2026-05-06T00:42:11 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC026_W024_2025-05-13 ===
2026-05-06T00:42:11 - INFO: Camera ID: 11481106
2026-05-06T00:42:11 - INFO: Wand ID: 
2026-05-06T00:42:11 - INFO: Recorded At: 2025-05-13T16:21:29Z

-------------------------------------------------------------------
                        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 858 stationary frames, 245 remain


→ 1000 (245 images) Phase 1:   0%|          | 0/245 [00:00<?, ?it/s]

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→ 1000 (245 images) Phase 1:  73%|███████▎  | 179/245 [00:02<00:00, 75.29it/s]

→ 1000 (245 images) Phase 1:  78%|███████▊  | 191/245 [00:03<00:00, 81.10it/s]

→ 1000 (245 images) Phase 1:  82%|████████▏ | 201/245 [00:03<00:00, 66.55it/s]

→ 1000 (245 images) Phase 1:  88%|████████▊ | 216/245 [00:03<00:00, 82.98it/s]

→ 1000 (245 images) Phase 1:  93%|█████████▎| 227/245 [00:03<00:00, 74.61it/s]

→ 1000 (245 images) Phase 1:  98%|█████████▊| 239/245 [00:03<00:00, 82.93it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/245 [00:00<?, ?it/s]

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→ 1000 Phase 2: corners:  17%|█▋        | 41/245 [00:00<00:02, 101.50it/s]

→ 1000 Phase 2: corners:  22%|██▏       | 53/245 [00:00<00:01, 104.40it/s]

→ 1000 Phase 2: corners:  27%|██▋       | 67/245 [00:00<00:01, 114.36it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 79/245 [00:00<00:01, 114.77it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 92/245 [00:00<00:01, 119.21it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 105/245 [00:01<00:01, 115.05it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 118/245 [00:01<00:01, 116.06it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 136/245 [00:01<00:00, 131.25it/s]

→ 1000 Phase 2: corners:  61%|██████    | 150/245 [00:01<00:00, 122.88it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 163/245 [00:01<00:00, 121.37it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 179/245 [00:01<00:00, 131.81it/s]

→ 1000 Phase 2: corners:  80%|████████  | 196/245 [00:01<00:00, 141.32it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 214/245 [00:01<00:00, 151.35it/s]

→ 1000 Phase 2: corners:  99%|█████████▉| 242/245 [00:01<00:00, 186.52it/s]

                                                                           [PASS2] Frame 0000251: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000252: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000253: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000256: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000257: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000258: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000260: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000263: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000344: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000350: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000351: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000352: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000353: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000354: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000355: fixed corners {1, 2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000357: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000358: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000360: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000361: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000362: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000363: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000367: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000368: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000369: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000445: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000450: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000454: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000792: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000793: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000794: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000795: fixed corners {0, 2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000797: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000798: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000799: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000800: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000802: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000803: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000804: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000806: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000807: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000812: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000929: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000930: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000931: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000932: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000933: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000940: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000941: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000942: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000943: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000945: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000946: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000947: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000948: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000951: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000952: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000953: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000954: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000955: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000959: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001061: fixed corners {1, 2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001064: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001065: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001066: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001067: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001073: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001074: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001075: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001076: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001077: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000253: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000255: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000259: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000263: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000352: fixed corners {1, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000355: fixed corners {0, 2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000356: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000357: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000362: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000369: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000791: fixed corners {0, 1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000794: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000799: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000804: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000806: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000807: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000931: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000932: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000942: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000947: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000953: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000959: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001059: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001061: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001062: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001063: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001066: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001075: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001076: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000252: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000253: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000258: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000259: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000352: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000354: fixed corners {0, 1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000355: fixed corners {1} (was_fixed=True, trusted=False)
[PASS2] Frame 0000356: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 46 frames fixed, 42 untrusted, 245 total


→ 1000 Phase 3: finalize:   0%|          | 0/245 [00:00<?, ?it/s]

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→ 1000 Phase 3: finalize:  60%|██████    | 148/245 [00:02<00:02, 47.65it/s]

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→ 1000 Phase 3: finalize:  64%|██████▍   | 158/245 [00:03<00:01, 48.42it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 164/245 [00:03<00:01, 49.48it/s]

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→ 1000 Phase 3: finalize:  72%|███████▏  | 176/245 [00:03<00:01, 51.86it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 182/245 [00:03<00:01, 52.65it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 188/245 [00:03<00:01, 53.17it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 194/245 [00:03<00:00, 53.51it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 200/245 [00:03<00:00, 53.86it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 206/245 [00:03<00:00, 54.08it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 212/245 [00:04<00:00, 54.35it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 218/245 [00:04<00:00, 54.46it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 224/245 [00:04<00:00, 54.49it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 230/245 [00:04<00:00, 54.37it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 236/245 [00:04<00:00, 54.01it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 242/245 [00:04<00:00, 53.80it/s]

                                                                           
[TIMING] 1000 — 245 frames
  Phase 1  radial detect   (parallel)  :   3.80s  (15.5 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.94s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   1.78s
  Phase 3  finalize        (sequential):   4.63s  (18.9 ms/frame)
    decode + undistort                 :   2.30s  (9.4 ms/frame)
    corner apply + refinement          :   1.12s  (4.6 ms/frame)
    segment                            :   0.99s  (4.0 ms/frame)
    smooth                             :   0.14s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  12.16s


Processing Rigs:  50%|█████     | 1/2 [00:17<00:17, 17.09s/it][INFO] Filtered 753 stationary frames, 238 remain


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→ 500 (238 images) Phase 1:  38%|███▊      | 91/238 [00:01<00:01, 81.11it/s]

→ 500 (238 images) Phase 1:  42%|████▏     | 101/238 [00:01<00:02, 60.93it/s]

→ 500 (238 images) Phase 1:  48%|████▊     | 114/238 [00:02<00:01, 74.19it/s]

→ 500 (238 images) Phase 1:  53%|█████▎    | 126/238 [00:02<00:01, 82.61it/s]

→ 500 (238 images) Phase 1:  57%|█████▋    | 136/238 [00:02<00:01, 61.35it/s]

→ 500 (238 images) Phase 1:  65%|██████▌   | 155/238 [00:02<00:00, 83.10it/s]

→ 500 (238 images) Phase 1:  70%|██████▉   | 166/238 [00:02<00:01, 64.75it/s]

→ 500 (238 images) Phase 1:  76%|███████▌  | 180/238 [00:02<00:00, 77.26it/s]

→ 500 (238 images) Phase 1:  80%|████████  | 191/238 [00:03<00:00, 82.15it/s]

→ 500 (238 images) Phase 1:  84%|████████▍ | 201/238 [00:03<00:00, 63.20it/s]

→ 500 (238 images) Phase 1:  93%|█████████▎| 221/238 [00:03<00:00, 85.10it/s]

→ 500 (238 images) Phase 1:  97%|█████████▋| 232/238 [00:03<00:00, 81.45it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/238 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:  33%|███▎      | 78/238 [00:00<00:01, 104.13it/s]

→ 500 Phase 2: corners:  37%|███▋      | 89/238 [00:01<00:01, 101.59it/s]

→ 500 Phase 2: corners:  43%|████▎     | 102/238 [00:01<00:01, 108.87it/s]

→ 500 Phase 2: corners:  49%|████▉     | 117/238 [00:01<00:01, 119.34it/s]

→ 500 Phase 2: corners:  55%|█████▍    | 130/238 [00:01<00:00, 120.40it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 149/238 [00:01<00:00, 137.49it/s]

→ 500 Phase 2: corners:  69%|██████▉   | 165/238 [00:01<00:00, 143.20it/s]

→ 500 Phase 2: corners:  76%|███████▌  | 180/238 [00:01<00:00, 143.04it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 195/238 [00:01<00:00, 139.50it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 210/238 [00:01<00:00, 129.52it/s]

→ 500 Phase 2: corners: 100%|█████████▉| 237/238 [00:02<00:00, 165.25it/s]

                                                                          [PASS2] Frame 0000439: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000440: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000441: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000442: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000441: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 2 untrusted, 238 total


→ 500 Phase 3: finalize:   0%|          | 0/238 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   3%|▎         | 6/238 [00:00<00:03, 58.59it/s]

→ 500 Phase 3: finalize:   5%|▌         | 12/238 [00:00<00:03, 59.02it/s]

→ 500 Phase 3: finalize:   8%|▊         | 19/238 [00:00<00:03, 62.29it/s]

→ 500 Phase 3: finalize:  11%|█         | 26/238 [00:00<00:03, 62.55it/s]

→ 500 Phase 3: finalize:  14%|█▍        | 33/238 [00:00<00:03, 63.35it/s]

→ 500 Phase 3: finalize:  17%|█▋        | 40/238 [00:00<00:03, 64.09it/s]

→ 500 Phase 3: finalize:  20%|█▉        | 47/238 [00:00<00:02, 64.08it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 54/238 [00:00<00:02, 64.46it/s]

→ 500 Phase 3: finalize:  26%|██▌       | 61/238 [00:00<00:02, 62.04it/s]

→ 500 Phase 3: finalize:  29%|██▊       | 68/238 [00:01<00:02, 58.55it/s]

→ 500 Phase 3: finalize:  31%|███       | 74/238 [00:01<00:02, 56.73it/s]

→ 500 Phase 3: finalize:  34%|███▎      | 80/238 [00:01<00:02, 55.12it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 86/238 [00:01<00:02, 53.46it/s]

→ 500 Phase 3: finalize:  39%|███▊      | 92/238 [00:01<00:02, 55.08it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 99/238 [00:01<00:02, 57.10it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 106/238 [00:01<00:02, 58.80it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 113/238 [00:01<00:02, 59.92it/s]

→ 500 Phase 3: finalize:  50%|█████     | 120/238 [00:02<00:01, 60.53it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 127/238 [00:02<00:01, 61.07it/s]

→ 500 Phase 3: finalize:  56%|█████▋    | 134/238 [00:02<00:01, 61.35it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 141/238 [00:02<00:01, 60.61it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 148/238 [00:02<00:01, 60.62it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 155/238 [00:02<00:01, 60.60it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 162/238 [00:02<00:01, 60.61it/s]

→ 500 Phase 3: finalize:  71%|███████   | 169/238 [00:02<00:01, 60.75it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 176/238 [00:02<00:01, 61.05it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 183/238 [00:03<00:00, 60.58it/s]

→ 500 Phase 3: finalize:  80%|███████▉  | 190/238 [00:03<00:00, 61.20it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 197/238 [00:03<00:00, 61.73it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 204/238 [00:03<00:00, 62.07it/s]

→ 500 Phase 3: finalize:  89%|████████▊ | 211/238 [00:03<00:00, 62.45it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 218/238 [00:03<00:00, 62.72it/s]

→ 500 Phase 3: finalize:  95%|█████████▍| 225/238 [00:03<00:00, 62.65it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 232/238 [00:03<00:00, 62.57it/s]

                                                                          
[TIMING] 500 — 238 frames
  Phase 1  radial detect   (parallel)  :   3.69s  (15.5 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.07s  (8.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.21s
  Phase 3  finalize        (sequential):   3.92s  (16.5 ms/frame)
    decode + undistort                 :   1.77s  (7.5 ms/frame)
    corner apply + refinement          :   0.94s  (3.9 ms/frame)
    segment                            :   0.98s  (4.1 ms/frame)
    smooth                             :   0.16s  (0.7 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.90s


Processing Rigs: 100%|██████████| 2/2 [00:30<00:00, 14.65s/it]
Processing Rigs: 100%|██████████| 2/2 [00:30<00:00, 15.02s/it]
[💾] Saved RigFrameDataset to /working/MDC026_W024_2025-05-13/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 483 frames from 2 rigs to /working/MDC026_W024_2025-05-13/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC026_W024_2025-05-13/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 245
[📋] 500: 100 frames selected from 238
[💾] Saved RigFrameDataset to /working/MDC026_W024_2025-05-13/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC026_W024_2025-05-13/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,  3.15s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<01:58,  3.12s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:55,  3.11s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:51,  3.10s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:48,  3.11s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:45,  3.11s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:21<01:42,  3.11s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:24<01:40,  3.14s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:38,  3.18s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:36,  3.20s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:34<01:32,  3.21s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:37<01:29,  3.21s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:41<01:26,  3.21s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:44<01:22,  3.17s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:47<01:18,  3.16s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:50<01:15,  3.14s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:53<01:11,  3.13s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:56<01:08,  3.12s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [00:59<01:05,  3.12s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:02<01:02,  3.12s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:06<00:59,  3.14s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:09<00:57,  3.17s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:12<00:53,  3.17s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:15<00:50,  3.17s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:18<00:47,  3.18s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:22<00:44,  3.18s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:25<00:41,  3.18s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:28<00:38,  3.18s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:31<00:35,  3.18s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:34<00:32,  3.21s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:38<00:28,  3.22s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:41<00:25,  3.21s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:44<00:22,  3.22s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:47<00:19,  3.22s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:51<00:16,  3.23s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:54<00:12,  3.22s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:57<00:09,  3.20s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:00<00:06,  3.20s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:03<00:03,  3.19s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:06<00:00,  3.19s/it]
                                                                                       
