2026-05-18T20:43:44 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-011_W042_2026_05_01 ===
2026-05-18T20:43:44 - INFO: Camera ID: 06PFM
2026-05-18T20:43:44 - INFO: Wand ID: 00001f0c517f
2026-05-18T20:43:44 - INFO: Recorded At: 2026-05-01T11:44:36Z

-------------------------------------------------------------------
                        Step 1: Digitization                       
-------------------------------------------------------------------

[🔄] 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 455 stationary frames, 589 remain

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                                                                           [A[PASS2] Frame 0000141: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000272: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000273: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000274: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000284: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000285: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000286: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000287: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000536: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000537: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000539: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000540: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000541: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000542: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000543: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000544: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000681: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000682: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000683: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000684: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000698: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000699: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000700: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000701: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000702: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000703: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000704: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000706: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000707: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000761: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000762: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000767: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000768: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000769: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000770: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000771: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001081: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001082: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001083: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001084: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001085: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001086: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001087: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001088: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001096: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001097: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001099: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000273: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000286: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000538: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000540: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000542: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000543: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000544: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000683: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000699: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000700: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000761: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000768: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000769: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000770: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001083: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001087: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000537: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000542: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000543: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000544: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Summary: 25 frames fixed, 27 untrusted, 588 total

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                                                                           [A
[TIMING] 1000 — 589 frames
  Phase 1  radial detect   (parallel)  :   8.83s  (15.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.03s  (6.9 ms/frame)
  Phase 2b corner fix      (sequential):   1.23s
  Phase 3  finalize        (sequential):  10.67s  (18.1 ms/frame)
    decode + undistort                 :   5.92s  (10.1 ms/frame)
    corner apply + refinement          :   1.98s  (3.4 ms/frame)
    segment                            :   2.25s  (3.8 ms/frame)
    smooth                             :   0.32s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  24.75s

Processing Rigs:  25%|██▌       | 1/4 [00:28<01:26, 28.95s/it][INFO] Filtered 354 stationary frames, 748 remain

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                                                                           [A[PASS2] Frame 0000288: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000289: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000291: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000292: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000293: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000294: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000395: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000396: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000397: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000398: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000854: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000855: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000856: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000857: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000858: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000859: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000860: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000883: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000997: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0001076: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001078: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001080: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001081: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001083: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001086: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001087: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001099: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001100: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001102: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001104: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001222: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001223: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001224: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001225: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000291: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000293: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000397: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000855: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000856: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000857: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000858: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000859: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000860: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000997: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0001080: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001102: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001224: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000857: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000858: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000997: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0001080: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 23 frames fixed, 19 untrusted, 747 total

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                                                                           [A
[TIMING] 1500 — 748 frames
  Phase 1  radial detect   (parallel)  :  11.16s  (14.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.38s  (7.2 ms/frame)
  Phase 2b corner fix      (sequential):   1.23s
  Phase 3  finalize        (sequential):  11.98s  (16.0 ms/frame)
    decode + undistort                 :   5.57s  (7.5 ms/frame)
    corner apply + refinement          :   2.80s  (3.7 ms/frame)
    segment                            :   2.96s  (4.0 ms/frame)
    smooth                             :   0.41s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  29.76s

Processing Rigs:  50%|█████     | 2/4 [01:02<01:03, 31.83s/it][INFO] Filtered 480 stationary frames, 495 remain

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                                                                            [A[PASS2] Frame 0000804: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000805: fixed corners {0, 1, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000934: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000943: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000946: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000947: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000948: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000952: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000953: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000954: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000956: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000964: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000965: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000966: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000967: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001085: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001086: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001087: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001088: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001160: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001161: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001162: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001163: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000803: fixed corners {0, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000804: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000805: fixed corners {0, 1, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000947: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000955: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000966: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001087: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001162: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000803: fixed corners {0, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000804: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000805: fixed corners {0, 1, 3} (was_fixed=False, trusted=False)
[PASS2] Summary: 10 frames fixed, 19 untrusted, 494 total

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                                                                            [A
[TIMING] 2000L — 495 frames
  Phase 1  radial detect   (parallel)  :   8.04s  (16.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.27s  (8.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.74s
  Phase 3  finalize        (sequential):   7.76s  (15.7 ms/frame)
    decode + undistort                 :   3.34s  (6.7 ms/frame)
    corner apply + refinement          :   2.03s  (4.1 ms/frame)
    segment                            :   1.95s  (3.9 ms/frame)
    smooth                             :   0.28s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  20.80s

Processing Rigs:  75%|███████▌  | 3/4 [01:27<00:28, 28.43s/it][INFO] Filtered 402 stationary frames, 606 remain

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                                                                            [A[PASS2] Frame 0000428: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000429: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000432: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000492: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000505: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000614: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000432: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000614: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000614: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 6 frames fixed, 4 untrusted, 605 total

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                                                                            [A
[TIMING] 2000R — 606 frames
  Phase 1  radial detect   (parallel)  :   9.77s  (16.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.58s  (7.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.51s
  Phase 3  finalize        (sequential):   9.41s  (15.5 ms/frame)
    decode + undistort                 :   4.05s  (6.7 ms/frame)
    corner apply + refinement          :   2.40s  (4.0 ms/frame)
    segment                            :   2.42s  (4.0 ms/frame)
    smooth                             :   0.34s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  24.27s

Processing Rigs: 100%|██████████| 4/4 [01:55<00:00, 28.30s/it]Processing Rigs: 100%|██████████| 4/4 [01:55<00:00, 28.82s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2434 frames from 4 rigs to /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 588
[📋] 1500: 100 frames selected from 747
[📋] 2000L: 100 frames selected from 494
[📋] 2000R: 100 frames selected from 605
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                      Step 2: Casing Detection                     
-------------------------------------------------------------------

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
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-------------------------------------------------------------------
                        Step 3: Calibration                        
-------------------------------------------------------------------

