2026-09-30T22:05:43 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC033_W017(6)_2026-09-28 ===
2026-09-30T22:05:43 - INFO: Camera ID: 15091875
2026-09-30T22:05:43 - INFO: Wand ID: 
2026-09-30T22:05:43 - INFO: Recorded At: 2026-09-28T13:02:56Z

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 486 stationary frames, 545 remain

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                                                                           [A[PASS2] Frame 0000425: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 1 untrusted, 545 total

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[TIMING] 1000 — 545 frames
  Phase 1  radial detect   (parallel)  :   8.63s  (15.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.82s  (7.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.24s
  Phase 3  finalize        (sequential):   9.85s  (18.1 ms/frame)
    decode + undistort                 :   5.45s  (10.0 ms/frame)
    corner apply + refinement          :   1.81s  (3.3 ms/frame)
    segment                            :   2.12s  (3.9 ms/frame)
    smooth                             :   0.30s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.54s

Processing Rigs:  25%|██▌       | 1/4 [00:27<01:21, 27.23s/it]
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                                                                            [A[PASS2] Frame 0000287: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000288: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000289: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000291: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000297: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000298: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000299: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000300: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000323: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000324: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000325: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000326: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000331: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000332: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000336: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000398: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000416: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000422: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000428: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000437: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000440: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000443: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000449: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000455: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000456: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000457: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000458: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000460: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000461: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000462: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000465: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000466: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000470: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000472: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000849: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000850: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000851: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000853: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000289: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000290: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000299: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000325: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000334: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000335: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000336: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000337: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000338: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000416: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000422: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000440: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000457: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000465: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000470: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000851: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000337: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000338: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000416: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000422: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000465: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 28 frames fixed, 35 untrusted, 445 total

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                                                                            [A
[TIMING] 2000R — 445 frames
  Phase 1  radial detect   (parallel)  :   7.03s  (15.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.50s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   1.39s
  Phase 3  finalize        (sequential):   8.26s  (18.6 ms/frame)
    decode + undistort                 :   4.30s  (9.7 ms/frame)
    corner apply + refinement          :   1.79s  (4.0 ms/frame)
    segment                            :   1.77s  (4.0 ms/frame)
    smooth                             :   0.25s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  20.18s

Processing Rigs:  50%|█████     | 2/4 [00:50<00:50, 25.00s/it][INFO] Filtered 690 stationary frames, 458 remain

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                                                                            [A[PASS2] Frame 0000397: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000403: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000527: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000908: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000909: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000910: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000911: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000918: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000919: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000920: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000921: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000923: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000924: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000925: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000927: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000928: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000929: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000930: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000931: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000932: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000933: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000934: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000935: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000937: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000938: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000939: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000940: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000941: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000942: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000944: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000945: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000947: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000953: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000959: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000965: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000968: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000971: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000974: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000977: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000980: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000983: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000986: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001053: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001054: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001057: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001058: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001059: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001060: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001061: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001062: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001063: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001064: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001065: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001066: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001067: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001068: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001069: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001070: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001071: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001072: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001079: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001080: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001081: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001082: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001095: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001096: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001097: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001098: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000910: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000924: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000929: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000930: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000931: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000934: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000939: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000940: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000941: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000947: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000950: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000956: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000971: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000977: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001052: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001053: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001059: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001062: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001066: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001067: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001071: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001081: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001097: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000921: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001052: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 43 frames fixed, 36 untrusted, 458 total

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                                                                            [A
[TIMING] 2000L — 458 frames
  Phase 1  radial detect   (parallel)  :   7.16s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.64s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   1.65s
  Phase 3  finalize        (sequential):   8.60s  (18.8 ms/frame)
    decode + undistort                 :   4.45s  (9.7 ms/frame)
    corner apply + refinement          :   1.87s  (4.1 ms/frame)
    segment                            :   1.85s  (4.1 ms/frame)
    smooth                             :   0.26s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  21.05s

Processing Rigs:  75%|███████▌  | 3/4 [01:15<00:24, 24.88s/it][INFO] Filtered 610 stationary frames, 713 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 0 untrusted, 713 total

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                                                                           [A
[TIMING] 1500 — 713 frames
  Phase 1  radial detect   (parallel)  :  11.14s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.35s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   0.14s
  Phase 3  finalize        (sequential):  12.93s  (18.1 ms/frame)
    decode + undistort                 :   6.81s  (9.6 ms/frame)
    corner apply + refinement          :   2.69s  (3.8 ms/frame)
    segment                            :   2.82s  (4.0 ms/frame)
    smooth                             :   0.38s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  29.57s

Processing Rigs: 100%|██████████| 4/4 [01:49<00:00, 28.46s/it]Processing Rigs: 100%|██████████| 4/4 [01:49<00:00, 27.33s/it]
[💾] Saved RigFrameDataset to /working/MDC033_W017(6)_2026-09-28/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2161 frames from 4 rigs to /working/MDC033_W017(6)_2026-09-28/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC033_W017(6)_2026-09-28/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 545
[📋] 2000R: 100 frames selected from 445
[📋] 2000L: 100 frames selected from 458
[📋] 1500: 100 frames selected from 713
[💾] Saved RigFrameDataset to /working/MDC033_W017(6)_2026-09-28/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC033_W017(6)_2026-09-28/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                        
-------------------------------------------------------------------