[✅] Casing detection complete: cx=998.0, cy=763.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   1%|          | 1/100 [00:00<00:26,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   1%|          | 1/100 [00:00<00:26,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   2%|▏         | 2/100 [00:00<00:26,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   3%|▎         | 3/100 [00:00<00:26,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   4%|▍         | 4/100 [00:00<00:26,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   5%|▌         | 5/100 [00:00<00:25,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:25,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   7%|▋         | 7/100 [00:00<00:25,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   8%|▊         | 8/100 [00:00<00:24,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   9%|▉         | 9/100 [00:00<00:24,  3.69it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  10%|█         | 10/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  10%|█         | 10/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  11%|█         | 11/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  13%|█▎        | 13/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  14%|█▍        | 14/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  15%|█▌        | 15/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.035 | Best: {'fx': 519.192} (0.035):  16%|█▌        | 16/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 519.192} (0.035):  17%|█▋        | 17/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.063 | Best: {'fx': 526.263} (0.063):  18%|█▊        | 18/100 [00:00<00:03, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.082 | Best: {'fx': 533.333} (0.082):  19%|█▉        | 19/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  20%|██        | 20/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.048 | Best: {'fx': 533.333} (0.082):  21%|██        | 21/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  22%|██▏       | 22/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.034 | Best: {'fx': 533.333} (0.082):  23%|██▎       | 23/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.026 | Best: {'fx': 533.333} (0.082):  24%|██▍       | 24/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.022 | Best: {'fx': 533.333} (0.082):  25%|██▌       | 25/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.082):  26%|██▌       | 26/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  27%|██▋       | 27/100 [00:00<00:02, 27.23it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  28%|██▊       | 28/100 [00:00<00:01, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.082):  28%|██▊       | 28/100 [00:00<00:01, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.082):  29%|██▉       | 29/100 [00:00<00:01, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  30%|███       | 30/100 [00:00<00:01, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.082):  31%|███       | 31/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.082):  32%|███▏      | 32/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.082):  33%|███▎      | 33/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  34%|███▍      | 34/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.082):  35%|███▌      | 35/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.082):  36%|███▌      | 36/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.082):  37%|███▋      | 37/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  38%|███▊      | 38/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.082):  39%|███▉      | 39/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.082):  40%|████      | 40/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  41%|████      | 41/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  42%|████▏     | 42/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.082):  43%|████▎     | 43/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.002 | Best: {'fx': 533.333} (0.082):  44%|████▍     | 44/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.082):  45%|████▌     | 45/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.082):  46%|████▌     | 46/100 [00:00<00:00, 69.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.082):  47%|████▋     | 47/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.082):  47%|████▋     | 47/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.082):  48%|████▊     | 48/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.082):  49%|████▉     | 49/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  50%|█████     | 50/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.082):  51%|█████     | 51/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.082):  52%|█████▏    | 52/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.082):  53%|█████▎    | 53/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.006 | Best: {'fx': 533.333} (0.082):  54%|█████▍    | 54/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  55%|█████▌    | 55/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  56%|█████▌    | 56/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  57%|█████▋    | 57/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  58%|█████▊    | 58/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  59%|█████▉    | 59/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  60%|██████    | 60/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  61%|██████    | 61/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  62%|██████▏   | 62/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  63%|██████▎   | 63/100 [00:00<00:00, 104.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  64%|██████▍   | 64/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  64%|██████▍   | 64/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.082):  65%|██████▌   | 65/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  66%|██████▌   | 66/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  67%|██████▋   | 67/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  68%|██████▊   | 68/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  69%|██████▉   | 69/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  70%|███████   | 70/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  71%|███████   | 71/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  72%|███████▏  | 72/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  73%|███████▎  | 73/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  74%|███████▍  | 74/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  75%|███████▌  | 75/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  76%|███████▌  | 76/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  77%|███████▋  | 77/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  78%|███████▊  | 78/100 [00:00<00:00, 122.12it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  79%|███████▉  | 79/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  79%|███████▉  | 79/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  80%|████████  | 80/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  81%|████████  | 81/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  82%|████████▏ | 82/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  83%|████████▎ | 83/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  84%|████████▍ | 84/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  85%|████████▌ | 85/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  86%|████████▌ | 86/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  87%|████████▋ | 87/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  88%|████████▊ | 88/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  89%|████████▉ | 89/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  90%|█████████ | 90/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  91%|█████████ | 91/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  92%|█████████▏| 92/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  93%|█████████▎| 93/100 [00:00<00:00, 124.34it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  94%|█████████▍| 94/100 [00:00<00:00, 124.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  94%|█████████▍| 94/100 [00:00<00:00, 124.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  95%|█████████▌| 95/100 [00:00<00:00, 124.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  96%|█████████▌| 96/100 [00:00<00:00, 124.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  97%|█████████▋| 97/100 [00:00<00:00, 124.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  98%|█████████▊| 98/100 [00:01<00:00, 124.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.082):  99%|█████████▉| 99/100 [00:01<00:00, 124.94it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.009 | Best: {'fx': 508.333} (0.009):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.009 | Best: {'fx': 508.333} (0.009):   2%|▏         | 1/50 [00:00<00:06,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.012 | Best: {'fx': 509.354} (0.012):   2%|▏         | 1/50 [00:00<00:06,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   4%|▍         | 2/50 [00:00<00:06,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.020 | Best: {'fx': 511.395} (0.020):   6%|▌         | 3/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.027 | Best: {'fx': 515.476} (0.027):   8%|▊         | 4/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.021 | Best: {'fx': 515.476} (0.027):  10%|█         | 5/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.031 | Best: {'fx': 517.517} (0.031):  12%|█▏        | 6/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.030 | Best: {'fx': 517.517} (0.031):  14%|█▍        | 7/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.024 | Best: {'fx': 517.517} (0.031):  16%|█▌        | 8/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.026 | Best: {'fx': 517.517} (0.031):  18%|█▊        | 9/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.036 | Best: {'fx': 519.558} (0.036):  20%|██        | 10/50 [00:00<00:05,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.033 | Best: {'fx': 519.558} (0.036):  22%|██▏       | 11/50 [00:00<00:04,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.038 | Best: {'fx': 520.578} (0.038):  24%|██▍       | 12/50 [00:00<00:04,  7.95it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.038 | Best: {'fx': 520.578} (0.038):  26%|██▌       | 13/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.041 | Best: {'fx': 521.599} (0.041):  26%|██▌       | 13/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.049 | Best: {'fx': 523.639} (0.049):  28%|██▊       | 14/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.045 | Best: {'fx': 523.639} (0.049):  30%|███       | 15/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.054 | Best: {'fx': 524.66} (0.054):  32%|███▏      | 16/50 [00:00<00:00, 64.48it/s] 
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.066 | Best: {'fx': 526.701} (0.066):  34%|███▍      | 17/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.060 | Best: {'fx': 526.701} (0.066):  36%|███▌      | 18/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.072 | Best: {'fx': 527.721} (0.072):  38%|███▊      | 19/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.078 | Best: {'fx': 528.741} (0.078):  40%|████      | 20/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.072 | Best: {'fx': 528.741} (0.078):  42%|████▏     | 21/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.082 | Best: {'fx': 529.762} (0.082):  44%|████▍     | 22/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.084 | Best: {'fx': 531.803} (0.084):  46%|████▌     | 23/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.081 | Best: {'fx': 531.803} (0.084):  48%|████▊     | 24/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.079 | Best: {'fx': 531.803} (0.084):  50%|█████     | 25/50 [00:00<00:00, 64.48it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.079 | Best: {'fx': 531.803} (0.084):  52%|█████▏    | 26/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.078 | Best: {'fx': 531.803} (0.084):  52%|█████▏    | 26/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.064 | Best: {'fx': 531.803} (0.084):  54%|█████▍    | 27/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.068 | Best: {'fx': 531.803} (0.084):  56%|█████▌    | 28/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.058 | Best: {'fx': 531.803} (0.084):  58%|█████▊    | 29/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.040 | Best: {'fx': 531.803} (0.084):  60%|██████    | 30/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.055 | Best: {'fx': 531.803} (0.084):  62%|██████▏   | 31/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.047 | Best: {'fx': 531.803} (0.084):  64%|██████▍   | 32/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.051 | Best: {'fx': 531.803} (0.084):  66%|██████▌   | 33/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.044 | Best: {'fx': 531.803} (0.084):  68%|██████▊   | 34/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.038 | Best: {'fx': 531.803} (0.084):  70%|███████   | 35/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.036 | Best: {'fx': 531.803} (0.084):  72%|███████▏  | 36/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.040 | Best: {'fx': 531.803} (0.084):  74%|███████▍  | 37/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.033 | Best: {'fx': 531.803} (0.084):  76%|███████▌  | 38/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.029 | Best: {'fx': 531.803} (0.084):  78%|███████▊  | 39/50 [00:00<00:00, 87.35it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.029 | Best: {'fx': 531.803} (0.084):  80%|████████  | 40/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.034 | Best: {'fx': 531.803} (0.084):  80%|████████  | 40/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.027 | Best: {'fx': 531.803} (0.084):  82%|████████▏ | 41/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.029 | Best: {'fx': 531.803} (0.084):  84%|████████▍ | 42/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.031 | Best: {'fx': 531.803} (0.084):  86%|████████▌ | 43/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.031 | Best: {'fx': 531.803} (0.084):  88%|████████▊ | 44/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.025 | Best: {'fx': 531.803} (0.084):  90%|█████████ | 45/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.025 | Best: {'fx': 531.803} (0.084):  92%|█████████▏| 46/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.024 | Best: {'fx': 531.803} (0.084):  94%|█████████▍| 47/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.026 | Best: {'fx': 531.803} (0.084):  96%|█████████▌| 48/50 [00:00<00:00, 105.84it/s]
Brute | Start: {'fx': 533.333} (0.082) | Current: 0.026 | Best: {'fx': 531.803} (0.084):  98%|█████████▊| 49/50 [00:00<00:00, 105.84it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 998.0
    py: 763.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization

Initial score: 11.1706: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 11.1706, picklable_objective(x0) = 11.1804

Best score: 11.1804: : 0iter [00:01, ?iter/s] 
Best score: 11.1804: : 1iter [00:01,  1.83s/iter]
Best score: 11.1804: : 1iter [00:01,  1.83s/iter]
Best score: 11.1804: : 2iter [00:01,  1.83s/iter]
Best score: 11.1804: : 3iter [00:01,  1.87iter/s]
Best score: 11.1804: : 3iter [00:02,  1.87iter/s]
Best score: 11.1804: : 4iter [00:02,  1.87iter/s]
Best score: 11.1804: : 5iter [00:02,  1.87iter/s]
Best score: 11.1804: : 6iter [00:02,  1.87iter/s]
Best score: 11.1804: : 7iter [00:02,  5.29iter/s]
Best score: 11.1804: : 7iter [00:02,  5.29iter/s]
Best score: 11.1804: : 8iter [00:02,  5.29iter/s]
Best score: 11.1804: : 9iter [00:02,  5.29iter/s]
Best score: 11.1804: : 10iter [00:02,  5.29iter/s]
Best score: 11.1804: : 11iter [00:02,  9.21iter/s]
Best score: 11.1804: : 11iter [00:02,  9.21iter/s]
Best score: 11.1804: : 12iter [00:02,  9.21iter/s]
Best score: 11.1804: : 13iter [00:02,  9.21iter/s]
Best score: 11.1804: : 14iter [00:02,  9.21iter/s]
Best score: 11.1804: : 15iter [00:02, 13.49iter/s]
Best score: 11.1804: : 15iter [00:02, 13.49iter/s]
Best score: 11.1804: : 16iter [00:02, 13.49iter/s]
Best score: 11.1804: : 17iter [00:02, 13.49iter/s]
Best score: 11.1804: : 18iter [00:02, 13.49iter/s]
Best score: 11.1804: : 19iter [00:02, 17.86iter/s]
Best score: 11.1804: : 19iter [00:02, 17.86iter/s]
Best score: 11.1804: : 20iter [00:02, 17.86iter/s]
Best score: 11.1804: : 21iter [00:02, 17.86iter/s]
Best score: 11.1804: : 22iter [00:02, 17.86iter/s]
Best score: 11.1804: : 23iter [00:02, 21.96iter/s]
Best score: 11.1804: : 23iter [00:02, 21.96iter/s]
Best score: 11.1804: : 24iter [00:02, 21.96iter/s]
Best score: 11.1804: : 25iter [00:02, 21.96iter/s]
Best score: 11.1804: : 26iter [00:02, 21.96iter/s]
Best score: 11.1804: : 27iter [00:02, 21.96iter/s]
Best score: 11.1804: : 28iter [00:02, 26.52iter/s]
Best score: 11.1804: : 28iter [00:02, 26.52iter/s]
Best score: 11.1804: : 29iter [00:02, 26.52iter/s]
Best score: 11.1804: : 30iter [00:02, 26.52iter/s]
Best score: 11.1804: : 31iter [00:02, 26.52iter/s]
Best score: 11.1804: : 32iter [00:02, 29.39iter/s]
Best score: 11.1804: : 32iter [00:02, 29.39iter/s]
Best score: 11.1804: : 33iter [00:02, 29.39iter/s]
Best score: 11.1804: : 34iter [00:02, 29.39iter/s]
Best score: 11.1804: : 35iter [00:02, 29.39iter/s]
Best score: 11.1804: : 36iter [00:02, 29.39iter/s]
Best score: 11.1804: : 37iter [00:02, 32.44iter/s]
Best score: 11.1804: : 37iter [00:02, 32.44iter/s]
Best score: 11.1804: : 38iter [00:02, 32.44iter/s]
Best score: 11.1804: : 39iter [00:02, 32.44iter/s]
Best score: 11.1804: : 40iter [00:02, 32.44iter/s]
Best score: 11.1804: : 41iter [00:02, 33.93iter/s]
Best score: 11.1804: : 41iter [00:02, 33.93iter/s]
Best score: 11.1804: : 42iter [00:03, 33.93iter/s]
Best score: 11.1804: : 43iter [00:03, 33.93iter/s]
Best score: 11.1804: : 44iter [00:03, 33.93iter/s]
Best score: 11.1804: : 45iter [00:03, 35.48iter/s]
Best score: 11.1804: : 45iter [00:03, 35.48iter/s]
Best score: 11.1804: : 46iter [00:03, 35.48iter/s]
Best score: 11.1804: : 47iter [00:03, 35.48iter/s]
Best score: 11.1804: : 48iter [00:03, 35.48iter/s]
[ITER 50] Best: 11.1804
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 763.0000
  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: 11.1804: : 49iter [00:03, 35.48iter/s]
Best score: 11.1804: : 50iter [00:03, 36.75iter/s]
Best score: 11.1804: : 50iter [00:03, 36.75iter/s]
Best score: 11.1804: : 51iter [00:03, 36.75iter/s]
Best score: 11.1804: : 52iter [00:03, 36.75iter/s]
Best score: 11.1804: : 53iter [00:03, 36.75iter/s]
Best score: 11.1804: : 54iter [00:03, 36.75iter/s]
Best score: 11.1804: : 55iter [00:03, 37.76iter/s]
Best score: 11.1804: : 55iter [00:03, 37.76iter/s]
Best score: 11.1804: : 56iter [00:03, 37.76iter/s]
Best score: 11.1804: : 57iter [00:03, 37.76iter/s]
Best score: 11.1804: : 58iter [00:03, 37.76iter/s]
Best score: 11.1804: : 59iter [00:03, 37.76iter/s]
Best score: 11.1804: : 60iter [00:03, 38.33iter/s]
Best score: 11.1804: : 60iter [00:03, 38.33iter/s]
Best score: 11.1804: : 61iter [00:03, 38.33iter/s]
Best score: 11.1804: : 62iter [00:03, 38.33iter/s]
Best score: 11.1804: : 63iter [00:03, 38.33iter/s]
Best score: 11.1804: : 64iter [00:03, 38.33iter/s]
Best score: 11.1804: : 65iter [00:03, 38.93iter/s]
Best score: 11.1804: : 65iter [00:03, 38.93iter/s]
Best score: 11.1804: : 66iter [00:03, 38.93iter/s]
Best score: 11.1804: : 67iter [00:03, 38.93iter/s]
Best score: 11.1804: : 68iter [00:03, 38.93iter/s]
Best score: 11.1804: : 69iter [00:03, 39.19iter/s]
Best score: 11.1804: : 69iter [00:03, 39.19iter/s]
Best score: 11.1804: : 70iter [00:03, 39.19iter/s]
Best score: 11.1804: : 71iter [00:03, 39.19iter/s]
Best score: 11.1804: : 72iter [00:03, 39.19iter/s]
Best score: 11.1804: : 73iter [00:03, 39.31iter/s]
Best score: 11.1804: : 73iter [00:03, 39.31iter/s]
Best score: 11.1804: : 74iter [00:03, 39.31iter/s]
Best score: 11.1804: : 75iter [00:03, 39.31iter/s]
Best score: 11.1804: : 76iter [00:03, 39.31iter/s]
Best score: 11.1804: : 77iter [00:03, 39.31iter/s]
Best score: 11.1804: : 78iter [00:03, 39.79iter/s]
Best score: 11.1804: : 78iter [00:03, 39.79iter/s]
Best score: 11.1804: : 79iter [00:03, 39.79iter/s]
Best score: 11.1804: : 80iter [00:03, 39.79iter/s]
Best score: 11.1804: : 81iter [00:03, 39.79iter/s]
Best score: 11.1804: : 82iter [00:04, 39.79iter/s]
Best score: 11.1804: : 83iter [00:04, 39.95iter/s]
Best score: 11.1804: : 83iter [00:04, 39.95iter/s]
Best score: 11.1804: : 84iter [00:04, 39.95iter/s]
Best score: 11.1804: : 85iter [00:04, 39.95iter/s]
Best score: 11.1804: : 86iter [00:04, 39.95iter/s]
Best score: 11.1804: : 87iter [00:04, 39.95iter/s]
Best score: 11.1804: : 88iter [00:04, 40.26iter/s]
Best score: 11.1804: : 88iter [00:04, 40.26iter/s]
Best score: 11.1804: : 89iter [00:04, 40.26iter/s]
Best score: 11.1804: : 90iter [00:04, 40.26iter/s]
Best score: 11.1804: : 91iter [00:04, 40.26iter/s]
Best score: 11.1804: : 92iter [00:04, 40.26iter/s]
Best score: 11.1804: : 93iter [00:04, 40.18iter/s]
Best score: 11.1804: : 93iter [00:04, 40.18iter/s]
Best score: 11.1804: : 94iter [00:04, 40.18iter/s]
Best score: 11.1804: : 95iter [00:04, 40.18iter/s]
Best score: 11.1804: : 96iter [00:04, 40.18iter/s]
Best score: 11.1804: : 97iter [00:04, 40.18iter/s]
Best score: 11.1804: : 98iter [00:04, 40.35iter/s]
Best score: 11.1804: : 98iter [00:04, 40.35iter/s]
[ITER 100] Best: 11.1804
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 763.0000
  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: 11.1804: : 99iter [00:04, 40.35iter/s]
Best score: 11.1804: : 100iter [00:04, 40.35iter/s]
Best score: 11.1804: : 101iter [00:04, 40.35iter/s]
Best score: 11.1804: : 102iter [00:04, 40.35iter/s]
Best score: 11.1804: : 103iter [00:04, 37.86iter/s]
Best score: 11.1804: : 103iter [00:04, 37.86iter/s]
Best score: 11.1804: : 104iter [00:04, 37.86iter/s]
Best score: 11.1804: : 105iter [00:04, 37.86iter/s]
Best score: 11.1804: : 106iter [00:04, 37.86iter/s]
Best score: 11.1804: : 107iter [00:04, 38.33iter/s]
Best score: 11.1804: : 107iter [00:04, 38.33iter/s]
Best score: 11.1804: : 108iter [00:04, 38.33iter/s]
Best score: 11.1804: : 109iter [00:04, 38.33iter/s]
Best score: 11.1804: : 110iter [00:04, 38.33iter/s]
Best score: 11.1804: : 111iter [00:04, 38.54iter/s]
Best score: 11.1804: : 111iter [00:04, 38.54iter/s]
Best score: 11.1804: : 112iter [00:04, 38.54iter/s]
Best score: 11.1804: : 113iter [00:04, 38.54iter/s]
Best score: 11.1804: : 114iter [00:04, 38.54iter/s]
Best score: 11.1804: : 115iter [00:04, 38.91iter/s]
Best score: 11.1804: : 115iter [00:04, 38.91iter/s]
Best score: 11.1804: : 116iter [00:04, 38.91iter/s]
Best score: 11.1804: : 117iter [00:04, 38.91iter/s]
Best score: 11.1804: : 118iter [00:04, 38.91iter/s]
Best score: 11.1804: : 119iter [00:04, 38.91iter/s]
Best score: 11.1804: : 119iter [00:04, 38.91iter/s]
Best score: 11.1804: : 120iter [00:04, 38.91iter/s]
Best score: 11.1804: : 121iter [00:05, 38.91iter/s]
Best score: 11.1804: : 122iter [00:05, 38.91iter/s]
Best score: 11.1804: : 123iter [00:05, 38.96iter/s]
Best score: 11.1804: : 123iter [00:05, 38.96iter/s]
Best score: 11.1804: : 124iter [00:05, 38.96iter/s]
Best score: 11.1804: : 125iter [00:05, 38.96iter/s]
Best score: 11.1804: : 126iter [00:05, 38.96iter/s]
Best score: 11.1804: : 127iter [00:05, 38.96iter/s]
Best score: 11.1804: : 128iter [00:05, 39.42iter/s]
Best score: 11.1804: : 128iter [00:05, 39.42iter/s]
Best score: 11.1804: : 129iter [00:05, 39.42iter/s]
Best score: 11.1804: : 130iter [00:05, 39.42iter/s]
Best score: 11.1804: : 131iter [00:05, 39.42iter/s]
Best score: 11.1804: : 132iter [00:05, 39.36iter/s]
Best score: 11.1804: : 132iter [00:05, 39.36iter/s]
Best score: 11.1804: : 133iter [00:05, 39.36iter/s]
Best score: 11.1804: : 134iter [00:05, 39.36iter/s]
Best score: 11.1804: : 135iter [00:05, 39.36iter/s]
Best score: 11.1804: : 136iter [00:05, 39.36iter/s]
Best score: 11.1804: : 137iter [00:05, 39.59iter/s]
Best score: 11.1804: : 137iter [00:05, 39.59iter/s]
Best score: 11.1804: : 138iter [00:05, 39.59iter/s]
Best score: 11.1804: : 139iter [00:05, 39.59iter/s]
Best score: 11.1804: : 140iter [00:05, 39.59iter/s]
Best score: 11.1804: : 141iter [00:05, 39.52iter/s]
Best score: 11.1804: : 141iter [00:05, 39.52iter/s]
Best score: 11.1804: : 142iter [00:05, 39.52iter/s]
Best score: 11.1804: : 143iter [00:05, 39.52iter/s]
Best score: 11.1804: : 144iter [00:05, 39.52iter/s]
Best score: 11.1804: : 145iter [00:05, 39.52iter/s]
Best score: 11.1804: : 146iter [00:05, 39.67iter/s]
Best score: 11.1804: : 146iter [00:05, 39.67iter/s]
Best score: 11.1804: : 147iter [00:05, 39.67iter/s]
Best score: 11.1804: : 148iter [00:05, 39.67iter/s]
[ITER 150] Best: 11.1804
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 763.0000
  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: 11.1804: : 149iter [00:05, 39.67iter/s]
Best score: 11.1804: : 150iter [00:05, 39.53iter/s]
Best score: 11.1804: : 150iter [00:05, 39.53iter/s]
Best score: 11.1804: : 151iter [00:05, 39.53iter/s]
Best score: 11.1804: : 152iter [00:05, 39.53iter/s]
Best score: 11.1804: : 153iter [00:05, 39.53iter/s]
Best score: 11.1804: : 154iter [00:05, 32.62iter/s]
Best score: 11.1804: : 154iter [00:05, 32.62iter/s]
Best score: 11.1804: : 155iter [00:05, 32.62iter/s]
Best score: 11.1804: : 156iter [00:05, 32.62iter/s]
Best score: 11.1804: : 157iter [00:05, 32.62iter/s]
Best score: 11.1804: : 158iter [00:06, 32.62iter/s]
Best score: 11.1804: : 159iter [00:06, 34.85iter/s]
Best score: 11.1804: : 159iter [00:06, 34.85iter/s]
Best score: 11.1804: : 160iter [00:06, 34.85iter/s]
Best score: 11.1804: : 161iter [00:06, 34.85iter/s]
Best score: 11.1804: : 162iter [00:06, 34.85iter/s]
Best score: 11.1804: : 163iter [00:06, 35.76iter/s]
Best score: 11.1804: : 163iter [00:06, 35.76iter/s]
Best score: 11.1804: : 164iter [00:06, 35.76iter/s]
Best score: 11.1804: : 165iter [00:06, 35.76iter/s]
Best score: 11.1804: : 166iter [00:06, 35.76iter/s]
Best score: 11.1804: : 167iter [00:06, 35.76iter/s]
Best score: 11.1804: : 168iter [00:06, 37.09iter/s]
Best score: 11.1804: : 168iter [00:06, 37.09iter/s]
Best score: 11.1804: : 169iter [00:06, 37.09iter/s]
Best score: 11.1804: : 170iter [00:06, 37.09iter/s]
Best score: 11.1804: : 171iter [00:06, 37.09iter/s]
Best score: 11.1804: : 172iter [00:06, 37.67iter/s]
Best score: 11.1804: : 172iter [00:06, 37.67iter/s]
Best score: 11.1804: : 173iter [00:06, 37.67iter/s]
Best score: 11.1804: : 174iter [00:06, 37.67iter/s]
Best score: 11.1804: : 175iter [00:06, 37.67iter/s]
Best score: 11.1804: : 176iter [00:06, 37.67iter/s]
Best score: 11.1804: : 177iter [00:06, 38.55iter/s]
Best score: 11.1804: : 177iter [00:06, 38.55iter/s]
Best score: 11.1804: : 178iter [00:06, 38.55iter/s]
Best score: 11.1804: : 179iter [00:06, 38.55iter/s]
Best score: 11.1804: : 180iter [00:06, 38.55iter/s]
Best score: 11.1804: : 181iter [00:06, 38.84iter/s]
Best score: 11.1804: : 181iter [00:06, 38.84iter/s]
Best score: 11.1804: : 182iter [00:06, 38.84iter/s]
Best score: 11.1804: : 183iter [00:06, 38.84iter/s]
Best score: 11.1804: : 184iter [00:06, 38.84iter/s]
Best score: 11.1804: : 185iter [00:06, 38.84iter/s]
Best score: 11.1804: : 186iter [00:06, 39.35iter/s]
Best score: 11.1804: : 186iter [00:06, 39.35iter/s]
Best score: 11.1804: : 187iter [00:06, 39.35iter/s]
Best score: 11.1804: : 188iter [00:06, 39.35iter/s]
Best score: 11.1804: : 189iter [00:06, 39.35iter/s]
Best score: 11.1804: : 190iter [00:06, 39.35iter/s]
Best score: 11.1804: : 191iter [00:06, 39.74iter/s]
Best score: 11.1804: : 191iter [00:06, 39.74iter/s]
Best score: 11.1804: : 192iter [00:06, 39.74iter/s]
Best score: 11.1804: : 193iter [00:06, 39.74iter/s]
Best score: 11.1804: : 194iter [00:06, 39.74iter/s]
Best score: 11.1804: : 195iter [00:06, 39.74iter/s]
Best score: 11.1804: : 196iter [00:06, 40.11iter/s]
Best score: 11.1804: : 196iter [00:06, 40.11iter/s]
Best score: 11.1804: : 197iter [00:06, 40.11iter/s]
Best score: 11.1804: : 198iter [00:07, 40.11iter/s]
[ITER 200] Best: 11.1804
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 763.0000
  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: 11.1804: : 199iter [00:07, 40.11iter/s]
Best score: 11.1804: : 200iter [00:07, 40.11iter/s]
Best score: 11.1804: : 201iter [00:07, 33.74iter/s]
Best score: 11.1804: : 201iter [00:07, 33.74iter/s]
Best score: 11.1804: : 202iter [00:07, 33.74iter/s]
Best score: 11.1804: : 203iter [00:07, 33.74iter/s]
Best score: 11.1804: : 204iter [00:07, 33.74iter/s]
Best score: 11.1804: : 205iter [00:07, 33.74iter/s]
Best score: 11.1804: : 206iter [00:07, 35.53iter/s]
Best score: 11.1804: : 206iter [00:07, 35.53iter/s]
Best score: 11.1804: : 207iter [00:07, 35.53iter/s]
Best score: 11.1804: : 208iter [00:07, 35.53iter/s]
Best score: 11.1804: : 209iter [00:07, 35.53iter/s]
Best score: 11.1804: : 210iter [00:07, 36.58iter/s]
Best score: 11.1804: : 210iter [00:07, 36.58iter/s]
Best score: 11.1804: : 211iter [00:07, 36.58iter/s]
Best score: 11.1804: : 212iter [00:07, 36.58iter/s]
Best score: 11.1804: : 213iter [00:07, 36.58iter/s]
Best score: 11.1804: : 214iter [00:07, 36.58iter/s]
Best score: 11.1804: : 215iter [00:07, 37.85iter/s]
Best score: 11.1804: : 215iter [00:07, 37.85iter/s]
Best score: 11.1804: : 216iter [00:07, 37.85iter/s]
Best score: 11.1804: : 217iter [00:07, 37.85iter/s]
Best score: 11.1804: : 218iter [00:07, 37.85iter/s]
Best score: 11.1804: : 219iter [00:07, 37.85iter/s]
Best score: 11.1804: : 220iter [00:07, 38.52iter/s]
Best score: 11.1804: : 220iter [00:07, 38.52iter/s]
Best score: 11.1804: : 221iter [00:07, 38.52iter/s]
Best score: 11.1804: : 222iter [00:07, 38.52iter/s]
Best score: 11.1804: : 223iter [00:07, 38.52iter/s]
Best score: 11.1804: : 224iter [00:07, 38.52iter/s]
Best score: 11.1804: : 225iter [00:07, 39.08iter/s]
Best score: 11.1804: : 225iter [00:07, 39.08iter/s]
Best score: 11.1804: : 226iter [00:07, 39.08iter/s]
Best score: 11.1804: : 227iter [00:07, 39.08iter/s]
Best score: 11.1804: : 228iter [00:07, 39.08iter/s]
Best score: 11.1804: : 229iter [00:07, 39.08iter/s]
Best score: 11.1804: : 230iter [00:07, 39.45iter/s]
Best score: 11.1804: : 230iter [00:07, 39.45iter/s]
Best score: 11.1804: : 231iter [00:07, 39.45iter/s]
Best score: 11.1804: : 232iter [00:07, 39.45iter/s]
Best score: 11.1804: : 233iter [00:07, 39.45iter/s]
Best score: 11.1804: : 234iter [00:07, 39.45iter/s]
Best score: 11.1804: : 235iter [00:07, 39.84iter/s]
Best score: 11.1804: : 235iter [00:08, 39.84iter/s]
Best score: 11.1804: : 236iter [00:08, 39.84iter/s]
Best score: 11.1804: : 237iter [00:08, 39.84iter/s]
Best score: 11.1804: : 238iter [00:08, 39.84iter/s]
Best score: 9.7091: : 239iter [00:08, 39.84iter/s] 
Best score: 9.7091: : 240iter [00:08, 39.86iter/s]
Best score: 9.7091: : 240iter [00:08, 39.86iter/s]
Best score: 9.7091: : 241iter [00:08, 39.86iter/s]
Best score: 9.7091: : 242iter [00:08, 39.86iter/s]
Best score: 9.7091: : 243iter [00:08, 39.86iter/s]
Best score: 9.7091: : 244iter [00:08, 39.86iter/s]
Best score: 9.7091: : 245iter [00:08, 40.22iter/s]
Best score: 9.7091: : 245iter [00:08, 40.22iter/s]
Best score: 9.7091: : 246iter [00:08, 40.22iter/s]
Best score: 9.7091: : 247iter [00:08, 40.22iter/s]
Best score: 9.7091: : 248iter [00:08, 40.22iter/s]
[ITER 250] Best: 5.9454
[PARAMS] Current best parameters:
  fx: 516.5124
  fy: 517.5252
  px: 996.6964
  py: 763.4481
  k1: -0.03402262
  k2: 0.03054438
  k3: 0.00478276
  k4: -0.00533636
  p1: 0.00573232
  p2: -0.00887048
  p3: -0.00625795
  p4: -0.01319336
  s1: 0.0058
  s2: 0.0203
  s3: -0.0004
  s4: 0.0125
  tilt_x: -0.0044
  tilt_y: 0.0000
  scale: 3.1669
  rotation: -0.4694

