05iter/s]Best score: 0.5127: : 252iter [00:54,  5.05iter/s]Best score: 0.5127: : 253iter [00:54,  5.11iter/s]Best score: 0.5127: : 253iter [00:54,  5.11iter/s]Best score: 0.5127: : 254iter [00:54,  5.15iter/s]Best score: 0.5127: : 254iter [00:54,  5.15iter/s]Best score: 0.5127: : 255iter [00:54,  5.16iter/s]Best score: 0.5127: : 255iter [00:54,  5.16iter/s]Best score: 0.5127: : 256iter [00:54,  5.18iter/s]Best score: 0.5127: : 256iter [00:54,  5.18iter/s]Best score: 0.5127: : 257iter [00:54,  5.02iter/s]Best score: 0.5127: : 257iter [00:55,  5.02iter/s]Best score: 0.5127: : 258iter [00:55,  4.98iter/s]Best score: 0.5127: : 258iter [00:55,  4.98iter/s]Best score: 0.5127: : 259iter [00:55,  5.03iter/s]Best score: 0.5060: : 259iter [00:55,  5.03iter/s]Best score: 0.5060: : 260iter [00:55,  4.98iter/s]Best score: 0.5060: : 260iter [00:55,  4.98iter/s]Best score: 0.5060: : 261iter [00:55,  5.07iter/s]Best score: 0.5060: : 261iter [00:55,  5.07iter/s]Best score: 0.5060: : 262iter [00:55,  4.99iter/s]Best score: 0.5060: : 262iter [00:56,  4.99iter/s]Best score: 0.5060: : 263iter [00:56,  4.94iter/s]Best score: 0.5060: : 263iter [00:56,  4.94iter/s]Best score: 0.5060: : 264iter [00:56,  4.96iter/s]Best score: 0.5060: : 264iter [00:56,  4.96iter/s]Best score: 0.5060: : 265iter [00:56,  5.07iter/s]Best score: 0.5060: : 265iter [00:56,  5.07iter/s]Best score: 0.5060: : 266iter [00:56,  5.19iter/s]Best score: 0.5060: : 266iter [00:56,  5.19iter/s]Best score: 0.5060: : 267iter [00:56,  5.18iter/s]Best score: 0.5060: : 267iter [00:57,  5.18iter/s]Best score: 0.5060: : 268iter [00:57,  5.15iter/s]Best score: 0.5060: : 268iter [00:57,  5.15iter/s]Best score: 0.5060: : 269iter [00:57,  5.17iter/s]Best score: 0.5056: : 269iter [00:57,  5.17iter/s]Best score: 0.5056: : 270iter [00:57,  5.07iter/s]Best score: 0.5056: : 270iter [00:57,  5.07iter/s]Best score: 0.5056: : 271iter [00:57,  5.14iter/s]Best score: 0.5056: : 271iter [00:57,  5.14iter/s]Best score: 0.5056: : 272iter [00:57,  5.18iter/s]Best score: 0.5056: : 272iter [00:58,  5.18iter/s]Best score: 0.5056: : 273iter [00:58,  5.25iter/s]Best score: 0.5056: : 273iter [00:58,  5.25iter/s]Best score: 0.5056: : 274iter [00:58,  5.26iter/s]Best score: 0.5056: : 274iter [00:58,  5.26iter/s]Best score: 0.5056: : 275iter [00:58,  5.24iter/s]Best score: 0.5056: : 275iter [00:58,  5.24iter/s]Best score: 0.5056: : 276iter [00:58,  5.28iter/s]Best score: 0.5056: : 276iter [00:58,  5.28iter/s]Best score: 0.5056: : 277iter [00:58,  5.34iter/s]Best score: 0.5056: : 277iter [00:59,  5.34iter/s]Best score: 0.5056: : 278iter [00:59,  5.33iter/s]Best score: 0.5056: : 278iter [00:59,  5.33iter/s]Best score: 0.5056: : 279iter [00:59,  5.34iter/s]Best score: 0.5054: : 279iter [00:59,  5.34iter/s]Best score: 0.5054: : 280iter [00:59,  5.14iter/s]Best score: 0.5054: : 280iter [00:59,  5.14iter/s]Best score: 0.5054: : 281iter [00:59,  5.19iter/s]Best score: 0.5054: : 281iter [00:59,  5.19iter/s]Best score: 0.5054: : 282iter [00:59,  5.22iter/s]Best score: 0.5054: : 282iter [00:59,  5.22iter/s]Best score: 0.5054: : 283iter [00:59,  5.24iter/s]Best score: 0.5054: : 283iter [01:00,  5.24iter/s]Best score: 0.5054: : 284iter [01:00,  5.28iter/s]Best score: 0.5054: : 284iter [01:00,  5.28iter/s]Best score: 0.5054: : 285iter [01:00,  5.24iter/s]Best score: 0.5054: : 285iter [01:00,  5.24iter/s]Best score: 0.5054: : 286iter [01:00,  5.25iter/s]Best score: 0.5054: : 286iter [01:00,  5.25iter/s]Best score: 0.5054: : 287iter [01:00,  5.23iter/s]Best score: 0.5054: : 287iter [01:00,  5.23iter/s]Best score: 0.5054: : 288iter [01:00,  5.23iter/s]Best score: 0.5054: : 288iter [01:01,  5.23iter/s]Best score: 0.5054: : 289iter [01:01,  5.12iter/s]Best score: 0.5042: : 289iter [01:01,  5.12iter/s]Best score: 0.5042: : 290iter [01:01,  5.07iter/s]Best score: 0.5042: : 290iter [01:01,  5.07iter/s]Best score: 0.5042: : 291iter [01:01,  5.13iter/s]Best score: 0.5042: : 291iter [01:01,  5.13iter/s]Best score: 0.5042: : 292iter [01:01,  5.22iter/s]Best score: 0.5042: : 292iter [01:01,  5.22iter/s]Best score: 0.5042: : 293iter [01:01,  5.24iter/s]Best score: 0.5042: : 293iter [01:02,  5.24iter/s]Best score: 0.5042: : 294iter [01:02,  5.27iter/s]Best score: 0.5042: : 294iter [01:02,  5.27iter/s]Best score: 0.5042: : 295iter [01:02,  5.31iter/s]Best score: 0.5042: : 295iter [01:02,  5.31iter/s]Best score: 0.5042: : 296iter [01:02,  5.35iter/s]Best score: 0.5042: : 296iter [01:02,  5.35iter/s]Best score: 0.5042: : 297iter [01:02,  5.35iter/s]Best score: 0.5042: : 297iter [01:02,  5.35iter/s]Best score: 0.5042: : 298iter [01:02,  5.42iter/s]Best score: 0.5042: : 298iter [01:03,  5.42iter/s]Best score: 0.5042: : 299iter [01:03,  5.41iter/s]
[ITER 300] Best: 0.5042
[PARAMS] Current best parameters:
  fx: 545.2138
  fy: 544.6883
  px: 1017.1871
  py: 763.7572
  k1: -0.01237005
  k2: -0.00898036
  k3: 0.01028446
  k4: -0.00347419
  p1: -0.00992546
  p2: -0.00891824
  p3: 0.00711775
  p4: 0.00008123
  scale: 2.9923
  rotation: -1.1774
Best score: 0.5042: : 299iter [01:03,  5.41iter/s]Best score: 0.5042: : 300iter [01:03,  5.23iter/s]Best score: 0.5042: : 300iter [01:03,  5.23iter/s]Best score: 0.5042: : 301iter [01:03,  5.31iter/s]Best score: 0.5042: : 301iter [01:03,  5.31iter/s]Best score: 0.5042: : 302iter [01:03,  5.34iter/s]Best score: 0.5042: : 302iter [01:03,  5.34iter/s]Best score: 0.5042: : 303iter [01:03,  5.38iter/s]Best score: 0.5042: : 303iter [01:03,  5.38iter/s]Best score: 0.5042: : 304iter [01:03,  5.38iter/s]Best score: 0.5042: : 304iter [01:04,  5.38iter/s]Best score: 0.5042: : 305iter [01:04,  5.22iter/s]Best score: 0.5042: : 305iter [01:04,  5.22iter/s]Best score: 0.5042: : 306iter [01:04,  5.19iter/s]Best score: 0.5042: : 306iter [01:04,  5.19iter/s]Best score: 0.5042: : 307iter [01:04,  5.26iter/s]Best score: 0.5042: : 307iter [01:04,  5.26iter/s]Best score: 0.5042: : 308iter [01:04,  5.31iter/s]Best score: 0.5042: : 308iter [01:04,  5.31iter/s]Best score: 0.5042: : 309iter [01:04,  5.32iter/s]Best score: 0.5038: : 309iter [01:05,  5.32iter/s]Best score: 0.5038: : 310iter [01:05,  5.12iter/s]Best score: 0.5038: : 310iter [01:05,  5.12iter/s]Best score: 0.5038: : 311iter [01:05,  5.18iter/s]Best score: 0.5038: : 311iter [01:05,  5.18iter/s]Best score: 0.5038: : 312iter [01:05,  5.11iter/s]Best score: 0.5038: : 312iter [01:05,  5.11iter/s]Best score: 0.5038: : 