🟢 Starting Calibration Pipeline

=== Processing MDC046_W048_2025-11-24 ===

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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 397 stationary frames, 289 remain


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→ 500 Phase 2: corners:  65%|██████▍   | 187/289 [02:51<01:32,  1.10it/s]

→ 500 Phase 2: corners:  65%|██████▌   | 188/289 [02:51<01:31,  1.10it/s]

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→ 500 Phase 2: corners:  66%|██████▌   | 191/289 [02:54<01:29,  1.09it/s]

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→ 500 Phase 2: corners:  69%|██████▊   | 198/289 [03:01<01:25,  1.06it/s]

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→ 500 Phase 2: corners:  69%|██████▉   | 200/289 [03:03<01:23,  1.06it/s]

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→ 500 Phase 2: corners:  73%|███████▎  | 210/289 [03:12<01:14,  1.06it/s]

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→ 500 Phase 2: corners:  74%|███████▎  | 213/289 [03:14<00:57,  1.32it/s]

→ 500 Phase 2: corners:  74%|███████▍  | 214/289 [03:15<01:00,  1.24it/s]

→ 500 Phase 2: corners:  74%|███████▍  | 215/289 [03:16<01:03,  1.17it/s]

→ 500 Phase 2: corners:  75%|███████▍  | 216/289 [03:17<01:03,  1.15it/s]

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→ 500 Phase 2: corners:  75%|███████▌  | 218/289 [03:18<01:02,  1.13it/s]

→ 500 Phase 2: corners:  76%|███████▌  | 219/289 [03:19<01:02,  1.12it/s]

→ 500 Phase 2: corners:  76%|███████▌  | 220/289 [03:20<01:01,  1.12it/s]

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→ 500 Phase 2: corners:  79%|███████▉  | 229/289 [03:28<00:54,  1.09it/s]

→ 500 Phase 2: corners:  80%|███████▉  | 230/289 [03:29<00:54,  1.09it/s]

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→ 500 Phase 2: corners:  83%|████████▎ | 239/289 [03:37<00:45,  1.10it/s]

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→ 500 Phase 2: corners:  83%|████████▎ | 241/289 [03:39<00:43,  1.11it/s]

→ 500 Phase 2: corners:  84%|████████▎ | 242/289 [03:40<00:42,  1.11it/s]

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→ 500 Phase 2: corners:  84%|████████▍ | 244/289 [03:42<00:40,  1.11it/s]

→ 500 Phase 2: corners:  85%|████████▍ | 245/289 [03:43<00:39,  1.11it/s]

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→ 500 Phase 2: corners:  86%|████████▌ | 248/289 [03:45<00:35,  1.16it/s]

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→ 500 Phase 2: corners:  87%|████████▋ | 250/289 [03:47<00:33,  1.17it/s]

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→ 500 Phase 2: corners:  87%|████████▋ | 252/289 [03:49<00:31,  1.19it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 253/289 [03:50<00:29,  1.21it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 254/289 [03:50<00:29,  1.20it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 255/289 [03:51<00:28,  1.20it/s]

→ 500 Phase 2: corners:  89%|████████▊ | 256/289 [03:52<00:27,  1.20it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 257/289 [03:53<00:26,  1.19it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 258/289 [03:54<00:26,  1.18it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 259/289 [03:55<00:25,  1.16it/s]

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→ 500 Phase 2: corners:  90%|█████████ | 261/289 [03:56<00:24,  1.16it/s]

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→ 500 Phase 2: corners:  91%|█████████▏| 264/289 [03:59<00:23,  1.06it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 265/289 [04:00<00:22,  1.06it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 266/289 [04:01<00:21,  1.05it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 267/289 [04:02<00:20,  1.07it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 268/289 [04:03<00:18,  1.11it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 269/289 [04:04<00:17,  1.13it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 270/289 [04:05<00:16,  1.15it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 271/289 [04:06<00:15,  1.16it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 272/289 [04:06<00:14,  1.18it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 273/289 [04:07<00:13,  1.20it/s]

→ 500 Phase 2: corners:  95%|█████████▍| 274/289 [04:08<00:12,  1.21it/s]

→ 500 Phase 2: corners:  95%|█████████▌| 275/289 [04:09<00:11,  1.23it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 276/289 [04:10<00:10,  1.23it/s]

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→ 500 Phase 2: corners:  96%|█████████▌| 278/289 [04:11<00:08,  1.22it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 279/289 [04:12<00:08,  1.21it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 280/289 [04:13<00:07,  1.21it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 281/289 [04:14<00:06,  1.21it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 282/289 [04:15<00:05,  1.21it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 283/289 [04:15<00:04,  1.22it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 284/289 [04:16<00:04,  1.20it/s]

→ 500 Phase 2: corners:  99%|█████████▊| 285/289 [04:17<00:03,  1.18it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 286/289 [04:18<00:02,  1.19it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 287/289 [04:19<00:01,  1.19it/s]

→ 500 Phase 2: corners: 100%|█████████▉| 288/289 [04:20<00:00,  1.19it/s]

→ 500 Phase 2: corners: 100%|██████████| 289/289 [04:20<00:00,  1.17it/s]

                                                                         [PASS2] Frame 0000440: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000441: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000442: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000443: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000444: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000445: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000453: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000454: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000455: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000458: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000493: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000494: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000498: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000499: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000500: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000501: fixed corners {0, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000522: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000574: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000575: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000576: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000442: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000443: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000444: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000455: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000493: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000500: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000502: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000575: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000576: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000440: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000441: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000442: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000500: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000502: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000576: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 16 frames fixed, 17 untrusted, 289 total


→ 500 Phase 3: finalize:   0%|          | 0/289 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   1%|          | 2/289 [00:00<00:23, 12.11it/s]

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→ 500 Phase 3: finalize:   6%|▌         | 16/289 [00:01<00:20, 13.32it/s]

→ 500 Phase 3: finalize:   6%|▌         | 18/289 [00:01<00:20, 13.32it/s]

→ 500 Phase 3: finalize:   7%|▋         | 20/289 [00:01<00:20, 13.35it/s]

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→ 500 Phase 3: finalize:  17%|█▋        | 50/289 [00:03<00:17, 13.49it/s]

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→ 500 Phase 3: finalize:  20%|██        | 58/289 [00:04<00:17, 13.52it/s]

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→ 500 Phase 3: finalize:  51%|█████     | 146/289 [00:11<00:11, 12.55it/s]

→ 500 Phase 3: finalize:  51%|█████     | 148/289 [00:11<00:11, 12.56it/s]

→ 500 Phase 3: finalize:  52%|█████▏    | 150/289 [00:11<00:10, 12.88it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 152/289 [00:11<00:10, 12.77it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 154/289 [00:11<00:10, 13.05it/s]

→ 500 Phase 3: finalize:  54%|█████▍    | 156/289 [00:11<00:10, 13.21it/s]

→ 500 Phase 3: finalize:  55%|█████▍    | 158/289 [00:12<00:09, 13.73it/s]

→ 500 Phase 3: finalize:  55%|█████▌    | 160/289 [00:12<00:09, 13.66it/s]

→ 500 Phase 3: finalize:  56%|█████▌    | 162/289 [00:12<00:09, 14.07it/s]

→ 500 Phase 3: finalize:  57%|█████▋    | 164/289 [00:12<00:09, 13.58it/s]

→ 500 Phase 3: finalize:  57%|█████▋    | 166/289 [00:12<00:08, 14.03it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 168/289 [00:12<00:08, 13.91it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 170/289 [00:12<00:08, 14.03it/s]

→ 500 Phase 3: finalize:  60%|█████▉    | 172/289 [00:13<00:08, 13.62it/s]

→ 500 Phase 3: finalize:  60%|██████    | 174/289 [00:13<00:08, 13.82it/s]

→ 500 Phase 3: finalize:  61%|██████    | 176/289 [00:13<00:08, 13.88it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 178/289 [00:13<00:07, 13.99it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 180/289 [00:13<00:07, 14.16it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 182/289 [00:13<00:07, 14.25it/s]

→ 500 Phase 3: finalize:  64%|██████▎   | 184/289 [00:13<00:07, 14.33it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 186/289 [00:14<00:07, 14.41it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 188/289 [00:14<00:07, 14.34it/s]

→ 500 Phase 3: finalize:  66%|██████▌   | 190/289 [00:14<00:06, 14.40it/s]

→ 500 Phase 3: finalize:  66%|██████▋   | 192/289 [00:14<00:06, 14.47it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 194/289 [00:14<00:06, 14.52it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 196/289 [00:14<00:06, 14.55it/s]

→ 500 Phase 3: finalize:  69%|██████▊   | 198/289 [00:14<00:06, 14.57it/s]

→ 500 Phase 3: finalize:  69%|██████▉   | 200/289 [00:14<00:06, 14.56it/s]

→ 500 Phase 3: finalize:  70%|██████▉   | 202/289 [00:15<00:06, 14.46it/s]

→ 500 Phase 3: finalize:  71%|███████   | 204/289 [00:15<00:05, 14.20it/s]

→ 500 Phase 3: finalize:  71%|███████▏  | 206/289 [00:15<00:05, 14.02it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 208/289 [00:15<00:05, 13.91it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 210/289 [00:15<00:05, 13.82it/s]

→ 500 Phase 3: finalize:  74%|███████▎  | 213/289 [00:15<00:04, 15.31it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 215/289 [00:16<00:04, 14.81it/s]

→ 500 Phase 3: finalize:  75%|███████▌  | 217/289 [00:16<00:04, 14.50it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 219/289 [00:16<00:04, 14.28it/s]

→ 500 Phase 3: finalize:  76%|███████▋  | 221/289 [00:16<00:04, 14.14it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 223/289 [00:16<00:04, 14.04it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 225/289 [00:16<00:04, 13.94it/s]

→ 500 Phase 3: finalize:  79%|███████▊  | 227/289 [00:16<00:04, 13.89it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 229/289 [00:17<00:04, 13.85it/s]

→ 500 Phase 3: finalize:  80%|███████▉  | 231/289 [00:17<00:04, 13.85it/s]

→ 500 Phase 3: finalize:  81%|████████  | 233/289 [00:17<00:04, 13.86it/s]

→ 500 Phase 3: finalize:  81%|████████▏ | 235/289 [00:17<00:03, 13.85it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 237/289 [00:17<00:03, 13.86it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 239/289 [00:17<00:03, 13.85it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 241/289 [00:17<00:03, 13.80it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 243/289 [00:18<00:03, 13.74it/s]

→ 500 Phase 3: finalize:  85%|████████▍ | 245/289 [00:18<00:03, 13.71it/s]

→ 500 Phase 3: finalize:  85%|████████▌ | 247/289 [00:18<00:03, 13.64it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 249/289 [00:18<00:02, 13.60it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 251/289 [00:18<00:02, 13.58it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 253/289 [00:18<00:02, 13.53it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 255/289 [00:18<00:02, 13.48it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 257/289 [00:19<00:02, 13.43it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 259/289 [00:19<00:02, 13.38it/s]

→ 500 Phase 3: finalize:  90%|█████████ | 261/289 [00:19<00:02, 13.38it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 263/289 [00:19<00:01, 13.20it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 265/289 [00:19<00:01, 13.17it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 267/289 [00:19<00:01, 13.14it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 269/289 [00:20<00:01, 13.19it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 271/289 [00:20<00:01, 13.22it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 273/289 [00:20<00:01, 13.24it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 275/289 [00:20<00:01, 13.25it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 277/289 [00:20<00:00, 13.29it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 279/289 [00:20<00:00, 13.28it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 281/289 [00:20<00:00, 13.29it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 283/289 [00:21<00:00, 13.28it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 285/289 [00:21<00:00, 13.28it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 287/289 [00:21<00:00, 13.26it/s]

→ 500 Phase 3: finalize: 100%|██████████| 289/289 [00:21<00:00, 13.26it/s]

                                                                          
Processing Rigs:  50%|█████     | 1/2 [05:13<05:13, 313.87s/it][INFO] Filtered 402 stationary frames, 228 remain


→ 1000 (228 images) Phase 1:   0%|          | 0/228 [00:00<?, ?it/s]

→ 1000 (228 images) Phase 1:   0%|          | 1/228 [00:03<12:34,  3.33s/it]

→ 1000 (228 images) Phase 1:  14%|█▍        | 32/228 [00:03<00:15, 12.97it/s]

→ 1000 (228 images) Phase 1:  21%|██        | 48/228 [00:06<00:20,  8.70it/s]

→ 1000 (228 images) Phase 1:  29%|██▊       | 65/228 [00:08<00:21,  7.62it/s]

→ 1000 (228 images) Phase 1:  36%|███▌      | 82/228 [00:08<00:12, 11.56it/s]

→ 1000 (228 images) Phase 1:  43%|████▎     | 97/228 [00:11<00:15,  8.71it/s]

→ 1000 (228 images) Phase 1:  52%|█████▏    | 118/228 [00:11<00:08, 13.74it/s]

→ 1000 (228 images) Phase 1:  57%|█████▋    | 129/228 [00:14<00:10,  9.08it/s]

→ 1000 (228 images) Phase 1:  65%|██████▍   | 148/228 [00:14<00:05, 13.67it/s]