Best score: 5.9454: : 249iter [00:08, 40.22iter/s]
Best score: 5.9454: : 250iter [00:08, 40.09iter/s]
Best score: 5.9454: : 250iter [00:08, 40.09iter/s]
Best score: 5.9454: : 251iter [00:08, 40.09iter/s]
Best score: 5.9454: : 252iter [00:08, 40.09iter/s]
Best score: 5.9454: : 253iter [00:08, 40.09iter/s]
Best score: 5.9454: : 254iter [00:08, 40.09iter/s]
Best score: 5.9454: : 255iter [00:08, 33.81iter/s]
Best score: 5.9454: : 255iter [00:08, 33.81iter/s]
Best score: 5.9454: : 256iter [00:08, 33.81iter/s]
Best score: 5.9454: : 257iter [00:08, 33.81iter/s]
Best score: 5.9454: : 258iter [00:08, 33.81iter/s]
Best score: 5.8535: : 259iter [00:08, 33.81iter/s]
Best score: 5.8535: : 260iter [00:08, 35.44iter/s]
Best score: 5.8535: : 260iter [00:08, 35.44iter/s]
Best score: 5.8535: : 261iter [00:08, 35.44iter/s]
Best score: 5.8535: : 262iter [00:08, 35.44iter/s]
Best score: 5.8535: : 263iter [00:08, 35.44iter/s]
Best score: 5.8535: : 264iter [00:08, 35.44iter/s]
Best score: 5.8535: : 265iter [00:08, 36.90iter/s]
Best score: 5.8535: : 265iter [00:08, 36.90iter/s]
Best score: 5.8535: : 266iter [00:08, 36.90iter/s]
Best score: 5.8535: : 267iter [00:08, 36.90iter/s]
Best score: 5.8535: : 268iter [00:08, 36.90iter/s]
Best score: 4.3492: : 269iter [00:08, 36.90iter/s]
Best score: 4.3492: : 270iter [00:08, 37.80iter/s]
Best score: 4.3492: : 270iter [00:08, 37.80iter/s]
Best score: 4.3492: : 271iter [00:08, 37.80iter/s]
Best score: 4.3492: : 272iter [00:09, 37.80iter/s]
Best score: 4.3492: : 273iter [00:09, 37.80iter/s]
Best score: 4.3492: : 274iter [00:09, 37.80iter/s]
Best score: 4.3492: : 275iter [00:09, 38.67iter/s]
Best score: 4.3492: : 275iter [00:09, 38.67iter/s]
Best score: 4.3492: : 276iter [00:09, 38.67iter/s]
Best score: 4.3492: : 277iter [00:09, 38.67iter/s]
Best score: 4.3492: : 278iter [00:09, 38.67iter/s]
Best score: 3.6659: : 279iter [00:09, 38.67iter/s]
Best score: 3.6659: : 280iter [00:09, 39.03iter/s]
Best score: 3.6659: : 280iter [00:09, 39.03iter/s]
Best score: 3.6659: : 281iter [00:09, 39.03iter/s]
Best score: 3.6659: : 282iter [00:09, 39.03iter/s]
Best score: 3.6659: : 283iter [00:09, 39.03iter/s]
Best score: 3.6659: : 284iter [00:09, 39.03iter/s]
Best score: 3.6659: : 285iter [00:09, 39.53iter/s]
Best score: 3.6659: : 285iter [00:09, 39.53iter/s]
Best score: 3.6659: : 286iter [00:09, 39.53iter/s]
Best score: 3.6659: : 287iter [00:09, 39.53iter/s]
Best score: 3.6659: : 288iter [00:09, 39.53iter/s]
Best score: 3.6659: : 289iter [00:09, 39.53iter/s]
Best score: 3.6659: : 290iter [00:09, 39.60iter/s]
Best score: 3.6659: : 290iter [00:09, 39.60iter/s]
Best score: 3.6659: : 291iter [00:09, 39.60iter/s]
Best score: 3.6659: : 292iter [00:09, 39.60iter/s]
Best score: 3.6659: : 293iter [00:09, 39.60iter/s]
Best score: 3.6659: : 294iter [00:09, 39.60iter/s]
Best score: 3.6659: : 295iter [00:09, 39.93iter/s]
Best score: 3.6659: : 295iter [00:09, 39.93iter/s]
Best score: 3.6659: : 296iter [00:09, 39.93iter/s]
Best score: 3.6659: : 297iter [00:09, 39.93iter/s]
Best score: 3.6659: : 298iter [00:09, 39.93iter/s]
[ITER 300] Best: 2.5534
[PARAMS] Current best parameters:
  fx: 515.0435
  fy: 514.3079
  px: 999.8124
  py: 761.5944
  k1: -0.01189082
  k2: 0.04757053
  k3: -0.02947084
  k4: 0.00585225
  p1: 0.00577025
  p2: -0.00503311
  p3: 0.01050734
  p4: -0.00185456
  s1: -0.0130
  s2: 0.0058
  s3: -0.0117
  s4: 0.0004
  tilt_x: -0.0146
  tilt_y: -0.0090
  scale: 3.0680
  rotation: -0.0472

Best score: 2.5534: : 299iter [00:09, 39.93iter/s]
Best score: 2.5534: : 300iter [00:09, 39.85iter/s]
Best score: 2.5534: : 300iter [00:09, 39.85iter/s]
Best score: 2.5534: : 301iter [00:09, 39.85iter/s]
Best score: 2.5534: : 302iter [00:09, 39.85iter/s]
Best score: 2.5534: : 303iter [00:09, 39.85iter/s]
Best score: 2.5534: : 304iter [00:09, 33.36iter/s]
Best score: 2.5534: : 304iter [00:09, 33.36iter/s]
Best score: 2.5534: : 305iter [00:09, 33.36iter/s]
Best score: 2.5534: : 306iter [00:09, 33.36iter/s]
Best score: 2.5534: : 307iter [00:09, 33.36iter/s]
Best score: 2.5534: : 308iter [00:09, 33.36iter/s]
Best score: 2.5534: : 309iter [00:09, 35.41iter/s]
Best score: 2.5534: : 309iter [00:10, 35.41iter/s]
Best score: 2.5534: : 310iter [00:10, 35.41iter/s]
Best score: 2.5534: : 311iter [00:10, 35.41iter/s]
Best score: 2.5534: : 312iter [00:10, 35.41iter/s]
Best score: 2.5534: : 313iter [00:10, 36.36iter/s]
Best score: 2.5534: : 313iter [00:10, 36.36iter/s]
Best score: 2.5534: : 314iter [00:10, 36.36iter/s]
Best score: 2.5534: : 315iter [00:10, 36.36iter/s]
Best score: 2.5534: : 316iter [00:10, 36.36iter/s]
Best score: 2.5534: : 317iter [00:10, 36.36iter/s]
Best score: 2.5534: : 318iter [00:10, 37.72iter/s]
Best score: 2.5534: : 318iter [00:10, 37.72iter/s]
Best score: 1.5759: : 319iter [00:10, 37.72iter/s]
Best score: 1.5759: : 320iter [00:10, 37.72iter/s]
Best score: 1.5759: : 321iter [00:10, 37.72iter/s]
Best score: 1.5759: : 322iter [00:10, 37.72iter/s]
Best score: 1.5759: : 323iter [00:10, 38.52iter/s]
Best score: 1.5759: : 323iter [00:10, 38.52iter/s]
Best score: 1.5759: : 324iter [00:10, 38.52iter/s]
Best score: 1.5759: : 325iter [00:10, 38.52iter/s]
Best score: 1.5759: : 326iter [00:10, 38.52iter/s]
Best score: 1.5759: : 327iter [00:10, 38.52iter/s]
Best score: 1.5759: : 328iter [00:10, 39.27iter/s]
Best score: 1.5759: : 328iter [00:10, 39.27iter/s]
Best score: 1.4147: : 329iter [00:10, 39.27iter/s]
Best score: 1.4147: : 330iter [00:10, 39.27iter/s]
Best score: 1.4147: : 331iter [00:10, 39.27iter/s]
Best score: 1.4147: : 332iter [00:10, 39.37iter/s]
Best score: 1.4147: : 332iter [00:10, 39.37iter/s]
Best score: 1.4147: : 333iter [00:10, 39.37iter/s]
Best score: 1.4147: : 334iter [00:10, 39.37iter/s]
Best score: 1.4147: : 335iter [00:10, 39.37iter/s]
Best score: 1.4147: : 336iter [00:10, 39.50iter/s]
Best score: 1.4147: : 336iter [00:10, 39.50iter/s]
Best score: 1.4147: : 337iter [00:10, 39.50iter/s]
Best score: 1.4147: : 338iter [00:10, 39.50iter/s]
Best score: 1.1888: : 339iter [00:10, 39.50iter/s]
Best score: 1.1888: : 340iter [00:10, 39.50iter/s]
Best score: 1.1888: : 341iter [00:10, 39.71iter/s]
Best score: 1.1888: : 341iter [00:10, 39.71iter/s]
Best score: 1.1888: : 342iter [00:10, 39.71iter/s]
Best score: 1.1888: : 343iter [00:10, 39.71iter/s]
Best score: 1.1888: : 344iter [00:10, 39.71iter/s]
Best score: 1.1888: : 345iter [00:10, 39.71iter/s]
Best score: 1.1888: : 346iter [00:10, 40.02iter/s]
Best score: 1.1888: : 346iter [00:10, 40.02iter/s]
Best score: 1.1888: : 347iter [00:10, 40.02iter/s]
Best score: 1.1888: : 348iter [00:10, 40.02iter/s]
[ITER 350] Best: 1.1598
[PARAMS] Current best parameters:
  fx: 529.2130
  fy: 529.0077
  px: 999.0440
  py: 761.8387
  k1: -0.03087561
  k2: 0.04493427
  k3: -0.02079797
  k4: 0.00272710
  p1: 0.00393033
  p2: -0.00493197
  p3: -0.00356819
  p4: -0.00498466
  s1: 0.0065
  s2: 0.0060
  s3: -0.0046
  s4: 0.0098
  tilt_x: -0.0133
  tilt_y: -0.0078
  scale: 3.0686
  rotation: -0.1932