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:26,  3.76it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:26,  3.76it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:26,  3.76it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:25,  3.76it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:07, 12.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:07, 12.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   5%|▌         | 5/100 [00:00<00:07, 12.35it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   6%|▌         | 6/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 356.566} (0.001):   6%|▌         | 6/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 356.566} (0.001):   7%|▋         | 7/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   8%|▊         | 8/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  11%|█         | 11/100 [00:00<00:07, 12.20it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:03, 23.01it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.003 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:03, 23.01it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 23.01it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  14%|█▍        | 14/100 [00:00<00:03, 23.01it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  15%|█▌        | 15/100 [00:00<00:04, 20.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 420.202} (0.006):  15%|█▌        | 15/100 [00:00<00:04, 20.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.005 | Best: {'fx': 420.202} (0.006):  16%|█▌        | 16/100 [00:00<00:04, 20.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:00<00:04, 20.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  18%|█▊        | 18/100 [00:00<00:04, 20.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:00<00:03, 20.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 434.343} (0.007):  20%|██        | 20/100 [00:00<00:03, 25.78it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:03, 25.78it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.012 | Best: {'fx': 455.556} (0.012):  21%|██        | 21/100 [00:01<00:03, 25.78it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.010 | Best: {'fx': 455.556} (0.012):  22%|██▏       | 22/100 [00:01<00:03, 25.78it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 25.78it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  24%|██▍       | 24/100 [00:01<00:02, 28.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 28.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 476.768} (0.020):  25%|██▌       | 25/100 [00:01<00:02, 28.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.030 | Best: {'fx': 490.909} (0.030):  26%|██▌       | 26/100 [00:01<00:02, 28.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.047 | Best: {'fx': 505.051} (0.047):  27%|██▋       | 27/100 [00:01<00:02, 28.67it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.047 | Best: {'fx': 505.051} (0.047):  28%|██▊       | 28/100 [00:01<00:02, 26.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 505.051} (0.047):  28%|██▊       | 28/100 [00:01<00:02, 26.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.038 | Best: {'fx': 505.051} (0.047):  29%|██▉       | 29/100 [00:01<00:02, 26.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.057 | Best: {'fx': 512.121} (0.057):  30%|███       | 30/100 [00:01<00:02, 26.46it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.057 | Best: {'fx': 512.121} (0.057):  31%|███       | 31/100 [00:01<00:02, 24.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.072 | Best: {'fx': 519.192} (0.072):  31%|███       | 31/100 [00:01<00:02, 24.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.093 | Best: {'fx': 526.263} (0.093):  32%|███▏      | 32/100 [00:01<00:02, 24.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.096 | Best: {'fx': 533.333} (0.096):  33%|███▎      | 33/100 [00:01<00:02, 24.43it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.096 | Best: {'fx': 533.333} (0.096):  34%|███▍      | 34/100 [00:01<00:03, 18.63it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.087 | Best: {'fx': 533.333} (0.096):  34%|███▍      | 34/100 [00:01<00:03, 18.63it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.059 | Best: {'fx': 533.333} (0.096):  35%|███▌      | 35/100 [00:01<00:03, 18.63it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.071 | Best: {'fx': 533.333} (0.096):  36%|███▌      | 36/100 [00:01<00:03, 18.63it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.071 | Best: {'fx': 533.333} (0.096):  37%|███▋      | 37/100 [00:01<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.047 | Best: {'fx': 533.333} (0.096):  37%|███▋      | 37/100 [00:01<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.043 | Best: {'fx': 533.333} (0.096):  38%|███▊      | 38/100 [00:01<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.036 | Best: {'fx': 533.333} (0.096):  39%|███▉      | 39/100 [00:01<00:03, 18.92it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.036 | Best: {'fx': 533.333} (0.096):  40%|████      | 40/100 [00:01<00:02, 20.06it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.052 | Best: {'fx': 533.333} (0.096):  40%|████      | 40/100 [00:02<00:02, 20.06it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.034 | Best: {'fx': 533.333} (0.096):  41%|████      | 41/100 [00:02<00:02, 20.06it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.040 | Best: {'fx': 533.333} (0.096):  42%|████▏     | 42/100 [00:02<00:02, 20.06it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.040 | Best: {'fx': 533.333} (0.096):  43%|████▎     | 43/100 [00:02<00:02, 20.99it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.038 | Best: {'fx': 533.333} (0.096):  43%|████▎     | 43/100 [00:02<00:02, 20.99it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.030 | Best: {'fx': 533.333} (0.096):  44%|████▍     | 44/100 [00:02<00:02, 20.99it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.033 | Best: {'fx': 533.333} (0.096):  45%|████▌     | 45/100 [00:02<00:02, 20.99it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.096):  46%|████▌     | 46/100 [00:02<00:02, 20.99it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.096):  47%|████▋     | 47/100 [00:02<00:02, 24.00it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.029 | Best: {'fx': 533.333} (0.096):  47%|████▋     | 47/100 [00:02<00:02, 24.00it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.096):  48%|████▊     | 48/100 [00:02<00:02, 24.00it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.031 | Best: {'fx': 533.333} (0.096):  49%|████▉     | 49/100 [00:02<00:02, 24.00it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 533.333} (0.096):  50%|█████     | 50/100 [00:02<00:02, 24.00it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 533.333} (0.096):  51%|█████     | 51/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.028 | Best: {'fx': 533.333} (0.096):  51%|█████     | 51/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.096):  52%|█████▏    | 52/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  53%|█████▎    | 53/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.096):  54%|█████▍    | 54/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.096):  55%|█████▌    | 55/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  56%|█████▌    | 56/100 [00:02<00:01, 26.28it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  57%|█████▋    | 57/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.096):  57%|█████▋    | 57/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.096):  58%|█████▊    | 58/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  59%|█████▉    | 59/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  60%|██████    | 60/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.096):  61%|██████    | 61/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.096):  62%|██████▏   | 62/100 [00:02<00:01, 30.54it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.096):  63%|██████▎   | 63/100 [00:02<00:01, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.096):  63%|██████▎   | 63/100 [00:02<00:01, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.096):  64%|██████▍   | 64/100 [00:02<00:01, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.096):  65%|██████▌   | 65/100 [00:02<00:01, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.096):  66%|██████▌   | 66/100 [00:02<00:01, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.096):  67%|██████▋   | 67/100 [00:02<00:00, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.096):  68%|██████▊   | 68/100 [00:02<00:00, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  69%|██████▉   | 69/100 [00:02<00:00, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.096):  70%|███████   | 70/100 [00:02<00:00, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  71%|███████   | 71/100 [00:02<00:00, 33.95it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  72%|███████▏  | 72/100 [00:02<00:00, 46.13it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  72%|███████▏  | 72/100 [00:02<00:00, 46.13it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  73%|███████▎  | 73/100 [00:02<00:00, 46.13it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  74%|███████▍  | 74/100 [00:02<00:00, 46.13it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  75%|███████▌  | 75/100 [00:02<00:00, 46.13it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  76%|███████▌  | 76/100 [00:02<00:00, 46.13it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  77%|███████▋  | 77/100 [00:02<00:00, 46.70it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  77%|███████▋  | 77/100 [00:02<00:00, 46.70it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  78%|███████▊  | 78/100 [00:02<00:00, 46.70it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.096):  79%|███████▉  | 79/100 [00:02<00:00, 46.70it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  80%|████████  | 80/100 [00:03<00:00, 46.70it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  81%|████████  | 81/100 [00:03<00:00, 46.70it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  82%|████████▏ | 82/100 [00:03<00:00, 34.81it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  82%|████████▏ | 82/100 [00:03<00:00, 34.81it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  83%|████████▎ | 83/100 [00:03<00:00, 34.81it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  84%|████████▍ | 84/100 [00:03<00:00, 34.81it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  85%|████████▌ | 85/100 [00:03<00:00, 34.81it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  86%|████████▌ | 86/100 [00:03<00:00, 34.81it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  87%|████████▋ | 87/100 [00:03<00:00, 35.51it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  87%|████████▋ | 87/100 [00:03<00:00, 35.51it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  88%|████████▊ | 88/100 [00:03<00:00, 35.51it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  89%|████████▉ | 89/100 [00:03<00:00, 35.51it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  90%|█████████ | 90/100 [00:03<00:00, 35.51it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  91%|█████████ | 91/100 [00:03<00:00, 33.65it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096):  91%|█████████ | 91/100 [00:03<00:00, 33.65it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096):  92%|█████████▏| 92/100 [00:03<00:00, 33.65it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  93%|█████████▎| 93/100 [00:03<00:00, 33.65it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096):  94%|█████████▍| 94/100 [00:03<00:00, 33.65it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  95%|█████████▌| 95/100 [00:03<00:00, 33.65it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  96%|█████████▌| 96/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  96%|█████████▌| 96/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096):  97%|█████████▋| 97/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096):  98%|█████████▊| 98/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096):  99%|█████████▉| 99/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.096): 100%|██████████| 100/100 [00:03<00:00, 30.32it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.051 | Best: {'fx': 508.333} (0.051):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.051 | Best: {'fx': 508.333} (0.051):   2%|▏         | 1/50 [00:00<00:28,  1.75it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.052 | Best: {'fx': 509.354} (0.052):   2%|▏         | 1/50 [00:00<00:28,  1.75it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.054 | Best: {'fx': 510.374} (0.054):   4%|▍         | 2/50 [00:00<00:27,  1.75it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.055 | Best: {'fx': 511.395} (0.055):   6%|▌         | 3/50 [00:00<00:26,  1.75it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.057 | Best: {'fx': 512.415} (0.057):   8%|▊         | 4/50 [00:00<00:26,  1.75it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.057 | Best: {'fx': 512.415} (0.057):  10%|█         | 5/50 [00:00<00:05,  8.59it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.059 | Best: {'fx': 513.435} (0.059):  10%|█         | 5/50 [00:00<00:05,  8.59it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.063 | Best: {'fx': 515.476} (0.063):  12%|█▏        | 6/50 [00:00<00:05,  8.59it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.066 | Best: {'fx': 516.497} (0.066):  14%|█▍        | 7/50 [00:00<00:05,  8.59it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.061 | Best: {'fx': 516.497} (0.066):  16%|█▌        | 8/50 [00:00<00:04,  8.59it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.061 | Best: {'fx': 516.497} (0.066):  18%|█▊        | 9/50 [00:00<00:02, 14.41it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.067 | Best: {'fx': 517.517} (0.067):  18%|█▊        | 9/50 [00:00<00:02, 14.41it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.073 | Best: {'fx': 519.558} (0.073):  20%|██        | 10/50 [00:00<00:02, 14.41it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.070 | Best: {'fx': 519.558} (0.073):  22%|██▏       | 11/50 [00:00<00:02, 14.41it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.074 | Best: {'fx': 520.578} (0.074):  24%|██▍       | 12/50 [00:00<00:02, 14.41it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.074 | Best: {'fx': 520.578} (0.074):  26%|██▌       | 13/50 [00:00<00:01, 18.57it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.079 | Best: {'fx': 522.619} (0.079):  26%|██▌       | 13/50 [00:01<00:01, 18.57it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.077 | Best: {'fx': 522.619} (0.079):  28%|██▊       | 14/50 [00:01<00:01, 18.57it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.087 | Best: {'fx': 524.66} (0.087):  30%|███       | 15/50 [00:01<00:01, 18.57it/s] Brute | Start: {'fx': 533.333} (0.096) | Current: 0.087 | Best: {'fx': 524.66} (0.087):  32%|███▏      | 16/50 [00:01<00:01, 18.60it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.094 | Best: {'fx': 526.701} (0.094):  32%|███▏      | 16/50 [00:01<00:01, 18.60it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.088 | Best: {'fx': 526.701} (0.094):  34%|███▍      | 17/50 [00:01<00:01, 18.60it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.082 | Best: {'fx': 526.701} (0.094):  36%|███▌      | 18/50 [00:01<00:01, 18.60it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.082 | Best: {'fx': 526.701} (0.094):  38%|███▊      | 19/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.097 | Best: {'fx': 530.782} (0.097):  38%|███▊      | 19/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.098 | Best: {'fx': 527.721} (0.098):  40%|████      | 20/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.088 | Best: {'fx': 527.721} (0.098):  42%|████▏     | 21/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.088 | Best: {'fx': 527.721} (0.098):  44%|████▍     | 22/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.097 | Best: {'fx': 527.721} (0.098):  44%|████▍     | 22/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.097 | Best: {'fx': 527.721} (0.098):  46%|████▌     | 23/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.096 | Best: {'fx': 527.721} (0.098):  48%|████▊     | 24/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.085 | Best: {'fx': 527.721} (0.098):  50%|█████     | 25/50 [00:01<00:01, 20.39it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.085 | Best: {'fx': 527.721} (0.098):  52%|█████▏    | 26/50 [00:01<00:01, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.084 | Best: {'fx': 527.721} (0.098):  52%|█████▏    | 26/50 [00:01<00:01, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.076 | Best: {'fx': 527.721} (0.098):  54%|█████▍    | 27/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.093 | Best: {'fx': 527.721} (0.098):  56%|█████▌    | 28/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.096 | Best: {'fx': 527.721} (0.098):  58%|█████▊    | 29/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.081 | Best: {'fx': 527.721} (0.098):  60%|██████    | 30/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.093 | Best: {'fx': 527.721} (0.098):  62%|██████▏   | 31/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.093 | Best: {'fx': 527.721} (0.098):  64%|██████▍   | 32/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.097 | Best: {'fx': 527.721} (0.098):  66%|██████▌   | 33/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.079 | Best: {'fx': 527.721} (0.098):  68%|██████▊   | 34/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.091 | Best: {'fx': 527.721} (0.098):  70%|███████   | 35/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.074 | Best: {'fx': 527.721} (0.098):  72%|███████▏  | 36/50 [00:01<00:00, 23.99it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.074 | Best: {'fx': 527.721} (0.098):  74%|███████▍  | 37/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.071 | Best: {'fx': 527.721} (0.098):  74%|███████▍  | 37/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.069 | Best: {'fx': 527.721} (0.098):  76%|███████▌  | 38/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.093 | Best: {'fx': 527.721} (0.098):  78%|███████▊  | 39/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.067 | Best: {'fx': 527.721} (0.098):  80%|████████  | 40/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.063 | Best: {'fx': 527.721} (0.098):  82%|████████▏ | 41/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.065 | Best: {'fx': 527.721} (0.098):  84%|████████▍ | 42/50 [00:01<00:00, 44.36it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.065 | Best: {'fx': 527.721} (0.098):  86%|████████▌ | 43/50 [00:01<00:00, 43.44it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.061 | Best: {'fx': 527.721} (0.098):  86%|████████▌ | 43/50 [00:01<00:00, 43.44it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.063 | Best: {'fx': 527.721} (0.098):  88%|████████▊ | 44/50 [00:01<00:00, 43.44it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.058 | Best: {'fx': 527.721} (0.098):  90%|█████████ | 45/50 [00:01<00:00, 43.44it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.060 | Best: {'fx': 527.721} (0.098):  92%|█████████▏| 46/50 [00:01<00:00, 43.44it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.056 | Best: {'fx': 527.721} (0.098):  94%|█████████▍| 47/50 [00:01<00:00, 43.44it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.056 | Best: {'fx': 527.721} (0.098):  96%|█████████▌| 48/50 [00:01<00:00, 40.67it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.057 | Best: {'fx': 527.721} (0.098):  96%|█████████▌| 48/50 [00:01<00:00, 40.67it/s]Brute | Start: {'fx': 533.333} (0.096) | Current: 0.055 | Best: {'fx': 527.721} (0.098):  98%|█████████▊| 49/50 [00:01<00:00, 40.67it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 527.7210884353742