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(0.043) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:07, 13.10it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   6%|▌         | 6/100 [00:00<00:07, 13.10it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   7%|▋         | 7/100 [00:00<00:07, 13.10it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   8%|▊         | 8/100 [00:00<00:05, 16.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:05, 16.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.002 | Best: {'fx': 370.707} (0.002):   9%|▉         | 9/100 [00:00<00:05, 16.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:05, 16.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  11%|█         | 11/100 [00:00<00:04, 19.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 19.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 19.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 19.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 19.43it/s] Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  15%|█▌        | 15/100 [00:00<00:04, 19.63it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:00<00:04, 19.63it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:04, 19.63it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:00<00:04, 19.63it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.007 | Best: {'fx': 427.273} (0.007):  18%|█▊        | 18/100 [00:01<00:04, 19.63it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.007 | Best: {'fx': 427.273} (0.007):  19%|█▉        | 19/100 [00:01<00:03, 23.00it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.010 | Best: {'fx': 441.414} (0.010):  19%|█▉        | 19/100 [00:01<00:03, 23.00it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.008 | Best: {'fx': 441.414} (0.010):  20%|██        | 20/100 [00:01<00:03, 23.00it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:01<00:03, 23.00it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.014 | Best: {'fx': 455.556} (0.014):  22%|██▏       | 22/100 [00:01<00:03, 23.00it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.016 | Best: {'fx': 462.626} (0.016):  23%|██▎       | 23/100 [00:01<00:03, 23.00it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.016 | Best: {'fx': 462.626} (0.016):  24%|██▍       | 24/100 [00:01<00:03, 24.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.027 | Best: {'fx': 483.838} (0.027):  24%|██▍       | 24/100 [00:01<00:03, 24.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 483.838} (0.027):  25%|██▌       | 25/100 [00:01<00:03, 24.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 483.838} (0.027):  26%|██▌       | 26/100 [00:01<00:03, 24.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 483.838} (0.027):  27%|██▋       | 27/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.041 | Best: {'fx': 497.98} (0.041):  27%|██▋       | 27/100 [00:01<00:02, 25.12it/s] Brute | Start: {'fx': 500.0} (0.043) | Current: 0.049 | Best: {'fx': 505.051} (0.049):  28%|██▊       | 28/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.033 | Best: {'fx': 505.051} (0.049):  29%|██▉       | 29/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.058 | Best: {'fx': 512.121} (0.058):  30%|███       | 30/100 [00:01<00:02, 25.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.058 | Best: {'fx': 512.121} (0.058):  31%|███       | 31/100 [00:01<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.064 | Best: {'fx': 526.263} (0.064):  31%|███       | 31/100 [00:01<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.067 | Best: {'fx': 519.192} (0.067):  32%|███▏      | 32/100 [00:01<00:03, 22.55it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.063 | Best: {'fx': 519.192} (0.067):  33%|███▎      | 33/100 [00:01<00:02, 22.55it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.063 | Best: {'fx': 519.192} (0.067):  34%|███▍      | 34/100 [00:01<00:03, 17.30it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.062 | Best: {'fx': 519.192} (0.067):  34%|███▍      | 34/100 [00:01<00:03, 17.30it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.043 | Best: {'fx': 519.192} (0.067):  35%|███▌      | 35/100 [00:01<00:03, 17.30it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.055 | Best: {'fx': 519.192} (0.067):  36%|███▌      | 36/100 [00:01<00:03, 17.30it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.039 | Best: {'fx': 519.192} (0.067):  37%|███▋      | 37/100 [00:01<00:03, 17.30it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.039 | Best: {'fx': 519.192} (0.067):  38%|███▊      | 38/100 [00:01<00:03, 19.89it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.046 | Best: {'fx': 519.192} (0.067):  38%|███▊      | 38/100 [00:02<00:03, 19.89it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.037 | Best: {'fx': 519.192} (0.067):  39%|███▉      | 39/100 [00:02<00:03, 19.89it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.051 | Best: {'fx': 519.192} (0.067):  40%|████      | 40/100 [00:02<00:03, 19.89it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.032 | Best: {'fx': 519.192} (0.067):  41%|████      | 41/100 [00:02<00:02, 19.89it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.032 | Best: {'fx': 519.192} (0.067):  42%|████▏     | 42/100 [00:02<00:02, 22.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.035 | Best: {'fx': 519.192} (0.067):  42%|████▏     | 42/100 [00:02<00:02, 22.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.030 | Best: {'fx': 519.192} (0.067):  43%|████▎     | 43/100 [00:02<00:02, 22.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.029 | Best: {'fx': 519.192} (0.067):  44%|████▍     | 44/100 [00:02<00:02, 22.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.029 | Best: {'fx': 519.192} (0.067):  45%|████▌     | 45/100 [00:02<00:02, 22.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.033 | Best: {'fx': 519.192} (0.067):  45%|████▌     | 45/100 [00:02<00:02, 22.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 519.192} (0.067):  46%|████▌     | 46/100 [00:02<00:02, 22.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 519.192} (0.067):  47%|████▋     | 47/100 [00:02<00:02, 22.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 519.192} (0.067):  48%|████▊     | 48/100 [00:02<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.027 | Best: {'fx': 519.192} (0.067):  48%|████▊     | 48/100 [00:02<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 519.192} (0.067):  49%|████▉     | 49/100 [00:02<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.027 | Best: {'fx': 519.192} (0.067):  50%|█████     | 50/100 [00:02<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 519.192} (0.067):  51%|█████     | 51/100 [00:02<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 519.192} (0.067):  52%|█████▏    | 52/100 [00:02<00:01, 25.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.026 | Best: {'fx': 519.192} (0.067):  52%|█████▏    | 52/100 [00:02<00:01, 25.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 519.192} (0.067):  53%|█████▎    | 53/100 [00:02<00:01, 25.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 519.192} (0.067):  54%|█████▍    | 54/100 [00:02<00:01, 25.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 519.192} (0.067):  55%|█████▌    | 55/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 519.192} (0.067):  55%|█████▌    | 55/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 519.192} (0.067):  56%|█████▌    | 56/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 519.192} (0.067):  57%|█████▋    | 57/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 519.192} (0.067):  58%|█████▊    | 58/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 519.192} (0.067):  59%|█████▉    | 59/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  60%|██████    | 60/100 [00:02<00:01, 25.54it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  61%|██████    | 61/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 519.192} (0.067):  61%|██████    | 61/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 519.192} (0.067):  62%|██████▏   | 62/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 519.192} (0.067):  63%|██████▎   | 63/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 519.192} (0.067):  64%|██████▍   | 64/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 519.192} (0.067):  65%|██████▌   | 65/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  66%|██████▌   | 66/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  67%|██████▋   | 67/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  68%|██████▊   | 68/100 [00:02<00:01, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 519.192} (0.067):  69%|██████▉   | 69/100 [00:02<00:00, 31.27it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 519.192} (0.067):  70%|███████   | 70/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 519.192} (0.067):  70%|███████   | 70/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  71%|███████   | 71/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  72%|███████▏  | 72/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  73%|███████▎  | 73/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 519.192} (0.067):  74%|███████▍  | 74/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  75%|███████▌  | 75/100 [00:02<00:00, 44.38it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  76%|███████▌  | 76/100 [00:02<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  76%|███████▌  | 76/100 [00:03<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  77%|███████▋  | 77/100 [00:03<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  78%|███████▊  | 78/100 [00:03<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  79%|███████▉  | 79/100 [00:03<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  80%|████████  | 80/100 [00:03<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  81%|████████  | 81/100 [00:03<00:00, 48.14it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  82%|████████▏ | 82/100 [00:03<00:00, 40.65it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  82%|████████▏ | 82/100 [00:03<00:00, 40.65it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 519.192} (0.067):  83%|████████▎ | 83/100 [00:03<00:00, 40.65it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  84%|████████▍ | 84/100 [00:03<00:00, 40.65it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  85%|████████▌ | 85/100 [00:03<00:00, 40.65it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  86%|████████▌ | 86/100 [00:03<00:00, 40.65it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  87%|████████▋ | 87/100 [00:03<00:00, 33.81it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  87%|████████▋ | 87/100 [00:03<00:00, 33.81it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  88%|████████▊ | 88/100 [00:03<00:00, 33.81it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  89%|████████▉ | 89/100 [00:03<00:00, 33.81it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  90%|█████████ | 90/100 [00:03<00:00, 33.81it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  91%|█████████ | 91/100 [00:03<00:00, 32.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  91%|█████████ | 91/100 [00:03<00:00, 32.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  92%|█████████▏| 92/100 [00:03<00:00, 32.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  93%|█████████▎| 93/100 [00:03<00:00, 32.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  94%|█████████▍| 94/100 [00:03<00:00, 32.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  95%|█████████▌| 95/100 [00:03<00:00, 32.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  96%|█████████▌| 96/100 [00:03<00:00, 35.90it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  96%|█████████▌| 96/100 [00:03<00:00, 35.90it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  97%|█████████▋| 97/100 [00:03<00:00, 35.90it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  98%|█████████▊| 98/100 [00:03<00:00, 35.90it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067):  99%|█████████▉| 99/100 [00:03<00:00, 35.90it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 519.192} (0.067): 100%|██████████| 100/100 [00:03<00:00, 31.04it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.039 | Best: {'fx': 495.212} (0.039):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.039 | Best: {'fx': 495.212} (0.039):   2%|▏         | 1/50 [00:00<00:29,  1.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.039 | Best: {'fx': 496.233} (0.039):   2%|▏         | 1/50 [00:00<00:29,  1.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.037 | Best: {'fx': 496.233} (0.039):   4%|▍         | 2/50 [00:00<00:29,  1.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.042 | Best: {'fx': 499.294} (0.042):   6%|▌         | 3/50 [00:00<00:28,  1.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.042 | Best: {'fx': 499.294} (0.042):   8%|▊         | 4/50 [00:00<00:06,  6.58it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.040 | Best: {'fx': 499.294} (0.042):   8%|▊         | 4/50 [00:00<00:06,  6.58it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.041 | Best: {'fx': 499.294} (0.042):  10%|█         | 5/50 [00:00<00:06,  6.58it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.043 | Best: {'fx': 500.314} (0.043):  12%|█▏        | 6/50 [00:00<00:06,  6.58it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.044 | Best: {'fx': 501.335} (0.044):  14%|█▍        | 7/50 [00:00<00:06,  6.58it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.048 | Best: {'fx': 504.396} (0.048):  16%|█▌        | 8/50 [00:00<00:06,  6.58it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.048 | Best: {'fx': 504.396} (0.048):  18%|█▊        | 9/50 [00:00<00:03, 13.24it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.050 | Best: {'fx': 506.437} (0.050):  18%|█▊        | 9/50 [00:00<00:03, 13.24it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.045 | Best: {'fx': 506.437} (0.050):  20%|██        | 10/50 [00:01<00:03, 13.24it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.053 | Best: {'fx': 508.478} (0.053):  22%|██▏       | 11/50 [00:01<00:02, 13.24it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.053 | Best: {'fx': 508.478} (0.053):  24%|██▍       | 12/50 [00:01<00:02, 14.44it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.052 | Best: {'fx': 508.478} (0.053):  24%|██▍       | 12/50 [00:01<00:02, 14.44it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.055 | Best: {'fx': 510.518} (0.055):  26%|██▌       | 13/50 [00:01<00:02, 14.44it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.047 | Best: {'fx': 510.518} (0.055):  28%|██▊       | 14/50 [00:01<00:02, 14.44it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.049 | Best: {'fx': 510.518} (0.055):  30%|███       | 15/50 [00:01<00:02, 14.44it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.049 | Best: {'fx': 510.518} (0.055):  32%|███▏      | 16/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.054 | Best: {'fx': 510.518} (0.055):  32%|███▏      | 16/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.058 | Best: {'fx': 512.559} (0.058):  34%|███▍      | 17/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.069 | Best: {'fx': 519.702} (0.069):  36%|███▌      | 18/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.056 | Best: {'fx': 519.702} (0.069):  38%|███▊      | 19/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.060 | Best: {'fx': 519.702} (0.069):  40%|████      | 20/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.063 | Best: {'fx': 519.702} (0.069):  42%|████▏     | 21/50 [00:01<00:01, 18.41it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.063 | Best: {'fx': 519.702} (0.069):  44%|████▍     | 22/50 [00:01<00:01, 23.73it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 519.702} (0.069):  44%|████▍     | 22/50 [00:01<00:01, 23.73it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.065 | Best: {'fx': 519.702} (0.069):  46%|████▌     | 23/50 [00:01<00:01, 23.73it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.066 | Best: {'fx': 519.702} (0.069):  48%|████▊     | 24/50 [00:01<00:01, 23.73it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.066 | Best: {'fx': 519.702} (0.069):  50%|█████     | 25/50 [00:01<00:01, 23.84it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.066 | Best: {'fx': 519.702} (0.069):  50%|█████     | 25/50 [00:01<00:01, 23.84it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 519.702} (0.069):  52%|█████▏    | 26/50 [00:01<00:01, 23.84it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.072 | Best: {'fx': 521.743} (0.072):  54%|█████▍    | 27/50 [00:01<00:00, 23.84it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.072 | Best: {'fx': 521.743} (0.072):  56%|█████▌    | 28/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.066 | Best: {'fx': 521.743} (0.072):  56%|█████▌    | 28/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.069 | Best: {'fx': 521.743} (0.072):  58%|█████▊    | 29/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.061 | Best: {'fx': 521.743} (0.072):  60%|██████    | 30/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.063 | Best: {'fx': 521.743} (0.072):  62%|██████▏   | 31/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.070 | Best: {'fx': 521.743} (0.072):  64%|██████▍   | 32/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.061 | Best: {'fx': 521.743} (0.072):  66%|██████▌   | 33/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.061 | Best: {'fx': 521.743} (0.072):  68%|██████▊   | 34/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.067 | Best: {'fx': 521.743} (0.072):  70%|███████   | 35/50 [00:01<00:00, 24.42it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.067 | Best: {'fx': 521.743} (0.072):  72%|███████▏  | 36/50 [00:01<00:00, 35.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.066 | Best: {'fx': 521.743} (0.072):  72%|███████▏  | 36/50 [00:01<00:00, 35.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.060 | Best: {'fx': 521.743} (0.072):  74%|███████▍  | 37/50 [00:01<00:00, 35.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  76%|███████▌  | 38/50 [00:01<00:00, 35.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  78%|███████▊  | 39/50 [00:01<00:00, 35.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  80%|████████  | 40/50 [00:01<00:00, 35.65it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  82%|████████▏ | 41/50 [00:01<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  82%|████████▏ | 41/50 [00:01<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  84%|████████▍ | 42/50 [00:01<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.062 | Best: {'fx': 521.743} (0.072):  86%|████████▌ | 43/50 [00:01<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.061 | Best: {'fx': 521.743} (0.072):  88%|████████▊ | 44/50 [00:01<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.063 | Best: {'fx': 521.743} (0.072):  90%|█████████ | 45/50 [00:01<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.061 | Best: {'fx': 521.743} (0.072):  92%|█████████▏| 46/50 [00:02<00:00, 36.62it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.061 | Best: {'fx': 521.743} (0.072):  94%|█████████▍| 47/50 [00:02<00:00, 39.06it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.058 | Best: {'fx': 521.743} (0.072):  94%|█████████▍| 47/50 [00:02<00:00, 39.06it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.063 | Best: {'fx': 521.743} (0.072):  96%|█████████▌| 48/50 [00:02<00:00, 39.06it/s]Brute | Start: {'fx': 519.192} (0.067) | Current: 0.059 | Best: {'fx': 521.743} (0.072):  98%|█████████▊| 49/50 [00:02<00:00, 39.06it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 521.7429396000824
    px: 882.0
    py: 919.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 80 frames will be used for optimization
  Rigs: 1000(1000mm):20/20, 1500(1500mm):20/20, 2000L(2000mm):20/20, 2000R(2000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 140.8034: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 140.8034, picklable_objective(x0) = 140.8855
Best score: 140.8855: : 0iter [00:02, ?iter/s] Best score: 140.8855: : 1iter [00:02,  2.02s/iter]Best score: 140.8855: : 1iter [00:02,  2.02s/iter]Best score: 140.8855: : 2iter [00:02,  2.02s/iter]Best score: 140.8855: : 3iter [00:02,  1.70iter/s]Best score: 140.8855: : 3iter [00:02,  1.70iter/s]Best score: 140.8855: : 4iter [00:02,  1.70iter/s]Best score: 140.8855: : 5iter [00:02,  3.03iter/s]Best score: 140.8855: : 5iter [00:02,  3.03iter/s]Best score: 140.8855: : 6iter [00:02,  3.03iter/s]Best score: 140.8855: : 7iter [00:02,  4.41iter/s]Best score: 140.8855: : 7iter [00:02,  4.41iter/s]Best score: 140.8855: : 8iter [00:02,  4.41iter/s]Best score: 140.8855: : 9iter [00:02,  5.74iter/s]Best score: 140.8855: : 9iter [00:02,  5.74iter/s]Best score: 140.8855: : 10iter [00:02,  5.74iter/s]Best score: 140.8855: : 11iter [00:02,  6.93iter/s]Best score: 140.8855: : 11iter [00:02,  6.93iter/s]Best score: 140.8855: : 12iter [00:03,  6.93iter/s]Best score: 140.8855: : 13iter [00:03,  7.99iter/s]Best score: 140.8855: : 13iter [00:03,  7.99iter/s]Best score: 140.8855: : 14iter [00:03,  7.99iter/s]Best score: 140.8855: : 15iter [00:03,  8.87iter/s]Best score: 140.8855: : 15iter [00:03,  8.87iter/s]Best score: 140.8855: : 16iter [00:03,  8.87iter/s]Best score: 140.8855: : 17iter [00:03,  9.55iter/s]Best score: 140.8855: : 17iter [00:03,  9.55iter/s]Best score: 140.8855: : 18iter [00:03,  9.55iter/s]Best score: 140.8855: : 19iter [00:03, 10.10iter/s]Best score: 140.8855: : 19iter [00:03, 10.10iter/s]Best score: 140.8855: : 20iter [00:03, 10.10iter/s]Best score: 140.8855: : 21iter [00:03, 10.37iter/s]Best score: 140.8855: : 21iter [00:03, 10.37iter/s]Best score: 140.8855: : 22iter [00:03, 10.37iter/s]Best score: 140.8855: : 23iter [00:03, 10.72iter/s]Best score: 140.8855: : 23iter [00:04, 10.72iter/s]Best score: 140.8855: : 24iter [00:04, 10.72iter/s]Best score: 140.8855: : 25iter [00:04, 10.97iter/s]Best score: 140.8855: : 25iter [00:04, 10.97iter/s]Best score: 140.8855: : 26iter [00:04, 10.97iter/s]Best score: 140.8855: : 27iter [00:04, 11.16iter/s]Best score: 140.8855: : 27iter [00:04, 11.16iter/s]Best score: 140.8855: : 28iter [00:04, 11.16iter/s]Best score: 140.8855: : 29iter [00:04, 11.30iter/s]Best score: 140.8855: : 29iter [00:04, 11.30iter/s]Best score: 140.8855: : 30iter [00:04, 11.30iter/s]Best score: 140.8855: : 31iter [00:04, 11.23iter/s]Best score: 140.8855: : 31iter [00:04, 11.23iter/s]Best score: 140.8855: : 32iter [00:04, 11.23iter/s]Best score: 140.8855: : 33iter [00:04, 11.38iter/s]Best score: 140.8855: : 33iter [00:04, 11.38iter/s]Best score: 140.8855: : 34iter [00:04, 11.38iter/s]Best score: 140.8855: : 35iter [00:04, 11.50iter/s]Best score: 140.8855: : 35iter [00:05, 11.50iter/s]Best score: 140.8855: : 36iter [00:05, 11.50iter/s]Best score: 140.8855: : 37iter [00:05, 11.57iter/s]Best score: 140.8855: : 37iter [00:05, 11.57iter/s]Best score: 140.8855: : 38iter [00:05, 11.57iter/s]Best score: 140.8855: : 39iter [00:05, 11.56iter/s]Best score: 129.7973: : 39iter [00:05, 11.56iter/s]Best score: 129.7973: : 40iter [00:05, 11.56iter/s]Best score: 129.7973: : 41iter [00:05, 11.39iter/s]Best score: 129.7973: : 41iter [00:05, 11.39iter/s]Best score: 129.7973: : 42iter [00:05, 11.39iter/s]Best score: 129.7973: : 43iter [00:05, 11.49iter/s]Best score: 129.7973: : 43iter [00:05, 11.49iter/s]Best score: 129.7973: : 44iter [00:05, 11.49iter/s]Best score: 129.7973: : 45iter [00:05, 11.52iter/s]Best score: 129.7973: : 45iter [00:05, 11.52iter/s]Best score: 129.7973: : 46iter [00:06, 11.52iter/s]Best score: 129.7973: : 47iter [00:06, 11.54iter/s]Best score: 129.7973: : 47iter [00:06, 11.54iter/s]Best score: 129.7973: : 48iter [00:06, 11.54iter/s]Best score: 129.7973: : 49iter [00:06, 11.58iter/s]