313iter [01:05,  5.15iter/s]Best score: 0.5038: : 313iter [01:05,  5.15iter/s]Best score: 0.5038: : 314iter [01:05,  5.07iter/s]Best score: 0.5038: : 314iter [01:06,  5.07iter/s]Best score: 0.5038: : 315iter [01:06,  5.11iter/s]Best score: 0.5038: : 315iter [01:06,  5.11iter/s]Best score: 0.5038: : 316iter [01:06,  5.09iter/s]Best score: 0.5038: : 316iter [01:06,  5.09iter/s]Best score: 0.5038: : 317iter [01:06,  5.02iter/s]Best score: 0.5038: : 317iter [01:06,  5.02iter/s]Best score: 0.5038: : 318iter [01:06,  5.14iter/s]Best score: 0.5038: : 318iter [01:06,  5.14iter/s]Best score: 0.5038: : 319iter [01:06,  5.18iter/s]Best score: 0.5037: : 319iter [01:07,  5.18iter/s]Best score: 0.5037: : 320iter [01:07,  5.08iter/s]Best score: 0.5037: : 320iter [01:07,  5.08iter/s]Best score: 0.5037: : 321iter [01:07,  5.15iter/s]Best score: 0.5037: : 321iter [01:07,  5.15iter/s]Best score: 0.5037: : 322iter [01:07,  5.22iter/s]Best score: 0.5037: : 322iter [01:07,  5.22iter/s]Best score: 0.5037: : 323iter [01:07,  5.26iter/s]Best score: 0.5037: : 323iter [01:07,  5.26iter/s]Best score: 0.5037: : 324iter [01:07,  5.27iter/s]Best score: 0.5037: : 324iter [01:08,  5.27iter/s]Best score: 0.5037: : 325iter [01:08,  5.22iter/s]Best score: 0.5037: : 325iter [01:08,  5.22iter/s]Best score: 0.5037: : 326iter [01:08,  5.27iter/s]Best score: 0.5037: : 326iter [01:08,  5.27iter/s]Best score: 0.5037: : 327iter [01:08,  5.11iter/s]Best score: 0.5037: : 327iter [01:08,  5.11iter/s]Best score: 0.5037: : 328iter [01:08,  5.20iter/s]Best score: 0.5037: : 328iter [01:08,  5.20iter/s]Best score: 0.5037: : 329iter [01:08,  5.19iter/s]Best score: 0.5037: : 329iter [01:09,  5.19iter/s]Best score: 0.5037: : 330iter [01:09,  3.94iter/s]Best score: 0.5037: : 330iter [01:09,  3.94iter/s]Best score: 0.5037: : 331iter [01:09,  4.24iter/s]Best score: 0.5037: : 331iter [01:09,  4.24iter/s]Best score: 0.5037: : 332iter [01:09,  4.42iter/s]Best score: 0.5037: : 332iter [01:09,  4.42iter/s]Best score: 0.5037: : 333iter [01:09,  4.61iter/s]Best score: 0.5037: : 333iter [01:09,  4.61iter/s]Best score: 0.5037: : 334iter [01:09,  4.73iter/s]Best score: 0.5037: : 334iter [01:10,  4.73iter/s]Best score: 0.5037: : 335iter [01:10,  4.86iter/s]Best score: 0.5037: : 335iter [01:10,  4.86iter/s]Best score: 0.5037: : 336iter [01:10,  4.93iter/s]Best score: 0.5037: : 336iter [01:10,  4.93iter/s]Best score: 0.5037: : 337iter [01:10,  5.05iter/s]Best score: 0.5037: : 337iter [01:10,  5.05iter/s]Best score: 0.5037: : 338iter [01:10,  5.16iter/s]Best score: 0.5037: : 338iter [01:10,  5.16iter/s]Best score: 0.5037: : 339iter [01:10,  5.24iter/s]Best score: 0.5036: : 339iter [01:11,  5.24iter/s]Best score: 0.5036: : 340iter [01:11,  5.13iter/s]Best score: 0.5036: : 340iter [01:11,  5.13iter/s]Best score: 0.5036: : 341iter [01:11,  5.28iter/s]Best score: 0.5036: : 341iter [01:11,  5.28iter/s]Best score: 0.5036: : 342iter [01:11,  5.25iter/s]Best score: 0.5036: : 342iter [01:11,  5.25iter/s]Best score: 0.5036: : 343iter [01:11,  5.22iter/s]Best score: 0.5036: : 343iter [01:11,  5.22iter/s]Best score: 0.5036: : 344iter [01:11,  5.29iter/s]Best score: 0.5036: : 344iter [01:12,  5.29iter/s]Best score: 0.5036: : 345iter [01:12,  5.27iter/s]Best score: 0.5036: : 345iter [01:12,  5.27iter/s]Best score: 0.5036: : 346iter [01:12,  5.32iter/s]Best score: 0.5036: : 346iter [01:12,  5.32iter/s]Best score: 0.5036: : 347iter [01:12,  5.35iter/s]Best score: 0.5036: : 347iter [01:12,  5.35iter/s]Best score: 0.5036: : 348iter [01:12,  5.40iter/s]Best score: 0.5036: : 348iter [01:12,  5.40iter/s]Best score: 0.5036: : 349iter [01:12,  5.38iter/s]
