🟢 Starting Calibration Pipeline

=== Processing MDC015_W068_2025-11-25 ===

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

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 1264 stationary frames, 234 remain


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→ 1000 Phase 2: corners:  79%|███████▊  | 184/234 [02:28<00:39,  1.25it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 185/234 [02:28<00:38,  1.27it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 186/234 [02:29<00:37,  1.27it/s]

→ 1000 Phase 2: corners:  80%|███████▉  | 187/234 [02:30<00:37,  1.26it/s]

→ 1000 Phase 2: corners:  80%|████████  | 188/234 [02:31<00:36,  1.27it/s]

→ 1000 Phase 2: corners:  81%|████████  | 189/234 [02:31<00:35,  1.26it/s]

→ 1000 Phase 2: corners:  81%|████████  | 190/234 [02:32<00:35,  1.26it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 191/234 [02:33<00:34,  1.25it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 192/234 [02:34<00:33,  1.24it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 193/234 [02:35<00:33,  1.24it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 194/234 [02:36<00:32,  1.24it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 195/234 [02:36<00:31,  1.24it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 196/234 [02:37<00:30,  1.23it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 197/234 [02:38<00:30,  1.22it/s]

→ 1000 Phase 2: corners:  85%|████████▍ | 198/234 [02:39<00:29,  1.23it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 199/234 [02:40<00:28,  1.22it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 200/234 [02:40<00:28,  1.21it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 201/234 [02:41<00:27,  1.21it/s]

→ 1000 Phase 2: corners:  86%|████████▋ | 202/234 [02:42<00:26,  1.20it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 203/234 [02:43<00:26,  1.19it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 204/234 [02:44<00:25,  1.19it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 205/234 [02:45<00:24,  1.18it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 206/234 [02:46<00:23,  1.17it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 207/234 [02:46<00:23,  1.17it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 208/234 [02:47<00:22,  1.16it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 209/234 [02:48<00:21,  1.16it/s]

→ 1000 Phase 2: corners:  90%|████████▉ | 210/234 [02:49<00:20,  1.17it/s]

→ 1000 Phase 2: corners:  90%|█████████ | 211/234 [02:50<00:19,  1.20it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 212/234 [02:51<00:18,  1.19it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 213/234 [02:51<00:17,  1.19it/s]

→ 1000 Phase 2: corners:  91%|█████████▏| 214/234 [02:52<00:16,  1.21it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 215/234 [02:53<00:15,  1.22it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 216/234 [02:54<00:14,  1.24it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 217/234 [02:55<00:13,  1.25it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 218/234 [02:55<00:12,  1.26it/s]

→ 1000 Phase 2: corners:  94%|█████████▎| 219/234 [02:56<00:11,  1.26it/s]

→ 1000 Phase 2: corners:  94%|█████████▍| 220/234 [02:57<00:11,  1.27it/s]

→ 1000 Phase 2: corners:  94%|█████████▍| 221/234 [02:58<00:10,  1.26it/s]

→ 1000 Phase 2: corners:  95%|█████████▍| 222/234 [02:59<00:09,  1.27it/s]

→ 1000 Phase 2: corners:  95%|█████████▌| 223/234 [02:59<00:08,  1.27it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 224/234 [03:00<00:07,  1.26it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 225/234 [03:01<00:07,  1.26it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 226/234 [03:02<00:06,  1.26it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 227/234 [03:03<00:05,  1.26it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 228/234 [03:03<00:04,  1.26it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 229/234 [03:04<00:03,  1.25it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 230/234 [03:05<00:03,  1.25it/s]

→ 1000 Phase 2: corners:  99%|█████████▊| 231/234 [03:06<00:02,  1.24it/s]

→ 1000 Phase 2: corners:  99%|█████████▉| 232/234 [03:07<00:01,  1.23it/s]

→ 1000 Phase 2: corners: 100%|█████████▉| 233/234 [03:07<00:00,  1.25it/s]

→ 1000 Phase 2: corners: 100%|██████████| 234/234 [03:08<00:00,  1.23it/s]

                                                                          [PASS2] Frame 0000664: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000665: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000666: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000667: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000668: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000669: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000670: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000671: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001042: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001043: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001044: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001045: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001050: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001051: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001052: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001053: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001054: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001055: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001056: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001196: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001197: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001198: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001199: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001200: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001201: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001202: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001203: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001204: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001205: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000666: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000670: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001041: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0001052: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001055: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001198: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001200: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001201: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001204: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001041: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Summary: 17 frames fixed, 18 untrusted, 234 total


→ 1000 Phase 3: finalize:   0%|          | 0/234 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   1%|          | 2/234 [00:00<00:19, 11.67it/s]

→ 1000 Phase 3: finalize:   2%|▏         | 4/234 [00:00<00:19, 12.09it/s]

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→ 1000 Phase 3: finalize:   4%|▍         | 10/234 [00:00<00:17, 12.68it/s]

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→ 1000 Phase 3: finalize:   6%|▌         | 14/234 [00:01<00:17, 12.86it/s]

→ 1000 Phase 3: finalize:   7%|▋         | 16/234 [00:01<00:16, 12.91it/s]

→ 1000 Phase 3: finalize:   8%|▊         | 18/234 [00:01<00:16, 12.90it/s]

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→ 1000 Phase 3: finalize:   9%|▉         | 22/234 [00:01<00:16, 12.93it/s]

→ 1000 Phase 3: finalize:  10%|█         | 24/234 [00:01<00:16, 12.97it/s]

→ 1000 Phase 3: finalize:  11%|█         | 26/234 [00:02<00:16, 12.99it/s]

→ 1000 Phase 3: finalize:  12%|█▏        | 28/234 [00:02<00:15, 13.00it/s]

→ 1000 Phase 3: finalize:  13%|█▎        | 30/234 [00:02<00:15, 12.96it/s]

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→ 1000 Phase 3: finalize:  18%|█▊        | 42/234 [00:03<00:14, 12.81it/s]

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→ 1000 Phase 3: finalize:  21%|██        | 48/234 [00:03<00:14, 12.68it/s]

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→ 1000 Phase 3: finalize:  24%|██▍       | 56/234 [00:04<00:14, 12.71it/s]

→ 1000 Phase 3: finalize:  25%|██▍       | 58/234 [00:04<00:13, 12.67it/s]

→ 1000 Phase 3: finalize:  26%|██▌       | 60/234 [00:04<00:13, 12.71it/s]

→ 1000 Phase 3: finalize:  26%|██▋       | 62/234 [00:04<00:13, 12.73it/s]

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→ 1000 Phase 3: finalize:  30%|██▉       | 70/234 [00:05<00:12, 12.70it/s]

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→ 1000 Phase 3: finalize:  34%|███▍      | 80/234 [00:06<00:12, 12.51it/s]

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→ 1000 Phase 3: finalize:  39%|███▉      | 92/234 [00:07<00:11, 12.38it/s]

→ 1000 Phase 3: finalize:  40%|████      | 94/234 [00:07<00:11, 12.38it/s]

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→ 1000 Phase 3: finalize:  42%|████▏     | 98/234 [00:07<00:11, 12.32it/s]

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→ 1000 Phase 3: finalize:  51%|█████▏    | 120/234 [00:09<00:08, 12.68it/s]

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→ 1000 Phase 3: finalize:  54%|█████▍    | 126/234 [00:09<00:08, 12.70it/s]

→ 1000 Phase 3: finalize:  55%|█████▍    | 128/234 [00:10<00:08, 12.76it/s]

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→ 1000 Phase 3: finalize:  58%|█████▊    | 136/234 [00:10<00:07, 12.95it/s]

→ 1000 Phase 3: finalize:  59%|█████▉    | 138/234 [00:10<00:07, 12.94it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 140/234 [00:11<00:07, 12.96it/s]

→ 1000 Phase 3: finalize:  61%|██████    | 142/234 [00:11<00:07, 12.97it/s]

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→ 1000 Phase 3: finalize:  63%|██████▎   | 148/234 [00:11<00:06, 12.98it/s]

→ 1000 Phase 3: finalize:  64%|██████▍   | 150/234 [00:11<00:06, 13.00it/s]

→ 1000 Phase 3: finalize:  65%|██████▍   | 152/234 [00:11<00:06, 13.04it/s]

→ 1000 Phase 3: finalize:  66%|██████▌   | 154/234 [00:12<00:06, 13.08it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 156/234 [00:12<00:05, 13.11it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 158/234 [00:12<00:05, 13.15it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 160/234 [00:12<00:05, 13.18it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 162/234 [00:12<00:05, 13.22it/s]

→ 1000 Phase 3: finalize:  70%|███████   | 164/234 [00:12<00:05, 13.26it/s]

→ 1000 Phase 3: finalize:  71%|███████   | 166/234 [00:13<00:05, 13.28it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 168/234 [00:13<00:04, 13.30it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 170/234 [00:13<00:04, 13.32it/s]

→ 1000 Phase 3: finalize:  74%|███████▎  | 172/234 [00:13<00:04, 13.34it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 174/234 [00:13<00:04, 13.36it/s]

→ 1000 Phase 3: finalize:  75%|███████▌  | 176/234 [00:13<00:04, 13.37it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 178/234 [00:13<00:04, 13.37it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 180/234 [00:14<00:04, 13.36it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 182/234 [00:14<00:03, 13.33it/s]

→ 1000 Phase 3: finalize:  79%|███████▊  | 184/234 [00:14<00:03, 13.28it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 186/234 [00:14<00:03, 13.24it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 188/234 [00:14<00:03, 13.23it/s]

→ 1000 Phase 3: finalize:  81%|████████  | 190/234 [00:14<00:03, 13.21it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 192/234 [00:14<00:03, 13.21it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 194/234 [00:15<00:03, 13.17it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 196/234 [00:15<00:02, 13.15it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 198/234 [00:15<00:02, 13.04it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 200/234 [00:15<00:02, 13.05it/s]

→ 1000 Phase 3: finalize:  86%|████████▋ | 202/234 [00:15<00:02, 13.02it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 204/234 [00:15<00:02, 13.04it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 206/234 [00:16<00:02, 13.03it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 208/234 [00:16<00:01, 13.07it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 210/234 [00:16<00:01, 13.05it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 212/234 [00:16<00:01, 13.07it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 214/234 [00:16<00:01, 13.06it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 216/234 [00:16<00:01, 13.04it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 218/234 [00:16<00:01, 13.03it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 220/234 [00:17<00:01, 13.01it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 222/234 [00:17<00:00, 12.99it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 224/234 [00:17<00:00, 12.96it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 226/234 [00:17<00:00, 12.94it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 228/234 [00:17<00:00, 12.94it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 230/234 [00:17<00:00, 12.94it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 232/234 [00:18<00:00, 12.94it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 234/234 [00:18<00:00, 12.93it/s]

                                                                           
Processing Rigs:  50%|█████     | 1/2 [03:55<03:55, 235.26s/it][INFO] Filtered 991 stationary frames, 227 remain


→ 500 (227 images) Phase 1:   0%|          | 0/227 [00:00<?, ?it/s]

→ 500 (227 images) Phase 1:   0%|          | 1/227 [00:03<12:35,  3.34s/it]

→ 500 (227 images) Phase 1:  15%|█▍        | 33/227 [00:06<00:30,  6.41it/s]

→ 500 (227 images) Phase 1:  26%|██▌       | 59/227 [00:06<00:12, 13.32it/s]

→ 500 (227 images) Phase 1:  32%|███▏      | 72/227 [00:08<00:17,  8.86it/s]

→ 500 (227 images) Phase 1:  43%|████▎     | 97/227 [00:11<00:14,  8.94it/s]