    px: 1009.0
    py: 748.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 80 frames will be used for optimization
  Rigs: 1000(1000mm):20/20, 2000R(2000mm):20/20, 2000L(2000mm):20/20, 1500(1500mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 21.8306: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 21.8306, picklable_objective(x0) = 21.6390
Best score: 21.6390: : 0iter [00:02, ?iter/s] Best score: 21.6390: : 1iter [00:02,  2.04s/iter]Best score: 21.6390: : 1iter [00:02,  2.04s/iter]Best score: 21.6390: : 2iter [00:02,  2.04s/iter]Best score: 21.6390: : 3iter [00:02,  1.68iter/s]Best score: 21.6390: : 3iter [00:02,  1.68iter/s]Best score: 21.6390: : 4iter [00:02,  1.68iter/s]Best score: 21.6390: : 5iter [00:02,  3.00iter/s]Best score: 21.6390: : 5iter [00:02,  3.00iter/s]Best score: 21.6390: : 6iter [00:02,  3.00iter/s]Best score: 21.6390: : 7iter [00:02,  4.36iter/s]Best score: 21.6390: : 7iter [00:02,  4.36iter/s]Best score: 21.6390: : 8iter [00:02,  4.36iter/s]Best score: 21.6390: : 9iter [00:02,  5.65iter/s]Best score: 21.6390: : 9iter [00:02,  5.65iter/s]Best score: 21.6390: : 10iter [00:02,  5.65iter/s]Best score: 21.6390: : 11iter [00:02,  6.81iter/s]Best score: 21.6390: : 11iter [00:03,  6.81iter/s]Best score: 21.6390: : 12iter [00:03,  6.81iter/s]Best score: 21.6390: : 13iter [00:03,  7.87iter/s]Best score: 21.6390: : 13iter [00:03,  7.87iter/s]Best score: 21.6390: : 14iter [00:03,  7.87iter/s]Best score: 21.6390: : 15iter [00:03,  8.78iter/s]Best score: 21.6390: : 15iter [00:03,  8.78iter/s]Best score: 21.6390: : 16iter [00:03,  8.78iter/s]Best score: 21.6390: : 17iter [00:03,  9.47iter/s]Best score: 21.6390: : 17iter [00:03,  9.47iter/s]Best score: 21.6390: : 18iter [00:03,  9.47iter/s]Best score: 21.6390: : 19iter [00:03, 10.04iter/s]Best score: 21.6390: : 19iter [00:03, 10.04iter/s]Best score: 21.6390: : 20iter [00:03, 10.04iter/s]Best score: 21.6390: : 21iter [00:03, 10.34iter/s]Best score: 21.6390: : 21iter [00:03, 10.34iter/s]Best score: 21.6390: : 22iter [00:03, 10.34iter/s]Best score: 21.6390: : 23iter [00:03, 10.71iter/s]Best score: 21.6390: : 23iter [00:04, 10.71iter/s]Best score: 21.6390: : 24iter [00:04, 10.71iter/s]Best score: 21.6390: : 25iter [00:04, 10.98iter/s]Best score: 21.6390: : 25iter [00:04, 10.98iter/s]Best score: 21.6390: : 26iter [00:04, 10.98iter/s]Best score: 21.6390: : 27iter [00:04, 11.13iter/s]Best score: 21.6390: : 27iter [00:04, 11.13iter/s]Best score: 21.6390: : 28iter [00:04, 11.13iter/s]Best score: 21.6390: : 29iter [00:04, 11.27iter/s]Best score: 21.6390: : 29iter [00:04, 11.27iter/s]Best score: 21.6390: : 30iter [00:04, 11.27iter/s]Best score: 21.6390: : 31iter [00:04, 11.20iter/s]Best score: 21.6390: : 31iter [00:04, 11.20iter/s]Best score: 21.6390: : 32iter [00:04, 11.20iter/s]Best score: 21.6390: : 33iter [00:04, 11.32iter/s]Best score: 21.6390: : 33iter [00:04, 11.32iter/s]Best score: 21.6390: : 34iter [00:05, 11.32iter/s]Best score: 21.6390: : 35iter [00:05, 11.41iter/s]Best score: 21.6390: : 35iter [00:05, 11.41iter/s]Best score: 21.6390: : 36iter [00:05, 11.41iter/s]Best score: 21.6390: : 37iter [00:05, 11.47iter/s]Best score: 21.6390: : 37iter [00:05, 11.47iter/s]Best score: 21.6390: : 38iter [00:05, 11.47iter/s]Best score: 21.6390: : 39iter [00:05, 11.51iter/s]Best score: 21.6390: : 39iter [00:05, 11.51iter/s]Best score: 21.6390: : 40iter [00:05, 11.51iter/s]Best score: 21.6390: : 41iter [00:05, 11.39iter/s]Best score: 21.6390: : 41iter [00:05, 11.39iter/s]Best score: 21.6390: : 42iter [00:05, 11.39iter/s]Best score: 21.6390: : 43iter [00:05, 11.46iter/s]Best score: 21.6390: : 43iter [00:05, 11.46iter/s]Best score: 21.6390: : 44iter [00:05, 11.46iter/s]Best score: 21.6390: : 45iter [00:05, 11.52iter/s]Best score: 21.6390: : 45iter [00:05, 11.52iter/s]Best score: 21.6390: : 46iter [00:06, 11.52iter/s]Best score: 21.6390: : 47iter [00:06, 11.49iter/s]Best score: 21.6390: : 47iter [00:06, 11.49iter/s]Best score: 21.6390: : 48iter [00:06, 11.49iter/s]Best score: 21.6390: : 49iter [00:06, 11.53iter/s]
[ITER 50] Best: 21.6390
[PARAMS] Current best parameters:
  fx: 527.7211
  fy: 527.7211
  px: 1009.0000
  py: 748.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 21.6390: : 49iter [00:06, 11.53iter/s]Best score: 21.6390: : 50iter [00:06, 11.53iter/s]Best score: 21.6390: : 51iter [00:06, 11.39iter/s]Best score: 21.6390: : 51iter [00:06, 11.39iter/s]Best score: 21.6390: : 52iter [00:06, 11.39iter/s]Best score: 21.6390: : 53iter [00:06, 11.46iter/s]Best score: 21.6390: : 53iter [00:06, 11.46iter/s]Best score: 21.6390: : 54iter [00:06, 11.46iter/s]Best score: 21.6390: : 55iter [00:06, 11.50iter/s]Best score: 21.6390: : 55iter [00:06, 11.50iter/s]Best score: 21.6390: : 56iter [00:06, 11.50iter/s]Best score: 21.6390: : 57iter [00:06, 11.54iter/s]Best score: 21.6390: : 57iter [00:07, 11.54iter/s]Best score: 21.6390: : 58iter [00:07, 11.54iter/s]Best score: 21.6390: : 59iter [00:07, 11.60iter/s]Best score: 21.6390: : 59iter [00:07, 11.60iter/s]Best score: 21.6390: : 60iter [00:07, 11.60iter/s]Best score: 21.6390: : 61iter [00:07, 11.41iter/s]Best score: 21.6390: : 61iter [00:07, 11.41iter/s]Best score: 21.6390: : 62iter [00:07, 11.41iter/s]Best score: 21.6390: : 63iter [00:07, 11.49iter/s]Best score: 21.6390: : 63iter [00:07, 11.49iter/s]Best score: 21.6390: : 64iter [00:07, 11.49iter/s]Best score: 21.6390: : 65iter [00:07, 11.42iter/s]Best score: 21.6390: : 65iter [00:07, 11.42iter/s]Best score: 21.6390: : 66iter [00:07, 11.42iter/s]Best score: 21.6390: : 67iter [00:07, 11.51iter/s]Best score: 21.6390: : 67iter [00:07, 11.51iter/s]Best score: 21.6390: : 68iter [00:07, 11.51iter/s]Best score: 21.6390: : 69iter [00:07, 11.53iter/s]Best score: 18.5947: : 69iter [00:08, 11.53iter/s]Best score: 18.5947: : 70iter [00:08, 11.53iter/s]Best score: 18.5947: : 71iter [00:08, 11.32iter/s]Best score: 18.5947: : 71iter [00:08, 11.32iter/s]Best score: 18.5947: : 72iter [00:08, 11.32iter/s]Best score: 18.5947: : 73iter [00:08, 11.46iter/s]Best score: 18.5947: : 73iter [00:08, 11.46iter/s]Best score: 18.5947: : 74iter [00:08, 11.46iter/s]Best score: 18.5947: : 75iter [00:08, 11.51iter/s]Best score: 18.5947: : 75iter [00:08, 11.51iter/s]Best score: 18.5947: : 76iter [00:08, 11.51iter/s]Best score: 18.5947: : 77iter [00:08, 11.53iter/s]Best score: 18.5947: : 77iter [00:08, 11.53iter/s]Best score: 18.5947: : 78iter [00:08, 11.53iter/s]Best score: 18.5947: : 79iter [00:08, 11.60iter/s]Best score: 12.8569: : 79iter [00:08, 11.60iter/s]Best score: 12.8569: : 80iter [00:09, 11.60iter/s]Best score: 12.8569: : 81iter [00:09, 11.45iter/s]Best score: 12.8569: : 81iter [00:09, 11.45iter/s]Best score: 12.8569: : 82iter [00:09, 11.45iter/s]Best score: 12.8569: : 83iter [00:09, 11.53iter/s]Best score: 12.8569: : 83iter [00:09, 11.53iter/s]Best score: 12.8569: : 84iter [00:09, 11.53iter/s]Best score: 12.8569: : 85iter [00:09, 11.58iter/s]Best score: 12.8569: : 85iter [00:09, 11.58iter/s]Best score: 12.8569: : 86iter [00:09, 11.58iter/s]Best score: 12.8569: : 87iter [00:09, 11.68iter/s]Best score: 12.8569: : 87iter [00:09, 11.68iter/s]Best score: 12.8569: : 88iter [00:09, 11.68iter/s]Best score: 12.8569: : 89iter [00:09, 11.72iter/s]Best score: 11.7170: : 89iter [00:09, 11.72iter/s]Best score: 11.7170: : 90iter [00:09, 11.72iter/s]Best score: 11.7170: : 91iter [00:09, 11.51iter/s]Best score: 11.7170: : 91iter [00:09, 11.51iter/s]Best score: 11.7170: : 92iter [00:10, 11.51iter/s]Best score: 11.7170: : 93iter [00:10, 11.52iter/s]Best score: 11.7170: : 93iter [00:10, 11.52iter/s]Best score: 11.7170: : 94iter [00:10, 11.52iter/s]Best score: 11.7170: : 95iter [00:10, 11.53iter/s]Best score: 11.7170: : 95iter [00:10, 11.53iter/s]Best score: 11.7170: : 96iter [00:10, 11.53iter/s]Best score: 11.7170: : 97iter [00:10, 11.58iter/s]Best score: 11.7170: : 97iter [00:10, 11.58iter/s]Best score: 11.7170: : 98iter [00:10, 11.58iter/s]Best score: 11.7170: : 99iter [00:10, 11.57iter/s]
[ITER 100] Best: 8.8441
[PARAMS] Current best parameters:
  fx: 564.8782
  fy: 566.7618
  px: 1019.7184
  py: 751.5187
  k1: -0.06353788
  k2: 0.01343469
  k3: 0.00729188
  k4: -0.00298420
  p1: 0.01296690
  p2: -0.00425292
  p3: -0.02491363
  p4: -0.01452555
  s1: 0.0375
  s2: 0.0045
  s3: 0.0063
  s4: 0.0137
  tilt_x: -0.0479
  tilt_y: -0.0691
  scale: 3.0748
  rotation: -0.0630
Best score: 8.8441: : 99iter [00:10, 11.57iter/s] Best score: 8.8441: : 100iter [00:10, 11.57iter/s]Best score: 8.8441: : 101iter [00:10,  9.95iter/s]Best score: 8.8441: : 101iter [00:10,  9.95iter/s]Best score: 8.8441: : 102iter [00:11,  9.95iter/s]Best score: 8.8441: : 103iter [00:11, 10.34iter/s]Best score: 8.8441: : 103iter [00:11, 10.34iter/s]Best score: 8.8441: : 104iter [00:11, 10.34iter/s]Best score: 8.8441: : 105iter [00:11, 10.69iter/s]Best score: 8.8441: : 105iter [00:11, 10.69iter/s]Best score: 8.8441: : 106iter [00:11, 10.69iter/s]Best score: 8.8441: : 107iter [00:11, 10.94iter/s]Best score: 8.8441: : 107iter [00:11, 10.94iter/s]Best score: 8.8441: : 108iter [00:11, 10.94iter/s]Best score: 8.8441: : 109iter [00:11, 11.08iter/s]Best score: 8.8441: : 109iter [00:11, 11.08iter/s]Best score: 8.8441: : 110iter [00:11, 11.08iter/s]Best score: 8.8441: : 111iter [00:11, 11.07iter/s]Best score: 8.8441: : 111iter [00:11, 11.07iter/s]Best score: 8.8441: : 112iter [00:11, 11.07iter/s]Best score: 8.8441: : 113iter [00:11, 11.21iter/s]Best score: 8.8441: : 113iter [00:11, 11.21iter/s]Best score: 8.8441: : 114iter [00:12, 11.21iter/s]Best score: 8.8441: : 115iter [00:12, 11.30iter/s]Best score: 8.8441: : 115iter [00:12, 11.30iter/s]Best score: 8.8441: : 116iter [00:12, 11.30iter/s]Best score: 8.8441: : 117iter [00:12, 11.28iter/s]Best score: 8.8441: : 117iter [00:12, 11.28iter/s]Best score: 8.8441: : 118iter [00:12, 11.28iter/s]Best score: 8.8441: : 119iter [00:12, 11.30iter/s]Best score: 8.8441: : 119iter [00:12, 11.30iter/s]Best score: 8.8441: : 120iter [00:12, 11.30iter/s]Best score: 8.8441: : 121iter [00:12, 11.14iter/s]Best score: 8.8441: : 121iter [00:12, 11.14iter/s]Best score: 8.8441: : 122iter [00:12, 11.14iter/s]Best score: 8.8441: : 123iter [00:12, 11.28iter/s]Best score: 8.8441: : 123iter [00:12, 11.28iter/s]Best score: 8.8441: : 124iter [00:12, 11.28iter/s]Best score: 8.8441: : 125iter [00:12, 11.36iter/s]Best score: 8.8441: : 125iter [00:13, 11.36iter/s]Best score: 8.8441: : 126iter [00:13, 11.36iter/s]Best score: 8.8441: : 127iter [00:13, 11.46iter/s]Best score: 8.8441: : 127iter [00:13, 11.46iter/s]Best score: 8.8441: : 128iter [00:13, 11.46iter/s]Best score: 8.8441: : 129iter [00:13, 11.47iter/s]Best score: 8.8441: : 129iter [00:13, 11.47iter/s]Best score: 8.8441: : 130iter [00:13, 11.47iter/s]Best score: 8.8441: : 131iter [00:13, 11.30iter/s]Best score: 8.8441: : 131iter [00:13, 11.30iter/s]Best score: 8.8441: : 132iter [00:13, 11.30iter/s]Best score: 8.8441: : 133iter [00:13, 11.39iter/s]Best score: 8.8441: : 133iter [00:13, 11.39iter/s]Best score: 8.8441: : 134iter [00:13, 11.39iter/s]Best score: 8.8441: : 135iter [00:13, 11.46iter/s]Best score: 8.8441: : 135iter [00:13, 11.46iter/s]Best score: 8.8441: : 136iter [00:13, 11.46iter/s]Best score: 8.8441: : 137iter [00:13, 11.50iter/s]Best score: 8.8441: : 137iter [00:14, 11.50iter/s]Best score: 8.8441: : 138iter [00:14, 11.50iter/s]Best score: 8.8441: : 139iter [00:14, 11.52iter/s]Best score: 8.8441: : 139iter [00:14, 11.52iter/s]Best score: 8.8441: : 140iter [00:14, 11.52iter/s]Best score: 8.8441: : 141iter [00:14, 11.33iter/s]Best score: 8.8441: : 141iter [00:14, 11.33iter/s]Best score: 8.8441: : 142iter [00:14, 11.33iter/s]Best score: 8.8441: : 143iter [00:14, 11.47iter/s]Best score: 8.8441: : 143iter [00:14, 11.47iter/s]Best score: 8.8441: : 144iter [00:14, 11.47iter/s]Best score: 8.8441: : 145iter [00:14, 11.54iter/s]Best score: 8.8441: : 145iter [00:14, 11.54iter/s]Best score: 8.8441: : 146iter [00:14, 11.54iter/s]Best score: 8.8441: : 147iter [00:14, 11.63iter/s]Best score: 8.8441: : 147iter [00:14, 11.63iter/s]Best score: 8.8441: : 148iter [00:15, 11.63iter/s]Best score: 8.8441: : 149iter [00:15, 11.67iter/s]
[ITER 150] Best: 8.8441
[PARAMS] Current best parameters:
  fx: 564.8782
  fy: 566.7618
  px: 1019.7184
  py: 751.5187
  k1: -0.06353788
  k2: 0.01343469
  k3: 0.00729188
  k4: -0.00298420
  p1: 0.01296690
  p2: -0.00425292
  p3: -0.02491363
  p4: -0.01452555
  s1: 0.0375
  s2: 0.0045
  s3: 0.0063
  s4: 0.0137
  tilt_x: -0.0479
  tilt_y: -0.0691
  scale: 3.0748
  rotation: -0.0630
Best score: 8.8441: : 149iter [00:15, 11.67iter/s]
[Periodic outlier @ iter 50] Removing 2000L frame 10 (score=211.16, threshold=76.18)
  Remaining frames: 79
Best score: 8.8441: : 150iter [00:15, 11.67iter/s]Best score: 8.8441: : 151iter [00:15,  6.56iter/s]Best score: 8.8441: : 151iter [00:15,  6.56iter/s]Best score: 8.8441: : 152iter [00:15,  6.56iter/s]Best score: 8.8441: : 153iter [00:15,  7.55iter/s]Best score: 8.8441: : 153iter [00:15,  7.55iter/s]Best score: 8.8441: : 154iter [00:15,  7.55iter/s]Best score: 8.8441: : 155iter [00:15,  8.42iter/s]Best score: 8.8441: : 155iter [00:16,  8.42iter/s]Best score: 8.8441: : 156iter [00:16,  8.42iter/s]Best score: 8.8441: : 157iter [00:16,  9.19iter/s]Best score: 8.8441: : 157iter [00:16,  9.19iter/s]Best score: 8.8441: : 158iter [00:16,  9.19iter/s]Best score: 8.8441: : 159iter [00:16,  9.81iter/s]Best score: 6.2816: : 159iter [00:16,  9.81iter/s]Best score: 6.2816: : 160iter [00:16,  9.81iter/s]Best score: 6.2816: : 161iter [00:16, 10.15iter/s]Best score: 6.2816: : 161iter [00:16, 10.15iter/s]Best score: 6.2816: : 162iter [00:16, 10.15iter/s]Best score: 6.2816: : 163iter [00:16, 10.55iter/s]Best score: 6.2816: : 163iter [00:16, 10.55iter/s]Best score: 6.2816: : 164iter [00:16, 10.55iter/s]Best score: 6.2816: : 165iter [00:16, 10.83iter/s]Best score: 6.2816: : 165iter [00:16, 10.83iter/s]Best score: 6.2816: : 166iter [00:17, 10.83iter/s]Best score: 6.2816: : 167iter [00:17, 11.09iter/s]Best score: 6.2816: : 167iter [00:17, 11.09iter/s]Best score: 6.2816: : 168iter [00:17, 11.09iter/s]Best score: 6.2816: : 169iter [00:17, 11.27iter/s]Best score: 6.2816: : 169iter [00:17, 11.27iter/s]Best score: 6.2816: : 170iter [00:17, 11.27iter/s]Best score: 6.2816: : 171iter [00:17, 11.20iter/s]Best score: 6.2816: : 171iter [00:17, 11.20iter/s]Best score: 6.2816: : 172iter [00:17, 11.20iter/s]Best score: 6.2816: : 173iter [00:17, 11.32iter/s]Best score: 6.2816: : 173iter [00:17, 11.32iter/s]Best score: 6.2816: : 174iter [00:17, 11.32iter/s]Best score: 6.2816: : 175iter [00:17, 11.44iter/s]Best score: 6.2816: : 175iter [00:17, 11.44iter/s]Best score: 6.2816: : 176iter [00:17, 11.44iter/s]Best score: 6.2816: : 177iter [00:17, 11.49iter/s]Best score: 6.2816: : 177iter [00:17, 11.49iter/s]Best score: 6.2816: : 178iter [00:18, 11.49iter/s]Best score: 6.2816: : 179iter [00:18, 11.47iter/s]Best score: 6.2816: : 179iter [00:18, 11.47iter/s]Best score: 6.2816: : 180iter [00:18, 11.47iter/s]Best score: 6.2816: : 181iter [00:18, 11.42iter/s]Best score: 6.2816: : 181iter [00:18, 11.42iter/s]Best score: 6.2816: : 182iter [00:18, 11.42iter/s]Best score: 6.2816: : 183iter [00:18, 11.54iter/s]Best score: 6.2816: : 183iter [00:18, 11.54iter/s]Best score: 6.2816: : 184iter [00:18, 11.54iter/s]Best score: 6.2816: : 185iter [00:18, 11.63iter/s]Best score: 6.2816: : 185iter [00:18, 11.63iter/s]Best score: 6.2816: : 186iter [00:18, 11.63iter/s]Best score: 6.2816: : 187iter [00:18, 11.69iter/s]Best score: 6.2816: : 187iter [00:18, 11.69iter/s]Best score: 6.2816: : 188iter [00:18, 11.69iter/s]Best score: 6.2816: : 189iter [00:18, 11.74iter/s]Best score: 6.2816: : 189iter [00:19, 11.74iter/s]Best score: 6.2816: : 190iter [00:19, 11.74iter/s]Best score: 6.2816: : 191iter [00:19, 11.55iter/s]Best score: 6.2816: : 191iter [00:19, 11.55iter/s]Best score: 6.2816: : 192iter [00:19, 11.55iter/s]Best score: 6.2816: : 193iter [00:19, 11.58iter/s]Best score: 6.2816: : 193iter [00:19, 11.58iter/s]Best score: 6.2816: : 194iter [00:19, 11.58iter/s]Best score: 6.2816: : 195iter [00:19, 11.66iter/s]Best score: 6.2816: : 195iter [00:19, 11.66iter/s]Best score: 6.2816: : 196iter [00:19, 11.66iter/s]Best score: 6.2816: : 197iter [00:19, 11.76iter/s]Best score: 6.2816: : 197iter [00:19, 11.76iter/s]Best score: 6.2816: : 198iter [00:19, 11.76iter/s]Best score: 6.2816: : 199iter [00:19, 11.86iter/s]
[ITER 200] Best: 6.2816
[PARAMS] Current best parameters:
  fx: 564.8782
  fy: 566.7618
  px: 1019.7184
  py: 751.5187
  k1: -0.06353788
  k2: 0.01343469
  k3: 0.00729188
  k4: -0.00298420
  p1: 0.01296690
  p2: -0.00425292
  p3: -0.02491363
  p4: -0.01452555
  s1: 0.0375
  s2: 0.0045
  s3: 0.0063
  s4: 0.0137
  tilt_x: -0.0479
  tilt_y: -0.0691
  scale: 3.0748
  rotation: -0.0630
