2026-05-06T01:38:41 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC013_W008_2025-05-23 ===
2026-05-06T01:38:41 - INFO: Camera ID: 14302213
2026-05-06T01:38:41 - INFO: Wand ID: 
2026-05-06T01:38:41 - INFO: Recorded At: 2025-05-23T10:38:24Z

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

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

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 1231 stationary frames, 230 remain


→ 1000 (230 images) Phase 1:   0%|          | 0/230 [00:00<?, ?it/s]

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→ 1000 (230 images) Phase 1:  22%|██▏       | 51/230 [00:01<00:03, 48.87it/s]

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→ 1000 (230 images) Phase 1:  42%|████▏     | 97/230 [00:01<00:02, 63.78it/s]

→ 1000 (230 images) Phase 1:  46%|████▌     | 106/230 [00:02<00:02, 60.86it/s]

→ 1000 (230 images) Phase 1:  53%|█████▎    | 123/230 [00:02<00:01, 78.71it/s]

→ 1000 (230 images) Phase 1:  58%|█████▊    | 134/230 [00:02<00:01, 64.81it/s]

→ 1000 (230 images) Phase 1:  64%|██████▍   | 147/230 [00:02<00:01, 70.51it/s]

→ 1000 (230 images) Phase 1:  70%|██████▉   | 160/230 [00:02<00:00, 80.48it/s]

→ 1000 (230 images) Phase 1:  74%|███████▍  | 170/230 [00:02<00:00, 65.84it/s]

→ 1000 (230 images) Phase 1:  80%|████████  | 184/230 [00:02<00:00, 78.98it/s]

→ 1000 (230 images) Phase 1:  84%|████████▍ | 194/230 [00:03<00:00, 67.43it/s]

→ 1000 (230 images) Phase 1:  88%|████████▊ | 203/230 [00:03<00:00, 68.25it/s]

→ 1000 (230 images) Phase 1:  96%|█████████▌| 220/230 [00:03<00:00, 85.33it/s]

→ 1000 (230 images) Phase 1: 100%|██████████| 230/230 [00:03<00:00, 88.27it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/230 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/230 [00:00<00:35,  6.45it/s]

→ 1000 Phase 2: corners:   7%|▋         | 16/230 [00:00<00:02, 71.97it/s]

→ 1000 Phase 2: corners:  13%|█▎        | 29/230 [00:00<00:02, 88.57it/s]

→ 1000 Phase 2: corners:  19%|█▊        | 43/230 [00:00<00:01, 105.61it/s]

→ 1000 Phase 2: corners:  25%|██▍       | 57/230 [00:00<00:01, 114.87it/s]

→ 1000 Phase 2: corners:  30%|███       | 69/230 [00:00<00:01, 105.77it/s]

→ 1000 Phase 2: corners:  36%|███▌      | 82/230 [00:00<00:01, 109.70it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 99/230 [00:00<00:01, 122.23it/s]

→ 1000 Phase 2: corners:  49%|████▊     | 112/230 [00:01<00:01, 117.52it/s]

→ 1000 Phase 2: corners:  55%|█████▌    | 127/230 [00:01<00:00, 124.72it/s]

→ 1000 Phase 2: corners:  63%|██████▎   | 145/230 [00:01<00:00, 138.96it/s]

→ 1000 Phase 2: corners:  70%|██████▉   | 160/230 [00:01<00:00, 134.10it/s]

→ 1000 Phase 2: corners:  77%|███████▋  | 177/230 [00:01<00:00, 143.57it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 192/230 [00:01<00:00, 135.63it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 213/230 [00:01<00:00, 152.74it/s]

                                                                           [PASS2] Frame 0001263: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001264: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001265: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001266: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001267: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001268: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001269: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001270: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001271: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001272: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001273: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001265: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001266: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001268: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001272: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 7 frames fixed, 5 untrusted, 230 total


→ 1000 Phase 3: finalize:   0%|          | 0/230 [00:00<?, ?it/s]

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→ 1000 Phase 3: finalize:  20%|██        | 47/230 [00:00<00:02, 63.36it/s]

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→ 1000 Phase 3: finalize:  42%|████▏     | 97/230 [00:01<00:02, 50.72it/s]

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→ 1000 Phase 3: finalize:  47%|████▋     | 109/230 [00:01<00:02, 49.62it/s]

→ 1000 Phase 3: finalize:  50%|████▉     | 114/230 [00:02<00:02, 49.31it/s]

→ 1000 Phase 3: finalize:  52%|█████▏    | 119/230 [00:02<00:02, 49.03it/s]

→ 1000 Phase 3: finalize:  54%|█████▍    | 124/230 [00:02<00:02, 48.90it/s]

→ 1000 Phase 3: finalize:  56%|█████▌    | 129/230 [00:02<00:02, 48.85it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 134/230 [00:02<00:01, 48.73it/s]

→ 1000 Phase 3: finalize:  60%|██████    | 139/230 [00:02<00:01, 48.68it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 144/230 [00:02<00:01, 48.61it/s]

→ 1000 Phase 3: finalize:  65%|██████▍   | 149/230 [00:02<00:01, 48.69it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 154/230 [00:02<00:01, 49.04it/s]

→ 1000 Phase 3: finalize:  70%|██████▉   | 160/230 [00:03<00:01, 49.45it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 166/230 [00:03<00:01, 50.33it/s]

→ 1000 Phase 3: finalize:  75%|███████▍  | 172/230 [00:03<00:01, 51.25it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 178/230 [00:03<00:01, 51.99it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 184/230 [00:03<00:00, 52.61it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 190/230 [00:03<00:00, 53.23it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 196/230 [00:03<00:00, 53.74it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 202/230 [00:03<00:00, 54.12it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 208/230 [00:03<00:00, 54.26it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 214/230 [00:04<00:00, 54.36it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 220/230 [00:04<00:00, 54.28it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 226/230 [00:04<00:00, 53.86it/s]

                                                                           
[TIMING] 1000 — 230 frames
  Phase 1  radial detect   (parallel)  :   3.60s  (15.7 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.82s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   0.38s
  Phase 3  finalize        (sequential):   4.32s  (18.8 ms/frame)
    decode + undistort                 :   2.15s  (9.3 ms/frame)
    corner apply + refinement          :   1.04s  (4.5 ms/frame)
    segment                            :   0.92s  (4.0 ms/frame)
    smooth                             :   0.13s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.13s


Processing Rigs:  50%|█████     | 1/2 [00:14<00:14, 14.68s/it][INFO] Filtered 1185 stationary frames, 234 remain


→ 500 (234 images) Phase 1:   0%|          | 0/234 [00:00<?, ?it/s]

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→ 500 (234 images) Phase 1:  11%|█         | 26/234 [00:00<00:03, 58.56it/s]

→ 500 (234 images) Phase 1:  16%|█▌        | 37/234 [00:01<00:04, 44.32it/s]

→ 500 (234 images) Phase 1:  25%|██▌       | 59/234 [00:01<00:02, 76.36it/s]

→ 500 (234 images) Phase 1:  31%|███       | 72/234 [00:01<00:02, 57.71it/s]

→ 500 (234 images) Phase 1:  40%|███▉      | 93/234 [00:01<00:01, 82.47it/s]

→ 500 (234 images) Phase 1:  46%|████▌     | 107/234 [00:01<00:01, 63.75it/s]

→ 500 (234 images) Phase 1:  55%|█████▍    | 128/234 [00:02<00:01, 82.32it/s]

→ 500 (234 images) Phase 1:  60%|██████    | 141/234 [00:02<00:01, 65.50it/s]

→ 500 (234 images) Phase 1:  68%|██████▊   | 160/234 [00:02<00:00, 83.13it/s]

→ 500 (234 images) Phase 1:  74%|███████▎  | 172/234 [00:02<00:00, 64.52it/s]

→ 500 (234 images) Phase 1:  82%|████████▏ | 192/234 [00:02<00:00, 83.99it/s]

→ 500 (234 images) Phase 1:  88%|████████▊ | 205/234 [00:03<00:00, 66.07it/s]

→ 500 (234 images) Phase 1:  96%|█████████▌| 225/234 [00:03<00:00, 79.89it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/234 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/234 [00:00<00:52,  4.45it/s]

→ 500 Phase 2: corners:   7%|▋         | 16/234 [00:00<00:03, 59.29it/s]

→ 500 Phase 2: corners:  13%|█▎        | 30/234 [00:00<00:02, 87.06it/s]

→ 500 Phase 2: corners:  19%|█▉        | 45/234 [00:00<00:01, 105.76it/s]

→ 500 Phase 2: corners:  26%|██▌       | 60/234 [00:00<00:01, 119.24it/s]

→ 500 Phase 2: corners:  32%|███▏      | 74/234 [00:00<00:01, 118.18it/s]

→ 500 Phase 2: corners:  38%|███▊      | 89/234 [00:00<00:01, 124.21it/s]

→ 500 Phase 2: corners:  44%|████▎     | 102/234 [00:00<00:01, 117.00it/s]

→ 500 Phase 2: corners:  50%|█████     | 118/234 [00:01<00:00, 125.27it/s]

→ 500 Phase 2: corners:  57%|█████▋    | 134/234 [00:01<00:00, 131.72it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 148/234 [00:01<00:00, 129.46it/s]

→ 500 Phase 2: corners:  69%|██████▉   | 162/234 [00:01<00:00, 120.39it/s]

→ 500 Phase 2: corners:  77%|███████▋  | 180/234 [00:01<00:00, 135.91it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 197/234 [00:01<00:00, 143.02it/s]

                                                                          [PASS2] Frame 0000183: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000409: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000410: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000411: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000424: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000425: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000426: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000427: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000430: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000431: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000434: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000436: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000438: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000561: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000562: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000563: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000565: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000570: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000738: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001096: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001109: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001110: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001267: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001274: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001281: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001282: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001283: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001284: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001285: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001287: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001288: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001444: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001445: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001446: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001447: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001448: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000408: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000435: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000436: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000437: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000564: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001109: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001283: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001287: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001447: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000408: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000435: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 26 frames fixed, 27 untrusted, 234 total


→ 500 Phase 3: finalize:   0%|          | 0/234 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   3%|▎         | 7/234 [00:00<00:03, 64.07it/s]

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→ 500 Phase 3: finalize:  12%|█▏        | 28/234 [00:00<00:03, 65.15it/s]

→ 500 Phase 3: finalize:  15%|█▍        | 35/234 [00:00<00:03, 65.90it/s]

→ 500 Phase 3: finalize:  18%|█▊        | 42/234 [00:00<00:02, 66.11it/s]

→ 500 Phase 3: finalize:  21%|██        | 49/234 [00:00<00:02, 63.91it/s]

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→ 500 Phase 3: finalize:  27%|██▋       | 63/234 [00:01<00:02, 58.12it/s]

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→ 500 Phase 3: finalize:  37%|███▋      | 87/234 [00:01<00:02, 54.48it/s]

→ 500 Phase 3: finalize:  40%|███▉      | 93/234 [00:01<00:02, 53.89it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 99/234 [00:01<00:02, 53.30it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 105/234 [00:01<00:02, 52.88it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 111/234 [00:01<00:02, 52.77it/s]

→ 500 Phase 3: finalize:  50%|█████     | 117/234 [00:02<00:02, 53.16it/s]

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→ 500 Phase 3: finalize:  55%|█████▌    | 129/234 [00:02<00:01, 53.61it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 135/234 [00:02<00:01, 53.71it/s]

→ 500 Phase 3: finalize:  60%|██████    | 141/234 [00:02<00:01, 53.78it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 147/234 [00:02<00:01, 53.79it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 153/234 [00:02<00:01, 53.68it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 159/234 [00:02<00:01, 53.09it/s]

→ 500 Phase 3: finalize:  71%|███████   | 165/234 [00:02<00:01, 53.14it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 171/234 [00:03<00:01, 53.33it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 177/234 [00:03<00:01, 53.48it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 183/234 [00:03<00:00, 53.49it/s]

→ 500 Phase 3: finalize:  81%|████████  | 189/234 [00:03<00:00, 53.50it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 195/234 [00:03<00:00, 53.61it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 201/234 [00:03<00:00, 53.55it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 207/234 [00:03<00:00, 54.11it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 213/234 [00:03<00:00, 54.11it/s]

→ 500 Phase 3: finalize:  94%|█████████▎| 219/234 [00:03<00:00, 53.93it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 225/234 [00:04<00:00, 53.89it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 231/234 [00:04<00:00, 53.88it/s]

                                                                          
[TIMING] 500 — 234 frames
  Phase 1  radial detect   (parallel)  :   3.58s  (15.3 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.81s  (7.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.81s
  Phase 3  finalize        (sequential):   4.22s  (18.0 ms/frame)
    decode + undistort                 :   2.17s  (9.3 ms/frame)
    corner apply + refinement          :   0.89s  (3.8 ms/frame)
    segment                            :   0.94s  (4.0 ms/frame)
    smooth                             :   0.14s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.42s


Processing Rigs: 100%|██████████| 2/2 [00:29<00:00, 14.77s/it]
Processing Rigs: 100%|██████████| 2/2 [00:29<00:00, 14.75s/it]
[💾] Saved RigFrameDataset to /working/MDC013_W008_2025-05-23/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 464 frames from 2 rigs to /working/MDC013_W008_2025-05-23/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC013_W008_2025-05-23/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 230
[📋] 500: 100 frames selected from 234
[💾] Saved RigFrameDataset to /working/MDC013_W008_2025-05-23/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC013_W008_2025-05-23/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<02:01,  3.12s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<01:57,  3.10s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:54,  3.09s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:51,  3.09s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:48,  3.09s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:44,  3.08s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:21<01:41,  3.08s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:24<01:38,  3.09s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:27<01:36,  3.11s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:33,  3.13s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:34<01:31,  3.15s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:37<01:28,  3.16s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:40<01:24,  3.14s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:43<01:20,  3.11s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:46<01:17,  3.10s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:49<01:14,  3.08s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:52<01:10,  3.08s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:55<01:07,  3.08s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [00:58<01:04,  3.08s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:00<00:52,  2.61s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:03<00:52,  2.76s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:06<00:51,  2.86s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:09<00:49,  2.94s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:12<00:47,  2.99s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:15<00:45,  3.03s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:19<00:42,  3.05s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:22<00:40,  3.09s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:25<00:37,  3.10s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:28<00:34,  3.12s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:31<00:31,  3.13s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:34<00:28,  3.12s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:37<00:25,  3.13s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:41<00:21,  3.14s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:44<00:18,  3.13s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:47<00:15,  3.12s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:50<00:12,  3.12s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:53<00:09,  3.11s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [01:56<00:06,  3.11s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [01:59<00:03,  3.10s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:02<00:00,  3.11s/it]
                                                                                       