→ 500 (227 images) Phase 1:  53%|█████▎    | 120/227 [00:11<00:07, 13.69it/s]

→ 500 (227 images) Phase 1:  58%|█████▊    | 131/227 [00:14<00:10,  9.32it/s]

→ 500 (227 images) Phase 1:  66%|██████▌   | 150/227 [00:14<00:05, 13.54it/s]

→ 500 (227 images) Phase 1:  71%|███████▏  | 162/227 [00:17<00:06,  9.29it/s]

→ 500 (227 images) Phase 1:  77%|███████▋  | 175/227 [00:17<00:04, 12.24it/s]

→ 500 (227 images) Phase 1:  85%|████████▍ | 192/227 [00:17<00:02, 17.43it/s]

→ 500 (227 images) Phase 1:  89%|████████▉ | 203/227 [00:20<00:02, 10.03it/s]

→ 500 (227 images) Phase 1:  97%|█████████▋| 221/227 [00:20<00:00, 15.03it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/227 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:  30%|██▉       | 67/227 [01:04<02:31,  1.05it/s]

→ 500 Phase 2: corners:  30%|██▉       | 68/227 [01:05<02:27,  1.08it/s]

→ 500 Phase 2: corners:  30%|███       | 69/227 [01:06<02:25,  1.09it/s]

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→ 500 Phase 2: corners:  86%|████████▌ | 195/227 [03:05<00:31,  1.01it/s]

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→ 500 Phase 2: corners:  88%|████████▊ | 200/227 [03:10<00:27,  1.01s/it]

→ 500 Phase 2: corners:  89%|████████▊ | 201/227 [03:11<00:26,  1.01s/it]

→ 500 Phase 2: corners:  89%|████████▉ | 202/227 [03:12<00:24,  1.01it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 203/227 [03:13<00:23,  1.01it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 204/227 [03:14<00:23,  1.00s/it]

→ 500 Phase 2: corners:  90%|█████████ | 205/227 [03:15<00:21,  1.01it/s]

→ 500 Phase 2: corners:  91%|█████████ | 206/227 [03:16<00:20,  1.02it/s]

→ 500 Phase 2: corners:  91%|█████████ | 207/227 [03:17<00:19,  1.04it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 208/227 [03:18<00:17,  1.08it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 209/227 [03:19<00:16,  1.07it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 210/227 [03:20<00:15,  1.09it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 211/227 [03:21<00:14,  1.10it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 212/227 [03:21<00:13,  1.11it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 213/227 [03:22<00:12,  1.09it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 214/227 [03:23<00:11,  1.09it/s]

→ 500 Phase 2: corners:  95%|█████████▍| 215/227 [03:24<00:10,  1.09it/s]

→ 500 Phase 2: corners:  95%|█████████▌| 216/227 [03:25<00:09,  1.10it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 217/227 [03:26<00:08,  1.11it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 218/227 [03:27<00:08,  1.11it/s]

→ 500 Phase 2: corners:  96%|█████████▋| 219/227 [03:28<00:07,  1.11it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 220/227 [03:29<00:06,  1.12it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 221/227 [03:30<00:05,  1.12it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 222/227 [03:30<00:04,  1.12it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 223/227 [03:31<00:03,  1.13it/s]

→ 500 Phase 2: corners:  99%|█████████▊| 224/227 [03:32<00:02,  1.11it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 225/227 [03:33<00:01,  1.10it/s]

→ 500 Phase 2: corners: 100%|█████████▉| 226/227 [03:34<00:00,  1.08it/s]

→ 500 Phase 2: corners: 100%|██████████| 227/227 [03:35<00:00,  1.08it/s]

                                                                         [PASS2] Frame 0001020: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001021: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001022: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001026: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001022: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 2 untrusted, 227 total


→ 500 Phase 3: finalize:   0%|          | 0/227 [00:00<?, ?it/s]

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→ 500 Phase 3: finalize:  11%|█         | 24/227 [00:01<00:16, 12.65it/s]

→ 500 Phase 3: finalize:  11%|█▏        | 26/227 [00:02<00:15, 12.65it/s]

→ 500 Phase 3: finalize:  12%|█▏        | 28/227 [00:02<00:15, 12.64it/s]

→ 500 Phase 3: finalize:  13%|█▎        | 30/227 [00:02<00:15, 12.65it/s]

→ 500 Phase 3: finalize:  14%|█▍        | 32/227 [00:02<00:15, 12.66it/s]

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→ 500 Phase 3: finalize:  17%|█▋        | 38/227 [00:02<00:14, 12.64it/s]

→ 500 Phase 3: finalize:  18%|█▊        | 40/227 [00:03<00:14, 12.64it/s]

→ 500 Phase 3: finalize:  19%|█▊        | 42/227 [00:03<00:14, 12.54it/s]

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→ 500 Phase 3: finalize:  35%|███▌      | 80/227 [00:06<00:11, 12.28it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 82/227 [00:06<00:11, 12.29it/s]

→ 500 Phase 3: finalize:  37%|███▋      | 84/227 [00:06<00:11, 12.28it/s]

→ 500 Phase 3: finalize:  38%|███▊      | 86/227 [00:06<00:11, 12.31it/s]

→ 500 Phase 3: finalize:  39%|███▉      | 88/227 [00:07<00:11, 12.34it/s]

→ 500 Phase 3: finalize:  40%|███▉      | 90/227 [00:07<00:11, 12.31it/s]

→ 500 Phase 3: finalize:  41%|████      | 92/227 [00:07<00:10, 12.33it/s]

→ 500 Phase 3: finalize:  41%|████▏     | 94/227 [00:07<00:10, 12.36it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 96/227 [00:07<00:10, 12.39it/s]

→ 500 Phase 3: finalize:  43%|████▎     | 98/227 [00:07<00:10, 12.44it/s]

→ 500 Phase 3: finalize:  44%|████▍     | 100/227 [00:07<00:09, 12.77it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 102/227 [00:08<00:09, 12.74it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 104/227 [00:08<00:09, 12.71it/s]

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→ 500 Phase 3: finalize:  48%|████▊     | 108/227 [00:08<00:09, 13.11it/s]

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→ 500 Phase 3: finalize:  61%|██████    | 138/227 [00:10<00:06, 13.69it/s]

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→ 500 Phase 3: finalize:  63%|██████▎   | 142/227 [00:11<00:06, 13.56it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 144/227 [00:11<00:06, 13.52it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 146/227 [00:11<00:05, 13.51it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 148/227 [00:11<00:05, 13.52it/s]

→ 500 Phase 3: finalize:  66%|██████▌   | 150/227 [00:11<00:05, 13.54it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 152/227 [00:11<00:05, 13.51it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 154/227 [00:12<00:05, 13.48it/s]

→ 500 Phase 3: finalize:  69%|██████▊   | 156/227 [00:12<00:05, 13.48it/s]

→ 500 Phase 3: finalize:  70%|██████▉   | 158/227 [00:12<00:05, 13.49it/s]

→ 500 Phase 3: finalize:  70%|███████   | 160/227 [00:12<00:04, 13.48it/s]

→ 500 Phase 3: finalize:  71%|███████▏  | 162/227 [00:12<00:04, 13.49it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 164/227 [00:12<00:04, 13.47it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 166/227 [00:12<00:04, 13.45it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 168/227 [00:13<00:04, 13.43it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 170/227 [00:13<00:04, 13.44it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 172/227 [00:13<00:04, 13.47it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 174/227 [00:13<00:03, 13.46it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 176/227 [00:13<00:03, 13.45it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 178/227 [00:13<00:03, 13.46it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 180/227 [00:13<00:03, 13.45it/s]

→ 500 Phase 3: finalize:  80%|████████  | 182/227 [00:14<00:03, 13.41it/s]

→ 500 Phase 3: finalize:  81%|████████  | 184/227 [00:14<00:03, 13.41it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 186/227 [00:14<00:03, 13.33it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 188/227 [00:14<00:02, 13.26it/s]

→ 500 Phase 3: finalize:  84%|████████▎ | 190/227 [00:14<00:02, 13.21it/s]

→ 500 Phase 3: finalize:  85%|████████▍ | 192/227 [00:14<00:02, 13.13it/s]

→ 500 Phase 3: finalize:  85%|████████▌ | 194/227 [00:15<00:02, 13.06it/s]

→ 500 Phase 3: finalize:  86%|████████▋ | 196/227 [00:15<00:02, 13.00it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 198/227 [00:15<00:02, 12.97it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 200/227 [00:15<00:02, 12.93it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 202/227 [00:15<00:01, 12.88it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 204/227 [00:15<00:01, 12.86it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 206/227 [00:15<00:01, 12.87it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 208/227 [00:16<00:01, 12.85it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 210/227 [00:16<00:01, 12.84it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 212/227 [00:16<00:01, 12.84it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 214/227 [00:16<00:01, 12.84it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 216/227 [00:16<00:00, 12.85it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 218/227 [00:16<00:00, 12.81it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 220/227 [00:17<00:00, 12.82it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 222/227 [00:17<00:00, 12.83it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 224/227 [00:17<00:00, 12.83it/s]

→ 500 Phase 3: finalize: 100%|█████████▉| 226/227 [00:17<00:00, 12.84it/s]

                                                                          
Processing Rigs: 100%|██████████| 2/2 [08:14<00:00, 249.19s/it]
Processing Rigs: 100%|██████████| 2/2 [08:14<00:00, 247.10s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 461 frames from 2 rigs to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 234
[📋] 500: 100 frames selected from 227
[💾] Saved RigFrameDataset to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                        Step 2: Calibration                        
-------------------------------------------------------------------

-> Calculating the casing center ...
[INFO] Using 20 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/20 [00:00<?, ?it/s]
Computing max image from bright frames:   5%|▌         | 1/20 [00:03<01:00,  3.21s/it]
Computing max image from bright frames:  10%|█         | 2/20 [00:06<00:56,  3.15s/it]
Computing max image from bright frames:  15%|█▌        | 3/20 [00:09<00:53,  3.13s/it]
Computing max image from bright frames:  20%|██        | 4/20 [00:12<00:49,  3.12s/it]
Computing max image from bright frames:  25%|██▌       | 5/20 [00:15<00:46,  3.11s/it]
Computing max image from bright frames:  30%|███       | 6/20 [00:18<00:43,  3.11s/it]
Computing max image from bright frames:  35%|███▌      | 7/20 [00:21<00:40,  3.12s/it]
Computing max image from bright frames:  40%|████      | 8/20 [00:24<00:37,  3.12s/it]
Computing max image from bright frames:  45%|████▌     | 9/20 [00:28<00:34,  3.11s/it]
Computing max image from bright frames:  50%|█████     | 10/20 [00:31<00:31,  3.15s/it]
Computing max image from bright frames:  55%|█████▌    | 11/20 [00:34<00:28,  3.17s/it]
Computing max image from bright frames:  60%|██████    | 12/20 [00:37<00:25,  3.18s/it]
Computing max image from bright frames:  65%|██████▌   | 13/20 [00:41<00:22,  3.21s/it]
Computing max image from bright frames:  70%|███████   | 14/20 [00:44<00:19,  3.22s/it]
Computing max image from bright frames:  75%|███████▌  | 15/20 [00:47<00:15,  3.19s/it]
Computing max image from bright frames:  80%|████████  | 16/20 [00:50<00:12,  3.17s/it]
Computing max image from bright frames:  85%|████████▌ | 17/20 [00:53<00:09,  3.15s/it]
Computing max image from bright frames:  90%|█████████ | 18/20 [00:56<00:06,  3.13s/it]
Computing max image from bright frames:  95%|█████████▌| 19/20 [00:59<00:03,  3.12s/it]
Computing max image from bright frames: 100%|██████████| 20/20 [01:02<00:00,  3.10s/it]
                                                                                       