Best score: 6.2816: : 199iter [00:19, 11.86iter/s]Best score: 6.2816: : 200iter [00:20, 11.86iter/s]Best score: 6.2816: : 201iter [00:20,  7.67iter/s]Best score: 6.2816: : 201iter [00:20,  7.67iter/s]Best score: 6.2816: : 202iter [00:20,  7.67iter/s]Best score: 6.2816: : 203iter [00:20,  8.57iter/s]Best score: 6.2816: : 203iter [00:20,  8.57iter/s]Best score: 6.2816: : 204iter [00:20,  8.57iter/s]Best score: 6.2816: : 205iter [00:20,  9.34iter/s]Best score: 6.2816: : 205iter [00:20,  9.34iter/s]Best score: 6.2816: : 206iter [00:20,  9.34iter/s]Best score: 6.2816: : 207iter [00:20,  9.96iter/s]Best score: 6.2816: : 207iter [00:20,  9.96iter/s]Best score: 6.2816: : 208iter [00:20,  9.96iter/s]Best score: 6.2816: : 209iter [00:20, 10.44iter/s]Best score: 6.2816: : 209iter [00:21, 10.44iter/s]Best score: 6.2816: : 210iter [00:21, 10.44iter/s]Best score: 6.2816: : 211iter [00:21, 10.63iter/s]Best score: 6.2816: : 211iter [00:21, 10.63iter/s]Best score: 6.2816: : 212iter [00:21, 10.63iter/s]Best score: 6.2816: : 213iter [00:21, 10.98iter/s]Best score: 6.2816: : 213iter [00:21, 10.98iter/s]Best score: 6.2816: : 214iter [00:21, 10.98iter/s]Best score: 6.2816: : 215iter [00:21, 11.22iter/s]Best score: 6.2816: : 215iter [00:21, 11.22iter/s]Best score: 6.2816: : 216iter [00:21, 11.22iter/s]Best score: 6.2816: : 217iter [00:21, 11.41iter/s]Best score: 6.2816: : 217iter [00:21, 11.41iter/s]Best score: 6.2816: : 218iter [00:21, 11.41iter/s]Best score: 6.2816: : 219iter [00:21, 11.54iter/s]Best score: 6.2816: : 219iter [00:21, 11.54iter/s]Best score: 6.2816: : 220iter [00:21, 11.54iter/s]Best score: 6.2816: : 221iter [00:21, 11.43iter/s]Best score: 6.2816: : 221iter [00:22, 11.43iter/s]Best score: 6.2816: : 222iter [00:22, 11.43iter/s]Best score: 6.2816: : 223iter [00:22, 11.64iter/s]Best score: 6.2816: : 223iter [00:22, 11.64iter/s]Best score: 6.2816: : 224iter [00:22, 11.64iter/s]Best score: 6.2816: : 225iter [00:22, 11.72iter/s]Best score: 6.2816: : 225iter [00:22, 11.72iter/s]Best score: 6.2816: : 226iter [00:22, 11.72iter/s]Best score: 6.2816: : 227iter [00:22, 11.81iter/s]Best score: 6.2816: : 227iter [00:22, 11.81iter/s]Best score: 6.2816: : 228iter [00:22, 11.81iter/s]Best score: 6.2816: : 229iter [00:22, 11.86iter/s]Best score: 6.0068: : 229iter [00:22, 11.86iter/s]Best score: 6.0068: : 230iter [00:22, 11.86iter/s]Best score: 6.0068: : 231iter [00:22, 11.69iter/s]Best score: 6.0068: : 231iter [00:22, 11.69iter/s]Best score: 6.0068: : 232iter [00:22, 11.69iter/s]Best score: 6.0068: : 233iter [00:22, 11.74iter/s]Best score: 6.0068: : 233iter [00:23, 11.74iter/s]Best score: 6.0068: : 234iter [00:23, 11.74iter/s]Best score: 6.0068: : 235iter [00:23, 11.80iter/s]Best score: 6.0068: : 235iter [00:23, 11.80iter/s]Best score: 6.0068: : 236iter [00:23, 11.80iter/s]Best score: 6.0068: : 237iter [00:23, 11.82iter/s]Best score: 6.0068: : 237iter [00:23, 11.82iter/s]Best score: 6.0068: : 238iter [00:23, 11.82iter/s]Best score: 6.0068: : 239iter [00:23, 11.81iter/s]Best score: 1.7900: : 239iter [00:23, 11.81iter/s]Best score: 1.7900: : 240iter [00:23, 11.81iter/s]Best score: 1.7900: : 241iter [00:23, 11.57iter/s]Best score: 1.7900: : 241iter [00:23, 11.57iter/s]Best score: 1.7900: : 242iter [00:23, 11.57iter/s]Best score: 1.7900: : 243iter [00:23, 11.63iter/s]Best score: 1.7900: : 243iter [00:23, 11.63iter/s]Best score: 1.7900: : 244iter [00:24, 11.63iter/s]Best score: 1.7900: : 245iter [00:24, 11.69iter/s]Best score: 1.7900: : 245iter [00:24, 11.69iter/s]Best score: 1.7900: : 246iter [00:24, 11.69iter/s]Best score: 1.7900: : 247iter [00:24, 11.74iter/s]Best score: 1.7900: : 247iter [00:24, 11.74iter/s]Best score: 1.7900: : 248iter [00:24, 11.74iter/s]Best score: 1.7900: : 249iter [00:24, 11.76iter/s]
[ITER 250] Best: 1.7900
[PARAMS] Current best parameters:
  fx: 560.1828
  fy: 560.2174
  px: 1020.6209
  py: 745.1562
  k1: -0.05242783
  k2: 0.02824266
  k3: -0.01400394
  k4: 0.00361858
  p1: -0.00339748
  p2: -0.00000136
  p3: -0.02516861
  p4: -0.01044135
  s1: 0.0335
  s2: 0.0069
  s3: 0.0183
  s4: 0.0119
  tilt_x: -0.0391
  tilt_y: -0.0489
  scale: 3.0200
  rotation: 0.0762
Best score: 1.7900: : 249iter [00:24, 11.76iter/s]Best score: 1.7900: : 250iter [00:24, 11.76iter/s]Best score: 1.7900: : 251iter [00:24,  7.65iter/s]Best score: 1.7900: : 251iter [00:24,  7.65iter/s]Best score: 1.7900: : 252iter [00:24,  7.65iter/s]Best score: 1.7900: : 253iter [00:24,  8.59iter/s]Best score: 1.7900: : 253iter [00:25,  8.59iter/s]Best score: 1.7900: : 254iter [00:25,  8.59iter/s]Best score: 1.7900: : 255iter [00:25,  9.40iter/s]Best score: 1.7900: : 255iter [00:25,  9.40iter/s]Best score: 1.7900: : 256iter [00:25,  9.40iter/s]Best score: 1.7900: : 257iter [00:25, 10.02iter/s]Best score: 1.7900: : 257iter [00:25, 10.02iter/s]Best score: 1.7900: : 258iter [00:25, 10.02iter/s]Best score: 1.7900: : 259iter [00:25, 10.51iter/s]Best score: 0.9196: : 259iter [00:25, 10.51iter/s]Best score: 0.9196: : 260iter [00:25, 10.51iter/s]Best score: 0.9196: : 261iter [00:25, 10.72iter/s]Best score: 0.9196: : 261iter [00:25, 10.72iter/s]Best score: 0.9196: : 262iter [00:25, 10.72iter/s]Best score: 0.9196: : 263iter [00:25, 11.03iter/s]Best score: 0.9196: : 263iter [00:25, 11.03iter/s]Best score: 0.9196: : 264iter [00:25, 11.03iter/s]Best score: 0.9196: : 265iter [00:25, 11.28iter/s]Best score: 0.9196: : 265iter [00:26, 11.28iter/s]Best score: 0.9196: : 266iter [00:26, 11.28iter/s]Best score: 0.9196: : 267iter [00:26, 11.46iter/s]Best score: 0.9196: : 267iter [00:26, 11.46iter/s]Best score: 0.9196: : 268iter [00:26, 11.46iter/s]Best score: 0.9196: : 269iter [00:26, 11.58iter/s]Best score: 0.9196: : 269iter [00:26, 11.58iter/s]Best score: 0.9196: : 270iter [00:26, 11.58iter/s]Best score: 0.9196: : 271iter [00:26, 11.44iter/s]Best score: 0.9196: : 271iter [00:26, 11.44iter/s]Best score: 0.9196: : 272iter [00:26, 11.44iter/s]Best score: 0.9196: : 273iter [00:26, 11.60iter/s]Best score: 0.9196: : 273iter [00:26, 11.60iter/s]Best score: 0.9196: : 274iter [00:26, 11.60iter/s]Best score: 0.9196: : 275iter [00:26, 11.68iter/s]Best score: 0.9196: : 275iter [00:26, 11.68iter/s]Best score: 0.9196: : 276iter [00:27, 11.68iter/s]Best score: 0.9196: : 277iter [00:27, 11.73iter/s]Best score: 0.9196: : 277iter [00:27, 11.73iter/s]Best score: 0.9196: : 278iter [00:27, 11.73iter/s]Best score: 0.9196: : 279iter [00:27, 11.79iter/s]Best score: 0.9196: : 279iter [00:27, 11.79iter/s]Best score: 0.9196: : 280iter [00:27, 11.79iter/s]Best score: 0.9196: : 281iter [00:27, 11.62iter/s]Best score: 0.9196: : 281iter [00:27, 11.62iter/s]Best score: 0.9196: : 282iter [00:27, 11.62iter/s]Best score: 0.9196: : 283iter [00:27, 11.71iter/s]Best score: 0.9196: : 283iter [00:27, 11.71iter/s]Best score: 0.9196: : 284iter [00:27, 11.71iter/s]Best score: 0.9196: : 285iter [00:27, 11.83iter/s]Best score: 0.9196: : 285iter [00:27, 11.83iter/s]Best score: 0.9196: : 286iter [00:27, 11.83iter/s]Best score: 0.9196: : 287iter [00:27, 11.84iter/s]Best score: 0.9196: : 287iter [00:27, 11.84iter/s]Best score: 0.9196: : 288iter [00:28, 11.84iter/s]Best score: 0.9196: : 289iter [00:28, 11.91iter/s]Best score: 0.8647: : 289iter [00:28, 11.91iter/s]Best score: 0.8647: : 290iter [00:28, 11.91iter/s]Best score: 0.8647: : 291iter [00:28, 11.69iter/s]Best score: 0.8647: : 291iter [00:28, 11.69iter/s]Best score: 0.8647: : 292iter [00:28, 11.69iter/s]Best score: 0.8647: : 293iter [00:28, 11.75iter/s]Best score: 0.8647: : 293iter [00:28, 11.75iter/s]Best score: 0.8647: : 294iter [00:28, 11.75iter/s]Best score: 0.8647: : 295iter [00:28, 11.81iter/s]Best score: 0.8647: : 295iter [00:28, 11.81iter/s]Best score: 0.8647: : 296iter [00:28, 11.81iter/s]Best score: 0.8647: : 297iter [00:28, 11.83iter/s]Best score: 0.8647: : 297iter [00:28, 11.83iter/s]Best score: 0.8647: : 298iter [00:28, 11.83iter/s]Best score: 0.8647: : 299iter [00:28, 11.86iter/s]Best score: 0.7235: : 299iter [00:28, 11.86iter/s]
[ITER 300] Best: 0.7235
[PARAMS] Current best parameters:
  fx: 550.0871
  fy: 549.3444
  px: 1022.3979
  py: 753.7379
  k1: -0.02764835
  k2: 0.02287134
  k3: -0.01729489
  k4: 0.00433144
  p1: 0.00374384
  p2: -0.00333096
  p3: -0.01953904
  p4: -0.01617582
  s1: 0.0315
  s2: 0.0112
  s3: 0.0314
  s4: 0.0076
  tilt_x: -0.0154
  tilt_y: -0.0648
  scale: 2.9702
  rotation: 0.0153
Best score: 0.7235: : 300iter [00:29, 11.86iter/s]Best score: 0.7235: : 301iter [00:29,  7.63iter/s]Best score: 0.7235: : 301iter [00:29,  7.63iter/s]Best score: 0.7235: : 302iter [00:29,  7.63iter/s]Best score: 0.7235: : 303iter [00:29,  8.56iter/s]Best score: 0.7235: : 303iter [00:29,  8.56iter/s]Best score: 0.7235: : 304iter [00:29,  8.56iter/s]Best score: 0.7235: : 305iter [00:29,  9.34iter/s]Best score: 0.7235: : 305iter [00:29,  9.34iter/s]Best score: 0.7235: : 306iter [00:29,  9.34iter/s]Best score: 0.7235: : 307iter [00:29,  9.94iter/s]Best score: 0.7235: : 307iter [00:29,  9.94iter/s]Best score: 0.7235: : 308iter [00:30,  9.94iter/s]Best score: 0.7235: : 309iter [00:30, 10.44iter/s]Best score: 0.6824: : 309iter [00:30, 10.44iter/s]Best score: 0.6824: : 310iter [00:30, 10.44iter/s]Best score: 0.6824: : 311iter [00:30,  8.26iter/s]Best score: 0.6824: : 311iter [00:30,  8.26iter/s]Best score: 0.6824: : 312iter [00:30,  8.26iter/s]Best score: 0.6824: : 313iter [00:30,  9.09iter/s]Best score: 0.6824: : 313iter [00:30,  9.09iter/s]Best score: 0.6824: : 314iter [00:30,  9.09iter/s]Best score: 0.6824: : 315iter [00:30,  9.76iter/s]Best score: 0.6824: : 315iter [00:30,  9.76iter/s]Best score: 0.6824: : 316iter [00:30,  9.76iter/s]Best score: 0.6824: : 317iter [00:30, 10.35iter/s]Best score: 0.6824: : 317iter [00:30, 10.35iter/s]Best score: 0.6824: : 318iter [00:31, 10.35iter/s]Best score: 0.6824: : 319iter [00:31, 10.79iter/s]Best score: 0.6795: : 319iter [00:31, 10.79iter/s]Best score: 0.6795: : 320iter [00:31, 10.79iter/s]Best score: 0.6795: : 321iter [00:31, 10.92iter/s]Best score: 0.6795: : 321iter [00:31, 10.92iter/s]Best score: 0.6795: : 322iter [00:31, 10.92iter/s]Best score: 0.6795: : 323iter [00:31, 11.17iter/s]Best score: 0.6795: : 323iter [00:31, 11.17iter/s]Best score: 0.6795: : 324iter [00:31, 11.17iter/s]Best score: 0.6795: : 325iter [00:31, 11.34iter/s]Best score: 0.6795: : 325iter [00:31, 11.34iter/s]Best score: 0.6795: : 326iter [00:31, 11.34iter/s]Best score: 0.6795: : 327iter [00:31, 11.41iter/s]Best score: 0.6795: : 327iter [00:31, 11.41iter/s]Best score: 0.6795: : 328iter [00:31, 11.41iter/s]Best score: 0.6795: : 329iter [00:31, 11.48iter/s]Best score: 0.5193: : 329iter [00:32, 11.48iter/s]Best score: 0.5193: : 330iter [00:32, 11.48iter/s]Best score: 0.5193: : 331iter [00:32, 11.38iter/s]Best score: 0.5193: : 331iter [00:32, 11.38iter/s]Best score: 0.5193: : 332iter [00:32, 11.38iter/s]Best score: 0.5193: : 333iter [00:32, 11.47iter/s]Best score: 0.5193: : 333iter [00:32, 11.47iter/s]Best score: 0.5193: : 334iter [00:32, 11.47iter/s]Best score: 0.5193: : 335iter [00:32, 11.59iter/s]Best score: 0.5193: : 335iter [00:32, 11.59iter/s]Best score: 0.5193: : 336iter [00:32, 11.59iter/s]Best score: 0.5193: : 337iter [00:32, 11.69iter/s]Best score: 0.5193: : 337iter [00:32, 11.69iter/s]Best score: 0.5193: : 338iter [00:32, 11.69iter/s]Best score: 0.5193: : 339iter [00:32, 11.76iter/s]Best score: 0.5193: : 339iter [00:32, 11.76iter/s]Best score: 0.5193: : 340iter [00:32, 11.76iter/s]Best score: 0.5193: : 341iter [00:32, 11.63iter/s]Best score: 0.5193: : 341iter [00:33, 11.63iter/s]Best score: 0.5193: : 342iter [00:33, 11.63iter/s]Best score: 0.5193: : 343iter [00:33, 11.76iter/s]Best score: 0.5193: : 343iter [00:33, 11.76iter/s]Best score: 0.5193: : 344iter [00:33, 11.76iter/s]Best score: 0.5193: : 345iter [00:33, 11.76iter/s]Best score: 0.5193: : 345iter [00:33, 11.76iter/s]Best score: 0.5193: : 346iter [00:33, 11.76iter/s]Best score: 0.5193: : 347iter [00:33, 11.79iter/s]Best score: 0.5193: : 347iter [00:33, 11.79iter/s]Best score: 0.5193: : 348iter [00:33, 11.79iter/s]Best score: 0.5193: : 349iter [00:33, 11.81iter/s]
[ITER 350] Best: 0.4569
[PARAMS] Current best parameters:
  fx: 554.4423
  fy: 553.7772
  px: 1025.0879
  py: 750.2565
  k1: -0.02933858
  k2: 0.01241153
  k3: -0.00610503
  k4: 0.00099477
  p1: -0.00426666
  p2: -0.00130008
  p3: -0.01964043
  p4: -0.00810280
  s1: 0.0312
  s2: 0.0096
  s3: 0.0243
  s4: 0.0069
  tilt_x: -0.0142
  tilt_y: -0.0741
  scale: 2.9670
  rotation: 0.1472
Best score: 0.4569: : 349iter [00:33, 11.81iter/s]Best score: 0.4569: : 350iter [00:34, 11.81iter/s]Best score: 0.4569: : 351iter [00:34,  7.53iter/s]Best score: 0.4569: : 351iter [00:34,  7.53iter/s]Best score: 0.4569: : 352iter [00:34,  7.53iter/s]Best score: 0.4569: : 353iter [00:34,  8.48iter/s]Best score: 0.4569: : 353iter [00:34,  8.48iter/s]Best score: 0.4569: : 354iter [00:34,  8.48iter/s]Best score: 0.4569: : 355iter [00:34,  9.31iter/s]Best score: 0.4569: : 355iter [00:34,  9.31iter/s]Best score: 0.4569: : 356iter [00:34,  9.31iter/s]Best score: 0.4569: : 357iter [00:34, 10.01iter/s]Best score: 0.4569: : 357iter [00:34, 10.01iter/s]Best score: 0.4569: : 358iter [00:34, 10.01iter/s]Best score: 0.4569: : 359iter [00:34, 10.55iter/s]Best score: 0.3760: : 359iter [00:34, 10.55iter/s]Best score: 0.3760: : 360iter [00:34, 10.55iter/s]Best score: 0.3760: : 361iter [00:34, 10.73iter/s]Best score: 0.3760: : 361iter [00:35, 10.73iter/s]Best score: 0.3760: : 362iter [00:35, 10.73iter/s]Best score: 0.3760: : 363iter [00:35, 11.04iter/s]Best score: 0.3760: : 363iter [00:35, 11.04iter/s]Best score: 0.3760: : 364iter [00:35, 11.04iter/s]Best score: 0.3760: : 365iter [00:35, 11.30iter/s]Best score: 0.3760: : 365iter [00:35, 11.30iter/s]Best score: 0.3760: : 366iter [00:35, 11.30iter/s]Best score: 0.3760: : 367iter [00:35, 11.49iter/s]Best score: 0.3760: : 367iter [00:35, 11.49iter/s]Best score: 0.3760: : 368iter [00:35, 11.49iter/s]Best score: 0.3760: : 369iter [00:35, 11.65iter/s]Best score: 0.3483: : 369iter [00:35, 11.65iter/s]Best score: 0.3483: : 370iter [00:35, 11.65iter/s]Best score: 0.3483: : 371iter [00:35, 11.53iter/s]Best score: 0.3483: : 371iter [00:35, 11.53iter/s]Best score: 0.3483: : 372iter [00:35, 11.53iter/s]Best score: 0.3483: : 373iter [00:35, 11.62iter/s]Best score: 0.3483: : 373iter [00:36, 11.62iter/s]Best score: 0.3483: : 374iter [00:36, 11.62iter/s]Best score: 0.3483: : 375iter [00:36, 11.75iter/s]Best score: 0.3483: : 375iter [00:36, 11.75iter/s]Best score: 0.3483: : 376iter [00:36, 11.75iter/s]Best score: 0.3483: : 377iter [00:36, 11.79iter/s]Best score: 0.3483: : 377iter [00:36, 11.79iter/s]Best score: 0.3483: : 378iter [00:36, 11.79iter/s]Best score: 0.3483: : 379iter [00:36, 11.79iter/s]Best score: 0.3483: : 379iter [00:36, 11.79iter/s]Best score: 0.3483: : 380iter [00:36, 11.79iter/s]Best score: 0.3483: : 381iter [00:36, 11.68iter/s]Best score: 0.3483: : 381iter [00:36, 11.68iter/s]Best score: 0.3483: : 382iter [00:36, 11.68iter/s]Best score: 0.3483: : 383iter [00:36, 11.77iter/s]Best score: 0.3483: : 383iter [00:36, 11.77iter/s]Best score: 0.3483: : 384iter [00:36, 11.77iter/s]Best score: 0.3483: : 385iter [00:36, 11.83iter/s]Best score: 0.3483: : 385iter [00:37, 11.83iter/s]Best score: 0.3483: : 386iter [00:37, 11.83iter/s]Best score: 0.3483: : 387iter [00:37, 11.93iter/s]Best score: 0.3483: : 387iter [00:37, 11.93iter/s]Best score: 0.3483: : 388iter [00:37, 11.93iter/s]Best score: 0.3483: : 389iter [00:37, 12.00iter/s]Best score: 0.3169: : 389iter [00:37, 12.00iter/s]Best score: 0.3169: : 390iter [00:37, 12.00iter/s]Best score: 0.3169: : 391iter [00:37, 11.86iter/s]Best score: 0.3169: : 391iter [00:37, 11.86iter/s]Best score: 0.3169: : 392iter [00:37, 11.86iter/s]Best score: 0.3169: : 393iter [00:37, 11.96iter/s]Best score: 0.3169: : 393iter [00:37, 11.96iter/s]Best score: 0.3169: : 394iter [00:37, 11.96iter/s]Best score: 0.3169: : 395iter [00:37, 11.90iter/s]Best score: 0.3169: : 395iter [00:37, 11.90iter/s]Best score: 0.3169: : 396iter [00:38, 11.90iter/s]Best score: 0.3169: : 397iter [00:38, 11.90iter/s]Best score: 0.3169: : 397iter [00:38, 11.90iter/s]Best score: 0.3169: : 398iter [00:38, 11.90iter/s]Best score: 0.3169: : 399iter [00:38, 11.97iter/s]Best score: 0.3169: : 399iter [00:38, 11.97iter/s]
[ITER 400] Best: 0.3169
[PARAMS] Current best parameters:
  fx: 558.8896
  fy: 558.1145
  px: 1022.0689
  py: 746.9126
  k1: -0.04046499
  k2: 0.02102275
  k3: -0.00922023
  k4: 0.00118829
  p1: -0.00684873
  p2: -0.00517063
  p3: -0.01668425
  p4: -0.00664319
  s1: 0.0296
  s2: 0.0070
  s3: 0.0223
  s4: 0.0039
  tilt_x: -0.0261
  tilt_y: -0.0660
  scale: 2.9758
  rotation: 0.1815
Best score: 0.3169: : 400iter [00:38, 11.97iter/s]Best score: 0.3169: : 401iter [00:38,  7.57iter/s]Best score: 0.3169: : 401iter [00:38,  7.57iter/s]Best score: 0.3169: : 402iter [00:38,  7.57iter/s]Best score: 0.3169: : 403iter [00:38,  8.52iter/s]Best score: 0.3169: : 403iter [00:38,  8.52iter/s]Best score: 0.3169: : 404iter [00:38,  8.52iter/s]Best score: 0.3169: : 405iter [00:38,  9.34iter/s]Best score: 0.3169: : 405iter [00:39,  9.34iter/s]Best score: 0.3169: : 406iter [00:39,  9.34iter/s]Best score: 0.3169: : 407iter [00:39, 10.05iter/s]Best score: 0.3169: : 407iter [00:39, 10.05iter/s]Best score: 0.3169: : 408iter [00:39, 10.05iter/s]Best score: 0.3169: : 409iter [00:39, 10.58iter/s]Best score: 0.3169: : 409iter [00:39, 10.58iter/s]Best score: 0.3169: : 410iter [00:39, 10.58iter/s]Best score: 0.3169: : 411iter [00:39, 10.86iter/s]Best score: 0.3169: : 411iter [00:39, 10.86iter/s]Best score: 0.3169: : 412iter [00:39, 10.86iter/s]Best score: 0.3169: : 413iter [00:39, 11.28iter/s]Best score: 0.3169: : 413iter [00:39, 11.28iter/s]Best score: 0.3169: : 414iter [00:39, 11.28iter/s]Best score: 0.3169: : 415iter [00:39, 11.57iter/s]Best score: 0.3169: : 415iter [00:39, 11.57iter/s]Best score: 0.3169: : 416iter [00:39, 11.57iter/s]Best score: 0.3169: : 417iter [00:39, 11.73iter/s]Best score: 0.3169: : 417iter [00:40, 11.73iter/s]Best score: 0.3169: : 418iter [00:40, 11.73iter/s]Best score: 0.3169: : 419iter [00:40, 11.88iter/s]Best score: 0.2751: : 419iter [00:40, 11.88iter/s]Best score: 0.2751: : 420iter [00:40, 11.88iter/s]Best score: 0.2751: : 421iter [00:40, 11.70iter/s]Best score: 0.2751: : 421iter [00:40, 11.70iter/s]Best score: 0.2751: : 422iter [00:40, 11.70iter/s]Best score: 0.2751: : 423iter [00:40, 11.79iter/s]Best score: 0.2751: : 423iter [00:40, 11.79iter/s]Best score: 0.2751: : 424iter [00:40, 11.79iter/s]Best score: 0.2751: : 425iter [00:40, 11.91iter/s]Best score: 0.2751: : 425iter [00:40, 11.91iter/s]Best score: 0.2751: : 426iter [00:40, 11.91iter/s]Best score: 0.2751: : 427iter [00:40, 11.99iter/s]Best score: 0.2751: : 427iter [00:40, 11.99iter/s]Best score: 0.2751: : 428iter [00:40, 11.99iter/s]Best score: 0.2751: : 429iter [00:40, 12.00iter/s]Best score: 0.2682: : 429iter [00:41, 12.00iter/s]Best score: 0.2682: : 430iter [00:41, 12.00iter/s]Best score: 0.2682: : 431iter [00:41, 11.86iter/s]Best score: 0.2682: : 431iter [00:41, 11.86iter/s]Best score: 0.2682: : 432iter [00:41, 11.86iter/s]Best score: 0.2682: : 433iter [00:41, 11.93iter/s]Best score: 0.2682: : 433iter [00:41, 11.93iter/s]Best score: 0.2682: : 434iter [00:41, 11.93iter/s]Best score: 0.2682: : 435iter [00:41, 11.93iter/s]Best score: 0.2682: : 435iter [00:41, 11.93iter/s]Best score: 0.2682: : 436iter [00:41, 11.93iter/s]Best score: 0.2682: : 437iter [00:41, 11.97iter/s]Best score: 0.2682: : 437iter [00:41, 11.97iter/s]Best score: 0.2682: : 438iter [00:41, 11.97iter/s]Best score: 0.2682: : 439iter [00:41, 12.01iter/s]Best score: 0.2511: : 439iter [00:41, 12.01iter/s]Best score: 0.2511: : 440iter [00:41, 12.01iter/s]Best score: 0.2511: : 441iter [00:41, 11.77iter/s]Best score: 0.2511: : 441iter [00:42, 11.77iter/s]Best score: 0.2511: : 442iter [00:42, 11.77iter/s]Best score: 0.2511: : 443iter [00:42, 11.88iter/s]Best score: 0.2511: : 443iter [00:42, 11.88iter/s]Best score: 0.2511: : 444iter [00:42, 11.88iter/s]Best score: 0.2511: : 445iter [00:42, 12.00iter/s]Best score: 0.2511: : 445iter [00:42, 12.00iter/s]Best score: 0.2511: : 446iter [00:42, 12.00iter/s]Best score: 0.2511: : 447iter [00:42, 12.07iter/s]Best score: 0.2511: : 447iter [00:42, 12.07iter/s]Best score: 0.2511: : 448iter [00:42, 12.07iter/s]Best score: 0.2511: : 449iter [00:42, 12.06iter/s]