→ 1000 (228 images) Phase 1:  70%|██████▉   | 159/228 [00:14<00:04, 17.04it/s]

→ 1000 (228 images) Phase 1:  75%|███████▍  | 170/228 [00:17<00:05,  9.78it/s]

→ 1000 (228 images) Phase 1:  80%|███████▉  | 182/228 [00:17<00:03, 13.09it/s]

→ 1000 (228 images) Phase 1:  84%|████████▍ | 192/228 [00:17<00:02, 16.64it/s]

→ 1000 (228 images) Phase 1:  88%|████████▊ | 201/228 [00:19<00:03,  8.68it/s]

→ 1000 (228 images) Phase 1:  94%|█████████▍| 215/228 [00:20<00:01, 12.90it/s]

→ 1000 (228 images) Phase 1:  99%|█████████▊| 225/228 [00:21<00:00, 10.71it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/228 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/228 [00:00<02:58,  1.27it/s]

→ 1000 Phase 2: corners:   1%|          | 2/228 [00:01<02:57,  1.27it/s]

→ 1000 Phase 2: corners:   1%|▏         | 3/228 [00:02<03:00,  1.25it/s]

→ 1000 Phase 2: corners:   2%|▏         | 4/228 [00:03<03:00,  1.24it/s]

→ 1000 Phase 2: corners:   2%|▏         | 5/228 [00:03<02:16,  1.63it/s]

→ 1000 Phase 2: corners:   3%|▎         | 6/228 [00:04<02:28,  1.49it/s]

→ 1000 Phase 2: corners:   3%|▎         | 7/228 [00:05<02:33,  1.44it/s]

→ 1000 Phase 2: corners:   4%|▎         | 8/228 [00:05<02:36,  1.40it/s]

→ 1000 Phase 2: corners:   4%|▍         | 9/228 [00:06<02:38,  1.38it/s]

→ 1000 Phase 2: corners:   4%|▍         | 10/228 [00:07<02:38,  1.38it/s]

→ 1000 Phase 2: corners:   5%|▍         | 11/228 [00:07<02:39,  1.36it/s]

→ 1000 Phase 2: corners:   5%|▌         | 12/228 [00:08<02:40,  1.35it/s]

→ 1000 Phase 2: corners:   6%|▌         | 13/228 [00:09<02:39,  1.35it/s]

→ 1000 Phase 2: corners:   6%|▌         | 14/228 [00:10<02:39,  1.34it/s]

→ 1000 Phase 2: corners:   7%|▋         | 15/228 [00:11<02:39,  1.33it/s]

→ 1000 Phase 2: corners:   7%|▋         | 16/228 [00:11<02:40,  1.32it/s]

→ 1000 Phase 2: corners:   7%|▋         | 17/228 [00:12<02:42,  1.30it/s]

→ 1000 Phase 2: corners:   8%|▊         | 18/228 [00:13<02:42,  1.30it/s]

→ 1000 Phase 2: corners:   8%|▊         | 19/228 [00:14<02:40,  1.30it/s]

→ 1000 Phase 2: corners:   9%|▉         | 20/228 [00:14<02:39,  1.31it/s]

→ 1000 Phase 2: corners:   9%|▉         | 21/228 [00:15<02:38,  1.31it/s]

→ 1000 Phase 2: corners:  10%|▉         | 22/228 [00:16<02:38,  1.30it/s]

→ 1000 Phase 2: corners:  10%|█         | 23/228 [00:17<02:41,  1.27it/s]

→ 1000 Phase 2: corners:  11%|█         | 24/228 [00:18<02:40,  1.27it/s]

→ 1000 Phase 2: corners:  11%|█         | 25/228 [00:18<02:36,  1.29it/s]

→ 1000 Phase 2: corners:  11%|█▏        | 26/228 [00:19<02:32,  1.32it/s]

→ 1000 Phase 2: corners:  12%|█▏        | 27/228 [00:20<02:33,  1.31it/s]

→ 1000 Phase 2: corners:  12%|█▏        | 28/228 [00:21<02:31,  1.32it/s]

→ 1000 Phase 2: corners:  13%|█▎        | 29/228 [00:21<02:30,  1.32it/s]

→ 1000 Phase 2: corners:  13%|█▎        | 30/228 [00:22<02:31,  1.30it/s]

→ 1000 Phase 2: corners:  14%|█▎        | 31/228 [00:23<02:32,  1.29it/s]

→ 1000 Phase 2: corners:  14%|█▍        | 32/228 [00:24<02:28,  1.32it/s]

→ 1000 Phase 2: corners:  14%|█▍        | 33/228 [00:24<02:29,  1.30it/s]

→ 1000 Phase 2: corners:  15%|█▍        | 34/228 [00:25<02:28,  1.31it/s]

→ 1000 Phase 2: corners:  15%|█▌        | 35/228 [00:26<02:26,  1.32it/s]

→ 1000 Phase 2: corners:  16%|█▌        | 36/228 [00:27<02:26,  1.31it/s]

→ 1000 Phase 2: corners:  16%|█▌        | 37/228 [00:27<02:26,  1.30it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 38/228 [00:28<02:27,  1.29it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 39/228 [00:29<02:24,  1.30it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 40/228 [00:30<02:25,  1.29it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 41/228 [00:30<02:23,  1.30it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 42/228 [00:31<02:25,  1.27it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 43/228 [00:32<02:23,  1.29it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 44/228 [00:33<02:20,  1.31it/s]

→ 1000 Phase 2: corners:  20%|█▉        | 45/228 [00:34<02:20,  1.30it/s]

→ 1000 Phase 2: corners:  20%|██        | 46/228 [00:34<02:18,  1.31it/s]

→ 1000 Phase 2: corners:  21%|██        | 47/228 [00:35<02:18,  1.30it/s]

→ 1000 Phase 2: corners:  21%|██        | 48/228 [00:36<02:16,  1.32it/s]

→ 1000 Phase 2: corners:  21%|██▏       | 49/228 [00:37<02:15,  1.32it/s]

→ 1000 Phase 2: corners:  22%|██▏       | 50/228 [00:37<02:16,  1.31it/s]

→ 1000 Phase 2: corners:  22%|██▏       | 51/228 [00:38<02:13,  1.33it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 52/228 [00:39<02:13,  1.32it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 53/228 [00:40<02:14,  1.30it/s]

→ 1000 Phase 2: corners:  24%|██▎       | 54/228 [00:40<02:12,  1.32it/s]

→ 1000 Phase 2: corners:  24%|██▍       | 55/228 [00:41<02:08,  1.34it/s]

→ 1000 Phase 2: corners:  25%|██▍       | 56/228 [00:42<02:09,  1.33it/s]

→ 1000 Phase 2: corners:  25%|██▌       | 57/228 [00:43<02:07,  1.34it/s]

→ 1000 Phase 2: corners:  25%|██▌       | 58/228 [00:43<02:08,  1.32it/s]

→ 1000 Phase 2: corners:  26%|██▌       | 59/228 [00:44<02:06,  1.33it/s]

→ 1000 Phase 2: corners:  26%|██▋       | 60/228 [00:45<02:07,  1.32it/s]

→ 1000 Phase 2: corners:  27%|██▋       | 61/228 [00:46<02:05,  1.33it/s]

→ 1000 Phase 2: corners:  27%|██▋       | 62/228 [00:46<02:04,  1.33it/s]

→ 1000 Phase 2: corners:  28%|██▊       | 63/228 [00:47<02:02,  1.34it/s]

→ 1000 Phase 2: corners:  28%|██▊       | 64/228 [00:48<02:02,  1.34it/s]

→ 1000 Phase 2: corners:  29%|██▊       | 65/228 [00:49<02:00,  1.36it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 66/228 [00:49<01:58,  1.37it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 67/228 [00:50<01:59,  1.35it/s]

→ 1000 Phase 2: corners:  30%|██▉       | 68/228 [00:51<01:58,  1.35it/s]

→ 1000 Phase 2: corners:  30%|███       | 69/228 [00:52<01:54,  1.39it/s]

→ 1000 Phase 2: corners:  31%|███       | 70/228 [00:52<01:54,  1.38it/s]

→ 1000 Phase 2: corners:  31%|███       | 71/228 [00:53<01:54,  1.37it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 72/228 [00:54<01:53,  1.38it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 73/228 [00:54<01:52,  1.37it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 74/228 [00:55<01:52,  1.37it/s]

→ 1000 Phase 2: corners:  33%|███▎      | 75/228 [00:56<01:52,  1.37it/s]

→ 1000 Phase 2: corners:  33%|███▎      | 76/228 [00:57<01:50,  1.37it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 77/228 [00:57<01:50,  1.37it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 78/228 [00:58<01:49,  1.37it/s]

→ 1000 Phase 2: corners:  35%|███▍      | 79/228 [00:59<01:49,  1.36it/s]

→ 1000 Phase 2: corners:  35%|███▌      | 80/228 [01:00<01:49,  1.36it/s]

→ 1000 Phase 2: corners:  36%|███▌      | 81/228 [01:00<01:47,  1.37it/s]

→ 1000 Phase 2: corners:  36%|███▌      | 82/228 [01:01<01:46,  1.37it/s]

→ 1000 Phase 2: corners:  36%|███▋      | 83/228 [01:02<01:45,  1.37it/s]

→ 1000 Phase 2: corners:  37%|███▋      | 84/228 [01:03<01:46,  1.35it/s]

→ 1000 Phase 2: corners:  37%|███▋      | 85/228 [01:03<01:45,  1.36it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 86/228 [01:04<01:44,  1.35it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 87/228 [01:05<01:43,  1.37it/s]

→ 1000 Phase 2: corners:  39%|███▊      | 88/228 [01:05<01:42,  1.37it/s]

→ 1000 Phase 2: corners:  39%|███▉      | 89/228 [01:06<01:44,  1.33it/s]

→ 1000 Phase 2: corners:  39%|███▉      | 90/228 [01:07<01:44,  1.31it/s]

→ 1000 Phase 2: corners:  40%|███▉      | 91/228 [01:08<01:44,  1.31it/s]

→ 1000 Phase 2: corners:  40%|████      | 92/228 [01:09<01:44,  1.30it/s]

→ 1000 Phase 2: corners:  41%|████      | 93/228 [01:09<01:44,  1.30it/s]

→ 1000 Phase 2: corners:  41%|████      | 94/228 [01:10<01:42,  1.31it/s]

→ 1000 Phase 2: corners:  42%|████▏     | 95/228 [01:11<01:42,  1.30it/s]

→ 1000 Phase 2: corners:  42%|████▏     | 96/228 [01:12<01:40,  1.31it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 97/228 [01:12<01:40,  1.30it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 98/228 [01:13<01:41,  1.28it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 99/228 [01:14<01:41,  1.28it/s]

→ 1000 Phase 2: corners:  44%|████▍     | 100/228 [01:15<01:39,  1.29it/s]

→ 1000 Phase 2: corners:  44%|████▍     | 101/228 [01:16<01:39,  1.28it/s]

→ 1000 Phase 2: corners:  45%|████▍     | 102/228 [01:16<01:38,  1.28it/s]

→ 1000 Phase 2: corners:  45%|████▌     | 103/228 [01:17<01:36,  1.30it/s]

→ 1000 Phase 2: corners:  46%|████▌     | 104/228 [01:18<01:34,  1.31it/s]

→ 1000 Phase 2: corners:  46%|████▌     | 105/228 [01:19<01:32,  1.33it/s]

→ 1000 Phase 2: corners:  46%|████▋     | 106/228 [01:19<01:32,  1.32it/s]

→ 1000 Phase 2: corners:  47%|████▋     | 107/228 [01:20<01:32,  1.31it/s]

→ 1000 Phase 2: corners:  47%|████▋     | 108/228 [01:21<01:32,  1.30it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 109/228 [01:22<01:31,  1.30it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 110/228 [01:22<01:31,  1.30it/s]

→ 1000 Phase 2: corners:  49%|████▊     | 111/228 [01:23<01:30,  1.30it/s]

→ 1000 Phase 2: corners:  49%|████▉     | 112/228 [01:24<01:29,  1.29it/s]

→ 1000 Phase 2: corners:  50%|████▉     | 113/228 [01:25<01:28,  1.30it/s]

→ 1000 Phase 2: corners:  50%|█████     | 114/228 [01:25<01:27,  1.31it/s]

→ 1000 Phase 2: corners:  50%|█████     | 115/228 [01:26<01:26,  1.31it/s]

→ 1000 Phase 2: corners:  51%|█████     | 116/228 [01:27<01:25,  1.31it/s]

→ 1000 Phase 2: corners:  51%|█████▏    | 117/228 [01:28<01:25,  1.29it/s]

→ 1000 Phase 2: corners:  52%|█████▏    | 118/228 [01:29<01:25,  1.29it/s]

→ 1000 Phase 2: corners:  52%|█████▏    | 119/228 [01:29<01:25,  1.28it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 120/228 [01:30<01:25,  1.26it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 121/228 [01:31<01:24,  1.26it/s]

→ 1000 Phase 2: corners:  54%|█████▎    | 122/228 [01:32<01:26,  1.23it/s]

→ 1000 Phase 2: corners:  54%|█████▍    | 123/228 [01:33<01:24,  1.25it/s]