Best score: 1.1598: : 349iter [00:10, 40.02iter/s]
Best score: 1.1598: : 350iter [00:11, 40.02iter/s]
Best score: 1.1598: : 351iter [00:11, 33.55iter/s]
Best score: 1.1598: : 351iter [00:11, 33.55iter/s]
Best score: 1.1598: : 352iter [00:11, 33.55iter/s]
Best score: 1.1598: : 353iter [00:11, 33.55iter/s]
Best score: 1.1598: : 354iter [00:11, 33.55iter/s]
Best score: 1.1598: : 355iter [00:11, 33.55iter/s]
Best score: 1.1598: : 356iter [00:11, 35.38iter/s]
Best score: 1.1598: : 356iter [00:11, 35.38iter/s]
Best score: 1.1598: : 357iter [00:11, 35.38iter/s]
Best score: 1.1598: : 358iter [00:11, 35.38iter/s]
Best score: 1.0053: : 359iter [00:11, 35.38iter/s]
Best score: 1.0053: : 360iter [00:11, 36.44iter/s]
Best score: 1.0053: : 360iter [00:11, 36.44iter/s]
Best score: 1.0053: : 361iter [00:11, 36.44iter/s]
Best score: 1.0053: : 362iter [00:11, 36.44iter/s]
Best score: 1.0053: : 363iter [00:11, 36.44iter/s]
Best score: 1.0053: : 364iter [00:11, 30.42iter/s]
Best score: 1.0053: : 364iter [00:11, 30.42iter/s]
Best score: 1.0053: : 365iter [00:11, 30.42iter/s]
Best score: 1.0053: : 366iter [00:11, 30.42iter/s]
Best score: 1.0053: : 367iter [00:11, 30.42iter/s]
Best score: 1.0053: : 368iter [00:11, 30.42iter/s]
Best score: 1.0053: : 369iter [00:11, 33.12iter/s]
Best score: 1.0053: : 369iter [00:11, 33.12iter/s]
Best score: 1.0053: : 370iter [00:11, 33.12iter/s]
Best score: 1.0053: : 371iter [00:11, 33.12iter/s]
Best score: 1.0053: : 372iter [00:11, 33.12iter/s]
Best score: 1.0053: : 373iter [00:11, 34.55iter/s]
Best score: 1.0053: : 373iter [00:11, 34.55iter/s]
Best score: 1.0053: : 374iter [00:11, 34.55iter/s]
Best score: 1.0053: : 375iter [00:11, 34.55iter/s]
Best score: 1.0053: : 376iter [00:11, 34.55iter/s]
Best score: 1.0053: : 377iter [00:11, 34.55iter/s]
Best score: 1.0053: : 378iter [00:11, 36.26iter/s]
Best score: 1.0053: : 378iter [00:11, 36.26iter/s]
Best score: 0.6917: : 379iter [00:11, 36.26iter/s]
Best score: 0.6917: : 380iter [00:11, 36.26iter/s]
Best score: 0.6917: : 381iter [00:11, 36.26iter/s]
Best score: 0.6917: : 382iter [00:11, 36.96iter/s]
Best score: 0.6917: : 382iter [00:11, 36.96iter/s]
Best score: 0.6917: : 383iter [00:12, 36.96iter/s]
Best score: 0.6917: : 384iter [00:12, 36.96iter/s]
Best score: 0.6917: : 385iter [00:12, 36.96iter/s]
Best score: 0.6917: : 386iter [00:12, 36.96iter/s]
Best score: 0.6917: : 387iter [00:12, 38.04iter/s]
Best score: 0.6917: : 387iter [00:12, 38.04iter/s]
Best score: 0.6917: : 388iter [00:12, 38.04iter/s]
Best score: 0.6167: : 389iter [00:12, 38.04iter/s]
Best score: 0.6167: : 390iter [00:12, 38.04iter/s]
Best score: 0.6167: : 391iter [00:12, 38.47iter/s]
Best score: 0.6167: : 391iter [00:12, 38.47iter/s]
Best score: 0.6167: : 392iter [00:12, 38.47iter/s]
Best score: 0.6167: : 393iter [00:12, 38.47iter/s]
Best score: 0.6167: : 394iter [00:12, 38.47iter/s]
Best score: 0.6167: : 395iter [00:12, 38.47iter/s]
Best score: 0.6167: : 396iter [00:12, 39.04iter/s]
Best score: 0.6167: : 396iter [00:12, 39.04iter/s]
Best score: 0.6167: : 397iter [00:12, 39.04iter/s]
Best score: 0.6167: : 398iter [00:12, 39.04iter/s]
[ITER 400] Best: 0.6167
[PARAMS] Current best parameters:
  fx: 532.4272
  fy: 532.2994
  px: 996.0106
  py: 764.6773
  k1: -0.00654502
  k2: 0.01418632
  k3: -0.00851772
  k4: 0.00105788
  p1: 0.00111203
  p2: -0.00727528
  p3: 0.01023929
  p4: -0.00196453
  s1: -0.0031
  s2: 0.0009
  s3: -0.0039
  s4: 0.0009
  tilt_x: 0.0091
  tilt_y: 0.0038
  scale: 3.0125
  rotation: -0.1679

Best score: 0.6167: : 399iter [00:12, 39.04iter/s]
Best score: 0.6167: : 400iter [00:12, 39.12iter/s]
Best score: 0.6167: : 400iter [00:12, 39.12iter/s]
Best score: 0.6167: : 401iter [00:12, 39.12iter/s]
Best score: 0.6167: : 402iter [00:12, 39.12iter/s]
Best score: 0.6167: : 403iter [00:12, 39.12iter/s]
Best score: 0.6167: : 404iter [00:12, 32.35iter/s]
Best score: 0.6167: : 404iter [00:12, 32.35iter/s]
Best score: 0.6167: : 405iter [00:12, 32.35iter/s]
Best score: 0.6167: : 406iter [00:12, 32.35iter/s]
Best score: 0.6167: : 407iter [00:12, 32.35iter/s]
Best score: 0.6167: : 408iter [00:12, 34.20iter/s]
Best score: 0.6167: : 408iter [00:12, 34.20iter/s]
Best score: 0.5894: : 409iter [00:12, 34.20iter/s]
Best score: 0.5894: : 410iter [00:12, 34.20iter/s]
Best score: 0.5894: : 411iter [00:12, 34.20iter/s]
Best score: 0.5894: : 412iter [00:12, 35.59iter/s]
Best score: 0.5894: : 412iter [00:12, 35.59iter/s]
Best score: 0.5894: : 413iter [00:12, 35.59iter/s]
Best score: 0.5894: : 414iter [00:12, 35.59iter/s]
Best score: 0.5894: : 415iter [00:12, 35.59iter/s]
Best score: 0.5894: : 416iter [00:12, 35.59iter/s]
Best score: 0.5894: : 417iter [00:12, 37.08iter/s]
Best score: 0.5894: : 417iter [00:12, 37.08iter/s]
Best score: 0.5894: : 418iter [00:12, 37.08iter/s]
Best score: 0.5894: : 419iter [00:12, 37.08iter/s]
Best score: 0.5894: : 420iter [00:13, 37.08iter/s]
Best score: 0.5894: : 421iter [00:13, 37.70iter/s]
Best score: 0.5894: : 421iter [00:13, 37.70iter/s]
Best score: 0.5894: : 422iter [00:13, 37.70iter/s]
Best score: 0.5894: : 423iter [00:13, 37.70iter/s]
Best score: 0.5894: : 424iter [00:13, 37.70iter/s]
Best score: 0.5894: : 425iter [00:13, 37.70iter/s]
Best score: 0.5894: : 426iter [00:13, 38.65iter/s]
Best score: 0.5894: : 426iter [00:13, 38.65iter/s]
Best score: 0.5894: : 427iter [00:13, 38.65iter/s]
Best score: 0.5894: : 428iter [00:13, 38.65iter/s]
Best score: 0.5894: : 429iter [00:13, 38.65iter/s]
Best score: 0.5894: : 430iter [00:13, 38.80iter/s]
Best score: 0.5894: : 430iter [00:13, 38.80iter/s]
Best score: 0.5894: : 431iter [00:13, 38.80iter/s]
Best score: 0.5894: : 432iter [00:13, 38.80iter/s]
Best score: 0.5894: : 433iter [00:13, 38.80iter/s]
Best score: 0.5894: : 434iter [00:13, 38.80iter/s]
Best score: 0.5894: : 435iter [00:13, 39.40iter/s]
Best score: 0.5894: : 435iter [00:13, 39.40iter/s]
Best score: 0.5894: : 436iter [00:13, 39.40iter/s]
Best score: 0.5894: : 437iter [00:13, 39.40iter/s]
Best score: 0.5894: : 438iter [00:13, 39.40iter/s]
Best score: 0.5894: : 439iter [00:13, 39.40iter/s]
Best score: 0.5894: : 440iter [00:13, 39.58iter/s]
Best score: 0.5894: : 440iter [00:13, 39.58iter/s]
Best score: 0.5894: : 441iter [00:13, 39.58iter/s]
Best score: 0.5894: : 442iter [00:13, 39.58iter/s]
Best score: 0.5894: : 443iter [00:13, 39.58iter/s]
Best score: 0.5894: : 444iter [00:13, 39.58iter/s]
Best score: 0.5894: : 445iter [00:13, 39.98iter/s]
Best score: 0.5894: : 445iter [00:13, 39.98iter/s]
Best score: 0.5894: : 446iter [00:13, 39.98iter/s]
Best score: 0.5894: : 447iter [00:13, 39.98iter/s]
Best score: 0.5894: : 448iter [00:13, 39.98iter/s]
[ITER 450] Best: 0.4923
[PARAMS] Current best parameters:
  fx: 518.7520
  fy: 518.1528
  px: 995.1644
  py: 762.1267
  k1: 0.00741562
  k2: -0.00046253
  k3: 0.00532105
  k4: -0.00259745
  p1: -0.00245313
  p2: -0.01178368
  p3: 0.01470241
  p4: -0.00078185
  s1: -0.0113
  s2: 0.0031
  s3: -0.0103
  s4: 0.0010
  tilt_x: 0.0033
  tilt_y: -0.0022
  scale: 3.0517
  rotation: -0.1756

Best score: 0.4923: : 449iter [00:13, 39.98iter/s]
Best score: 0.4923: : 450iter [00:13, 39.81iter/s]
Best score: 0.4923: : 450iter [00:13, 39.81iter/s]
Best score: 0.4923: : 451iter [00:13, 39.81iter/s]
Best score: 0.4923: : 452iter [00:13, 39.81iter/s]
Best score: 0.4923: : 453iter [00:13, 39.81iter/s]
Best score: 0.4923: : 454iter [00:13, 33.21iter/s]
Best score: 0.4923: : 454iter [00:13, 33.21iter/s]
Best score: 0.4923: : 455iter [00:13, 33.21iter/s]
Best score: 0.4923: : 456iter [00:13, 33.21iter/s]
Best score: 0.4923: : 457iter [00:14, 33.21iter/s]
Best score: 0.4923: : 458iter [00:14, 33.21iter/s]
Best score: 0.4923: : 459iter [00:14, 35.38iter/s]
Best score: 0.4463: : 459iter [00:14, 35.38iter/s]
Best score: 0.4463: : 460iter [00:14, 35.38iter/s]
Best score: 0.4463: : 461iter [00:14, 35.38iter/s]
Best score: 0.4463: : 462iter [00:14, 35.38iter/s]
Best score: 0.4463: : 463iter [00:14, 36.41iter/s]
Best score: 0.4463: : 463iter [00:14, 36.41iter/s]
Best score: 0.4463: : 464iter [00:14, 36.41iter/s]
Best score: 0.4463: : 465iter [00:14, 36.41iter/s]
Best score: 0.4463: : 466iter [00:14, 36.41iter/s]
Best score: 0.4463: : 467iter [00:14, 36.41iter/s]
Best score: 0.4463: : 468iter [00:14, 37.73iter/s]
Best score: 0.4463: : 468iter [00:14, 37.73iter/s]
Best score: 0.4463: : 469iter [00:14, 37.73iter/s]
Best score: 0.4463: : 470iter [00:14, 37.73iter/s]
Best score: 0.4463: : 471iter [00:14, 37.73iter/s]
Best score: 0.4463: : 472iter [00:14, 37.73iter/s]
Best score: 0.4463: : 473iter [00:14, 38.50iter/s]
Best score: 0.4463: : 473iter [00:14, 38.50iter/s]
Best score: 0.4463: : 474iter [00:14, 38.50iter/s]
Best score: 0.4463: : 475iter [00:14, 38.50iter/s]
Best score: 0.4463: : 476iter [00:14, 38.50iter/s]
Best score: 0.4463: : 477iter [00:14, 38.50iter/s]
Best score: 0.4463: : 478iter [00:14, 39.23iter/s]
Best score: 0.4463: : 478iter [00:14, 39.23iter/s]
Best score: 0.4463: : 479iter [00:14, 39.23iter/s]
Best score: 0.4463: : 480iter [00:14, 39.23iter/s]
Best score: 0.4463: : 481iter [00:14, 39.23iter/s]
Best score: 0.4463: : 482iter [00:14, 39.39iter/s]
Best score: 0.4463: : 482iter [00:14, 39.39iter/s]
Best score: 0.4463: : 483iter [00:14, 39.39iter/s]
Best score: 0.4463: : 484iter [00:14, 39.39iter/s]
Best score: 0.4463: : 485iter [00:14, 39.39iter/s]
Best score: 0.4463: : 486iter [00:14, 39.39iter/s]
Best score: 0.4463: : 487iter [00:14, 39.76iter/s]
Best score: 0.4463: : 487iter [00:14, 39.76iter/s]
Best score: 0.4463: : 488iter [00:14, 39.76iter/s]
Best score: 0.4463: : 489iter [00:14, 39.76iter/s]
Best score: 0.4463: : 490iter [00:14, 39.76iter/s]
Best score: 0.4463: : 491iter [00:14, 39.69iter/s]
Best score: 0.4463: : 491iter [00:14, 39.69iter/s]
Best score: 0.4463: : 492iter [00:14, 39.69iter/s]
Best score: 0.4463: : 493iter [00:14, 39.69iter/s]
Best score: 0.4463: : 494iter [00:14, 39.69iter/s]
Best score: 0.4463: : 495iter [00:14, 39.69iter/s]
Best score: 0.4463: : 496iter [00:14, 40.02iter/s]
Best score: 0.4463: : 496iter [00:14, 40.02iter/s]
Best score: 0.4463: : 497iter [00:15, 40.02iter/s]
Best score: 0.4463: : 498iter [00:15, 40.02iter/s]
[ITER 500] Best: 0.4463
[PARAMS] Current best parameters:
  fx: 521.5992
  fy: 521.1046
  px: 994.6431
  py: 763.4621
  k1: 0.00145115
  k2: 0.01253982
  k3: -0.00554876
  k4: 0.00010863
  p1: -0.00050038
  p2: -0.00874629
  p3: 0.01487882
  p4: -0.00127648
  s1: -0.0084
  s2: 0.0004
  s3: -0.0082
  s4: 0.0006
  tilt_x: 0.0056
  tilt_y: 0.0078
  scale: 3.0366
  rotation: -0.2432

