2026-05-12T04:10:28 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC027_W031_2025-03-24 ===
2026-05-12T04:10:28 - INFO: Camera ID: 15091869
2026-05-12T04:10:28 - INFO: Wand ID: 
2026-05-12T04:10:28 - INFO: Recorded At: 2025-03-23T19:16:47Z

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 248 stationary frames, 411 remain


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→ 1000 Phase 2: corners: 100%|█████████▉| 409/411 [00:02<00:00, 190.08it/s]

                                                                           [PASS2] Frame 0000096: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000100: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000125: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000126: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000127: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000128: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000129: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000130: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000131: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000132: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000299: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000300: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000301: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000302: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000322: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000323: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000324: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000325: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000329: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000330: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000331: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000332: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000334: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000341: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000355: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000378: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000379: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000380: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000381: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000382: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000383: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000384: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000388: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000391: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000392: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000393: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000409: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000410: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000411: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000412: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000638: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000639: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000640: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000641: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000642: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000643: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000644: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000645: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000646: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000647: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000649: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000650: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000651: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000652: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000653: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000654: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000655: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000656: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000126: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000127: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000131: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000324: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000331: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000341: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000380: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000381: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000384: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000385: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000386: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000387: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000390: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000411: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000640: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000642: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000646: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000651: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000655: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000123: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000389: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000390: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Summary: 36 frames fixed, 39 untrusted, 411 total


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→ 1000 Phase 3: finalize:  97%|█████████▋| 400/411 [00:08<00:00, 45.22it/s]

→ 1000 Phase 3: finalize:  99%|█████████▊| 405/411 [00:08<00:00, 45.46it/s]

→ 1000 Phase 3: finalize: 100%|█████████▉| 410/411 [00:09<00:00, 45.61it/s]

                                                                           
[TIMING] 1000 — 411 frames
  Phase 1  radial detect   (parallel)  :   6.42s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.93s  (7.1 ms/frame)
  Phase 2b corner fix      (sequential):   1.52s
  Phase 3  finalize        (sequential):   9.08s  (22.1 ms/frame)
    decode + undistort                 :   5.16s  (12.5 ms/frame)
    corner apply + refinement          :   1.88s  (4.6 ms/frame)
    segment                            :   1.68s  (4.1 ms/frame)
    smooth                             :   0.23s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  19.96s


Processing Rigs:  50%|█████     | 1/2 [00:23<00:23, 23.96s/it][INFO] Filtered 167 stationary frames, 333 remain


→ 500 (333 images) Phase 1:   0%|          | 0/333 [00:00<?, ?it/s]

→ 500 (333 images) Phase 1:   0%|          | 1/333 [00:00<03:43,  1.49it/s]

→ 500 (333 images) Phase 1:   7%|▋         | 24/333 [00:00<00:07, 41.35it/s]

→ 500 (333 images) Phase 1:  11%|█▏        | 38/333 [00:01<00:07, 38.40it/s]

→ 500 (333 images) Phase 1:  18%|█▊        | 61/333 [00:01<00:04, 66.05it/s]

→ 500 (333 images) Phase 1:  23%|██▎       | 75/333 [00:01<00:04, 54.63it/s]

→ 500 (333 images) Phase 1:  28%|██▊       | 94/333 [00:01<00:03, 74.48it/s]

→ 500 (333 images) Phase 1:  32%|███▏      | 107/333 [00:02<00:03, 58.50it/s]

→ 500 (333 images) Phase 1:  37%|███▋      | 124/333 [00:02<00:02, 73.67it/s]

→ 500 (333 images) Phase 1:  41%|████      | 136/333 [00:02<00:03, 58.61it/s]

→ 500 (333 images) Phase 1:  47%|████▋     | 155/333 [00:02<00:02, 76.55it/s]

→ 500 (333 images) Phase 1:  50%|█████     | 167/333 [00:02<00:02, 60.70it/s]

→ 500 (333 images) Phase 1:  55%|█████▍    | 183/333 [00:03<00:02, 74.99it/s]

→ 500 (333 images) Phase 1:  58%|█████▊    | 194/333 [00:03<00:02, 57.74it/s]

→ 500 (333 images) Phase 1:  64%|██████▍   | 214/333 [00:03<00:01, 78.22it/s]

→ 500 (333 images) Phase 1:  68%|██████▊   | 226/333 [00:03<00:01, 60.91it/s]

→ 500 (333 images) Phase 1:  73%|███████▎  | 242/333 [00:03<00:01, 75.58it/s]

→ 500 (333 images) Phase 1:  77%|███████▋  | 257/333 [00:04<00:01, 63.83it/s]

→ 500 (333 images) Phase 1:  81%|████████▏ | 271/333 [00:04<00:00, 74.62it/s]

→ 500 (333 images) Phase 1:  86%|████████▌ | 287/333 [00:04<00:00, 89.84it/s]

→ 500 (333 images) Phase 1:  90%|████████▉ | 299/333 [00:04<00:00, 64.89it/s]

→ 500 (333 images) Phase 1:  96%|█████████▌| 320/333 [00:04<00:00, 86.95it/s]

→ 500 (333 images) Phase 1: 100%|██████████| 333/333 [00:05<00:00, 74.80it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/333 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/333 [00:00<01:38,  3.37it/s]

→ 500 Phase 2: corners:   5%|▍         | 16/333 [00:00<00:06, 49.72it/s]

→ 500 Phase 2: corners:   8%|▊         | 27/333 [00:00<00:04, 66.47it/s]

→ 500 Phase 2: corners:  13%|█▎        | 42/333 [00:00<00:03, 91.51it/s]

→ 500 Phase 2: corners:  17%|█▋        | 56/333 [00:00<00:02, 103.69it/s]

→ 500 Phase 2: corners:  21%|██▏       | 71/333 [00:00<00:02, 113.98it/s]

→ 500 Phase 2: corners:  25%|██▌       | 84/333 [00:00<00:02, 114.33it/s]

→ 500 Phase 2: corners:  29%|██▉       | 98/333 [00:01<00:01, 118.98it/s]

→ 500 Phase 2: corners:  33%|███▎      | 111/333 [00:01<00:01, 116.57it/s]

→ 500 Phase 2: corners:  38%|███▊      | 125/333 [00:01<00:01, 117.82it/s]

→ 500 Phase 2: corners:  41%|████▏     | 138/333 [00:01<00:01, 118.97it/s]

→ 500 Phase 2: corners:  45%|████▌     | 151/333 [00:01<00:01, 118.51it/s]

→ 500 Phase 2: corners:  50%|████▉     | 165/333 [00:01<00:01, 120.21it/s]

→ 500 Phase 2: corners:  54%|█████▍    | 181/333 [00:01<00:01, 131.00it/s]

→ 500 Phase 2: corners:  59%|█████▉    | 198/333 [00:01<00:00, 139.76it/s]

→ 500 Phase 2: corners:  64%|██████▍   | 213/333 [00:01<00:00, 141.87it/s]

→ 500 Phase 2: corners:  69%|██████▉   | 229/333 [00:02<00:00, 146.12it/s]

→ 500 Phase 2: corners:  74%|███████▎  | 245/333 [00:02<00:00, 145.67it/s]

→ 500 Phase 2: corners:  78%|███████▊  | 260/333 [00:02<00:00, 145.19it/s]

→ 500 Phase 2: corners:  83%|████████▎ | 276/333 [00:02<00:00, 147.15it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 294/333 [00:02<00:00, 154.45it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 310/333 [00:02<00:00, 147.90it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 328/333 [00:02<00:00, 156.75it/s]

                                                                          [PASS2] Frame 0000250: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000251: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000252: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000253: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000484: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000485: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000486: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000487: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000252: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 7 untrusted, 333 total


→ 500 Phase 3: finalize:   0%|          | 0/333 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   2%|▏         | 5/333 [00:00<00:06, 47.48it/s]

→ 500 Phase 3: finalize:   3%|▎         | 11/333 [00:00<00:06, 51.48it/s]

→ 500 Phase 3: finalize:   5%|▌         | 17/333 [00:00<00:05, 52.99it/s]

→ 500 Phase 3: finalize:   7%|▋         | 23/333 [00:00<00:05, 53.86it/s]

→ 500 Phase 3: finalize:   9%|▊         | 29/333 [00:00<00:05, 54.22it/s]

→ 500 Phase 3: finalize:  11%|█         | 35/333 [00:00<00:05, 54.33it/s]

→ 500 Phase 3: finalize:  12%|█▏        | 41/333 [00:00<00:05, 54.37it/s]

→ 500 Phase 3: finalize:  14%|█▍        | 47/333 [00:00<00:05, 54.40it/s]

→ 500 Phase 3: finalize:  16%|█▌        | 53/333 [00:00<00:05, 54.91it/s]

→ 500 Phase 3: finalize:  18%|█▊        | 59/333 [00:01<00:04, 55.24it/s]

→ 500 Phase 3: finalize:  20%|█▉        | 65/333 [00:01<00:05, 52.43it/s]

→ 500 Phase 3: finalize:  21%|██▏       | 71/333 [00:01<00:05, 50.50it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 77/333 [00:01<00:05, 49.17it/s]

→ 500 Phase 3: finalize:  25%|██▍       | 82/333 [00:01<00:05, 48.39it/s]

→ 500 Phase 3: finalize:  26%|██▌       | 87/333 [00:01<00:05, 47.86it/s]

→ 500 Phase 3: finalize:  28%|██▊       | 92/333 [00:01<00:05, 47.46it/s]

→ 500 Phase 3: finalize:  29%|██▉       | 97/333 [00:01<00:04, 47.21it/s]

→ 500 Phase 3: finalize:  31%|███       | 102/333 [00:02<00:04, 46.98it/s]

→ 500 Phase 3: finalize:  32%|███▏      | 107/333 [00:02<00:04, 46.75it/s]

→ 500 Phase 3: finalize:  34%|███▎      | 112/333 [00:02<00:04, 46.58it/s]

→ 500 Phase 3: finalize:  35%|███▌      | 117/333 [00:02<00:04, 46.51it/s]

→ 500 Phase 3: finalize:  37%|███▋      | 122/333 [00:02<00:04, 46.09it/s]

→ 500 Phase 3: finalize:  38%|███▊      | 127/333 [00:02<00:04, 45.85it/s]

→ 500 Phase 3: finalize:  40%|███▉      | 132/333 [00:02<00:04, 45.77it/s]

→ 500 Phase 3: finalize:  41%|████      | 137/333 [00:02<00:04, 45.84it/s]

→ 500 Phase 3: finalize:  43%|████▎     | 142/333 [00:02<00:04, 45.97it/s]

→ 500 Phase 3: finalize:  44%|████▍     | 147/333 [00:02<00:04, 46.07it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 152/333 [00:03<00:03, 46.33it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 157/333 [00:03<00:03, 46.52it/s]

→ 500 Phase 3: finalize:  49%|████▊     | 162/333 [00:03<00:03, 46.68it/s]

→ 500 Phase 3: finalize:  50%|█████     | 167/333 [00:03<00:03, 46.77it/s]

→ 500 Phase 3: finalize:  52%|█████▏    | 172/333 [00:03<00:03, 46.69it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 177/333 [00:03<00:03, 46.67it/s]

→ 500 Phase 3: finalize:  55%|█████▍    | 182/333 [00:03<00:03, 47.03it/s]

→ 500 Phase 3: finalize:  56%|█████▌    | 187/333 [00:03<00:03, 46.95it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 192/333 [00:03<00:02, 47.08it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 197/333 [00:04<00:02, 47.09it/s]

→ 500 Phase 3: finalize:  61%|██████    | 202/333 [00:04<00:02, 47.16it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 207/333 [00:04<00:02, 47.04it/s]

→ 500 Phase 3: finalize:  64%|██████▎   | 212/333 [00:04<00:02, 46.08it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 217/333 [00:04<00:02, 45.84it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 222/333 [00:04<00:02, 45.31it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 227/333 [00:04<00:02, 45.05it/s]

→ 500 Phase 3: finalize:  70%|██████▉   | 232/333 [00:04<00:02, 44.94it/s]

→ 500 Phase 3: finalize:  71%|███████   | 237/333 [00:04<00:02, 45.18it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 242/333 [00:05<00:01, 45.54it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 247/333 [00:05<00:01, 45.62it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 252/333 [00:05<00:01, 46.01it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 257/333 [00:05<00:01, 46.28it/s]

→ 500 Phase 3: finalize:  79%|███████▊  | 262/333 [00:05<00:01, 46.47it/s]

→ 500 Phase 3: finalize:  80%|████████  | 267/333 [00:05<00:01, 46.54it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 272/333 [00:05<00:01, 46.57it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 277/333 [00:05<00:01, 46.68it/s]

→ 500 Phase 3: finalize:  85%|████████▍ | 282/333 [00:05<00:01, 46.63it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 287/333 [00:06<00:00, 46.71it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 292/333 [00:06<00:00, 46.61it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 297/333 [00:06<00:00, 46.31it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 302/333 [00:06<00:00, 46.08it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 307/333 [00:06<00:00, 46.02it/s]

→ 500 Phase 3: finalize:  94%|█████████▎| 312/333 [00:06<00:00, 45.87it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 317/333 [00:06<00:00, 45.98it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 322/333 [00:06<00:00, 47.05it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 327/333 [00:06<00:00, 46.79it/s]

→ 500 Phase 3: finalize: 100%|█████████▉| 332/333 [00:06<00:00, 46.54it/s]

                                                                          
[TIMING] 500 — 333 frames
  Phase 1  radial detect   (parallel)  :   5.25s  (15.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.78s  (8.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.32s
  Phase 3  finalize        (sequential):   7.00s  (21.0 ms/frame)
    decode + undistort                 :   4.12s  (12.4 ms/frame)
    corner apply + refinement          :   1.21s  (3.6 ms/frame)
    segment                            :   1.32s  (4.0 ms/frame)
    smooth                             :   0.25s  (0.7 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  15.35s


Processing Rigs: 100%|██████████| 2/2 [00:42<00:00, 20.70s/it]
Processing Rigs: 100%|██████████| 2/2 [00:42<00:00, 21.19s/it]
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 744 frames from 2 rigs to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 411
[📋] 500: 100 frames selected from 333
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC027_W031_2025-03-24/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<02:05,  3.22s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:01,  3.21s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:58,  3.20s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:55,  3.20s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:51,  3.20s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:19<01:48,  3.20s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:45,  3.20s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:42,  3.19s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:38,  3.19s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:35,  3.19s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:35<01:32,  3.20s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:38<01:29,  3.20s/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<01:23,  3.21s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:48<01:20,  3.20s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:51<01:17,  3.21s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:54<01:13,  3.22s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:57<01:10,  3.22s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:00<01:07,  3.21s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:04<01:04,  3.21s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:07<01:01,  3.21s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:10<00:57,  3.21s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:13<00:54,  3.20s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:16<00:51,  3.20s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:20<00:48,  3.21s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:23<00:44,  3.21s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:26<00:41,  3.20s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:29<00:38,  3.20s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:32<00:35,  3.19s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:36<00:31,  3.19s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:39<00:28,  3.20s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:42<00:25,  3.20s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:45<00:22,  3.21s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:48<00:19,  3.21s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:52<00:16,  3.20s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:55<00:12,  3.20s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:58<00:09,  3.20s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:01<00:06,  3.19s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:04<00:03,  3.19s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:08<00:00,  3.18s/it]
                                                                                       
[✅] Casing detection complete: cx=1023.0, cy=750.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:08,  9.90it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  24%|██▍       | 24/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  25%|██▌       | 25/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 505.051} (0.011):  26%|██▌       | 26/100 [00:00<00:01, 64.88it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 505.051} (0.011):  27%|██▋       | 27/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 505.051} (0.011):  27%|██▋       | 27/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.020 | Best: {'fx': 512.121} (0.020):  28%|██▊       | 28/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.002 | Best: {'fx': 512.121} (0.020):  29%|██▉       | 29/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 512.121} (0.020):  30%|███       | 30/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.052 | Best: {'fx': 526.263} (0.052):  31%|███       | 31/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.036 | Best: {'fx': 526.263} (0.052):  32%|███▏      | 32/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.062 | Best: {'fx': 533.333} (0.062):  33%|███▎      | 33/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.041 | Best: {'fx': 533.333} (0.062):  34%|███▍      | 34/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 533.333} (0.062):  35%|███▌      | 35/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.062):  36%|███▌      | 36/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.062):  37%|███▋      | 37/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.062):  38%|███▊      | 38/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.062):  39%|███▉      | 39/100 [00:00<00:00, 90.64it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.062):  40%|████      | 40/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.062):  40%|████      | 40/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.062):  41%|████      | 41/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.062):  42%|████▏     | 42/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.062):  43%|████▎     | 43/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.062):  44%|████▍     | 44/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.062):  45%|████▌     | 45/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.062):  46%|████▌     | 46/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.062):  47%|████▋     | 47/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.062):  48%|████▊     | 48/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  49%|████▉     | 49/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.062):  50%|█████     | 50/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.062):  51%|█████     | 51/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.062):  52%|█████▏    | 52/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  53%|█████▎    | 53/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  54%|█████▍    | 54/100 [00:00<00:00, 104.47it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  55%|█████▌    | 55/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  55%|█████▌    | 55/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  56%|█████▌    | 56/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  57%|█████▋    | 57/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  58%|█████▊    | 58/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  59%|█████▉    | 59/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  60%|██████    | 60/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  61%|██████    | 61/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  62%|██████▏   | 62/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  63%|██████▎   | 63/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  64%|██████▍   | 64/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.062):  65%|██████▌   | 65/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  66%|██████▌   | 66/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  67%|██████▋   | 67/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  68%|██████▊   | 68/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  69%|██████▉   | 69/100 [00:00<00:00, 119.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  70%|███████   | 70/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  70%|███████   | 70/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  71%|███████   | 71/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  72%|███████▏  | 72/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  73%|███████▎  | 73/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  74%|███████▍  | 74/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  75%|███████▌  | 75/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  76%|███████▌  | 76/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  77%|███████▋  | 77/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  78%|███████▊  | 78/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  79%|███████▉  | 79/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  80%|████████  | 80/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  81%|████████  | 81/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  82%|████████▏ | 82/100 [00:00<00:00, 128.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  83%|████████▎ | 83/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  83%|████████▎ | 83/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  84%|████████▍ | 84/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  85%|████████▌ | 85/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  86%|████████▌ | 86/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  87%|████████▋ | 87/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  88%|████████▊ | 88/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  89%|████████▉ | 89/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  90%|█████████ | 90/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  91%|█████████ | 91/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  92%|█████████▏| 92/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  93%|█████████▎| 93/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  94%|█████████▍| 94/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  95%|█████████▌| 95/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  96%|█████████▌| 96/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  97%|█████████▋| 97/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  98%|█████████▊| 98/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062):  99%|█████████▉| 99/100 [00:00<00:00, 126.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.062): 100%|██████████| 100/100 [00:00<00:00, 136.66it/s]
                                                                                                                                         