[ITER 450] Best: 0.2340
[PARAMS] Current best parameters:
  fx: 560.9350
  fy: 560.1137
  px: 1020.4675
  py: 749.1070
  k1: -0.03803915
  k2: 0.01228891
  k3: -0.00306399
  k4: -0.00015411
  p1: -0.00955591
  p2: -0.00538275
  p3: -0.00493647
  p4: -0.00645096
  s1: 0.0171
  s2: 0.0062
  s3: 0.0131
  s4: 0.0057
  tilt_x: -0.0099
  tilt_y: -0.0591
  scale: 2.9681
  rotation: 0.1872
Best score: 0.2340: : 449iter [00:42, 12.06iter/s]Best score: 0.2340: : 450iter [00:43, 12.06iter/s]Best score: 0.2340: : 451iter [00:43,  7.66iter/s]Best score: 0.2340: : 451iter [00:43,  7.66iter/s]Best score: 0.2340: : 452iter [00:43,  7.66iter/s]Best score: 0.2340: : 453iter [00:43,  8.60iter/s]Best score: 0.2340: : 453iter [00:43,  8.60iter/s]Best score: 0.2340: : 454iter [00:43,  8.60iter/s]Best score: 0.2340: : 455iter [00:43,  9.38iter/s]Best score: 0.2340: : 455iter [00:43,  9.38iter/s]Best score: 0.2340: : 456iter [00:43,  9.38iter/s]Best score: 0.2340: : 457iter [00:43, 10.06iter/s]Best score: 0.2340: : 457iter [00:43, 10.06iter/s]Best score: 0.2340: : 458iter [00:43, 10.06iter/s]Best score: 0.2340: : 459iter [00:43, 10.62iter/s]Best score: 0.2340: : 459iter [00:43, 10.62iter/s]Best score: 0.2340: : 460iter [00:43, 10.62iter/s]Best score: 0.2340: : 461iter [00:43, 10.87iter/s]Best score: 0.2340: : 461iter [00:44, 10.87iter/s]Best score: 0.2340: : 462iter [00:44, 10.87iter/s]Best score: 0.2340: : 463iter [00:44, 11.26iter/s]Best score: 0.2340: : 463iter [00:44, 11.26iter/s]Best score: 0.2340: : 464iter [00:44, 11.26iter/s]Best score: 0.2340: : 465iter [00:44, 11.47iter/s]Best score: 0.2340: : 465iter [00:44, 11.47iter/s]Best score: 0.2340: : 466iter [00:44, 11.47iter/s]Best score: 0.2340: : 467iter [00:44, 11.67iter/s]Best score: 0.2340: : 467iter [00:44, 11.67iter/s]Best score: 0.2340: : 468iter [00:44, 11.67iter/s]Best score: 0.2340: : 469iter [00:44, 11.83iter/s]Best score: 0.2340: : 469iter [00:44, 11.83iter/s]Best score: 0.2340: : 470iter [00:44, 11.83iter/s]Best score: 0.2340: : 471iter [00:44, 11.75iter/s]Best score: 0.2340: : 471iter [00:44, 11.75iter/s]Best score: 0.2340: : 472iter [00:44, 11.75iter/s]Best score: 0.2340: : 473iter [00:44, 11.85iter/s]Best score: 0.2340: : 473iter [00:45, 11.85iter/s]Best score: 0.2340: : 474iter [00:45, 11.85iter/s]Best score: 0.2340: : 475iter [00:45, 11.93iter/s]Best score: 0.2340: : 475iter [00:45, 11.93iter/s]Best score: 0.2340: : 476iter [00:45, 11.93iter/s]Best score: 0.2340: : 477iter [00:45, 11.96iter/s]Best score: 0.2340: : 477iter [00:45, 11.96iter/s]Best score: 0.2340: : 478iter [00:45, 11.96iter/s]Best score: 0.2340: : 479iter [00:45, 12.00iter/s]Best score: 0.2329: : 479iter [00:45, 12.00iter/s]Best score: 0.2329: : 480iter [00:45, 12.00iter/s]Best score: 0.2329: : 481iter [00:45, 11.86iter/s]Best score: 0.2329: : 481iter [00:45, 11.86iter/s]Best score: 0.2329: : 482iter [00:45, 11.86iter/s]Best score: 0.2329: : 483iter [00:45, 11.96iter/s]Best score: 0.2329: : 483iter [00:45, 11.96iter/s]Best score: 0.2329: : 484iter [00:45, 11.96iter/s]Best score: 0.2329: : 485iter [00:45, 12.02iter/s]Best score: 0.2329: : 485iter [00:46, 12.02iter/s]Best score: 0.2329: : 486iter [00:46, 12.02iter/s]Best score: 0.2329: : 487iter [00:46, 12.07iter/s]Best score: 0.2329: : 487iter [00:46, 12.07iter/s]Best score: 0.2329: : 488iter [00:46, 12.07iter/s]Best score: 0.2329: : 489iter [00:46, 12.09iter/s]Best score: 0.2311: : 489iter [00:46, 12.09iter/s]Best score: 0.2311: : 490iter [00:46, 12.09iter/s]Best score: 0.2311: : 491iter [00:46, 11.92iter/s]Best score: 0.2311: : 491iter [00:46, 11.92iter/s]Best score: 0.2311: : 492iter [00:46, 11.92iter/s]Best score: 0.2311: : 493iter [00:46, 11.99iter/s]Best score: 0.2311: : 493iter [00:46, 11.99iter/s]Best score: 0.2311: : 494iter [00:46, 11.99iter/s]Best score: 0.2311: : 495iter [00:46, 11.94iter/s]Best score: 0.2311: : 495iter [00:46, 11.94iter/s]Best score: 0.2311: : 496iter [00:46, 11.94iter/s]Best score: 0.2311: : 497iter [00:46, 11.98iter/s]Best score: 0.2311: : 497iter [00:47, 11.98iter/s]Best score: 0.2311: : 498iter [00:47, 11.98iter/s]Best score: 0.2311: : 499iter [00:47, 11.97iter/s]
[ITER 500] Best: 0.2236
[PARAMS] Current best parameters:
  fx: 562.0807
  fy: 561.3164
  px: 1019.7209
  py: 749.2657
  k1: -0.03726234
  k2: 0.00887828
  k3: -0.00118401
  k4: -0.00040833
  p1: -0.01251167
  p2: -0.00369497
  p3: -0.00117364
  p4: -0.00590300
  s1: 0.0103
  s2: 0.0063
  s3: 0.0122
  s4: 0.0057
  tilt_x: -0.0032
  tilt_y: -0.0520
  scale: 2.9660
  rotation: 0.1761
Best score: 0.2236: : 499iter [00:47, 11.97iter/s]Best score: 0.2236: : 500iter [00:47, 11.97iter/s]Best score: 0.2236: : 501iter [00:47,  7.61iter/s]Best score: 0.2236: : 501iter [00:47,  7.61iter/s]Best score: 0.2236: : 502iter [00:47,  7.61iter/s]Best score: 0.2236: : 503iter [00:47,  8.53iter/s]Best score: 0.2236: : 503iter [00:47,  8.53iter/s]Best score: 0.2236: : 504iter [00:47,  8.53iter/s]Best score: 0.2236: : 505iter [00:47,  9.31iter/s]Best score: 0.2236: : 505iter [00:48,  9.31iter/s]Best score: 0.2236: : 506iter [00:48,  9.31iter/s]Best score: 0.2236: : 507iter [00:48,  9.98iter/s]Best score: 0.2236: : 507iter [00:48,  9.98iter/s]Best score: 0.2236: : 508iter [00:48,  9.98iter/s]Best score: 0.2236: : 509iter [00:48, 10.47iter/s]Best score: 0.2236: : 509iter [00:48, 10.47iter/s]Best score: 0.2236: : 510iter [00:48, 10.47iter/s]Best score: 0.2236: : 511iter [00:48, 10.74iter/s]Best score: 0.2236: : 511iter [00:48, 10.74iter/s]Best score: 0.2236: : 512iter [00:48, 10.74iter/s]Best score: 0.2236: : 513iter [00:48, 11.12iter/s]Best score: 0.2236: : 513iter [00:48, 11.12iter/s]Best score: 0.2236: : 514iter [00:48, 11.12iter/s]Best score: 0.2236: : 515iter [00:48, 11.39iter/s]Best score: 0.2236: : 515iter [00:48, 11.39iter/s]Best score: 0.2236: : 516iter [00:48, 11.39iter/s]Best score: 0.2236: : 517iter [00:48, 11.56iter/s]Best score: 0.2236: : 517iter [00:49, 11.56iter/s]Best score: 0.2236: : 518iter [00:49, 11.56iter/s]Best score: 0.2236: : 519iter [00:49, 11.67iter/s]Best score: 0.2236: : 519iter [00:49, 11.67iter/s]Best score: 0.2236: : 520iter [00:49, 11.67iter/s]Best score: 0.2236: : 521iter [00:49, 11.57iter/s]Best score: 0.2236: : 521iter [00:49, 11.57iter/s]Best score: 0.2236: : 522iter [00:49, 11.57iter/s]Best score: 0.2236: : 523iter [00:49, 11.69iter/s]Best score: 0.2236: : 523iter [00:49, 11.69iter/s]Best score: 0.2236: : 524iter [00:49, 11.69iter/s]Best score: 0.2236: : 525iter [00:49, 11.73iter/s]Best score: 0.2236: : 525iter [00:49, 11.73iter/s]Best score: 0.2236: : 526iter [00:49, 11.73iter/s]Best score: 0.2236: : 527iter [00:49, 11.76iter/s]Best score: 0.2236: : 527iter [00:49, 11.76iter/s]Best score: 0.2236: : 528iter [00:49, 11.76iter/s]Best score: 0.2236: : 529iter [00:49, 11.87iter/s]Best score: 0.2145: : 529iter [00:50, 11.87iter/s]Best score: 0.2145: : 530iter [00:50, 11.87iter/s]Best score: 0.2145: : 531iter [00:50, 11.72iter/s]Best score: 0.2145: : 531iter [00:50, 11.72iter/s]Best score: 0.2145: : 532iter [00:50, 11.72iter/s]Best score: 0.2145: : 533iter [00:50, 11.77iter/s]Best score: 0.2145: : 533iter [00:50, 11.77iter/s]Best score: 0.2145: : 534iter [00:50, 11.77iter/s]Best score: 0.2145: : 535iter [00:50, 11.87iter/s]Best score: 0.2145: : 535iter [00:50, 11.87iter/s]Best score: 0.2145: : 536iter [00:50, 11.87iter/s]Best score: 0.2145: : 537iter [00:50, 11.95iter/s]Best score: 0.2145: : 537iter [00:50, 11.95iter/s]Best score: 0.2145: : 538iter [00:50, 11.95iter/s]Best score: 0.2145: : 539iter [00:50, 12.02iter/s]Best score: 0.2043: : 539iter [00:50, 12.02iter/s]Best score: 0.2043: : 540iter [00:50, 12.02iter/s]Best score: 0.2043: : 541iter [00:50, 11.81iter/s]Best score: 0.2043: : 541iter [00:51, 11.81iter/s]Best score: 0.2043: : 542iter [00:51, 11.81iter/s]Best score: 0.2043: : 543iter [00:51, 11.92iter/s]Best score: 0.2043: : 543iter [00:51, 11.92iter/s]Best score: 0.2043: : 544iter [00:51, 11.92iter/s]Best score: 0.2043: : 545iter [00:51, 11.95iter/s]Best score: 0.2043: : 545iter [00:51, 11.95iter/s]Best score: 0.2043: : 546iter [00:51, 11.95iter/s]Best score: 0.2043: : 547iter [00:51, 12.04iter/s]Best score: 0.2043: : 547iter [00:51, 12.04iter/s]Best score: 0.2043: : 548iter [00:51, 12.04iter/s]Best score: 0.2043: : 549iter [00:51, 12.09iter/s]
[ITER 550] Best: 0.2039
[PARAMS] Current best parameters:
  fx: 561.9682
  fy: 561.4132
  px: 1014.8620
  py: 752.3301
  k1: -0.03353253
  k2: 0.00025288
  k3: 0.00577044
  k4: -0.00228253
  p1: -0.01376202
  p2: -0.01042065
  p3: 0.00793108
  p4: -0.00639628
  s1: 0.0038
  s2: 0.0058
  s3: 0.0068
  s4: 0.0068
  tilt_x: 0.0150
  tilt_y: -0.0417
  scale: 2.9637
  rotation: 0.1285
Best score: 0.2039: : 549iter [00:51, 12.09iter/s]Best score: 0.2039: : 550iter [00:52, 12.09iter/s]Best score: 0.2039: : 551iter [00:52,  7.67iter/s]Best score: 0.2039: : 551iter [00:52,  7.67iter/s]Best score: 0.2039: : 552iter [00:52,  7.67iter/s]Best score: 0.2039: : 553iter [00:52,  8.64iter/s]Best score: 0.2039: : 553iter [00:52,  8.64iter/s]Best score: 0.2039: : 554iter [00:52,  8.64iter/s]Best score: 0.2039: : 555iter [00:52,  9.47iter/s]Best score: 0.2039: : 555iter [00:52,  9.47iter/s]Best score: 0.2039: : 556iter [00:52,  9.47iter/s]Best score: 0.2039: : 557iter [00:52, 10.09iter/s]Best score: 0.2039: : 557iter [00:52, 10.09iter/s]Best score: 0.2039: : 558iter [00:52, 10.09iter/s]Best score: 0.2039: : 559iter [00:52, 10.57iter/s]Best score: 0.2035: : 559iter [00:52, 10.57iter/s]Best score: 0.2035: : 560iter [00:52, 10.57iter/s]Best score: 0.2035: : 561iter [00:52, 10.85iter/s]Best score: 0.2035: : 561iter [00:53, 10.85iter/s]Best score: 0.2035: : 562iter [00:53, 10.85iter/s]Best score: 0.2035: : 563iter [00:53, 11.24iter/s]Best score: 0.2035: : 563iter [00:53, 11.24iter/s]Best score: 0.2035: : 564iter [00:53, 11.24iter/s]Best score: 0.2035: : 565iter [00:53, 11.50iter/s]Best score: 0.2035: : 565iter [00:53, 11.50iter/s]Best score: 0.2035: : 566iter [00:53, 11.50iter/s]Best score: 0.2035: : 567iter [00:53, 11.72iter/s]Best score: 0.2035: : 567iter [00:53, 11.72iter/s]Best score: 0.2035: : 568iter [00:53, 11.72iter/s]Best score: 0.2035: : 569iter [00:53, 11.86iter/s]Best score: 0.2035: : 569iter [00:53, 11.86iter/s]Best score: 0.2035: : 570iter [00:53, 11.86iter/s]Best score: 0.2035: : 571iter [00:53, 11.79iter/s]Best score: 0.2035: : 571iter [00:53, 11.79iter/s]Best score: 0.2035: : 572iter [00:53, 11.79iter/s]Best score: 0.2035: : 573iter [00:53, 11.93iter/s]Best score: 0.2035: : 573iter [00:54, 11.93iter/s]Best score: 0.2035: : 574iter [00:54, 11.93iter/s]Best score: 0.2035: : 575iter [00:54, 12.03iter/s]Best score: 0.2035: : 575iter [00:54, 12.03iter/s]Best score: 0.2035: : 576iter [00:54, 12.03iter/s]Best score: 0.2035: : 577iter [00:54, 12.11iter/s]Best score: 0.2035: : 577iter [00:54, 12.11iter/s]Best score: 0.2035: : 578iter [00:54, 12.11iter/s]Best score: 0.2035: : 579iter [00:54, 12.11iter/s]Best score: 0.2009: : 579iter [00:54, 12.11iter/s]Best score: 0.2009: : 580iter [00:54, 12.11iter/s]Best score: 0.2009: : 581iter [00:54, 11.84iter/s]Best score: 0.2009: : 581iter [00:54, 11.84iter/s]Best score: 0.2009: : 582iter [00:54, 11.84iter/s]Best score: 0.2009: : 583iter [00:54, 11.95iter/s]Best score: 0.2009: : 583iter [00:54, 11.95iter/s]Best score: 0.2009: : 584iter [00:54, 11.95iter/s]Best score: 0.2009: : 585iter [00:54, 12.04iter/s]Best score: 0.2009: : 585iter [00:55, 12.04iter/s]Best score: 0.2009: : 586iter [00:55, 12.04iter/s]Best score: 0.2009: : 587iter [00:55, 12.10iter/s]Best score: 0.2009: : 587iter [00:55, 12.10iter/s]Best score: 0.2009: : 588iter [00:55, 12.10iter/s]Best score: 0.2009: : 589iter [00:55, 12.16iter/s]Best score: 0.1964: : 589iter [00:55, 12.16iter/s]Best score: 0.1964: : 590iter [00:55, 12.16iter/s]Best score: 0.1964: : 591iter [00:55, 11.99iter/s]Best score: 0.1964: : 591iter [00:55, 11.99iter/s]Best score: 0.1964: : 592iter [00:55, 11.99iter/s]Best score: 0.1964: : 593iter [00:55, 12.04iter/s]Best score: 0.1964: : 593iter [00:55, 12.04iter/s]Best score: 0.1964: : 594iter [00:55, 12.04iter/s]Best score: 0.1964: : 595iter [00:55, 12.05iter/s]Best score: 0.1964: : 595iter [00:55, 12.05iter/s]Best score: 0.1964: : 596iter [00:55, 12.05iter/s]Best score: 0.1964: : 597iter [00:55, 11.99iter/s]Best score: 0.1964: : 597iter [00:56, 11.99iter/s]Best score: 0.1964: : 598iter [00:56, 11.99iter/s]Best score: 0.1964: : 599iter [00:56, 11.99iter/s]
[ITER 600] Best: 0.1964
[PARAMS] Current best parameters:
  fx: 563.1150
  fy: 562.6200
  px: 1013.4009
  py: 752.3331
  k1: -0.03290522
  k2: -0.00111726
  k3: 0.00546104
  k4: -0.00192398
  p1: -0.01248941
  p2: -0.01089418
  p3: 0.00584361
  p4: -0.00640686
  s1: 0.0044
  s2: 0.0061
  s3: 0.0071
  s4: 0.0064
  tilt_x: 0.0127
  tilt_y: -0.0354
  scale: 2.9599
  rotation: 0.1284
Best score: 0.1964: : 599iter [00:56, 11.99iter/s]Best score: 0.1964: : 600iter [00:56, 11.99iter/s]Best score: 0.1964: : 601iter [00:56,  7.65iter/s]Best score: 0.1964: : 601iter [00:56,  7.65iter/s]Best score: 0.1964: : 602iter [00:56,  7.65iter/s]Best score: 0.1964: : 603iter [00:56,  8.62iter/s]Best score: 0.1964: : 603iter [00:56,  8.62iter/s]Best score: 0.1964: : 604iter [00:56,  8.62iter/s]Best score: 0.1964: : 605iter [00:56,  9.45iter/s]Best score: 0.1964: : 605iter [00:57,  9.45iter/s]Best score: 0.1964: : 606iter [00:57,  9.45iter/s]Best score: 0.1964: : 607iter [00:57, 10.14iter/s]Best score: 0.1964: : 607iter [00:57, 10.14iter/s]Best score: 0.1964: : 608iter [00:57, 10.14iter/s]Best score: 0.1964: : 609iter [00:57, 10.68iter/s]Best score: 0.1964: : 609iter [00:57, 10.68iter/s]Best score: 0.1964: : 610iter [00:57, 10.68iter/s]Best score: 0.1964: : 611iter [00:57, 10.93iter/s]Best score: 0.1964: : 611iter [00:57, 10.93iter/s]Best score: 0.1964: : 612iter [00:57, 10.93iter/s]Best score: 0.1964: : 613iter [00:57, 11.27iter/s]Best score: 0.1964: : 613iter [00:57, 11.27iter/s]Best score: 0.1964: : 614iter [00:57, 11.27iter/s]Best score: 0.1964: : 615iter [00:57, 11.53iter/s]Best score: 0.1964: : 615iter [00:57, 11.53iter/s]Best score: 0.1964: : 616iter [00:57, 11.53iter/s]Best score: 0.1964: : 617iter [00:57, 11.74iter/s]Best score: 0.1964: : 617iter [00:58, 11.74iter/s]Best score: 0.1964: : 618iter [00:58, 11.74iter/s]Best score: 0.1964: : 619iter [00:58, 11.88iter/s]Best score: 0.1946: : 619iter [00:58, 11.88iter/s]Best score: 0.1946: : 620iter [00:58, 11.88iter/s]Best score: 0.1946: : 621iter [00:58, 11.77iter/s]Best score: 0.1946: : 621iter [00:58, 11.77iter/s]Best score: 0.1946: : 622iter [00:58, 11.77iter/s]Best score: 0.1946: : 623iter [00:58, 11.90iter/s]Best score: 0.1946: : 623iter [00:58, 11.90iter/s]Best score: 0.1946: : 624iter [00:58, 11.90iter/s]Best score: 0.1946: : 625iter [00:58, 11.99iter/s]Best score: 0.1946: : 625iter [00:58, 11.99iter/s]Best score: 0.1946: : 626iter [00:58, 11.99iter/s]Best score: 0.1946: : 627iter [00:58, 12.01iter/s]Best score: 0.1946: : 627iter [00:58, 12.01iter/s]Best score: 0.1946: : 628iter [00:58, 12.01iter/s]Best score: 0.1946: : 629iter [00:58, 12.07iter/s]Best score: 0.1940: : 629iter [00:59, 12.07iter/s]Best score: 0.1940: : 630iter [00:59, 12.07iter/s]Best score: 0.1940: : 631iter [00:59, 11.88iter/s]Best score: 0.1940: : 631iter [00:59, 11.88iter/s]Best score: 0.1940: : 632iter [00:59, 11.88iter/s]Best score: 0.1940: : 633iter [00:59, 11.93iter/s]Best score: 0.1940: : 633iter [00:59, 11.93iter/s]Best score: 0.1940: : 634iter [00:59, 11.93iter/s]Best score: 0.1940: : 635iter [00:59, 12.02iter/s]Best score: 0.1940: : 635iter [00:59, 12.02iter/s]Best score: 0.1940: : 636iter [00:59, 12.02iter/s]Best score: 0.1940: : 637iter [00:59, 12.05iter/s]Best score: 0.1940: : 637iter [00:59, 12.05iter/s]Best score: 0.1940: : 638iter [01:00, 12.05iter/s]Best score: 0.1940: : 639iter [01:00,  7.58iter/s]Best score: 0.1933: : 639iter [01:00,  7.58iter/s]Best score: 0.1933: : 640iter [01:00,  7.58iter/s]Best score: 0.1933: : 641iter [01:00,  8.40iter/s]Best score: 0.1933: : 641iter [01:00,  8.40iter/s]Best score: 0.1933: : 642iter [01:00,  8.40iter/s]Best score: 0.1933: : 643iter [01:00,  9.22iter/s]Best score: 0.1933: : 643iter [01:00,  9.22iter/s]Best score: 0.1933: : 644iter [01:00,  9.22iter/s]Best score: 0.1933: : 645iter [01:00,  9.88iter/s]Best score: 0.1933: : 645iter [01:00,  9.88iter/s]Best score: 0.1933: : 646iter [01:00,  9.88iter/s]Best score: 0.1933: : 647iter [01:00, 10.44iter/s]Best score: 0.1933: : 647iter [01:00, 10.44iter/s]Best score: 0.1933: : 648iter [01:00, 10.44iter/s]Best score: 0.1933: : 649iter [01:00, 10.93iter/s]
[ITER 650] Best: 0.1930
[PARAMS] Current best parameters:
  fx: 562.7250
  fy: 562.2397
  px: 1012.3922
  py: 751.0905
  k1: -0.03652691
  k2: 0.00657871
  k3: 0.00019228
  k4: -0.00070080
  p1: -0.01424124
  p2: -0.01140090
  p3: 0.00594735
  p4: -0.00409968
  s1: 0.0037
  s2: 0.0038
  s3: 0.0081
  s4: 0.0040
  tilt_x: 0.0098
  tilt_y: -0.0315
  scale: 2.9615
  rotation: 0.1472