[ITER 50] Best: 105.2029
[PARAMS] Current best parameters:
  fx: 494.4340
  fy: 499.3571
  px: 872.1727
  py: 962.8993
  k1: -0.02055345
  k2: 0.01992749
  k3: 0.03148135
  k4: -0.01531218
  p1: 0.00970044
  p2: -0.01916619
  p3: 0.00388980
  p4: -0.00190038
  s1: -0.0098
  s2: -0.0100
  s3: 0.0412
  s4: 0.0163
  tilt_x: -0.0076
  tilt_y: -0.0053
  scale: 2.9983
  rotation: -0.1033
Best score: 105.2029: : 49iter [00:06, 11.58iter/s]Best score: 105.2029: : 50iter [00:06, 11.58iter/s]Best score: 105.2029: : 51iter [00:06, 11.41iter/s]Best score: 105.2029: : 51iter [00:06, 11.41iter/s]Best score: 105.2029: : 52iter [00:06, 11.41iter/s]Best score: 105.2029: : 53iter [00:06, 11.51iter/s]Best score: 105.2029: : 53iter [00:06, 11.51iter/s]Best score: 105.2029: : 54iter [00:06, 11.51iter/s]Best score: 105.2029: : 55iter [00:06, 11.53iter/s]Best score: 105.2029: : 55iter [00:06, 11.53iter/s]Best score: 105.2029: : 56iter [00:06, 11.53iter/s]Best score: 105.2029: : 57iter [00:06, 11.57iter/s]Best score: 105.2029: : 57iter [00:06, 11.57iter/s]Best score: 105.2029: : 58iter [00:07, 11.57iter/s]Best score: 105.2029: : 59iter [00:07, 11.65iter/s]Best score: 66.2598: : 59iter [00:07, 11.65iter/s] Best score: 66.2598: : 60iter [00:07, 11.65iter/s]Best score: 66.2598: : 61iter [00:07, 11.49iter/s]Best score: 66.2598: : 61iter [00:07, 11.49iter/s]Best score: 66.2598: : 62iter [00:07, 11.49iter/s]Best score: 66.2598: : 63iter [00:07, 11.54iter/s]Best score: 66.2598: : 63iter [00:07, 11.54iter/s]Best score: 66.2598: : 64iter [00:07, 11.54iter/s]Best score: 66.2598: : 65iter [00:07, 11.66iter/s]Best score: 66.2598: : 65iter [00:07, 11.66iter/s]Best score: 66.2598: : 66iter [00:07, 11.66iter/s]Best score: 66.2598: : 67iter [00:07, 11.64iter/s]Best score: 66.2598: : 67iter [00:07, 11.64iter/s]Best score: 66.2598: : 68iter [00:07, 11.64iter/s]Best score: 66.2598: : 69iter [00:07, 11.67iter/s]Best score: 16.8208: : 69iter [00:08, 11.67iter/s]Best score: 16.8208: : 70iter [00:08, 11.67iter/s]Best score: 16.8208: : 71iter [00:08, 11.51iter/s]Best score: 16.8208: : 71iter [00:08, 11.51iter/s]Best score: 16.8208: : 72iter [00:08, 11.51iter/s]Best score: 16.8208: : 73iter [00:08, 11.61iter/s]Best score: 16.8208: : 73iter [00:08, 11.61iter/s]Best score: 16.8208: : 74iter [00:08, 11.61iter/s]Best score: 16.8208: : 75iter [00:08, 11.65iter/s]Best score: 16.8208: : 75iter [00:08, 11.65iter/s]Best score: 16.8208: : 76iter [00:08, 11.65iter/s]Best score: 16.8208: : 77iter [00:08, 11.69iter/s]Best score: 16.8208: : 77iter [00:08, 11.69iter/s]Best score: 16.8208: : 78iter [00:08, 11.69iter/s]Best score: 16.8208: : 79iter [00:08, 11.76iter/s]Best score: 11.0210: : 79iter [00:08, 11.76iter/s]Best score: 11.0210: : 80iter [00:08, 11.76iter/s]Best score: 11.0210: : 81iter [00:08, 11.61iter/s]Best score: 11.0210: : 81iter [00:09, 11.61iter/s]Best score: 11.0210: : 82iter [00:09, 11.61iter/s]Best score: 11.0210: : 83iter [00:09, 11.66iter/s]Best score: 11.0210: : 83iter [00:09, 11.66iter/s]Best score: 11.0210: : 84iter [00:09, 11.66iter/s]Best score: 11.0210: : 85iter [00:09, 11.72iter/s]Best score: 11.0210: : 85iter [00:09, 11.72iter/s]Best score: 11.0210: : 86iter [00:09, 11.72iter/s]Best score: 11.0210: : 87iter [00:09, 11.78iter/s]Best score: 11.0210: : 87iter [00:09, 11.78iter/s]Best score: 11.0210: : 88iter [00:09, 11.78iter/s]Best score: 11.0210: : 89iter [00:09, 11.79iter/s]Best score: 11.0210: : 89iter [00:09, 11.79iter/s]Best score: 11.0210: : 90iter [00:09, 11.79iter/s]Best score: 11.0210: : 91iter [00:09, 11.64iter/s]Best score: 11.0210: : 91iter [00:09, 11.64iter/s]Best score: 11.0210: : 92iter [00:09, 11.64iter/s]Best score: 11.0210: : 93iter [00:09, 11.74iter/s]Best score: 11.0210: : 93iter [00:10, 11.74iter/s]Best score: 11.0210: : 94iter [00:10, 11.74iter/s]Best score: 11.0210: : 95iter [00:10, 11.78iter/s]Best score: 11.0210: : 95iter [00:10, 11.78iter/s]Best score: 11.0210: : 96iter [00:10, 11.78iter/s]Best score: 11.0210: : 97iter [00:10, 11.79iter/s]Best score: 11.0210: : 97iter [00:10, 11.79iter/s]Best score: 11.0210: : 98iter [00:10, 11.79iter/s]Best score: 11.0210: : 99iter [00:10, 11.82iter/s]
[ITER 100] Best: 6.0806
[PARAMS] Current best parameters:
  fx: 498.7641
  fy: 499.9997
  px: 874.6367
  py: 941.3140
  k1: -0.02240190
  k2: 0.04892585
  k3: -0.02574480
  k4: 0.00672696
  p1: -0.00018791
  p2: -0.01675403
  p3: -0.01597565
  p4: -0.00965139
  s1: 0.0102
  s2: 0.0117
  s3: 0.0202
  s4: 0.0136
  tilt_x: 0.0116
  tilt_y: 0.0086
  scale: 3.0630
  rotation: -1.5353
Best score: 6.0806: : 99iter [00:10, 11.82iter/s] Best score: 6.0806: : 100iter [00:10, 11.82iter/s]Best score: 6.0806: : 101iter [00:10, 10.09iter/s]Best score: 6.0806: : 101iter [00:10, 10.09iter/s]Best score: 6.0806: : 102iter [00:10, 10.09iter/s]Best score: 6.0806: : 103iter [00:10, 10.47iter/s]Best score: 6.0806: : 103iter [00:10, 10.47iter/s]Best score: 6.0806: : 104iter [00:11, 10.47iter/s]Best score: 6.0806: : 105iter [00:11, 10.75iter/s]Best score: 6.0806: : 105iter [00:11, 10.75iter/s]Best score: 6.0806: : 106iter [00:11, 10.75iter/s]Best score: 6.0806: : 107iter [00:11, 10.96iter/s]Best score: 6.0806: : 107iter [00:11, 10.96iter/s]Best score: 6.0806: : 108iter [00:11, 10.96iter/s]Best score: 6.0806: : 109iter [00:11, 11.15iter/s]Best score: 6.0806: : 109iter [00:11, 11.15iter/s]Best score: 6.0806: : 110iter [00:11, 11.15iter/s]Best score: 6.0806: : 111iter [00:11, 11.11iter/s]Best score: 6.0806: : 111iter [00:11, 11.11iter/s]Best score: 6.0806: : 112iter [00:11, 11.11iter/s]Best score: 6.0806: : 113iter [00:11, 11.24iter/s]Best score: 6.0806: : 113iter [00:11, 11.24iter/s]Best score: 6.0806: : 114iter [00:11, 11.24iter/s]Best score: 6.0806: : 115iter [00:11, 11.33iter/s]Best score: 6.0806: : 115iter [00:12, 11.33iter/s]Best score: 6.0806: : 116iter [00:12, 11.33iter/s]Best score: 6.0806: : 117iter [00:12, 11.41iter/s]Best score: 6.0806: : 117iter [00:12, 11.41iter/s]Best score: 6.0806: : 118iter [00:12, 11.41iter/s]Best score: 6.0806: : 119iter [00:12, 11.40iter/s]Best score: 6.0806: : 119iter [00:12, 11.40iter/s]Best score: 6.0806: : 120iter [00:12, 11.40iter/s]Best score: 6.0806: : 121iter [00:12, 11.24iter/s]Best score: 6.0806: : 121iter [00:12, 11.24iter/s]Best score: 6.0806: : 122iter [00:12, 11.24iter/s]Best score: 6.0806: : 123iter [00:12, 11.33iter/s]Best score: 6.0806: : 123iter [00:12, 11.33iter/s]Best score: 6.0806: : 124iter [00:12, 11.33iter/s]Best score: 6.0806: : 125iter [00:12, 11.39iter/s]Best score: 6.0806: : 125iter [00:12, 11.39iter/s]Best score: 6.0806: : 126iter [00:13, 11.39iter/s]Best score: 6.0806: : 127iter [00:13, 11.45iter/s]Best score: 6.0806: : 127iter [00:13, 11.45iter/s]Best score: 6.0806: : 128iter [00:13, 11.45iter/s]Best score: 6.0806: : 129iter [00:13, 11.48iter/s]Best score: 6.0806: : 129iter [00:13, 11.48iter/s]Best score: 6.0806: : 130iter [00:13, 11.48iter/s]Best score: 6.0806: : 131iter [00:13, 11.32iter/s]Best score: 6.0806: : 131iter [00:13, 11.32iter/s]Best score: 6.0806: : 132iter [00:13, 11.32iter/s]Best score: 6.0806: : 133iter [00:13, 11.38iter/s]Best score: 6.0806: : 133iter [00:13, 11.38iter/s]Best score: 6.0806: : 134iter [00:13, 11.38iter/s]Best score: 6.0806: : 135iter [00:13, 11.44iter/s]Best score: 6.0806: : 135iter [00:13, 11.44iter/s]Best score: 6.0806: : 136iter [00:13, 11.44iter/s]Best score: 6.0806: : 137iter [00:13, 11.46iter/s]Best score: 6.0806: : 137iter [00:13, 11.46iter/s]Best score: 6.0806: : 138iter [00:14, 11.46iter/s]Best score: 6.0806: : 139iter [00:14, 11.50iter/s]Best score: 6.0806: : 139iter [00:14, 11.50iter/s]Best score: 6.0806: : 140iter [00:14, 11.50iter/s]Best score: 6.0806: : 141iter [00:14, 11.38iter/s]Best score: 6.0806: : 141iter [00:14, 11.38iter/s]Best score: 6.0806: : 142iter [00:14, 11.38iter/s]Best score: 6.0806: : 143iter [00:14, 11.50iter/s]Best score: 6.0806: : 143iter [00:14, 11.50iter/s]Best score: 6.0806: : 144iter [00:14, 11.50iter/s]Best score: 6.0806: : 145iter [00:14, 11.54iter/s]Best score: 6.0806: : 145iter [00:14, 11.54iter/s]Best score: 6.0806: : 146iter [00:14, 11.54iter/s]Best score: 6.0806: : 147iter [00:14, 11.56iter/s]Best score: 6.0806: : 147iter [00:14, 11.56iter/s]Best score: 6.0806: : 148iter [00:14, 11.56iter/s]Best score: 6.0806: : 149iter [00:14, 11.57iter/s]
[ITER 150] Best: 6.0806
[PARAMS] Current best parameters:
  fx: 498.7641
  fy: 499.9997
  px: 874.6367
  py: 941.3140
  k1: -0.02240190
  k2: 0.04892585
  k3: -0.02574480
  k4: 0.00672696
  p1: -0.00018791
  p2: -0.01675403
  p3: -0.01597565
  p4: -0.00965139
  s1: 0.0102
  s2: 0.0117
  s3: 0.0202
  s4: 0.0136
  tilt_x: 0.0116
  tilt_y: 0.0086
  scale: 3.0630
  rotation: -1.5353
Best score: 6.0806: : 149iter [00:15, 11.57iter/s]Best score: 6.0806: : 150iter [00:15, 11.57iter/s]Best score: 6.0806: : 151iter [00:15,  7.51iter/s]Best score: 6.0806: : 151iter [00:15,  7.51iter/s]Best score: 6.0806: : 152iter [00:15,  7.51iter/s]Best score: 6.0806: : 153iter [00:15,  8.44iter/s]Best score: 6.0806: : 153iter [00:15,  8.44iter/s]Best score: 6.0806: : 154iter [00:15,  8.44iter/s]Best score: 6.0806: : 155iter [00:15,  9.22iter/s]Best score: 6.0806: : 155iter [00:15,  9.22iter/s]Best score: 6.0806: : 156iter [00:15,  9.22iter/s]Best score: 6.0806: : 157iter [00:15,  9.85iter/s]Best score: 6.0806: : 157iter [00:16,  9.85iter/s]Best score: 6.0806: : 158iter [00:16,  9.85iter/s]Best score: 6.0806: : 159iter [00:16, 10.32iter/s]Best score: 6.0806: : 159iter [00:16, 10.32iter/s]Best score: 6.0806: : 160iter [00:16, 10.32iter/s]Best score: 6.0806: : 161iter [00:16, 10.49iter/s]Best score: 6.0806: : 161iter [00:16, 10.49iter/s]Best score: 6.0806: : 162iter [00:16, 10.49iter/s]Best score: 6.0806: : 163iter [00:16, 10.83iter/s]Best score: 6.0806: : 163iter [00:16, 10.83iter/s]Best score: 6.0806: : 164iter [00:16, 10.83iter/s]Best score: 6.0806: : 165iter [00:16, 11.07iter/s]Best score: 6.0806: : 165iter [00:16, 11.07iter/s]Best score: 6.0806: : 166iter [00:16, 11.07iter/s]Best score: 6.0806: : 167iter [00:16, 11.24iter/s]Best score: 6.0806: : 167iter [00:16, 11.24iter/s]Best score: 6.0806: : 168iter [00:16, 11.24iter/s]Best score: 6.0806: : 169iter [00:16, 11.34iter/s]Best score: 6.0806: : 169iter [00:17, 11.34iter/s]Best score: 6.0806: : 170iter [00:17, 11.34iter/s]Best score: 6.0806: : 171iter [00:17, 11.27iter/s]Best score: 6.0806: : 171iter [00:17, 11.27iter/s]Best score: 6.0806: : 172iter [00:17, 11.27iter/s]Best score: 6.0806: : 173iter [00:17, 11.37iter/s]Best score: 6.0806: : 173iter [00:17, 11.37iter/s]Best score: 6.0806: : 174iter [00:17, 11.37iter/s]Best score: 6.0806: : 175iter [00:17, 11.42iter/s]Best score: 6.0806: : 175iter [00:17, 11.42iter/s]Best score: 6.0806: : 176iter [00:17, 11.42iter/s]Best score: 6.0806: : 177iter [00:17, 11.52iter/s]Best score: 6.0806: : 177iter [00:17, 11.52iter/s]Best score: 6.0806: : 178iter [00:17, 11.52iter/s]Best score: 6.0806: : 179iter [00:17, 11.62iter/s]Best score: 6.0806: : 179iter [00:17, 11.62iter/s]Best score: 6.0806: : 180iter [00:18, 11.62iter/s]Best score: 6.0806: : 181iter [00:18, 11.44iter/s]Best score: 6.0806: : 181iter [00:18, 11.44iter/s]Best score: 6.0806: : 182iter [00:18, 11.44iter/s]Best score: 6.0806: : 183iter [00:18, 11.59iter/s]Best score: 6.0806: : 183iter [00:18, 11.59iter/s]Best score: 6.0806: : 184iter [00:18, 11.59iter/s]Best score: 6.0806: : 185iter [00:18,  8.81iter/s]Best score: 6.0806: : 185iter [00:18,  8.81iter/s]Best score: 6.0806: : 186iter [00:18,  8.81iter/s]Best score: 6.0806: : 187iter [00:18,  9.51iter/s]Best score: 6.0806: : 187iter [00:18,  9.51iter/s]Best score: 6.0806: : 188iter [00:18,  9.51iter/s]Best score: 6.0806: : 189iter [00:18, 10.09iter/s]Best score: 6.0806: : 189iter [00:18, 10.09iter/s]Best score: 6.0806: : 190iter [00:19, 10.09iter/s]Best score: 6.0806: : 191iter [00:19, 10.38iter/s]Best score: 6.0806: : 191iter [00:19, 10.38iter/s]Best score: 6.0806: : 192iter [00:19, 10.38iter/s]Best score: 6.0806: : 193iter [00:19, 10.74iter/s]Best score: 6.0806: : 193iter [00:19, 10.74iter/s]Best score: 6.0806: : 194iter [00:19, 10.74iter/s]Best score: 6.0806: : 195iter [00:19, 11.02iter/s]Best score: 6.0806: : 195iter [00:19, 11.02iter/s]Best score: 6.0806: : 196iter [00:19, 11.02iter/s]Best score: 6.0806: : 197iter [00:19, 11.18iter/s]Best score: 6.0806: : 197iter [00:19, 11.18iter/s]Best score: 6.0806: : 198iter [00:19, 11.18iter/s]Best score: 6.0806: : 199iter [00:19, 11.37iter/s]
[ITER 200] Best: 6.0806
[PARAMS] Current best parameters:
  fx: 498.7641
  fy: 499.9997
  px: 874.6367
  py: 941.3140
  k1: -0.02240190
  k2: 0.04892585
  k3: -0.02574480
  k4: 0.00672696
  p1: -0.00018791
  p2: -0.01675403
  p3: -0.01597565
  p4: -0.00965139
  s1: 0.0102
  s2: 0.0117
  s3: 0.0202
  s4: 0.0136
  tilt_x: 0.0116
  tilt_y: 0.0086
  scale: 3.0630
  rotation: -1.5353
Best score: 6.0806: : 199iter [00:19, 11.37iter/s]Best score: 6.0806: : 200iter [00:20, 11.37iter/s]Best score: 6.0806: : 201iter [00:20,  7.43iter/s]Best score: 6.0806: : 201iter [00:20,  7.43iter/s]Best score: 6.0806: : 202iter [00:20,  7.43iter/s]Best score: 6.0806: : 203iter [00:20,  8.35iter/s]Best score: 6.0806: : 203iter [00:20,  8.35iter/s]Best score: 6.0806: : 204iter [00:20,  8.35iter/s]Best score: 6.0806: : 205iter [00:20,  9.14iter/s]Best score: 6.0806: : 205iter [00:20,  9.14iter/s]Best score: 6.0806: : 206iter [00:20,  9.14iter/s]Best score: 6.0806: : 207iter [00:20,  9.80iter/s]Best score: 6.0806: : 207iter [00:20,  9.80iter/s]Best score: 6.0806: : 208iter [00:20,  9.80iter/s]Best score: 6.0806: : 209iter [00:20, 10.32iter/s]Best score: 6.0806: : 209iter [00:20, 10.32iter/s]Best score: 6.0806: : 210iter [00:21, 10.32iter/s]Best score: 6.0806: : 211iter [00:21, 10.58iter/s]Best score: 6.0806: : 211iter [00:21, 10.58iter/s]Best score: 6.0806: : 212iter [00:21, 10.58iter/s]Best score: 6.0806: : 213iter [00:21, 10.90iter/s]Best score: 6.0806: : 213iter [00:21, 10.90iter/s]Best score: 6.0806: : 214iter [00:21, 10.90iter/s]Best score: 6.0806: : 215iter [00:21, 11.19iter/s]Best score: 6.0806: : 215iter [00:21, 11.19iter/s]Best score: 6.0806: : 216iter [00:21, 11.19iter/s]Best score: 6.0806: : 217iter [00:21, 11.41iter/s]Best score: 6.0806: : 217iter [00:21, 11.41iter/s]Best score: 6.0806: : 218iter [00:21, 11.41iter/s]Best score: 6.0806: : 219iter [00:21, 11.52iter/s]Best score: 6.0806: : 219iter [00:21, 11.52iter/s]Best score: 6.0806: : 220iter [00:21, 11.52iter/s]Best score: 6.0806: : 221iter [00:21, 11.39iter/s]Best score: 6.0806: : 221iter [00:22, 11.39iter/s]Best score: 6.0806: : 222iter [00:22, 11.39iter/s]Best score: 6.0806: : 223iter [00:22, 11.51iter/s]Best score: 6.0806: : 223iter [00:22, 11.51iter/s]Best score: 6.0806: : 224iter [00:22, 11.51iter/s]Best score: 6.0806: : 225iter [00:22, 11.60iter/s]Best score: 6.0806: : 225iter [00:22, 11.60iter/s]Best score: 6.0806: : 226iter [00:22, 11.60iter/s]Best score: 6.0806: : 227iter [00:22, 11.67iter/s]Best score: 6.0806: : 227iter [00:22, 11.67iter/s]Best score: 6.0806: : 228iter [00:22, 11.67iter/s]Best score: 6.0806: : 229iter [00:22, 11.68iter/s]Best score: 6.0806: : 229iter [00:22, 11.68iter/s]Best score: 6.0806: : 230iter [00:22, 11.68iter/s]Best score: 6.0806: : 231iter [00:22, 11.49iter/s]Best score: 6.0806: : 231iter [00:22, 11.49iter/s]Best score: 6.0806: : 232iter [00:22, 11.49iter/s]Best score: 6.0806: : 233iter [00:22, 11.57iter/s]Best score: 6.0806: : 233iter [00:23, 11.57iter/s]Best score: 6.0806: : 234iter [00:23, 11.57iter/s]Best score: 6.0806: : 235iter [00:23, 11.68iter/s]Best score: 6.0806: : 235iter [00:23, 11.68iter/s]Best score: 6.0806: : 236iter [00:23, 11.68iter/s]Best score: 6.0806: : 237iter [00:23, 11.69iter/s]Best score: 6.0806: : 237iter [00:23, 11.69iter/s]Best score: 6.0806: : 238iter [00:23, 11.69iter/s]Best score: 6.0806: : 239iter [00:23, 11.73iter/s]Best score: 5.8325: : 239iter [00:23, 11.73iter/s]Best score: 5.8325: : 240iter [00:23, 11.73iter/s]Best score: 5.8325: : 241iter [00:23, 11.52iter/s]Best score: 5.8325: : 241iter [00:23, 11.52iter/s]Best score: 5.8325: : 242iter [00:23, 11.52iter/s]Best score: 5.8325: : 243iter [00:23, 11.55iter/s]Best score: 5.8325: : 243iter [00:23, 11.55iter/s]Best score: 5.8325: : 244iter [00:23, 11.55iter/s]Best score: 5.8325: : 245iter [00:23, 11.64iter/s]Best score: 5.8325: : 245iter [00:24, 11.64iter/s]Best score: 5.8325: : 246iter [00:24, 11.64iter/s]Best score: 5.8325: : 247iter [00:24, 11.67iter/s]Best score: 5.8325: : 247iter [00:24, 11.67iter/s]Best score: 5.8325: : 248iter [00:24, 11.67iter/s]Best score: 5.8325: : 249iter [00:24, 11.69iter/s]
[ITER 250] Best: 4.6386
[PARAMS] Current best parameters:
  fx: 457.4742
  fy: 459.4700
  px: 871.7976
  py: 945.2985
  k1: 0.03084219
  k2: 0.09156241
  k3: -0.07840679
  k4: 0.02085549
  p1: -0.00712709
  p2: -0.02787686
  p3: -0.01026435
  p4: -0.00094767
  s1: 0.0049
  s2: 0.0091
  s3: 0.0151
  s4: 0.0102
  tilt_x: 0.0413
  tilt_y: 0.0058
  scale: 3.0108
  rotation: -1.3355
Best score: 4.6386: : 249iter [00:24, 11.69iter/s]
[Periodic outlier @ iter 150] Removing 2000R frame 13 (score=24.03, threshold=18.89)
  Remaining frames: 79
Best score: 4.6386: : 250iter [00:24, 11.69iter/s]Best score: 4.6386: : 251iter [00:24,  6.49iter/s]Best score: 4.6386: : 251iter [00:25,  6.49iter/s]Best score: 4.6386: : 252iter [00:25,  6.49iter/s]Best score: 4.6386: : 253iter [00:25,  7.48iter/s]Best score: 4.6386: : 253iter [00:25,  7.48iter/s]Best score: 4.6386: : 254iter [00:25,  7.48iter/s]Best score: 4.6386: : 255iter [00:25,  8.39iter/s]Best score: 4.6386: : 255iter [00:25,  8.39iter/s]Best score: 4.6386: : 256iter [00:25,  8.39iter/s]Best score: 4.6386: : 257iter [00:25,  9.18iter/s]Best score: 4.6386: : 257iter [00:25,  9.18iter/s]Best score: 4.6386: : 258iter [00:25,  9.18iter/s]Best score: 4.6386: : 259iter [00:25,  9.84iter/s]Best score: 4.4110: : 259iter [00:25,  9.84iter/s]Best score: 4.4110: : 260iter [00:25,  9.84iter/s]Best score: 4.4110: : 261iter [00:25, 10.20iter/s]Best score: 4.4110: : 261iter [00:25, 10.20iter/s]Best score: 4.4110: : 262iter [00:25, 10.20iter/s]Best score: 4.4110: : 263iter [00:25, 10.61iter/s]Best score: 4.4110: : 263iter [00:26, 10.61iter/s]Best score: 4.4110: : 264iter [00:26, 10.61iter/s]Best score: 4.4110: : 265iter [00:26, 10.88iter/s]Best score: 4.4110: : 265iter [00:26, 10.88iter/s]Best score: 4.4110: : 266iter [00:26, 10.88iter/s]Best score: 4.4110: : 267iter [00:26, 11.13iter/s]Best score: 4.4110: : 267iter [00:26, 11.13iter/s]Best score: 4.4110: : 268iter [00:26, 11.13iter/s]Best score: 4.4110: : 269iter [00:26, 11.29iter/s]Best score: 4.4110: : 269iter [00:26, 11.29iter/s]Best score: 4.4110: : 270iter [00:26, 11.29iter/s]Best score: 4.4110: : 271iter [00:26, 11.25iter/s]Best score: 4.4110: : 271iter [00:26, 11.25iter/s]Best score: 4.4110: : 272iter [00:26, 11.25iter/s]Best score: 4.4110: : 273iter [00:26, 11.36iter/s]Best score: 4.4110: : 273iter [00:26, 11.36iter/s]Best score: 4.4110: : 274iter [00:27, 11.36iter/s]Best score: 4.4110: : 275iter [00:27, 11.45iter/s]Best score: 4.4110: : 275iter [00:27, 11.45iter/s]Best score: 4.4110: : 276iter [00:27, 11.45iter/s]Best score: 4.4110: : 277iter [00:27, 11.50iter/s]Best score: 4.4110: : 277iter [00:27, 11.50iter/s]Best score: 4.4110: : 278iter [00:27, 11.50iter/s]Best score: 4.4110: : 279iter [00:27, 11.60iter/s]Best score: 4.4110: : 279iter [00:27, 11.60iter/s]Best score: 4.4110: : 280iter [00:27, 11.60iter/s]Best score: 4.4110: : 281iter [00:27, 11.46iter/s]Best score: 4.4110: : 281iter [00:27, 11.46iter/s]Best score: 4.4110: : 282iter [00:27, 11.46iter/s]Best score: 4.4110: : 283iter [00:27, 11.51iter/s]Best score: 4.4110: : 283iter [00:27, 11.51iter/s]Best score: 4.4110: : 284iter [00:27, 11.51iter/s]Best score: 4.4110: : 285iter [00:27, 11.60iter/s]Best score: 4.4110: : 285iter [00:27, 11.60iter/s]Best score: 4.4110: : 286iter [00:28, 11.60iter/s]Best score: 4.4110: : 287iter [00:28, 11.67iter/s]Best score: 4.4110: : 287iter [00:28, 11.67iter/s]Best score: 4.4110: : 288iter [00:28, 11.67iter/s]Best score: 4.4110: : 289iter [00:28, 11.73iter/s]Best score: 4.4110: : 289iter [00:28, 11.73iter/s]Best score: 4.4110: : 290iter [00:28, 11.73iter/s]Best score: 4.4110: : 291iter [00:28, 11.55iter/s]Best score: 4.4110: : 291iter [00:28, 11.55iter/s]Best score: 4.4110: : 292iter [00:28, 11.55iter/s]Best score: 4.4110: : 293iter [00:28, 11.55iter/s]Best score: 4.4110: : 293iter [00:28, 11.55iter/s]Best score: 4.4110: : 294iter [00:28, 11.55iter/s]Best score: 4.4110: : 295iter [00:28, 11.61iter/s]Best score: 4.4110: : 295iter [00:28, 11.61iter/s]Best score: 4.4110: : 296iter [00:28, 11.61iter/s]Best score: 4.4110: : 297iter [00:28, 11.69iter/s]Best score: 4.4110: : 297iter [00:29, 11.69iter/s]Best score: 4.4110: : 298iter [00:29, 11.69iter/s]Best score: 4.4110: : 299iter [00:29, 11.77iter/s]