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   2%|▏         | 1/50 [00:00<00:05,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.014 | Best: {'fx': 510.374} (0.015):   2%|▏         | 1/50 [00:00<00:05,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   4%|▍         | 2/50 [00:00<00:05,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.018 | Best: {'fx': 511.395} (0.018):   6%|▌         | 3/50 [00:00<00:05,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.023 | Best: {'fx': 514.456} (0.023):   8%|▊         | 4/50 [00:00<00:05,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.021 | Best: {'fx': 514.456} (0.023):  10%|█         | 5/50 [00:00<00:04,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.022 | Best: {'fx': 514.456} (0.023):  12%|█▏        | 6/50 [00:00<00:04,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.031 | Best: {'fx': 516.497} (0.031):  14%|█▍        | 7/50 [00:00<00:04,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.028 | Best: {'fx': 516.497} (0.031):  16%|█▌        | 8/50 [00:00<00:04,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.032 | Best: {'fx': 517.517} (0.032):  18%|█▊        | 9/50 [00:00<00:04,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.039 | Best: {'fx': 520.578} (0.039):  20%|██        | 10/50 [00:00<00:04,  9.07it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.039 | Best: {'fx': 520.578} (0.039):  22%|██▏       | 11/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.034 | Best: {'fx': 520.578} (0.039):  22%|██▏       | 11/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.036 | Best: {'fx': 520.578} (0.039):  24%|██▍       | 12/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.046 | Best: {'fx': 523.639} (0.046):  26%|██▌       | 13/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.042 | Best: {'fx': 523.639} (0.046):  28%|██▊       | 14/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.052 | Best: {'fx': 525.68} (0.052):  30%|███       | 15/50 [00:00<00:00, 59.22it/s] 
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.045 | Best: {'fx': 525.68} (0.052):  32%|███▏      | 16/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.054 | Best: {'fx': 526.701} (0.054):  34%|███▍      | 17/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.062 | Best: {'fx': 527.721} (0.062):  36%|███▌      | 18/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.050 | Best: {'fx': 527.721} (0.062):  38%|███▊      | 19/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.064 | Best: {'fx': 528.741} (0.064):  40%|████      | 20/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.066 | Best: {'fx': 530.782} (0.066):  42%|████▏     | 21/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.064 | Best: {'fx': 530.782} (0.066):  44%|████▍     | 22/50 [00:00<00:00, 59.22it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.064 | Best: {'fx': 530.782} (0.066):  46%|████▌     | 23/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.061 | Best: {'fx': 530.782} (0.066):  46%|████▌     | 23/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.067 | Best: {'fx': 531.803} (0.067):  48%|████▊     | 24/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.049 | Best: {'fx': 531.803} (0.067):  50%|█████     | 25/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.058 | Best: {'fx': 531.803} (0.067):  52%|█████▏    | 26/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.042 | Best: {'fx': 531.803} (0.067):  54%|█████▍    | 27/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.045 | Best: {'fx': 531.803} (0.067):  56%|█████▌    | 28/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.057 | Best: {'fx': 531.803} (0.067):  58%|█████▊    | 29/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.055 | Best: {'fx': 531.803} (0.067):  60%|██████    | 30/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.044 | Best: {'fx': 531.803} (0.067):  62%|██████▏   | 31/50 [00:00<00:00, 83.85it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.044 | Best: {'fx': 531.803} (0.067):  64%|██████▍   | 32/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.040 | Best: {'fx': 531.803} (0.067):  64%|██████▍   | 32/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.035 | Best: {'fx': 531.803} (0.067):  66%|██████▌   | 33/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.036 | Best: {'fx': 531.803} (0.067):  68%|██████▊   | 34/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.035 | Best: {'fx': 531.803} (0.067):  70%|███████   | 35/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.033 | Best: {'fx': 531.803} (0.067):  72%|███████▏  | 36/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.028 | Best: {'fx': 531.803} (0.067):  74%|███████▍  | 37/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.031 | Best: {'fx': 531.803} (0.067):  76%|███████▌  | 38/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.028 | Best: {'fx': 531.803} (0.067):  78%|███████▊  | 39/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.032 | Best: {'fx': 531.803} (0.067):  80%|████████  | 40/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.029 | Best: {'fx': 531.803} (0.067):  82%|████████▏ | 41/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.027 | Best: {'fx': 531.803} (0.067):  84%|████████▍ | 42/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.023 | Best: {'fx': 531.803} (0.067):  86%|████████▌ | 43/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.025 | Best: {'fx': 531.803} (0.067):  88%|████████▊ | 44/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.026 | Best: {'fx': 531.803} (0.067):  90%|█████████ | 45/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.025 | Best: {'fx': 531.803} (0.067):  92%|█████████▏| 46/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.023 | Best: {'fx': 531.803} (0.067):  94%|█████████▍| 47/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.024 | Best: {'fx': 531.803} (0.067):  96%|█████████▌| 48/50 [00:00<00:00, 82.73it/s]
Brute | Start: {'fx': 533.333} (0.062) | Current: 0.022 | Best: {'fx': 531.803} (0.067):  98%|█████████▊| 49/50 [00:00<00:00, 82.73it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1023.0
    py: 750.0
    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: 34.0858: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 34.0858, picklable_objective(x0) = 34.0893

Best score: 34.0893: : 0iter [00:01, ?iter/s] 
Best score: 34.0893: : 1iter [00:01,  1.94s/iter]
Best score: 34.0893: : 1iter [00:01,  1.94s/iter]
Best score: 34.0893: : 2iter [00:01,  1.94s/iter]
Best score: 34.0893: : 3iter [00:02,  1.94s/iter]
Best score: 34.0893: : 4iter [00:02,  1.94s/iter]
Best score: 34.0893: : 5iter [00:02,  3.22iter/s]
Best score: 34.0893: : 5iter [00:02,  3.22iter/s]
Best score: 34.0893: : 6iter [00:02,  3.22iter/s]
Best score: 34.0893: : 7iter [00:02,  3.22iter/s]
Best score: 34.0893: : 8iter [00:02,  3.22iter/s]
Best score: 34.0893: : 9iter [00:02,  6.49iter/s]
Best score: 34.0893: : 9iter [00:02,  6.49iter/s]
Best score: 34.0893: : 10iter [00:02,  6.49iter/s]
Best score: 34.0893: : 11iter [00:02,  6.49iter/s]
Best score: 34.0893: : 12iter [00:02,  6.49iter/s]
Best score: 34.0893: : 13iter [00:02, 10.24iter/s]
Best score: 34.0893: : 13iter [00:02, 10.24iter/s]
Best score: 34.0893: : 14iter [00:02, 10.24iter/s]
Best score: 34.0893: : 15iter [00:02, 10.24iter/s]
Best score: 34.0893: : 16iter [00:02, 10.24iter/s]
Best score: 34.0893: : 17iter [00:02, 14.35iter/s]
Best score: 34.0893: : 17iter [00:02, 14.35iter/s]
Best score: 34.0893: : 18iter [00:02, 14.35iter/s]
Best score: 34.0893: : 19iter [00:02, 14.35iter/s]
Best score: 34.0893: : 20iter [00:02, 14.35iter/s]
Best score: 34.0893: : 21iter [00:02, 18.50iter/s]
Best score: 34.0893: : 21iter [00:02, 18.50iter/s]
Best score: 34.0893: : 22iter [00:02, 18.50iter/s]
Best score: 34.0893: : 23iter [00:02, 18.50iter/s]
Best score: 34.0893: : 24iter [00:02, 18.50iter/s]
Best score: 34.0893: : 25iter [00:02, 18.50iter/s]
Best score: 34.0893: : 26iter [00:02, 23.37iter/s]
Best score: 34.0893: : 26iter [00:02, 23.37iter/s]
Best score: 34.0893: : 27iter [00:02, 23.37iter/s]
Best score: 34.0893: : 28iter [00:02, 23.37iter/s]
Best score: 34.0893: : 29iter [00:02, 23.37iter/s]
Best score: 34.0893: : 30iter [00:02, 26.67iter/s]
Best score: 34.0893: : 30iter [00:02, 26.67iter/s]
Best score: 34.0893: : 31iter [00:02, 26.67iter/s]
Best score: 34.0893: : 32iter [00:02, 26.67iter/s]
Best score: 34.0893: : 33iter [00:02, 26.67iter/s]
Best score: 34.0893: : 34iter [00:02, 29.62iter/s]
Best score: 34.0893: : 34iter [00:02, 29.62iter/s]
Best score: 34.0893: : 35iter [00:02, 29.62iter/s]
Best score: 34.0893: : 36iter [00:02, 29.62iter/s]
Best score: 34.0893: : 37iter [00:02, 29.62iter/s]
Best score: 34.0893: : 38iter [00:02, 32.10iter/s]
Best score: 34.0893: : 38iter [00:02, 32.10iter/s]
Best score: 34.0893: : 39iter [00:02, 32.10iter/s]
Best score: 34.0893: : 40iter [00:02, 32.10iter/s]
Best score: 34.0893: : 41iter [00:02, 32.10iter/s]
Best score: 34.0893: : 42iter [00:02, 33.93iter/s]
Best score: 34.0893: : 42iter [00:03, 33.93iter/s]
Best score: 34.0893: : 43iter [00:03, 33.93iter/s]
Best score: 34.0893: : 44iter [00:03, 33.93iter/s]
Best score: 34.0893: : 45iter [00:03, 33.93iter/s]
Best score: 34.0893: : 46iter [00:03, 35.47iter/s]
Best score: 34.0893: : 46iter [00:03, 35.47iter/s]
Best score: 34.0893: : 47iter [00:03, 35.47iter/s]
Best score: 34.0893: : 48iter [00:03, 35.47iter/s]
[ITER 50] Best: 34.0893
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1023.0000
  py: 750.0000
  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: 34.0893: : 49iter [00:03, 35.47iter/s]
Best score: 34.0893: : 50iter [00:03, 36.63iter/s]
Best score: 34.0893: : 50iter [00:03, 36.63iter/s]
Best score: 34.0893: : 51iter [00:03, 36.63iter/s]
Best score: 34.0893: : 52iter [00:03, 36.63iter/s]
Best score: 34.0893: : 53iter [00:03, 36.63iter/s]
Best score: 34.0893: : 54iter [00:03, 36.63iter/s]
Best score: 34.0893: : 55iter [00:03, 37.77iter/s]
Best score: 34.0893: : 55iter [00:03, 37.77iter/s]
Best score: 34.0893: : 56iter [00:03, 37.77iter/s]
Best score: 34.0893: : 57iter [00:03, 37.77iter/s]
Best score: 34.0893: : 58iter [00:03, 37.77iter/s]
Best score: 34.0893: : 59iter [00:03, 38.35iter/s]
Best score: 34.0893: : 59iter [00:03, 38.35iter/s]
Best score: 34.0893: : 60iter [00:03, 38.35iter/s]
Best score: 34.0893: : 61iter [00:03, 38.35iter/s]
Best score: 34.0893: : 62iter [00:03, 38.35iter/s]
Best score: 34.0893: : 63iter [00:03, 38.59iter/s]
Best score: 34.0893: : 63iter [00:03, 38.59iter/s]
Best score: 34.0893: : 64iter [00:03, 38.59iter/s]
Best score: 34.0893: : 65iter [00:03, 38.59iter/s]
Best score: 34.0893: : 66iter [00:03, 38.59iter/s]
Best score: 34.0893: : 67iter [00:03, 38.59iter/s]
Best score: 34.0893: : 68iter [00:03, 39.06iter/s]
Best score: 34.0893: : 68iter [00:03, 39.06iter/s]
Best score: 34.0893: : 69iter [00:03, 39.06iter/s]
Best score: 34.0893: : 70iter [00:03, 39.06iter/s]
Best score: 34.0893: : 71iter [00:03, 39.06iter/s]
Best score: 34.0893: : 72iter [00:03, 39.10iter/s]
Best score: 34.0893: : 72iter [00:03, 39.10iter/s]
Best score: 34.0893: : 73iter [00:03, 39.10iter/s]
Best score: 34.0893: : 74iter [00:03, 39.10iter/s]
Best score: 34.0893: : 75iter [00:03, 39.10iter/s]
Best score: 34.0893: : 76iter [00:03, 39.03iter/s]
Best score: 34.0893: : 76iter [00:03, 39.03iter/s]
Best score: 34.0893: : 77iter [00:03, 39.03iter/s]
Best score: 34.0893: : 78iter [00:03, 39.03iter/s]
Best score: 34.0893: : 79iter [00:03, 39.03iter/s]
Best score: 34.0893: : 80iter [00:03, 39.10iter/s]
Best score: 34.0893: : 80iter [00:03, 39.10iter/s]
Best score: 34.0893: : 81iter [00:03, 39.10iter/s]
Best score: 34.0893: : 82iter [00:04, 39.10iter/s]
Best score: 34.0893: : 83iter [00:04, 39.10iter/s]
Best score: 34.0893: : 84iter [00:04, 39.10iter/s]
Best score: 34.0893: : 85iter [00:04, 39.50iter/s]
Best score: 34.0893: : 85iter [00:04, 39.50iter/s]
Best score: 34.0893: : 86iter [00:04, 39.50iter/s]
Best score: 34.0893: : 87iter [00:04, 39.50iter/s]
Best score: 34.0893: : 88iter [00:04, 39.50iter/s]
Best score: 34.0893: : 89iter [00:04, 39.50iter/s]
Best score: 34.0893: : 90iter [00:04, 39.50iter/s]
Best score: 34.0893: : 90iter [00:04, 39.50iter/s]
Best score: 34.0893: : 91iter [00:04, 39.50iter/s]
Best score: 34.0893: : 92iter [00:04, 39.50iter/s]
Best score: 34.0893: : 93iter [00:04, 39.50iter/s]
Best score: 34.0893: : 94iter [00:04, 39.50iter/s]
Best score: 34.0893: : 95iter [00:04, 39.75iter/s]
Best score: 34.0893: : 95iter [00:04, 39.75iter/s]
Best score: 34.0893: : 96iter [00:04, 39.75iter/s]
Best score: 34.0893: : 97iter [00:04, 39.75iter/s]
Best score: 34.0893: : 98iter [00:04, 39.75iter/s]
[ITER 100] Best: 34.0893
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1023.0000
  py: 750.0000
  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: 34.0893: : 99iter [00:04, 39.75iter/s]
Best score: 34.0893: : 100iter [00:04, 39.82iter/s]
Best score: 34.0893: : 100iter [00:04, 39.82iter/s]
Best score: 34.0893: : 101iter [00:04, 39.82iter/s]
Best score: 34.0893: : 102iter [00:04, 39.82iter/s]
Best score: 34.0893: : 103iter [00:04, 39.82iter/s]
Best score: 34.0893: : 104iter [00:04, 37.34iter/s]
Best score: 34.0893: : 104iter [00:04, 37.34iter/s]
Best score: 34.0893: : 105iter [00:04, 37.34iter/s]
Best score: 34.0893: : 106iter [00:04, 37.34iter/s]
Best score: 34.0893: : 107iter [00:04, 37.34iter/s]
Best score: 34.0893: : 108iter [00:04, 37.98iter/s]
Best score: 34.0893: : 108iter [00:04, 37.98iter/s]
Best score: 34.0893: : 109iter [00:04, 37.98iter/s]
Best score: 34.0893: : 110iter [00:04, 37.98iter/s]
Best score: 34.0893: : 111iter [00:04, 37.98iter/s]
Best score: 34.0893: : 112iter [00:04, 38.03iter/s]
Best score: 34.0893: : 112iter [00:04, 38.03iter/s]
Best score: 34.0893: : 113iter [00:04, 38.03iter/s]
Best score: 34.0893: : 114iter [00:04, 38.03iter/s]
Best score: 34.0893: : 115iter [00:04, 38.03iter/s]
Best score: 34.0893: : 116iter [00:04, 38.39iter/s]
Best score: 34.0893: : 116iter [00:04, 38.39iter/s]
Best score: 34.0893: : 117iter [00:04, 38.39iter/s]
Best score: 34.0893: : 118iter [00:04, 38.39iter/s]
Best score: 34.0893: : 119iter [00:04, 38.39iter/s]
Best score: 34.0893: : 120iter [00:04, 38.51iter/s]
Best score: 34.0893: : 120iter [00:05, 38.51iter/s]
Best score: 34.0893: : 121iter [00:05, 38.51iter/s]
Best score: 34.0893: : 122iter [00:05, 38.51iter/s]
Best score: 34.0893: : 123iter [00:05, 38.51iter/s]
Best score: 34.0893: : 124iter [00:05, 38.83iter/s]
Best score: 34.0893: : 124iter [00:05, 38.83iter/s]
Best score: 34.0893: : 125iter [00:05, 38.83iter/s]
Best score: 34.0893: : 126iter [00:05, 38.83iter/s]
Best score: 34.0893: : 127iter [00:05, 38.83iter/s]
Best score: 34.0893: : 128iter [00:05, 39.08iter/s]
Best score: 34.0893: : 128iter [00:05, 39.08iter/s]
Best score: 34.0893: : 129iter [00:05, 39.08iter/s]
Best score: 34.0893: : 130iter [00:05, 39.08iter/s]
Best score: 34.0893: : 131iter [00:05, 39.08iter/s]
Best score: 34.0893: : 132iter [00:05, 39.04iter/s]
Best score: 34.0893: : 132iter [00:05, 39.04iter/s]
Best score: 34.0893: : 133iter [00:05, 39.04iter/s]
Best score: 34.0893: : 134iter [00:05, 39.04iter/s]
Best score: 34.0893: : 135iter [00:05, 39.04iter/s]
Best score: 34.0893: : 136iter [00:05, 39.16iter/s]
Best score: 34.0893: : 136iter [00:05, 39.16iter/s]
Best score: 34.0893: : 137iter [00:05, 39.16iter/s]
Best score: 34.0893: : 138iter [00:05, 39.16iter/s]
Best score: 34.0893: : 139iter [00:05, 39.16iter/s]
Best score: 34.0893: : 140iter [00:05, 39.04iter/s]
Best score: 34.0893: : 140iter [00:05, 39.04iter/s]
Best score: 34.0893: : 141iter [00:05, 39.04iter/s]
Best score: 34.0893: : 142iter [00:05, 39.04iter/s]
Best score: 34.0893: : 143iter [00:05, 39.04iter/s]
Best score: 34.0893: : 144iter [00:05, 39.31iter/s]
Best score: 34.0893: : 144iter [00:05, 39.31iter/s]
Best score: 34.0893: : 145iter [00:05, 39.31iter/s]
Best score: 34.0893: : 146iter [00:05, 39.31iter/s]
Best score: 34.0893: : 147iter [00:05, 39.31iter/s]
Best score: 34.0893: : 148iter [00:05, 39.38iter/s]
Best score: 34.0893: : 148iter [00:05, 39.38iter/s]
[ITER 150] Best: 34.0893
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1023.0000
  py: 750.0000
  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: 34.0893: : 149iter [00:05, 39.38iter/s]
Best score: 34.0893: : 150iter [00:05, 39.38iter/s]
Best score: 34.0893: : 151iter [00:05, 39.38iter/s]
Best score: 34.0893: : 152iter [00:05, 31.91iter/s]
Best score: 34.0893: : 152iter [00:05, 31.91iter/s]
Best score: 34.0893: : 153iter [00:05, 31.91iter/s]
Best score: 34.0893: : 154iter [00:05, 31.91iter/s]
Best score: 34.0893: : 155iter [00:05, 31.91iter/s]
Best score: 34.0893: : 156iter [00:05, 33.83iter/s]
Best score: 34.0893: : 156iter [00:05, 33.83iter/s]
Best score: 34.0893: : 157iter [00:06, 33.83iter/s]
Best score: 34.0893: : 158iter [00:06, 33.83iter/s]
Best score: 34.0893: : 159iter [00:06, 33.83iter/s]
Best score: 34.0893: : 160iter [00:06, 35.03iter/s]
Best score: 34.0893: : 160iter [00:06, 35.03iter/s]
Best score: 34.0893: : 161iter [00:06, 35.03iter/s]
Best score: 34.0893: : 162iter [00:06, 35.03iter/s]
Best score: 34.0893: : 163iter [00:06, 35.03iter/s]
Best score: 34.0893: : 164iter [00:06, 36.27iter/s]
Best score: 34.0893: : 164iter [00:06, 36.27iter/s]
Best score: 34.0893: : 165iter [00:06, 36.27iter/s]
Best score: 34.0893: : 166iter [00:06, 36.27iter/s]
Best score: 34.0893: : 167iter [00:06, 36.27iter/s]
Best score: 34.0893: : 168iter [00:06, 37.21iter/s]
Best score: 34.0893: : 168iter [00:06, 37.21iter/s]
Best score: 34.0893: : 169iter [00:06, 37.21iter/s]
Best score: 34.0893: : 170iter [00:06, 37.21iter/s]
Best score: 34.0893: : 171iter [00:06, 37.21iter/s]
Best score: 34.0893: : 172iter [00:06, 37.67iter/s]
Best score: 34.0893: : 172iter [00:06, 37.67iter/s]
Best score: 34.0893: : 173iter [00:06, 37.67iter/s]
Best score: 34.0893: : 174iter [00:06, 37.67iter/s]
Best score: 34.0893: : 175iter [00:06, 37.67iter/s]
Best score: 34.0893: : 176iter [00:06, 38.19iter/s]
Best score: 34.0893: : 176iter [00:06, 38.19iter/s]
Best score: 34.0893: : 177iter [00:06, 38.19iter/s]
Best score: 34.0893: : 178iter [00:06, 38.19iter/s]
Best score: 34.0893: : 179iter [00:06, 38.19iter/s]
Best score: 34.0893: : 180iter [00:06, 38.41iter/s]
Best score: 34.0893: : 180iter [00:06, 38.41iter/s]
Best score: 34.0893: : 181iter [00:06, 38.41iter/s]
Best score: 34.0893: : 182iter [00:06, 38.41iter/s]
Best score: 34.0893: : 183iter [00:06, 38.41iter/s]
Best score: 34.0893: : 184iter [00:06, 38.71iter/s]
Best score: 34.0893: : 184iter [00:06, 38.71iter/s]
Best score: 34.0893: : 185iter [00:06, 38.71iter/s]
Best score: 34.0893: : 186iter [00:06, 38.71iter/s]
Best score: 34.0893: : 187iter [00:06, 38.71iter/s]
Best score: 34.0893: : 188iter [00:06, 38.92iter/s]
Best score: 34.0893: : 188iter [00:06, 38.92iter/s]
Best score: 34.0893: : 189iter [00:06, 38.92iter/s]
Best score: 34.0893: : 190iter [00:06, 38.92iter/s]
Best score: 34.0893: : 191iter [00:06, 38.92iter/s]
Best score: 34.0893: : 192iter [00:06, 38.51iter/s]
Best score: 34.0893: : 192iter [00:06, 38.51iter/s]
Best score: 34.0893: : 193iter [00:06, 38.51iter/s]
Best score: 34.0893: : 194iter [00:06, 38.51iter/s]
Best score: 34.0893: : 195iter [00:06, 38.51iter/s]
Best score: 34.0893: : 196iter [00:06, 38.74iter/s]
Best score: 34.0893: : 196iter [00:07, 38.74iter/s]
Best score: 34.0893: : 197iter [00:07, 38.74iter/s]
Best score: 34.0893: : 198iter [00:07, 38.74iter/s]
[ITER 200] Best: 34.0893
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1023.0000
  py: 750.0000
  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: 34.0893: : 199iter [00:07, 38.74iter/s]
Best score: 34.0893: : 200iter [00:07, 38.76iter/s]
Best score: 34.0893: : 200iter [00:07, 38.76iter/s]
Best score: 34.0893: : 201iter [00:07, 38.76iter/s]
Best score: 34.0893: : 202iter [00:07, 38.76iter/s]
Best score: 34.0893: : 203iter [00:07, 38.76iter/s]
Best score: 34.0893: : 204iter [00:07, 31.88iter/s]
Best score: 34.0893: : 204iter [00:07, 31.88iter/s]
Best score: 34.0893: : 205iter [00:07, 31.88iter/s]
Best score: 34.0893: : 206iter [00:07, 31.88iter/s]
Best score: 34.0893: : 207iter [00:07, 31.88iter/s]
Best score: 34.0893: : 208iter [00:07, 33.82iter/s]
Best score: 34.0893: : 208iter [00:07, 33.82iter/s]
Best score: 34.0893: : 209iter [00:07, 33.82iter/s]
Best score: 34.0893: : 210iter [00:07, 33.82iter/s]
Best score: 34.0893: : 211iter [00:07, 33.82iter/s]
Best score: 34.0893: : 212iter [00:07, 35.20iter/s]
Best score: 34.0893: : 212iter [00:07, 35.20iter/s]
Best score: 34.0893: : 213iter [00:07, 35.20iter/s]
Best score: 34.0893: : 214iter [00:07, 35.20iter/s]
Best score: 34.0893: : 215iter [00:07, 35.20iter/s]
Best score: 34.0893: : 216iter [00:07, 36.47iter/s]
Best score: 34.0893: : 216iter [00:07, 36.47iter/s]
Best score: 34.0893: : 217iter [00:07, 36.47iter/s]
Best score: 34.0893: : 218iter [00:07, 36.47iter/s]
Best score: 34.0893: : 219iter [00:07, 36.47iter/s]
Best score: 34.0893: : 220iter [00:07, 37.18iter/s]
Best score: 34.0893: : 220iter [00:07, 37.18iter/s]
Best score: 34.0893: : 221iter [00:07, 37.18iter/s]
Best score: 34.0893: : 222iter [00:07, 37.18iter/s]
Best score: 34.0893: : 223iter [00:07, 37.18iter/s]
Best score: 34.0893: : 224iter [00:07, 37.18iter/s]
Best score: 34.0893: : 225iter [00:07, 38.20iter/s]
Best score: 34.0893: : 225iter [00:07, 38.20iter/s]
Best score: 34.0893: : 226iter [00:07, 38.20iter/s]
Best score: 34.0893: : 227iter [00:07, 38.20iter/s]
Best score: 34.0893: : 228iter [00:07, 38.20iter/s]
Best score: 34.0893: : 229iter [00:07, 38.20iter/s]
Best score: 34.0893: : 230iter [00:07, 38.62iter/s]
Best score: 34.0893: : 230iter [00:07, 38.62iter/s]
Best score: 34.0893: : 231iter [00:07, 38.62iter/s]
Best score: 34.0893: : 232iter [00:08, 38.62iter/s]
Best score: 34.0893: : 233iter [00:08, 38.62iter/s]
Best score: 34.0893: : 234iter [00:08, 38.62iter/s]
Best score: 34.0893: : 235iter [00:08, 39.21iter/s]
Best score: 34.0893: : 235iter [00:08, 39.21iter/s]
Best score: 34.0893: : 236iter [00:08, 39.21iter/s]
Best score: 34.0893: : 237iter [00:08, 39.21iter/s]
Best score: 34.0893: : 238iter [00:08, 39.21iter/s]
Best score: 34.0893: : 239iter [00:08, 39.21iter/s]
Best score: 34.0893: : 240iter [00:08, 39.36iter/s]
Best score: 34.0893: : 240iter [00:08, 39.36iter/s]
Best score: 34.0893: : 241iter [00:08, 39.36iter/s]
Best score: 34.0893: : 242iter [00:08, 39.36iter/s]
Best score: 34.0893: : 243iter [00:08, 39.36iter/s]
Best score: 34.0893: : 244iter [00:08, 39.36iter/s]
Best score: 34.0893: : 245iter [00:08, 39.65iter/s]
Best score: 34.0893: : 245iter [00:08, 39.65iter/s]
Best score: 34.0893: : 246iter [00:08, 39.65iter/s]
Best score: 34.0893: : 247iter [00:08, 39.65iter/s]
Best score: 34.0893: : 248iter [00:08, 39.65iter/s]
[ITER 250] Best: 29.6523
[PARAMS] Current best parameters:
  fx: 551.3705
  fy: 551.4681
  px: 1028.0022
  py: 761.9198
  k1: -0.00074685
  k2: -0.00649863
  k3: -0.00772587
  k4: 0.00407429
  p1: -0.00107986
  p2: 0.02434243
  p3: 0.01015173
  p4: 0.00639423
  s1: -0.0196
  s2: 0.0025
  s3: -0.0094
  s4: 0.0006
  tilt_x: 0.0299
  tilt_y: 0.0226
  scale: 2.9144
  rotation: -0.3970