[✅] Casing detection complete: cx=987.0, cy=751.0

-------------------------------------------------------------------
                        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<00:26,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:26,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:25,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:25,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:25,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:25,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:24,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:24,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:24,  3.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:03, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:03, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:03, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:03, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:02, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:02, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:02, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:02, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:02, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  17%|█▋        | 17/100 [00:00<00:02, 29.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  18%|█▊        | 18/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 519.192} (0.033):  18%|█▊        | 18/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.069 | Best: {'fx': 533.333} (0.069):  19%|█▉        | 19/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.051 | Best: {'fx': 533.333} (0.069):  20%|██        | 20/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  21%|██        | 21/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  22%|██▏       | 22/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.041 | Best: {'fx': 533.333} (0.069):  23%|██▎       | 23/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.069):  24%|██▍       | 24/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 533.333} (0.069):  25%|██▌       | 25/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.024 | Best: {'fx': 533.333} (0.069):  26%|██▌       | 26/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  27%|██▋       | 27/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.069):  28%|██▊       | 28/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  29%|██▉       | 29/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.069):  30%|███       | 30/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  31%|███       | 31/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  32%|███▏      | 32/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  33%|███▎      | 33/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.069):  34%|███▍      | 34/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.069):  35%|███▌      | 35/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.069):  36%|███▌      | 36/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  37%|███▋      | 37/100 [00:00<00:01, 47.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  38%|███▊      | 38/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.069):  38%|███▊      | 38/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.069):  39%|███▉      | 39/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.069):  40%|████      | 40/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.069):  41%|████      | 41/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.069):  42%|████▏     | 42/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.069):  43%|████▎     | 43/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.069):  44%|████▍     | 44/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.069):  45%|████▌     | 45/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.069):  46%|████▌     | 46/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.069):  47%|████▋     | 47/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  48%|████▊     | 48/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.069):  49%|████▉     | 49/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.069):  50%|█████     | 50/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.069):  51%|█████     | 51/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.069):  52%|█████▏    | 52/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  53%|█████▎    | 53/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.069):  54%|█████▍    | 54/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  55%|█████▌    | 55/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  56%|█████▌    | 56/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  57%|█████▋    | 57/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  58%|█████▊    | 58/100 [00:00<00:00, 92.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  59%|█████▉    | 59/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  59%|█████▉    | 59/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  60%|██████    | 60/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  61%|██████    | 61/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.069):  62%|██████▏   | 62/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  63%|██████▎   | 63/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  64%|██████▍   | 64/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  65%|██████▌   | 65/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  66%|██████▌   | 66/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  67%|██████▋   | 67/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  68%|██████▊   | 68/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  69%|██████▉   | 69/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  70%|███████   | 70/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  71%|███████   | 71/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  72%|███████▏  | 72/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  73%|███████▎  | 73/100 [00:00<00:00, 123.14it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  74%|███████▍  | 74/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  74%|███████▍  | 74/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  75%|███████▌  | 75/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  76%|███████▌  | 76/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  77%|███████▋  | 77/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  78%|███████▊  | 78/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  79%|███████▉  | 79/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  80%|████████  | 80/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  81%|████████  | 81/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  82%|████████▏ | 82/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  83%|████████▎ | 83/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  84%|████████▍ | 84/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  85%|████████▌ | 85/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  86%|████████▌ | 86/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  87%|████████▋ | 87/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  88%|████████▊ | 88/100 [00:00<00:00, 129.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  89%|████████▉ | 89/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  89%|████████▉ | 89/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  90%|█████████ | 90/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  91%|█████████ | 91/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  92%|█████████▏| 92/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  93%|█████████▎| 93/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  94%|█████████▍| 94/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  95%|█████████▌| 95/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  96%|█████████▌| 96/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  97%|█████████▋| 97/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  98%|█████████▊| 98/100 [00:00<00:00, 132.74it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.069):  99%|█████████▉| 99/100 [00:00<00:00, 132.74it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.015 | Best: {'fx': 508.333} (0.015):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.015 | Best: {'fx': 508.333} (0.015):   2%|▏         | 1/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.015 | Best: {'fx': 509.354} (0.015):   2%|▏         | 1/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.017 | Best: {'fx': 510.374} (0.017):   4%|▍         | 2/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.019 | Best: {'fx': 511.395} (0.019):   6%|▌         | 3/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.024 | Best: {'fx': 513.435} (0.024):   8%|▊         | 4/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.021 | Best: {'fx': 513.435} (0.024):  10%|█         | 5/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.025 | Best: {'fx': 514.456} (0.025):  12%|█▏        | 6/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.028 | Best: {'fx': 515.476} (0.028):  14%|█▍        | 7/50 [00:00<00:05,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.029 | Best: {'fx': 516.497} (0.029):  16%|█▌        | 8/50 [00:00<00:04,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:04,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  20%|██        | 10/50 [00:00<00:04,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.042 | Best: {'fx': 523.639} (0.042):  22%|██▏       | 11/50 [00:00<00:04,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.033 | Best: {'fx': 523.639} (0.042):  24%|██▍       | 12/50 [00:00<00:04,  8.52it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.033 | Best: {'fx': 523.639} (0.042):  26%|██▌       | 13/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.036 | Best: {'fx': 523.639} (0.042):  26%|██▌       | 13/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.037 | Best: {'fx': 523.639} (0.042):  28%|██▊       | 14/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.040 | Best: {'fx': 523.639} (0.042):  30%|███       | 15/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.045 | Best: {'fx': 524.66} (0.045):  32%|███▏      | 16/50 [00:00<00:00, 68.59it/s] 
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.048 | Best: {'fx': 525.68} (0.048):  34%|███▍      | 17/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.055 | Best: {'fx': 527.721} (0.055):  36%|███▌      | 18/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.051 | Best: {'fx': 527.721} (0.055):  38%|███▊      | 19/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.059 | Best: {'fx': 528.741} (0.059):  40%|████      | 20/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.066 | Best: {'fx': 530.782} (0.066):  42%|████▏     | 21/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.062 | Best: {'fx': 530.782} (0.066):  44%|████▍     | 22/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.059 | Best: {'fx': 530.782} (0.066):  46%|████▌     | 23/50 [00:00<00:00, 68.59it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.059 | Best: {'fx': 530.782} (0.066):  48%|████▊     | 24/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.072 | Best: {'fx': 531.803} (0.072):  48%|████▊     | 24/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.071 | Best: {'fx': 531.803} (0.072):  50%|█████     | 25/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.069 | Best: {'fx': 531.803} (0.072):  52%|█████▏    | 26/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.059 | Best: {'fx': 531.803} (0.072):  54%|█████▍    | 27/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.048 | Best: {'fx': 531.803} (0.072):  56%|█████▌    | 28/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.044 | Best: {'fx': 531.803} (0.072):  58%|█████▊    | 29/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.052 | Best: {'fx': 531.803} (0.072):  60%|██████    | 30/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.042 | Best: {'fx': 531.803} (0.072):  62%|██████▏   | 31/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.041 | Best: {'fx': 531.803} (0.072):  64%|██████▍   | 32/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.038 | Best: {'fx': 531.803} (0.072):  66%|██████▌   | 33/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.036 | Best: {'fx': 531.803} (0.072):  68%|██████▊   | 34/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.035 | Best: {'fx': 531.803} (0.072):  70%|███████   | 35/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.033 | Best: {'fx': 531.803} (0.072):  72%|███████▏  | 36/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.030 | Best: {'fx': 531.803} (0.072):  74%|███████▍  | 37/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.033 | Best: {'fx': 531.803} (0.072):  76%|███████▌  | 38/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.031 | Best: {'fx': 531.803} (0.072):  78%|███████▊  | 39/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.027 | Best: {'fx': 531.803} (0.072):  80%|████████  | 40/50 [00:00<00:00, 82.68it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.027 | Best: {'fx': 531.803} (0.072):  82%|████████▏ | 41/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.029 | Best: {'fx': 531.803} (0.072):  82%|████████▏ | 41/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.026 | Best: {'fx': 531.803} (0.072):  84%|████████▍ | 42/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.028 | Best: {'fx': 531.803} (0.072):  86%|████████▌ | 43/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.024 | Best: {'fx': 531.803} (0.072):  88%|████████▊ | 44/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.023 | Best: {'fx': 531.803} (0.072):  90%|█████████ | 45/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.025 | Best: {'fx': 531.803} (0.072):  92%|█████████▏| 46/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.026 | Best: {'fx': 531.803} (0.072):  94%|█████████▍| 47/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.024 | Best: {'fx': 531.803} (0.072):  96%|█████████▌| 48/50 [00:00<00:00, 109.14it/s]
Brute | Start: {'fx': 533.333} (0.069) | Current: 0.023 | Best: {'fx': 531.803} (0.072):  98%|█████████▊| 49/50 [00:00<00:00, 109.14it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 987.0
    py: 751.0
    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: 75.4602: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 75.4602, picklable_objective(x0) = 75.4380

Best score: 75.4380: : 0iter [00:01, ?iter/s] 
Best score: 75.4380: : 1iter [00:01,  1.84s/iter]
Best score: 75.4380: : 1iter [00:01,  1.84s/iter]
Best score: 75.4380: : 2iter [00:02,  1.84s/iter]
Best score: 75.4380: : 3iter [00:02,  1.86iter/s]
Best score: 75.4380: : 3iter [00:02,  1.86iter/s]
Best score: 75.4380: : 4iter [00:02,  1.86iter/s]
Best score: 75.4380: : 5iter [00:02,  1.86iter/s]
Best score: 75.4380: : 6iter [00:02,  1.86iter/s]
Best score: 75.4380: : 7iter [00:02,  5.27iter/s]
Best score: 75.4380: : 7iter [00:02,  5.27iter/s]
Best score: 75.4380: : 8iter [00:02,  5.27iter/s]
Best score: 75.4380: : 9iter [00:02,  5.27iter/s]
Best score: 75.4380: : 10iter [00:02,  5.27iter/s]
Best score: 75.4380: : 11iter [00:02,  9.19iter/s]
Best score: 75.4380: : 11iter [00:02,  9.19iter/s]
Best score: 75.4380: : 12iter [00:02,  9.19iter/s]
Best score: 75.4380: : 13iter [00:02,  9.19iter/s]
Best score: 75.4380: : 14iter [00:02,  9.19iter/s]
Best score: 75.4380: : 15iter [00:02,  9.19iter/s]
Best score: 75.4380: : 16iter [00:02, 14.40iter/s]
Best score: 75.4380: : 16iter [00:02, 14.40iter/s]
Best score: 75.4380: : 17iter [00:02, 14.40iter/s]
Best score: 75.4380: : 18iter [00:02, 14.40iter/s]
Best score: 75.4380: : 19iter [00:02, 14.40iter/s]
Best score: 75.4380: : 20iter [00:02, 18.46iter/s]
Best score: 75.4380: : 20iter [00:02, 18.46iter/s]
Best score: 75.4380: : 21iter [00:02, 18.46iter/s]
Best score: 75.4380: : 22iter [00:02, 18.46iter/s]
Best score: 75.4380: : 23iter [00:02, 18.46iter/s]
Best score: 75.4380: : 24iter [00:02, 18.46iter/s]
Best score: 75.4380: : 25iter [00:02, 23.31iter/s]
Best score: 75.4380: : 25iter [00:02, 23.31iter/s]
Best score: 75.4380: : 26iter [00:02, 23.31iter/s]
Best score: 75.4380: : 27iter [00:02, 23.31iter/s]
Best score: 75.4380: : 28iter [00:02, 23.31iter/s]
Best score: 75.4380: : 29iter [00:02, 23.31iter/s]
Best score: 75.4380: : 30iter [00:02, 27.26iter/s]
Best score: 75.4380: : 30iter [00:02, 27.26iter/s]
Best score: 75.4380: : 31iter [00:02, 27.26iter/s]
Best score: 75.4380: : 32iter [00:02, 27.26iter/s]
Best score: 75.4380: : 33iter [00:02, 27.26iter/s]
Best score: 75.4380: : 34iter [00:02, 29.97iter/s]
Best score: 75.4380: : 34iter [00:02, 29.97iter/s]
Best score: 75.4380: : 35iter [00:02, 29.97iter/s]
Best score: 75.4380: : 36iter [00:02, 29.97iter/s]
Best score: 75.4380: : 37iter [00:02, 29.97iter/s]
Best score: 75.4380: : 38iter [00:02, 29.97iter/s]
Best score: 75.4380: : 39iter [00:02, 32.84iter/s]
Best score: 75.4380: : 39iter [00:02, 32.84iter/s]
Best score: 75.4380: : 40iter [00:02, 32.84iter/s]
Best score: 75.4380: : 41iter [00:02, 32.84iter/s]
Best score: 75.4380: : 42iter [00:03, 32.84iter/s]
Best score: 75.4380: : 43iter [00:03, 34.49iter/s]
Best score: 75.4380: : 43iter [00:03, 34.49iter/s]
Best score: 75.4380: : 44iter [00:03, 34.49iter/s]
Best score: 75.4380: : 45iter [00:03, 34.49iter/s]
Best score: 75.4380: : 46iter [00:03, 34.49iter/s]
Best score: 75.4380: : 47iter [00:03, 34.49iter/s]
Best score: 75.4380: : 48iter [00:03, 36.17iter/s]
Best score: 75.4380: : 48iter [00:03, 36.17iter/s]
[ITER 50] Best: 75.4380
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 987.0000
  py: 751.0000
  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: 75.4380: : 49iter [00:03, 36.17iter/s]
Best score: 75.4380: : 50iter [00:03, 36.17iter/s]
Best score: 75.4380: : 51iter [00:03, 36.17iter/s]
Best score: 75.4380: : 52iter [00:03, 36.93iter/s]
Best score: 75.4380: : 52iter [00:03, 36.93iter/s]
Best score: 75.4380: : 53iter [00:03, 36.93iter/s]
Best score: 75.4380: : 54iter [00:03, 36.93iter/s]
Best score: 75.4380: : 55iter [00:03, 36.93iter/s]
Best score: 75.4380: : 56iter [00:03, 36.93iter/s]
Best score: 75.4380: : 57iter [00:03, 37.93iter/s]
Best score: 75.4380: : 57iter [00:03, 37.93iter/s]
Best score: 75.4380: : 58iter [00:03, 37.93iter/s]
Best score: 75.4380: : 59iter [00:03, 37.93iter/s]
Best score: 75.4380: : 60iter [00:03, 37.93iter/s]
Best score: 75.4380: : 61iter [00:03, 38.36iter/s]
Best score: 75.4380: : 61iter [00:03, 38.36iter/s]
Best score: 75.4380: : 62iter [00:03, 38.36iter/s]
Best score: 75.4380: : 63iter [00:03, 38.36iter/s]
Best score: 75.4380: : 64iter [00:03, 38.36iter/s]
Best score: 75.4380: : 65iter [00:03, 38.36iter/s]
Best score: 75.4380: : 66iter [00:03, 38.91iter/s]
Best score: 75.4380: : 66iter [00:03, 38.91iter/s]
Best score: 75.4380: : 67iter [00:03, 38.91iter/s]
Best score: 75.4380: : 68iter [00:03, 38.91iter/s]
Best score: 75.4380: : 69iter [00:03, 38.91iter/s]
Best score: 75.4380: : 70iter [00:03, 38.94iter/s]
Best score: 75.4380: : 70iter [00:03, 38.94iter/s]
Best score: 75.4380: : 71iter [00:03, 38.94iter/s]
Best score: 75.4380: : 72iter [00:03, 38.94iter/s]
Best score: 75.4380: : 73iter [00:03, 38.94iter/s]
Best score: 75.4380: : 74iter [00:03, 38.94iter/s]
Best score: 75.4380: : 75iter [00:03, 39.34iter/s]
Best score: 75.4380: : 75iter [00:03, 39.34iter/s]
Best score: 75.4380: : 76iter [00:03, 39.34iter/s]
Best score: 75.4380: : 77iter [00:03, 39.34iter/s]
Best score: 75.4380: : 78iter [00:03, 39.34iter/s]
Best score: 75.4380: : 79iter [00:03, 39.34iter/s]
Best score: 75.4380: : 80iter [00:03, 39.56iter/s]
Best score: 75.4380: : 80iter [00:03, 39.56iter/s]
Best score: 75.4380: : 81iter [00:03, 39.56iter/s]
Best score: 75.4380: : 82iter [00:04, 39.56iter/s]
Best score: 75.4380: : 83iter [00:04, 39.56iter/s]
Best score: 75.4380: : 84iter [00:04, 39.66iter/s]
Best score: 75.4380: : 84iter [00:04, 39.66iter/s]
Best score: 75.4380: : 85iter [00:04, 39.66iter/s]
Best score: 75.4380: : 86iter [00:04, 39.66iter/s]
Best score: 75.4380: : 87iter [00:04, 39.66iter/s]
Best score: 75.4380: : 88iter [00:04, 39.37iter/s]
Best score: 75.4380: : 88iter [00:04, 39.37iter/s]
Best score: 75.4380: : 89iter [00:04, 39.37iter/s]
Best score: 75.4380: : 90iter [00:04, 39.37iter/s]
Best score: 75.4380: : 91iter [00:04, 39.37iter/s]
Best score: 75.4380: : 92iter [00:04, 39.27iter/s]
Best score: 75.4380: : 92iter [00:04, 39.27iter/s]
Best score: 75.4380: : 93iter [00:04, 39.27iter/s]
Best score: 75.4380: : 94iter [00:04, 39.27iter/s]
Best score: 75.4380: : 95iter [00:04, 39.27iter/s]
Best score: 75.4380: : 96iter [00:04, 39.27iter/s]
Best score: 75.4380: : 97iter [00:04, 39.61iter/s]
Best score: 75.4380: : 97iter [00:04, 39.61iter/s]
Best score: 75.4380: : 98iter [00:04, 39.61iter/s]
[ITER 100] Best: 75.4380
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 987.0000
  py: 751.0000
  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: 75.4380: : 99iter [00:04, 39.61iter/s]
Best score: 75.4380: : 100iter [00:04, 39.61iter/s]
Best score: 75.4380: : 101iter [00:04, 37.19iter/s]
Best score: 75.4380: : 101iter [00:04, 37.19iter/s]
Best score: 75.4380: : 102iter [00:04, 37.19iter/s]
Best score: 75.4380: : 103iter [00:04, 37.19iter/s]
Best score: 75.4380: : 104iter [00:04, 37.19iter/s]
Best score: 75.4380: : 105iter [00:04, 37.55iter/s]
Best score: 75.4380: : 105iter [00:04, 37.55iter/s]
Best score: 75.4380: : 106iter [00:04, 37.55iter/s]
Best score: 75.4380: : 107iter [00:04, 37.55iter/s]
Best score: 75.4380: : 108iter [00:04, 37.55iter/s]
Best score: 75.4380: : 109iter [00:04, 38.19iter/s]
Best score: 75.4380: : 109iter [00:04, 38.19iter/s]
Best score: 75.4380: : 110iter [00:04, 38.19iter/s]
Best score: 75.4380: : 111iter [00:04, 38.19iter/s]
Best score: 75.4380: : 112iter [00:04, 38.19iter/s]
Best score: 75.4380: : 113iter [00:04, 38.39iter/s]
Best score: 75.4380: : 113iter [00:04, 38.39iter/s]
Best score: 75.4380: : 114iter [00:04, 38.39iter/s]
Best score: 75.4380: : 115iter [00:04, 38.39iter/s]
Best score: 75.4380: : 116iter [00:04, 38.39iter/s]
Best score: 75.4380: : 117iter [00:04, 38.83iter/s]
Best score: 75.4380: : 117iter [00:04, 38.83iter/s]
Best score: 75.4380: : 118iter [00:04, 38.83iter/s]
Best score: 75.4380: : 119iter [00:04, 38.83iter/s]
Best score: 75.4380: : 120iter [00:05, 38.83iter/s]
Best score: 75.4380: : 121iter [00:05, 39.06iter/s]
Best score: 75.4380: : 121iter [00:05, 39.06iter/s]
Best score: 75.4380: : 122iter [00:05, 39.06iter/s]
Best score: 75.4380: : 123iter [00:05, 39.06iter/s]
Best score: 75.4380: : 124iter [00:05, 39.06iter/s]
Best score: 75.4380: : 125iter [00:05, 39.06iter/s]
Best score: 75.4380: : 126iter [00:05, 39.41iter/s]
Best score: 75.4380: : 126iter [00:05, 39.41iter/s]
Best score: 75.4380: : 127iter [00:05, 39.41iter/s]
Best score: 75.4380: : 128iter [00:05, 39.41iter/s]
Best score: 75.4380: : 129iter [00:05, 39.41iter/s]
Best score: 75.4380: : 130iter [00:05, 39.44iter/s]
Best score: 75.4380: : 130iter [00:05, 39.44iter/s]
Best score: 75.4380: : 131iter [00:05, 39.44iter/s]
Best score: 75.4380: : 132iter [00:05, 39.44iter/s]
Best score: 75.4380: : 133iter [00:05, 39.44iter/s]
Best score: 75.4380: : 134iter [00:05, 39.48iter/s]
Best score: 75.4380: : 134iter [00:05, 39.48iter/s]
Best score: 75.4380: : 135iter [00:05, 39.48iter/s]
Best score: 75.4380: : 136iter [00:05, 39.48iter/s]
Best score: 75.4380: : 137iter [00:05, 39.48iter/s]
Best score: 75.4380: : 138iter [00:05, 39.56iter/s]
Best score: 75.4380: : 138iter [00:05, 39.56iter/s]
Best score: 75.4380: : 139iter [00:05, 39.56iter/s]
Best score: 75.4380: : 140iter [00:05, 39.56iter/s]
Best score: 75.4380: : 141iter [00:05, 39.56iter/s]
Best score: 75.4380: : 142iter [00:05, 39.56iter/s]
Best score: 75.4380: : 142iter [00:05, 39.56iter/s]
Best score: 75.4380: : 143iter [00:05, 39.56iter/s]
Best score: 75.4380: : 144iter [00:05, 39.56iter/s]
Best score: 75.4380: : 145iter [00:05, 39.56iter/s]
Best score: 75.4380: : 146iter [00:05, 39.66iter/s]
Best score: 75.4380: : 146iter [00:05, 39.66iter/s]
Best score: 75.4380: : 147iter [00:05, 39.66iter/s]
Best score: 75.4380: : 148iter [00:05, 39.66iter/s]
[ITER 150] Best: 75.4380
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 987.0000
  py: 751.0000
  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: 75.4380: : 149iter [00:05, 39.66iter/s]
Best score: 75.4380: : 150iter [00:05, 39.65iter/s]
Best score: 75.4380: : 150iter [00:05, 39.65iter/s]
Best score: 75.4380: : 151iter [00:05, 39.65iter/s]
Best score: 75.4380: : 152iter [00:05, 39.65iter/s]
Best score: 75.4380: : 153iter [00:05, 39.65iter/s]
Best score: 75.4380: : 154iter [00:05, 32.58iter/s]
Best score: 75.4380: : 154iter [00:05, 32.58iter/s]
Best score: 75.4380: : 155iter [00:05, 32.58iter/s]
Best score: 75.4380: : 156iter [00:05, 32.58iter/s]
Best score: 75.4380: : 157iter [00:06, 32.58iter/s]
Best score: 75.4380: : 158iter [00:06, 32.58iter/s]
Best score: 75.4380: : 159iter [00:06, 34.91iter/s]
Best score: 75.4380: : 159iter [00:06, 34.91iter/s]
Best score: 75.4380: : 160iter [00:06, 34.91iter/s]
Best score: 75.4380: : 161iter [00:06, 34.91iter/s]
Best score: 75.4380: : 162iter [00:06, 34.91iter/s]
Best score: 75.4380: : 163iter [00:06, 36.16iter/s]
Best score: 75.4380: : 163iter [00:06, 36.16iter/s]
Best score: 75.4380: : 164iter [00:06, 36.16iter/s]
Best score: 75.4380: : 165iter [00:06, 36.16iter/s]
Best score: 75.4380: : 166iter [00:06, 36.16iter/s]
Best score: 75.4380: : 167iter [00:06, 36.16iter/s]
Best score: 75.4380: : 168iter [00:06, 37.53iter/s]
Best score: 75.4380: : 168iter [00:06, 37.53iter/s]
Best score: 62.9112: : 169iter [00:06, 37.53iter/s]
Best score: 62.9112: : 170iter [00:06, 37.53iter/s]
Best score: 62.9112: : 171iter [00:06, 37.53iter/s]
Best score: 62.9112: : 172iter [00:06, 38.01iter/s]
Best score: 62.9112: : 172iter [00:06, 38.01iter/s]
Best score: 62.9112: : 173iter [00:06, 38.01iter/s]
Best score: 62.9112: : 174iter [00:06, 38.01iter/s]
Best score: 62.9112: : 175iter [00:06, 38.01iter/s]
Best score: 62.9112: : 176iter [00:06, 38.01iter/s]
Best score: 62.9112: : 177iter [00:06, 38.66iter/s]
Best score: 62.9112: : 177iter [00:06, 38.66iter/s]
Best score: 62.9112: : 178iter [00:06, 38.66iter/s]
Best score: 58.5244: : 179iter [00:06, 38.66iter/s]
Best score: 58.5244: : 180iter [00:06, 38.66iter/s]
Best score: 58.5244: : 181iter [00:06, 38.96iter/s]
Best score: 58.5244: : 181iter [00:06, 38.96iter/s]
Best score: 58.5244: : 182iter [00:06, 38.96iter/s]
Best score: 58.5244: : 183iter [00:06, 38.96iter/s]
Best score: 58.5244: : 184iter [00:06, 38.96iter/s]
Best score: 58.5244: : 185iter [00:06, 38.96iter/s]
Best score: 58.5244: : 186iter [00:06, 39.48iter/s]
Best score: 58.5244: : 186iter [00:06, 39.48iter/s]
Best score: 58.5244: : 187iter [00:06, 39.48iter/s]
Best score: 58.5244: : 188iter [00:06, 39.48iter/s]
Best score: 20.4503: : 189iter [00:06, 39.48iter/s]
Best score: 20.4503: : 190iter [00:06, 39.59iter/s]
Best score: 20.4503: : 190iter [00:06, 39.59iter/s]
Best score: 20.4503: : 191iter [00:06, 39.59iter/s]
Best score: 20.4503: : 192iter [00:06, 39.59iter/s]
Best score: 20.4503: : 193iter [00:06, 39.59iter/s]
Best score: 20.4503: : 194iter [00:06, 39.59iter/s]
Best score: 20.4503: : 195iter [00:06, 39.94iter/s]
Best score: 20.4503: : 195iter [00:06, 39.94iter/s]
Best score: 20.4503: : 196iter [00:06, 39.94iter/s]
Best score: 20.4503: : 197iter [00:06, 39.94iter/s]
Best score: 20.4503: : 198iter [00:07, 39.94iter/s]
[ITER 200] Best: 20.4503
[PARAMS] Current best parameters:
  fx: 519.9659
  fy: 520.1577
  px: 993.6358
  py: 754.2199
  k1: 0.12626801
  k2: -0.07931415
  k3: 0.00274153
  k4: 0.00453811
  p1: -0.00760737
  p2: 0.01742715
  p3: -0.02628101
  p4: 0.02401817
  s1: -0.0168
  s2: -0.0076
  s3: -0.0087
  s4: -0.0067
  tilt_x: 0.0579
  tilt_y: 0.0233
  scale: 2.7288
  rotation: 0.7027