→ 1000 Phase 2: corners:  54%|█████▍    | 124/228 [01:33<01:22,  1.26it/s]

→ 1000 Phase 2: corners:  55%|█████▍    | 125/228 [01:34<01:19,  1.29it/s]

→ 1000 Phase 2: corners:  55%|█████▌    | 126/228 [01:35<01:19,  1.28it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 127/228 [01:36<01:20,  1.26it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 128/228 [01:37<01:19,  1.26it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 129/228 [01:37<01:18,  1.26it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 130/228 [01:38<01:18,  1.25it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 131/228 [01:39<01:17,  1.24it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 132/228 [01:40<01:17,  1.24it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 133/228 [01:41<01:15,  1.26it/s]

→ 1000 Phase 2: corners:  59%|█████▉    | 134/228 [01:41<01:13,  1.27it/s]

→ 1000 Phase 2: corners:  59%|█████▉    | 135/228 [01:42<01:12,  1.28it/s]

→ 1000 Phase 2: corners:  60%|█████▉    | 136/228 [01:43<01:11,  1.29it/s]

→ 1000 Phase 2: corners:  60%|██████    | 137/228 [01:44<01:10,  1.30it/s]

→ 1000 Phase 2: corners:  61%|██████    | 138/228 [01:44<01:09,  1.30it/s]

→ 1000 Phase 2: corners:  61%|██████    | 139/228 [01:45<01:07,  1.31it/s]

→ 1000 Phase 2: corners:  61%|██████▏   | 140/228 [01:46<01:07,  1.31it/s]

→ 1000 Phase 2: corners:  62%|██████▏   | 141/228 [01:47<01:06,  1.31it/s]

→ 1000 Phase 2: corners:  62%|██████▏   | 142/228 [01:47<01:06,  1.30it/s]

→ 1000 Phase 2: corners:  63%|██████▎   | 143/228 [01:48<01:05,  1.30it/s]

→ 1000 Phase 2: corners:  63%|██████▎   | 144/228 [01:49<01:05,  1.28it/s]

→ 1000 Phase 2: corners:  64%|██████▎   | 145/228 [01:50<01:05,  1.28it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 146/228 [01:51<01:03,  1.30it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 147/228 [01:51<01:01,  1.31it/s]

→ 1000 Phase 2: corners:  65%|██████▍   | 148/228 [01:52<01:01,  1.31it/s]

→ 1000 Phase 2: corners:  65%|██████▌   | 149/228 [01:53<01:00,  1.30it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 150/228 [01:54<01:00,  1.29it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 151/228 [01:54<01:01,  1.25it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 152/228 [01:55<01:01,  1.24it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 153/228 [01:56<01:01,  1.23it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 154/228 [01:57<01:01,  1.20it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 155/228 [01:58<01:00,  1.21it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 156/228 [01:59<00:58,  1.22it/s]

→ 1000 Phase 2: corners:  69%|██████▉   | 157/228 [01:59<00:57,  1.23it/s]

→ 1000 Phase 2: corners:  69%|██████▉   | 158/228 [02:00<00:56,  1.24it/s]

→ 1000 Phase 2: corners:  70%|██████▉   | 159/228 [02:01<00:55,  1.24it/s]

→ 1000 Phase 2: corners:  70%|███████   | 160/228 [02:02<00:55,  1.23it/s]

→ 1000 Phase 2: corners:  71%|███████   | 161/228 [02:03<00:53,  1.26it/s]

→ 1000 Phase 2: corners:  71%|███████   | 162/228 [02:03<00:53,  1.25it/s]

→ 1000 Phase 2: corners:  71%|███████▏  | 163/228 [02:04<00:52,  1.24it/s]

→ 1000 Phase 2: corners:  72%|███████▏  | 164/228 [02:05<00:50,  1.26it/s]

→ 1000 Phase 2: corners:  72%|███████▏  | 165/228 [02:06<00:48,  1.29it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 166/228 [02:06<00:48,  1.29it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 167/228 [02:07<00:46,  1.30it/s]

→ 1000 Phase 2: corners:  74%|███████▎  | 168/228 [02:08<00:46,  1.29it/s]

→ 1000 Phase 2: corners:  74%|███████▍  | 169/228 [02:09<00:46,  1.28it/s]

→ 1000 Phase 2: corners:  75%|███████▍  | 170/228 [02:10<00:44,  1.30it/s]

→ 1000 Phase 2: corners:  75%|███████▌  | 171/228 [02:10<00:43,  1.31it/s]

→ 1000 Phase 2: corners:  75%|███████▌  | 172/228 [02:11<00:42,  1.31it/s]

→ 1000 Phase 2: corners:  76%|███████▌  | 173/228 [02:12<00:41,  1.32it/s]

→ 1000 Phase 2: corners:  76%|███████▋  | 174/228 [02:13<00:41,  1.31it/s]

→ 1000 Phase 2: corners:  77%|███████▋  | 175/228 [02:13<00:40,  1.32it/s]

→ 1000 Phase 2: corners:  77%|███████▋  | 176/228 [02:14<00:39,  1.32it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 177/228 [02:15<00:38,  1.32it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 178/228 [02:16<00:38,  1.31it/s]

→ 1000 Phase 2: corners:  79%|███████▊  | 179/228 [02:16<00:37,  1.30it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 180/228 [02:17<00:36,  1.31it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 181/228 [02:18<00:35,  1.31it/s]

→ 1000 Phase 2: corners:  80%|███████▉  | 182/228 [02:19<00:35,  1.29it/s]

→ 1000 Phase 2: corners:  80%|████████  | 183/228 [02:20<00:34,  1.29it/s]

→ 1000 Phase 2: corners:  81%|████████  | 184/228 [02:20<00:34,  1.28it/s]

→ 1000 Phase 2: corners:  81%|████████  | 185/228 [02:21<00:32,  1.31it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 186/228 [02:22<00:31,  1.35it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 187/228 [02:22<00:29,  1.37it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 188/228 [02:23<00:29,  1.36it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 189/228 [02:24<00:29,  1.34it/s]

→ 1000 Phase 2: corners:  83%|████████▎ | 190/228 [02:25<00:28,  1.33it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 191/228 [02:25<00:27,  1.33it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 192/228 [02:26<00:27,  1.33it/s]

→ 1000 Phase 2: corners:  85%|████████▍ | 193/228 [02:27<00:26,  1.33it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 194/228 [02:28<00:26,  1.30it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 195/228 [02:29<00:25,  1.29it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 196/228 [02:29<00:24,  1.32it/s]

→ 1000 Phase 2: corners:  86%|████████▋ | 197/228 [02:30<00:24,  1.29it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 198/228 [02:31<00:22,  1.32it/s]

→ 1000 Phase 2: corners:  87%|████████▋ | 199/228 [02:32<00:21,  1.32it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 200/228 [02:32<00:20,  1.34it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 201/228 [02:33<00:19,  1.35it/s]

→ 1000 Phase 2: corners:  89%|████████▊ | 202/228 [02:34<00:18,  1.37it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 203/228 [02:35<00:18,  1.34it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 204/228 [02:35<00:17,  1.34it/s]

→ 1000 Phase 2: corners:  90%|████████▉ | 205/228 [02:36<00:16,  1.37it/s]

→ 1000 Phase 2: corners:  90%|█████████ | 206/228 [02:37<00:16,  1.32it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 207/228 [02:38<00:15,  1.34it/s]

→ 1000 Phase 2: corners:  91%|█████████ | 208/228 [02:38<00:14,  1.35it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 209/228 [02:39<00:14,  1.35it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 210/228 [02:40<00:13,  1.34it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 211/228 [02:40<00:12,  1.35it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 212/228 [02:41<00:11,  1.33it/s]

→ 1000 Phase 2: corners:  93%|█████████▎| 213/228 [02:42<00:11,  1.29it/s]

→ 1000 Phase 2: corners:  94%|█████████▍| 214/228 [02:43<00:10,  1.31it/s]

→ 1000 Phase 2: corners:  94%|█████████▍| 215/228 [02:44<00:10,  1.28it/s]

→ 1000 Phase 2: corners:  95%|█████████▍| 216/228 [02:44<00:09,  1.30it/s]

→ 1000 Phase 2: corners:  95%|█████████▌| 217/228 [02:45<00:08,  1.29it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 218/228 [02:46<00:07,  1.29it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 219/228 [02:47<00:06,  1.31it/s]

→ 1000 Phase 2: corners:  96%|█████████▋| 220/228 [02:47<00:06,  1.33it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 221/228 [02:48<00:05,  1.33it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 222/228 [02:49<00:04,  1.32it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 223/228 [02:50<00:03,  1.33it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 224/228 [02:50<00:03,  1.32it/s]

→ 1000 Phase 2: corners:  99%|█████████▊| 225/228 [02:51<00:02,  1.32it/s]

→ 1000 Phase 2: corners:  99%|█████████▉| 226/228 [02:52<00:01,  1.29it/s]

→ 1000 Phase 2: corners: 100%|█████████▉| 227/228 [02:53<00:00,  1.29it/s]

→ 1000 Phase 2: corners: 100%|██████████| 228/228 [02:54<00:00,  1.29it/s]

                                                                          [PASS2] Frame 0000289: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000303: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000306: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000317: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000574: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000615: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000628: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000629: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000630: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000631: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000306: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000581: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000630: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 6 frames fixed, 7 untrusted, 228 total


→ 1000 Phase 3: finalize:   0%|          | 0/228 [00:00<?, ?it/s]

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→ 1000 Phase 3: finalize:   7%|▋         | 16/228 [00:01<00:15, 13.36it/s]

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→ 1000 Phase 3: finalize:  30%|██▉       | 68/228 [00:05<00:12, 13.17it/s]

→ 1000 Phase 3: finalize:  31%|███       | 70/228 [00:05<00:11, 13.17it/s]

→ 1000 Phase 3: finalize:  32%|███▏      | 72/228 [00:05<00:11, 13.14it/s]

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→ 1000 Phase 3: finalize:  40%|████      | 92/228 [00:06<00:10, 12.95it/s]

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→ 1000 Phase 3: finalize:  42%|████▏     | 96/228 [00:07<00:10, 12.99it/s]

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→ 1000 Phase 3: finalize:  61%|██████    | 138/228 [00:10<00:06, 12.99it/s]

→ 1000 Phase 3: finalize:  61%|██████▏   | 140/228 [00:10<00:06, 13.03it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 142/228 [00:10<00:06, 13.06it/s]

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→ 1000 Phase 3: finalize:  64%|██████▍   | 146/228 [00:11<00:06, 13.13it/s]

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→ 1000 Phase 3: finalize:  68%|██████▊   | 156/228 [00:11<00:05, 13.25it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 158/228 [00:12<00:05, 13.26it/s]

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→ 1000 Phase 3: finalize:  72%|███████▏  | 164/228 [00:12<00:04, 13.28it/s]

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→ 1000 Phase 3: finalize:  74%|███████▎  | 168/228 [00:12<00:04, 13.36it/s]

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→ 1000 Phase 3: finalize:  80%|███████▉  | 182/228 [00:13<00:03, 13.55it/s]

→ 1000 Phase 3: finalize:  81%|████████  | 184/228 [00:13<00:03, 13.58it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 186/228 [00:14<00:03, 13.60it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 188/228 [00:14<00:02, 13.58it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 190/228 [00:14<00:02, 13.55it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 192/228 [00:14<00:02, 13.52it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 194/228 [00:14<00:02, 13.48it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 196/228 [00:14<00:02, 13.44it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 198/228 [00:15<00:02, 13.40it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 200/228 [00:15<00:02, 13.38it/s]

→ 1000 Phase 3: finalize:  89%|████████▊ | 202/228 [00:15<00:01, 13.37it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 204/228 [00:15<00:01, 13.32it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 206/228 [00:15<00:01, 13.33it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 208/228 [00:15<00:01, 13.33it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 210/228 [00:15<00:01, 13.30it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 212/228 [00:16<00:01, 13.26it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 214/228 [00:16<00:01, 13.23it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 216/228 [00:16<00:00, 13.19it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 218/228 [00:16<00:00, 13.20it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 220/228 [00:16<00:00, 13.21it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 222/228 [00:16<00:00, 13.18it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 224/228 [00:16<00:00, 13.17it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 226/228 [00:17<00:00, 13.17it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 228/228 [00:17<00:00, 13.15it/s]

                                                                           
Processing Rigs: 100%|██████████| 2/2 [08:49<00:00, 255.96s/it]
Processing Rigs: 100%|██████████| 2/2 [08:49<00:00, 264.65s/it]
[💾] Saved RigFrameDataset to /working/MDC046_W048_2025-11-24/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 517 frames from 2 rigs to /working/MDC046_W048_2025-11-24/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC046_W048_2025-11-24/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 289
[📋] 1000: 100 frames selected from 228
[💾] Saved RigFrameDataset to /working/MDC046_W048_2025-11-24/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC046_W048_2025-11-24/calibration/debug/1_digitized/rig_frames.json