Best score: 29.6523: : 249iter [00:08, 39.65iter/s]
Best score: 29.6523: : 250iter [00:08, 39.65iter/s]
Best score: 29.6523: : 250iter [00:08, 39.65iter/s]
Best score: 29.6523: : 251iter [00:08, 39.65iter/s]
Best score: 29.6523: : 252iter [00:08, 39.65iter/s]
Best score: 29.6523: : 253iter [00:08, 39.65iter/s]
Best score: 29.6523: : 254iter [00:08, 33.19iter/s]
Best score: 29.6523: : 254iter [00:08, 33.19iter/s]
Best score: 29.6523: : 255iter [00:08, 33.19iter/s]
Best score: 29.6523: : 256iter [00:08, 33.19iter/s]
Best score: 29.6523: : 257iter [00:08, 33.19iter/s]
Best score: 29.6523: : 258iter [00:08, 33.19iter/s]
Best score: 29.6523: : 259iter [00:08, 35.21iter/s]
Best score: 29.6523: : 259iter [00:08, 35.21iter/s]
Best score: 29.6523: : 260iter [00:08, 35.21iter/s]
Best score: 29.6523: : 261iter [00:08, 35.21iter/s]
Best score: 29.6523: : 262iter [00:08, 35.21iter/s]
Best score: 29.6523: : 263iter [00:08, 36.19iter/s]
Best score: 29.6523: : 263iter [00:08, 36.19iter/s]
Best score: 29.6523: : 264iter [00:08, 36.19iter/s]
Best score: 29.6523: : 265iter [00:08, 36.19iter/s]
Best score: 29.6523: : 266iter [00:08, 36.19iter/s]
Best score: 29.6523: : 267iter [00:08, 37.09iter/s]
Best score: 29.6523: : 267iter [00:08, 37.09iter/s]
Best score: 29.6523: : 268iter [00:08, 37.09iter/s]
Best score: 29.6523: : 269iter [00:09, 37.09iter/s]
Best score: 29.6523: : 270iter [00:09, 37.09iter/s]
Best score: 29.6523: : 271iter [00:09, 37.69iter/s]
Best score: 29.6523: : 271iter [00:09, 37.69iter/s]
Best score: 29.6523: : 272iter [00:09, 37.69iter/s]
Best score: 29.6523: : 273iter [00:09, 37.69iter/s]
Best score: 29.6523: : 274iter [00:09, 37.69iter/s]
Best score: 29.6523: : 275iter [00:09, 37.69iter/s]
Best score: 29.6523: : 276iter [00:09, 38.57iter/s]
Best score: 29.6523: : 276iter [00:09, 38.57iter/s]
Best score: 29.6523: : 277iter [00:09, 38.57iter/s]
Best score: 29.6523: : 278iter [00:09, 38.57iter/s]
Best score: 10.7150: : 279iter [00:09, 38.57iter/s]
Best score: 10.7150: : 280iter [00:09, 38.82iter/s]
Best score: 10.7150: : 280iter [00:09, 38.82iter/s]
Best score: 10.7150: : 281iter [00:09, 38.82iter/s]
Best score: 10.7150: : 282iter [00:09, 38.82iter/s]
Best score: 10.7150: : 283iter [00:09, 38.82iter/s]
Best score: 10.7150: : 284iter [00:09, 39.10iter/s]
Best score: 10.7150: : 284iter [00:09, 39.10iter/s]
Best score: 10.7150: : 285iter [00:09, 39.10iter/s]
Best score: 10.7150: : 286iter [00:09, 39.10iter/s]
Best score: 10.7150: : 287iter [00:09, 39.10iter/s]
Best score: 10.7150: : 288iter [00:09, 39.10iter/s]
Best score: 10.7150: : 289iter [00:09, 39.52iter/s]
Best score: 10.7150: : 289iter [00:09, 39.52iter/s]
Best score: 10.7150: : 290iter [00:09, 39.52iter/s]
Best score: 10.7150: : 291iter [00:09, 39.52iter/s]
Best score: 10.7150: : 292iter [00:09, 39.52iter/s]
Best score: 10.7150: : 293iter [00:09, 39.47iter/s]
Best score: 10.7150: : 293iter [00:09, 39.47iter/s]
Best score: 10.7150: : 294iter [00:09, 39.47iter/s]
Best score: 10.7150: : 295iter [00:09, 39.47iter/s]
Best score: 10.7150: : 296iter [00:09, 39.47iter/s]
Best score: 10.7150: : 297iter [00:09, 39.47iter/s]
Best score: 10.7150: : 298iter [00:09, 39.74iter/s]
Best score: 10.7150: : 298iter [00:09, 39.74iter/s]
[ITER 300] Best: 9.1500
[PARAMS] Current best parameters:
  fx: 543.2851
  fy: 540.9618
  px: 1021.2207
  py: 757.8581
  k1: -0.01688320
  k2: 0.00220314
  k3: 0.00136710
  k4: -0.00046110
  p1: 0.01303892
  p2: 0.00846453
  p3: 0.00022295
  p4: -0.00244469
  s1: 0.0129
  s2: -0.0112
  s3: -0.0180
  s4: 0.0128
  tilt_x: -0.0115
  tilt_y: 0.0083
  scale: 3.0501
  rotation: -0.3933

Best score: 9.1500: : 299iter [00:09, 39.74iter/s] 
Best score: 9.1500: : 300iter [00:09, 39.74iter/s]
Best score: 9.1500: : 301iter [00:09, 39.74iter/s]
Best score: 9.1500: : 302iter [00:09, 26.82iter/s]
Best score: 9.1500: : 302iter [00:10, 26.82iter/s]
Best score: 9.1500: : 303iter [00:10, 26.82iter/s]
Best score: 9.1500: : 304iter [00:10, 26.82iter/s]
Best score: 9.1500: : 305iter [00:10, 26.82iter/s]
Best score: 9.1500: : 306iter [00:10, 26.82iter/s]
Best score: 9.1500: : 307iter [00:10, 30.22iter/s]
Best score: 9.1500: : 307iter [00:10, 30.22iter/s]
Best score: 9.1500: : 308iter [00:10, 30.22iter/s]
Best score: 5.7494: : 309iter [00:10, 30.22iter/s]
Best score: 5.7494: : 310iter [00:10, 30.22iter/s]
Best score: 5.7494: : 311iter [00:10, 32.28iter/s]
Best score: 5.7494: : 311iter [00:10, 32.28iter/s]
Best score: 5.7494: : 312iter [00:10, 32.28iter/s]
Best score: 5.7494: : 313iter [00:10, 32.28iter/s]
Best score: 5.7494: : 314iter [00:10, 32.28iter/s]
Best score: 5.7494: : 315iter [00:10, 32.28iter/s]
Best score: 5.7494: : 316iter [00:10, 34.57iter/s]
Best score: 5.7494: : 316iter [00:10, 34.57iter/s]
Best score: 5.7494: : 317iter [00:10, 34.57iter/s]
Best score: 5.7494: : 318iter [00:10, 34.57iter/s]
Best score: 5.7494: : 319iter [00:10, 34.57iter/s]
Best score: 5.7494: : 320iter [00:10, 35.74iter/s]
Best score: 5.7494: : 320iter [00:10, 35.74iter/s]
Best score: 5.7494: : 321iter [00:10, 35.74iter/s]
Best score: 5.7494: : 322iter [00:10, 35.74iter/s]
Best score: 5.7494: : 323iter [00:10, 35.74iter/s]
Best score: 5.7494: : 324iter [00:10, 35.74iter/s]
Best score: 5.7494: : 325iter [00:10, 37.24iter/s]
Best score: 5.7494: : 325iter [00:10, 37.24iter/s]
Best score: 5.7494: : 326iter [00:10, 37.24iter/s]
Best score: 5.7494: : 327iter [00:10, 37.24iter/s]
Best score: 5.7494: : 328iter [00:10, 37.24iter/s]
Best score: 3.1696: : 329iter [00:10, 37.24iter/s]
Best score: 3.1696: : 330iter [00:10, 38.01iter/s]
Best score: 3.1696: : 330iter [00:10, 38.01iter/s]
Best score: 3.1696: : 331iter [00:10, 38.01iter/s]
Best score: 3.1696: : 332iter [00:10, 38.01iter/s]
Best score: 3.1696: : 333iter [00:10, 38.01iter/s]
Best score: 3.1696: : 334iter [00:10, 38.01iter/s]
Best score: 3.1696: : 335iter [00:10, 38.65iter/s]
Best score: 3.1696: : 335iter [00:10, 38.65iter/s]
Best score: 3.1696: : 336iter [00:10, 38.65iter/s]
Best score: 3.1696: : 337iter [00:10, 38.65iter/s]
Best score: 3.1696: : 338iter [00:10, 38.65iter/s]
Best score: 3.1696: : 339iter [00:10, 38.94iter/s]
Best score: 2.3376: : 339iter [00:10, 38.94iter/s]
Best score: 2.3376: : 340iter [00:10, 38.94iter/s]
Best score: 2.3376: : 341iter [00:10, 38.94iter/s]
Best score: 2.3376: : 342iter [00:11, 38.94iter/s]
Best score: 2.3376: : 343iter [00:11, 39.03iter/s]
Best score: 2.3376: : 343iter [00:11, 39.03iter/s]
Best score: 2.3376: : 344iter [00:11, 39.03iter/s]
Best score: 2.3376: : 345iter [00:11, 39.03iter/s]
Best score: 2.3376: : 346iter [00:11, 39.03iter/s]
Best score: 2.3376: : 347iter [00:11, 39.03iter/s]
Best score: 2.3376: : 348iter [00:11, 39.56iter/s]
Best score: 2.3376: : 348iter [00:11, 39.56iter/s]
[ITER 350] Best: 2.3376
[PARAMS] Current best parameters:
  fx: 563.8499
  fy: 564.3619
  px: 1027.6446
  py: 759.7722
  k1: -0.04955158
  k2: 0.06346456
  k3: -0.05075435
  k4: 0.01259219
  p1: 0.01391908
  p2: 0.02308945
  p3: -0.00169261
  p4: -0.01218412
  s1: 0.0018
  s2: 0.0024
  s3: 0.0034
  s4: 0.0061
  tilt_x: -0.0005
  tilt_y: 0.0103
  scale: 2.9534
  rotation: 0.0168

