🟢 Starting Calibration Pipeline

=== Processing MDC015_W038_2025-05-14 ===

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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 1127 stationary frames, 256 remain


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→ 500 Phase 2: corners:  71%|███████   | 182/256 [02:47<01:05,  1.12it/s]

→ 500 Phase 2: corners:  71%|███████▏  | 183/256 [02:48<01:04,  1.14it/s]

→ 500 Phase 2: corners:  72%|███████▏  | 184/256 [02:48<01:04,  1.12it/s]

→ 500 Phase 2: corners:  72%|███████▏  | 185/256 [02:49<01:04,  1.10it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 186/256 [02:50<01:03,  1.11it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 187/256 [02:51<01:02,  1.11it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 188/256 [02:52<01:01,  1.10it/s]

→ 500 Phase 2: corners:  74%|███████▍  | 189/256 [02:53<01:02,  1.08it/s]

→ 500 Phase 2: corners:  74%|███████▍  | 190/256 [02:54<01:01,  1.07it/s]

→ 500 Phase 2: corners:  75%|███████▍  | 191/256 [02:55<01:00,  1.07it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 192/256 [02:56<01:00,  1.06it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 193/256 [02:57<01:00,  1.04it/s]

→ 500 Phase 2: corners:  76%|███████▌  | 194/256 [02:58<01:01,  1.01it/s]

→ 500 Phase 2: corners:  76%|███████▌  | 195/256 [02:59<01:00,  1.02it/s]

→ 500 Phase 2: corners:  77%|███████▋  | 196/256 [03:00<00:59,  1.01it/s]

→ 500 Phase 2: corners:  77%|███████▋  | 197/256 [03:01<00:58,  1.01it/s]

→ 500 Phase 2: corners:  77%|███████▋  | 198/256 [03:02<00:58,  1.01s/it]

→ 500 Phase 2: corners:  78%|███████▊  | 199/256 [03:03<00:56,  1.01it/s]

→ 500 Phase 2: corners:  78%|███████▊  | 200/256 [03:04<00:54,  1.03it/s]

→ 500 Phase 2: corners:  79%|███████▊  | 201/256 [03:05<00:52,  1.05it/s]

→ 500 Phase 2: corners:  79%|███████▉  | 202/256 [03:06<00:50,  1.06it/s]

→ 500 Phase 2: corners:  79%|███████▉  | 203/256 [03:07<00:50,  1.05it/s]

→ 500 Phase 2: corners:  80%|███████▉  | 204/256 [03:08<00:48,  1.07it/s]

→ 500 Phase 2: corners:  80%|████████  | 205/256 [03:09<00:46,  1.09it/s]

→ 500 Phase 2: corners:  80%|████████  | 206/256 [03:09<00:45,  1.09it/s]

→ 500 Phase 2: corners:  81%|████████  | 207/256 [03:10<00:45,  1.08it/s]

→ 500 Phase 2: corners:  81%|████████▏ | 208/256 [03:11<00:44,  1.09it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 209/256 [03:12<00:42,  1.10it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 210/256 [03:13<00:42,  1.09it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 211/256 [03:14<00:41,  1.09it/s]

→ 500 Phase 2: corners:  83%|████████▎ | 212/256 [03:15<00:40,  1.09it/s]

→ 500 Phase 2: corners:  83%|████████▎ | 213/256 [03:16<00:38,  1.10it/s]

→ 500 Phase 2: corners:  84%|████████▎ | 214/256 [03:17<00:37,  1.11it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 215/256 [03:18<00:36,  1.11it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 216/256 [03:19<00:36,  1.10it/s]

→ 500 Phase 2: corners:  85%|████████▍ | 217/256 [03:19<00:35,  1.11it/s]

→ 500 Phase 2: corners:  85%|████████▌ | 218/256 [03:20<00:34,  1.11it/s]

→ 500 Phase 2: corners:  86%|████████▌ | 219/256 [03:21<00:33,  1.11it/s]

→ 500 Phase 2: corners:  86%|████████▌ | 220/256 [03:22<00:33,  1.08it/s]

→ 500 Phase 2: corners:  86%|████████▋ | 221/256 [03:23<00:32,  1.09it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 222/256 [03:24<00:31,  1.09it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 223/256 [03:25<00:30,  1.08it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 224/256 [03:26<00:29,  1.07it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 225/256 [03:27<00:29,  1.06it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 226/256 [03:28<00:28,  1.06it/s]

→ 500 Phase 2: corners:  89%|████████▊ | 227/256 [03:29<00:27,  1.07it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 228/256 [03:30<00:26,  1.07it/s]

→ 500 Phase 2: corners:  89%|████████▉ | 229/256 [03:31<00:25,  1.07it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 230/256 [03:32<00:24,  1.07it/s]

→ 500 Phase 2: corners:  90%|█████████ | 231/256 [03:32<00:23,  1.08it/s]

→ 500 Phase 2: corners:  91%|█████████ | 232/256 [03:33<00:22,  1.07it/s]

→ 500 Phase 2: corners:  91%|█████████ | 233/256 [03:34<00:21,  1.08it/s]

→ 500 Phase 2: corners:  91%|█████████▏| 234/256 [03:35<00:20,  1.07it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 235/256 [03:36<00:19,  1.08it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 236/256 [03:37<00:18,  1.08it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 237/256 [03:38<00:17,  1.10it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 238/256 [03:39<00:16,  1.09it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 239/256 [03:40<00:15,  1.10it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 240/256 [03:41<00:14,  1.09it/s]

→ 500 Phase 2: corners:  94%|█████████▍| 241/256 [03:42<00:13,  1.10it/s]

→ 500 Phase 2: corners:  95%|█████████▍| 242/256 [03:43<00:12,  1.09it/s]

→ 500 Phase 2: corners:  95%|█████████▍| 243/256 [03:43<00:11,  1.09it/s]

→ 500 Phase 2: corners:  95%|█████████▌| 244/256 [03:44<00:10,  1.10it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 245/256 [03:45<00:10,  1.09it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 246/256 [03:46<00:09,  1.09it/s]

→ 500 Phase 2: corners:  96%|█████████▋| 247/256 [03:47<00:08,  1.09it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 248/256 [03:48<00:07,  1.09it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 249/256 [03:49<00:06,  1.09it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 250/256 [03:50<00:05,  1.09it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 251/256 [03:51<00:04,  1.09it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 252/256 [03:52<00:03,  1.09it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 253/256 [03:53<00:02,  1.09it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 254/256 [03:54<00:01,  1.09it/s]

→ 500 Phase 2: corners: 100%|█████████▉| 255/256 [03:54<00:00,  1.08it/s]

→ 500 Phase 2: corners: 100%|██████████| 256/256 [03:55<00:00,  1.09it/s]

                                                                         [PASS2] Summary: 0 frames fixed, 0 untrusted, 256 total


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→ 500 Phase 3: finalize:  20%|██        | 52/256 [00:04<00:16, 12.16it/s]

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→ 500 Phase 3: finalize:  75%|███████▌  | 192/256 [00:15<00:04, 13.80it/s]

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→ 500 Phase 3: finalize:  82%|████████▏ | 210/256 [00:16<00:03, 13.61it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 212/256 [00:16<00:03, 13.51it/s]

→ 500 Phase 3: finalize:  84%|████████▎ | 214/256 [00:16<00:03, 13.39it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 216/256 [00:17<00:02, 13.36it/s]

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→ 500 Phase 3: finalize:  87%|████████▋ | 222/256 [00:17<00:02, 13.22it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 224/256 [00:17<00:02, 13.17it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 226/256 [00:17<00:02, 13.13it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 228/256 [00:18<00:02, 13.11it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 230/256 [00:18<00:01, 13.11it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 232/256 [00:18<00:01, 13.09it/s]

→ 500 Phase 3: finalize:  91%|█████████▏| 234/256 [00:18<00:01, 13.08it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 236/256 [00:18<00:01, 13.08it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 238/256 [00:18<00:01, 13.09it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 240/256 [00:18<00:01, 13.07it/s]

→ 500 Phase 3: finalize:  95%|█████████▍| 242/256 [00:19<00:01, 13.07it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 244/256 [00:19<00:00, 13.05it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 246/256 [00:19<00:00, 13.05it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 248/256 [00:19<00:00, 13.04it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 250/256 [00:19<00:00, 13.03it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 252/256 [00:19<00:00, 13.04it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 254/256 [00:20<00:00, 13.03it/s]

→ 500 Phase 3: finalize: 100%|██████████| 256/256 [00:20<00:00, 13.03it/s]

                                                                          
Processing Rigs:  50%|█████     | 1/2 [04:45<04:45, 285.77s/it][INFO] Filtered 1187 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<12:30,  3.31s/it]

→ 1000 (228 images) Phase 1:  14%|█▍        | 33/228 [00:06<00:30,  6.47it/s]

→ 1000 (228 images) Phase 1:  25%|██▌       | 58/228 [00:06<00:12, 13.10it/s]

→ 1000 (228 images) Phase 1:  30%|██▉       | 68/228 [00:08<00:19,  8.34it/s]

→ 1000 (228 images) Phase 1:  39%|███▉      | 90/228 [00:08<00:09, 13.92it/s]

→ 1000 (228 images) Phase 1:  44%|████▍     | 101/228 [00:11<00:14,  9.02it/s]

→ 1000 (228 images) Phase 1:  53%|█████▎    | 120/228 [00:11<00:07, 13.74it/s]

→ 1000 (228 images) Phase 1:  57%|█████▋    | 131/228 [00:14<00:10,  8.98it/s]

→ 1000 (228 images) Phase 1:  65%|██████▌   | 149/228 [00:14<00:05, 13.43it/s]

→ 1000 (228 images) Phase 1:  70%|███████   | 160/228 [00:14<00:04, 16.41it/s]

→ 1000 (228 images) Phase 1:  74%|███████▍  | 169/228 [00:17<00:06,  9.25it/s]

→ 1000 (228 images) Phase 1:  81%|████████  | 184/228 [00:17<00:03, 13.48it/s]

→ 1000 (228 images) Phase 1:  85%|████████▍ | 193/228 [00:19<00:04,  8.23it/s]

→ 1000 (228 images) Phase 1:  90%|████████▉ | 205/228 [00:19<00:02, 11.42it/s]

→ 1000 (228 images) Phase 1:  95%|█████████▍| 216/228 [00:20<00:00, 15.14it/s]

→ 1000 (228 images) Phase 1:  98%|█████████▊| 224/228 [00:20<00:00, 18.10it/s]

                                                                              

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

→ 1000 Phase 2: corners:   0%|          | 1/228 [00:00<02:59,  1.26it/s]

→ 1000 Phase 2: corners:   1%|          | 2/228 [00:01<02:58,  1.27it/s]

→ 1000 Phase 2: corners:   1%|▏         | 3/228 [00:02<02:54,  1.29it/s]

→ 1000 Phase 2: corners:   2%|▏         | 4/228 [00:03<02:51,  1.31it/s]

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→ 1000 Phase 2: corners:   3%|▎         | 6/228 [00:04<02:49,  1.31it/s]

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→ 1000 Phase 2: corners:   5%|▌         | 12/228 [00:09<02:45,  1.31it/s]

→ 1000 Phase 2: corners:   6%|▌         | 13/228 [00:10<02:44,  1.31it/s]

→ 1000 Phase 2: corners:   6%|▌         | 14/228 [00:10<02:45,  1.29it/s]

→ 1000 Phase 2: corners:   7%|▋         | 15/228 [00:11<02:51,  1.24it/s]

→ 1000 Phase 2: corners:   7%|▋         | 16/228 [00:12<02:51,  1.23it/s]

→ 1000 Phase 2: corners:   7%|▋         | 17/228 [00:13<02:48,  1.25it/s]

→ 1000 Phase 2: corners:   8%|▊         | 18/228 [00:14<02:44,  1.27it/s]

→ 1000 Phase 2: corners:   8%|▊         | 19/228 [00:14<02:43,  1.28it/s]

→ 1000 Phase 2: corners:   9%|▉         | 20/228 [00:15<02:44,  1.26it/s]

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→ 1000 Phase 2: corners:  10%|▉         | 22/228 [00:17<02:42,  1.26it/s]

→ 1000 Phase 2: corners:  10%|█         | 23/228 [00:18<02:43,  1.25it/s]

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

→ 1000 Phase 2: corners:  11%|█         | 25/228 [00:19<02:38,  1.28it/s]