Best score: 20.4503: : 199iter [00:07, 39.94iter/s]
Best score: 20.4503: : 200iter [00:07, 40.03iter/s]
Best score: 20.4503: : 200iter [00:07, 40.03iter/s]
Best score: 20.4503: : 201iter [00:07, 40.03iter/s]
Best score: 20.4503: : 202iter [00:07, 40.03iter/s]
Best score: 20.4503: : 203iter [00:07, 40.03iter/s]
Best score: 20.4503: : 204iter [00:07, 40.03iter/s]
Best score: 20.4503: : 205iter [00:07, 33.85iter/s]
Best score: 20.4503: : 205iter [00:07, 33.85iter/s]
Best score: 20.4503: : 206iter [00:07, 33.85iter/s]
Best score: 20.4503: : 207iter [00:07, 33.85iter/s]
Best score: 20.4503: : 208iter [00:07, 33.85iter/s]
Best score: 19.7212: : 209iter [00:07, 33.85iter/s]
Best score: 19.7212: : 210iter [00:07, 35.64iter/s]
Best score: 19.7212: : 210iter [00:07, 35.64iter/s]
Best score: 19.7212: : 211iter [00:07, 35.64iter/s]
Best score: 19.7212: : 212iter [00:07, 35.64iter/s]
Best score: 19.7212: : 213iter [00:07, 35.64iter/s]
Best score: 19.7212: : 214iter [00:07, 35.64iter/s]
Best score: 19.7212: : 215iter [00:07, 37.17iter/s]
Best score: 19.7212: : 215iter [00:07, 37.17iter/s]
Best score: 19.7212: : 216iter [00:07, 37.17iter/s]
Best score: 19.7212: : 217iter [00:07, 37.17iter/s]
Best score: 19.7212: : 218iter [00:07, 37.17iter/s]
Best score: 13.6912: : 219iter [00:07, 37.17iter/s]
Best score: 13.6912: : 220iter [00:07, 38.07iter/s]
Best score: 13.6912: : 220iter [00:07, 38.07iter/s]
Best score: 13.6912: : 221iter [00:07, 38.07iter/s]
Best score: 13.6912: : 222iter [00:07, 38.07iter/s]
Best score: 13.6912: : 223iter [00:07, 38.07iter/s]
Best score: 13.6912: : 224iter [00:07, 38.07iter/s]
Best score: 13.6912: : 225iter [00:07, 39.05iter/s]
Best score: 13.6912: : 225iter [00:07, 39.05iter/s]
Best score: 13.6912: : 226iter [00:07, 39.05iter/s]
Best score: 13.6912: : 227iter [00:07, 39.05iter/s]
Best score: 13.6912: : 228iter [00:07, 39.05iter/s]
Best score: 13.6912: : 229iter [00:07, 39.05iter/s]
Best score: 13.6912: : 230iter [00:07, 39.32iter/s]
Best score: 13.6912: : 230iter [00:07, 39.32iter/s]
Best score: 13.6912: : 231iter [00:07, 39.32iter/s]
Best score: 13.6912: : 232iter [00:07, 39.32iter/s]
Best score: 13.6912: : 233iter [00:07, 39.32iter/s]
Best score: 13.6912: : 234iter [00:07, 39.32iter/s]
Best score: 13.6912: : 235iter [00:07, 39.71iter/s]
Best score: 13.6912: : 235iter [00:08, 39.71iter/s]
Best score: 13.6912: : 236iter [00:08, 39.71iter/s]
Best score: 13.6912: : 237iter [00:08, 39.71iter/s]
Best score: 13.6912: : 238iter [00:08, 39.71iter/s]
Best score: 12.6773: : 239iter [00:08, 39.71iter/s]
Best score: 12.6773: : 240iter [00:08, 39.74iter/s]
Best score: 12.6773: : 240iter [00:08, 39.74iter/s]
Best score: 12.6773: : 241iter [00:08, 39.74iter/s]
Best score: 12.6773: : 242iter [00:08, 39.74iter/s]
Best score: 12.6773: : 243iter [00:08, 39.74iter/s]
Best score: 12.6773: : 244iter [00:08, 39.74iter/s]
Best score: 12.6773: : 245iter [00:08, 40.03iter/s]
Best score: 12.6773: : 245iter [00:08, 40.03iter/s]
Best score: 12.6773: : 246iter [00:08, 40.03iter/s]
Best score: 12.6773: : 247iter [00:08, 40.03iter/s]
Best score: 12.6773: : 248iter [00:08, 40.03iter/s]
[ITER 250] Best: 10.4969
[PARAMS] Current best parameters:
  fx: 538.7925
  fy: 537.6112
  px: 991.4113
  py: 761.3796
  k1: 0.04206254
  k2: -0.03666045
  k3: 0.00397235
  k4: 0.00085926
  p1: 0.00325801
  p2: 0.01851090
  p3: -0.01930961
  p4: 0.01456074
  s1: -0.0151
  s2: -0.0035
  s3: -0.0027
  s4: 0.0027
  tilt_x: 0.0716
  tilt_y: 0.0448
  scale: 2.9448
  rotation: 0.6126

Best score: 10.4969: : 249iter [00:08, 40.03iter/s]
Best score: 10.4969: : 250iter [00:08, 39.96iter/s]
Best score: 10.4969: : 250iter [00:08, 39.96iter/s]
Best score: 10.4969: : 251iter [00:08, 39.96iter/s]
Best score: 10.4969: : 252iter [00:08, 39.96iter/s]
Best score: 10.4969: : 253iter [00:08, 39.96iter/s]
Best score: 10.4969: : 254iter [00:08, 39.96iter/s]
Best score: 10.4969: : 255iter [00:08, 33.87iter/s]
Best score: 10.4969: : 255iter [00:08, 33.87iter/s]
Best score: 10.4969: : 256iter [00:08, 33.87iter/s]
Best score: 10.4969: : 257iter [00:08, 33.87iter/s]
Best score: 10.4969: : 258iter [00:08, 33.87iter/s]
Best score: 10.4969: : 259iter [00:08, 33.87iter/s]
Best score: 10.4969: : 260iter [00:08, 35.57iter/s]
Best score: 10.4969: : 260iter [00:08, 35.57iter/s]
Best score: 10.4969: : 261iter [00:08, 35.57iter/s]
Best score: 10.4969: : 262iter [00:08, 35.57iter/s]
Best score: 10.4969: : 263iter [00:08, 35.57iter/s]
Best score: 10.4969: : 264iter [00:08, 35.57iter/s]
Best score: 10.4969: : 265iter [00:08, 36.99iter/s]
Best score: 10.4969: : 265iter [00:08, 36.99iter/s]
Best score: 10.4969: : 266iter [00:08, 36.99iter/s]
Best score: 10.4969: : 267iter [00:08, 36.99iter/s]
Best score: 10.4969: : 268iter [00:08, 36.99iter/s]
Best score: 7.1690: : 269iter [00:08, 36.99iter/s] 
Best score: 7.1690: : 270iter [00:08, 37.83iter/s]
Best score: 7.1690: : 270iter [00:08, 37.83iter/s]
Best score: 7.1690: : 271iter [00:08, 37.83iter/s]
Best score: 7.1690: : 272iter [00:09, 37.83iter/s]
Best score: 7.1690: : 273iter [00:09, 37.83iter/s]
Best score: 7.1690: : 274iter [00:09, 37.83iter/s]
Best score: 7.1690: : 275iter [00:09, 38.64iter/s]
Best score: 7.1690: : 275iter [00:09, 38.64iter/s]
Best score: 7.1690: : 276iter [00:09, 38.64iter/s]
Best score: 7.1690: : 277iter [00:09, 38.64iter/s]
Best score: 7.1690: : 278iter [00:09, 38.64iter/s]
Best score: 4.9614: : 279iter [00:09, 38.64iter/s]
Best score: 4.9614: : 280iter [00:09, 39.08iter/s]
Best score: 4.9614: : 280iter [00:09, 39.08iter/s]
Best score: 4.9614: : 281iter [00:09, 39.08iter/s]
Best score: 4.9614: : 282iter [00:09, 39.08iter/s]
Best score: 4.9614: : 283iter [00:09, 39.08iter/s]
Best score: 4.9614: : 284iter [00:09, 39.08iter/s]
Best score: 4.9614: : 285iter [00:09, 39.65iter/s]
Best score: 4.9614: : 285iter [00:09, 39.65iter/s]
Best score: 4.9614: : 286iter [00:09, 39.65iter/s]
Best score: 4.9614: : 287iter [00:09, 39.65iter/s]
Best score: 4.9614: : 288iter [00:09, 39.65iter/s]
Best score: 3.7687: : 289iter [00:09, 39.65iter/s]
Best score: 3.7687: : 290iter [00:09, 39.65iter/s]
Best score: 3.7687: : 290iter [00:09, 39.65iter/s]
Best score: 3.7687: : 291iter [00:09, 39.65iter/s]
Best score: 3.7687: : 292iter [00:09, 39.65iter/s]
Best score: 3.7687: : 293iter [00:09, 39.65iter/s]
Best score: 3.7687: : 294iter [00:09, 39.65iter/s]
Best score: 3.7687: : 295iter [00:09, 40.00iter/s]
Best score: 3.7687: : 295iter [00:09, 40.00iter/s]
Best score: 3.7687: : 296iter [00:09, 40.00iter/s]
Best score: 3.7687: : 297iter [00:09, 40.00iter/s]
Best score: 3.7687: : 298iter [00:09, 40.00iter/s]
[ITER 300] Best: 2.3586
[PARAMS] Current best parameters:
  fx: 536.8030
  fy: 537.3909
  px: 986.0960
  py: 751.1924
  k1: -0.00119128
  k2: 0.00573738
  k3: -0.00715151
  k4: 0.00124826
  p1: 0.00286334
  p2: 0.00177199
  p3: -0.01278196
  p4: 0.00819579
  s1: 0.0369
  s2: -0.0217
  s3: 0.0147
  s4: -0.0094
  tilt_x: 0.0117
  tilt_y: 0.0455
  scale: 3.0228
  rotation: 0.5011

Best score: 2.3586: : 299iter [00:09, 40.00iter/s]
Best score: 2.3586: : 300iter [00:09, 40.06iter/s]
Best score: 2.3586: : 300iter [00:09, 40.06iter/s]
Best score: 2.3586: : 301iter [00:09, 40.06iter/s]
Best score: 2.3586: : 302iter [00:09, 40.06iter/s]
Best score: 2.3586: : 303iter [00:09, 40.06iter/s]
Best score: 2.3586: : 304iter [00:09, 40.06iter/s]
Best score: 2.3586: : 305iter [00:09, 34.00iter/s]
Best score: 2.3586: : 305iter [00:09, 34.00iter/s]
Best score: 2.3586: : 306iter [00:09, 34.00iter/s]
Best score: 2.3586: : 307iter [00:09, 34.00iter/s]
Best score: 2.3586: : 308iter [00:09, 34.00iter/s]
Best score: 1.8095: : 309iter [00:09, 34.00iter/s]
Best score: 1.8095: : 310iter [00:09, 35.61iter/s]
Best score: 1.8095: : 310iter [00:10, 35.61iter/s]
Best score: 1.8095: : 311iter [00:10, 35.61iter/s]
Best score: 1.8095: : 312iter [00:10, 35.61iter/s]
Best score: 1.8095: : 313iter [00:10, 35.61iter/s]
Best score: 1.8095: : 314iter [00:10, 35.61iter/s]
Best score: 1.8095: : 315iter [00:10, 37.06iter/s]
Best score: 1.8095: : 315iter [00:10, 37.06iter/s]
Best score: 1.8095: : 316iter [00:10, 37.06iter/s]
Best score: 1.8095: : 317iter [00:10, 37.06iter/s]
Best score: 1.8095: : 318iter [00:10, 37.06iter/s]
Best score: 1.8095: : 319iter [00:10, 37.06iter/s]
Best score: 1.8095: : 320iter [00:10, 37.81iter/s]
Best score: 1.8095: : 320iter [00:10, 37.81iter/s]
Best score: 1.8095: : 321iter [00:10, 37.81iter/s]
Best score: 1.8095: : 322iter [00:10, 37.81iter/s]
Best score: 1.8095: : 323iter [00:10, 37.81iter/s]
Best score: 1.8095: : 324iter [00:10, 37.81iter/s]
Best score: 1.8095: : 325iter [00:10, 38.56iter/s]
Best score: 1.8095: : 325iter [00:10, 38.56iter/s]
Best score: 1.8095: : 326iter [00:10, 38.56iter/s]
Best score: 1.8095: : 327iter [00:10, 38.56iter/s]
Best score: 1.8095: : 328iter [00:10, 38.56iter/s]
Best score: 1.8095: : 329iter [00:10, 38.56iter/s]
Best score: 1.8095: : 330iter [00:10, 39.00iter/s]
Best score: 1.8095: : 330iter [00:10, 39.00iter/s]
Best score: 1.8095: : 331iter [00:10, 39.00iter/s]
Best score: 1.8095: : 332iter [00:10, 39.00iter/s]
Best score: 1.8095: : 333iter [00:10, 39.00iter/s]
Best score: 1.8095: : 334iter [00:10, 39.00iter/s]
Best score: 1.8095: : 335iter [00:10, 39.53iter/s]
Best score: 1.8095: : 335iter [00:10, 39.53iter/s]
Best score: 1.8095: : 336iter [00:10, 39.53iter/s]
Best score: 1.8095: : 337iter [00:10, 39.53iter/s]
Best score: 1.8095: : 338iter [00:10, 39.53iter/s]
Best score: 1.2662: : 339iter [00:10, 39.53iter/s]
Best score: 1.2662: : 340iter [00:10, 39.72iter/s]
Best score: 1.2662: : 340iter [00:10, 39.72iter/s]
Best score: 1.2662: : 341iter [00:10, 39.72iter/s]
Best score: 1.2662: : 342iter [00:10, 39.72iter/s]
Best score: 1.2662: : 343iter [00:10, 39.72iter/s]
Best score: 1.2662: : 344iter [00:10, 39.72iter/s]
Best score: 1.2662: : 345iter [00:10, 40.13iter/s]
Best score: 1.2662: : 345iter [00:10, 40.13iter/s]
Best score: 1.2662: : 346iter [00:10, 40.13iter/s]
Best score: 1.2662: : 347iter [00:10, 40.13iter/s]
Best score: 1.2662: : 348iter [00:10, 40.13iter/s]
[ITER 350] Best: 1.1346
[PARAMS] Current best parameters:
  fx: 540.2157
  fy: 540.1299
  px: 981.9461
  py: 746.4389
  k1: -0.01684141
  k2: 0.00213976
  k3: 0.00555705
  k4: -0.00262607
  p1: -0.00223419
  p2: -0.00270231
  p3: -0.01142627
  p4: 0.00629935
  s1: 0.0159
  s2: -0.0110
  s3: 0.0113
  s4: -0.0049
  tilt_x: -0.0027
  tilt_y: 0.0576
  scale: 3.0704
  rotation: 0.6167