  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:11,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:11,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   4%|▍         | 4/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   5%|▌         | 5/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   6%|▌         | 6/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   7%|▋         | 7/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  12%|█▏        | 12/100 [00:00<00:09,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  13%|█▎        | 13/100 [00:00<00:09,  8.81it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  20%|██        | 20/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  21%|██        | 21/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  24%|██▍       | 24/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  25%|██▌       | 25/100 [00:00<00:01, 64.62it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 483.838} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.006 | Best: {'fx': 505.051} (0.006):  27%|██▋       | 27/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.020 | Best: {'fx': 512.121} (0.020):  28%|██▊       | 28/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 512.121} (0.020):  29%|██▉       | 29/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 512.121} (0.020):  30%|███       | 30/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.034 | Best: {'fx': 519.192} (0.034):  31%|███       | 31/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.020 | Best: {'fx': 519.192} (0.034):  32%|███▏      | 32/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.056 | Best: {'fx': 526.263} (0.056):  33%|███▎      | 33/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.018 | Best: {'fx': 526.263} (0.056):  34%|███▍      | 34/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.076 | Best: {'fx': 533.333} (0.076):  35%|███▌      | 35/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.052 | Best: {'fx': 533.333} (0.076):  36%|███▌      | 36/100 [00:00<00:00, 83.49it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.052 | Best: {'fx': 533.333} (0.076):  37%|███▋      | 37/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.035 | Best: {'fx': 533.333} (0.076):  37%|███▋      | 37/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.027 | Best: {'fx': 533.333} (0.076):  38%|███▊      | 38/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.076):  39%|███▉      | 39/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.023 | Best: {'fx': 533.333} (0.076):  40%|████      | 40/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.076):  41%|████      | 41/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.076):  42%|████▏     | 42/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.076):  43%|████▎     | 43/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.076):  44%|████▍     | 44/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.076):  45%|████▌     | 45/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.076):  46%|████▌     | 46/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  47%|████▋     | 47/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.076):  48%|████▊     | 48/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.076):  49%|████▉     | 49/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  50%|█████     | 50/100 [00:00<00:00, 91.86it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  51%|█████     | 51/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  51%|█████     | 51/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  52%|█████▏    | 52/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  53%|█████▎    | 53/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  54%|█████▍    | 54/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  55%|█████▌    | 55/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  56%|█████▌    | 56/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  57%|█████▋    | 57/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  58%|█████▊    | 58/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  59%|█████▉    | 59/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  60%|██████    | 60/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  61%|██████    | 61/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  62%|██████▏   | 62/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  63%|██████▎   | 63/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  64%|██████▍   | 64/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  65%|██████▌   | 65/100 [00:00<00:00, 107.15it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  66%|██████▌   | 66/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  66%|██████▌   | 66/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  67%|██████▋   | 67/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  68%|██████▊   | 68/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  69%|██████▉   | 69/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  70%|███████   | 70/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  71%|███████   | 71/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  72%|███████▏  | 72/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  73%|███████▎  | 73/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  74%|███████▍  | 74/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  75%|███████▌  | 75/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  76%|███████▌  | 76/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  77%|███████▋  | 77/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  78%|███████▊  | 78/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  79%|███████▉  | 79/100 [00:00<00:00, 116.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  80%|████████  | 80/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  80%|████████  | 80/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  81%|████████  | 81/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  82%|████████▏ | 82/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  83%|████████▎ | 83/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  84%|████████▍ | 84/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  85%|████████▌ | 85/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  86%|████████▌ | 86/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  87%|████████▋ | 87/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  88%|████████▊ | 88/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  89%|████████▉ | 89/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  90%|█████████ | 90/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  91%|█████████ | 91/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  92%|█████████▏| 92/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  93%|█████████▎| 93/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  94%|█████████▍| 94/100 [00:00<00:00, 128.27it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  94%|█████████▍| 94/100 [00:00<00:00, 128.27it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  95%|█████████▌| 95/100 [00:00<00:00, 128.27it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  96%|█████████▌| 96/100 [00:00<00:00, 128.27it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  97%|█████████▋| 97/100 [00:00<00:00, 128.27it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  98%|█████████▊| 98/100 [00:00<00:00, 128.27it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  99%|█████████▉| 99/100 [00:00<00:00, 128.27it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.017 | Best: {'fx': 510.374} (0.017):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.017 | Best: {'fx': 510.374} (0.017):   2%|▏         | 1/50 [00:00<00:05,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.012 | Best: {'fx': 510.374} (0.017):   2%|▏         | 1/50 [00:00<00:05,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.014 | Best: {'fx': 510.374} (0.017):   4%|▍         | 2/50 [00:00<00:05,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.017 | Best: {'fx': 511.395} (0.017):   6%|▌         | 3/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.022 | Best: {'fx': 512.415} (0.022):   8%|▊         | 4/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.023 | Best: {'fx': 513.435} (0.023):  10%|█         | 5/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.024 | Best: {'fx': 514.456} (0.024):  12%|█▏        | 6/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  14%|█▍        | 7/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.026 | Best: {'fx': 517.517} (0.030):  16%|█▌        | 8/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.028 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.034 | Best: {'fx': 519.558} (0.034):  20%|██        | 10/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.031 | Best: {'fx': 519.558} (0.034):  22%|██▏       | 11/50 [00:00<00:04,  9.49it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.031 | Best: {'fx': 519.558} (0.034):  24%|██▍       | 12/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  24%|██▍       | 12/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.041 | Best: {'fx': 522.619} (0.041):  26%|██▌       | 13/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.036 | Best: {'fx': 522.619} (0.041):  28%|██▊       | 14/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.046 | Best: {'fx': 523.639} (0.046):  30%|███       | 15/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.050 | Best: {'fx': 524.66} (0.050):  32%|███▏      | 16/50 [00:00<00:00, 64.33it/s] 
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.052 | Best: {'fx': 525.68} (0.052):  34%|███▍      | 17/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.056 | Best: {'fx': 526.701} (0.056):  36%|███▌      | 18/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.062 | Best: {'fx': 527.721} (0.062):  38%|███▊      | 19/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.069 | Best: {'fx': 528.741} (0.069):  40%|████      | 20/50 [00:00<00:00, 64.33it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.069 | Best: {'fx': 528.741} (0.069):  42%|████▏     | 21/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.075 | Best: {'fx': 530.782} (0.075):  42%|████▏     | 21/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.074 | Best: {'fx': 530.782} (0.075):  44%|████▍     | 22/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.074 | Best: {'fx': 530.782} (0.075):  46%|████▌     | 23/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.072 | Best: {'fx': 530.782} (0.075):  48%|████▊     | 24/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.073 | Best: {'fx': 530.782} (0.075):  50%|█████     | 25/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.081 | Best: {'fx': 533.844} (0.081):  52%|█████▏    | 26/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.060 | Best: {'fx': 533.844} (0.081):  54%|█████▍    | 27/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.067 | Best: {'fx': 533.844} (0.081):  56%|█████▌    | 28/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.055 | Best: {'fx': 533.844} (0.081):  58%|█████▊    | 29/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.054 | Best: {'fx': 533.844} (0.081):  60%|██████    | 30/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.050 | Best: {'fx': 533.844} (0.081):  62%|██████▏   | 31/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.067 | Best: {'fx': 533.844} (0.081):  64%|██████▍   | 32/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.046 | Best: {'fx': 533.844} (0.081):  66%|██████▌   | 33/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.044 | Best: {'fx': 533.844} (0.081):  68%|██████▊   | 34/50 [00:00<00:00, 74.80it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.044 | Best: {'fx': 533.844} (0.081):  70%|███████   | 35/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.040 | Best: {'fx': 533.844} (0.081):  70%|███████   | 35/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.036 | Best: {'fx': 533.844} (0.081):  72%|███████▏  | 36/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.031 | Best: {'fx': 533.844} (0.081):  74%|███████▍  | 37/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.039 | Best: {'fx': 533.844} (0.081):  76%|███████▌  | 38/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.037 | Best: {'fx': 533.844} (0.081):  78%|███████▊  | 39/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.034 | Best: {'fx': 533.844} (0.081):  80%|████████  | 40/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.032 | Best: {'fx': 533.844} (0.081):  82%|████████▏ | 41/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.030 | Best: {'fx': 533.844} (0.081):  84%|████████▍ | 42/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.029 | Best: {'fx': 533.844} (0.081):  86%|████████▌ | 43/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.028 | Best: {'fx': 533.844} (0.081):  88%|████████▊ | 44/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.027 | Best: {'fx': 533.844} (0.081):  90%|█████████ | 45/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.027 | Best: {'fx': 533.844} (0.081):  92%|█████████▏| 46/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.025 | Best: {'fx': 533.844} (0.081):  94%|█████████▍| 47/50 [00:00<00:00, 98.19it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.025 | Best: {'fx': 533.844} (0.081):  96%|█████████▌| 48/50 [00:00<00:00, 105.10it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.026 | Best: {'fx': 533.844} (0.081):  96%|█████████▌| 48/50 [00:00<00:00, 105.10it/s]
Brute | Start: {'fx': 533.333} (0.076) | Current: 0.024 | Best: {'fx': 533.844} (0.081):  98%|█████████▊| 49/50 [00:00<00:00, 105.10it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.843537414966
    px: 1001.0
    py: 747.0
    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

  [Ring preference] diameter=3500 mm  →  radius=1349.6 px

[🔲] 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: 24.0970: 0iter [00:00, ?iter/s]  [Ring mask] 25,918/25,918 points within ring radius 1349.6 px
[DEBUG] objective(x0) = 24.0970, picklable_objective(x0) = 24.0950

