2026-05-17T23:08:07 - INFO: Slick 0.0.8a11

🟢 Starting Calibration Pipeline

=== Processing MDC015_W068_2026-04-03 ===
2026-05-17T23:08:07 - INFO: Camera ID: 14302215
2026-05-17T23:08:07 - INFO: Wand ID: 
2026-05-17T23:08:07 - INFO: Recorded At: 2026-04-13T16:13:37Z

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

[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 1052 stationary frames, 215 remain


→ 500 (215 images) Phase 1:   0%|          | 0/215 [00:00<?, ?it/s]

→ 500 (215 images) Phase 1:   0%|          | 1/215 [00:00<01:56,  1.84it/s]

→ 500 (215 images) Phase 1:  15%|█▌        | 33/215 [00:01<00:04, 38.14it/s]

→ 500 (215 images) Phase 1:  18%|█▊        | 39/215 [00:01<00:04, 40.55it/s]

→ 500 (215 images) Phase 1:  30%|███       | 65/215 [00:01<00:02, 55.92it/s]

→ 500 (215 images) Phase 1:  33%|███▎      | 71/215 [00:01<00:02, 55.14it/s]

→ 500 (215 images) Phase 1:  45%|████▌     | 97/215 [00:01<00:01, 65.14it/s]

→ 500 (215 images) Phase 1:  48%|████▊     | 104/215 [00:02<00:01, 64.08it/s]

→ 500 (215 images) Phase 1:  60%|██████    | 129/215 [00:02<00:01, 69.61it/s]

→ 500 (215 images) Phase 1:  64%|██████▎   | 137/215 [00:02<00:01, 69.02it/s]

→ 500 (215 images) Phase 1:  75%|███████▍  | 161/215 [00:02<00:00, 71.28it/s]

→ 500 (215 images) Phase 1:  79%|███████▊  | 169/215 [00:02<00:00, 71.42it/s]

→ 500 (215 images) Phase 1:  90%|████████▉ | 193/215 [00:03<00:00, 85.55it/s]

→ 500 (215 images) Phase 1:  94%|█████████▍| 202/215 [00:03<00:00, 83.56it/s]

→ 500 (215 images) Phase 1: 100%|██████████| 215/215 [00:03<00:00, 92.31it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/215 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/215 [00:00<00:38,  5.61it/s]

→ 500 Phase 2: corners:   8%|▊         | 18/215 [00:00<00:02, 78.62it/s]

→ 500 Phase 2: corners:  13%|█▎        | 29/215 [00:00<00:02, 83.54it/s]

→ 500 Phase 2: corners:  19%|█▉        | 41/215 [00:00<00:01, 95.47it/s]

→ 500 Phase 2: corners:  25%|██▍       | 53/215 [00:00<00:01, 100.36it/s]

→ 500 Phase 2: corners:  32%|███▏      | 68/215 [00:00<00:01, 108.93it/s]

→ 500 Phase 2: corners:  37%|███▋      | 80/215 [00:00<00:01, 111.76it/s]

→ 500 Phase 2: corners:  43%|████▎     | 92/215 [00:00<00:01, 104.83it/s]

→ 500 Phase 2: corners:  51%|█████     | 110/215 [00:01<00:00, 125.81it/s]

→ 500 Phase 2: corners:  58%|█████▊    | 124/215 [00:01<00:00, 126.13it/s]

→ 500 Phase 2: corners:  66%|██████▌   | 142/215 [00:01<00:00, 138.53it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 157/215 [00:01<00:00, 127.95it/s]

→ 500 Phase 2: corners:  80%|████████  | 173/215 [00:01<00:00, 135.19it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 188/215 [00:01<00:00, 136.55it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 213/215 [00:01<00:00, 167.74it/s]

                                                                          [PASS2] Summary: 0 frames fixed, 0 untrusted, 215 total


→ 500 Phase 3: finalize:   0%|          | 0/215 [00:00<?, ?it/s]

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→ 500 Phase 3: finalize:  22%|██▏       | 47/215 [00:00<00:03, 52.97it/s]

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→ 500 Phase 3: finalize:  30%|███       | 65/215 [00:01<00:02, 53.58it/s]

→ 500 Phase 3: finalize:  33%|███▎      | 71/215 [00:01<00:02, 52.97it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 77/215 [00:01<00:02, 52.86it/s]

→ 500 Phase 3: finalize:  39%|███▊      | 83/215 [00:01<00:02, 52.83it/s]

→ 500 Phase 3: finalize:  41%|████▏     | 89/215 [00:01<00:02, 52.91it/s]

→ 500 Phase 3: finalize:  44%|████▍     | 95/215 [00:01<00:02, 52.91it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 101/215 [00:01<00:02, 52.96it/s]

→ 500 Phase 3: finalize:  50%|████▉     | 107/215 [00:02<00:02, 53.26it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 113/215 [00:02<00:01, 53.38it/s]

→ 500 Phase 3: finalize:  55%|█████▌    | 119/215 [00:02<00:01, 53.33it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 125/215 [00:02<00:01, 53.03it/s]

→ 500 Phase 3: finalize:  61%|██████    | 131/215 [00:02<00:01, 52.59it/s]

→ 500 Phase 3: finalize:  64%|██████▎   | 137/215 [00:02<00:01, 52.65it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 143/215 [00:02<00:01, 52.87it/s]

→ 500 Phase 3: finalize:  69%|██████▉   | 149/215 [00:02<00:01, 53.11it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 155/215 [00:02<00:01, 53.31it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 161/215 [00:03<00:01, 53.42it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 167/215 [00:03<00:00, 53.46it/s]

→ 500 Phase 3: finalize:  80%|████████  | 173/215 [00:03<00:00, 53.50it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 179/215 [00:03<00:00, 53.53it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 185/215 [00:03<00:00, 53.68it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 191/215 [00:03<00:00, 53.72it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 197/215 [00:03<00:00, 53.72it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 203/215 [00:03<00:00, 53.65it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 209/215 [00:03<00:00, 53.37it/s]

→ 500 Phase 3: finalize: 100%|██████████| 215/215 [00:04<00:00, 53.44it/s]

                                                                          
[TIMING] 500 — 215 frames
  Phase 1  radial detect   (parallel)  :   3.40s  (15.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.79s  (8.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.04s
  Phase 3  finalize        (sequential):   4.05s  (18.8 ms/frame)
    decode + undistort                 :   2.19s  (10.2 ms/frame)
    corner apply + refinement          :   0.82s  (3.8 ms/frame)
    segment                            :   0.85s  (4.0 ms/frame)
    smooth                             :   0.12s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.29s


Processing Rigs:  50%|█████     | 1/2 [00:14<00:14, 14.94s/it][INFO] Filtered 1060 stationary frames, 234 remain


→ 1000 (234 images) Phase 1:   0%|          | 0/234 [00:00<?, ?it/s]

→ 1000 (234 images) Phase 1:   0%|          | 1/234 [00:00<02:15,  1.72it/s]

→ 1000 (234 images) Phase 1:  14%|█▍        | 33/234 [00:00<00:05, 40.18it/s]

→ 1000 (234 images) Phase 1:  27%|██▋       | 64/234 [00:01<00:02, 77.21it/s]

→ 1000 (234 images) Phase 1:  33%|███▎      | 78/234 [00:01<00:02, 60.10it/s]

→ 1000 (234 images) Phase 1:  41%|████▏     | 97/234 [00:01<00:02, 56.86it/s]

→ 1000 (234 images) Phase 1:  51%|█████     | 119/234 [00:01<00:01, 77.72it/s]

→ 1000 (234 images) Phase 1:  56%|█████▋    | 132/234 [00:02<00:01, 61.05it/s]

→ 1000 (234 images) Phase 1:  67%|██████▋   | 157/234 [00:02<00:00, 86.60it/s]

→ 1000 (234 images) Phase 1:  74%|███████▎  | 172/234 [00:02<00:00, 67.51it/s]

→ 1000 (234 images) Phase 1:  82%|████████▏ | 193/234 [00:03<00:00, 63.94it/s]

→ 1000 (234 images) Phase 1:  91%|█████████ | 213/234 [00:03<00:00, 81.10it/s]

→ 1000 (234 images) Phase 1:  97%|█████████▋| 226/234 [00:03<00:00, 77.65it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/234 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/234 [00:00<00:48,  4.85it/s]

→ 1000 Phase 2: corners:   6%|▋         | 15/234 [00:00<00:03, 58.23it/s]

→ 1000 Phase 2: corners:  11%|█         | 26/234 [00:00<00:02, 76.89it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 39/234 [00:00<00:02, 94.92it/s]

→ 1000 Phase 2: corners:  22%|██▏       | 51/234 [00:00<00:01, 100.99it/s]

→ 1000 Phase 2: corners:  29%|██▊       | 67/234 [00:00<00:01, 114.88it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 80/234 [00:00<00:01, 111.24it/s]

→ 1000 Phase 2: corners:  39%|███▉      | 92/234 [00:00<00:01, 107.73it/s]

→ 1000 Phase 2: corners:  45%|████▌     | 106/234 [00:01<00:01, 116.73it/s]

→ 1000 Phase 2: corners:  50%|█████     | 118/234 [00:01<00:01, 111.86it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 134/234 [00:01<00:00, 121.24it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 150/234 [00:01<00:00, 129.64it/s]

→ 1000 Phase 2: corners:  71%|███████   | 165/234 [00:01<00:00, 131.49it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 182/234 [00:01<00:00, 141.25it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 199/234 [00:01<00:00, 145.91it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 215/234 [00:01<00:00, 149.51it/s]

                                                                           [PASS2] Frame 0000182: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000413: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000414: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000415: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000416: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000417: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000418: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000419: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000420: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000421: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000426: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000427: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000428: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000548: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000565: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000566: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000567: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000568: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000706: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000707: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000708: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000709: fixed corners {0, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000710: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000711: fixed corners {1, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000712: fixed corners {1, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000713: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000714: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000715: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001049: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001050: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001051: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001052: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001053: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001054: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001055: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001056: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001057: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001058: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001059: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001060: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001061: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001062: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001063: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001064: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001065: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001067: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001068: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001175: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001176: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001177: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001178: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001179: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001181: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001182: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001183: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001184: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001203: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001204: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001205: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001206: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001208: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001317: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001318: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001319: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001321: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001322: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001323: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001324: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001326: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001327: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001328: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001330: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001331: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001332: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001336: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001337: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001338: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001339: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000420: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000428: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000707: fixed corners {1, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000714: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001050: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001068: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001177: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001178: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001183: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001205: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001318: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001323: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001328: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001329: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001331: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001338: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000417: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000418: fixed corners {0, 1, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000707: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001050: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001329: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Summary: 45 frames fixed, 55 untrusted, 234 total


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→ 1000 Phase 3: finalize:  33%|███▎      | 78/234 [00:01<00:03, 51.34it/s]

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→ 1000 Phase 3: finalize:  38%|███▊      | 90/234 [00:01<00:02, 49.40it/s]

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→ 1000 Phase 3: finalize:  45%|████▍     | 105/234 [00:02<00:02, 48.34it/s]

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→ 1000 Phase 3: finalize:  53%|█████▎    | 125/234 [00:02<00:02, 47.82it/s]

→ 1000 Phase 3: finalize:  56%|█████▌    | 130/234 [00:02<00:02, 47.75it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 135/234 [00:02<00:02, 47.77it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 140/234 [00:02<00:01, 47.65it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 145/234 [00:02<00:01, 47.70it/s]

→ 1000 Phase 3: finalize:  64%|██████▍   | 150/234 [00:02<00:01, 48.13it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 156/234 [00:03<00:01, 48.74it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 162/234 [00:03<00:01, 50.28it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 168/234 [00:03<00:01, 51.62it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 174/234 [00:03<00:01, 52.12it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 180/234 [00:03<00:01, 52.62it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 186/234 [00:03<00:00, 52.98it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 192/234 [00:03<00:00, 53.28it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 198/234 [00:03<00:00, 53.47it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 204/234 [00:03<00:00, 53.77it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 210/234 [00:04<00:00, 54.03it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 216/234 [00:04<00:00, 54.11it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 222/234 [00:04<00:00, 53.86it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 228/234 [00:04<00:00, 53.65it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 234/234 [00:04<00:00, 53.34it/s]

                                                                           
[TIMING] 1000 — 234 frames
  Phase 1  radial detect   (parallel)  :   3.57s  (15.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.99s  (8.5 ms/frame)
  Phase 2b corner fix      (sequential):   1.73s
  Phase 3  finalize        (sequential):   4.55s  (19.4 ms/frame)
    decode + undistort                 :   2.38s  (10.2 ms/frame)
    corner apply + refinement          :   1.02s  (4.4 ms/frame)
    segment                            :   0.93s  (4.0 ms/frame)
    smooth                             :   0.13s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  11.83s


Processing Rigs: 100%|██████████| 2/2 [00:31<00:00, 15.62s/it]
Processing Rigs: 100%|██████████| 2/2 [00:31<00:00, 15.52s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W068_2026-04-03/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 449 frames from 2 rigs to /working/MDC015_W068_2026-04-03/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W068_2026-04-03/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 215
[📋] 1000: 100 frames selected from 234
[💾] Saved RigFrameDataset to /working/MDC015_W068_2026-04-03/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W068_2026-04-03/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:03<02:02,  3.14s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<01:57,  3.10s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:54,  3.08s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:50,  3.08s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:47,  3.08s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:44,  3.08s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:21<01:41,  3.08s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:24<01:41,  3.16s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:38,  3.19s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:36,  3.22s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:34<01:34,  3.25s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:38<01:31,  3.27s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:41<01:27,  3.24s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:44<01:25,  3.30s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:47<01:20,  3.24s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:50<01:16,  3.19s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:53<01:12,  3.16s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:57<01:08,  3.13s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:00<01:05,  3.12s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:03<01:02,  3.11s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:06<00:59,  3.11s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:09<00:55,  3.09s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:12<00:52,  3.09s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:15<00:49,  3.09s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:18<00:46,  3.09s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:21<00:43,  3.09s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:24<00:40,  3.09s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:27<00:37,  3.09s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:30<00:33,  3.08s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:34<00:31,  3.16s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:37<00:28,  3.14s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:40<00:25,  3.15s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:43<00:21,  3.14s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:46<00:18,  3.15s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:49<00:15,  3.13s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:53<00:12,  3.11s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:56<00:09,  3.09s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [01:59<00:06,  3.09s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:02<00:03,  3.10s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:05<00:00,  3.09s/it]
                                                                                       
[✅] Casing detection complete: cx=999.0, cy=743.0

-------------------------------------------------------------------
                        Step 3: Calibration                        
-------------------------------------------------------------------