[ITER 300] Best: 4.4110
[PARAMS] Current best parameters:
  fx: 457.4742
  fy: 459.4700
  px: 871.7976
  py: 945.2985
  k1: 0.03084219
  k2: 0.09156241
  k3: -0.07840679
  k4: 0.02085549
  p1: -0.00712709
  p2: -0.02787686
  p3: -0.01026435
  p4: -0.00094767
  s1: 0.0049
  s2: 0.0091
  s3: 0.0151
  s4: 0.0102
  tilt_x: 0.0413
  tilt_y: 0.0058
  scale: 3.0108
  rotation: -1.3355
Best score: 4.4110: : 299iter [00:29, 11.77iter/s]Best score: 4.4110: : 300iter [00:29, 11.77iter/s]Best score: 4.4110: : 301iter [00:29,  7.57iter/s]Best score: 4.4110: : 301iter [00:29,  7.57iter/s]Best score: 4.4110: : 302iter [00:29,  7.57iter/s]Best score: 4.4110: : 303iter [00:29,  8.48iter/s]Best score: 4.4110: : 303iter [00:29,  8.48iter/s]Best score: 4.4110: : 304iter [00:29,  8.48iter/s]Best score: 4.4110: : 305iter [00:29,  9.28iter/s]Best score: 4.4110: : 305iter [00:29,  9.28iter/s]Best score: 4.4110: : 306iter [00:30,  9.28iter/s]Best score: 4.4110: : 307iter [00:30,  9.92iter/s]Best score: 4.4110: : 307iter [00:30,  9.92iter/s]Best score: 4.4110: : 308iter [00:30,  9.92iter/s]Best score: 4.4110: : 309iter [00:30, 10.44iter/s]Best score: 4.4110: : 309iter [00:30, 10.44iter/s]Best score: 4.4110: : 310iter [00:30, 10.44iter/s]Best score: 4.4110: : 311iter [00:30, 10.71iter/s]Best score: 4.4110: : 311iter [00:30, 10.71iter/s]Best score: 4.4110: : 312iter [00:30, 10.71iter/s]Best score: 4.4110: : 313iter [00:30, 11.06iter/s]Best score: 4.4110: : 313iter [00:30, 11.06iter/s]Best score: 4.4110: : 314iter [00:30, 11.06iter/s]Best score: 4.4110: : 315iter [00:30, 11.30iter/s]Best score: 4.4110: : 315iter [00:30, 11.30iter/s]Best score: 4.4110: : 316iter [00:30, 11.30iter/s]Best score: 4.4110: : 317iter [00:30, 11.43iter/s]Best score: 4.4110: : 317iter [00:31, 11.43iter/s]Best score: 4.4110: : 318iter [00:31, 11.43iter/s]Best score: 4.4110: : 319iter [00:31, 11.58iter/s]Best score: 4.4110: : 319iter [00:31, 11.58iter/s]Best score: 4.4110: : 320iter [00:31, 11.58iter/s]Best score: 4.4110: : 321iter [00:31, 11.50iter/s]Best score: 4.4110: : 321iter [00:31, 11.50iter/s]Best score: 4.4110: : 322iter [00:31, 11.50iter/s]Best score: 4.4110: : 323iter [00:31, 11.65iter/s]Best score: 4.4110: : 323iter [00:31, 11.65iter/s]Best score: 4.4110: : 324iter [00:31, 11.65iter/s]Best score: 4.4110: : 325iter [00:31, 11.78iter/s]Best score: 4.4110: : 325iter [00:31, 11.78iter/s]Best score: 4.4110: : 326iter [00:31, 11.78iter/s]Best score: 4.4110: : 327iter [00:31, 11.79iter/s]Best score: 4.4110: : 327iter [00:31, 11.79iter/s]Best score: 4.4110: : 328iter [00:31, 11.79iter/s]Best score: 4.4110: : 329iter [00:31, 11.83iter/s]Best score: 4.4110: : 329iter [00:32, 11.83iter/s]Best score: 4.4110: : 330iter [00:32, 11.83iter/s]Best score: 4.4110: : 331iter [00:32, 11.67iter/s]Best score: 4.4110: : 331iter [00:32, 11.67iter/s]Best score: 4.4110: : 332iter [00:32, 11.67iter/s]Best score: 4.4110: : 333iter [00:32, 11.76iter/s]Best score: 4.4110: : 333iter [00:32, 11.76iter/s]Best score: 4.4110: : 334iter [00:32, 11.76iter/s]Best score: 4.4110: : 335iter [00:32, 11.83iter/s]Best score: 4.4110: : 335iter [00:32, 11.83iter/s]Best score: 4.4110: : 336iter [00:32, 11.83iter/s]Best score: 4.4110: : 337iter [00:32, 11.86iter/s]Best score: 4.4110: : 337iter [00:32, 11.86iter/s]Best score: 4.4110: : 338iter [00:32, 11.86iter/s]Best score: 4.4110: : 339iter [00:32, 11.91iter/s]Best score: 4.4110: : 339iter [00:32, 11.91iter/s]Best score: 4.4110: : 340iter [00:32, 11.91iter/s]Best score: 4.4110: : 341iter [00:32, 11.79iter/s]Best score: 4.4110: : 341iter [00:33, 11.79iter/s]Best score: 4.4110: : 342iter [00:33, 11.79iter/s]Best score: 4.4110: : 343iter [00:33, 11.89iter/s]Best score: 4.4110: : 343iter [00:33, 11.89iter/s]Best score: 4.4110: : 344iter [00:33, 11.89iter/s]Best score: 4.4110: : 345iter [00:33, 11.90iter/s]Best score: 4.4110: : 345iter [00:33, 11.90iter/s]Best score: 4.4110: : 346iter [00:33, 11.90iter/s]Best score: 4.4110: : 347iter [00:33, 11.96iter/s]Best score: 4.4110: : 347iter [00:33, 11.96iter/s]Best score: 4.4110: : 348iter [00:33, 11.96iter/s]Best score: 4.4110: : 349iter [00:33, 12.00iter/s]
[ITER 350] Best: 2.8611
[PARAMS] Current best parameters:
  fx: 457.7947
  fy: 458.1349
  px: 878.0250
  py: 941.4670
  k1: 0.04711376
  k2: 0.06700921
  k3: -0.04892242
  k4: 0.01002299
  p1: -0.00646358
  p2: -0.02535029
  p3: -0.01414071
  p4: 0.00189666
  s1: 0.0130
  s2: 0.0035
  s3: 0.0192
  s4: 0.0065
  tilt_x: 0.0322
  tilt_y: -0.0128
  scale: 2.9159
  rotation: -1.0321
Best score: 2.8611: : 349iter [00:33, 12.00iter/s]Best score: 2.8611: : 350iter [00:34, 12.00iter/s]Best score: 2.8611: : 351iter [00:34,  7.67iter/s]Best score: 2.8611: : 351iter [00:34,  7.67iter/s]Best score: 2.8611: : 352iter [00:34,  7.67iter/s]Best score: 2.8611: : 353iter [00:34,  8.59iter/s]Best score: 2.8611: : 353iter [00:34,  8.59iter/s]Best score: 2.8611: : 354iter [00:34,  8.59iter/s]Best score: 2.8611: : 355iter [00:34,  9.39iter/s]Best score: 2.8611: : 355iter [00:34,  9.39iter/s]Best score: 2.8611: : 356iter [00:34,  9.39iter/s]Best score: 2.8611: : 357iter [00:34, 10.03iter/s]Best score: 2.8611: : 357iter [00:34, 10.03iter/s]Best score: 2.8611: : 358iter [00:34, 10.03iter/s]Best score: 2.8611: : 359iter [00:34, 10.55iter/s]Best score: 2.1388: : 359iter [00:34, 10.55iter/s]Best score: 2.1388: : 360iter [00:34, 10.55iter/s]Best score: 2.1388: : 361iter [00:34, 10.77iter/s]Best score: 2.1388: : 361iter [00:35, 10.77iter/s]Best score: 2.1388: : 362iter [00:35, 10.77iter/s]Best score: 2.1388: : 363iter [00:35, 11.14iter/s]Best score: 2.1388: : 363iter [00:35, 11.14iter/s]Best score: 2.1388: : 364iter [00:35, 11.14iter/s]Best score: 2.1388: : 365iter [00:35, 11.37iter/s]Best score: 2.1388: : 365iter [00:35, 11.37iter/s]Best score: 2.1388: : 366iter [00:35, 11.37iter/s]Best score: 2.1388: : 367iter [00:35, 11.50iter/s]Best score: 2.1388: : 367iter [00:35, 11.50iter/s]Best score: 2.1388: : 368iter [00:35, 11.50iter/s]Best score: 2.1388: : 369iter [00:35, 11.67iter/s]Best score: 2.1388: : 369iter [00:35, 11.67iter/s]Best score: 2.1388: : 370iter [00:35, 11.67iter/s]Best score: 2.1388: : 371iter [00:35, 11.54iter/s]Best score: 2.1388: : 371iter [00:35, 11.54iter/s]Best score: 2.1388: : 372iter [00:35, 11.54iter/s]Best score: 2.1388: : 373iter [00:35, 11.67iter/s]Best score: 2.1388: : 373iter [00:36, 11.67iter/s]Best score: 2.1388: : 374iter [00:36, 11.67iter/s]Best score: 2.1388: : 375iter [00:36, 11.79iter/s]Best score: 2.1388: : 375iter [00:36, 11.79iter/s]Best score: 2.1388: : 376iter [00:36, 11.79iter/s]Best score: 2.1388: : 377iter [00:36, 11.88iter/s]Best score: 2.1388: : 377iter [00:36, 11.88iter/s]Best score: 2.1388: : 378iter [00:36, 11.88iter/s]Best score: 2.1388: : 379iter [00:36, 11.95iter/s]Best score: 2.1388: : 379iter [00:36, 11.95iter/s]Best score: 2.1388: : 380iter [00:36, 11.95iter/s]Best score: 2.1388: : 381iter [00:36, 11.76iter/s]Best score: 2.1388: : 381iter [00:36, 11.76iter/s]Best score: 2.1388: : 382iter [00:36, 11.76iter/s]Best score: 2.1388: : 383iter [00:36, 11.85iter/s]Best score: 2.1388: : 383iter [00:36, 11.85iter/s]Best score: 2.1388: : 384iter [00:36, 11.85iter/s]Best score: 2.1388: : 385iter [00:36, 11.91iter/s]Best score: 2.1388: : 385iter [00:37, 11.91iter/s]Best score: 2.1388: : 386iter [00:37, 11.91iter/s]Best score: 2.1388: : 387iter [00:37, 11.90iter/s]Best score: 2.1388: : 387iter [00:37, 11.90iter/s]Best score: 2.1388: : 388iter [00:37, 11.90iter/s]Best score: 2.1388: : 389iter [00:37, 11.93iter/s]Best score: 1.6881: : 389iter [00:37, 11.93iter/s]Best score: 1.6881: : 390iter [00:37, 11.93iter/s]Best score: 1.6881: : 391iter [00:37, 11.77iter/s]Best score: 1.6881: : 391iter [00:37, 11.77iter/s]Best score: 1.6881: : 392iter [00:37, 11.77iter/s]Best score: 1.6881: : 393iter [00:37, 11.82iter/s]Best score: 1.6881: : 393iter [00:37, 11.82iter/s]Best score: 1.6881: : 394iter [00:37, 11.82iter/s]Best score: 1.6881: : 395iter [00:37, 11.89iter/s]Best score: 1.6881: : 395iter [00:37, 11.89iter/s]Best score: 1.6881: : 396iter [00:37, 11.89iter/s]Best score: 1.6881: : 397iter [00:37, 11.94iter/s]Best score: 1.6881: : 397iter [00:38, 11.94iter/s]Best score: 1.6881: : 398iter [00:38, 11.94iter/s]Best score: 1.6881: : 399iter [00:38, 12.01iter/s]
[ITER 400] Best: 1.5382
[PARAMS] Current best parameters:
  fx: 472.1820
  fy: 471.6001
  px: 877.9882
  py: 946.3276
  k1: 0.03620922
  k2: 0.06431326
  k3: -0.05198740
  k4: 0.01190409
  p1: -0.00671773
  p2: -0.01730019
  p3: -0.01864772
  p4: 0.00472776
  s1: 0.0129
  s2: -0.0016
  s3: 0.0324
  s4: -0.0011
  tilt_x: 0.0536
  tilt_y: 0.0015
  scale: 2.9173
  rotation: -1.4599
Best score: 1.5382: : 399iter [00:38, 12.01iter/s]Best score: 1.5382: : 400iter [00:38, 12.01iter/s]Best score: 1.5382: : 401iter [00:38,  7.61iter/s]Best score: 1.5382: : 401iter [00:38,  7.61iter/s]Best score: 1.5382: : 402iter [00:38,  7.61iter/s]Best score: 1.5382: : 403iter [00:38,  8.55iter/s]Best score: 1.5382: : 403iter [00:38,  8.55iter/s]Best score: 1.5382: : 404iter [00:38,  8.55iter/s]Best score: 1.5382: : 405iter [00:38,  9.37iter/s]Best score: 1.5382: : 405iter [00:39,  9.37iter/s]Best score: 1.5382: : 406iter [00:39,  9.37iter/s]Best score: 1.5382: : 407iter [00:39, 10.05iter/s]Best score: 1.5382: : 407iter [00:39, 10.05iter/s]Best score: 1.5382: : 408iter [00:39, 10.05iter/s]Best score: 1.5382: : 409iter [00:39, 10.60iter/s]Best score: 1.2885: : 409iter [00:39, 10.60iter/s]Best score: 1.2885: : 410iter [00:39, 10.60iter/s]Best score: 1.2885: : 411iter [00:39, 10.84iter/s]Best score: 1.2885: : 411iter [00:39, 10.84iter/s]Best score: 1.2885: : 412iter [00:39, 10.84iter/s]Best score: 1.2885: : 413iter [00:39, 11.18iter/s]Best score: 1.2885: : 413iter [00:39, 11.18iter/s]Best score: 1.2885: : 414iter [00:39, 11.18iter/s]Best score: 1.2885: : 415iter [00:39, 11.37iter/s]Best score: 1.2885: : 415iter [00:39, 11.37iter/s]Best score: 1.2885: : 416iter [00:39, 11.37iter/s]Best score: 1.2885: : 417iter [00:39, 11.53iter/s]Best score: 1.2885: : 417iter [00:40, 11.53iter/s]Best score: 1.2885: : 418iter [00:40, 11.53iter/s]Best score: 1.2885: : 419iter [00:40, 11.66iter/s]Best score: 1.1193: : 419iter [00:40, 11.66iter/s]Best score: 1.1193: : 420iter [00:40, 11.66iter/s]Best score: 1.1193: : 421iter [00:40, 11.60iter/s]Best score: 1.1193: : 421iter [00:40, 11.60iter/s]Best score: 1.1193: : 422iter [00:40, 11.60iter/s]Best score: 1.1193: : 423iter [00:40, 11.71iter/s]Best score: 1.1193: : 423iter [00:40, 11.71iter/s]Best score: 1.1193: : 424iter [00:40, 11.71iter/s]Best score: 1.1193: : 425iter [00:40, 11.76iter/s]Best score: 1.1193: : 425iter [00:40, 11.76iter/s]Best score: 1.1193: : 426iter [00:40, 11.76iter/s]Best score: 1.1193: : 427iter [00:40, 11.83iter/s]Best score: 1.1193: : 427iter [00:40, 11.83iter/s]Best score: 1.1193: : 428iter [00:40, 11.83iter/s]Best score: 1.1193: : 429iter [00:40, 11.89iter/s]Best score: 1.1193: : 429iter [00:41, 11.89iter/s]Best score: 1.1193: : 430iter [00:41, 11.89iter/s]Best score: 1.1193: : 431iter [00:41, 11.67iter/s]Best score: 1.1193: : 431iter [00:41, 11.67iter/s]Best score: 1.1193: : 432iter [00:41, 11.67iter/s]Best score: 1.1193: : 433iter [00:41, 11.76iter/s]Best score: 1.1193: : 433iter [00:41, 11.76iter/s]Best score: 1.1193: : 434iter [00:41, 11.76iter/s]Best score: 1.1193: : 435iter [00:41, 11.87iter/s]Best score: 1.1193: : 435iter [00:41, 11.87iter/s]Best score: 1.1193: : 436iter [00:41, 11.87iter/s]Best score: 1.1193: : 437iter [00:41, 11.92iter/s]Best score: 1.1193: : 437iter [00:41, 11.92iter/s]Best score: 1.1193: : 438iter [00:41, 11.92iter/s]Best score: 1.1193: : 439iter [00:41, 11.96iter/s]Best score: 1.0883: : 439iter [00:41, 11.96iter/s]Best score: 1.0883: : 440iter [00:41, 11.96iter/s]Best score: 1.0883: : 441iter [00:41, 11.79iter/s]Best score: 1.0883: : 441iter [00:42, 11.79iter/s]Best score: 1.0883: : 442iter [00:42, 11.79iter/s]Best score: 1.0883: : 443iter [00:42, 11.85iter/s]Best score: 1.0883: : 443iter [00:42, 11.85iter/s]Best score: 1.0883: : 444iter [00:42, 11.85iter/s]Best score: 1.0883: : 445iter [00:42, 11.93iter/s]Best score: 1.0883: : 445iter [00:42, 11.93iter/s]Best score: 1.0883: : 446iter [00:42, 11.93iter/s]Best score: 1.0883: : 447iter [00:42, 11.95iter/s]Best score: 1.0883: : 447iter [00:42, 11.95iter/s]Best score: 1.0883: : 448iter [00:42, 11.95iter/s]Best score: 1.0883: : 449iter [00:42, 12.00iter/s]
[ITER 450] Best: 1.0883
[PARAMS] Current best parameters:
  fx: 473.8491
  fy: 474.2560
  px: 880.4317
  py: 945.8889
  k1: 0.03950801
  k2: 0.05357869
  k3: -0.04564935
  k4: 0.01073091
  p1: -0.01077854
  p2: -0.01828976
  p3: -0.02071443
  p4: 0.00230449
  s1: 0.0182
  s2: 0.0022
  s3: 0.0431
  s4: -0.0027
  tilt_x: 0.0606
  tilt_y: -0.0122
  scale: 2.9188
  rotation: -1.1681
Best score: 1.0883: : 449iter [00:42, 12.00iter/s]Best score: 1.0883: : 450iter [00:43, 12.00iter/s]Best score: 1.0883: : 451iter [00:43,  7.64iter/s]Best score: 1.0883: : 451iter [00:43,  7.64iter/s]Best score: 1.0883: : 452iter [00:43,  7.64iter/s]Best score: 1.0883: : 453iter [00:43,  8.58iter/s]Best score: 1.0883: : 453iter [00:43,  8.58iter/s]Best score: 1.0883: : 454iter [00:43,  8.58iter/s]Best score: 1.0883: : 455iter [00:43,  9.36iter/s]Best score: 1.0883: : 455iter [00:43,  9.36iter/s]Best score: 1.0883: : 456iter [00:43,  9.36iter/s]Best score: 1.0883: : 457iter [00:43, 10.05iter/s]Best score: 1.0883: : 457iter [00:43, 10.05iter/s]Best score: 1.0883: : 458iter [00:43, 10.05iter/s]Best score: 1.0883: : 459iter [00:43, 10.59iter/s]Best score: 0.8101: : 459iter [00:43, 10.59iter/s]Best score: 0.8101: : 460iter [00:43, 10.59iter/s]Best score: 0.8101: : 461iter [00:43, 10.81iter/s]Best score: 0.8101: : 461iter [00:44, 10.81iter/s]Best score: 0.8101: : 462iter [00:44, 10.81iter/s]Best score: 0.8101: : 463iter [00:44, 11.13iter/s]Best score: 0.8101: : 463iter [00:44, 11.13iter/s]Best score: 0.8101: : 464iter [00:44, 11.13iter/s]Best score: 0.8101: : 465iter [00:44, 11.38iter/s]Best score: 0.8101: : 465iter [00:44, 11.38iter/s]Best score: 0.8101: : 466iter [00:44, 11.38iter/s]Best score: 0.8101: : 467iter [00:44, 11.55iter/s]Best score: 0.8101: : 467iter [00:44, 11.55iter/s]Best score: 0.8101: : 468iter [00:44, 11.55iter/s]Best score: 0.8101: : 469iter [00:44, 11.72iter/s]Best score: 0.7588: : 469iter [00:44, 11.72iter/s]Best score: 0.7588: : 470iter [00:44, 11.72iter/s]Best score: 0.7588: : 471iter [00:44, 11.63iter/s]Best score: 0.7588: : 471iter [00:44, 11.63iter/s]Best score: 0.7588: : 472iter [00:44, 11.63iter/s]Best score: 0.7588: : 473iter [00:44, 11.72iter/s]Best score: 0.7588: : 473iter [00:45, 11.72iter/s]Best score: 0.7588: : 474iter [00:45, 11.72iter/s]Best score: 0.7588: : 475iter [00:45, 11.74iter/s]Best score: 0.7588: : 475iter [00:45, 11.74iter/s]Best score: 0.7588: : 476iter [00:45, 11.74iter/s]Best score: 0.7588: : 477iter [00:45, 11.78iter/s]Best score: 0.7588: : 477iter [00:45, 11.78iter/s]Best score: 0.7588: : 478iter [00:45, 11.78iter/s]Best score: 0.7588: : 479iter [00:45, 11.87iter/s]Best score: 0.6568: : 479iter [00:45, 11.87iter/s]Best score: 0.6568: : 480iter [00:45, 11.87iter/s]Best score: 0.6568: : 481iter [00:45, 11.73iter/s]Best score: 0.6568: : 481iter [00:45, 11.73iter/s]Best score: 0.6568: : 482iter [00:45, 11.73iter/s]Best score: 0.6568: : 483iter [00:45, 11.84iter/s]Best score: 0.6568: : 483iter [00:45, 11.84iter/s]Best score: 0.6568: : 484iter [00:45, 11.84iter/s]Best score: 0.6568: : 485iter [00:45, 11.87iter/s]Best score: 0.6568: : 485iter [00:46, 11.87iter/s]Best score: 0.6568: : 486iter [00:46, 11.87iter/s]Best score: 0.6568: : 487iter [00:46, 11.90iter/s]Best score: 0.6568: : 487iter [00:46, 11.90iter/s]Best score: 0.6568: : 488iter [00:46, 11.90iter/s]Best score: 0.6568: : 489iter [00:46, 11.95iter/s]Best score: 0.6415: : 489iter [00:46, 11.95iter/s]Best score: 0.6415: : 490iter [00:46, 11.95iter/s]Best score: 0.6415: : 491iter [00:46, 11.79iter/s]Best score: 0.6415: : 491iter [00:46, 11.79iter/s]Best score: 0.6415: : 492iter [00:46, 11.79iter/s]Best score: 0.6415: : 493iter [00:46, 11.84iter/s]Best score: 0.6415: : 493iter [00:46, 11.84iter/s]Best score: 0.6415: : 494iter [00:46, 11.84iter/s]Best score: 0.6415: : 495iter [00:46, 11.84iter/s]Best score: 0.6415: : 495iter [00:46, 11.84iter/s]Best score: 0.6415: : 496iter [00:47, 11.84iter/s]Best score: 0.6415: : 497iter [00:47, 11.89iter/s]Best score: 0.6415: : 497iter [00:47, 11.89iter/s]Best score: 0.6415: : 498iter [00:47, 11.89iter/s]Best score: 0.6415: : 499iter [00:47, 11.81iter/s]
[ITER 500] Best: 0.5632
[PARAMS] Current best parameters:
  fx: 477.4492
  fy: 477.9989
  px: 887.6720
  py: 952.1694
  k1: 0.00912112
  k2: 0.08437924
  k3: -0.05830193
  k4: 0.01257728
  p1: -0.01259916
  p2: -0.00926399
  p3: -0.00700532
  p4: 0.00040613
  s1: 0.0145
  s2: -0.0024
  s3: 0.0360
  s4: -0.0004
  tilt_x: 0.0908
  tilt_y: -0.0187
  scale: 2.9719
  rotation: -1.3062
Best score: 0.5632: : 499iter [00:47, 11.81iter/s]Best score: 0.5632: : 500iter [00:47, 11.81iter/s]Best score: 0.5632: : 501iter [00:47,  7.61iter/s]Best score: 0.5632: : 501iter [00:47,  7.61iter/s]Best score: 0.5632: : 502iter [00:47,  7.61iter/s]Best score: 0.5632: : 503iter [00:47,  8.54iter/s]Best score: 0.5632: : 503iter [00:47,  8.54iter/s]Best score: 0.5632: : 504iter [00:47,  8.54iter/s]Best score: 0.5632: : 505iter [00:47,  9.37iter/s]Best score: 0.5632: : 505iter [00:48,  9.37iter/s]Best score: 0.5632: : 506iter [00:48,  9.37iter/s]Best score: 0.5632: : 507iter [00:48, 10.06iter/s]Best score: 0.5632: : 507iter [00:48, 10.06iter/s]Best score: 0.5632: : 508iter [00:48, 10.06iter/s]Best score: 0.5632: : 509iter [00:48, 10.58iter/s]Best score: 0.4341: : 509iter [00:48, 10.58iter/s]Best score: 0.4341: : 510iter [00:48, 10.58iter/s]Best score: 0.4341: : 511iter [00:48, 10.81iter/s]Best score: 0.4341: : 511iter [00:48, 10.81iter/s]Best score: 0.4341: : 512iter [00:48, 10.81iter/s]Best score: 0.4341: : 513iter [00:48, 11.13iter/s]Best score: 0.4341: : 513iter [00:48, 11.13iter/s]Best score: 0.4341: : 514iter [00:48, 11.13iter/s]Best score: 0.4341: : 515iter [00:48, 11.41iter/s]Best score: 0.4341: : 515iter [00:48, 11.41iter/s]Best score: 0.4341: : 516iter [00:49, 11.41iter/s]Best score: 0.4341: : 517iter [00:49, 11.56iter/s]Best score: 0.4341: : 517iter [00:49, 11.56iter/s]Best score: 0.4341: : 518iter [00:49, 11.56iter/s]Best score: 0.4341: : 519iter [00:49, 11.65iter/s]Best score: 0.4341: : 519iter [00:49, 11.65iter/s]Best score: 0.4341: : 520iter [00:49, 11.65iter/s]Best score: 0.4341: : 521iter [00:49, 11.59iter/s]Best score: 0.4341: : 521iter [00:49, 11.59iter/s]Best score: 0.4341: : 522iter [00:49, 11.59iter/s]Best score: 0.4341: : 523iter [00:49, 11.71iter/s]Best score: 0.4341: : 523iter [00:49, 11.71iter/s]Best score: 0.4341: : 524iter [00:49, 11.71iter/s]Best score: 0.4341: : 525iter [00:49, 11.81iter/s]Best score: 0.4341: : 525iter [00:49, 11.81iter/s]Best score: 0.4341: : 526iter [00:49, 11.81iter/s]Best score: 0.4341: : 527iter [00:49, 11.86iter/s]Best score: 0.4341: : 527iter [00:49, 11.86iter/s]Best score: 0.4341: : 528iter [00:50, 11.86iter/s]Best score: 0.4341: : 529iter [00:50, 11.87iter/s]Best score: 0.4341: : 529iter [00:50, 11.87iter/s]Best score: 0.4341: : 530iter [00:50, 11.87iter/s]Best score: 0.4341: : 531iter [00:50, 11.65iter/s]Best score: 0.4341: : 531iter [00:50, 11.65iter/s]Best score: 0.4341: : 532iter [00:50, 11.65iter/s]Best score: 0.4341: : 533iter [00:50, 11.77iter/s]Best score: 0.4341: : 533iter [00:50, 11.77iter/s]Best score: 0.4341: : 534iter [00:50, 11.77iter/s]Best score: 0.4341: : 535iter [00:50, 11.86iter/s]Best score: 0.4341: : 535iter [00:50, 11.86iter/s]Best score: 0.4341: : 536iter [00:50, 11.86iter/s]Best score: 0.4341: : 537iter [00:50, 11.89iter/s]Best score: 0.4341: : 537iter [00:50, 11.89iter/s]Best score: 0.4341: : 538iter [00:50, 11.89iter/s]Best score: 0.4341: : 539iter [00:50, 11.87iter/s]Best score: 0.4341: : 539iter [00:50, 11.87iter/s]Best score: 0.4341: : 540iter [00:51, 11.87iter/s]Best score: 0.4341: : 541iter [00:51, 11.74iter/s]Best score: 0.4341: : 541iter [00:51, 11.74iter/s]Best score: 0.4341: : 542iter [00:51, 11.74iter/s]Best score: 0.4341: : 543iter [00:51, 11.81iter/s]Best score: 0.4341: : 543iter [00:51, 11.81iter/s]Best score: 0.4341: : 544iter [00:51, 11.81iter/s]Best score: 0.4341: : 545iter [00:51, 11.91iter/s]Best score: 0.4341: : 545iter [00:51, 11.91iter/s]Best score: 0.4341: : 546iter [00:51, 11.91iter/s]Best score: 0.4341: : 547iter [00:51, 11.97iter/s]Best score: 0.4341: : 547iter [00:51, 11.97iter/s]Best score: 0.4341: : 548iter [00:51, 11.97iter/s]Best score: 0.4341: : 549iter [00:51, 11.98iter/s]
[ITER 550] Best: 0.4341
[PARAMS] Current best parameters:
  fx: 481.0031
  fy: 481.3385
  px: 889.6455
  py: 952.5466
  k1: 0.01824547
  k2: 0.06409552
  k3: -0.04608002
  k4: 0.00998365
  p1: -0.01376294
  p2: -0.01136667
  p3: -0.00923090
  p4: 0.00403181
  s1: 0.0209
  s2: -0.0057
  s3: 0.0437
  s4: -0.0068
  tilt_x: 0.0927
  tilt_y: -0.0310
  scale: 2.9604
  rotation: -1.2985