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

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

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:03<02:10,  3.34s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:04,  3.28s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<02:01,  3.29s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:13<01:58,  3.30s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:54,  3.27s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:19<01:51,  3.28s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:23<01:48,  3.30s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:26<01:46,  3.32s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:29<01:43,  3.33s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:33<01:40,  3.34s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:36<01:35,  3.31s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:39<01:32,  3.31s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:42<01:28,  3.28s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:46<01:25,  3.29s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:49<01:22,  3.30s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:52<01:19,  3.29s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:56<01:15,  3.29s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:59<01:12,  3.29s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:02<01:08,  3.28s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:05<01:05,  3.26s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:09<01:01,  3.26s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:12<00:58,  3.27s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:15<00:55,  3.26s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:18<00:52,  3.26s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:22<00:48,  3.26s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:25<00:45,  3.26s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:28<00:42,  3.28s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:32<00:39,  3.31s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:35<00:36,  3.34s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:38<00:33,  3.36s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:42<00:30,  3.37s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:45<00:26,  3.33s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:48<00:23,  3.30s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:52<00:19,  3.28s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:55<00:16,  3.27s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:58<00:13,  3.27s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [02:01<00:09,  3.27s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:05<00:06,  3.26s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:08<00:03,  3.26s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:11<00:00,  3.18s/it]
                                                                                       

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Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:08,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:08,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:08,  9.94it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  24%|██▍       | 24/100 [00:00<00:00, 76.76it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  25%|██▌       | 25/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 483.838} (0.001):  25%|██▌       | 25/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 490.909} (0.001):  26%|██▌       | 26/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 490.909} (0.001):  27%|██▋       | 27/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 512.121} (0.017):  28%|██▊       | 28/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 512.121} (0.017):  29%|██▉       | 29/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.025 | Best: {'fx': 554.545} (0.025):  30%|███       | 30/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.007 | Best: {'fx': 554.545} (0.025):  31%|███       | 31/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.031 | Best: {'fx': 547.475} (0.031):  32%|███▏      | 32/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.036 | Best: {'fx': 519.192} (0.036):  33%|███▎      | 33/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.065 | Best: {'fx': 526.263} (0.065):  34%|███▍      | 34/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.067 | Best: {'fx': 533.333} (0.067):  35%|███▌      | 35/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.044 | Best: {'fx': 533.333} (0.067):  36%|███▌      | 36/100 [00:00<00:00, 84.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.044 | Best: {'fx': 533.333} (0.067):  37%|███▋      | 37/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.019 | Best: {'fx': 533.333} (0.067):  37%|███▋      | 37/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.067):  38%|███▊      | 38/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.067):  39%|███▉      | 39/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.067):  40%|████      | 40/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.067):  41%|████      | 41/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.067):  42%|████▏     | 42/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.067):  43%|████▎     | 43/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.067):  44%|████▍     | 44/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.067):  45%|████▌     | 45/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  46%|████▌     | 46/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  47%|████▋     | 47/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  48%|████▊     | 48/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  49%|████▉     | 49/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  50%|█████     | 50/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  51%|█████     | 51/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  52%|█████▏    | 52/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  53%|█████▎    | 53/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  54%|█████▍    | 54/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  55%|█████▌    | 55/100 [00:00<00:00, 97.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  56%|█████▌    | 56/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  56%|█████▌    | 56/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  57%|█████▋    | 57/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  58%|█████▊    | 58/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  59%|█████▉    | 59/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  60%|██████    | 60/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  61%|██████    | 61/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  62%|██████▏   | 62/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  63%|██████▎   | 63/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  64%|██████▍   | 64/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  65%|██████▌   | 65/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  66%|██████▌   | 66/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  67%|██████▋   | 67/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  68%|██████▊   | 68/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  69%|██████▉   | 69/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  70%|███████   | 70/100 [00:00<00:00, 127.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  71%|███████   | 71/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  71%|███████   | 71/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  72%|███████▏  | 72/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  73%|███████▎  | 73/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  74%|███████▍  | 74/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  75%|███████▌  | 75/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  76%|███████▌  | 76/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  77%|███████▋  | 77/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  78%|███████▊  | 78/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  79%|███████▉  | 79/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  80%|████████  | 80/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  81%|████████  | 81/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  82%|████████▏ | 82/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  83%|████████▎ | 83/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  84%|████████▍ | 84/100 [00:00<00:00, 132.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  85%|████████▌ | 85/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  85%|████████▌ | 85/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  86%|████████▌ | 86/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  87%|████████▋ | 87/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  88%|████████▊ | 88/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  89%|████████▉ | 89/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  90%|█████████ | 90/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  91%|█████████ | 91/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  92%|█████████▏| 92/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  93%|█████████▎| 93/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  94%|█████████▍| 94/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  95%|█████████▌| 95/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  96%|█████████▌| 96/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  97%|█████████▋| 97/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  98%|█████████▊| 98/100 [00:00<00:00, 129.10it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  99%|█████████▉| 99/100 [00:00<00:00, 129.10it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.011 | Best: {'fx': 508.333} (0.011):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.011 | Best: {'fx': 508.333} (0.011):   2%|▏         | 1/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.014 | Best: {'fx': 510.374} (0.014):   2%|▏         | 1/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.011 | Best: {'fx': 510.374} (0.014):   4%|▍         | 2/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.029 | Best: {'fx': 515.476} (0.029):   6%|▌         | 3/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.017 | Best: {'fx': 515.476} (0.029):   8%|▊         | 4/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.018 | Best: {'fx': 515.476} (0.029):  10%|█         | 5/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.032 | Best: {'fx': 517.517} (0.032):  12%|█▏        | 6/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.025 | Best: {'fx': 517.517} (0.032):  14%|█▍        | 7/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.031 | Best: {'fx': 517.517} (0.032):  16%|█▌        | 8/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.028 | Best: {'fx': 517.517} (0.032):  18%|█▊        | 9/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 518.537} (0.034):  20%|██        | 10/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.043 | Best: {'fx': 521.599} (0.043):  22%|██▏       | 11/50 [00:00<00:04,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 521.599} (0.043):  24%|██▍       | 12/50 [00:00<00:04,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.052 | Best: {'fx': 523.639} (0.052):  26%|██▌       | 13/50 [00:00<00:04,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.039 | Best: {'fx': 523.639} (0.052):  28%|██▊       | 14/50 [00:00<00:04,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.039 | Best: {'fx': 523.639} (0.052):  30%|███       | 15/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.054 | Best: {'fx': 524.66} (0.054):  30%|███       | 15/50 [00:00<00:00, 73.99it/s] 
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.047 | Best: {'fx': 524.66} (0.054):  32%|███▏      | 16/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.063 | Best: {'fx': 526.701} (0.063):  34%|███▍      | 17/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.065 | Best: {'fx': 525.68} (0.065):  36%|███▌      | 18/50 [00:00<00:00, 73.99it/s] 
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.073 | Best: {'fx': 527.721} (0.073):  38%|███▊      | 19/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.076 | Best: {'fx': 528.741} (0.076):  40%|████      | 20/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.061 | Best: {'fx': 528.741} (0.076):  42%|████▏     | 21/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.077 | Best: {'fx': 529.762} (0.077):  44%|████▍     | 22/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.077 | Best: {'fx': 529.762} (0.077):  46%|████▌     | 23/50 [00:00<00:00, 73.99it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.077 | Best: {'fx': 529.762} (0.077):  48%|████▊     | 24/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.053 | Best: {'fx': 529.762} (0.077):  48%|████▊     | 24/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.073 | Best: {'fx': 529.762} (0.077):  50%|█████     | 25/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.068 | Best: {'fx': 529.762} (0.077):  52%|█████▏    | 26/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.050 | Best: {'fx': 529.762} (0.077):  54%|█████▍    | 27/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.065 | Best: {'fx': 529.762} (0.077):  56%|█████▌    | 28/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.047 | Best: {'fx': 529.762} (0.077):  58%|█████▊    | 29/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.040 | Best: {'fx': 529.762} (0.077):  60%|██████    | 30/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.058 | Best: {'fx': 529.762} (0.077):  62%|██████▏   | 31/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.039 | Best: {'fx': 529.762} (0.077):  64%|██████▍   | 32/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.045 | Best: {'fx': 529.762} (0.077):  66%|██████▌   | 33/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 529.762} (0.077):  68%|██████▊   | 34/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.033 | Best: {'fx': 529.762} (0.077):  70%|███████   | 35/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.031 | Best: {'fx': 529.762} (0.077):  72%|███████▏  | 36/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.036 | Best: {'fx': 529.762} (0.077):  74%|███████▍  | 37/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 529.762} (0.077):  76%|███████▌  | 38/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.032 | Best: {'fx': 529.762} (0.077):  78%|███████▊  | 39/50 [00:00<00:00, 80.12it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.032 | Best: {'fx': 529.762} (0.077):  80%|████████  | 40/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.030 | Best: {'fx': 529.762} (0.077):  80%|████████  | 40/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.028 | Best: {'fx': 529.762} (0.077):  82%|████████▏ | 41/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.027 | Best: {'fx': 529.762} (0.077):  84%|████████▍ | 42/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.029 | Best: {'fx': 529.762} (0.077):  86%|████████▌ | 43/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.025 | Best: {'fx': 529.762} (0.077):  88%|████████▊ | 44/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.026 | Best: {'fx': 529.762} (0.077):  90%|█████████ | 45/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.024 | Best: {'fx': 529.762} (0.077):  92%|█████████▏| 46/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.026 | Best: {'fx': 529.762} (0.077):  94%|█████████▍| 47/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.023 | Best: {'fx': 529.762} (0.077):  96%|█████████▌| 48/50 [00:00<00:00, 108.72it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.023 | Best: {'fx': 529.762} (0.077):  98%|█████████▊| 49/50 [00:00<00:00, 108.72it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.7619047619048
    px: 982.0
    py: 749.0
    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=1375.6 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: 29.0433: 0iter [00:00, ?iter/s]  [Ring mask] 25,848/25,848 points within ring radius 1375.6 px
[DEBUG] objective(x0) = 29.0433, picklable_objective(x0) = 29.0604

Best score: 29.0604: : 0iter [00:01, ?iter/s] 
Best score: 29.0604: : 1iter [00:01,  1.93s/iter]
Best score: 29.0604: : 1iter [00:01,  1.93s/iter]
Best score: 29.0604: : 2iter [00:02,  1.93s/iter]
Best score: 29.0604: : 3iter [00:02,  1.93s/iter]
Best score: 29.0604: : 4iter [00:02,  2.49iter/s]
Best score: 29.0604: : 4iter [00:02,  2.49iter/s]
Best score: 29.0604: : 5iter [00:02,  2.49iter/s]
Best score: 29.0604: : 6iter [00:02,  2.49iter/s]
Best score: 29.0604: : 7iter [00:02,  4.76iter/s]
Best score: 29.0604: : 7iter [00:02,  4.76iter/s]
Best score: 29.0604: : 8iter [00:02,  4.76iter/s]
Best score: 29.0604: : 9iter [00:02,  4.76iter/s]
Best score: 29.0604: : 10iter [00:02,  7.20iter/s]
Best score: 29.0604: : 10iter [00:02,  7.20iter/s]
Best score: 29.0604: : 11iter [00:02,  7.20iter/s]
Best score: 29.0604: : 12iter [00:02,  7.20iter/s]
Best score: 29.0604: : 13iter [00:02,  9.73iter/s]
Best score: 29.0604: : 13iter [00:02,  9.73iter/s]
Best score: 29.0604: : 14iter [00:02,  9.73iter/s]
Best score: 29.0604: : 15iter [00:02,  9.73iter/s]
Best score: 29.0604: : 16iter [00:02, 12.17iter/s]
Best score: 29.0604: : 16iter [00:02, 12.17iter/s]
Best score: 29.0604: : 17iter [00:02, 12.17iter/s]
Best score: 29.0604: : 18iter [00:02, 12.17iter/s]
Best score: 29.0604: : 19iter [00:02, 14.38iter/s]
Best score: 29.0604: : 19iter [00:02, 14.38iter/s]
Best score: 29.0604: : 20iter [00:02, 14.38iter/s]
Best score: 29.0604: : 21iter [00:02, 14.38iter/s]
Best score: 29.0604: : 22iter [00:02, 16.18iter/s]
Best score: 29.0604: : 22iter [00:02, 16.18iter/s]
Best score: 29.0604: : 23iter [00:02, 16.18iter/s]
Best score: 29.0604: : 24iter [00:03, 16.18iter/s]
Best score: 29.0604: : 25iter [00:03, 17.70iter/s]
Best score: 29.0604: : 25iter [00:03, 17.70iter/s]
Best score: 29.0604: : 26iter [00:03, 17.70iter/s]
Best score: 29.0604: : 27iter [00:03, 17.70iter/s]
Best score: 29.0604: : 28iter [00:03, 19.01iter/s]
Best score: 29.0604: : 28iter [00:03, 19.01iter/s]
Best score: 29.0604: : 29iter [00:03, 19.01iter/s]
Best score: 29.0604: : 30iter [00:03, 19.01iter/s]
Best score: 29.0604: : 31iter [00:03, 19.78iter/s]
Best score: 29.0604: : 31iter [00:03, 19.78iter/s]
Best score: 29.0604: : 32iter [00:03, 19.78iter/s]
Best score: 29.0604: : 33iter [00:03, 19.78iter/s]
Best score: 29.0604: : 34iter [00:03, 20.58iter/s]
Best score: 29.0604: : 34iter [00:03, 20.58iter/s]
Best score: 29.0604: : 35iter [00:03, 20.58iter/s]
Best score: 29.0604: : 36iter [00:03, 20.58iter/s]
Best score: 29.0604: : 37iter [00:03, 21.17iter/s]
Best score: 29.0604: : 37iter [00:03, 21.17iter/s]
Best score: 29.0604: : 38iter [00:03, 21.17iter/s]
Best score: 29.0604: : 39iter [00:03, 21.17iter/s]
Best score: 29.0604: : 40iter [00:03, 21.39iter/s]
Best score: 29.0604: : 40iter [00:03, 21.39iter/s]
Best score: 29.0604: : 41iter [00:03, 21.39iter/s]
Best score: 29.0604: : 42iter [00:03, 21.39iter/s]
Best score: 29.0604: : 43iter [00:03, 21.80iter/s]
Best score: 29.0604: : 43iter [00:03, 21.80iter/s]
Best score: 29.0604: : 44iter [00:03, 21.80iter/s]
Best score: 29.0604: : 45iter [00:03, 21.80iter/s]
Best score: 29.0604: : 46iter [00:03, 22.11iter/s]
Best score: 29.0604: : 46iter [00:03, 22.11iter/s]
Best score: 29.0604: : 47iter [00:04, 22.11iter/s]
Best score: 29.0604: : 48iter [00:04, 22.11iter/s]
Best score: 29.0604: : 49iter [00:04, 22.06iter/s]
[ITER 50] Best: 22.7520
[PARAMS] Current best parameters:
  fx: 554.2716
  fy: 549.9622
  px: 975.5080
  py: 755.7754
  k1: 0.00843001
  k2: 0.00122087
  k3: -0.02598772
  k4: 0.00995115
  p1: 0.00183510
  p2: -0.00905325
  p3: 0.00196433
  p4: -0.00340212
  scale: 2.8845
  rotation: 0.2464

