
🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-002_W043(6)_2026-03-05 ===

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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 429 stationary frames, 357 remain

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                                                                           [A[PASS2] Frame 0000351: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000352: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000353: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000354: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000355: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000356: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000357: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000358: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000454: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000455: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000456: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000457: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000460: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000461: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000472: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000474: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000476: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000477: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000478: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000484: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000485: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000487: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000498: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000516: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000930: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000931: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000932: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000933: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000353: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000463: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000471: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000472: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000476: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000477: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000478: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000932: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000471: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000472: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000476: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000477: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000478: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 17 frames fixed, 23 untrusted, 356 total

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                                                                            [AProcessing Rigs:  25%|██▌       | 1/4 [05:53<17:40, 353.61s/it][INFO] Filtered 440 stationary frames, 303 remain

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                                                                           [A[PASS2] Frame 0000799: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000955: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000956: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000957: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000958: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000959: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000960: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000961: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000963: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000964: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001047: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001048: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001049: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001050: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000799: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000956: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000957: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000962: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000963: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001049: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000799: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000962: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 11 frames fixed, 8 untrusted, 302 total

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                                                                            [AProcessing Rigs:  50%|█████     | 2/4 [10:53<10:44, 322.28s/it][INFO] Filtered 706 stationary frames, 406 remain

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                                                                          [A[PASS2] Frame 0000448: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000449: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000450: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001413: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001414: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001415: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001416: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000450: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001415: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 5 frames fixed, 6 untrusted, 405 total

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                                                                           [AProcessing Rigs:  75%|███████▌  | 3/4 [17:45<06:02, 362.92s/it][INFO] Filtered 492 stationary frames, 319 remain

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                                                                          [A[PASS2] Frame 0000359: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000368: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000491: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000492: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000493: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000494: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000781: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000782: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000783: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000784: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000785: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000786: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000845: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000846: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000847: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000848: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000849: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000850: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000851: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000852: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000853: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000854: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000855: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000856: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000857: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000858: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000859: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000928: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000929: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000930: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000931: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000932: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000942: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000960: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001027: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001028: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001029: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001213: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001214: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001215: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001216: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000359: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000368: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000491: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000492: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000493: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000782: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000783: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000784: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000785: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000845: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000846: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000847: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000848: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000852: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000853: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000856: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000858: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000930: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000931: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001027: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001028: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001215: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000359: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000491: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000492: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000846: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000847: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000848: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000855: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000856: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 30 frames fixed, 19 untrusted, 318 total