Best score: 0.4341: : 549iter [00:51, 11.98iter/s]Best score: 0.4341: : 550iter [00:52, 11.98iter/s]Best score: 0.4341: : 551iter [00:52,  7.61iter/s]Best score: 0.4341: : 551iter [00:52,  7.61iter/s]Best score: 0.4341: : 552iter [00:52,  7.61iter/s]Best score: 0.4341: : 553iter [00:52,  8.56iter/s]Best score: 0.4341: : 553iter [00:52,  8.56iter/s]Best score: 0.4341: : 554iter [00:52,  8.56iter/s]Best score: 0.4341: : 555iter [00:52,  9.39iter/s]Best score: 0.4341: : 555iter [00:52,  9.39iter/s]Best score: 0.4341: : 556iter [00:52,  9.39iter/s]Best score: 0.4341: : 557iter [00:52, 10.07iter/s]Best score: 0.4341: : 557iter [00:52, 10.07iter/s]Best score: 0.4341: : 558iter [00:52, 10.07iter/s]Best score: 0.4341: : 559iter [00:52, 10.61iter/s]Best score: 0.4087: : 559iter [00:52, 10.61iter/s]Best score: 0.4087: : 560iter [00:53, 10.61iter/s]Best score: 0.4087: : 561iter [00:53, 10.84iter/s]Best score: 0.4087: : 561iter [00:53, 10.84iter/s]Best score: 0.4087: : 562iter [00:53, 10.84iter/s]Best score: 0.4087: : 563iter [00:53, 11.18iter/s]Best score: 0.4087: : 563iter [00:53, 11.18iter/s]Best score: 0.4087: : 564iter [00:53, 11.18iter/s]Best score: 0.4087: : 565iter [00:53, 11.40iter/s]Best score: 0.4087: : 565iter [00:53, 11.40iter/s]Best score: 0.4087: : 566iter [00:53, 11.40iter/s]Best score: 0.4087: : 567iter [00:53,  8.82iter/s]Best score: 0.4087: : 567iter [00:53,  8.82iter/s]Best score: 0.4087: : 568iter [00:53,  8.82iter/s]Best score: 0.4087: : 569iter [00:53,  9.57iter/s]Best score: 0.4087: : 569iter [00:53,  9.57iter/s]Best score: 0.4087: : 570iter [00:54,  9.57iter/s]Best score: 0.4087: : 571iter [00:54, 10.04iter/s]Best score: 0.4087: : 571iter [00:54, 10.04iter/s]Best score: 0.4087: : 572iter [00:54, 10.04iter/s]Best score: 0.4087: : 573iter [00:54, 10.59iter/s]Best score: 0.4087: : 573iter [00:54, 10.59iter/s]Best score: 0.4087: : 574iter [00:54, 10.59iter/s]Best score: 0.4087: : 575iter [00:54, 11.03iter/s]Best score: 0.4087: : 575iter [00:54, 11.03iter/s]Best score: 0.4087: : 576iter [00:54, 11.03iter/s]Best score: 0.4087: : 577iter [00:54, 11.32iter/s]Best score: 0.4087: : 577iter [00:54, 11.32iter/s]Best score: 0.4087: : 578iter [00:54, 11.32iter/s]Best score: 0.4087: : 579iter [00:54, 11.52iter/s]Best score: 0.4000: : 579iter [00:54, 11.52iter/s]Best score: 0.4000: : 580iter [00:54, 11.52iter/s]Best score: 0.4000: : 581iter [00:54, 11.50iter/s]Best score: 0.4000: : 581iter [00:54, 11.50iter/s]Best score: 0.4000: : 582iter [00:55, 11.50iter/s]Best score: 0.4000: : 583iter [00:55, 11.65iter/s]Best score: 0.4000: : 583iter [00:55, 11.65iter/s]Best score: 0.4000: : 584iter [00:55, 11.65iter/s]Best score: 0.4000: : 585iter [00:55, 11.73iter/s]Best score: 0.4000: : 585iter [00:55, 11.73iter/s]Best score: 0.4000: : 586iter [00:55, 11.73iter/s]Best score: 0.4000: : 587iter [00:55, 11.81iter/s]Best score: 0.4000: : 587iter [00:55, 11.81iter/s]Best score: 0.4000: : 588iter [00:55, 11.81iter/s]Best score: 0.4000: : 589iter [00:55, 11.90iter/s]Best score: 0.3891: : 589iter [00:55, 11.90iter/s]Best score: 0.3891: : 590iter [00:55, 11.90iter/s]Best score: 0.3891: : 591iter [00:55, 11.77iter/s]Best score: 0.3891: : 591iter [00:55, 11.77iter/s]Best score: 0.3891: : 592iter [00:55, 11.77iter/s]Best score: 0.3891: : 593iter [00:55, 11.86iter/s]Best score: 0.3891: : 593iter [00:55, 11.86iter/s]Best score: 0.3891: : 594iter [00:56, 11.86iter/s]Best score: 0.3891: : 595iter [00:56, 11.93iter/s]Best score: 0.3891: : 595iter [00:56, 11.93iter/s]Best score: 0.3891: : 596iter [00:56, 11.93iter/s]Best score: 0.3891: : 597iter [00:56, 11.93iter/s]Best score: 0.3891: : 597iter [00:56, 11.93iter/s]Best score: 0.3891: : 598iter [00:56, 11.93iter/s]Best score: 0.3891: : 599iter [00:56, 11.98iter/s]
[ITER 600] Best: 0.3847
[PARAMS] Current best parameters:
  fx: 481.6279
  fy: 481.9752
  px: 888.3267
  py: 950.1130
  k1: 0.02443329
  k2: 0.05497611
  k3: -0.04149581
  k4: 0.00910141
  p1: -0.01544161
  p2: -0.01439413
  p3: -0.00998379
  p4: 0.00493768
  s1: 0.0203
  s2: -0.0047
  s3: 0.0416
  s4: -0.0068
  tilt_x: 0.0862
  tilt_y: -0.0314
  scale: 2.9484
  rotation: -1.2775
Best score: 0.3847: : 599iter [00:56, 11.98iter/s]Best score: 0.3847: : 600iter [00:56, 11.98iter/s]Best score: 0.3847: : 601iter [00:56,  7.62iter/s]Best score: 0.3847: : 601iter [00:56,  7.62iter/s]Best score: 0.3847: : 602iter [00:57,  7.62iter/s]Best score: 0.3847: : 603iter [00:57,  8.56iter/s]Best score: 0.3847: : 603iter [00:57,  8.56iter/s]Best score: 0.3847: : 604iter [00:57,  8.56iter/s]Best score: 0.3847: : 605iter [00:57,  9.39iter/s]Best score: 0.3847: : 605iter [00:57,  9.39iter/s]Best score: 0.3847: : 606iter [00:57,  9.39iter/s]Best score: 0.3847: : 607iter [00:57, 10.08iter/s]Best score: 0.3847: : 607iter [00:57, 10.08iter/s]Best score: 0.3847: : 608iter [00:57, 10.08iter/s]Best score: 0.3847: : 609iter [00:57, 10.62iter/s]Best score: 0.3763: : 609iter [00:57, 10.62iter/s]Best score: 0.3763: : 610iter [00:57, 10.62iter/s]Best score: 0.3763: : 611iter [00:57, 10.78iter/s]Best score: 0.3763: : 611iter [00:57, 10.78iter/s]Best score: 0.3763: : 612iter [00:57, 10.78iter/s]Best score: 0.3763: : 613iter [00:57, 11.13iter/s]Best score: 0.3763: : 613iter [00:57, 11.13iter/s]Best score: 0.3763: : 614iter [00:58, 11.13iter/s]Best score: 0.3763: : 615iter [00:58, 11.39iter/s]Best score: 0.3763: : 615iter [00:58, 11.39iter/s]Best score: 0.3763: : 616iter [00:58, 11.39iter/s]Best score: 0.3763: : 617iter [00:58, 11.59iter/s]Best score: 0.3763: : 617iter [00:58, 11.59iter/s]Best score: 0.3763: : 618iter [00:58, 11.59iter/s]Best score: 0.3763: : 619iter [00:58, 11.76iter/s]Best score: 0.3547: : 619iter [00:58, 11.76iter/s]Best score: 0.3547: : 620iter [00:58, 11.76iter/s]Best score: 0.3547: : 621iter [00:58, 11.68iter/s]Best score: 0.3547: : 621iter [00:58, 11.68iter/s]Best score: 0.3547: : 622iter [00:58, 11.68iter/s]Best score: 0.3547: : 623iter [00:58, 11.81iter/s]Best score: 0.3547: : 623iter [00:58, 11.81iter/s]Best score: 0.3547: : 624iter [00:58, 11.81iter/s]Best score: 0.3547: : 625iter [00:58, 11.90iter/s]Best score: 0.3547: : 625iter [00:58, 11.90iter/s]Best score: 0.3547: : 626iter [00:59, 11.90iter/s]Best score: 0.3547: : 627iter [00:59, 11.97iter/s]Best score: 0.3547: : 627iter [00:59, 11.97iter/s]Best score: 0.3547: : 628iter [00:59, 11.97iter/s]Best score: 0.3547: : 629iter [00:59, 12.00iter/s]Best score: 0.3490: : 629iter [00:59, 12.00iter/s]Best score: 0.3490: : 630iter [00:59, 12.00iter/s]Best score: 0.3490: : 631iter [00:59, 11.81iter/s]Best score: 0.3490: : 631iter [00:59, 11.81iter/s]Best score: 0.3490: : 632iter [00:59, 11.81iter/s]Best score: 0.3490: : 633iter [00:59, 11.90iter/s]Best score: 0.3490: : 633iter [00:59, 11.90iter/s]Best score: 0.3490: : 634iter [00:59, 11.90iter/s]Best score: 0.3490: : 635iter [00:59, 11.96iter/s]Best score: 0.3490: : 635iter [00:59, 11.96iter/s]Best score: 0.3490: : 636iter [00:59, 11.96iter/s]Best score: 0.3490: : 637iter [00:59, 12.01iter/s]Best score: 0.3490: : 637iter [00:59, 12.01iter/s]Best score: 0.3490: : 638iter [01:00, 12.01iter/s]Best score: 0.3490: : 639iter [01:00, 12.03iter/s]Best score: 0.3478: : 639iter [01:00, 12.03iter/s]Best score: 0.3478: : 640iter [01:00, 12.03iter/s]Best score: 0.3478: : 641iter [01:00, 11.86iter/s]Best score: 0.3478: : 641iter [01:00, 11.86iter/s]Best score: 0.3478: : 642iter [01:00, 11.86iter/s]Best score: 0.3478: : 643iter [01:00, 11.95iter/s]Best score: 0.3478: : 643iter [01:00, 11.95iter/s]Best score: 0.3478: : 644iter [01:00, 11.95iter/s]Best score: 0.3478: : 645iter [01:00, 11.99iter/s]Best score: 0.3478: : 645iter [01:00, 11.99iter/s]Best score: 0.3478: : 646iter [01:00, 11.99iter/s]Best score: 0.3478: : 647iter [01:00, 12.03iter/s]Best score: 0.3478: : 647iter [01:00, 12.03iter/s]Best score: 0.3478: : 648iter [01:00, 12.03iter/s]Best score: 0.3478: : 649iter [01:00, 12.05iter/s]
[ITER 650] Best: 0.3447
[PARAMS] Current best parameters:
  fx: 477.9904
  fy: 478.0630
  px: 887.9980
  py: 948.4323
  k1: 0.02164673
  k2: 0.05930726
  k3: -0.04019728
  k4: 0.00804275
  p1: -0.01761375
  p2: -0.01329476
  p3: -0.00621015
  p4: 0.00323201
  s1: 0.0177
  s2: -0.0043
  s3: 0.0382
  s4: -0.0048
  tilt_x: 0.0832
  tilt_y: -0.0269
  scale: 2.9605
  rotation: -1.2756
Best score: 0.3447: : 649iter [01:00, 12.05iter/s]Best score: 0.3447: : 650iter [01:01, 12.05iter/s]Best score: 0.3447: : 651iter [01:01,  7.67iter/s]Best score: 0.3447: : 651iter [01:01,  7.67iter/s]Best score: 0.3447: : 652iter [01:01,  7.67iter/s]Best score: 0.3447: : 653iter [01:01,  8.60iter/s]Best score: 0.3447: : 653iter [01:01,  8.60iter/s]Best score: 0.3447: : 654iter [01:01,  8.60iter/s]Best score: 0.3447: : 655iter [01:01,  9.41iter/s]Best score: 0.3447: : 655iter [01:01,  9.41iter/s]Best score: 0.3447: : 656iter [01:01,  9.41iter/s]Best score: 0.3447: : 657iter [01:01, 10.08iter/s]Best score: 0.3447: : 657iter [01:01, 10.08iter/s]Best score: 0.3447: : 658iter [01:02, 10.08iter/s]Best score: 0.3447: : 659iter [01:02, 10.59iter/s]Best score: 0.3310: : 659iter [01:02, 10.59iter/s]Best score: 0.3310: : 660iter [01:02, 10.59iter/s]Best score: 0.3310: : 661iter [01:02, 10.74iter/s]Best score: 0.3310: : 661iter [01:02, 10.74iter/s]Best score: 0.3310: : 662iter [01:02, 10.74iter/s]Best score: 0.3310: : 663iter [01:02, 11.08iter/s]Best score: 0.3310: : 663iter [01:02, 11.08iter/s]Best score: 0.3310: : 664iter [01:02, 11.08iter/s]Best score: 0.3310: : 665iter [01:02, 11.36iter/s]Best score: 0.3310: : 665iter [01:02, 11.36iter/s]Best score: 0.3310: : 666iter [01:02, 11.36iter/s]Best score: 0.3310: : 667iter [01:02, 11.59iter/s]Best score: 0.3310: : 667iter [01:02, 11.59iter/s]Best score: 0.3310: : 668iter [01:02, 11.59iter/s]Best score: 0.3310: : 669iter [01:02, 11.74iter/s]Best score: 0.3286: : 669iter [01:02, 11.74iter/s]Best score: 0.3286: : 670iter [01:03, 11.74iter/s]Best score: 0.3286: : 671iter [01:03, 11.66iter/s]Best score: 0.3286: : 671iter [01:03, 11.66iter/s]Best score: 0.3286: : 672iter [01:03, 11.66iter/s]Best score: 0.3286: : 673iter [01:03, 11.79iter/s]Best score: 0.3286: : 673iter [01:03, 11.79iter/s]Best score: 0.3286: : 674iter [01:03, 11.79iter/s]Best score: 0.3286: : 675iter [01:03, 11.90iter/s]Best score: 0.3286: : 675iter [01:03, 11.90iter/s]Best score: 0.3286: : 676iter [01:03, 11.90iter/s]Best score: 0.3286: : 677iter [01:03, 11.92iter/s]Best score: 0.3286: : 677iter [01:03, 11.92iter/s]Best score: 0.3286: : 678iter [01:03, 11.92iter/s]Best score: 0.3286: : 679iter [01:03, 11.88iter/s]Best score: 0.3183: : 679iter [01:03, 11.88iter/s]Best score: 0.3183: : 680iter [01:03, 11.88iter/s]Best score: 0.3183: : 681iter [01:03, 11.70iter/s]Best score: 0.3183: : 681iter [01:03, 11.70iter/s]Best score: 0.3183: : 682iter [01:04, 11.70iter/s]Best score: 0.3183: : 683iter [01:04, 11.82iter/s]Best score: 0.3183: : 683iter [01:04, 11.82iter/s]Best score: 0.3183: : 684iter [01:04, 11.82iter/s]Best score: 0.3183: : 685iter [01:04, 11.86iter/s]Best score: 0.3183: : 685iter [01:04, 11.86iter/s]Best score: 0.3183: : 686iter [01:04, 11.86iter/s]Best score: 0.3183: : 687iter [01:04, 11.93iter/s]Best score: 0.3183: : 687iter [01:04, 11.93iter/s]Best score: 0.3183: : 688iter [01:04, 11.93iter/s]Best score: 0.3183: : 689iter [01:04, 11.95iter/s]Best score: 0.3092: : 689iter [01:04, 11.95iter/s]Best score: 0.3092: : 690iter [01:04, 11.95iter/s]Best score: 0.3092: : 691iter [01:04, 11.80iter/s]Best score: 0.3092: : 691iter [01:04, 11.80iter/s]Best score: 0.3092: : 692iter [01:04, 11.80iter/s]Best score: 0.3092: : 693iter [01:04, 11.85iter/s]Best score: 0.3092: : 693iter [01:04, 11.85iter/s]Best score: 0.3092: : 694iter [01:05, 11.85iter/s]Best score: 0.3092: : 695iter [01:05, 11.87iter/s]Best score: 0.3092: : 695iter [01:05, 11.87iter/s]Best score: 0.3092: : 696iter [01:05, 11.87iter/s]Best score: 0.3092: : 697iter [01:05, 11.91iter/s]Best score: 0.3092: : 697iter [01:05, 11.91iter/s]Best score: 0.3092: : 698iter [01:05, 11.91iter/s]Best score: 0.3092: : 699iter [01:05, 11.95iter/s]
[ITER 700] Best: 0.3092
[PARAMS] Current best parameters:
  fx: 475.6480
  fy: 475.6053
  px: 884.4025
  py: 942.0819
  k1: 0.02966748
  k2: 0.04990360
  k3: -0.03485692
  k4: 0.00701571
  p1: -0.02475640
  p2: -0.01618168
  p3: 0.00662998
  p4: 0.00397777
  s1: 0.0040
  s2: -0.0051
  s3: 0.0286
  s4: -0.0055
  tilt_x: 0.0702
  tilt_y: -0.0193
  scale: 2.9560
  rotation: -1.2563
Best score: 0.3092: : 699iter [01:05, 11.95iter/s]Best score: 0.3092: : 700iter [01:05, 11.95iter/s]Best score: 0.3092: : 701iter [01:05,  7.64iter/s]Best score: 0.3092: : 701iter [01:05,  7.64iter/s]Best score: 0.3092: : 702iter [01:06,  7.64iter/s]Best score: 0.3092: : 703iter [01:06,  8.59iter/s]Best score: 0.3092: : 703iter [01:06,  8.59iter/s]Best score: 0.3092: : 704iter [01:06,  8.59iter/s]Best score: 0.3092: : 705iter [01:06,  9.41iter/s]Best score: 0.3092: : 705iter [01:06,  9.41iter/s]Best score: 0.3092: : 706iter [01:06,  9.41iter/s]Best score: 0.3092: : 707iter [01:06, 10.10iter/s]Best score: 0.3092: : 707iter [01:06, 10.10iter/s]Best score: 0.3092: : 708iter [01:06, 10.10iter/s]Best score: 0.3092: : 709iter [01:06, 10.64iter/s]Best score: 0.3017: : 709iter [01:06, 10.64iter/s]Best score: 0.3017: : 710iter [01:06, 10.64iter/s]Best score: 0.3017: : 711iter [01:06, 10.89iter/s]Best score: 0.3017: : 711iter [01:06, 10.89iter/s]Best score: 0.3017: : 712iter [01:06, 10.89iter/s]Best score: 0.3017: : 713iter [01:06, 11.23iter/s]Best score: 0.3017: : 713iter [01:06, 11.23iter/s]Best score: 0.3017: : 714iter [01:07, 11.23iter/s]Best score: 0.3017: : 715iter [01:07, 11.43iter/s]Best score: 0.3017: : 715iter [01:07, 11.43iter/s]Best score: 0.3017: : 716iter [01:07, 11.43iter/s]Best score: 0.3017: : 717iter [01:07, 11.62iter/s]Best score: 0.3017: : 717iter [01:07, 11.62iter/s]Best score: 0.3017: : 718iter [01:07, 11.62iter/s]Best score: 0.3017: : 719iter [01:07, 11.76iter/s]Best score: 0.2965: : 719iter [01:07, 11.76iter/s]Best score: 0.2965: : 720iter [01:07, 11.76iter/s]Best score: 0.2965: : 721iter [01:07, 11.65iter/s]Best score: 0.2965: : 721iter [01:07, 11.65iter/s]Best score: 0.2965: : 722iter [01:07, 11.65iter/s]Best score: 0.2965: : 723iter [01:07, 11.77iter/s]Best score: 0.2965: : 723iter [01:07, 11.77iter/s]Best score: 0.2965: : 724iter [01:07, 11.77iter/s]Best score: 0.2965: : 725iter [01:07, 11.83iter/s]Best score: 0.2965: : 725iter [01:07, 11.83iter/s]Best score: 0.2965: : 726iter [01:08, 11.83iter/s]Best score: 0.2965: : 727iter [01:08, 11.92iter/s]Best score: 0.2965: : 727iter [01:08, 11.92iter/s]Best score: 0.2965: : 728iter [01:08, 11.92iter/s]Best score: 0.2965: : 729iter [01:08, 11.91iter/s]Best score: 0.2758: : 729iter [01:08, 11.91iter/s]Best score: 0.2758: : 730iter [01:08, 11.91iter/s]Best score: 0.2758: : 731iter [01:08, 11.78iter/s]Best score: 0.2758: : 731iter [01:08, 11.78iter/s]Best score: 0.2758: : 732iter [01:08, 11.78iter/s]Best score: 0.2758: : 733iter [01:08, 11.89iter/s]Best score: 0.2758: : 733iter [01:08, 11.89iter/s]Best score: 0.2758: : 734iter [01:08, 11.89iter/s]Best score: 0.2758: : 735iter [01:08, 11.97iter/s]Best score: 0.2758: : 735iter [01:08, 11.97iter/s]Best score: 0.2758: : 736iter [01:08, 11.97iter/s]Best score: 0.2758: : 737iter [01:08, 12.02iter/s]Best score: 0.2758: : 737iter [01:08, 12.02iter/s]Best score: 0.2758: : 738iter [01:09, 12.02iter/s]Best score: 0.2758: : 739iter [01:09, 12.04iter/s]Best score: 0.2724: : 739iter [01:09, 12.04iter/s]Best score: 0.2724: : 740iter [01:09, 12.04iter/s]Best score: 0.2724: : 741iter [01:09, 11.78iter/s]Best score: 0.2724: : 741iter [01:09, 11.78iter/s]Best score: 0.2724: : 742iter [01:09, 11.78iter/s]Best score: 0.2724: : 743iter [01:09, 11.87iter/s]Best score: 0.2724: : 743iter [01:09, 11.87iter/s]Best score: 0.2724: : 744iter [01:09, 11.87iter/s]Best score: 0.2724: : 745iter [01:09, 11.95iter/s]Best score: 0.2724: : 745iter [01:09, 11.95iter/s]Best score: 0.2724: : 746iter [01:09, 11.95iter/s]Best score: 0.2724: : 747iter [01:09, 12.00iter/s]Best score: 0.2724: : 747iter [01:09, 12.00iter/s]Best score: 0.2724: : 748iter [01:09, 12.00iter/s]Best score: 0.2724: : 749iter [01:09, 12.02iter/s]
[ITER 750] Best: 0.2647
[PARAMS] Current best parameters:
  fx: 469.2051
  fy: 468.9538
  px: 881.6396
  py: 935.9653
  k1: 0.04525282
  k2: 0.03358490
  k3: -0.02272453
  k4: 0.00382945
  p1: -0.02626396
  p2: -0.01726687
  p3: 0.01149646
  p4: 0.00584846
  s1: -0.0037
  s2: -0.0054
  s3: 0.0171
  s4: -0.0062
  tilt_x: 0.0478
  tilt_y: -0.0106
  scale: 2.9434
  rotation: -1.2149
Best score: 0.2647: : 749iter [01:09, 12.02iter/s]Best score: 0.2647: : 750iter [01:10, 12.02iter/s]Best score: 0.2647: : 751iter [01:10,  7.63iter/s]Best score: 0.2647: : 751iter [01:10,  7.63iter/s]Best score: 0.2647: : 752iter [01:10,  7.63iter/s]Best score: 0.2647: : 753iter [01:10,  8.59iter/s]Best score: 0.2647: : 753iter [01:10,  8.59iter/s]Best score: 0.2647: : 754iter [01:10,  8.59iter/s]Best score: 0.2647: : 755iter [01:10,  9.40iter/s]Best score: 0.2647: : 755iter [01:10,  9.40iter/s]Best score: 0.2647: : 756iter [01:10,  9.40iter/s]Best score: 0.2647: : 757iter [01:10, 10.09iter/s]Best score: 0.2647: : 757iter [01:10, 10.09iter/s]Best score: 0.2647: : 758iter [01:11, 10.09iter/s]Best score: 0.2647: : 759iter [01:11, 10.62iter/s]Best score: 0.2647: : 759iter [01:11, 10.62iter/s]Best score: 0.2647: : 760iter [01:11, 10.62iter/s]Best score: 0.2647: : 761iter [01:11, 10.83iter/s]Best score: 0.2647: : 761iter [01:11, 10.83iter/s]Best score: 0.2647: : 762iter [01:11, 10.83iter/s]Best score: 0.2647: : 763iter [01:11, 11.18iter/s]Best score: 0.2647: : 763iter [01:11, 11.18iter/s]Best score: 0.2647: : 764iter [01:11, 11.18iter/s]Best score: 0.2647: : 765iter [01:11, 11.43iter/s]Best score: 0.2647: : 765iter [01:11, 11.43iter/s]Best score: 0.2647: : 766iter [01:11, 11.43iter/s]Best score: 0.2647: : 767iter [01:11, 11.63iter/s]Best score: 0.2647: : 767iter [01:11, 11.63iter/s]Best score: 0.2647: : 768iter [01:11, 11.63iter/s]Best score: 0.2647: : 769iter [01:11, 11.77iter/s]Best score: 0.2536: : 769iter [01:11, 11.77iter/s]Best score: 0.2536: : 770iter [01:12, 11.77iter/s]Best score: 0.2536: : 771iter [01:12, 11.65iter/s]Best score: 0.2536: : 771iter [01:12, 11.65iter/s]Best score: 0.2536: : 772iter [01:12, 11.65iter/s]Best score: 0.2536: : 773iter [01:12, 11.72iter/s]Best score: 0.2536: : 773iter [01:12, 11.72iter/s]Best score: 0.2536: : 774iter [01:12, 11.72iter/s]Best score: 0.2536: : 775iter [01:12, 11.83iter/s]Best score: 0.2536: : 775iter [01:12, 11.83iter/s]Best score: 0.2536: : 776iter [01:12, 11.83iter/s]Best score: 0.2536: : 777iter [01:12, 11.89iter/s]Best score: 0.2536: : 777iter [01:12, 11.89iter/s]Best score: 0.2536: : 778iter [01:12, 11.89iter/s]Best score: 0.2536: : 779iter [01:12, 11.94iter/s]Best score: 0.2536: : 779iter [01:12, 11.94iter/s]Best score: 0.2536: : 780iter [01:12, 11.94iter/s]Best score: 0.2536: : 781iter [01:12, 11.77iter/s]Best score: 0.2536: : 781iter [01:12, 11.77iter/s]Best score: 0.2536: : 782iter [01:13, 11.77iter/s]Best score: 0.2536: : 783iter [01:13, 11.86iter/s]Best score: 0.2536: : 783iter [01:13, 11.86iter/s]Best score: 0.2536: : 784iter [01:13, 11.86iter/s]Best score: 0.2536: : 785iter [01:13, 11.91iter/s]Best score: 0.2536: : 785iter [01:13, 11.91iter/s]Best score: 0.2536: : 786iter [01:13, 11.91iter/s]Best score: 0.2536: : 787iter [01:13, 11.98iter/s]Best score: 0.2536: : 787iter [01:13, 11.98iter/s]Best score: 0.2536: : 788iter [01:13, 11.98iter/s]Best score: 0.2536: : 789iter [01:13, 11.96iter/s]Best score: 0.2502: : 789iter [01:13, 11.96iter/s]Best score: 0.2502: : 790iter [01:13, 11.96iter/s]Best score: 0.2502: : 791iter [01:13, 11.72iter/s]Best score: 0.2502: : 791iter [01:13, 11.72iter/s]Best score: 0.2502: : 792iter [01:13, 11.72iter/s]Best score: 0.2502: : 793iter [01:13, 11.83iter/s]Best score: 0.2502: : 793iter [01:13, 11.83iter/s]Best score: 0.2502: : 794iter [01:14, 11.83iter/s]Best score: 0.2502: : 795iter [01:14, 11.91iter/s]Best score: 0.2502: : 795iter [01:14, 11.91iter/s]Best score: 0.2502: : 796iter [01:14, 11.91iter/s]Best score: 0.2502: : 797iter [01:14, 11.94iter/s]Best score: 0.2502: : 797iter [01:14, 11.94iter/s]Best score: 0.2502: : 798iter [01:14, 11.94iter/s]Best score: 0.2502: : 799iter [01:14, 12.00iter/s]
[ITER 800] Best: 0.2502
[PARAMS] Current best parameters:
  fx: 469.2440
  fy: 469.0119
  px: 884.1921
  py: 936.0608
  k1: 0.05036487
  k2: 0.03323805
  k3: -0.02660891
  k4: 0.00531541
  p1: -0.02081126
  p2: -0.01719377
  p3: 0.01372462
  p4: 0.00380427
  s1: -0.0025
  s2: -0.0042
  s3: 0.0082
  s4: -0.0035
  tilt_x: 0.0380
  tilt_y: -0.0208
  scale: 2.9308
  rotation: -1.2044
Best score: 0.2502: : 799iter [01:14, 12.00iter/s]Best score: 0.2502: : 800iter [01:14, 12.00iter/s]Best score: 0.2502: : 801iter [01:14,  7.66iter/s]Best score: 0.2502: : 801iter [01:14,  7.66iter/s]Best score: 0.2502: : 802iter [01:15,  7.66iter/s]Best score: 0.2502: : 803iter [01:15,  8.58iter/s]Best score: 0.2502: : 803iter [01:15,  8.58iter/s]Best score: 0.2502: : 804iter [01:15,  8.58iter/s]Best score: 0.2502: : 805iter [01:15,  9.41iter/s]Best score: 0.2502: : 805iter [01:15,  9.41iter/s]Best score: 0.2502: : 806iter [01:15,  9.41iter/s]Best score: 0.2502: : 807iter [01:15, 10.09iter/s]Best score: 0.2502: : 807iter [01:15, 10.09iter/s]Best score: 0.2502: : 808iter [01:15, 10.09iter/s]Best score: 0.2502: : 809iter [01:15, 10.63iter/s]Best score: 0.2482: : 809iter [01:15, 10.63iter/s]Best score: 0.2482: : 810iter [01:15, 10.63iter/s]Best score: 0.2482: : 811iter [01:15, 10.87iter/s]Best score: 0.2482: : 811iter [01:15, 10.87iter/s]Best score: 0.2482: : 812iter [01:15, 10.87iter/s]Best score: 0.2482: : 813iter [01:15, 11.22iter/s]Best score: 0.2482: : 813iter [01:15, 11.22iter/s]Best score: 0.2482: : 814iter [01:16, 11.22iter/s]Best score: 0.2482: : 815iter [01:16, 11.48iter/s]Best score: 0.2482: : 815iter [01:16, 11.48iter/s]Best score: 0.2482: : 816iter [01:16, 11.48iter/s]Best score: 0.2482: : 817iter [01:16, 11.63iter/s]Best score: 0.2482: : 817iter [01:16, 11.63iter/s]Best score: 0.2482: : 818iter [01:16, 11.63iter/s]Best score: 0.2482: : 819iter [01:16, 11.74iter/s]Best score: 0.2331: : 819iter [01:16, 11.74iter/s]Best score: 0.2331: : 820iter [01:16, 11.74iter/s]Best score: 0.2331: : 821iter [01:16, 11.61iter/s]Best score: 0.2331: : 821iter [01:16, 11.61iter/s]Best score: 0.2331: : 822iter [01:16, 11.61iter/s]Best score: 0.2331: : 823iter [01:16, 11.77iter/s]Best score: 0.2331: : 823iter [01:16, 11.77iter/s]Best score: 0.2331: : 824iter [01:16, 11.77iter/s]Best score: 0.2331: : 825iter [01:16, 11.84iter/s]Best score: 0.2331: : 825iter [01:16, 11.84iter/s]Best score: 0.2331: : 826iter [01:17, 11.84iter/s]Best score: 0.2331: : 827iter [01:17, 11.93iter/s]Best score: 0.2331: : 827iter [01:17, 11.93iter/s]Best score: 0.2331: : 828iter [01:17, 11.93iter/s]Best score: 0.2331: : 829iter [01:17, 11.91iter/s]Best score: 0.2323: : 829iter [01:17, 11.91iter/s]Best score: 0.2323: : 830iter [01:17, 11.91iter/s]Best score: 0.2323: : 831iter [01:17, 11.75iter/s]Best score: 0.2323: : 831iter [01:17, 11.75iter/s]Best score: 0.2323: : 832iter [01:17, 11.75iter/s]Best score: 0.2323: : 833iter [01:17, 11.86iter/s]Best score: 0.2323: : 833iter [01:17, 11.86iter/s]Best score: 0.2323: : 834iter [01:17, 11.86iter/s]Best score: 0.2323: : 835iter [01:17, 11.94iter/s]Best score: 0.2323: : 835iter [01:17, 11.94iter/s]Best score: 0.2323: : 836iter [01:17, 11.94iter/s]Best score: 0.2323: : 837iter [01:17, 12.00iter/s]Best score: 0.2323: : 837iter [01:17, 12.00iter/s]Best score: 0.2323: : 838iter [01:18, 12.00iter/s]Best score: 0.2323: : 839iter [01:18, 12.02iter/s]Best score: 0.2318: : 839iter [01:18, 12.02iter/s]Best score: 0.2318: : 840iter [01:18, 12.02iter/s]Best score: 0.2318: : 841iter [01:18, 11.85iter/s]Best score: 0.2318: : 841iter [01:18, 11.85iter/s]Best score: 0.2318: : 842iter [01:18, 11.85iter/s]Best score: 0.2318: : 843iter [01:18, 11.89iter/s]Best score: 0.2318: : 843iter [01:18, 11.89iter/s]Best score: 0.2318: : 844iter [01:18, 11.89iter/s]Best score: 0.2318: : 845iter [01:18, 11.97iter/s]Best score: 0.2318: : 845iter [01:18, 11.97iter/s]Best score: 0.2318: : 846iter [01:18, 11.97iter/s]Best score: 0.2318: : 847iter [01:18, 12.02iter/s]Best score: 0.2318: : 847iter [01:18, 12.02iter/s]Best score: 0.2318: : 848iter [01:18, 12.02iter/s]Best score: 0.2318: : 849iter [01:18, 11.99iter/s]
