🟢 Starting Calibration Pipeline

=== Processing MDCP3D-083WF-W028(6)_2025-11-20 ===

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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 4 las0.zip files to process

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→ 1500 (888 images):  72%|███████▏  | 637/888 [01:28<00:21, 11.47it/s]

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→ 1500 (888 images):  73%|███████▎  | 648/888 [01:31<00:44,  5.42it/s]

→ 1500 (888 images):  73%|███████▎  | 650/888 [01:31<00:38,  6.16it/s]

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→ 1500 (888 images):  74%|███████▍  | 655/888 [01:31<00:23,  9.87it/s]

→ 1500 (888 images):  74%|███████▍  | 657/888 [01:31<00:20, 11.12it/s]

→ 1500 (888 images):  74%|███████▍  | 660/888 [01:31<00:16, 13.55it/s]

→ 1500 (888 images):  75%|███████▍  | 664/888 [01:31<00:12, 17.76it/s]

→ 1500 (888 images):  75%|███████▌  | 667/888 [01:32<00:12, 17.45it/s]

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→ 1500 (888 images):  76%|███████▌  | 674/888 [01:35<01:13,  2.93it/s]

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→ 1500 (888 images):  79%|███████▉  | 700/888 [01:36<00:12, 15.60it/s]

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→ 1500 (888 images):  81%|████████  | 719/888 [01:40<00:17,  9.87it/s]

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→ 1500 (888 images):  82%|████████▏ | 726/888 [01:40<00:11, 14.60it/s]

→ 1500 (888 images):  82%|████████▏ | 730/888 [01:40<00:09, 16.88it/s]

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→ 1500 (888 images):  95%|█████████▍| 840/888 [01:57<00:07,  6.30it/s]

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→ 1500 (888 images):  95%|█████████▌| 844/888 [01:57<00:05,  8.43it/s]

→ 1500 (888 images):  95%|█████████▌| 846/888 [01:57<00:04, 10.00it/s]

→ 1500 (888 images):  96%|█████████▌| 849/888 [01:57<00:03, 12.74it/s]

→ 1500 (888 images):  96%|█████████▌| 852/888 [01:57<00:02, 15.44it/s]

→ 1500 (888 images):  96%|█████████▌| 854/888 [01:57<00:02, 14.97it/s]

→ 1500 (888 images):  96%|█████████▋| 856/888 [01:58<00:02, 15.45it/s]

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→ 1500 (888 images):  97%|█████████▋| 860/888 [01:58<00:01, 15.93it/s]

→ 1500 (888 images):  97%|█████████▋| 862/888 [01:58<00:02, 10.01it/s]

→ 1500 (888 images):  97%|█████████▋| 864/888 [01:58<00:02,  8.72it/s]

→ 1500 (888 images):  98%|█████████▊| 866/888 [01:59<00:04,  4.58it/s]

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→ 1500 (888 images):  98%|█████████▊| 869/888 [02:00<00:03,  4.95it/s]

→ 1500 (888 images):  98%|█████████▊| 870/888 [02:00<00:03,  5.47it/s]

→ 1500 (888 images):  98%|█████████▊| 873/888 [02:00<00:01,  9.16it/s]

→ 1500 (888 images):  99%|█████████▊| 875/888 [02:00<00:01, 10.17it/s]

→ 1500 (888 images):  99%|█████████▉| 880/888 [02:00<00:00, 17.44it/s]

→ 1500 (888 images):  99%|█████████▉| 883/888 [02:01<00:00, 15.87it/s]

→ 1500 (888 images): 100%|█████████▉| 885/888 [02:01<00:00, 16.09it/s]

→ 1500 (888 images): 100%|█████████▉| 887/888 [02:01<00:00, 14.74it/s]

                                                                      
📁 Processing Rigs:  25%|██▌       | 1/4 [02:01<06:05, 121.69s/it]

→ 2000L (915 images):   0%|          | 0/915 [00:00<?, ?it/s]

→ 2000L (915 images):   0%|          | 1/915 [00:03<48:19,  3.17s/it]

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→ 2000L (915 images):  75%|███████▌  | 689/915 [01:35<00:22, 10.11it/s]

→ 2000L (915 images):  76%|███████▌  | 691/915 [01:35<00:21, 10.27it/s]

→ 2000L (915 images):  76%|███████▌  | 693/915 [01:35<00:19, 11.56it/s]

→ 2000L (915 images):  76%|███████▌  | 695/915 [01:35<00:17, 12.37it/s]

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→ 2000L (915 images):  77%|███████▋  | 700/915 [01:36<00:19, 11.12it/s]

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→ 2000L (915 images):  77%|███████▋  | 705/915 [01:37<00:42,  4.97it/s]

→ 2000L (915 images):  77%|███████▋  | 706/915 [01:38<00:58,  3.57it/s]

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→ 2000L (915 images):  78%|███████▊  | 710/915 [01:38<00:39,  5.25it/s]

→ 2000L (915 images):  78%|███████▊  | 711/915 [01:38<00:40,  5.10it/s]

→ 2000L (915 images):  78%|███████▊  | 714/915 [01:39<00:26,  7.69it/s]

→ 2000L (915 images):  78%|███████▊  | 716/915 [01:39<00:22,  8.86it/s]

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→ 2000L (915 images):  79%|███████▊  | 720/915 [01:39<00:22,  8.66it/s]

→ 2000L (915 images):  79%|███████▉  | 723/915 [01:39<00:16, 11.44it/s]

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→ 2000L (915 images):  80%|███████▉  | 728/915 [01:40<00:13, 13.77it/s]

→ 2000L (915 images):  80%|███████▉  | 730/915 [01:40<00:14, 12.87it/s]

→ 2000L (915 images):  80%|████████  | 732/915 [01:40<00:15, 12.01it/s]

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→ 2000L (915 images):  82%|████████▏ | 750/915 [01:43<00:18,  8.96it/s]

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→ 2000L (915 images):  83%|████████▎ | 763/915 [01:44<00:10, 14.29it/s]

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→ 2000L (915 images):  85%|████████▌ | 780/915 [01:47<00:14,  9.49it/s]

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→ 2000L (915 images):  86%|████████▌ | 784/915 [01:48<00:14,  9.19it/s]

→ 2000L (915 images):  86%|████████▌ | 786/915 [01:48<00:12, 10.22it/s]

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→ 2000L (915 images):  88%|████████▊ | 808/915 [01:51<00:17,  6.08it/s]

→ 2000L (915 images):  89%|████████▊ | 810/915 [01:51<00:13,  7.62it/s]

→ 2000L (915 images):  89%|████████▊ | 812/915 [01:52<00:11,  8.98it/s]

→ 2000L (915 images):  89%|████████▉ | 814/915 [01:52<00:12,  8.09it/s]

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→ 2000L (915 images):  90%|█████████ | 826/915 [01:53<00:07, 11.88it/s]

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→ 2000L (915 images):  92%|█████████▏| 838/915 [01:55<00:15,  4.95it/s]

→ 2000L (915 images):  92%|█████████▏| 840/915 [01:56<00:12,  5.78it/s]

→ 2000L (915 images):  92%|█████████▏| 843/915 [01:56<00:08,  8.25it/s]

→ 2000L (915 images):  92%|█████████▏| 845/915 [01:56<00:07,  8.84it/s]

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→ 2000L (915 images):  94%|█████████▍| 861/915 [01:57<00:04, 12.20it/s]

→ 2000L (915 images):  94%|█████████▍| 863/915 [01:58<00:05, 10.35it/s]

→ 2000L (915 images):  95%|█████████▍| 865/915 [01:58<00:09,  5.17it/s]

→ 2000L (915 images):  95%|█████████▍| 866/915 [01:59<00:13,  3.60it/s]

→ 2000L (915 images):  95%|█████████▍| 867/915 [02:00<00:15,  3.10it/s]

→ 2000L (915 images):  95%|█████████▍| 869/915 [02:00<00:10,  4.34it/s]

→ 2000L (915 images):  95%|█████████▌| 872/915 [02:00<00:06,  6.34it/s]

→ 2000L (915 images):  96%|█████████▌| 875/915 [02:00<00:04,  9.03it/s]

→ 2000L (915 images):  96%|█████████▌| 877/915 [02:00<00:03, 10.13it/s]

→ 2000L (915 images):  96%|█████████▌| 879/915 [02:01<00:04,  8.80it/s]

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→ 2000L (915 images):  97%|█████████▋| 884/915 [02:01<00:02, 11.19it/s]

→ 2000L (915 images):  97%|█████████▋| 886/915 [02:01<00:03,  9.13it/s]

→ 2000L (915 images):  97%|█████████▋| 888/915 [02:01<00:02, 10.56it/s]

→ 2000L (915 images):  97%|█████████▋| 891/915 [02:02<00:01, 12.21it/s]

→ 2000L (915 images):  98%|█████████▊| 893/915 [02:02<00:01, 12.68it/s]

→ 2000L (915 images):  98%|█████████▊| 895/915 [02:02<00:01, 12.55it/s]

→ 2000L (915 images):  98%|█████████▊| 897/915 [02:02<00:02,  7.27it/s]

→ 2000L (915 images):  98%|█████████▊| 899/915 [02:03<00:02,  6.36it/s]

→ 2000L (915 images):  98%|█████████▊| 901/915 [02:03<00:01,  7.29it/s]

→ 2000L (915 images):  99%|█████████▊| 902/915 [02:03<00:01,  7.62it/s]

→ 2000L (915 images):  99%|█████████▉| 904/915 [02:03<00:01,  9.23it/s]

→ 2000L (915 images):  99%|█████████▉| 908/915 [02:03<00:00, 12.54it/s]

→ 2000L (915 images):  99%|█████████▉| 910/915 [02:04<00:00, 13.35it/s]

→ 2000L (915 images): 100%|█████████▉| 912/915 [02:04<00:00, 12.11it/s]

                                                                       
📁 Processing Rigs:  50%|█████     | 2/4 [04:06<04:06, 123.29s/it]

→ 2000R (838 images):   0%|          | 0/838 [00:00<?, ?it/s]

→ 2000R (838 images):   0%|          | 1/838 [00:03<44:29,  3.19s/it]

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→ 2000R (838 images):   1%|          | 7/838 [00:04<06:15,  2.21it/s]

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→ 2000R (838 images):   3%|▎         | 24/838 [00:05<01:09, 11.65it/s]

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→ 2000R (838 images):   7%|▋         | 55/838 [00:09<00:57, 13.71it/s]

→ 2000R (838 images):   7%|▋         | 58/838 [00:09<00:51, 15.23it/s]

→ 2000R (838 images):   7%|▋         | 61/838 [00:09<00:48, 16.09it/s]