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                                                                           [AProcessing Rigs: 100%|██████████| 4/4 [23:27<00:00, 354.62s/it]Processing Rigs: 100%|██████████| 4/4 [23:27<00:00, 351.78s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1381 frames from 4 rigs to /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 2000L: 100 frames selected from 356
[📋] 2000R: 100 frames selected from 302
[📋] 1500: 100 frames selected from 405
[📋] 1000: 100 frames selected from 318
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-002_W043(6)_2026-03-05/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, 79.23it/s]Computing max image from bright frames:  21%|██▏       | 17/80 [00:00<00:00, 84.41it/s]Computing max image from bright frames:  34%|███▍      | 27/80 [00:00<00:00, 88.95it/s]Computing max image from bright frames:  45%|████▌     | 36/80 [00:00<00:00, 89.24it/s]Computing max image from bright frames:  57%|█████▊    | 46/80 [00:00<00:00, 90.95it/s]Computing max image from bright frames:  70%|███████   | 56/80 [00:00<00:00, 89.39it/s]Computing max image from bright frames:  82%|████████▎ | 66/80 [00:00<00:00, 90.26it/s]Computing max image from bright frames:  95%|█████████▌| 76/80 [00:00<00:00, 90.62it/s]                                                                                       [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 24, black threshold: 72
[INFO] Found 5907 casing boundary candidate points
[RESULT] Detected casing: center=(894, 910), radius=896, inliers=2503/5907
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(0.002):  11%|█         | 11/100 [00:00<00:04, 18.59it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 18.59it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 18.59it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 18.59it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 19.28it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.005 | Best: {'fx': 398.99} (0.005):  14%|█▍        | 14/100 [00:00<00:04, 19.28it/s] Brute | Start: {'fx': 500.0} (0.047) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:00<00:04, 19.28it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:01<00:04, 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| Current: 0.013 | Best: {'fx': 455.556} (0.013):  22%|██▏       | 22/100 [00:01<00:03, 20.71it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  23%|██▎       | 23/100 [00:01<00:03, 21.09it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.016 | Best: {'fx': 462.626} (0.016):  23%|██▎       | 23/100 [00:01<00:03, 21.09it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.018 | Best: {'fx': 469.697} (0.018):  24%|██▍       | 24/100 [00:01<00:03, 21.09it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 476.768} (0.022):  25%|██▌       | 25/100 [00:01<00:03, 21.09it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 476.768} (0.022):  26%|██▌       | 26/100 [00:01<00:03, 20.72it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.027 | Best: {'fx': 483.838} (0.027):  26%|██▌       | 26/100 [00:01<00:03, 20.72it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.034 | Best: {'fx': 490.909} (0.034):  27%|██▋       | 27/100 [00:01<00:03, 20.72it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.046 | Best: {'fx': 497.98} (0.046):  28%|██▊       | 28/100 [00:01<00:03, 20.72it/s] Brute | Start: {'fx': 500.0} (0.047) | Current: 0.046 | Best: {'fx': 497.98} (0.046):  29%|██▉       | 29/100 [00:01<00:03, 20.67it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.052 | Best: {'fx': 505.051} (0.052):  29%|██▉       | 29/100 [00:01<00:03, 20.67it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.061 | Best: {'fx': 512.121} (0.061):  30%|███       | 30/100 [00:01<00:03, 20.67it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.070 | Best: {'fx': 519.192} (0.070):  31%|███       | 31/100 [00:01<00:03, 20.67it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.070 | Best: {'fx': 519.192} (0.070):  32%|███▏      | 32/100 [00:01<00:04, 14.34it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.076 | Best: {'fx': 526.263} (0.076):  32%|███▏      | 32/100 [00:01<00:04, 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| Current: 0.045 | Best: {'fx': 526.263} (0.076):  39%|███▉      | 39/100 [00:02<00:03, 17.46it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.042 | Best: {'fx': 526.263} (0.076):  39%|███▉      | 39/100 [00:02<00:03, 17.46it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.040 | Best: {'fx': 526.263} (0.076):  40%|████      | 40/100 [00:02<00:03, 17.46it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.037 | Best: {'fx': 526.263} (0.076):  41%|████      | 41/100 [00:02<00:03, 17.46it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.035 | Best: {'fx': 526.263} (0.076):  42%|████▏     | 42/100 [00:02<00:03, 17.46it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.034 | Best: {'fx': 526.263} (0.076):  43%|████▎     | 43/100 [00:02<00:03, 17.46it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.034 | Best: {'fx': 526.263} (0.076):  44%|████▍     | 44/100 [00:02<00:02, 20.88it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.031 | Best: {'fx': 526.263} (0.076):  44%|████▍     | 44/100 [00:02<00:02, 20.88it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.032 | Best: {'fx': 526.263} (0.076):  45%|████▌     | 45/100 [00:02<00:02, 20.88it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.029 | Best: {'fx': 526.263} (0.076):  46%|████▌     | 46/100 [00:02<00:02, 20.88it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.029 | Best: {'fx': 526.263} (0.076):  47%|████▋     | 47/100 [00:02<00:02, 19.24it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.030 | Best: {'fx': 526.263} (0.076):  47%|████▋     | 47/100 [00:02<00:02, 19.24it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.026 | Best: {'fx': 526.263} (0.076):  48%|████▊     | 48/100 [00:02<00:02, 19.24it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.028 | Best: {'fx': 526.263} (0.076):  49%|████▉     | 49/100 [00:02<00:02, 19.24it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.028 | Best: {'fx': 526.263} (0.076):  50%|█████     | 50/100 [00:02<00:02, 20.65it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.029 | Best: {'fx': 526.263} (0.076):  50%|█████     | 50/100 [00:02<00:02, 20.65it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.026 | Best: {'fx': 526.263} (0.076):  51%|█████     | 51/100 [00:02<00:02, 20.65it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.025 | Best: {'fx': 526.263} (0.076):  52%|█████▏    | 52/100 [00:02<00:02, 20.65it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.025 | Best: {'fx': 526.263} (0.076):  53%|█████▎    | 53/100 [00:02<00:02, 22.15it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.027 | Best: {'fx': 526.263} (0.076):  53%|█████▎    | 53/100 [00:02<00:02, 22.15it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.025 | Best: {'fx': 526.263} (0.076):  54%|█████▍    | 54/100 [00:02<00:02, 22.15it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.025 | Best: {'fx': 526.263} (0.076):  55%|█████▌    | 55/100 [00:02<00:02, 22.15it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.025 | Best: {'fx': 526.263} (0.076):  56%|█████▌    | 56/100 [00:02<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.024 | Best: {'fx': 526.263} (0.076):  56%|█████▌    | 56/100 [00:02<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  57%|█████▋    | 57/100 [00:02<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  58%|█████▊    | 58/100 [00:02<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.024 | Best: {'fx': 526.263} (0.076):  59%|█████▉    | 59/100 [00:02<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  60%|██████    | 60/100 [00:03<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  61%|██████    | 61/100 [00:03<00:01, 23.91it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  62%|██████▏   | 62/100 [00:03<00:01, 31.94it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.024 | Best: {'fx': 526.263} (0.076):  62%|██████▏   | 62/100 [00:03<00:01, 31.94it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  63%|██████▎   | 63/100 [00:03<00:01, 31.94it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.023 | Best: {'fx': 526.263} (0.076):  64%|██████▍   | 64/100 [00:03<00:01, 31.94it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.023 | Best: {'fx': 526.263} (0.076):  65%|██████▌   | 65/100 [00:03<00:01, 31.94it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.023 | Best: {'fx': 526.263} (0.076):  66%|██████▌   | 66/100 [00:03<00:01, 31.94it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.023 | Best: {'fx': 526.263} (0.076):  67%|██████▋   | 67/100 [00:03<00:00, 34.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.022 | Best: {'fx': 526.263} (0.076):  67%|██████▋   | 67/100 [00:03<00:00, 34.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  68%|██████▊   | 68/100 [00:03<00:00, 34.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  69%|██████▉   | 69/100 [00:03<00:00, 34.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  70%|███████   | 70/100 [00:03<00:00, 34.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  71%|███████   | 71/100 [00:03<00:00, 34.