Best score: 1.1346: : 349iter [00:10, 40.13iter/s]
Best score: 1.1346: : 350iter [00:10, 40.11iter/s]
Best score: 1.1346: : 350iter [00:11, 40.11iter/s]
Best score: 1.1346: : 351iter [00:11, 40.11iter/s]
Best score: 1.1346: : 352iter [00:11, 40.11iter/s]
Best score: 1.1346: : 353iter [00:11, 40.11iter/s]
Best score: 1.1346: : 354iter [00:11, 40.11iter/s]
Best score: 1.1346: : 355iter [00:11, 34.13iter/s]
Best score: 1.1346: : 355iter [00:11, 34.13iter/s]
Best score: 1.1346: : 356iter [00:11, 34.13iter/s]
Best score: 1.1346: : 357iter [00:11, 34.13iter/s]
Best score: 1.1346: : 358iter [00:11, 34.13iter/s]
Best score: 1.1346: : 359iter [00:11, 34.13iter/s]
Best score: 1.1346: : 360iter [00:11, 35.74iter/s]
Best score: 1.1346: : 360iter [00:11, 35.74iter/s]
Best score: 1.1346: : 361iter [00:11, 35.74iter/s]
Best score: 1.1346: : 362iter [00:11, 35.74iter/s]
Best score: 1.1346: : 363iter [00:11, 35.74iter/s]
Best score: 1.1346: : 364iter [00:11, 30.37iter/s]
Best score: 1.1346: : 364iter [00:11, 30.37iter/s]
Best score: 1.1346: : 365iter [00:11, 30.37iter/s]
Best score: 1.1346: : 366iter [00:11, 30.37iter/s]
Best score: 1.1346: : 367iter [00:11, 30.37iter/s]
Best score: 1.1346: : 368iter [00:11, 30.37iter/s]
Best score: 1.1346: : 369iter [00:11, 33.04iter/s]
Best score: 0.9180: : 369iter [00:11, 33.04iter/s]
Best score: 0.9180: : 370iter [00:11, 33.04iter/s]
Best score: 0.9180: : 371iter [00:11, 33.04iter/s]
Best score: 0.9180: : 372iter [00:11, 33.04iter/s]
Best score: 0.9180: : 373iter [00:11, 34.54iter/s]
Best score: 0.9180: : 373iter [00:11, 34.54iter/s]
Best score: 0.9180: : 374iter [00:11, 34.54iter/s]
Best score: 0.9180: : 375iter [00:11, 34.54iter/s]
Best score: 0.9180: : 376iter [00:11, 34.54iter/s]
Best score: 0.9180: : 377iter [00:11, 34.54iter/s]
Best score: 0.9180: : 378iter [00:11, 36.37iter/s]
Best score: 0.9180: : 378iter [00:11, 36.37iter/s]
Best score: 0.8477: : 379iter [00:11, 36.37iter/s]
Best score: 0.8477: : 380iter [00:11, 36.37iter/s]
Best score: 0.8477: : 381iter [00:11, 36.37iter/s]
Best score: 0.8477: : 382iter [00:11, 37.22iter/s]
Best score: 0.8477: : 382iter [00:11, 37.22iter/s]
Best score: 0.8477: : 383iter [00:11, 37.22iter/s]
Best score: 0.8477: : 384iter [00:12, 37.22iter/s]
Best score: 0.8477: : 385iter [00:12, 37.22iter/s]
Best score: 0.8477: : 386iter [00:12, 37.22iter/s]
Best score: 0.8477: : 387iter [00:12, 38.18iter/s]
Best score: 0.8477: : 387iter [00:12, 38.18iter/s]
Best score: 0.8477: : 388iter [00:12, 38.18iter/s]
Best score: 0.8477: : 389iter [00:12, 38.18iter/s]
Best score: 0.8477: : 390iter [00:12, 38.18iter/s]
Best score: 0.8477: : 391iter [00:12, 38.60iter/s]
Best score: 0.8477: : 391iter [00:12, 38.60iter/s]
Best score: 0.8477: : 392iter [00:12, 38.60iter/s]
Best score: 0.8477: : 393iter [00:12, 38.60iter/s]
Best score: 0.8477: : 394iter [00:12, 38.60iter/s]
Best score: 0.8477: : 395iter [00:12, 38.60iter/s]
Best score: 0.8477: : 396iter [00:12, 39.40iter/s]
Best score: 0.8477: : 396iter [00:12, 39.40iter/s]
Best score: 0.8477: : 397iter [00:12, 39.40iter/s]
Best score: 0.8477: : 398iter [00:12, 39.40iter/s]
[ITER 400] Best: 0.8097
[PARAMS] Current best parameters:
  fx: 534.1081
  fy: 533.6334
  px: 989.0327
  py: 744.3311
  k1: 0.00913251
  k2: -0.01385192
  k3: 0.00821305
  k4: -0.00249695
  p1: -0.00169474
  p2: -0.00018702
  p3: -0.01047998
  p4: 0.00069697
  s1: 0.0164
  s2: -0.0033
  s3: 0.0073
  s4: 0.0001
  tilt_x: -0.0126
  tilt_y: 0.0296
  scale: 3.0248
  rotation: 0.3931

Best score: 0.8097: : 399iter [00:12, 39.40iter/s]
Best score: 0.8097: : 400iter [00:12, 39.38iter/s]
Best score: 0.8097: : 400iter [00:12, 39.38iter/s]
Best score: 0.8097: : 401iter [00:12, 39.38iter/s]
Best score: 0.8097: : 402iter [00:12, 39.38iter/s]
Best score: 0.8097: : 403iter [00:12, 39.38iter/s]
Best score: 0.8097: : 404iter [00:12, 32.80iter/s]
Best score: 0.8097: : 404iter [00:12, 32.80iter/s]
Best score: 0.8097: : 405iter [00:12, 32.80iter/s]
Best score: 0.8097: : 406iter [00:12, 32.80iter/s]
Best score: 0.8097: : 407iter [00:12, 32.80iter/s]
Best score: 0.8097: : 408iter [00:12, 32.80iter/s]
Best score: 0.8097: : 409iter [00:12, 34.96iter/s]
Best score: 0.8097: : 409iter [00:12, 34.96iter/s]
Best score: 0.8097: : 410iter [00:12, 34.96iter/s]
Best score: 0.8097: : 411iter [00:12, 34.96iter/s]
Best score: 0.8097: : 412iter [00:12, 34.96iter/s]
Best score: 0.8097: : 413iter [00:12, 34.96iter/s]
Best score: 0.8097: : 414iter [00:12, 36.52iter/s]
Best score: 0.8097: : 414iter [00:12, 36.52iter/s]
Best score: 0.8097: : 415iter [00:12, 36.52iter/s]
Best score: 0.8097: : 416iter [00:12, 36.52iter/s]
Best score: 0.8097: : 417iter [00:12, 36.52iter/s]
Best score: 0.8097: : 418iter [00:12, 36.52iter/s]
Best score: 0.8097: : 419iter [00:12, 37.89iter/s]
Best score: 0.6677: : 419iter [00:12, 37.89iter/s]
Best score: 0.6677: : 420iter [00:12, 37.89iter/s]
Best score: 0.6677: : 421iter [00:13, 37.89iter/s]
Best score: 0.6677: : 422iter [00:13, 37.89iter/s]
Best score: 0.6677: : 423iter [00:13, 38.33iter/s]
Best score: 0.6677: : 423iter [00:13, 38.33iter/s]
Best score: 0.6677: : 424iter [00:13, 38.33iter/s]
Best score: 0.6677: : 425iter [00:13, 38.33iter/s]
Best score: 0.6677: : 426iter [00:13, 38.33iter/s]
Best score: 0.6677: : 427iter [00:13, 38.33iter/s]
Best score: 0.6677: : 428iter [00:13, 38.89iter/s]
Best score: 0.6677: : 428iter [00:13, 38.89iter/s]
Best score: 0.6055: : 429iter [00:13, 38.89iter/s]
Best score: 0.6055: : 430iter [00:13, 38.89iter/s]
Best score: 0.6055: : 431iter [00:13, 38.89iter/s]
Best score: 0.6055: : 432iter [00:13, 39.11iter/s]
Best score: 0.6055: : 432iter [00:13, 39.11iter/s]
Best score: 0.6055: : 433iter [00:13, 39.11iter/s]
Best score: 0.6055: : 434iter [00:13, 39.11iter/s]
Best score: 0.6055: : 435iter [00:13, 39.11iter/s]
Best score: 0.6055: : 436iter [00:13, 39.11iter/s]
Best score: 0.6055: : 437iter [00:13, 39.69iter/s]
Best score: 0.6055: : 437iter [00:13, 39.69iter/s]
Best score: 0.6055: : 438iter [00:13, 39.69iter/s]
Best score: 0.5203: : 439iter [00:13, 39.69iter/s]
Best score: 0.5203: : 440iter [00:13, 39.69iter/s]
Best score: 0.5203: : 441iter [00:13, 39.53iter/s]
Best score: 0.5203: : 441iter [00:13, 39.53iter/s]
Best score: 0.5203: : 442iter [00:13, 39.53iter/s]
Best score: 0.5203: : 443iter [00:13, 39.53iter/s]
Best score: 0.5203: : 444iter [00:13, 39.53iter/s]
Best score: 0.5203: : 445iter [00:13, 39.53iter/s]
Best score: 0.5203: : 446iter [00:13, 39.90iter/s]
Best score: 0.5203: : 446iter [00:13, 39.90iter/s]
Best score: 0.5203: : 447iter [00:13, 39.90iter/s]
Best score: 0.5203: : 448iter [00:13, 39.90iter/s]
[ITER 450] Best: 0.5203
[PARAMS] Current best parameters:
  fx: 526.5811
  fy: 526.1368
  px: 994.3019
  py: 747.1269
  k1: 0.00346296
  k2: -0.00685771
  k3: 0.01087861
  k4: -0.00417862
  p1: -0.00081019
  p2: 0.00149018
  p3: -0.01017277
  p4: 0.00332882
  s1: 0.0192
  s2: -0.0053
  s3: 0.0085
  s4: -0.0028
  tilt_x: -0.0013
  tilt_y: 0.0047
  scale: 3.0538
  rotation: 0.4798

Best score: 0.5203: : 449iter [00:13, 39.90iter/s]
Best score: 0.5203: : 450iter [00:13, 39.80iter/s]
Best score: 0.5203: : 450iter [00:13, 39.80iter/s]
Best score: 0.5203: : 451iter [00:13, 39.80iter/s]
Best score: 0.5203: : 452iter [00:13, 39.80iter/s]
Best score: 0.5203: : 453iter [00:13, 39.80iter/s]
Best score: 0.5203: : 454iter [00:13, 32.98iter/s]
Best score: 0.5203: : 454iter [00:13, 32.98iter/s]
Best score: 0.5203: : 455iter [00:13, 32.98iter/s]
Best score: 0.5203: : 456iter [00:13, 32.98iter/s]
Best score: 0.5203: : 457iter [00:13, 32.98iter/s]
Best score: 0.5203: : 458iter [00:14, 32.98iter/s]
Best score: 0.5203: : 459iter [00:14, 35.24iter/s]
Best score: 0.5203: : 459iter [00:14, 35.24iter/s]
Best score: 0.5203: : 460iter [00:14, 35.24iter/s]
Best score: 0.5203: : 461iter [00:14, 35.24iter/s]
Best score: 0.5203: : 462iter [00:14, 35.24iter/s]
Best score: 0.5203: : 463iter [00:14, 36.41iter/s]
Best score: 0.5203: : 463iter [00:14, 36.41iter/s]
Best score: 0.5203: : 464iter [00:14, 36.41iter/s]
Best score: 0.5203: : 465iter [00:14, 36.41iter/s]
Best score: 0.5203: : 466iter [00:14, 36.41iter/s]
Best score: 0.5203: : 467iter [00:14, 36.41iter/s]
Best score: 0.5203: : 468iter [00:14, 37.78iter/s]
Best score: 0.5203: : 468iter [00:14, 37.78iter/s]
Best score: 0.5203: : 469iter [00:14, 37.78iter/s]
Best score: 0.5203: : 470iter [00:14, 37.78iter/s]
Best score: 0.5203: : 471iter [00:14, 37.78iter/s]
Best score: 0.5203: : 472iter [00:14, 38.27iter/s]
Best score: 0.5203: : 472iter [00:14, 38.27iter/s]
Best score: 0.5203: : 473iter [00:14, 38.27iter/s]
Best score: 0.5203: : 474iter [00:14, 38.27iter/s]
Best score: 0.5203: : 475iter [00:14, 38.27iter/s]
Best score: 0.5203: : 476iter [00:14, 38.27iter/s]
Best score: 0.5203: : 477iter [00:14, 38.93iter/s]
Best score: 0.5203: : 477iter [00:14, 38.93iter/s]
Best score: 0.5203: : 478iter [00:14, 38.93iter/s]
Best score: 0.4966: : 479iter [00:14, 38.93iter/s]
Best score: 0.4966: : 480iter [00:14, 38.93iter/s]
Best score: 0.4966: : 481iter [00:14, 39.04iter/s]
Best score: 0.4966: : 481iter [00:14, 39.04iter/s]
Best score: 0.4966: : 482iter [00:14, 39.04iter/s]
Best score: 0.4966: : 483iter [00:14, 39.04iter/s]
Best score: 0.4966: : 484iter [00:14, 39.04iter/s]
Best score: 0.4966: : 485iter [00:14, 39.29iter/s]
Best score: 0.4966: : 485iter [00:14, 39.29iter/s]
Best score: 0.4966: : 486iter [00:14, 39.29iter/s]
Best score: 0.4966: : 487iter [00:14, 39.29iter/s]
Best score: 0.4966: : 488iter [00:14, 39.29iter/s]
Best score: 0.4375: : 489iter [00:14, 39.29iter/s]
Best score: 0.4375: : 490iter [00:14, 39.41iter/s]
Best score: 0.4375: : 490iter [00:14, 39.41iter/s]
Best score: 0.4375: : 491iter [00:14, 39.41iter/s]
Best score: 0.4375: : 492iter [00:14, 39.41iter/s]
Best score: 0.4375: : 493iter [00:14, 39.41iter/s]
Best score: 0.4375: : 494iter [00:14, 39.41iter/s]
Best score: 0.4375: : 495iter [00:14, 39.84iter/s]
Best score: 0.4375: : 495iter [00:14, 39.84iter/s]
Best score: 0.4375: : 496iter [00:14, 39.84iter/s]
Best score: 0.4375: : 497iter [00:14, 39.84iter/s]
Best score: 0.4375: : 498iter [00:15, 39.84iter/s]
[ITER 500] Best: 0.4375
[PARAMS] Current best parameters:
  fx: 530.8932
  fy: 530.6830
  px: 990.8219
  py: 748.0389
  k1: 0.00049878
  k2: 0.00146610
  k3: 0.00154286
  k4: -0.00159845
  p1: -0.00282006
  p2: 0.00041223
  p3: -0.01088339
  p4: 0.00307339
  s1: 0.0178
  s2: -0.0048
  s3: 0.0113
  s4: -0.0027
  tilt_x: 0.0083
  tilt_y: 0.0207
  scale: 3.0310
  rotation: 0.5058