→ 2000R (838 images):   8%|▊         | 64/838 [00:10<00:44, 17.36it/s]

→ 2000R (838 images):   8%|▊         | 67/838 [00:12<04:03,  3.16it/s]

→ 2000R (838 images):   8%|▊         | 69/838 [00:13<03:25,  3.74it/s]

→ 2000R (838 images):   8%|▊         | 71/838 [00:13<02:46,  4.61it/s]

→ 2000R (838 images):   9%|▊         | 73/838 [00:13<02:19,  5.50it/s]

→ 2000R (838 images):   9%|▉         | 76/838 [00:13<01:41,  7.54it/s]

→ 2000R (838 images):   9%|▉         | 79/838 [00:13<01:18,  9.68it/s]

→ 2000R (838 images):  10%|▉         | 82/838 [00:13<01:04, 11.80it/s]

→ 2000R (838 images):  10%|█         | 85/838 [00:13<00:55, 13.61it/s]

→ 2000R (838 images):  11%|█         | 88/838 [00:14<00:48, 15.42it/s]

→ 2000R (838 images):  11%|█         | 91/838 [00:14<00:45, 16.26it/s]

→ 2000R (838 images):  11%|█         | 94/838 [00:14<00:43, 17.12it/s]

→ 2000R (838 images):  11%|█▏        | 96/838 [00:14<00:43, 16.98it/s]

→ 2000R (838 images):  12%|█▏        | 98/838 [00:16<04:17,  2.88it/s]

→ 2000R (838 images):  12%|█▏        | 100/838 [00:17<03:31,  3.49it/s]

→ 2000R (838 images):  12%|█▏        | 102/838 [00:17<02:51,  4.29it/s]

→ 2000R (838 images):  12%|█▏        | 104/838 [00:17<02:19,  5.27it/s]

→ 2000R (838 images):  13%|█▎        | 106/838 [00:17<01:52,  6.52it/s]

→ 2000R (838 images):  13%|█▎        | 109/838 [00:17<01:24,  8.58it/s]

→ 2000R (838 images):  13%|█▎        | 111/838 [00:17<01:14,  9.80it/s]

→ 2000R (838 images):  13%|█▎        | 113/838 [00:18<01:05, 11.15it/s]

→ 2000R (838 images):  14%|█▎        | 115/838 [00:18<00:57, 12.48it/s]

→ 2000R (838 images):  14%|█▍        | 117/838 [00:18<00:52, 13.65it/s]

→ 2000R (838 images):  14%|█▍        | 120/838 [00:18<00:45, 15.89it/s]

→ 2000R (838 images):  15%|█▍        | 123/838 [00:18<00:41, 17.27it/s]

→ 2000R (838 images):  15%|█▍        | 125/838 [00:18<00:39, 17.84it/s]

→ 2000R (838 images):  15%|█▌        | 127/838 [00:18<00:41, 17.07it/s]

→ 2000R (838 images):  15%|█▌        | 129/838 [00:20<02:52,  4.11it/s]

→ 2000R (838 images):  16%|█▌        | 131/838 [00:21<03:51,  3.05it/s]

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→ 2000R (838 images):  16%|█▌        | 135/838 [00:21<02:18,  5.09it/s]

→ 2000R (838 images):  16%|█▋        | 138/838 [00:21<01:32,  7.57it/s]

→ 2000R (838 images):  17%|█▋        | 140/838 [00:21<01:17,  8.97it/s]

→ 2000R (838 images):  17%|█▋        | 142/838 [00:22<01:11,  9.67it/s]

→ 2000R (838 images):  17%|█▋        | 145/838 [00:22<00:59, 11.69it/s]

→ 2000R (838 images):  18%|█▊        | 147/838 [00:22<00:56, 12.34it/s]

→ 2000R (838 images):  18%|█▊        | 149/838 [00:22<00:50, 13.74it/s]

→ 2000R (838 images):  18%|█▊        | 152/838 [00:22<00:42, 16.17it/s]

→ 2000R (838 images):  18%|█▊        | 155/838 [00:22<00:38, 17.59it/s]

→ 2000R (838 images):  19%|█▊        | 157/838 [00:22<00:39, 17.18it/s]

→ 2000R (838 images):  19%|█▉        | 159/838 [00:23<00:39, 17.40it/s]

→ 2000R (838 images):  19%|█▉        | 161/838 [00:24<02:42,  4.16it/s]

→ 2000R (838 images):  19%|█▉        | 163/838 [00:25<03:32,  3.18it/s]

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→ 2000R (838 images):  20%|█▉        | 165/838 [00:25<02:58,  3.77it/s]

→ 2000R (838 images):  20%|█▉        | 167/838 [00:26<02:13,  5.04it/s]

→ 2000R (838 images):  20%|██        | 169/838 [00:26<01:41,  6.62it/s]

→ 2000R (838 images):  20%|██        | 171/838 [00:26<01:21,  8.20it/s]

→ 2000R (838 images):  21%|██        | 173/838 [00:26<01:14,  8.93it/s]

→ 2000R (838 images):  21%|██        | 175/838 [00:26<01:03, 10.46it/s]

→ 2000R (838 images):  21%|██        | 177/838 [00:26<00:54, 12.06it/s]

→ 2000R (838 images):  21%|██▏       | 179/838 [00:26<00:48, 13.66it/s]

→ 2000R (838 images):  22%|██▏       | 181/838 [00:26<00:44, 14.80it/s]

→ 2000R (838 images):  22%|██▏       | 183/838 [00:27<00:41, 15.78it/s]

→ 2000R (838 images):  22%|██▏       | 185/838 [00:27<00:40, 16.18it/s]

→ 2000R (838 images):  22%|██▏       | 188/838 [00:27<00:36, 17.73it/s]

→ 2000R (838 images):  23%|██▎       | 190/838 [00:27<00:37, 17.44it/s]

→ 2000R (838 images):  23%|██▎       | 192/838 [00:27<00:39, 16.39it/s]

→ 2000R (838 images):  23%|██▎       | 194/838 [00:29<03:46,  2.84it/s]

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→ 2000R (838 images):  24%|██▎       | 198/838 [00:30<02:39,  4.02it/s]

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→ 2000R (838 images):  25%|██▍       | 206/838 [00:30<01:09,  9.05it/s]

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→ 2000R (838 images):  98%|█████████▊| 819/838 [01:53<00:01, 13.46it/s]

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→ 2000R (838 images): 100%|█████████▉| 835/838 [01:54<00:00,  7.89it/s]

→ 2000R (838 images): 100%|█████████▉| 837/838 [01:55<00:00,  7.78it/s]

                                                                       
📁 Processing Rigs:  75%|███████▌  | 3/4 [06:01<01:59, 119.64s/it]

→ 1000 (591 images):   0%|          | 0/591 [00:00<?, ?it/s]

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→ 1000 (591 images):  20%|██        | 121/591 [00:18<00:23, 19.70it/s]

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→ 1000 (591 images):  99%|█████████▉| 588/591 [01:22<00:00, 11.38it/s]

                                                                      
📁 Processing Rigs: 100%|██████████| 4/4 [07:24<00:00, 105.06s/it]
📁 Processing Rigs: 100%|██████████| 4/4 [07:24<00:00, 111.03s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 3228 frames from 4 rigs to /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 887
[📋] 2000L: 100 frames selected from 914
[📋] 2000R: 100 frames selected from 837
[📋] 1000: 100 frames selected from 590
[💾] Saved RigFrameDataset to /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/1_digitized/rig_frames.json

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

[INFO] Using 80 total sampled bright frames

Averaging bright frames:   0%|          | 0/80 [00:00<?, ?it/s]
Averaging bright frames:   4%|▍         | 3/80 [00:00<00:02, 26.75it/s]
Averaging bright frames:   9%|▉         | 7/80 [00:00<00:02, 33.76it/s]
Averaging bright frames:  14%|█▍        | 11/80 [00:00<00:01, 35.60it/s]
Averaging bright frames:  19%|█▉        | 15/80 [00:00<00:01, 36.65it/s]
Averaging bright frames:  24%|██▍       | 19/80 [00:00<00:01, 37.23it/s]
Averaging bright frames:  29%|██▉       | 23/80 [00:00<00:01, 38.01it/s]
Averaging bright frames:  34%|███▍      | 27/80 [00:00<00:01, 38.08it/s]
Averaging bright frames:  39%|███▉      | 31/80 [00:00<00:01, 38.58it/s]
Averaging bright frames:  44%|████▍     | 35/80 [00:00<00:01, 38.85it/s]
Averaging bright frames:  49%|████▉     | 39/80 [00:01<00:01, 39.03it/s]
Averaging bright frames:  54%|█████▍    | 43/80 [00:01<00:00, 39.30it/s]
Averaging bright frames:  59%|█████▉    | 47/80 [00:01<00:00, 39.43it/s]
Averaging bright frames:  64%|██████▍   | 51/80 [00:01<00:00, 39.32it/s]
Averaging bright frames:  69%|██████▉   | 55/80 [00:01<00:00, 39.11it/s]
Averaging bright frames:  74%|███████▍  | 59/80 [00:01<00:00, 39.17it/s]
Averaging bright frames:  79%|███████▉  | 63/80 [00:01<00:00, 39.18it/s]
Averaging bright frames:  84%|████████▍ | 67/80 [00:01<00:00, 39.37it/s]
Averaging bright frames:  89%|████████▉ | 71/80 [00:01<00:00, 39.43it/s]
Averaging bright frames:  95%|█████████▌| 76/80 [00:01<00:00, 39.86it/s]
Averaging bright frames: 100%|██████████| 80/80 [00:02<00:00, 39.83it/s]
                                                                        
Total time to process (v2): 0.06
[RESULT] Combined: Center=(866, 966), Radius=882