→ 1000 Phase 2: corners:  11%|█▏        | 26/228 [00:20<02:38,  1.28it/s]

→ 1000 Phase 2: corners:  12%|█▏        | 27/228 [00:21<02:38,  1.27it/s]

→ 1000 Phase 2: corners:  12%|█▏        | 28/228 [00:21<02:34,  1.29it/s]

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→ 1000 Phase 2: corners:  13%|█▎        | 30/228 [00:23<02:35,  1.27it/s]

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→ 1000 Phase 2: corners:  14%|█▍        | 32/228 [00:25<02:35,  1.26it/s]

→ 1000 Phase 2: corners:  14%|█▍        | 33/228 [00:25<02:35,  1.26it/s]

→ 1000 Phase 2: corners:  15%|█▍        | 34/228 [00:26<02:33,  1.26it/s]

→ 1000 Phase 2: corners:  15%|█▌        | 35/228 [00:27<02:33,  1.26it/s]

→ 1000 Phase 2: corners:  16%|█▌        | 36/228 [00:28<02:33,  1.25it/s]

→ 1000 Phase 2: corners:  16%|█▌        | 37/228 [00:29<02:32,  1.25it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 38/228 [00:29<02:30,  1.26it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 39/228 [00:30<02:31,  1.25it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 40/228 [00:31<02:29,  1.26it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 41/228 [00:32<02:30,  1.25it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 42/228 [00:33<02:30,  1.23it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 43/228 [00:33<02:30,  1.23it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 44/228 [00:34<02:28,  1.24it/s]

→ 1000 Phase 2: corners:  20%|█▉        | 45/228 [00:35<02:28,  1.23it/s]

→ 1000 Phase 2: corners:  20%|██        | 46/228 [00:36<02:29,  1.22it/s]

→ 1000 Phase 2: corners:  21%|██        | 47/228 [00:37<02:27,  1.23it/s]

→ 1000 Phase 2: corners:  21%|██        | 48/228 [00:37<02:25,  1.24it/s]

→ 1000 Phase 2: corners:  21%|██▏       | 49/228 [00:38<02:26,  1.22it/s]

→ 1000 Phase 2: corners:  22%|██▏       | 50/228 [00:39<02:26,  1.21it/s]

→ 1000 Phase 2: corners:  22%|██▏       | 51/228 [00:40<02:26,  1.21it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 52/228 [00:41<02:24,  1.22it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 53/228 [00:42<02:22,  1.23it/s]

→ 1000 Phase 2: corners:  24%|██▎       | 54/228 [00:42<02:19,  1.25it/s]

→ 1000 Phase 2: corners:  24%|██▍       | 55/228 [00:43<02:19,  1.24it/s]

→ 1000 Phase 2: corners:  25%|██▍       | 56/228 [00:44<02:15,  1.27it/s]

→ 1000 Phase 2: corners:  25%|██▌       | 57/228 [00:45<02:14,  1.27it/s]

→ 1000 Phase 2: corners:  25%|██▌       | 58/228 [00:46<02:15,  1.26it/s]

→ 1000 Phase 2: corners:  26%|██▌       | 59/228 [00:46<02:14,  1.26it/s]

→ 1000 Phase 2: corners:  26%|██▋       | 60/228 [00:47<02:13,  1.26it/s]

→ 1000 Phase 2: corners:  27%|██▋       | 61/228 [00:48<02:12,  1.26it/s]

→ 1000 Phase 2: corners:  27%|██▋       | 62/228 [00:49<02:10,  1.27it/s]

→ 1000 Phase 2: corners:  28%|██▊       | 63/228 [00:49<02:09,  1.28it/s]

→ 1000 Phase 2: corners:  28%|██▊       | 64/228 [00:50<02:07,  1.29it/s]

→ 1000 Phase 2: corners:  29%|██▊       | 65/228 [00:51<02:06,  1.29it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 66/228 [00:52<02:05,  1.29it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 67/228 [00:52<02:02,  1.31it/s]

→ 1000 Phase 2: corners:  30%|██▉       | 68/228 [00:53<02:01,  1.32it/s]

→ 1000 Phase 2: corners:  30%|███       | 69/228 [00:54<02:00,  1.32it/s]

→ 1000 Phase 2: corners:  31%|███       | 70/228 [00:55<02:00,  1.31it/s]

→ 1000 Phase 2: corners:  31%|███       | 71/228 [00:56<01:59,  1.32it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 72/228 [00:56<02:00,  1.29it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 73/228 [00:57<01:59,  1.29it/s]

→ 1000 Phase 2: corners:  32%|███▏      | 74/228 [00:58<01:59,  1.29it/s]

→ 1000 Phase 2: corners:  33%|███▎      | 75/228 [00:59<01:58,  1.30it/s]

→ 1000 Phase 2: corners:  33%|███▎      | 76/228 [00:59<01:58,  1.28it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 77/228 [01:00<01:57,  1.29it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 78/228 [01:01<01:56,  1.28it/s]

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→ 1000 Phase 2: corners:  36%|███▌      | 81/228 [01:03<01:53,  1.29it/s]

→ 1000 Phase 2: corners:  36%|███▌      | 82/228 [01:04<01:54,  1.27it/s]

→ 1000 Phase 2: corners:  36%|███▋      | 83/228 [01:05<01:54,  1.26it/s]

→ 1000 Phase 2: corners:  37%|███▋      | 84/228 [01:06<01:56,  1.24it/s]

→ 1000 Phase 2: corners:  37%|███▋      | 85/228 [01:07<01:53,  1.27it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 86/228 [01:07<01:52,  1.26it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 87/228 [01:08<01:53,  1.24it/s]

→ 1000 Phase 2: corners:  39%|███▊      | 88/228 [01:09<01:54,  1.22it/s]

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→ 1000 Phase 2: corners:  39%|███▉      | 90/228 [01:11<01:52,  1.23it/s]

→ 1000 Phase 2: corners:  40%|███▉      | 91/228 [01:11<01:51,  1.23it/s]

→ 1000 Phase 2: corners:  40%|████      | 92/228 [01:12<01:49,  1.24it/s]

→ 1000 Phase 2: corners:  41%|████      | 93/228 [01:13<01:48,  1.25it/s]

→ 1000 Phase 2: corners:  41%|████      | 94/228 [01:14<01:47,  1.25it/s]

→ 1000 Phase 2: corners:  42%|████▏     | 95/228 [01:15<01:46,  1.25it/s]

→ 1000 Phase 2: corners:  42%|████▏     | 96/228 [01:15<01:45,  1.25it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 97/228 [01:16<01:43,  1.27it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 98/228 [01:17<01:42,  1.27it/s]

→ 1000 Phase 2: corners:  43%|████▎     | 99/228 [01:18<01:42,  1.26it/s]

→ 1000 Phase 2: corners:  44%|████▍     | 100/228 [01:19<01:41,  1.26it/s]

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→ 1000 Phase 2: corners:  45%|████▍     | 102/228 [01:20<01:39,  1.27it/s]

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→ 1000 Phase 2: corners:  46%|████▌     | 105/228 [01:22<01:36,  1.27it/s]

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→ 1000 Phase 2: corners:  47%|████▋     | 107/228 [01:24<01:35,  1.27it/s]

→ 1000 Phase 2: corners:  47%|████▋     | 108/228 [01:25<01:34,  1.27it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 109/228 [01:26<01:34,  1.26it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 110/228 [01:26<01:33,  1.26it/s]

→ 1000 Phase 2: corners:  49%|████▊     | 111/228 [01:27<01:33,  1.25it/s]

→ 1000 Phase 2: corners:  49%|████▉     | 112/228 [01:28<01:33,  1.23it/s]

→ 1000 Phase 2: corners:  50%|████▉     | 113/228 [01:29<01:32,  1.25it/s]

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→ 1000 Phase 2: corners:  51%|█████     | 116/228 [01:31<01:28,  1.26it/s]

→ 1000 Phase 2: corners:  51%|█████▏    | 117/228 [01:32<01:27,  1.26it/s]

→ 1000 Phase 2: corners:  52%|█████▏    | 118/228 [01:33<01:27,  1.26it/s]

→ 1000 Phase 2: corners:  52%|█████▏    | 119/228 [01:34<01:26,  1.26it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 120/228 [01:34<01:26,  1.25it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 121/228 [01:35<01:24,  1.26it/s]

→ 1000 Phase 2: corners:  54%|█████▎    | 122/228 [01:36<01:24,  1.25it/s]

→ 1000 Phase 2: corners:  54%|█████▍    | 123/228 [01:37<01:23,  1.26it/s]

→ 1000 Phase 2: corners:  54%|█████▍    | 124/228 [01:38<01:22,  1.26it/s]

→ 1000 Phase 2: corners:  55%|█████▍    | 125/228 [01:38<01:21,  1.26it/s]

→ 1000 Phase 2: corners:  55%|█████▌    | 126/228 [01:39<01:20,  1.26it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 127/228 [01:40<01:19,  1.27it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 128/228 [01:41<01:19,  1.27it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 129/228 [01:42<01:17,  1.27it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 130/228 [01:42<01:17,  1.26it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 131/228 [01:43<01:17,  1.26it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 132/228 [01:44<01:17,  1.24it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 133/228 [01:45<01:17,  1.23it/s]

→ 1000 Phase 2: corners:  59%|█████▉    | 134/228 [01:46<01:17,  1.21it/s]

→ 1000 Phase 2: corners:  59%|█████▉    | 135/228 [01:46<01:16,  1.22it/s]

→ 1000 Phase 2: corners:  60%|█████▉    | 136/228 [01:47<01:15,  1.22it/s]

→ 1000 Phase 2: corners:  60%|██████    | 137/228 [01:48<01:12,  1.25it/s]

→ 1000 Phase 2: corners:  61%|██████    | 138/228 [01:49<01:12,  1.25it/s]

→ 1000 Phase 2: corners:  61%|██████    | 139/228 [01:50<01:11,  1.25it/s]

→ 1000 Phase 2: corners:  61%|██████▏   | 140/228 [01:50<01:10,  1.25it/s]

→ 1000 Phase 2: corners:  62%|██████▏   | 141/228 [01:51<01:07,  1.28it/s]

→ 1000 Phase 2: corners:  62%|██████▏   | 142/228 [01:52<01:05,  1.32it/s]

→ 1000 Phase 2: corners:  63%|██████▎   | 143/228 [01:53<01:04,  1.32it/s]

→ 1000 Phase 2: corners:  63%|██████▎   | 144/228 [01:53<01:02,  1.34it/s]

→ 1000 Phase 2: corners:  64%|██████▎   | 145/228 [01:54<01:00,  1.36it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 146/228 [01:55<01:01,  1.33it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 147/228 [01:56<01:00,  1.34it/s]

→ 1000 Phase 2: corners:  65%|██████▍   | 148/228 [01:56<00:59,  1.34it/s]

→ 1000 Phase 2: corners:  65%|██████▌   | 149/228 [01:57<00:58,  1.35it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 150/228 [01:58<00:57,  1.35it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 151/228 [01:59<00:57,  1.35it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 152/228 [01:59<00:56,  1.34it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 153/228 [02:00<00:56,  1.34it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 154/228 [02:01<00:54,  1.35it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 155/228 [02:02<00:54,  1.34it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 156/228 [02:02<00:53,  1.34it/s]

→ 1000 Phase 2: corners:  69%|██████▉   | 157/228 [02:03<00:53,  1.34it/s]

→ 1000 Phase 2: corners:  69%|██████▉   | 158/228 [02:04<00:52,  1.33it/s]

→ 1000 Phase 2: corners:  70%|██████▉   | 159/228 [02:05<00:52,  1.32it/s]

→ 1000 Phase 2: corners:  70%|███████   | 160/228 [02:05<00:51,  1.32it/s]

→ 1000 Phase 2: corners:  71%|███████   | 161/228 [02:06<00:50,  1.32it/s]

→ 1000 Phase 2: corners:  71%|███████   | 162/228 [02:07<00:50,  1.31it/s]

→ 1000 Phase 2: corners:  71%|███████▏  | 163/228 [02:08<00:50,  1.28it/s]

→ 1000 Phase 2: corners:  72%|███████▏  | 164/228 [02:09<00:50,  1.27it/s]

→ 1000 Phase 2: corners:  72%|███████▏  | 165/228 [02:09<00:49,  1.28it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 166/228 [02:10<00:49,  1.25it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 167/228 [02:11<00:49,  1.24it/s]