Best score: 2.3376: : 349iter [00:11, 39.56iter/s]
Best score: 2.3376: : 350iter [00:11, 39.56iter/s]
Best score: 2.3376: : 351iter [00:11, 39.56iter/s]
Best score: 2.3376: : 352iter [00:11, 32.69iter/s]
Best score: 2.3376: : 352iter [00:11, 32.69iter/s]
Best score: 2.3376: : 353iter [00:11, 32.69iter/s]
Best score: 2.3376: : 354iter [00:11, 32.69iter/s]
Best score: 2.3376: : 355iter [00:11, 32.69iter/s]
Best score: 2.3376: : 356iter [00:11, 32.69iter/s]
Best score: 2.3376: : 357iter [00:11, 34.87iter/s]
Best score: 2.3376: : 357iter [00:11, 34.87iter/s]
Best score: 2.3376: : 358iter [00:11, 34.87iter/s]
Best score: 1.7014: : 359iter [00:11, 34.87iter/s]
Best score: 1.7014: : 360iter [00:11, 34.87iter/s]
Best score: 1.7014: : 361iter [00:11, 36.00iter/s]
Best score: 1.7014: : 361iter [00:11, 36.00iter/s]
Best score: 1.7014: : 362iter [00:11, 36.00iter/s]
Best score: 1.7014: : 363iter [00:11, 36.00iter/s]
Best score: 1.7014: : 364iter [00:11, 36.00iter/s]
Best score: 1.7014: : 365iter [00:11, 36.00iter/s]
Best score: 1.7014: : 366iter [00:11, 37.34iter/s]
Best score: 1.7014: : 366iter [00:11, 37.34iter/s]
Best score: 1.7014: : 367iter [00:11, 37.34iter/s]
Best score: 1.7014: : 368iter [00:11, 37.34iter/s]
Best score: 1.5821: : 369iter [00:11, 37.34iter/s]
Best score: 1.5821: : 370iter [00:11, 37.87iter/s]
Best score: 1.5821: : 370iter [00:11, 37.87iter/s]
Best score: 1.5821: : 371iter [00:11, 37.87iter/s]
Best score: 1.5821: : 372iter [00:11, 37.87iter/s]
Best score: 1.5821: : 373iter [00:11, 37.87iter/s]
Best score: 1.5821: : 374iter [00:11, 37.87iter/s]
Best score: 1.5821: : 375iter [00:11, 38.64iter/s]
Best score: 1.5821: : 375iter [00:11, 38.64iter/s]
Best score: 1.5821: : 376iter [00:11, 38.64iter/s]
Best score: 1.5821: : 377iter [00:11, 38.64iter/s]
Best score: 1.5821: : 378iter [00:11, 38.64iter/s]
Best score: 1.2331: : 379iter [00:12, 38.64iter/s]
Best score: 1.2331: : 380iter [00:12, 38.99iter/s]
Best score: 1.2331: : 380iter [00:12, 38.99iter/s]
Best score: 1.2331: : 381iter [00:12, 38.99iter/s]
Best score: 1.2331: : 382iter [00:12, 38.99iter/s]
Best score: 1.2331: : 383iter [00:12, 38.99iter/s]
Best score: 1.2331: : 384iter [00:12, 38.99iter/s]
Best score: 1.2331: : 385iter [00:12, 39.57iter/s]
Best score: 1.2331: : 385iter [00:12, 39.57iter/s]
Best score: 1.2331: : 386iter [00:12, 39.57iter/s]
Best score: 1.2331: : 387iter [00:12, 39.57iter/s]
Best score: 1.2331: : 388iter [00:12, 39.57iter/s]
Best score: 0.8157: : 389iter [00:12, 39.57iter/s]
Best score: 0.8157: : 390iter [00:12, 39.56iter/s]
Best score: 0.8157: : 390iter [00:12, 39.56iter/s]
Best score: 0.8157: : 391iter [00:12, 39.56iter/s]
Best score: 0.8157: : 392iter [00:12, 39.56iter/s]
Best score: 0.8157: : 393iter [00:12, 39.56iter/s]
Best score: 0.8157: : 394iter [00:12, 39.56iter/s]
Best score: 0.8157: : 395iter [00:12, 39.75iter/s]
Best score: 0.8157: : 395iter [00:12, 39.75iter/s]
Best score: 0.8157: : 396iter [00:12, 39.75iter/s]
Best score: 0.8157: : 397iter [00:12, 39.75iter/s]
Best score: 0.8157: : 398iter [00:12, 39.75iter/s]
[ITER 400] Best: 0.8157
[PARAMS] Current best parameters:
  fx: 546.9850
  fy: 546.8078
  px: 1023.1237
  py: 756.5097
  k1: -0.02093703
  k2: 0.03082123
  k3: -0.02594641
  k4: 0.00609301
  p1: 0.01505271
  p2: 0.01183845
  p3: -0.00391842
  p4: -0.00921824
  s1: 0.0092
  s2: -0.0003
  s3: -0.0080
  s4: 0.0084
  tilt_x: -0.0199
  tilt_y: 0.0098
  scale: 2.9879
  rotation: -0.2196

Best score: 0.8157: : 399iter [00:12, 39.75iter/s]
Best score: 0.8157: : 400iter [00:12, 39.67iter/s]
Best score: 0.8157: : 400iter [00:12, 39.67iter/s]
Best score: 0.8157: : 401iter [00:12, 39.67iter/s]
Best score: 0.8157: : 402iter [00:12, 39.67iter/s]
Best score: 0.8157: : 403iter [00:12, 39.67iter/s]
Best score: 0.8157: : 404iter [00:12, 33.29iter/s]
Best score: 0.8157: : 404iter [00:12, 33.29iter/s]
Best score: 0.8157: : 405iter [00:12, 33.29iter/s]
Best score: 0.8157: : 406iter [00:12, 33.29iter/s]
Best score: 0.8157: : 407iter [00:12, 33.29iter/s]
Best score: 0.8157: : 408iter [00:12, 33.29iter/s]
Best score: 0.8157: : 409iter [00:12, 35.23iter/s]
Best score: 0.6844: : 409iter [00:12, 35.23iter/s]
Best score: 0.6844: : 410iter [00:12, 35.23iter/s]
Best score: 0.6844: : 411iter [00:12, 35.23iter/s]
Best score: 0.6844: : 412iter [00:12, 35.23iter/s]
Best score: 0.6844: : 413iter [00:12, 36.24iter/s]
Best score: 0.6844: : 413iter [00:12, 36.24iter/s]
Best score: 0.6844: : 414iter [00:12, 36.24iter/s]
Best score: 0.6844: : 415iter [00:12, 36.24iter/s]
Best score: 0.6844: : 416iter [00:13, 36.24iter/s]
Best score: 0.6844: : 417iter [00:13, 36.24iter/s]
Best score: 0.6844: : 418iter [00:13, 37.49iter/s]
Best score: 0.6844: : 418iter [00:13, 37.49iter/s]
Best score: 0.6844: : 419iter [00:13, 37.49iter/s]
Best score: 0.6844: : 420iter [00:13, 37.49iter/s]
Best score: 0.6844: : 421iter [00:13, 37.49iter/s]
Best score: 0.6844: : 422iter [00:13, 37.92iter/s]
Best score: 0.6844: : 422iter [00:13, 37.92iter/s]
Best score: 0.6844: : 423iter [00:13, 37.92iter/s]
Best score: 0.6844: : 424iter [00:13, 37.92iter/s]
Best score: 0.6844: : 425iter [00:13, 37.92iter/s]
Best score: 0.6844: : 426iter [00:13, 37.92iter/s]
Best score: 0.6844: : 427iter [00:13, 38.68iter/s]
Best score: 0.6844: : 427iter [00:13, 38.68iter/s]
Best score: 0.6844: : 428iter [00:13, 38.68iter/s]
Best score: 0.4078: : 429iter [00:13, 38.68iter/s]
Best score: 0.4078: : 430iter [00:13, 38.68iter/s]
Best score: 0.4078: : 431iter [00:13, 38.80iter/s]
Best score: 0.4078: : 431iter [00:13, 38.80iter/s]
Best score: 0.4078: : 432iter [00:13, 38.80iter/s]
Best score: 0.4078: : 433iter [00:13, 38.80iter/s]
Best score: 0.4078: : 434iter [00:13, 38.80iter/s]
Best score: 0.4078: : 435iter [00:13, 39.12iter/s]
Best score: 0.4078: : 435iter [00:13, 39.12iter/s]
Best score: 0.4078: : 436iter [00:13, 39.12iter/s]
Best score: 0.4078: : 437iter [00:13, 39.12iter/s]
Best score: 0.4078: : 438iter [00:13, 39.12iter/s]
Best score: 0.4078: : 439iter [00:13, 39.12iter/s]
Best score: 0.4078: : 440iter [00:13, 39.41iter/s]
Best score: 0.4078: : 440iter [00:13, 39.41iter/s]
Best score: 0.4078: : 441iter [00:13, 39.41iter/s]
Best score: 0.4078: : 442iter [00:13, 39.41iter/s]
Best score: 0.4078: : 443iter [00:13, 39.41iter/s]
Best score: 0.4078: : 444iter [00:13, 39.41iter/s]
Best score: 0.4078: : 445iter [00:13, 39.66iter/s]
Best score: 0.4078: : 445iter [00:13, 39.66iter/s]
Best score: 0.4078: : 446iter [00:13, 39.66iter/s]
Best score: 0.4078: : 447iter [00:13, 39.66iter/s]
Best score: 0.4078: : 448iter [00:13, 39.66iter/s]
[ITER 450] Best: 0.3329
[PARAMS] Current best parameters:
  fx: 533.6873
  fy: 533.4035
  px: 1020.3185
  py: 754.1507
  k1: 0.03135699
  k2: -0.04219050
  k3: 0.01876536
  k4: -0.00335929
  p1: 0.01043623
  p2: -0.00580324
  p3: -0.00482187
  p4: 0.00021585
  s1: 0.0080
  s2: 0.0005
  s3: -0.0059
  s4: 0.0002
  tilt_x: -0.0232
  tilt_y: -0.0120
  scale: 2.9990
  rotation: -0.1563

Best score: 0.3329: : 449iter [00:13, 39.66iter/s]
Best score: 0.3329: : 450iter [00:13, 39.60iter/s]
Best score: 0.3329: : 450iter [00:13, 39.60iter/s]
Best score: 0.3329: : 451iter [00:13, 39.60iter/s]
Best score: 0.3329: : 452iter [00:14, 39.60iter/s]
Best score: 0.3329: : 453iter [00:14, 39.60iter/s]
Best score: 0.3329: : 454iter [00:14, 32.87iter/s]
Best score: 0.3329: : 454iter [00:14, 32.87iter/s]
Best score: 0.3329: : 455iter [00:14, 32.87iter/s]
Best score: 0.3329: : 456iter [00:14, 32.87iter/s]
Best score: 0.3329: : 457iter [00:14, 32.87iter/s]
Best score: 0.3329: : 458iter [00:14, 32.87iter/s]
Best score: 0.3329: : 459iter [00:14, 34.93iter/s]
Best score: 0.3329: : 459iter [00:14, 34.93iter/s]
Best score: 0.3329: : 460iter [00:14, 34.93iter/s]
Best score: 0.3329: : 461iter [00:14, 34.93iter/s]
Best score: 0.3329: : 462iter [00:14, 34.93iter/s]
Best score: 0.3329: : 463iter [00:14, 36.09iter/s]
Best score: 0.3329: : 463iter [00:14, 36.09iter/s]
Best score: 0.3329: : 464iter [00:14, 36.09iter/s]
Best score: 0.3329: : 465iter [00:14, 36.09iter/s]
Best score: 0.3329: : 466iter [00:14, 36.09iter/s]
Best score: 0.3329: : 467iter [00:14, 36.09iter/s]
Best score: 0.3329: : 468iter [00:14, 37.29iter/s]
Best score: 0.3329: : 468iter [00:14, 37.29iter/s]
Best score: 0.2548: : 469iter [00:14, 37.29iter/s]
Best score: 0.2548: : 470iter [00:14, 37.29iter/s]
Best score: 0.2548: : 471iter [00:14, 37.29iter/s]
Best score: 0.2548: : 472iter [00:14, 37.64iter/s]
Best score: 0.2548: : 472iter [00:14, 37.64iter/s]
Best score: 0.2548: : 473iter [00:14, 37.64iter/s]
Best score: 0.2548: : 474iter [00:14, 37.64iter/s]
Best score: 0.2548: : 475iter [00:14, 37.64iter/s]
Best score: 0.2548: : 476iter [00:14, 37.64iter/s]
Best score: 0.2548: : 477iter [00:14, 38.55iter/s]
Best score: 0.2548: : 477iter [00:14, 38.55iter/s]
Best score: 0.2548: : 478iter [00:14, 38.55iter/s]
Best score: 0.2353: : 479iter [00:14, 38.55iter/s]
Best score: 0.2353: : 480iter [00:14, 38.55iter/s]
Best score: 0.2353: : 481iter [00:14, 38.79iter/s]
Best score: 0.2353: : 481iter [00:14, 38.79iter/s]
Best score: 0.2353: : 482iter [00:14, 38.79iter/s]
Best score: 0.2353: : 483iter [00:14, 38.79iter/s]
Best score: 0.2353: : 484iter [00:14, 38.79iter/s]
Best score: 0.2353: : 485iter [00:14, 38.79iter/s]
Best score: 0.2353: : 486iter [00:14, 39.31iter/s]
Best score: 0.2353: : 486iter [00:14, 39.31iter/s]
Best score: 0.2353: : 487iter [00:14, 39.31iter/s]
Best score: 0.2353: : 488iter [00:14, 39.31iter/s]
Best score: 0.2353: : 489iter [00:14, 39.31iter/s]
Best score: 0.2353: : 490iter [00:14, 39.38iter/s]
Best score: 0.2353: : 490iter [00:14, 39.38iter/s]
Best score: 0.2353: : 491iter [00:14, 39.38iter/s]
Best score: 0.2353: : 492iter [00:15, 39.38iter/s]
Best score: 0.2353: : 493iter [00:15, 39.38iter/s]
Best score: 0.2353: : 494iter [00:15, 39.38iter/s]
Best score: 0.2353: : 495iter [00:15, 39.75iter/s]
Best score: 0.2353: : 495iter [00:15, 39.75iter/s]
Best score: 0.2353: : 496iter [00:15, 39.75iter/s]
Best score: 0.2353: : 497iter [00:15, 39.75iter/s]
Best score: 0.2353: : 498iter [00:15, 39.75iter/s]
[ITER 500] Best: 0.2337
[PARAMS] Current best parameters:
  fx: 534.1201
  fy: 534.0533
  px: 1020.9592
  py: 754.5381
  k1: 0.02005807
  k2: -0.02314290
  k3: 0.00753160
  k4: -0.00112312
  p1: 0.01093402
  p2: -0.00208091
  p3: -0.00452663
  p4: -0.00171364
  s1: 0.0028
  s2: 0.0030
  s3: -0.0061
  s4: 0.0018
  tilt_x: -0.0219
  tilt_y: -0.0082
  scale: 2.9991
  rotation: -0.2034

Best score: 0.2337: : 499iter [00:15, 39.75iter/s]
Best score: 0.2337: : 500iter [00:15, 39.58iter/s]
Best score: 0.2337: : 500iter [00:15, 39.58iter/s]
Best score: 0.2337: : 501iter [00:15, 39.58iter/s]
Best score: 0.2337: : 502iter [00:15, 39.58iter/s]
Best score: 0.2337: : 503iter [00:15, 39.58iter/s]
Best score: 0.2337: : 504iter [00:15, 33.06iter/s]
Best score: 0.2337: : 504iter [00:15, 33.06iter/s]
Best score: 0.2337: : 505iter [00:15, 33.06iter/s]
Best score: 0.2337: : 506iter [00:15, 33.06iter/s]
Best score: 0.2337: : 507iter [00:15, 33.06iter/s]
Best score: 0.2337: : 508iter [00:15, 33.06iter/s]
Best score: 0.2337: : 509iter [00:15, 35.12iter/s]
Best score: 0.2313: : 509iter [00:15, 35.12iter/s]
Best score: 0.2313: : 510iter [00:15, 35.12iter/s]
Best score: 0.2313: : 511iter [00:15, 35.12iter/s]
Best score: 0.2313: : 512iter [00:15, 35.12iter/s]
Best score: 0.2313: : 513iter [00:15, 36.11iter/s]
Best score: 0.2313: : 513iter [00:15, 36.11iter/s]
Best score: 0.2313: : 514iter [00:15, 36.11iter/s]
Best score: 0.2313: : 515iter [00:15, 36.11iter/s]
Best score: 0.2313: : 516iter [00:15, 36.11iter/s]
Best score: 0.2313: : 517iter [00:15, 36.11iter/s]
Best score: 0.2313: : 518iter [00:15, 37.45iter/s]
Best score: 0.2313: : 518iter [00:15, 37.45iter/s]
Best score: 0.2230: : 519iter [00:15, 37.45iter/s]
Best score: 0.2230: : 520iter [00:15, 37.45iter/s]
Best score: 0.2230: : 521iter [00:15, 37.45iter/s]
Best score: 0.2230: : 522iter [00:15, 37.93iter/s]
Best score: 0.2230: : 522iter [00:15, 37.93iter/s]
Best score: 0.2230: : 523iter [00:15, 37.93iter/s]
Best score: 0.2230: : 524iter [00:15, 37.93iter/s]
Best score: 0.2230: : 525iter [00:15, 37.93iter/s]
Best score: 0.2230: : 526iter [00:15, 37.93iter/s]
Best score: 0.2230: : 527iter [00:15, 38.73iter/s]
Best score: 0.2230: : 527iter [00:15, 38.73iter/s]
Best score: 0.2230: : 528iter [00:15, 38.73iter/s]
Best score: 0.2173: : 529iter [00:16, 38.73iter/s]
Best score: 0.2173: : 530iter [00:16, 38.73iter/s]
Best score: 0.2173: : 531iter [00:16, 38.76iter/s]
Best score: 0.2173: : 531iter [00:16, 38.76iter/s]
Best score: 0.2173: : 532iter [00:16, 38.76iter/s]
Best score: 0.2173: : 533iter [00:16, 38.76iter/s]
Best score: 0.2173: : 534iter [00:16, 38.76iter/s]
Best score: 0.2173: : 535iter [00:16, 38.76iter/s]
Best score: 0.2173: : 536iter [00:16, 39.33iter/s]
Best score: 0.2173: : 536iter [00:16, 39.33iter/s]
Best score: 0.2173: : 537iter [00:16, 39.33iter/s]
Best score: 0.2173: : 538iter [00:16, 39.33iter/s]
Best score: 0.1999: : 539iter [00:16, 39.33iter/s]
Best score: 0.1999: : 540iter [00:16, 39.24iter/s]
Best score: 0.1999: : 540iter [00:16, 39.24iter/s]
Best score: 0.1999: : 541iter [00:16, 39.24iter/s]
Best score: 0.1999: : 542iter [00:16, 39.24iter/s]
Best score: 0.1999: : 543iter [00:16, 39.24iter/s]
Best score: 0.1999: : 544iter [00:16, 39.24iter/s]
Best score: 0.1999: : 545iter [00:16, 39.56iter/s]
Best score: 0.1999: : 545iter [00:16, 39.56iter/s]
Best score: 0.1999: : 546iter [00:16, 39.56iter/s]
Best score: 0.1999: : 547iter [00:16, 39.56iter/s]
Best score: 0.1999: : 548iter [00:16, 39.56iter/s]
[ITER 550] Best: 0.1999
[PARAMS] Current best parameters:
  fx: 537.0975
  fy: 537.1017
  px: 1020.8333
  py: 753.3426
  k1: 0.02269787
  k2: -0.03347182
  k3: 0.01487672
  k4: -0.00289516
  p1: 0.00759927
  p2: -0.00459789
  p3: -0.00342565
  p4: 0.00050985
  s1: 0.0058
  s2: 0.0001
  s3: -0.0033
  s4: -0.0014
  tilt_x: -0.0215
  tilt_y: -0.0123
  scale: 2.9919
  rotation: -0.2029