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  72%|███████▏  | 72/100 [00:03<00:00, 34.44it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  72%|███████▏  | 72/100 [00:03<00:00, 34.44it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.021 | Best: {'fx': 526.263} (0.076):  73%|███████▎  | 73/100 [00:03<00:00, 34.44it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  74%|███████▍  | 74/100 [00:03<00:00, 34.44it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  75%|███████▌  | 75/100 [00:03<00:00, 34.44it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  76%|███████▌  | 76/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  76%|███████▌  | 76/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  77%|███████▋  | 77/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  78%|███████▊  | 78/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  79%|███████▉  | 79/100 [00:03<00:00, 32.31it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  80%|████████  | 80/100 [00:03<00:00, 28.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  80%|████████  | 80/100 [00:03<00:00, 28.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  81%|████████  | 81/100 [00:03<00:00, 28.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  82%|████████▏ | 82/100 [00:03<00:00, 28.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  83%|████████▎ | 83/100 [00:03<00:00, 28.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  84%|████████▍ | 84/100 [00:03<00:00, 30.21it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.020 | Best: {'fx': 526.263} (0.076):  84%|████████▍ | 84/100 [00:03<00:00, 30.21it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  85%|████████▌ | 85/100 [00:03<00:00, 30.21it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  86%|████████▌ | 86/100 [00:03<00:00, 30.21it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  87%|████████▋ | 87/100 [00:03<00:00, 30.21it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  88%|████████▊ | 88/100 [00:03<00:00, 28.60it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  88%|████████▊ | 88/100 [00:04<00:00, 28.60it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  89%|████████▉ | 89/100 [00:04<00:00, 28.60it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  90%|█████████ | 90/100 [00:04<00:00, 28.60it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  91%|█████████ | 91/100 [00:04<00:00, 23.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  91%|█████████ | 91/100 [00:04<00:00, 23.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  92%|█████████▏| 92/100 [00:04<00:00, 23.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  93%|█████████▎| 93/100 [00:04<00:00, 23.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  94%|█████████▍| 94/100 [00:04<00:00, 23.52it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  95%|█████████▌| 95/100 [00:04<00:00, 25.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  95%|█████████▌| 95/100 [00:04<00:00, 25.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  96%|█████████▌| 96/100 [00:04<00:00, 25.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  97%|█████████▋| 97/100 [00:04<00:00, 25.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  98%|█████████▊| 98/100 [00:04<00:00, 25.56it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  99%|█████████▉| 99/100 [00:04<00:00, 22.25it/s]Brute | Start: {'fx': 500.0} (0.047) | Current: 0.019 | Best: {'fx': 526.263} (0.076):  99%|█████████▉| 99/100 [00:04<00:00, 22.25it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.050 | Best: {'fx': 502.283} (0.050):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.050 | Best: {'fx': 502.283} (0.050):   2%|▏         | 1/50 [00:00<00:27,  1.79it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.049 | Best: {'fx': 502.283} (0.050):   2%|▏         | 1/50 [00:00<00:27,  1.79it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.050 | Best: {'fx': 503.303} (0.050):   4%|▍         | 2/50 [00:00<00:26,  1.79it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.052 | Best: {'fx': 504.324} (0.052):   6%|▌         | 3/50 [00:00<00:26,  1.79it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.052 | Best: {'fx': 504.324} (0.052):   8%|▊         | 4/50 [00:00<00:06,  6.73it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.053 | Best: {'fx': 505.344} (0.053):   8%|▊         | 4/50 [00:00<00:06,  6.73it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.054 | Best: {'fx': 506.365} (0.054):  10%|█         | 5/50 [00:00<00:06,  6.73it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.056 | Best: {'fx': 507.385} (0.056):  12%|█▏        | 6/50 [00:00<00:06,  6.73it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.056 | Best: {'fx': 507.385} (0.056):  14%|█▍        | 7/50 [00:00<00:03, 11.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.057 | Best: {'fx': 508.405} (0.057):  14%|█▍        | 7/50 [00:00<00:03, 11.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.058 | Best: {'fx': 509.426} (0.058):  16%|█▌        | 8/50 [00:00<00:03, 11.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.060 | Best: {'fx': 510.446} (0.060):  18%|█▊        | 9/50 [00:01<00:03, 11.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.060 | Best: {'fx': 510.446} (0.060):  20%|██        | 10/50 [00:01<00:03, 12.87it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.060 | Best: {'fx': 511.467} (0.060):  20%|██        | 10/50 [00:01<00:03, 12.87it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.061 | Best: {'fx': 512.487} (0.061):  22%|██▏       | 11/50 [00:01<00:03, 12.87it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.061 | Best: {'fx': 512.487} (0.061):  24%|██▍       | 12/50 [00:01<00:02, 13.85it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.062 | Best: {'fx': 513.508} (0.062):  24%|██▍       | 12/50 [00:01<00:02, 13.85it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.062 | Best: {'fx': 514.528} (0.062):  26%|██▌       | 13/50 [00:01<00:02, 13.85it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.063 | Best: {'fx': 515.548} (0.063):  28%|██▊       | 14/50 [00:01<00:02, 13.85it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.063 | Best: {'fx': 515.548} (0.063):  30%|███       | 15/50 [00:01<00:02, 15.81it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.065 | Best: {'fx': 516.569} (0.065):  30%|███       | 15/50 [00:01<00:02, 15.81it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.067 | Best: {'fx': 517.589} (0.067):  32%|███▏      | 16/50 [00:01<00:02, 15.81it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.068 | Best: {'fx': 518.61} (0.068):  34%|███▍      | 17/50 [00:01<00:02, 15.81it/s] Brute | Start: {'fx': 526.263} (0.076) | Current: 0.068 | Best: {'fx': 518.61} (0.068):  36%|███▌      | 18/50 [00:01<00:01, 16.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.070 | Best: {'fx': 519.63} (0.070):  36%|███▌      | 18/50 [00:01<00:01, 16.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.072 | Best: {'fx': 520.65} (0.072):  38%|███▊      | 19/50 [00:01<00:01, 16.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.072 | Best: {'fx': 520.65} (0.072):  40%|████      | 20/50 [00:01<00:01, 16.62it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.076 | Best: {'fx': 521.671} (0.076):  40%|████      | 20/50 [00:01<00:01, 16.62it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.077 | Best: {'fx': 522.691} (0.077):  42%|████▏     | 21/50 [00:01<00:01, 16.62it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.076 | Best: {'fx': 522.691} (0.077):  44%|████▍     | 22/50 [00:01<00:01, 16.62it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 522.691} (0.077):  46%|████▌     | 23/50 [00:01<00:01, 16.62it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 522.691} (0.077):  48%|████▊     | 24/50 [00:01<00:01, 17.43it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 522.691} (0.077):  48%|████▊     | 24/50 [00:01<00:01, 17.43it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 522.691} (0.077):  50%|█████     | 25/50 [00:01<00:01, 17.43it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 522.691} (0.077):  52%|█████▏    | 26/50 [00:01<00:01, 17.68it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.078 | Best: {'fx': 532.895} (0.078):  52%|█████▏    | 26/50 [00:01<00:01, 17.68it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.076 | Best: {'fx': 532.895} (0.078):  54%|█████▍    | 27/50 [00:01<00:01, 17.68it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.076 | Best: {'fx': 532.895} (0.078):  56%|█████▌    | 28/50 [00:01<00:01, 17.68it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 532.895} (0.078):  58%|█████▊    | 29/50 [00:01<00:01, 17.68it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.076 | Best: {'fx': 532.895} (0.078):  60%|██████    | 30/50 [00:01<00:01, 17.68it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.076 | Best: {'fx': 532.895} (0.078):  62%|██████▏   | 31/50 [00:01<00:00, 24.55it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 532.895} (0.078):  62%|██████▏   | 31/50 [00:01<00:00, 24.55it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.077 | Best: {'fx': 532.895} (0.078):  64%|██████▍   | 32/50 [00:02<00:00, 24.55it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.078 | Best: {'fx': 531.875} (0.078):  66%|██████▌   | 33/50 [00:02<00:00, 24.55it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 531.875} (0.078):  68%|██████▊   | 34/50 [00:02<00:00, 24.55it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.075 | Best: {'fx': 531.875} (0.078):  70%|███████   | 35/50 [00:02<00:00, 26.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.074 | Best: {'fx': 531.875} (0.078):  70%|███████   | 35/50 [00:02<00:00, 26.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.073 | Best: {'fx': 531.875} (0.078):  72%|███████▏  | 36/50 [00:02<00:00, 26.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.074 | Best: {'fx': 531.875} (0.078):  74%|███████▍  | 37/50 [00:02<00:00, 26.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.072 | Best: {'fx': 531.875} (0.078):  76%|███████▌  | 38/50 [00:02<00:00, 26.27it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.072 | Best: {'fx': 531.875} (0.078):  78%|███████▊  | 39/50 [00:02<00:00, 27.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.070 | Best: {'fx': 531.875} (0.078):  78%|███████▊  | 39/50 [00:02<00:00, 27.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.072 | Best: {'fx': 531.875} (0.078):  80%|████████  | 40/50 [00:02<00:00, 27.