[ITER 850] Best: 0.2296
[PARAMS] Current best parameters:
  fx: 465.0033
  fy: 464.9195
  px: 884.7972
  py: 933.6094
  k1: 0.05663758
  k2: 0.02433274
  k3: -0.01755527
  k4: 0.00269821
  p1: -0.01760725
  p2: -0.02039700
  p3: 0.01530663
  p4: 0.00601774
  s1: -0.0001
  s2: -0.0062
  s3: 0.0012
  s4: -0.0057
  tilt_x: 0.0215
  tilt_y: -0.0296
  scale: 2.9333
  rotation: -1.2054
Best score: 0.2296: : 849iter [01:18, 11.99iter/s]Best score: 0.2296: : 850iter [01:19, 11.99iter/s]Best score: 0.2296: : 851iter [01:19,  7.50iter/s]Best score: 0.2296: : 851iter [01:19,  7.50iter/s]Best score: 0.2296: : 852iter [01:19,  7.50iter/s]Best score: 0.2296: : 853iter [01:19,  8.44iter/s]Best score: 0.2296: : 853iter [01:19,  8.44iter/s]Best score: 0.2296: : 854iter [01:19,  8.44iter/s]Best score: 0.2296: : 855iter [01:19,  9.29iter/s]Best score: 0.2296: : 855iter [01:19,  9.29iter/s]Best score: 0.2296: : 856iter [01:19,  9.29iter/s]Best score: 0.2296: : 857iter [01:19,  9.99iter/s]Best score: 0.2296: : 857iter [01:19,  9.99iter/s]Best score: 0.2296: : 858iter [01:20,  9.99iter/s]Best score: 0.2296: : 859iter [01:20, 10.51iter/s]Best score: 0.2296: : 859iter [01:20, 10.51iter/s]Best score: 0.2296: : 860iter [01:20, 10.51iter/s]Best score: 0.2296: : 861iter [01:20, 10.74iter/s]Best score: 0.2296: : 861iter [01:20, 10.74iter/s]Best score: 0.2296: : 862iter [01:20, 10.74iter/s]Best score: 0.2296: : 863iter [01:20, 11.10iter/s]Best score: 0.2296: : 863iter [01:20, 11.10iter/s]Best score: 0.2296: : 864iter [01:20, 11.10iter/s]Best score: 0.2296: : 865iter [01:20, 11.39iter/s]Best score: 0.2296: : 865iter [01:20, 11.39iter/s]Best score: 0.2296: : 866iter [01:20, 11.39iter/s]Best score: 0.2296: : 867iter [01:20, 11.60iter/s]Best score: 0.2296: : 867iter [01:20, 11.60iter/s]Best score: 0.2296: : 868iter [01:20, 11.60iter/s]Best score: 0.2296: : 869iter [01:20, 11.75iter/s]Best score: 0.2281: : 869iter [01:20, 11.75iter/s]Best score: 0.2281: : 870iter [01:21, 11.75iter/s]Best score: 0.2281: : 871iter [01:21, 11.66iter/s]Best score: 0.2281: : 871iter [01:21, 11.66iter/s]Best score: 0.2281: : 872iter [01:21, 11.66iter/s]Best score: 0.2281: : 873iter [01:21, 11.79iter/s]Best score: 0.2281: : 873iter [01:21, 11.79iter/s]Best score: 0.2281: : 874iter [01:21, 11.79iter/s]Best score: 0.2281: : 875iter [01:21, 11.89iter/s]Best score: 0.2281: : 875iter [01:21, 11.89iter/s]Best score: 0.2281: : 876iter [01:21, 11.89iter/s]Best score: 0.2281: : 877iter [01:21, 11.97iter/s]Best score: 0.2281: : 877iter [01:21, 11.97iter/s]Best score: 0.2281: : 878iter [01:21, 11.97iter/s]Best score: 0.2281: : 879iter [01:21, 12.02iter/s]Best score: 0.2241: : 879iter [01:21, 12.02iter/s]Best score: 0.2241: : 880iter [01:21, 12.02iter/s]Best score: 0.2241: : 881iter [01:21, 11.74iter/s]Best score: 0.2241: : 881iter [01:21, 11.74iter/s]Best score: 0.2241: : 882iter [01:22, 11.74iter/s]Best score: 0.2241: : 883iter [01:22, 11.85iter/s]Best score: 0.2241: : 883iter [01:22, 11.85iter/s]Best score: 0.2241: : 884iter [01:22, 11.85iter/s]Best score: 0.2241: : 885iter [01:22, 11.92iter/s]Best score: 0.2241: : 885iter [01:22, 11.92iter/s]Best score: 0.2241: : 886iter [01:22, 11.92iter/s]Best score: 0.2241: : 887iter [01:22, 11.98iter/s]Best score: 0.2241: : 887iter [01:22, 11.98iter/s]Best score: 0.2241: : 888iter [01:22, 11.98iter/s]Best score: 0.2241: : 889iter [01:22, 12.03iter/s]Best score: 0.2190: : 889iter [01:22, 12.03iter/s]Best score: 0.2190: : 890iter [01:22, 12.03iter/s]Best score: 0.2190: : 891iter [01:22, 11.86iter/s]Best score: 0.2190: : 891iter [01:22, 11.86iter/s]Best score: 0.2190: : 892iter [01:22, 11.86iter/s]Best score: 0.2190: : 893iter [01:22, 11.92iter/s]Best score: 0.2190: : 893iter [01:22, 11.92iter/s]Best score: 0.2190: : 894iter [01:23, 11.92iter/s]Best score: 0.2190: : 895iter [01:23, 11.93iter/s]Best score: 0.2190: : 895iter [01:23, 11.93iter/s]Best score: 0.2190: : 896iter [01:23, 11.93iter/s]Best score: 0.2190: : 897iter [01:23, 12.00iter/s]Best score: 0.2190: : 897iter [01:23, 12.00iter/s]Best score: 0.2190: : 898iter [01:23, 12.00iter/s]Best score: 0.2190: : 899iter [01:23, 12.03iter/s]
[ITER 900] Best: 0.2182
[PARAMS] Current best parameters:
  fx: 462.4616
  fy: 462.3253
  px: 885.6158
  py: 932.1704
  k1: 0.08012160
  k2: -0.01316821
  k3: 0.00691672
  k4: -0.00282305
  p1: -0.01703368
  p2: -0.01778302
  p3: 0.00986753
  p4: 0.00814415
  s1: 0.0038
  s2: -0.0086
  s3: 0.0055
  s4: -0.0081
  tilt_x: 0.0145
  tilt_y: -0.0278
  scale: 2.9223
  rotation: -1.1906
Best score: 0.2182: : 899iter [01:23, 12.03iter/s]Best score: 0.2182: : 900iter [01:23, 12.03iter/s]Best score: 0.2182: : 901iter [01:23,  7.64iter/s]Best score: 0.2182: : 901iter [01:23,  7.64iter/s]Best score: 0.2182: : 902iter [01:24,  7.64iter/s]Best score: 0.2182: : 903iter [01:24,  8.58iter/s]Best score: 0.2182: : 903iter [01:24,  8.58iter/s]Best score: 0.2182: : 904iter [01:24,  8.58iter/s]Best score: 0.2182: : 905iter [01:24,  9.36iter/s]Best score: 0.2182: : 905iter [01:24,  9.36iter/s]Best score: 0.2182: : 906iter [01:24,  9.36iter/s]Best score: 0.2182: : 907iter [01:24, 10.04iter/s]Best score: 0.2182: : 907iter [01:24, 10.04iter/s]Best score: 0.2182: : 908iter [01:24, 10.04iter/s]Best score: 0.2182: : 909iter [01:24, 10.59iter/s]Best score: 0.2172: : 909iter [01:24, 10.59iter/s]Best score: 0.2172: : 910iter [01:24, 10.59iter/s]Best score: 0.2172: : 911iter [01:24, 10.84iter/s]Best score: 0.2172: : 911iter [01:24, 10.84iter/s]Best score: 0.2172: : 912iter [01:24, 10.84iter/s]Best score: 0.2172: : 913iter [01:24, 11.17iter/s]Best score: 0.2172: : 913iter [01:24, 11.17iter/s]Best score: 0.2172: : 914iter [01:25, 11.17iter/s]Best score: 0.2172: : 915iter [01:25, 11.43iter/s]Best score: 0.2172: : 915iter [01:25, 11.43iter/s]Best score: 0.2172: : 916iter [01:25, 11.43iter/s]Best score: 0.2172: : 917iter [01:25, 11.64iter/s]Best score: 0.2172: : 917iter [01:25, 11.64iter/s]Best score: 0.2172: : 918iter [01:25, 11.64iter/s]Best score: 0.2172: : 919iter [01:25, 11.73iter/s]Best score: 0.2172: : 919iter [01:25, 11.73iter/s]Best score: 0.2172: : 920iter [01:25, 11.73iter/s]Best score: 0.2172: : 921iter [01:25, 11.55iter/s]Best score: 0.2172: : 921iter [01:25, 11.55iter/s]Best score: 0.2172: : 922iter [01:25, 11.55iter/s]Best score: 0.2172: : 923iter [01:25, 11.68iter/s]Best score: 0.2172: : 923iter [01:25, 11.68iter/s]Best score: 0.2172: : 924iter [01:25, 11.68iter/s]Best score: 0.2172: : 925iter [01:25, 11.80iter/s]Best score: 0.2172: : 925iter [01:25, 11.80iter/s]Best score: 0.2172: : 926iter [01:26, 11.80iter/s]Best score: 0.2172: : 927iter [01:26, 11.87iter/s]Best score: 0.2172: : 927iter [01:26, 11.87iter/s]Best score: 0.2172: : 928iter [01:26, 11.87iter/s]Best score: 0.2172: : 929iter [01:26, 11.93iter/s]Best score: 0.2172: : 929iter [01:26, 11.93iter/s]Best score: 0.2172: : 930iter [01:26, 11.93iter/s]Best score: 0.2172: : 931iter [01:26, 11.74iter/s]Best score: 0.2172: : 931iter [01:26, 11.74iter/s]Best score: 0.2172: : 932iter [01:26, 11.74iter/s]Best score: 0.2172: : 933iter [01:26, 11.84iter/s]Best score: 0.2172: : 933iter [01:26, 11.84iter/s]Best score: 0.2172: : 934iter [01:26, 11.84iter/s]Best score: 0.2172: : 935iter [01:26, 11.92iter/s]Best score: 0.2172: : 935iter [01:26, 11.92iter/s]Best score: 0.2172: : 936iter [01:26, 11.92iter/s]Best score: 0.2172: : 937iter [01:26, 11.92iter/s]Best score: 0.2172: : 937iter [01:26, 11.92iter/s]Best score: 0.2172: : 938iter [01:27, 11.92iter/s]Best score: 0.2172: : 939iter [01:27, 11.92iter/s]Best score: 0.2164: : 939iter [01:27, 11.92iter/s]Best score: 0.2164: : 940iter [01:27, 11.92iter/s]Best score: 0.2164: : 941iter [01:27, 11.71iter/s]Best score: 0.2164: : 941iter [01:27, 11.71iter/s]Best score: 0.2164: : 942iter [01:27, 11.71iter/s]Best score: 0.2164: : 943iter [01:27, 11.83iter/s]Best score: 0.2164: : 943iter [01:27, 11.83iter/s]Best score: 0.2164: : 944iter [01:27, 11.83iter/s]Best score: 0.2164: : 945iter [01:27, 11.92iter/s]Best score: 0.2164: : 945iter [01:27, 11.92iter/s]Best score: 0.2164: : 946iter [01:27, 11.92iter/s]Best score: 0.2164: : 947iter [01:27, 11.97iter/s]Best score: 0.2164: : 947iter [01:27, 11.97iter/s]Best score: 0.2164: : 948iter [01:27, 11.97iter/s]Best score: 0.2164: : 949iter [01:27, 12.02iter/s]
[ITER 950] Best: 0.2147
[PARAMS] Current best parameters:
  fx: 461.4416
  fy: 461.3171
  px: 885.5572
  py: 932.1028
  k1: 0.09375829
  k2: -0.03106928
  k3: 0.01619002
  k4: -0.00451398
  p1: -0.01511943
  p2: -0.01635100
  p3: 0.00467903
  p4: 0.00747310
  s1: 0.0071
  s2: -0.0077
  s3: 0.0087
  s4: -0.0075
  tilt_x: 0.0104
  tilt_y: -0.0248
  scale: 2.9104
  rotation: -1.1855
Best score: 0.2147: : 949iter [01:27, 12.02iter/s]Best score: 0.2147: : 950iter [01:28, 12.02iter/s]Best score: 0.2147: : 951iter [01:28,  7.60iter/s]Best score: 0.2147: : 951iter [01:28,  7.60iter/s]Best score: 0.2147: : 952iter [01:28,  7.60iter/s]Best score: 0.2147: : 953iter [01:28,  8.53iter/s]Best score: 0.2147: : 953iter [01:28,  8.53iter/s]Best score: 0.2147: : 954iter [01:28,  8.53iter/s]Best score: 0.2147: : 955iter [01:28,  9.36iter/s]Best score: 0.2147: : 955iter [01:28,  9.36iter/s]Best score: 0.2147: : 956iter [01:28,  9.36iter/s]Best score: 0.2147: : 957iter [01:28, 10.05iter/s]Best score: 0.2147: : 957iter [01:28, 10.05iter/s]Best score: 0.2147: : 958iter [01:29, 10.05iter/s]Best score: 0.2147: : 959iter [01:29, 10.59iter/s]Best score: 0.2135: : 959iter [01:29, 10.59iter/s]Best score: 0.2135: : 960iter [01:29, 10.59iter/s]Best score: 0.2135: : 961iter [01:29, 10.82iter/s]Best score: 0.2135: : 961iter [01:29, 10.82iter/s]Best score: 0.2135: : 962iter [01:29, 10.82iter/s]Best score: 0.2135: : 963iter [01:29, 11.14iter/s]Best score: 0.2135: : 963iter [01:29, 11.14iter/s]Best score: 0.2135: : 964iter [01:29, 11.14iter/s]Best score: 0.2135: : 965iter [01:29, 11.39iter/s]Best score: 0.2135: : 965iter [01:29, 11.39iter/s]Best score: 0.2135: : 966iter [01:29, 11.39iter/s]Best score: 0.2135: : 967iter [01:29, 11.62iter/s]Best score: 0.2135: : 967iter [01:29, 11.62iter/s]Best score: 0.2135: : 968iter [01:29, 11.62iter/s]Best score: 0.2135: : 969iter [01:29, 11.76iter/s]Best score: 0.2135: : 969iter [01:29, 11.76iter/s]Best score: 0.2135: : 970iter [01:30, 11.76iter/s]Best score: 0.2135: : 971iter [01:30, 11.66iter/s]Best score: 0.2135: : 971iter [01:30, 11.66iter/s]Best score: 0.2135: : 972iter [01:30, 11.66iter/s]Best score: 0.2135: : 973iter [01:30, 11.80iter/s]Best score: 0.2135: : 973iter [01:30, 11.80iter/s]Best score: 0.2135: : 974iter [01:30, 11.80iter/s]Best score: 0.2135: : 975iter [01:30, 11.91iter/s]Best score: 0.2135: : 975iter [01:30, 11.91iter/s]Best score: 0.2135: : 976iter [01:30, 11.91iter/s]Best score: 0.2135: : 977iter [01:30, 11.98iter/s]Best score: 0.2135: : 977iter [01:30, 11.98iter/s]Best score: 0.2135: : 978iter [01:30, 11.98iter/s]Best score: 0.2135: : 979iter [01:30, 12.04iter/s]Best score: 0.2134: : 979iter [01:30, 12.04iter/s]Best score: 0.2134: : 980iter [01:30, 12.04iter/s]Best score: 0.2134: : 981iter [01:30, 11.84iter/s]Best score: 0.2134: : 981iter [01:30, 11.84iter/s]Best score: 0.2134: : 982iter [01:31, 11.84iter/s]Best score: 0.2134: : 983iter [01:31, 11.92iter/s]Best score: 0.2134: : 983iter [01:31, 11.92iter/s]Best score: 0.2134: : 984iter [01:31, 11.92iter/s]Best score: 0.2134: : 985iter [01:31, 11.97iter/s]Best score: 0.2134: : 985iter [01:31, 11.97iter/s]Best score: 0.2134: : 986iter [01:31, 11.97iter/s]Best score: 0.2134: : 987iter [01:31, 12.01iter/s]Best score: 0.2134: : 987iter [01:31, 12.01iter/s]Best score: 0.2134: : 988iter [01:31, 12.01iter/s]Best score: 0.2134: : 989iter [01:31, 12.06iter/s]Best score: 0.2127: : 989iter [01:31, 12.06iter/s]Best score: 0.2127: : 990iter [01:31, 12.06iter/s]Best score: 0.2127: : 991iter [01:31, 11.89iter/s]Best score: 0.2127: : 991iter [01:31, 11.89iter/s]Best score: 0.2127: : 992iter [01:31, 11.89iter/s]Best score: 0.2127: : 993iter [01:31, 11.93iter/s]Best score: 0.2127: : 993iter [01:31, 11.93iter/s]Best score: 0.2127: : 994iter [01:32, 11.93iter/s]Best score: 0.2127: : 995iter [01:32, 11.93iter/s]Best score: 0.2127: : 995iter [01:32, 11.93iter/s]Best score: 0.2127: : 996iter [01:32, 11.93iter/s]Best score: 0.2127: : 997iter [01:32, 11.94iter/s]Best score: 0.2127: : 997iter [01:32, 11.94iter/s]Best score: 0.2127: : 998iter [01:32, 11.94iter/s]Best score: 0.2127: : 999iter [01:32, 11.93iter/s]
[ITER 1000] Best: 0.2123
[PARAMS] Current best parameters:
  fx: 460.1482
  fy: 460.0574
  px: 886.7500
  py: 932.4203
  k1: 0.10077455
  k2: -0.03689097
  k3: 0.01794745
  k4: -0.00460838
  p1: -0.01224420
  p2: -0.01599726
  p3: 0.00019765
  p4: 0.00643136
  s1: 0.0123
  s2: -0.0066
  s3: 0.0098
  s4: -0.0060
  tilt_x: 0.0062
  tilt_y: -0.0289
  scale: 2.9038
  rotation: -1.1842
Best score: 0.2123: : 999iter [01:32, 11.93iter/s]Best score: 0.2123: : 1000iter [01:32, 11.93iter/s]Best score: 0.2123: : 1001iter [01:32,  7.57iter/s]Best score: 0.2123: : 1001iter [01:32,  7.57iter/s]Best score: 0.2123: : 1002iter [01:33,  7.57iter/s]Best score: 0.2123: : 1003iter [01:33,  8.54iter/s]Best score: 0.2123: : 1003iter [01:33,  8.54iter/s]Best score: 0.2123: : 1004iter [01:33,  8.54iter/s]Best score: 0.2123: : 1005iter [01:33,  9.34iter/s]Best score: 0.2123: : 1005iter [01:33,  9.34iter/s]Best score: 0.2123: : 1006iter [01:33,  9.34iter/s]Best score: 0.2123: : 1007iter [01:33, 10.03iter/s]Best score: 0.2123: : 1007iter [01:33, 10.03iter/s]Best score: 0.2123: : 1008iter [01:33, 10.03iter/s]Best score: 0.2123: : 1009iter [01:33, 10.57iter/s]Best score: 0.2123: : 1009iter [01:33, 10.57iter/s]Best score: 0.2123: : 1010iter [01:33, 10.57iter/s]Best score: 0.2123: : 1011iter [01:33, 10.80iter/s]Best score: 0.2123: : 1011iter [01:33, 10.80iter/s]Best score: 0.2123: : 1012iter [01:33, 10.80iter/s]Best score: 0.2123: : 1013iter [01:33, 11.13iter/s]Best score: 0.2123: : 1013iter [01:33, 11.13iter/s]Best score: 0.2123: : 1014iter [01:34, 11.13iter/s]Best score: 0.2123: : 1015iter [01:34, 11.33iter/s]Best score: 0.2123: : 1015iter [01:34, 11.33iter/s]Best score: 0.2123: : 1016iter [01:34, 11.33iter/s]Best score: 0.2123: : 1017iter [01:34, 11.52iter/s]Best score: 0.2123: : 1017iter [01:34, 11.52iter/s]Best score: 0.2123: : 1018iter [01:34, 11.52iter/s]Best score: 0.2123: : 1019iter [01:34, 11.70iter/s]Best score: 0.2115: : 1019iter [01:34, 11.70iter/s]Best score: 0.2115: : 1020iter [01:34, 11.70iter/s]Best score: 0.2115: : 1021iter [01:34, 11.62iter/s]Best score: 0.2115: : 1021iter [01:34, 11.62iter/s]Best score: 0.2115: : 1022iter [01:34, 11.62iter/s]Best score: 0.2115: : 1023iter [01:34,  8.97iter/s]Best score: 0.2115: : 1023iter [01:35,  8.97iter/s]Best score: 0.2115: : 1024iter [01:35,  8.97iter/s]Best score: 0.2115: : 1025iter [01:35,  9.68iter/s]Best score: 0.2115: : 1025iter [01:35,  9.68iter/s]Best score: 0.2115: : 1026iter [01:35,  9.68iter/s]Best score: 0.2115: : 1027iter [01:35, 10.30iter/s]Best score: 0.2115: : 1027iter [01:35, 10.30iter/s]Best score: 0.2115: : 1028iter [01:35, 10.30iter/s]Best score: 0.2115: : 1029iter [01:35, 10.76iter/s]Best score: 0.2114: : 1029iter [01:35, 10.76iter/s]Best score: 0.2114: : 1030iter [01:35, 10.76iter/s]Best score: 0.2114: : 1031iter [01:35, 10.96iter/s]Best score: 0.2114: : 1031iter [01:35, 10.96iter/s]Best score: 0.2114: : 1032iter [01:35, 10.96iter/s]Best score: 0.2114: : 1033iter [01:35, 11.28iter/s]Best score: 0.2114: : 1033iter [01:35, 11.28iter/s]Best score: 0.2114: : 1034iter [01:35, 11.28iter/s]Best score: 0.2114: : 1035iter [01:35, 11.51iter/s]Best score: 0.2114: : 1035iter [01:36, 11.51iter/s]Best score: 0.2114: : 1036iter [01:36, 11.51iter/s]Best score: 0.2114: : 1037iter [01:36, 11.69iter/s]Best score: 0.2114: : 1037iter [01:36, 11.69iter/s]Best score: 0.2114: : 1038iter [01:36, 11.69iter/s]Best score: 0.2114: : 1039iter [01:36, 11.82iter/s]Best score: 0.2110: : 1039iter [01:36, 11.82iter/s]Best score: 0.2110: : 1040iter [01:36, 11.82iter/s]Best score: 0.2110: : 1041iter [01:36, 11.72iter/s]Best score: 0.2110: : 1041iter [01:36, 11.72iter/s]Best score: 0.2110: : 1042iter [01:36, 11.72iter/s]Best score: 0.2110: : 1043iter [01:36, 11.84iter/s]Best score: 0.2110: : 1043iter [01:36, 11.84iter/s]Best score: 0.2110: : 1044iter [01:36, 11.84iter/s]Best score: 0.2110: : 1045iter [01:36, 11.92iter/s]Best score: 0.2110: : 1045iter [01:36, 11.92iter/s]Best score: 0.2110: : 1046iter [01:36, 11.92iter/s]Best score: 0.2110: : 1047iter [01:36, 11.97iter/s]Best score: 0.2110: : 1047iter [01:37, 11.97iter/s]Best score: 0.2110: : 1048iter [01:37, 11.97iter/s]Best score: 0.2110: : 1049iter [01:37, 12.01iter/s]
[ITER 1050] Best: 0.2100
[PARAMS] Current best parameters:
  fx: 457.4563
  fy: 457.4772
  px: 887.0571
  py: 932.7985
  k1: 0.10212378
  k2: -0.03425735
  k3: 0.01627633
  k4: -0.00423517
  p1: -0.00847758
  p2: -0.01658304
  p3: -0.00359024
  p4: 0.00591967
  s1: 0.0175
  s2: -0.0063
  s3: 0.0100
  s4: -0.0051
  tilt_x: 0.0001
  tilt_y: -0.0311
  scale: 2.9058
  rotation: -1.1755
Best score: 0.2100: : 1049iter [01:37, 12.01iter/s]Best score: 0.2100: : 1050iter [01:37, 12.01iter/s]Best score: 0.2100: : 1051iter [01:37,  7.66iter/s]Best score: 0.2100: : 1051iter [01:37,  7.66iter/s]Best score: 0.2100: : 1052iter [01:37,  7.66iter/s]Best score: 0.2100: : 1053iter [01:37,  8.61iter/s]Best score: 0.2100: : 1053iter [01:37,  8.61iter/s]Best score: 0.2100: : 1054iter [01:37,  8.61iter/s]Best score: 0.2100: : 1055iter [01:37,  9.42iter/s]Best score: 0.2100: : 1055iter [01:37,  9.42iter/s]Best score: 0.2100: : 1056iter [01:38,  9.42iter/s]Best score: 0.2100: : 1057iter [01:38, 10.03iter/s]Best score: 0.2100: : 1057iter [01:38, 10.03iter/s]Best score: 0.2100: : 1058iter [01:38, 10.03iter/s]Best score: 0.2100: : 1059iter [01:38, 10.53iter/s]Best score: 0.2100: : 1059iter [01:38, 10.53iter/s]Best score: 0.2100: : 1060iter [01:38, 10.53iter/s]Best score: 0.2100: : 1061iter [01:38, 10.79iter/s]Best score: 0.2100: : 1061iter [01:38, 10.79iter/s]Best score: 0.2100: : 1062iter [01:38, 10.79iter/s]Best score: 0.2100: : 1063iter [01:38, 11.17iter/s]Best score: 0.2100: : 1063iter [01:38, 11.17iter/s]Best score: 0.2100: : 1064iter [01:38, 11.17iter/s]Best score: 0.2100: : 1065iter [01:38, 11.41iter/s]Best score: 0.2100: : 1065iter [01:38, 11.41iter/s]Best score: 0.2100: : 1066iter [01:38, 11.41iter/s]Best score: 0.2100: : 1067iter [01:38, 11.54iter/s]Best score: 0.2100: : 1067iter [01:38, 11.54iter/s]Best score: 0.2100: : 1068iter [01:39, 11.54iter/s]Best score: 0.2100: : 1069iter [01:39, 11.70iter/s]Best score: 0.2100: : 1069iter [01:39, 11.70iter/s]Best score: 0.2100: : 1070iter [01:39, 11.70iter/s]Best score: 0.2100: : 1071iter [01:39, 11.63iter/s]Best score: 0.2100: : 1071iter [01:39, 11.63iter/s]Best score: 0.2100: : 1072iter [01:39, 11.63iter/s]Best score: 0.2100: : 1073iter [01:39, 11.76iter/s]Best score: 0.2100: : 1073iter [01:39, 11.76iter/s]Best score: 0.2100: : 1074iter [01:39, 11.76iter/s]Best score: 0.2100: : 1075iter [01:39, 11.87iter/s]Best score: 0.2100: : 1075iter [01:39, 11.87iter/s]Best score: 0.2100: : 1076iter [01:39, 11.87iter/s]Best score: 0.2100: : 1077iter [01:39, 11.94iter/s]Best score: 0.2100: : 1077iter [01:39, 11.94iter/s]Best score: 0.2100: : 1078iter [01:39, 11.94iter/s]Best score: 0.2100: : 1079iter [01:39, 11.99iter/s]Best score: 0.2100: : 1079iter [01:40, 11.99iter/s]Best score: 0.2100: : 1080iter [01:40, 11.99iter/s]Best score: 0.2100: : 1081iter [01:40, 11.76iter/s]Best score: 0.2100: : 1081iter [01:40, 11.76iter/s]Best score: 0.2100: : 1082iter [01:40, 11.76iter/s]Best score: 0.2100: : 1083iter [01:40, 11.82iter/s]Best score: 0.2100: : 1083iter [01:40, 11.82iter/s]Best score: 0.2100: : 1084iter [01:40, 11.82iter/s]Best score: 0.2100: : 1085iter [01:40, 11.85iter/s]Best score: 0.2100: : 1085iter [01:40, 11.85iter/s]Best score: 0.2100: : 1086iter [01:40, 11.85iter/s]Best score: 0.2100: : 1087iter [01:40, 11.91iter/s]Best score: 0.2100: : 1087iter [01:40, 11.91iter/s]Best score: 0.2100: : 1088iter [01:40, 11.91iter/s]Best score: 0.2100: : 1089iter [01:40, 11.92iter/s]Best score: 0.2100: : 1089iter [01:40, 11.92iter/s]Best score: 0.2100: : 1090iter [01:40, 11.92iter/s]Best score: 0.2100: : 1091iter [01:40, 11.78iter/s]Best score: 0.2100: : 1091iter [01:41, 11.78iter/s]Best score: 0.2100: : 1092iter [01:41, 11.78iter/s]Best score: 0.2100: : 1093iter [01:41, 11.79iter/s]Best score: 0.2100: : 1093iter [01:41, 11.79iter/s]Best score: 0.2100: : 1094iter [01:41, 11.79iter/s]Best score: 0.2100: : 1095iter [01:41, 11.80iter/s]Best score: 0.2100: : 1095iter [01:41, 11.80iter/s]Best score: 0.2100: : 1096iter [01:41, 11.80iter/s]Best score: 0.2100: : 1097iter [01:41, 11.86iter/s]Best score: 0.2100: : 1097iter [01:41, 11.86iter/s]Best score: 0.2100: : 1098iter [01:41, 11.86iter/s]Best score: 0.2100: : 1099iter [01:41, 11.89iter/s]
[ITER 1100] Best: 0.2094
[PARAMS] Current best parameters:
  fx: 456.9290
  fy: 456.9519
  px: 887.0227
  py: 932.8532
  k1: 0.10460769
  k2: -0.03760208
  k3: 0.01854592
  k4: -0.00478644
  p1: -0.00835108
  p2: -0.01583816
  p3: -0.00482273
  p4: 0.00569964
  s1: 0.0181
  s2: -0.0062
  s3: 0.0118
  s4: -0.0053
  tilt_x: 0.0001
  tilt_y: -0.0295
  scale: 2.9043
  rotation: -1.1803
Best score: 0.2094: : 1099iter [01:41, 11.89iter/s]
[📈] Running L-BFGS-B polish after 1100 DE iterations...
Best score: 0.2094: : 1100iter [01:42, 10.78iter/s]
  Polish improved score: 0.209438 -> 0.208699