Best score: 0.4375: : 499iter [00:15, 39.84iter/s]
Best score: 0.4375: : 500iter [00:15, 39.91iter/s]
Best score: 0.4375: : 500iter [00:15, 39.91iter/s]
Best score: 0.4375: : 501iter [00:15, 39.91iter/s]
Best score: 0.4375: : 502iter [00:15, 39.91iter/s]
Best score: 0.4375: : 503iter [00:15, 39.91iter/s]
Best score: 0.4375: : 504iter [00:15, 33.38iter/s]
Best score: 0.4375: : 504iter [00:15, 33.38iter/s]
Best score: 0.4375: : 505iter [00:15, 33.38iter/s]
Best score: 0.4375: : 506iter [00:15, 33.38iter/s]
Best score: 0.4375: : 507iter [00:15, 33.38iter/s]
Best score: 0.4375: : 508iter [00:15, 33.38iter/s]
Best score: 0.4375: : 509iter [00:15, 35.45iter/s]
Best score: 0.4375: : 509iter [00:15, 35.45iter/s]
Best score: 0.4375: : 510iter [00:15, 35.45iter/s]
Best score: 0.4375: : 511iter [00:15, 35.45iter/s]
Best score: 0.4375: : 512iter [00:15, 35.45iter/s]
Best score: 0.4375: : 513iter [00:15, 36.47iter/s]
Best score: 0.4375: : 513iter [00:15, 36.47iter/s]
Best score: 0.4375: : 514iter [00:15, 36.47iter/s]
Best score: 0.4375: : 515iter [00:15, 36.47iter/s]
Best score: 0.4375: : 516iter [00:15, 36.47iter/s]
Best score: 0.4375: : 517iter [00:15, 36.47iter/s]
Best score: 0.4375: : 518iter [00:15, 37.70iter/s]
Best score: 0.4375: : 518iter [00:15, 37.70iter/s]
Best score: 0.4375: : 519iter [00:15, 37.70iter/s]
Best score: 0.4375: : 520iter [00:15, 37.70iter/s]
Best score: 0.4375: : 521iter [00:15, 37.70iter/s]
Best score: 0.4375: : 522iter [00:15, 38.11iter/s]
Best score: 0.4375: : 522iter [00:15, 38.11iter/s]
Best score: 0.4375: : 523iter [00:15, 38.11iter/s]
Best score: 0.4375: : 524iter [00:15, 38.11iter/s]
Best score: 0.4375: : 525iter [00:15, 38.11iter/s]
Best score: 0.4375: : 526iter [00:15, 38.11iter/s]
Best score: 0.4375: : 527iter [00:15, 39.06iter/s]
Best score: 0.4375: : 527iter [00:15, 39.06iter/s]
Best score: 0.4375: : 528iter [00:15, 39.06iter/s]
Best score: 0.4375: : 529iter [00:15, 39.06iter/s]
Best score: 0.4375: : 530iter [00:15, 39.06iter/s]
Best score: 0.4375: : 531iter [00:15, 39.23iter/s]
Best score: 0.4375: : 531iter [00:15, 39.23iter/s]
Best score: 0.4375: : 532iter [00:15, 39.23iter/s]
Best score: 0.4375: : 533iter [00:15, 39.23iter/s]
Best score: 0.4375: : 534iter [00:15, 39.23iter/s]
Best score: 0.4375: : 535iter [00:16, 39.23iter/s]
Best score: 0.4375: : 536iter [00:16, 39.73iter/s]
Best score: 0.4375: : 536iter [00:16, 39.73iter/s]
Best score: 0.4375: : 537iter [00:16, 39.73iter/s]
Best score: 0.4375: : 538iter [00:16, 39.73iter/s]
Best score: 0.4375: : 539iter [00:16, 39.73iter/s]
Best score: 0.4375: : 540iter [00:16, 39.80iter/s]
Best score: 0.4375: : 540iter [00:16, 39.80iter/s]
Best score: 0.4375: : 541iter [00:16, 39.80iter/s]
Best score: 0.4375: : 542iter [00:16, 39.80iter/s]
Best score: 0.4375: : 543iter [00:16, 39.80iter/s]
Best score: 0.4375: : 544iter [00:16, 39.80iter/s]
Best score: 0.4375: : 545iter [00:16, 40.15iter/s]
Best score: 0.4375: : 545iter [00:16, 40.15iter/s]
Best score: 0.4375: : 546iter [00:16, 40.15iter/s]
Best score: 0.4375: : 547iter [00:16, 40.15iter/s]
Best score: 0.4375: : 548iter [00:16, 40.15iter/s]
[ITER 550] Best: 0.4375
[PARAMS] Current best parameters:
  fx: 530.8932
  fy: 530.6830
  px: 990.8219
  py: 748.0389
  k1: 0.00049878
  k2: 0.00146610
  k3: 0.00154286
  k4: -0.00159845
  p1: -0.00282006
  p2: 0.00041223
  p3: -0.01088339
  p4: 0.00307339
  s1: 0.0178
  s2: -0.0048
  s3: 0.0113
  s4: -0.0027
  tilt_x: 0.0083
  tilt_y: 0.0207
  scale: 3.0310
  rotation: 0.5058

Best score: 0.4375: : 549iter [00:16, 40.15iter/s]
Best score: 0.4375: : 550iter [00:16, 40.10iter/s]
Best score: 0.4375: : 550iter [00:16, 40.10iter/s]
Best score: 0.4375: : 551iter [00:16, 40.10iter/s]
Best score: 0.4375: : 552iter [00:16, 40.10iter/s]
Best score: 0.4375: : 553iter [00:16, 40.10iter/s]
Best score: 0.4375: : 554iter [00:16, 40.10iter/s]
Best score: 0.4375: : 555iter [00:16, 33.78iter/s]
Best score: 0.4375: : 555iter [00:16, 33.78iter/s]
Best score: 0.4375: : 556iter [00:16, 33.78iter/s]
Best score: 0.4375: : 557iter [00:16, 33.78iter/s]
Best score: 0.4375: : 558iter [00:16, 33.78iter/s]
Best score: 0.4136: : 559iter [00:16, 33.78iter/s]
Best score: 0.4136: : 560iter [00:16, 35.48iter/s]
Best score: 0.4136: : 560iter [00:16, 35.48iter/s]
Best score: 0.4136: : 561iter [00:16, 35.48iter/s]
Best score: 0.4136: : 562iter [00:16, 35.48iter/s]
Best score: 0.4136: : 563iter [00:16, 35.48iter/s]
Best score: 0.4136: : 564iter [00:16, 35.48iter/s]
Best score: 0.4136: : 565iter [00:16, 37.07iter/s]
Best score: 0.4136: : 565iter [00:16, 37.07iter/s]
Best score: 0.4136: : 566iter [00:16, 37.07iter/s]
Best score: 0.4136: : 567iter [00:16, 37.07iter/s]
Best score: 0.4136: : 568iter [00:16, 37.07iter/s]
Best score: 0.4068: : 569iter [00:16, 37.07iter/s]
Best score: 0.4068: : 570iter [00:16, 38.06iter/s]
Best score: 0.4068: : 570iter [00:16, 38.06iter/s]
Best score: 0.4068: : 571iter [00:16, 38.06iter/s]
Best score: 0.4068: : 572iter [00:16, 38.06iter/s]
Best score: 0.4068: : 573iter [00:17, 38.06iter/s]
Best score: 0.4068: : 574iter [00:17, 38.06iter/s]
Best score: 0.4068: : 575iter [00:17, 38.87iter/s]
Best score: 0.4068: : 575iter [00:17, 38.87iter/s]
Best score: 0.4068: : 576iter [00:17, 38.87iter/s]
Best score: 0.4068: : 577iter [00:17, 38.87iter/s]
Best score: 0.4068: : 578iter [00:17, 38.87iter/s]
Best score: 0.3956: : 579iter [00:17, 38.87iter/s]
Best score: 0.3956: : 580iter [00:17, 39.17iter/s]
Best score: 0.3956: : 580iter [00:17, 39.17iter/s]
Best score: 0.3956: : 581iter [00:17, 39.17iter/s]
Best score: 0.3956: : 582iter [00:17, 39.17iter/s]
Best score: 0.3956: : 583iter [00:17, 39.17iter/s]
Best score: 0.3956: : 584iter [00:17, 39.17iter/s]
Best score: 0.3956: : 585iter [00:17, 39.62iter/s]
Best score: 0.3956: : 585iter [00:17, 39.62iter/s]
Best score: 0.3956: : 586iter [00:17, 39.62iter/s]
Best score: 0.3956: : 587iter [00:17, 39.62iter/s]
Best score: 0.3956: : 588iter [00:17, 39.62iter/s]
Best score: 0.3822: : 589iter [00:17, 39.62iter/s]
Best score: 0.3822: : 590iter [00:17, 39.61iter/s]
Best score: 0.3822: : 590iter [00:17, 39.61iter/s]
Best score: 0.3822: : 591iter [00:17, 39.61iter/s]
Best score: 0.3822: : 592iter [00:17, 39.61iter/s]
Best score: 0.3822: : 593iter [00:17, 39.61iter/s]
Best score: 0.3822: : 594iter [00:17, 39.61iter/s]
Best score: 0.3822: : 595iter [00:17, 39.87iter/s]
Best score: 0.3822: : 595iter [00:17, 39.87iter/s]
Best score: 0.3822: : 596iter [00:17, 39.87iter/s]
Best score: 0.3822: : 597iter [00:17, 39.87iter/s]
Best score: 0.3822: : 598iter [00:17, 39.87iter/s]
[ITER 600] Best: 0.3822
[PARAMS] Current best parameters:
  fx: 532.0520
  fy: 531.9285
  px: 991.1561
  py: 747.6270
  k1: -0.00350384
  k2: 0.00666104
  k3: -0.00293457
  k4: -0.00026014
  p1: -0.00269621
  p2: -0.00039233
  p3: -0.01223655
  p4: 0.00420275
  s1: 0.0234
  s2: -0.0077
  s3: 0.0136
  s4: -0.0041
  tilt_x: 0.0058
  tilt_y: 0.0177
  scale: 3.0421
  rotation: 0.5037

Best score: 0.3822: : 599iter [00:17, 39.87iter/s]
Best score: 0.3822: : 600iter [00:17, 39.84iter/s]
Best score: 0.3822: : 600iter [00:17, 39.84iter/s]
Best score: 0.3822: : 601iter [00:17, 39.84iter/s]
Best score: 0.3822: : 602iter [00:17, 39.84iter/s]
Best score: 0.3822: : 603iter [00:17, 39.84iter/s]
Best score: 0.3822: : 604iter [00:17, 33.62iter/s]
Best score: 0.3822: : 604iter [00:17, 33.62iter/s]
Best score: 0.3822: : 605iter [00:17, 33.62iter/s]
Best score: 0.3822: : 606iter [00:17, 33.62iter/s]
Best score: 0.3822: : 607iter [00:17, 33.62iter/s]
Best score: 0.3822: : 608iter [00:17, 33.62iter/s]
Best score: 0.3822: : 609iter [00:17, 35.58iter/s]
Best score: 0.3809: : 609iter [00:17, 35.58iter/s]
Best score: 0.3809: : 610iter [00:18, 35.58iter/s]
Best score: 0.3809: : 611iter [00:18, 35.58iter/s]
Best score: 0.3809: : 612iter [00:18, 35.58iter/s]
Best score: 0.3809: : 613iter [00:18, 35.58iter/s]
Best score: 0.3809: : 614iter [00:18, 36.91iter/s]
Best score: 0.3809: : 614iter [00:18, 36.91iter/s]
Best score: 0.3809: : 615iter [00:18, 36.91iter/s]
Best score: 0.3809: : 616iter [00:18, 36.91iter/s]
Best score: 0.3809: : 617iter [00:18, 36.91iter/s]
Best score: 0.3809: : 618iter [00:18, 36.91iter/s]
Best score: 0.3809: : 619iter [00:18, 38.11iter/s]
Best score: 0.3757: : 619iter [00:18, 38.11iter/s]
Best score: 0.3757: : 620iter [00:18, 38.11iter/s]
Best score: 0.3757: : 621iter [00:18, 38.11iter/s]
Best score: 0.3757: : 622iter [00:18, 38.11iter/s]
Best score: 0.3757: : 623iter [00:18, 38.53iter/s]
Best score: 0.3757: : 623iter [00:18, 38.53iter/s]
Best score: 0.3757: : 624iter [00:18, 38.53iter/s]
Best score: 0.3757: : 625iter [00:18, 38.53iter/s]
Best score: 0.3757: : 626iter [00:18, 38.53iter/s]
Best score: 0.3757: : 627iter [00:18, 38.53iter/s]
Best score: 0.3757: : 628iter [00:18, 39.19iter/s]
Best score: 0.3757: : 628iter [00:18, 39.19iter/s]
Best score: 0.3757: : 629iter [00:18, 39.19iter/s]
Best score: 0.3757: : 630iter [00:18, 39.19iter/s]
Best score: 0.3757: : 631iter [00:18, 39.19iter/s]
Best score: 0.3757: : 632iter [00:18, 39.39iter/s]
Best score: 0.3757: : 632iter [00:18, 39.39iter/s]
Best score: 0.3757: : 633iter [00:18, 39.39iter/s]
Best score: 0.3757: : 634iter [00:18, 39.39iter/s]
Best score: 0.3757: : 635iter [00:18, 39.39iter/s]
Best score: 0.3757: : 636iter [00:18, 39.39iter/s]
Best score: 0.3757: : 637iter [00:18, 39.94iter/s]
Best score: 0.3757: : 637iter [00:18, 39.94iter/s]
Best score: 0.3757: : 638iter [00:18, 39.94iter/s]
Best score: 0.3757: : 639iter [00:18, 39.94iter/s]
Best score: 0.3757: : 640iter [00:18, 39.94iter/s]
Best score: 0.3757: : 641iter [00:18, 39.94iter/s]
Best score: 0.3757: : 642iter [00:18, 40.00iter/s]
Best score: 0.3757: : 642iter [00:18, 40.00iter/s]
Best score: 0.3757: : 643iter [00:18, 40.00iter/s]
Best score: 0.3757: : 644iter [00:18, 40.00iter/s]
Best score: 0.3757: : 645iter [00:18, 40.00iter/s]
Best score: 0.3757: : 646iter [00:18, 40.00iter/s]
Best score: 0.3757: : 647iter [00:18, 40.35iter/s]
Best score: 0.3757: : 647iter [00:18, 40.35iter/s]
Best score: 0.3757: : 648iter [00:18, 40.35iter/s]
[ITER 650] Best: 0.3757
[PARAMS] Current best parameters:
  fx: 533.8434
  fy: 533.6881
  px: 991.1922
  py: 747.1943
  k1: -0.00357347
  k2: 0.00418320
  k3: -0.00138907
  k4: -0.00059511
  p1: -0.00313600
  p2: 0.00026912
  p3: -0.01221197
  p4: 0.00445350
  s1: 0.0226
  s2: -0.0077
  s3: 0.0142
  s4: -0.0046
  tilt_x: 0.0041
  tilt_y: 0.0190
  scale: 3.0382
  rotation: 0.4938

Best score: 0.3757: : 649iter [00:18, 40.35iter/s]
Best score: 0.3757: : 650iter [00:19, 40.35iter/s]
Best score: 0.3757: : 651iter [00:19, 40.35iter/s]
Best score: 0.3757: : 652iter [00:19, 33.75iter/s]
Best score: 0.3757: : 652iter [00:19, 33.75iter/s]
Best score: 0.3757: : 653iter [00:19, 33.75iter/s]
Best score: 0.3757: : 654iter [00:19, 33.75iter/s]
Best score: 0.3757: : 655iter [00:19, 33.75iter/s]
Best score: 0.3757: : 656iter [00:19, 33.75iter/s]
Best score: 0.3757: : 657iter [00:19, 35.59iter/s]
Best score: 0.3757: : 657iter [00:19, 35.59iter/s]
Best score: 0.3757: : 658iter [00:19, 35.59iter/s]
Best score: 0.3757: : 659iter [00:19, 35.59iter/s]
Best score: 0.3757: : 660iter [00:19, 35.59iter/s]
Best score: 0.3757: : 661iter [00:19, 36.55iter/s]
Best score: 0.3757: : 661iter [00:19, 36.55iter/s]
Best score: 0.3757: : 662iter [00:19, 36.55iter/s]
Best score: 0.3757: : 663iter [00:19, 36.55iter/s]
Best score: 0.3757: : 664iter [00:19, 36.55iter/s]
Best score: 0.3757: : 665iter [00:19, 36.55iter/s]
Best score: 0.3757: : 666iter [00:19, 37.74iter/s]
Best score: 0.3757: : 666iter [00:19, 37.74iter/s]
Best score: 0.3757: : 667iter [00:19, 37.74iter/s]
Best score: 0.3757: : 668iter [00:19, 37.74iter/s]
Best score: 0.3742: : 669iter [00:19, 37.74iter/s]
Best score: 0.3742: : 670iter [00:19, 38.18iter/s]
Best score: 0.3742: : 670iter [00:19, 38.18iter/s]
Best score: 0.3742: : 671iter [00:19, 38.18iter/s]
Best score: 0.3742: : 672iter [00:19, 38.18iter/s]
Best score: 0.3742: : 673iter [00:19, 38.18iter/s]
Best score: 0.3742: : 674iter [00:19, 38.18iter/s]
Best score: 0.3742: : 675iter [00:19, 38.99iter/s]
Best score: 0.3742: : 675iter [00:19, 38.99iter/s]
Best score: 0.3742: : 676iter [00:19, 38.99iter/s]
Best score: 0.3742: : 677iter [00:19, 38.99iter/s]
Best score: 0.3742: : 678iter [00:19, 38.99iter/s]
Best score: 0.3729: : 679iter [00:19, 38.99iter/s]
Best score: 0.3729: : 680iter [00:19, 39.22iter/s]
Best score: 0.3729: : 680iter [00:19, 39.22iter/s]
Best score: 0.3729: : 681iter [00:19, 39.22iter/s]
Best score: 0.3729: : 682iter [00:19, 39.22iter/s]
Best score: 0.3729: : 683iter [00:19, 39.22iter/s]
Best score: 0.3729: : 684iter [00:19, 32.13iter/s]
Best score: 0.3729: : 684iter [00:20, 32.13iter/s]
Best score: 0.3729: : 685iter [00:20, 32.13iter/s]
Best score: 0.3729: : 686iter [00:20, 32.13iter/s]
Best score: 0.3729: : 687iter [00:20, 32.13iter/s]
Best score: 0.3729: : 688iter [00:20, 32.13iter/s]
Best score: 0.3729: : 689iter [00:20, 34.51iter/s]
Best score: 0.3708: : 689iter [00:20, 34.51iter/s]
Best score: 0.3708: : 690iter [00:20, 34.51iter/s]
Best score: 0.3708: : 691iter [00:20, 34.51iter/s]
Best score: 0.3708: : 692iter [00:20, 34.51iter/s]
Best score: 0.3708: : 693iter [00:20, 35.72iter/s]
Best score: 0.3708: : 693iter [00:20, 35.72iter/s]
Best score: 0.3708: : 694iter [00:20, 35.72iter/s]
Best score: 0.3708: : 695iter [00:20, 35.72iter/s]
Best score: 0.3708: : 696iter [00:20, 35.72iter/s]
Best score: 0.3708: : 697iter [00:20, 36.44iter/s]
Best score: 0.3708: : 697iter [00:20, 36.44iter/s]
Best score: 0.3708: : 698iter [00:20, 36.44iter/s]
[ITER 700] Best: 0.3708
Best score: 0.3708: : 699iter [00:20, 36.44iter/s]
[PARAMS] Current best parameters:
  fx: 532.7635
  fy: 532.6592
  px: 991.5189
  py: 746.3840
  k1: -0.00375658
  k2: 0.00598450
  k3: -0.00246594
  k4: -0.00037827
  p1: -0.00293048
  p2: -0.00025632
  p3: -0.01168430
  p4: 0.00420476
  s1: 0.0229
  s2: -0.0076
  s3: 0.0134
  s4: -0.0048
  tilt_x: -0.0003
  tilt_y: 0.0162
  scale: 3.0409
  rotation: 0.5015