[ITER 350] Best: 0.5031
[PARAMS] Current best parameters:
  fx: 548.0279
  fy: 547.4162
  px: 1017.2356
  py: 763.7325
  k1: -0.01507166
  k2: -0.00990315
  k3: 0.01151530
  k4: -0.00380736
  p1: -0.00999016
  p2: -0.00882371
  p3: 0.00707697
  p4: 0.00012314
  scale: 2.9919
  rotation: -1.1804
Best score: 0.5031: : 349iter [01:13,  5.38iter/s]Best score: 0.5031: : 350iter [01:13,  5.22iter/s]Best score: 0.5031: : 350iter [01:13,  5.22iter/s]Best score: 0.5031: : 351iter [01:13,  5.26iter/s]Best score: 0.5031: : 351iter [01:13,  5.26iter/s]Best score: 0.5031: : 352iter [01:13,  5.26iter/s]Best score: 0.5031: : 352iter [01:13,  5.26iter/s]Best score: 0.5031: : 353iter [01:13,  5.32iter/s]Best score: 0.5031: : 353iter [01:13,  5.32iter/s]Best score: 0.5031: : 354iter [01:13,  5.32iter/s]Best score: 0.5031: : 354iter [01:13,  5.32iter/s]Best score: 0.5031: : 355iter [01:13,  5.36iter/s]Best score: 0.5031: : 355iter [01:14,  5.36iter/s]Best score: 0.5031: : 356iter [01:14,  5.34iter/s]Best score: 0.5031: : 356iter [01:14,  5.34iter/s]Best score: 0.5031: : 357iter [01:14,  5.27iter/s]Best score: 0.5031: : 357iter [01:14,  5.27iter/s]Best score: 0.5031: : 358iter [01:14,  5.27iter/s]Best score: 0.5031: : 358iter [01:14,  5.27iter/s]Best score: 0.5031: : 359iter [01:14,  5.39iter/s]Best score: 0.5031: : 359iter [01:14,  5.39iter/s]Best score: 0.5031: : 360iter [01:14,  5.24iter/s]Best score: 0.5031: : 360iter [01:15,  5.24iter/s]Best score: 0.5031: : 361iter [01:15,  5.22iter/s]Best score: 0.5031: : 361iter [01:15,  5.22iter/s]Best score: 0.5031: : 362iter [01:15,  5.33iter/s]Best score: 0.5031: : 362iter [01:15,  5.33iter/s]Best score: 0.5031: : 363iter [01:15,  5.27iter/s]Best score: 0.5031: : 363iter [01:15,  5.27iter/s]Best score: 0.5031: : 364iter [01:15,  5.30iter/s]Best score: 0.5031: : 364iter [01:15,  5.30iter/s]Best score: 0.5031: : 365iter [01:15,  5.35iter/s]Best score: 0.5031: : 365iter [01:16,  5.35iter/s]Best score: 0.5031: : 366iter [01:16,  5.39iter/s]Best score: 0.5031: : 366iter [01:16,  5.39iter/s]Best score: 0.5031: : 367iter [01:16,  5.37iter/s]Best score: 0.5031: : 367iter [01:16,  5.37iter/s]Best score: 0.5031: : 368iter [01:16,  5.37iter/s]Best score: 0.5031: : 368iter [01:16,  5.37iter/s]Best score: 0.5031: : 369iter [01:16,  5.26iter/s]Best score: 0.5030: : 369iter [01:16,  5.26iter/s]Best score: 0.5030: : 370iter [01:16,  5.17iter/s]Best score: 0.5030: : 370iter [01:16,  5.17iter/s]Best score: 0.5030: : 371iter [01:16,  5.13iter/s]Best score: 0.5030: : 371iter [01:17,  5.13iter/s]Best score: 0.5030: : 372iter [01:17,  5.20iter/s]Best score: 0.5030: : 372iter [01:17,  5.20iter/s]Best score: 0.5030: : 373iter [01:17,  5.28iter/s]Best score: 0.5030: : 373iter [01:17,  5.28iter/s]Best