Best score: 22.7520: : 49iter [00:04, 22.06iter/s]
Best score: 22.7520: : 50iter [00:04, 22.06iter/s]
Best score: 22.7520: : 51iter [00:04, 22.06iter/s]
Best score: 22.7520: : 52iter [00:04, 22.02iter/s]
Best score: 22.7520: : 52iter [00:04, 22.02iter/s]
Best score: 22.7520: : 53iter [00:04, 22.02iter/s]
Best score: 22.7520: : 54iter [00:04, 22.02iter/s]
Best score: 22.7520: : 55iter [00:04, 22.18iter/s]
Best score: 22.7520: : 55iter [00:04, 22.18iter/s]
Best score: 22.7520: : 56iter [00:04, 22.18iter/s]
Best score: 22.7520: : 57iter [00:04, 22.18iter/s]
Best score: 22.7520: : 58iter [00:04, 22.29iter/s]
Best score: 22.7520: : 58iter [00:04, 22.29iter/s]
Best score: 17.6251: : 59iter [00:04, 22.29iter/s]
Best score: 17.6251: : 60iter [00:04, 22.29iter/s]
Best score: 17.6251: : 61iter [00:04, 22.14iter/s]
Best score: 17.6251: : 61iter [00:04, 22.14iter/s]
Best score: 17.6251: : 62iter [00:04, 22.14iter/s]
Best score: 17.6251: : 63iter [00:04, 22.14iter/s]
Best score: 17.6251: : 64iter [00:04, 22.30iter/s]
Best score: 17.6251: : 64iter [00:04, 22.30iter/s]
Best score: 17.6251: : 65iter [00:04, 22.30iter/s]
Best score: 17.6251: : 66iter [00:04, 22.30iter/s]
Best score: 17.6251: : 67iter [00:04, 22.39iter/s]
Best score: 17.6251: : 67iter [00:04, 22.39iter/s]
Best score: 17.6251: : 68iter [00:04, 22.39iter/s]
Best score: 12.4082: : 69iter [00:05, 22.39iter/s]
Best score: 12.4082: : 70iter [00:05, 22.23iter/s]
Best score: 12.4082: : 70iter [00:05, 22.23iter/s]
Best score: 12.4082: : 71iter [00:05, 22.23iter/s]
Best score: 12.4082: : 72iter [00:05, 22.23iter/s]
Best score: 12.4082: : 73iter [00:05, 22.39iter/s]
Best score: 12.4082: : 73iter [00:05, 22.39iter/s]
Best score: 12.4082: : 74iter [00:05, 22.39iter/s]
Best score: 12.4082: : 75iter [00:05, 22.39iter/s]
Best score: 12.4082: : 76iter [00:05, 22.48iter/s]
Best score: 12.4082: : 76iter [00:05, 22.48iter/s]
Best score: 12.4082: : 77iter [00:05, 22.48iter/s]
Best score: 12.4082: : 78iter [00:05, 22.48iter/s]
Best score: 12.4082: : 79iter [00:05, 22.51iter/s]
Best score: 4.1902: : 79iter [00:05, 22.51iter/s] 
Best score: 4.1902: : 80iter [00:05, 22.51iter/s]
Best score: 4.1902: : 81iter [00:05, 22.51iter/s]
Best score: 4.1902: : 82iter [00:05, 22.26iter/s]
Best score: 4.1902: : 82iter [00:05, 22.26iter/s]
Best score: 4.1902: : 83iter [00:05, 22.26iter/s]
Best score: 4.1902: : 84iter [00:05, 22.26iter/s]
Best score: 4.1902: : 85iter [00:05, 22.39iter/s]
Best score: 4.1902: : 85iter [00:05, 22.39iter/s]
Best score: 4.1902: : 86iter [00:05, 22.39iter/s]
Best score: 4.1902: : 87iter [00:05, 22.39iter/s]
Best score: 4.1902: : 88iter [00:05, 22.46iter/s]
Best score: 4.1902: : 88iter [00:05, 22.46iter/s]
Best score: 3.6490: : 89iter [00:05, 22.46iter/s]
Best score: 3.6490: : 90iter [00:05, 22.46iter/s]
Best score: 3.6490: : 91iter [00:05, 22.19iter/s]
Best score: 3.6490: : 91iter [00:06, 22.19iter/s]
Best score: 3.6490: : 92iter [00:06, 22.19iter/s]
Best score: 3.6490: : 93iter [00:06, 22.19iter/s]
Best score: 3.6490: : 94iter [00:06, 22.34iter/s]
Best score: 3.6490: : 94iter [00:06, 22.34iter/s]
Best score: 3.6490: : 95iter [00:06, 22.34iter/s]
Best score: 3.6490: : 96iter [00:06, 22.34iter/s]
Best score: 3.6490: : 97iter [00:06, 22.46iter/s]
Best score: 3.6490: : 97iter [00:06, 22.46iter/s]
Best score: 3.6490: : 98iter [00:06, 22.46iter/s]
[ITER 100] Best: 1.7623
[PARAMS] Current best parameters:
  fx: 534.5615
  fy: 535.3215
  px: 982.1715
  py: 753.3352
  k1: 0.01208603
  k2: -0.01003013
  k3: 0.00294536
  k4: -0.00120909
  p1: -0.00028216
  p2: 0.00376616
  p3: -0.00361719
  p4: 0.00190151
  scale: 2.9141
  rotation: -0.3715

Best score: 1.7623: : 99iter [00:06, 22.46iter/s]
Best score: 1.7623: : 100iter [00:06, 22.32iter/s]
Best score: 1.7623: : 100iter [00:06, 22.32iter/s]
Best score: 1.7623: : 101iter [00:06, 22.32iter/s]
Best score: 1.7623: : 102iter [00:06, 22.32iter/s]
Best score: 1.7623: : 103iter [00:06, 22.43iter/s]
Best score: 1.7623: : 103iter [00:06, 22.43iter/s]
Best score: 1.7623: : 104iter [00:06, 22.43iter/s]
Best score: 1.7623: : 105iter [00:06, 22.43iter/s]
Best score: 1.7623: : 106iter [00:06, 22.50iter/s]
Best score: 1.7623: : 106iter [00:06, 22.50iter/s]
Best score: 1.7623: : 107iter [00:06, 22.50iter/s]
Best score: 1.7623: : 108iter [00:06, 22.50iter/s]
Best score: 1.7623: : 109iter [00:06, 22.55iter/s]
Best score: 1.1265: : 109iter [00:06, 22.55iter/s]
Best score: 1.1265: : 110iter [00:06, 22.55iter/s]
Best score: 1.1265: : 111iter [00:06, 22.55iter/s]
Best score: 1.1265: : 112iter [00:06, 22.32iter/s]
Best score: 1.1265: : 112iter [00:06, 22.32iter/s]
Best score: 1.1265: : 113iter [00:06, 22.32iter/s]
Best score: 1.1265: : 114iter [00:07, 22.32iter/s]
Best score: 1.1265: : 115iter [00:07, 22.39iter/s]
Best score: 1.1265: : 115iter [00:07, 22.39iter/s]
Best score: 1.1265: : 116iter [00:07, 22.39iter/s]
Best score: 1.1265: : 117iter [00:07, 22.39iter/s]
Best score: 1.1265: : 118iter [00:07, 22.43iter/s]
Best score: 1.1265: : 118iter [00:07, 22.43iter/s]
Best score: 0.6997: : 119iter [00:07, 22.43iter/s]
Best score: 0.6997: : 120iter [00:07, 22.43iter/s]
Best score: 0.6997: : 121iter [00:07, 22.26iter/s]
Best score: 0.6997: : 121iter [00:07, 22.26iter/s]
Best score: 0.6997: : 122iter [00:07, 22.26iter/s]
Best score: 0.6997: : 123iter [00:07, 22.26iter/s]
Best score: 0.6997: : 124iter [00:07, 22.34iter/s]
Best score: 0.6997: : 124iter [00:07, 22.34iter/s]
Best score: 0.6997: : 125iter [00:07, 22.34iter/s]
Best score: 0.6997: : 126iter [00:07, 22.34iter/s]
Best score: 0.6997: : 127iter [00:07, 22.38iter/s]
Best score: 0.6997: : 127iter [00:07, 22.38iter/s]
Best score: 0.6997: : 128iter [00:07, 22.38iter/s]
Best score: 0.6997: : 129iter [00:07, 22.38iter/s]
Best score: 0.6997: : 130iter [00:07, 22.25iter/s]
Best score: 0.6997: : 130iter [00:07, 22.25iter/s]
Best score: 0.6997: : 131iter [00:07, 22.25iter/s]
Best score: 0.6997: : 132iter [00:07, 22.25iter/s]
Best score: 0.6997: : 133iter [00:07, 22.37iter/s]
Best score: 0.6997: : 133iter [00:07, 22.37iter/s]
Best score: 0.6997: : 134iter [00:07, 22.37iter/s]
Best score: 0.6997: : 135iter [00:07, 22.37iter/s]
Best score: 0.6997: : 136iter [00:07, 22.44iter/s]
Best score: 0.6997: : 136iter [00:08, 22.44iter/s]
Best score: 0.6997: : 137iter [00:08, 22.44iter/s]
Best score: 0.6997: : 138iter [00:08, 22.44iter/s]
Best score: 0.6997: : 139iter [00:08, 22.49iter/s]
Best score: 0.6266: : 139iter [00:08, 22.49iter/s]
Best score: 0.6266: : 140iter [00:08, 22.49iter/s]
Best score: 0.6266: : 141iter [00:08, 22.49iter/s]
Best score: 0.6266: : 142iter [00:08, 22.34iter/s]
Best score: 0.6266: : 142iter [00:08, 22.34iter/s]
Best score: 0.6266: : 143iter [00:08, 22.34iter/s]
Best score: 0.6266: : 144iter [00:08, 22.34iter/s]
Best score: 0.6266: : 145iter [00:08, 22.51iter/s]
Best score: 0.6266: : 145iter [00:08, 22.51iter/s]
Best score: 0.6266: : 146iter [00:08, 22.51iter/s]
Best score: 0.6266: : 147iter [00:08, 22.51iter/s]
Best score: 0.6266: : 148iter [00:08, 22.63iter/s]
Best score: 0.6266: : 148iter [00:08, 22.63iter/s]
[ITER 150] Best: 0.5091
[PARAMS] Current best parameters:
  fx: 535.9397
  fy: 535.3738
  px: 983.7672
  py: 753.1068
  k1: 0.00088691
  k2: -0.00386792
  k3: 0.00280695
  k4: -0.00141650
  p1: 0.00015530
  p2: 0.00725939
  p3: -0.00416992
  p4: 0.00018589
  scale: 2.9505
  rotation: -0.4543