Best score: 0.3708: : 700iter [00:20, 36.44iter/s]
Best score: 0.3708: : 701iter [00:20, 30.79iter/s]
Best score: 0.3708: : 701iter [00:20, 30.79iter/s]
Best score: 0.3708: : 702iter [00:20, 30.79iter/s]
Best score: 0.3708: : 703iter [00:20, 30.79iter/s]
Best score: 0.3708: : 704iter [00:20, 30.79iter/s]
Best score: 0.3708: : 705iter [00:20, 30.79iter/s]
Best score: 0.3708: : 706iter [00:20, 33.42iter/s]
Best score: 0.3708: : 706iter [00:20, 33.42iter/s]
Best score: 0.3708: : 707iter [00:20, 33.42iter/s]
Best score: 0.3708: : 708iter [00:20, 33.42iter/s]
Best score: 0.3708: : 709iter [00:20, 33.42iter/s]
Best score: 0.3708: : 710iter [00:20, 34.81iter/s]
Best score: 0.3708: : 710iter [00:20, 34.81iter/s]
Best score: 0.3708: : 711iter [00:20, 34.81iter/s]
Best score: 0.3708: : 712iter [00:20, 34.81iter/s]
Best score: 0.3708: : 713iter [00:20, 34.81iter/s]
Best score: 0.3708: : 714iter [00:20, 34.81iter/s]
Best score: 0.3708: : 715iter [00:20, 36.53iter/s]
Best score: 0.3708: : 715iter [00:20, 36.53iter/s]
Best score: 0.3708: : 716iter [00:20, 36.53iter/s]
Best score: 0.3708: : 717iter [00:20, 36.53iter/s]
Best score: 0.3708: : 718iter [00:20, 36.53iter/s]
Best score: 0.3708: : 719iter [00:20, 36.53iter/s]
Best score: 0.3708: : 720iter [00:20, 37.53iter/s]
Best score: 0.3708: : 720iter [00:20, 37.53iter/s]
Best score: 0.3708: : 721iter [00:21, 37.53iter/s]
Best score: 0.3708: : 722iter [00:21, 37.53iter/s]
Best score: 0.3708: : 723iter [00:21, 37.53iter/s]
Best score: 0.3708: : 724iter [00:21, 37.53iter/s]
Best score: 0.3708: : 725iter [00:21, 38.46iter/s]
Best score: 0.3708: : 725iter [00:21, 38.46iter/s]
Best score: 0.3708: : 726iter [00:21, 38.46iter/s]
Best score: 0.3708: : 727iter [00:21, 38.46iter/s]
Best score: 0.3708: : 728iter [00:21, 38.46iter/s]
Best score: 0.3708: : 729iter [00:21, 38.46iter/s]
Best score: 0.3708: : 730iter [00:21, 39.01iter/s]
Best score: 0.3708: : 730iter [00:21, 39.01iter/s]
Best score: 0.3708: : 731iter [00:21, 39.01iter/s]
Best score: 0.3708: : 732iter [00:21, 39.01iter/s]
Best score: 0.3708: : 733iter [00:21, 39.01iter/s]
Best score: 0.3708: : 734iter [00:21, 39.01iter/s]
Best score: 0.3708: : 735iter [00:21, 39.57iter/s]
Best score: 0.3708: : 735iter [00:21, 39.57iter/s]
Best score: 0.3708: : 736iter [00:21, 39.57iter/s]
Best score: 0.3708: : 737iter [00:21, 39.57iter/s]
Best score: 0.3708: : 738iter [00:21, 39.57iter/s]
Best score: 0.3708: : 739iter [00:21, 39.57iter/s]
Best score: 0.3708: : 740iter [00:21, 39.66iter/s]
Best score: 0.3708: : 740iter [00:21, 39.66iter/s]
Best score: 0.3708: : 741iter [00:21, 39.66iter/s]
Best score: 0.3708: : 742iter [00:21, 39.66iter/s]
Best score: 0.3708: : 743iter [00:21, 39.66iter/s]
Best score: 0.3708: : 744iter [00:21, 39.66iter/s]
Best score: 0.3708: : 745iter [00:21, 40.03iter/s]
Best score: 0.3708: : 745iter [00:21, 40.03iter/s]
Best score: 0.3708: : 746iter [00:21, 40.03iter/s]
Best score: 0.3708: : 747iter [00:21, 40.03iter/s]
Best score: 0.3708: : 748iter [00:21, 40.03iter/s]
[ITER 750] Best: 0.3707
[PARAMS] Current best parameters:
  fx: 532.7027
  fy: 532.5927
  px: 991.6378
  py: 746.1495
  k1: -0.00213487
  k2: 0.00389458
  k3: -0.00144359
  k4: -0.00056048
  p1: -0.00326770
  p2: -0.00025143
  p3: -0.01166220
  p4: 0.00435137
  s1: 0.0229
  s2: -0.0077
  s3: 0.0135
  s4: -0.0049
  tilt_x: -0.0008
  tilt_y: 0.0156
  scale: 3.0388
  rotation: 0.5034

Best score: 0.3707: : 749iter [00:21, 40.03iter/s]
Best score: 0.3707: : 750iter [00:21, 40.06iter/s]
Best score: 0.3707: : 750iter [00:21, 40.06iter/s]
Best score: 0.3707: : 751iter [00:21, 40.06iter/s]
Best score: 0.3707: : 752iter [00:21, 40.06iter/s]
Best score: 0.3707: : 753iter [00:21, 40.06iter/s]
Best score: 0.3707: : 754iter [00:21, 40.06iter/s]
Best score: 0.3707: : 755iter [00:21, 33.92iter/s]
Best score: 0.3707: : 755iter [00:21, 33.92iter/s]
Best score: 0.3707: : 756iter [00:21, 33.92iter/s]
Best score: 0.3707: : 757iter [00:21, 33.92iter/s]
Best score: 0.3707: : 758iter [00:22, 33.92iter/s]
Best score: 0.3701: : 759iter [00:22, 33.92iter/s]
Best score: 0.3701: : 760iter [00:22, 35.57iter/s]
Best score: 0.3701: : 760iter [00:22, 35.57iter/s]
Best score: 0.3701: : 761iter [00:22, 35.57iter/s]
Best score: 0.3701: : 762iter [00:22, 35.57iter/s]
Best score: 0.3701: : 763iter [00:22, 35.57iter/s]
Best score: 0.3701: : 764iter [00:22, 35.57iter/s]
Best score: 0.3701: : 765iter [00:22, 37.03iter/s]
Best score: 0.3701: : 765iter [00:22, 37.03iter/s]
Best score: 0.3701: : 766iter [00:22, 37.03iter/s]
Best score: 0.3701: : 767iter [00:22, 37.03iter/s]
Best score: 0.3701: : 768iter [00:22, 37.03iter/s]
Best score: 0.3697: : 769iter [00:22, 37.03iter/s]
Best score: 0.3697: : 770iter [00:22, 37.95iter/s]
Best score: 0.3697: : 770iter [00:22, 37.95iter/s]
Best score: 0.3697: : 771iter [00:22, 37.95iter/s]
Best score: 0.3697: : 772iter [00:22, 37.95iter/s]
Best score: 0.3697: : 773iter [00:22, 37.95iter/s]
Best score: 0.3697: : 774iter [00:22, 37.95iter/s]
Best score: 0.3697: : 775iter [00:22, 38.67iter/s]
Best score: 0.3697: : 775iter [00:22, 38.67iter/s]
Best score: 0.3697: : 776iter [00:22, 38.67iter/s]
Best score: 0.3697: : 777iter [00:22, 38.67iter/s]
Best score: 0.3697: : 778iter [00:22, 38.67iter/s]
Best score: 0.3697: : 779iter [00:22, 38.67iter/s]
Best score: 0.3697: : 780iter [00:22, 38.99iter/s]
Best score: 0.3697: : 780iter [00:22, 38.99iter/s]
Best score: 0.3697: : 781iter [00:22, 38.99iter/s]
Best score: 0.3697: : 782iter [00:22, 38.99iter/s]
Best score: 0.3697: : 783iter [00:22, 38.99iter/s]
Best score: 0.3697: : 784iter [00:22, 38.99iter/s]
Best score: 0.3697: : 785iter [00:22, 39.47iter/s]
Best score: 0.3697: : 785iter [00:22, 39.47iter/s]
Best score: 0.3697: : 786iter [00:22, 39.47iter/s]
Best score: 0.3697: : 787iter [00:22, 39.47iter/s]
Best score: 0.3697: : 788iter [00:22, 39.47iter/s]
Best score: 0.3694: : 789iter [00:22, 39.47iter/s]
Best score: 0.3694: : 790iter [00:22, 39.50iter/s]
Best score: 0.3694: : 790iter [00:22, 39.50iter/s]
Best score: 0.3694: : 791iter [00:22, 39.50iter/s]
Best score: 0.3694: : 792iter [00:22, 39.50iter/s]
Best score: 0.3694: : 793iter [00:22, 39.50iter/s]
Best score: 0.3694: : 794iter [00:22, 39.50iter/s]
Best score: 0.3694: : 795iter [00:22, 40.05iter/s]
Best score: 0.3694: : 795iter [00:22, 40.05iter/s]
Best score: 0.3694: : 796iter [00:22, 40.05iter/s]
Best score: 0.3694: : 797iter [00:22, 40.05iter/s]
Best score: 0.3694: : 798iter [00:22, 40.05iter/s]
[ITER 800] Best: 0.3694
[PARAMS] Current best parameters:
  fx: 532.8631
  fy: 532.7445
  px: 991.4574
  py: 746.5394
  k1: -0.00275589
  k2: 0.00492377
  k3: -0.00208928
  k4: -0.00043432
  p1: -0.00306308
  p2: -0.00011459
  p3: -0.01105805
  p4: 0.00427202
  s1: 0.0220
  s2: -0.0076
  s3: 0.0130
  s4: -0.0048
  tilt_x: 0.0007
  tilt_y: 0.0168
  scale: 3.0383
  rotation: 0.5009

Best score: 0.3694: : 799iter [00:23, 40.05iter/s]
Best score: 0.3694: : 800iter [00:23, 39.89iter/s]
Best score: 0.3694: : 800iter [00:23, 39.89iter/s]
Best score: 0.3694: : 801iter [00:23, 39.89iter/s]
Best score: 0.3694: : 802iter [00:23, 39.89iter/s]
Best score: 0.3694: : 803iter [00:23, 39.89iter/s]
Best score: 0.3694: : 804iter [00:23, 39.89iter/s]
Best score: 0.3694: : 805iter [00:23, 33.79iter/s]
Best score: 0.3694: : 805iter [00:23, 33.79iter/s]
Best score: 0.3694: : 806iter [00:23, 33.79iter/s]
Best score: 0.3694: : 807iter [00:23, 33.79iter/s]
Best score: 0.3694: : 808iter [00:23, 33.79iter/s]
Best score: 0.3694: : 809iter [00:23, 33.79iter/s]
Best score: 0.3694: : 810iter [00:23, 35.47iter/s]
Best score: 0.3694: : 810iter [00:23, 35.47iter/s]
Best score: 0.3694: : 811iter [00:23, 35.47iter/s]
Best score: 0.3694: : 812iter [00:23, 35.47iter/s]
Best score: 0.3694: : 813iter [00:23, 35.47iter/s]
Best score: 0.3694: : 814iter [00:23, 35.47iter/s]
Best score: 0.3694: : 815iter [00:23, 36.98iter/s]
Best score: 0.3694: : 815iter [00:23, 36.98iter/s]
Best score: 0.3694: : 816iter [00:23, 36.98iter/s]
Best score: 0.3694: : 817iter [00:23, 36.98iter/s]
Best score: 0.3694: : 818iter [00:23, 36.98iter/s]
Best score: 0.3694: : 819iter [00:23, 36.98iter/s]
Best score: 0.3694: : 820iter [00:23, 37.85iter/s]
Best score: 0.3694: : 820iter [00:23, 37.85iter/s]
Best score: 0.3694: : 821iter [00:23, 37.85iter/s]
Best score: 0.3694: : 822iter [00:23, 37.85iter/s]
Best score: 0.3694: : 823iter [00:23, 37.85iter/s]
Best score: 0.3694: : 824iter [00:23, 38.32iter/s]
Best score: 0.3694: : 824iter [00:23, 38.32iter/s]
Best score: 0.3694: : 825iter [00:23, 38.32iter/s]
Best score: 0.3694: : 826iter [00:23, 38.32iter/s]
Best score: 0.3694: : 827iter [00:23, 38.32iter/s]
Best score: 0.3694: : 828iter [00:23, 38.32iter/s]
Best score: 0.3694: : 829iter [00:23, 39.15iter/s]
Best score: 0.3694: : 829iter [00:23, 39.15iter/s]
Best score: 0.3694: : 830iter [00:23, 39.15iter/s]
Best score: 0.3694: : 831iter [00:23, 39.15iter/s]
Best score: 0.3694: : 832iter [00:23, 39.15iter/s]
Best score: 0.3694: : 833iter [00:23, 39.27iter/s]
Best score: 0.3694: : 833iter [00:23, 39.27iter/s]
Best score: 0.3694: : 834iter [00:23, 39.27iter/s]
Best score: 0.3694: : 835iter [00:23, 39.27iter/s]
Best score: 0.3694: : 836iter [00:24, 39.27iter/s]
Best score: 0.3694: : 837iter [00:24, 39.27iter/s]
Best score: 0.3694: : 838iter [00:24, 39.69iter/s]
Best score: 0.3694: : 838iter [00:24, 39.69iter/s]
Best score: 0.3694: : 839iter [00:24, 39.69iter/s]
Best score: 0.3694: : 840iter [00:24, 39.69iter/s]
Best score: 0.3694: : 841iter [00:24, 39.69iter/s]
Best score: 0.3694: : 842iter [00:24, 39.54iter/s]
Best score: 0.3694: : 842iter [00:24, 39.54iter/s]
Best score: 0.3694: : 843iter [00:24, 39.54iter/s]
Best score: 0.3694: : 844iter [00:24, 39.54iter/s]
Best score: 0.3694: : 845iter [00:24, 39.54iter/s]
Best score: 0.3694: : 846iter [00:24, 39.54iter/s]
Best score: 0.3694: : 847iter [00:24, 39.84iter/s]
Best score: 0.3694: : 847iter [00:24, 39.84iter/s]
Best score: 0.3694: : 848iter [00:24, 39.84iter/s]
[ITER 850] Best: 0.3693
[PARAMS] Current best parameters:
  fx: 532.2258
  fy: 532.1084
  px: 991.5439
  py: 746.2003
  k1: -0.00250343
  k2: 0.00522488
  k3: -0.00210784
  k4: -0.00046923
  p1: -0.00326946
  p2: -0.00040920
  p3: -0.01113072
  p4: 0.00451474
  s1: 0.0223
  s2: -0.0078
  s3: 0.0130
  s4: -0.0051
  tilt_x: -0.0006
  tilt_y: 0.0157
  scale: 3.0390
  rotation: 0.5020