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   1%|          | 1/100 [00:00<00:26,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   1%|          | 1/100 [00:00<00:26,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   2%|▏         | 2/100 [00:00<00:26,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   3%|▎         | 3/100 [00:00<00:26,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   4%|▍         | 4/100 [00:00<00:25,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   5%|▌         | 5/100 [00:00<00:25,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:25,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   7%|▋         | 7/100 [00:00<00:24,  3.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   8%|▊         | 8/100 [00:00<00:04, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   8%|▊         | 8/100 [00:00<00:04, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   9%|▉         | 9/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  10%|█         | 10/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  11%|█         | 11/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.063 | Best: {'fx': 526.263} (0.063):  13%|█▎        | 13/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.033 | Best: {'fx': 526.263} (0.063):  14%|█▍        | 14/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.099 | Best: {'fx': 533.333} (0.099):  15%|█▌        | 15/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  16%|█▌        | 16/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  17%|█▋        | 17/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.033 | Best: {'fx': 533.333} (0.099):  18%|█▊        | 18/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.048 | Best: {'fx': 533.333} (0.099):  19%|█▉        | 19/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  20%|██        | 20/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.027 | Best: {'fx': 533.333} (0.099):  21%|██        | 21/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  22%|██▏       | 22/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.099):  23%|██▎       | 23/100 [00:00<00:03, 23.00it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.099):  24%|██▍       | 24/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  24%|██▍       | 24/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  25%|██▌       | 25/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.099):  26%|██▌       | 26/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  27%|██▋       | 27/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.020 | Best: {'fx': 533.333} (0.099):  28%|██▊       | 28/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.018 | Best: {'fx': 533.333} (0.099):  29%|██▉       | 29/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  30%|███       | 30/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  31%|███       | 31/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.099):  32%|███▏      | 32/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  33%|███▎      | 33/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  34%|███▍      | 34/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.099):  35%|███▌      | 35/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.099):  36%|███▌      | 36/100 [00:00<00:01, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  37%|███▋      | 37/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.099):  38%|███▊      | 38/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.099):  39%|███▉      | 39/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.099):  40%|████      | 40/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.099):  41%|████      | 41/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.099):  42%|████▏     | 42/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.099):  43%|████▎     | 43/100 [00:00<00:00, 63.54it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.099):  44%|████▍     | 44/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.099):  44%|████▍     | 44/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.099):  45%|████▌     | 45/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.099):  46%|████▌     | 46/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.099):  47%|████▋     | 47/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 533.333} (0.099):  48%|████▊     | 48/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.099):  49%|████▉     | 49/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.099):  50%|█████     | 50/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  51%|█████     | 51/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.099):  52%|█████▏    | 52/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.099):  53%|█████▎    | 53/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.005 | Best: {'fx': 533.333} (0.099):  54%|█████▍    | 54/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  55%|█████▌    | 55/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  56%|█████▌    | 56/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  57%|█████▋    | 57/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  58%|█████▊    | 58/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  59%|█████▉    | 59/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  60%|██████    | 60/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  61%|██████    | 61/100 [00:00<00:00, 103.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  62%|██████▏   | 62/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  62%|██████▏   | 62/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  63%|██████▎   | 63/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  64%|██████▍   | 64/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.099):  65%|██████▌   | 65/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  66%|██████▌   | 66/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  67%|██████▋   | 67/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  68%|██████▊   | 68/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  69%|██████▉   | 69/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  70%|███████   | 70/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  71%|███████   | 71/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  72%|███████▏  | 72/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  73%|███████▎  | 73/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  74%|███████▍  | 74/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  75%|███████▌  | 75/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  76%|███████▌  | 76/100 [00:00<00:00, 125.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  77%|███████▋  | 77/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  77%|███████▋  | 77/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  78%|███████▊  | 78/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  79%|███████▉  | 79/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  80%|████████  | 80/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  81%|████████  | 81/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  82%|████████▏ | 82/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  83%|████████▎ | 83/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  84%|████████▍ | 84/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  85%|████████▌ | 85/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  86%|████████▌ | 86/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  87%|████████▋ | 87/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  88%|████████▊ | 88/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  89%|████████▉ | 89/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  90%|█████████ | 90/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  91%|█████████ | 91/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  92%|█████████▏| 92/100 [00:00<00:00, 127.28it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  93%|█████████▎| 93/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  93%|█████████▎| 93/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  94%|█████████▍| 94/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  95%|█████████▌| 95/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  96%|█████████▌| 96/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  97%|█████████▋| 97/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  98%|█████████▊| 98/100 [00:00<00:00, 135.63it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.099):  99%|█████████▉| 99/100 [00:00<00:00, 135.63it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   2%|▏         | 1/50 [00:00<00:06,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.014 | Best: {'fx': 509.354} (0.014):   2%|▏         | 1/50 [00:00<00:06,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.018 | Best: {'fx': 510.374} (0.018):   4%|▍         | 2/50 [00:00<00:06,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.021 | Best: {'fx': 511.395} (0.021):   6%|▌         | 3/50 [00:00<00:06,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.026 | Best: {'fx': 515.476} (0.026):   8%|▊         | 4/50 [00:00<00:06,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.022 | Best: {'fx': 515.476} (0.026):  10%|█         | 5/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.024 | Best: {'fx': 515.476} (0.026):  12%|█▏        | 6/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.025 | Best: {'fx': 515.476} (0.026):  14%|█▍        | 7/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  16%|█▌        | 8/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.028 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.032 | Best: {'fx': 518.537} (0.032):  20%|██        | 10/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.035 | Best: {'fx': 519.558} (0.035):  22%|██▏       | 11/50 [00:00<00:05,  7.56it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.035 | Best: {'fx': 519.558} (0.035):  24%|██▍       | 12/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.042 | Best: {'fx': 522.619} (0.042):  24%|██▍       | 12/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.037 | Best: {'fx': 522.619} (0.042):  26%|██▌       | 13/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.040 | Best: {'fx': 522.619} (0.042):  28%|██▊       | 14/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.047 | Best: {'fx': 523.639} (0.047):  30%|███       | 15/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.052 | Best: {'fx': 524.66} (0.052):  32%|███▏      | 16/50 [00:00<00:00, 60.07it/s] 
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.065 | Best: {'fx': 526.701} (0.065):  34%|███▍      | 17/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.059 | Best: {'fx': 526.701} (0.065):  36%|███▌      | 18/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.100 | Best: {'fx': 530.782} (0.100):  38%|███▊      | 19/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.077 | Best: {'fx': 530.782} (0.100):  40%|████      | 20/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.088 | Best: {'fx': 530.782} (0.100):  42%|████▏     | 21/50 [00:00<00:00, 60.07it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.088 | Best: {'fx': 530.782} (0.100):  44%|████▍     | 22/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.098 | Best: {'fx': 530.782} (0.100):  44%|████▍     | 22/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.080 | Best: {'fx': 530.782} (0.100):  46%|████▌     | 23/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.098 | Best: {'fx': 530.782} (0.100):  48%|████▊     | 24/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.095 | Best: {'fx': 530.782} (0.100):  50%|█████     | 25/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.072 | Best: {'fx': 530.782} (0.100):  52%|█████▏    | 26/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.088 | Best: {'fx': 530.782} (0.100):  54%|█████▍    | 27/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.065 | Best: {'fx': 530.782} (0.100):  56%|█████▌    | 28/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.055 | Best: {'fx': 530.782} (0.100):  58%|█████▊    | 29/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.061 | Best: {'fx': 530.782} (0.100):  60%|██████    | 30/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.051 | Best: {'fx': 530.782} (0.100):  62%|██████▏   | 31/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.047 | Best: {'fx': 530.782} (0.100):  64%|██████▍   | 32/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.045 | Best: {'fx': 530.782} (0.100):  66%|██████▌   | 33/50 [00:00<00:00, 75.69it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.045 | Best: {'fx': 530.782} (0.100):  68%|██████▊   | 34/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.041 | Best: {'fx': 530.782} (0.100):  68%|██████▊   | 34/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.040 | Best: {'fx': 530.782} (0.100):  70%|███████   | 35/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.037 | Best: {'fx': 530.782} (0.100):  72%|███████▏  | 36/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.033 | Best: {'fx': 530.782} (0.100):  74%|███████▍  | 37/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.036 | Best: {'fx': 530.782} (0.100):  76%|███████▌  | 38/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.031 | Best: {'fx': 530.782} (0.100):  78%|███████▊  | 39/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.034 | Best: {'fx': 530.782} (0.100):  80%|████████  | 40/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.030 | Best: {'fx': 530.782} (0.100):  82%|████████▏ | 41/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.029 | Best: {'fx': 530.782} (0.100):  84%|████████▍ | 42/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.028 | Best: {'fx': 530.782} (0.100):  86%|████████▌ | 43/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.028 | Best: {'fx': 530.782} (0.100):  88%|████████▊ | 44/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.024 | Best: {'fx': 530.782} (0.100):  90%|█████████ | 45/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.027 | Best: {'fx': 530.782} (0.100):  92%|█████████▏| 46/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.025 | Best: {'fx': 530.782} (0.100):  94%|█████████▍| 47/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.026 | Best: {'fx': 530.782} (0.100):  96%|█████████▌| 48/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.024 | Best: {'fx': 530.782} (0.100):  98%|█████████▊| 49/50 [00:00<00:00, 91.72it/s]
Brute | Start: {'fx': 533.333} (0.099) | Current: 0.024 | Best: {'fx': 530.782} (0.100): 100%|██████████| 50/50 [00:00<00:00, 112.56it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 999.0
    py: 743.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 1 incomplete frames): 19 frames will be used for optimization
  Rigs: 1000(1000mm):19/20
  Total: 19/20 frames used for optimization

Initial score: 2.7448: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 2.7448, picklable_objective(x0) = 2.7370