  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.46it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:28,  3.46it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.46it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.46it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:27,  3.46it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   5%|▌         | 5/100 [00:00<00:06, 13.88it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.001 | Best: {'fx': 342.424} (0.001):   5%|▌         | 5/100 [00:00<00:06, 13.88it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   6%|▌         | 6/100 [00:00<00:06, 13.88it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:06, 13.88it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.000 | Best: {'fx': 349.495} (0.001):   8%|▊         | 8/100 [00:00<00:04, 18.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:04, 18.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.002 | Best: {'fx': 363.636} (0.002):   9%|▉         | 9/100 [00:00<00:04, 18.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  10%|█         | 10/100 [00:00<00:04, 18.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  11%|█         | 11/100 [00:00<00:04, 18.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:03, 23.55it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.002 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:03, 23.55it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 23.55it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:03, 23.55it/s] 
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 23.55it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  16%|█▌        | 16/100 [00:00<00:03, 27.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 27.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:00<00:03, 27.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:00<00:02, 27.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:00<00:02, 27.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  20%|██        | 20/100 [00:00<00:02, 27.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  21%|██        | 21/100 [00:00<00:02, 27.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.010 | Best: {'fx': 455.556} (0.013):  21%|██        | 21/100 [00:00<00:02, 27.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.009 | Best: {'fx': 455.556} (0.013):  22%|██▏       | 22/100 [00:00<00:02, 27.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:02, 27.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 27.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  25%|██▌       | 25/100 [00:01<00:02, 26.15it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.024 | Best: {'fx': 483.838} (0.024):  25%|██▌       | 25/100 [00:01<00:02, 26.15it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 483.838} (0.024):  26%|██▌       | 26/100 [00:01<00:02, 26.15it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.037 | Best: {'fx': 497.98} (0.037):  27%|██▋       | 27/100 [00:01<00:02, 26.15it/s] 
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  28%|██▊       | 28/100 [00:01<00:02, 26.15it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.032 | Best: {'fx': 505.051} (0.046):  29%|██▉       | 29/100 [00:01<00:02, 26.15it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.057 | Best: {'fx': 512.121} (0.057):  30%|███       | 30/100 [00:01<00:02, 26.15it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.057 | Best: {'fx': 512.121} (0.057):  31%|███       | 31/100 [00:01<00:02, 33.56it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.110 | Best: {'fx': 533.333} (0.110):  31%|███       | 31/100 [00:01<00:02, 33.56it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.067 | Best: {'fx': 533.333} (0.110):  32%|███▏      | 32/100 [00:01<00:02, 33.56it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.044 | Best: {'fx': 533.333} (0.110):  33%|███▎      | 33/100 [00:01<00:01, 33.56it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.076 | Best: {'fx': 533.333} (0.110):  34%|███▍      | 34/100 [00:01<00:01, 33.56it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.076 | Best: {'fx': 533.333} (0.110):  35%|███▌      | 35/100 [00:01<00:03, 20.01it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.056 | Best: {'fx': 533.333} (0.110):  35%|███▌      | 35/100 [00:01<00:03, 20.01it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.108 | Best: {'fx': 533.333} (0.110):  36%|███▌      | 36/100 [00:01<00:03, 20.01it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.084 | Best: {'fx': 533.333} (0.110):  37%|███▋      | 37/100 [00:01<00:03, 20.01it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.050 | Best: {'fx': 533.333} (0.110):  38%|███▊      | 38/100 [00:01<00:03, 20.01it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.050 | Best: {'fx': 533.333} (0.110):  39%|███▉      | 39/100 [00:01<00:02, 23.20it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.035 | Best: {'fx': 533.333} (0.110):  39%|███▉      | 39/100 [00:01<00:02, 23.20it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.032 | Best: {'fx': 533.333} (0.110):  40%|████      | 40/100 [00:01<00:02, 23.20it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.041 | Best: {'fx': 533.333} (0.110):  41%|████      | 41/100 [00:01<00:02, 23.20it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.028 | Best: {'fx': 533.333} (0.110):  42%|████▏     | 42/100 [00:01<00:02, 23.20it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.028 | Best: {'fx': 533.333} (0.110):  43%|████▎     | 43/100 [00:01<00:02, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.031 | Best: {'fx': 533.333} (0.110):  43%|████▎     | 43/100 [00:01<00:02, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.027 | Best: {'fx': 533.333} (0.110):  44%|████▍     | 44/100 [00:01<00:02, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.037 | Best: {'fx': 533.333} (0.110):  45%|████▌     | 45/100 [00:01<00:02, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.029 | Best: {'fx': 533.333} (0.110):  46%|████▌     | 46/100 [00:01<00:02, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.033 | Best: {'fx': 533.333} (0.110):  47%|████▋     | 47/100 [00:01<00:02, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.025 | Best: {'fx': 533.333} (0.110):  48%|████▊     | 48/100 [00:01<00:01, 26.03it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.025 | Best: {'fx': 533.333} (0.110):  49%|████▉     | 49/100 [00:01<00:01, 32.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.025 | Best: {'fx': 533.333} (0.110):  49%|████▉     | 49/100 [00:02<00:01, 32.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.026 | Best: {'fx': 533.333} (0.110):  50%|█████     | 50/100 [00:02<00:01, 32.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.024 | Best: {'fx': 533.333} (0.110):  51%|█████     | 51/100 [00:02<00:01, 32.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.026 | Best: {'fx': 533.333} (0.110):  52%|█████▏    | 52/100 [00:02<00:01, 32.17it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.026 | Best: {'fx': 533.333} (0.110):  53%|█████▎    | 53/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 533.333} (0.110):  53%|█████▎    | 53/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.023 | Best: {'fx': 533.333} (0.110):  54%|█████▍    | 54/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.024 | Best: {'fx': 533.333} (0.110):  55%|█████▌    | 55/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  56%|█████▌    | 56/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.024 | Best: {'fx': 533.333} (0.110):  57%|█████▋    | 57/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 533.333} (0.110):  58%|█████▊    | 58/100 [00:02<00:01, 29.13it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 533.333} (0.110):  59%|█████▉    | 59/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 533.333} (0.110):  59%|█████▉    | 59/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 533.333} (0.110):  60%|██████    | 60/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 533.333} (0.110):  61%|██████    | 61/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 533.333} (0.110):  62%|██████▏   | 62/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  63%|██████▎   | 63/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.021 | Best: {'fx': 533.333} (0.110):  64%|██████▍   | 64/100 [00:02<00:01, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  65%|██████▌   | 65/100 [00:02<00:00, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  66%|██████▌   | 66/100 [00:02<00:00, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  67%|██████▋   | 67/100 [00:02<00:00, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.022 | Best: {'fx': 533.333} (0.110):  68%|██████▊   | 68/100 [00:02<00:00, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  69%|██████▉   | 69/100 [00:02<00:00, 35.47it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  70%|███████   | 70/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  70%|███████   | 70/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  71%|███████   | 71/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  72%|███████▏  | 72/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.020 | Best: {'fx': 533.333} (0.110):  73%|███████▎  | 73/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  74%|███████▍  | 74/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  75%|███████▌  | 75/100 [00:02<00:00, 50.58it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  76%|███████▌  | 76/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  76%|███████▌  | 76/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  77%|███████▋  | 77/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  78%|███████▊  | 78/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.019 | Best: {'fx': 533.333} (0.110):  79%|███████▉  | 79/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  80%|████████  | 80/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  81%|████████  | 81/100 [00:02<00:00, 49.60it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  82%|████████▏ | 82/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  82%|████████▏ | 82/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  83%|████████▎ | 83/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  84%|████████▍ | 84/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  85%|████████▌ | 85/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  86%|████████▌ | 86/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  87%|████████▋ | 87/100 [00:02<00:00, 52.12it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  88%|████████▊ | 88/100 [00:02<00:00, 44.35it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  88%|████████▊ | 88/100 [00:02<00:00, 44.35it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  89%|████████▉ | 89/100 [00:02<00:00, 44.35it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  90%|█████████ | 90/100 [00:02<00:00, 44.35it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  91%|█████████ | 91/100 [00:02<00:00, 44.35it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  92%|█████████▏| 92/100 [00:02<00:00, 44.35it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  93%|█████████▎| 93/100 [00:02<00:00, 44.66it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  93%|█████████▎| 93/100 [00:02<00:00, 44.66it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  94%|█████████▍| 94/100 [00:02<00:00, 44.66it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  95%|█████████▌| 95/100 [00:02<00:00, 44.66it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.017 | Best: {'fx': 533.333} (0.110):  96%|█████████▌| 96/100 [00:03<00:00, 44.66it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  97%|█████████▋| 97/100 [00:03<00:00, 44.66it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  98%|█████████▊| 98/100 [00:03<00:00, 37.95it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.018 | Best: {'fx': 533.333} (0.110):  98%|█████████▊| 98/100 [00:03<00:00, 37.95it/s]
Brute | Start: {'fx': 500.0} (0.040) | Current: 0.017 | Best: {'fx': 533.333} (0.110):  99%|█████████▉| 99/100 [00:03<00:00, 37.95it/s]
                                                                                                                                       