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.066 | Best: {'fx': 531.875} (0.078):  82%|████████▏ | 41/50 [00:02<00:00, 27.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.068 | Best: {'fx': 531.875} (0.078):  84%|████████▍ | 42/50 [00:02<00:00, 27.70it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.068 | Best: {'fx': 531.875} (0.078):  86%|████████▌ | 43/50 [00:02<00:00, 29.74it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.065 | Best: {'fx': 531.875} (0.078):  86%|████████▌ | 43/50 [00:02<00:00, 29.74it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.064 | Best: {'fx': 531.875} (0.078):  88%|████████▊ | 44/50 [00:02<00:00, 29.74it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.064 | Best: {'fx': 531.875} (0.078):  90%|█████████ | 45/50 [00:02<00:00, 29.74it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.062 | Best: {'fx': 531.875} (0.078):  92%|█████████▏| 46/50 [00:02<00:00, 29.74it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.062 | Best: {'fx': 531.875} (0.078):  94%|█████████▍| 47/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.062 | Best: {'fx': 531.875} (0.078):  94%|█████████▍| 47/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.061 | Best: {'fx': 531.875} (0.078):  96%|█████████▌| 48/50 [00:02<00:00, 28.46it/s]Brute | Start: {'fx': 526.263} (0.076) | Current: 0.059 | Best: {'fx': 531.875} (0.078):  98%|█████████▊| 49/50 [00:02<00:00, 28.46it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8748711605855
    px: 894.0
    py: 910.0
    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 1 incomplete frames): 79 frames will be used for optimization
  Rigs: 2000L(2000mm):19/20, 2000R(2000mm):20/20, 1500(1500mm):20/20, 1000(1000mm):20/20
  Total: 79/80 frames used for optimization
Initial score: 68.7543: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 68.7543, picklable_objective(x0) = 68.3868
Best score: 68.3868: : 0iter [00:02, ?iter/s] Best score: 68.3868: : 1iter [00:02,  2.02s/iter]Best score: 68.3868: : 1iter [00:02,  2.02s/iter]Best score: 68.3868: : 2iter [00:02,  1.08iter/s]Best score: 68.3868: : 2iter [00:02,  1.08iter/s]Best score: 68.3868: : 3iter [00:02,  1.73iter/s]Best score: 68.3868: : 3iter [00:02,  1.73iter/s]Best score: 68.3868: : 4iter [00:02,  2.43iter/s]Best score: 68.3868: : 4iter [00:02,  2.43iter/s]Best score: 68.3868: : 5iter [00:02,  3.12iter/s]Best score: 68.3868: : 5iter [00:02,  3.12iter/s]Best score: 68.3868: : 6iter [00:02,  3.77iter/s]Best score: 68.3868: : 6iter [00:02,  3.77iter/s]Best score: 68.3868: : 7iter [00:02,  4.34iter/s]Best score: 68.3868: : 7iter [00:03,  4.34iter/s]Best score: 68.3868: : 8iter [00:03,  4.83iter/s]Best score: 68.3868: : 8iter [00:03,  4.83iter/s]Best score: 68.3868: : 9iter [00:03,  5.21iter/s]Best score: 50.7305: : 9iter [00:03,  5.21iter/s]Best score: 50.7305: : 10iter [00:03,  5.36iter/s]Best score: 50.7305: : 10iter [00:03,  5.36iter/s]Best score: 50.7305: : 11iter [00:03,  5.62iter/s]Best score: 50.7305: : 11iter [00:03,  5.62iter/s]Best score: 50.7305: : 12iter [00:03,  5.82iter/s]Best score: 50.7305: : 12iter [00:03,  5.82iter/s]Best score: 50.7305: : 13iter [00:03,  5.95iter/s]Best score: 50.7305: : 13iter [00:04,  5.95iter/s]Best score: 50.7305: : 14iter [00:04,  6.05iter/s]Best score: 50.7305: : 14iter [00:04,  6.05iter/s]Best score: 50.7305: : 15iter [00:04,  6.12iter/s]Best score: 50.7305: : 15iter [00:04,  6.12iter/s]Best score: 50.7305: : 16iter [00:04,  6.17iter/s]Best score: 50.7305: : 16iter [00:04,  6.17iter/s]Best score: 50.7305: : 17iter [00:04,  6.22iter/s]Best score: 50.7305: : 17iter [00:04,  6.22iter/s]Best score: 50.7305: : 18iter [00:04,  6.26iter/s]Best score: 50.7305: : 18iter [00:04,  6.26iter/s]Best score: 50.7305: : 19iter [00:04,  6.28iter/s]Best score: 36.0646: : 19iter [00:05,  6.28iter/s]Best score: 36.0646: : 20iter [00:05,  6.11iter/s]Best score: 36.0646: : 20iter [00:05,  6.11iter/s]Best score: 36.0646: : 21iter [00:05,  6.16iter/s]Best score: 36.0646: : 21iter [00:05,  6.16iter/s]Best score: 36.0646: : 22iter [00:05,  6.21iter/s]Best score: 36.0646: : 22iter [00:05,  6.21iter/s]Best score: 36.0646: : 23iter [00:05,  6.23iter/s]Best score: 36.0646: : 23iter [00:05,  6.23iter/s]Best score: 36.0646: : 24iter [00:05,  6.28iter/s]Best score: 36.0646: : 24iter [00:05,  6.28iter/s]Best score: 36.0646: : 25iter [00:05,  6.31iter/s]Best score: 36.0646: : 25iter [00:06,  6.31iter/s]Best score: 36.0646: : 26iter [00:06,  6.34iter/s]Best score: 36.0646: : 26iter [00:06,  6.34iter/s]Best score: 36.0646: : 27iter [00:06,  6.35iter/s]Best score: 36.0646: : 27iter [00:06,  6.35iter/s]Best score: 36.0646: : 28iter [00:06,  6.33iter/s]Best score: 36.0646: : 28iter [00:06,  6.33iter/s]Best score: 36.0646: : 29iter [00:06,  6.33iter/s]Best score: 23.8001: : 29iter [00:06,  6.33iter/s]Best score: 23.8001: : 30iter [00:06,  6.15iter/s]Best score: 23.8001: : 30iter [00:06,  6.15iter/s]Best score: 23.8001: : 31iter [00:06,  6.20iter/s]Best score: 23.8001: : 31iter [00:06,  6.20iter/s]Best score: 23.8001: : 32iter [00:06,  6.23iter/s]Best score: 23.8001: : 32iter [00:07,  6.23iter/s]Best score: 23.8001: : 33iter [00:07,  6.26iter/s]Best score: 23.8001: : 33iter [00:07,  6.26iter/s]Best score: 23.8001: : 34iter [00:07,  6.28iter/s]Best score: 23.8001: : 34iter [00:07,  6.28iter/s]Best score: 23.8001: : 35iter [00:07,  6.32iter/s]Best score: 23.8001: : 35iter [00:07,  6.32iter/s]Best score: 23.8001: : 36iter [00:07,  6.33iter/s]Best score: 23.8001: : 36iter [00:07,  6.33iter/s]Best score: 23.8001: : 37iter [00:07,  6.33iter/s]Best score: 23.8001: : 37iter [00:07,  6.33iter/s]Best score: 23.8001: : 38iter [00:07,  6.34iter/s]Best score: 23.8001: : 38iter [00:08,  6.34iter/s]Best score: 23.8001: : 39iter [00:08,  6.36iter/s]Best score: 23.8001: : 39iter [00:08,  6.36iter/s]Best score: 23.8001: : 40iter [00:08,  6.15iter/s]Best score: 23.8001: : 40iter [00:08,  6.15iter/s]Best score: 23.8001: : 41iter [00:08,  6.20iter/s]Best score: 23.8001: : 41iter [00:08,  6.20iter/s]Best score: 23.8001: : 42iter [00:08,  6.24iter/s]Best score: 23.8001: : 42iter [00:08,  6.24iter/s]Best score: 23.8001: : 43iter [00:08,  6.27iter/s]Best score: 23.8001: : 43iter [00:08,  6.27iter/s]Best score: 23.8001: : 44iter [00:08,  6.30iter/s]Best score: 23.8001: : 44iter [00:09,  6.30iter/s]Best score: 23.8001: : 45iter [00:09,  6.34iter/s]Best score: 23.8001: : 45iter [00:09,  6.34iter/s]Best score: 23.8001: : 46iter [00:09,  6.32iter/s]Best score: 23.8001: : 46iter [00:09,  6.32iter/s]Best score: 23.8001: : 47iter [00:09,  6.33iter/s]Best score: 23.8001: : 47iter [00:09,  6.33iter/s]Best score: 23.8001: : 48iter [00:09,  6.33iter/s]Best score: 23.8001: : 48iter [00:09,  6.33iter/s]Best score: 23.8001: : 49iter [00:09,  6.33iter/s]
[ITER 50] Best: 23.8001
[PARAMS] Current best parameters:
  fx: 559.2285
  fy: 556.9220
  px: 892.8779
  py: 925.2997
  k1: -0.02153335
  k2: 0.00422710
  k3: -0.00110299
  k4: -0.00189784
  p1: -0.00458239
  p2: 0.00073426
  p3: 0.00565576
  p4: 0.00239116
  scale: 2.8976
  rotation: -0.3181
Best score: 23.8001: : 49iter [00:09,  6.33iter/s]Best score: 23.8001: : 50iter [00:09,  6.14iter/s]Best score: 23.8001: : 50iter [00:10,  6.14iter/s]Best score: 23.8001: : 51iter [00:10,  6.21iter/s]Best score: 23.8001: : 51iter [00:10,  6.21iter/s]Best score: 23.8001: : 52iter [00:10,  6.26iter/s]Best score: 23.8001: : 52iter [00:10,  6.26iter/s]Best score: 23.8001: : 53iter [00:10,  6.30iter/s]Best score: 23.8001: : 53iter [00:10,  6.30iter/s]Best score: 23.8001: : 54iter [00:10,  6.33iter/s]Best score: 23.8001: : 54iter [00:10,  6.33iter/s]Best score: 23.8001: : 55iter [00:10,  6.34iter/s]Best score: 23.8001: : 55iter [00:10,  6.34iter/s]Best score: 23.8001: : 56iter [00:10,  6.35iter/s]Best score: 23.8001: : 56iter [00:10,  6.35iter/s]Best score: 23.8001: : 57iter [00:10,  6.37iter/s]Best score: 23.8001: : 57iter [00:11,  6.37iter/s]Best score: 23.8001: : 58iter [00:11,  6.39iter/s]Best score: 23.8001: : 58iter [00:11,  6.39iter/s]Best score: 23.8001: : 59iter [00:11,  6.40iter/s]Best score: 4.8972: : 59iter [00:11,  6.40iter/s] Best score: 4.8972: : 60iter [00:11,  6.19iter/s]Best score: 4.8972: : 60iter [00:11,  6.19iter/s]Best score: 4.8972: : 61iter [00:11,  6.23iter/s]Best score: 4.8972: : 61iter [00:11,  6.23iter/s]Best score: 4.8972: : 62iter [00:11,  6.29iter/s]Best score: 4.8972: : 62iter [00:11,  6.29iter/s]Best score: 4.8972: : 63iter [00:11,  6.29iter/s]Best score: 4.8972: : 63iter [00:12,  6.29iter/s]Best score: 4.8972: : 64iter [00:12,  6.30iter/s]Best score: 4.8972: : 64iter [00:12,  6.30iter/s]Best score: 4.8972: : 65iter [00:12,  6.33iter/s]Best score: 4.8972: : 65iter [00:12,  6.33iter/s]Best score: 4.8972: : 66iter [00:12,  6.35iter/s]Best score: 4.8972: : 66iter [00:12,  6.35iter/s]Best score: 4.8972: : 67iter [00:12,  6.36iter/s]Best score: 4.8972: : 67iter [00:12,  6.36iter/s]Best score: 4.8972: : 68iter [00:12,  6.36iter/s]Best score: 4.8972: : 68iter [00:12,  6.36iter/s]Best score: 4.8972: : 69iter [00:12,  6.36iter/s]Best score: 2.4751: : 69iter [00:13,  6.36iter/s]Best score: 2.4751: : 70iter [00:13,  6.17iter/s]Best score: 2.4751: : 70iter [00:13,  6.17iter/s]Best score: 2.4751: : 71iter [00:13,  6.21iter/s]Best score: 2.4751: : 71iter [00:13,  6.21iter/s]Best score: 2.4751: : 72iter [00:13,  6.24iter/s]Best score: 2.4751: : 72iter [00:13,  6.24iter/s]Best score: 2.4751: : 73iter [00:13,  6.27iter/s]Best score: 2.4751: : 73iter [00:13,  6.27iter/s]Best score: 2.4751: : 74iter [00:13,  6.30iter/s]Best score: 2.4751: : 74iter [00:13,  6.30iter/s]Best score: 2.4751: : 75iter [00:13,  6.30iter/s]Best score: 2.4751: : 75iter [00:13,  6.30iter/s]Best score: 2.4751: : 76iter [00:13,  6.30iter/s]Best score: 2.4751: : 76iter [00:14,  6.30iter/s]Best score: 2.4751: : 77iter [00:14,  6.31iter/s]Best score: 2.4751: : 77iter [00:14,  6.31iter/s]Best score: 2.4751: : 78iter [00:14,  6.31iter/s]Best score: 2.4751: : 78iter [00:14,  6.31iter/s]Best score: 2.4751: : 79iter [00:14,  6.32iter/s]Best score: 2.4751: : 79iter [00:14,  6.32iter/s]Best score: 2.4751: : 80iter [00:14,  6.14iter/s]Best score: 2.4751: : 80iter [00:14,  6.14iter/s]Best score: 2.4751: : 81iter [00:14,  6.21iter/s]Best score: 2.4751: : 81iter [00:14,  6.21iter/s]Best score: 2.4751: : 82iter [00:14,  6.26iter/s]Best score: 2.4751: : 82iter [00:15,  6.26iter/s]Best score: 2.4751: : 83iter [00:15,  6.33iter/s]Best score: 2.4751: : 83iter [00:15,  6.33iter/s]Best score: 2.4751: : 84iter [00:15,  6.36iter/s]Best score: 2.4751: : 84iter [00:15,  6.36iter/s]Best score: 2.4751: : 85iter [00:15,  6.38iter/s]Best score: 2.4751: : 85iter [00:15,  6.38iter/s]Best score: 2.4751: : 86iter [00:15,  6.38iter/s]Best score: 2.4751: : 86iter [00:15,  6.38iter/s]Best score: 2.4751: : 87iter [00:15,  6.40iter/s]Best score: 2.4751: : 87iter [00:15,  6.40iter/s]Best score: 2.4751: : 88iter [00:15,  6.41iter/s]Best score: 2.4751: : 88iter [00:16,  6.41iter/s]Best score: 2.4751: : 89iter [00:16,  6.41iter/s]Best score: 1.1140: : 89iter [00:16,  6.41iter/s]Best score: 1.1140: : 90iter [00:16,  6.21iter/s]Best score: 1.1140: : 90iter [00:16,  6.21iter/s]Best score: 1.1140: : 91iter [00:16,  6.27iter/s]Best score: 1.1140: : 91iter [00:16,  6.27iter/s]Best score: 1.1140: : 92iter [00:16,  6.30iter/s]Best score: 1.1140: : 92iter [00:16,  6.30iter/s]Best score: 1.1140: : 93iter [00:16,  6.33iter/s]Best score: 1.1140: : 93iter [00:16,  6.33iter/s]Best score: 1.1140: : 94iter [00:16,  6.34iter/s]Best score: 1.1140: : 94iter [00:16,  6.34iter/s]Best score: 1.1140: : 95iter [00:16,  6.36iter/s]Best score: 1.1140: : 95iter [00:17,  6.36iter/s]Best score: 1.1140: : 96iter [00:17,  6.38iter/s]Best score: 1.1140: : 96iter [00:17,  6.38iter/s]Best score: 1.1140: : 97iter [00:17,  6.38iter/s]Best score: 1.1140: : 97iter [00:17,  6.38iter/s]Best score: 1.1140: : 98iter [00:17,  4.83iter/s]Best score: 1.1140: : 98iter [00:17,  4.83iter/s]Best score: 1.1140: : 99iter [00:17,  5.22iter/s]
[ITER 100] Best: 0.6990
[PARAMS] Current best parameters:
  fx: 523.1039
  fy: 523.5111
  px: 887.1844
  py: 918.7769
  k1: -0.00876591
  k2: 0.03484491
  k3: -0.02466093
  k4: 0.00515161
  p1: -0.00894942
  p2: -0.00722367
  p3: -0.00526896
  p4: 0.00539232
  scale: 2.9905
  rotation: -0.4554
Best score: 0.6990: : 99iter [00:17,  5.22iter/s]Best score: 0.6990: : 100iter [00:17,  5.36iter/s]Best score: 0.6990: : 100iter [00:18,  5.36iter/s]Best score: 0.6990: : 101iter [00:18,  5.63iter/s]Best score: 0.6990: : 101iter [00:18,  5.63iter/s]Best score: 0.6990: : 102iter [00:18,  5.83iter/s]Best score: 0.6990: : 102iter [00:18,  5.83iter/s]Best score: 0.6990: : 103iter [00:18,  5.99iter/s]Best score: 0.6990: : 103iter [00:18,  5.99iter/s]Best score: 0.6990: : 104iter [00:18,  6.12iter/s]Best score: 0.6990: : 104iter [00:18,  6.12iter/s]Best score: 0.6990: : 105iter [00:18,  6.19iter/s]Best score: 0.6990: : 105iter [00:18,  6.19iter/s]Best score: 0.6990: : 106iter [00:18,  6.26iter/s]Best score: 0.6990: : 106iter [00:19,  6.26iter/s]Best score: 0.6990: : 107iter [00:19,  6.31iter/s]Best score: 0.6990: : 107iter [00:19,  6.31iter/s]Best score: 0.6990: : 108iter [00:19,  6.34iter/s]Best score: 0.6990: : 108iter [00:19,  6.34iter/s]Best score: 0.6990: : 109iter [00:19,  6.35iter/s]Best score: 0.6247: : 109iter [00:19,  6.35iter/s]Best score: 0.6247: : 110iter [00:19,  6.16iter/s]Best score: 0.6247: : 110iter [00:19,  6.16iter/s]Best score: 0.6247: : 111iter [00:19,  6.23iter/s]Best score: 0.6247: : 111iter [00:19,  6.23iter/s]Best score: 0.6247: : 112iter [00:19,  6.27iter/s]Best score: 0.6247: : 112iter [00:19,  6.27iter/s]Best score: 0.6247: : 113iter [00:19,  6.31iter/s]Best score: 0.6247: : 113iter [00:20,  6.31iter/s]Best score: 0.6247: : 114iter [00:20,  6.33iter/s]Best score: 0.6247: : 114iter [00:20,  6.33iter/s]Best score: 0.6247: : 115iter [00:20,  6.34iter/s]Best score: 0.6247: : 115iter [00:20,  6.34iter/s]Best score: 0.6247: : 116iter [00:20,  6.35iter/s]Best score: 0.6247: : 116iter [00:20,  6.35iter/s]Best score: 0.6247: : 117iter [00:20,  6.35iter/s]Best score: 0.6247: : 117iter [00:20,  6.35iter/s]Best score: 0.6247: : 118iter [00:20,  6.37iter/s]Best score: 0.6247: : 118iter [00:20,  6.37iter/s]Best score: 0.6247: : 119iter [00:20,  6.38iter/s]Best score: 0.4919: : 119iter [00:21,  6.38iter/s]Best score: 0.4919: : 120iter [00:21,  6.19iter/s]Best score: 0.4919: : 120iter [00:21,  6.19iter/s]Best score: 0.4919: : 121iter [00:21,  6.25iter/s]Best score: 0.4919: : 121iter [00:21,  6.25iter/s]Best score: 0.4919: : 122iter [00:21,  6.29iter/s]Best score: 0.4919: : 122iter [00:21,  6.29iter/s]Best score: 0.4919: : 123iter [00:21,  6.32iter/s]Best score: 0.4919: : 123iter [00:21,  6.32iter/s]Best score: 0.4919: : 124iter [00:21,  6.35iter/s]Best score: 0.4919: : 124iter [00:21,  6.35iter/s]Best score: 0.4919: : 125iter [00:21,  6.37iter/s]Best score: 0.4919: : 125iter [00:22,  6.37iter/s]Best score: 0.4919: : 126iter [00:22,  6.38iter/s]Best score: 0.4919: : 126iter [00:22,  6.38iter/s]Best score: 0.4919: : 127iter [00:22,  6.38iter/s]Best score: 0.4919: : 127iter [00:22,  6.38iter/s]Best score: 0.4919: : 128iter [00:22,  6.39iter/s]Best score: 0.4919: : 128iter [00:22,  6.39iter/s]Best score: 0.4919: : 129iter [00:22,  6.39iter/s]Best score: 0.3399: : 129iter [00:22,  6.39iter/s]Best score: 0.3399: : 130iter [00:22,  6.19iter/s]Best score: 0.3399: : 130iter [00:22,  6.19iter/s]Best score: 0.3399: : 131iter [00:22,  6.25iter/s]Best score: 0.3399: : 131iter [00:22,  6.25iter/s]Best score: 0.3399: : 132iter [00:22,  6.28iter/s]Best score: 0.3399: : 132iter [00:23,  6.28iter/s]Best score: 0.3399: : 133iter [00:23,  6.31iter/s]Best score: 0.3399: : 133iter [00:23,  6.31iter/s]Best score: 0.3399: : 134iter [00:23,  6.31iter/s]Best score: 0.3399: : 134iter [00:23,  6.31iter/s]Best score: 0.3399: : 135iter [00:23,  6.33iter/s]Best score: 0.3399: : 135iter [00:23,  6.33iter/s]Best score: 0.3399: : 136iter [00:23,  6.35iter/s]Best score: 0.3399: : 136iter [00:23,  6.35iter/s]Best score: 0.3399: : 137iter [00:23,  6.37iter/s]Best score: 0.3399: : 137iter [00:23,  6.37iter/s]Best score: 0.3399: : 138iter [00:23,  6.38iter/s]Best score: 0.3399: : 138iter [00:24,  6.38iter/s]Best score: 0.3399: : 139iter [00:24,  6.39iter/s]Best score: 0.3278: : 139iter [00:24,  6.39iter/s]Best score: 0.3278: : 140iter [00:24,  6.19iter/s]Best score: 0.3278: : 140iter [00:24,  6.19iter/s]Best score: 0.3278: : 141iter [00:24,  6.24iter/s]Best score: 0.3278: : 141iter [00:24,  6.24iter/s]Best score: 0.3278: : 142iter [00:24,  6.28iter/s]Best score: 0.3278: : 142iter [00:24,  6.28iter/s]Best score: 0.3278: : 143iter [00:24,  6.33iter/s]Best score: 0.3278: : 143iter [00:24,  6.33iter/s]Best score: 0.3278: : 144iter [00:24,  6.35iter/s]Best score: 0.3278: : 144iter [00:25,  6.35iter/s]Best score: 0.3278: : 145iter [00:25,  6.37iter/s]Best score: 0.3278: : 145iter [00:25,  6.37iter/s]Best score: 0.3278: : 146iter [00:25,  6.38iter/s]Best score: 0.3278: : 146iter [00:25,  6.38iter/s]Best score: 0.3278: : 147iter [00:25,  6.39iter/s]Best score: 0.3278: : 147iter [00:25,  6.39iter/s]Best score: 0.3278: : 148iter [00:25,  6.40iter/s]Best score: 0.3278: : 148iter [00:25,  6.40iter/s]Best score: 0.3278: : 149iter [00:25,  6.40iter/s]
[ITER 150] Best: 0.3278
[PARAMS] Current best parameters:
  fx: 528.9151
  fy: 528.9568
  px: 887.4916
  py: 920.7954
  k1: -0.01348733
  k2: 0.03674560
  k3: -0.02791688
  k4: 0.00630625
  p1: -0.00439169
  p2: -0.00737628
  p3: -0.00672169
  p4: 0.00496539
  scale: 2.9906
  rotation: -0.4579
Best score: 0.3278: : 149iter [00:25,  6.40iter/s]Best score: 0.3278: : 150iter [00:25,  6.21iter/s]Best score: 0.3278: : 150iter [00:25,  6.21iter/s]Best score: 0.3278: : 151iter [00:25,  6.26iter/s]Best score: 0.3278: : 151iter [00:26,  6.26iter/s]Best score: 0.3278: : 152iter [00:26,  6.31iter/s]Best score: 0.3278: : 152iter [00:26,  6.31iter/s]Best score: 0.3278: : 153iter [00:26,  6.33iter/s]Best score: 0.3278: : 153iter [00:26,  6.33iter/s]Best score: 0.3278: : 154iter [00:26,  6.35iter/s]Best score: 0.3278: : 154iter [00:26,  6.35iter/s]Best score: 0.3278: : 155iter [00:26,  6.36iter/s]Best score: 0.3278: : 155iter [00:26,  6.36iter/s]Best score: 0.3278: : 156iter [00:26,  6.37iter/s]Best score: 0.3278: : 156iter [00:26,  6.37iter/s]Best score: 0.3278: : 157iter [00:26,  6.38iter/s]Best score: 0.3278: : 157iter [00:27,  6.38iter/s]Best score: 0.3278: : 158iter [00:27,  6.39iter/s]Best score: 0.3278: : 158iter [00:27,  6.39iter/s]Best score: 0.3278: : 159iter [00:27,  6.39iter/s]Best score: 0.2536: : 159iter [00:27,  6.39iter/s]Best score: 0.2536: : 160iter [00:27,  6.19iter/s]Best score: 0.2536: : 160iter [00:27,  6.19iter/s]Best score: 0.2536: : 161iter [00:27,  6.26iter/s]Best score: 0.2536: : 161iter [00:27,  6.26iter/s]Best score: 0.2536: : 162iter [00:27,  6.30iter/s]Best score: 0.2536: : 162iter [00:27,  6.30iter/s]Best score: 0.2536: : 163iter [00:27,  6.32iter/s]Best score: 0.2536: : 163iter [00:28,  6.32iter/s]Best score: 0.2536: : 164iter [00:28,  6.35iter/s]Best score: 0.2536: : 164iter [00:28,  6.35iter/s]Best score: 0.2536: : 165iter [00:28,  6.37iter/s]Best score: 0.2536: : 165iter [00:28,  6.37iter/s]Best score: 0.2536: : 166iter [00:28,  6.39iter/s]Best score: 0.2536: : 166iter [00:28,  6.39iter/s]Best score: 0.2536: : 167iter [00:28,  6.39iter/s]Best score: 0.2536: : 167iter [00:28,  6.39iter/s]Best score: 0.2536: : 168iter [00:28,  6.40iter/s]Best score: 0.2536: : 168iter [00:28,  6.40iter/s]Best score: 0.2536: : 169iter [00:28,  6.38iter/s]Best score: 0.2536: : 169iter [00:28,  6.38iter/s]Best score: 0.2536: : 170iter [00:28,  6.19iter/s]Best score: 0.2536: : 170iter [00:29,  6.19iter/s]Best score: 0.2536: : 171iter [00:29,  6.25iter/s]Best score: 0.2536: : 171iter [00:29,  6.25iter/s]Best score: 0.2536: : 172iter [00:29,  6.29iter/s]Best score: 0.2536: : 172iter [00:29,  6.29iter/s]Best score: 0.2536: : 173iter [00:29,  6.31iter/s]Best score: 