Best score: 2.7370: : 0iter [00:01, ?iter/s] 
Best score: 2.7370: : 1iter [00:01,  1.85s/iter]
Best score: 2.7370: : 1iter [00:01,  1.85s/iter]
Best score: 2.7370: : 2iter [00:01,  1.85s/iter]
Best score: 2.7370: : 3iter [00:02,  1.85s/iter]
Best score: 2.7370: : 4iter [00:02,  2.50iter/s]
Best score: 2.7370: : 4iter [00:02,  2.50iter/s]
Best score: 2.7370: : 5iter [00:02,  2.50iter/s]
Best score: 2.7370: : 6iter [00:02,  2.50iter/s]
Best score: 2.7370: : 7iter [00:02,  2.50iter/s]
Best score: 2.7370: : 8iter [00:02,  2.50iter/s]
Best score: 2.7370: : 9iter [00:02,  6.56iter/s]
Best score: 2.7370: : 9iter [00:02,  6.56iter/s]
Best score: 2.7370: : 10iter [00:02,  6.56iter/s]
Best score: 2.7370: : 11iter [00:02,  6.56iter/s]
Best score: 2.7370: : 12iter [00:02,  6.56iter/s]
Best score: 2.7370: : 13iter [00:02,  6.56iter/s]
Best score: 2.7370: : 14iter [00:02, 11.06iter/s]
Best score: 2.7370: : 14iter [00:02, 11.06iter/s]
Best score: 2.7370: : 15iter [00:02, 11.06iter/s]
Best score: 2.7370: : 16iter [00:02, 11.06iter/s]
Best score: 2.7370: : 17iter [00:02, 11.06iter/s]
Best score: 2.7370: : 18iter [00:02, 11.06iter/s]
Best score: 2.7370: : 19iter [00:02, 15.80iter/s]
Best score: 2.7370: : 19iter [00:02, 15.80iter/s]
Best score: 2.7370: : 20iter [00:02, 15.80iter/s]
Best score: 2.7370: : 21iter [00:02, 15.80iter/s]
Best score: 2.7370: : 22iter [00:02, 15.80iter/s]
Best score: 2.7370: : 23iter [00:02, 15.80iter/s]
Best score: 2.7370: : 24iter [00:02, 20.44iter/s]
Best score: 2.7370: : 24iter [00:02, 20.44iter/s]
Best score: 2.7370: : 25iter [00:02, 20.44iter/s]
Best score: 2.7370: : 26iter [00:02, 20.44iter/s]
Best score: 2.7370: : 27iter [00:02, 20.44iter/s]
Best score: 2.7370: : 28iter [00:02, 20.44iter/s]
Best score: 2.7370: : 29iter [00:02, 24.79iter/s]
Best score: 2.7370: : 29iter [00:02, 24.79iter/s]
Best score: 2.7370: : 30iter [00:02, 24.79iter/s]
Best score: 2.7370: : 31iter [00:02, 24.79iter/s]
Best score: 2.7370: : 32iter [00:02, 24.79iter/s]
Best score: 2.7370: : 33iter [00:02, 24.79iter/s]
Best score: 2.7370: : 34iter [00:02, 28.48iter/s]
Best score: 2.7370: : 34iter [00:02, 28.48iter/s]
Best score: 2.7370: : 35iter [00:02, 28.48iter/s]
Best score: 2.7370: : 36iter [00:02, 28.48iter/s]
Best score: 2.7370: : 37iter [00:02, 28.48iter/s]
Best score: 2.7370: : 38iter [00:02, 28.48iter/s]
Best score: 2.7370: : 39iter [00:02, 31.68iter/s]
Best score: 2.7370: : 39iter [00:02, 31.68iter/s]
Best score: 2.7370: : 40iter [00:02, 31.68iter/s]
Best score: 2.7370: : 41iter [00:02, 31.68iter/s]
Best score: 2.7370: : 42iter [00:02, 31.68iter/s]
Best score: 2.7370: : 43iter [00:03, 31.68iter/s]
Best score: 2.7370: : 44iter [00:03, 34.13iter/s]
Best score: 2.7370: : 44iter [00:03, 34.13iter/s]
Best score: 2.7370: : 45iter [00:03, 34.13iter/s]
Best score: 2.7370: : 46iter [00:03, 34.13iter/s]
Best score: 2.7370: : 47iter [00:03, 34.13iter/s]
Best score: 2.7370: : 48iter [00:03, 34.13iter/s]
Best score: 2.7370: : 49iter [00:03, 36.13iter/s]
[ITER 50] Best: 2.7370
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 999.0000
  py: 743.0000
  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: 2.7370: : 49iter [00:03, 36.13iter/s]
Best score: 2.7370: : 50iter [00:03, 36.13iter/s]
Best score: 2.7370: : 51iter [00:03, 36.13iter/s]
Best score: 2.7370: : 52iter [00:03, 36.13iter/s]
Best score: 2.7370: : 53iter [00:03, 36.13iter/s]
Best score: 2.7370: : 54iter [00:03, 37.42iter/s]
Best score: 2.7370: : 54iter [00:03, 37.42iter/s]
Best score: 2.7370: : 55iter [00:03, 37.42iter/s]
Best score: 2.7370: : 56iter [00:03, 37.42iter/s]
Best score: 2.7370: : 57iter [00:03, 37.42iter/s]
Best score: 2.7370: : 58iter [00:03, 37.42iter/s]
Best score: 2.7370: : 59iter [00:03, 38.48iter/s]
Best score: 2.7370: : 59iter [00:03, 38.48iter/s]
Best score: 2.7370: : 60iter [00:03, 38.48iter/s]
Best score: 2.7370: : 61iter [00:03, 38.48iter/s]
Best score: 2.7370: : 62iter [00:03, 38.48iter/s]
Best score: 2.7370: : 63iter [00:03, 38.48iter/s]
Best score: 2.7370: : 64iter [00:03, 39.16iter/s]
Best score: 2.7370: : 64iter [00:03, 39.16iter/s]
Best score: 2.7370: : 65iter [00:03, 39.16iter/s]
Best score: 2.7370: : 66iter [00:03, 39.16iter/s]
Best score: 2.7370: : 67iter [00:03, 39.16iter/s]
Best score: 2.7370: : 68iter [00:03, 39.16iter/s]
Best score: 2.7370: : 69iter [00:03, 39.90iter/s]
Best score: 2.7370: : 69iter [00:03, 39.90iter/s]
Best score: 2.7370: : 70iter [00:03, 39.90iter/s]
Best score: 2.7370: : 71iter [00:03, 39.90iter/s]
Best score: 2.7370: : 72iter [00:03, 39.90iter/s]
Best score: 2.7370: : 73iter [00:03, 39.90iter/s]
Best score: 2.7370: : 74iter [00:03, 40.31iter/s]
Best score: 2.7370: : 74iter [00:03, 40.31iter/s]
Best score: 2.7370: : 75iter [00:03, 40.31iter/s]
Best score: 2.7370: : 76iter [00:03, 40.31iter/s]
Best score: 2.7370: : 77iter [00:03, 40.31iter/s]
Best score: 2.7370: : 78iter [00:03, 40.31iter/s]
Best score: 2.7370: : 79iter [00:03, 40.68iter/s]
Best score: 2.7370: : 79iter [00:03, 40.68iter/s]
Best score: 2.7370: : 80iter [00:03, 40.68iter/s]
Best score: 2.7370: : 81iter [00:03, 40.68iter/s]
Best score: 2.7370: : 82iter [00:03, 40.68iter/s]
Best score: 2.7370: : 83iter [00:03, 40.68iter/s]
Best score: 2.7370: : 84iter [00:03, 40.82iter/s]
Best score: 2.7370: : 84iter [00:03, 40.82iter/s]
Best score: 2.7370: : 85iter [00:04, 40.82iter/s]
Best score: 2.7370: : 86iter [00:04, 40.82iter/s]
Best score: 2.7370: : 87iter [00:04, 40.82iter/s]
Best score: 2.7370: : 88iter [00:04, 40.82iter/s]
Best score: 2.7370: : 89iter [00:04, 41.18iter/s]
Best score: 2.7370: : 89iter [00:04, 41.18iter/s]
Best score: 2.7370: : 90iter [00:04, 41.18iter/s]
Best score: 2.7370: : 91iter [00:04, 41.18iter/s]
Best score: 2.7370: : 92iter [00:04, 41.18iter/s]
Best score: 2.7370: : 93iter [00:04, 41.18iter/s]
Best score: 2.7370: : 94iter [00:04, 41.13iter/s]
Best score: 2.7370: : 94iter [00:04, 41.13iter/s]
Best score: 2.7370: : 95iter [00:04, 41.13iter/s]
Best score: 2.7370: : 96iter [00:04, 41.13iter/s]
Best score: 2.7370: : 97iter [00:04, 41.13iter/s]
Best score: 2.7370: : 98iter [00:04, 41.13iter/s]
Best score: 2.7370: : 99iter [00:04, 41.37iter/s]
[ITER 100] Best: 2.7370
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 999.0000
  py: 743.0000
  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: 2.7370: : 99iter [00:04, 41.37iter/s]
Best score: 2.7370: : 100iter [00:04, 41.37iter/s]
Best score: 2.7370: : 101iter [00:04, 41.37iter/s]
Best score: 2.7370: : 102iter [00:04, 41.37iter/s]
Best score: 2.7370: : 103iter [00:04, 41.37iter/s]
Best score: 2.7370: : 104iter [00:04, 39.00iter/s]
Best score: 2.7370: : 104iter [00:04, 39.00iter/s]
Best score: 2.7370: : 105iter [00:04, 39.00iter/s]
Best score: 2.7370: : 106iter [00:04, 39.00iter/s]
Best score: 2.7370: : 107iter [00:04, 39.00iter/s]
Best score: 2.7370: : 108iter [00:04, 39.00iter/s]
Best score: 2.7370: : 109iter [00:04, 39.62iter/s]
Best score: 2.7370: : 109iter [00:04, 39.62iter/s]
Best score: 2.7370: : 110iter [00:04, 39.62iter/s]
Best score: 2.7370: : 111iter [00:04, 39.62iter/s]
Best score: 2.7370: : 112iter [00:04, 39.62iter/s]
Best score: 2.7370: : 113iter [00:04, 39.62iter/s]
Best score: 2.7370: : 114iter [00:04, 39.92iter/s]
Best score: 2.7370: : 114iter [00:04, 39.92iter/s]
Best score: 2.7370: : 115iter [00:04, 39.92iter/s]
Best score: 2.7370: : 116iter [00:04, 39.92iter/s]
Best score: 2.7370: : 117iter [00:04, 39.92iter/s]
Best score: 2.7370: : 118iter [00:04, 39.92iter/s]
Best score: 2.7370: : 119iter [00:04, 40.50iter/s]
Best score: 2.7370: : 119iter [00:04, 40.50iter/s]
Best score: 2.7370: : 120iter [00:04, 40.50iter/s]
Best score: 2.7370: : 121iter [00:04, 40.50iter/s]
Best score: 2.7370: : 122iter [00:04, 40.50iter/s]
Best score: 2.7370: : 123iter [00:04, 40.50iter/s]
Best score: 2.7370: : 124iter [00:04, 40.68iter/s]
Best score: 2.7370: : 124iter [00:04, 40.68iter/s]
Best score: 2.7370: : 125iter [00:05, 40.68iter/s]
Best score: 2.7370: : 126iter [00:05, 40.68iter/s]
Best score: 2.7370: : 127iter [00:05, 40.68iter/s]
Best score: 2.7370: : 128iter [00:05, 40.68iter/s]
Best score: 2.7370: : 129iter [00:05, 40.86iter/s]
Best score: 2.7370: : 129iter [00:05, 40.86iter/s]
Best score: 2.7370: : 130iter [00:05, 40.86iter/s]
Best score: 2.7370: : 131iter [00:05, 40.86iter/s]
Best score: 2.7370: : 132iter [00:05, 40.86iter/s]
Best score: 2.7370: : 133iter [00:05, 40.86iter/s]
Best score: 2.7370: : 134iter [00:05, 40.85iter/s]
Best score: 2.7370: : 134iter [00:05, 40.85iter/s]
Best score: 2.7370: : 135iter [00:05, 40.85iter/s]
Best score: 2.7370: : 136iter [00:05, 40.85iter/s]
Best score: 2.7370: : 137iter [00:05, 40.85iter/s]
Best score: 2.7370: : 138iter [00:05, 40.85iter/s]
Best score: 2.7370: : 139iter [00:05, 41.07iter/s]
Best score: 2.7370: : 139iter [00:05, 41.07iter/s]
Best score: 2.7370: : 140iter [00:05, 41.07iter/s]
Best score: 2.7370: : 141iter [00:05, 41.07iter/s]
Best score: 2.7370: : 142iter [00:05, 41.07iter/s]
Best score: 2.7370: : 143iter [00:05, 41.07iter/s]
Best score: 2.7370: : 144iter [00:05, 41.13iter/s]
Best score: 2.7370: : 144iter [00:05, 41.13iter/s]
Best score: 2.7370: : 145iter [00:05, 41.13iter/s]
Best score: 2.7370: : 146iter [00:05, 41.13iter/s]
Best score: 2.7370: : 147iter [00:05, 41.13iter/s]
Best score: 2.7370: : 148iter [00:05, 41.13iter/s]
Best score: 2.7370: : 149iter [00:05, 41.28iter/s]
[ITER 150] Best: 2.7370
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 999.0000
  py: 743.0000
  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: 2.7370: : 149iter [00:05, 41.28iter/s]
Best score: 2.7370: : 150iter [00:05, 41.28iter/s]
Best score: 2.7370: : 151iter [00:05, 41.28iter/s]
Best score: 2.7370: : 152iter [00:05, 41.28iter/s]
Best score: 2.7370: : 153iter [00:05, 41.28iter/s]
Best score: 2.7370: : 154iter [00:05, 34.89iter/s]
Best score: 2.7370: : 154iter [00:05, 34.89iter/s]
Best score: 2.7370: : 155iter [00:05, 34.89iter/s]
Best score: 2.7370: : 156iter [00:05, 34.89iter/s]
Best score: 2.7370: : 157iter [00:05, 34.89iter/s]
Best score: 2.7370: : 158iter [00:05, 34.89iter/s]
Best score: 2.7370: : 159iter [00:05, 36.68iter/s]
Best score: 2.7370: : 159iter [00:05, 36.68iter/s]
Best score: 2.7370: : 160iter [00:05, 36.68iter/s]
Best score: 2.7370: : 161iter [00:05, 36.68iter/s]
Best score: 2.7370: : 162iter [00:05, 36.68iter/s]
Best score: 2.7370: : 163iter [00:06, 36.68iter/s]
Best score: 2.7370: : 164iter [00:06, 37.88iter/s]
Best score: 2.7370: : 164iter [00:06, 37.88iter/s]
Best score: 2.7370: : 165iter [00:06, 37.88iter/s]
Best score: 2.7370: : 166iter [00:06, 37.88iter/s]
Best score: 2.7370: : 167iter [00:06, 37.88iter/s]
Best score: 2.7370: : 168iter [00:06, 37.88iter/s]
Best score: 2.7370: : 169iter [00:06, 38.95iter/s]
Best score: 2.7370: : 169iter [00:06, 38.95iter/s]
Best score: 2.7370: : 170iter [00:06, 38.95iter/s]
Best score: 2.7370: : 171iter [00:06, 38.95iter/s]
Best score: 2.7370: : 172iter [00:06, 38.95iter/s]
Best score: 2.7370: : 173iter [00:06, 38.95iter/s]
Best score: 2.7370: : 174iter [00:06, 39.45iter/s]
Best score: 2.7370: : 174iter [00:06, 39.45iter/s]
Best score: 2.7370: : 175iter [00:06, 39.45iter/s]
Best score: 2.7370: : 176iter [00:06, 39.45iter/s]
Best score: 2.7370: : 177iter [00:06, 39.45iter/s]
Best score: 2.7370: : 178iter [00:06, 39.45iter/s]
Best score: 2.7370: : 179iter [00:06, 40.11iter/s]
Best score: 2.7370: : 179iter [00:06, 40.11iter/s]
Best score: 2.7370: : 180iter [00:06, 40.11iter/s]
Best score: 2.7370: : 181iter [00:06, 40.11iter/s]
Best score: 2.7370: : 182iter [00:06, 40.11iter/s]
Best score: 2.7370: : 183iter [00:06, 40.11iter/s]
Best score: 2.7370: : 184iter [00:06, 40.03iter/s]
Best score: 2.7370: : 184iter [00:06, 40.03iter/s]
Best score: 2.7370: : 185iter [00:06, 40.03iter/s]
Best score: 2.7370: : 186iter [00:06, 40.03iter/s]
Best score: 2.7370: : 187iter [00:06, 40.03iter/s]
Best score: 2.7370: : 188iter [00:06, 40.03iter/s]
Best score: 2.7370: : 189iter [00:06, 40.33iter/s]
Best score: 2.7370: : 189iter [00:06, 40.33iter/s]
Best score: 2.7370: : 190iter [00:06, 40.33iter/s]
Best score: 2.7370: : 191iter [00:06, 40.33iter/s]
Best score: 2.7370: : 192iter [00:06, 40.33iter/s]
Best score: 2.7370: : 193iter [00:06, 40.33iter/s]
Best score: 2.7370: : 194iter [00:06, 40.46iter/s]
Best score: 2.7370: : 194iter [00:06, 40.46iter/s]
Best score: 2.7370: : 195iter [00:06, 40.46iter/s]
Best score: 2.7370: : 196iter [00:06, 40.46iter/s]
Best score: 2.7370: : 197iter [00:06, 40.46iter/s]
Best score: 2.7370: : 198iter [00:06, 40.46iter/s]
Best score: 2.7370: : 199iter [00:06, 40.80iter/s]
[ITER 200] Best: 2.7370
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 999.0000
  py: 743.0000
  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: 2.7370: : 199iter [00:06, 40.80iter/s]
Best score: 2.7370: : 200iter [00:06, 40.80iter/s]
Best score: 2.7370: : 201iter [00:07, 40.80iter/s]
Best score: 2.7370: : 202iter [00:07, 40.80iter/s]
Best score: 2.7370: : 203iter [00:07, 40.80iter/s]
Best score: 2.7370: : 204iter [00:07, 34.73iter/s]
Best score: 2.7370: : 204iter [00:07, 34.73iter/s]
Best score: 2.7370: : 205iter [00:07, 34.73iter/s]
Best score: 2.7370: : 206iter [00:07, 34.73iter/s]
Best score: 2.7370: : 207iter [00:07, 34.73iter/s]
Best score: 2.7370: : 208iter [00:07, 34.73iter/s]
Best score: 2.7370: : 209iter [00:07, 36.70iter/s]
Best score: 2.7370: : 209iter [00:07, 36.70iter/s]
Best score: 2.7370: : 210iter [00:07, 36.70iter/s]
Best score: 2.7370: : 211iter [00:07, 36.70iter/s]
Best score: 2.7370: : 212iter [00:07, 36.70iter/s]
Best score: 2.7370: : 213iter [00:07, 36.70iter/s]
Best score: 2.7370: : 214iter [00:07, 38.04iter/s]
Best score: 2.7370: : 214iter [00:07, 38.04iter/s]
Best score: 2.7370: : 215iter [00:07, 38.04iter/s]
Best score: 2.7370: : 216iter [00:07, 38.04iter/s]
Best score: 2.7370: : 217iter [00:07, 38.04iter/s]
Best score: 2.7370: : 218iter [00:07, 38.04iter/s]
Best score: 2.7370: : 219iter [00:07, 39.27iter/s]
Best score: 2.7370: : 219iter [00:07, 39.27iter/s]
Best score: 2.7370: : 220iter [00:07, 39.27iter/s]
Best score: 2.7370: : 221iter [00:07, 39.27iter/s]
Best score: 2.7370: : 222iter [00:07, 39.27iter/s]
Best score: 2.7370: : 223iter [00:07, 39.27iter/s]
Best score: 2.7370: : 224iter [00:07, 33.04iter/s]
Best score: 2.7370: : 224iter [00:07, 33.04iter/s]
Best score: 2.7370: : 225iter [00:07, 33.04iter/s]
Best score: 2.7370: : 226iter [00:07, 33.04iter/s]
Best score: 2.7370: : 227iter [00:07, 33.04iter/s]
Best score: 2.7370: : 228iter [00:07, 33.04iter/s]
Best score: 2.7370: : 229iter [00:07, 35.40iter/s]
Best score: 2.7370: : 229iter [00:07, 35.40iter/s]
Best score: 2.7370: : 230iter [00:07, 35.40iter/s]
Best score: 2.7370: : 231iter [00:07, 35.40iter/s]
Best score: 2.7370: : 232iter [00:07, 35.40iter/s]
Best score: 2.7370: : 233iter [00:07, 35.40iter/s]
Best score: 2.7370: : 234iter [00:07, 36.99iter/s]
Best score: 2.7370: : 234iter [00:07, 36.99iter/s]
Best score: 2.7370: : 235iter [00:07, 36.99iter/s]
Best score: 2.7370: : 236iter [00:07, 36.99iter/s]
Best score: 2.7370: : 237iter [00:07, 36.99iter/s]
Best score: 2.7370: : 238iter [00:07, 36.99iter/s]
Best score: 2.7370: : 239iter [00:07, 38.40iter/s]
Best score: 2.7370: : 239iter [00:08, 38.40iter/s]
Best score: 2.7370: : 240iter [00:08, 38.40iter/s]
Best score: 2.7370: : 241iter [00:08, 38.40iter/s]
Best score: 2.7370: : 242iter [00:08, 38.40iter/s]
Best score: 2.7370: : 243iter [00:08, 38.40iter/s]
Best score: 2.7370: : 244iter [00:08, 39.25iter/s]
Best score: 2.7370: : 244iter [00:08, 39.25iter/s]
Best score: 2.7370: : 245iter [00:08, 39.25iter/s]
Best score: 2.7370: : 246iter [00:08, 39.25iter/s]
Best score: 2.7370: : 247iter [00:08, 39.25iter/s]
Best score: 2.7370: : 248iter [00:08, 39.25iter/s]
Best score: 2.7370: : 249iter [00:08, 40.16iter/s]
[ITER 250] Best: 2.7370
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 999.0000
  py: 743.0000
  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: 2.7370: : 249iter [00:08, 40.16iter/s]
Best score: 2.7370: : 250iter [00:08, 40.16iter/s]
Best score: 2.7370: : 251iter [00:08, 40.16iter/s]
Best score: 2.7370: : 252iter [00:08, 40.16iter/s]
Best score: 2.7370: : 253iter [00:08, 40.16iter/s]
Best score: 2.7370: : 254iter [00:08, 34.53iter/s]
Best score: 2.7370: : 254iter [00:08, 34.53iter/s]
Best score: 2.7370: : 255iter [00:08, 34.53iter/s]
Best score: 2.7370: : 256iter [00:08, 34.53iter/s]
Best score: 2.7370: : 257iter [00:08, 34.53iter/s]
Best score: 2.7370: : 258iter [00:08, 34.53iter/s]
Best score: 2.7370: : 259iter [00:08, 36.53iter/s]
Best score: 2.7370: : 259iter [00:08, 36.53iter/s]
Best score: 2.7370: : 260iter [00:08, 36.53iter/s]
Best score: 2.7370: : 261iter [00:08, 36.53iter/s]
Best score: 2.7370: : 262iter [00:08, 36.53iter/s]
Best score: 2.7370: : 263iter [00:08, 36.53iter/s]
Best score: 2.7370: : 264iter [00:08, 37.93iter/s]
Best score: 2.7370: : 264iter [00:08, 37.93iter/s]
Best score: 2.7370: : 265iter [00:08, 37.93iter/s]
Best score: 2.7370: : 266iter [00:08, 37.93iter/s]
Best score: 2.7370: : 267iter [00:08, 37.93iter/s]
Best score: 2.7370: : 268iter [00:08, 37.93iter/s]
Best score: 2.7370: : 269iter [00:08, 39.14iter/s]
Best score: 2.7370: : 269iter [00:08, 39.14iter/s]
Best score: 2.7370: : 270iter [00:08, 39.14iter/s]
Best score: 2.7370: : 271iter [00:08, 39.14iter/s]
Best score: 2.7370: : 272iter [00:08, 39.14iter/s]
Best score: 2.7370: : 273iter [00:08, 39.14iter/s]
Best score: 2.7370: : 274iter [00:08, 39.80iter/s]
Best score: 2.7370: : 274iter [00:08, 39.80iter/s]
Best score: 2.7370: : 275iter [00:08, 39.80iter/s]
Best score: 2.7370: : 276iter [00:08, 39.80iter/s]
Best score: 2.7370: : 277iter [00:08, 39.80iter/s]
Best score: 2.7370: : 278iter [00:09, 39.80iter/s]
Best score: 2.7370: : 279iter [00:09, 40.65iter/s]
Best score: 2.7370: : 279iter [00:09, 40.65iter/s]
Best score: 2.7370: : 280iter [00:09, 40.65iter/s]
Best score: 2.7370: : 281iter [00:09, 40.65iter/s]
Best score: 2.7370: : 282iter [00:09, 40.65iter/s]
Best score: 2.7370: : 283iter [00:09, 40.65iter/s]
Best score: 2.7370: : 284iter [00:09, 41.06iter/s]
Best score: 2.7370: : 284iter [00:09, 41.06iter/s]
Best score: 2.7370: : 285iter [00:09, 41.06iter/s]
Best score: 2.7370: : 286iter [00:09, 41.06iter/s]
Best score: 2.7370: : 287iter [00:09, 41.06iter/s]
Best score: 2.7370: : 288iter [00:09, 41.06iter/s]
Best score: 2.7370: : 289iter [00:09, 41.55iter/s]
Best score: 2.3292: : 289iter [00:09, 41.55iter/s]
Best score: 2.3292: : 290iter [00:09, 41.55iter/s]
Best score: 2.3292: : 291iter [00:09, 41.55iter/s]
Best score: 2.3292: : 292iter [00:09, 41.55iter/s]
Best score: 2.3292: : 293iter [00:09, 41.55iter/s]
Best score: 2.3292: : 294iter [00:09, 41.63iter/s]
Best score: 2.3292: : 294iter [00:09, 41.63iter/s]
Best score: 2.3292: : 295iter [00:09, 41.63iter/s]
Best score: 2.3292: : 296iter [00:09, 41.63iter/s]
Best score: 2.3292: : 297iter [00:09, 41.63iter/s]
Best score: 2.3292: : 298iter [00:09, 41.63iter/s]
Best score: 2.3292: : 299iter [00:09, 42.02iter/s]
[ITER 300] Best: 1.9786
[PARAMS] Current best parameters:
  fx: 531.0221
  fy: 530.6799
  px: 994.2645
  py: 747.0844
  k1: -0.00594278
  k2: 0.01101040
  k3: -0.00335561
  k4: -0.00083105
  p1: 0.00016539
  p2: -0.00220438
  p3: -0.00856989
  p4: -0.00102575
  s1: 0.0126
  s2: -0.0011
  s3: 0.0043
  s4: 0.0054
  tilt_x: 0.0208
  tilt_y: 0.0234
  scale: 3.0103
  rotation: 0.0992