Best score: 24.0950: : 0iter [00:01, ?iter/s] 
Best score: 24.0950: : 1iter [00:01,  1.94s/iter]
Best score: 24.0950: : 1iter [00:01,  1.94s/iter]
Best score: 24.0950: : 2iter [00:02,  1.94s/iter]
Best score: 24.0950: : 3iter [00:02,  1.94s/iter]
Best score: 24.0950: : 4iter [00:02,  2.47iter/s]
Best score: 24.0950: : 4iter [00:02,  2.47iter/s]
Best score: 24.0950: : 5iter [00:02,  2.47iter/s]
Best score: 24.0950: : 6iter [00:02,  2.47iter/s]
Best score: 24.0950: : 7iter [00:02,  4.72iter/s]
Best score: 24.0950: : 7iter [00:02,  4.72iter/s]
Best score: 24.0950: : 8iter [00:02,  4.72iter/s]
Best score: 24.0950: : 9iter [00:02,  4.72iter/s]
Best score: 24.0950: : 10iter [00:02,  7.13iter/s]
Best score: 24.0950: : 10iter [00:02,  7.13iter/s]
Best score: 24.0950: : 11iter [00:02,  7.13iter/s]
Best score: 24.0950: : 12iter [00:02,  7.13iter/s]
Best score: 24.0950: : 13iter [00:02,  9.62iter/s]
Best score: 24.0950: : 13iter [00:02,  9.62iter/s]
Best score: 24.0950: : 14iter [00:02,  9.62iter/s]
Best score: 24.0950: : 15iter [00:02,  9.62iter/s]
Best score: 24.0950: : 16iter [00:02, 12.03iter/s]
Best score: 24.0950: : 16iter [00:02, 12.03iter/s]
Best score: 24.0950: : 17iter [00:02, 12.03iter/s]
Best score: 24.0950: : 18iter [00:02, 12.03iter/s]
Best score: 24.0950: : 19iter [00:02, 14.19iter/s]
Best score: 24.0950: : 19iter [00:02, 14.19iter/s]
Best score: 24.0950: : 20iter [00:02, 14.19iter/s]
Best score: 24.0950: : 21iter [00:02, 14.19iter/s]
Best score: 24.0950: : 22iter [00:02, 15.92iter/s]
Best score: 24.0950: : 22iter [00:02, 15.92iter/s]
Best score: 24.0950: : 23iter [00:02, 15.92iter/s]
Best score: 24.0950: : 24iter [00:03, 15.92iter/s]
Best score: 24.0950: : 25iter [00:03, 17.47iter/s]
Best score: 24.0950: : 25iter [00:03, 17.47iter/s]
Best score: 24.0950: : 26iter [00:03, 17.47iter/s]
Best score: 24.0950: : 27iter [00:03, 17.47iter/s]
Best score: 24.0950: : 28iter [00:03, 18.69iter/s]
Best score: 24.0950: : 28iter [00:03, 18.69iter/s]
Best score: 24.0950: : 29iter [00:03, 18.69iter/s]
Best score: 24.0950: : 30iter [00:03, 18.69iter/s]
Best score: 24.0950: : 31iter [00:03, 19.45iter/s]
Best score: 24.0950: : 31iter [00:03, 19.45iter/s]
Best score: 24.0950: : 32iter [00:03, 19.45iter/s]
Best score: 24.0950: : 33iter [00:03, 19.45iter/s]
Best score: 24.0950: : 34iter [00:03, 20.17iter/s]
Best score: 24.0950: : 34iter [00:03, 20.17iter/s]
Best score: 24.0950: : 35iter [00:03, 20.17iter/s]
Best score: 24.0950: : 36iter [00:03, 20.17iter/s]
Best score: 24.0950: : 37iter [00:03, 20.75iter/s]
Best score: 24.0950: : 37iter [00:03, 20.75iter/s]
Best score: 24.0950: : 38iter [00:03, 20.75iter/s]
Best score: 18.8842: : 39iter [00:03, 20.75iter/s]
Best score: 18.8842: : 40iter [00:03, 20.93iter/s]
Best score: 18.8842: : 40iter [00:03, 20.93iter/s]
Best score: 18.8842: : 41iter [00:03, 20.93iter/s]
Best score: 18.8842: : 42iter [00:03, 20.93iter/s]
Best score: 18.8842: : 43iter [00:03, 21.26iter/s]
Best score: 18.8842: : 43iter [00:03, 21.26iter/s]
Best score: 18.8842: : 44iter [00:03, 21.26iter/s]
Best score: 18.8842: : 45iter [00:03, 21.26iter/s]
Best score: 18.8842: : 46iter [00:03, 21.54iter/s]
Best score: 18.8842: : 46iter [00:04, 21.54iter/s]
Best score: 18.8842: : 47iter [00:04, 21.54iter/s]
Best score: 18.8842: : 48iter [00:04, 21.54iter/s]
Best score: 18.8842: : 49iter [00:04, 21.76iter/s]
[ITER 50] Best: 18.8842
[PARAMS] Current best parameters:
  fx: 556.7846
  fy: 556.0528
  px: 1001.2217
  py: 744.6807
  k1: -0.01963727
  k2: 0.00123423
  k3: 0.00171828
  k4: -0.00154699
  p1: -0.00388474
  p2: -0.00136002
  p3: 0.00369108
  p4: 0.00018435
  scale: 2.9923
  rotation: 0.8708

Best score: 18.8842: : 49iter [00:04, 21.76iter/s]
Best score: 18.8842: : 50iter [00:04, 21.76iter/s]
Best score: 18.8842: : 51iter [00:04, 21.76iter/s]
Best score: 18.8842: : 52iter [00:04, 21.73iter/s]
Best score: 18.8842: : 52iter [00:04, 21.73iter/s]
Best score: 18.8842: : 53iter [00:04, 21.73iter/s]
Best score: 18.8842: : 54iter [00:04, 21.73iter/s]
Best score: 18.8842: : 55iter [00:04, 21.88iter/s]
Best score: 18.8842: : 55iter [00:04, 21.88iter/s]
Best score: 18.8842: : 56iter [00:04, 21.88iter/s]
Best score: 18.8842: : 57iter [00:04, 21.88iter/s]
Best score: 18.8842: : 58iter [00:04, 22.01iter/s]
Best score: 18.8842: : 58iter [00:04, 22.01iter/s]
Best score: 18.8842: : 59iter [00:04, 22.01iter/s]
Best score: 18.8842: : 60iter [00:04, 22.01iter/s]
Best score: 18.8842: : 61iter [00:04, 21.87iter/s]
Best score: 18.8842: : 61iter [00:04, 21.87iter/s]
Best score: 18.8842: : 62iter [00:04, 21.87iter/s]
Best score: 18.8842: : 63iter [00:04, 21.87iter/s]
Best score: 18.8842: : 64iter [00:04, 21.93iter/s]
Best score: 18.8842: : 64iter [00:04, 21.93iter/s]
Best score: 18.8842: : 65iter [00:04, 21.93iter/s]
Best score: 18.8842: : 66iter [00:04, 21.93iter/s]
Best score: 18.8842: : 67iter [00:04, 21.98iter/s]
Best score: 18.8842: : 67iter [00:04, 21.98iter/s]
Best score: 18.8842: : 68iter [00:05, 21.98iter/s]
Best score: 9.4139: : 69iter [00:05, 21.98iter/s] 
Best score: 9.4139: : 70iter [00:05, 21.81iter/s]
Best score: 9.4139: : 70iter [00:05, 21.81iter/s]
Best score: 9.4139: : 71iter [00:05, 21.81iter/s]
Best score: 9.4139: : 72iter [00:05, 21.81iter/s]
Best score: 9.4139: : 73iter [00:05, 21.86iter/s]
Best score: 9.4139: : 73iter [00:05, 21.86iter/s]
Best score: 9.4139: : 74iter [00:05, 21.86iter/s]
Best score: 9.4139: : 75iter [00:05, 21.86iter/s]
Best score: 9.4139: : 76iter [00:05, 21.90iter/s]
Best score: 9.4139: : 76iter [00:05, 21.90iter/s]
Best score: 9.4139: : 77iter [00:05, 21.90iter/s]
Best score: 9.4139: : 78iter [00:05, 21.90iter/s]
Best score: 9.4139: : 79iter [00:05, 22.04iter/s]
Best score: 9.4139: : 79iter [00:05, 22.04iter/s]
Best score: 9.4139: : 80iter [00:05, 22.04iter/s]
Best score: 9.4139: : 81iter [00:05, 22.04iter/s]
Best score: 9.4139: : 82iter [00:05, 21.89iter/s]
Best score: 9.4139: : 82iter [00:05, 21.89iter/s]
Best score: 9.4139: : 83iter [00:05, 21.89iter/s]
Best score: 9.4139: : 84iter [00:05, 21.89iter/s]
Best score: 9.4139: : 85iter [00:05, 21.94iter/s]
Best score: 9.4139: : 85iter [00:05, 21.94iter/s]
Best score: 9.4139: : 86iter [00:05, 21.94iter/s]
Best score: 9.4139: : 87iter [00:05, 21.94iter/s]
Best score: 9.4139: : 88iter [00:05, 22.00iter/s]
Best score: 9.4139: : 88iter [00:05, 22.00iter/s]
Best score: 9.4139: : 89iter [00:06, 22.00iter/s]
Best score: 9.4139: : 90iter [00:06, 22.00iter/s]
Best score: 9.4139: : 91iter [00:06, 21.86iter/s]
Best score: 9.4139: : 91iter [00:06, 21.86iter/s]
Best score: 9.4139: : 92iter [00:06, 21.86iter/s]
Best score: 9.4139: : 93iter [00:06, 21.86iter/s]
Best score: 9.4139: : 94iter [00:06, 21.97iter/s]
Best score: 9.4139: : 94iter [00:06, 21.97iter/s]
Best score: 9.4139: : 95iter [00:06, 21.97iter/s]
Best score: 9.4139: : 96iter [00:06, 21.97iter/s]
Best score: 9.4139: : 97iter [00:06, 22.07iter/s]
Best score: 9.4139: : 97iter [00:06, 22.07iter/s]
Best score: 9.4139: : 98iter [00:06, 22.07iter/s]
[ITER 100] Best: 7.6865
[PARAMS] Current best parameters:
  fx: 569.1807
  fy: 569.1475
  px: 996.9834
  py: 742.3610
  k1: -0.03121847
  k2: -0.01046039
  k3: 0.00811305
  k4: -0.00115441
  p1: -0.00592275
  p2: -0.00636379
  p3: 0.00001545
  p4: 0.00356107
  scale: 3.0334
  rotation: 0.0436