score: 0.5030: : 374iter [01:17,  5.25iter/s]Best score: 0.5030: : 374iter [01:17,  5.25iter/s]Best score: 0.5030: : 375iter [01:17,  5.30iter/s]Best score: 0.5030: : 375iter [01:17,  5.30iter/s]Best score: 0.5030: : 376iter [01:17,  5.36iter/s]Best score: 0.5030: : 376iter [01:18,  5.36iter/s]Best score: 0.5030: : 377iter [01:18,  5.39iter/s]Best score: 0.5030: : 377iter [01:18,  5.39iter/s]Best score: 0.5030: : 378iter [01:18,  5.34iter/s]Best score: 0.5030: : 378iter [01:18,  5.34iter/s]Best score: 0.5030: : 379iter [01:18,  5.22iter/s]Best score: 0.5030: : 379iter [01:18,  5.22iter/s]Best score: 0.5030: : 380iter [01:18,  5.13iter/s]Best score: 0.5030: : 380iter [01:18,  5.13iter/s]Best score: 0.5030: : 381iter [01:18,  5.19iter/s]Best score: 0.5030: : 381iter [01:19,  5.19iter/s]Best score: 0.5030: : 382iter [01:19,  5.22iter/s]Best score: 0.5030: : 382iter [01:19,  5.22iter/s]Best score: 0.5030: : 383iter [01:19,  5.33iter/s]Best score: 0.5030: : 383iter [01:19,  5.33iter/s]Best score: 0.5030: : 384iter [01:19,  5.27iter/s]Best score: 0.5030: : 384iter [01:19,  5.27iter/s]Best score: 0.5030: : 385iter [01:19,  5.32iter/s]Best score: 0.5030: : 385iter [01:19,  5.32iter/s]Best score: 0.5030: : 386iter [01:19,  5.39iter/s]Best score: 0.5030: : 386iter [01:19,  5.39iter/s]Best score: 0.5030: : 387iter [01:19,  5.32iter/s]Best score: 0.5030: : 387iter [01:20,  5.32iter/s]Best score: 0.5030: : 388iter [01:20,  5.37iter/s]Best score: 0.5030: : 388iter [01:20,  5.37iter/s]Best score: 0.5030: : 389iter [01:20,  5.27iter/s]Best score: 0.5030: : 389iter [01:20,  5.27iter/s]Best score: 0.5030: : 390iter [01:20,  5.17iter/s]Best score: 0.5030: : 390iter [01:20,  5.17iter/s]Best score: 0.5030: : 391iter [01:20,  5.27iter/s]Best score: 0.5030: : 391iter [01:20,  5.27iter/s]Best score: 0.5030: : 392iter [01:20,  5.33iter/s]Best score: 0.5030: : 392iter [01:21,  5.33iter/s]Best score: 0.5030: : 393iter [01:21,  5.35iter/s]Best score: 0.5030: : 393iter [01:21,  5.35iter/s]Best score: 0.5030: : 394iter [01:21,  5.38iter/s]Best score: 0.5030: : 394iter [01:21,  5.38iter/s]Best score: 0.5030: : 395iter [01:21,  5.44iter/s]Best score: 0.5030: : 395iter [01:21,  5.44iter/s]Best score: 0.5030: : 396iter [01:21,  5.45iter/s]Best score: 0.5030: : 396iter [01:21,  5.45iter/s]Best score: 0.5030: : 397iter [01:21,  5.46iter/s]Best score: 0.5030: : 397iter [01:22,  5.46iter/s]Best score: 0.5030: : 398iter [01:22,  5.35iter/s]Best score: 0.5030: : 398iter [01:22,  5.35iter/s]Best score: 0.5030: : 399iter [01:22,  5.38iter/s]
[ITER 400] Best: 0.5028
[PARAMS] Current best parameters:
  fx: 546.1652
  fy: 545.5562
  px: 1017.2158
  py: 763.7426
  k1: -0.01143013
  k2: -0.01169807
  k3: 0.01180277
  k4: -0.00375599
  p1: -0.00998168
  p2: -0.00886608
  p3: 0.00727796
  p4: 0.00000280
  scale: 2.9898
  rotation: -1.1796
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[01:31,  5.34iter/s]Best score: 0.5025: : 448iter [01:31,  5.34iter/s]Best score: 0.5025: : 449iter [01:31,  5.39iter/s]
[ITER 450] Best: 0.5024
[PARAMS] Current best parameters:
  fx: 543.5229
  fy: 542.9031
  px: 1017.2399
  py: 763.7122
  k1: -0.00478656
  k2: -0.01841184
  k3: 0.01578354