Best score: 1.9786: : 299iter [00:09, 42.02iter/s]
Best score: 1.9786: : 300iter [00:09, 42.02iter/s]
Best score: 1.9786: : 301iter [00:09, 42.02iter/s]
Best score: 1.9786: : 302iter [00:09, 42.02iter/s]
Best score: 1.9786: : 303iter [00:09, 42.02iter/s]
Best score: 1.9786: : 304iter [00:09, 35.35iter/s]
Best score: 1.9786: : 304iter [00:09, 35.35iter/s]
Best score: 1.9786: : 305iter [00:09, 35.35iter/s]
Best score: 1.9786: : 306iter [00:09, 35.35iter/s]
Best score: 1.9786: : 307iter [00:09, 35.35iter/s]
Best score: 1.9786: : 308iter [00:09, 35.35iter/s]
Best score: 1.9786: : 309iter [00:09, 37.21iter/s]
Best score: 1.9017: : 309iter [00:09, 37.21iter/s]
Best score: 1.9017: : 310iter [00:09, 37.21iter/s]
Best score: 1.9017: : 311iter [00:09, 37.21iter/s]
Best score: 1.9017: : 312iter [00:09, 37.21iter/s]
Best score: 1.9017: : 313iter [00:09, 37.21iter/s]
Best score: 1.9017: : 314iter [00:09, 38.37iter/s]
Best score: 1.9017: : 314iter [00:09, 38.37iter/s]
Best score: 1.9017: : 315iter [00:09, 38.37iter/s]
Best score: 1.9017: : 316iter [00:09, 38.37iter/s]
Best score: 1.9017: : 317iter [00:09, 38.37iter/s]
Best score: 1.9017: : 318iter [00:10, 38.37iter/s]
Best score: 1.9017: : 319iter [00:10, 39.46iter/s]
Best score: 1.9017: : 319iter [00:10, 39.46iter/s]
Best score: 1.9017: : 320iter [00:10, 39.46iter/s]
Best score: 1.9017: : 321iter [00:10, 39.46iter/s]
Best score: 1.9017: : 322iter [00:10, 39.46iter/s]
Best score: 1.9017: : 323iter [00:10, 39.46iter/s]
Best score: 1.9017: : 324iter [00:10, 40.12iter/s]
Best score: 1.9017: : 324iter [00:10, 40.12iter/s]
Best score: 1.9017: : 325iter [00:10, 40.12iter/s]
Best score: 1.9017: : 326iter [00:10, 40.12iter/s]
Best score: 1.9017: : 327iter [00:10, 40.12iter/s]
Best score: 1.9017: : 328iter [00:10, 40.12iter/s]
Best score: 1.9017: : 329iter [00:10, 40.87iter/s]
Best score: 1.9017: : 329iter [00:10, 40.87iter/s]
Best score: 1.9017: : 330iter [00:10, 40.87iter/s]
Best score: 1.9017: : 331iter [00:10, 40.87iter/s]
Best score: 1.9017: : 332iter [00:10, 40.87iter/s]
Best score: 1.9017: : 333iter [00:10, 40.87iter/s]
Best score: 1.9017: : 334iter [00:10, 41.10iter/s]
Best score: 1.9017: : 334iter [00:10, 41.10iter/s]
Best score: 1.9017: : 335iter [00:10, 41.10iter/s]
Best score: 1.9017: : 336iter [00:10, 41.10iter/s]
Best score: 1.9017: : 337iter [00:10, 41.10iter/s]
Best score: 1.9017: : 338iter [00:10, 41.10iter/s]
Best score: 1.9017: : 339iter [00:10, 41.50iter/s]
Best score: 1.5308: : 339iter [00:10, 41.50iter/s]
Best score: 1.5308: : 340iter [00:10, 41.50iter/s]
Best score: 1.5308: : 341iter [00:10, 41.50iter/s]
Best score: 1.5308: : 342iter [00:10, 41.50iter/s]
Best score: 1.5308: : 343iter [00:10, 41.50iter/s]
Best score: 1.5308: : 344iter [00:10, 41.49iter/s]
Best score: 1.5308: : 344iter [00:10, 41.49iter/s]
Best score: 1.5308: : 345iter [00:10, 41.49iter/s]
Best score: 1.5308: : 346iter [00:10, 41.49iter/s]
Best score: 1.5308: : 347iter [00:10, 41.49iter/s]
Best score: 1.5308: : 348iter [00:10, 41.49iter/s]
Best score: 1.5308: : 349iter [00:10, 41.76iter/s]
[ITER 350] Best: 1.3826
[PARAMS] Current best parameters:
  fx: 533.7080
  fy: 532.9066
  px: 991.6024
  py: 745.5978
  k1: -0.00990088
  k2: -0.00644871
  k3: 0.01538283
  k4: -0.00554643
  p1: -0.00502791
  p2: -0.00487949
  p3: 0.00268084
  p4: -0.00110581
  s1: -0.0054
  s2: 0.0009
  s3: 0.0054
  s4: 0.0005
  tilt_x: 0.0217
  tilt_y: 0.0280
  scale: 3.0302
  rotation: 0.1544

Best score: 1.3826: : 349iter [00:10, 41.76iter/s]
Best score: 1.3826: : 350iter [00:10, 41.76iter/s]
Best score: 1.3826: : 351iter [00:10, 41.76iter/s]
Best score: 1.3826: : 352iter [00:10, 41.76iter/s]
Best score: 1.3826: : 353iter [00:10, 41.76iter/s]
Best score: 1.3826: : 354iter [00:10, 35.04iter/s]
Best score: 1.3826: : 354iter [00:10, 35.04iter/s]
Best score: 1.3826: : 355iter [00:10, 35.04iter/s]
Best score: 1.3826: : 356iter [00:11, 35.04iter/s]
Best score: 1.3826: : 357iter [00:11, 35.04iter/s]
Best score: 1.3826: : 358iter [00:11, 35.04iter/s]
Best score: 1.3826: : 359iter [00:11, 37.09iter/s]
Best score: 1.2395: : 359iter [00:11, 37.09iter/s]
Best score: 1.2395: : 360iter [00:11, 37.09iter/s]
Best score: 1.2395: : 361iter [00:11, 37.09iter/s]
Best score: 1.2395: : 362iter [00:11, 37.09iter/s]
Best score: 1.2395: : 363iter [00:11, 37.09iter/s]
Best score: 1.2395: : 364iter [00:11, 38.24iter/s]
Best score: 1.2395: : 364iter [00:11, 38.24iter/s]
Best score: 1.2395: : 365iter [00:11, 38.24iter/s]
Best score: 1.2395: : 366iter [00:11, 38.24iter/s]
Best score: 1.2395: : 367iter [00:11, 38.24iter/s]
Best score: 1.2395: : 368iter [00:11, 38.24iter/s]
Best score: 1.2395: : 369iter [00:11, 39.43iter/s]
Best score: 1.1221: : 369iter [00:11, 39.43iter/s]
Best score: 1.1221: : 370iter [00:11, 39.43iter/s]
Best score: 1.1221: : 371iter [00:11, 39.43iter/s]
Best score: 1.1221: : 372iter [00:11, 39.43iter/s]
Best score: 1.1221: : 373iter [00:11, 39.43iter/s]
Best score: 1.1221: : 374iter [00:11, 40.04iter/s]
Best score: 1.1221: : 374iter [00:11, 40.04iter/s]
Best score: 1.1221: : 375iter [00:11, 40.04iter/s]
Best score: 1.1221: : 376iter [00:11, 40.04iter/s]
Best score: 1.1221: : 377iter [00:11, 40.04iter/s]
Best score: 1.1221: : 378iter [00:11, 40.04iter/s]
Best score: 1.1221: : 379iter [00:11, 40.73iter/s]
Best score: 1.1221: : 379iter [00:11, 40.73iter/s]
Best score: 1.1221: : 380iter [00:11, 40.73iter/s]
Best score: 1.1221: : 381iter [00:11, 40.73iter/s]
Best score: 1.1221: : 382iter [00:11, 40.73iter/s]
Best score: 1.1221: : 383iter [00:11, 40.73iter/s]
Best score: 1.1221: : 384iter [00:11, 40.87iter/s]
Best score: 1.1221: : 384iter [00:11, 40.87iter/s]
Best score: 1.1221: : 385iter [00:11, 40.87iter/s]
Best score: 1.1221: : 386iter [00:11, 40.87iter/s]
Best score: 1.1221: : 387iter [00:11, 40.87iter/s]
Best score: 1.1221: : 388iter [00:11, 40.87iter/s]
Best score: 1.1221: : 389iter [00:11, 41.33iter/s]
Best score: 1.0532: : 389iter [00:11, 41.33iter/s]
Best score: 1.0532: : 390iter [00:11, 41.33iter/s]
Best score: 1.0532: : 391iter [00:11, 41.33iter/s]
Best score: 1.0532: : 392iter [00:11, 41.33iter/s]
Best score: 1.0532: : 393iter [00:11, 41.33iter/s]
Best score: 1.0532: : 394iter [00:11, 41.30iter/s]
Best score: 1.0532: : 394iter [00:11, 41.30iter/s]
Best score: 1.0532: : 395iter [00:11, 41.30iter/s]
Best score: 1.0532: : 396iter [00:11, 41.30iter/s]
Best score: 1.0532: : 397iter [00:11, 41.30iter/s]
Best score: 1.0532: : 398iter [00:12, 41.30iter/s]
Best score: 1.0532: : 399iter [00:12, 41.66iter/s]
[ITER 400] Best: 0.8405
[PARAMS] Current best parameters:
  fx: 524.4154
  fy: 524.1128
  px: 995.4617
  py: 744.3120
  k1: 0.01653981
  k2: -0.00776624
  k3: 0.00392019
  k4: -0.00165729
  p1: -0.00486379
  p2: -0.00708649
  p3: -0.00410591
  p4: 0.00807400
  s1: 0.0088
  s2: -0.0087
  s3: 0.0059
  s4: -0.0074
  tilt_x: 0.0172
  tilt_y: 0.0027
  scale: 2.9773
  rotation: -0.0877

Best score: 0.8405: : 399iter [00:12, 41.66iter/s]
[Periodic outlier @ iter 300] Removing 1000 frame 13 (score=4.15, threshold=1.45)
  Remaining frames: 18

Best score: 0.8405: : 400iter [00:12, 41.66iter/s]
Best score: 0.8405: : 401iter [00:12, 41.66iter/s]
Best score: 0.8405: : 402iter [00:12, 41.66iter/s]
Best score: 0.8405: : 403iter [00:12, 41.66iter/s]
Best score: 0.8405: : 404iter [00:12, 32.87iter/s]
Best score: 0.8405: : 404iter [00:12, 32.87iter/s]
Best score: 0.8405: : 405iter [00:12, 32.87iter/s]
Best score: 0.8405: : 406iter [00:12, 32.87iter/s]
Best score: 0.8405: : 407iter [00:12, 32.87iter/s]
Best score: 0.8405: : 408iter [00:12, 32.87iter/s]
Best score: 0.8405: : 409iter [00:12, 35.49iter/s]
Best score: 0.6477: : 409iter [00:12, 35.49iter/s]
Best score: 0.6477: : 410iter [00:12, 35.49iter/s]
Best score: 0.6477: : 411iter [00:12, 35.49iter/s]
Best score: 0.6477: : 412iter [00:12, 35.49iter/s]
Best score: 0.6477: : 413iter [00:12, 35.49iter/s]
Best score: 0.6477: : 414iter [00:12, 37.33iter/s]
Best score: 0.6477: : 414iter [00:12, 37.33iter/s]
Best score: 0.6477: : 415iter [00:12, 37.33iter/s]
Best score: 0.6477: : 416iter [00:12, 37.33iter/s]
Best score: 0.6477: : 417iter [00:12, 37.33iter/s]
Best score: 0.6477: : 418iter [00:12, 37.33iter/s]
Best score: 0.6477: : 419iter [00:12, 38.98iter/s]
Best score: 0.6477: : 419iter [00:12, 38.98iter/s]
Best score: 0.6477: : 420iter [00:12, 38.98iter/s]
Best score: 0.6477: : 421iter [00:12, 38.98iter/s]
Best score: 0.6477: : 422iter [00:12, 38.98iter/s]
Best score: 0.6477: : 423iter [00:12, 38.98iter/s]
Best score: 0.6477: : 424iter [00:12, 40.11iter/s]
Best score: 0.6477: : 424iter [00:12, 40.11iter/s]
Best score: 0.6477: : 425iter [00:12, 40.11iter/s]
Best score: 0.6477: : 426iter [00:12, 40.11iter/s]
Best score: 0.6477: : 427iter [00:12, 40.11iter/s]
Best score: 0.6477: : 428iter [00:12, 40.11iter/s]
Best score: 0.6477: : 429iter [00:12, 41.03iter/s]
Best score: 0.6477: : 429iter [00:12, 41.03iter/s]
Best score: 0.6477: : 430iter [00:12, 41.03iter/s]
Best score: 0.6477: : 431iter [00:12, 41.03iter/s]
Best score: 0.6477: : 432iter [00:12, 41.03iter/s]
Best score: 0.6477: : 433iter [00:12, 41.03iter/s]
Best score: 0.6477: : 434iter [00:12, 41.63iter/s]
Best score: 0.6477: : 434iter [00:12, 41.63iter/s]
Best score: 0.6477: : 435iter [00:12, 41.63iter/s]
Best score: 0.6477: : 436iter [00:12, 41.63iter/s]
Best score: 0.6477: : 437iter [00:13, 41.63iter/s]
Best score: 0.6477: : 438iter [00:13, 41.63iter/s]
Best score: 0.6477: : 439iter [00:13, 42.22iter/s]
Best score: 0.6477: : 439iter [00:13, 42.22iter/s]
Best score: 0.6477: : 440iter [00:13, 42.22iter/s]
Best score: 0.6477: : 441iter [00:13, 42.22iter/s]
Best score: 0.6477: : 442iter [00:13, 42.22iter/s]
Best score: 0.6477: : 443iter [00:13, 42.22iter/s]
Best score: 0.6477: : 444iter [00:13, 34.61iter/s]
Best score: 0.6477: : 444iter [00:13, 34.61iter/s]
Best score: 0.6477: : 445iter [00:13, 34.61iter/s]
Best score: 0.6477: : 446iter [00:13, 34.61iter/s]
Best score: 0.6477: : 447iter [00:13, 34.61iter/s]
Best score: 0.6477: : 448iter [00:13, 34.61iter/s]
Best score: 0.6477: : 449iter [00:13, 36.83iter/s]
[ITER 450] Best: 0.6477
[PARAMS] Current best parameters:
  fx: 524.4154
  fy: 524.1128
  px: 995.4617
  py: 744.3120
  k1: 0.01653981
  k2: -0.00776624
  k3: 0.00392019
  k4: -0.00165729
  p1: -0.00486379
  p2: -0.00708649
  p3: -0.00410591
  p4: 0.00807400
  s1: 0.0088
  s2: -0.0087
  s3: 0.0059
  s4: -0.0074
  tilt_x: 0.0172
  tilt_y: 0.0027
  scale: 2.9773
  rotation: -0.0877

Best score: 0.6477: : 449iter [00:13, 36.83iter/s]
[Periodic outlier @ iter 350] Removing 1000 frame 5 (score=3.00, threshold=1.43)
  Remaining frames: 17

Best score: 0.6477: : 450iter [00:13, 36.83iter/s]
Best score: 0.6477: : 451iter [00:13, 36.83iter/s]
Best score: 0.6477: : 452iter [00:13, 36.83iter/s]
Best score: 0.6477: : 453iter [00:13, 30.45iter/s]
Best score: 0.6477: : 453iter [00:13, 30.45iter/s]
Best score: 0.6477: : 454iter [00:13, 30.45iter/s]
Best score: 0.6477: : 455iter [00:13, 30.45iter/s]
Best score: 0.6477: : 456iter [00:13, 30.45iter/s]
Best score: 0.6477: : 457iter [00:13, 30.45iter/s]
Best score: 0.6477: : 458iter [00:13, 33.96iter/s]
Best score: 0.6477: : 458iter [00:13, 33.96iter/s]
Best score: 0.4989: : 459iter [00:13, 33.96iter/s]
Best score: 0.4989: : 460iter [00:13, 33.96iter/s]
Best score: 0.4989: : 461iter [00:13, 33.96iter/s]
Best score: 0.4989: : 462iter [00:13, 33.96iter/s]
Best score: 0.4989: : 463iter [00:13, 36.64iter/s]
Best score: 0.4989: : 463iter [00:13, 36.64iter/s]
Best score: 0.4989: : 464iter [00:13, 36.64iter/s]
Best score: 0.4989: : 465iter [00:13, 36.64iter/s]
Best score: 0.4989: : 466iter [00:13, 36.64iter/s]
Best score: 0.4989: : 467iter [00:13, 36.64iter/s]
Best score: 0.4989: : 468iter [00:13, 39.02iter/s]
Best score: 0.4989: : 468iter [00:13, 39.02iter/s]
Best score: 0.4989: : 469iter [00:13, 39.02iter/s]
Best score: 0.4989: : 470iter [00:13, 39.02iter/s]
Best score: 0.4989: : 471iter [00:13, 39.02iter/s]
Best score: 0.4989: : 472iter [00:14, 39.02iter/s]
Best score: 0.4989: : 473iter [00:14, 40.68iter/s]
Best score: 0.4989: : 473iter [00:14, 40.68iter/s]
Best score: 0.4989: : 474iter [00:14, 40.68iter/s]
Best score: 0.4989: : 475iter [00:14, 40.68iter/s]
Best score: 0.4989: : 476iter [00:14, 40.68iter/s]
Best score: 0.4989: : 477iter [00:14, 40.68iter/s]
Best score: 0.4989: : 478iter [00:14, 42.12iter/s]
Best score: 0.4989: : 478iter [00:14, 42.12iter/s]
Best score: 0.4989: : 479iter [00:14, 42.12iter/s]
Best score: 0.4989: : 480iter [00:14, 42.12iter/s]
Best score: 0.4989: : 481iter [00:14, 42.12iter/s]
Best score: 0.4989: : 482iter [00:14, 42.12iter/s]
Best score: 0.4989: : 483iter [00:14, 42.82iter/s]
Best score: 0.4989: : 483iter [00:14, 42.82iter/s]
Best score: 0.4989: : 484iter [00:14, 42.82iter/s]
Best score: 0.4989: : 485iter [00:14, 42.82iter/s]
Best score: 0.4989: : 486iter [00:14, 42.82iter/s]
Best score: 0.4989: : 487iter [00:14, 42.82iter/s]
Best score: 0.4989: : 488iter [00:14, 43.69iter/s]
Best score: 0.4989: : 488iter [00:14, 43.69iter/s]
Best score: 0.4989: : 489iter [00:14, 43.69iter/s]
Best score: 0.4989: : 490iter [00:14, 43.69iter/s]
Best score: 0.4989: : 491iter [00:14, 43.69iter/s]
Best score: 0.4989: : 492iter [00:14, 43.69iter/s]
Best score: 0.4989: : 493iter [00:14, 44.17iter/s]
Best score: 0.4989: : 493iter [00:14, 44.17iter/s]
Best score: 0.4989: : 494iter [00:14, 44.17iter/s]
Best score: 0.4989: : 495iter [00:14, 44.17iter/s]
Best score: 0.4989: : 496iter [00:14, 44.17iter/s]
Best score: 0.4989: : 497iter [00:14, 44.17iter/s]
Best score: 0.4989: : 498iter [00:14, 44.56iter/s]
Best score: 0.4989: : 498iter [00:14, 44.56iter/s]
[ITER 500] Best: 0.4989
[PARAMS] Current best parameters:
  fx: 524.4154
  fy: 524.1128
  px: 995.4617
  py: 744.3120
  k1: 0.01653981
  k2: -0.00776624
  k3: 0.00392019
  k4: -0.00165729
  p1: -0.00486379
  p2: -0.00708649
  p3: -0.00410591
  p4: 0.00807400
  s1: 0.0088
  s2: -0.0087
  s3: 0.0059
  s4: -0.0074
  tilt_x: 0.0172
  tilt_y: 0.0027
  scale: 2.9773
  rotation: -0.0877