→ 1000 Phase 2: corners:  74%|███████▎  | 168/228 [02:12<00:49,  1.22it/s]

→ 1000 Phase 2: corners:  74%|███████▍  | 169/228 [02:13<00:49,  1.20it/s]

→ 1000 Phase 2: corners:  75%|███████▍  | 170/228 [02:14<00:48,  1.20it/s]

→ 1000 Phase 2: corners:  75%|███████▌  | 171/228 [02:14<00:47,  1.20it/s]

→ 1000 Phase 2: corners:  75%|███████▌  | 172/228 [02:15<00:46,  1.21it/s]

→ 1000 Phase 2: corners:  76%|███████▌  | 173/228 [02:16<00:45,  1.20it/s]

→ 1000 Phase 2: corners:  76%|███████▋  | 174/228 [02:17<00:45,  1.18it/s]

→ 1000 Phase 2: corners:  77%|███████▋  | 175/228 [02:18<00:45,  1.17it/s]

→ 1000 Phase 2: corners:  77%|███████▋  | 176/228 [02:19<00:44,  1.17it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 177/228 [02:19<00:43,  1.18it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 178/228 [02:20<00:42,  1.19it/s]

→ 1000 Phase 2: corners:  79%|███████▊  | 179/228 [02:21<00:41,  1.19it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 180/228 [02:22<00:40,  1.18it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 181/228 [02:23<00:39,  1.19it/s]

→ 1000 Phase 2: corners:  80%|███████▉  | 182/228 [02:24<00:38,  1.20it/s]

→ 1000 Phase 2: corners:  80%|████████  | 183/228 [02:24<00:36,  1.22it/s]

→ 1000 Phase 2: corners:  81%|████████  | 184/228 [02:25<00:36,  1.20it/s]

→ 1000 Phase 2: corners:  81%|████████  | 185/228 [02:26<00:35,  1.22it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 186/228 [02:27<00:34,  1.23it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 187/228 [02:28<00:32,  1.24it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 188/228 [02:28<00:32,  1.25it/s]

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→ 1000 Phase 2: corners:  83%|████████▎ | 190/228 [02:30<00:30,  1.25it/s]

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→ 1000 Phase 2: corners: 100%|██████████| 228/228 [03:00<00:00,  1.32it/s]

                                                                          [PASS2] Frame 0000341: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000342: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000345: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001124: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001125: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001126: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001127: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001286: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001287: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001289: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001126: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 6 frames fixed, 6 untrusted, 228 total


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→ 1000 Phase 3: finalize: 100%|██████████| 228/228 [00:17<00:00, 13.01it/s]

                                                                           
Processing Rigs: 100%|██████████| 2/2 [08:30<00:00, 249.60s/it]
Processing Rigs: 100%|██████████| 2/2 [08:30<00:00, 255.03s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 484 frames from 2 rigs to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 256
[📋] 1000: 100 frames selected from 228
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W038_2025-05-14/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:  25%|██▌       | 10/40 [00:00<00:00, 90.54it/s]
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Computing max image from bright frames:  80%|████████  | 32/40 [00:00<00:00, 102.92it/s]
                                                                                        
[INFO] Computed max image from 40 bright frames
[INFO] Corner median intensity: 20, black threshold: 60
[INFO] Saved casing detection debug images to /working/MDC015_W038_2025-05-14/calibration/debug/2_calibration/casing_detection
[INFO] Found 20785 casing boundary candidate points
[RESULT] Detected casing: center=(996, 734), radius=834, inliers=2080/20785

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Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.42it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.42it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  13%|█▎        | 13/100 [00:00<00:10,  8.42it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  14%|█▍        | 14/100 [00:00<00:10,  8.42it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  15%|█▌        | 15/100 [00:00<00:10,  8.42it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  16%|█▌        | 16/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  16%|█▌        | 16/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  20%|██        | 20/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  21%|██        | 21/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 84.66it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 505.051} (0.009):  26%|██▌       | 26/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 505.051} (0.009):  27%|██▋       | 27/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 505.051} (0.009):  28%|██▊       | 28/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.055 | Best: {'fx': 526.263} (0.055):  29%|██▉       | 29/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 526.263} (0.055):  30%|███       | 30/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 526.263} (0.055):  31%|███       | 31/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.049 | Best: {'fx': 526.263} (0.055):  32%|███▏      | 32/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.030 | Best: {'fx': 526.263} (0.055):  33%|███▎      | 33/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.019 | Best: {'fx': 526.263} (0.055):  34%|███▍      | 34/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.038 | Best: {'fx': 526.263} (0.055):  35%|███▌      | 35/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.025 | Best: {'fx': 526.263} (0.055):  36%|███▌      | 36/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.029 | Best: {'fx': 526.263} (0.055):  37%|███▋      | 37/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.021 | Best: {'fx': 526.263} (0.055):  38%|███▊      | 38/100 [00:00<00:00, 84.30it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.021 | Best: {'fx': 526.263} (0.055):  39%|███▉      | 39/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 526.263} (0.055):  39%|███▉      | 39/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 526.263} (0.055):  40%|████      | 40/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 526.263} (0.055):  41%|████      | 41/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 526.263} (0.055):  42%|████▏     | 42/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 526.263} (0.055):  43%|████▎     | 43/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 526.263} (0.055):  44%|████▍     | 44/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 526.263} (0.055):  45%|████▌     | 45/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 526.263} (0.055):  46%|████▌     | 46/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 526.263} (0.055):  47%|████▋     | 47/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 526.263} (0.055):  48%|████▊     | 48/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 526.263} (0.055):  49%|████▉     | 49/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 526.263} (0.055):  50%|█████     | 50/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  51%|█████     | 51/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 526.263} (0.055):  52%|█████▏    | 52/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 526.263} (0.055):  53%|█████▎    | 53/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  54%|█████▍    | 54/100 [00:00<00:00, 101.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  55%|█████▌    | 55/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  55%|█████▌    | 55/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  56%|█████▌    | 56/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  57%|█████▋    | 57/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  58%|█████▊    | 58/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  59%|█████▉    | 59/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  60%|██████    | 60/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  61%|██████    | 61/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  62%|██████▏   | 62/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  63%|██████▎   | 63/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  64%|██████▍   | 64/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  65%|██████▌   | 65/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 526.263} (0.055):  66%|██████▌   | 66/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  67%|██████▋   | 67/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  68%|██████▊   | 68/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  69%|██████▉   | 69/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  70%|███████   | 70/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  71%|███████   | 71/100 [00:00<00:00, 118.67it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  72%|███████▏  | 72/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  72%|███████▏  | 72/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  73%|███████▎  | 73/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  74%|███████▍  | 74/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  75%|███████▌  | 75/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  76%|███████▌  | 76/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  77%|███████▋  | 77/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  78%|███████▊  | 78/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  79%|███████▉  | 79/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  80%|████████  | 80/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  81%|████████  | 81/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  82%|████████▏ | 82/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  83%|████████▎ | 83/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  84%|████████▍ | 84/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  85%|████████▌ | 85/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  86%|████████▌ | 86/100 [00:00<00:00, 130.21it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  87%|████████▋ | 87/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  87%|████████▋ | 87/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  88%|████████▊ | 88/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  89%|████████▉ | 89/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  90%|█████████ | 90/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  91%|█████████ | 91/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  92%|█████████▏| 92/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  93%|█████████▎| 93/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  94%|█████████▍| 94/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  95%|█████████▌| 95/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  96%|█████████▌| 96/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  97%|█████████▋| 97/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  98%|█████████▊| 98/100 [00:00<00:00, 135.04it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 526.263} (0.055):  99%|█████████▉| 99/100 [00:00<00:00, 135.04it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.005 | Best: {'fx': 501.263} (0.005):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.005 | Best: {'fx': 501.263} (0.005):   2%|▏         | 1/50 [00:00<00:06,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.010 | Best: {'fx': 507.385} (0.010):   2%|▏         | 1/50 [00:00<00:06,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.006 | Best: {'fx': 507.385} (0.010):   4%|▍         | 2/50 [00:00<00:06,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.007 | Best: {'fx': 507.385} (0.010):   6%|▌         | 3/50 [00:00<00:06,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.008 | Best: {'fx': 507.385} (0.010):   8%|▊         | 4/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.013 | Best: {'fx': 510.446} (0.013):  10%|█         | 5/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.010 | Best: {'fx': 510.446} (0.013):  12%|█▏        | 6/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.009 | Best: {'fx': 510.446} (0.013):  14%|█▍        | 7/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.010 | Best: {'fx': 510.446} (0.013):  16%|█▌        | 8/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.011 | Best: {'fx': 510.446} (0.013):  18%|█▊        | 9/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.013 | Best: {'fx': 511.467} (0.013):  20%|██        | 10/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.014 | Best: {'fx': 512.487} (0.014):  22%|██▏       | 11/50 [00:00<00:05,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.015 | Best: {'fx': 513.508} (0.015):  24%|██▍       | 12/50 [00:00<00:04,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.024 | Best: {'fx': 515.548} (0.024):  26%|██▌       | 13/50 [00:00<00:04,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.018 | Best: {'fx': 515.548} (0.024):  28%|██▊       | 14/50 [00:00<00:04,  7.73it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.018 | Best: {'fx': 515.548} (0.024):  30%|███       | 15/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.030 | Best: {'fx': 517.589} (0.030):  30%|███       | 15/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.032 | Best: {'fx': 519.63} (0.032):  32%|███▏      | 16/50 [00:00<00:00, 74.39it/s] 
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.026 | Best: {'fx': 519.63} (0.032):  34%|███▍      | 17/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.029 | Best: {'fx': 519.63} (0.032):  36%|███▌      | 18/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.040 | Best: {'fx': 522.691} (0.040):  38%|███▊      | 19/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.038 | Best: {'fx': 522.691} (0.040):  40%|████      | 20/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.034 | Best: {'fx': 522.691} (0.040):  42%|████▏     | 21/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.064 | Best: {'fx': 527.793} (0.064):  44%|████▍     | 22/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.043 | Best: {'fx': 527.793} (0.064):  46%|████▌     | 23/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.050 | Best: {'fx': 527.793} (0.064):  48%|████▊     | 24/50 [00:00<00:00, 74.39it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.050 | Best: {'fx': 527.793} (0.064):  50%|█████     | 25/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.056 | Best: {'fx': 527.793} (0.064):  50%|█████     | 25/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.058 | Best: {'fx': 527.793} (0.064):  52%|█████▏    | 26/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.059 | Best: {'fx': 527.793} (0.064):  54%|█████▍    | 27/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.052 | Best: {'fx': 527.793} (0.064):  56%|█████▌    | 28/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.050 | Best: {'fx': 527.793} (0.064):  58%|█████▊    | 29/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.062 | Best: {'fx': 527.793} (0.064):  60%|██████    | 30/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.051 | Best: {'fx': 527.793} (0.064):  62%|██████▏   | 31/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.062 | Best: {'fx': 527.793} (0.064):  64%|██████▍   | 32/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.046 | Best: {'fx': 527.793} (0.064):  66%|██████▌   | 33/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.045 | Best: {'fx': 527.793} (0.064):  68%|██████▊   | 34/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.042 | Best: {'fx': 527.793} (0.064):  70%|███████   | 35/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.051 | Best: {'fx': 527.793} (0.064):  72%|███████▏  | 36/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.040 | Best: {'fx': 527.793} (0.064):  74%|███████▍  | 37/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.043 | Best: {'fx': 527.793} (0.064):  76%|███████▌  | 38/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.037 | Best: {'fx': 527.793} (0.064):  78%|███████▊  | 39/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.038 | Best: {'fx': 527.793} (0.064):  80%|████████  | 40/50 [00:00<00:00, 84.79it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.038 | Best: {'fx': 527.793} (0.064):  82%|████████▏ | 41/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.034 | Best: {'fx': 527.793} (0.064):  82%|████████▏ | 41/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.034 | Best: {'fx': 527.793} (0.064):  84%|████████▍ | 42/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.031 | Best: {'fx': 527.793} (0.064):  86%|████████▌ | 43/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.029 | Best: {'fx': 527.793} (0.064):  88%|████████▊ | 44/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.032 | Best: {'fx': 527.793} (0.064):  90%|█████████ | 45/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.028 | Best: {'fx': 527.793} (0.064):  92%|█████████▏| 46/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.030 | Best: {'fx': 527.793} (0.064):  94%|█████████▍| 47/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.028 | Best: {'fx': 527.793} (0.064):  96%|█████████▌| 48/50 [00:00<00:00, 112.09it/s]
Brute | Start: {'fx': 526.263} (0.055) | Current: 0.027 | Best: {'fx': 527.793} (0.064):  98%|█████████▊| 49/50 [00:00<00:00, 112.09it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 527.7932385075243
    px: 996.0
    py: 734.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