Best score: 7.6865: : 99iter [00:06, 22.07iter/s]
Best score: 7.6865: : 100iter [00:06, 21.86iter/s]
Best score: 7.6865: : 100iter [00:06, 21.86iter/s]
Best score: 7.6865: : 101iter [00:06, 21.86iter/s]
Best score: 7.6865: : 102iter [00:06, 21.86iter/s]
Best score: 7.6865: : 103iter [00:06, 22.00iter/s]
Best score: 7.6865: : 103iter [00:06, 22.00iter/s]
Best score: 7.6865: : 104iter [00:06, 22.00iter/s]
Best score: 7.6865: : 105iter [00:06, 22.00iter/s]
Best score: 7.6865: : 106iter [00:06, 22.08iter/s]
Best score: 7.6865: : 106iter [00:06, 22.08iter/s]
Best score: 7.6865: : 107iter [00:06, 22.08iter/s]
Best score: 7.6865: : 108iter [00:06, 22.08iter/s]
Best score: 7.6865: : 109iter [00:06, 22.17iter/s]
Best score: 2.6269: : 109iter [00:06, 22.17iter/s]
Best score: 2.6269: : 110iter [00:06, 22.17iter/s]
Best score: 2.6269: : 111iter [00:06, 22.17iter/s]
Best score: 2.6269: : 112iter [00:07, 21.98iter/s]
Best score: 2.6269: : 112iter [00:07, 21.98iter/s]
Best score: 2.6269: : 113iter [00:07, 21.98iter/s]
Best score: 2.6269: : 114iter [00:07, 21.98iter/s]
Best score: 2.6269: : 115iter [00:07, 22.06iter/s]
Best score: 2.6269: : 115iter [00:07, 22.06iter/s]
Best score: 2.6269: : 116iter [00:07, 22.06iter/s]
Best score: 2.6269: : 117iter [00:07, 22.06iter/s]
Best score: 2.6269: : 118iter [00:07, 22.13iter/s]
Best score: 2.6269: : 118iter [00:07, 22.13iter/s]
Best score: 2.1637: : 119iter [00:07, 22.13iter/s]
Best score: 2.1637: : 120iter [00:07, 22.13iter/s]
Best score: 2.1637: : 121iter [00:07, 21.95iter/s]
Best score: 2.1637: : 121iter [00:07, 21.95iter/s]
Best score: 2.1637: : 122iter [00:07, 21.95iter/s]
Best score: 2.1637: : 123iter [00:07, 21.95iter/s]
Best score: 2.1637: : 124iter [00:07, 22.14iter/s]
Best score: 2.1637: : 124iter [00:07, 22.14iter/s]
Best score: 2.1637: : 125iter [00:07, 22.14iter/s]
Best score: 2.1637: : 126iter [00:07, 22.14iter/s]
Best score: 2.1637: : 127iter [00:07, 22.21iter/s]
Best score: 2.1637: : 127iter [00:07, 22.21iter/s]
Best score: 2.1637: : 128iter [00:07, 22.21iter/s]
Best score: 0.9914: : 129iter [00:07, 22.21iter/s]
Best score: 0.9914: : 130iter [00:07, 22.03iter/s]
Best score: 0.9914: : 130iter [00:07, 22.03iter/s]
Best score: 0.9914: : 131iter [00:07, 22.03iter/s]
Best score: 0.9914: : 132iter [00:07, 22.03iter/s]
Best score: 0.9914: : 133iter [00:07, 22.08iter/s]
Best score: 0.9914: : 133iter [00:07, 22.08iter/s]
Best score: 0.9914: : 134iter [00:08, 22.08iter/s]
Best score: 0.9914: : 135iter [00:08, 22.08iter/s]
Best score: 0.9914: : 136iter [00:08, 22.18iter/s]
Best score: 0.9914: : 136iter [00:08, 22.18iter/s]
Best score: 0.9914: : 137iter [00:08, 22.18iter/s]
Best score: 0.9914: : 138iter [00:08, 22.18iter/s]
Best score: 0.9914: : 139iter [00:08, 22.21iter/s]
Best score: 0.9914: : 139iter [00:08, 22.21iter/s]
Best score: 0.9914: : 140iter [00:08, 22.21iter/s]
Best score: 0.9914: : 141iter [00:08, 22.21iter/s]
Best score: 0.9914: : 142iter [00:08, 22.00iter/s]
Best score: 0.9914: : 142iter [00:08, 22.00iter/s]
Best score: 0.9914: : 143iter [00:08, 22.00iter/s]
Best score: 0.9914: : 144iter [00:08, 22.00iter/s]
Best score: 0.9914: : 145iter [00:08, 22.06iter/s]
Best score: 0.9914: : 145iter [00:08, 22.06iter/s]
Best score: 0.9914: : 146iter [00:08, 22.06iter/s]
Best score: 0.9914: : 147iter [00:08, 22.06iter/s]
Best score: 0.9914: : 148iter [00:08, 22.16iter/s]
Best score: 0.9914: : 148iter [00:08, 22.16iter/s]
[ITER 150] Best: 0.8527
[PARAMS] Current best parameters:
  fx: 579.4185
  fy: 578.7488
  px: 997.4586
  py: 740.2894
  k1: -0.03640021
  k2: -0.01472552
  k3: 0.01139053
  k4: -0.00232255
  p1: -0.00853547
  p2: -0.00910330
  p3: 0.00428533
  p4: 0.00142739
  scale: 3.0238
  rotation: 0.0981

Best score: 0.8527: : 149iter [00:08, 22.16iter/s]
Best score: 0.8527: : 150iter [00:08, 22.16iter/s]
Best score: 0.8527: : 151iter [00:08, 22.01iter/s]
Best score: 0.8527: : 151iter [00:08, 22.01iter/s]
Best score: 0.8527: : 152iter [00:08, 22.01iter/s]
Best score: 0.8527: : 153iter [00:08, 22.01iter/s]
Best score: 0.8527: : 154iter [00:08, 22.17iter/s]
Best score: 0.8527: : 154iter [00:08, 22.17iter/s]
Best score: 0.8527: : 155iter [00:08, 22.17iter/s]
Best score: 0.8527: : 156iter [00:09, 22.17iter/s]
Best score: 0.8527: : 157iter [00:09, 22.22iter/s]
Best score: 0.8527: : 157iter [00:09, 22.22iter/s]
Best score: 0.8527: : 158iter [00:09, 22.22iter/s]
Best score: 0.7071: : 159iter [00:09, 22.22iter/s]
Best score: 0.7071: : 160iter [00:09, 22.00iter/s]
Best score: 0.7071: : 160iter [00:09, 22.00iter/s]
Best score: 0.7071: : 161iter [00:09, 22.00iter/s]
Best score: 0.7071: : 162iter [00:09, 22.00iter/s]
Best score: 0.7071: : 163iter [00:09, 22.06iter/s]
Best score: 0.7071: : 163iter [00:09, 22.06iter/s]
Best score: 0.7071: : 164iter [00:09, 22.06iter/s]
Best score: 0.7071: : 165iter [00:09, 22.06iter/s]
Best score: 0.7071: : 166iter [00:09, 22.11iter/s]
Best score: 0.7071: : 166iter [00:09, 22.11iter/s]
Best score: 0.7071: : 167iter [00:09, 22.11iter/s]
Best score: 0.7071: : 168iter [00:09, 22.11iter/s]
Best score: 0.7071: : 169iter [00:09, 22.18iter/s]
Best score: 0.7071: : 169iter [00:09, 22.18iter/s]
Best score: 0.7071: : 170iter [00:09, 22.18iter/s]
Best score: 0.7071: : 171iter [00:09, 22.18iter/s]
Best score: 0.7071: : 172iter [00:09, 22.03iter/s]
Best score: 0.7071: : 172iter [00:09, 22.03iter/s]
Best score: 0.7071: : 173iter [00:09, 22.03iter/s]
Best score: 0.7071: : 174iter [00:09, 22.03iter/s]
Best score: 0.7071: : 175iter [00:09, 22.13iter/s]
Best score: 0.7071: : 175iter [00:09, 22.13iter/s]
Best score: 0.7071: : 176iter [00:09, 22.13iter/s]
Best score: 0.7071: : 177iter [00:09, 22.13iter/s]
Best score: 0.7071: : 178iter [00:09, 22.21iter/s]
Best score: 0.7071: : 178iter [00:10, 22.21iter/s]
Best score: 0.6858: : 179iter [00:10, 22.21iter/s]
Best score: 0.6858: : 180iter [00:10, 22.21iter/s]
Best score: 0.6858: : 181iter [00:10, 22.04iter/s]
Best score: 0.6858: : 181iter [00:10, 22.04iter/s]
Best score: 0.6858: : 182iter [00:10, 22.04iter/s]
Best score: 0.6858: : 183iter [00:10, 22.04iter/s]
Best score: 0.6858: : 184iter [00:10, 22.13iter/s]
Best score: 0.6858: : 184iter [00:10, 22.13iter/s]
Best score: 0.6858: : 185iter [00:10, 22.13iter/s]
Best score: 0.6858: : 186iter [00:10, 22.13iter/s]
Best score: 0.6858: : 187iter [00:10, 22.22iter/s]
Best score: 0.6858: : 187iter [00:10, 22.22iter/s]
Best score: 0.6858: : 188iter [00:10, 22.22iter/s]
Best score: 0.4757: : 189iter [00:10, 22.22iter/s]
Best score: 0.4757: : 190iter [00:10, 17.80iter/s]
Best score: 0.4757: : 190iter [00:10, 17.80iter/s]
Best score: 0.4757: : 191iter [00:10, 17.80iter/s]
Best score: 0.4757: : 192iter [00:10, 17.80iter/s]
Best score: 0.4757: : 193iter [00:10, 18.98iter/s]
Best score: 0.4757: : 193iter [00:10, 18.98iter/s]
Best score: 0.4757: : 194iter [00:10, 18.98iter/s]
Best score: 0.4757: : 195iter [00:10, 18.98iter/s]
Best score: 0.4757: : 196iter [00:10, 19.92iter/s]
Best score: 0.4757: : 196iter [00:10, 19.92iter/s]
Best score: 0.4757: : 197iter [00:10, 19.92iter/s]
Best score: 0.4757: : 198iter [00:11, 19.92iter/s]
Best score: 0.4757: : 199iter [00:11, 20.61iter/s]
[ITER 200] Best: 0.4389
[PARAMS] Current best parameters:
  fx: 585.8697
  fy: 585.5183
  px: 998.0545
  py: 741.5478
  k1: -0.04100839
  k2: -0.00955257
  k3: 0.00601536
  k4: -0.00091618
  p1: -0.00698009
  p2: -0.00749782
  p3: 0.00539108
  p4: 0.00077781
  scale: 3.0032
  rotation: 0.0829

Best score: 0.4389: : 199iter [00:11, 20.61iter/s]
Best score: 0.4389: : 200iter [00:11, 20.61iter/s]
Best score: 0.4389: : 201iter [00:11, 20.61iter/s]
Best score: 0.4389: : 202iter [00:11, 20.96iter/s]
Best score: 0.4389: : 202iter [00:11, 20.96iter/s]
Best score: 0.4389: : 203iter [00:11, 20.96iter/s]
Best score: 0.4389: : 204iter [00:11, 20.96iter/s]
Best score: 0.4389: : 205iter [00:11, 21.33iter/s]
Best score: 0.4389: : 205iter [00:11, 21.33iter/s]
Best score: 0.4389: : 206iter [00:11, 21.33iter/s]
Best score: 0.4389: : 207iter [00:11, 21.33iter/s]
Best score: 0.4389: : 208iter [00:11, 21.65iter/s]
Best score: 0.4389: : 208iter [00:11, 21.65iter/s]
Best score: 0.4055: : 209iter [00:11, 21.65iter/s]
Best score: 0.4055: : 210iter [00:11, 21.65iter/s]
Best score: 0.4055: : 211iter [00:11, 21.61iter/s]
Best score: 0.4055: : 211iter [00:11, 21.61iter/s]
Best score: 0.4055: : 212iter [00:11, 21.61iter/s]
Best score: 0.4055: : 213iter [00:11, 21.61iter/s]
Best score: 0.4055: : 214iter [00:11, 21.81iter/s]
Best score: 0.4055: : 214iter [00:11, 21.81iter/s]
Best score: 0.4055: : 215iter [00:11, 21.81iter/s]
Best score: 0.4055: : 216iter [00:11, 21.81iter/s]
Best score: 0.4055: : 217iter [00:11, 21.99iter/s]
Best score: 0.4055: : 217iter [00:11, 21.99iter/s]
Best score: 0.4055: : 218iter [00:11, 21.99iter/s]
Best score: 0.3820: : 219iter [00:11, 21.99iter/s]
Best score: 0.3820: : 220iter [00:11, 21.93iter/s]
Best score: 0.3820: : 220iter [00:12, 21.93iter/s]
Best score: 0.3820: : 221iter [00:12, 21.93iter/s]
Best score: 0.3820: : 222iter [00:12, 21.93iter/s]
Best score: 0.3820: : 223iter [00:12, 22.13iter/s]
Best score: 0.3820: : 223iter [00:12, 22.13iter/s]
Best score: 0.3820: : 224iter [00:12, 22.13iter/s]
Best score: 0.3820: : 225iter [00:12, 22.13iter/s]
Best score: 0.3820: : 226iter [00:12, 22.17iter/s]
Best score: 0.3820: : 226iter [00:12, 22.17iter/s]
Best score: 0.3820: : 227iter [00:12, 22.17iter/s]
Best score: 0.3820: : 228iter [00:12, 22.17iter/s]
Best score: 0.3820: : 229iter [00:12, 22.25iter/s]
Best score: 0.3578: : 229iter [00:12, 22.25iter/s]
Best score: 0.3578: : 230iter [00:12, 22.25iter/s]
Best score: 0.3578: : 231iter [00:12, 22.25iter/s]
Best score: 0.3578: : 232iter [00:12, 22.05iter/s]
Best score: 0.3578: : 232iter [00:12, 22.05iter/s]
Best score: 0.3578: : 233iter [00:12, 22.05iter/s]
Best score: 0.3578: : 234iter [00:12, 22.05iter/s]
Best score: 0.3578: : 235iter [00:12, 22.16iter/s]
Best score: 0.3578: : 235iter [00:12, 22.16iter/s]
Best score: 0.3578: : 236iter [00:12, 22.16iter/s]
Best score: 0.3578: : 237iter [00:12, 22.16iter/s]
Best score: 0.3578: : 238iter [00:12, 22.24iter/s]
Best score: 0.3578: : 238iter [00:12, 22.24iter/s]
Best score: 0.3504: : 239iter [00:12, 22.24iter/s]
Best score: 0.3504: : 240iter [00:12, 22.24iter/s]
Best score: 0.3504: : 241iter [00:12, 22.10iter/s]
Best score: 0.3504: : 241iter [00:12, 22.10iter/s]
Best score: 0.3504: : 242iter [00:13, 22.10iter/s]
Best score: 0.3504: : 243iter [00:13, 22.10iter/s]
Best score: 0.3504: : 244iter [00:13, 22.20iter/s]
Best score: 0.3504: : 244iter [00:13, 22.20iter/s]
Best score: 0.3504: : 245iter [00:13, 22.20iter/s]
Best score: 0.3504: : 246iter [00:13, 22.20iter/s]
Best score: 0.3504: : 247iter [00:13, 22.26iter/s]
Best score: 0.3504: : 247iter [00:13, 22.26iter/s]
Best score: 0.3504: : 248iter [00:13, 22.26iter/s]
[ITER 250] Best: 0.2417
[PARAMS] Current best parameters:
  fx: 581.9836
  fy: 581.4890
  px: 998.2201
  py: 740.8093
  k1: -0.04314133
  k2: -0.00262927
  k3: 0.00320808
  k4: -0.00071455
  p1: -0.00823954
  p2: -0.00695134
  p3: 0.00543150
  p4: -0.00007507
  scale: 3.0156
  rotation: 0.0705