Best score: 0.4989: : 499iter [00:14, 44.56iter/s]
Best score: 0.4989: : 500iter [00:14, 44.56iter/s]
Best score: 0.4989: : 501iter [00:14, 44.56iter/s]
Best score: 0.4989: : 502iter [00:14, 44.56iter/s]
Best score: 0.4989: : 503iter [00:14, 37.41iter/s]
Best score: 0.4989: : 503iter [00:14, 37.41iter/s]
Best score: 0.4989: : 504iter [00:14, 37.41iter/s]
Best score: 0.4989: : 505iter [00:14, 37.41iter/s]
Best score: 0.4989: : 506iter [00:14, 37.41iter/s]
Best score: 0.4989: : 507iter [00:14, 37.41iter/s]
Best score: 0.4989: : 508iter [00:14, 39.62iter/s]
Best score: 0.4989: : 508iter [00:14, 39.62iter/s]
Best score: 0.4989: : 509iter [00:14, 39.62iter/s]
Best score: 0.4989: : 510iter [00:14, 39.62iter/s]
Best score: 0.4989: : 511iter [00:14, 39.62iter/s]
Best score: 0.4989: : 512iter [00:14, 39.62iter/s]
Best score: 0.4989: : 513iter [00:14, 41.08iter/s]
Best score: 0.4989: : 513iter [00:14, 41.08iter/s]
Best score: 0.4989: : 514iter [00:14, 41.08iter/s]
Best score: 0.4989: : 515iter [00:15, 41.08iter/s]
Best score: 0.4989: : 516iter [00:15, 41.08iter/s]
Best score: 0.4989: : 517iter [00:15, 41.08iter/s]
Best score: 0.4989: : 518iter [00:15, 42.22iter/s]
Best score: 0.4989: : 518iter [00:15, 42.22iter/s]
Best score: 0.4989: : 519iter [00:15, 42.22iter/s]
Best score: 0.4989: : 520iter [00:15, 42.22iter/s]
Best score: 0.4989: : 521iter [00:15, 42.22iter/s]
Best score: 0.4989: : 522iter [00:15, 42.22iter/s]
Best score: 0.4989: : 523iter [00:15, 43.01iter/s]
Best score: 0.4989: : 523iter [00:15, 43.01iter/s]
Best score: 0.4989: : 524iter [00:15, 43.01iter/s]
Best score: 0.4989: : 525iter [00:15, 43.01iter/s]
Best score: 0.4989: : 526iter [00:15, 43.01iter/s]
Best score: 0.4989: : 527iter [00:15, 43.01iter/s]
Best score: 0.4989: : 528iter [00:15, 43.91iter/s]
Best score: 0.4989: : 528iter [00:15, 43.91iter/s]
Best score: 0.4989: : 529iter [00:15, 43.91iter/s]
Best score: 0.4989: : 530iter [00:15, 43.91iter/s]
Best score: 0.4989: : 531iter [00:15, 43.91iter/s]
Best score: 0.4989: : 532iter [00:15, 43.91iter/s]
Best score: 0.4989: : 533iter [00:15, 44.41iter/s]
Best score: 0.4989: : 533iter [00:15, 44.41iter/s]
Best score: 0.4989: : 534iter [00:15, 44.41iter/s]
Best score: 0.4989: : 535iter [00:15, 44.41iter/s]
Best score: 0.4989: : 536iter [00:15, 44.41iter/s]
Best score: 0.4989: : 537iter [00:15, 44.41iter/s]
Best score: 0.4989: : 538iter [00:15, 45.06iter/s]
Best score: 0.4989: : 538iter [00:15, 45.06iter/s]
Best score: 0.4989: : 539iter [00:15, 45.06iter/s]
Best score: 0.4989: : 540iter [00:15, 45.06iter/s]
Best score: 0.4989: : 541iter [00:15, 45.06iter/s]
Best score: 0.4989: : 542iter [00:15, 45.06iter/s]
Best score: 0.4989: : 543iter [00:15, 45.14iter/s]
Best score: 0.4989: : 543iter [00:15, 45.14iter/s]
Best score: 0.4989: : 544iter [00:15, 45.14iter/s]
Best score: 0.4989: : 545iter [00:15, 45.14iter/s]
Best score: 0.4989: : 546iter [00:15, 45.14iter/s]
Best score: 0.4989: : 547iter [00:15, 45.14iter/s]
Best score: 0.4989: : 548iter [00:15, 45.57iter/s]
Best score: 0.4989: : 548iter [00:15, 45.57iter/s]
[ITER 550] Best: 0.4989
[PARAMS] Current best parameters:
  fx: 524.4154
  fy: 524.1128
  px: 995.4617
  py: 744.3120
  k1: 0.01653981
  k2: -0.00776624
  k3: 0.00392019
  k4: -0.00165729
  p1: -0.00486379
  p2: -0.00708649
  p3: -0.00410591
  p4: 0.00807400
  s1: 0.0088
  s2: -0.0087
  s3: 0.0059
  s4: -0.0074
  tilt_x: 0.0172
  tilt_y: 0.0027
  scale: 2.9773
  rotation: -0.0877

Best score: 0.4989: : 549iter [00:15, 45.57iter/s]
Best score: 0.4989: : 550iter [00:15, 45.57iter/s]
Best score: 0.4989: : 551iter [00:15, 45.57iter/s]
Best score: 0.4989: : 552iter [00:15, 45.57iter/s]
Best score: 0.4989: : 553iter [00:15, 38.10iter/s]
Best score: 0.4989: : 553iter [00:15, 38.10iter/s]
Best score: 0.4989: : 554iter [00:15, 38.10iter/s]
Best score: 0.4989: : 555iter [00:15, 38.10iter/s]
Best score: 0.4989: : 556iter [00:15, 38.10iter/s]
Best score: 0.4989: : 557iter [00:16, 38.10iter/s]
Best score: 0.4989: : 558iter [00:16, 40.32iter/s]
Best score: 0.4989: : 558iter [00:16, 40.32iter/s]
Best score: 0.4989: : 559iter [00:16, 40.32iter/s]
Best score: 0.4989: : 560iter [00:16, 40.32iter/s]
Best score: 0.4989: : 561iter [00:16, 40.32iter/s]
Best score: 0.4989: : 562iter [00:16, 40.32iter/s]
Best score: 0.4989: : 563iter [00:16, 41.80iter/s]
Best score: 0.4989: : 563iter [00:16, 41.80iter/s]
Best score: 0.4989: : 564iter [00:16, 41.80iter/s]
Best score: 0.4989: : 565iter [00:16, 41.80iter/s]
Best score: 0.4989: : 566iter [00:16, 41.80iter/s]
Best score: 0.4989: : 567iter [00:16, 41.80iter/s]
Best score: 0.4989: : 568iter [00:16, 43.18iter/s]
Best score: 0.4989: : 568iter [00:16, 43.18iter/s]
Best score: 0.4989: : 569iter [00:16, 43.18iter/s]
Best score: 0.4989: : 570iter [00:16, 43.18iter/s]
Best score: 0.4989: : 571iter [00:16, 43.18iter/s]
Best score: 0.4989: : 572iter [00:16, 43.18iter/s]
Best score: 0.4989: : 573iter [00:16, 44.02iter/s]
Best score: 0.4989: : 573iter [00:16, 44.02iter/s]
Best score: 0.4989: : 574iter [00:16, 44.02iter/s]
Best score: 0.4989: : 575iter [00:16, 44.02iter/s]
Best score: 0.4989: : 576iter [00:16, 44.02iter/s]
Best score: 0.4989: : 577iter [00:16, 44.02iter/s]
Best score: 0.4989: : 578iter [00:16, 44.69iter/s]
Best score: 0.4989: : 578iter [00:16, 44.69iter/s]
Best score: 0.4989: : 579iter [00:16, 44.69iter/s]
Best score: 0.4989: : 580iter [00:16, 44.69iter/s]
Best score: 0.4989: : 581iter [00:16, 44.69iter/s]
Best score: 0.4989: : 582iter [00:16, 44.69iter/s]
Best score: 0.4989: : 583iter [00:16, 44.82iter/s]
Best score: 0.4989: : 583iter [00:16, 44.82iter/s]
Best score: 0.4989: : 584iter [00:16, 44.82iter/s]
Best score: 0.4989: : 585iter [00:16, 44.82iter/s]
Best score: 0.4989: : 586iter [00:16, 44.82iter/s]
Best score: 0.4989: : 587iter [00:16, 44.82iter/s]
Best score: 0.4989: : 588iter [00:16, 45.21iter/s]
Best score: 0.4989: : 588iter [00:16, 45.21iter/s]
Best score: 0.4989: : 589iter [00:16, 45.21iter/s]
Best score: 0.4989: : 590iter [00:16, 45.21iter/s]
Best score: 0.4989: : 591iter [00:16, 45.21iter/s]
Best score: 0.4989: : 592iter [00:16, 45.21iter/s]
Best score: 0.4989: : 593iter [00:16, 44.44iter/s]
Best score: 0.4989: : 593iter [00:16, 44.44iter/s]
Best score: 0.4989: : 594iter [00:16, 44.44iter/s]
Best score: 0.4989: : 595iter [00:16, 44.44iter/s]
Best score: 0.4989: : 596iter [00:16, 44.44iter/s]
Best score: 0.4989: : 597iter [00:16, 44.44iter/s]
Best score: 0.4989: : 598iter [00:16, 45.12iter/s]
Best score: 0.4989: : 598iter [00:16, 45.12iter/s]
[ITER 600] Best: 0.4989
[PARAMS] Current best parameters:
  fx: 524.4154
  fy: 524.1128
  px: 995.4617
  py: 744.3120
  k1: 0.01653981
  k2: -0.00776624
  k3: 0.00392019
  k4: -0.00165729
  p1: -0.00486379
  p2: -0.00708649
  p3: -0.00410591
  p4: 0.00807400
  s1: 0.0088
  s2: -0.0087
  s3: 0.0059
  s4: -0.0074
  tilt_x: 0.0172
  tilt_y: 0.0027
  scale: 2.9773
  rotation: -0.0877

Best score: 0.4989: : 599iter [00:16, 45.12iter/s]
Best score: 0.4989: : 600iter [00:17, 45.12iter/s]
Best score: 0.4989: : 601iter [00:17, 45.12iter/s]
Best score: 0.4989: : 602iter [00:17, 45.12iter/s]
Best score: 0.4989: : 603iter [00:17, 37.89iter/s]
Best score: 0.4989: : 603iter [00:17, 37.89iter/s]
Best score: 0.4989: : 604iter [00:17, 37.89iter/s]
Best score: 0.4989: : 605iter [00:17, 37.89iter/s]
Best score: 0.4989: : 606iter [00:17, 37.89iter/s]
Best score: 0.4989: : 607iter [00:17, 37.89iter/s]
Best score: 0.4989: : 608iter [00:17, 40.14iter/s]
Best score: 0.4989: : 608iter [00:17, 40.14iter/s]
Best score: 0.4989: : 609iter [00:17, 40.14iter/s]
Best score: 0.4989: : 610iter [00:17, 40.14iter/s]
Best score: 0.4989: : 611iter [00:17, 40.14iter/s]
Best score: 0.4989: : 612iter [00:17, 40.14iter/s]
Best score: 0.4989: : 613iter [00:17, 41.80iter/s]
Best score: 0.4989: : 613iter [00:17, 41.80iter/s]
Best score: 0.4989: : 614iter [00:17, 41.80iter/s]
Best score: 0.4989: : 615iter [00:17, 41.80iter/s]
Best score: 0.4989: : 616iter [00:17, 41.80iter/s]
Best score: 0.4989: : 617iter [00:17, 41.80iter/s]
Best score: 0.4989: : 618iter [00:17, 43.26iter/s]
Best score: 0.4989: : 618iter [00:17, 43.26iter/s]
Best score: 0.4989: : 619iter [00:17, 43.26iter/s]
Best score: 0.4989: : 620iter [00:17, 43.26iter/s]
Best score: 0.4989: : 621iter [00:17, 43.26iter/s]
Best score: 0.4989: : 622iter [00:17, 43.26iter/s]
Best score: 0.4989: : 623iter [00:17, 43.92iter/s]
Best score: 0.4989: : 623iter [00:17, 43.92iter/s]
Best score: 0.4989: : 624iter [00:17, 43.92iter/s]
Best score: 0.4989: : 625iter [00:17, 43.92iter/s]
Best score: 0.4989: : 626iter [00:17, 43.92iter/s]
Best score: 0.4989: : 627iter [00:17, 43.92iter/s]
Best score: 0.4989: : 628iter [00:17, 44.70iter/s]
Best score: 0.4989: : 628iter [00:17, 44.70iter/s]
Best score: 0.4989: : 629iter [00:17, 44.70iter/s]
Best score: 0.4989: : 630iter [00:17, 44.70iter/s]
Best score: 0.4989: : 631iter [00:17, 44.70iter/s]
Best score: 0.4989: : 632iter [00:17, 44.70iter/s]
Best score: 0.4989: : 633iter [00:17, 45.01iter/s]
Best score: 0.4989: : 633iter [00:17, 45.01iter/s]
Best score: 0.4989: : 634iter [00:17, 45.01iter/s]
Best score: 0.4989: : 635iter [00:17, 45.01iter/s]
Best score: 0.4989: : 636iter [00:17, 45.01iter/s]
Best score: 0.4989: : 637iter [00:17, 45.01iter/s]
Best score: 0.4989: : 638iter [00:17, 45.46iter/s]
Best score: 0.4989: : 638iter [00:17, 45.46iter/s]
Best score: 0.4989: : 639iter [00:17, 45.46iter/s]
Best score: 0.4989: : 640iter [00:17, 45.46iter/s]
Best score: 0.4989: : 641iter [00:17, 45.46iter/s]
Best score: 0.4989: : 642iter [00:17, 45.46iter/s]
Best score: 0.4989: : 643iter [00:17, 45.71iter/s]
Best score: 0.4989: : 643iter [00:17, 45.71iter/s]
Best score: 0.4989: : 644iter [00:17, 45.71iter/s]
Best score: 0.4989: : 645iter [00:17, 45.71iter/s]
Best score: 0.4989: : 646iter [00:18, 45.71iter/s]
Best score: 0.4989: : 647iter [00:18, 45.71iter/s]
Best score: 0.4989: : 648iter [00:18, 46.09iter/s]
Best score: 0.4989: : 648iter [00:18, 46.09iter/s]
[ITER 650] Best: 0.4989
[PARAMS] Current best parameters:
  fx: 524.4154
  fy: 524.1128
  px: 995.4617
  py: 744.3120
  k1: 0.01653981
  k2: -0.00776624
  k3: 0.00392019
  k4: -0.00165729
  p1: -0.00486379
  p2: -0.00708649
  p3: -0.00410591
  p4: 0.00807400
  s1: 0.0088
  s2: -0.0087
  s3: 0.0059
  s4: -0.0074
  tilt_x: 0.0172
  tilt_y: 0.0027
  scale: 2.9773
  rotation: -0.0877