Best score: 0.1999: : 549iter [00:16, 39.56iter/s]
Best score: 0.1999: : 550iter [00:16, 39.53iter/s]
Best score: 0.1999: : 550iter [00:16, 39.53iter/s]
Best score: 0.1999: : 551iter [00:16, 39.53iter/s]
Best score: 0.1999: : 552iter [00:16, 39.53iter/s]
Best score: 0.1999: : 553iter [00:16, 39.53iter/s]
Best score: 0.1999: : 554iter [00:16, 33.07iter/s]
Best score: 0.1999: : 554iter [00:16, 33.07iter/s]
Best score: 0.1999: : 555iter [00:16, 33.07iter/s]
Best score: 0.1999: : 556iter [00:16, 33.07iter/s]
Best score: 0.1999: : 557iter [00:16, 33.07iter/s]
Best score: 0.1999: : 558iter [00:16, 34.69iter/s]
Best score: 0.1999: : 558iter [00:16, 34.69iter/s]
Best score: 0.1999: : 559iter [00:16, 34.69iter/s]
Best score: 0.1999: : 560iter [00:16, 34.69iter/s]
Best score: 0.1999: : 561iter [00:16, 34.69iter/s]
Best score: 0.1999: : 562iter [00:16, 35.65iter/s]
Best score: 0.1999: : 562iter [00:16, 35.65iter/s]
Best score: 0.1999: : 563iter [00:16, 35.65iter/s]
Best score: 0.1999: : 564iter [00:16, 35.65iter/s]
Best score: 0.1999: : 565iter [00:16, 35.65iter/s]
Best score: 0.1999: : 566iter [00:17, 35.65iter/s]
Best score: 0.1999: : 567iter [00:17, 37.10iter/s]
Best score: 0.1999: : 567iter [00:17, 37.10iter/s]
Best score: 0.1999: : 568iter [00:17, 37.10iter/s]
Best score: 0.1980: : 569iter [00:17, 37.10iter/s]
Best score: 0.1980: : 570iter [00:17, 37.10iter/s]
Best score: 0.1980: : 571iter [00:17, 37.68iter/s]
Best score: 0.1980: : 571iter [00:17, 37.68iter/s]
Best score: 0.1980: : 572iter [00:17, 37.68iter/s]
Best score: 0.1980: : 573iter [00:17, 37.68iter/s]
Best score: 0.1980: : 574iter [00:17, 37.68iter/s]
Best score: 0.1980: : 575iter [00:17, 37.68iter/s]
Best score: 0.1980: : 576iter [00:17, 38.51iter/s]
Best score: 0.1980: : 576iter [00:17, 38.51iter/s]
Best score: 0.1980: : 577iter [00:17, 38.51iter/s]
Best score: 0.1980: : 578iter [00:17, 38.51iter/s]
Best score: 0.1925: : 579iter [00:17, 38.51iter/s]
Best score: 0.1925: : 580iter [00:17, 38.73iter/s]
Best score: 0.1925: : 580iter [00:17, 38.73iter/s]
Best score: 0.1925: : 581iter [00:17, 38.73iter/s]
Best score: 0.1925: : 582iter [00:17, 38.73iter/s]
Best score: 0.1925: : 583iter [00:17, 38.73iter/s]
Best score: 0.1925: : 584iter [00:17, 38.73iter/s]
Best score: 0.1925: : 585iter [00:17, 39.26iter/s]
Best score: 0.1925: : 585iter [00:17, 39.26iter/s]
Best score: 0.1925: : 586iter [00:17, 39.26iter/s]
Best score: 0.1925: : 587iter [00:17, 39.26iter/s]
Best score: 0.1925: : 588iter [00:17, 39.26iter/s]
Best score: 0.1925: : 589iter [00:17, 39.26iter/s]
Best score: 0.1925: : 590iter [00:17, 39.34iter/s]
Best score: 0.1925: : 590iter [00:17, 39.34iter/s]
Best score: 0.1925: : 591iter [00:17, 39.34iter/s]
Best score: 0.1925: : 592iter [00:17, 39.34iter/s]
Best score: 0.1925: : 593iter [00:17, 39.34iter/s]
Best score: 0.1925: : 594iter [00:17, 39.34iter/s]
Best score: 0.1925: : 595iter [00:17, 39.59iter/s]
Best score: 0.1925: : 595iter [00:17, 39.59iter/s]
Best score: 0.1925: : 596iter [00:17, 39.59iter/s]
Best score: 0.1925: : 597iter [00:17, 39.59iter/s]
Best score: 0.1925: : 598iter [00:17, 39.59iter/s]
[ITER 600] Best: 0.1925
[PARAMS] Current best parameters:
  fx: 537.9890
  fy: 537.9932
  px: 1022.4058
  py: 753.0285
  k1: 0.02893507
  k2: -0.04277406
  k3: 0.01927336
  k4: -0.00361400
  p1: 0.00577735
  p2: -0.00525342
  p3: -0.00381303
  p4: 0.00112737
  s1: 0.0084
  s2: -0.0007
  s3: -0.0016
  s4: -0.0020
  tilt_x: -0.0195
  tilt_y: -0.0220
  scale: 2.9817
  rotation: -0.1790

Best score: 0.1925: : 599iter [00:17, 39.59iter/s]
Best score: 0.1925: : 600iter [00:17, 39.50iter/s]
Best score: 0.1925: : 600iter [00:17, 39.50iter/s]
Best score: 0.1925: : 601iter [00:17, 39.50iter/s]
Best score: 0.1925: : 602iter [00:17, 39.50iter/s]
Best score: 0.1925: : 603iter [00:18, 39.50iter/s]
Best score: 0.1925: : 604iter [00:18, 33.04iter/s]
Best score: 0.1925: : 604iter [00:18, 33.04iter/s]
Best score: 0.1925: : 605iter [00:18, 33.04iter/s]
Best score: 0.1925: : 606iter [00:18, 33.04iter/s]
Best score: 0.1925: : 607iter [00:18, 33.04iter/s]
Best score: 0.1925: : 608iter [00:18, 33.04iter/s]
Best score: 0.1925: : 609iter [00:18, 35.05iter/s]
Best score: 0.1925: : 609iter [00:18, 35.05iter/s]
Best score: 0.1925: : 610iter [00:18, 35.05iter/s]
Best score: 0.1925: : 611iter [00:18, 35.05iter/s]
Best score: 0.1925: : 612iter [00:18, 35.05iter/s]
Best score: 0.1925: : 613iter [00:18, 36.15iter/s]
Best score: 0.1925: : 613iter [00:18, 36.15iter/s]
Best score: 0.1925: : 614iter [00:18, 36.15iter/s]
Best score: 0.1925: : 615iter [00:18, 36.15iter/s]
Best score: 0.1925: : 616iter [00:18, 36.15iter/s]
Best score: 0.1925: : 617iter [00:18, 36.15iter/s]
Best score: 0.1925: : 618iter [00:18, 37.43iter/s]
Best score: 0.1925: : 618iter [00:18, 37.43iter/s]
Best score: 0.1919: : 619iter [00:18, 37.43iter/s]
Best score: 0.1919: : 620iter [00:18, 37.43iter/s]
Best score: 0.1919: : 621iter [00:18, 37.43iter/s]
Best score: 0.1919: : 622iter [00:18, 30.78iter/s]
Best score: 0.1919: : 622iter [00:18, 30.78iter/s]
Best score: 0.1919: : 623iter [00:18, 30.78iter/s]
Best score: 0.1919: : 624iter [00:18, 30.78iter/s]
Best score: 0.1919: : 625iter [00:18, 30.78iter/s]
Best score: 0.1919: : 626iter [00:18, 30.78iter/s]
Best score: 0.1919: : 627iter [00:18, 33.36iter/s]
Best score: 0.1919: : 627iter [00:18, 33.36iter/s]
Best score: 0.1919: : 628iter [00:18, 33.36iter/s]
Best score: 0.1911: : 629iter [00:18, 33.36iter/s]
Best score: 0.1911: : 630iter [00:18, 33.36iter/s]
Best score: 0.1911: : 631iter [00:18, 34.55iter/s]
Best score: 0.1911: : 631iter [00:18, 34.55iter/s]
Best score: 0.1911: : 632iter [00:18, 34.55iter/s]
Best score: 0.1911: : 633iter [00:18, 34.55iter/s]
Best score: 0.1911: : 634iter [00:18, 34.55iter/s]
Best score: 0.1911: : 635iter [00:18, 34.55iter/s]
Best score: 0.1911: : 636iter [00:18, 36.23iter/s]
Best score: 0.1911: : 636iter [00:18, 36.23iter/s]
Best score: 0.1911: : 637iter [00:18, 36.23iter/s]
Best score: 0.1911: : 638iter [00:18, 36.23iter/s]
Best score: 0.1906: : 639iter [00:19, 36.23iter/s]
Best score: 0.1906: : 640iter [00:19, 36.95iter/s]
Best score: 0.1906: : 640iter [00:19, 36.95iter/s]
Best score: 0.1906: : 641iter [00:19, 36.95iter/s]
Best score: 0.1906: : 642iter [00:19, 36.95iter/s]
Best score: 0.1906: : 643iter [00:19, 36.95iter/s]
Best score: 0.1906: : 644iter [00:19, 37.72iter/s]
Best score: 0.1906: : 644iter [00:19, 37.72iter/s]
Best score: 0.1906: : 645iter [00:19, 37.72iter/s]
Best score: 0.1906: : 646iter [00:19, 37.72iter/s]
Best score: 0.1906: : 647iter [00:19, 37.72iter/s]
Best score: 0.1906: : 648iter [00:19, 37.72iter/s]
Best score: 0.1906: : 649iter [00:19, 38.55iter/s]
[ITER 650] Best: 0.1886
[PARAMS] Current best parameters:
  fx: 537.7413
  fy: 537.7383
  px: 1022.1700
  py: 752.8158
  k1: 0.02737668
  k2: -0.03709412
  k3: 0.01468233
  k4: -0.00249480
  p1: 0.00510515
  p2: -0.00546001
  p3: -0.00321685
  p4: 0.00115154
  s1: 0.0078
  s2: -0.0007
  s3: -0.0022
  s4: -0.0017
  tilt_x: -0.0191
  tilt_y: -0.0212
  scale: 2.9788
  rotation: -0.1906

Best score: 0.1886: : 649iter [00:19, 38.55iter/s]
Best score: 0.1886: : 650iter [00:19, 38.55iter/s]
Best score: 0.1886: : 651iter [00:19, 38.55iter/s]
Best score: 0.1886: : 652iter [00:19, 38.55iter/s]
Best score: 0.1886: : 653iter [00:19, 31.79iter/s]
Best score: 0.1886: : 653iter [00:19, 31.79iter/s]
Best score: 0.1886: : 654iter [00:19, 31.79iter/s]
Best score: 0.1886: : 655iter [00:19, 31.79iter/s]
Best score: 0.1886: : 656iter [00:19, 31.79iter/s]
Best score: 0.1886: : 657iter [00:19, 31.79iter/s]
Best score: 0.1886: : 658iter [00:19, 34.19iter/s]
Best score: 0.1886: : 658iter [00:19, 34.19iter/s]
Best score: 0.1877: : 659iter [00:19, 34.19iter/s]
Best score: 0.1877: : 660iter [00:19, 34.19iter/s]
Best score: 0.1877: : 661iter [00:19, 34.19iter/s]
Best score: 0.1877: : 662iter [00:19, 35.33iter/s]
Best score: 0.1877: : 662iter [00:19, 35.33iter/s]
Best score: 0.1877: : 663iter [00:19, 35.33iter/s]
Best score: 0.1877: : 664iter [00:19, 35.33iter/s]
Best score: 0.1877: : 665iter [00:19, 35.33iter/s]
Best score: 0.1877: : 666iter [00:19, 35.33iter/s]
Best score: 0.1877: : 667iter [00:19, 36.81iter/s]
Best score: 0.1877: : 667iter [00:19, 36.81iter/s]
Best score: 0.1877: : 668iter [00:19, 36.81iter/s]
Best score: 0.1877: : 669iter [00:19, 36.81iter/s]
Best score: 0.1877: : 670iter [00:19, 36.81iter/s]
Best score: 0.1877: : 671iter [00:19, 37.36iter/s]
Best score: 0.1877: : 671iter [00:19, 37.36iter/s]
Best score: 0.1877: : 672iter [00:19, 37.36iter/s]
Best score: 0.1877: : 673iter [00:19, 37.36iter/s]
Best score: 0.1877: : 674iter [00:19, 37.36iter/s]
Best score: 0.1877: : 675iter [00:20, 37.36iter/s]
Best score: 0.1877: : 676iter [00:20, 38.32iter/s]
Best score: 0.1877: : 676iter [00:20, 38.32iter/s]
Best score: 0.1877: : 677iter [00:20, 38.32iter/s]
Best score: 0.1877: : 678iter [00:20, 38.32iter/s]
Best score: 0.1877: : 679iter [00:20, 38.32iter/s]
Best score: 0.1877: : 680iter [00:20, 38.65iter/s]
Best score: 0.1877: : 680iter [00:20, 38.65iter/s]
Best score: 0.1877: : 681iter [00:20, 38.65iter/s]
Best score: 0.1877: : 682iter [00:20, 38.65iter/s]
Best score: 0.1877: : 683iter [00:20, 38.65iter/s]
Best score: 0.1877: : 684iter [00:20, 38.65iter/s]
Best score: 0.1877: : 685iter [00:20, 39.11iter/s]
Best score: 0.1877: : 685iter [00:20, 39.11iter/s]
Best score: 0.1877: : 686iter [00:20, 39.11iter/s]
Best score: 0.1877: : 687iter [00:20, 39.11iter/s]
Best score: 0.1877: : 688iter [00:20, 39.11iter/s]
Best score: 0.1877: : 689iter [00:20, 39.11iter/s]
Best score: 0.1877: : 690iter [00:20, 39.25iter/s]
Best score: 0.1877: : 690iter [00:20, 39.25iter/s]
Best score: 0.1877: : 691iter [00:20, 39.25iter/s]
Best score: 0.1877: : 692iter [00:20, 39.25iter/s]
Best score: 0.1877: : 693iter [00:20, 39.25iter/s]
Best score: 0.1877: : 694iter [00:20, 39.25iter/s]
Best score: 0.1877: : 695iter [00:20, 39.53iter/s]
Best score: 0.1877: : 695iter [00:20, 39.53iter/s]
Best score: 0.1877: : 696iter [00:20, 39.53iter/s]
Best score: 0.1877: : 697iter [00:20, 39.53iter/s]
Best score: 0.1877: : 698iter [00:20, 39.53iter/s]
[ITER 700] Best: 0.1866
[PARAMS] Current best parameters:
  fx: 538.9707
  fy: 538.9586
  px: 1022.0268
  py: 752.0892
  k1: 0.02130495
  k2: -0.03118012
  k3: 0.01214028
  k4: -0.00208462
  p1: 0.00452585
  p2: -0.00579391
  p3: -0.00234142
  p4: 0.00096226
  s1: 0.0072
  s2: -0.0005
  s3: -0.0032
  s4: -0.0014
  tilt_x: -0.0218
  tilt_y: -0.0211
  scale: 2.9863
  rotation: -0.1768