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 99,600 → 93,773 within 3500mm ring
  Ring polish did not improve score: 0.208699
  Success: True
  Tangential distortion: p1=-0.008343, p2=-0.015740, p3=-0.005124, p4=0.005680
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2087
[✅] Parameters copied to: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 4: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 456.92901164195825
    fy: 456.9519503234129
    px: 887.0227220122929
    py: 932.8532235320055
    k1: 0.1044595750124265
    k2: -0.037542048457395415
    k3: 0.018659066923295033
    k4: -0.004845817500260578
    p1: -0.008343207100389346
    p2: -0.01573953802463046
    p3: -0.005124114212716338
    p4: 0.005679519736335822
    scale: 2.9045104525731493
    rotation: -1.1802335139550715
    mmpp: 3.3478045425184138

[🔄] Converting 2434 frames to PTD, creating debug images for 400 calibration frames...
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                                                                 [A📁 Rigs:  50%|█████     | 2/4 [00:13<00:13,  6.73s/rig]
   🔄 2000L:   0%|          | 0/494 [00:00<?, ?frame/s][A
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   🔄 2000L:  98%|█████████▊| 483/494 [00:02<00:00, 167.40frame/s][A
                                                                  [A📁 Rigs:  75%|███████▌  | 3/4 [00:18<00:05,  5.93s/rig]
   🔄 2000R:   0%|          | 0/605 [00:00<?, ?frame/s][A
   🔄 2000R:   3%|▎         | 18/605 [00:00<00:03, 176.08frame/s][A
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   🔄 2000R:  58%|█████▊    | 352/605 [00:01<00:01, 179.37frame/s][A
   🔄 2000R:  61%|██████▏   | 371/605 [00:02<00:01, 179.93frame/s][A
   🔄 2000R:  64%|██████▍   | 390/605 [00:02<00:01, 180.40frame/s][A
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   🔄 2000R:  86%|████████▋ | 523/605 [00:02<00:00, 183.04frame/s][A
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   🔄 2000R:  93%|█████████▎| 561/605 [00:03<00:00, 181.68frame/s][A
   🔄 2000R:  96%|█████████▌| 580/605 [00:03<00:00, 181.80frame/s][A
   🔄 2000R:  99%|█████████▉| 599/605 [00:03<00:00, 180.71frame/s][A
                                                                  [A📁 Rigs: 100%|██████████| 4/4 [00:24<00:00,  5.96s/rig]📁 Rigs: 100%|██████████| 4/4 [00:24<00:00,  6.04s/rig]
[✅] Saved PTDs: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/4_undistorted/output/ptd
📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]
   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 1000:  19%|█▉        | 19/100 [00:00<00:00, 182.73frame/s][A
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                                                                [A📁 Rigs:  25%|██▌       | 1/4 [00:00<00:02,  1.14rig/s]
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   🔄 1500:  91%|█████████ | 91/100 [00:00<00:00, 178.58frame/s][A
                                                                [A📁 Rigs:  50%|█████     | 2/4 [00:01<00:01,  1.11rig/s]
   🔄 2000L:   0%|          | 0/100 [00:00<?, ?frame/s][A
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   🔄 2000L:  94%|█████████▍| 94/100 [00:00<00:00, 179.74frame/s][A
                                                                 [A📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.01rig/s]
   🔄 2000R:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 2000R:  18%|█▊        | 18/100 [00:00<00:00, 174.44frame/s][A
   🔄 2000R:  36%|███▌      | 36/100 [00:00<00:00, 174.98frame/s][A
   🔄 2000R:  54%|█████▍    | 54/100 [00:00<00:00, 174.99frame/s][A
   🔄 2000R:  72%|███████▏  | 72/100 [00:00<00:00, 176.62frame/s][A
   🔄 2000R:  91%|█████████ | 91/100 [00:00<00:00, 178.05frame/s][A
                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.04rig/s]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.05rig/s]
[✅] Saved PTDs: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/4_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:   2%|▏         | 79387/3240000 [00:00<00:03, 793863.19px/s]📤 Writing LUT:   5%|▌         | 164633/3240000 [00:00<00:03, 828331.08px/s]📤 Writing LUT:   8%|▊         | 254738/3240000 [00:00<00:03, 861535.43px/s]📤 Writing LUT:  11%|█         | 342618/3240000 [00:00<00:03, 868349.15px/s]📤 Writing LUT:  13%|█▎        | 429513/3240000 [00:00<00:03, 868563.13px/s]📤 Writing LUT:  16%|█▌        | 517366/3240000 [00:00<00:03, 871949.32px/s]📤 Writing LUT:  19%|█▊        | 606173/3240000 [00:00<00:03, 877217.75px/s]📤 Writing LUT:  21%|██▏       | 693961/3240000 [00:00<00:02, 877426.84px/s]📤 Writing LUT:  24%|██▍       | 782010/3240000 [00:00<00:02, 878382.73px/s]📤 Writing LUT:  27%|██▋       | 871840/3240000 [00:01<00:02, 884528.58px/s]📤 Writing LUT:  30%|██▉       | 962154/3240000 [00:01<00:02, 890223.56px/s]📤 Writing LUT:  32%|███▏      | 1051177/3240000 [00:01<00:02, 888595.86px/s]📤 Writing LUT:  35%|███▌      | 1140073/3240000 [00:01<00:02, 888705.65px/s]📤 Writing LUT:  38%|███▊      | 1230554/3240000 [00:01<00:02, 893563.28px/s]📤 Writing LUT:  41%|████      | 1319912/3240000 [00:01<00:02, 891038.17px/s]📤 Writing LUT:  44%|████▎     | 1410783/3240000 [00:01<00:02, 896343.51px/s]📤 Writing LUT:  46%|████▋     | 1501074/3240000 [00:01<00:01, 898314.32px/s]📤 Writing LUT:  49%|████▉     | 1590908/3240000 [00:01<00:01, 896356.37px/s]📤 Writing LUT:  52%|█████▏    | 1680546/3240000 [00:01<00:01, 895623.44px/s]📤 Writing LUT:  55%|█████▍    | 1770110/3240000 [00:02<00:01, 890346.76px/s]📤 Writing LUT:  57%|█████▋    | 1859452/3240000 [00:02<00:01, 891262.98px/s]📤 Writing LUT:  60%|██████    | 1949348/3240000 [00:02<00:01, 893561.48px/s]📤 Writing LUT:  63%|██████▎   | 2038709/3240000 [00:02<00:01, 890707.72px/s]📤 Writing LUT:  66%|██████▌   | 2129012/3240000 [00:02<00:01, 894389.17px/s]📤 Writing LUT:  68%|██████▊   | 2218952/3240000 [00:02<00:01, 895887.81px/s]📤 Writing LUT:  71%|███████▏  | 2308734/3240000 [00:02<00:01, 896463.38px/s]📤 Writing LUT:  74%|███████▍  | 2398554/3240000 [00:02<00:00, 896982.62px/s]📤 Writing LUT:  77%|███████▋  | 2488255/3240000 [00:02<00:00, 895442.65px/s]📤 Writing LUT:  80%|███████▉  | 2577802/3240000 [00:02<00:00, 895079.19px/s]📤 Writing LUT:  82%|████████▏ | 2669406/3240000 [00:03<00:00, 901356.91px/s]📤 Writing LUT:  85%|████████▌ | 2759748/3240000 [00:03<00:00, 901972.82px/s]📤 Writing LUT:  88%|████████▊ | 2849947/3240000 [00:03<00:00, 901821.61px/s]📤 Writing LUT:  91%|█████████ | 2940261/3240000 [00:03<00:00, 902216.47px/s]📤 Writing LUT:  94%|█████████▎| 3030615/3240000 [00:03<00:00, 902610.37px/s]📤 Writing LUT:  96%|█████████▋| 3121459/3240000 [00:03<00:00, 904356.35px/s]📤 Writing LUT:  99%|█████████▉| 3215659/3240000 [00:03<00:00, 915645.74px/s]📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 891779.37px/s]
[✅] LUT written to: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/4_undistorted/output/LUT.lut
[✅] LUT copied to: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/MDCP3D-SL-011_W042_unknown_2026_05_01.lut