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

Initial score: 194.1778: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 194.1778, picklable_objective(x0) = 194.2224

Best score: 194.2224: : 0iter [00:01, ?iter/s] 
Best score: 194.2224: : 1iter [00:01,  1.92s/iter]
Best score: 194.2224: : 1iter [00:01,  1.92s/iter]
Best score: 194.2224: : 2iter [00:02,  1.92s/iter]
Best score: 194.2224: : 3iter [00:02,  1.92s/iter]
Best score: 194.2224: : 4iter [00:02,  2.50iter/s]
Best score: 194.2224: : 4iter [00:02,  2.50iter/s]
Best score: 194.2224: : 5iter [00:02,  2.50iter/s]
Best score: 194.2224: : 6iter [00:02,  2.50iter/s]
Best score: 194.2224: : 7iter [00:02,  4.76iter/s]
Best score: 194.2224: : 7iter [00:02,  4.76iter/s]
Best score: 194.2224: : 8iter [00:02,  4.76iter/s]
Best score: 173.9783: : 9iter [00:02,  4.76iter/s]
Best score: 173.9783: : 10iter [00:02,  7.16iter/s]
Best score: 173.9783: : 10iter [00:02,  7.16iter/s]
Best score: 173.9783: : 11iter [00:02,  7.16iter/s]
Best score: 173.9783: : 12iter [00:02,  7.16iter/s]
Best score: 173.9783: : 13iter [00:02,  9.65iter/s]
Best score: 173.9783: : 13iter [00:02,  9.65iter/s]
Best score: 173.9783: : 14iter [00:02,  9.65iter/s]
Best score: 173.9783: : 15iter [00:02,  9.65iter/s]
Best score: 173.9783: : 16iter [00:02, 12.02iter/s]
Best score: 173.9783: : 16iter [00:02, 12.02iter/s]
Best score: 173.9783: : 17iter [00:02, 12.02iter/s]
Best score: 173.9783: : 18iter [00:02, 12.02iter/s]
Best score: 173.9783: : 19iter [00:02, 14.20iter/s]
Best score: 96.9519: : 19iter [00:02, 14.20iter/s] 
Best score: 96.9519: : 20iter [00:02, 14.20iter/s]
Best score: 96.9519: : 21iter [00:02, 14.20iter/s]
Best score: 96.9519: : 22iter [00:02, 15.91iter/s]
Best score: 96.9519: : 22iter [00:02, 15.91iter/s]
Best score: 96.9519: : 23iter [00:02, 15.91iter/s]
Best score: 96.9519: : 24iter [00:03, 15.91iter/s]
Best score: 96.9519: : 25iter [00:03, 17.39iter/s]
Best score: 96.9519: : 25iter [00:03, 17.39iter/s]
Best score: 96.9519: : 26iter [00:03, 17.39iter/s]
Best score: 96.9519: : 27iter [00:03, 17.39iter/s]
Best score: 96.9519: : 28iter [00:03, 18.57iter/s]
Best score: 96.9519: : 28iter [00:03, 18.57iter/s]
Best score: 96.9519: : 29iter [00:03, 18.57iter/s]
Best score: 96.9519: : 30iter [00:03, 18.57iter/s]
Best score: 96.9519: : 31iter [00:03, 19.27iter/s]
Best score: 96.9519: : 31iter [00:03, 19.27iter/s]
Best score: 96.9519: : 32iter [00:03, 19.27iter/s]
Best score: 96.9519: : 33iter [00:03, 19.27iter/s]
Best score: 96.9519: : 34iter [00:03, 20.02iter/s]
Best score: 96.9519: : 34iter [00:03, 20.02iter/s]
Best score: 96.9519: : 35iter [00:03, 20.02iter/s]
Best score: 96.9519: : 36iter [00:03, 20.02iter/s]
Best score: 96.9519: : 37iter [00:03, 20.58iter/s]
Best score: 96.9519: : 37iter [00:03, 20.58iter/s]
Best score: 96.9519: : 38iter [00:03, 20.58iter/s]
Best score: 83.4304: : 39iter [00:03, 20.58iter/s]
Best score: 83.4304: : 40iter [00:03, 20.79iter/s]
Best score: 83.4304: : 40iter [00:03, 20.79iter/s]
Best score: 83.4304: : 41iter [00:03, 20.79iter/s]
Best score: 83.4304: : 42iter [00:03, 20.79iter/s]
Best score: 83.4304: : 43iter [00:03, 21.16iter/s]
Best score: 83.4304: : 43iter [00:03, 21.16iter/s]
Best score: 83.4304: : 44iter [00:03, 21.16iter/s]
Best score: 83.4304: : 45iter [00:03, 21.16iter/s]
Best score: 83.4304: : 46iter [00:03, 21.41iter/s]
Best score: 83.4304: : 46iter [00:04, 21.41iter/s]
Best score: 83.4304: : 47iter [00:04, 21.41iter/s]
Best score: 83.4304: : 48iter [00:04, 21.41iter/s]
Best score: 83.4304: : 49iter [00:04, 21.59iter/s]
[ITER 50] Best: 39.4663
[PARAMS] Current best parameters:
  fx: 538.3589
  fy: 536.0389
  px: 1000.7148
  py: 744.4198
  k1: -0.02602502
  k2: 0.01036927
  k3: -0.02541618
  k4: 0.01259375
  p1: -0.00443456
  p2: 0.00343990
  p3: 0.00748863
  p4: -0.00109167
  scale: 3.1855
  rotation: -0.6622

Best score: 39.4663: : 49iter [00:04, 21.59iter/s]
Best score: 39.4663: : 50iter [00:04, 21.59iter/s]
Best score: 39.4663: : 51iter [00:04, 21.59iter/s]
Best score: 39.4663: : 52iter [00:04, 21.49iter/s]
Best score: 39.4663: : 52iter [00:04, 21.49iter/s]
Best score: 39.4663: : 53iter [00:04, 21.49iter/s]
Best score: 39.4663: : 54iter [00:04, 21.49iter/s]
Best score: 39.4663: : 55iter [00:04, 21.66iter/s]
Best score: 39.4663: : 55iter [00:04, 21.66iter/s]
Best score: 39.4663: : 56iter [00:04, 21.66iter/s]
Best score: 39.4663: : 57iter [00:04, 21.66iter/s]
Best score: 39.4663: : 58iter [00:04, 21.84iter/s]
Best score: 39.4663: : 58iter [00:04, 21.84iter/s]
Best score: 17.4942: : 59iter [00:04, 21.84iter/s]
Best score: 17.4942: : 60iter [00:04, 21.84iter/s]
Best score: 17.4942: : 61iter [00:04, 21.65iter/s]
Best score: 17.4942: : 61iter [00:04, 21.65iter/s]
Best score: 17.4942: : 62iter [00:04, 21.65iter/s]
Best score: 17.4942: : 63iter [00:04, 21.65iter/s]
Best score: 17.4942: : 64iter [00:04, 21.70iter/s]
Best score: 17.4942: : 64iter [00:04, 21.70iter/s]
Best score: 17.4942: : 65iter [00:04, 21.70iter/s]
Best score: 17.4942: : 66iter [00:04, 21.70iter/s]
Best score: 17.4942: : 67iter [00:04, 21.79iter/s]
Best score: 17.4942: : 67iter [00:05, 21.79iter/s]
Best score: 17.4942: : 68iter [00:05, 21.79iter/s]
Best score: 10.6684: : 69iter [00:05, 21.79iter/s]
Best score: 10.6684: : 70iter [00:05, 21.63iter/s]
Best score: 10.6684: : 70iter [00:05, 21.63iter/s]
Best score: 10.6684: : 71iter [00:05, 21.63iter/s]
Best score: 10.6684: : 72iter [00:05, 21.63iter/s]
Best score: 10.6684: : 73iter [00:05, 21.78iter/s]
Best score: 10.6684: : 73iter [00:05, 21.78iter/s]
Best score: 10.6684: : 74iter [00:05, 21.78iter/s]
Best score: 10.6684: : 75iter [00:05, 21.78iter/s]
Best score: 10.6684: : 76iter [00:05, 21.84iter/s]
Best score: 10.6684: : 76iter [00:05, 21.84iter/s]
Best score: 10.6684: : 77iter [00:05, 21.84iter/s]
Best score: 10.6684: : 78iter [00:05, 21.84iter/s]
Best score: 10.6684: : 79iter [00:05, 21.97iter/s]
Best score: 10.6684: : 79iter [00:05, 21.97iter/s]
Best score: 10.6684: : 80iter [00:05, 21.97iter/s]
Best score: 10.6684: : 81iter [00:05, 21.97iter/s]
Best score: 10.6684: : 82iter [00:05, 21.77iter/s]
Best score: 10.6684: : 82iter [00:05, 21.77iter/s]
Best score: 10.6684: : 83iter [00:05, 21.77iter/s]
Best score: 10.6684: : 84iter [00:05, 21.77iter/s]
Best score: 10.6684: : 85iter [00:05, 21.93iter/s]
Best score: 10.6684: : 85iter [00:05, 21.93iter/s]
Best score: 10.6684: : 86iter [00:05, 21.93iter/s]
Best score: 10.6684: : 87iter [00:05, 21.93iter/s]
Best score: 10.6684: : 88iter [00:05, 22.03iter/s]
Best score: 10.6684: : 88iter [00:05, 22.03iter/s]
Best score: 3.8068: : 89iter [00:06, 22.03iter/s] 
Best score: 3.8068: : 90iter [00:06, 22.03iter/s]
Best score: 3.8068: : 91iter [00:06, 21.78iter/s]
Best score: 3.8068: : 91iter [00:06, 21.78iter/s]
Best score: 3.8068: : 92iter [00:06, 21.78iter/s]
Best score: 3.8068: : 93iter [00:06, 21.78iter/s]
Best score: 3.8068: : 94iter [00:06, 21.94iter/s]
Best score: 3.8068: : 94iter [00:06, 21.94iter/s]
Best score: 3.8068: : 95iter [00:06, 21.94iter/s]
Best score: 3.8068: : 96iter [00:06, 21.94iter/s]
Best score: 3.8068: : 97iter [00:06, 22.02iter/s]
Best score: 3.8068: : 97iter [00:06, 22.02iter/s]
Best score: 3.8068: : 98iter [00:06, 22.02iter/s]
[ITER 100] Best: 1.7257
[PARAMS] Current best parameters:
  fx: 535.2106
  fy: 534.0080
  px: 997.4296
  py: 742.7851
  k1: -0.01674885
  k2: 0.01372527
  k3: 0.00265778
  k4: -0.00366125
  p1: -0.00424731
  p2: -0.00261637
  p3: 0.00003274
  p4: 0.00000927
  scale: 2.8935
  rotation: -0.6031