  k4: -0.00464314
  p1: -0.00999622
  p2: -0.00877650
  p3: 0.00714881
  p4: 0.00006836
  scale: 2.9873
  rotation: -1.1835
Best score: 0.5024: : 449iter [01:31,  5.39iter/s]Best score: 0.5024: : 450iter [01:31,  5.29iter/s]Best score: 0.5024: : 450iter [01:31,  5.29iter/s]Best score: 0.5024: : 451iter [01:31,  5.35iter/s]Best score: 0.5024: : 451iter [01:32,  5.35iter/s]Best score: 0.5024: : 452iter [01:32,  5.37iter/s]Best score: 0.5024: : 452iter [01:32,  5.37iter/s]Best score: 0.5024: : 453iter [01:32,  5.40iter/s]Best score: 0.5024: : 453iter [01:32,  5.40iter/s]Best score: 0.5024: : 454iter [01:32,  5.44iter/s]Best score: 0.5024: : 454iter [01:32,  5.44iter/s]Best score: 0.5024: : 455iter [01:32,  5.41iter/s]Best score: 0.5024: : 455iter [01:32,  5.41iter/s]Best score: 0.5024: : 456iter [01:32,  5.43iter/s]Best score: 0.5024: : 456iter [01:33,  5.43iter/s]Best score: 0.5024: : 457iter [01:33,  5.47iter/s]Best score: 0.5024: : 457iter [01:33,  5.47iter/s]Best score: 0.5024: : 458iter [01:33,  5.45iter/s]Best score: 0.5024: : 458iter [01:33,  5.45iter/s]Best score: 0.5024: : 459iter 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score: 0.5024: : 488iter [01:39,  5.33iter/s]Best score: 0.5024: : 489iter [01:39,  5.37iter/s]Best score: 0.5024: : 489iter [01:39,  5.37iter/s]Best score: 0.5024: : 490iter [01:39,  5.17iter/s]Best score: 0.5024: : 490iter [01:39,  5.17iter/s]Best score: 0.5024: : 491iter [01:39,  5.15iter/s]Best score: 0.5024: : 491iter [01:39,  5.15iter/s]Best score: 0.5024: : 492iter [01:39,  5.15iter/s]Best score: 0.5024: : 492iter [01:39,  5.15iter/s]Best score: 0.5024: : 493iter [01:39,  5.18iter/s]Best score: 0.5024: : 493iter [01:40,  5.18iter/s]Best score: 0.5024: : 494iter [01:40,  5.29iter/s]Best score: 0.5024: : 494iter [01:40,  5.29iter/s]Best score: 0.5024: : 495iter [01:40,  5.36iter/s]Best score: 0.5024: : 495iter [01:40,  5.36iter/s]Best score: 0.5024: : 496iter [01:40,  5.35iter/s]Best score: 0.5024: : 496iter [01:40,  5.35iter/s]Best score: 0.5024: : 497iter [01:40,  5.37iter/s]Best score: 0.5024: : 497iter [01:40,  5.37iter/s]Best score: 0.5024: : 498iter [01:40,  5.41iter/s]Best score: 0.5024: : 498iter [01:41,  5.41iter/s]Best score: 0.5024: : 499iter [01:41,  5.25iter/s]
[ITER 500] Best: 0.5023
[PARAMS] Current best parameters:
  fx: 542.6269
  fy: 542.0034
  px: 1017.2111
  py: 763.7134
  k1: -0.00302215
  k2: -0.01980280
  k3: 0.01650500
  k4: -0.00478540
  p1: -0.00999684
  p2: -0.00881027
  p3: 0.00707519
  p4: 0.00014208
  scale: 2.9870
  rotation: -1.1843
Best score: 0.5023: : 499iter [01:41,  5.25iter/s]Best score: 0.5023: : 500iter [01:41,  5.17iter/s]Best score: 0.5023: : 500iter [01:41,  4.94iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.502348 -> 0.502328
  Success: True
  Tangential distortion: p1=-0.009995, p2=-0.008809, p3=0.007077, p4=0.000143
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.5023
[✅] Parameters copied to: /working/MDC028_W009(6)_2024-07-23/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 542.6268790935572
    fy: 542.0033565160983
    px: 1017.2111473338013
    py: 763.7133660456899
    k1: -0.0030204605344810098
    k2: -0.019800015229174368