-------------------------------------------------------------------
                          Step 5: Heatmap                          
-------------------------------------------------------------------

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)
📁 Rigs (heatmap):   0%|          | 0/4 [00:00<?, ?rig/s]
   🔥 1000:   0%|          | 0/588 [00:00<?, ?frame/s][A
   🔥 1000:   1%|▏         | 8/588 [00:00<00:07, 78.58frame/s][A
   🔥 1000:   3%|▎         | 17/588 [00:00<00:06, 83.59frame/s][A
   🔥 1000:   4%|▍         | 26/588 [00:00<00:06, 84.89frame/s][A
   🔥 1000:   6%|▌         | 35/588 [00:00<00:06, 86.69frame/s][A
   🔥 1000:   8%|▊         | 45/588 [00:00<00:06, 89.31frame/s][A
   🔥 1000:   9%|▉         | 55/588 [00:00<00:05, 91.64frame/s][A
   🔥 1000:  11%|█         | 65/588 [00:00<00:05, 92.92frame/s][A
   🔥 1000:  13%|█▎        | 75/588 [00:00<00:05, 93.64frame/s][A
   🔥 1000:  14%|█▍        | 85/588 [00:00<00:05, 94.24frame/s][A
   🔥 1000:  16%|█▌        | 95/588 [00:01<00:05, 92.36frame/s][A
   🔥 1000:  18%|█▊        | 105/588 [00:01<00:05, 90.83frame/s][A
   🔥 1000:  20%|█▉        | 115/588 [00:01<00:05, 89.85frame/s][A
   🔥 1000:  21%|██        | 124/588 [00:01<00:05, 89.14frame/s][A
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                                                                [A[✅] 1000: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/1000_heatmap_fitted.png
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                                                                [A[✅] 1500: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/1500_heatmap_fitted.png
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                                                                 [A[✅] 2000L: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
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                                                                 [A[✅] 2000R: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:33<00:00,  8.32s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:33<00:00,  8.33s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDCP3D-SL-011_W042_2026_05_01/calibration/debug/5_heatmap
[🏁] Finished processing MDCP3D-SL-011_W042_2026_05_01


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