Best score: 1.7257: : 99iter [00:06, 22.02iter/s]
Best score: 1.7257: : 100iter [00:06, 21.87iter/s]
Best score: 1.7257: : 100iter [00:06, 21.87iter/s]
Best score: 1.7257: : 101iter [00:06, 21.87iter/s]
Best score: 1.7257: : 102iter [00:06, 21.87iter/s]
Best score: 1.7257: : 103iter [00:06, 21.97iter/s]
Best score: 1.7257: : 103iter [00:06, 21.97iter/s]
Best score: 1.7257: : 104iter [00:06, 21.97iter/s]
Best score: 1.7257: : 105iter [00:06, 21.97iter/s]
Best score: 1.7257: : 106iter [00:06, 22.07iter/s]
Best score: 1.7257: : 106iter [00:06, 22.07iter/s]
Best score: 1.7257: : 107iter [00:06, 22.07iter/s]
Best score: 1.7257: : 108iter [00:06, 22.07iter/s]
Best score: 1.7257: : 109iter [00:06, 22.18iter/s]
Best score: 1.1055: : 109iter [00:06, 22.18iter/s]
Best score: 1.1055: : 110iter [00:06, 22.18iter/s]
Best score: 1.1055: : 111iter [00:07, 22.18iter/s]
Best score: 1.1055: : 112iter [00:07, 22.02iter/s]
Best score: 1.1055: : 112iter [00:07, 22.02iter/s]
Best score: 1.1055: : 113iter [00:07, 22.02iter/s]
Best score: 1.1055: : 114iter [00:07, 22.02iter/s]
Best score: 1.1055: : 115iter [00:07, 22.14iter/s]
Best score: 1.1055: : 115iter [00:07, 22.14iter/s]
Best score: 1.1055: : 116iter [00:07, 22.14iter/s]
Best score: 1.1055: : 117iter [00:07, 22.14iter/s]
Best score: 1.1055: : 118iter [00:07, 22.26iter/s]
Best score: 1.1055: : 118iter [00:07, 22.26iter/s]
Best score: 0.8958: : 119iter [00:07, 22.26iter/s]
Best score: 0.8958: : 120iter [00:07, 22.26iter/s]
Best score: 0.8958: : 121iter [00:07, 22.10iter/s]
Best score: 0.8958: : 121iter [00:07, 22.10iter/s]
Best score: 0.8958: : 122iter [00:07, 22.10iter/s]
Best score: 0.8958: : 123iter [00:07, 22.10iter/s]
Best score: 0.8958: : 124iter [00:07, 22.21iter/s]
Best score: 0.8958: : 124iter [00:07, 22.21iter/s]
Best score: 0.8958: : 125iter [00:07, 22.21iter/s]
Best score: 0.8958: : 126iter [00:07, 22.21iter/s]
Best score: 0.8958: : 127iter [00:07, 22.20iter/s]
Best score: 0.8958: : 127iter [00:07, 22.20iter/s]
Best score: 0.8958: : 128iter [00:07, 22.20iter/s]
Best score: 0.5768: : 129iter [00:07, 22.20iter/s]
Best score: 0.5768: : 130iter [00:07, 22.00iter/s]
Best score: 0.5768: : 130iter [00:07, 22.00iter/s]
Best score: 0.5768: : 131iter [00:07, 22.00iter/s]
Best score: 0.5768: : 132iter [00:07, 22.00iter/s]
Best score: 0.5768: : 133iter [00:07, 22.07iter/s]
Best score: 0.5768: : 133iter [00:07, 22.07iter/s]
Best score: 0.5768: : 134iter [00:08, 22.07iter/s]
Best score: 0.5768: : 135iter [00:08, 22.07iter/s]
Best score: 0.5768: : 136iter [00:08, 22.20iter/s]
Best score: 0.5768: : 136iter [00:08, 22.20iter/s]
Best score: 0.5768: : 137iter [00:08, 22.20iter/s]
Best score: 0.5768: : 138iter [00:08, 22.20iter/s]
Best score: 0.5768: : 139iter [00:08, 22.24iter/s]
Best score: 0.5768: : 139iter [00:08, 22.24iter/s]
Best score: 0.5768: : 140iter [00:08, 22.24iter/s]
Best score: 0.5768: : 141iter [00:08, 22.24iter/s]
Best score: 0.5768: : 142iter [00:08, 22.06iter/s]
Best score: 0.5768: : 142iter [00:08, 22.06iter/s]
Best score: 0.5768: : 143iter [00:08, 22.06iter/s]
Best score: 0.5768: : 144iter [00:08, 22.06iter/s]
Best score: 0.5768: : 145iter [00:08, 22.18iter/s]
Best score: 0.5768: : 145iter [00:08, 22.18iter/s]
Best score: 0.5768: : 146iter [00:08, 22.18iter/s]
Best score: 0.5768: : 147iter [00:08, 22.18iter/s]
Best score: 0.5768: : 148iter [00:08, 22.11iter/s]
Best score: 0.5768: : 148iter [00:08, 22.11iter/s]
[ITER 150] Best: 0.5768
[PARAMS] Current best parameters:
  fx: 534.7140
  fy: 533.8653
  px: 997.9662
  py: 743.2319
  k1: -0.00057723
  k2: 0.00653937
  k3: -0.00692723
  k4: 0.00130249
  p1: -0.00356153
  p2: -0.00130971
  p3: -0.00046187
  p4: 0.00082560
  scale: 2.8811
  rotation: -0.4943

Best score: 0.5768: : 149iter [00:08, 22.11iter/s]
Best score: 0.5768: : 150iter [00:08, 22.11iter/s]
Best score: 0.5768: : 151iter [00:08, 21.89iter/s]
Best score: 0.5768: : 151iter [00:08, 21.89iter/s]
Best score: 0.5768: : 152iter [00:08, 21.89iter/s]
Best score: 0.5768: : 153iter [00:08, 21.89iter/s]
Best score: 0.5768: : 154iter [00:08, 21.99iter/s]
Best score: 0.5768: : 154iter [00:08, 21.99iter/s]
Best score: 0.5768: : 155iter [00:08, 21.99iter/s]
Best score: 0.5768: : 156iter [00:09, 21.99iter/s]
Best score: 0.5768: : 157iter [00:09, 22.11iter/s]
Best score: 0.5768: : 157iter [00:09, 22.11iter/s]
Best score: 0.5768: : 158iter [00:09, 22.11iter/s]
Best score: 0.4910: : 159iter [00:09, 22.11iter/s]
Best score: 0.4910: : 160iter [00:09, 21.98iter/s]
Best score: 0.4910: : 160iter [00:09, 21.98iter/s]
Best score: 0.4910: : 161iter [00:09, 21.98iter/s]
Best score: 0.4910: : 162iter [00:09, 21.98iter/s]
Best score: 0.4910: : 163iter [00:09, 22.06iter/s]
Best score: 0.4910: : 163iter [00:09, 22.06iter/s]
Best score: 0.4910: : 164iter [00:09, 22.06iter/s]
Best score: 0.4910: : 165iter [00:09, 22.06iter/s]
Best score: 0.4910: : 166iter [00:09, 22.14iter/s]
Best score: 0.4910: : 166iter [00:09, 22.14iter/s]
Best score: 0.4910: : 167iter [00:09, 22.14iter/s]
Best score: 0.4910: : 168iter [00:09, 22.14iter/s]
Best score: 0.4910: : 169iter [00:09, 22.21iter/s]
Best score: 0.4485: : 169iter [00:09, 22.21iter/s]
Best score: 0.4485: : 170iter [00:09, 22.21iter/s]
Best score: 0.4485: : 171iter [00:09, 22.21iter/s]
Best score: 0.4485: : 172iter [00:09, 22.01iter/s]
Best score: 0.4485: : 172iter [00:09, 22.01iter/s]
Best score: 0.4485: : 173iter [00:09, 22.01iter/s]
Best score: 0.4485: : 174iter [00:09, 22.01iter/s]
Best score: 0.4485: : 175iter [00:09, 22.14iter/s]
Best score: 0.4485: : 175iter [00:09, 22.14iter/s]
Best score: 0.4485: : 176iter [00:09, 22.14iter/s]
Best score: 0.4485: : 177iter [00:09, 22.14iter/s]
Best score: 0.4485: : 178iter [00:09, 22.25iter/s]
Best score: 0.4485: : 178iter [00:10, 22.25iter/s]
Best score: 0.4199: : 179iter [00:10, 22.25iter/s]
Best score: 0.4199: : 180iter [00:10, 22.25iter/s]
Best score: 0.4199: : 181iter [00:10, 22.07iter/s]
Best score: 0.4199: : 181iter [00:10, 22.07iter/s]
Best score: 0.4199: : 182iter [00:10, 22.07iter/s]
Best score: 0.4199: : 183iter [00:10, 22.07iter/s]
Best score: 0.4199: : 184iter [00:10, 22.18iter/s]
Best score: 0.4199: : 184iter [00:10, 22.18iter/s]
Best score: 0.4199: : 185iter [00:10, 22.18iter/s]
Best score: 0.4199: : 186iter [00:10, 22.18iter/s]
Best score: 0.4199: : 187iter [00:10, 22.29iter/s]
Best score: 0.4199: : 187iter [00:10, 22.29iter/s]
Best score: 0.4199: : 188iter [00:10, 22.29iter/s]
Best score: 0.4064: : 189iter [00:10, 22.29iter/s]
Best score: 0.4064: : 190iter [00:10, 22.13iter/s]
Best score: 0.4064: : 190iter [00:10, 22.13iter/s]
Best score: 0.4064: : 191iter [00:10, 22.13iter/s]
Best score: 0.4064: : 192iter [00:10, 22.13iter/s]
Best score: 0.4064: : 193iter [00:10, 22.20iter/s]
Best score: 0.4064: : 193iter [00:10, 22.20iter/s]
Best score: 0.4064: : 194iter [00:10, 22.20iter/s]
Best score: 0.4064: : 195iter [00:10, 22.20iter/s]
Best score: 0.4064: : 196iter [00:10, 22.26iter/s]
Best score: 0.4064: : 196iter [00:10, 22.26iter/s]
Best score: 0.4064: : 197iter [00:10, 22.26iter/s]
Best score: 0.4064: : 198iter [00:10, 22.26iter/s]
Best score: 0.4064: : 199iter [00:10, 22.32iter/s]
[ITER 200] Best: 0.3953
[PARAMS] Current best parameters:
  fx: 526.8094
  fy: 526.3822
  px: 995.7877
  py: 741.8805
  k1: 0.00692894
  k2: 0.00579487
  k3: -0.00490542
  k4: 0.00029250
  p1: -0.00675053
  p2: -0.00653732
  p3: 0.00302421
  p4: 0.00034749
  scale: 2.8757
  rotation: -0.4566

Best score: 0.3953: : 199iter [00:10, 22.32iter/s]
Best score: 0.3953: : 200iter [00:11, 22.32iter/s]
Best score: 0.3953: : 201iter [00:11, 22.32iter/s]
Best score: 0.3953: : 202iter [00:11, 22.19iter/s]
Best score: 0.3953: : 202iter [00:11, 22.19iter/s]
Best score: 0.3953: : 203iter [00:11, 22.19iter/s]
Best score: 0.3953: : 204iter [00:11, 22.19iter/s]
Best score: 0.3953: : 205iter [00:11, 22.26iter/s]
Best score: 0.3953: : 205iter [00:11, 22.26iter/s]
Best score: 0.3953: : 206iter [00:11, 22.26iter/s]
Best score: 0.3953: : 207iter [00:11, 22.26iter/s]
Best score: 0.3953: : 208iter [00:11, 22.31iter/s]
Best score: 0.3953: : 208iter [00:11, 22.31iter/s]
Best score: 0.3769: : 209iter [00:11, 22.31iter/s]
Best score: 0.3769: : 210iter [00:11, 22.31iter/s]
Best score: 0.3769: : 211iter [00:11, 22.05iter/s]
Best score: 0.3769: : 211iter [00:11, 22.05iter/s]
Best score: 0.3769: : 212iter [00:11, 22.05iter/s]
Best score: 0.3769: : 213iter [00:11, 22.05iter/s]
Best score: 0.3769: : 214iter [00:11, 22.14iter/s]
Best score: 0.3769: : 214iter [00:11, 22.14iter/s]
Best score: 0.3769: : 215iter [00:11, 22.14iter/s]
Best score: 0.3769: : 216iter [00:11, 22.14iter/s]
Best score: 0.3769: : 217iter [00:11, 22.23iter/s]
Best score: 0.3769: : 217iter [00:11, 22.23iter/s]
Best score: 0.3769: : 218iter [00:11, 22.23iter/s]
Best score: 0.3705: : 219iter [00:11, 22.23iter/s]
Best score: 0.3705: : 220iter [00:11, 21.98iter/s]
Best score: 0.3705: : 220iter [00:11, 21.98iter/s]
Best score: 0.3705: : 221iter [00:11, 21.98iter/s]
Best score: 0.3705: : 222iter [00:12, 21.98iter/s]
Best score: 0.3705: : 223iter [00:12, 22.18iter/s]
Best score: 0.3705: : 223iter [00:12, 22.18iter/s]
Best score: 0.3705: : 224iter [00:12, 22.18iter/s]
Best score: 0.3705: : 225iter [00:12, 22.18iter/s]
Best score: 0.3705: : 226iter [00:12, 22.27iter/s]
Best score: 0.3705: : 226iter [00:12, 22.27iter/s]
Best score: 0.3705: : 227iter [00:12, 22.27iter/s]
Best score: 0.3705: : 228iter [00:12, 22.27iter/s]
Best score: 0.3705: : 229iter [00:12, 22.27iter/s]
Best score: 0.3700: : 229iter [00:12, 22.27iter/s]
Best score: 0.3700: : 230iter [00:12, 22.27iter/s]
Best score: 0.3700: : 231iter [00:12, 22.27iter/s]
Best score: 0.3700: : 232iter [00:12, 22.12iter/s]
Best score: 0.3700: : 232iter [00:12, 22.12iter/s]
Best score: 0.3700: : 233iter [00:12, 22.12iter/s]
Best score: 0.3700: : 234iter [00:12, 22.12iter/s]
Best score: 0.3700: : 235iter [00:12, 22.22iter/s]
Best score: 0.3700: : 235iter [00:12, 22.22iter/s]
Best score: 0.3700: : 236iter [00:12, 22.22iter/s]
Best score: 0.3700: : 237iter [00:12, 22.22iter/s]
Best score: 0.3700: : 238iter [00:12, 22.29iter/s]
Best score: 0.3700: : 238iter [00:12, 22.29iter/s]
Best score: 0.3659: : 239iter [00:12, 22.29iter/s]
Best score: 0.3659: : 240iter [00:12, 22.29iter/s]
Best score: 0.3659: : 241iter [00:12, 22.05iter/s]
Best score: 0.3659: : 241iter [00:12, 22.05iter/s]
Best score: 0.3659: : 242iter [00:12, 22.05iter/s]
Best score: 0.3659: : 243iter [00:12, 22.05iter/s]
Best score: 0.3659: : 244iter [00:12, 22.11iter/s]
Best score: 0.3659: : 244iter [00:13, 22.11iter/s]
Best score: 0.3659: : 245iter [00:13, 22.11iter/s]
Best score: 0.3659: : 246iter [00:13, 22.11iter/s]
Best score: 0.3659: : 247iter [00:13, 22.15iter/s]
Best score: 0.3659: : 247iter [00:13, 22.15iter/s]
Best score: 0.3659: : 248iter [00:13, 22.15iter/s]
[ITER 250] Best: 0.3612
[PARAMS] Current best parameters:
  fx: 527.0303
  fy: 526.5666
  px: 995.3991
  py: 741.6493
  k1: 0.00415797
  k2: 0.00935549
  k3: -0.00747202
  k4: 0.00102212
  p1: -0.00738421
  p2: -0.00735223
  p3: 0.00478535
  p4: -0.00022975
  scale: 2.8834
  rotation: -0.4564