    k3: 0.01650500993861556
    k4: -0.0047868823677112565
    p1: -0.009994872366460798
    p2: -0.008809250312478973
    p3: 0.007076766970178476
    p4: 0.0001430883413148721
    scale: 2.987041654996043
    rotation: -1.1842747075154558
    mmpp: 3.4295176563106846
    nmpp: 3429517.6563106845

[🔄] Converting 1772 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDC028_W009(6)_2024-07-23/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC028_W009(6)_2024-07-23/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
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[✅] LUT written to: /working/MDC028_W009(6)_2024-07-23/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC028_W009(6)_2024-07-23' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC028_W009(6)_2024-07-23/calibration/MDC028_W009(6)_2024-07-23_unknown.lut

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

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                                                                  [A[✅] 2000L: /working/MDC028_W009(6)_2024-07-23/calibration/debug/4_heatmap/2000L_heatmap.png
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                                                                 [A[✅] 1500: /working/MDC028_W009(6)_2024-07-23/calibration/debug/4_heatmap/1500_heatmap.png
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   🔥 2000R:  61%|██████▏   | 376/612 [00:02<00:01, 173.49frame/s][A
   🔥 2000R:  64%|██████▍   | 394/612 [00:02<00:01, 172.51frame/s][A
   🔥 2000R:  67%|██████▋   | 412/612 [00:02<00:01, 171.16frame/s][A
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   🔥 2000R:  76%|███████▌  | 464/612 [00:02<00:00, 168.50frame/s][A
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   🔥 2000R:  84%|████████▍ | 517/612 [00:03<00:00, 171.00frame/s][A
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   🔥 2000R:  90%|█████████ | 553/612 [00:03<00:00, 172.39frame/s][A
   🔥 2000R:  93%|█████████▎| 571/612 [00:03<00:00, 173.02frame/s][A
   🔥 2000R:  96%|█████████▌| 589/612 [00:03<00:00, 171.54frame/s][A
   🔥 2000R:  99%|█████████▉| 607/612 [00:03<00:00, 169.98frame/s][A
                                                                  [A[✅] 2000R: /working/MDC028_W009(6)_2024-07-23/calibration/debug/4_heatmap/2000R_heatmap.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:11<00:04,  4.10s/rig]
   🔥 1000:   0%|          | 0/239 [00:00<?, ?frame/s][A
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   🔥 1000:  77%|███████▋  | 185/239 [00:01<00:00, 176.41frame/s][A
   🔥 1000:  85%|████████▌ | 204/239 [00:01<00:00, 179.08frame/s][A
   🔥 1000:  93%|█████████▎| 223/239 [00:01<00:00, 180.76frame/s][A
                                                                 [A[✅] 1000: /working/MDC028_W009(6)_2024-07-23/calibration/debug/4_heatmap/1000_heatmap.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:13<00:00,  3.15s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:13<00:00,  3.42s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC028_W009(6)_2024-07-23/calibration/debug/4_heatmap
[🏁] Finished processing MDC028_W009(6)_2024-07-23


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