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.050 | Best: {'fx': 508.333} (0.050):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.050 | Best: {'fx': 508.333} (0.050):   2%|▏         | 1/50 [00:00<00:24,  1.97it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.055 | Best: {'fx': 511.395} (0.055):   2%|▏         | 1/50 [00:00<00:24,  1.97it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.058 | Best: {'fx': 512.415} (0.058):   4%|▍         | 2/50 [00:00<00:24,  1.97it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.058 | Best: {'fx': 512.415} (0.058):   6%|▌         | 3/50 [00:00<00:08,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.060 | Best: {'fx': 513.435} (0.060):   6%|▌         | 3/50 [00:00<00:08,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.051 | Best: {'fx': 513.435} (0.060):   8%|▊         | 4/50 [00:00<00:08,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.062 | Best: {'fx': 514.456} (0.062):  10%|█         | 5/50 [00:00<00:07,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.065 | Best: {'fx': 515.476} (0.065):  12%|█▏        | 6/50 [00:00<00:07,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.065 | Best: {'fx': 515.476} (0.065):  14%|█▍        | 7/50 [00:00<00:03, 13.32it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.053 | Best: {'fx': 515.476} (0.065):  14%|█▍        | 7/50 [00:00<00:03, 13.32it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.070 | Best: {'fx': 517.517} (0.070):  16%|█▌        | 8/50 [00:00<00:03, 13.32it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.067 | Best: {'fx': 517.517} (0.070):  18%|█▊        | 9/50 [00:00<00:03, 13.32it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.073 | Best: {'fx': 518.537} (0.073):  20%|██        | 10/50 [00:00<00:03, 13.32it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.073 | Best: {'fx': 518.537} (0.073):  22%|██▏       | 11/50 [00:00<00:02, 18.60it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.086 | Best: {'fx': 521.599} (0.086):  22%|██▏       | 11/50 [00:00<00:02, 18.60it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.077 | Best: {'fx': 521.599} (0.086):  24%|██▍       | 12/50 [00:00<00:02, 18.60it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.081 | Best: {'fx': 521.599} (0.086):  26%|██▌       | 13/50 [00:00<00:01, 18.60it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.090 | Best: {'fx': 522.619} (0.090):  28%|██▊       | 14/50 [00:00<00:01, 18.60it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.090 | Best: {'fx': 522.619} (0.090):  30%|███       | 15/50 [00:00<00:01, 22.45it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.096 | Best: {'fx': 523.639} (0.096):  30%|███       | 15/50 [00:00<00:01, 22.45it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.101 | Best: {'fx': 524.66} (0.101):  32%|███▏      | 16/50 [00:01<00:01, 22.45it/s] 
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.110 | Best: {'fx': 526.701} (0.110):  34%|███▍      | 17/50 [00:01<00:01, 22.45it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.110 | Best: {'fx': 526.701} (0.110):  36%|███▌      | 18/50 [00:01<00:01, 22.10it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.112 | Best: {'fx': 527.721} (0.112):  36%|███▌      | 18/50 [00:01<00:01, 22.10it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.105 | Best: {'fx': 527.721} (0.112):  38%|███▊      | 19/50 [00:01<00:01, 22.10it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.113 | Best: {'fx': 528.741} (0.113):  40%|████      | 20/50 [00:01<00:01, 22.10it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.115 | Best: {'fx': 529.762} (0.115):  42%|████▏     | 21/50 [00:01<00:01, 22.10it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.117 | Best: {'fx': 530.782} (0.117):  44%|████▍     | 22/50 [00:01<00:01, 22.10it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.117 | Best: {'fx': 530.782} (0.117):  46%|████▌     | 23/50 [00:01<00:01, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.112 | Best: {'fx': 530.782} (0.117):  46%|████▌     | 23/50 [00:01<00:01, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.084 | Best: {'fx': 530.782} (0.117):  48%|████▊     | 24/50 [00:01<00:01, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.113 | Best: {'fx': 530.782} (0.117):  50%|█████     | 25/50 [00:01<00:00, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.082 | Best: {'fx': 530.782} (0.117):  52%|█████▏    | 26/50 [00:01<00:00, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.109 | Best: {'fx': 530.782} (0.117):  54%|█████▍    | 27/50 [00:01<00:00, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.078 | Best: {'fx': 530.782} (0.117):  56%|█████▌    | 28/50 [00:01<00:00, 25.05it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.078 | Best: {'fx': 530.782} (0.117):  58%|█████▊    | 29/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.101 | Best: {'fx': 530.782} (0.117):  58%|█████▊    | 29/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.099 | Best: {'fx': 530.782} (0.117):  60%|██████    | 30/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.074 | Best: {'fx': 530.782} (0.117):  62%|██████▏   | 31/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.094 | Best: {'fx': 530.782} (0.117):  64%|██████▍   | 32/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.075 | Best: {'fx': 530.782} (0.117):  66%|██████▌   | 33/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.071 | Best: {'fx': 530.782} (0.117):  68%|██████▊   | 34/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.091 | Best: {'fx': 530.782} (0.117):  70%|███████   | 35/50 [00:01<00:00, 32.86it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.091 | Best: {'fx': 530.782} (0.117):  72%|███████▏  | 36/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.068 | Best: {'fx': 530.782} (0.117):  72%|███████▏  | 36/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.065 | Best: {'fx': 530.782} (0.117):  74%|███████▍  | 37/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.088 | Best: {'fx': 530.782} (0.117):  76%|███████▌  | 38/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.067 | Best: {'fx': 530.782} (0.117):  78%|███████▊  | 39/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.063 | Best: {'fx': 530.782} (0.117):  80%|████████  | 40/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.060 | Best: {'fx': 530.782} (0.117):  82%|████████▏ | 41/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.056 | Best: {'fx': 530.782} (0.117):  84%|████████▍ | 42/50 [00:01<00:00, 38.59it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.056 | Best: {'fx': 530.782} (0.117):  86%|████████▌ | 43/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.061 | Best: {'fx': 530.782} (0.117):  86%|████████▌ | 43/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.059 | Best: {'fx': 530.782} (0.117):  88%|████████▊ | 44/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.057 | Best: {'fx': 530.782} (0.117):  90%|█████████ | 45/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.054 | Best: {'fx': 530.782} (0.117):  92%|█████████▏| 46/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.053 | Best: {'fx': 530.782} (0.117):  94%|█████████▍| 47/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.052 | Best: {'fx': 530.782} (0.117):  96%|█████████▌| 48/50 [00:01<00:00, 39.62it/s]
Brute | Start: {'fx': 533.333} (0.110) | Current: 0.055 | Best: {'fx': 530.782} (0.117):  98%|█████████▊| 49/50 [00:01<00:00, 39.62it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 866.0
    py: 966.0
    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 7 frames per rig (excluded 0 incomplete frames): 28 frames will be used for optimization
  Rigs: 1500(1500mm):7/20, 2000L(2000mm):7/20, 2000R(2000mm):7/20, 1000(1000mm):7/20
  Total: 28/80 frames used for optimization

Initial score: 3.6388: 0iter [00:00, ?iter/s]
Initial score: 3.6388: 1iter [00:03,  3.56s/iter]
Initial score: 3.6388: 2iter [00:05,  2.50s/iter]
Initial score: 3.6388: 3iter [00:07,  2.13s/iter]
Initial score: 3.6388: 4iter [00:08,  1.93s/iter]
Initial score: 3.6388: 5iter [00:10,  1.85s/iter]
Initial score: 3.6388: 6iter [00:12,  1.80s/iter]
Initial score: 3.6388: 7iter [00:13,  1.78s/iter]
Initial score: 3.6388: 8iter [00:15,  1.78s/iter]
Initial score: 3.6388: 9iter [00:17,  1.74s/iter]
Initial score: 3.6388: 10iter [00:18,  1.74s/iter]
Initial score: 3.6388: 11iter [00:20,  1.75s/iter]
Initial score: 3.6388: 12iter [00:22,  1.74s/iter]
Initial score: 3.6388: 13iter [00:24,  1.73s/iter]
Initial score: 3.6388: 14iter [00:25,  1.74s/iter]
Initial score: 3.6388: 15iter [00:27,  1.72s/iter]
Initial score: 3.6388: 16iter [00:29,  1.72s/iter]
Initial score: 3.6388: 17iter [00:31,  1.73s/iter]
Initial score: 3.6388: 18iter [00:32,  1.76s/iter]
Initial score: 3.6388: 19iter [00:34,  1.72s/iter]
Initial score: 3.6388: 20iter [00:36,  1.73s/iter]
Initial score: 3.6388: 21iter [00:37,  1.72s/iter]
Initial score: 3.6388: 22iter [00:39,  1.73s/iter]
Initial score: 3.6388: 23iter [00:41,  1.73s/iter]
Initial score: 3.6388: 24iter [00:43,  1.73s/iter]
Initial score: 3.6388: 25iter [00:44,  1.72s/iter]
Initial score: 3.6388: 26iter [00:46,  1.71s/iter]
Initial score: 3.6388: 27iter [00:48,  1.72s/iter]
Initial score: 3.6388: 28iter [00:49,  1.70s/iter]
Initial score: 3.6388: 29iter [00:51,  1.71s/iter]
Initial score: 3.6388: 30iter [00:53,  1.70s/iter]
Initial score: 3.6388: 31iter [00:55,  1.72s/iter]
Initial score: 3.6388: 32iter [00:56,  1.71s/iter]
Initial score: 3.6388: 33iter [00:58,  1.73s/iter]
Initial score: 3.6388: 34iter [01:00,  1.73s/iter]
Initial score: 3.6388: 35iter [01:02,  1.71s/iter]
Initial score: 3.6388: 36iter [01:03,  1.69s/iter]
Initial score: 3.6388: 37iter [01:05,  1.73s/iter]
Initial score: 3.6388: 38iter [01:07,  1.70s/iter]
Initial score: 3.6388: 39iter [01:08,  1.72s/iter]
Initial score: 3.6388: 40iter [01:10,  1.71s/iter]
Initial score: 3.6388: 41iter [01:12,  1.70s/iter]
Initial score: 3.6388: 42iter [01:13,  1.72s/iter]
Initial score: 3.6388: 43iter [01:15,  1.72s/iter]
Initial score: 3.6388: 44iter [01:17,  1.72s/iter]
Initial score: 3.6388: 45iter [01:19,  1.73s/iter]
Initial score: 3.6388: 46iter [01:20,  1.72s/iter]
Initial score: 3.6388: 47iter [01:22,  1.72s/iter]
Initial score: 3.6388: 48iter [01:24,  1.74s/iter]
Initial score: 3.6388: 49iter [01:26,  1.73s/iter]
[ITER 50] Current best score: 3.6388
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 866.0000
  py: 966.0000
  k1: 0.00000000
  k2: 0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  scale: 3.0000
  rotation: 0.0000