Best score: 0.1930: : 649iter [01:01, 10.93iter/s]Best score: 0.1930: : 650iter [01:01, 10.93iter/s]Best score: 0.1930: : 651iter [01:01,  7.32iter/s]Best score: 0.1930: : 651iter [01:01,  7.32iter/s]Best score: 0.1930: : 652iter [01:01,  7.32iter/s]Best score: 0.1930: : 653iter [01:01,  8.32iter/s]Best score: 0.1930: : 653iter [01:01,  8.32iter/s]Best score: 0.1930: : 654iter [01:01,  8.32iter/s]Best score: 0.1930: : 655iter [01:01,  9.20iter/s]Best score: 0.1930: : 655iter [01:01,  9.20iter/s]Best score: 0.1930: : 656iter [01:01,  9.20iter/s]Best score: 0.1930: : 657iter [01:01,  9.93iter/s]Best score: 0.1930: : 657iter [01:01,  9.93iter/s]Best score: 0.1930: : 658iter [01:02,  9.93iter/s]Best score: 0.1930: : 659iter [01:02, 10.49iter/s]Best score: 0.1928: : 659iter [01:02, 10.49iter/s]Best score: 0.1928: : 660iter [01:02, 10.49iter/s]Best score: 0.1928: : 661iter [01:02, 10.80iter/s]Best score: 0.1928: : 661iter [01:02, 10.80iter/s]Best score: 0.1928: : 662iter [01:02, 10.80iter/s]Best score: 0.1928: : 663iter [01:02, 11.20iter/s]Best score: 0.1928: : 663iter [01:02, 11.20iter/s]Best score: 0.1928: : 664iter [01:02, 11.20iter/s]Best score: 0.1928: : 665iter [01:02, 11.42iter/s]Best score: 0.1928: : 665iter [01:02, 11.42iter/s]Best score: 0.1928: : 666iter [01:02, 11.42iter/s]Best score: 0.1928: : 667iter [01:02, 11.59iter/s]Best score: 0.1928: : 667iter [01:02, 11.59iter/s]Best score: 0.1928: : 668iter [01:02, 11.59iter/s]Best score: 0.1928: : 669iter [01:02, 11.77iter/s]Best score: 0.1922: : 669iter [01:02, 11.77iter/s]Best score: 0.1922: : 670iter [01:03, 11.77iter/s]Best score: 0.1922: : 671iter [01:03, 11.68iter/s]Best score: 0.1922: : 671iter [01:03, 11.68iter/s]Best score: 0.1922: : 672iter [01:03, 11.68iter/s]Best score: 0.1922: : 673iter [01:03, 11.84iter/s]Best score: 0.1922: : 673iter [01:03, 11.84iter/s]Best score: 0.1922: : 674iter [01:03, 11.84iter/s]Best score: 0.1922: : 675iter [01:03, 11.95iter/s]Best score: 0.1922: : 675iter [01:03, 11.95iter/s]Best score: 0.1922: : 676iter [01:03, 11.95iter/s]Best score: 0.1922: : 677iter [01:03, 11.99iter/s]Best score: 0.1922: : 677iter [01:03, 11.99iter/s]Best score: 0.1922: : 678iter [01:03, 11.99iter/s]Best score: 0.1922: : 679iter [01:03, 12.07iter/s]Best score: 0.1922: : 679iter [01:03, 12.07iter/s]Best score: 0.1922: : 680iter [01:03, 12.07iter/s]Best score: 0.1922: : 681iter [01:03, 11.87iter/s]Best score: 0.1922: : 681iter [01:03, 11.87iter/s]Best score: 0.1922: : 682iter [01:04, 11.87iter/s]Best score: 0.1922: : 683iter [01:04, 11.99iter/s]Best score: 0.1922: : 683iter [01:04, 11.99iter/s]Best score: 0.1922: : 684iter [01:04, 11.99iter/s]Best score: 0.1922: : 685iter [01:04, 12.06iter/s]Best score: 0.1922: : 685iter [01:04, 12.06iter/s]Best score: 0.1922: : 686iter [01:04, 12.06iter/s]Best score: 0.1922: : 687iter [01:04, 12.07iter/s]Best score: 0.1922: : 687iter [01:04, 12.07iter/s]Best score: 0.1922: : 688iter [01:04, 12.07iter/s]Best score: 0.1922: : 689iter [01:04, 12.02iter/s]Best score: 0.1922: : 689iter [01:04, 12.02iter/s]Best score: 0.1922: : 690iter [01:04, 12.02iter/s]Best score: 0.1922: : 691iter [01:04, 11.88iter/s]Best score: 0.1922: : 691iter [01:04, 11.88iter/s]Best score: 0.1922: : 692iter [01:04, 11.88iter/s]Best score: 0.1922: : 693iter [01:04, 11.99iter/s]Best score: 0.1922: : 693iter [01:04, 11.99iter/s]Best score: 0.1922: : 694iter [01:05, 11.99iter/s]Best score: 0.1922: : 695iter [01:05, 12.07iter/s]Best score: 0.1922: : 695iter [01:05, 12.07iter/s]Best score: 0.1922: : 696iter [01:05, 12.07iter/s]Best score: 0.1922: : 697iter [01:05, 12.13iter/s]Best score: 0.1922: : 697iter [01:05, 12.13iter/s]Best score: 0.1922: : 698iter [01:05, 12.13iter/s]Best score: 0.1922: : 699iter [01:05, 12.17iter/s]
[ITER 700] Best: 0.1922
[PARAMS] Current best parameters:
  fx: 562.2569
  fy: 561.7981
  px: 1012.4204
  py: 751.0722
  k1: -0.03563031
  k2: 0.00516029
  k3: 0.00145502
  k4: -0.00106572
  p1: -0.01392584
  p2: -0.01265984
  p3: 0.00792805
  p4: -0.00458207
  s1: 0.0029
  s2: 0.0045
  s3: 0.0061
  s4: 0.0040
  tilt_x: 0.0092
  tilt_y: -0.0341
  scale: 2.9624
  rotation: 0.1446
Best score: 0.1922: : 699iter [01:05, 12.17iter/s]Best score: 0.1922: : 700iter [01:05, 12.17iter/s]Best score: 0.1922: : 701iter [01:05,  7.65iter/s]Best score: 0.1922: : 701iter [01:05,  7.65iter/s]Best score: 0.1922: : 702iter [01:06,  7.65iter/s]Best score: 0.1922: : 703iter [01:06,  8.60iter/s]Best score: 0.1922: : 703iter [01:06,  8.60iter/s]Best score: 0.1922: : 704iter [01:06,  8.60iter/s]Best score: 0.1922: : 705iter [01:06,  9.38iter/s]Best score: 0.1922: : 705iter [01:06,  9.38iter/s]Best score: 0.1922: : 706iter [01:06,  9.38iter/s]Best score: 0.1922: : 707iter [01:06, 10.02iter/s]Best score: 0.1922: : 707iter [01:06, 10.02iter/s]Best score: 0.1922: : 708iter [01:06, 10.02iter/s]Best score: 0.1922: : 709iter [01:06, 10.52iter/s]Best score: 0.1922: : 709iter [01:06, 10.52iter/s]Best score: 0.1922: : 710iter [01:06, 10.52iter/s]Best score: 0.1922: : 711iter [01:06, 10.73iter/s]Best score: 0.1922: : 711iter [01:06, 10.73iter/s]Best score: 0.1922: : 712iter [01:06, 10.73iter/s]Best score: 0.1922: : 713iter [01:06, 11.10iter/s]Best score: 0.1922: : 713iter [01:06, 11.10iter/s]Best score: 0.1922: : 714iter [01:07, 11.10iter/s]Best score: 0.1922: : 715iter [01:07, 11.34iter/s]Best score: 0.1922: : 715iter [01:07, 11.34iter/s]Best score: 0.1922: : 716iter [01:07, 11.34iter/s]Best score: 0.1922: : 717iter [01:07, 11.51iter/s]Best score: 0.1922: : 717iter [01:07, 11.51iter/s]Best score: 0.1922: : 718iter [01:07, 11.51iter/s]Best score: 0.1922: : 719iter [01:07, 11.70iter/s]Best score: 0.1922: : 719iter [01:07, 11.70iter/s]Best score: 0.1922: : 720iter [01:07, 11.70iter/s]Best score: 0.1922: : 721iter [01:07, 11.61iter/s]Best score: 0.1922: : 721iter [01:07, 11.61iter/s]Best score: 0.1922: : 722iter [01:07, 11.61iter/s]Best score: 0.1922: : 723iter [01:07, 11.69iter/s]Best score: 0.1922: : 723iter [01:07, 11.69iter/s]Best score: 0.1922: : 724iter [01:07, 11.69iter/s]Best score: 0.1922: : 725iter [01:07, 11.77iter/s]Best score: 0.1922: : 725iter [01:07, 11.77iter/s]Best score: 0.1922: : 726iter [01:08, 11.77iter/s]Best score: 0.1922: : 727iter [01:08, 11.85iter/s]Best score: 0.1922: : 727iter [01:08, 11.85iter/s]Best score: 0.1922: : 728iter [01:08, 11.85iter/s]Best score: 0.1922: : 729iter [01:08, 11.97iter/s]Best score: 0.1921: : 729iter [01:08, 11.97iter/s]Best score: 0.1921: : 730iter [01:08, 11.97iter/s]Best score: 0.1921: : 731iter [01:08, 11.84iter/s]Best score: 0.1921: : 731iter [01:08, 11.84iter/s]Best score: 0.1921: : 732iter [01:08, 11.84iter/s]Best score: 0.1921: : 733iter [01:08, 11.93iter/s]Best score: 0.1921: : 733iter [01:08, 11.93iter/s]Best score: 0.1921: : 734iter [01:08, 11.93iter/s]Best score: 0.1921: : 735iter [01:08, 11.95iter/s]Best score: 0.1921: : 735iter [01:08, 11.95iter/s]Best score: 0.1921: : 736iter [01:08, 11.95iter/s]Best score: 0.1921: : 737iter [01:08, 12.01iter/s]Best score: 0.1921: : 737iter [01:08, 12.01iter/s]Best score: 0.1921: : 738iter [01:09, 12.01iter/s]Best score: 0.1921: : 739iter [01:09, 11.99iter/s]Best score: 0.1921: : 739iter [01:09, 11.99iter/s]Best score: 0.1921: : 740iter [01:09, 11.99iter/s]Best score: 0.1921: : 741iter [01:09, 11.77iter/s]Best score: 0.1921: : 741iter [01:09, 11.77iter/s]Best score: 0.1921: : 742iter [01:09, 11.77iter/s]Best score: 0.1921: : 743iter [01:09, 11.85iter/s]Best score: 0.1921: : 743iter [01:09, 11.85iter/s]Best score: 0.1921: : 744iter [01:09, 11.85iter/s]Best score: 0.1921: : 745iter [01:09, 11.95iter/s]Best score: 0.1921: : 745iter [01:09, 11.95iter/s]Best score: 0.1921: : 746iter [01:09, 11.95iter/s]Best score: 0.1921: : 747iter [01:09, 12.03iter/s]Best score: 0.1921: : 747iter [01:09, 12.03iter/s]Best score: 0.1921: : 748iter [01:09, 12.03iter/s]Best score: 0.1921: : 749iter [01:09, 12.08iter/s]
[ITER 750] Best: 0.1921
[PARAMS] Current best parameters:
  fx: 562.3174
  fy: 561.8838
  px: 1012.1998
  py: 750.3945
  k1: -0.03860139
  k2: 0.01113441
  k3: -0.00295746
  k4: 0.00005229
  p1: -0.01314463
  p2: -0.01257136
  p3: 0.00512622
  p4: -0.00328439
  s1: 0.0052
  s2: 0.0034
  s3: 0.0071
  s4: 0.0030
  tilt_x: 0.0044
  tilt_y: -0.0330
  scale: 2.9636
  rotation: 0.1513
Best score: 0.1921: : 749iter [01:09, 12.08iter/s]Best score: 0.1921: : 750iter [01:10, 12.08iter/s]Best score: 0.1921: : 751iter [01:10,  7.68iter/s]Best score: 0.1921: : 751iter [01:10,  7.68iter/s]Best score: 0.1921: : 752iter [01:10,  7.68iter/s]Best score: 0.1921: : 753iter [01:10,  8.63iter/s]Best score: 0.1921: : 753iter [01:10,  8.63iter/s]Best score: 0.1921: : 754iter [01:10,  8.63iter/s]Best score: 0.1921: : 755iter [01:10,  9.44iter/s]Best score: 0.1921: : 755iter [01:10,  9.44iter/s]Best score: 0.1921: : 756iter [01:10,  9.44iter/s]Best score: 0.1921: : 757iter [01:10, 10.11iter/s]Best score: 0.1921: : 757iter [01:10, 10.11iter/s]Best score: 0.1921: : 758iter [01:11, 10.11iter/s]Best score: 0.1921: : 759iter [01:11, 10.65iter/s]Best score: 0.1920: : 759iter [01:11, 10.65iter/s]Best score: 0.1920: : 760iter [01:11, 10.65iter/s]Best score: 0.1920: : 761iter [01:11, 10.90iter/s]Best score: 0.1920: : 761iter [01:11, 10.90iter/s]Best score: 0.1920: : 762iter [01:11, 10.90iter/s]Best score: 0.1920: : 763iter [01:11, 11.27iter/s]Best score: 0.1920: : 763iter [01:11, 11.27iter/s]Best score: 0.1920: : 764iter [01:11, 11.27iter/s]Best score: 0.1920: : 765iter [01:11, 11.51iter/s]Best score: 0.1920: : 765iter [01:11, 11.51iter/s]Best score: 0.1920: : 766iter [01:11, 11.51iter/s]Best score: 0.1920: : 767iter [01:11, 11.63iter/s]Best score: 0.1920: : 767iter [01:11, 11.63iter/s]Best score: 0.1920: : 768iter [01:11, 11.63iter/s]Best score: 0.1920: : 769iter [01:11, 11.77iter/s]Best score: 0.1918: : 769iter [01:11, 11.77iter/s]Best score: 0.1918: : 770iter [01:12, 11.77iter/s]Best score: 0.1918: : 771iter [01:12, 11.62iter/s]Best score: 0.1918: : 771iter [01:12, 11.62iter/s]Best score: 0.1918: : 772iter [01:12, 11.62iter/s]Best score: 0.1918: : 773iter [01:12, 11.75iter/s]Best score: 0.1918: : 773iter [01:12, 11.75iter/s]Best score: 0.1918: : 774iter [01:12, 11.75iter/s]Best score: 0.1918: : 775iter [01:12, 11.88iter/s]Best score: 0.1918: : 775iter [01:12, 11.88iter/s]Best score: 0.1918: : 776iter [01:12, 11.88iter/s]Best score: 0.1918: : 777iter [01:12, 11.93iter/s]Best score: 0.1918: : 777iter [01:12, 11.93iter/s]Best score: 0.1918: : 778iter [01:12, 11.93iter/s]Best score: 0.1918: : 779iter [01:12, 12.01iter/s]Best score: 0.1918: : 779iter [01:12, 12.01iter/s]Best score: 0.1918: : 780iter [01:12, 12.01iter/s]Best score: 0.1918: : 781iter [01:12, 11.78iter/s]Best score: 0.1918: : 781iter [01:12, 11.78iter/s]Best score: 0.1918: : 782iter [01:13, 11.78iter/s]Best score: 0.1918: : 783iter [01:13, 11.90iter/s]Best score: 0.1918: : 783iter [01:13, 11.90iter/s]Best score: 0.1918: : 784iter [01:13, 11.90iter/s]Best score: 0.1918: : 785iter [01:13, 11.95iter/s]Best score: 0.1918: : 785iter [01:13, 11.95iter/s]Best score: 0.1918: : 786iter [01:13, 11.95iter/s]Best score: 0.1918: : 787iter [01:13, 11.91iter/s]Best score: 0.1918: : 787iter [01:13, 11.91iter/s]Best score: 0.1918: : 788iter [01:13, 11.91iter/s]Best score: 0.1918: : 789iter [01:13, 12.00iter/s]Best score: 0.1916: : 789iter [01:13, 12.00iter/s]Best score: 0.1916: : 790iter [01:13, 12.00iter/s]Best score: 0.1916: : 791iter [01:13, 11.82iter/s]Best score: 0.1916: : 791iter [01:13, 11.82iter/s]Best score: 0.1916: : 792iter [01:13, 11.82iter/s]Best score: 0.1916: : 793iter [01:13, 11.96iter/s]Best score: 0.1916: : 793iter [01:13, 11.96iter/s]Best score: 0.1916: : 794iter [01:14, 11.96iter/s]Best score: 0.1916: : 795iter [01:14, 12.06iter/s]Best score: 0.1916: : 795iter [01:14, 12.06iter/s]Best score: 0.1916: : 796iter [01:14, 12.06iter/s]Best score: 0.1916: : 797iter [01:14, 12.12iter/s]Best score: 0.1916: : 797iter [01:14, 12.12iter/s]Best score: 0.1916: : 798iter [01:14, 12.12iter/s]Best score: 0.1916: : 799iter [01:14, 12.15iter/s]
[ITER 800] Best: 0.1916
[PARAMS] Current best parameters:
  fx: 562.0796
  fy: 561.6695
  px: 1012.6183
  py: 750.4908
  k1: -0.03827431
  k2: 0.01129318
  k3: -0.00323107
  k4: 0.00013659
  p1: -0.01210165
  p2: -0.01248233
  p3: 0.00381540
  p4: -0.00322493
  s1: 0.0070
  s2: 0.0031
  s3: 0.0077
  s4: 0.0027
  tilt_x: 0.0027
  tilt_y: -0.0348
  scale: 2.9634
  rotation: 0.1527
Best score: 0.1916: : 799iter [01:14, 12.15iter/s]Best score: 0.1916: : 800iter [01:14, 12.15iter/s]Best score: 0.1916: : 801iter [01:14,  7.69iter/s]Best score: 0.1916: : 801iter [01:14,  7.69iter/s]Best score: 0.1916: : 802iter [01:15,  7.69iter/s]Best score: 0.1916: : 803iter [01:15,  8.61iter/s]Best score: 0.1916: : 803iter [01:15,  8.61iter/s]Best score: 0.1916: : 804iter [01:15,  8.61iter/s]Best score: 0.1916: : 805iter [01:15,  9.42iter/s]Best score: 0.1916: : 805iter [01:15,  9.42iter/s]Best score: 0.1916: : 806iter [01:15,  9.42iter/s]Best score: 0.1916: : 807iter [01:15, 10.12iter/s]Best score: 0.1916: : 807iter [01:15, 10.12iter/s]Best score: 0.1916: : 808iter [01:15, 10.12iter/s]Best score: 0.1916: : 809iter [01:15, 10.67iter/s]Best score: 0.1916: : 809iter [01:15, 10.67iter/s]Best score: 0.1916: : 810iter [01:15, 10.67iter/s]Best score: 0.1916: : 811iter [01:15, 10.92iter/s]Best score: 0.1916: : 811iter [01:15, 10.92iter/s]Best score: 0.1916: : 812iter [01:15, 10.92iter/s]Best score: 0.1916: : 813iter [01:15, 11.29iter/s]Best score: 0.1916: : 813iter [01:15, 11.29iter/s]Best score: 0.1916: : 814iter [01:16, 11.29iter/s]Best score: 0.1916: : 815iter [01:16, 11.55iter/s]Best score: 0.1916: : 815iter [01:16, 11.55iter/s]Best score: 0.1916: : 816iter [01:16, 11.55iter/s]Best score: 0.1916: : 817iter [01:16, 11.72iter/s]Best score: 0.1916: : 817iter [01:16, 11.72iter/s]Best score: 0.1916: : 818iter [01:16, 11.72iter/s]Best score: 0.1916: : 819iter [01:16, 11.86iter/s]Best score: 0.1916: : 819iter [01:16, 11.86iter/s]Best score: 0.1916: : 820iter [01:16, 11.86iter/s]Best score: 0.1916: : 821iter [01:16, 11.77iter/s]Best score: 0.1916: : 821iter [01:16, 11.77iter/s]Best score: 0.1916: : 822iter [01:16, 11.77iter/s]Best score: 0.1916: : 823iter [01:16, 11.88iter/s]Best score: 0.1916: : 823iter [01:16, 11.88iter/s]Best score: 0.1916: : 824iter [01:16, 11.88iter/s]Best score: 0.1916: : 825iter [01:16, 11.98iter/s]Best score: 0.1916: : 825iter [01:16, 11.98iter/s]Best score: 0.1916: : 826iter [01:17, 11.98iter/s]Best score: 0.1916: : 827iter [01:17, 12.04iter/s]Best score: 0.1916: : 827iter [01:17, 12.04iter/s]Best score: 0.1916: : 828iter [01:17, 12.04iter/s]Best score: 0.1916: : 829iter [01:17, 12.02iter/s]Best score: 0.1916: : 829iter [01:17, 12.02iter/s]Best score: 0.1916: : 830iter [01:17, 12.02iter/s]Best score: 0.1916: : 831iter [01:17, 11.89iter/s]Best score: 0.1916: : 831iter [01:17, 11.89iter/s]Best score: 0.1916: : 832iter [01:17, 11.89iter/s]Best score: 0.1916: : 833iter [01:17, 12.01iter/s]Best score: 0.1916: : 833iter [01:17, 12.01iter/s]Best score: 0.1916: : 834iter [01:17, 12.01iter/s]Best score: 0.1916: : 835iter [01:17, 12.05iter/s]Best score: 0.1916: : 835iter [01:17, 12.05iter/s]Best score: 0.1916: : 836iter [01:17, 12.05iter/s]Best score: 0.1916: : 837iter [01:17, 12.04iter/s]Best score: 0.1916: : 837iter [01:17, 12.04iter/s]Best score: 0.1916: : 838iter [01:18, 12.04iter/s]Best score: 0.1916: : 839iter [01:18, 12.07iter/s]Best score: 0.1913: : 839iter [01:18, 12.07iter/s]Best score: 0.1913: : 840iter [01:18, 12.07iter/s]Best score: 0.1913: : 841iter [01:18, 11.86iter/s]Best score: 0.1913: : 841iter [01:18, 11.86iter/s]Best score: 0.1913: : 842iter [01:18, 11.86iter/s]Best score: 0.1913: : 843iter [01:18, 11.97iter/s]Best score: 0.1913: : 843iter [01:18, 11.97iter/s]Best score: 0.1913: : 844iter [01:18, 11.97iter/s]Best score: 0.1913: : 845iter [01:18, 12.00iter/s]Best score: 0.1913: : 845iter [01:18, 12.00iter/s]Best score: 0.1913: : 846iter [01:18, 12.00iter/s]Best score: 0.1913: : 847iter [01:18, 11.98iter/s]Best score: 0.1913: : 847iter [01:18, 11.98iter/s]Best score: 0.1913: : 848iter [01:18, 11.98iter/s]Best score: 0.1913: : 849iter [01:18, 12.01iter/s]
[ITER 850] Best: 0.1912
[PARAMS] Current best parameters:
  fx: 561.6218
  fy: 561.2223
  px: 1012.9868
  py: 750.0183
  k1: -0.03944186
  k2: 0.01396166
  k3: -0.00490548
  k4: 0.00050464
  p1: -0.01096579
  p2: -0.01207378
  p3: 0.00021797
  p4: -0.00193427
  s1: 0.0106
  s2: 0.0018
  s3: 0.0096
  s4: 0.0014
  tilt_x: -0.0018
  tilt_y: -0.0357
  scale: 2.9652
  rotation: 0.1534
Best score: 0.1912: : 849iter [01:18, 12.01iter/s]Best score: 0.1912: : 850iter [01:19, 12.01iter/s]Best score: 0.1912: : 851iter [01:19,  7.61iter/s]Best score: 0.1912: : 851iter [01:19,  7.61iter/s]Best score: 0.1912: : 852iter [01:19,  7.61iter/s]Best score: 0.1912: : 853iter [01:19,  8.56iter/s]Best score: 0.1912: : 853iter [01:19,  8.56iter/s]Best score: 0.1912: : 854iter [01:19,  8.56iter/s]Best score: 0.1912: : 855iter [01:19,  9.41iter/s]Best score: 0.1912: : 855iter [01:19,  9.41iter/s]Best score: 0.1912: : 856iter [01:19,  9.41iter/s]Best score: 0.1912: : 857iter [01:19, 10.13iter/s]Best score: 0.1912: : 857iter [01:19, 10.13iter/s]Best score: 0.1912: : 858iter [01:19, 10.13iter/s]Best score: 0.1912: : 859iter [01:19, 10.68iter/s]Best score: 0.1912: : 859iter [01:20, 10.68iter/s]Best score: 0.1912: : 860iter [01:20, 10.68iter/s]Best score: 0.1912: : 861iter [01:20, 10.93iter/s]Best score: 0.1912: : 861iter [01:20, 10.93iter/s]Best score: 0.1912: : 862iter [01:20, 10.93iter/s]Best score: 0.1912: : 863iter [01:20, 11.21iter/s]Best score: 0.1912: : 863iter [01:20, 11.21iter/s]Best score: 0.1912: : 864iter [01:20, 11.21iter/s]Best score: 0.1912: : 865iter [01:20, 11.41iter/s]Best score: 0.1912: : 865iter [01:20, 11.41iter/s]Best score: 0.1912: : 866iter [01:20, 11.41iter/s]Best score: 0.1912: : 867iter [01:20, 11.65iter/s]Best score: 0.1912: : 867iter [01:20, 11.65iter/s]Best score: 0.1912: : 868iter [01:20, 11.65iter/s]Best score: 0.1912: : 869iter [01:20, 11.82iter/s]Best score: 0.1912: : 869iter [01:20, 11.82iter/s]Best score: 0.1912: : 870iter [01:21, 11.82iter/s]Best score: 0.1912: : 871iter [01:21, 11.75iter/s]Best score: 0.1912: : 871iter [01:21, 11.75iter/s]Best score: 0.1912: : 872iter [01:21, 11.75iter/s]Best score: 0.1912: : 873iter [01:21, 11.91iter/s]Best score: 0.1912: : 873iter [01:21, 11.91iter/s]Best score: 0.1912: : 874iter [01:21, 11.91iter/s]Best score: 0.1912: : 875iter [01:21, 12.00iter/s]Best score: 0.1912: : 875iter [01:21, 12.00iter/s]Best score: 0.1912: : 876iter [01:21, 12.00iter/s]Best score: 0.1912: : 877iter [01:21, 12.07iter/s]Best score: 0.1912: : 877iter [01:21, 12.07iter/s]Best score: 0.1912: : 878iter [01:21, 12.07iter/s]Best score: 0.1912: : 879iter [01:21, 12.08iter/s]Best score: 0.1912: : 879iter [01:21, 12.08iter/s]Best score: 0.1912: : 880iter [01:21, 12.08iter/s]Best score: 0.1912: : 881iter [01:21, 11.87iter/s]Best score: 0.1912: : 881iter [01:21, 11.87iter/s]Best score: 0.1912: : 882iter [01:21, 11.87iter/s]Best score: 0.1912: : 883iter [01:21, 11.92iter/s]Best score: 0.1912: : 883iter [01:22, 11.92iter/s]Best score: 0.1912: : 884iter [01:22, 11.92iter/s]Best score: 0.1912: : 885iter [01:22, 11.97iter/s]Best score: 0.1912: : 885iter [01:22, 11.97iter/s]Best score: 0.1912: : 886iter [01:22, 11.97iter/s]Best score: 0.1912: : 887iter [01:22, 11.99iter/s]Best score: 0.1912: : 887iter [01:22, 11.99iter/s]Best score: 0.1912: : 888iter [01:22, 11.99iter/s]Best score: 0.1912: : 889iter [01:22, 12.01iter/s]Best score: 0.1909: : 889iter [01:22, 12.01iter/s]Best score: 0.1909: : 890iter [01:22, 12.01iter/s]Best score: 0.1909: : 891iter [01:22, 11.83iter/s]Best score: 0.1909: : 891iter [01:22, 11.83iter/s]Best score: 0.1909: : 892iter [01:22, 11.83iter/s]Best score: 0.1909: : 893iter [01:22, 11.90iter/s]Best score: 0.1909: : 893iter [01:22, 11.90iter/s]Best score: 0.1909: : 894iter [01:23, 11.90iter/s]Best score: 0.1909: : 895iter [01:23, 11.97iter/s]Best score: 0.1909: : 895iter [01:23, 11.97iter/s]Best score: 0.1909: : 896iter [01:23, 11.97iter/s]Best score: 0.1909: : 897iter [01:23, 12.03iter/s]Best score: 0.1909: : 897iter [01:23, 12.03iter/s]Best score: 0.1909: : 898iter [01:23, 12.03iter/s]Best score: 0.1909: : 899iter [01:23, 12.09iter/s]