Best score: 0.3693: : 849iter [00:24, 39.84iter/s]
Best score: 0.3693: : 850iter [00:24, 39.84iter/s]
Best score: 0.3693: : 851iter [00:24, 32.91iter/s]
Best score: 0.3693: : 851iter [00:24, 32.91iter/s]
Best score: 0.3693: : 852iter [00:24, 32.91iter/s]
Best score: 0.3693: : 853iter [00:24, 32.91iter/s]
Best score: 0.3693: : 854iter [00:24, 32.91iter/s]
Best score: 0.3693: : 855iter [00:24, 32.91iter/s]
Best score: 0.3693: : 856iter [00:24, 35.10iter/s]
Best score: 0.3693: : 856iter [00:24, 35.10iter/s]
Best score: 0.3693: : 857iter [00:24, 35.10iter/s]
Best score: 0.3693: : 858iter [00:24, 35.10iter/s]
Best score: 0.3689: : 859iter [00:24, 35.10iter/s]
Best score: 0.3689: : 860iter [00:24, 36.19iter/s]
Best score: 0.3689: : 860iter [00:24, 36.19iter/s]
Best score: 0.3689: : 861iter [00:24, 36.19iter/s]
Best score: 0.3689: : 862iter [00:24, 36.19iter/s]
Best score: 0.3689: : 863iter [00:24, 36.19iter/s]
Best score: 0.3689: : 864iter [00:24, 36.19iter/s]
Best score: 0.3689: : 865iter [00:24, 37.58iter/s]
Best score: 0.3689: : 865iter [00:24, 37.58iter/s]
Best score: 0.3689: : 866iter [00:24, 37.58iter/s]
Best score: 0.3689: : 867iter [00:24, 37.58iter/s]
Best score: 0.3689: : 868iter [00:24, 37.58iter/s]
Best score: 0.3688: : 869iter [00:24, 37.58iter/s]
Best score: 0.3688: : 870iter [00:24, 38.32iter/s]
Best score: 0.3688: : 870iter [00:24, 38.32iter/s]
Best score: 0.3688: : 871iter [00:24, 38.32iter/s]
Best score: 0.3688: : 872iter [00:24, 38.32iter/s]
Best score: 0.3688: : 873iter [00:25, 38.32iter/s]
Best score: 0.3688: : 874iter [00:25, 38.32iter/s]
Best score: 0.3688: : 875iter [00:25, 39.08iter/s]
Best score: 0.3688: : 875iter [00:25, 39.08iter/s]
Best score: 0.3688: : 876iter [00:25, 39.08iter/s]
Best score: 0.3688: : 877iter [00:25, 39.08iter/s]
Best score: 0.3688: : 878iter [00:25, 39.08iter/s]
Best score: 0.3688: : 879iter [00:25, 39.08iter/s]
Best score: 0.3688: : 880iter [00:25, 39.42iter/s]
Best score: 0.3688: : 880iter [00:25, 39.42iter/s]
Best score: 0.3688: : 881iter [00:25, 39.42iter/s]
Best score: 0.3688: : 882iter [00:25, 39.42iter/s]
Best score: 0.3688: : 883iter [00:25, 39.42iter/s]
Best score: 0.3688: : 884iter [00:25, 39.42iter/s]
Best score: 0.3688: : 885iter [00:25, 39.80iter/s]
Best score: 0.3688: : 885iter [00:25, 39.80iter/s]
Best score: 0.3688: : 886iter [00:25, 39.80iter/s]
Best score: 0.3688: : 887iter [00:25, 39.80iter/s]
Best score: 0.3688: : 888iter [00:25, 39.80iter/s]
Best score: 0.3688: : 889iter [00:25, 39.80iter/s]
Best score: 0.3688: : 890iter [00:25, 39.63iter/s]
Best score: 0.3688: : 890iter [00:25, 39.63iter/s]
Best score: 0.3688: : 891iter [00:25, 39.63iter/s]
Best score: 0.3688: : 892iter [00:25, 39.63iter/s]
Best score: 0.3688: : 893iter [00:25, 39.63iter/s]
Best score: 0.3688: : 894iter [00:25, 39.63iter/s]
Best score: 0.3688: : 895iter [00:25, 39.85iter/s]
Best score: 0.3688: : 895iter [00:25, 39.85iter/s]
Best score: 0.3688: : 896iter [00:25, 39.85iter/s]
Best score: 0.3688: : 897iter [00:25, 39.85iter/s]
Best score: 0.3688: : 898iter [00:25, 39.85iter/s]
[ITER 900] Best: 0.3687
[PARAMS] Current best parameters:
  fx: 532.0000
  fy: 531.9055
  px: 992.1404
  py: 746.1449
  k1: -0.00629362
  k2: 0.01047483
  k3: -0.00475409
  k4: 0.00003472
  p1: -0.00316683
  p2: -0.00096606
  p3: -0.01234982
  p4: 0.00492499
  s1: 0.0244
  s2: -0.0081
  s3: 0.0141
  s4: -0.0055
  tilt_x: -0.0011
  tilt_y: 0.0115
  scale: 3.0452
  rotation: 0.5008

Best score: 0.3687: : 899iter [00:25, 39.85iter/s]
Best score: 0.3687: : 900iter [00:25, 39.73iter/s]
Best score: 0.3687: : 900iter [00:25, 39.73iter/s]
Best score: 0.3687: : 901iter [00:25, 39.73iter/s]
Best score: 0.3687: : 902iter [00:25, 39.73iter/s]
Best score: 0.3687: : 903iter [00:25, 39.73iter/s]
Best score: 0.3687: : 904iter [00:25, 33.38iter/s]
Best score: 0.3687: : 904iter [00:25, 33.38iter/s]
Best score: 0.3687: : 905iter [00:25, 33.38iter/s]
Best score: 0.3687: : 906iter [00:25, 33.38iter/s]
Best score: 0.3687: : 907iter [00:25, 33.38iter/s]
Best score: 0.3687: : 908iter [00:25, 34.90iter/s]
Best score: 0.3687: : 908iter [00:25, 34.90iter/s]
Best score: 0.3686: : 909iter [00:25, 34.90iter/s]
Best score: 0.3686: : 910iter [00:26, 34.90iter/s]
Best score: 0.3686: : 911iter [00:26, 34.90iter/s]
Best score: 0.3686: : 912iter [00:26, 36.07iter/s]
Best score: 0.3686: : 912iter [00:26, 36.07iter/s]
Best score: 0.3686: : 913iter [00:26, 36.07iter/s]
Best score: 0.3686: : 914iter [00:26, 36.07iter/s]
Best score: 0.3686: : 915iter [00:26, 36.07iter/s]
Best score: 0.3686: : 916iter [00:26, 36.07iter/s]
Best score: 0.3686: : 917iter [00:26, 37.44iter/s]
Best score: 0.3686: : 917iter [00:26, 37.44iter/s]
Best score: 0.3686: : 918iter [00:26, 37.44iter/s]
Best score: 0.3686: : 919iter [00:26, 37.44iter/s]
Best score: 0.3686: : 920iter [00:26, 37.44iter/s]
Best score: 0.3686: : 921iter [00:26, 38.03iter/s]
Best score: 0.3686: : 921iter [00:26, 38.03iter/s]
Best score: 0.3686: : 922iter [00:26, 38.03iter/s]
Best score: 0.3686: : 923iter [00:26, 38.03iter/s]
Best score: 0.3686: : 924iter [00:26, 38.03iter/s]
Best score: 0.3686: : 925iter [00:26, 38.03iter/s]
Best score: 0.3686: : 926iter [00:26, 38.87iter/s]
Best score: 0.3686: : 926iter [00:26, 38.87iter/s]
Best score: 0.3686: : 927iter [00:26, 38.87iter/s]
Best score: 0.3686: : 928iter [00:26, 38.87iter/s]
Best score: 0.3685: : 929iter [00:26, 38.87iter/s]
Best score: 0.3685: : 930iter [00:26, 38.87iter/s]
Best score: 0.3685: : 930iter [00:26, 38.87iter/s]
Best score: 0.3685: : 931iter [00:26, 38.87iter/s]
Best score: 0.3685: : 932iter [00:26, 38.87iter/s]
Best score: 0.3685: : 933iter [00:26, 38.87iter/s]
Best score: 0.3685: : 934iter [00:26, 38.74iter/s]
Best score: 0.3685: : 934iter [00:26, 38.74iter/s]
Best score: 0.3685: : 935iter [00:26, 38.74iter/s]
Best score: 0.3685: : 936iter [00:26, 38.74iter/s]
Best score: 0.3685: : 937iter [00:26, 38.74iter/s]
Best score: 0.3685: : 938iter [00:26, 38.74iter/s]
Best score: 0.3685: : 939iter [00:26, 39.34iter/s]
Best score: 0.3683: : 939iter [00:26, 39.34iter/s]
Best score: 0.3683: : 940iter [00:26, 39.34iter/s]
Best score: 0.3683: : 941iter [00:26, 39.34iter/s]
Best score: 0.3683: : 942iter [00:26, 39.34iter/s]
Best score: 0.3683: : 943iter [00:26, 39.33iter/s]
Best score: 0.3683: : 943iter [00:26, 39.33iter/s]
Best score: 0.3683: : 944iter [00:26, 39.33iter/s]
Best score: 0.3683: : 945iter [00:26, 39.33iter/s]
Best score: 0.3683: : 946iter [00:26, 39.33iter/s]
Best score: 0.3683: : 947iter [00:26, 39.33iter/s]
Best score: 0.3683: : 948iter [00:26, 39.78iter/s]
Best score: 0.3683: : 948iter [00:26, 39.78iter/s]
[ITER 950] Best: 0.3682
[PARAMS] Current best parameters:
  fx: 532.4096
  fy: 532.2928
  px: 991.7441
  py: 746.3868
  k1: -0.01117614
  k2: 0.01669032
  k3: -0.00779223
  k4: 0.00055850
  p1: -0.00343123
  p2: -0.00068286
  p3: -0.01121888
  p4: 0.00507775
  s1: 0.0227
  s2: -0.0083
  s3: 0.0134
  s4: -0.0056
  tilt_x: 0.0006
  tilt_y: 0.0142
  scale: 3.0501
  rotation: 0.5009

Best score: 0.3682: : 949iter [00:26, 39.78iter/s]
Best score: 0.3682: : 950iter [00:27, 39.78iter/s]
Best score: 0.3682: : 951iter [00:27, 39.78iter/s]
Best score: 0.3682: : 952iter [00:27, 32.85iter/s]
Best score: 0.3682: : 952iter [00:27, 32.85iter/s]
Best score: 0.3682: : 953iter [00:27, 32.85iter/s]
Best score: 0.3682: : 954iter [00:27, 32.85iter/s]
Best score: 0.3682: : 955iter [00:27, 32.85iter/s]
Best score: 0.3682: : 956iter [00:27, 32.85iter/s]
Best score: 0.3682: : 957iter [00:27, 34.99iter/s]
Best score: 0.3682: : 957iter [00:27, 34.99iter/s]
Best score: 0.3682: : 958iter [00:27, 34.99iter/s]
Best score: 0.3680: : 959iter [00:27, 34.99iter/s]
Best score: 0.3680: : 960iter [00:27, 34.99iter/s]
Best score: 0.3680: : 961iter [00:27, 36.10iter/s]
Best score: 0.3680: : 961iter [00:27, 36.10iter/s]
Best score: 0.3680: : 962iter [00:27, 36.10iter/s]
Best score: 0.3680: : 963iter [00:27, 36.10iter/s]
Best score: 0.3680: : 964iter [00:27, 36.10iter/s]
Best score: 0.3680: : 965iter [00:27, 36.10iter/s]
Best score: 0.3680: : 966iter [00:27, 37.41iter/s]
Best score: 0.3680: : 966iter [00:27, 37.41iter/s]
Best score: 0.3680: : 967iter [00:27, 37.41iter/s]
Best score: 0.3680: : 968iter [00:27, 37.41iter/s]
Best score: 0.3677: : 969iter [00:27, 37.41iter/s]
Best score: 0.3677: : 970iter [00:27, 37.95iter/s]
Best score: 0.3677: : 970iter [00:27, 37.95iter/s]
Best score: 0.3677: : 971iter [00:27, 37.95iter/s]
Best score: 0.3677: : 972iter [00:27, 37.95iter/s]
Best score: 0.3677: : 973iter [00:27, 37.95iter/s]
Best score: 0.3677: : 974iter [00:27, 38.45iter/s]
Best score: 0.3677: : 974iter [00:27, 38.45iter/s]
Best score: 0.3677: : 975iter [00:27, 38.45iter/s]
Best score: 0.3677: : 976iter [00:27, 38.45iter/s]
Best score: 0.3677: : 977iter [00:27, 38.45iter/s]
Best score: 0.3677: : 978iter [00:27, 38.45iter/s]
Best score: 0.3677: : 979iter [00:27, 39.08iter/s]
Best score: 0.3676: : 979iter [00:27, 39.08iter/s]
Best score: 0.3676: : 980iter [00:27, 39.08iter/s]
Best score: 0.3676: : 981iter [00:27, 39.08iter/s]
Best score: 0.3676: : 982iter [00:27, 39.08iter/s]
Best score: 0.3676: : 983iter [00:27, 39.28iter/s]
Best score: 0.3676: : 983iter [00:27, 39.28iter/s]
Best score: 0.3676: : 984iter [00:27, 39.28iter/s]
Best score: 0.3676: : 985iter [00:27, 39.28iter/s]
Best score: 0.3676: : 986iter [00:27, 39.28iter/s]
Best score: 0.3676: : 987iter [00:28, 39.28iter/s]
Best score: 0.3676: : 988iter [00:28, 39.80iter/s]
Best score: 0.3676: : 988iter [00:28, 39.80iter/s]
Best score: 0.3676: : 989iter [00:28, 39.80iter/s]
Best score: 0.3676: : 990iter [00:28, 39.80iter/s]
Best score: 0.3676: : 991iter [00:28, 39.80iter/s]
Best score: 0.3676: : 992iter [00:28, 39.80iter/s]
Best score: 0.3676: : 993iter [00:28, 39.95iter/s]
Best score: 0.3676: : 993iter [00:28, 39.95iter/s]
Best score: 0.3676: : 994iter [00:28, 39.95iter/s]
Best score: 0.3676: : 995iter [00:28, 39.95iter/s]
Best score: 0.3676: : 996iter [00:28, 39.95iter/s]
Best score: 0.3676: : 997iter [00:28, 39.95iter/s]
Best score: 0.3676: : 998iter [00:28, 40.10iter/s]
Best score: 0.3676: : 998iter [00:28, 40.10iter/s]
[ITER 1000] Best: 0.3674
[PARAMS] Current best parameters:
  fx: 532.8331
  fy: 532.7188
  px: 991.9846
  py: 746.0376
  k1: -0.02061540
  k2: 0.02860839
  k3: -0.01350649
  k4: 0.00155152
  p1: -0.00474457
  p2: -0.00066446
  p3: -0.01027010
  p4: 0.00602693
  s1: 0.0220
  s2: -0.0093
  s3: 0.0137
  s4: -0.0067
  tilt_x: 0.0014
  tilt_y: 0.0130
  scale: 3.0621
  rotation: 0.4987

Best score: 0.3674: : 999iter [00:28, 40.10iter/s]
Best score: 0.3674: : 1000iter [00:28, 40.10iter/s]
Best score: 0.3674: : 1001iter [00:28, 40.10iter/s]
Best score: 0.3674: : 1002iter [00:28, 40.10iter/s]
Best score: 0.3674: : 1003iter [00:28, 28.81iter/s]
Best score: 0.3674: : 1003iter [00:28, 28.81iter/s]
Best score: 0.3674: : 1004iter [00:28, 28.81iter/s]
Best score: 0.3674: : 1005iter [00:28, 28.81iter/s]
Best score: 0.3674: : 1006iter [00:28, 28.81iter/s]
Best score: 0.3674: : 1007iter [00:28, 28.81iter/s]
Best score: 0.3674: : 1008iter [00:28, 31.67iter/s]
Best score: 0.3674: : 1008iter [00:28, 31.67iter/s]
Best score: 0.3673: : 1009iter [00:28, 31.67iter/s]
Best score: 0.3673: : 1010iter [00:28, 31.67iter/s]
Best score: 0.3673: : 1011iter [00:28, 31.67iter/s]
Best score: 0.3673: : 1012iter [00:28, 33.46iter/s]
Best score: 0.3673: : 1012iter [00:28, 33.46iter/s]
Best score: 0.3673: : 1013iter [00:28, 33.46iter/s]
Best score: 0.3673: : 1014iter [00:28, 33.46iter/s]
Best score: 0.3673: : 1015iter [00:28, 33.46iter/s]
Best score: 0.3673: : 1016iter [00:28, 33.46iter/s]
Best score: 0.3673: : 1017iter [00:28, 35.43iter/s]
Best score: 0.3673: : 1017iter [00:28, 35.43iter/s]
Best score: 0.3673: : 1018iter [00:28, 35.43iter/s]
Best score: 0.3671: : 1019iter [00:28, 35.43iter/s]
Best score: 0.3671: : 1020iter [00:28, 35.43iter/s]
Best score: 0.3671: : 1021iter [00:28, 36.48iter/s]
Best score: 0.3671: : 1021iter [00:29, 36.48iter/s]
Best score: 0.3671: : 1022iter [00:29, 36.48iter/s]
Best score: 0.3671: : 1023iter [00:29, 36.48iter/s]
Best score: 0.3671: : 1024iter [00:29, 36.48iter/s]
Best score: 0.3671: : 1025iter [00:29, 36.48iter/s]
Best score: 0.3671: : 1026iter [00:29, 37.72iter/s]
Best score: 0.3671: : 1026iter [00:29, 37.72iter/s]
Best score: 0.3671: : 1027iter [00:29, 37.72iter/s]
Best score: 0.3671: : 1028iter [00:29, 37.72iter/s]
Best score: 0.3670: : 1029iter [00:29, 37.72iter/s]
Best score: 0.3670: : 1030iter [00:29, 38.14iter/s]
Best score: 0.3670: : 1030iter [00:29, 38.14iter/s]
Best score: 0.3670: : 1031iter [00:29, 38.14iter/s]
Best score: 0.3670: : 1032iter [00:29, 38.14iter/s]
Best score: 0.3670: : 1033iter [00:29, 38.14iter/s]
Best score: 0.3670: : 1034iter [00:29, 38.14iter/s]
Best score: 0.3670: : 1035iter [00:29, 38.94iter/s]
Best score: 0.3670: : 1035iter [00:29, 38.94iter/s]
Best score: 0.3670: : 1036iter [00:29, 38.94iter/s]
Best score: 0.3670: : 1037iter [00:29, 38.94iter/s]
Best score: 0.3670: : 1038iter [00:29, 38.94iter/s]
Best score: 0.3669: : 1039iter [00:29, 38.94iter/s]
Best score: 0.3669: : 1040iter [00:29, 39.22iter/s]
Best score: 0.3669: : 1040iter [00:29, 39.22iter/s]
Best score: 0.3669: : 1041iter [00:29, 39.22iter/s]
Best score: 0.3669: : 1042iter [00:29, 39.22iter/s]
Best score: 0.3669: : 1043iter [00:29, 39.22iter/s]
Best score: 0.3669: : 1044iter [00:29, 39.22iter/s]
Best score: 0.3669: : 1045iter [00:29, 39.68iter/s]
Best score: 0.3669: : 1045iter [00:29, 39.68iter/s]
Best score: 0.3669: : 1046iter [00:29, 39.68iter/s]
Best score: 0.3669: : 1047iter [00:29, 39.68iter/s]
Best score: 0.3669: : 1048iter [00:29, 39.68iter/s]
[ITER 1050] Best: 0.3667
[PARAMS] Current best parameters:
  fx: 531.4726
  fy: 531.3647
  px: 991.6488
  py: 745.8651
  k1: -0.02240291
  k2: 0.03313629
  k3: -0.01593309
  k4: 0.00199036
  p1: -0.00501271
  p2: -0.00113590
  p3: -0.00915211
  p4: 0.00585639
  s1: 0.0210
  s2: -0.0091
  s3: 0.0126
  s4: -0.0065
  tilt_x: 0.0011
  tilt_y: 0.0137
  scale: 3.0665
  rotation: 0.4979