Initial score: 3.6388: 50iter [01:28,  1.79s/iter]
Initial score: 3.6388: 51iter [01:29,  1.74s/iter]
Initial score: 3.6388: 52iter [01:31,  1.75s/iter]
Initial score: 3.6388: 53iter [01:33,  1.73s/iter]
Initial score: 3.6388: 54iter [01:34,  1.74s/iter]
Initial score: 3.6388: 55iter [01:36,  1.71s/iter]
Initial score: 3.6388: 56iter [01:38,  1.73s/iter]
Initial score: 3.6388: 57iter [01:39,  1.71s/iter]
Initial score: 3.6388: 58iter [01:41,  1.71s/iter]
Initial score: 3.6388: 59iter [01:43,  1.74s/iter]
Initial score: 3.6388: 60iter [01:45,  1.71s/iter]
Initial score: 3.6388: 61iter [01:46,  1.73s/iter]
Initial score: 3.6388: 62iter [01:48,  1.73s/iter]
Initial score: 3.6388: 63iter [01:50,  1.74s/iter]
Initial score: 3.6388: 64iter [01:52,  1.73s/iter]
Initial score: 3.6388: 65iter [01:53,  1.73s/iter]
Initial score: 3.6388: 66iter [01:55,  1.72s/iter]
Best score: 3.5707: : 66iter [01:57,  1.72s/iter] 
Best score: 3.5707: : 67iter [01:57,  1.74s/iter]
Best score: 3.5707: : 68iter [01:59,  1.75s/iter]
Best score: 3.5707: : 69iter [02:00,  1.75s/iter]
Best score: 3.5707: : 70iter [02:02,  1.72s/iter]
Best score: 3.5707: : 71iter [02:04,  1.72s/iter]
Best score: 3.5707: : 72iter [02:05,  1.72s/iter]
Best score: 3.5707: : 73iter [02:07,  1.71s/iter]
Best score: 3.5707: : 74iter [02:09,  1.74s/iter]
Best score: 3.5707: : 75iter [02:11,  1.76s/iter]
Best score: 3.5707: : 76iter [02:12,  1.76s/iter]
Best score: 3.5707: : 77iter [02:14,  1.74s/iter]
Best score: 3.5707: : 78iter [02:16,  1.72s/iter]
Best score: 2.5911: : 78iter [02:18,  1.72s/iter]
Best score: 2.5911: : 79iter [02:18,  1.72s/iter]
Best score: 2.2323: : 79iter [02:19,  1.72s/iter]
Best score: 2.2323: : 80iter [02:19,  1.72s/iter]
Best score: 2.2323: : 81iter [02:21,  1.72s/iter]
Best score: 2.2323: : 82iter [02:23,  1.76s/iter]
Best score: 2.2323: : 83iter [02:25,  1.75s/iter]
Best score: 1.7706: : 83iter [02:26,  1.75s/iter]
Best score: 1.7706: : 84iter [02:26,  1.72s/iter]
Best score: 1.7706: : 85iter [02:28,  1.70s/iter]
Best score: 1.7706: : 86iter [02:30,  1.71s/iter]
Best score: 1.7706: : 87iter [02:31,  1.73s/iter]
Best score: 1.7706: : 88iter [02:33,  1.72s/iter]
Best score: 1.7706: : 89iter [02:35,  1.70s/iter]
Best score: 1.7706: : 90iter [02:36,  1.69s/iter]
Best score: 1.7706: : 91iter [02:38,  1.70s/iter]
Best score: 1.7706: : 92iter [02:40,  1.69s/iter]
Best score: 1.7706: : 93iter [02:42,  1.71s/iter]
Best score: 1.7706: : 94iter [02:43,  1.71s/iter]
Best score: 1.7706: : 95iter [02:45,  1.69s/iter]
Best score: 1.7706: : 96iter [02:47,  1.68s/iter]
Best score: 1.7706: : 97iter [02:48,  1.70s/iter]
Best score: 1.7706: : 98iter [02:50,  1.71s/iter]
Best score: 1.7706: : 99iter [02:52,  1.73s/iter]
[ITER 100] Current best score: 2.0863
[PARAMS] Current best parameters:
  fx: 572.5132
  fy: 573.3060
  px: 865.1262
  py: 964.2981
  k1: -0.04903777
  k2: 0.02252183
  k3: -0.01071000
  k4: 0.00103013
  p1: -0.00307856
  p2: -0.00366650
  p3: 0.00111688
  p4: 0.00204142
  scale: 2.9541
  rotation: 0.1960

Best score: 1.7706: : 100iter [02:54,  1.80s/iter]
Best score: 1.7706: : 101iter [02:55,  1.76s/iter]
Best score: 1.7706: : 102iter [02:57,  1.74s/iter]
Best score: 1.7706: : 103iter [02:59,  1.74s/iter]
Best score: 1.7706: : 104iter [03:01,  1.73s/iter]
Best score: 1.7706: : 105iter [03:02,  1.68s/iter]
Best score: 1.7706: : 106iter [03:04,  1.72s/iter]
Best score: 1.7706: : 107iter [03:06,  1.72s/iter]
Best score: 1.7706: : 108iter [03:07,  1.72s/iter]
Best score: 1.7706: : 109iter [03:09,  1.73s/iter]
Best score: 1.0371: : 109iter [03:11,  1.73s/iter]
Best score: 1.0371: : 110iter [03:11,  1.74s/iter]
Best score: 1.0371: : 111iter [03:13,  1.71s/iter]
Best score: 1.0371: : 112iter [03:14,  1.67s/iter]
Best score: 1.0371: : 113iter [03:16,  1.73s/iter]
Best score: 1.0371: : 114iter [03:18,  1.71s/iter]
Best score: 1.0371: : 115iter [03:19,  1.72s/iter]
Best score: 1.0371: : 116iter [03:21,  1.71s/iter]
Best score: 1.0371: : 117iter [03:23,  1.71s/iter]
Best score: 1.0371: : 118iter [03:25,  1.69s/iter]
Best score: 1.0371: : 119iter [03:26,  1.71s/iter]
Best score: 1.0371: : 120iter [03:28,  1.69s/iter]
Best score: 1.0371: : 121iter [03:30,  1.71s/iter]
Best score: 1.0371: : 122iter [03:31,  1.72s/iter]
Best score: 1.0371: : 123iter [03:33,  1.74s/iter]
Best score: 1.0371: : 124iter [03:35,  1.71s/iter]
Best score: 1.0371: : 125iter [03:37,  1.74s/iter]
Best score: 1.0371: : 126iter [03:38,  1.73s/iter]
Best score: 1.0371: : 127iter [03:40,  1.71s/iter]
Best score: 1.0371: : 128iter [03:42,  1.69s/iter]
Best score: 1.0371: : 129iter [03:43,  1.70s/iter]
Best score: 1.0371: : 130iter [03:45,  1.68s/iter]
Best score: 1.0371: : 131iter [03:47,  1.68s/iter]
Best score: 1.0371: : 132iter [03:48,  1.69s/iter]
Best score: 1.0371: : 133iter [03:50,  1.69s/iter]
Best score: 1.0371: : 134iter [03:52,  1.71s/iter]
Best score: 1.0371: : 135iter [03:54,  1.73s/iter]
Best score: 1.0371: : 136iter [03:55,  1.71s/iter]
Best score: 1.0371: : 137iter [03:57,  1.71s/iter]
Best score: 1.0371: : 138iter [03:59,  1.71s/iter]
Best score: 1.0371: : 139iter [04:00,  1.72s/iter]
Best score: 1.0371: : 140iter [04:02,  1.71s/iter]
Best score: 1.0371: : 141iter [04:04,  1.72s/iter]
Best score: 1.0371: : 142iter [04:06,  1.71s/iter]
Best score: 1.0371: : 143iter [04:07,  1.71s/iter]
Best score: 1.0371: : 144iter [04:09,  1.69s/iter]
Best score: 1.0371: : 145iter [04:11,  1.74s/iter]
Best score: 1.0371: : 146iter [04:12,  1.73s/iter]
Best score: 1.0371: : 147iter [04:14,  1.74s/iter]
Best score: 0.3188: : 147iter [04:16,  1.74s/iter]
Best score: 0.3188: : 148iter [04:16,  1.73s/iter]
Best score: 0.3188: : 149iter [04:18,  1.74s/iter]
[ITER 150] Current best score: 0.3188
[PARAMS] Current best parameters:
  fx: 568.7905
  fy: 569.6507
  px: 861.5699
  py: 962.8507
  k1: -0.04736557
  k2: 0.01655426
  k3: -0.00772719
  k4: 0.00147229
  p1: -0.00420685
  p2: -0.00972510
  p3: 0.00255363
  p4: -0.00218213
  scale: 2.9848
  rotation: 0.2654

Best score: 0.3188: : 150iter [04:20,  1.80s/iter]
Best score: 0.3188: : 151iter [04:21,  1.78s/iter]
Best score: 0.3188: : 152iter [04:23,  1.74s/iter]
Best score: 0.3188: : 153iter [04:25,  1.73s/iter]
Best score: 0.3188: : 154iter [04:27,  1.74s/iter]
Best score: 0.3188: : 155iter [04:28,  1.73s/iter]
Best score: 0.3188: : 156iter [04:30,  1.72s/iter]
Best score: 0.3188: : 157iter [04:32,  1.71s/iter]
Best score: 0.3188: : 158iter [04:33,  1.72s/iter]
Best score: 0.3188: : 159iter [04:35,  1.69s/iter]
Best score: 0.3188: : 160iter [04:37,  1.71s/iter]
Best score: 0.3188: : 161iter [04:38,  1.71s/iter]
Best score: 0.3188: : 162iter [04:40,  1.72s/iter]
Best score: 0.3188: : 163iter [04:42,  1.70s/iter]
Best score: 0.3188: : 164iter [04:44,  1.72s/iter]
Best score: 0.3188: : 165iter [04:45,  1.71s/iter]
Best score: 0.3188: : 166iter [04:47,  1.71s/iter]
Best score: 0.3188: : 167iter [04:49,  1.71s/iter]
Best score: 0.3188: : 168iter [04:50,  1.69s/iter]
Best score: 0.3188: : 169iter [04:52,  1.68s/iter]
Best score: 0.3188: : 170iter [04:54,  1.70s/iter]
Best score: 0.3188: : 171iter [04:56,  1.72s/iter]
Best score: 0.3188: : 172iter [04:57,  1.73s/iter]
Best score: 0.3188: : 173iter [04:59,  1.73s/iter]
Best score: 0.3188: : 174iter [05:01,  1.72s/iter]
Best score: 0.3188: : 175iter [05:02,  1.74s/iter]
Best score: 0.3188: : 176iter [05:04,  1.74s/iter]
Best score: 0.3188: : 177iter [05:06,  1.73s/iter]
Best score: 0.3188: : 178iter [05:08,  1.71s/iter]
Best score: 0.3188: : 179iter [05:09,  1.71s/iter]
Best score: 0.3188: : 180iter [05:11,  1.70s/iter]
Best score: 0.3188: : 181iter [05:13,  1.72s/iter]
Best score: 0.3188: : 182iter [05:14,  1.71s/iter]
Best score: 0.3188: : 183iter [05:16,  1.71s/iter]
Best score: 0.3188: : 184iter [05:18,  1.72s/iter]
Best score: 0.3188: : 185iter [05:20,  1.72s/iter]
Best score: 0.3188: : 186iter [05:21,  1.71s/iter]
Best score: 0.3188: : 187iter [05:23,  1.72s/iter]
Best score: 0.3188: : 188iter [05:25,  1.70s/iter]
Best score: 0.3188: : 189iter [05:26,  1.71s/iter]
Best score: 0.3188: : 190iter [05:28,  1.71s/iter]
Best score: 0.3188: : 191iter [05:30,  1.72s/iter]
Best score: 0.3188: : 192iter [05:32,  1.71s/iter]
Best score: 0.3188: : 193iter [05:33,  1.71s/iter]
Best score: 0.3188: : 194iter [05:35,  1.72s/iter]
Best score: 0.3188: : 195iter [05:37,  1.72s/iter]
Best score: 0.3188: : 196iter [05:38,  1.72s/iter]
Best score: 0.3188: : 197iter [05:40,  1.71s/iter]
Best score: 0.3188: : 198iter [05:42,  1.72s/iter]
Best score: 0.3188: : 199iter [05:44,  1.71s/iter]
[ITER 200] Current best score: 0.3188
[PARAMS] Current best parameters:
  fx: 568.7905
  fy: 569.6507
  px: 861.5699
  py: 962.8507
  k1: -0.04736557
  k2: 0.01655426
  k3: -0.00772719
  k4: 0.00147229
  p1: -0.00420685
  p2: -0.00972510
  p3: 0.00255363
  p4: -0.00218213
  scale: 2.9848
  rotation: 0.2654