Best score: 0.1866: : 699iter [00:20, 39.53iter/s]
Best score: 0.1866: : 700iter [00:20, 39.42iter/s]
Best score: 0.1866: : 700iter [00:20, 39.42iter/s]
Best score: 0.1866: : 701iter [00:20, 39.42iter/s]
Best score: 0.1866: : 702iter [00:20, 39.42iter/s]
Best score: 0.1866: : 703iter [00:20, 39.42iter/s]
Best score: 0.1866: : 704iter [00:20, 32.99iter/s]
Best score: 0.1866: : 704iter [00:20, 32.99iter/s]
Best score: 0.1866: : 705iter [00:20, 32.99iter/s]
Best score: 0.1866: : 706iter [00:20, 32.99iter/s]
Best score: 0.1866: : 707iter [00:20, 32.99iter/s]
Best score: 0.1866: : 708iter [00:20, 32.99iter/s]
Best score: 0.1866: : 709iter [00:20, 35.05iter/s]
Best score: 0.1851: : 709iter [00:20, 35.05iter/s]
Best score: 0.1851: : 710iter [00:20, 35.05iter/s]
Best score: 0.1851: : 711iter [00:20, 35.05iter/s]
Best score: 0.1851: : 712iter [00:21, 35.05iter/s]
Best score: 0.1851: : 713iter [00:21, 36.02iter/s]
Best score: 0.1851: : 713iter [00:21, 36.02iter/s]
Best score: 0.1851: : 714iter [00:21, 36.02iter/s]
Best score: 0.1851: : 715iter [00:21, 36.02iter/s]
Best score: 0.1851: : 716iter [00:21, 36.02iter/s]
Best score: 0.1851: : 717iter [00:21, 36.02iter/s]
Best score: 0.1851: : 718iter [00:21, 37.40iter/s]
Best score: 0.1851: : 718iter [00:21, 37.40iter/s]
Best score: 0.1834: : 719iter [00:21, 37.40iter/s]
Best score: 0.1834: : 720iter [00:21, 37.40iter/s]
Best score: 0.1834: : 721iter [00:21, 37.40iter/s]
Best score: 0.1834: : 722iter [00:21, 37.93iter/s]
Best score: 0.1834: : 722iter [00:21, 37.93iter/s]
Best score: 0.1834: : 723iter [00:21, 37.93iter/s]
Best score: 0.1834: : 724iter [00:21, 37.93iter/s]
Best score: 0.1834: : 725iter [00:21, 37.93iter/s]
Best score: 0.1834: : 726iter [00:21, 37.93iter/s]
Best score: 0.1834: : 727iter [00:21, 38.67iter/s]
Best score: 0.1834: : 727iter [00:21, 38.67iter/s]
Best score: 0.1834: : 728iter [00:21, 38.67iter/s]
Best score: 0.1834: : 729iter [00:21, 38.67iter/s]
Best score: 0.1834: : 730iter [00:21, 38.67iter/s]
Best score: 0.1834: : 731iter [00:21, 38.82iter/s]
Best score: 0.1834: : 731iter [00:21, 38.82iter/s]
Best score: 0.1834: : 732iter [00:21, 38.82iter/s]
Best score: 0.1834: : 733iter [00:21, 38.82iter/s]
Best score: 0.1834: : 734iter [00:21, 38.82iter/s]
Best score: 0.1834: : 735iter [00:21, 38.82iter/s]
Best score: 0.1834: : 736iter [00:21, 39.27iter/s]
Best score: 0.1834: : 736iter [00:21, 39.27iter/s]
Best score: 0.1834: : 737iter [00:21, 39.27iter/s]
Best score: 0.1834: : 738iter [00:21, 39.27iter/s]
Best score: 0.1834: : 739iter [00:21, 39.27iter/s]
Best score: 0.1834: : 740iter [00:21, 39.15iter/s]
Best score: 0.1834: : 740iter [00:21, 39.15iter/s]
Best score: 0.1834: : 741iter [00:21, 39.15iter/s]
Best score: 0.1834: : 742iter [00:21, 39.15iter/s]
Best score: 0.1834: : 743iter [00:21, 39.15iter/s]
Best score: 0.1834: : 744iter [00:21, 39.37iter/s]
Best score: 0.1834: : 744iter [00:21, 39.37iter/s]
Best score: 0.1834: : 745iter [00:21, 39.37iter/s]
Best score: 0.1834: : 746iter [00:21, 39.37iter/s]
Best score: 0.1834: : 747iter [00:21, 39.37iter/s]
Best score: 0.1834: : 748iter [00:21, 39.37iter/s]
Best score: 0.1834: : 749iter [00:21, 39.60iter/s]
[ITER 750] Best: 0.1834
[PARAMS] Current best parameters:
  fx: 541.9664
  fy: 541.8888
  px: 1021.9848
  py: 752.3377
  k1: 0.00142792
  k2: -0.00739926
  k3: 0.00017338
  k4: 0.00010968
  p1: 0.00671882
  p2: -0.00288119
  p3: -0.00110080
  p4: 0.00080643
  s1: 0.0029
  s2: -0.0004
  s3: -0.0063
  s4: -0.0012
  tilt_x: -0.0250
  tilt_y: -0.0152
  scale: 3.0047
  rotation: -0.1877

Best score: 0.1834: : 749iter [00:21, 39.60iter/s]
Best score: 0.1834: : 750iter [00:22, 39.60iter/s]
Best score: 0.1834: : 751iter [00:22, 39.60iter/s]
Best score: 0.1834: : 752iter [00:22, 39.60iter/s]
Best score: 0.1834: : 753iter [00:22, 32.57iter/s]
Best score: 0.1834: : 753iter [00:22, 32.57iter/s]
Best score: 0.1834: : 754iter [00:22, 32.57iter/s]
Best score: 0.1834: : 755iter [00:22, 32.57iter/s]
Best score: 0.1834: : 756iter [00:22, 32.57iter/s]
Best score: 0.1834: : 757iter [00:22, 32.57iter/s]
Best score: 0.1834: : 758iter [00:22, 34.74iter/s]
Best score: 0.1834: : 758iter [00:22, 34.74iter/s]
Best score: 0.1823: : 759iter [00:22, 34.74iter/s]
Best score: 0.1823: : 760iter [00:22, 34.74iter/s]
Best score: 0.1823: : 761iter [00:22, 34.74iter/s]
Best score: 0.1823: : 762iter [00:22, 35.87iter/s]
Best score: 0.1823: : 762iter [00:22, 35.87iter/s]
Best score: 0.1823: : 763iter [00:22, 35.87iter/s]
Best score: 0.1823: : 764iter [00:22, 35.87iter/s]
Best score: 0.1823: : 765iter [00:22, 35.87iter/s]
Best score: 0.1823: : 766iter [00:22, 35.87iter/s]
Best score: 0.1823: : 767iter [00:22, 37.32iter/s]
Best score: 0.1823: : 767iter [00:22, 37.32iter/s]
Best score: 0.1823: : 768iter [00:22, 37.32iter/s]
Best score: 0.1816: : 769iter [00:22, 37.32iter/s]
Best score: 0.1816: : 770iter [00:22, 37.32iter/s]
Best score: 0.1816: : 771iter [00:22, 37.81iter/s]
Best score: 0.1816: : 771iter [00:22, 37.81iter/s]
Best score: 0.1816: : 772iter [00:22, 37.81iter/s]
Best score: 0.1816: : 773iter [00:22, 37.81iter/s]
Best score: 0.1816: : 774iter [00:22, 37.81iter/s]
Best score: 0.1816: : 775iter [00:22, 38.37iter/s]
Best score: 0.1816: : 775iter [00:22, 38.37iter/s]
Best score: 0.1816: : 776iter [00:22, 38.37iter/s]
Best score: 0.1816: : 777iter [00:22, 38.37iter/s]
Best score: 0.1816: : 778iter [00:22, 38.37iter/s]
Best score: 0.1814: : 779iter [00:22, 38.37iter/s]
Best score: 0.1814: : 780iter [00:22, 38.75iter/s]
Best score: 0.1814: : 780iter [00:22, 38.75iter/s]
Best score: 0.1814: : 781iter [00:22, 38.75iter/s]
Best score: 0.1814: : 782iter [00:22, 38.75iter/s]
Best score: 0.1814: : 783iter [00:22, 38.75iter/s]
Best score: 0.1814: : 784iter [00:22, 39.06iter/s]
Best score: 0.1814: : 784iter [00:22, 39.06iter/s]
Best score: 0.1814: : 785iter [00:22, 39.06iter/s]
Best score: 0.1814: : 786iter [00:22, 39.06iter/s]
Best score: 0.1814: : 787iter [00:22, 39.06iter/s]
Best score: 0.1814: : 788iter [00:22, 39.06iter/s]
Best score: 0.1814: : 789iter [00:22, 39.50iter/s]
Best score: 0.1814: : 789iter [00:23, 39.50iter/s]
Best score: 0.1814: : 790iter [00:23, 39.50iter/s]
Best score: 0.1814: : 791iter [00:23, 39.50iter/s]
Best score: 0.1814: : 792iter [00:23, 39.50iter/s]
Best score: 0.1814: : 793iter [00:23, 39.31iter/s]
Best score: 0.1814: : 793iter [00:23, 39.31iter/s]
Best score: 0.1814: : 794iter [00:23, 39.31iter/s]
Best score: 0.1814: : 795iter [00:23, 39.31iter/s]
Best score: 0.1814: : 796iter [00:23, 39.31iter/s]
Best score: 0.1814: : 797iter [00:23, 39.49iter/s]
Best score: 0.1814: : 797iter [00:23, 39.49iter/s]
Best score: 0.1814: : 798iter [00:23, 39.49iter/s]
[ITER 800] Best: 0.1811
[PARAMS] Current best parameters:
  fx: 542.6637
  fy: 542.5956
  px: 1022.1104
  py: 752.2074
  k1: -0.00919365
  k2: 0.00630460
  k3: -0.00660461
  k4: 0.00130956
  p1: 0.00803998
  p2: -0.00149181
  p3: -0.00115824
  p4: -0.00018170
  s1: 0.0014
  s2: 0.0008
  s3: -0.0072
  s4: -0.0005
  tilt_x: -0.0283
  tilt_y: -0.0132
  scale: 3.0166
  rotation: -0.1889

Best score: 0.1811: : 799iter [00:23, 39.49iter/s]
Best score: 0.1811: : 800iter [00:23, 39.49iter/s]
Best score: 0.1811: : 801iter [00:23, 32.24iter/s]
Best score: 0.1811: : 801iter [00:23, 32.24iter/s]
Best score: 0.1811: : 802iter [00:23, 32.24iter/s]
Best score: 0.1811: : 803iter [00:23, 32.24iter/s]
Best score: 0.1811: : 804iter [00:23, 32.24iter/s]
Best score: 0.1811: : 805iter [00:23, 32.24iter/s]
Best score: 0.1811: : 806iter [00:23, 34.53iter/s]
Best score: 0.1811: : 806iter [00:23, 34.53iter/s]
Best score: 0.1811: : 807iter [00:23, 34.53iter/s]
Best score: 0.1811: : 808iter [00:23, 34.53iter/s]
Best score: 0.1810: : 809iter [00:23, 34.53iter/s]
Best score: 0.1810: : 810iter [00:23, 35.74iter/s]
Best score: 0.1810: : 810iter [00:23, 35.74iter/s]
Best score: 0.1810: : 811iter [00:23, 35.74iter/s]
Best score: 0.1810: : 812iter [00:23, 35.74iter/s]
Best score: 0.1810: : 813iter [00:23, 35.74iter/s]
Best score: 0.1810: : 814iter [00:23, 35.74iter/s]
Best score: 0.1810: : 815iter [00:23, 37.14iter/s]
Best score: 0.1810: : 815iter [00:23, 37.14iter/s]
Best score: 0.1810: : 816iter [00:23, 37.14iter/s]
Best score: 0.1810: : 817iter [00:23, 37.14iter/s]
Best score: 0.1810: : 818iter [00:23, 37.14iter/s]
Best score: 0.1802: : 819iter [00:23, 37.14iter/s]
Best score: 0.1802: : 820iter [00:23, 37.95iter/s]
Best score: 0.1802: : 820iter [00:23, 37.95iter/s]
Best score: 0.1802: : 821iter [00:23, 37.95iter/s]
Best score: 0.1802: : 822iter [00:23, 37.95iter/s]
Best score: 0.1802: : 823iter [00:23, 37.95iter/s]
Best score: 0.1802: : 824iter [00:23, 38.47iter/s]
Best score: 0.1802: : 824iter [00:23, 38.47iter/s]
Best score: 0.1802: : 825iter [00:24, 38.47iter/s]
Best score: 0.1802: : 826iter [00:24, 38.47iter/s]
Best score: 0.1802: : 827iter [00:24, 38.47iter/s]
Best score: 0.1802: : 828iter [00:24, 38.47iter/s]
Best score: 0.1802: : 829iter [00:24, 39.11iter/s]
Best score: 0.1802: : 829iter [00:24, 39.11iter/s]
Best score: 0.1802: : 830iter [00:24, 39.11iter/s]
Best score: 0.1802: : 831iter [00:24, 39.11iter/s]
Best score: 0.1802: : 832iter [00:24, 39.11iter/s]
Best score: 0.1802: : 833iter [00:24, 39.05iter/s]
Best score: 0.1802: : 833iter [00:24, 39.05iter/s]
Best score: 0.1802: : 834iter [00:24, 39.05iter/s]
Best score: 0.1802: : 835iter [00:24, 39.05iter/s]
Best score: 0.1802: : 836iter [00:24, 39.05iter/s]
Best score: 0.1802: : 837iter [00:24, 39.05iter/s]
Best score: 0.1802: : 838iter [00:24, 39.47iter/s]
Best score: 0.1802: : 838iter [00:24, 39.47iter/s]
Best score: 0.1802: : 839iter [00:24, 39.47iter/s]
Best score: 0.1802: : 840iter [00:24, 39.47iter/s]
Best score: 0.1802: : 841iter [00:24, 39.47iter/s]
Best score: 0.1802: : 842iter [00:24, 39.40iter/s]
Best score: 0.1802: : 842iter [00:24, 39.40iter/s]
Best score: 0.1802: : 843iter [00:24, 39.40iter/s]
Best score: 0.1802: : 844iter [00:24, 39.40iter/s]
Best score: 0.1802: : 845iter [00:24, 39.40iter/s]
Best score: 0.1802: : 846iter [00:24, 39.40iter/s]
Best score: 0.1802: : 847iter [00:24, 39.59iter/s]
Best score: 0.1802: : 847iter [00:24, 39.59iter/s]
Best score: 0.1802: : 848iter [00:24, 39.59iter/s]
[ITER 850] Best: 0.1800
[PARAMS] Current best parameters:
  fx: 544.8384
  fy: 544.7931
  px: 1022.0145
  py: 751.8621
  k1: -0.02273680
  k2: 0.02241908
  k3: -0.01487877
  k4: 0.00287525
  p1: 0.00828613
  p2: 0.00023120
  p3: -0.00040429
  p4: -0.00093408
  s1: -0.0010
  s2: 0.0015
  s3: -0.0082
  s4: -0.0001
  tilt_x: -0.0306
  tilt_y: -0.0091
  scale: 3.0302
  rotation: -0.1932

Best score: 0.1800: : 849iter [00:24, 39.59iter/s]
Best score: 0.1800: : 850iter [00:24, 39.59iter/s]
Best score: 0.1800: : 851iter [00:24, 32.79iter/s]
Best score: 0.1800: : 851iter [00:24, 32.79iter/s]
Best score: 0.1800: : 852iter [00:24, 32.79iter/s]
Best score: 0.1800: : 853iter [00:24, 32.79iter/s]
Best score: 0.1800: : 854iter [00:24, 32.79iter/s]
Best score: 0.1800: : 855iter [00:24, 34.35iter/s]
Best score: 0.1800: : 855iter [00:24, 34.35iter/s]
Best score: 0.1800: : 856iter [00:24, 34.35iter/s]
Best score: 0.1800: : 857iter [00:24, 34.35iter/s]
Best score: 0.1800: : 858iter [00:24, 34.35iter/s]
Best score: 0.1800: : 859iter [00:24, 34.35iter/s]
Best score: 0.1800: : 860iter [00:24, 35.98iter/s]
Best score: 0.1800: : 860iter [00:24, 35.98iter/s]
Best score: 0.1800: : 861iter [00:24, 35.98iter/s]
Best score: 0.1800: : 862iter [00:25, 35.98iter/s]
Best score: 0.1800: : 863iter [00:25, 35.98iter/s]
Best score: 0.1800: : 864iter [00:25, 35.98iter/s]
Best score: 0.1800: : 865iter [00:25, 37.35iter/s]
Best score: 0.1800: : 865iter [00:25, 37.35iter/s]
Best score: 0.1800: : 866iter [00:25, 37.35iter/s]
Best score: 0.1800: : 867iter [00:25, 37.35iter/s]
Best score: 0.1800: : 868iter [00:25, 37.35iter/s]
Best score: 0.1797: : 869iter [00:25, 37.35iter/s]
Best score: 0.1797: : 870iter [00:25, 38.00iter/s]
Best score: 0.1797: : 870iter [00:25, 38.00iter/s]
Best score: 0.1797: : 871iter [00:25, 38.00iter/s]
Best score: 0.1797: : 872iter [00:25, 38.00iter/s]
Best score: 0.1797: : 873iter [00:25, 38.00iter/s]
Best score: 0.1797: : 874iter [00:25, 38.00iter/s]
Best score: 0.1797: : 875iter [00:25, 38.80iter/s]
Best score: 0.1797: : 875iter [00:25, 38.80iter/s]
Best score: 0.1797: : 876iter [00:25, 38.80iter/s]
Best score: 0.1797: : 877iter [00:25, 38.80iter/s]
Best score: 0.1797: : 878iter [00:25, 38.80iter/s]
Best score: 0.1797: : 879iter [00:25, 38.80iter/s]
Best score: 0.1797: : 880iter [00:25, 38.99iter/s]
Best score: 0.1797: : 880iter [00:25, 38.99iter/s]
Best score: 0.1797: : 881iter [00:25, 38.99iter/s]
Best score: 0.1797: : 882iter [00:25, 38.99iter/s]
Best score: 0.1797: : 883iter [00:25, 38.99iter/s]
Best score: 0.1797: : 884iter [00:25, 38.99iter/s]
Best score: 0.1797: : 885iter [00:25, 39.30iter/s]
Best score: 0.1797: : 885iter [00:25, 39.30iter/s]
Best score: 0.1797: : 886iter [00:25, 39.30iter/s]
Best score: 0.1797: : 887iter [00:25, 39.30iter/s]
Best score: 0.1797: : 888iter [00:25, 39.30iter/s]
Best score: 0.1797: : 889iter [00:25, 39.38iter/s]
Best score: 0.1796: : 889iter [00:25, 39.38iter/s]
Best score: 0.1796: : 890iter [00:25, 39.38iter/s]
Best score: 0.1796: : 891iter [00:25, 39.38iter/s]
Best score: 0.1796: : 892iter [00:25, 39.38iter/s]
Best score: 0.1796: : 893iter [00:25, 38.90iter/s]
Best score: 0.1796: : 893iter [00:25, 38.90iter/s]
Best score: 0.1796: : 894iter [00:25, 38.90iter/s]
Best score: 0.1796: : 895iter [00:25, 38.90iter/s]
Best score: 0.1796: : 896iter [00:25, 38.90iter/s]
Best score: 0.1796: : 897iter [00:25, 38.90iter/s]
Best score: 0.1796: : 898iter [00:25, 39.33iter/s]
Best score: 0.1796: : 898iter [00:25, 39.33iter/s]
[ITER 900] Best: 0.1796
[PARAMS] Current best parameters:
  fx: 544.0898
  fy: 544.0338
  px: 1021.5347
  py: 751.8531
  k1: -0.02472649
  k2: 0.02719220
  k3: -0.01774482
  k4: 0.00343916
  p1: 0.00865891
  p2: 0.00036946
  p3: -0.00011098
  p4: -0.00114308
  s1: -0.0021
  s2: 0.0018
  s3: -0.0090
  s4: 0.0003
  tilt_x: -0.0314
  tilt_y: -0.0065
  scale: 3.0314
  rotation: -0.1977