Best score: 0.5091: : 149iter [00:08, 22.63iter/s]
Best score: 0.5091: : 150iter [00:08, 22.63iter/s]
Best score: 0.5091: : 151iter [00:08, 22.45iter/s]
Best score: 0.5091: : 151iter [00:08, 22.45iter/s]
Best score: 0.5091: : 152iter [00:08, 22.45iter/s]
Best score: 0.5091: : 153iter [00:08, 22.45iter/s]
Best score: 0.5091: : 154iter [00:08, 22.56iter/s]
Best score: 0.5091: : 154iter [00:08, 22.56iter/s]
Best score: 0.5091: : 155iter [00:08, 22.56iter/s]
Best score: 0.5091: : 156iter [00:08, 22.56iter/s]
Best score: 0.5091: : 157iter [00:08, 22.61iter/s]
Best score: 0.5091: : 157iter [00:08, 22.61iter/s]
Best score: 0.5091: : 158iter [00:08, 22.61iter/s]
Best score: 0.4741: : 159iter [00:09, 22.61iter/s]
Best score: 0.4741: : 160iter [00:09, 22.39iter/s]
Best score: 0.4741: : 160iter [00:09, 22.39iter/s]
Best score: 0.4741: : 161iter [00:09, 22.39iter/s]
Best score: 0.4741: : 162iter [00:09, 22.39iter/s]
Best score: 0.4741: : 163iter [00:09, 22.52iter/s]
Best score: 0.4741: : 163iter [00:09, 22.52iter/s]
Best score: 0.4741: : 164iter [00:09, 22.52iter/s]
Best score: 0.4741: : 165iter [00:09, 22.52iter/s]
Best score: 0.4741: : 166iter [00:09, 22.63iter/s]
Best score: 0.4741: : 166iter [00:09, 22.63iter/s]
Best score: 0.4741: : 167iter [00:09, 22.63iter/s]
Best score: 0.4741: : 168iter [00:09, 22.63iter/s]
Best score: 0.4741: : 169iter [00:09, 22.71iter/s]
Best score: 0.4049: : 169iter [00:09, 22.71iter/s]
Best score: 0.4049: : 170iter [00:09, 22.71iter/s]
Best score: 0.4049: : 171iter [00:09, 22.71iter/s]
Best score: 0.4049: : 172iter [00:09, 22.53iter/s]
Best score: 0.4049: : 172iter [00:09, 22.53iter/s]
Best score: 0.4049: : 173iter [00:09, 22.53iter/s]
Best score: 0.4049: : 174iter [00:09, 22.53iter/s]
Best score: 0.4049: : 175iter [00:09, 22.56iter/s]
Best score: 0.4049: : 175iter [00:09, 22.56iter/s]
Best score: 0.4049: : 176iter [00:09, 22.56iter/s]
Best score: 0.4049: : 177iter [00:09, 22.56iter/s]
Best score: 0.4049: : 178iter [00:09, 22.62iter/s]
Best score: 0.4049: : 178iter [00:09, 22.62iter/s]
Best score: 0.3965: : 179iter [00:09, 22.62iter/s]
Best score: 0.3965: : 180iter [00:09, 22.62iter/s]
Best score: 0.3965: : 181iter [00:09, 22.46iter/s]
Best score: 0.3965: : 181iter [00:10, 22.46iter/s]
Best score: 0.3965: : 182iter [00:10, 22.46iter/s]
Best score: 0.3965: : 183iter [00:10, 22.46iter/s]
Best score: 0.3965: : 184iter [00:10, 22.56iter/s]
Best score: 0.3965: : 184iter [00:10, 22.56iter/s]
Best score: 0.3965: : 185iter [00:10, 22.56iter/s]
Best score: 0.3965: : 186iter [00:10, 22.56iter/s]
Best score: 0.3965: : 187iter [00:10, 22.65iter/s]
Best score: 0.3965: : 187iter [00:10, 22.65iter/s]
Best score: 0.3965: : 188iter [00:10, 22.65iter/s]
Best score: 0.3497: : 189iter [00:10, 22.65iter/s]
Best score: 0.3497: : 190iter [00:10, 22.45iter/s]
Best score: 0.3497: : 190iter [00:10, 22.45iter/s]
Best score: 0.3497: : 191iter [00:10, 22.45iter/s]
Best score: 0.3497: : 192iter [00:10, 22.45iter/s]
Best score: 0.3497: : 193iter [00:10, 22.50iter/s]
Best score: 0.3497: : 193iter [00:10, 22.50iter/s]
Best score: 0.3497: : 194iter [00:10, 22.50iter/s]
Best score: 0.3497: : 195iter [00:10, 22.50iter/s]
Best score: 0.3497: : 196iter [00:10, 22.59iter/s]
Best score: 0.3497: : 196iter [00:10, 22.59iter/s]
Best score: 0.3497: : 197iter [00:10, 22.59iter/s]
Best score: 0.3497: : 198iter [00:10, 22.59iter/s]
Best score: 0.3497: : 199iter [00:10, 22.62iter/s]
[ITER 200] Best: 0.3372
[PARAMS] Current best parameters:
  fx: 532.5049
  fy: 532.2990
  px: 982.5503
  py: 753.9025
  k1: 0.00408605
  k2: -0.00259504
  k3: 0.00053167
  k4: -0.00067675
  p1: 0.00223823
  p2: 0.00491275
  p3: -0.00396031
  p4: -0.00047968
  scale: 2.9605
  rotation: -0.4151

Best score: 0.3372: : 199iter [00:10, 22.62iter/s]
Best score: 0.3372: : 200iter [00:10, 22.62iter/s]
Best score: 0.3372: : 201iter [00:10, 22.62iter/s]
Best score: 0.3372: : 202iter [00:10, 22.40iter/s]
Best score: 0.3372: : 202iter [00:10, 22.40iter/s]
Best score: 0.3372: : 203iter [00:10, 22.40iter/s]
Best score: 0.3372: : 204iter [00:11, 22.40iter/s]
Best score: 0.3372: : 205iter [00:11, 22.48iter/s]
Best score: 0.3372: : 205iter [00:11, 22.48iter/s]
Best score: 0.3372: : 206iter [00:11, 22.48iter/s]
Best score: 0.3372: : 207iter [00:11, 22.48iter/s]
Best score: 0.3372: : 208iter [00:11, 22.55iter/s]
Best score: 0.3372: : 208iter [00:11, 22.55iter/s]
Best score: 0.3253: : 209iter [00:11, 22.55iter/s]
Best score: 0.3253: : 210iter [00:11, 22.55iter/s]
Best score: 0.3253: : 211iter [00:11, 22.40iter/s]
Best score: 0.3253: : 211iter [00:11, 22.40iter/s]
Best score: 0.3253: : 212iter [00:11, 22.40iter/s]
Best score: 0.3253: : 213iter [00:11, 22.40iter/s]
Best score: 0.3253: : 214iter [00:11, 22.55iter/s]
Best score: 0.3253: : 214iter [00:11, 22.55iter/s]
Best score: 0.3253: : 215iter [00:11, 22.55iter/s]
Best score: 0.3253: : 216iter [00:11, 22.55iter/s]
Best score: 0.3253: : 217iter [00:11, 22.64iter/s]
Best score: 0.3253: : 217iter [00:11, 22.64iter/s]
Best score: 0.3253: : 218iter [00:11, 22.64iter/s]
Best score: 0.3224: : 219iter [00:11, 22.64iter/s]
Best score: 0.3224: : 220iter [00:11, 22.49iter/s]
Best score: 0.3224: : 220iter [00:11, 22.49iter/s]
Best score: 0.3224: : 221iter [00:11, 22.49iter/s]
Best score: 0.3224: : 222iter [00:11, 22.49iter/s]
Best score: 0.3224: : 223iter [00:11, 22.60iter/s]
Best score: 0.3224: : 223iter [00:11, 22.60iter/s]
Best score: 0.3224: : 224iter [00:11, 22.60iter/s]
Best score: 0.3224: : 225iter [00:11, 22.60iter/s]
Best score: 0.3224: : 226iter [00:11, 22.63iter/s]
Best score: 0.3224: : 226iter [00:12, 22.63iter/s]
Best score: 0.3224: : 227iter [00:12, 22.63iter/s]
Best score: 0.3224: : 228iter [00:12, 22.63iter/s]
Best score: 0.3224: : 229iter [00:12, 22.68iter/s]
Best score: 0.3224: : 229iter [00:12, 22.68iter/s]
Best score: 0.3224: : 230iter [00:12, 22.68iter/s]
Best score: 0.3224: : 231iter [00:12, 22.68iter/s]
Best score: 0.3224: : 232iter [00:12, 22.46iter/s]
Best score: 0.3224: : 232iter [00:12, 22.46iter/s]
Best score: 0.3224: : 233iter [00:12, 22.46iter/s]
Best score: 0.3224: : 234iter [00:12, 22.46iter/s]
Best score: 0.3224: : 235iter [00:12, 22.58iter/s]
Best score: 0.3224: : 235iter [00:12, 22.58iter/s]
Best score: 0.3224: : 236iter [00:12, 22.58iter/s]
Best score: 0.3224: : 237iter [00:12, 22.58iter/s]
Best score: 0.3224: : 238iter [00:12, 22.66iter/s]
Best score: 0.3224: : 238iter [00:12, 22.66iter/s]
Best score: 0.3215: : 239iter [00:12, 22.66iter/s]
Best score: 0.3215: : 240iter [00:12, 22.66iter/s]
Best score: 0.3215: : 241iter [00:12, 22.41iter/s]
Best score: 0.3215: : 241iter [00:12, 22.41iter/s]
Best score: 0.3215: : 242iter [00:12, 22.41iter/s]
Best score: 0.3215: : 243iter [00:12, 22.41iter/s]
Best score: 0.3215: : 244iter [00:12, 22.51iter/s]
Best score: 0.3215: : 244iter [00:12, 22.51iter/s]
Best score: 0.3215: : 245iter [00:12, 22.51iter/s]
Best score: 0.3215: : 246iter [00:12, 22.51iter/s]
Best score: 0.3215: : 247iter [00:12, 22.56iter/s]
Best score: 0.3215: : 247iter [00:12, 22.56iter/s]
Best score: 0.3215: : 248iter [00:12, 22.56iter/s]
[ITER 250] Best: 0.3181
[PARAMS] Current best parameters:
  fx: 532.6195
  fy: 532.4040
  px: 982.2625
  py: 753.7418
  k1: 0.00743749
  k2: -0.00328832
  k3: -0.00139269
  k4: 0.00008430
  p1: 0.00175829
  p2: 0.00433523
  p3: -0.00318690
  p4: -0.00066027
  scale: 2.9490
  rotation: -0.4039

Best score: 0.3181: : 249iter [00:13, 22.56iter/s]
Best score: 0.3181: : 250iter [00:13, 22.32iter/s]
Best score: 0.3181: : 250iter [00:13, 22.32iter/s]
Best score: 0.3181: : 251iter [00:13, 22.32iter/s]
Best score: 0.3181: : 252iter [00:13, 22.32iter/s]
Best score: 0.3181: : 253iter [00:13, 22.47iter/s]
Best score: 0.3181: : 253iter [00:13, 22.47iter/s]
Best score: 0.3181: : 254iter [00:13, 22.47iter/s]
Best score: 0.3181: : 255iter [00:13, 22.47iter/s]
Best score: 0.3181: : 256iter [00:13, 22.57iter/s]
Best score: 0.3181: : 256iter [00:13, 22.57iter/s]
Best score: 0.3181: : 257iter [00:13, 22.57iter/s]
Best score: 0.3181: : 258iter [00:13, 22.57iter/s]
Best score: 0.3181: : 259iter [00:13, 22.66iter/s]
Best score: 0.3180: : 259iter [00:13, 22.66iter/s]
Best score: 0.3180: : 260iter [00:13, 22.66iter/s]
Best score: 0.3180: : 261iter [00:13, 22.66iter/s]
Best score: 0.3180: : 262iter [00:13, 22.44iter/s]
Best score: 0.3180: : 262iter [00:13, 22.44iter/s]
Best score: 0.3180: : 263iter [00:13, 22.44iter/s]
Best score: 0.3180: : 264iter [00:13, 22.44iter/s]
Best score: 0.3180: : 265iter [00:13, 22.52iter/s]
Best score: 0.3180: : 265iter [00:13, 22.52iter/s]
Best score: 0.3180: : 266iter [00:13, 22.52iter/s]
Best score: 0.3180: : 267iter [00:13, 22.52iter/s]
Best score: 0.3180: : 268iter [00:13, 22.58iter/s]
Best score: 0.3180: : 268iter [00:13, 22.58iter/s]
Best score: 0.3156: : 269iter [00:13, 22.58iter/s]
Best score: 0.3156: : 270iter [00:13, 22.58iter/s]
Best score: 0.3156: : 271iter [00:13, 22.33iter/s]
Best score: 0.3156: : 271iter [00:14, 22.33iter/s]
Best score: 0.3156: : 272iter [00:14, 22.33iter/s]
Best score: 0.3156: : 273iter [00:14, 22.33iter/s]
Best score: 0.3156: : 274iter [00:14, 22.47iter/s]
Best score: 0.3156: : 274iter [00:14, 22.47iter/s]
Best score: 0.3156: : 275iter [00:14, 22.47iter/s]
Best score: 0.3156: : 276iter [00:14, 22.47iter/s]
Best score: 0.3156: : 277iter [00:14, 22.54iter/s]
Best score: 0.3156: : 277iter [00:14, 22.54iter/s]
Best score: 0.3156: : 278iter [00:14, 22.54iter/s]
Best score: 0.3156: : 279iter [00:14, 22.54iter/s]
Best score: 0.3156: : 280iter [00:14, 22.40iter/s]
Best score: 0.3156: : 280iter [00:14, 22.40iter/s]
Best score: 0.3156: : 281iter [00:14, 22.40iter/s]
Best score: 0.3156: : 282iter [00:14, 22.40iter/s]
Best score: 0.3156: : 283iter [00:14, 22.39iter/s]
Best score: 0.3156: : 283iter [00:14, 22.39iter/s]
Best score: 0.3156: : 284iter [00:14, 22.39iter/s]
Best score: 0.3156: : 285iter [00:14, 22.39iter/s]
Best score: 0.3156: : 286iter [00:14, 22.51iter/s]
Best score: 0.3156: : 286iter [00:14, 22.51iter/s]
Best score: 0.3156: : 287iter [00:14, 22.51iter/s]
Best score: 0.3156: : 288iter [00:14, 22.51iter/s]
Best score: 0.3156: : 289iter [00:14, 22.56iter/s]
Best score: 0.3146: : 289iter [00:14, 22.56iter/s]
Best score: 0.3146: : 290iter [00:14, 22.56iter/s]
Best score: 0.3146: : 291iter [00:14, 22.56iter/s]
Best score: 0.3146: : 292iter [00:14, 22.18iter/s]
Best score: 0.3146: : 292iter [00:14, 22.18iter/s]
Best score: 0.3146: : 293iter [00:14, 22.18iter/s]
Best score: 0.3146: : 294iter [00:15, 22.18iter/s]
Best score: 0.3146: : 295iter [00:15, 22.37iter/s]
Best score: 0.3146: : 295iter [00:15, 22.37iter/s]
Best score: 0.3146: : 296iter [00:15, 22.37iter/s]
Best score: 0.3146: : 297iter [00:15, 22.37iter/s]
Best score: 0.3146: : 298iter [00:15, 22.49iter/s]
Best score: 0.3146: : 298iter [00:15, 22.49iter/s]
[ITER 300] Best: 0.3146
[PARAMS] Current best parameters:
  fx: 531.8287
  fy: 531.5667
  px: 981.3430
  py: 753.0064
  k1: 0.00753541
  k2: -0.00160492
  k3: -0.00265884
  k4: 0.00038410
  p1: -0.00014295
  p2: 0.00208812
  p3: -0.00190523
  p4: -0.00058380
  scale: 2.9484
  rotation: -0.4068