Best score: 0.3612: : 249iter [00:13, 22.15iter/s]
Best score: 0.3612: : 250iter [00:13, 22.00iter/s]
Best score: 0.3612: : 250iter [00:13, 22.00iter/s]
Best score: 0.3612: : 251iter [00:13, 22.00iter/s]
Best score: 0.3612: : 252iter [00:13, 22.00iter/s]
Best score: 0.3612: : 253iter [00:13, 22.04iter/s]
Best score: 0.3612: : 253iter [00:13, 22.04iter/s]
Best score: 0.3612: : 254iter [00:13, 22.04iter/s]
Best score: 0.3612: : 255iter [00:13, 22.04iter/s]
Best score: 0.3612: : 256iter [00:13, 22.10iter/s]
Best score: 0.3612: : 256iter [00:13, 22.10iter/s]
Best score: 0.3612: : 257iter [00:13, 22.10iter/s]
Best score: 0.3612: : 258iter [00:13, 22.10iter/s]
Best score: 0.3612: : 259iter [00:13, 22.11iter/s]
Best score: 0.3594: : 259iter [00:13, 22.11iter/s]
Best score: 0.3594: : 260iter [00:13, 22.11iter/s]
Best score: 0.3594: : 261iter [00:13, 22.11iter/s]
Best score: 0.3594: : 262iter [00:13, 21.92iter/s]
Best score: 0.3594: : 262iter [00:13, 21.92iter/s]
Best score: 0.3594: : 263iter [00:13, 21.92iter/s]
Best score: 0.3594: : 264iter [00:13, 21.92iter/s]
Best score: 0.3594: : 265iter [00:13, 22.04iter/s]
Best score: 0.3594: : 265iter [00:13, 22.04iter/s]
Best score: 0.3594: : 266iter [00:14, 22.04iter/s]
Best score: 0.3594: : 267iter [00:14, 22.04iter/s]
Best score: 0.3594: : 268iter [00:14, 22.19iter/s]
Best score: 0.3594: : 268iter [00:14, 22.19iter/s]
Best score: 0.3594: : 269iter [00:14, 22.19iter/s]
Best score: 0.3594: : 270iter [00:14, 22.19iter/s]
Best score: 0.3594: : 271iter [00:14, 22.02iter/s]
Best score: 0.3594: : 271iter [00:14, 22.02iter/s]
Best score: 0.3594: : 272iter [00:14, 22.02iter/s]
Best score: 0.3594: : 273iter [00:14, 22.02iter/s]
Best score: 0.3594: : 274iter [00:14, 22.12iter/s]
Best score: 0.3594: : 274iter [00:14, 22.12iter/s]
Best score: 0.3594: : 275iter [00:14, 22.12iter/s]
Best score: 0.3594: : 276iter [00:14, 22.12iter/s]
Best score: 0.3594: : 277iter [00:14, 22.21iter/s]
Best score: 0.3594: : 277iter [00:14, 22.21iter/s]
Best score: 0.3594: : 278iter [00:14, 22.21iter/s]
Best score: 0.3594: : 279iter [00:14, 22.21iter/s]
Best score: 0.3594: : 280iter [00:14, 22.05iter/s]
Best score: 0.3594: : 280iter [00:14, 22.05iter/s]
Best score: 0.3594: : 281iter [00:14, 22.05iter/s]
Best score: 0.3594: : 282iter [00:14, 22.05iter/s]
Best score: 0.3594: : 283iter [00:14, 22.18iter/s]
Best score: 0.3594: : 283iter [00:14, 22.18iter/s]
Best score: 0.3594: : 284iter [00:14, 22.18iter/s]
Best score: 0.3594: : 285iter [00:14, 22.18iter/s]
Best score: 0.3594: : 286iter [00:14, 22.28iter/s]
Best score: 0.3594: : 286iter [00:14, 22.28iter/s]
Best score: 0.3594: : 287iter [00:14, 22.28iter/s]
Best score: 0.3594: : 288iter [00:14, 22.28iter/s]
Best score: 0.3594: : 289iter [00:14, 22.32iter/s]
Best score: 0.3594: : 289iter [00:15, 22.32iter/s]
Best score: 0.3594: : 290iter [00:15, 22.32iter/s]
Best score: 0.3594: : 291iter [00:15, 22.32iter/s]
Best score: 0.3594: : 292iter [00:15, 22.15iter/s]
Best score: 0.3594: : 292iter [00:15, 22.15iter/s]
Best score: 0.3594: : 293iter [00:15, 22.15iter/s]
Best score: 0.3594: : 294iter [00:15, 22.15iter/s]
Best score: 0.3594: : 295iter [00:15, 22.26iter/s]
Best score: 0.3594: : 295iter [00:15, 22.26iter/s]
Best score: 0.3594: : 296iter [00:15, 22.26iter/s]
Best score: 0.3594: : 297iter [00:15, 22.26iter/s]
Best score: 0.3594: : 298iter [00:15, 22.29iter/s]
Best score: 0.3594: : 298iter [00:15, 22.29iter/s]
[ITER 300] Best: 0.3576
[PARAMS] Current best parameters:
  fx: 523.4196
  fy: 522.9238
  px: 995.4112
  py: 741.4717
  k1: 0.00489283
  k2: 0.01215604
  k3: -0.00817314
  k4: 0.00094188
  p1: -0.00774716
  p2: -0.00731375
  p3: 0.00541113
  p4: -0.00045203
  scale: 2.8896
  rotation: -0.4634

Best score: 0.3576: : 299iter [00:15, 22.29iter/s]
Best score: 0.3576: : 300iter [00:15, 22.29iter/s]
Best score: 0.3576: : 301iter [00:15, 22.12iter/s]
Best score: 0.3576: : 301iter [00:15, 22.12iter/s]
Best score: 0.3576: : 302iter [00:15, 22.12iter/s]
Best score: 0.3576: : 303iter [00:15, 22.12iter/s]
Best score: 0.3576: : 304iter [00:15, 22.21iter/s]
Best score: 0.3576: : 304iter [00:15, 22.21iter/s]
Best score: 0.3576: : 305iter [00:15, 22.21iter/s]
Best score: 0.3576: : 306iter [00:15, 22.21iter/s]
Best score: 0.3576: : 307iter [00:15, 22.30iter/s]
Best score: 0.3576: : 307iter [00:15, 22.30iter/s]
Best score: 0.3576: : 308iter [00:15, 22.30iter/s]
Best score: 0.3575: : 309iter [00:15, 22.30iter/s]
Best score: 0.3575: : 310iter [00:15, 22.06iter/s]
Best score: 0.3575: : 310iter [00:15, 22.06iter/s]
Best score: 0.3575: : 311iter [00:16, 22.06iter/s]
Best score: 0.3575: : 312iter [00:16, 22.06iter/s]
Best score: 0.3575: : 313iter [00:16, 18.24iter/s]
Best score: 0.3575: : 313iter [00:16, 18.24iter/s]
Best score: 0.3575: : 314iter [00:16, 18.24iter/s]
Best score: 0.3575: : 315iter [00:16, 18.24iter/s]
Best score: 0.3575: : 316iter [00:16, 19.32iter/s]
Best score: 0.3575: : 316iter [00:16, 19.32iter/s]
Best score: 0.3575: : 317iter [00:16, 19.32iter/s]
Best score: 0.3575: : 318iter [00:16, 19.32iter/s]
Best score: 0.3575: : 319iter [00:16, 20.16iter/s]
Best score: 0.3571: : 319iter [00:16, 20.16iter/s]
Best score: 0.3571: : 320iter [00:16, 20.16iter/s]
Best score: 0.3571: : 321iter [00:16, 20.16iter/s]
Best score: 0.3571: : 322iter [00:16, 20.59iter/s]
Best score: 0.3571: : 322iter [00:16, 20.59iter/s]
Best score: 0.3571: : 323iter [00:16, 20.59iter/s]
Best score: 0.3571: : 324iter [00:16, 20.59iter/s]
Best score: 0.3571: : 325iter [00:16, 21.08iter/s]
Best score: 0.3571: : 325iter [00:16, 21.08iter/s]
Best score: 0.3571: : 326iter [00:16, 21.08iter/s]
Best score: 0.3571: : 327iter [00:16, 21.08iter/s]
Best score: 0.3571: : 328iter [00:16, 21.44iter/s]
Best score: 0.3571: : 328iter [00:16, 21.44iter/s]
Best score: 0.3565: : 329iter [00:16, 21.44iter/s]
Best score: 0.3565: : 330iter [00:16, 21.44iter/s]
Best score: 0.3565: : 331iter [00:16, 21.52iter/s]
Best score: 0.3565: : 331iter [00:17, 21.52iter/s]
Best score: 0.3565: : 332iter [00:17, 21.52iter/s]
Best score: 0.3565: : 333iter [00:17, 21.52iter/s]
Best score: 0.3565: : 334iter [00:17, 21.78iter/s]
Best score: 0.3565: : 334iter [00:17, 21.78iter/s]
Best score: 0.3565: : 335iter [00:17, 21.78iter/s]
Best score: 0.3565: : 336iter [00:17, 21.78iter/s]
Best score: 0.3565: : 337iter [00:17, 21.82iter/s]
Best score: 0.3565: : 337iter [00:17, 21.82iter/s]
Best score: 0.3565: : 338iter [00:17, 21.82iter/s]
Best score: 0.3565: : 339iter [00:17, 21.82iter/s]
Best score: 0.3565: : 340iter [00:17, 21.75iter/s]
Best score: 0.3565: : 340iter [00:17, 21.75iter/s]
Best score: 0.3565: : 341iter [00:17, 21.75iter/s]
Best score: 0.3565: : 342iter [00:17, 21.75iter/s]
Best score: 0.3565: : 343iter [00:17, 21.93iter/s]
Best score: 0.3565: : 343iter [00:17, 21.93iter/s]
Best score: 0.3565: : 344iter [00:17, 21.93iter/s]
Best score: 0.3565: : 345iter [00:17, 21.93iter/s]
Best score: 0.3565: : 346iter [00:17, 22.08iter/s]
Best score: 0.3565: : 346iter [00:17, 22.08iter/s]
Best score: 0.3565: : 347iter [00:17, 22.08iter/s]
Best score: 0.3565: : 348iter [00:17, 22.08iter/s]
Best score: 0.3565: : 349iter [00:17, 22.20iter/s]
[ITER 350] Best: 0.3565
[PARAMS] Current best parameters:
  fx: 521.3404
  fy: 520.8768
  px: 995.0700
  py: 741.1427
  k1: -0.00161476
  k2: 0.02444233
  k3: -0.01466275
  k4: 0.00213231
  p1: -0.00850329
  p2: -0.00812028
  p3: 0.00575543
  p4: -0.00033436
  scale: 2.8999
  rotation: -0.4606