[ITER 900] Best: 0.1907
[PARAMS] Current best parameters:
  fx: 561.5313
  fy: 561.2015
  px: 1012.5937
  py: 749.9313
  k1: -0.04036024
  k2: 0.01573505
  k3: -0.00611348
  k4: 0.00079930
  p1: -0.00975264
  p2: -0.01202569
  p3: -0.00344203
  p4: -0.00094789
  s1: 0.0140
  s2: 0.0007
  s3: 0.0117
  s4: 0.0007
  tilt_x: -0.0046
  tilt_y: -0.0337
  scale: 2.9655
  rotation: 0.1569
Best score: 0.1907: : 899iter [01:23, 12.09iter/s]Best score: 0.1907: : 900iter [01:23, 12.09iter/s]Best score: 0.1907: : 901iter [01:23,  7.65iter/s]Best score: 0.1907: : 901iter [01:23,  7.65iter/s]Best score: 0.1907: : 902iter [01:23,  7.65iter/s]Best score: 0.1907: : 903iter [01:23,  8.61iter/s]Best score: 0.1907: : 903iter [01:24,  8.61iter/s]Best score: 0.1907: : 904iter [01:24,  8.61iter/s]Best score: 0.1907: : 905iter [01:24,  9.42iter/s]Best score: 0.1907: : 905iter [01:24,  9.42iter/s]Best score: 0.1907: : 906iter [01:24,  9.42iter/s]Best score: 0.1907: : 907iter [01:24, 10.11iter/s]Best score: 0.1907: : 907iter [01:24, 10.11iter/s]Best score: 0.1907: : 908iter [01:24, 10.11iter/s]Best score: 0.1907: : 909iter [01:24, 10.67iter/s]Best score: 0.1904: : 909iter [01:24, 10.67iter/s]Best score: 0.1904: : 910iter [01:24, 10.67iter/s]Best score: 0.1904: : 911iter [01:24, 10.91iter/s]Best score: 0.1904: : 911iter [01:24, 10.91iter/s]Best score: 0.1904: : 912iter [01:24, 10.91iter/s]Best score: 0.1904: : 913iter [01:24, 11.27iter/s]Best score: 0.1904: : 913iter [01:24, 11.27iter/s]Best score: 0.1904: : 914iter [01:24, 11.27iter/s]Best score: 0.1904: : 915iter [01:24, 11.53iter/s]Best score: 0.1904: : 915iter [01:25, 11.53iter/s]Best score: 0.1904: : 916iter [01:25, 11.53iter/s]Best score: 0.1904: : 917iter [01:25, 11.70iter/s]Best score: 0.1904: : 917iter [01:25, 11.70iter/s]Best score: 0.1904: : 918iter [01:25, 11.70iter/s]Best score: 0.1904: : 919iter [01:25, 11.80iter/s]Best score: 0.1903: : 919iter [01:25, 11.80iter/s]Best score: 0.1903: : 920iter [01:25, 11.80iter/s]Best score: 0.1903: : 921iter [01:25, 11.72iter/s]Best score: 0.1903: : 921iter [01:25, 11.72iter/s]Best score: 0.1903: : 922iter [01:25, 11.72iter/s]Best score: 0.1903: : 923iter [01:25, 11.83iter/s]Best score: 0.1903: : 923iter [01:25, 11.83iter/s]Best score: 0.1903: : 924iter [01:25, 11.83iter/s]Best score: 0.1903: : 925iter [01:25, 11.95iter/s]Best score: 0.1903: : 925iter [01:25, 11.95iter/s]Best score: 0.1903: : 926iter [01:25, 11.95iter/s]Best score: 0.1903: : 927iter [01:25, 11.98iter/s]Best score: 0.1903: : 927iter [01:26, 11.98iter/s]Best score: 0.1903: : 928iter [01:26, 11.98iter/s]Best score: 0.1903: : 929iter [01:26, 12.02iter/s]Best score: 0.1903: : 929iter [01:26, 12.02iter/s]Best score: 0.1903: : 930iter [01:26, 12.02iter/s]Best score: 0.1903: : 931iter [01:26, 11.84iter/s]Best score: 0.1903: : 931iter [01:26, 11.84iter/s]Best score: 0.1903: : 932iter [01:26, 11.84iter/s]Best score: 0.1903: : 933iter [01:26, 11.88iter/s]Best score: 0.1903: : 933iter [01:26, 11.88iter/s]Best score: 0.1903: : 934iter [01:26, 11.88iter/s]Best score: 0.1903: : 935iter [01:26, 11.91iter/s]Best score: 0.1903: : 935iter [01:26, 11.91iter/s]Best score: 0.1903: : 936iter [01:26, 11.91iter/s]Best score: 0.1903: : 937iter [01:26, 11.89iter/s]Best score: 0.1903: : 937iter [01:26, 11.89iter/s]Best score: 0.1903: : 938iter [01:26, 11.89iter/s]Best score: 0.1903: : 939iter [01:26, 11.94iter/s]Best score: 0.1902: : 939iter [01:27, 11.94iter/s]Best score: 0.1902: : 940iter [01:27, 11.94iter/s]Best score: 0.1902: : 941iter [01:27, 11.84iter/s]Best score: 0.1902: : 941iter [01:27, 11.84iter/s]Best score: 0.1902: : 942iter [01:27, 11.84iter/s]Best score: 0.1902: : 943iter [01:27, 11.90iter/s]Best score: 0.1902: : 943iter [01:27, 11.90iter/s]Best score: 0.1902: : 944iter [01:27, 11.90iter/s]Best score: 0.1902: : 945iter [01:27, 11.96iter/s]Best score: 0.1902: : 945iter [01:27, 11.96iter/s]Best score: 0.1902: : 946iter [01:27, 11.96iter/s]Best score: 0.1902: : 947iter [01:27, 11.93iter/s]Best score: 0.1902: : 947iter [01:27, 11.93iter/s]Best score: 0.1902: : 948iter [01:27, 11.93iter/s]Best score: 0.1902: : 949iter [01:27, 12.01iter/s]
[ITER 950] Best: 0.1900
[PARAMS] Current best parameters:
  fx: 563.2226
  fy: 562.9464
  px: 1012.5724
  py: 750.3530
  k1: -0.04037772
  k2: 0.01203737
  k3: -0.00327678
  k4: 0.00013586
  p1: -0.00805912
  p2: -0.00961072
  p3: -0.01154634
  p4: 0.00034897
  s1: 0.0194
  s2: -0.0005
  s3: 0.0188
  s4: -0.0008
  tilt_x: -0.0060
  tilt_y: -0.0288
  scale: 2.9634
  rotation: 0.1589
Best score: 0.1900: : 949iter [01:27, 12.01iter/s]Best score: 0.1900: : 950iter [01:28, 12.01iter/s]Best score: 0.1900: : 951iter [01:28,  7.59iter/s]Best score: 0.1900: : 951iter [01:28,  7.59iter/s]Best score: 0.1900: : 952iter [01:28,  7.59iter/s]Best score: 0.1900: : 953iter [01:28,  8.55iter/s]Best score: 0.1900: : 953iter [01:28,  8.55iter/s]Best score: 0.1900: : 954iter [01:28,  8.55iter/s]Best score: 0.1900: : 955iter [01:28,  9.37iter/s]Best score: 0.1900: : 955iter [01:28,  9.37iter/s]Best score: 0.1900: : 956iter [01:28,  9.37iter/s]Best score: 0.1900: : 957iter [01:28, 10.05iter/s]Best score: 0.1900: : 957iter [01:28, 10.05iter/s]Best score: 0.1900: : 958iter [01:28, 10.05iter/s]Best score: 0.1900: : 959iter [01:28, 10.61iter/s]Best score: 0.1898: : 959iter [01:29, 10.61iter/s]Best score: 0.1898: : 960iter [01:29, 10.61iter/s]Best score: 0.1898: : 961iter [01:29, 10.88iter/s]Best score: 0.1898: : 961iter [01:29, 10.88iter/s]Best score: 0.1898: : 962iter [01:29, 10.88iter/s]Best score: 0.1898: : 963iter [01:29, 11.23iter/s]Best score: 0.1898: : 963iter [01:29, 11.23iter/s]Best score: 0.1898: : 964iter [01:29, 11.23iter/s]Best score: 0.1898: : 965iter [01:29, 11.51iter/s]Best score: 0.1898: : 965iter [01:29, 11.51iter/s]Best score: 0.1898: : 966iter [01:29, 11.51iter/s]Best score: 0.1898: : 967iter [01:29, 11.69iter/s]Best score: 0.1898: : 967iter [01:29, 11.69iter/s]Best score: 0.1898: : 968iter [01:29, 11.69iter/s]Best score: 0.1898: : 969iter [01:29, 11.77iter/s]Best score: 0.1897: : 969iter [01:29, 11.77iter/s]Best score: 0.1897: : 970iter [01:29, 11.77iter/s]Best score: 0.1897: : 971iter [01:29, 11.71iter/s]Best score: 0.1897: : 971iter [01:30, 11.71iter/s]Best score: 0.1897: : 972iter [01:30, 11.71iter/s]Best score: 0.1897: : 973iter [01:30, 11.85iter/s]Best score: 0.1897: : 973iter [01:30, 11.85iter/s]Best score: 0.1897: : 974iter [01:30, 11.85iter/s]Best score: 0.1897: : 975iter [01:30, 11.92iter/s]Best score: 0.1897: : 975iter [01:30, 11.92iter/s]Best score: 0.1897: : 976iter [01:30, 11.92iter/s]Best score: 0.1897: : 977iter [01:30, 11.92iter/s]Best score: 0.1897: : 977iter [01:30, 11.92iter/s]Best score: 0.1897: : 978iter [01:30, 11.92iter/s]Best score: 0.1897: : 979iter [01:30, 11.96iter/s]Best score: 0.1897: : 979iter [01:30, 11.96iter/s]Best score: 0.1897: : 980iter [01:30, 11.96iter/s]Best score: 0.1897: : 981iter [01:30, 11.85iter/s]Best score: 0.1897: : 981iter [01:30, 11.85iter/s]Best score: 0.1897: : 982iter [01:30, 11.85iter/s]Best score: 0.1897: : 983iter [01:30, 11.91iter/s]Best score: 0.1897: : 983iter [01:31, 11.91iter/s]Best score: 0.1897: : 984iter [01:31, 11.91iter/s]Best score: 0.1897: : 985iter [01:31, 11.94iter/s]Best score: 0.1897: : 985iter [01:31, 11.94iter/s]Best score: 0.1897: : 986iter [01:31, 11.94iter/s]Best score: 0.1897: : 987iter [01:31, 12.01iter/s]Best score: 0.1897: : 987iter [01:31, 12.01iter/s]Best score: 0.1897: : 988iter [01:31, 12.01iter/s]Best score: 0.1897: : 989iter [01:31, 12.08iter/s]Best score: 0.1897: : 989iter [01:31, 12.08iter/s]Best score: 0.1897: : 990iter [01:31, 12.08iter/s]Best score: 0.1897: : 991iter [01:31, 11.90iter/s]Best score: 0.1897: : 991iter [01:31, 11.90iter/s]Best score: 0.1897: : 992iter [01:31, 11.90iter/s]Best score: 0.1897: : 993iter [01:31, 11.95iter/s]Best score: 0.1897: : 993iter [01:31, 11.95iter/s]Best score: 0.1897: : 994iter [01:31, 11.95iter/s]Best score: 0.1897: : 995iter [01:31, 12.01iter/s]Best score: 0.1897: : 995iter [01:32, 12.01iter/s]Best score: 0.1897: : 996iter [01:32, 12.01iter/s]Best score: 0.1897: : 997iter [01:32, 11.97iter/s]Best score: 0.1897: : 997iter [01:32, 11.97iter/s]Best score: 0.1897: : 998iter [01:32, 11.97iter/s]Best score: 0.1897: : 999iter [01:32, 12.02iter/s]
[ITER 1000] Best: 0.1897
[PARAMS] Current best parameters:
  fx: 562.6297
  fy: 562.4042
  px: 1013.2427
  py: 750.2350
  k1: -0.03936416
  k2: 0.01198792
  k3: -0.00375694
  k4: 0.00033617
  p1: -0.00605239
  p2: -0.00893920
  p3: -0.01392905
  p4: 0.00059298
  s1: 0.0219
  s2: -0.0008
  s3: 0.0189
  s4: -0.0008
  tilt_x: -0.0107
  tilt_y: -0.0307
  scale: 2.9632
  rotation: 0.1608
Best score: 0.1897: : 999iter [01:32, 12.02iter/s]Best score: 0.1897: : 1000iter [01:32, 12.02iter/s]Best score: 0.1897: : 1001iter [01:32,  7.61iter/s]Best score: 0.1897: : 1001iter [01:32,  7.61iter/s]Best score: 0.1897: : 1002iter [01:32,  7.61iter/s]Best score: 0.1897: : 1003iter [01:32,  8.54iter/s]Best score: 0.1897: : 1003iter [01:33,  8.54iter/s]Best score: 0.1897: : 1004iter [01:33,  8.54iter/s]Best score: 0.1897: : 1005iter [01:33,  9.39iter/s]Best score: 0.1897: : 1005iter [01:33,  9.39iter/s]Best score: 0.1897: : 1006iter [01:33,  9.39iter/s]Best score: 0.1897: : 1007iter [01:33, 10.09iter/s]Best score: 0.1897: : 1007iter [01:33, 10.09iter/s]Best score: 0.1897: : 1008iter [01:33, 10.09iter/s]Best score: 0.1897: : 1009iter [01:33, 10.62iter/s]Best score: 0.1896: : 1009iter [01:33, 10.62iter/s]Best score: 0.1896: : 1010iter [01:33, 10.62iter/s]Best score: 0.1896: : 1011iter [01:33, 10.87iter/s]Best score: 0.1896: : 1011iter [01:33, 10.87iter/s]Best score: 0.1896: : 1012iter [01:33, 10.87iter/s]Best score: 0.1896: : 1013iter [01:33, 11.19iter/s]Best score: 0.1896: : 1013iter [01:33, 11.19iter/s]Best score: 0.1896: : 1014iter [01:33, 11.19iter/s]Best score: 0.1896: : 1015iter [01:33, 11.49iter/s]Best score: 0.1896: : 1015iter [01:34, 11.49iter/s]Best score: 0.1896: : 1016iter [01:34, 11.49iter/s]Best score: 0.1896: : 1017iter [01:34, 11.59iter/s]Best score: 0.1896: : 1017iter [01:34, 11.59iter/s]Best score: 0.1896: : 1018iter [01:34, 11.59iter/s]Best score: 0.1896: : 1019iter [01:34, 11.77iter/s]Best score: 0.1896: : 1019iter [01:34, 11.77iter/s]Best score: 0.1896: : 1020iter [01:34, 11.77iter/s]Best score: 0.1896: : 1021iter [01:34, 11.66iter/s]Best score: 0.1896: : 1021iter [01:34, 11.66iter/s]Best score: 0.1896: : 1022iter [01:34, 11.66iter/s]Best score: 0.1896: : 1023iter [01:34, 11.81iter/s]Best score: 0.1896: : 1023iter [01:34, 11.81iter/s]Best score: 0.1896: : 1024iter [01:34, 11.81iter/s]Best score: 0.1896: : 1025iter [01:34, 11.88iter/s]Best score: 0.1896: : 1025iter [01:34, 11.88iter/s]Best score: 0.1896: : 1026iter [01:34, 11.88iter/s]Best score: 0.1896: : 1027iter [01:34, 11.99iter/s]Best score: 0.1896: : 1027iter [01:35, 11.99iter/s]Best score: 0.1896: : 1028iter [01:35, 11.99iter/s]Best score: 0.1896: : 1029iter [01:35, 12.06iter/s]Best score: 0.1896: : 1029iter [01:35, 12.06iter/s]Best score: 0.1896: : 1030iter [01:35, 12.06iter/s]Best score: 0.1896: : 1031iter [01:35, 11.86iter/s]Best score: 0.1896: : 1031iter [01:35, 11.86iter/s]Best score: 0.1896: : 1032iter [01:35, 11.86iter/s]Best score: 0.1896: : 1033iter [01:35, 11.96iter/s]Best score: 0.1896: : 1033iter [01:35, 11.96iter/s]Best score: 0.1896: : 1034iter [01:35, 11.96iter/s]Best score: 0.1896: : 1035iter [01:35, 12.06iter/s]Best score: 0.1896: : 1035iter [01:35, 12.06iter/s]Best score: 0.1896: : 1036iter [01:35, 12.06iter/s]Best score: 0.1896: : 1037iter [01:35, 12.12iter/s]Best score: 0.1896: : 1037iter [01:35, 12.12iter/s]Best score: 0.1896: : 1038iter [01:35, 12.12iter/s]Best score: 0.1896: : 1039iter [01:35, 12.12iter/s]Best score: 0.1894: : 1039iter [01:36, 12.12iter/s]Best score: 0.1894: : 1040iter [01:36, 12.12iter/s]Best score: 0.1894: : 1041iter [01:36, 11.94iter/s]Best score: 0.1894: : 1041iter [01:36, 11.94iter/s]Best score: 0.1894: : 1042iter [01:36, 11.94iter/s]Best score: 0.1894: : 1043iter [01:36, 12.01iter/s]Best score: 0.1894: : 1043iter [01:36, 12.01iter/s]Best score: 0.1894: : 1044iter [01:36, 12.01iter/s]Best score: 0.1894: : 1045iter [01:36, 12.03iter/s]Best score: 0.1894: : 1045iter [01:36, 12.03iter/s]Best score: 0.1894: : 1046iter [01:36, 12.03iter/s]Best score: 0.1894: : 1047iter [01:36, 12.02iter/s]Best score: 0.1894: : 1047iter [01:36, 12.02iter/s]Best score: 0.1894: : 1048iter [01:36, 12.02iter/s]Best score: 0.1894: : 1049iter [01:36, 12.06iter/s]
[ITER 1050] Best: 0.1894
[PARAMS] Current best parameters:
  fx: 562.8410
  fy: 562.5942
  px: 1013.1025
  py: 750.0562
  k1: -0.03734336
  k2: 0.00782057
  k3: -0.00094971
  k4: -0.00031439
  p1: -0.00660139
  p2: -0.00809667
  p3: -0.01367425
  p4: 0.00019622
  s1: 0.0207
  s2: -0.0003
  s3: 0.0191
  s4: -0.0005
  tilt_x: -0.0105
  tilt_y: -0.0283
  scale: 2.9615
  rotation: 0.1621
Best score: 0.1894: : 1049iter [01:36, 12.06iter/s]Best score: 0.1894: : 1050iter [01:37, 12.06iter/s]Best score: 0.1894: : 1051iter [01:37,  7.62iter/s]Best score: 0.1894: : 1051iter [01:37,  7.62iter/s]Best score: 0.1894: : 1052iter [01:37,  7.62iter/s]Best score: 0.1894: : 1053iter [01:37,  8.59iter/s]Best score: 0.1894: : 1053iter [01:37,  8.59iter/s]Best score: 0.1894: : 1054iter [01:37,  8.59iter/s]Best score: 0.1894: : 1055iter [01:37,  9.37iter/s]Best score: 0.1894: : 1055iter [01:37,  9.37iter/s]Best score: 0.1894: : 1056iter [01:37,  9.37iter/s]Best score: 0.1894: : 1057iter [01:37, 10.02iter/s]Best score: 0.1894: : 1057iter [01:37, 10.02iter/s]Best score: 0.1894: : 1058iter [01:37, 10.02iter/s]Best score: 0.1894: : 1059iter [01:37, 10.53iter/s]Best score: 0.1894: : 1059iter [01:38, 10.53iter/s]Best score: 0.1894: : 1060iter [01:38, 10.53iter/s]Best score: 0.1894: : 1061iter [01:38, 10.82iter/s]Best score: 0.1894: : 1061iter [01:38, 10.82iter/s]Best score: 0.1894: : 1062iter [01:38, 10.82iter/s]Best score: 0.1894: : 1063iter [01:38, 11.19iter/s]Best score: 0.1894: : 1063iter [01:38, 11.19iter/s]Best score: 0.1894: : 1064iter [01:38, 11.19iter/s]Best score: 0.1894: : 1065iter [01:38, 11.49iter/s]Best score: 0.1894: : 1065iter [01:38, 11.49iter/s]Best score: 0.1894: : 1066iter [01:38, 11.49iter/s]Best score: 0.1894: : 1067iter [01:38, 11.72iter/s]Best score: 0.1894: : 1067iter [01:38, 11.72iter/s]Best score: 0.1894: : 1068iter [01:38, 11.72iter/s]Best score: 0.1894: : 1069iter [01:38, 11.85iter/s]Best score: 0.1894: : 1069iter [01:38, 11.85iter/s]Best score: 0.1894: : 1070iter [01:38, 11.85iter/s]Best score: 0.1894: : 1071iter [01:38, 11.77iter/s]Best score: 0.1894: : 1071iter [01:39, 11.77iter/s]Best score: 0.1894: : 1072iter [01:39, 11.77iter/s]Best score: 0.1894: : 1073iter [01:39, 11.86iter/s]Best score: 0.1894: : 1073iter [01:39, 11.86iter/s]Best score: 0.1894: : 1074iter [01:39, 11.86iter/s]Best score: 0.1894: : 1075iter [01:39, 11.96iter/s]Best score: 0.1894: : 1075iter [01:39, 11.96iter/s]Best score: 0.1894: : 1076iter [01:39, 11.96iter/s]Best score: 0.1894: : 1077iter [01:39, 12.04iter/s]Best score: 0.1894: : 1077iter [01:39, 12.04iter/s]Best score: 0.1894: : 1078iter [01:39, 12.04iter/s]Best score: 0.1894: : 1079iter [01:39, 12.10iter/s]Best score: 0.1894: : 1079iter [01:39, 12.10iter/s]Best score: 0.1894: : 1080iter [01:39, 12.10iter/s]Best score: 0.1894: : 1081iter [01:39, 11.94iter/s]Best score: 0.1894: : 1081iter [01:39, 11.94iter/s]Best score: 0.1894: : 1082iter [01:39, 11.94iter/s]Best score: 0.1894: : 1083iter [01:39, 12.03iter/s]Best score: 0.1894: : 1083iter [01:40, 12.03iter/s]Best score: 0.1894: : 1084iter [01:40, 12.03iter/s]Best score: 0.1894: : 1085iter [01:40, 11.98iter/s]Best score: 0.1894: : 1085iter [01:40, 11.98iter/s]Best score: 0.1894: : 1086iter [01:40, 11.98iter/s]Best score: 0.1894: : 1087iter [01:40, 12.01iter/s]Best score: 0.1894: : 1087iter [01:40, 12.01iter/s]Best score: 0.1894: : 1088iter [01:40, 12.01iter/s]Best score: 0.1894: : 1089iter [01:40, 12.07iter/s]Best score: 0.1893: : 1089iter [01:40, 12.07iter/s]Best score: 0.1893: : 1090iter [01:40, 12.07iter/s]Best score: 0.1893: : 1091iter [01:40, 11.90iter/s]Best score: 0.1893: : 1091iter [01:40, 11.90iter/s]Best score: 0.1893: : 1092iter [01:40, 11.90iter/s]Best score: 0.1893: : 1093iter [01:40, 12.01iter/s]Best score: 0.1893: : 1093iter [01:40, 12.01iter/s]Best score: 0.1893: : 1094iter [01:40, 12.01iter/s]Best score: 0.1893: : 1095iter [01:40, 12.08iter/s]Best score: 0.1893: : 1095iter [01:41, 12.08iter/s]Best score: 0.1893: : 1096iter [01:41, 12.08iter/s]Best score: 0.1893: : 1097iter [01:41, 12.05iter/s]Best score: 0.1893: : 1097iter [01:41, 12.05iter/s]Best score: 0.1893: : 1098iter [01:41, 12.05iter/s]Best score: 0.1893: : 1099iter [01:41, 12.04iter/s]
[ITER 1100] Best: 0.1892
[PARAMS] Current best parameters:
  fx: 564.0581
  fy: 563.7934
  px: 1013.4000
  py: 750.0743
  k1: -0.03846055
  k2: 0.00729756
  k3: -0.00025097
  k4: -0.00051671
  p1: -0.00630782
  p2: -0.00775523
  p3: -0.01408273
  p4: 0.00013659
  s1: 0.0212
  s2: -0.0003
  s3: 0.0192
  s4: -0.0005
  tilt_x: -0.0110
  tilt_y: -0.0291
  scale: 2.9613
  rotation: 0.1624
Best score: 0.1892: : 1099iter [01:41, 12.04iter/s]Best score: 0.1892: : 1100iter [01:41, 10.82iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.189198 -> 0.188985