Best score: 0.3188: : 200iter [05:46,  1.78s/iter]
Best score: 0.3188: : 201iter [05:47,  1.76s/iter]
Best score: 0.3188: : 202iter [05:49,  1.76s/iter]
Best score: 0.3188: : 203iter [05:51,  1.75s/iter]
Best score: 0.3188: : 204iter [05:52,  1.73s/iter]
Best score: 0.3188: : 205iter [05:54,  1.71s/iter]
Best score: 0.3188: : 206iter [05:56,  1.72s/iter]
Best score: 0.3188: : 207iter [05:58,  1.72s/iter]
Best score: 0.3188: : 208iter [05:59,  1.73s/iter]
Best score: 0.3188: : 209iter [06:01,  1.73s/iter]
Best score: 0.3188: : 210iter [06:03,  1.74s/iter]
Best score: 0.3188: : 211iter [06:05,  1.73s/iter]
Best score: 0.3188: : 212iter [06:06,  1.74s/iter]
Best score: 0.3188: : 213iter [06:08,  1.73s/iter]
Best score: 0.3188: : 214iter [06:10,  1.72s/iter]
Best score: 0.3188: : 215iter [06:11,  1.73s/iter]
Best score: 0.3188: : 216iter [06:13,  1.70s/iter]
Best score: 0.3188: : 217iter [06:15,  1.70s/iter]
Best score: 0.3188: : 218iter [06:16,  1.69s/iter]
Best score: 0.3188: : 219iter [06:18,  1.73s/iter]
Best score: 0.3188: : 220iter [06:20,  1.74s/iter]
Best score: 0.3188: : 221iter [06:22,  1.75s/iter]
Best score: 0.3188: : 222iter [06:24,  1.74s/iter]
Best score: 0.3188: : 223iter [06:25,  1.74s/iter]
Best score: 0.3188: : 224iter [06:27,  1.75s/iter]
Best score: 0.3188: : 225iter [06:29,  1.75s/iter]
Best score: 0.3188: : 226iter [06:30,  1.72s/iter]
Best score: 0.3188: : 227iter [06:32,  1.73s/iter]
Best score: 0.3188: : 228iter [06:34,  1.72s/iter]
Best score: 0.3188: : 229iter [06:36,  1.73s/iter]
Best score: 0.3188: : 230iter [06:37,  1.72s/iter]
Best score: 0.3188: : 231iter [06:39,  1.72s/iter]
Best score: 0.3188: : 232iter [06:41,  1.73s/iter]
Best score: 0.3188: : 233iter [06:43,  1.75s/iter]
Best score: 0.3188: : 234iter [06:44,  1.78s/iter]
Best score: 0.3188: : 235iter [06:46,  1.75s/iter]
Best score: 0.3188: : 236iter [06:48,  1.75s/iter]
Best score: 0.3188: : 237iter [06:50,  1.73s/iter]
Best score: 0.3188: : 238iter [06:51,  1.72s/iter]
Best score: 0.3188: : 239iter [06:53,  1.69s/iter]
Best score: 0.3188: : 240iter [06:55,  1.72s/iter]
Best score: 0.3188: : 241iter [06:56,  1.73s/iter]
Best score: 0.3188: : 242iter [06:58,  1.70s/iter]
Best score: 0.3188: : 243iter [07:00,  1.71s/iter]
Best score: 0.3188: : 244iter [07:01,  1.70s/iter]
Best score: 0.3188: : 245iter [07:03,  1.71s/iter]
Best score: 0.3188: : 246iter [07:05,  1.71s/iter]
Best score: 0.3188: : 247iter [07:07,  1.70s/iter]
Best score: 0.3188: : 248iter [07:08,  1.67s/iter]
Best score: 0.3188: : 249iter [07:10,  1.70s/iter]
[ITER 250] Current best score: 0.3188
[PARAMS] Current best parameters:
  fx: 568.7905
  fy: 569.6507
  px: 861.5699
  py: 962.8507
  k1: -0.04736557
  k2: 0.01655426
  k3: -0.00772719
  k4: 0.00147229
  p1: -0.00420685
  p2: -0.00972510
  p3: 0.00255363
  p4: -0.00218213
  scale: 2.9848
  rotation: 0.2654

Best score: 0.3188: : 250iter [07:12,  1.76s/iter]
Best score: 0.3188: : 251iter [07:14,  1.74s/iter]
Best score: 0.3188: : 252iter [07:15,  1.74s/iter]
Best score: 0.3188: : 253iter [07:17,  1.73s/iter]
Best score: 0.3188: : 254iter [07:19,  1.74s/iter]
Best score: 0.3188: : 255iter [07:20,  1.72s/iter]
Best score: 0.3188: : 256iter [07:22,  1.73s/iter]
Best score: 0.3188: : 257iter [07:24,  1.71s/iter]
Best score: 0.3188: : 258iter [07:26,  1.72s/iter]
Best score: 0.3188: : 259iter [07:27,  1.72s/iter]
Best score: 0.3188: : 260iter [07:29,  1.73s/iter]
Best score: 0.3188: : 261iter [07:31,  1.72s/iter]
Best score: 0.3188: : 262iter [07:32,  1.72s/iter]
Best score: 0.3188: : 263iter [07:34,  1.71s/iter]
Best score: 0.3188: : 264iter [07:36,  1.72s/iter]
Best score: 0.3188: : 265iter [07:38,  1.73s/iter]
Best score: 0.3188: : 266iter [07:39,  1.73s/iter]
Best score: 0.3188: : 267iter [07:41,  1.73s/iter]
Best score: 0.3188: : 268iter [07:43,  1.74s/iter]
Best score: 0.3188: : 269iter [07:45,  1.71s/iter]
Best score: 0.3188: : 270iter [07:46,  1.73s/iter]
Best score: 0.3188: : 271iter [07:48,  1.74s/iter]
Best score: 0.3188: : 272iter [07:50,  1.72s/iter]
Best score: 0.3188: : 273iter [07:51,  1.71s/iter]
Best score: 0.3188: : 274iter [07:53,  1.72s/iter]
Best score: 0.3188: : 275iter [07:55,  1.73s/iter]
Best score: 0.3188: : 276iter [07:57,  1.72s/iter]
Best score: 0.3188: : 277iter [07:58,  1.73s/iter]
Best score: 0.3188: : 278iter [08:00,  1.75s/iter]
Best score: 0.3188: : 279iter [08:02,  1.73s/iter]
Best score: 0.3188: : 280iter [08:03,  1.70s/iter]
Best score: 0.3188: : 281iter [08:05,  1.70s/iter]
Best score: 0.3188: : 282iter [08:07,  1.68s/iter]
Best score: 0.3188: : 283iter [08:09,  1.69s/iter]
Best score: 0.3188: : 284iter [08:10,  1.69s/iter]
Best score: 0.3188: : 285iter [08:12,  1.68s/iter]
Best score: 0.3188: : 286iter [08:14,  1.71s/iter]
Best score: 0.3188: : 287iter [08:15,  1.71s/iter]
Best score: 0.3188: : 288iter [08:17,  1.75s/iter]
Best score: 0.3188: : 289iter [08:19,  1.76s/iter]
Best score: 0.3188: : 290iter [08:21,  1.73s/iter]
Best score: 0.3188: : 291iter [08:22,  1.74s/iter]
Best score: 0.3188: : 292iter [08:24,  1.74s/iter]
Best score: 0.3188: : 293iter [08:26,  1.75s/iter]
Best score: 0.3188: : 294iter [08:28,  1.75s/iter]
Best score: 0.3188: : 295iter [08:29,  1.75s/iter]
Best score: 0.3188: : 296iter [08:31,  1.74s/iter]
Best score: 0.3188: : 297iter [08:33,  1.73s/iter]
Best score: 0.3188: : 298iter [08:35,  1.75s/iter]
Best score: 0.3188: : 299iter [08:36,  1.73s/iter]
[ITER 300] Current best score: 0.3694
[PARAMS] Current best parameters:
  fx: 561.6697
  fy: 561.6640
  px: 861.0062
  py: 961.9321
  k1: -0.03906026
  k2: 0.01265849
  k3: -0.00323869
  k4: -0.00033331
  p1: -0.00575054
  p2: -0.00945416
  p3: 0.00160866
  p4: -0.00242158
  scale: 2.9891
  rotation: 0.2153

Best score: 0.3188: : 300iter [08:38,  1.77s/iter]
Best score: 0.3188: : 301iter [08:40,  1.76s/iter]
Best score: 0.3188: : 302iter [08:42,  1.79s/iter]
Best score: 0.3188: : 303iter [08:43,  1.76s/iter]
Best score: 0.3188: : 304iter [08:45,  1.74s/iter]
Best score: 0.3188: : 305iter [08:47,  1.71s/iter]
Best score: 0.3188: : 306iter [08:49,  1.72s/iter]
Best score: 0.3188: : 307iter [08:50,  1.73s/iter]
Best score: 0.3188: : 308iter [08:52,  1.73s/iter]
Best score: 0.3188: : 309iter [08:54,  1.73s/iter]
Best score: 0.3188: : 310iter [08:55,  1.73s/iter]
Best score: 0.3188: : 311iter [08:57,  1.72s/iter]
Best score: 0.3188: : 312iter [08:59,  1.73s/iter]
Best score: 0.3188: : 313iter [09:01,  1.73s/iter]
Best score: 0.3188: : 314iter [09:02,  1.72s/iter]
Best score: 0.3188: : 315iter [09:04,  1.74s/iter]
Best score: 0.3188: : 316iter [09:06,  1.75s/iter]
Best score: 0.3188: : 317iter [09:08,  1.74s/iter]
Best score: 0.3188: : 318iter [09:09,  1.73s/iter]
Best score: 0.3188: : 319iter [09:11,  1.72s/iter]
Best score: 0.3188: : 320iter [09:13,  1.71s/iter]
Best score: 0.3188: : 321iter [09:15,  1.73s/iter]
Best score: 0.3188: : 322iter [09:16,  1.71s/iter]
Best score: 0.3188: : 323iter [09:18,  1.73s/iter]
Best score: 0.3188: : 324iter [09:20,  1.74s/iter]
Best score: 0.3188: : 325iter [09:21,  1.74s/iter]
Best score: 0.3188: : 326iter [09:23,  1.72s/iter]
Best score: 0.3188: : 327iter [09:25,  1.71s/iter]
Best score: 0.3188: : 328iter [09:27,  1.73s/iter]
Best score: 0.3188: : 329iter [09:28,  1.73s/iter]
Best score: 0.3188: : 330iter [09:30,  1.75s/iter]
Best score: 0.3188: : 331iter [09:32,  1.75s/iter]
Best score: 0.3188: : 332iter [09:34,  1.76s/iter]
Best score: 0.3188: : 333iter [09:35,  1.73s/iter]
Best score: 0.3188: : 334iter [09:37,  1.73s/iter]
Best score: 0.3188: : 335iter [09:39,  1.74s/iter]
Best score: 0.3188: : 336iter [09:40,  1.72s/iter]
Best score: 0.3188: : 337iter [09:42,  1.73s/iter]
Best score: 0.3188: : 338iter [09:44,  1.72s/iter]
Best score: 0.3188: : 339iter [09:46,  1.72s/iter]
Best score: 0.3188: : 340iter [09:47,  1.71s/iter]
Best score: 0.3188: : 341iter [09:49,  1.71s/iter]
Best score: 0.3188: : 342iter [09:51,  1.72s/iter]
Best score: 0.3188: : 343iter [09:53,  1.72s/iter]
Best score: 0.3188: : 344iter [09:54,  1.73s/iter]
Best score: 0.3188: : 345iter [09:56,  1.74s/iter]
Best score: 0.3188: : 346iter [09:58,  1.75s/iter]
Best score: 0.3188: : 347iter [10:00,  1.74s/iter]
Best score: 0.3188: : 348iter [10:01,  1.73s/iter]
Best score: 0.2824: : 348iter [10:03,  1.73s/iter]
Best score: 0.2824: : 349iter [10:03,  1.71s/iter]
[ITER 350] Current best score: 0.2824
[PARAMS] Current best parameters:
  fx: 574.5565
  fy: 574.5670
  px: 862.4141
  py: 961.6475
  k1: -0.04521597
  k2: 0.00403882
  k3: 0.00096213
  k4: -0.00058132
  p1: -0.00620587
  p2: -0.00746451
  p3: 0.00165218
  p4: -0.00267810
  scale: 2.9782
  rotation: 0.2900