Best score: 0.1796: : 899iter [00:25, 39.33iter/s]
Best score: 0.1796: : 900iter [00:26, 39.33iter/s]
Best score: 0.1796: : 901iter [00:26, 39.33iter/s]
Best score: 0.1796: : 902iter [00:26, 32.43iter/s]
Best score: 0.1796: : 902iter [00:26, 32.43iter/s]
Best score: 0.1796: : 903iter [00:26, 32.43iter/s]
Best score: 0.1796: : 904iter [00:26, 32.43iter/s]
Best score: 0.1796: : 905iter [00:26, 32.43iter/s]
Best score: 0.1796: : 906iter [00:26, 32.43iter/s]
Best score: 0.1796: : 907iter [00:26, 34.59iter/s]
Best score: 0.1796: : 907iter [00:26, 34.59iter/s]
Best score: 0.1796: : 908iter [00:26, 34.59iter/s]
Best score: 0.1796: : 909iter [00:26, 34.59iter/s]
Best score: 0.1796: : 910iter [00:26, 34.59iter/s]
Best score: 0.1796: : 911iter [00:26, 35.82iter/s]
Best score: 0.1796: : 911iter [00:26, 35.82iter/s]
Best score: 0.1796: : 912iter [00:26, 35.82iter/s]
Best score: 0.1796: : 913iter [00:26, 35.82iter/s]
Best score: 0.1796: : 914iter [00:26, 35.82iter/s]
Best score: 0.1796: : 915iter [00:26, 35.82iter/s]
Best score: 0.1796: : 916iter [00:26, 37.32iter/s]
Best score: 0.1796: : 916iter [00:26, 37.32iter/s]
Best score: 0.1796: : 917iter [00:26, 37.32iter/s]
Best score: 0.1796: : 918iter [00:26, 37.32iter/s]
Best score: 0.1796: : 919iter [00:26, 37.32iter/s]
Best score: 0.1796: : 920iter [00:26, 37.89iter/s]
Best score: 0.1796: : 920iter [00:26, 37.89iter/s]
Best score: 0.1796: : 921iter [00:26, 37.89iter/s]
Best score: 0.1796: : 922iter [00:26, 37.89iter/s]
Best score: 0.1796: : 923iter [00:26, 37.89iter/s]
Best score: 0.1796: : 924iter [00:26, 37.89iter/s]
Best score: 0.1796: : 925iter [00:26, 38.73iter/s]
Best score: 0.1796: : 925iter [00:26, 38.73iter/s]
Best score: 0.1796: : 926iter [00:26, 38.73iter/s]
Best score: 0.1796: : 927iter [00:26, 38.73iter/s]
Best score: 0.1796: : 928iter [00:26, 38.73iter/s]
Best score: 0.1794: : 929iter [00:26, 38.73iter/s]
Best score: 0.1794: : 930iter [00:26, 38.99iter/s]
Best score: 0.1794: : 930iter [00:26, 38.99iter/s]
Best score: 0.1794: : 931iter [00:26, 38.99iter/s]
Best score: 0.1794: : 932iter [00:26, 38.99iter/s]
Best score: 0.1794: : 933iter [00:26, 38.99iter/s]
Best score: 0.1794: : 934iter [00:26, 39.21iter/s]
Best score: 0.1794: : 934iter [00:26, 39.21iter/s]
Best score: 0.1794: : 935iter [00:27, 39.21iter/s]
Best score: 0.1794: : 936iter [00:27, 39.21iter/s]
Best score: 0.1794: : 937iter [00:27, 39.21iter/s]
Best score: 0.1794: : 938iter [00:27, 31.69iter/s]
Best score: 0.1794: : 938iter [00:27, 31.69iter/s]
Best score: 0.1794: : 939iter [00:27, 31.69iter/s]
Best score: 0.1794: : 940iter [00:27, 31.69iter/s]
Best score: 0.1794: : 941iter [00:27, 31.69iter/s]
Best score: 0.1794: : 942iter [00:27, 33.45iter/s]
Best score: 0.1794: : 942iter [00:27, 33.45iter/s]
Best score: 0.1794: : 943iter [00:27, 33.45iter/s]
Best score: 0.1794: : 944iter [00:27, 33.45iter/s]
Best score: 0.1794: : 945iter [00:27, 33.45iter/s]
Best score: 0.1794: : 946iter [00:27, 33.45iter/s]
Best score: 0.1794: : 947iter [00:27, 35.43iter/s]
Best score: 0.1794: : 947iter [00:27, 35.43iter/s]
Best score: 0.1794: : 948iter [00:27, 35.43iter/s]
[ITER 950] Best: 0.1794
[PARAMS] Current best parameters:
  fx: 544.4579
  fy: 544.4165
  px: 1021.8142
  py: 751.6887
  k1: -0.02452531
  k2: 0.02633076
  k3: -0.01726414
  k4: 0.00335249
  p1: 0.00802541
  p2: 0.00009471
  p3: 0.00002388
  p4: -0.00113608
  s1: -0.0018
  s2: 0.0018
  s3: -0.0087
  s4: 0.0003
  tilt_x: -0.0310
  tilt_y: -0.0085
  scale: 3.0308
  rotation: -0.1978

Best score: 0.1794: : 949iter [00:27, 35.43iter/s]
Best score: 0.1794: : 950iter [00:27, 35.43iter/s]
Best score: 0.1794: : 951iter [00:27, 30.29iter/s]
Best score: 0.1794: : 951iter [00:27, 30.29iter/s]
Best score: 0.1794: : 952iter [00:27, 30.29iter/s]
Best score: 0.1794: : 953iter [00:27, 30.29iter/s]
Best score: 0.1794: : 954iter [00:27, 30.29iter/s]
Best score: 0.1794: : 955iter [00:27, 30.29iter/s]
Best score: 0.1794: : 956iter [00:27, 33.09iter/s]
Best score: 0.1794: : 956iter [00:27, 33.09iter/s]
Best score: 0.1794: : 957iter [00:27, 33.09iter/s]
Best score: 0.1794: : 958iter [00:27, 33.09iter/s]
Best score: 0.1794: : 959iter [00:27, 33.09iter/s]
Best score: 0.1794: : 960iter [00:27, 34.59iter/s]
Best score: 0.1794: : 960iter [00:27, 34.59iter/s]
Best score: 0.1794: : 961iter [00:27, 34.59iter/s]
Best score: 0.1794: : 962iter [00:27, 34.59iter/s]
Best score: 0.1794: : 963iter [00:27, 34.59iter/s]
Best score: 0.1794: : 964iter [00:27, 35.92iter/s]
Best score: 0.1794: : 964iter [00:27, 35.92iter/s]
Best score: 0.1794: : 965iter [00:27, 35.92iter/s]
Best score: 0.1794: : 966iter [00:27, 35.92iter/s]
Best score: 0.1794: : 967iter [00:27, 35.92iter/s]
Best score: 0.1794: : 968iter [00:27, 35.92iter/s]
Best score: 0.1794: : 969iter [00:27, 37.24iter/s]
Best score: 0.1794: : 969iter [00:27, 37.24iter/s]
Best score: 0.1794: : 970iter [00:27, 37.24iter/s]
Best score: 0.1794: : 971iter [00:28, 37.24iter/s]
Best score: 0.1794: : 972iter [00:28, 37.24iter/s]
Best score: 0.1794: : 973iter [00:28, 37.67iter/s]
Best score: 0.1794: : 973iter [00:28, 37.67iter/s]
Best score: 0.1794: : 974iter [00:28, 37.67iter/s]
Best score: 0.1794: : 975iter [00:28, 37.67iter/s]
Best score: 0.1794: : 976iter [00:28, 37.67iter/s]
Best score: 0.1794: : 977iter [00:28, 37.67iter/s]
Best score: 0.1794: : 978iter [00:28, 38.55iter/s]
Best score: 0.1794: : 978iter [00:28, 38.55iter/s]
Best score: 0.1794: : 979iter [00:28, 38.55iter/s]
Best score: 0.1794: : 980iter [00:28, 38.55iter/s]
Best score: 0.1794: : 981iter [00:28, 38.55iter/s]
Best score: 0.1794: : 982iter [00:28, 38.74iter/s]
Best score: 0.1794: : 982iter [00:28, 38.74iter/s]
Best score: 0.1794: : 983iter [00:28, 38.74iter/s]
Best score: 0.1794: : 984iter [00:28, 38.74iter/s]
Best score: 0.1794: : 985iter [00:28, 38.74iter/s]
Best score: 0.1794: : 986iter [00:28, 38.98iter/s]
Best score: 0.1794: : 986iter [00:28, 38.98iter/s]
Best score: 0.1794: : 987iter [00:28, 38.98iter/s]
Best score: 0.1794: : 988iter [00:28, 38.98iter/s]
Best score: 0.1794: : 989iter [00:28, 38.98iter/s]
Best score: 0.1794: : 990iter [00:28, 38.97iter/s]
Best score: 0.1794: : 990iter [00:28, 38.97iter/s]
Best score: 0.1794: : 991iter [00:28, 38.97iter/s]
Best score: 0.1794: : 992iter [00:28, 38.97iter/s]
Best score: 0.1794: : 993iter [00:28, 38.97iter/s]
Best score: 0.1794: : 994iter [00:28, 39.22iter/s]
Best score: 0.1794: : 994iter [00:28, 39.22iter/s]
Best score: 0.1794: : 995iter [00:28, 39.22iter/s]
Best score: 0.1794: : 996iter [00:28, 39.22iter/s]
Best score: 0.1794: : 997iter [00:28, 39.22iter/s]
Best score: 0.1794: : 998iter [00:28, 39.22iter/s]
Best score: 0.1794: : 999iter [00:28, 39.67iter/s]
[ITER 1000] Best: 0.1793
[PARAMS] Current best parameters:
  fx: 544.6743
  fy: 544.6263
  px: 1021.7024
  py: 751.8488
  k1: -0.02502758
  k2: 0.02645565
  k3: -0.01718184
  k4: 0.00331993
  p1: 0.00822682
  p2: 0.00024615
  p3: 0.00021218
  p4: -0.00116973
  s1: -0.0021
  s2: 0.0018
  s3: -0.0091
  s4: 0.0004
  tilt_x: -0.0305
  tilt_y: -0.0076
  scale: 3.0315
  rotation: -0.1980

Best score: 0.1793: : 999iter [00:28, 39.67iter/s]
Best score: 0.1793: : 1000iter [00:28, 39.67iter/s]
Best score: 0.1793: : 1001iter [00:28, 39.67iter/s]
Best score: 0.1793: : 1002iter [00:28, 39.67iter/s]
Best score: 0.1793: : 1003iter [00:28, 32.46iter/s]
Best score: 0.1793: : 1003iter [00:28, 32.46iter/s]
Best score: 0.1793: : 1004iter [00:28, 32.46iter/s]
Best score: 0.1793: : 1005iter [00:28, 32.46iter/s]
Best score: 0.1793: : 1006iter [00:28, 32.46iter/s]
Best score: 0.1793: : 1007iter [00:28, 32.46iter/s]
Best score: 0.1793: : 1008iter [00:28, 34.71iter/s]
Best score: 0.1793: : 1008iter [00:29, 34.71iter/s]
Best score: 0.1792: : 1009iter [00:29, 34.71iter/s]
Best score: 0.1792: : 1010iter [00:29, 34.71iter/s]
Best score: 0.1792: : 1011iter [00:29, 34.71iter/s]
Best score: 0.1792: : 1012iter [00:29, 35.75iter/s]
Best score: 0.1792: : 1012iter [00:29, 35.75iter/s]
Best score: 0.1792: : 1013iter [00:29, 35.75iter/s]
Best score: 0.1792: : 1014iter [00:29, 35.75iter/s]
Best score: 0.1792: : 1015iter [00:29, 35.75iter/s]
Best score: 0.1792: : 1016iter [00:29, 36.84iter/s]
Best score: 0.1792: : 1016iter [00:29, 36.84iter/s]
Best score: 0.1792: : 1017iter [00:29, 36.84iter/s]
Best score: 0.1792: : 1018iter [00:29, 36.84iter/s]
Best score: 0.1792: : 1019iter [00:29, 36.84iter/s]
Best score: 0.1792: : 1020iter [00:29, 37.36iter/s]
Best score: 0.1792: : 1020iter [00:29, 37.36iter/s]
Best score: 0.1792: : 1021iter [00:29, 37.36iter/s]
Best score: 0.1792: : 1022iter [00:29, 37.36iter/s]
Best score: 0.1792: : 1023iter [00:29, 37.36iter/s]
Best score: 0.1792: : 1024iter [00:29, 37.36iter/s]
Best score: 0.1792: : 1025iter [00:29, 38.20iter/s]
Best score: 0.1792: : 1025iter [00:29, 38.20iter/s]
Best score: 0.1792: : 1026iter [00:29, 38.20iter/s]
Best score: 0.1792: : 1027iter [00:29, 38.20iter/s]
Best score: 0.1792: : 1028iter [00:29, 38.20iter/s]
Best score: 0.1792: : 1029iter [00:29, 38.58iter/s]
Best score: 0.1792: : 1029iter [00:29, 38.58iter/s]
Best score: 0.1792: : 1030iter [00:29, 38.58iter/s]
Best score: 0.1792: : 1031iter [00:29, 38.58iter/s]
Best score: 0.1792: : 1032iter [00:29, 38.58iter/s]
Best score: 0.1792: : 1033iter [00:29, 38.65iter/s]
Best score: 0.1792: : 1033iter [00:29, 38.65iter/s]
Best score: 0.1792: : 1034iter [00:29, 38.65iter/s]
Best score: 0.1792: : 1035iter [00:29, 38.65iter/s]
Best score: 0.1792: : 1036iter [00:29, 38.65iter/s]
Best score: 0.1792: : 1037iter [00:29, 38.65iter/s]
Best score: 0.1792: : 1038iter [00:29, 39.23iter/s]
Best score: 0.1792: : 1038iter [00:29, 39.23iter/s]
Best score: 0.1791: : 1039iter [00:29, 39.23iter/s]
Best score: 0.1791: : 1040iter [00:29, 39.23iter/s]
Best score: 0.1791: : 1041iter [00:29, 39.23iter/s]
Best score: 0.1791: : 1042iter [00:29, 39.07iter/s]
Best score: 0.1791: : 1042iter [00:29, 39.07iter/s]
Best score: 0.1791: : 1043iter [00:29, 39.07iter/s]
Best score: 0.1791: : 1044iter [00:29, 39.07iter/s]
Best score: 0.1791: : 1045iter [00:29, 39.07iter/s]
Best score: 0.1791: : 1046iter [00:29, 39.07iter/s]
Best score: 0.1791: : 1047iter [00:29, 39.56iter/s]
Best score: 0.1791: : 1047iter [00:29, 39.56iter/s]
Best score: 0.1791: : 1048iter [00:30, 39.56iter/s]
[ITER 1050] Best: 0.1790
[PARAMS] Current best parameters:
  fx: 546.1239
  fy: 546.0897
  px: 1022.2274
  py: 751.1160
  k1: -0.02647610
  k2: 0.02625142
  k3: -0.01687924
  k4: 0.00325772
  p1: 0.00547992
  p2: -0.00076298
  p3: 0.00106802
  p4: -0.00111594
  s1: -0.0015
  s2: 0.0018
  s3: -0.0076
  s4: 0.0002
  tilt_x: -0.0290
  tilt_y: -0.0123
  scale: 3.0313
  rotation: -0.1909

Best score: 0.1790: : 1049iter [00:30, 39.56iter/s]
Best score: 0.1790: : 1050iter [00:30, 39.56iter/s]
Best score: 0.1790: : 1051iter [00:30, 32.56iter/s]
Best score: 0.1790: : 1051iter [00:30, 32.56iter/s]
Best score: 0.1790: : 1052iter [00:30, 32.56iter/s]
Best score: 0.1790: : 1053iter [00:30, 32.56iter/s]
Best score: 0.1790: : 1054iter [00:30, 32.56iter/s]
Best score: 0.1790: : 1055iter [00:30, 32.56iter/s]
Best score: 0.1790: : 1056iter [00:30, 34.79iter/s]
Best score: 0.1790: : 1056iter [00:30, 34.79iter/s]
Best score: 0.1790: : 1057iter [00:30, 34.79iter/s]
Best score: 0.1790: : 1058iter [00:30, 34.79iter/s]
Best score: 0.1788: : 1059iter [00:30, 34.79iter/s]
Best score: 0.1788: : 1060iter [00:30, 35.88iter/s]
Best score: 0.1788: : 1060iter [00:30, 35.88iter/s]
Best score: 0.1788: : 1061iter [00:30, 35.88iter/s]
Best score: 0.1788: : 1062iter [00:30, 35.88iter/s]
Best score: 0.1788: : 1063iter [00:30, 35.88iter/s]
Best score: 0.1788: : 1064iter [00:30, 35.88iter/s]
Best score: 0.1788: : 1065iter [00:30, 37.11iter/s]
Best score: 0.1788: : 1065iter [00:30, 37.11iter/s]
Best score: 0.1788: : 1066iter [00:30, 37.11iter/s]
Best score: 0.1788: : 1067iter [00:30, 37.11iter/s]
Best score: 0.1788: : 1068iter [00:30, 37.11iter/s]
Best score: 0.1788: : 1069iter [00:30, 37.78iter/s]
Best score: 0.1787: : 1069iter [00:30, 37.78iter/s]
Best score: 0.1787: : 1070iter [00:30, 37.78iter/s]
Best score: 0.1787: : 1071iter [00:30, 37.78iter/s]
Best score: 0.1787: : 1072iter [00:30, 37.78iter/s]
Best score: 0.1787: : 1073iter [00:30, 38.07iter/s]
Best score: 0.1787: : 1073iter [00:30, 38.07iter/s]
Best score: 0.1787: : 1074iter [00:30, 38.07iter/s]
Best score: 0.1787: : 1075iter [00:30, 38.07iter/s]
Best score: 0.1787: : 1076iter [00:30, 38.07iter/s]
Best score: 0.1787: : 1077iter [00:30, 38.49iter/s]
Best score: 0.1787: : 1077iter [00:30, 38.49iter/s]
Best score: 0.1787: : 1078iter [00:30, 38.49iter/s]
Best score: 0.1785: : 1079iter [00:30, 38.49iter/s]
Best score: 0.1785: : 1080iter [00:30, 38.49iter/s]
Best score: 0.1785: : 1081iter [00:30, 38.62iter/s]
Best score: 0.1785: : 1081iter [00:30, 38.62iter/s]
Best score: 0.1785: : 1082iter [00:30, 38.62iter/s]
Best score: 0.1785: : 1083iter [00:30, 38.62iter/s]
Best score: 0.1785: : 1084iter [00:31, 38.62iter/s]
Best score: 0.1785: : 1085iter [00:31, 38.62iter/s]
Best score: 0.1785: : 1086iter [00:31, 39.07iter/s]
Best score: 0.1785: : 1086iter [00:31, 39.07iter/s]
Best score: 0.1785: : 1087iter [00:31, 39.07iter/s]
Best score: 0.1785: : 1088iter [00:31, 39.07iter/s]
Best score: 0.1785: : 1089iter [00:31, 39.07iter/s]
Best score: 0.1785: : 1090iter [00:31, 39.08iter/s]
Best score: 0.1785: : 1090iter [00:31, 39.08iter/s]
Best score: 0.1785: : 1091iter [00:31, 39.08iter/s]
Best score: 0.1785: : 1092iter [00:31, 39.08iter/s]
Best score: 0.1785: : 1093iter [00:31, 39.08iter/s]
Best score: 0.1785: : 1094iter [00:31, 39.08iter/s]
Best score: 0.1785: : 1095iter [00:31, 39.35iter/s]
Best score: 0.1785: : 1095iter [00:31, 39.35iter/s]
Best score: 0.1785: : 1096iter [00:31, 39.35iter/s]
Best score: 0.1785: : 1097iter [00:31, 39.35iter/s]
Best score: 0.1785: : 1098iter [00:31, 39.35iter/s]
Best score: 0.1785: : 1099iter [00:31, 39.41iter/s]
[ITER 1100] Best: 0.1784
[PARAMS] Current best parameters:
  fx: 547.8254
  fy: 547.8126
  px: 1022.1202
  py: 750.8386
  k1: -0.03340793
  k2: 0.03398266
  k3: -0.02094579
  k4: 0.00405429
  p1: 0.00382549
  p2: -0.00089946
  p3: 0.00245570
  p4: -0.00179253
  s1: -0.0028
  s2: 0.0024
  s3: -0.0077
  s4: 0.0010
  tilt_x: -0.0272
  tilt_y: -0.0121
  scale: 3.0361
  rotation: -0.1944

Best score: 0.1784: : 1099iter [00:31, 39.41iter/s]
[📈] Running L-BFGS-B polish after 1100 DE iterations...