Best score: 0.2417: : 249iter [00:13, 22.26iter/s]
Best score: 0.2417: : 250iter [00:13, 22.11iter/s]
Best score: 0.2417: : 250iter [00:13, 22.11iter/s]
Best score: 0.2417: : 251iter [00:13, 22.11iter/s]
Best score: 0.2417: : 252iter [00:13, 22.11iter/s]
Best score: 0.2417: : 253iter [00:13, 22.16iter/s]
Best score: 0.2417: : 253iter [00:13, 22.16iter/s]
Best score: 0.2417: : 254iter [00:13, 22.16iter/s]
Best score: 0.2417: : 255iter [00:13, 22.16iter/s]
Best score: 0.2417: : 256iter [00:13, 22.21iter/s]
Best score: 0.2417: : 256iter [00:13, 22.21iter/s]
Best score: 0.2417: : 257iter [00:13, 22.21iter/s]
Best score: 0.2417: : 258iter [00:13, 22.21iter/s]
Best score: 0.2417: : 259iter [00:13, 22.29iter/s]
Best score: 0.2417: : 259iter [00:13, 22.29iter/s]
Best score: 0.2417: : 260iter [00:13, 22.29iter/s]
Best score: 0.2417: : 261iter [00:13, 22.29iter/s]
Best score: 0.2417: : 262iter [00:13, 22.09iter/s]
Best score: 0.2417: : 262iter [00:13, 22.09iter/s]
Best score: 0.2417: : 263iter [00:13, 22.09iter/s]
Best score: 0.2417: : 264iter [00:14, 22.09iter/s]
Best score: 0.2417: : 265iter [00:14, 22.14iter/s]
Best score: 0.2417: : 265iter [00:14, 22.14iter/s]
Best score: 0.2417: : 266iter [00:14, 22.14iter/s]
Best score: 0.2417: : 267iter [00:14, 22.14iter/s]
Best score: 0.2417: : 268iter [00:14, 22.24iter/s]
Best score: 0.2417: : 268iter [00:14, 22.24iter/s]
Best score: 0.2337: : 269iter [00:14, 22.24iter/s]
Best score: 0.2337: : 270iter [00:14, 22.24iter/s]
Best score: 0.2337: : 271iter [00:14, 22.11iter/s]
Best score: 0.2337: : 271iter [00:14, 22.11iter/s]
Best score: 0.2337: : 272iter [00:14, 22.11iter/s]
Best score: 0.2337: : 273iter [00:14, 22.11iter/s]
Best score: 0.2337: : 274iter [00:14, 22.08iter/s]
Best score: 0.2337: : 274iter [00:14, 22.08iter/s]
Best score: 0.2337: : 275iter [00:14, 22.08iter/s]
Best score: 0.2337: : 276iter [00:14, 22.08iter/s]
Best score: 0.2337: : 277iter [00:14, 22.17iter/s]
Best score: 0.2337: : 277iter [00:14, 22.17iter/s]
Best score: 0.2337: : 278iter [00:14, 22.17iter/s]
Best score: 0.2337: : 279iter [00:14, 22.17iter/s]
Best score: 0.2337: : 280iter [00:14, 22.03iter/s]
Best score: 0.2337: : 280iter [00:14, 22.03iter/s]
Best score: 0.2337: : 281iter [00:14, 22.03iter/s]
Best score: 0.2337: : 282iter [00:14, 22.03iter/s]
Best score: 0.2337: : 283iter [00:14, 22.13iter/s]
Best score: 0.2337: : 283iter [00:14, 22.13iter/s]
Best score: 0.2337: : 284iter [00:14, 22.13iter/s]
Best score: 0.2337: : 285iter [00:14, 22.13iter/s]
Best score: 0.2337: : 286iter [00:14, 22.22iter/s]
Best score: 0.2337: : 286iter [00:15, 22.22iter/s]
Best score: 0.2337: : 287iter [00:15, 22.22iter/s]
Best score: 0.2337: : 288iter [00:15, 22.22iter/s]
Best score: 0.2337: : 289iter [00:15, 22.29iter/s]
Best score: 0.2226: : 289iter [00:15, 22.29iter/s]
Best score: 0.2226: : 290iter [00:15, 22.29iter/s]
Best score: 0.2226: : 291iter [00:15, 22.29iter/s]
Best score: 0.2226: : 292iter [00:15, 22.07iter/s]
Best score: 0.2226: : 292iter [00:15, 22.07iter/s]
Best score: 0.2226: : 293iter [00:15, 22.07iter/s]
Best score: 0.2226: : 294iter [00:15, 22.07iter/s]
Best score: 0.2226: : 295iter [00:15, 22.18iter/s]
Best score: 0.2226: : 295iter [00:15, 22.18iter/s]
Best score: 0.2226: : 296iter [00:15, 22.18iter/s]
Best score: 0.2226: : 297iter [00:15, 22.18iter/s]
Best score: 0.2226: : 298iter [00:15, 22.18iter/s]
Best score: 0.2226: : 298iter [00:15, 22.18iter/s]
[ITER 300] Best: 0.2224
[PARAMS] Current best parameters:
  fx: 582.8382
  fy: 582.3605
  px: 998.3430
  py: 741.1855
  k1: -0.04670303
  k2: -0.00065574
  k3: 0.00362126
  k4: -0.00107365
  p1: -0.00726738
  p2: -0.00619741
  p3: 0.00449134
  p4: 0.00021892
  scale: 3.0199
  rotation: 0.0386

Best score: 0.2224: : 299iter [00:15, 22.18iter/s]
Best score: 0.2224: : 300iter [00:15, 22.18iter/s]
Best score: 0.2224: : 301iter [00:15, 22.03iter/s]
Best score: 0.2224: : 301iter [00:15, 22.03iter/s]
Best score: 0.2224: : 302iter [00:15, 22.03iter/s]
Best score: 0.2224: : 303iter [00:15, 22.03iter/s]
Best score: 0.2224: : 304iter [00:15, 22.12iter/s]
Best score: 0.2224: : 304iter [00:15, 22.12iter/s]
Best score: 0.2224: : 305iter [00:15, 22.12iter/s]
Best score: 0.2224: : 306iter [00:15, 22.12iter/s]
Best score: 0.2224: : 307iter [00:15, 22.23iter/s]
Best score: 0.2224: : 307iter [00:15, 22.23iter/s]
Best score: 0.2224: : 308iter [00:15, 22.23iter/s]
Best score: 0.2224: : 309iter [00:16, 22.23iter/s]
Best score: 0.2224: : 310iter [00:16, 22.04iter/s]
Best score: 0.2224: : 310iter [00:16, 22.04iter/s]
Best score: 0.2224: : 311iter [00:16, 22.04iter/s]
Best score: 0.2224: : 312iter [00:16, 22.04iter/s]
Best score: 0.2224: : 313iter [00:16, 22.13iter/s]
Best score: 0.2224: : 313iter [00:16, 22.13iter/s]
Best score: 0.2224: : 314iter [00:16, 22.13iter/s]
Best score: 0.2224: : 315iter [00:16, 22.13iter/s]
Best score: 0.2224: : 316iter [00:16, 22.20iter/s]
Best score: 0.2224: : 316iter [00:16, 22.20iter/s]
Best score: 0.2224: : 317iter [00:16, 22.20iter/s]
Best score: 0.2224: : 318iter [00:16, 22.20iter/s]
Best score: 0.2224: : 319iter [00:16, 22.28iter/s]
Best score: 0.1996: : 319iter [00:16, 22.28iter/s]
Best score: 0.1996: : 320iter [00:16, 22.28iter/s]
Best score: 0.1996: : 321iter [00:16, 22.28iter/s]
Best score: 0.1996: : 322iter [00:16, 22.10iter/s]
Best score: 0.1996: : 322iter [00:16, 22.10iter/s]
Best score: 0.1996: : 323iter [00:16, 22.10iter/s]
Best score: 0.1996: : 324iter [00:16, 22.10iter/s]
Best score: 0.1996: : 325iter [00:16, 22.19iter/s]
Best score: 0.1996: : 325iter [00:16, 22.19iter/s]
Best score: 0.1996: : 326iter [00:16, 22.19iter/s]
Best score: 0.1996: : 327iter [00:16, 22.19iter/s]
Best score: 0.1996: : 328iter [00:16, 22.29iter/s]
Best score: 0.1996: : 328iter [00:16, 22.29iter/s]
Best score: 0.1984: : 329iter [00:16, 22.29iter/s]
Best score: 0.1984: : 330iter [00:16, 22.29iter/s]
Best score: 0.1984: : 331iter [00:16, 22.10iter/s]
Best score: 0.1984: : 331iter [00:17, 22.10iter/s]
Best score: 0.1984: : 332iter [00:17, 22.10iter/s]
Best score: 0.1984: : 333iter [00:17, 22.10iter/s]
Best score: 0.1984: : 334iter [00:17, 22.21iter/s]
Best score: 0.1984: : 334iter [00:17, 22.21iter/s]
Best score: 0.1984: : 335iter [00:17, 22.21iter/s]
Best score: 0.1984: : 336iter [00:17, 22.21iter/s]
Best score: 0.1984: : 337iter [00:17, 22.25iter/s]
Best score: 0.1984: : 337iter [00:17, 22.25iter/s]
Best score: 0.1984: : 338iter [00:17, 22.25iter/s]
Best score: 0.1963: : 339iter [00:17, 22.25iter/s]
Best score: 0.1963: : 340iter [00:17, 22.08iter/s]
Best score: 0.1963: : 340iter [00:17, 22.08iter/s]
Best score: 0.1963: : 341iter [00:17, 22.08iter/s]
Best score: 0.1963: : 342iter [00:17, 22.08iter/s]
Best score: 0.1963: : 343iter [00:17, 21.90iter/s]
Best score: 0.1963: : 343iter [00:17, 21.90iter/s]
Best score: 0.1963: : 344iter [00:17, 21.90iter/s]
Best score: 0.1963: : 345iter [00:17, 21.90iter/s]
Best score: 0.1963: : 346iter [00:17, 22.06iter/s]
Best score: 0.1963: : 346iter [00:17, 22.06iter/s]
Best score: 0.1963: : 347iter [00:17, 22.06iter/s]
Best score: 0.1963: : 348iter [00:17, 22.06iter/s]
Best score: 0.1963: : 349iter [00:17, 22.16iter/s]
[ITER 350] Best: 0.1945
[PARAMS] Current best parameters:
  fx: 582.7606
  fy: 582.1819
  px: 1000.0242
  py: 742.5452
  k1: -0.05216117
  k2: 0.00907881
  k3: -0.00265285
  k4: 0.00034853
  p1: -0.00326224
  p2: -0.00159367
  p3: 0.00360224
  p4: -0.00127590
  scale: 3.0257