Best score: 0.4463: : 499iter [00:15, 40.02iter/s]
Best score: 0.4463: : 500iter [00:15, 40.02iter/s]
Best score: 0.4463: : 501iter [00:15, 33.45iter/s]
Best score: 0.4463: : 501iter [00:15, 33.45iter/s]
Best score: 0.4463: : 502iter [00:15, 33.45iter/s]
Best score: 0.4463: : 503iter [00:15, 33.45iter/s]
Best score: 0.4463: : 504iter [00:15, 33.45iter/s]
Best score: 0.4463: : 505iter [00:15, 33.45iter/s]
Best score: 0.4463: : 506iter [00:15, 35.42iter/s]
Best score: 0.4463: : 506iter [00:15, 35.42iter/s]
Best score: 0.4463: : 507iter [00:15, 35.42iter/s]
Best score: 0.4463: : 508iter [00:15, 35.42iter/s]
Best score: 0.4463: : 509iter [00:15, 35.42iter/s]
Best score: 0.4463: : 510iter [00:15, 36.32iter/s]
Best score: 0.4463: : 510iter [00:15, 36.32iter/s]
Best score: 0.4463: : 511iter [00:15, 36.32iter/s]
Best score: 0.4463: : 512iter [00:15, 36.32iter/s]
Best score: 0.4463: : 513iter [00:15, 36.32iter/s]
Best score: 0.4463: : 514iter [00:15, 36.32iter/s]
Best score: 0.4463: : 515iter [00:15, 37.65iter/s]
Best score: 0.4463: : 515iter [00:15, 37.65iter/s]
Best score: 0.4463: : 516iter [00:15, 37.65iter/s]
Best score: 0.4463: : 517iter [00:15, 37.65iter/s]
Best score: 0.4463: : 518iter [00:15, 37.65iter/s]
Best score: 0.3482: : 519iter [00:15, 37.65iter/s]
Best score: 0.3482: : 520iter [00:15, 38.19iter/s]
Best score: 0.3482: : 520iter [00:15, 38.19iter/s]
Best score: 0.3482: : 521iter [00:15, 38.19iter/s]
Best score: 0.3482: : 522iter [00:15, 38.19iter/s]
Best score: 0.3482: : 523iter [00:15, 38.19iter/s]
Best score: 0.3482: : 524iter [00:15, 38.19iter/s]
Best score: 0.3482: : 525iter [00:15, 38.84iter/s]
Best score: 0.3482: : 525iter [00:15, 38.84iter/s]
Best score: 0.3482: : 526iter [00:15, 38.84iter/s]
Best score: 0.3482: : 527iter [00:15, 38.84iter/s]
Best score: 0.3482: : 528iter [00:15, 38.84iter/s]
Best score: 0.3482: : 529iter [00:15, 38.84iter/s]
Best score: 0.3482: : 530iter [00:15, 39.06iter/s]
Best score: 0.3482: : 530iter [00:15, 39.06iter/s]
Best score: 0.3482: : 531iter [00:15, 39.06iter/s]
Best score: 0.3482: : 532iter [00:15, 39.06iter/s]
Best score: 0.3482: : 533iter [00:15, 39.06iter/s]
Best score: 0.3482: : 534iter [00:16, 39.06iter/s]
Best score: 0.3482: : 535iter [00:16, 39.52iter/s]
Best score: 0.3482: : 535iter [00:16, 39.52iter/s]
Best score: 0.3482: : 536iter [00:16, 39.52iter/s]
Best score: 0.3482: : 537iter [00:16, 39.52iter/s]
Best score: 0.3482: : 538iter [00:16, 39.52iter/s]
Best score: 0.3413: : 539iter [00:16, 39.52iter/s]
Best score: 0.3413: : 540iter [00:16, 39.68iter/s]
Best score: 0.3413: : 540iter [00:16, 39.68iter/s]
Best score: 0.3413: : 541iter [00:16, 39.68iter/s]
Best score: 0.3413: : 542iter [00:16, 39.68iter/s]
Best score: 0.3413: : 543iter [00:16, 39.68iter/s]
Best score: 0.3413: : 544iter [00:16, 39.68iter/s]
Best score: 0.3413: : 545iter [00:16, 39.95iter/s]
Best score: 0.3413: : 545iter [00:16, 39.95iter/s]
Best score: 0.3413: : 546iter [00:16, 39.95iter/s]
Best score: 0.3413: : 547iter [00:16, 39.95iter/s]
Best score: 0.3413: : 548iter [00:16, 39.95iter/s]
[ITER 550] Best: 0.3201
[PARAMS] Current best parameters:
  fx: 513.0249
  fy: 512.4240
  px: 999.0404
  py: 757.2506
  k1: -0.02941106
  k2: 0.08123137
  k3: -0.04733166
  k4: 0.00861321
  p1: 0.00327726
  p2: 0.00198023
  p3: 0.01253144
  p4: -0.00777297
  s1: -0.0139
  s2: 0.0077
  s3: -0.0142
  s4: 0.0064
  tilt_x: -0.0310
  tilt_y: 0.0074
  scale: 3.0675
  rotation: -0.2248

Best score: 0.3201: : 549iter [00:16, 39.95iter/s]
Best score: 0.3201: : 550iter [00:16, 39.93iter/s]
Best score: 0.3201: : 550iter [00:16, 39.93iter/s]
Best score: 0.3201: : 551iter [00:16, 39.93iter/s]
Best score: 0.3201: : 552iter [00:16, 39.93iter/s]
Best score: 0.3201: : 553iter [00:16, 39.93iter/s]
Best score: 0.3201: : 554iter [00:16, 39.93iter/s]
Best score: 0.3201: : 555iter [00:16, 33.96iter/s]
Best score: 0.3201: : 555iter [00:16, 33.96iter/s]
Best score: 0.3201: : 556iter [00:16, 33.96iter/s]
Best score: 0.3201: : 557iter [00:16, 33.96iter/s]
Best score: 0.3201: : 558iter [00:16, 33.96iter/s]
Best score: 0.3049: : 559iter [00:16, 33.96iter/s]
Best score: 0.3049: : 560iter [00:16, 35.54iter/s]
Best score: 0.3049: : 560iter [00:16, 35.54iter/s]
Best score: 0.3049: : 561iter [00:16, 35.54iter/s]
Best score: 0.3049: : 562iter [00:16, 35.54iter/s]
Best score: 0.3049: : 563iter [00:16, 35.54iter/s]
Best score: 0.3049: : 564iter [00:16, 35.54iter/s]
Best score: 0.3049: : 565iter [00:16, 36.97iter/s]
Best score: 0.3049: : 565iter [00:16, 36.97iter/s]
Best score: 0.3049: : 566iter [00:16, 36.97iter/s]
Best score: 0.3049: : 567iter [00:16, 36.97iter/s]
Best score: 0.3049: : 568iter [00:16, 36.97iter/s]
Best score: 0.2943: : 569iter [00:16, 36.97iter/s]
Best score: 0.2943: : 570iter [00:16, 37.71iter/s]
Best score: 0.2943: : 570iter [00:16, 37.71iter/s]
Best score: 0.2943: : 571iter [00:17, 37.71iter/s]
Best score: 0.2943: : 572iter [00:17, 37.71iter/s]
Best score: 0.2943: : 573iter [00:17, 37.71iter/s]
Best score: 0.2943: : 574iter [00:17, 37.71iter/s]
Best score: 0.2943: : 575iter [00:17, 38.54iter/s]
Best score: 0.2943: : 575iter [00:17, 38.54iter/s]
Best score: 0.2943: : 576iter [00:17, 38.54iter/s]
Best score: 0.2943: : 577iter [00:17, 38.54iter/s]
Best score: 0.2943: : 578iter [00:17, 38.54iter/s]
Best score: 0.2878: : 579iter [00:17, 38.54iter/s]
Best score: 0.2878: : 580iter [00:17, 38.96iter/s]
Best score: 0.2878: : 580iter [00:17, 38.96iter/s]
Best score: 0.2878: : 581iter [00:17, 38.96iter/s]
Best score: 0.2878: : 582iter [00:17, 38.96iter/s]
Best score: 0.2878: : 583iter [00:17, 38.96iter/s]
Best score: 0.2878: : 584iter [00:17, 38.96iter/s]
Best score: 0.2878: : 585iter [00:17, 39.52iter/s]
Best score: 0.2878: : 585iter [00:17, 39.52iter/s]
Best score: 0.2878: : 586iter [00:17, 39.52iter/s]
Best score: 0.2878: : 587iter [00:17, 39.52iter/s]
Best score: 0.2878: : 588iter [00:17, 39.52iter/s]
Best score: 0.2878: : 589iter [00:17, 39.52iter/s]
Best score: 0.2878: : 590iter [00:17, 39.59iter/s]
Best score: 0.2878: : 590iter [00:17, 39.59iter/s]
Best score: 0.2878: : 591iter [00:17, 39.59iter/s]
Best score: 0.2878: : 592iter [00:17, 39.59iter/s]
Best score: 0.2878: : 593iter [00:17, 39.59iter/s]
Best score: 0.2878: : 594iter [00:17, 39.59iter/s]
Best score: 0.2878: : 595iter [00:17, 39.92iter/s]
Best score: 0.2878: : 595iter [00:17, 39.92iter/s]
Best score: 0.2878: : 596iter [00:17, 39.92iter/s]
Best score: 0.2878: : 597iter [00:17, 39.92iter/s]
Best score: 0.2878: : 598iter [00:17, 39.92iter/s]
[ITER 600] Best: 0.2878
[PARAMS] Current best parameters:
  fx: 513.1327
  fy: 512.5416
  px: 1000.5313
  py: 758.5917
  k1: -0.02636205
  k2: 0.07963543
  k3: -0.04789676
  k4: 0.00894082
  p1: 0.00238496
  p2: 0.00017437
  p3: 0.01505632
  p4: -0.00643857
  s1: -0.0134
  s2: 0.0063
  s3: -0.0164
  s4: 0.0053
  tilt_x: -0.0222
  tilt_y: -0.0034
  scale: 3.0552
  rotation: -0.2187

Best score: 0.2878: : 599iter [00:17, 39.92iter/s]
Best score: 0.2878: : 600iter [00:17, 39.75iter/s]
Best score: 0.2878: : 600iter [00:17, 39.75iter/s]
Best score: 0.2878: : 601iter [00:17, 39.75iter/s]
Best score: 0.2878: : 602iter [00:17, 39.75iter/s]
Best score: 0.2878: : 603iter [00:17, 39.75iter/s]
Best score: 0.2878: : 604iter [00:17, 33.40iter/s]
Best score: 0.2878: : 604iter [00:17, 33.40iter/s]
Best score: 0.2878: : 605iter [00:17, 33.40iter/s]
Best score: 0.2878: : 606iter [00:17, 33.40iter/s]
Best score: 0.2878: : 607iter [00:17, 33.40iter/s]
Best score: 0.2878: : 608iter [00:17, 33.40iter/s]
Best score: 0.2878: : 609iter [00:17, 35.47iter/s]
Best score: 0.2694: : 609iter [00:18, 35.47iter/s]
Best score: 0.2694: : 610iter [00:18, 35.47iter/s]
Best score: 0.2694: : 611iter [00:18, 35.47iter/s]
Best score: 0.2694: : 612iter [00:18, 35.47iter/s]
Best score: 0.2694: : 613iter [00:18, 36.55iter/s]
Best score: 0.2694: : 613iter [00:18, 36.55iter/s]
Best score: 0.2694: : 614iter [00:18, 36.55iter/s]
Best score: 0.2694: : 615iter [00:18, 36.55iter/s]
Best score: 0.2694: : 616iter [00:18, 36.55iter/s]
Best score: 0.2694: : 617iter [00:18, 36.55iter/s]
Best score: 0.2694: : 618iter [00:18, 37.84iter/s]
Best score: 0.2694: : 618iter [00:18, 37.84iter/s]
Best score: 0.2566: : 619iter [00:18, 37.84iter/s]
Best score: 0.2566: : 620iter [00:18, 37.84iter/s]
Best score: 0.2566: : 621iter [00:18, 37.84iter/s]
Best score: 0.2566: : 622iter [00:18, 38.31iter/s]
Best score: 0.2566: : 622iter [00:18, 38.31iter/s]
Best score: 0.2566: : 623iter [00:18, 38.31iter/s]
Best score: 0.2566: : 624iter [00:18, 38.31iter/s]
Best score: 0.2566: : 625iter [00:18, 38.31iter/s]
Best score: 0.2566: : 626iter [00:18, 38.31iter/s]
Best score: 0.2566: : 627iter [00:18, 39.00iter/s]
Best score: 0.2566: : 627iter [00:18, 39.00iter/s]
Best score: 0.2566: : 628iter [00:18, 39.00iter/s]
Best score: 0.2473: : 629iter [00:18, 39.00iter/s]
Best score: 0.2473: : 630iter [00:18, 39.00iter/s]
Best score: 0.2473: : 631iter [00:18, 39.17iter/s]
Best score: 0.2473: : 631iter [00:18, 39.17iter/s]
Best score: 0.2473: : 632iter [00:18, 39.17iter/s]
Best score: 0.2473: : 633iter [00:18, 39.17iter/s]
Best score: 0.2473: : 634iter [00:18, 39.17iter/s]
Best score: 0.2473: : 635iter [00:18, 39.17iter/s]
Best score: 0.2473: : 636iter [00:18, 39.66iter/s]
Best score: 0.2473: : 636iter [00:18, 39.66iter/s]
Best score: 0.2473: : 637iter [00:18, 39.66iter/s]
Best score: 0.2473: : 638iter [00:18, 39.66iter/s]
Best score: 0.2473: : 639iter [00:18, 39.66iter/s]
Best score: 0.2473: : 640iter [00:18, 39.65iter/s]
Best score: 0.2473: : 640iter [00:18, 39.65iter/s]
Best score: 0.2473: : 641iter [00:18, 39.65iter/s]
Best score: 0.2473: : 642iter [00:18, 39.65iter/s]
Best score: 0.2473: : 643iter [00:18, 39.65iter/s]
Best score: 0.2473: : 644iter [00:18, 39.65iter/s]
Best score: 0.2473: : 645iter [00:18, 40.05iter/s]
Best score: 0.2473: : 645iter [00:18, 40.05iter/s]
Best score: 0.2473: : 646iter [00:18, 40.05iter/s]
Best score: 0.2473: : 647iter [00:18, 40.05iter/s]
Best score: 0.2473: : 648iter [00:18, 40.05iter/s]
[ITER 650] Best: 0.2473
[PARAMS] Current best parameters:
  fx: 511.8109
  fy: 511.2421
  px: 998.1514
  py: 757.9138
  k1: -0.03440348
  k2: 0.08977450
  k3: -0.05162543
  k4: 0.00940246
  p1: 0.00652522
  p2: 0.00632650
  p3: 0.00596409
  p4: -0.00686655
  s1: -0.0127
  s2: 0.0065
  s3: -0.0113
  s4: 0.0059
  tilt_x: -0.0343
  tilt_y: 0.0199
  scale: 3.0726
  rotation: -0.2481

Best score: 0.2473: : 649iter [00:19, 40.05iter/s]
Best score: 0.2473: : 650iter [00:19, 40.05iter/s]
Best score: 0.2473: : 650iter [00:19, 40.05iter/s]
Best score: 0.2473: : 651iter [00:19, 40.05iter/s]
Best score: 0.2473: : 652iter [00:19, 40.05iter/s]
Best score: 0.2473: : 653iter [00:19, 40.05iter/s]
Best score: 0.2473: : 654iter [00:19, 40.05iter/s]
Best score: 0.2473: : 655iter [00:19, 33.71iter/s]
Best score: 0.2473: : 655iter [00:19, 33.71iter/s]
Best score: 0.2473: : 656iter [00:19, 33.71iter/s]
Best score: 0.2473: : 657iter [00:19, 33.71iter/s]
Best score: 0.2473: : 658iter [00:19, 33.71iter/s]
Best score: 0.2458: : 659iter [00:19, 33.71iter/s]
Best score: 0.2458: : 660iter [00:19, 35.40iter/s]
Best score: 0.2458: : 660iter [00:19, 35.40iter/s]
Best score: 0.2458: : 661iter [00:19, 35.40iter/s]
Best score: 0.2458: : 662iter [00:19, 35.40iter/s]
Best score: 0.2458: : 663iter [00:19, 35.40iter/s]
Best score: 0.2458: : 664iter [00:19, 35.40iter/s]
Best score: 0.2458: : 665iter [00:19, 36.88iter/s]
Best score: 0.2458: : 665iter [00:19, 36.88iter/s]
Best score: 0.2458: : 666iter [00:19, 36.88iter/s]
Best score: 0.2458: : 667iter [00:19, 36.88iter/s]
Best score: 0.2458: : 668iter [00:19, 36.88iter/s]
Best score: 0.2428: : 669iter [00:19, 36.88iter/s]
Best score: 0.2428: : 670iter [00:19, 37.74iter/s]
Best score: 0.2428: : 670iter [00:19, 37.74iter/s]
Best score: 0.2428: : 671iter [00:19, 37.74iter/s]
Best score: 0.2428: : 672iter [00:19, 37.74iter/s]
Best score: 0.2428: : 673iter [00:19, 37.74iter/s]
Best score: 0.2428: : 674iter [00:19, 37.74iter/s]
Best score: 0.2428: : 675iter [00:19, 38.45iter/s]
Best score: 0.2428: : 675iter [00:19, 38.45iter/s]
Best score: 0.2428: : 676iter [00:19, 38.45iter/s]
Best score: 0.2428: : 677iter [00:19, 38.45iter/s]
Best score: 0.2428: : 678iter [00:19, 38.45iter/s]
Best score: 0.2329: : 679iter [00:19, 38.45iter/s]
Best score: 0.2329: : 680iter [00:19, 38.79iter/s]
Best score: 0.2329: : 680iter [00:19, 38.79iter/s]
Best score: 0.2329: : 681iter [00:19, 38.79iter/s]
Best score: 0.2329: : 682iter [00:19, 38.79iter/s]
Best score: 0.2329: : 683iter [00:20, 38.79iter/s]
Best score: 0.2329: : 684iter [00:20, 32.37iter/s]
Best score: 0.2329: : 684iter [00:20, 32.37iter/s]
Best score: 0.2329: : 685iter [00:20, 32.37iter/s]
Best score: 0.2329: : 686iter [00:20, 32.37iter/s]
Best score: 0.2329: : 687iter [00:20, 32.37iter/s]
Best score: 0.2329: : 688iter [00:20, 32.37iter/s]
Best score: 0.2329: : 689iter [00:20, 34.55iter/s]
Best score: 0.2329: : 689iter [00:20, 34.55iter/s]
Best score: 0.2329: : 690iter [00:20, 34.55iter/s]
Best score: 0.2329: : 691iter [00:20, 34.55iter/s]
Best score: 0.2329: : 692iter [00:20, 34.55iter/s]
Best score: 0.2329: : 693iter [00:20, 35.83iter/s]
Best score: 0.2329: : 693iter [00:20, 35.83iter/s]
Best score: 0.2329: : 694iter [00:20, 35.83iter/s]
Best score: 0.2329: : 695iter [00:20, 35.83iter/s]
Best score: 0.2329: : 696iter [00:20, 35.83iter/s]
Best score: 0.2329: : 697iter [00:20, 35.83iter/s]
Best score: 0.2329: : 698iter [00:20, 37.28iter/s]
Best score: 0.2329: : 698iter [00:20, 37.28iter/s]
[ITER 700] Best: 0.2282
[PARAMS] Current best parameters:
  fx: 508.9049
  fy: 508.3579
  px: 996.3898
  py: 756.6627
  k1: -0.03958931
  k2: 0.09581722
  k3: -0.05197411
  k4: 0.00897348
  p1: 0.00750306
  p2: 0.00462907
  p3: 0.00405867
  p4: -0.00657247
  s1: -0.0110
  s2: 0.0063
  s3: -0.0120
  s4: 0.0057
  tilt_x: -0.0420
  tilt_y: 0.0248
  scale: 3.0934
  rotation: -0.2667