Best score: 0.3146: : 299iter [00:15, 22.49iter/s]
Best score: 0.3146: : 300iter [00:15, 22.49iter/s]
Best score: 0.3146: : 301iter [00:15, 22.34iter/s]
Best score: 0.3146: : 301iter [00:15, 22.34iter/s]
Best score: 0.3146: : 302iter [00:15, 22.34iter/s]
Best score: 0.3146: : 303iter [00:15, 22.34iter/s]
Best score: 0.3146: : 304iter [00:15, 22.49iter/s]
Best score: 0.3146: : 304iter [00:15, 22.49iter/s]
Best score: 0.3146: : 305iter [00:15, 22.49iter/s]
Best score: 0.3146: : 306iter [00:15, 22.49iter/s]
Best score: 0.3146: : 307iter [00:15, 22.53iter/s]
Best score: 0.3146: : 307iter [00:15, 22.53iter/s]
Best score: 0.3146: : 308iter [00:15, 22.53iter/s]
Best score: 0.3145: : 309iter [00:15, 22.53iter/s]
Best score: 0.3145: : 310iter [00:15, 22.37iter/s]
Best score: 0.3145: : 310iter [00:15, 22.37iter/s]
Best score: 0.3145: : 311iter [00:15, 22.37iter/s]
Best score: 0.3145: : 312iter [00:15, 22.37iter/s]
Best score: 0.3145: : 313iter [00:15, 22.47iter/s]
Best score: 0.3145: : 313iter [00:15, 22.47iter/s]
Best score: 0.3145: : 314iter [00:15, 22.47iter/s]
Best score: 0.3145: : 315iter [00:15, 22.47iter/s]
Best score: 0.3145: : 316iter [00:15, 22.56iter/s]
Best score: 0.3145: : 316iter [00:16, 22.56iter/s]
Best score: 0.3145: : 317iter [00:16, 22.56iter/s]
Best score: 0.3145: : 318iter [00:16, 22.56iter/s]
Best score: 0.3145: : 319iter [00:16, 22.66iter/s]
Best score: 0.3144: : 319iter [00:16, 22.66iter/s]
Best score: 0.3144: : 320iter [00:16, 22.66iter/s]
Best score: 0.3144: : 321iter [00:16, 22.66iter/s]
Best score: 0.3144: : 322iter [00:16, 22.50iter/s]
Best score: 0.3144: : 322iter [00:16, 22.50iter/s]
Best score: 0.3144: : 323iter [00:16, 22.50iter/s]
Best score: 0.3144: : 324iter [00:16, 22.50iter/s]
Best score: 0.3144: : 325iter [00:16, 22.61iter/s]
Best score: 0.3144: : 325iter [00:16, 22.61iter/s]
Best score: 0.3144: : 326iter [00:16, 22.61iter/s]
Best score: 0.3144: : 327iter [00:16, 22.61iter/s]
Best score: 0.3144: : 328iter [00:16, 22.68iter/s]
Best score: 0.3144: : 328iter [00:16, 22.68iter/s]
Best score: 0.3137: : 329iter [00:16, 22.68iter/s]
Best score: 0.3137: : 330iter [00:16, 22.68iter/s]
Best score: 0.3137: : 331iter [00:16, 22.41iter/s]
Best score: 0.3137: : 331iter [00:16, 22.41iter/s]
Best score: 0.3137: : 332iter [00:16, 22.41iter/s]
Best score: 0.3137: : 333iter [00:16, 22.41iter/s]
Best score: 0.3137: : 334iter [00:16, 22.48iter/s]
Best score: 0.3137: : 334iter [00:16, 22.48iter/s]
Best score: 0.3137: : 335iter [00:16, 22.48iter/s]
Best score: 0.3137: : 336iter [00:16, 22.48iter/s]
Best score: 0.3137: : 337iter [00:16, 22.55iter/s]
Best score: 0.3137: : 337iter [00:16, 22.55iter/s]
Best score: 0.3137: : 338iter [00:16, 22.55iter/s]
Best score: 0.3137: : 339iter [00:17, 22.55iter/s]
Best score: 0.3137: : 340iter [00:17, 22.36iter/s]
Best score: 0.3137: : 340iter [00:17, 22.36iter/s]
Best score: 0.3137: : 341iter [00:17, 22.36iter/s]
Best score: 0.3137: : 342iter [00:17, 22.36iter/s]
Best score: 0.3137: : 343iter [00:17, 22.49iter/s]
Best score: 0.3137: : 343iter [00:17, 22.49iter/s]
Best score: 0.3137: : 344iter [00:17, 22.49iter/s]
Best score: 0.3137: : 345iter [00:17, 22.49iter/s]
Best score: 0.3137: : 346iter [00:17, 22.56iter/s]
Best score: 0.3137: : 346iter [00:17, 22.56iter/s]
Best score: 0.3137: : 347iter [00:17, 22.56iter/s]
Best score: 0.3137: : 348iter [00:17, 22.56iter/s]
Best score: 0.3137: : 349iter [00:17, 22.63iter/s]
[ITER 350] Best: 0.3133
[PARAMS] Current best parameters:
  fx: 532.9287
  fy: 532.6464
  px: 979.7988
  py: 751.4638
  k1: 0.00012701
  k2: 0.00706076
  k3: -0.00695676
  k4: 0.00116650
  p1: -0.00403109
  p2: -0.00174474
  p3: 0.00036680
  p4: -0.00043180
  scale: 2.9555
  rotation: -0.4031

Best score: 0.3133: : 349iter [00:17, 22.63iter/s]
Best score: 0.3133: : 350iter [00:17, 22.63iter/s]
Best score: 0.3133: : 351iter [00:17, 22.63iter/s]
Best score: 0.3133: : 352iter [00:17, 22.46iter/s]
Best score: 0.3133: : 352iter [00:17, 22.46iter/s]
Best score: 0.3133: : 353iter [00:17, 22.46iter/s]
Best score: 0.3133: : 354iter [00:17, 22.46iter/s]
Best score: 0.3133: : 355iter [00:17, 22.56iter/s]
Best score: 0.3133: : 355iter [00:17, 22.56iter/s]
Best score: 0.3133: : 356iter [00:17, 22.56iter/s]
Best score: 0.3133: : 357iter [00:17, 22.56iter/s]
Best score: 0.3133: : 358iter [00:17, 22.66iter/s]
Best score: 0.3133: : 358iter [00:17, 22.66iter/s]
Best score: 0.3128: : 359iter [00:17, 22.66iter/s]
Best score: 0.3128: : 360iter [00:17, 22.66iter/s]
Best score: 0.3128: : 361iter [00:17, 22.48iter/s]
Best score: 0.3128: : 361iter [00:18, 22.48iter/s]
Best score: 0.3128: : 362iter [00:18, 22.48iter/s]
Best score: 0.3128: : 363iter [00:18, 22.48iter/s]
Best score: 0.3128: : 364iter [00:18, 22.54iter/s]
Best score: 0.3128: : 364iter [00:18, 22.54iter/s]
Best score: 0.3128: : 365iter [00:18, 22.54iter/s]
Best score: 0.3128: : 366iter [00:18, 22.54iter/s]
Best score: 0.3128: : 367iter [00:18, 22.54iter/s]
Best score: 0.3128: : 367iter [00:18, 22.54iter/s]
Best score: 0.3128: : 368iter [00:18, 22.54iter/s]
Best score: 0.3117: : 369iter [00:18, 22.54iter/s]
Best score: 0.3117: : 370iter [00:18, 22.40iter/s]
Best score: 0.3117: : 370iter [00:18, 22.40iter/s]
Best score: 0.3117: : 371iter [00:18, 22.40iter/s]
Best score: 0.3117: : 372iter [00:18, 22.40iter/s]
Best score: 0.3117: : 373iter [00:18, 22.49iter/s]
Best score: 0.3117: : 373iter [00:18, 22.49iter/s]
Best score: 0.3117: : 374iter [00:18, 22.49iter/s]
Best score: 0.3117: : 375iter [00:18, 22.49iter/s]
Best score: 0.3117: : 376iter [00:18, 22.57iter/s]
Best score: 0.3117: : 376iter [00:18, 22.57iter/s]
Best score: 0.3117: : 377iter [00:18, 22.57iter/s]
Best score: 0.3117: : 378iter [00:18, 22.57iter/s]
Best score: 0.3117: : 379iter [00:18, 22.62iter/s]
Best score: 0.3113: : 379iter [00:18, 22.62iter/s]
Best score: 0.3113: : 380iter [00:18, 22.62iter/s]
Best score: 0.3113: : 381iter [00:18, 22.62iter/s]
Best score: 0.3113: : 382iter [00:18, 22.45iter/s]
Best score: 0.3113: : 382iter [00:18, 22.45iter/s]
Best score: 0.3113: : 383iter [00:18, 22.45iter/s]
Best score: 0.3113: : 384iter [00:19, 22.45iter/s]
Best score: 0.3113: : 385iter [00:19, 22.50iter/s]
Best score: 0.3113: : 385iter [00:19, 22.50iter/s]
Best score: 0.3113: : 386iter [00:19, 22.50iter/s]
Best score: 0.3113: : 387iter [00:19, 22.50iter/s]
Best score: 0.3113: : 388iter [00:19, 22.56iter/s]
Best score: 0.3113: : 388iter [00:19, 22.56iter/s]
Best score: 0.3108: : 389iter [00:19, 22.56iter/s]
Best score: 0.3108: : 390iter [00:19, 22.56iter/s]
Best score: 0.3108: : 391iter [00:19, 22.38iter/s]
Best score: 0.3108: : 391iter [00:19, 22.38iter/s]
Best score: 0.3108: : 392iter [00:19, 22.38iter/s]
Best score: 0.3108: : 393iter [00:19, 22.38iter/s]
Best score: 0.3108: : 394iter [00:19, 22.47iter/s]
Best score: 0.3108: : 394iter [00:19, 22.47iter/s]
Best score: 0.3108: : 395iter [00:19, 22.47iter/s]
Best score: 0.3108: : 396iter [00:19, 22.47iter/s]
Best score: 0.3108: : 397iter [00:19, 22.59iter/s]
Best score: 0.3108: : 397iter [00:19, 22.59iter/s]
Best score: 0.3108: : 398iter [00:19, 22.59iter/s]
[ITER 400] Best: 0.3089
[PARAMS] Current best parameters:
  fx: 541.2525
  fy: 540.9635
  px: 979.4009
  py: 751.1510
  k1: -0.04302066
  k2: 0.05728696
  k3: -0.03298531
  k4: 0.00611662
  p1: -0.00493192
  p2: -0.00274644
  p3: 0.00081940
  p4: -0.00036698
  scale: 2.9913
  rotation: -0.4020