Best score: 0.3667: : 1049iter [00:29, 39.68iter/s]
Best score: 0.3667: : 1050iter [00:29, 39.75iter/s]
Best score: 0.3667: : 1050iter [00:29, 39.75iter/s]
Best score: 0.3667: : 1051iter [00:29, 39.75iter/s]
Best score: 0.3667: : 1052iter [00:29, 39.75iter/s]
Best score: 0.3667: : 1053iter [00:29, 39.75iter/s]
Best score: 0.3667: : 1054iter [00:29, 33.26iter/s]
Best score: 0.3667: : 1054iter [00:29, 33.26iter/s]
Best score: 0.3667: : 1055iter [00:29, 33.26iter/s]
Best score: 0.3667: : 1056iter [00:29, 33.26iter/s]
Best score: 0.3667: : 1057iter [00:29, 33.26iter/s]
Best score: 0.3667: : 1058iter [00:30, 33.26iter/s]
Best score: 0.3667: : 1059iter [00:30, 35.23iter/s]
Best score: 0.3667: : 1059iter [00:30, 35.23iter/s]
Best score: 0.3667: : 1060iter [00:30, 35.23iter/s]
Best score: 0.3667: : 1061iter [00:30, 35.23iter/s]
Best score: 0.3667: : 1062iter [00:30, 35.23iter/s]
Best score: 0.3667: : 1063iter [00:30, 36.20iter/s]
Best score: 0.3667: : 1063iter [00:30, 36.20iter/s]
Best score: 0.3667: : 1064iter [00:30, 36.20iter/s]
Best score: 0.3667: : 1065iter [00:30, 36.20iter/s]
Best score: 0.3667: : 1066iter [00:30, 36.20iter/s]
Best score: 0.3667: : 1067iter [00:30, 36.20iter/s]
Best score: 0.3667: : 1068iter [00:30, 37.52iter/s]
Best score: 0.3667: : 1068iter [00:30, 37.52iter/s]
Best score: 0.3667: : 1069iter [00:30, 37.52iter/s]
Best score: 0.3667: : 1070iter [00:30, 37.52iter/s]
Best score: 0.3667: : 1071iter [00:30, 37.52iter/s]
Best score: 0.3667: : 1072iter [00:30, 38.05iter/s]
Best score: 0.3667: : 1072iter [00:30, 38.05iter/s]
Best score: 0.3667: : 1073iter [00:30, 38.05iter/s]
Best score: 0.3667: : 1074iter [00:30, 38.05iter/s]
Best score: 0.3667: : 1075iter [00:30, 38.05iter/s]
Best score: 0.3667: : 1076iter [00:30, 38.05iter/s]
Best score: 0.3667: : 1077iter [00:30, 38.84iter/s]
Best score: 0.3667: : 1077iter [00:30, 38.84iter/s]
Best score: 0.3667: : 1078iter [00:30, 38.84iter/s]
Best score: 0.3667: : 1079iter [00:30, 38.84iter/s]
Best score: 0.3667: : 1080iter [00:30, 38.84iter/s]
Best score: 0.3667: : 1081iter [00:30, 39.10iter/s]
Best score: 0.3667: : 1081iter [00:30, 39.10iter/s]
Best score: 0.3667: : 1082iter [00:30, 39.10iter/s]
Best score: 0.3667: : 1083iter [00:30, 39.10iter/s]
Best score: 0.3667: : 1084iter [00:30, 39.10iter/s]
Best score: 0.3667: : 1085iter [00:30, 39.10iter/s]
Best score: 0.3667: : 1086iter [00:30, 39.61iter/s]
Best score: 0.3667: : 1086iter [00:30, 39.61iter/s]
Best score: 0.3667: : 1087iter [00:30, 39.61iter/s]
Best score: 0.3667: : 1088iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1089iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1090iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1090iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1091iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1092iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1093iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1094iter [00:30, 39.61iter/s]
Best score: 0.3666: : 1095iter [00:30, 40.10iter/s]
Best score: 0.3666: : 1095iter [00:30, 40.10iter/s]
Best score: 0.3666: : 1096iter [00:30, 40.10iter/s]
Best score: 0.3666: : 1097iter [00:30, 40.10iter/s]
Best score: 0.3666: : 1098iter [00:31, 40.10iter/s]
[ITER 1100] Best: 0.3664
[PARAMS] Current best parameters:
  fx: 530.5453
  fy: 530.4339
  px: 991.7321
  py: 745.6264
  k1: -0.03140606
  k2: 0.04710792
  k3: -0.02314419
  k4: 0.00330303
  p1: -0.00581973
  p2: -0.00109381
  p3: -0.00404274
  p4: 0.00495826
  s1: 0.0158
  s2: -0.0081
  s3: 0.0079
  s4: -0.0055
  tilt_x: 0.0016
  tilt_y: 0.0133
  scale: 3.0793
  rotation: 0.4896

Best score: 0.3664: : 1099iter [00:31, 40.10iter/s]
Best score: 0.3664: : 1100iter [00:31, 40.03iter/s]
Best score: 0.3664: : 1100iter [00:31, 35.35iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.366418 -> 0.366296

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,821 → 25,821 within 3500mm ring
  Ring polish did not improve score: 0.366296
  Success: True
  Tangential distortion: p1=-0.005820, p2=-0.001095, p3=-0.004042, p4=0.004959
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3663
[✅] Parameters copied to: /working/MDC013_W008_2025-05-23/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 530.5452936731463
    fy: 530.433939588427
    px: 991.7320680498615
    py: 745.6264298247958
    k1: -0.031400938078040876
    k2: 0.04711179946778223
    k3: -0.023146255786965095
    k4: 0.003302016835246047
    p1: -0.005820439764105345
    p2: -0.0010945708837686347
    p3: -0.004041902500212175
    p4: 0.004958966758497789
    scale: 3.079296857999514
    rotation: 0.48963682062509706
    mmpp: 1.2926159314913557
    nmpp: 1292615.9314913556

[🔄] Converting 464 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/230 [00:00<?, ?frame/s]

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   🔄 1000:  23%|██▎       | 54/230 [00:00<00:01, 174.13frame/s]

   🔄 1000:  31%|███▏      | 72/230 [00:00<00:00, 174.42frame/s]

   🔄 1000:  39%|███▉      | 90/230 [00:00<00:00, 173.82frame/s]

   🔄 1000:  47%|████▋     | 108/230 [00:00<00:00, 172.96frame/s]

   🔄 1000:  55%|█████▍    | 126/230 [00:00<00:00, 172.57frame/s]

   🔄 1000:  63%|██████▎   | 144/230 [00:00<00:00, 172.19frame/s]

   🔄 1000:  70%|███████   | 162/230 [00:00<00:00, 172.20frame/s]

   🔄 1000:  78%|███████▊  | 180/230 [00:01<00:00, 173.21frame/s]

   🔄 1000:  86%|████████▌ | 198/230 [00:01<00:00, 173.72frame/s]

   🔄 1000:  94%|█████████▍| 216/230 [00:01<00:00, 174.09frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.40s/rig]

   🔄 500:   0%|          | 0/234 [00:00<?, ?frame/s]

   🔄 500:   8%|▊         | 18/234 [00:00<00:01, 173.23frame/s]

   🔄 500:  15%|█▌        | 36/234 [00:00<00:01, 174.69frame/s]

   🔄 500:  23%|██▎       | 54/234 [00:00<00:01, 176.02frame/s]

   🔄 500:  31%|███       | 72/234 [00:00<00:00, 176.71frame/s]

   🔄 500:  38%|███▊      | 90/234 [00:00<00:00, 176.65frame/s]

   🔄 500:  47%|████▋     | 109/234 [00:00<00:00, 177.90frame/s]

   🔄 500:  55%|█████▍    | 128/234 [00:00<00:00, 180.03frame/s]

   🔄 500:  63%|██████▎   | 147/234 [00:00<00:00, 180.92frame/s]

   🔄 500:  71%|███████   | 166/234 [00:00<00:00, 179.88frame/s]

   🔄 500:  79%|███████▊  | 184/234 [00:01<00:00, 178.68frame/s]

   🔄 500:  86%|████████▋ | 202/234 [00:01<00:00, 177.23frame/s]

   🔄 500:  94%|█████████▍| 220/234 [00:01<00:00, 176.26frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.41s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.41s/rig]
[✅] Saved PTDs: /working/MDC013_W008_2025-05-23/calibration/debug/4_undistorted/output/ptd

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

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   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 176.53frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 176.79frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 173.69frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 172.79frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 173.74frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.10rig/s]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 174.13frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 174.58frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 176.61frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 177.00frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 176.14frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.03rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.04rig/s]
[✅] Saved PTDs: /working/MDC013_W008_2025-05-23/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%|▎         | 79121/3080448 [00:00<00:03, 791203.21px/s]
📤 Writing LUT:   5%|▌         | 166224/3080448 [00:00<00:03, 838153.40px/s]
📤 Writing LUT:   8%|▊         | 255416/3080448 [00:00<00:03, 862701.76px/s]
📤 Writing LUT:  11%|█         | 345553/3080448 [00:00<00:03, 877965.32px/s]
📤 Writing LUT:  14%|█▍        | 433350/3080448 [00:00<00:03, 877677.55px/s]
📤 Writing LUT:  17%|█▋        | 521313/3080448 [00:00<00:02, 878337.25px/s]
📤 Writing LUT:  20%|█▉        | 609431/3080448 [00:00<00:02, 879262.63px/s]
📤 Writing LUT:  23%|██▎       | 698911/3080448 [00:00<00:02, 884206.72px/s]
📤 Writing LUT:  26%|██▌       | 787332/3080448 [00:00<00:02, 881030.66px/s]
📤 Writing LUT:  28%|██▊       | 875438/3080448 [00:01<00:02, 876039.95px/s]
📤 Writing LUT:  31%|███▏      | 963822/3080448 [00:01<00:02, 878413.17px/s]
📤 Writing LUT:  34%|███▍      | 1051670/3080448 [00:01<00:02, 877530.94px/s]
📤 Writing LUT:  37%|███▋      | 1139428/3080448 [00:01<00:02, 877036.60px/s]
📤 Writing LUT:  40%|███▉      | 1227135/3080448 [00:01<00:02, 877014.79px/s]
📤 Writing LUT:  43%|████▎     | 1314839/3080448 [00:01<00:02, 874649.49px/s]
📤 Writing LUT:  46%|████▌     | 1403551/3080448 [00:01<00:01, 878389.73px/s]
📤 Writing LUT:  48%|████▊     | 1491393/3080448 [00:01<00:01, 877042.17px/s]
📤 Writing LUT:  51%|█████▏    | 1579100/3080448 [00:01<00:01, 876534.71px/s]
📤 Writing LUT:  54%|█████▍    | 1667815/3080448 [00:01<00:01, 879713.34px/s]
📤 Writing LUT:  57%|█████▋    | 1755789/3080448 [00:02<00:01, 878360.64px/s]
📤 Writing LUT:  60%|█████▉    | 1844323/3080448 [00:02<00:01, 880448.09px/s]
📤 Writing LUT:  63%|██████▎   | 1932498/3080448 [00:02<00:01, 880836.58px/s]
📤 Writing LUT:  66%|██████▌   | 2021074/3080448 [00:02<00:01, 882311.39px/s]
📤 Writing LUT:  69%|██████▊   | 2110612/3080448 [00:02<00:01, 886226.82px/s]
📤 Writing LUT:  71%|███████▏  | 2200616/3080448 [00:02<00:00, 890368.73px/s]
📤 Writing LUT:  74%|███████▍  | 2290061/3080448 [00:02<00:00, 891592.38px/s]
📤 Writing LUT:  77%|███████▋  | 2379221/3080448 [00:02<00:00, 891315.11px/s]
📤 Writing LUT:  80%|████████  | 2468353/3080448 [00:02<00:00, 885051.36px/s]
📤 Writing LUT:  83%|████████▎ | 2558572/3080448 [00:02<00:00, 890163.40px/s]
📤 Writing LUT:  86%|████████▌ | 2647599/3080448 [00:03<00:00, 885881.02px/s]
📤 Writing LUT:  89%|████████▉ | 2736198/3080448 [00:03<00:00, 885415.18px/s]
📤 Writing LUT:  92%|█████████▏| 2825036/3080448 [00:03<00:00, 886291.99px/s]
📤 Writing LUT:  95%|█████████▍| 2913671/3080448 [00:03<00:00, 881888.17px/s]
📤 Writing LUT:  97%|█████████▋| 3001868/3080448 [00:03<00:00, 879876.09px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 879986.83px/s]
[✅] LUT written to: /working/MDC013_W008_2025-05-23/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC013_W008_2025-05-23' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC013_W008_2025-05-23/calibration/MDC013_W008_2025-05-23_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%|          | 0/230 [00:00<?, ?frame/s]

   🔥 1000:   3%|▎         | 8/230 [00:00<00:02, 79.90frame/s]

   🔥 1000:   7%|▋         | 17/230 [00:00<00:02, 85.65frame/s]

   🔥 1000:  11%|█▏        | 26/230 [00:00<00:02, 87.08frame/s]

   🔥 1000:  15%|█▌        | 35/230 [00:00<00:02, 87.07frame/s]

   🔥 1000:  19%|█▉        | 44/230 [00:00<00:02, 86.12frame/s]

   🔥 1000:  23%|██▎       | 53/230 [00:00<00:02, 85.16frame/s]

   🔥 1000:  27%|██▋       | 62/230 [00:00<00:02, 82.82frame/s]

   🔥 1000:  31%|███       | 71/230 [00:00<00:01, 82.00frame/s]

   🔥 1000:  35%|███▍      | 80/230 [00:00<00:01, 81.49frame/s]

   🔥 1000:  39%|███▊      | 89/230 [00:01<00:01, 82.82frame/s]

   🔥 1000:  43%|████▎     | 98/230 [00:01<00:01, 83.26frame/s]

   🔥 1000:  47%|████▋     | 107/230 [00:01<00:01, 82.97frame/s]

   🔥 1000:  50%|█████     | 116/230 [00:01<00:01, 82.24frame/s]

   🔥 1000:  54%|█████▍    | 125/230 [00:01<00:01, 81.99frame/s]

   🔥 1000:  58%|█████▊    | 134/230 [00:01<00:01, 81.52frame/s]

   🔥 1000:  62%|██████▏   | 143/230 [00:01<00:01, 82.27frame/s]

   🔥 1000:  66%|██████▌   | 152/230 [00:01<00:00, 81.83frame/s]

   🔥 1000:  70%|███████   | 161/230 [00:01<00:00, 82.41frame/s]

   🔥 1000:  74%|███████▍  | 171/230 [00:02<00:00, 85.42frame/s]

   🔥 1000:  78%|███████▊  | 180/230 [00:02<00:00, 86.18frame/s]

   🔥 1000:  82%|████████▏ | 189/230 [00:02<00:00, 85.87frame/s]

   🔥 1000:  86%|████████▌ | 198/230 [00:02<00:00, 85.35frame/s]

   🔥 1000:  90%|█████████ | 207/230 [00:02<00:00, 86.15frame/s]

   🔥 1000:  94%|█████████▍| 216/230 [00:02<00:00, 87.21frame/s]

   🔥 1000:  98%|█████████▊| 225/230 [00:02<00:00, 86.72frame/s]

                                                                [✅] 1000: /working/MDC013_W008_2025-05-23/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC013_W008_2025-05-23/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.64s/rig]

   🔥 500:   0%|          | 0/234 [00:00<?, ?frame/s]

   🔥 500:   3%|▎         | 8/234 [00:00<00:03, 74.67frame/s]

   🔥 500:   7%|▋         | 16/234 [00:00<00:02, 77.56frame/s]

   🔥 500:  10%|█         | 24/234 [00:00<00:02, 77.59frame/s]

   🔥 500:  14%|█▎        | 32/234 [00:00<00:02, 77.94frame/s]

   🔥 500:  17%|█▋        | 40/234 [00:00<00:02, 78.65frame/s]

   🔥 500:  21%|██        | 49/234 [00:00<00:02, 79.96frame/s]

   🔥 500:  25%|██▍       | 58/234 [00:00<00:02, 82.90frame/s]

   🔥 500:  29%|██▉       | 68/234 [00:00<00:01, 86.07frame/s]

   🔥 500:  33%|███▎      | 77/234 [00:00<00:01, 86.17frame/s]

   🔥 500:  37%|███▋      | 86/234 [00:01<00:01, 84.27frame/s]

   🔥 500:  41%|████      | 95/234 [00:01<00:01, 82.81frame/s]

   🔥 500:  44%|████▍     | 104/234 [00:01<00:01, 81.10frame/s]

   🔥 500:  48%|████▊     | 113/234 [00:01<00:01, 80.80frame/s]

   🔥 500:  52%|█████▏    | 122/234 [00:01<00:01, 80.80frame/s]

   🔥 500:  56%|█████▌    | 131/234 [00:01<00:01, 79.78frame/s]

   🔥 500:  59%|█████▉    | 139/234 [00:01<00:01, 78.25frame/s]

   🔥 500:  63%|██████▎   | 147/234 [00:01<00:01, 77.36frame/s]

   🔥 500:  66%|██████▌   | 155/234 [00:01<00:01, 76.17frame/s]

   🔥 500:  70%|██████▉   | 163/234 [00:02<00:00, 77.18frame/s]

   🔥 500:  73%|███████▎  | 171/234 [00:02<00:00, 77.88frame/s]

   🔥 500:  77%|███████▋  | 180/234 [00:02<00:00, 78.79frame/s]

   🔥 500:  80%|████████  | 188/234 [00:02<00:00, 78.71frame/s]

   🔥 500:  84%|████████▍ | 196/234 [00:02<00:00, 78.51frame/s]

   🔥 500:  87%|████████▋ | 204/234 [00:02<00:00, 78.72frame/s]

   🔥 500:  91%|█████████ | 212/234 [00:02<00:00, 78.65frame/s]

   🔥 500:  94%|█████████▍| 220/234 [00:02<00:00, 78.86frame/s]

   🔥 500:  97%|█████████▋| 228/234 [00:02<00:00, 79.11frame/s]

                                                               [✅] 500: /working/MDC013_W008_2025-05-23/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC013_W008_2025-05-23/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.71s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.85s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC013_W008_2025-05-23/calibration/debug/5_heatmap
[🏁] Finished processing MDC013_W008_2025-05-23


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