Best score: 0.2282: : 699iter [00:20, 37.28iter/s]
Best score: 0.2282: : 700iter [00:20, 37.28iter/s]
Best score: 0.2282: : 701iter [00:20, 37.28iter/s]
Best score: 0.2282: : 702iter [00:20, 31.70iter/s]
Best score: 0.2282: : 702iter [00:20, 31.70iter/s]
Best score: 0.2282: : 703iter [00:20, 31.70iter/s]
Best score: 0.2282: : 704iter [00:20, 31.70iter/s]
Best score: 0.2282: : 705iter [00:20, 31.70iter/s]
Best score: 0.2282: : 706iter [00:20, 31.70iter/s]
Best score: 0.2282: : 707iter [00:20, 34.06iter/s]
Best score: 0.2282: : 707iter [00:20, 34.06iter/s]
Best score: 0.2282: : 708iter [00:20, 34.06iter/s]
Best score: 0.2266: : 709iter [00:20, 34.06iter/s]
Best score: 0.2266: : 710iter [00:20, 34.06iter/s]
Best score: 0.2266: : 711iter [00:20, 35.43iter/s]
Best score: 0.2266: : 711iter [00:20, 35.43iter/s]
Best score: 0.2266: : 712iter [00:20, 35.43iter/s]
Best score: 0.2266: : 713iter [00:20, 35.43iter/s]
Best score: 0.2266: : 714iter [00:20, 35.43iter/s]
Best score: 0.2266: : 715iter [00:20, 36.59iter/s]
Best score: 0.2266: : 715iter [00:20, 36.59iter/s]
Best score: 0.2266: : 716iter [00:20, 36.59iter/s]
Best score: 0.2266: : 717iter [00:20, 36.59iter/s]
Best score: 0.2266: : 718iter [00:20, 36.59iter/s]
Best score: 0.2260: : 719iter [00:20, 36.59iter/s]
Best score: 0.2260: : 720iter [00:20, 37.62iter/s]
Best score: 0.2260: : 720iter [00:21, 37.62iter/s]
Best score: 0.2260: : 721iter [00:21, 37.62iter/s]
Best score: 0.2260: : 722iter [00:21, 37.62iter/s]
Best score: 0.2260: : 723iter [00:21, 37.62iter/s]
Best score: 0.2260: : 724iter [00:21, 37.62iter/s]
Best score: 0.2260: : 725iter [00:21, 38.48iter/s]
Best score: 0.2260: : 725iter [00:21, 38.48iter/s]
Best score: 0.2260: : 726iter [00:21, 38.48iter/s]
Best score: 0.2260: : 727iter [00:21, 38.48iter/s]
Best score: 0.2260: : 728iter [00:21, 38.48iter/s]
Best score: 0.2260: : 729iter [00:21, 38.48iter/s]
Best score: 0.2260: : 730iter [00:21, 38.87iter/s]
Best score: 0.2260: : 730iter [00:21, 38.87iter/s]
Best score: 0.2260: : 731iter [00:21, 38.87iter/s]
Best score: 0.2260: : 732iter [00:21, 38.87iter/s]
Best score: 0.2260: : 733iter [00:21, 38.87iter/s]
Best score: 0.2260: : 734iter [00:21, 38.87iter/s]
Best score: 0.2260: : 735iter [00:21, 39.39iter/s]
Best score: 0.2260: : 735iter [00:21, 39.39iter/s]
Best score: 0.2260: : 736iter [00:21, 39.39iter/s]
Best score: 0.2260: : 737iter [00:21, 39.39iter/s]
Best score: 0.2260: : 738iter [00:21, 39.39iter/s]
Best score: 0.2260: : 739iter [00:21, 39.39iter/s]
Best score: 0.2260: : 740iter [00:21, 39.51iter/s]
Best score: 0.2260: : 740iter [00:21, 39.51iter/s]
Best score: 0.2260: : 741iter [00:21, 39.51iter/s]
Best score: 0.2260: : 742iter [00:21, 39.51iter/s]
Best score: 0.2260: : 743iter [00:21, 39.51iter/s]
Best score: 0.2260: : 744iter [00:21, 39.51iter/s]
Best score: 0.2260: : 745iter [00:21, 39.95iter/s]
Best score: 0.2260: : 745iter [00:21, 39.95iter/s]
Best score: 0.2260: : 746iter [00:21, 39.95iter/s]
Best score: 0.2260: : 747iter [00:21, 39.95iter/s]
Best score: 0.2260: : 748iter [00:21, 39.95iter/s]
[ITER 750] Best: 0.2210
[PARAMS] Current best parameters:
  fx: 504.7934
  fy: 504.2352
  px: 997.3679
  py: 756.6875
  k1: -0.03758966
  k2: 0.09577732
  k3: -0.05032092
  k4: 0.00832807
  p1: 0.00651911
  p2: 0.00429214
  p3: 0.00608817
  p4: -0.00728382
  s1: -0.0117
  s2: 0.0070
  s3: -0.0132
  s4: 0.0064
  tilt_x: -0.0395
  tilt_y: 0.0192
  scale: 3.1017
  rotation: -0.2485

Best score: 0.2210: : 749iter [00:21, 39.95iter/s]
Best score: 0.2210: : 750iter [00:21, 39.97iter/s]
Best score: 0.2210: : 750iter [00:21, 39.97iter/s]
Best score: 0.2210: : 751iter [00:21, 39.97iter/s]
Best score: 0.2210: : 752iter [00:21, 39.97iter/s]
Best score: 0.2210: : 753iter [00:21, 39.97iter/s]
Best score: 0.2210: : 754iter [00:21, 39.97iter/s]
Best score: 0.2210: : 755iter [00:21, 33.86iter/s]
Best score: 0.2210: : 755iter [00:21, 33.86iter/s]
Best score: 0.2210: : 756iter [00:21, 33.86iter/s]
Best score: 0.2210: : 757iter [00:22, 33.86iter/s]
Best score: 0.2210: : 758iter [00:22, 33.86iter/s]
Best score: 0.2210: : 759iter [00:22, 33.86iter/s]
Best score: 0.2210: : 760iter [00:22, 35.44iter/s]
Best score: 0.2210: : 760iter [00:22, 35.44iter/s]
Best score: 0.2210: : 761iter [00:22, 35.44iter/s]
Best score: 0.2210: : 762iter [00:22, 35.44iter/s]
Best score: 0.2210: : 763iter [00:22, 35.44iter/s]
Best score: 0.2210: : 764iter [00:22, 35.44iter/s]
Best score: 0.2210: : 765iter [00:22, 36.90iter/s]
Best score: 0.2210: : 765iter [00:22, 36.90iter/s]
Best score: 0.2210: : 766iter [00:22, 36.90iter/s]
Best score: 0.2210: : 767iter [00:22, 36.90iter/s]
Best score: 0.2210: : 768iter [00:22, 36.90iter/s]
Best score: 0.2190: : 769iter [00:22, 36.90iter/s]
Best score: 0.2190: : 770iter [00:22, 37.81iter/s]
Best score: 0.2190: : 770iter [00:22, 37.81iter/s]
Best score: 0.2190: : 771iter [00:22, 37.81iter/s]
Best score: 0.2190: : 772iter [00:22, 37.81iter/s]
Best score: 0.2190: : 773iter [00:22, 37.81iter/s]
Best score: 0.2190: : 774iter [00:22, 37.81iter/s]
Best score: 0.2190: : 775iter [00:22, 38.77iter/s]
Best score: 0.2190: : 775iter [00:22, 38.77iter/s]
Best score: 0.2190: : 776iter [00:22, 38.77iter/s]
Best score: 0.2190: : 777iter [00:22, 38.77iter/s]
Best score: 0.2190: : 778iter [00:22, 38.77iter/s]
Best score: 0.2184: : 779iter [00:22, 38.77iter/s]
Best score: 0.2184: : 780iter [00:22, 39.16iter/s]
Best score: 0.2184: : 780iter [00:22, 39.16iter/s]
Best score: 0.2184: : 781iter [00:22, 39.16iter/s]
Best score: 0.2184: : 782iter [00:22, 39.16iter/s]
Best score: 0.2184: : 783iter [00:22, 39.16iter/s]
Best score: 0.2184: : 784iter [00:22, 39.16iter/s]
Best score: 0.2184: : 785iter [00:22, 39.49iter/s]
Best score: 0.2184: : 785iter [00:22, 39.49iter/s]
Best score: 0.2184: : 786iter [00:22, 39.49iter/s]
Best score: 0.2184: : 787iter [00:22, 39.49iter/s]
Best score: 0.2184: : 788iter [00:22, 39.49iter/s]
Best score: 0.2184: : 789iter [00:22, 39.49iter/s]
Best score: 0.2184: : 790iter [00:22, 39.62iter/s]
Best score: 0.2184: : 790iter [00:22, 39.62iter/s]
Best score: 0.2184: : 791iter [00:22, 39.62iter/s]
Best score: 0.2184: : 792iter [00:22, 39.62iter/s]
Best score: 0.2184: : 793iter [00:22, 39.62iter/s]
Best score: 0.2184: : 794iter [00:22, 39.62iter/s]
Best score: 0.2184: : 795iter [00:22, 39.83iter/s]
Best score: 0.2184: : 795iter [00:22, 39.83iter/s]
Best score: 0.2184: : 796iter [00:22, 39.83iter/s]
Best score: 0.2184: : 797iter [00:23, 39.83iter/s]
Best score: 0.2184: : 798iter [00:23, 39.83iter/s]
[ITER 800] Best: 0.2184
[PARAMS] Current best parameters:
  fx: 505.7401
  fy: 505.2153
  px: 997.3127
  py: 756.3787
  k1: -0.03808022
  k2: 0.09488412
  k3: -0.04980591
  k4: 0.00824472
  p1: 0.00658994
  p2: 0.00345603
  p3: 0.00375555
  p4: -0.00682722
  s1: -0.0087
  s2: 0.0066
  s3: -0.0111
  s4: 0.0059
  tilt_x: -0.0413
  tilt_y: 0.0178
  scale: 3.1020
  rotation: -0.2512

Best score: 0.2184: : 799iter [00:23, 39.83iter/s]
Best score: 0.2184: : 800iter [00:23, 39.71iter/s]
Best score: 0.2184: : 800iter [00:23, 39.71iter/s]
Best score: 0.2184: : 801iter [00:23, 39.71iter/s]
Best score: 0.2184: : 802iter [00:23, 39.71iter/s]
Best score: 0.2184: : 803iter [00:23, 39.71iter/s]
Best score: 0.2184: : 804iter [00:23, 33.31iter/s]
Best score: 0.2184: : 804iter [00:23, 33.31iter/s]
Best score: 0.2184: : 805iter [00:23, 33.31iter/s]
Best score: 0.2184: : 806iter [00:23, 33.31iter/s]
Best score: 0.2184: : 807iter [00:23, 33.31iter/s]
Best score: 0.2184: : 808iter [00:23, 34.79iter/s]
Best score: 0.2184: : 808iter [00:23, 34.79iter/s]
Best score: 0.2176: : 809iter [00:23, 34.79iter/s]
Best score: 0.2176: : 810iter [00:23, 34.79iter/s]
Best score: 0.2176: : 811iter [00:23, 34.79iter/s]
Best score: 0.2176: : 812iter [00:23, 36.00iter/s]
Best score: 0.2176: : 812iter [00:23, 36.00iter/s]
Best score: 0.2176: : 813iter [00:23, 36.00iter/s]
Best score: 0.2176: : 814iter [00:23, 36.00iter/s]
Best score: 0.2176: : 815iter [00:23, 36.00iter/s]
Best score: 0.2176: : 816iter [00:23, 36.00iter/s]
Best score: 0.2176: : 817iter [00:23, 37.53iter/s]
Best score: 0.2176: : 817iter [00:23, 37.53iter/s]
Best score: 0.2176: : 818iter [00:23, 37.53iter/s]
Best score: 0.2158: : 819iter [00:23, 37.53iter/s]
Best score: 0.2158: : 820iter [00:23, 37.53iter/s]
Best score: 0.2158: : 821iter [00:23, 38.13iter/s]
Best score: 0.2158: : 821iter [00:23, 38.13iter/s]
Best score: 0.2158: : 822iter [00:23, 38.13iter/s]
Best score: 0.2158: : 823iter [00:23, 38.13iter/s]
Best score: 0.2158: : 824iter [00:23, 38.13iter/s]
Best score: 0.2158: : 825iter [00:23, 38.13iter/s]
Best score: 0.2158: : 826iter [00:23, 39.02iter/s]
Best score: 0.2158: : 826iter [00:23, 39.02iter/s]
Best score: 0.2158: : 827iter [00:23, 39.02iter/s]
Best score: 0.2158: : 828iter [00:23, 39.02iter/s]
Best score: 0.2144: : 829iter [00:23, 39.02iter/s]
Best score: 0.2144: : 830iter [00:23, 39.02iter/s]
Best score: 0.2144: : 831iter [00:23, 39.35iter/s]
Best score: 0.2144: : 831iter [00:23, 39.35iter/s]
Best score: 0.2144: : 832iter [00:23, 39.35iter/s]
Best score: 0.2144: : 833iter [00:23, 39.35iter/s]
Best score: 0.2144: : 834iter [00:24, 39.35iter/s]
Best score: 0.2144: : 835iter [00:24, 39.35iter/s]
Best score: 0.2144: : 836iter [00:24, 39.71iter/s]
Best score: 0.2144: : 836iter [00:24, 39.71iter/s]
Best score: 0.2144: : 837iter [00:24, 39.71iter/s]
Best score: 0.2144: : 838iter [00:24, 39.71iter/s]
Best score: 0.2139: : 839iter [00:24, 39.71iter/s]
Best score: 0.2139: : 840iter [00:24, 39.75iter/s]
Best score: 0.2139: : 840iter [00:24, 39.75iter/s]
Best score: 0.2139: : 841iter [00:24, 39.75iter/s]
Best score: 0.2139: : 842iter [00:24, 39.75iter/s]
Best score: 0.2139: : 843iter [00:24, 39.75iter/s]
Best score: 0.2139: : 844iter [00:24, 39.75iter/s]
Best score: 0.2139: : 845iter [00:24, 40.06iter/s]
Best score: 0.2139: : 845iter [00:24, 40.06iter/s]
Best score: 0.2139: : 846iter [00:24, 40.06iter/s]
Best score: 0.2139: : 847iter [00:24, 40.06iter/s]
Best score: 0.2139: : 848iter [00:24, 40.06iter/s]
[ITER 850] Best: 0.2135
[PARAMS] Current best parameters:
  fx: 502.2409
  fy: 501.7753
  px: 997.6365
  py: 754.8760
  k1: -0.04822750
  k2: 0.11495155
  k3: -0.06043098
  k4: 0.01018464
  p1: 0.00733940
  p2: 0.00508230
  p3: 0.00170171
  p4: -0.00731860
  s1: -0.0082
  s2: 0.0072
  s3: -0.0115
  s4: 0.0066
  tilt_x: -0.0499
  tilt_y: 0.0194
  scale: 3.1174
  rotation: -0.2497