Best score: 0.4989: : 649iter [00:18, 46.09iter/s]
Best score: 0.4989: : 650iter [00:18, 46.09iter/s]
Best score: 0.4989: : 651iter [00:18, 46.09iter/s]
Best score: 0.4989: : 652iter [00:18, 46.09iter/s]
Best score: 0.4989: : 653iter [00:18, 38.39iter/s]
Best score: 0.4989: : 653iter [00:18, 38.39iter/s]
Best score: 0.4989: : 654iter [00:18, 38.39iter/s]
Best score: 0.4989: : 655iter [00:18, 38.39iter/s]
Best score: 0.4989: : 656iter [00:18, 38.39iter/s]
Best score: 0.4989: : 657iter [00:18, 38.39iter/s]
Best score: 0.4989: : 658iter [00:18, 40.54iter/s]
Best score: 0.4989: : 658iter [00:18, 40.54iter/s]
Best score: 0.4813: : 659iter [00:18, 40.54iter/s]
Best score: 0.4813: : 660iter [00:18, 40.54iter/s]
Best score: 0.4813: : 661iter [00:18, 40.54iter/s]
Best score: 0.4813: : 662iter [00:18, 40.54iter/s]
Best score: 0.4813: : 663iter [00:18, 34.46iter/s]
Best score: 0.4813: : 663iter [00:18, 34.46iter/s]
Best score: 0.4813: : 664iter [00:18, 34.46iter/s]
Best score: 0.4813: : 665iter [00:18, 34.46iter/s]
Best score: 0.4813: : 666iter [00:18, 34.46iter/s]
Best score: 0.4813: : 667iter [00:18, 34.46iter/s]
Best score: 0.4813: : 668iter [00:18, 37.31iter/s]
Best score: 0.4813: : 668iter [00:18, 37.31iter/s]
Best score: 0.4813: : 669iter [00:18, 37.31iter/s]
Best score: 0.4813: : 670iter [00:18, 37.31iter/s]
Best score: 0.4813: : 671iter [00:18, 37.31iter/s]
Best score: 0.4813: : 672iter [00:18, 37.31iter/s]
Best score: 0.4813: : 673iter [00:18, 39.41iter/s]
Best score: 0.4813: : 673iter [00:18, 39.41iter/s]
Best score: 0.4813: : 674iter [00:18, 39.41iter/s]
Best score: 0.4813: : 675iter [00:18, 39.41iter/s]
Best score: 0.4813: : 676iter [00:18, 39.41iter/s]
Best score: 0.4813: : 677iter [00:18, 39.41iter/s]
Best score: 0.4813: : 678iter [00:18, 41.34iter/s]
Best score: 0.4813: : 678iter [00:18, 41.34iter/s]
Best score: 0.4813: : 679iter [00:18, 41.34iter/s]
Best score: 0.4813: : 680iter [00:18, 41.34iter/s]
Best score: 0.4813: : 681iter [00:18, 41.34iter/s]
Best score: 0.4813: : 682iter [00:18, 41.34iter/s]
Best score: 0.4813: : 683iter [00:18, 42.47iter/s]
Best score: 0.4813: : 683iter [00:18, 42.47iter/s]
Best score: 0.4813: : 684iter [00:18, 42.47iter/s]
Best score: 0.4813: : 685iter [00:19, 42.47iter/s]
Best score: 0.4813: : 686iter [00:19, 42.47iter/s]
Best score: 0.4813: : 687iter [00:19, 42.47iter/s]
Best score: 0.4813: : 688iter [00:19, 43.46iter/s]
Best score: 0.4813: : 688iter [00:19, 43.46iter/s]
Best score: 0.4813: : 689iter [00:19, 43.46iter/s]
Best score: 0.4813: : 690iter [00:19, 43.46iter/s]
Best score: 0.4813: : 691iter [00:19, 43.46iter/s]
Best score: 0.4813: : 692iter [00:19, 43.46iter/s]
Best score: 0.4813: : 693iter [00:19, 44.10iter/s]
Best score: 0.4813: : 693iter [00:19, 44.10iter/s]
Best score: 0.4813: : 694iter [00:19, 44.10iter/s]
Best score: 0.4813: : 695iter [00:19, 44.10iter/s]
Best score: 0.4813: : 696iter [00:19, 44.10iter/s]
Best score: 0.4813: : 697iter [00:19, 44.10iter/s]
Best score: 0.4813: : 698iter [00:19, 44.83iter/s]
Best score: 0.4813: : 698iter [00:19, 44.83iter/s]
[ITER 700] Best: 0.4813
[PARAMS] Current best parameters:
  fx: 534.8371
  fy: 534.2710
  px: 995.8853
  py: 741.8665
  k1: 0.00799608
  k2: 0.00342763
  k3: -0.00970517
  k4: 0.00250500
  p1: -0.00650953
  p2: -0.00589667
  p3: -0.00661260
  p4: 0.01047472
  s1: 0.0150
  s2: -0.0129
  s3: 0.0094
  s4: -0.0090
  tilt_x: 0.0077
  tilt_y: 0.0034
  scale: 2.9516
  rotation: -0.0105

Best score: 0.4813: : 699iter [00:19, 44.83iter/s]
Best score: 0.4813: : 700iter [00:19, 44.83iter/s]
Best score: 0.4813: : 701iter [00:19, 44.83iter/s]
Best score: 0.4813: : 702iter [00:19, 44.83iter/s]
Best score: 0.4813: : 703iter [00:19, 37.63iter/s]
Best score: 0.4813: : 703iter [00:19, 37.63iter/s]
Best score: 0.4813: : 704iter [00:19, 37.63iter/s]
Best score: 0.4813: : 705iter [00:19, 37.63iter/s]
Best score: 0.4813: : 706iter [00:19, 37.63iter/s]
Best score: 0.4813: : 707iter [00:19, 37.63iter/s]
Best score: 0.4813: : 708iter [00:19, 39.85iter/s]
Best score: 0.4813: : 708iter [00:19, 39.85iter/s]
Best score: 0.2942: : 709iter [00:19, 39.85iter/s]
Best score: 0.2942: : 710iter [00:19, 39.85iter/s]
Best score: 0.2942: : 711iter [00:19, 39.85iter/s]
Best score: 0.2942: : 712iter [00:19, 39.85iter/s]
Best score: 0.2942: : 713iter [00:19, 41.33iter/s]
Best score: 0.2942: : 713iter [00:19, 41.33iter/s]
Best score: 0.2942: : 714iter [00:19, 41.33iter/s]
Best score: 0.2942: : 715iter [00:19, 41.33iter/s]
Best score: 0.2942: : 716iter [00:19, 41.33iter/s]
Best score: 0.2942: : 717iter [00:19, 41.33iter/s]
Best score: 0.2942: : 718iter [00:19, 42.78iter/s]
Best score: 0.2942: : 718iter [00:19, 42.78iter/s]
Best score: 0.2942: : 719iter [00:19, 42.78iter/s]
Best score: 0.2942: : 720iter [00:19, 42.78iter/s]
Best score: 0.2942: : 721iter [00:19, 42.78iter/s]
Best score: 0.2942: : 722iter [00:19, 42.78iter/s]
Best score: 0.2942: : 723iter [00:19, 43.69iter/s]
Best score: 0.2942: : 723iter [00:19, 43.69iter/s]
Best score: 0.2942: : 724iter [00:19, 43.69iter/s]
Best score: 0.2942: : 725iter [00:19, 43.69iter/s]
Best score: 0.2942: : 726iter [00:19, 43.69iter/s]
Best score: 0.2942: : 727iter [00:20, 43.69iter/s]
Best score: 0.2942: : 728iter [00:20, 44.54iter/s]
Best score: 0.2942: : 728iter [00:20, 44.54iter/s]
Best score: 0.2847: : 729iter [00:20, 44.54iter/s]
Best score: 0.2847: : 730iter [00:20, 44.54iter/s]
Best score: 0.2847: : 731iter [00:20, 44.54iter/s]
Best score: 0.2847: : 732iter [00:20, 44.54iter/s]
Best score: 0.2847: : 733iter [00:20, 44.75iter/s]
Best score: 0.2847: : 733iter [00:20, 44.75iter/s]
Best score: 0.2847: : 734iter [00:20, 44.75iter/s]
Best score: 0.2847: : 735iter [00:20, 44.75iter/s]
Best score: 0.2847: : 736iter [00:20, 44.75iter/s]
Best score: 0.2847: : 737iter [00:20, 44.75iter/s]
Best score: 0.2847: : 738iter [00:20, 45.18iter/s]
Best score: 0.2847: : 738iter [00:20, 45.18iter/s]
Best score: 0.2840: : 739iter [00:20, 45.18iter/s]
Best score: 0.2840: : 740iter [00:20, 45.18iter/s]
Best score: 0.2840: : 741iter [00:20, 45.18iter/s]
Best score: 0.2840: : 742iter [00:20, 45.18iter/s]
Best score: 0.2840: : 743iter [00:20, 45.30iter/s]
Best score: 0.2840: : 743iter [00:20, 45.30iter/s]
Best score: 0.2840: : 744iter [00:20, 45.30iter/s]
Best score: 0.2840: : 745iter [00:20, 45.30iter/s]
Best score: 0.2840: : 746iter [00:20, 45.30iter/s]
Best score: 0.2840: : 747iter [00:20, 45.30iter/s]
Best score: 0.2840: : 748iter [00:20, 45.70iter/s]
Best score: 0.2840: : 748iter [00:20, 45.70iter/s]
[ITER 750] Best: 0.2498
[PARAMS] Current best parameters:
  fx: 533.1878
  fy: 532.5541
  px: 999.0451
  py: 742.2284
  k1: 0.01131767
  k2: 0.00154370
  k3: -0.00917930
  k4: 0.00246765
  p1: 0.00004823
  p2: -0.00359305
  p3: -0.01005491
  p4: 0.00702799
  s1: 0.0111
  s2: -0.0061
  s3: 0.0053
  s4: -0.0058
  tilt_x: -0.0032
  tilt_y: -0.0076
  scale: 2.9516
  rotation: 0.0069

Best score: 0.2498: : 749iter [00:20, 45.70iter/s]
Best score: 0.2498: : 750iter [00:20, 45.70iter/s]
Best score: 0.2498: : 751iter [00:20, 45.70iter/s]
Best score: 0.2498: : 752iter [00:20, 45.70iter/s]
Best score: 0.2498: : 753iter [00:20, 38.16iter/s]
Best score: 0.2498: : 753iter [00:20, 38.16iter/s]
Best score: 0.2498: : 754iter [00:20, 38.16iter/s]
Best score: 0.2498: : 755iter [00:20, 38.16iter/s]
Best score: 0.2498: : 756iter [00:20, 38.16iter/s]
Best score: 0.2498: : 757iter [00:20, 38.16iter/s]
Best score: 0.2498: : 758iter [00:20, 40.30iter/s]
Best score: 0.2498: : 758iter [00:20, 40.30iter/s]
Best score: 0.2498: : 759iter [00:20, 40.30iter/s]
Best score: 0.2498: : 760iter [00:20, 40.30iter/s]
Best score: 0.2498: : 761iter [00:20, 40.30iter/s]
Best score: 0.2498: : 762iter [00:20, 40.30iter/s]
Best score: 0.2498: : 763iter [00:20, 41.51iter/s]
Best score: 0.2498: : 763iter [00:20, 41.51iter/s]
Best score: 0.2498: : 764iter [00:20, 41.51iter/s]
Best score: 0.2498: : 765iter [00:20, 41.51iter/s]
Best score: 0.2498: : 766iter [00:20, 41.51iter/s]
Best score: 0.2498: : 767iter [00:20, 41.51iter/s]
Best score: 0.2498: : 768iter [00:20, 42.81iter/s]
Best score: 0.2498: : 768iter [00:20, 42.81iter/s]
Best score: 0.2498: : 769iter [00:20, 42.81iter/s]
Best score: 0.2498: : 770iter [00:21, 42.81iter/s]
Best score: 0.2498: : 771iter [00:21, 42.81iter/s]
Best score: 0.2498: : 772iter [00:21, 42.81iter/s]
Best score: 0.2498: : 773iter [00:21, 43.62iter/s]
Best score: 0.2498: : 773iter [00:21, 43.62iter/s]
Best score: 0.2498: : 774iter [00:21, 43.62iter/s]
Best score: 0.2498: : 775iter [00:21, 43.62iter/s]
Best score: 0.2498: : 776iter [00:21, 43.62iter/s]
Best score: 0.2498: : 777iter [00:21, 43.62iter/s]
Best score: 0.2498: : 778iter [00:21, 44.38iter/s]
Best score: 0.2498: : 778iter [00:21, 44.38iter/s]
Best score: 0.2370: : 779iter [00:21, 44.38iter/s]
Best score: 0.2370: : 780iter [00:21, 44.38iter/s]
Best score: 0.2370: : 781iter [00:21, 44.38iter/s]
Best score: 0.2370: : 782iter [00:21, 44.38iter/s]
Best score: 0.2370: : 783iter [00:21, 44.77iter/s]
Best score: 0.2370: : 783iter [00:21, 44.77iter/s]
Best score: 0.2370: : 784iter [00:21, 44.77iter/s]
Best score: 0.2370: : 785iter [00:21, 44.77iter/s]
Best score: 0.2370: : 786iter [00:21, 44.77iter/s]
Best score: 0.2370: : 787iter [00:21, 44.77iter/s]
Best score: 0.2370: : 788iter [00:21, 45.32iter/s]
Best score: 0.2370: : 788iter [00:21, 45.32iter/s]
Best score: 0.2297: : 789iter [00:21, 45.32iter/s]
Best score: 0.2297: : 790iter [00:21, 45.32iter/s]
Best score: 0.2297: : 791iter [00:21, 45.32iter/s]
Best score: 0.2297: : 792iter [00:21, 45.32iter/s]
Best score: 0.2297: : 793iter [00:21, 45.36iter/s]
Best score: 0.2297: : 793iter [00:21, 45.36iter/s]
Best score: 0.2297: : 794iter [00:21, 45.36iter/s]
Best score: 0.2297: : 795iter [00:21, 45.36iter/s]
Best score: 0.2297: : 796iter [00:21, 45.36iter/s]
Best score: 0.2297: : 797iter [00:21, 45.36iter/s]
Best score: 0.2297: : 798iter [00:21, 45.51iter/s]
Best score: 0.2297: : 798iter [00:21, 45.51iter/s]
[ITER 800] Best: 0.2282
[PARAMS] Current best parameters:
  fx: 523.6113
  fy: 523.1081
  px: 998.8926
  py: 740.9917
  k1: 0.01705155
  k2: -0.00358464
  k3: -0.00124203
  k4: -0.00002818
  p1: -0.00022666
  p2: -0.00296458
  p3: -0.00555965
  p4: 0.00465685
  s1: 0.0097
  s2: -0.0054
  s3: 0.0055
  s4: -0.0060
  tilt_x: -0.0092
  tilt_y: -0.0062
  scale: 2.9770
  rotation: -0.0084

Best score: 0.2282: : 799iter [00:21, 45.51iter/s]
Best score: 0.2282: : 800iter [00:21, 45.51iter/s]
Best score: 0.2282: : 801iter [00:21, 45.51iter/s]
Best score: 0.2282: : 802iter [00:21, 45.51iter/s]
Best score: 0.2282: : 803iter [00:21, 38.18iter/s]
Best score: 0.2282: : 803iter [00:21, 38.18iter/s]
Best score: 0.2282: : 804iter [00:21, 38.18iter/s]
Best score: 0.2282: : 805iter [00:21, 38.18iter/s]
Best score: 0.2282: : 806iter [00:21, 38.18iter/s]
Best score: 0.2282: : 807iter [00:21, 38.18iter/s]
Best score: 0.2282: : 808iter [00:21, 40.39iter/s]
Best score: 0.2282: : 808iter [00:21, 40.39iter/s]
Best score: 0.1839: : 809iter [00:21, 40.39iter/s]
Best score: 0.1839: : 810iter [00:21, 40.39iter/s]
Best score: 0.1839: : 811iter [00:21, 40.39iter/s]
Best score: 0.1839: : 812iter [00:22, 40.39iter/s]
Best score: 0.1839: : 813iter [00:22, 41.86iter/s]
Best score: 0.1839: : 813iter [00:22, 41.86iter/s]
Best score: 0.1839: : 814iter [00:22, 41.86iter/s]
Best score: 0.1839: : 815iter [00:22, 41.86iter/s]
Best score: 0.1839: : 816iter [00:22, 41.86iter/s]
Best score: 0.1839: : 817iter [00:22, 41.86iter/s]
Best score: 0.1839: : 818iter [00:22, 43.27iter/s]
Best score: 0.1839: : 818iter [00:22, 43.27iter/s]
Best score: 0.1839: : 819iter [00:22, 43.27iter/s]
Best score: 0.1839: : 820iter [00:22, 43.27iter/s]
Best score: 0.1839: : 821iter [00:22, 43.27iter/s]
Best score: 0.1839: : 822iter [00:22, 43.27iter/s]
Best score: 0.1839: : 823iter [00:22, 43.81iter/s]
Best score: 0.1839: : 823iter [00:22, 43.81iter/s]
Best score: 0.1839: : 824iter [00:22, 43.81iter/s]
Best score: 0.1839: : 825iter [00:22, 43.81iter/s]
Best score: 0.1839: : 826iter [00:22, 43.81iter/s]
Best score: 0.1839: : 827iter [00:22, 43.81iter/s]
Best score: 0.1839: : 828iter [00:22, 44.53iter/s]
Best score: 0.1839: : 828iter [00:22, 44.53iter/s]
Best score: 0.1778: : 829iter [00:22, 44.53iter/s]
Best score: 0.1778: : 830iter [00:22, 44.53iter/s]
Best score: 0.1778: : 831iter [00:22, 44.53iter/s]
Best score: 0.1778: : 832iter [00:22, 44.53iter/s]
Best score: 0.1778: : 833iter [00:22, 44.97iter/s]
Best score: 0.1778: : 833iter [00:22, 44.97iter/s]
Best score: 0.1778: : 834iter [00:22, 44.97iter/s]
Best score: 0.1778: : 835iter [00:22, 44.97iter/s]
Best score: 0.1778: : 836iter [00:22, 44.97iter/s]
Best score: 0.1778: : 837iter [00:22, 44.97iter/s]
Best score: 0.1778: : 838iter [00:22, 45.24iter/s]
Best score: 0.1778: : 838iter [00:22, 45.24iter/s]
Best score: 0.1730: : 839iter [00:22, 45.24iter/s]
Best score: 0.1730: : 840iter [00:22, 45.24iter/s]
Best score: 0.1730: : 841iter [00:22, 45.24iter/s]
Best score: 0.1730: : 842iter [00:22, 45.24iter/s]
Best score: 0.1730: : 843iter [00:22, 45.04iter/s]
Best score: 0.1730: : 843iter [00:22, 45.04iter/s]
Best score: 0.1730: : 844iter [00:22, 45.04iter/s]
Best score: 0.1730: : 845iter [00:22, 45.04iter/s]
Best score: 0.1730: : 846iter [00:22, 45.04iter/s]
Best score: 0.1730: : 847iter [00:22, 45.04iter/s]
Best score: 0.1730: : 848iter [00:22, 45.41iter/s]
Best score: 0.1730: : 848iter [00:22, 45.41iter/s]
[ITER 850] Best: 0.1730
[PARAMS] Current best parameters:
  fx: 523.8665
  fy: 523.3415
  px: 997.8974
  py: 740.0625
  k1: 0.01460094
  k2: 0.00351724
  k3: -0.00652953
  k4: 0.00112752
  p1: 0.00581754
  p2: -0.00170878
  p3: -0.01224604
  p4: 0.00291038
  s1: 0.0109
  s2: -0.0022
  s3: 0.0030
  s4: -0.0031
  tilt_x: -0.0262
  tilt_y: 0.0016
  scale: 2.9710
  rotation: -0.0201