Best score: 0.2824: : 350iter [10:05,  1.79s/iter]
Best score: 0.2824: : 351iter [10:07,  1.79s/iter]
Best score: 0.2824: : 352iter [10:08,  1.76s/iter]
Best score: 0.2824: : 353iter [10:10,  1.75s/iter]
Best score: 0.2824: : 354iter [10:12,  1.73s/iter]
Best score: 0.2824: : 355iter [10:13,  1.72s/iter]
Best score: 0.2824: : 356iter [10:15,  1.75s/iter]
Best score: 0.2824: : 357iter [10:17,  1.74s/iter]
Best score: 0.2824: : 358iter [10:19,  1.74s/iter]
Best score: 0.2824: : 359iter [10:21,  1.76s/iter]
Best score: 0.2824: : 360iter [10:22,  1.74s/iter]
Best score: 0.2824: : 361iter [10:24,  1.73s/iter]
Best score: 0.2824: : 362iter [10:26,  1.75s/iter]
Best score: 0.2824: : 363iter [10:27,  1.75s/iter]
Best score: 0.2824: : 364iter [10:29,  1.74s/iter]
Best score: 0.2824: : 365iter [10:31,  1.74s/iter]
Best score: 0.2824: : 366iter [10:33,  1.74s/iter]
Best score: 0.2824: : 367iter [10:34,  1.74s/iter]
Best score: 0.2824: : 368iter [10:36,  1.76s/iter]
Best score: 0.2824: : 369iter [10:38,  1.72s/iter]
Best score: 0.2824: : 370iter [10:40,  1.75s/iter]
Best score: 0.2824: : 371iter [10:41,  1.74s/iter]
Best score: 0.2824: : 372iter [10:43,  1.73s/iter]
Best score: 0.2824: : 373iter [10:45,  1.73s/iter]
Best score: 0.2824: : 374iter [10:47,  1.74s/iter]
Best score: 0.2824: : 375iter [10:48,  1.74s/iter]
Best score: 0.2824: : 376iter [10:50,  1.73s/iter]
Best score: 0.2824: : 377iter [10:52,  1.70s/iter]
Best score: 0.2824: : 378iter [10:53,  1.70s/iter]
Best score: 0.2824: : 379iter [10:55,  1.71s/iter]
Best score: 0.2824: : 380iter [10:57,  1.72s/iter]
Best score: 0.2824: : 381iter [10:59,  1.73s/iter]
Best score: 0.2824: : 382iter [11:00,  1.73s/iter]
Best score: 0.2824: : 383iter [11:02,  1.72s/iter]
Best score: 0.2824: : 384iter [11:04,  1.73s/iter]
Best score: 0.2824: : 385iter [11:05,  1.72s/iter]
Best score: 0.2824: : 386iter [11:07,  1.71s/iter]
Best score: 0.2824: : 387iter [11:09,  1.73s/iter]
Best score: 0.2824: : 388iter [11:11,  1.72s/iter]
Best score: 0.2824: : 389iter [11:12,  1.72s/iter]
Best score: 0.2824: : 390iter [11:14,  1.71s/iter]
Best score: 0.2824: : 391iter [11:16,  1.74s/iter]
Best score: 0.2824: : 392iter [11:18,  1.73s/iter]
Best score: 0.2824: : 393iter [11:19,  1.73s/iter]
Best score: 0.2824: : 394iter [11:21,  1.74s/iter]
Best score: 0.2824: : 395iter [11:23,  1.72s/iter]
Best score: 0.2824: : 396iter [11:24,  1.72s/iter]
Best score: 0.2733: : 396iter [11:26,  1.72s/iter]
Best score: 0.2733: : 397iter [11:26,  1.69s/iter]
Best score: 0.2733: : 398iter [11:28,  1.70s/iter]
Best score: 0.2733: : 399iter [11:30,  1.71s/iter]
[ITER 400] Current best score: 0.2733
[PARAMS] Current best parameters:
  fx: 570.4627
  fy: 570.8354
  px: 862.2756
  py: 961.3517
  k1: -0.03922769
  k2: -0.00172249
  k3: 0.00499203
  k4: -0.00154834
  p1: -0.00662341
  p2: -0.00772683
  p3: 0.00163770
  p4: -0.00237151
  scale: 2.9821
  rotation: 0.2459

Best score: 0.2733: : 400iter [11:31,  1.77s/iter]
Best score: 0.2733: : 401iter [11:33,  1.75s/iter]
Best score: 0.2733: : 402iter [11:35,  1.74s/iter]
Best score: 0.2733: : 403iter [11:37,  1.76s/iter]
Best score: 0.2733: : 404iter [11:38,  1.73s/iter]
Best score: 0.2733: : 405iter [11:40,  1.73s/iter]
Best score: 0.2268: : 405iter [11:42,  1.73s/iter]
Best score: 0.2268: : 406iter [11:42,  1.70s/iter]
Best score: 0.2268: : 407iter [11:43,  1.72s/iter]
Best score: 0.2268: : 408iter [11:45,  1.71s/iter]
Best score: 0.2268: : 409iter [11:47,  1.71s/iter]
Best score: 0.2268: : 410iter [11:49,  1.71s/iter]
Best score: 0.2268: : 411iter [11:50,  1.72s/iter]
Best score: 0.2268: : 412iter [11:52,  1.68s/iter]
Best score: 0.2268: : 413iter [11:54,  1.70s/iter]
Best score: 0.2268: : 414iter [11:55,  1.69s/iter]
Best score: 0.2268: : 415iter [11:57,  1.72s/iter]
Best score: 0.2268: : 416iter [11:59,  1.72s/iter]
Best score: 0.2268: : 417iter [12:00,  1.70s/iter]
Best score: 0.2268: : 418iter [12:02,  1.72s/iter]
Best score: 0.2268: : 419iter [12:04,  1.68s/iter]
Best score: 0.2268: : 420iter [12:06,  1.69s/iter]
Best score: 0.2268: : 421iter [12:07,  1.70s/iter]
Best score: 0.2268: : 422iter [12:09,  1.71s/iter]
Best score: 0.2268: : 423iter [12:11,  1.71s/iter]
Best score: 0.2268: : 424iter [12:12,  1.71s/iter]
Best score: 0.2268: : 425iter [12:14,  1.72s/iter]
Best score: 0.2268: : 426iter [12:16,  1.72s/iter]
Best score: 0.2268: : 427iter [12:18,  1.71s/iter]
Best score: 0.2268: : 428iter [12:19,  1.72s/iter]
Best score: 0.2268: : 429iter [12:21,  1.72s/iter]
Best score: 0.2268: : 430iter [12:23,  1.74s/iter]
Best score: 0.2268: : 431iter [12:25,  1.74s/iter]
Best score: 0.2268: : 432iter [12:26,  1.75s/iter]
Best score: 0.2268: : 433iter [12:28,  1.72s/iter]
Best score: 0.2268: : 434iter [12:30,  1.72s/iter]
Best score: 0.2268: : 435iter [12:31,  1.75s/iter]
Best score: 0.2268: : 436iter [12:33,  1.73s/iter]
Best score: 0.2268: : 437iter [12:35,  1.72s/iter]
Best score: 0.2268: : 438iter [12:37,  1.73s/iter]
Best score: 0.2268: : 439iter [12:38,  1.75s/iter]
Best score: 0.2268: : 440iter [12:40,  1.74s/iter]
Best score: 0.2268: : 441iter [12:42,  1.70s/iter]
Best score: 0.2268: : 442iter [12:43,  1.70s/iter]
Best score: 0.2268: : 443iter [12:45,  1.71s/iter]
Best score: 0.2268: : 444iter [12:47,  1.73s/iter]
Best score: 0.2268: : 445iter [12:49,  1.73s/iter]
Best score: 0.2268: : 446iter [12:50,  1.69s/iter]
Best score: 0.2268: : 447iter [12:52,  1.73s/iter]
Best score: 0.2268: : 448iter [12:54,  1.73s/iter]
Best score: 0.2268: : 449iter [12:56,  1.73s/iter]
[ITER 450] Current best score: 0.2340
[PARAMS] Current best parameters:
  fx: 566.6919
  fy: 566.9099
  px: 862.6342
  py: 962.2469
  k1: -0.03057503
  k2: -0.01445674
  k3: 0.01508125
  k4: -0.00424543
  p1: -0.00444399
  p2: -0.00667798
  p3: -0.00022064
  p4: -0.00157027
  scale: 2.9845
  rotation: 0.2793