Best score: 0.2135: : 849iter [00:24, 40.06iter/s]
Best score: 0.2135: : 850iter [00:24, 39.96iter/s]
Best score: 0.2135: : 850iter [00:24, 39.96iter/s]
Best score: 0.2135: : 851iter [00:24, 39.96iter/s]
Best score: 0.2135: : 852iter [00:24, 39.96iter/s]
Best score: 0.2135: : 853iter [00:24, 39.96iter/s]
Best score: 0.2135: : 854iter [00:24, 39.96iter/s]
Best score: 0.2135: : 855iter [00:24, 33.56iter/s]
Best score: 0.2135: : 855iter [00:24, 33.56iter/s]
Best score: 0.2135: : 856iter [00:24, 33.56iter/s]
Best score: 0.2135: : 857iter [00:24, 33.56iter/s]
Best score: 0.2135: : 858iter [00:24, 33.56iter/s]
Best score: 0.2134: : 859iter [00:24, 33.56iter/s]
Best score: 0.2134: : 860iter [00:24, 35.17iter/s]
Best score: 0.2134: : 860iter [00:24, 35.17iter/s]
Best score: 0.2134: : 861iter [00:24, 35.17iter/s]
Best score: 0.2134: : 862iter [00:24, 35.17iter/s]
Best score: 0.2134: : 863iter [00:24, 35.17iter/s]
Best score: 0.2134: : 864iter [00:24, 35.17iter/s]
Best score: 0.2134: : 865iter [00:24, 36.70iter/s]
Best score: 0.2134: : 865iter [00:24, 36.70iter/s]
Best score: 0.2134: : 866iter [00:24, 36.70iter/s]
Best score: 0.2134: : 867iter [00:24, 36.70iter/s]
Best score: 0.2134: : 868iter [00:24, 36.70iter/s]
Best score: 0.2134: : 869iter [00:24, 36.70iter/s]
Best score: 0.2134: : 870iter [00:24, 37.58iter/s]
Best score: 0.2134: : 870iter [00:24, 37.58iter/s]
Best score: 0.2134: : 871iter [00:25, 37.58iter/s]
Best score: 0.2134: : 872iter [00:25, 37.58iter/s]
Best score: 0.2134: : 873iter [00:25, 37.58iter/s]
Best score: 0.2134: : 874iter [00:25, 37.58iter/s]
Best score: 0.2134: : 875iter [00:25, 38.58iter/s]
Best score: 0.2134: : 875iter [00:25, 38.58iter/s]
Best score: 0.2134: : 876iter [00:25, 38.58iter/s]
Best score: 0.2134: : 877iter [00:25, 38.58iter/s]
Best score: 0.2134: : 878iter [00:25, 38.58iter/s]
Best score: 0.2127: : 879iter [00:25, 38.58iter/s]
Best score: 0.2127: : 880iter [00:25, 38.94iter/s]
Best score: 0.2127: : 880iter [00:25, 38.94iter/s]
Best score: 0.2127: : 881iter [00:25, 38.94iter/s]
Best score: 0.2127: : 882iter [00:25, 38.94iter/s]
Best score: 0.2127: : 883iter [00:25, 38.94iter/s]
Best score: 0.2127: : 884iter [00:25, 38.94iter/s]
Best score: 0.2127: : 885iter [00:25, 39.38iter/s]
Best score: 0.2127: : 885iter [00:25, 39.38iter/s]
Best score: 0.2127: : 886iter [00:25, 39.38iter/s]
Best score: 0.2127: : 887iter [00:25, 39.38iter/s]
Best score: 0.2127: : 888iter [00:25, 39.38iter/s]
Best score: 0.2125: : 889iter [00:25, 39.38iter/s]
Best score: 0.2125: : 890iter [00:25, 39.44iter/s]
Best score: 0.2125: : 890iter [00:25, 39.44iter/s]
Best score: 0.2125: : 891iter [00:25, 39.44iter/s]
Best score: 0.2125: : 892iter [00:25, 39.44iter/s]
Best score: 0.2125: : 893iter [00:25, 39.44iter/s]
Best score: 0.2125: : 894iter [00:25, 39.44iter/s]
Best score: 0.2125: : 895iter [00:25, 39.88iter/s]
Best score: 0.2125: : 895iter [00:25, 39.88iter/s]
Best score: 0.2125: : 896iter [00:25, 39.88iter/s]
Best score: 0.2125: : 897iter [00:25, 39.88iter/s]
Best score: 0.2125: : 898iter [00:25, 39.88iter/s]
[ITER 900] Best: 0.2122
[PARAMS] Current best parameters:
  fx: 502.4941
  fy: 502.0818
  px: 997.9670
  py: 754.3231
  k1: -0.05435061
  k2: 0.12288288
  k3: -0.06434044
  k4: 0.01087649
  p1: 0.00750792
  p2: 0.00566696
  p3: -0.00125471
  p4: -0.00690157
  s1: -0.0059
  s2: 0.0069
  s3: -0.0095
  s4: 0.0064
  tilt_x: -0.0527
  tilt_y: 0.0189
  scale: 3.1247
  rotation: -0.2530

Best score: 0.2122: : 899iter [00:25, 39.88iter/s]
Best score: 0.2122: : 900iter [00:25, 39.88iter/s]
Best score: 0.2122: : 900iter [00:25, 39.88iter/s]
Best score: 0.2122: : 901iter [00:25, 39.88iter/s]
Best score: 0.2122: : 902iter [00:25, 39.88iter/s]
Best score: 0.2122: : 903iter [00:25, 39.88iter/s]
Best score: 0.2122: : 904iter [00:25, 33.34iter/s]
Best score: 0.2122: : 904iter [00:25, 33.34iter/s]
Best score: 0.2122: : 905iter [00:25, 33.34iter/s]
Best score: 0.2122: : 906iter [00:25, 33.34iter/s]
Best score: 0.2122: : 907iter [00:25, 33.34iter/s]
Best score: 0.2122: : 908iter [00:26, 33.34iter/s]
Best score: 0.2122: : 909iter [00:26, 35.31iter/s]
Best score: 0.2121: : 909iter [00:26, 35.31iter/s]
Best score: 0.2121: : 910iter [00:26, 35.31iter/s]
Best score: 0.2121: : 911iter [00:26, 35.31iter/s]
Best score: 0.2121: : 912iter [00:26, 35.31iter/s]
Best score: 0.2121: : 913iter [00:26, 36.27iter/s]
Best score: 0.2121: : 913iter [00:26, 36.27iter/s]
Best score: 0.2121: : 914iter [00:26, 36.27iter/s]
Best score: 0.2121: : 915iter [00:26, 36.27iter/s]
Best score: 0.2121: : 916iter [00:26, 36.27iter/s]
Best score: 0.2121: : 917iter [00:26, 36.27iter/s]
Best score: 0.2121: : 918iter [00:26, 37.60iter/s]
Best score: 0.2121: : 918iter [00:26, 37.60iter/s]
Best score: 0.2117: : 919iter [00:26, 37.60iter/s]
Best score: 0.2117: : 920iter [00:26, 37.60iter/s]
Best score: 0.2117: : 921iter [00:26, 37.60iter/s]
Best score: 0.2117: : 922iter [00:26, 38.14iter/s]
Best score: 0.2117: : 922iter [00:26, 38.14iter/s]
Best score: 0.2117: : 923iter [00:26, 38.14iter/s]
Best score: 0.2117: : 924iter [00:26, 38.14iter/s]
Best score: 0.2117: : 925iter [00:26, 38.14iter/s]
Best score: 0.2117: : 926iter [00:26, 38.14iter/s]
Best score: 0.2117: : 927iter [00:26, 38.78iter/s]
Best score: 0.2117: : 927iter [00:26, 38.78iter/s]
Best score: 0.2117: : 928iter [00:26, 38.78iter/s]
Best score: 0.2116: : 929iter [00:26, 38.78iter/s]
Best score: 0.2116: : 930iter [00:26, 38.78iter/s]
Best score: 0.2116: : 931iter [00:26, 38.95iter/s]
Best score: 0.2116: : 931iter [00:26, 38.95iter/s]
Best score: 0.2116: : 932iter [00:26, 38.95iter/s]
Best score: 0.2116: : 933iter [00:26, 38.95iter/s]
Best score: 0.2116: : 934iter [00:26, 38.95iter/s]
Best score: 0.2116: : 935iter [00:26, 38.95iter/s]
Best score: 0.2116: : 936iter [00:26, 39.31iter/s]
Best score: 0.2116: : 936iter [00:26, 39.31iter/s]
Best score: 0.2116: : 937iter [00:26, 39.31iter/s]
Best score: 0.2116: : 938iter [00:26, 39.31iter/s]
Best score: 0.2115: : 939iter [00:26, 39.31iter/s]
Best score: 0.2115: : 940iter [00:26, 39.37iter/s]
Best score: 0.2115: : 940iter [00:26, 39.37iter/s]
Best score: 0.2115: : 941iter [00:26, 39.37iter/s]
Best score: 0.2115: : 942iter [00:26, 39.37iter/s]
Best score: 0.2115: : 943iter [00:26, 39.37iter/s]
Best score: 0.2115: : 944iter [00:26, 39.37iter/s]
Best score: 0.2115: : 945iter [00:26, 39.82iter/s]
Best score: 0.2115: : 945iter [00:26, 39.82iter/s]
Best score: 0.2115: : 946iter [00:26, 39.82iter/s]
Best score: 0.2115: : 947iter [00:26, 39.82iter/s]
Best score: 0.2115: : 948iter [00:27, 39.82iter/s]
[ITER 950] Best: 0.2114
[PARAMS] Current best parameters:
  fx: 501.2241
  fy: 500.8032
  px: 997.9517
  py: 754.4199
  k1: -0.05518498
  k2: 0.12571436
  k3: -0.06571637
  k4: 0.01110182
  p1: 0.00791818
  p2: 0.00694103
  p3: -0.00156553
  p4: -0.00721155
  s1: -0.0067
  s2: 0.0071
  s3: -0.0094
  s4: 0.0067
  tilt_x: -0.0531
  tilt_y: 0.0215
  scale: 3.1280
  rotation: -0.2518

Best score: 0.2114: : 949iter [00:27, 39.82iter/s]
Best score: 0.2114: : 950iter [00:27, 39.80iter/s]
Best score: 0.2114: : 950iter [00:27, 39.80iter/s]
Best score: 0.2114: : 951iter [00:27, 39.80iter/s]
Best score: 0.2114: : 952iter [00:27, 39.80iter/s]
Best score: 0.2114: : 953iter [00:27, 39.80iter/s]
Best score: 0.2114: : 954iter [00:27, 33.14iter/s]
Best score: 0.2114: : 954iter [00:27, 33.14iter/s]
Best score: 0.2114: : 955iter [00:27, 33.14iter/s]
Best score: 0.2114: : 956iter [00:27, 33.14iter/s]
Best score: 0.2114: : 957iter [00:27, 33.14iter/s]
Best score: 0.2114: : 958iter [00:27, 33.14iter/s]
Best score: 0.2114: : 959iter [00:27, 35.31iter/s]
Best score: 0.2113: : 959iter [00:27, 35.31iter/s]
Best score: 0.2113: : 960iter [00:27, 35.31iter/s]
Best score: 0.2113: : 961iter [00:27, 35.31iter/s]
Best score: 0.2113: : 962iter [00:27, 35.31iter/s]
Best score: 0.2113: : 963iter [00:27, 36.38iter/s]
Best score: 0.2113: : 963iter [00:27, 36.38iter/s]
Best score: 0.2113: : 964iter [00:27, 36.38iter/s]
Best score: 0.2113: : 965iter [00:27, 36.38iter/s]
Best score: 0.2113: : 966iter [00:27, 36.38iter/s]
Best score: 0.2113: : 967iter [00:27, 36.38iter/s]
Best score: 0.2113: : 968iter [00:27, 37.65iter/s]
Best score: 0.2113: : 968iter [00:27, 37.65iter/s]
Best score: 0.2113: : 969iter [00:27, 37.65iter/s]
Best score: 0.2113: : 970iter [00:27, 37.65iter/s]
Best score: 0.2113: : 971iter [00:27, 37.65iter/s]
Best score: 0.2113: : 972iter [00:27, 38.15iter/s]
Best score: 0.2113: : 972iter [00:27, 38.15iter/s]
Best score: 0.2113: : 973iter [00:27, 38.15iter/s]
Best score: 0.2113: : 974iter [00:27, 38.15iter/s]
Best score: 0.2113: : 975iter [00:27, 38.15iter/s]
Best score: 0.2113: : 976iter [00:27, 38.15iter/s]
Best score: 0.2113: : 977iter [00:27, 38.89iter/s]
Best score: 0.2113: : 977iter [00:27, 38.89iter/s]
Best score: 0.2113: : 978iter [00:27, 38.89iter/s]
Best score: 0.2113: : 979iter [00:27, 38.89iter/s]
Best score: 0.2113: : 980iter [00:27, 38.89iter/s]
Best score: 0.2113: : 981iter [00:27, 39.07iter/s]
Best score: 0.2113: : 981iter [00:27, 39.07iter/s]
Best score: 0.2113: : 982iter [00:27, 39.07iter/s]
Best score: 0.2113: : 983iter [00:27, 39.07iter/s]
Best score: 0.2113: : 984iter [00:27, 39.07iter/s]
Best score: 0.2113: : 985iter [00:28, 39.07iter/s]
Best score: 0.2113: : 986iter [00:28, 39.40iter/s]
Best score: 0.2113: : 986iter [00:28, 39.40iter/s]
Best score: 0.2113: : 987iter [00:28, 39.40iter/s]
Best score: 0.2113: : 988iter [00:28, 39.40iter/s]
Best score: 0.2112: : 989iter [00:28, 39.40iter/s]
Best score: 0.2112: : 990iter [00:28, 39.47iter/s]
Best score: 0.2112: : 990iter [00:28, 39.47iter/s]
Best score: 0.2112: : 991iter [00:28, 39.47iter/s]
Best score: 0.2112: : 992iter [00:28, 39.47iter/s]
Best score: 0.2112: : 993iter [00:28, 39.47iter/s]
Best score: 0.2112: : 994iter [00:28, 39.47iter/s]
Best score: 0.2112: : 995iter [00:28, 39.74iter/s]
Best score: 0.2112: : 995iter [00:28, 39.74iter/s]
Best score: 0.2112: : 996iter [00:28, 39.74iter/s]
Best score: 0.2112: : 997iter [00:28, 39.74iter/s]
Best score: 0.2112: : 998iter [00:28, 39.74iter/s]
[ITER 1000] Best: 0.2112
[PARAMS] Current best parameters:
  fx: 501.8967
  fy: 501.4822
  px: 997.6431
  py: 753.8106
  k1: -0.05666522
  k2: 0.12740465
  k3: -0.06688420
  k4: 0.01137441
  p1: 0.00760002
  p2: 0.00608538
  p3: -0.00087163
  p4: -0.00679170
  s1: -0.0069
  s2: 0.0067
  s3: -0.0103
  s4: 0.0063
  tilt_x: -0.0553
  tilt_y: 0.0212
  scale: 3.1282
  rotation: -0.2535

Best score: 0.2112: : 999iter [00:28, 39.74iter/s]
Best score: 0.2112: : 1000iter [00:28, 39.71iter/s]
Best score: 0.2112: : 1000iter [00:28, 39.71iter/s]
Best score: 0.2112: : 1001iter [00:28, 39.71iter/s]
Best score: 0.2112: : 1002iter [00:28, 39.71iter/s]
Best score: 0.2112: : 1003iter [00:28, 39.71iter/s]
Best score: 0.2112: : 1004iter [00:28, 27.70iter/s]
Best score: 0.2112: : 1004iter [00:28, 27.70iter/s]
Best score: 0.2112: : 1005iter [00:28, 27.70iter/s]
Best score: 0.2112: : 1006iter [00:28, 27.70iter/s]
Best score: 0.2112: : 1007iter [00:28, 27.70iter/s]
Best score: 0.2112: : 1008iter [00:28, 27.70iter/s]
Best score: 0.2112: : 1009iter [00:28, 30.92iter/s]
Best score: 0.2112: : 1009iter [00:28, 30.92iter/s]
Best score: 0.2112: : 1010iter [00:28, 30.92iter/s]
Best score: 0.2112: : 1011iter [00:28, 30.92iter/s]
Best score: 0.2112: : 1012iter [00:28, 30.92iter/s]
Best score: 0.2112: : 1013iter [00:28, 32.87iter/s]
Best score: 0.2112: : 1013iter [00:28, 32.87iter/s]
Best score: 0.2112: : 1014iter [00:28, 32.87iter/s]
Best score: 0.2112: : 1015iter [00:28, 32.87iter/s]
Best score: 0.2112: : 1016iter [00:28, 32.87iter/s]
Best score: 0.2112: : 1017iter [00:28, 32.87iter/s]
Best score: 0.2112: : 1018iter [00:28, 35.08iter/s]
Best score: 0.2112: : 1018iter [00:28, 35.08iter/s]
Best score: 0.2112: : 1019iter [00:29, 35.08iter/s]
Best score: 0.2112: : 1020iter [00:29, 35.08iter/s]
Best score: 0.2112: : 1021iter [00:29, 35.08iter/s]
Best score: 0.2112: : 1022iter [00:29, 36.14iter/s]
Best score: 0.2112: : 1022iter [00:29, 36.14iter/s]
Best score: 0.2112: : 1023iter [00:29, 36.14iter/s]
Best score: 0.2112: : 1024iter [00:29, 36.14iter/s]
Best score: 0.2112: : 1025iter [00:29, 36.14iter/s]
Best score: 0.2112: : 1026iter [00:29, 36.14iter/s]
Best score: 0.2112: : 1027iter [00:29, 37.47iter/s]
Best score: 0.2112: : 1027iter [00:29, 37.47iter/s]
Best score: 0.2112: : 1028iter [00:29, 37.47iter/s]
Best score: 0.2112: : 1029iter [00:29, 37.47iter/s]
Best score: 0.2112: : 1030iter [00:29, 37.47iter/s]
Best score: 0.2112: : 1031iter [00:29, 37.93iter/s]
Best score: 0.2112: : 1031iter [00:29, 37.93iter/s]
Best score: 0.2112: : 1032iter [00:29, 37.93iter/s]
Best score: 0.2112: : 1033iter [00:29, 37.93iter/s]
Best score: 0.2112: : 1034iter [00:29, 37.93iter/s]
Best score: 0.2112: : 1035iter [00:29, 37.93iter/s]
Best score: 0.2112: : 1036iter [00:29, 38.65iter/s]
Best score: 0.2112: : 1036iter [00:29, 38.65iter/s]
Best score: 0.2112: : 1037iter [00:29, 38.65iter/s]
Best score: 0.2112: : 1038iter [00:29, 38.65iter/s]
Best score: 0.2111: : 1039iter [00:29, 38.65iter/s]
Best score: 0.2111: : 1040iter [00:29, 38.90iter/s]
Best score: 0.2111: : 1040iter [00:29, 38.90iter/s]
Best score: 0.2111: : 1041iter [00:29, 38.90iter/s]
Best score: 0.2111: : 1042iter [00:29, 38.90iter/s]
Best score: 0.2111: : 1043iter [00:29, 38.90iter/s]
Best score: 0.2111: : 1044iter [00:29, 38.90iter/s]
Best score: 0.2111: : 1045iter [00:29, 39.25iter/s]
Best score: 0.2111: : 1045iter [00:29, 39.25iter/s]
Best score: 0.2111: : 1046iter [00:29, 39.25iter/s]
Best score: 0.2111: : 1047iter [00:29, 39.25iter/s]
Best score: 0.2111: : 1048iter [00:29, 39.25iter/s]
[ITER 1050] Best: 0.2111
[PARAMS] Current best parameters:
  fx: 501.0434
  fy: 500.6354
  px: 997.8695
  py: 753.9901
  k1: -0.05614827
  k2: 0.12744583
  k3: -0.06664954
  k4: 0.01127390
  p1: 0.00763104
  p2: 0.00688567
  p3: -0.00159145
  p4: -0.00726357
  s1: -0.0066
  s2: 0.0071
  s3: -0.0094
  s4: 0.0068
  tilt_x: -0.0546
  tilt_y: 0.0217
  scale: 3.1294
  rotation: -0.2511