Best score: 0.3565: : 349iter [00:17, 22.20iter/s]
Best score: 0.3565: : 350iter [00:17, 22.20iter/s]
Best score: 0.3565: : 351iter [00:17, 22.20iter/s]
Best score: 0.3565: : 352iter [00:17, 22.03iter/s]
Best score: 0.3565: : 352iter [00:17, 22.03iter/s]
Best score: 0.3565: : 353iter [00:18, 22.03iter/s]
Best score: 0.3565: : 354iter [00:18, 22.03iter/s]
Best score: 0.3565: : 355iter [00:18, 22.14iter/s]
Best score: 0.3565: : 355iter [00:18, 22.14iter/s]
Best score: 0.3565: : 356iter [00:18, 22.14iter/s]
Best score: 0.3565: : 357iter [00:18, 22.14iter/s]
Best score: 0.3565: : 358iter [00:18, 22.23iter/s]
Best score: 0.3565: : 358iter [00:18, 22.23iter/s]
Best score: 0.3564: : 359iter [00:18, 22.23iter/s]
Best score: 0.3564: : 360iter [00:18, 22.23iter/s]
Best score: 0.3564: : 361iter [00:18, 22.05iter/s]
Best score: 0.3564: : 361iter [00:18, 22.05iter/s]
Best score: 0.3564: : 362iter [00:18, 22.05iter/s]
Best score: 0.3564: : 363iter [00:18, 22.05iter/s]
Best score: 0.3564: : 364iter [00:18, 22.14iter/s]
Best score: 0.3564: : 364iter [00:18, 22.14iter/s]
Best score: 0.3564: : 365iter [00:18, 22.14iter/s]
Best score: 0.3564: : 366iter [00:18, 22.14iter/s]
Best score: 0.3564: : 367iter [00:18, 22.23iter/s]
Best score: 0.3564: : 367iter [00:18, 22.23iter/s]
Best score: 0.3564: : 368iter [00:18, 22.23iter/s]
Best score: 0.3560: : 369iter [00:18, 22.23iter/s]
Best score: 0.3560: : 370iter [00:18, 22.03iter/s]
Best score: 0.3560: : 370iter [00:18, 22.03iter/s]
Best score: 0.3560: : 371iter [00:18, 22.03iter/s]
Best score: 0.3560: : 372iter [00:18, 22.03iter/s]
Best score: 0.3560: : 373iter [00:18, 22.15iter/s]
Best score: 0.3560: : 373iter [00:18, 22.15iter/s]
Best score: 0.3560: : 374iter [00:18, 22.15iter/s]
Best score: 0.3560: : 375iter [00:19, 22.15iter/s]
Best score: 0.3560: : 376iter [00:19, 22.22iter/s]
Best score: 0.3560: : 376iter [00:19, 22.22iter/s]
Best score: 0.3560: : 377iter [00:19, 22.22iter/s]
Best score: 0.3560: : 378iter [00:19, 22.22iter/s]
Best score: 0.3560: : 379iter [00:19, 22.23iter/s]
Best score: 0.3559: : 379iter [00:19, 22.23iter/s]
Best score: 0.3559: : 380iter [00:19, 22.23iter/s]
Best score: 0.3559: : 381iter [00:19, 22.23iter/s]
Best score: 0.3559: : 382iter [00:19, 22.04iter/s]
Best score: 0.3559: : 382iter [00:19, 22.04iter/s]
Best score: 0.3559: : 383iter [00:19, 22.04iter/s]
Best score: 0.3559: : 384iter [00:19, 22.04iter/s]
Best score: 0.3559: : 385iter [00:19, 22.16iter/s]
Best score: 0.3559: : 385iter [00:19, 22.16iter/s]
Best score: 0.3559: : 386iter [00:19, 22.16iter/s]
Best score: 0.3559: : 387iter [00:19, 22.16iter/s]
Best score: 0.3559: : 388iter [00:19, 22.22iter/s]
Best score: 0.3559: : 388iter [00:19, 22.22iter/s]
Best score: 0.3556: : 389iter [00:19, 22.22iter/s]
Best score: 0.3556: : 390iter [00:19, 22.22iter/s]
Best score: 0.3556: : 391iter [00:19, 22.02iter/s]
Best score: 0.3556: : 391iter [00:19, 22.02iter/s]
Best score: 0.3556: : 392iter [00:19, 22.02iter/s]
Best score: 0.3556: : 393iter [00:19, 22.02iter/s]
Best score: 0.3556: : 394iter [00:19, 22.14iter/s]
Best score: 0.3556: : 394iter [00:19, 22.14iter/s]
Best score: 0.3556: : 395iter [00:19, 22.14iter/s]
Best score: 0.3556: : 396iter [00:19, 22.14iter/s]
Best score: 0.3556: : 397iter [00:19, 22.20iter/s]
Best score: 0.3556: : 397iter [00:20, 22.20iter/s]
Best score: 0.3556: : 398iter [00:20, 22.20iter/s]
[ITER 400] Best: 0.3556
[PARAMS] Current best parameters:
  fx: 518.4381
  fy: 517.9647
  px: 995.0931
  py: 741.1756
  k1: -0.01210063
  k2: 0.04362189
  k3: -0.02469001
  k4: 0.00395365
  p1: -0.00835946
  p2: -0.00800911
  p3: 0.00563048
  p4: -0.00033627
  scale: 2.9151
  rotation: -0.4598

Best score: 0.3556: : 399iter [00:20, 22.20iter/s]
Best score: 0.3556: : 400iter [00:20, 21.98iter/s]
Best score: 0.3556: : 400iter [00:20, 21.98iter/s]
Best score: 0.3556: : 401iter [00:20, 21.98iter/s]
Best score: 0.3556: : 402iter [00:20, 21.98iter/s]
Best score: 0.3556: : 403iter [00:20, 22.09iter/s]
Best score: 0.3556: : 403iter [00:20, 22.09iter/s]
Best score: 0.3556: : 404iter [00:20, 22.09iter/s]
Best score: 0.3556: : 405iter [00:20, 22.09iter/s]
Best score: 0.3556: : 406iter [00:20, 22.12iter/s]
Best score: 0.3556: : 406iter [00:20, 22.12iter/s]
Best score: 0.3556: : 407iter [00:20, 22.12iter/s]
Best score: 0.3556: : 408iter [00:20, 22.12iter/s]
Best score: 0.3556: : 409iter [00:20, 22.17iter/s]
Best score: 0.3555: : 409iter [00:20, 22.17iter/s]
Best score: 0.3555: : 410iter [00:20, 22.17iter/s]
Best score: 0.3555: : 411iter [00:20, 22.17iter/s]
Best score: 0.3555: : 412iter [00:20, 22.01iter/s]
Best score: 0.3555: : 412iter [00:20, 22.01iter/s]
Best score: 0.3555: : 413iter [00:20, 22.01iter/s]
Best score: 0.3555: : 414iter [00:20, 22.01iter/s]
Best score: 0.3555: : 415iter [00:20, 22.10iter/s]
Best score: 0.3555: : 415iter [00:20, 22.10iter/s]
Best score: 0.3555: : 416iter [00:20, 22.10iter/s]
Best score: 0.3555: : 417iter [00:20, 22.10iter/s]
Best score: 0.3555: : 418iter [00:20, 22.15iter/s]
Best score: 0.3555: : 418iter [00:20, 22.15iter/s]
Best score: 0.3555: : 419iter [00:21, 22.15iter/s]
Best score: 0.3555: : 420iter [00:21, 22.15iter/s]
Best score: 0.3555: : 421iter [00:21, 22.01iter/s]
Best score: 0.3555: : 421iter [00:21, 22.01iter/s]
Best score: 0.3555: : 422iter [00:21, 22.01iter/s]
Best score: 0.3555: : 423iter [00:21, 22.01iter/s]
Best score: 0.3555: : 424iter [00:21, 22.16iter/s]
Best score: 0.3555: : 424iter [00:21, 22.16iter/s]
Best score: 0.3555: : 425iter [00:21, 22.16iter/s]
Best score: 0.3555: : 426iter [00:21, 22.16iter/s]
Best score: 0.3555: : 427iter [00:21, 22.24iter/s]
Best score: 0.3555: : 427iter [00:21, 22.24iter/s]
Best score: 0.3555: : 428iter [00:21, 22.24iter/s]
Best score: 0.3555: : 429iter [00:21, 22.24iter/s]
Best score: 0.3555: : 430iter [00:21, 22.05iter/s]
Best score: 0.3555: : 430iter [00:21, 22.05iter/s]
Best score: 0.3555: : 431iter [00:21, 22.05iter/s]
Best score: 0.3555: : 432iter [00:21, 22.05iter/s]
Best score: 0.3555: : 433iter [00:21, 22.14iter/s]
Best score: 0.3555: : 433iter [00:21, 22.14iter/s]
Best score: 0.3555: : 434iter [00:21, 22.14iter/s]
Best score: 0.3555: : 435iter [00:21, 22.14iter/s]
Best score: 0.3555: : 436iter [00:21, 22.18iter/s]
Best score: 0.3555: : 436iter [00:21, 22.18iter/s]
Best score: 0.3555: : 437iter [00:21, 22.18iter/s]
Best score: 0.3555: : 438iter [00:21, 22.18iter/s]
Best score: 0.3555: : 439iter [00:21, 22.23iter/s]
Best score: 0.3555: : 439iter [00:21, 22.23iter/s]
Best score: 0.3555: : 440iter [00:21, 22.23iter/s]
Best score: 0.3555: : 441iter [00:22, 22.23iter/s]
Best score: 0.3555: : 442iter [00:22, 22.09iter/s]
Best score: 0.3555: : 442iter [00:22, 22.09iter/s]
Best score: 0.3555: : 443iter [00:22, 22.09iter/s]
Best score: 0.3555: : 444iter [00:22, 22.09iter/s]
Best score: 0.3555: : 445iter [00:22, 22.20iter/s]
Best score: 0.3555: : 445iter [00:22, 22.20iter/s]
Best score: 0.3555: : 446iter [00:22, 22.20iter/s]
Best score: 0.3555: : 447iter [00:22, 22.20iter/s]
Best score: 0.3555: : 448iter [00:22, 22.33iter/s]
Best score: 0.3555: : 448iter [00:22, 22.33iter/s]
[ITER 450] Best: 0.3555
[PARAMS] Current best parameters:
  fx: 519.1327
  fy: 518.6621
  px: 995.0945
  py: 741.1722
  k1: -0.01522699
  k2: 0.04707065
  k3: -0.02650929
  k4: 0.00431244
  p1: -0.00838198
  p2: -0.00801095
  p3: 0.00568942
  p4: -0.00035890
  scale: 2.9178
  rotation: -0.4606