Best score: 0.3089: : 399iter [00:19, 22.59iter/s]
Best score: 0.3089: : 400iter [00:19, 22.41iter/s]
Best score: 0.3089: : 400iter [00:19, 22.41iter/s]
Best score: 0.3089: : 401iter [00:19, 22.41iter/s]
Best score: 0.3089: : 402iter [00:19, 22.41iter/s]
Best score: 0.3089: : 403iter [00:19, 22.49iter/s]
Best score: 0.3089: : 403iter [00:19, 22.49iter/s]
Best score: 0.3089: : 404iter [00:19, 22.49iter/s]
Best score: 0.3089: : 405iter [00:19, 22.49iter/s]
Best score: 0.3089: : 406iter [00:19, 22.52iter/s]
Best score: 0.3089: : 406iter [00:20, 22.52iter/s]
Best score: 0.3089: : 407iter [00:20, 22.52iter/s]
Best score: 0.3089: : 408iter [00:20, 22.52iter/s]
Best score: 0.3089: : 409iter [00:20, 22.49iter/s]
Best score: 0.3070: : 409iter [00:20, 22.49iter/s]
Best score: 0.3070: : 410iter [00:20, 22.49iter/s]
Best score: 0.3070: : 411iter [00:20, 22.49iter/s]
Best score: 0.3070: : 412iter [00:20, 22.23iter/s]
Best score: 0.3070: : 412iter [00:20, 22.23iter/s]
Best score: 0.3070: : 413iter [00:20, 22.23iter/s]
Best score: 0.3070: : 414iter [00:20, 22.23iter/s]
Best score: 0.3070: : 415iter [00:20, 22.30iter/s]
Best score: 0.3070: : 415iter [00:20, 22.30iter/s]
Best score: 0.3070: : 416iter [00:20, 22.30iter/s]
Best score: 0.3070: : 417iter [00:20, 22.30iter/s]
Best score: 0.3070: : 418iter [00:20, 22.40iter/s]
Best score: 0.3070: : 418iter [00:20, 22.40iter/s]
Best score: 0.3068: : 419iter [00:20, 22.40iter/s]
Best score: 0.3068: : 420iter [00:20, 22.40iter/s]
Best score: 0.3068: : 421iter [00:20, 22.23iter/s]
Best score: 0.3068: : 421iter [00:20, 22.23iter/s]
Best score: 0.3068: : 422iter [00:20, 22.23iter/s]
Best score: 0.3068: : 423iter [00:20, 22.23iter/s]
Best score: 0.3068: : 424iter [00:20, 22.32iter/s]
Best score: 0.3068: : 424iter [00:20, 22.32iter/s]
Best score: 0.3068: : 425iter [00:20, 22.32iter/s]
Best score: 0.3068: : 426iter [00:20, 22.32iter/s]
Best score: 0.3068: : 427iter [00:20, 22.38iter/s]
Best score: 0.3068: : 427iter [00:20, 22.38iter/s]
Best score: 0.3068: : 428iter [00:20, 22.38iter/s]
Best score: 0.3065: : 429iter [00:21, 22.38iter/s]
Best score: 0.3065: : 430iter [00:21, 22.20iter/s]
Best score: 0.3065: : 430iter [00:21, 22.20iter/s]
Best score: 0.3065: : 431iter [00:21, 22.20iter/s]
Best score: 0.3065: : 432iter [00:21, 22.20iter/s]
Best score: 0.3065: : 433iter [00:21, 22.37iter/s]
Best score: 0.3065: : 433iter [00:21, 22.37iter/s]
Best score: 0.3065: : 434iter [00:21, 22.37iter/s]
Best score: 0.3065: : 435iter [00:21, 22.37iter/s]
Best score: 0.3065: : 436iter [00:21, 22.40iter/s]
Best score: 0.3065: : 436iter [00:21, 22.40iter/s]
Best score: 0.3065: : 437iter [00:21, 22.40iter/s]
Best score: 0.3065: : 438iter [00:21, 22.40iter/s]
Best score: 0.3065: : 439iter [00:21, 22.46iter/s]
Best score: 0.3065: : 439iter [00:21, 22.46iter/s]
Best score: 0.3065: : 440iter [00:21, 22.46iter/s]
Best score: 0.3065: : 441iter [00:21, 22.46iter/s]
Best score: 0.3065: : 442iter [00:21, 22.24iter/s]
Best score: 0.3065: : 442iter [00:21, 22.24iter/s]
Best score: 0.3065: : 443iter [00:21, 22.24iter/s]
Best score: 0.3065: : 444iter [00:21, 22.24iter/s]
Best score: 0.3065: : 445iter [00:21, 18.39iter/s]
Best score: 0.3065: : 445iter [00:21, 18.39iter/s]
Best score: 0.3065: : 446iter [00:21, 18.39iter/s]
Best score: 0.3065: : 447iter [00:21, 18.39iter/s]
Best score: 0.3065: : 448iter [00:21, 19.45iter/s]
Best score: 0.3065: : 448iter [00:21, 19.45iter/s]
[ITER 450] Best: 0.3063
[PARAMS] Current best parameters:
  fx: 541.8484
  fy: 541.5595
  px: 979.6107
  py: 751.2737
  k1: -0.05656161
  k2: 0.07381430
  k3: -0.04026250
  k4: 0.00721570
  p1: -0.00457865
  p2: -0.00229651
  p3: 0.00084434
  p4: -0.00050211
  scale: 3.0076
  rotation: -0.3997

Best score: 0.3063: : 449iter [00:22, 19.45iter/s]
Best score: 0.3063: : 450iter [00:22, 19.45iter/s]
Best score: 0.3063: : 451iter [00:22, 20.09iter/s]
Best score: 0.3063: : 451iter [00:22, 20.09iter/s]
Best score: 0.3063: : 452iter [00:22, 20.09iter/s]
Best score: 0.3063: : 453iter [00:22, 20.09iter/s]
Best score: 0.3063: : 454iter [00:22, 20.77iter/s]
Best score: 0.3063: : 454iter [00:22, 20.77iter/s]
Best score: 0.3063: : 455iter [00:22, 20.77iter/s]
Best score: 0.3063: : 456iter [00:22, 20.77iter/s]
Best score: 0.3063: : 457iter [00:22, 21.29iter/s]
Best score: 0.3063: : 457iter [00:22, 21.29iter/s]
Best score: 0.3063: : 458iter [00:22, 21.29iter/s]
Best score: 0.3063: : 459iter [00:22, 21.29iter/s]
Best score: 0.3063: : 460iter [00:22, 21.47iter/s]
Best score: 0.3063: : 460iter [00:22, 21.47iter/s]
Best score: 0.3063: : 461iter [00:22, 21.47iter/s]
Best score: 0.3063: : 462iter [00:22, 21.47iter/s]
Best score: 0.3063: : 463iter [00:22, 21.82iter/s]
Best score: 0.3063: : 463iter [00:22, 21.82iter/s]
Best score: 0.3063: : 464iter [00:22, 21.82iter/s]
Best score: 0.3063: : 465iter [00:22, 21.82iter/s]
Best score: 0.3063: : 466iter [00:22, 22.04iter/s]
Best score: 0.3063: : 466iter [00:22, 22.04iter/s]
Best score: 0.3063: : 467iter [00:22, 22.04iter/s]
Best score: 0.3063: : 468iter [00:22, 22.04iter/s]
Best score: 0.3063: : 469iter [00:22, 22.21iter/s]
Best score: 0.3060: : 469iter [00:22, 22.21iter/s]
Best score: 0.3060: : 470iter [00:22, 22.21iter/s]
Best score: 0.3060: : 471iter [00:23, 22.21iter/s]
Best score: 0.3060: : 472iter [00:23, 22.02iter/s]
Best score: 0.3060: : 472iter [00:23, 22.02iter/s]
Best score: 0.3060: : 473iter [00:23, 22.02iter/s]
Best score: 0.3060: : 474iter [00:23, 22.02iter/s]
Best score: 0.3060: : 475iter [00:23, 22.18iter/s]
Best score: 0.3060: : 475iter [00:23, 22.18iter/s]
Best score: 0.3060: : 476iter [00:23, 22.18iter/s]
Best score: 0.3060: : 477iter [00:23, 22.18iter/s]
Best score: 0.3060: : 478iter [00:23, 22.28iter/s]
Best score: 0.3060: : 478iter [00:23, 22.28iter/s]
Best score: 0.3060: : 479iter [00:23, 22.28iter/s]
Best score: 0.3060: : 480iter [00:23, 22.28iter/s]
Best score: 0.3060: : 481iter [00:23, 22.07iter/s]
Best score: 0.3060: : 481iter [00:23, 22.07iter/s]
Best score: 0.3060: : 482iter [00:23, 22.07iter/s]
Best score: 0.3060: : 483iter [00:23, 22.07iter/s]
Best score: 0.3060: : 484iter [00:23, 22.17iter/s]
Best score: 0.3060: : 484iter [00:23, 22.17iter/s]
Best score: 0.3060: : 485iter [00:23, 22.17iter/s]
Best score: 0.3060: : 486iter [00:23, 22.17iter/s]
Best score: 0.3060: : 487iter [00:23, 22.25iter/s]
Best score: 0.3060: : 487iter [00:23, 22.25iter/s]
Best score: 0.3060: : 488iter [00:23, 22.25iter/s]
Best score: 0.3059: : 489iter [00:23, 22.25iter/s]
Best score: 0.3059: : 490iter [00:23, 22.12iter/s]
Best score: 0.3059: : 490iter [00:23, 22.12iter/s]
Best score: 0.3059: : 491iter [00:23, 22.12iter/s]
Best score: 0.3059: : 492iter [00:23, 22.12iter/s]
Best score: 0.3059: : 493iter [00:23, 22.22iter/s]
Best score: 0.3059: : 493iter [00:24, 22.22iter/s]
Best score: 0.3059: : 494iter [00:24, 22.22iter/s]
Best score: 0.3059: : 495iter [00:24, 22.22iter/s]
Best score: 0.3059: : 496iter [00:24, 22.23iter/s]
Best score: 0.3059: : 496iter [00:24, 22.23iter/s]
Best score: 0.3059: : 497iter [00:24, 22.23iter/s]
Best score: 0.3059: : 498iter [00:24, 22.23iter/s]
Best score: 0.3059: : 499iter [00:24, 22.30iter/s]
[ITER 500] Best: 0.3059
[PARAMS] Current best parameters:
  fx: 543.0238
  fy: 542.7375
  px: 979.2783
  py: 751.0460
  k1: -0.06187374
  k2: 0.07951233
  k3: -0.04303070
  k4: 0.00771037
  p1: -0.00520357
  p2: -0.00307937
  p3: 0.00117002
  p4: -0.00043648
  scale: 3.0123
  rotation: -0.4012

Best score: 0.3059: : 499iter [00:24, 22.30iter/s]
Best score: 0.3059: : 500iter [00:24, 20.59iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.305890 -> 0.305811
  Success: True
  Tangential distortion: p1=-0.005202, p2=-0.003080, p3=0.001170, p4=-0.000436
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3058
[✅] Parameters copied to: /working/MDC046_W048_2025-11-24/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 543.0238470526243
    fy: 542.7375164492478
    px: 979.2783131940394
    py: 751.0459560387037
    k1: -0.06186893570730932
    k2: 0.07951225700246882
    k3: -0.04302811811442276
    k4: 0.007707538004889071
    p1: -0.005202342085338323
    p2: -0.0030799809947827206
    p3: 0.0011702914652502257
    p4: -0.0004360313941254442
    scale: 3.01232114749119
    rotation: -0.4012475347177511
    mmpp: 1.2722088637099276
    nmpp: 1272208.8637099275

[🔄] Converting 517 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:05<00:00,  2.60s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:05<00:00,  2.64s/rig]
[✅] Saved PTDs: /working/MDC046_W048_2025-11-24/calibration/debug/3_undistorted/output/ptd

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   🔄 1000:  92%|█████████▏| 92/100 [00:00<00:00, 176.76frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.02rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.04rig/s]
[✅] Saved PTDs: /working/MDC046_W048_2025-11-24/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.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
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📤 Writing LUT:  89%|████████▉ | 2735941/3080448 [00:03<00:00, 866527.37px/s]
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📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 855463.94px/s]
[✅] LUT written to: /working/MDC046_W048_2025-11-24/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC046_W048_2025-11-24' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC046_W048_2025-11-24/calibration/MDC046_W048_2025-11-24_unknown.lut

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


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   🔥 500:  98%|█████████▊| 283/289 [00:01<00:00, 158.92frame/s]

                                                                [✅] 500: /working/MDC046_W048_2025-11-24/calibration/debug/4_heatmap/500_heatmap.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:02<00:02,  2.87s/rig]

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   🔥 1000:  84%|████████▍ | 192/228 [00:01<00:00, 155.95frame/s]

   🔥 1000:  91%|█████████ | 208/228 [00:01<00:00, 155.82frame/s]

   🔥 1000:  98%|█████████▊| 224/228 [00:01<00:00, 155.93frame/s]

                                                                 [✅] 1000: /working/MDC046_W048_2025-11-24/calibration/debug/4_heatmap/1000_heatmap.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:07<00:00,  3.67s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:07<00:00,  3.55s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC046_W048_2025-11-24/calibration/debug/4_heatmap
[🏁] Finished processing MDC046_W048_2025-11-24


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