Best score: 0.2111: : 1049iter [00:29, 39.25iter/s]
Best score: 0.2111: : 1050iter [00:29, 39.37iter/s]
Best score: 0.2111: : 1050iter [00:29, 39.37iter/s]
Best score: 0.2111: : 1051iter [00:29, 39.37iter/s]
Best score: 0.2111: : 1052iter [00:29, 39.37iter/s]
Best score: 0.2111: : 1053iter [00:29, 39.37iter/s]
Best score: 0.2111: : 1054iter [00:29, 32.93iter/s]
Best score: 0.2111: : 1054iter [00:29, 32.93iter/s]
Best score: 0.2111: : 1055iter [00:29, 32.93iter/s]
Best score: 0.2111: : 1056iter [00:30, 32.93iter/s]
Best score: 0.2111: : 1057iter [00:30, 32.93iter/s]
Best score: 0.2111: : 1058iter [00:30, 32.93iter/s]
Best score: 0.2111: : 1059iter [00:30, 35.10iter/s]
Best score: 0.2111: : 1059iter [00:30, 35.10iter/s]
Best score: 0.2111: : 1060iter [00:30, 35.10iter/s]
Best score: 0.2111: : 1061iter [00:30, 35.10iter/s]
Best score: 0.2111: : 1062iter [00:30, 35.10iter/s]
Best score: 0.2111: : 1063iter [00:30, 36.19iter/s]
Best score: 0.2111: : 1063iter [00:30, 36.19iter/s]
Best score: 0.2111: : 1064iter [00:30, 36.19iter/s]
Best score: 0.2111: : 1065iter [00:30, 36.19iter/s]
Best score: 0.2111: : 1066iter [00:30, 36.19iter/s]
Best score: 0.2111: : 1067iter [00:30, 36.19iter/s]
Best score: 0.2111: : 1068iter [00:30, 37.54iter/s]
Best score: 0.2111: : 1068iter [00:30, 37.54iter/s]
Best score: 0.2111: : 1069iter [00:30, 37.54iter/s]
Best score: 0.2111: : 1070iter [00:30, 37.54iter/s]
Best score: 0.2111: : 1071iter [00:30, 37.54iter/s]
Best score: 0.2111: : 1072iter [00:30, 38.17iter/s]
Best score: 0.2111: : 1072iter [00:30, 38.17iter/s]
Best score: 0.2111: : 1073iter [00:30, 38.17iter/s]
Best score: 0.2111: : 1074iter [00:30, 38.17iter/s]
Best score: 0.2111: : 1075iter [00:30, 38.17iter/s]
Best score: 0.2111: : 1076iter [00:30, 38.17iter/s]
Best score: 0.2111: : 1077iter [00:30, 39.01iter/s]
Best score: 0.2111: : 1077iter [00:30, 39.01iter/s]
Best score: 0.2111: : 1078iter [00:30, 39.01iter/s]
Best score: 0.2109: : 1079iter [00:30, 39.01iter/s]
Best score: 0.2109: : 1080iter [00:30, 39.01iter/s]
Best score: 0.2109: : 1081iter [00:30, 39.04iter/s]
Best score: 0.2109: : 1081iter [00:30, 39.04iter/s]
Best score: 0.2109: : 1082iter [00:30, 39.04iter/s]
Best score: 0.2109: : 1083iter [00:30, 39.04iter/s]
Best score: 0.2109: : 1084iter [00:30, 39.04iter/s]
Best score: 0.2109: : 1085iter [00:30, 39.28iter/s]
Best score: 0.2109: : 1085iter [00:30, 39.28iter/s]
Best score: 0.2109: : 1086iter [00:30, 39.28iter/s]
Best score: 0.2109: : 1087iter [00:30, 39.28iter/s]
Best score: 0.2109: : 1088iter [00:30, 39.28iter/s]
Best score: 0.2109: : 1089iter [00:30, 39.28iter/s]
Best score: 0.2109: : 1090iter [00:30, 39.37iter/s]
Best score: 0.2109: : 1090iter [00:30, 39.37iter/s]
Best score: 0.2109: : 1091iter [00:30, 39.37iter/s]
Best score: 0.2109: : 1092iter [00:30, 39.37iter/s]
Best score: 0.2109: : 1093iter [00:30, 39.37iter/s]
Best score: 0.2109: : 1094iter [00:30, 39.37iter/s]
Best score: 0.2109: : 1095iter [00:30, 39.71iter/s]
Best score: 0.2109: : 1095iter [00:30, 39.71iter/s]
Best score: 0.2109: : 1096iter [00:31, 39.71iter/s]
Best score: 0.2109: : 1097iter [00:31, 39.71iter/s]
Best score: 0.2109: : 1098iter [00:31, 39.71iter/s]
[ITER 1100] Best: 0.2109
[PARAMS] Current best parameters:
  fx: 501.3131
  fy: 500.8964
  px: 997.8438
  py: 754.0762
  k1: -0.05492406
  k2: 0.12554430
  k3: -0.06579267
  k4: 0.01114371
  p1: 0.00760269
  p2: 0.00652525
  p3: -0.00091629
  p4: -0.00718466
  s1: -0.0070
  s2: 0.0071
  s3: -0.0100
  s4: 0.0067
  tilt_x: -0.0541
  tilt_y: 0.0211
  scale: 3.1272
  rotation: -0.2521

Best score: 0.2109: : 1099iter [00:31, 39.71iter/s]
Best score: 0.2109: : 1100iter [00:31, 39.65iter/s]
Best score: 0.2109: : 1100iter [00:31, 35.29iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.210937 -> 0.210921

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,895 → 25,895 within 3500mm ring
  Ring polish improved score: 0.210921 -> 0.210381
  Success: True
  Tangential distortion: p1=0.007603, p2=0.006527, p3=-0.000917, p4=-0.007185
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2104
[✅] Parameters copied to: /working/MDC026_W024_2025-05-13/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 501.3130659282206
    fy: 500.8963566437438
    px: 997.8438053327718
    py: 754.0761859535606
    k1: -0.05492569333854976
    k2: 0.12554304695519472
    k3: -0.06579245159152977
    k4: 0.011144204681363484
    p1: 0.0076028613862788525
    p2: 0.006527285789722315
    p3: -0.0009167418583497755
    p4: -0.007184900941274677
    scale: 3.12717480398862
    rotation: -0.252077244361314
    mmpp: 1.2843444455963444
    nmpp: 1284344.4455963443

[🔄] Converting 483 frames to PTD, creating debug images for 200 calibration frames...

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   🔄 1000:  81%|████████  | 198/245 [00:01<00:00, 175.20frame/s]

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   🔄 1000:  96%|█████████▌| 234/245 [00:01<00:00, 175.66frame/s]

                                                                 
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   🔄 500:  61%|██████▏   | 146/238 [00:00<00:00, 178.90frame/s]

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   🔄 500:  76%|███████▋  | 182/238 [00:01<00:00, 177.94frame/s]

   🔄 500:  84%|████████▍ | 200/238 [00:01<00:00, 177.04frame/s]

   🔄 500:  92%|█████████▏| 218/238 [00:01<00:00, 176.29frame/s]

   🔄 500:  99%|█████████▉| 236/238 [00:01<00:00, 175.65frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.48s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.48s/rig]
[✅] Saved PTDs: /working/MDC026_W024_2025-05-13/calibration/debug/4_undistorted/output/ptd

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   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 173.08frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.07rig/s]

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   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 175.06frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.01rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.02rig/s]
[✅] Saved PTDs: /working/MDC026_W024_2025-05-13/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 88244/3080448 [00:00<00:03, 882436.63px/s]
📤 Writing LUT:   6%|▌         | 182420/3080448 [00:00<00:03, 917330.62px/s]
📤 Writing LUT:   9%|▉         | 277099/3080448 [00:00<00:03, 930778.71px/s]
📤 Writing LUT:  12%|█▏        | 370177/3080448 [00:00<00:02, 911625.85px/s]
📤 Writing LUT:  15%|█▍        | 461399/3080448 [00:00<00:02, 903719.24px/s]
📤 Writing LUT:  18%|█▊        | 551811/3080448 [00:00<00:02, 899301.14px/s]
📤 Writing LUT:  21%|██        | 641766/3080448 [00:00<00:02, 890092.92px/s]
📤 Writing LUT:  24%|██▎       | 730799/3080448 [00:00<00:02, 880684.51px/s]
📤 Writing LUT:  27%|██▋       | 818890/3080448 [00:00<00:02, 874276.64px/s]
📤 Writing LUT:  29%|██▉       | 906332/3080448 [00:01<00:02, 869303.78px/s]
📤 Writing LUT:  32%|███▏      | 993486/3080448 [00:01<00:02, 869975.30px/s]
📤 Writing LUT:  35%|███▌      | 1082047/3080448 [00:01<00:02, 874674.61px/s]
📤 Writing LUT:  38%|███▊      | 1170151/3080448 [00:01<00:02, 876583.89px/s]
📤 Writing LUT:  41%|████      | 1257818/3080448 [00:01<00:02, 874581.50px/s]
📤 Writing LUT:  44%|████▎     | 1345282/3080448 [00:01<00:01, 873590.36px/s]
📤 Writing LUT:  47%|████▋     | 1432645/3080448 [00:01<00:01, 867615.42px/s]
📤 Writing LUT:  49%|████▉     | 1519566/3080448 [00:01<00:01, 868088.20px/s]
📤 Writing LUT:  52%|█████▏    | 1608040/3080448 [00:01<00:01, 873060.67px/s]
📤 Writing LUT:  55%|█████▌    | 1697284/3080448 [00:01<00:01, 878855.71px/s]
📤 Writing LUT:  58%|█████▊    | 1787526/3080448 [00:02<00:01, 885907.39px/s]
📤 Writing LUT:  61%|██████    | 1877465/3080448 [00:02<00:01, 889945.51px/s]
📤 Writing LUT:  64%|██████▍   | 1966466/3080448 [00:02<00:01, 888908.79px/s]
📤 Writing LUT:  67%|██████▋   | 2055362/3080448 [00:02<00:01, 886561.44px/s]
📤 Writing LUT:  70%|██████▉   | 2144023/3080448 [00:02<00:01, 881871.27px/s]
📤 Writing LUT:  72%|███████▏  | 2232218/3080448 [00:02<00:00, 876950.25px/s]
📤 Writing LUT:  75%|███████▌  | 2319922/3080448 [00:02<00:00, 876908.14px/s]
📤 Writing LUT:  78%|███████▊  | 2407619/3080448 [00:02<00:00, 874253.17px/s]
📤 Writing LUT:  81%|████████  | 2497507/3080448 [00:02<00:00, 881595.86px/s]
📤 Writing LUT:  84%|████████▍ | 2586879/3080448 [00:02<00:00, 885214.04px/s]
📤 Writing LUT:  87%|████████▋ | 2676788/3080448 [00:03<00:00, 889362.08px/s]
📤 Writing LUT:  90%|████████▉ | 2766250/3080448 [00:03<00:00, 890933.85px/s]
📤 Writing LUT:  93%|█████████▎| 2855859/3080448 [00:03<00:00, 892475.49px/s]
📤 Writing LUT:  96%|█████████▌| 2945111/3080448 [00:03<00:00, 891562.19px/s]
📤 Writing LUT:  99%|█████████▊| 3034944/3080448 [00:03<00:00, 893587.29px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 884821.33px/s]
[✅] LUT written to: /working/MDC026_W024_2025-05-13/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC026_W024_2025-05-13' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC026_W024_2025-05-13/calibration/MDC026_W024_2025-05-13_unknown.lut

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

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

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

   🔥 1000:   0%|          | 0/245 [00:00<?, ?frame/s]

   🔥 1000:   4%|▎         | 9/245 [00:00<00:02, 84.34frame/s]

   🔥 1000:   8%|▊         | 19/245 [00:00<00:02, 88.60frame/s]

   🔥 1000:  11%|█▏        | 28/245 [00:00<00:02, 88.88frame/s]

   🔥 1000:  15%|█▌        | 37/245 [00:00<00:02, 88.04frame/s]

   🔥 1000:  19%|█▉        | 46/245 [00:00<00:02, 86.93frame/s]

   🔥 1000:  22%|██▏       | 55/245 [00:00<00:02, 85.35frame/s]

   🔥 1000:  26%|██▌       | 64/245 [00:00<00:02, 84.59frame/s]

   🔥 1000:  30%|██▉       | 73/245 [00:00<00:02, 84.47frame/s]

   🔥 1000:  34%|███▍      | 83/245 [00:00<00:01, 86.38frame/s]

   🔥 1000:  38%|███▊      | 93/245 [00:01<00:01, 88.02frame/s]

   🔥 1000:  42%|████▏     | 102/245 [00:01<00:01, 88.22frame/s]

   🔥 1000:  45%|████▌     | 111/245 [00:01<00:01, 86.81frame/s]

   🔥 1000:  49%|████▉     | 120/245 [00:01<00:01, 86.55frame/s]

   🔥 1000:  53%|█████▎    | 129/245 [00:01<00:01, 87.01frame/s]

   🔥 1000:  56%|█████▋    | 138/245 [00:01<00:01, 87.87frame/s]

   🔥 1000:  60%|██████    | 147/245 [00:01<00:01, 87.74frame/s]

   🔥 1000:  64%|██████▎   | 156/245 [00:01<00:01, 86.33frame/s]

   🔥 1000:  67%|██████▋   | 165/245 [00:01<00:00, 86.90frame/s]

   🔥 1000:  71%|███████   | 174/245 [00:01<00:00, 87.77frame/s]

   🔥 1000:  75%|███████▍  | 183/245 [00:02<00:00, 87.83frame/s]

   🔥 1000:  78%|███████▊  | 192/245 [00:02<00:00, 86.84frame/s]

   🔥 1000:  82%|████████▏ | 201/245 [00:02<00:00, 86.27frame/s]

   🔥 1000:  86%|████████▌ | 210/245 [00:02<00:00, 86.20frame/s]

   🔥 1000:  90%|████████▉ | 220/245 [00:02<00:00, 87.58frame/s]

   🔥 1000:  94%|█████████▍| 230/245 [00:02<00:00, 88.81frame/s]

   🔥 1000:  98%|█████████▊| 239/245 [00:02<00:00, 88.78frame/s]

                                                                [✅] 1000: /working/MDC026_W024_2025-05-13/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC026_W024_2025-05-13/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.73s/rig]

   🔥 500:   0%|          | 0/238 [00:00<?, ?frame/s]

   🔥 500:   3%|▎         | 8/238 [00:00<00:02, 76.84frame/s]

   🔥 500:   7%|▋         | 17/238 [00:00<00:02, 79.18frame/s]

   🔥 500:  11%|█         | 25/238 [00:00<00:02, 79.44frame/s]

   🔥 500:  14%|█▍        | 34/238 [00:00<00:02, 79.98frame/s]

   🔥 500:  18%|█▊        | 43/238 [00:00<00:02, 80.75frame/s]

   🔥 500:  22%|██▏       | 52/238 [00:00<00:02, 83.32frame/s]

   🔥 500:  26%|██▌       | 61/238 [00:00<00:02, 83.65frame/s]

   🔥 500:  29%|██▉       | 70/238 [00:00<00:02, 83.01frame/s]

   🔥 500:  33%|███▎      | 79/238 [00:00<00:01, 81.50frame/s]

   🔥 500:  37%|███▋      | 88/238 [00:01<00:01, 79.99frame/s]

   🔥 500:  41%|████      | 97/238 [00:01<00:01, 79.49frame/s]

   🔥 500:  45%|████▍     | 106/238 [00:01<00:01, 80.59frame/s]

   🔥 500:  48%|████▊     | 115/238 [00:01<00:01, 81.21frame/s]

   🔥 500:  52%|█████▏    | 124/238 [00:01<00:01, 80.39frame/s]

   🔥 500:  56%|█████▌    | 133/238 [00:01<00:01, 79.88frame/s]

   🔥 500:  59%|█████▉    | 141/238 [00:01<00:01, 79.51frame/s]

   🔥 500:  63%|██████▎   | 149/238 [00:01<00:01, 78.82frame/s]

   🔥 500:  66%|██████▌   | 157/238 [00:01<00:01, 77.92frame/s]

   🔥 500:  69%|██████▉   | 165/238 [00:02<00:00, 77.27frame/s]

   🔥 500:  73%|███████▎  | 173/238 [00:02<00:00, 77.32frame/s]

   🔥 500:  76%|███████▌  | 181/238 [00:02<00:00, 76.70frame/s]

   🔥 500:  79%|███████▉  | 189/238 [00:02<00:00, 77.45frame/s]

   🔥 500:  83%|████████▎ | 198/238 [00:02<00:00, 78.97frame/s]

   🔥 500:  87%|████████▋ | 207/238 [00:02<00:00, 79.40frame/s]

   🔥 500:  90%|█████████ | 215/238 [00:02<00:00, 79.22frame/s]

   🔥 500:  94%|█████████▎| 223/238 [00:02<00:00, 77.40frame/s]

   🔥 500:  97%|█████████▋| 231/238 [00:02<00:00, 78.03frame/s]

                                                               [✅] 500: /working/MDC026_W024_2025-05-13/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC026_W024_2025-05-13/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.78s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.93s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC026_W024_2025-05-13/calibration/debug/5_heatmap
[🏁] Finished processing MDC026_W024_2025-05-13


================================================================================================================================
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