Best score: 0.1730: : 849iter [00:22, 45.41iter/s]
Best score: 0.1730: : 850iter [00:22, 45.41iter/s]
Best score: 0.1730: : 851iter [00:22, 45.41iter/s]
Best score: 0.1730: : 852iter [00:22, 45.41iter/s]
Best score: 0.1730: : 853iter [00:22, 38.06iter/s]
Best score: 0.1730: : 853iter [00:22, 38.06iter/s]
Best score: 0.1730: : 854iter [00:22, 38.06iter/s]
Best score: 0.1730: : 855iter [00:23, 38.06iter/s]
Best score: 0.1730: : 856iter [00:23, 38.06iter/s]
Best score: 0.1730: : 857iter [00:23, 38.06iter/s]
Best score: 0.1730: : 858iter [00:23, 40.32iter/s]
Best score: 0.1730: : 858iter [00:23, 40.32iter/s]
Best score: 0.1730: : 859iter [00:23, 40.32iter/s]
Best score: 0.1730: : 860iter [00:23, 40.32iter/s]
Best score: 0.1730: : 861iter [00:23, 40.32iter/s]
Best score: 0.1730: : 862iter [00:23, 40.32iter/s]
Best score: 0.1730: : 863iter [00:23, 41.81iter/s]
Best score: 0.1730: : 863iter [00:23, 41.81iter/s]
Best score: 0.1730: : 864iter [00:23, 41.81iter/s]
Best score: 0.1730: : 865iter [00:23, 41.81iter/s]
Best score: 0.1730: : 866iter [00:23, 41.81iter/s]
Best score: 0.1730: : 867iter [00:23, 41.81iter/s]
Best score: 0.1730: : 868iter [00:23, 43.16iter/s]
Best score: 0.1730: : 868iter [00:23, 43.16iter/s]
Best score: 0.1687: : 869iter [00:23, 43.16iter/s]
Best score: 0.1687: : 870iter [00:23, 43.16iter/s]
Best score: 0.1687: : 871iter [00:23, 43.16iter/s]
Best score: 0.1687: : 872iter [00:23, 43.16iter/s]
Best score: 0.1687: : 873iter [00:23, 43.82iter/s]
Best score: 0.1687: : 873iter [00:23, 43.82iter/s]
Best score: 0.1687: : 874iter [00:23, 43.82iter/s]
Best score: 0.1687: : 875iter [00:23, 43.82iter/s]
Best score: 0.1687: : 876iter [00:23, 43.82iter/s]
Best score: 0.1687: : 877iter [00:23, 43.82iter/s]
Best score: 0.1687: : 878iter [00:23, 44.66iter/s]
Best score: 0.1687: : 878iter [00:23, 44.66iter/s]
Best score: 0.1647: : 879iter [00:23, 44.66iter/s]
Best score: 0.1647: : 880iter [00:23, 44.66iter/s]
Best score: 0.1647: : 881iter [00:23, 44.66iter/s]
Best score: 0.1647: : 882iter [00:23, 44.66iter/s]
Best score: 0.1647: : 883iter [00:23, 44.81iter/s]
Best score: 0.1647: : 883iter [00:23, 44.81iter/s]
Best score: 0.1647: : 884iter [00:23, 44.81iter/s]
Best score: 0.1647: : 885iter [00:23, 44.81iter/s]
Best score: 0.1647: : 886iter [00:23, 44.81iter/s]
Best score: 0.1647: : 887iter [00:23, 44.81iter/s]
Best score: 0.1647: : 888iter [00:23, 36.51iter/s]
Best score: 0.1647: : 888iter [00:23, 36.51iter/s]
Best score: 0.1604: : 889iter [00:23, 36.51iter/s]
Best score: 0.1604: : 890iter [00:23, 36.51iter/s]
Best score: 0.1604: : 891iter [00:23, 36.51iter/s]
Best score: 0.1604: : 892iter [00:23, 36.51iter/s]
Best score: 0.1604: : 893iter [00:23, 38.75iter/s]
Best score: 0.1604: : 893iter [00:23, 38.75iter/s]
Best score: 0.1604: : 894iter [00:23, 38.75iter/s]
Best score: 0.1604: : 895iter [00:23, 38.75iter/s]
Best score: 0.1604: : 896iter [00:23, 38.75iter/s]
Best score: 0.1604: : 897iter [00:24, 38.75iter/s]
Best score: 0.1604: : 898iter [00:24, 40.72iter/s]
Best score: 0.1604: : 898iter [00:24, 40.72iter/s]
[ITER 900] Best: 0.1570
[PARAMS] Current best parameters:
  fx: 521.2876
  fy: 520.8964
  px: 998.9452
  py: 740.6513
  k1: 0.02029370
  k2: -0.00233108
  k3: -0.00302121
  k4: 0.00036400
  p1: 0.00559130
  p2: -0.00071285
  p3: -0.01078148
  p4: 0.00019012
  s1: 0.0113
  s2: -0.0004
  s3: 0.0029
  s4: -0.0007
  tilt_x: -0.0227
  tilt_y: -0.0014
  scale: 2.9735
  rotation: -0.0305

Best score: 0.1570: : 899iter [00:24, 40.72iter/s]
Best score: 0.1570: : 900iter [00:24, 40.72iter/s]
Best score: 0.1570: : 901iter [00:24, 40.72iter/s]
Best score: 0.1570: : 902iter [00:24, 40.72iter/s]
Best score: 0.1570: : 903iter [00:24, 35.77iter/s]
Best score: 0.1570: : 903iter [00:24, 35.77iter/s]
Best score: 0.1570: : 904iter [00:24, 35.77iter/s]
Best score: 0.1570: : 905iter [00:24, 35.77iter/s]
Best score: 0.1570: : 906iter [00:24, 35.77iter/s]
Best score: 0.1570: : 907iter [00:24, 35.77iter/s]
Best score: 0.1570: : 908iter [00:24, 38.32iter/s]
Best score: 0.1570: : 908iter [00:24, 38.32iter/s]
Best score: 0.1570: : 909iter [00:24, 38.32iter/s]
Best score: 0.1570: : 910iter [00:24, 38.32iter/s]
Best score: 0.1570: : 911iter [00:24, 38.32iter/s]
Best score: 0.1570: : 912iter [00:24, 38.32iter/s]
Best score: 0.1570: : 913iter [00:24, 40.28iter/s]
Best score: 0.1570: : 913iter [00:24, 40.28iter/s]
Best score: 0.1570: : 914iter [00:24, 40.28iter/s]
Best score: 0.1570: : 915iter [00:24, 40.28iter/s]
Best score: 0.1570: : 916iter [00:24, 40.28iter/s]
Best score: 0.1570: : 917iter [00:24, 40.28iter/s]
Best score: 0.1570: : 918iter [00:24, 41.95iter/s]
Best score: 0.1570: : 918iter [00:24, 41.95iter/s]
Best score: 0.1567: : 919iter [00:24, 41.95iter/s]
Best score: 0.1567: : 920iter [00:24, 41.95iter/s]
Best score: 0.1567: : 921iter [00:24, 41.95iter/s]
Best score: 0.1567: : 922iter [00:24, 41.95iter/s]
Best score: 0.1567: : 923iter [00:24, 42.97iter/s]
Best score: 0.1567: : 923iter [00:24, 42.97iter/s]
Best score: 0.1567: : 924iter [00:24, 42.97iter/s]
Best score: 0.1567: : 925iter [00:24, 42.97iter/s]
Best score: 0.1567: : 926iter [00:24, 42.97iter/s]
Best score: 0.1567: : 927iter [00:24, 42.97iter/s]
Best score: 0.1567: : 928iter [00:24, 44.04iter/s]
Best score: 0.1567: : 928iter [00:24, 44.04iter/s]
Best score: 0.1567: : 929iter [00:24, 44.04iter/s]
Best score: 0.1567: : 930iter [00:24, 44.04iter/s]
Best score: 0.1567: : 931iter [00:24, 44.04iter/s]
Best score: 0.1567: : 932iter [00:24, 44.04iter/s]
Best score: 0.1567: : 933iter [00:24, 44.65iter/s]
Best score: 0.1567: : 933iter [00:24, 44.65iter/s]
Best score: 0.1567: : 934iter [00:24, 44.65iter/s]
Best score: 0.1567: : 935iter [00:24, 44.65iter/s]
Best score: 0.1567: : 936iter [00:24, 44.65iter/s]
Best score: 0.1567: : 937iter [00:24, 44.65iter/s]
Best score: 0.1567: : 938iter [00:24, 45.25iter/s]
Best score: 0.1567: : 938iter [00:24, 45.25iter/s]
Best score: 0.1506: : 939iter [00:24, 45.25iter/s]
Best score: 0.1506: : 940iter [00:25, 45.25iter/s]
Best score: 0.1506: : 941iter [00:25, 45.25iter/s]
Best score: 0.1506: : 942iter [00:25, 45.25iter/s]
Best score: 0.1506: : 943iter [00:25, 45.31iter/s]
Best score: 0.1506: : 943iter [00:25, 45.31iter/s]
Best score: 0.1506: : 944iter [00:25, 45.31iter/s]
Best score: 0.1506: : 945iter [00:25, 45.31iter/s]
Best score: 0.1506: : 946iter [00:25, 45.31iter/s]
Best score: 0.1506: : 947iter [00:25, 45.31iter/s]
Best score: 0.1506: : 948iter [00:25, 45.72iter/s]
Best score: 0.1506: : 948iter [00:25, 45.72iter/s]
[ITER 950] Best: 0.1506
[PARAMS] Current best parameters:
  fx: 520.1325
  fy: 519.7017
  px: 998.4953
  py: 741.1503
  k1: 0.01912374
  k2: -0.00060146
  k3: -0.00270372
  k4: 0.00002454
  p1: 0.00678265
  p2: -0.00006295
  p3: -0.01091463
  p4: -0.00026505
  s1: 0.0101
  s2: 0.0002
  s3: 0.0024
  s4: -0.0004
  tilt_x: -0.0224
  tilt_y: 0.0023
  scale: 2.9763
  rotation: -0.0282

Best score: 0.1506: : 949iter [00:25, 45.72iter/s]
Best score: 0.1506: : 950iter [00:25, 45.72iter/s]
Best score: 0.1506: : 951iter [00:25, 45.72iter/s]
Best score: 0.1506: : 952iter [00:25, 45.72iter/s]
Best score: 0.1506: : 953iter [00:25, 38.37iter/s]
Best score: 0.1506: : 953iter [00:25, 38.37iter/s]
Best score: 0.1506: : 954iter [00:25, 38.37iter/s]
Best score: 0.1506: : 955iter [00:25, 38.37iter/s]
Best score: 0.1506: : 956iter [00:25, 38.37iter/s]
Best score: 0.1506: : 957iter [00:25, 38.37iter/s]
Best score: 0.1506: : 958iter [00:25, 40.48iter/s]
Best score: 0.1506: : 958iter [00:25, 40.48iter/s]
Best score: 0.1500: : 959iter [00:25, 40.48iter/s]
Best score: 0.1500: : 960iter [00:25, 40.48iter/s]
Best score: 0.1500: : 961iter [00:25, 40.48iter/s]
Best score: 0.1500: : 962iter [00:25, 40.48iter/s]
Best score: 0.1500: : 963iter [00:25, 41.86iter/s]
Best score: 0.1500: : 963iter [00:25, 41.86iter/s]
Best score: 0.1500: : 964iter [00:25, 41.86iter/s]
Best score: 0.1500: : 965iter [00:25, 41.86iter/s]
Best score: 0.1500: : 966iter [00:25, 41.86iter/s]
Best score: 0.1500: : 967iter [00:25, 41.86iter/s]
Best score: 0.1500: : 968iter [00:25, 43.26iter/s]
Best score: 0.1500: : 968iter [00:25, 43.26iter/s]
Best score: 0.1500: : 969iter [00:25, 43.26iter/s]
Best score: 0.1500: : 970iter [00:25, 43.26iter/s]
Best score: 0.1500: : 971iter [00:25, 43.26iter/s]
Best score: 0.1500: : 972iter [00:25, 43.26iter/s]
Best score: 0.1500: : 973iter [00:25, 43.93iter/s]
Best score: 0.1500: : 973iter [00:25, 43.93iter/s]
Best score: 0.1500: : 974iter [00:25, 43.93iter/s]
Best score: 0.1500: : 975iter [00:25, 43.93iter/s]
Best score: 0.1500: : 976iter [00:25, 43.93iter/s]
Best score: 0.1500: : 977iter [00:25, 43.93iter/s]
Best score: 0.1500: : 978iter [00:25, 44.64iter/s]
Best score: 0.1500: : 978iter [00:25, 44.64iter/s]
Best score: 0.1498: : 979iter [00:25, 44.64iter/s]
Best score: 0.1498: : 980iter [00:25, 44.64iter/s]
Best score: 0.1498: : 981iter [00:25, 44.64iter/s]
Best score: 0.1498: : 982iter [00:25, 44.64iter/s]
Best score: 0.1498: : 983iter [00:25, 44.56iter/s]
Best score: 0.1498: : 983iter [00:26, 44.56iter/s]
Best score: 0.1498: : 984iter [00:26, 44.56iter/s]
Best score: 0.1498: : 985iter [00:26, 44.56iter/s]
Best score: 0.1498: : 986iter [00:26, 44.56iter/s]
Best score: 0.1498: : 987iter [00:26, 44.56iter/s]
Best score: 0.1498: : 988iter [00:26, 45.16iter/s]
Best score: 0.1498: : 988iter [00:26, 45.16iter/s]
Best score: 0.1498: : 989iter [00:26, 45.16iter/s]
Best score: 0.1498: : 990iter [00:26, 45.16iter/s]
Best score: 0.1498: : 991iter [00:26, 45.16iter/s]
Best score: 0.1498: : 992iter [00:26, 45.16iter/s]
Best score: 0.1498: : 993iter [00:26, 45.17iter/s]
Best score: 0.1498: : 993iter [00:26, 45.17iter/s]
Best score: 0.1498: : 994iter [00:26, 45.17iter/s]
Best score: 0.1498: : 995iter [00:26, 45.17iter/s]
Best score: 0.1498: : 996iter [00:26, 45.17iter/s]
Best score: 0.1498: : 997iter [00:26, 45.17iter/s]
Best score: 0.1498: : 998iter [00:26, 45.47iter/s]
Best score: 0.1498: : 998iter [00:26, 45.47iter/s]
[ITER 1000] Best: 0.1459
[PARAMS] Current best parameters:
  fx: 520.4022
  fy: 520.0062
  px: 998.4679
  py: 741.1732
  k1: 0.01568711
  k2: 0.00241206
  k3: -0.00352326
  k4: 0.00006590
  p1: 0.00876684
  p2: 0.00081551
  p3: -0.01304496
  p4: -0.00005245
  s1: 0.0110
  s2: 0.0002
  s3: 0.0020
  s4: -0.0004
  tilt_x: -0.0261
  tilt_y: 0.0039
  scale: 2.9820
  rotation: -0.0372