0.2536: : 173iter [00:29,  6.31iter/s]Best score: 0.2536: : 174iter [00:29,  6.33iter/s]Best score: 0.2536: : 174iter [00:29,  6.33iter/s]Best score: 0.2536: : 175iter [00:29,  6.36iter/s]Best score: 0.2536: : 175iter [00:29,  6.36iter/s]Best score: 0.2536: : 176iter [00:29,  6.37iter/s]Best score: 0.2536: : 176iter [00:30,  6.37iter/s]Best score: 0.2536: : 177iter [00:30,  6.38iter/s]Best score: 0.2536: : 177iter [00:30,  6.38iter/s]Best score: 0.2536: : 178iter [00:30,  6.37iter/s]Best score: 0.2536: : 178iter [00:30,  6.37iter/s]Best score: 0.2536: : 179iter [00:30,  6.36iter/s]Best score: 0.2536: : 179iter [00:30,  6.36iter/s]Best score: 0.2536: : 180iter [00:30,  6.18iter/s]Best score: 0.2536: : 180iter [00:30,  6.18iter/s]Best score: 0.2536: : 181iter [00:30,  6.23iter/s]Best score: 0.2536: : 181iter [00:30,  6.23iter/s]Best score: 0.2536: : 182iter [00:30,  6.29iter/s]Best score: 0.2536: : 182iter [00:31,  6.29iter/s]Best score: 0.2536: : 183iter [00:31,  6.33iter/s]Best score: 0.2536: : 183iter [00:31,  6.33iter/s]Best score: 0.2536: : 184iter [00:31,  6.36iter/s]Best score: 0.2536: : 184iter [00:31,  6.36iter/s]Best score: 0.2536: : 185iter [00:31,  6.39iter/s]Best score: 0.2536: : 185iter [00:31,  6.39iter/s]Best score: 0.2536: : 186iter [00:31,  6.38iter/s]Best score: 0.2536: : 186iter [00:31,  6.38iter/s]Best score: 0.2536: : 187iter [00:31,  6.37iter/s]Best score: 0.2536: : 187iter [00:31,  6.37iter/s]Best score: 0.2536: : 188iter [00:31,  6.37iter/s]Best score: 0.2536: : 188iter [00:31,  6.37iter/s]Best score: 0.2536: : 189iter [00:31,  6.37iter/s]Best score: 0.2536: : 189iter [00:32,  6.37iter/s]Best score: 0.2536: : 190iter [00:32,  6.18iter/s]Best score: 0.2536: : 190iter [00:32,  6.18iter/s]Best score: 0.2536: : 191iter [00:32,  6.24iter/s]Best score: 0.2536: : 191iter [00:32,  6.24iter/s]Best score: 0.2536: : 192iter [00:32,  6.27iter/s]Best score: 0.2536: : 192iter [00:32,  6.27iter/s]Best score: 0.2536: : 193iter [00:32,  6.31iter/s]Best score: 0.2536: : 193iter [00:32,  6.31iter/s]Best score: 0.2536: : 194iter [00:32,  6.33iter/s]Best score: 0.2536: : 194iter [00:32,  6.33iter/s]Best score: 0.2536: : 195iter [00:32,  6.35iter/s]Best score: 0.2536: : 195iter [00:33,  6.35iter/s]Best score: 0.2536: : 196iter [00:33,  6.36iter/s]Best score: 0.2536: : 196iter [00:33,  6.36iter/s]Best score: 0.2536: : 197iter [00:33,  6.38iter/s]Best score: 0.2536: : 197iter [00:33,  6.38iter/s]Best score: 0.2536: : 198iter [00:33,  6.39iter/s]Best score: 0.2536: : 198iter [00:33,  6.39iter/s]Best score: 0.2536: : 199iter [00:33,  6.38iter/s]
[ITER 200] Best: 0.2469
[PARAMS] Current best parameters:
  fx: 515.3795
  fy: 515.2407
  px: 886.9834
  py: 920.7328
  k1: -0.00282843
  k2: 0.04521637
  k3: -0.03712148
  k4: 0.00909573
  p1: -0.00391750
  p2: -0.00808737
  p3: -0.00683404
  p4: 0.00369092
  scale: 3.0015
  rotation: -0.4913
Best score: 0.2469: : 199iter [00:33,  6.38iter/s]Best score: 0.2469: : 200iter [00:33,  6.17iter/s]Best score: 0.2469: : 200iter [00:33,  6.17iter/s]Best score: 0.2469: : 201iter [00:33,  6.25iter/s]Best score: 0.2469: : 201iter [00:34,  6.25iter/s]Best score: 0.2469: : 202iter [00:34,  6.29iter/s]Best score: 0.2469: : 202iter [00:34,  6.29iter/s]Best score: 0.2469: : 203iter [00:34,  6.31iter/s]Best score: 0.2469: : 203iter [00:34,  6.31iter/s]Best score: 0.2469: : 204iter [00:34,  6.35iter/s]Best score: 0.2469: : 204iter [00:34,  6.35iter/s]Best score: 0.2469: : 205iter [00:34,  6.38iter/s]Best score: 0.2469: : 205iter [00:34,  6.38iter/s]Best score: 0.2469: : 206iter [00:34,  6.38iter/s]Best score: 0.2469: : 206iter [00:34,  6.38iter/s]Best score: 0.2469: : 207iter [00:34,  6.37iter/s]Best score: 0.2469: : 207iter [00:34,  6.37iter/s]Best score: 0.2469: : 208iter [00:34,  6.38iter/s]Best score: 0.2469: : 208iter [00:35,  6.38iter/s]Best score: 0.2469: : 209iter [00:35,  6.39iter/s]Best score: 0.2410: : 209iter [00:35,  6.39iter/s]Best score: 0.2410: : 210iter [00:35,  6.20iter/s]Best score: 0.2410: : 210iter [00:35,  6.20iter/s]Best score: 0.2410: : 211iter [00:35,  6.25iter/s]Best score: 0.2410: : 211iter [00:35,  6.25iter/s]Best score: 0.2410: : 212iter [00:35,  6.28iter/s]Best score: 0.2410: : 212iter [00:35,  6.28iter/s]Best score: 0.2410: : 213iter [00:35,  6.31iter/s]Best score: 0.2410: : 213iter [00:35,  6.31iter/s]Best score: 0.2410: : 214iter [00:35,  6.33iter/s]Best score: 0.2410: : 214iter [00:36,  6.33iter/s]Best score: 0.2410: : 215iter [00:36,  6.35iter/s]Best score: 0.2410: : 215iter [00:36,  6.35iter/s]Best score: 0.2410: : 216iter [00:36,  6.36iter/s]Best score: 0.2410: : 216iter [00:36,  6.36iter/s]Best score: 0.2410: : 217iter [00:36,  6.36iter/s]Best score: 0.2410: : 217iter [00:36,  6.36iter/s]Best score: 0.2410: : 218iter [00:36,  6.37iter/s]Best score: 0.2410: : 218iter [00:36,  6.37iter/s]Best score: 0.2410: : 219iter [00:36,  6.37iter/s]Best score: 0.2304: : 219iter [00:36,  6.37iter/s]Best score: 0.2304: : 220iter [00:36,  6.18iter/s]Best score: 0.2304: : 220iter [00:37,  6.18iter/s]Best score: 0.2304: : 221iter [00:37,  6.25iter/s]Best score: 0.2304: : 221iter [00:37,  6.25iter/s]Best score: 0.2304: : 222iter [00:37,  6.29iter/s]Best score: 0.2304: : 222iter [00:37,  6.29iter/s]Best score: 0.2304: : 223iter [00:37,  6.32iter/s]Best score: 0.2304: : 223iter [00:37,  6.32iter/s]Best score: 0.2304: : 224iter [00:37,  6.34iter/s]Best score: 0.2304: : 224iter [00:37,  6.34iter/s]Best score: 0.2304: : 225iter [00:37,  6.36iter/s]Best score: 0.2304: : 225iter [00:37,  6.36iter/s]Best score: 0.2304: : 226iter [00:37,  6.35iter/s]Best score: 0.2304: : 226iter [00:38,  6.35iter/s]Best score: 0.2304: : 227iter [00:38,  6.36iter/s]Best score: 0.2304: : 227iter [00:38,  6.36iter/s]Best score: 0.2304: : 228iter [00:38,  6.37iter/s]Best score: 0.2304: : 228iter [00:38,  6.37iter/s]Best score: 0.2304: : 229iter [00:38,  6.39iter/s]Best score: 0.2287: : 229iter [00:38,  6.39iter/s]Best score: 0.2287: : 230iter [00:38,  6.19iter/s]Best score: 0.2287: : 230iter [00:38,  6.19iter/s]Best score: 0.2287: : 231iter [00:38,  6.24iter/s]Best score: 0.2287: : 231iter [00:38,  6.24iter/s]Best score: 0.2287: : 232iter [00:38,  6.28iter/s]Best score: 0.2287: : 232iter [00:38,  6.28iter/s]Best score: 0.2287: : 233iter [00:38,  6.32iter/s]Best score: 0.2287: : 233iter [00:39,  6.32iter/s]Best score: 0.2287: : 234iter [00:39,  6.33iter/s]Best score: 0.2287: : 234iter [00:39,  6.33iter/s]Best score: 0.2287: : 235iter [00:39,  6.35iter/s]Best score: 0.2287: : 235iter [00:39,  6.35iter/s]Best score: 0.2287: : 236iter [00:39,  6.35iter/s]Best score: 0.2287: : 236iter [00:39,  6.35iter/s]Best score: 0.2287: : 237iter [00:39,  6.37iter/s]Best score: 0.2287: : 237iter [00:39,  6.37iter/s]Best score: 0.2287: : 238iter [00:39,  6.38iter/s]Best score: 0.2287: : 238iter [00:39,  6.38iter/s]Best score: 0.2287: : 239iter [00:39,  6.39iter/s]Best score: 0.2182: : 239iter [00:40,  6.39iter/s]Best score: 0.2182: : 240iter [00:40,  6.19iter/s]Best score: 0.2182: : 240iter [00:40,  6.19iter/s]Best score: 0.2182: : 241iter [00:40,  6.26iter/s]Best score: 0.2182: : 241iter [00:40,  6.26iter/s]Best score: 0.2182: : 242iter [00:40,  6.29iter/s]Best score: 0.2182: : 242iter [00:40,  6.29iter/s]Best score: 0.2182: : 243iter [00:40,  6.33iter/s]Best score: 0.2182: : 243iter [00:40,  6.33iter/s]Best score: 0.2182: : 244iter [00:40,  6.36iter/s]Best score: 0.2182: : 244iter [00:40,  6.36iter/s]Best score: 0.2182: : 245iter [00:40,  6.38iter/s]Best score: 0.2182: : 245iter [00:41,  6.38iter/s]Best score: 0.2182: : 246iter [00:41,  6.38iter/s]Best score: 0.2182: : 246iter [00:41,  6.38iter/s]Best score: 0.2182: : 247iter [00:41,  6.37iter/s]Best score: 0.2182: : 247iter [00:41,  6.37iter/s]Best score: 0.2182: : 248iter [00:41,  6.38iter/s]Best score: 0.2182: : 248iter [00:41,  6.38iter/s]Best score: 0.2182: : 249iter [00:41,  6.37iter/s]
[ITER 250] Best: 0.2128
[PARAMS] Current best parameters:
  fx: 524.1432
  fy: 523.9083
  px: 886.4500
  py: 919.9049
  k1: -0.00671363
  k2: 0.02935720
  k3: -0.02244176
  k4: 0.00507159
  p1: -0.00623557
  p2: -0.00947160
  p3: -0.00420635
  p4: 0.00220494
  scale: 3.0003
  rotation: -0.4974
Best score: 0.2128: : 249iter [00:41,  6.37iter/s]Best score: 0.2128: : 250iter [00:41,  6.18iter/s]Best score: 0.2128: : 250iter [00:41,  6.18iter/s]Best score: 0.2128: : 251iter [00:41,  6.25iter/s]Best score: 0.2128: : 251iter [00:41,  6.25iter/s]Best score: 0.2128: : 252iter [00:41,  6.30iter/s]Best score: 0.2128: : 252iter [00:42,  6.30iter/s]Best score: 0.2128: : 253iter [00:42,  6.33iter/s]Best score: 0.2128: : 253iter [00:42,  6.33iter/s]Best score: 0.2128: : 254iter [00:42,  6.34iter/s]Best score: 0.2128: : 254iter [00:42,  6.34iter/s]Best score: 0.2128: : 255iter [00:42,  6.35iter/s]Best score: 0.2128: : 255iter [00:42,  6.35iter/s]Best score: 0.2128: : 256iter [00:42,  6.36iter/s]Best score: 0.2128: : 256iter [00:42,  6.36iter/s]Best score: 0.2128: : 257iter [00:42,  6.38iter/s]Best score: 0.2128: : 257iter [00:42,  6.38iter/s]Best score: 0.2128: : 258iter [00:42,  6.38iter/s]Best score: 0.2128: : 258iter [00:43,  6.38iter/s]Best score: 0.2128: : 259iter [00:43,  6.39iter/s]Best score: 0.2128: : 259iter [00:43,  6.39iter/s]Best score: 0.2128: : 260iter [00:43,  6.20iter/s]Best score: 0.2128: : 260iter [00:43,  6.20iter/s]Best score: 0.2128: : 261iter [00:43,  6.26iter/s]Best score: 0.2128: : 261iter [00:43,  6.26iter/s]Best score: 0.2128: : 262iter [00:43,  6.29iter/s]Best score: 0.2128: : 262iter [00:43,  6.29iter/s]Best score: 0.2128: : 263iter [00:43,  6.32iter/s]Best score: 0.2128: : 263iter [00:43,  6.32iter/s]Best score: 0.2128: : 264iter [00:43,  6.33iter/s]Best score: 0.2128: : 264iter [00:44,  6.33iter/s]Best score: 0.2128: : 265iter [00:44,  6.34iter/s]Best score: 0.2128: : 265iter [00:44,  6.34iter/s]Best score: 0.2128: : 266iter [00:44,  6.36iter/s]Best score: 0.2128: : 266iter [00:44,  6.36iter/s]Best score: 0.2128: : 267iter [00:44,  6.37iter/s]Best score: 0.2128: : 267iter [00:44,  6.37iter/s]Best