Best score: 0.3555: : 449iter [00:22, 22.33iter/s]
Best score: 0.3555: : 450iter [00:22, 22.33iter/s]
Best score: 0.3555: : 451iter [00:22, 22.16iter/s]
Best score: 0.3555: : 451iter [00:22, 22.16iter/s]
Best score: 0.3555: : 452iter [00:22, 22.16iter/s]
Best score: 0.3555: : 453iter [00:22, 22.16iter/s]
Best score: 0.3555: : 454iter [00:22, 22.27iter/s]
Best score: 0.3555: : 454iter [00:22, 22.27iter/s]
Best score: 0.3555: : 455iter [00:22, 22.27iter/s]
Best score: 0.3555: : 456iter [00:22, 22.27iter/s]
Best score: 0.3555: : 457iter [00:22, 22.27iter/s]
Best score: 0.3555: : 457iter [00:22, 22.27iter/s]
Best score: 0.3555: : 458iter [00:22, 22.27iter/s]
Best score: 0.3555: : 459iter [00:22, 22.27iter/s]
Best score: 0.3555: : 460iter [00:22, 22.08iter/s]
Best score: 0.3555: : 460iter [00:22, 22.08iter/s]
Best score: 0.3555: : 461iter [00:22, 22.08iter/s]
Best score: 0.3555: : 462iter [00:22, 22.08iter/s]
Best score: 0.3555: : 463iter [00:22, 22.11iter/s]
Best score: 0.3555: : 463iter [00:22, 22.11iter/s]
Best score: 0.3555: : 464iter [00:23, 22.11iter/s]
Best score: 0.3555: : 465iter [00:23, 22.11iter/s]
Best score: 0.3555: : 466iter [00:23, 22.12iter/s]
Best score: 0.3555: : 466iter [00:23, 22.12iter/s]
Best score: 0.3555: : 467iter [00:23, 22.12iter/s]
Best score: 0.3555: : 468iter [00:23, 22.12iter/s]
Best score: 0.3555: : 469iter [00:23, 22.23iter/s]
Best score: 0.3554: : 469iter [00:23, 22.23iter/s]
Best score: 0.3554: : 470iter [00:23, 22.23iter/s]
Best score: 0.3554: : 471iter [00:23, 22.23iter/s]
Best score: 0.3554: : 472iter [00:23, 22.05iter/s]
Best score: 0.3554: : 472iter [00:23, 22.05iter/s]
Best score: 0.3554: : 473iter [00:23, 22.05iter/s]
Best score: 0.3554: : 474iter [00:23, 22.05iter/s]
Best score: 0.3554: : 475iter [00:23, 22.09iter/s]
Best score: 0.3554: : 475iter [00:23, 22.09iter/s]
Best score: 0.3554: : 476iter [00:23, 22.09iter/s]
Best score: 0.3554: : 477iter [00:23, 22.09iter/s]
Best score: 0.3554: : 478iter [00:23, 22.17iter/s]
Best score: 0.3554: : 478iter [00:23, 22.17iter/s]
Best score: 0.3554: : 479iter [00:23, 22.17iter/s]
Best score: 0.3554: : 480iter [00:23, 22.17iter/s]
Best score: 0.3554: : 481iter [00:23, 22.00iter/s]
Best score: 0.3554: : 481iter [00:23, 22.00iter/s]
Best score: 0.3554: : 482iter [00:23, 22.00iter/s]
Best score: 0.3554: : 483iter [00:23, 22.00iter/s]
Best score: 0.3554: : 484iter [00:23, 22.09iter/s]
Best score: 0.3554: : 484iter [00:23, 22.09iter/s]
Best score: 0.3554: : 485iter [00:23, 22.09iter/s]
Best score: 0.3554: : 486iter [00:24, 22.09iter/s]
Best score: 0.3554: : 487iter [00:24, 22.20iter/s]
Best score: 0.3554: : 487iter [00:24, 22.20iter/s]
Best score: 0.3554: : 488iter [00:24, 22.20iter/s]
Best score: 0.3554: : 489iter [00:24, 22.20iter/s]
Best score: 0.3554: : 490iter [00:24, 22.00iter/s]
Best score: 0.3554: : 490iter [00:24, 22.00iter/s]
Best score: 0.3554: : 491iter [00:24, 22.00iter/s]
Best score: 0.3554: : 492iter [00:24, 22.00iter/s]
Best score: 0.3554: : 493iter [00:24, 22.12iter/s]
Best score: 0.3554: : 493iter [00:24, 22.12iter/s]
Best score: 0.3554: : 494iter [00:24, 22.12iter/s]
Best score: 0.3554: : 495iter [00:24, 22.12iter/s]
Best score: 0.3554: : 496iter [00:24, 22.23iter/s]
Best score: 0.3554: : 496iter [00:24, 22.23iter/s]
Best score: 0.3554: : 497iter [00:24, 22.23iter/s]
Best score: 0.3554: : 498iter [00:24, 22.23iter/s]
Best score: 0.3554: : 499iter [00:24, 22.26iter/s]
[ITER 500] Best: 0.3554
[PARAMS] Current best parameters:
  fx: 517.8818
  fy: 517.4151
  px: 995.0900
  py: 741.1720
  k1: -0.01715404
  k2: 0.05137877
  k3: -0.02862238
  k4: 0.00466072
  p1: -0.00836768
  p2: -0.00800448
  p3: 0.00564651
  p4: -0.00033391
  scale: 2.9218
  rotation: -0.4605

Best score: 0.3554: : 499iter [00:24, 22.26iter/s]
Best score: 0.3554: : 500iter [00:24, 20.30iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.355432 -> 0.355420
  Success: True
  Tangential distortion: p1=-0.008368, p2=-0.008005, p3=0.005646, p4=-0.000334
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3554
[✅] Parameters copied to: /working/MDC015_W038_2025-05-14/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 517.8817591737856
    fy: 517.4150963339023
    px: 995.0899770221007
    py: 741.1720039774341
    k1: -0.0171553630217446
    k2: 0.05137844628323608
    k3: -0.028622129122297585
    k4: 0.004660789023997321
    p1: -0.008368101679725659
    p2: -0.008004910487269737
    p3: 0.00564642068684266
    p4: -0.0003339142154307372
    scale: 2.9217748530022414
    rotation: -0.4605291403660892
    mmpp: 1.2363133699737558
    nmpp: 1236313.369973756

[🔄] Converting 484 frames to PTD, creating debug images for 200 calibration frames...

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   🔄 500:  98%|█████████▊| 252/256 [00:01<00:00, 174.14frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.74s/rig]

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   🔄 1000:  32%|███▏      | 72/228 [00:00<00:00, 175.53frame/s]

   🔄 1000:  39%|███▉      | 90/228 [00:00<00:00, 175.28frame/s]

   🔄 1000:  47%|████▋     | 108/228 [00:00<00:00, 175.12frame/s]

   🔄 1000:  55%|█████▌    | 126/228 [00:00<00:00, 175.20frame/s]

   🔄 1000:  63%|██████▎   | 144/228 [00:00<00:00, 175.84frame/s]

   🔄 1000:  71%|███████   | 162/228 [00:00<00:00, 176.33frame/s]

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   🔄 1000:  87%|████████▋ | 198/228 [00:01<00:00, 176.96frame/s]

   🔄 1000:  95%|█████████▍| 216/228 [00:01<00:00, 176.84frame/s]

                                                                 
📁 Rigs: 100%|██████████| 2/2 [00:05<00:00,  2.49s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:05<00:00,  2.52s/rig]
[✅] Saved PTDs: /working/MDC015_W038_2025-05-14/calibration/debug/3_undistorted/output/ptd

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   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 174.08frame/s]

                                                               
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.03s/rig]

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   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 176.75frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 175.54frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 175.95frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 176.97frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
[✅] Saved PTDs: /working/MDC015_W038_2025-05-14/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

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📤 Writing LUT:  62%|██████▏   | 1918560/3080448 [00:02<00:01, 879164.77px/s]
📤 Writing LUT:  65%|██████▌   | 2007601/3080448 [00:02<00:01, 882528.24px/s]
📤 Writing LUT:  68%|██████▊   | 2095860/3080448 [00:02<00:01, 876965.89px/s]
📤 Writing LUT:  71%|███████   | 2183568/3080448 [00:02<00:01, 874082.24px/s]
📤 Writing LUT:  74%|███████▎  | 2270985/3080448 [00:02<00:00, 870248.53px/s]
📤 Writing LUT:  77%|███████▋  | 2359847/3080448 [00:02<00:00, 875718.11px/s]
📤 Writing LUT:  80%|███████▉  | 2449789/3080448 [00:02<00:00, 882790.05px/s]
📤 Writing LUT:  82%|████████▏ | 2540731/3080448 [00:02<00:00, 890748.24px/s]
📤 Writing LUT:  85%|████████▌ | 2629969/3080448 [00:03<00:00, 891235.50px/s]
📤 Writing LUT:  88%|████████▊ | 2719614/3080448 [00:03<00:00, 892796.02px/s]
📤 Writing LUT:  91%|█████████ | 2809847/3080448 [00:03<00:00, 895650.96px/s]
📤 Writing LUT:  94%|█████████▍| 2899878/3080448 [00:03<00:00, 897047.06px/s]
📤 Writing LUT:  97%|█████████▋| 2989586/3080448 [00:03<00:00, 896338.95px/s]
📤 Writing LUT: 100%|█████████▉| 3079223/3080448 [00:03<00:00, 895533.28px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 876582.93px/s]
[✅] LUT written to: /working/MDC015_W038_2025-05-14/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W038_2025-05-14' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W038_2025-05-14/calibration/MDC015_W038_2025-05-14_unknown.lut

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


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

   🔥 500:   0%|          | 0/256 [00:00<?, ?frame/s]

   🔥 500:   6%|▋         | 16/256 [00:00<00:01, 158.86frame/s]

   🔥 500:  13%|█▎        | 33/256 [00:00<00:01, 162.95frame/s]

   🔥 500:  20%|█▉        | 50/256 [00:00<00:01, 164.87frame/s]

   🔥 500:  26%|██▌       | 67/256 [00:00<00:01, 165.76frame/s]

   🔥 500:  33%|███▎      | 84/256 [00:00<00:01, 165.99frame/s]

   🔥 500:  39%|███▉      | 101/256 [00:00<00:00, 165.69frame/s]

   🔥 500:  46%|████▌     | 118/256 [00:00<00:00, 165.50frame/s]

   🔥 500:  53%|█████▎    | 135/256 [00:00<00:00, 165.50frame/s]

   🔥 500:  59%|█████▉    | 152/256 [00:00<00:00, 165.70frame/s]

   🔥 500:  66%|██████▌   | 169/256 [00:01<00:00, 166.10frame/s]

   🔥 500:  73%|███████▎  | 186/256 [00:01<00:00, 166.23frame/s]

   🔥 500:  79%|███████▉  | 203/256 [00:01<00:00, 166.40frame/s]

   🔥 500:  86%|████████▌ | 220/256 [00:01<00:00, 166.31frame/s]

   🔥 500:  93%|█████████▎| 237/256 [00:01<00:00, 166.36frame/s]

   🔥 500:  99%|█████████▉| 254/256 [00:01<00:00, 165.97frame/s]

                                                                [✅] 500: /working/MDC015_W038_2025-05-14/calibration/debug/4_heatmap/500_heatmap.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:02<00:02,  2.05s/rig]

   🔥 1000:   0%|          | 0/228 [00:00<?, ?frame/s]

   🔥 1000:   7%|▋         | 17/228 [00:00<00:01, 164.36frame/s]

   🔥 1000:  15%|█▍        | 34/228 [00:00<00:01, 166.19frame/s]

   🔥 1000:  22%|██▏       | 51/228 [00:00<00:01, 166.78frame/s]

   🔥 1000:  30%|██▉       | 68/228 [00:00<00:00, 166.92frame/s]

   🔥 1000:  37%|███▋      | 85/228 [00:00<00:00, 166.25frame/s]

   🔥 1000:  45%|████▍     | 102/228 [00:00<00:00, 165.62frame/s]

   🔥 1000:  52%|█████▏    | 119/228 [00:00<00:00, 165.35frame/s]

   🔥 1000:  60%|█████▉    | 136/228 [00:00<00:00, 165.69frame/s]

   🔥 1000:  67%|██████▋   | 153/228 [00:00<00:00, 165.74frame/s]

   🔥 1000:  75%|███████▍  | 170/228 [00:01<00:00, 166.46frame/s]

   🔥 1000:  82%|████████▏ | 187/228 [00:01<00:00, 167.10frame/s]

   🔥 1000:  89%|████████▉ | 204/228 [00:01<00:00, 167.60frame/s]

   🔥 1000:  97%|█████████▋| 222/228 [00:01<00:00, 168.37frame/s]

                                                                 [✅] 1000: /working/MDC015_W038_2025-05-14/calibration/debug/4_heatmap/1000_heatmap.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:04<00:00,  2.04s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:04<00:00,  2.04s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC015_W038_2025-05-14/calibration/debug/4_heatmap
[🏁] Finished processing MDC015_W038_2025-05-14


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