Best score: 0.1459: : 999iter [00:26, 45.47iter/s]
Best score: 0.1459: : 1000iter [00:26, 45.47iter/s]
Best score: 0.1459: : 1001iter [00:26, 45.47iter/s]
Best score: 0.1459: : 1002iter [00:26, 45.47iter/s]
Best score: 0.1459: : 1003iter [00:26, 38.29iter/s]
Best score: 0.1459: : 1003iter [00:26, 38.29iter/s]
Best score: 0.1459: : 1004iter [00:26, 38.29iter/s]
Best score: 0.1459: : 1005iter [00:26, 38.29iter/s]
Best score: 0.1459: : 1006iter [00:26, 38.29iter/s]
Best score: 0.1459: : 1007iter [00:26, 38.29iter/s]
Best score: 0.1459: : 1008iter [00:26, 40.31iter/s]
Best score: 0.1459: : 1008iter [00:26, 40.31iter/s]
Best score: 0.1459: : 1009iter [00:26, 40.31iter/s]
Best score: 0.1459: : 1010iter [00:26, 40.31iter/s]
Best score: 0.1459: : 1011iter [00:26, 40.31iter/s]
Best score: 0.1459: : 1012iter [00:26, 40.31iter/s]
Best score: 0.1459: : 1013iter [00:26, 41.77iter/s]
Best score: 0.1459: : 1013iter [00:26, 41.77iter/s]
Best score: 0.1459: : 1014iter [00:26, 41.77iter/s]
Best score: 0.1459: : 1015iter [00:26, 41.77iter/s]
Best score: 0.1459: : 1016iter [00:26, 41.77iter/s]
Best score: 0.1459: : 1017iter [00:26, 41.77iter/s]
Best score: 0.1459: : 1018iter [00:26, 43.06iter/s]
Best score: 0.1459: : 1018iter [00:26, 43.06iter/s]
Best score: 0.1457: : 1019iter [00:26, 43.06iter/s]
Best score: 0.1457: : 1020iter [00:26, 43.06iter/s]
Best score: 0.1457: : 1021iter [00:26, 43.06iter/s]
Best score: 0.1457: : 1022iter [00:26, 43.06iter/s]
Best score: 0.1457: : 1023iter [00:26, 43.69iter/s]
Best score: 0.1457: : 1023iter [00:26, 43.69iter/s]
Best score: 0.1457: : 1024iter [00:26, 43.69iter/s]
Best score: 0.1457: : 1025iter [00:27, 43.69iter/s]
Best score: 0.1457: : 1026iter [00:27, 43.69iter/s]
Best score: 0.1457: : 1027iter [00:27, 43.69iter/s]
Best score: 0.1457: : 1028iter [00:27, 44.54iter/s]
Best score: 0.1457: : 1028iter [00:27, 44.54iter/s]
Best score: 0.1457: : 1029iter [00:27, 44.54iter/s]
Best score: 0.1457: : 1030iter [00:27, 44.54iter/s]
Best score: 0.1457: : 1031iter [00:27, 44.54iter/s]
Best score: 0.1457: : 1032iter [00:27, 44.54iter/s]
Best score: 0.1457: : 1033iter [00:27, 44.74iter/s]
Best score: 0.1457: : 1033iter [00:27, 44.74iter/s]
Best score: 0.1457: : 1034iter [00:27, 44.74iter/s]
Best score: 0.1457: : 1035iter [00:27, 44.74iter/s]
Best score: 0.1457: : 1036iter [00:27, 44.74iter/s]
Best score: 0.1457: : 1037iter [00:27, 44.74iter/s]
Best score: 0.1457: : 1038iter [00:27, 45.41iter/s]
Best score: 0.1457: : 1038iter [00:27, 45.41iter/s]
Best score: 0.1445: : 1039iter [00:27, 45.41iter/s]
Best score: 0.1445: : 1040iter [00:27, 45.41iter/s]
Best score: 0.1445: : 1041iter [00:27, 45.41iter/s]
Best score: 0.1445: : 1042iter [00:27, 45.41iter/s]
Best score: 0.1445: : 1043iter [00:27, 45.38iter/s]
Best score: 0.1445: : 1043iter [00:27, 45.38iter/s]
Best score: 0.1445: : 1044iter [00:27, 45.38iter/s]
Best score: 0.1445: : 1045iter [00:27, 45.38iter/s]
Best score: 0.1445: : 1046iter [00:27, 45.38iter/s]
Best score: 0.1445: : 1047iter [00:27, 45.38iter/s]
Best score: 0.1445: : 1048iter [00:27, 45.68iter/s]
Best score: 0.1445: : 1048iter [00:27, 45.68iter/s]
[ITER 1050] Best: 0.1445
[PARAMS] Current best parameters:
  fx: 520.2638
  fy: 519.8415
  px: 998.4586
  py: 741.4690
  k1: 0.01536170
  k2: 0.00288159
  k3: -0.00388180
  k4: 0.00019538
  p1: 0.00954079
  p2: 0.00072593
  p3: -0.01367440
  p4: -0.00042601
  s1: 0.0126
  s2: 0.0002
  s3: 0.0023
  s4: -0.0002
  tilt_x: -0.0261
  tilt_y: 0.0040
  scale: 2.9836
  rotation: -0.0303

Best score: 0.1445: : 1049iter [00:27, 45.68iter/s]
Best score: 0.1445: : 1050iter [00:27, 45.68iter/s]
Best score: 0.1445: : 1051iter [00:27, 45.68iter/s]
Best score: 0.1445: : 1052iter [00:27, 45.68iter/s]
Best score: 0.1445: : 1053iter [00:27, 38.13iter/s]
Best score: 0.1445: : 1053iter [00:27, 38.13iter/s]
Best score: 0.1445: : 1054iter [00:27, 38.13iter/s]
Best score: 0.1445: : 1055iter [00:27, 38.13iter/s]
Best score: 0.1445: : 1056iter [00:27, 38.13iter/s]
Best score: 0.1445: : 1057iter [00:27, 38.13iter/s]
Best score: 0.1445: : 1058iter [00:27, 40.32iter/s]
Best score: 0.1445: : 1058iter [00:27, 40.32iter/s]
Best score: 0.1436: : 1059iter [00:27, 40.32iter/s]
Best score: 0.1436: : 1060iter [00:27, 40.32iter/s]
Best score: 0.1436: : 1061iter [00:27, 40.32iter/s]
Best score: 0.1436: : 1062iter [00:27, 40.32iter/s]
Best score: 0.1436: : 1063iter [00:27, 41.73iter/s]
Best score: 0.1436: : 1063iter [00:27, 41.73iter/s]
Best score: 0.1436: : 1064iter [00:27, 41.73iter/s]
Best score: 0.1436: : 1065iter [00:27, 41.73iter/s]
Best score: 0.1436: : 1066iter [00:27, 41.73iter/s]
Best score: 0.1436: : 1067iter [00:27, 41.73iter/s]
Best score: 0.1436: : 1068iter [00:27, 43.12iter/s]
Best score: 0.1436: : 1068iter [00:28, 43.12iter/s]
Best score: 0.1436: : 1069iter [00:28, 43.12iter/s]
Best score: 0.1436: : 1070iter [00:28, 43.12iter/s]
Best score: 0.1436: : 1071iter [00:28, 43.12iter/s]
Best score: 0.1436: : 1072iter [00:28, 43.12iter/s]
Best score: 0.1436: : 1073iter [00:28, 43.72iter/s]
Best score: 0.1436: : 1073iter [00:28, 43.72iter/s]
Best score: 0.1436: : 1074iter [00:28, 43.72iter/s]
Best score: 0.1436: : 1075iter [00:28, 43.72iter/s]
Best score: 0.1436: : 1076iter [00:28, 43.72iter/s]
Best score: 0.1436: : 1077iter [00:28, 43.72iter/s]
Best score: 0.1436: : 1078iter [00:28, 44.47iter/s]
Best score: 0.1436: : 1078iter [00:28, 44.47iter/s]
Best score: 0.1416: : 1079iter [00:28, 44.47iter/s]
Best score: 0.1416: : 1080iter [00:28, 44.47iter/s]
Best score: 0.1416: : 1081iter [00:28, 44.47iter/s]
Best score: 0.1416: : 1082iter [00:28, 44.47iter/s]
Best score: 0.1416: : 1083iter [00:28, 44.76iter/s]
Best score: 0.1416: : 1083iter [00:28, 44.76iter/s]
Best score: 0.1416: : 1084iter [00:28, 44.76iter/s]
Best score: 0.1416: : 1085iter [00:28, 44.76iter/s]
Best score: 0.1416: : 1086iter [00:28, 44.76iter/s]
Best score: 0.1416: : 1087iter [00:28, 44.76iter/s]
Best score: 0.1416: : 1088iter [00:28, 45.22iter/s]
Best score: 0.1416: : 1088iter [00:28, 45.22iter/s]
Best score: 0.1412: : 1089iter [00:28, 45.22iter/s]
Best score: 0.1412: : 1090iter [00:28, 45.22iter/s]
Best score: 0.1412: : 1091iter [00:28, 45.22iter/s]
Best score: 0.1412: : 1092iter [00:28, 45.22iter/s]
Best score: 0.1412: : 1093iter [00:28, 45.24iter/s]
Best score: 0.1412: : 1093iter [00:28, 45.24iter/s]
Best score: 0.1412: : 1094iter [00:28, 45.24iter/s]
Best score: 0.1412: : 1095iter [00:28, 45.24iter/s]
Best score: 0.1412: : 1096iter [00:28, 45.24iter/s]
Best score: 0.1412: : 1097iter [00:28, 45.24iter/s]
Best score: 0.1412: : 1098iter [00:28, 45.34iter/s]
Best score: 0.1412: : 1098iter [00:28, 45.34iter/s]
[ITER 1100] Best: 0.1407
[PARAMS] Current best parameters:
  fx: 520.5076
  fy: 520.1159
  px: 998.0429
  py: 741.1448
  k1: 0.01139053
  k2: 0.00681993
  k3: -0.00498505
  k4: 0.00020714
  p1: 0.01053564
  p2: 0.00082316
  p3: -0.01501831
  p4: 0.00009613
  s1: 0.0133
  s2: -0.0002
  s3: 0.0025
  s4: -0.0007
  tilt_x: -0.0298
  tilt_y: 0.0062
  scale: 2.9887
  rotation: -0.0428

Best score: 0.1407: : 1099iter [00:28, 45.34iter/s]
Best score: 0.1407: : 1100iter [00:28, 38.25iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.140723 -> 0.140053

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 22,009 → 22,009 within 3500mm ring
  Ring polish improved score: 0.140053 -> 0.139789
  Success: True
  Tangential distortion: p1=0.010538, p2=0.000814, p3=-0.015022, p4=0.000095
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1398
[✅] Parameters copied to: /working/MDC015_W068_2026-04-03/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 520.5075809432707
    fy: 520.1159225963079
    px: 998.0428890797818
    py: 741.1447787701344
    k1: 0.011397229964091964
    k2: 0.006823274784423765
    k3: -0.0049872775424639046
    k4: 0.00020509537452609728
    p1: 0.010537557683057561
    p2: 0.0008138231631783477
    p3: -0.015022359647416439
    p4: 9.453563767426202e-05
    scale: 2.988835053714047
    rotation: -0.04281080013491995
    mmpp: 1.2692575691509675

[🔄] Converting 449 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.27s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.27s/rig]
[✅] Saved PTDs: /working/MDC015_W068_2026-04-03/calibration/debug/4_undistorted/output/ptd

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📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.00s/rig]
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[✅] Saved PTDs: /working/MDC015_W068_2026-04-03/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.

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📤 Writing LUT:  88%|████████▊ | 2700788/3080448 [00:03<00:00, 876257.79px/s]
📤 Writing LUT:  91%|█████████ | 2789422/3080448 [00:03<00:00, 879269.44px/s]
📤 Writing LUT:  93%|█████████▎| 2877353/3080448 [00:03<00:00, 877849.85px/s]
📤 Writing LUT:  96%|█████████▋| 2965141/3080448 [00:03<00:00, 875523.65px/s]
📤 Writing LUT:  99%|█████████▉| 3052837/3080448 [00:03<00:00, 875949.95px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 869546.12px/s]
[✅] LUT written to: /working/MDC015_W068_2026-04-03/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W068_2026-04-03' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W068_2026-04-03/calibration/MDC015_W068_2026-04-03_unknown.lut

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

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)

📁 Rigs (heatmap):   0%|          | 0/2 [00:00<?, ?rig/s]

   🔥 500:   0%|          | 0/215 [00:00<?, ?frame/s]

   🔥 500:   4%|▎         | 8/215 [00:00<00:02, 72.21frame/s]

   🔥 500:   7%|▋         | 16/215 [00:00<00:02, 75.14frame/s]

   🔥 500:  11%|█         | 24/215 [00:00<00:02, 76.43frame/s]

   🔥 500:  15%|█▍        | 32/215 [00:00<00:02, 77.06frame/s]

   🔥 500:  19%|█▊        | 40/215 [00:00<00:02, 77.06frame/s]

   🔥 500:  22%|██▏       | 48/215 [00:00<00:02, 76.83frame/s]

   🔥 500:  26%|██▌       | 56/215 [00:00<00:02, 76.53frame/s]

   🔥 500:  30%|██▉       | 64/215 [00:00<00:01, 76.60frame/s]

   🔥 500:  33%|███▎      | 72/215 [00:00<00:01, 76.34frame/s]

   🔥 500:  37%|███▋      | 80/215 [00:01<00:01, 75.97frame/s]

   🔥 500:  41%|████      | 88/215 [00:01<00:01, 75.07frame/s]

   🔥 500:  45%|████▍     | 96/215 [00:01<00:01, 74.85frame/s]

   🔥 500:  48%|████▊     | 104/215 [00:01<00:01, 75.82frame/s]

   🔥 500:  52%|█████▏    | 112/215 [00:01<00:01, 76.11frame/s]

   🔥 500:  56%|█████▌    | 120/215 [00:01<00:01, 76.09frame/s]

   🔥 500:  60%|█████▉    | 128/215 [00:01<00:01, 76.45frame/s]

   🔥 500:  63%|██████▎   | 136/215 [00:01<00:01, 76.34frame/s]

   🔥 500:  67%|██████▋   | 144/215 [00:01<00:00, 76.31frame/s]

   🔥 500:  71%|███████   | 152/215 [00:01<00:00, 76.49frame/s]

   🔥 500:  74%|███████▍  | 160/215 [00:02<00:00, 76.14frame/s]

   🔥 500:  78%|███████▊  | 168/215 [00:02<00:00, 76.48frame/s]

   🔥 500:  82%|████████▏ | 176/215 [00:02<00:00, 76.71frame/s]

   🔥 500:  86%|████████▌ | 184/215 [00:02<00:00, 77.00frame/s]

   🔥 500:  89%|████████▉ | 192/215 [00:02<00:00, 77.29frame/s]

   🔥 500:  93%|█████████▎| 200/215 [00:02<00:00, 77.54frame/s]

   🔥 500:  97%|█████████▋| 208/215 [00:02<00:00, 77.64frame/s]

                                                               [✅] 500: /working/MDC015_W068_2026-04-03/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC015_W068_2026-04-03/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:03<00:03,  3.99s/rig]

   🔥 1000:   0%|          | 0/234 [00:00<?, ?frame/s]

   🔥 1000:   3%|▎         | 8/234 [00:00<00:03, 74.49frame/s]

   🔥 1000:   8%|▊         | 18/234 [00:00<00:02, 86.49frame/s]

   🔥 1000:  12%|█▏        | 27/234 [00:00<00:02, 87.36frame/s]

   🔥 1000:  15%|█▌        | 36/234 [00:00<00:02, 88.06frame/s]

   🔥 1000:  19%|█▉        | 45/234 [00:00<00:02, 86.38frame/s]

   🔥 1000:  23%|██▎       | 54/234 [00:00<00:02, 85.14frame/s]

   🔥 1000:  27%|██▋       | 63/234 [00:00<00:02, 82.12frame/s]

   🔥 1000:  31%|███       | 72/234 [00:00<00:01, 82.35frame/s]

   🔥 1000:  35%|███▍      | 81/234 [00:00<00:01, 82.55frame/s]

   🔥 1000:  38%|███▊      | 90/234 [00:01<00:01, 83.53frame/s]

   🔥 1000:  42%|████▏     | 99/234 [00:01<00:01, 83.71frame/s]

   🔥 1000:  46%|████▌     | 108/234 [00:01<00:01, 84.11frame/s]

   🔥 1000:  50%|█████     | 117/234 [00:01<00:01, 85.24frame/s]

   🔥 1000:  54%|█████▍    | 126/234 [00:01<00:01, 86.02frame/s]

   🔥 1000:  58%|█████▊    | 135/234 [00:01<00:01, 86.17frame/s]

   🔥 1000:  62%|██████▏   | 144/234 [00:01<00:01, 85.59frame/s]

   🔥 1000:  65%|██████▌   | 153/234 [00:01<00:00, 84.62frame/s]

   🔥 1000:  69%|██████▉   | 162/234 [00:01<00:00, 84.27frame/s]

   🔥 1000:  73%|███████▎  | 171/234 [00:02<00:00, 84.78frame/s]

   🔥 1000:  77%|███████▋  | 180/234 [00:02<00:00, 85.96frame/s]

   🔥 1000:  81%|████████  | 190/234 [00:02<00:00, 87.38frame/s]

   🔥 1000:  85%|████████▌ | 200/234 [00:02<00:00, 88.45frame/s]

   🔥 1000:  90%|████████▉ | 210/234 [00:02<00:00, 89.01frame/s]

   🔥 1000:  94%|█████████▎| 219/234 [00:02<00:00, 89.22frame/s]

   🔥 1000:  97%|█████████▋| 228/234 [00:02<00:00, 88.81frame/s]

                                                                [✅] 1000: /working/MDC015_W068_2026-04-03/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC015_W068_2026-04-03/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.99s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.84s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC015_W068_2026-04-03/calibration/debug/5_heatmap
[🏁] Finished processing MDC015_W068_2026-04-03


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