score: 0.2128: : 268iter [00:44,  6.37iter/s]Best score: 0.2128: : 268iter [00:44,  6.37iter/s]Best score: 0.2128: : 269iter [00:44,  6.37iter/s]Best score: 0.2097: : 269iter [00:44,  6.37iter/s]Best score: 0.2097: : 270iter [00:44,  6.19iter/s]Best score: 0.2097: : 270iter [00:44,  6.19iter/s]Best score: 0.2097: : 271iter [00:44,  6.25iter/s]Best score: 0.2097: : 271iter [00:45,  6.25iter/s]Best score: 0.2097: : 272iter [00:45,  6.29iter/s]Best score: 0.2097: : 272iter [00:45,  6.29iter/s]Best score: 0.2097: : 273iter [00:45,  6.32iter/s]Best score: 0.2097: : 273iter [00:45,  6.32iter/s]Best score: 0.2097: : 274iter [00:45,  6.35iter/s]Best score: 0.2097: : 274iter [00:45,  6.35iter/s]Best score: 0.2097: : 275iter [00:45,  6.34iter/s]Best score: 0.2097: : 275iter [00:45,  6.34iter/s]Best score: 0.2097: : 276iter [00:45,  6.36iter/s]Best score: 0.2097: : 276iter [00:45,  6.36iter/s]Best score: 0.2097: : 277iter [00:45,  6.36iter/s]Best score: 0.2097: : 277iter [00:46,  6.36iter/s]Best score: 0.2097: : 278iter [00:46,  6.38iter/s]Best score: 0.2097: : 278iter [00:46,  6.38iter/s]Best score: 0.2097: : 279iter [00:46,  6.38iter/s]Best score: 0.2097: : 279iter [00:46,  6.38iter/s]Best score: 0.2097: : 280iter [00:46,  6.19iter/s]Best score: 0.2097: : 280iter [00:46,  6.19iter/s]Best score: 0.2097: : 281iter [00:46,  6.26iter/s]Best score: 0.2097: : 281iter [00:46,  6.26iter/s]Best score: 0.2097: : 282iter [00:46,  6.30iter/s]Best score: 0.2097: : 282iter [00:46,  6.30iter/s]Best score: 0.2097: : 283iter [00:46,  6.31iter/s]Best score: 0.2097: : 283iter [00:47,  6.31iter/s]Best score: 0.2097: : 284iter [00:47,  6.33iter/s]Best score: 0.2097: : 284iter [00:47,  6.33iter/s]Best score: 0.2097: : 285iter [00:47,  6.33iter/s]Best score: 0.2097: : 285iter [00:47,  6.33iter/s]Best score: 0.2097: : 286iter [00:47,  6.35iter/s]Best score: 0.2097: : 286iter [00:47,  6.35iter/s]Best score: 0.2097: : 287iter [00:47,  6.36iter/s]Best score: 0.2097: : 287iter [00:47,  6.36iter/s]Best score: 0.2097: : 288iter [00:47,  6.36iter/s]Best score: 0.2097: : 288iter [00:47,  6.36iter/s]Best score: 0.2097: : 289iter [00:47,  6.38iter/s]Best score: 0.2077: : 289iter [00:47,  6.38iter/s]Best score: 0.2077: : 290iter [00:47,  6.17iter/s]Best score: 0.2077: : 290iter [00:48,  6.17iter/s]Best score: 0.2077: : 291iter [00:48,  6.24iter/s]Best score: 0.2077: : 291iter [00:48,  6.24iter/s]Best score: 0.2077: : 292iter [00:48,  6.28iter/s]Best score: 0.2077: : 292iter [00:48,  6.28iter/s]Best score: 0.2077: : 293iter [00:48,  6.30iter/s]Best score: 0.2077: : 293iter [00:48,  6.30iter/s]Best score: 0.2077: : 294iter [00:48,  6.32iter/s]Best score: 0.2077: : 294iter [00:48,  6.32iter/s]Best score: 0.2077: : 295iter [00:48,  6.34iter/s]Best score: 0.2077: : 295iter [00:48,  6.34iter/s]Best score: 0.2077: : 296iter [00:48,  6.35iter/s]Best score: 0.2077: : 296iter [00:49,  6.35iter/s]Best score: 0.2077: : 297iter [00:49,  6.38iter/s]Best score: 0.2077: : 297iter [00:49,  6.38iter/s]Best score: 0.2077: : 298iter [00:49,  6.39iter/s]Best score: 0.2077: : 298iter [00:49,  6.39iter/s]Best score: 0.2077: : 299iter [00:49,  6.40iter/s]
[ITER 300] Best: 0.2069
[PARAMS] Current best parameters:
  fx: 529.8586
  fy: 529.7230
  px: 886.4973
  py: 920.4494
  k1: -0.00684881
  k2: 0.01727898
  k3: -0.01365245
  k4: 0.00308354
  p1: -0.00536328
  p2: -0.00946563
  p3: -0.00348949
  p4: 0.00195076
  scale: 2.9961
  rotation: -0.5098
Best score: 0.2069: : 299iter [00:49,  6.40iter/s]Best score: 0.2069: : 300iter [00:49,  6.20iter/s]Best score: 0.2069: : 300iter [00:49,  6.20iter/s]Best score: 0.2069: : 301iter [00:49,  6.26iter/s]Best score: 0.2069: : 301iter [00:49,  6.26iter/s]Best score: 0.2069: : 302iter [00:49,  6.30iter/s]Best score: 0.2069: : 302iter [00:50,  6.30iter/s]Best score: 0.2069: : 303iter [00:50,  6.34iter/s]Best score: 0.2069: : 303iter [00:50,  6.34iter/s]Best score: 0.2069: : 304iter [00:50,  6.35iter/s]Best score: 0.2069: : 304iter [00:50,  6.35iter/s]Best score: 0.2069: : 305iter [00:50,  6.37iter/s]Best score: 0.2069: : 305iter [00:50,  6.37iter/s]Best score: 0.2069: : 306iter [00:50,  6.38iter/s]Best score: 0.2069: : 306iter [00:50,  6.38iter/s]Best score: 0.2069: : 307iter [00:50,  6.38iter/s]Best score: 0.2069: : 307iter [00:50,  6.38iter/s]Best score: 0.2069: : 308iter [00:50,  6.39iter/s]Best score: 0.2069: : 308iter [00:50,  6.39iter/s]Best score: 0.2069: : 309iter [00:50,  6.40iter/s]Best score: 0.2059: : 309iter [00:51,  6.40iter/s]Best score: 0.2059: : 310iter [00:51,  6.20iter/s]Best score: 0.2059: : 310iter [00:51,  6.20iter/s]Best score: 0.2059: : 311iter [00:51,  6.27iter/s]Best score: 0.2059: : 311iter [00:51,  6.27iter/s]Best score: 0.2059: : 312iter [00:51,  6.31iter/s]Best score: 0.2059: : 312iter [00:51,  6.31iter/s]Best score: 0.2059: : 313iter [00:51,  6.34iter/s]Best score: 0.2059: : 313iter [00:51,  6.34iter/s]Best score: 0.2059: : 314iter [00:51,  6.36iter/s]Best score: 0.2059: : 314iter [00:51,  6.36iter/s]Best score: 0.2059: : 315iter [00:51,  6.37iter/s]Best score: 0.2059: : 315iter [00:52,  6.37iter/s]Best score: 0.2059: : 316iter [00:52,  6.37iter/s]Best score: 0.2059: : 316iter [00:52,  6.37iter/s]Best score: 0.2059: : 317iter [00:52,  6.36iter/s]Best score: 0.2059: : 317iter [00:52,  6.36iter/s]Best score: 0.2059: : 318iter [00:52,  6.38iter/s]Best score: 0.2059: : 318iter [00:52,  6.38iter/s]Best score: 0.2059: : 319iter [00:52,  6.38iter/s]Best score: 0.2054: : 319iter [00:52,  6.38iter/s]Best score: 0.2054: : 320iter [00:52,  6.20iter/s]Best score: 0.2054: : 320iter [00:52,  6.20iter/s]Best score: 0.2054: : 321iter [00:52,  6.27iter/s]Best score: 0.2054: : 321iter [00:53,  6.27iter/s]Best score: 0.2054: : 322iter [00:53,  6.31iter/s]Best score: 0.2054: : 322iter [00:53,  6.31iter/s]Best score: 0.2054: : 323iter [00:53,  6.33iter/s]Best score: 0.2054: : 323iter [00:53,  6.33iter/s]Best score: 0.2054: : 324iter [00:53,  6.35iter/s]Best score: 0.2054: : 324iter [00:53,  6.35iter/s]Best score: 0.2054: : 325iter [00:53,  6.36iter/s]Best score: 0.2054: : 325iter [00:53,  6.36iter/s]Best score: 0.2054: : 326iter [00:53,  6.37iter/s]Best score: 0.2054: : 326iter [00:53,  6.37iter/s]Best score: 0.2054: : 327iter [00:53,  6.38iter/s]Best score: 0.2054: : 327iter [00:53,  6.38iter/s]Best score: 0.2054: : 328iter [00:53,  6.39iter/s]Best score: 0.2054: : 328iter [00:54,  6.39iter/s]Best score: 0.2054: : 329iter [00:54,  6.39iter/s]Best score: 0.2051: : 329iter [00:54,  6.39iter/s]Best score: 0.2051: : 330iter [00:54,  6.20iter/s]Best score: 0.2051: : 330iter [00:54,  6.20iter/s]Best score: 0.2051: : 331iter [00:54,  6.25iter/s]Best score: 0.2051: : 331iter [00:54,  6.25iter/s]Best score: 0.2051: : 332iter [00:54,  6.28iter/s]Best score: 0.2051: : 332iter [00:54,  6.28iter/s]Best score: 0.2051: : 333iter [00:54,  6.31iter/s]Best score: 0.2051: : 333iter [00:54,  6.31iter/s]Best score: 0.2051: : 334iter [00:54,  6.32iter/s]Best score: 0.2051: : 334iter [00:55,  6.32iter/s]Best score: 0.2051: : 335iter [00:55,  6.34iter/s]Best score: 0.2051: : 335iter [00:55,  6.34iter/s]Best score: 0.2051: : 336iter [00:55,  6.35iter/s]Best score: 0.2051: : 336iter [00:55,  6.35iter/s]Best score: 0.2051: : 337iter [00:55,  6.37iter/s]Best score: 0.2051: : 337iter [00:55,  6.37iter/s]Best score: 0.2051: : 338iter [00:55,  6.39iter/s]Best score: 0.2051: : 338iter [00:55,  6.39iter/s]Best score: 0.2051: : 339iter [00:55,  6.40iter/s]Best score: 0.2051: : 339iter [00:55,  6.40iter/s]Best score: 0.2051: : 340iter [00:55,  6.21iter/s]Best score: 0.2051: : 340iter [00:56,  6.21iter/s]Best score: 0.2051: : 341iter [00:56,  6.26iter/s]Best score: 0.2051: : 341iter [00:56,  6.26iter/s]Best score: 0.2051: : 342iter [00:56,  6.30iter/s]Best score: 0.2051: : 342iter [00:56,  6.30iter/s]Best score: 0.2051: : 343iter [00:56,  6.33iter/s]Best score: 0.2051: : 343iter [00:56,  6.33iter/s]Best score: 0.2051: : 344iter [00:56,  6.34iter/s]Best score: 0.2051: : 344iter [00:56,  6.34iter/s]Best score: 0.2051: : 345iter [00:56,  6.37iter/s]Best score: 0.2051: : 345iter [00:56,  6.37iter/s]Best score: 0.2051: : 346iter [00:56,  6.37iter/s]Best score: 0.2051: : 346iter [00:56,  6.37iter/s]Best score: 0.2051: : 347iter [00:56,  6.39iter/s]Best score: 0.2051: : 347iter [00:57,  6.39iter/s]Best score: 0.2051: : 348iter [00:57,  6.39iter/s]Best score: 0.2051: : 348iter [00:57,  6.39iter/s]Best score: 0.2051: : 349iter [00:57,  6.39iter/s]
[ITER 350] Best: 0.2050
[PARAMS] Current best parameters:
  fx: 531.5892
  fy: 531.4171
  px: 886.6398
  py: 920.3256
  k1: -0.00846286
  k2: 0.01685938
  k3: -0.01304087
  k4: 0.00289565
  p1: -0.00559763
  p2: -0.00933529
  p3: -0.00349915
  p4: 0.00198183
  scale: 2.9949
  rotation: -0.5105