Best score: 0.1784: : 1100iter [00:31, 34.95iter/s]
  Polish improved score: 0.178449 -> 0.178445

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 26,106 → 26,106 within 3500mm ring
  Ring polish did not improve score: 0.178445
  Success: True
  Tangential distortion: p1=0.003826, p2=-0.000899, p3=0.002456, p4=-0.001793
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1784
[✅] Parameters copied to: /working/MDC027_W031_2025-03-24/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 547.825426863345
    fy: 547.8126484860447
    px: 1022.1201514626564
    py: 750.8386401424794
    k1: -0.033407831769699615
    k2: 0.03398274038733154
    k3: -0.020945736440254343
    k4: 0.004054312318564911
    p1: 0.00382550062872458
    p2: -0.0008994409358521001
    p3: 0.0024557145836710758
    p4: -0.0017925145177347932
    scale: 3.036130143343588
    rotation: -0.19444744315441823
    mmpp: 1.3017657263453897
    nmpp: 1301765.7263453896

[🔄] Converting 744 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/411 [00:00<?, ?frame/s]

   🔄 1000:   4%|▍         | 18/411 [00:00<00:02, 173.94frame/s]

   🔄 1000:   9%|▉         | 36/411 [00:00<00:02, 171.80frame/s]

   🔄 1000:  13%|█▎        | 54/411 [00:00<00:02, 172.42frame/s]

   🔄 1000:  18%|█▊        | 72/411 [00:00<00:01, 173.04frame/s]

   🔄 1000:  22%|██▏       | 90/411 [00:00<00:01, 173.52frame/s]

   🔄 1000:  26%|██▋       | 108/411 [00:00<00:01, 173.85frame/s]

   🔄 1000:  31%|███       | 126/411 [00:00<00:01, 173.68frame/s]

   🔄 1000:  35%|███▌      | 144/411 [00:00<00:01, 173.89frame/s]

   🔄 1000:  39%|███▉      | 162/411 [00:00<00:01, 174.74frame/s]

   🔄 1000:  44%|████▍     | 180/411 [00:01<00:01, 175.38frame/s]

   🔄 1000:  48%|████▊     | 198/411 [00:01<00:01, 175.12frame/s]

   🔄 1000:  53%|█████▎    | 216/411 [00:01<00:01, 175.35frame/s]

   🔄 1000:  57%|█████▋    | 234/411 [00:01<00:01, 175.35frame/s]

   🔄 1000:  61%|██████▏   | 252/411 [00:01<00:00, 175.45frame/s]

   🔄 1000:  66%|██████▌   | 270/411 [00:01<00:00, 174.87frame/s]

   🔄 1000:  70%|███████   | 288/411 [00:01<00:00, 174.19frame/s]

   🔄 1000:  74%|███████▍  | 306/411 [00:01<00:00, 173.87frame/s]

   🔄 1000:  79%|███████▉  | 324/411 [00:01<00:00, 173.62frame/s]

   🔄 1000:  83%|████████▎ | 342/411 [00:01<00:00, 173.57frame/s]

   🔄 1000:  88%|████████▊ | 360/411 [00:02<00:00, 173.59frame/s]

   🔄 1000:  92%|█████████▏| 378/411 [00:02<00:00, 173.35frame/s]

   🔄 1000:  96%|█████████▋| 396/411 [00:02<00:00, 173.04frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:04<00:04,  4.35s/rig]

   🔄 500:   0%|          | 0/333 [00:00<?, ?frame/s]

   🔄 500:   5%|▌         | 18/333 [00:00<00:01, 175.09frame/s]

   🔄 500:  11%|█         | 36/333 [00:00<00:01, 176.21frame/s]

   🔄 500:  17%|█▋        | 55/333 [00:00<00:01, 178.29frame/s]

   🔄 500:  22%|██▏       | 74/333 [00:00<00:01, 180.80frame/s]

   🔄 500:  28%|██▊       | 93/333 [00:00<00:01, 182.38frame/s]

   🔄 500:  34%|███▎      | 112/333 [00:00<00:01, 182.56frame/s]

   🔄 500:  39%|███▉      | 131/333 [00:00<00:01, 181.55frame/s]

   🔄 500:  45%|████▌     | 150/333 [00:00<00:01, 180.56frame/s]

   🔄 500:  51%|█████     | 169/333 [00:00<00:00, 180.30frame/s]

   🔄 500:  56%|█████▋    | 188/333 [00:01<00:00, 179.61frame/s]

   🔄 500:  62%|██████▏   | 206/333 [00:01<00:00, 179.23frame/s]

   🔄 500:  67%|██████▋   | 224/333 [00:01<00:00, 178.37frame/s]

   🔄 500:  73%|███████▎  | 242/333 [00:01<00:00, 178.03frame/s]

   🔄 500:  78%|███████▊  | 261/333 [00:01<00:00, 179.07frame/s]

   🔄 500:  84%|████████▍ | 280/333 [00:01<00:00, 180.30frame/s]

   🔄 500:  90%|████████▉ | 299/333 [00:01<00:00, 180.11frame/s]

   🔄 500:  95%|█████████▌| 318/333 [00:01<00:00, 178.79frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:07<00:00,  3.66s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:07<00:00,  3.77s/rig]
[✅] Saved PTDs: /working/MDC027_W031_2025-03-24/calibration/debug/4_undistorted/output/ptd

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

   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 1000:  17%|█▋        | 17/100 [00:00<00:00, 166.89frame/s]

   🔄 1000:  34%|███▍      | 34/100 [00:00<00:00, 166.40frame/s]

   🔄 1000:  51%|█████     | 51/100 [00:00<00:00, 167.32frame/s]

   🔄 1000:  68%|██████▊   | 68/100 [00:00<00:00, 166.57frame/s]

   🔄 1000:  85%|████████▌ | 85/100 [00:00<00:00, 164.69frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.06s/rig]

   🔄 500:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 500:  17%|█▋        | 17/100 [00:00<00:00, 163.31frame/s]

   🔄 500:  35%|███▌      | 35/100 [00:00<00:00, 172.36frame/s]

   🔄 500:  53%|█████▎    | 53/100 [00:00<00:00, 171.11frame/s]

   🔄 500:  71%|███████   | 71/100 [00:00<00:00, 169.20frame/s]

   🔄 500:  89%|████████▉ | 89/100 [00:00<00:00, 172.71frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
[✅] Saved PTDs: /working/MDC027_W031_2025-03-24/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%|▎         | 81398/3080448 [00:00<00:03, 813971.07px/s]
📤 Writing LUT:   6%|▌         | 170756/3080448 [00:00<00:03, 860797.71px/s]
📤 Writing LUT:   9%|▊         | 264909/3080448 [00:00<00:03, 897656.16px/s]
📤 Writing LUT:  12%|█▏        | 356790/3080448 [00:00<00:03, 906006.05px/s]
📤 Writing LUT:  15%|█▍        | 451162/3080448 [00:00<00:02, 919604.64px/s]
📤 Writing LUT:  18%|█▊        | 545379/3080448 [00:00<00:02, 927274.14px/s]
📤 Writing LUT:  21%|██        | 638107/3080448 [00:00<00:02, 918736.82px/s]
📤 Writing LUT:  24%|██▎       | 730837/3080448 [00:00<00:02, 921443.95px/s]
📤 Writing LUT:  27%|██▋       | 824017/3080448 [00:00<00:02, 924663.52px/s]
📤 Writing LUT:  30%|██▉       | 916494/3080448 [00:01<00:02, 921324.85px/s]
📤 Writing LUT:  33%|███▎      | 1008636/3080448 [00:01<00:02, 915426.64px/s]
📤 Writing LUT:  36%|███▌      | 1100191/3080448 [00:01<00:02, 914267.22px/s]
📤 Writing LUT:  39%|███▊      | 1192000/3080448 [00:01<00:02, 915416.94px/s]
📤 Writing LUT:  42%|████▏     | 1285058/3080448 [00:01<00:01, 919975.73px/s]
📤 Writing LUT:  45%|████▍     | 1377062/3080448 [00:01<00:01, 916365.32px/s]
📤 Writing LUT:  48%|████▊     | 1470108/3080448 [00:01<00:01, 920586.60px/s]
📤 Writing LUT:  51%|█████     | 1562174/3080448 [00:01<00:01, 918590.10px/s]
📤 Writing LUT:  54%|█████▎    | 1654039/3080448 [00:01<00:01, 914580.82px/s]
📤 Writing LUT:  57%|█████▋    | 1745504/3080448 [00:01<00:01, 912698.62px/s]
📤 Writing LUT:  60%|█████▉    | 1836778/3080448 [00:02<00:01, 911305.45px/s]
📤 Writing LUT:  63%|██████▎   | 1927912/3080448 [00:02<00:01, 907398.33px/s]
📤 Writing LUT:  66%|██████▌   | 2019440/3080448 [00:02<00:01, 909748.03px/s]
📤 Writing LUT:  69%|██████▊   | 2111331/3080448 [00:02<00:01, 912483.84px/s]
📤 Writing LUT:  72%|███████▏  | 2205004/3080448 [00:02<00:00, 919733.54px/s]
📤 Writing LUT:  75%|███████▍  | 2296983/3080448 [00:02<00:00, 919267.13px/s]
📤 Writing LUT:  78%|███████▊  | 2388914/3080448 [00:02<00:00, 918439.61px/s]
📤 Writing LUT:  81%|████████  | 2480850/3080448 [00:02<00:00, 918712.83px/s]
📤 Writing LUT:  84%|████████▎ | 2573522/3080448 [00:02<00:00, 921111.34px/s]
📤 Writing LUT:  87%|████████▋ | 2668408/3080448 [00:02<00:00, 929427.66px/s]
📤 Writing LUT:  90%|████████▉ | 2762889/3080448 [00:03<00:00, 934036.89px/s]
📤 Writing LUT:  93%|█████████▎| 2857823/3080448 [00:03<00:00, 938625.28px/s]
📤 Writing LUT:  96%|█████████▌| 2953385/3080448 [00:03<00:00, 943718.25px/s]
📤 Writing LUT:  99%|█████████▉| 3048382/3080448 [00:03<00:00, 945590.91px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 920380.62px/s]
[✅] LUT written to: /working/MDC027_W031_2025-03-24/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC027_W031_2025-03-24' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC027_W031_2025-03-24/calibration/MDC027_W031_2025-03-24_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%|          | 0/411 [00:00<?, ?frame/s]

   🔥 1000:   2%|▏         | 9/411 [00:00<00:04, 85.21frame/s]

   🔥 1000:   4%|▍         | 18/411 [00:00<00:04, 87.85frame/s]

   🔥 1000:   7%|▋         | 28/411 [00:00<00:04, 89.73frame/s]

   🔥 1000:   9%|▉         | 37/411 [00:00<00:04, 89.72frame/s]

   🔥 1000:  11%|█         | 46/411 [00:00<00:04, 89.63frame/s]

   🔥 1000:  13%|█▎        | 55/411 [00:00<00:03, 89.09frame/s]

   🔥 1000:  16%|█▌        | 64/411 [00:00<00:03, 89.13frame/s]

   🔥 1000:  18%|█▊        | 73/411 [00:00<00:03, 89.13frame/s]

   🔥 1000:  20%|█▉        | 82/411 [00:00<00:03, 89.12frame/s]

   🔥 1000:  22%|██▏       | 91/411 [00:01<00:03, 89.16frame/s]

   🔥 1000:  24%|██▍       | 100/411 [00:01<00:03, 89.15frame/s]

   🔥 1000:  27%|██▋       | 110/411 [00:01<00:03, 89.75frame/s]

   🔥 1000:  29%|██▉       | 120/411 [00:01<00:03, 90.27frame/s]

   🔥 1000:  32%|███▏      | 130/411 [00:01<00:03, 88.98frame/s]

   🔥 1000:  34%|███▍      | 139/411 [00:01<00:03, 87.46frame/s]

   🔥 1000:  36%|███▌      | 148/411 [00:01<00:03, 86.07frame/s]

   🔥 1000:  38%|███▊      | 157/411 [00:01<00:02, 85.99frame/s]

   🔥 1000:  40%|████      | 166/411 [00:01<00:02, 86.80frame/s]

   🔥 1000:  43%|████▎     | 175/411 [00:01<00:02, 86.25frame/s]

   🔥 1000:  45%|████▍     | 184/411 [00:02<00:02, 85.97frame/s]

   🔥 1000:  47%|████▋     | 193/411 [00:02<00:02, 85.62frame/s]

   🔥 1000:  49%|████▉     | 202/411 [00:02<00:02, 85.46frame/s]

   🔥 1000:  51%|█████▏    | 211/411 [00:02<00:02, 85.10frame/s]

   🔥 1000:  54%|█████▎    | 220/411 [00:02<00:02, 86.43frame/s]

   🔥 1000:  56%|█████▌    | 229/411 [00:02<00:02, 86.42frame/s]

   🔥 1000:  58%|█████▊    | 238/411 [00:02<00:01, 86.55frame/s]

   🔥 1000:  60%|██████    | 247/411 [00:02<00:01, 85.95frame/s]

   🔥 1000:  62%|██████▏   | 256/411 [00:02<00:01, 85.84frame/s]

   🔥 1000:  64%|██████▍   | 265/411 [00:03<00:01, 85.46frame/s]

   🔥 1000:  67%|██████▋   | 274/411 [00:03<00:01, 86.47frame/s]

   🔥 1000:  69%|██████▉   | 283/411 [00:03<00:01, 86.91frame/s]

   🔥 1000:  71%|███████   | 292/411 [00:03<00:01, 86.99frame/s]

   🔥 1000:  73%|███████▎  | 301/411 [00:03<00:01, 86.00frame/s]

   🔥 1000:  75%|███████▌  | 310/411 [00:03<00:01, 86.33frame/s]

   🔥 1000:  78%|███████▊  | 319/411 [00:03<00:01, 87.19frame/s]

   🔥 1000:  80%|████████  | 329/411 [00:03<00:00, 88.19frame/s]

   🔥 1000:  82%|████████▏ | 339/411 [00:03<00:00, 88.97frame/s]

   🔥 1000:  85%|████████▍ | 349/411 [00:03<00:00, 89.55frame/s]

   🔥 1000:  87%|████████▋ | 359/411 [00:04<00:00, 89.84frame/s]

   🔥 1000:  90%|████████▉ | 368/411 [00:04<00:00, 88.47frame/s]

   🔥 1000:  92%|█████████▏| 377/411 [00:04<00:00, 87.81frame/s]

   🔥 1000:  94%|█████████▍| 386/411 [00:04<00:00, 87.99frame/s]

   🔥 1000:  96%|█████████▌| 395/411 [00:04<00:00, 87.68frame/s]

   🔥 1000:  98%|█████████▊| 404/411 [00:04<00:00, 87.46frame/s]

                                                                [✅] 1000: /working/MDC027_W031_2025-03-24/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC027_W031_2025-03-24/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:07<00:07,  7.62s/rig]

   🔥 500:   0%|          | 0/333 [00:00<?, ?frame/s]

   🔥 500:   3%|▎         | 9/333 [00:00<00:03, 86.50frame/s]

   🔥 500:   6%|▌         | 19/333 [00:00<00:03, 89.56frame/s]

   🔥 500:   9%|▊         | 29/333 [00:00<00:03, 90.28frame/s]

   🔥 500:  12%|█▏        | 39/333 [00:00<00:03, 90.89frame/s]

   🔥 500:  15%|█▍        | 49/333 [00:00<00:03, 92.14frame/s]

   🔥 500:  18%|█▊        | 59/333 [00:00<00:02, 93.76frame/s]

   🔥 500:  21%|██        | 69/333 [00:00<00:02, 94.63frame/s]

   🔥 500:  24%|██▎       | 79/333 [00:00<00:02, 94.93frame/s]

   🔥 500:  27%|██▋       | 89/333 [00:00<00:02, 93.86frame/s]

   🔥 500:  30%|██▉       | 99/333 [00:01<00:02, 93.54frame/s]

   🔥 500:  33%|███▎      | 109/333 [00:01<00:02, 93.86frame/s]

   🔥 500:  36%|███▌      | 119/333 [00:01<00:02, 93.11frame/s]

   🔥 500:  39%|███▊      | 129/333 [00:01<00:02, 90.46frame/s]

   🔥 500:  42%|████▏     | 139/333 [00:01<00:02, 88.25frame/s]

   🔥 500:  44%|████▍     | 148/333 [00:01<00:02, 86.89frame/s]

   🔥 500:  47%|████▋     | 157/333 [00:01<00:02, 86.13frame/s]

   🔥 500:  50%|████▉     | 166/333 [00:01<00:01, 85.58frame/s]

   🔥 500:  53%|█████▎    | 175/333 [00:01<00:01, 86.56frame/s]

   🔥 500:  56%|█████▌    | 185/333 [00:02<00:01, 87.90frame/s]

   🔥 500:  59%|█████▊    | 195/333 [00:02<00:01, 88.80frame/s]

   🔥 500:  61%|██████▏   | 204/333 [00:02<00:01, 88.95frame/s]

   🔥 500:  64%|██████▍   | 213/333 [00:02<00:01, 87.52frame/s]

   🔥 500:  67%|██████▋   | 222/333 [00:02<00:01, 87.18frame/s]

   🔥 500:  69%|██████▉   | 231/333 [00:02<00:01, 85.92frame/s]

   🔥 500:  72%|███████▏  | 240/333 [00:02<00:01, 86.04frame/s]

   🔥 500:  75%|███████▍  | 249/333 [00:02<00:00, 86.07frame/s]

   🔥 500:  78%|███████▊  | 259/333 [00:02<00:00, 88.07frame/s]

   🔥 500:  81%|████████  | 269/333 [00:03<00:00, 90.33frame/s]

   🔥 500:  84%|████████▍ | 279/333 [00:03<00:00, 92.01frame/s]

   🔥 500:  87%|████████▋ | 289/333 [00:03<00:00, 92.70frame/s]

   🔥 500:  90%|████████▉ | 299/333 [00:03<00:00, 92.58frame/s]

   🔥 500:  93%|█████████▎| 309/333 [00:03<00:00, 92.26frame/s]

   🔥 500:  96%|█████████▌| 319/333 [00:03<00:00, 91.91frame/s]

   🔥 500:  99%|█████████▉| 329/333 [00:03<00:00, 93.56frame/s]

                                                               [✅] 500: /working/MDC027_W031_2025-03-24/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC027_W031_2025-03-24/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:12<00:00,  6.01s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:12<00:00,  6.25s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC027_W031_2025-03-24/calibration/debug/5_heatmap
[🏁] Finished processing MDC027_W031_2025-03-24


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