Best score: 0.1945: : 349iter [00:17, 22.16iter/s]  rotation: 0.0435

Best score: 0.1945: : 350iter [00:17, 22.16iter/s]
Best score: 0.1945: : 351iter [00:17, 22.16iter/s]
Best score: 0.1945: : 352iter [00:17, 21.98iter/s]
Best score: 0.1945: : 352iter [00:17, 21.98iter/s]
Best score: 0.1945: : 353iter [00:18, 21.98iter/s]
Best score: 0.1945: : 354iter [00:18, 21.98iter/s]
Best score: 0.1945: : 355iter [00:18, 22.14iter/s]
Best score: 0.1945: : 355iter [00:18, 22.14iter/s]
Best score: 0.1945: : 356iter [00:18, 22.14iter/s]
Best score: 0.1945: : 357iter [00:18, 22.14iter/s]
Best score: 0.1945: : 358iter [00:18, 22.15iter/s]
Best score: 0.1945: : 358iter [00:18, 22.15iter/s]
Best score: 0.1945: : 359iter [00:18, 22.15iter/s]
Best score: 0.1945: : 360iter [00:18, 22.15iter/s]
Best score: 0.1945: : 361iter [00:18, 22.02iter/s]
Best score: 0.1945: : 361iter [00:18, 22.02iter/s]
Best score: 0.1945: : 362iter [00:18, 22.02iter/s]
Best score: 0.1945: : 363iter [00:18, 22.02iter/s]
Best score: 0.1945: : 364iter [00:18, 22.12iter/s]
Best score: 0.1945: : 364iter [00:18, 22.12iter/s]
Best score: 0.1945: : 365iter [00:18, 22.12iter/s]
Best score: 0.1945: : 366iter [00:18, 22.12iter/s]
Best score: 0.1945: : 367iter [00:18, 22.15iter/s]
Best score: 0.1945: : 367iter [00:18, 22.15iter/s]
Best score: 0.1945: : 368iter [00:18, 22.15iter/s]
Best score: 0.1933: : 369iter [00:18, 22.15iter/s]
Best score: 0.1933: : 370iter [00:18, 21.98iter/s]
Best score: 0.1933: : 370iter [00:18, 21.98iter/s]
Best score: 0.1933: : 371iter [00:18, 21.98iter/s]
Best score: 0.1933: : 372iter [00:18, 21.98iter/s]
Best score: 0.1933: : 373iter [00:18, 22.13iter/s]
Best score: 0.1933: : 373iter [00:18, 22.13iter/s]
Best score: 0.1933: : 374iter [00:18, 22.13iter/s]
Best score: 0.1933: : 375iter [00:19, 22.13iter/s]
Best score: 0.1933: : 376iter [00:19, 22.25iter/s]
Best score: 0.1933: : 376iter [00:19, 22.25iter/s]
Best score: 0.1933: : 377iter [00:19, 22.25iter/s]
Best score: 0.1933: : 378iter [00:19, 22.25iter/s]
Best score: 0.1933: : 379iter [00:19, 22.30iter/s]
Best score: 0.1918: : 379iter [00:19, 22.30iter/s]
Best score: 0.1918: : 380iter [00:19, 22.30iter/s]
Best score: 0.1918: : 381iter [00:19, 22.30iter/s]
Best score: 0.1918: : 382iter [00:19, 22.05iter/s]
Best score: 0.1918: : 382iter [00:19, 22.05iter/s]
Best score: 0.1918: : 383iter [00:19, 22.05iter/s]
Best score: 0.1918: : 384iter [00:19, 22.05iter/s]
Best score: 0.1918: : 385iter [00:19, 22.12iter/s]
Best score: 0.1918: : 385iter [00:19, 22.12iter/s]
Best score: 0.1918: : 386iter [00:19, 22.12iter/s]
Best score: 0.1918: : 387iter [00:19, 22.12iter/s]
Best score: 0.1918: : 388iter [00:19, 22.26iter/s]
Best score: 0.1918: : 388iter [00:19, 22.26iter/s]
Best score: 0.1915: : 389iter [00:19, 22.26iter/s]
Best score: 0.1915: : 390iter [00:19, 22.26iter/s]
Best score: 0.1915: : 391iter [00:19, 22.06iter/s]
Best score: 0.1915: : 391iter [00:19, 22.06iter/s]
Best score: 0.1915: : 392iter [00:19, 22.06iter/s]
Best score: 0.1915: : 393iter [00:19, 22.06iter/s]
Best score: 0.1915: : 394iter [00:19, 22.21iter/s]
Best score: 0.1915: : 394iter [00:19, 22.21iter/s]
Best score: 0.1915: : 395iter [00:19, 22.21iter/s]
Best score: 0.1915: : 396iter [00:19, 22.21iter/s]
Best score: 0.1915: : 397iter [00:19, 22.25iter/s]
Best score: 0.1915: : 397iter [00:20, 22.25iter/s]
Best score: 0.1915: : 398iter [00:20, 22.25iter/s]
[ITER 400] Best: 0.1911
[PARAMS] Current best parameters:
  fx: 583.2736
  fy: 582.6668
  px: 999.9496
  py: 742.2463
  k1: -0.05160752
  k2: 0.00662880
  k3: -0.00080396
  k4: -0.00008823
  p1: -0.00406309
  p2: -0.00183875
  p3: 0.00326664
  p4: -0.00089565
  scale: 3.0260
  rotation: 0.0456

Best score: 0.1911: : 399iter [00:20, 22.25iter/s]
Best score: 0.1911: : 400iter [00:20, 22.08iter/s]
Best score: 0.1911: : 400iter [00:20, 22.08iter/s]
Best score: 0.1911: : 401iter [00:20, 22.08iter/s]
Best score: 0.1911: : 402iter [00:20, 22.08iter/s]
Best score: 0.1911: : 403iter [00:20, 22.16iter/s]
Best score: 0.1911: : 403iter [00:20, 22.16iter/s]
Best score: 0.1911: : 404iter [00:20, 22.16iter/s]
Best score: 0.1911: : 405iter [00:20, 22.16iter/s]
Best score: 0.1911: : 406iter [00:20, 22.21iter/s]
Best score: 0.1911: : 406iter [00:20, 22.21iter/s]
Best score: 0.1911: : 407iter [00:20, 22.21iter/s]
Best score: 0.1911: : 408iter [00:20, 22.21iter/s]
Best score: 0.1911: : 409iter [00:20, 18.33iter/s]
Best score: 0.1910: : 409iter [00:20, 18.33iter/s]
Best score: 0.1910: : 410iter [00:20, 18.33iter/s]
Best score: 0.1910: : 411iter [00:20, 18.33iter/s]
Best score: 0.1910: : 412iter [00:20, 19.24iter/s]
Best score: 0.1910: : 412iter [00:20, 19.24iter/s]
Best score: 0.1910: : 413iter [00:20, 19.24iter/s]
Best score: 0.1910: : 414iter [00:20, 19.24iter/s]
Best score: 0.1910: : 415iter [00:20, 20.06iter/s]
Best score: 0.1910: : 415iter [00:20, 20.06iter/s]
Best score: 0.1910: : 416iter [00:20, 20.06iter/s]
Best score: 0.1910: : 417iter [00:21, 20.06iter/s]
Best score: 0.1910: : 418iter [00:21, 20.70iter/s]
Best score: 0.1910: : 418iter [00:21, 20.70iter/s]
Best score: 0.1910: : 419iter [00:21, 20.70iter/s]
Best score: 0.1910: : 420iter [00:21, 20.70iter/s]
Best score: 0.1910: : 421iter [00:21, 20.97iter/s]
Best score: 0.1910: : 421iter [00:21, 20.97iter/s]
Best score: 0.1910: : 422iter [00:21, 20.97iter/s]
Best score: 0.1910: : 423iter [00:21, 20.97iter/s]
Best score: 0.1910: : 424iter [00:21, 21.39iter/s]
Best score: 0.1910: : 424iter [00:21, 21.39iter/s]
Best score: 0.1910: : 425iter [00:21, 21.39iter/s]
Best score: 0.1910: : 426iter [00:21, 21.39iter/s]
Best score: 0.1910: : 427iter [00:21, 21.64iter/s]
Best score: 0.1910: : 427iter [00:21, 21.64iter/s]
Best score: 0.1910: : 428iter [00:21, 21.64iter/s]
Best score: 0.1903: : 429iter [00:21, 21.64iter/s]
Best score: 0.1903: : 430iter [00:21, 21.62iter/s]
Best score: 0.1903: : 430iter [00:21, 21.62iter/s]
Best score: 0.1903: : 431iter [00:21, 21.62iter/s]
Best score: 0.1903: : 432iter [00:21, 21.62iter/s]
Best score: 0.1903: : 433iter [00:21, 21.83iter/s]
Best score: 0.1903: : 433iter [00:21, 21.83iter/s]
Best score: 0.1903: : 434iter [00:21, 21.83iter/s]
Best score: 0.1903: : 435iter [00:21, 21.83iter/s]
Best score: 0.1903: : 436iter [00:21, 21.97iter/s]
Best score: 0.1903: : 436iter [00:21, 21.97iter/s]
Best score: 0.1903: : 437iter [00:21, 21.97iter/s]
Best score: 0.1903: : 438iter [00:21, 21.97iter/s]
Best score: 0.1903: : 439iter [00:21, 22.08iter/s]
Best score: 0.1903: : 439iter [00:22, 22.08iter/s]
Best score: 0.1903: : 440iter [00:22, 22.08iter/s]
Best score: 0.1903: : 441iter [00:22, 22.08iter/s]
Best score: 0.1903: : 442iter [00:22, 21.98iter/s]
Best score: 0.1903: : 442iter [00:22, 21.98iter/s]
Best score: 0.1903: : 443iter [00:22, 21.98iter/s]
Best score: 0.1903: : 444iter [00:22, 21.98iter/s]
Best score: 0.1903: : 445iter [00:22, 22.11iter/s]
Best score: 0.1903: : 445iter [00:22, 22.11iter/s]
Best score: 0.1903: : 446iter [00:22, 22.11iter/s]
Best score: 0.1903: : 447iter [00:22, 22.11iter/s]
Best score: 0.1903: : 448iter [00:22, 22.24iter/s]
Best score: 0.1903: : 448iter [00:22, 22.24iter/s]
[ITER 450] Best: 0.1898
[PARAMS] Current best parameters:
  fx: 582.1775
  fy: 581.5623
  px: 1000.2806
  py: 742.5967
  k1: -0.05403187
  k2: 0.01287573
  k3: -0.00488688
  k4: 0.00080352
  p1: -0.00307147
  p2: -0.00088065
  p3: 0.00216618
  p4: -0.00061761
  scale: 3.0290
  rotation: 0.0386