Best score: 0.2050: : 349iter [00:57,  6.39iter/s]Best score: 0.2050: : 350iter [00:57,  6.19iter/s]Best score: 0.2050: : 350iter [00:57,  6.19iter/s]Best score: 0.2050: : 351iter [00:57,  6.24iter/s]Best score: 0.2050: : 351iter [00:57,  6.24iter/s]Best score: 0.2050: : 352iter [00:57,  6.28iter/s]Best score: 0.2050: : 352iter [00:57,  6.28iter/s]Best score: 0.2050: : 353iter [00:57,  6.31iter/s]Best score: 0.2050: : 353iter [00:58,  6.31iter/s]Best score: 0.2050: : 354iter [00:58,  6.33iter/s]Best score: 0.2050: : 354iter [00:58,  6.33iter/s]Best score: 0.2050: : 355iter [00:58,  6.35iter/s]Best score: 0.2050: : 355iter [00:58,  6.35iter/s]Best score: 0.2050: : 356iter [00:58,  6.37iter/s]Best score: 0.2050: : 356iter [00:58,  6.37iter/s]Best score: 0.2050: : 357iter [00:58,  6.37iter/s]Best score: 0.2050: : 357iter [00:58,  6.37iter/s]Best score: 0.2050: : 358iter [00:58,  6.39iter/s]Best score: 0.2050: : 358iter [00:58,  6.39iter/s]Best score: 0.2050: : 359iter [00:58,  6.38iter/s]Best score: 0.2050: : 359iter [00:59,  6.38iter/s]Best score: 0.2050: : 360iter [00:59,  6.18iter/s]Best score: 0.2050: : 360iter [00:59,  6.18iter/s]Best score: 0.2050: : 361iter [00:59,  6.24iter/s]Best score: 0.2050: : 361iter [00:59,  6.24iter/s]Best score: 0.2050: : 362iter [00:59,  6.29iter/s]Best score: 0.2050: : 362iter [00:59,  6.29iter/s]Best score: 0.2050: : 363iter [00:59,  6.32iter/s]Best score: 0.2050: : 363iter [00:59,  6.32iter/s]Best score: 0.2050: : 364iter [00:59,  6.36iter/s]Best score: 0.2050: : 364iter [00:59,  6.36iter/s]Best score: 0.2050: : 365iter [00:59,  6.38iter/s]Best score: 0.2050: : 365iter [00:59,  6.38iter/s]Best score: 0.2050: : 366iter [00:59,  6.39iter/s]Best score: 0.2050: : 366iter [01:00,  6.39iter/s]Best score: 0.2050: : 367iter [01:00,  6.38iter/s]Best score: 0.2050: : 367iter [01:00,  6.38iter/s]Best score: 0.2050: : 368iter [01:00,  6.39iter/s]Best score: 0.2050: : 368iter [01:00,  6.39iter/s]Best score: 0.2050: : 369iter [01:00,  6.39iter/s]Best score: 0.2048: : 369iter [01:00,  6.39iter/s]Best score: 0.2048: : 370iter [01:00,  6.20iter/s]Best score: 0.2048: : 370iter [01:00,  6.20iter/s]Best score: 0.2048: : 371iter [01:00,  6.25iter/s]Best score: 0.2048: : 371iter [01:00,  6.25iter/s]Best score: 0.2048: : 372iter [01:00,  6.29iter/s]Best score: 0.2048: : 372iter [01:01,  6.29iter/s]Best score: 0.2048: : 373iter [01:01,  6.32iter/s]Best score: 0.2048: : 373iter [01:01,  6.32iter/s]Best score: 0.2048: : 374iter [01:01,  6.33iter/s]Best score: 0.2048: : 374iter [01:01,  6.33iter/s]Best score: 0.2048: : 375iter [01:01,  6.33iter/s]Best score: 0.2048: : 375iter [01:01,  6.33iter/s]Best score: 0.2048: : 376iter [01:01,  6.31iter/s]Best score: 0.2048: : 376iter [01:01,  6.31iter/s]Best score: 0.2048: : 377iter [01:01,  6.32iter/s]Best score: 0.2048: : 377iter [01:01,  6.32iter/s]Best score: 0.2048: : 378iter [01:01,  6.33iter/s]Best score: 0.2048: : 378iter [01:02,  6.33iter/s]Best score: 0.2048: : 379iter [01:02,  6.34iter/s]Best score: 0.2047: : 379iter [01:02,  6.34iter/s]Best score: 0.2047: : 380iter [01:02,  6.15iter/s]Best score: 0.2047: : 380iter [01:02,  6.15iter/s]Best score: 0.2047: : 381iter [01:02,  6.21iter/s]Best score: 0.2047: : 381iter [01:02,  6.21iter/s]Best score: 0.2047: : 382iter [01:02,  6.25iter/s]Best score: 0.2047: : 382iter [01:02,  6.25iter/s]Best score: 0.2047: : 383iter [01:02,  6.28iter/s]Best score: 0.2047: : 383iter [01:02,  6.28iter/s]Best score: 0.2047: : 384iter [01:02,  6.31iter/s]Best score: 0.2047: : 384iter [01:02,  6.31iter/s]Best score: 0.2047: : 385iter [01:02,  6.33iter/s]Best score: 0.2047: : 385iter [01:03,  6.33iter/s]Best score: 0.2047: : 386iter [01:03,  6.35iter/s]Best score: 0.2047: : 386iter [01:03,  6.35iter/s]Best score: 0.2047: : 387iter [01:03,  6.35iter/s]Best score: 0.2047: : 387iter [01:03,  6.35iter/s]Best score: 0.2047: : 388iter [01:03,  6.35iter/s]Best score: 0.2047: : 388iter [01:03,  6.35iter/s]Best score: 0.2047: : 389iter [01:03,  6.36iter/s]Best score: 0.2047: : 389iter [01:03,  6.36iter/s]Best score: 0.2047: : 390iter [01:03,  6.17iter/s]Best score: 0.2047: : 390iter [01:03,  6.17iter/s]Best score: 0.2047: : 391iter [01:03,  6.23iter/s]Best score: 0.2047: : 391iter [01:04,  6.23iter/s]Best score: 0.2047: : 392iter [01:04,  6.27iter/s]Best score: 0.2047: : 392iter [01:04,  6.27iter/s]Best score: 0.2047: : 393iter [01:04,  6.30iter/s]Best score: 0.2047: : 393iter [01:04,  6.30iter/s]Best score: 0.2047: : 394iter [01:04,  6.32iter/s]Best score: 0.2047: : 394iter [01:04,  6.32iter/s]Best score: 0.2047: : 395iter [01:04,  6.33iter/s]Best score: 0.2047: : 395iter [01:04,  6.33iter/s]Best score: 0.2047: : 396iter [01:04,  6.35iter/s]Best score: 0.2047: : 396iter [01:04,  6.35iter/s]Best score: 0.2047: : 397iter [01:04,  6.35iter/s]Best score: 0.2047: : 397iter [01:05,  6.35iter/s]Best score: 0.2047: : 398iter [01:05,  6.35iter/s]Best score: 0.2047: : 398iter [01:05,  6.35iter/s]Best score: 0.2047: : 399iter [01:05,  6.36iter/s]
[ITER 400] Best: 0.2047
[PARAMS] Current best parameters:
  fx: 531.6548
  fy: 531.4854
  px: 886.5228
  py: 920.1263
  k1: -0.00583339
  k2: 0.01243616
  k3: -0.01047419
  k4: 0.00238296
  p1: -0.00607029
  p2: -0.00962074
  p3: -0.00333506
  p4: 0.00201961
  scale: 2.9928
  rotation: -0.5120
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[01:12,  6.36iter/s]Best score: 0.2046: : 448iter [01:13,  6.36iter/s]Best score: 0.2046: : 449iter [01:13,  6.37iter/s]
[ITER 450] Best: 0.2046
[PARAMS] Current best parameters:
  fx: 532.6989
  fy: 532.5424
  px: 886.4383
  py: 920.0609
  k1: -0.00789170
  k2: 0.01397019
  k3: -0.01128447
  k4: 0.00255402
  p1: -0.00624383
  p2: -0.00982684
  p3: -0.00323994
  p4: 0.00202154
  scale: 2.9932
  rotation: -0.5113
Best score: 0.2046: : 449iter [01:13,  6.37iter/s]Best score: 0.2046: : 450iter [01:13,  6.17iter/s]Best score: 0.2046: : 450iter [01:13,  6.17iter/s]Best score: 0.2046: : 451iter [01:13,  6.23iter/s]Best score: 0.2046: : 451iter [01:13,  6.23iter/s]Best score: 0.2046: : 452iter [01:13,  6.27iter/s]Best score: 0.2046: : 452iter [01:13,  6.27iter/s]Best score: 0.2046: : 453iter [01:13,  6.29iter/s]Best score: 0.2046: : 453iter [01:13,  6.29iter/s]Best score: 0.2046: : 454iter [01:13,  6.31iter/s]Best score: 0.2046: : 454iter [01:14,  6.31iter/s]Best score: 0.2046: : 455iter [01:14,  6.33iter/s]Best score: 0.2046: : 455iter [01:14,  6.33iter/s]Best score: 0.2046: : 456iter [01:14,  6.35iter/s]Best score: 0.2046: : 456iter [01:14,  6.35iter/s]Best score: 0.2046: : 457iter [01:14,  6.36iter/s]Best score: 0.2046: : 457iter [01:14,  6.36iter/s]Best score: 0.2046: : 458iter [01:14,  6.36iter/s]Best score: 0.2046: : 458iter [01:14,  6.36iter/s]Best score: 0.2046: : 459iter 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[01:20,  6.35iter/s]Best score: 0.2046: : 498iter [01:21,  6.35iter/s]Best score: 0.2046: : 499iter [01:21,  6.36iter/s]
[ITER 500] Best: 0.2046
[PARAMS] Current best parameters:
  fx: 532.7276
  fy: 532.5669
  px: 886.4409
  py: 920.0835
  k1: -0.00766507
  k2: 0.01346434
  k3: -0.01093111
  k4: 0.00247087
  p1: -0.00619300
  p2: -0.00982059
  p3: -0.00327217
  p4: 0.00203388
  scale: 2.9931
  rotation: -0.5108
Best score: 0.2046: : 499iter [01:21,  6.36iter/s]Best score: 0.2046: : 500iter [01:21,  6.16iter/s]Best score: 0.2046: : 500iter [01:21,  6.15iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.204597 -> 0.204595
  Success: True
  Tangential distortion: p1=-0.006193, p2=-0.009821, p3=-0.003272, p4=0.002034
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2046
[✅] Parameters copied to: /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 532.7275841377865
    fy: 532.566861685143
    px: 886.4409418614918
    py: 920.0834700061564
    k1: -0.007664976811817052
    k2: 0.013464450335484993
    k3: -0.010931004556382615
    k4: 0.002470900152100863
    p1: -0.0061930053862053
    p2: -0.009820595032731742
    p3: -0.003272182327704066
    p4: 0.0020338572734451836
    scale: 2.993130071923698
    rotation: -0.5107956378052108
    mmpp: 3.4515765531317117
    nmpp: 3451576.5531317117

[🔄] Converting 1381 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
📤 Writing LUT:   0%|          | 0/3240000 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 81310/3240000 [00:00<00:03, 813098.84px/s]📤 Writing LUT:   5%|▌         | 173204/3240000 [00:00<00:03, 875353.89px/s]📤 Writing LUT:   8%|▊         | 264883/3240000 [00:00<00:03, 894269.95px/s]📤 Writing LUT:  11%|█         | 358438/3240000 [00:00<00:03, 910565.40px/s]📤 Writing LUT:  14%|█▍        | 451627/3240000 [00:00<00:03, 918254.70px/s]📤 Writing LUT:  17%|█▋        | 543837/3240000 [00:00<00:02, 919558.67px/s]📤 Writing LUT:  20%|█▉        | 636117/3240000 [00:00<00:02, 920617.90px/s]📤 Writing LUT:  22%|██▏       | 728179/3240000 [00:00<00:02, 919411.58px/s]📤 Writing LUT:  25%|██▌       | 820121/3240000 [00:00<00:02, 915018.92px/s]📤 Writing LUT:  28%|██▊       | 911628/3240000 [00:01<00:02, 914190.39px/s]📤 Writing LUT:  31%|███       | 1003050/3240000 [00:01<00:02, 911608.25px/s]📤 Writing LUT:  34%|███▍      | 1095397/3240000 [00:01<00:02, 915189.50px/s]📤 Writing LUT:  37%|███▋      | 1186920/3240000 [00:01<00:02, 914012.71px/s]📤 Writing LUT:  39%|███▉      | 1278324/3240000 [00:01<00:02, 911619.13px/s]📤 Writing LUT:  42%|████▏     | 1371469/3240000 [00:01<00:02, 917572.14px/s]📤 Writing LUT:  45%|████▌     | 1464677/3240000 [00:01<00:01, 921925.23px/s]📤 Writing LUT:  48%|████▊     | 1557169/3240000 [00:01<00:01, 922822.96px/s]📤 Writing LUT:  51%|█████     | 1650065/3240000 [00:01<00:01, 924664.18px/s]📤 Writing LUT:  54%|█████▍    | 1742833/3240000 [00:01<00:01, 925567.38px/s]📤 Writing LUT:  57%|█████▋    | 1835392/3240000 [00:02<00:01, 919602.57px/s]📤 Writing LUT:  59%|█████▉    | 1927362/3240000 [00:02<00:01, 917572.45px/s]📤 Writing LUT:  62%|██████▏   | 2019443/3240000 [00:02<00:01, 918538.13px/s]📤 Writing LUT:  65%|██████▌   | 2111624/3240000 [00:02<00:01, 919513.46px/s]📤 Writing LUT:  68%|██████▊   | 2203580/3240000 [00:02<00:01, 918057.52px/s]📤 Writing LUT:  71%|███████   | 2296472/3240000 [00:02<00:01, 921306.32px/s]📤 Writing LUT:  74%|███████▎  | 2388606/3240000 [00:02<00:00, 916706.24px/s]📤 Writing LUT:  77%|███████▋  | 2480284/3240000 [00:02<00:00, 916087.54px/s]📤 Writing LUT:  79%|███████▉  | 2571898/3240000 [00:02<00:00, 912431.79px/s]📤 Writing LUT:  82%|████████▏ | 2663147/3240000 [00:02<00:00, 911723.73px/s]📤 Writing LUT:  85%|████████▌ | 2754323/3240000 [00:03<00:00, 910476.40px/s]📤 Writing LUT:  88%|████████▊ | 2847237/3240000 [00:03<00:00, 916051.65px/s]📤 Writing LUT:  91%|█████████ | 2939767/3240000 [00:03<00:00, 918815.88px/s]📤 Writing LUT:  94%|█████████▎| 3032236/3240000 [00:03<00:00, 920572.22px/s]📤 Writing LUT:  96%|█████████▋| 3124296/3240000 [00:03<00:00, 919406.65px/s]📤 Writing LUT:  99%|█████████▉| 3218652/3240000 [00:03<00:00, 926637.35px/s]📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 916464.47px/s]
[✅] LUT written to: /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-SL-002_W043(6)_2026-03-05' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-SL-002_W043(6)_2026-03-05/calibration/MDCP3D-SL-002_W043(6)_2026-03-05_unknown.lut
[🏁] Finished processing MDCP3D-SL-002_W043(6)_2026-03-05


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