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 99,312 → 92,418 within 3500mm ring
  Ring polish did not improve score: 0.188985
  Success: True
  Tangential distortion: p1=-0.006256, p2=-0.007751, p3=-0.014139, p4=0.000139
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1890
[✅] Parameters copied to: /working/MDC033_W017(6)_2026-09-28/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 564.0580696646862
    fy: 563.7934038290094
    px: 1013.4000048008521
    py: 750.0742759398858
    k1: -0.038528210368742814
    k2: 0.0073162351192021585
    k3: -0.0001873188670895999
    k4: -0.0005453848046143537
    p1: -0.006256423425535261
    p2: -0.007750659316984584
    p3: -0.014139377867104792
    p4: 0.00013929904614705328
    scale: 2.9612480628469506
    rotation: 0.16241793593084664
    mmpp: 3.3810651573354975

[🔄] Converting 2161 frames to PTD, creating debug images for 400 calibration frames...
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                                                                  [A📁 Rigs:  50%|█████     | 2/4 [00:09<00:09,  4.90s/rig]
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                                                                  [A📁 Rigs:  75%|███████▌  | 3/4 [00:14<00:04,  4.67s/rig]
   🔄 1500:   0%|          | 0/713 [00:00<?, ?frame/s][A
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                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:21<00:00,  5.68s/rig]📁 Rigs: 100%|██████████| 4/4 [00:21<00:00,  5.40s/rig]
[✅] Saved PTDs: /working/MDC033_W017(6)_2026-09-28/calibration/debug/4_undistorted/output/ptd
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                                                                 [A📁 Rigs:  50%|█████     | 2/4 [00:01<00:01,  1.10rig/s]
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                                                                 [A📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:01,  1.00s/rig]
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   🔄 1500:  90%|█████████ | 90/100 [00:00<00:00, 177.33frame/s][A
                                                                [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.03rig/s]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.04rig/s]
[✅] Saved PTDs: /working/MDC033_W017(6)_2026-09-28/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%|          | 0/3080448 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 79083/3080448 [00:00<00:03, 790828.87px/s]📤 Writing LUT:   5%|▌         | 163450/3080448 [00:00<00:03, 821905.41px/s]📤 Writing LUT:   8%|▊         | 248859/3080448 [00:00<00:03, 836600.05px/s]📤 Writing LUT:  11%|█         | 333795/3080448 [00:00<00:03, 841633.87px/s]📤 Writing LUT:  14%|█▎        | 418842/3080448 [00:00<00:03, 844819.79px/s]📤 Writing LUT:  16%|█▋        | 504111/3080448 [00:00<00:03, 847494.56px/s]📤 Writing LUT:  19%|█▉        | 589412/3080448 [00:00<00:02, 849294.63px/s]📤 Writing LUT:  22%|██▏       | 675045/3080448 [00:00<00:02, 851532.02px/s]📤 Writing LUT:  25%|██▍       | 760208/3080448 [00:00<00:02, 851561.00px/s]📤 Writing LUT:  27%|██▋       | 845726/3080448 [00:01<00:02, 852676.00px/s]📤 Writing LUT:  30%|███       | 930994/3080448 [00:01<00:02, 852118.50px/s]📤 Writing LUT:  33%|███▎      | 1016443/3080448 [00:01<00:02, 852839.23px/s]📤 Writing LUT:  36%|███▌      | 1101907/3080448 [00:01<00:02, 853384.20px/s]📤 Writing LUT:  39%|███▊      | 1187246/3080448 [00:01<00:02, 851386.05px/s]📤 Writing LUT:  41%|████▏     | 1272473/3080448 [00:01<00:02, 851648.60px/s]📤 Writing LUT:  44%|████▍     | 1361791/3080448 [00:01<00:01, 864131.61px/s]📤 Writing LUT:  47%|████▋     | 1451617/3080448 [00:01<00:01, 874384.57px/s]📤 Writing LUT:  50%|████▉     | 1539776/3080448 [00:01<00:01, 876548.01px/s]📤 Writing LUT:  53%|█████▎    | 1627965/3080448 [00:01<00:01, 878150.74px/s]📤 Writing LUT:  56%|█████▌    | 1716394/3080448 [00:02<00:01, 879991.16px/s]📤 Writing LUT:  59%|█████▊    | 1804395/3080448 [00:02<00:01, 877952.98px/s]📤 Writing LUT:  61%|██████▏   | 1892193/3080448 [00:02<00:01, 877026.07px/s]📤 Writing LUT:  64%|██████▍   | 1979897/3080448 [00:02<00:01, 873517.10px/s]📤 Writing LUT:  67%|██████▋   | 2067253/3080448 [00:02<00:01, 872616.97px/s]📤 Writing LUT:  70%|██████▉   | 2154558/3080448 [00:02<00:01, 872745.42px/s]📤 Writing LUT:  73%|███████▎  | 2241938/3080448 [00:02<00:00, 873059.39px/s]📤 Writing LUT:  76%|███████▌  | 2329729/3080448 [00:02<00:00, 874509.80px/s]📤 Writing LUT:  78%|███████▊  | 2417182/3080448 [00:02<00:00, 874433.64px/s]📤 Writing LUT:  81%|████████▏ | 2505348/3080448 [00:02<00:00, 876599.18px/s]📤 Writing LUT:  84%|████████▍ | 2593831/3080448 [00:03<00:00, 879065.83px/s]📤 Writing LUT:  87%|████████▋ | 2682075/3080448 [00:03<00:00, 880076.62px/s]📤 Writing LUT:  90%|████████▉ | 2770427/3080448 [00:03<00:00, 881107.67px/s]📤 Writing LUT:  93%|█████████▎| 2858668/3080448 [00:03<00:00, 881497.90px/s]📤 Writing LUT:  96%|█████████▌| 2947543/3080448 [00:03<00:00, 883672.81px/s]📤 Writing LUT:  99%|█████████▊| 3036967/3080448 [00:03<00:00, 886840.34px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 867580.70px/s]
[✅] LUT written to: /working/MDC033_W017(6)_2026-09-28/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC033_W017(6)_2026-09-28' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC033_W017(6)_2026-09-28/calibration/MDC033_W017(6)_2026-09-28_unknown.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)
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                                                                [A[✅] 1000: /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/1000_heatmap_fitted.png
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                                                                 [A[✅] 2000R: /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
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                                                                 [A[✅] 2000L: /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
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   🔥 1500:  86%|████████▋ | 615/713 [00:07<00:01, 85.94frame/s][A
   🔥 1500:  88%|████████▊ | 625/713 [00:07<00:01, 87.72frame/s][A
   🔥 1500:  89%|████████▉ | 634/713 [00:07<00:00, 87.44frame/s][A
   🔥 1500:  90%|█████████ | 643/713 [00:07<00:00, 87.36frame/s][A
   🔥 1500:  91%|█████████▏| 652/713 [00:07<00:00, 87.04frame/s][A
   🔥 1500:  93%|█████████▎| 661/713 [00:08<00:00, 86.89frame/s][A
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   🔥 1500:  99%|█████████▉| 706/713 [00:08<00:00, 86.66frame/s][A
                                                                [A[✅] 1500: /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap/1500_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:30<00:00,  8.02s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:30<00:00,  7.55s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC033_W017(6)_2026-09-28/calibration/debug/5_heatmap
[🏁] Finished processing MDC033_W017(6)_2026-09-28


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