Best score: 0.1898: : 449iter [00:22, 22.24iter/s]
Best score: 0.1898: : 450iter [00:22, 22.24iter/s]
Best score: 0.1898: : 451iter [00:22, 22.05iter/s]
Best score: 0.1898: : 451iter [00:22, 22.05iter/s]
Best score: 0.1898: : 452iter [00:22, 22.05iter/s]
Best score: 0.1898: : 453iter [00:22, 22.05iter/s]
Best score: 0.1898: : 454iter [00:22, 22.14iter/s]
Best score: 0.1898: : 454iter [00:22, 22.14iter/s]
Best score: 0.1898: : 455iter [00:22, 22.14iter/s]
Best score: 0.1898: : 456iter [00:22, 22.14iter/s]
Best score: 0.1898: : 457iter [00:22, 22.22iter/s]
Best score: 0.1898: : 457iter [00:22, 22.22iter/s]
Best score: 0.1898: : 458iter [00:22, 22.22iter/s]
Best score: 0.1896: : 459iter [00:22, 22.22iter/s]
Best score: 0.1896: : 460iter [00:22, 22.01iter/s]
Best score: 0.1896: : 460iter [00:22, 22.01iter/s]
Best score: 0.1896: : 461iter [00:23, 22.01iter/s]
Best score: 0.1896: : 462iter [00:23, 22.01iter/s]
Best score: 0.1896: : 463iter [00:23, 22.14iter/s]
Best score: 0.1896: : 463iter [00:23, 22.14iter/s]
Best score: 0.1896: : 464iter [00:23, 22.14iter/s]
Best score: 0.1896: : 465iter [00:23, 22.14iter/s]
Best score: 0.1896: : 466iter [00:23, 22.23iter/s]
Best score: 0.1896: : 466iter [00:23, 22.23iter/s]
Best score: 0.1896: : 467iter [00:23, 22.23iter/s]
Best score: 0.1896: : 468iter [00:23, 22.23iter/s]
Best score: 0.1896: : 469iter [00:23, 22.30iter/s]
Best score: 0.1895: : 469iter [00:23, 22.30iter/s]
Best score: 0.1895: : 470iter [00:23, 22.30iter/s]
Best score: 0.1895: : 471iter [00:23, 22.30iter/s]
Best score: 0.1895: : 472iter [00:23, 22.04iter/s]
Best score: 0.1895: : 472iter [00:23, 22.04iter/s]
Best score: 0.1895: : 473iter [00:23, 22.04iter/s]
Best score: 0.1895: : 474iter [00:23, 22.04iter/s]
Best score: 0.1895: : 475iter [00:23, 22.16iter/s]
Best score: 0.1895: : 475iter [00:23, 22.16iter/s]
Best score: 0.1895: : 476iter [00:23, 22.16iter/s]
Best score: 0.1895: : 477iter [00:23, 22.16iter/s]
Best score: 0.1895: : 478iter [00:23, 22.28iter/s]
Best score: 0.1895: : 478iter [00:23, 22.28iter/s]
Best score: 0.1891: : 479iter [00:23, 22.28iter/s]
Best score: 0.1891: : 480iter [00:23, 22.28iter/s]
Best score: 0.1891: : 481iter [00:23, 22.11iter/s]
Best score: 0.1891: : 481iter [00:23, 22.11iter/s]
Best score: 0.1891: : 482iter [00:23, 22.11iter/s]
Best score: 0.1891: : 483iter [00:23, 22.11iter/s]
Best score: 0.1891: : 484iter [00:23, 22.22iter/s]
Best score: 0.1891: : 484iter [00:24, 22.22iter/s]
Best score: 0.1891: : 485iter [00:24, 22.22iter/s]
Best score: 0.1891: : 486iter [00:24, 22.22iter/s]
Best score: 0.1891: : 487iter [00:24, 22.24iter/s]
Best score: 0.1891: : 487iter [00:24, 22.24iter/s]
Best score: 0.1891: : 488iter [00:24, 22.24iter/s]
Best score: 0.1889: : 489iter [00:24, 22.24iter/s]
Best score: 0.1889: : 490iter [00:24, 22.03iter/s]
Best score: 0.1889: : 490iter [00:24, 22.03iter/s]
Best score: 0.1889: : 491iter [00:24, 22.03iter/s]
Best score: 0.1889: : 492iter [00:24, 22.03iter/s]
Best score: 0.1889: : 493iter [00:24, 21.94iter/s]
Best score: 0.1889: : 493iter [00:24, 21.94iter/s]
Best score: 0.1889: : 494iter [00:24, 21.94iter/s]
Best score: 0.1889: : 495iter [00:24, 21.94iter/s]
Best score: 0.1889: : 496iter [00:24, 22.10iter/s]
Best score: 0.1889: : 496iter [00:24, 22.10iter/s]
Best score: 0.1889: : 497iter [00:24, 22.10iter/s]
Best score: 0.1889: : 498iter [00:24, 22.10iter/s]
Best score: 0.1889: : 499iter [00:24, 22.20iter/s]
[ITER 500] Best: 0.1888
[PARAMS] Current best parameters:
  fx: 581.5840
  fy: 580.9866
  px: 1000.0540
  py: 742.4776
  k1: -0.06712005
  k2: 0.03398040
  k3: -0.01668643
  k4: 0.00310899
  p1: -0.00340325
  p2: -0.00146089
  p3: 0.00244846
  p4: -0.00063121
  scale: 3.0436
  rotation: 0.0416

Best score: 0.1888: : 499iter [00:24, 22.20iter/s]
Best score: 0.1888: : 500iter [00:24, 20.22iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.188764 -> 0.188692
  Success: True
  Tangential distortion: p1=-0.003398, p2=-0.001460, p3=0.002450, p4=-0.000628
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1887
[✅] Parameters copied to: /working/MDC015_W068_2025-11-25/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 3: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 581.5840389182512
    fy: 580.9866399439372
    px: 1000.0540484192765
    py: 742.4775595894057
    k1: -0.06712073366764754
    k2: 0.03398044188429378
    k3: -0.0166847190394816
    k4: 0.0031086561528150813
    p1: -0.0033983234609199706
    p2: -0.0014603003196479553
    p3: 0.002450489196873622
    p4: -0.0006284019493376802
    scale: 3.043608992815589
    rotation: 0.041623532248798806
    mmpp: 1.296710033586721
    nmpp: 1296710.033586721

[🔄] Converting 461 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.40s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.42s/rig]
[✅] Saved PTDs: /working/MDC015_W068_2025-11-25/calibration/debug/3_undistorted/output/ptd

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📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.03s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.03s/rig]
[✅] Saved PTDs: /working/MDC015_W068_2025-11-25/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

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📤 Writing LUT:  77%|███████▋  | 2370673/3080448 [00:02<00:00, 879351.60px/s]
📤 Writing LUT:  80%|███████▉  | 2458780/3080448 [00:02<00:00, 879863.45px/s]
📤 Writing LUT:  83%|████████▎ | 2546833/3080448 [00:02<00:00, 880062.66px/s]
📤 Writing LUT:  86%|████████▌ | 2635420/3080448 [00:03<00:00, 881802.15px/s]
📤 Writing LUT:  88%|████████▊ | 2723602/3080448 [00:03<00:00, 881596.71px/s]
📤 Writing LUT:  91%|█████████▏| 2811763/3080448 [00:03<00:00, 879428.86px/s]
📤 Writing LUT:  94%|█████████▍| 2899708/3080448 [00:03<00:00, 875968.01px/s]
📤 Writing LUT:  97%|█████████▋| 2987309/3080448 [00:03<00:00, 875489.52px/s]
📤 Writing LUT: 100%|█████████▉| 3076070/3080448 [00:03<00:00, 879110.84px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 876635.15px/s]
[✅] LUT written to: /working/MDC015_W068_2025-11-25/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W068_2025-11-25' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W068_2025-11-25/calibration/MDC015_W068_2025-11-25_unknown.lut

-------------------------------------------------------------------
                          Step 4: Heatmap                          
-------------------------------------------------------------------


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

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

   🔥 1000:   6%|▋         | 15/234 [00:00<00:01, 146.86frame/s]

   🔥 1000:  13%|█▎        | 31/234 [00:00<00:01, 151.93frame/s]

   🔥 1000:  20%|██        | 47/234 [00:00<00:01, 152.89frame/s]

   🔥 1000:  27%|██▋       | 63/234 [00:00<00:01, 153.62frame/s]

   🔥 1000:  34%|███▍      | 79/234 [00:00<00:01, 153.19frame/s]

   🔥 1000:  41%|████      | 95/234 [00:00<00:00, 152.28frame/s]

   🔥 1000:  47%|████▋     | 111/234 [00:00<00:00, 151.79frame/s]

   🔥 1000:  54%|█████▍    | 127/234 [00:00<00:00, 150.73frame/s]

   🔥 1000:  61%|██████    | 143/234 [00:00<00:00, 150.84frame/s]

   🔥 1000:  68%|██████▊   | 159/234 [00:01<00:00, 151.69frame/s]

   🔥 1000:  75%|███████▍  | 175/234 [00:01<00:00, 153.19frame/s]

   🔥 1000:  82%|████████▏ | 191/234 [00:01<00:00, 153.64frame/s]

   🔥 1000:  88%|████████▊ | 207/234 [00:01<00:00, 153.62frame/s]

   🔥 1000:  95%|█████████▌| 223/234 [00:01<00:00, 153.98frame/s]

                                                                 [✅] 1000: /working/MDC015_W068_2025-11-25/calibration/debug/4_heatmap/1000_heatmap.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:04<00:04,  4.38s/rig]

   🔥 500:   0%|          | 0/227 [00:00<?, ?frame/s]

   🔥 500:   7%|▋         | 15/227 [00:00<00:01, 146.32frame/s]

   🔥 500:  14%|█▎        | 31/227 [00:00<00:01, 148.96frame/s]

   🔥 500:  21%|██        | 47/227 [00:00<00:01, 149.85frame/s]

   🔥 500:  28%|██▊       | 63/227 [00:00<00:01, 150.74frame/s]

   🔥 500:  35%|███▍      | 79/227 [00:00<00:00, 151.26frame/s]

   🔥 500:  42%|████▏     | 95/227 [00:00<00:00, 153.23frame/s]

   🔥 500:  49%|████▉     | 112/227 [00:00<00:00, 156.44frame/s]

   🔥 500:  56%|█████▋    | 128/227 [00:00<00:00, 157.44frame/s]

   🔥 500:  63%|██████▎   | 144/227 [00:00<00:00, 157.45frame/s]

   🔥 500:  70%|███████   | 160/227 [00:01<00:00, 155.53frame/s]

   🔥 500:  78%|███████▊  | 176/227 [00:01<00:00, 153.99frame/s]

   🔥 500:  85%|████████▍ | 192/227 [00:01<00:00, 152.87frame/s]

   🔥 500:  92%|█████████▏| 208/227 [00:01<00:00, 152.31frame/s]

   🔥 500:  99%|█████████▊| 224/227 [00:01<00:00, 151.71frame/s]

                                                                [✅] 500: /working/MDC015_W068_2025-11-25/calibration/debug/4_heatmap/500_heatmap.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:07<00:00,  3.38s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:07<00:00,  3.53s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC015_W068_2025-11-25/calibration/debug/4_heatmap
[🏁] Finished processing MDC015_W068_2025-11-25


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