Best score: 0.2268: : 450iter [12:58,  1.79s/iter]
Best score: 0.2268: : 451iter [12:59,  1.78s/iter]
Best score: 0.2268: : 452iter [13:01,  1.78s/iter]
Best score: 0.2268: : 453iter [13:03,  1.76s/iter]
Best score: 0.2268: : 454iter [13:04,  1.75s/iter]
Best score: 0.2268: : 455iter [13:06,  1.74s/iter]
Best score: 0.2215: : 455iter [13:08,  1.74s/iter]
Best score: 0.2215: : 456iter [13:08,  1.72s/iter]
Best score: 0.2215: : 457iter [13:10,  1.72s/iter]
Best score: 0.2215: : 458iter [13:11,  1.74s/iter]
Best score: 0.2215: : 459iter [13:13,  1.73s/iter]
Best score: 0.2215: : 460iter [13:15,  1.76s/iter]
Best score: 0.2215: : 461iter [13:17,  1.75s/iter]
Best score: 0.2215: : 462iter [13:18,  1.76s/iter]
Best score: 0.2215: : 463iter [13:20,  1.77s/iter]
Best score: 0.2215: : 464iter [13:22,  1.75s/iter]
Best score: 0.2215: : 465iter [13:24,  1.73s/iter]
Best score: 0.2215: : 466iter [13:25,  1.72s/iter]
Best score: 0.2215: : 467iter [13:27,  1.73s/iter]
Best score: 0.2215: : 468iter [13:29,  1.72s/iter]
Best score: 0.2215: : 469iter [13:30,  1.72s/iter]
Best score: 0.2215: : 470iter [13:32,  1.72s/iter]
Best score: 0.2215: : 471iter [13:34,  1.74s/iter]
Best score: 0.2215: : 472iter [13:36,  1.74s/iter]
Best score: 0.2215: : 473iter [13:37,  1.73s/iter]
Best score: 0.2215: : 474iter [13:39,  1.70s/iter]
Best score: 0.2215: : 475iter [13:41,  1.71s/iter]
Best score: 0.2215: : 476iter [13:43,  1.72s/iter]
Best score: 0.2215: : 477iter [13:44,  1.72s/iter]
Best score: 0.2215: : 478iter [13:46,  1.72s/iter]
Best score: 0.2215: : 479iter [13:48,  1.71s/iter]
Best score: 0.2215: : 480iter [13:49,  1.72s/iter]
Best score: 0.2215: : 481iter [13:51,  1.73s/iter]
Best score: 0.2215: : 482iter [13:53,  1.74s/iter]
Best score: 0.2215: : 483iter [13:55,  1.72s/iter]
Best score: 0.2215: : 484iter [13:56,  1.72s/iter]
Best score: 0.2215: : 485iter [13:58,  1.73s/iter]
Best score: 0.2215: : 486iter [14:00,  1.73s/iter]
Best score: 0.2215: : 487iter [14:02,  1.73s/iter]
Best score: 0.2215: : 488iter [14:03,  1.74s/iter]
Best score: 0.2215: : 489iter [14:05,  1.74s/iter]
Best score: 0.2215: : 490iter [14:07,  1.72s/iter]
Best score: 0.2215: : 491iter [14:08,  1.74s/iter]
Best score: 0.2215: : 492iter [14:10,  1.73s/iter]
Best score: 0.2215: : 493iter [14:12,  1.73s/iter]
Best score: 0.2215: : 494iter [14:14,  1.73s/iter]
Best score: 0.2215: : 495iter [14:15,  1.74s/iter]
Best score: 0.2215: : 496iter [14:17,  1.71s/iter]
Best score: 0.2215: : 497iter [14:19,  1.70s/iter]
Best score: 0.2215: : 498iter [14:20,  1.70s/iter]
Best score: 0.2215: : 499iter [14:22,  1.73s/iter]
[ITER 500] Current best score: 0.2366
[PARAMS] Current best parameters:
  fx: 570.2465
  fy: 570.3501
  px: 862.4625
  py: 962.1276
  k1: -0.03806978
  k2: -0.00516125
  k3: 0.00840446
  k4: -0.00257265
  p1: -0.00511095
  p2: -0.00721577
  p3: 0.00077353
  p4: -0.00231264
  scale: 2.9835
  rotation: 0.2847

Best score: 0.2215: : 500iter [14:24,  1.79s/iter]
Best score: 0.2215: : 500iter [16:13,  1.95s/iter]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.209098 -> 0.196601
  Success: True
  Tangential distortion: p1=-0.004790, p2=-0.006568, p3=-0.001735, p4=-0.001461
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1966
[✅] Parameters copied to: /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 570.2464652907656
    fy: 570.3500817373836
    px: 862.462505100767
    py: 962.1276293049369
    k1: -0.03786203405162157
    k2: -0.005242370120939254
    k3: 0.008100813961864274
    k4: -0.002391190149625878
    p1: -0.004790274691479424
    p2: -0.006568343311704894
    p3: -0.001735371764089468
    p4: -0.0014613612389350644
    scale: 2.9835392228732327
    rotation: 0.28471200651174283
    mmpp: 3.4328580605940067
    nmpp: 3432858.0605940064

[🔄] Converting 3228 frames to PTD, creating debug images for 400 calibration frames...

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📁 Rigs:  50%|█████     | 2/4 [00:17<00:17,  8.96s/rig]

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📁 Rigs:  75%|███████▌  | 3/4 [00:26<00:08,  8.64s/rig]

   🔄 1000:   0%|          | 0/590 [00:00<?, ?frame/s]

   🔄 1000:   3%|▎         | 19/590 [00:00<00:03, 184.73frame/s]

   🔄 1000:   6%|▋         | 38/590 [00:00<00:02, 185.74frame/s]

   🔄 1000:  10%|▉         | 57/590 [00:00<00:02, 186.11frame/s]

   🔄 1000:  13%|█▎        | 76/590 [00:00<00:02, 186.90frame/s]

   🔄 1000:  16%|█▌        | 95/590 [00:00<00:02, 186.80frame/s]

   🔄 1000:  19%|█▉        | 114/590 [00:00<00:02, 185.99frame/s]

   🔄 1000:  23%|██▎       | 133/590 [00:00<00:02, 185.93frame/s]

   🔄 1000:  26%|██▌       | 152/590 [00:00<00:02, 185.22frame/s]

   🔄 1000:  29%|██▉       | 171/590 [00:00<00:02, 184.93frame/s]

   🔄 1000:  32%|███▏      | 190/590 [00:01<00:02, 184.73frame/s]

   🔄 1000:  35%|███▌      | 209/590 [00:01<00:02, 185.78frame/s]

   🔄 1000:  39%|███▊      | 228/590 [00:01<00:01, 185.60frame/s]

   🔄 1000:  42%|████▏     | 247/590 [00:01<00:01, 184.85frame/s]

   🔄 1000:  45%|████▌     | 266/590 [00:01<00:01, 184.91frame/s]

   🔄 1000:  48%|████▊     | 285/590 [00:01<00:01, 184.92frame/s]

   🔄 1000:  52%|█████▏    | 304/590 [00:01<00:01, 184.91frame/s]

   🔄 1000:  55%|█████▍    | 323/590 [00:01<00:01, 184.66frame/s]

   🔄 1000:  58%|█████▊    | 342/590 [00:01<00:01, 184.55frame/s]

   🔄 1000:  61%|██████    | 361/590 [00:01<00:01, 184.30frame/s]

   🔄 1000:  64%|██████▍   | 380/590 [00:02<00:01, 184.90frame/s]

   🔄 1000:  68%|██████▊   | 399/590 [00:02<00:01, 184.48frame/s]

   🔄 1000:  71%|███████   | 418/590 [00:02<00:00, 184.08frame/s]

   🔄 1000:  74%|███████▍  | 437/590 [00:02<00:00, 184.47frame/s]

   🔄 1000:  77%|███████▋  | 456/590 [00:02<00:00, 184.84frame/s]

   🔄 1000:  81%|████████  | 475/590 [00:02<00:00, 185.09frame/s]

   🔄 1000:  84%|████████▎ | 494/590 [00:02<00:00, 185.19frame/s]

   🔄 1000:  87%|████████▋ | 513/590 [00:02<00:00, 185.72frame/s]

   🔄 1000:  90%|█████████ | 532/590 [00:02<00:00, 186.16frame/s]

   🔄 1000:  93%|█████████▎| 551/590 [00:02<00:00, 186.52frame/s]

   🔄 1000:  97%|█████████▋| 570/590 [00:03<00:00, 186.96frame/s]

   🔄 1000: 100%|█████████▉| 589/590 [00:03<00:00, 187.34frame/s]

                                                                 
📁 Rigs: 100%|██████████| 4/4 [00:31<00:00,  7.48s/rig]
📁 Rigs: 100%|██████████| 4/4 [00:31<00:00,  7.97s/rig]
[✅] Saved PTDs: /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/3_undistorted/output/ptd

📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]

   🔄 1500:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 1500:  18%|█▊        | 18/100 [00:00<00:00, 175.67frame/s]

   🔄 1500:  37%|███▋      | 37/100 [00:00<00:00, 179.07frame/s]

   🔄 1500:  56%|█████▌    | 56/100 [00:00<00:00, 180.42frame/s]

   🔄 1500:  75%|███████▌  | 75/100 [00:00<00:00, 180.80frame/s]

   🔄 1500:  94%|█████████▍| 94/100 [00:00<00:00, 181.53frame/s]

                                                                
📁 Rigs:  25%|██▌       | 1/4 [00:00<00:02,  1.12rig/s]

   🔄 2000L:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 2000L:  18%|█▊        | 18/100 [00:00<00:00, 177.97frame/s]

   🔄 2000L:  37%|███▋      | 37/100 [00:00<00:00, 179.85frame/s]

   🔄 2000L:  55%|█████▌    | 55/100 [00:00<00:00, 179.71frame/s]

   🔄 2000L:  73%|███████▎  | 73/100 [00:00<00:00, 179.71frame/s]

   🔄 2000L:  91%|█████████ | 91/100 [00:00<00:00, 179.32frame/s]

                                                                 
📁 Rigs:  50%|█████     | 2/4 [00:01<00:01,  1.10rig/s]

   🔄 2000R:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 2000R:  18%|█▊        | 18/100 [00:00<00:00, 179.44frame/s]

   🔄 2000R:  37%|███▋      | 37/100 [00:00<00:00, 179.91frame/s]

   🔄 2000R:  56%|█████▌    | 56/100 [00:00<00:00, 179.79frame/s]

   🔄 2000R:  75%|███████▌  | 75/100 [00:00<00:00, 180.85frame/s]

   🔄 2000R:  94%|█████████▍| 94/100 [00:00<00:00, 181.14frame/s]

                                                                 
📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.03rig/s]

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

   🔄 1000:  19%|█▉        | 19/100 [00:00<00:00, 184.74frame/s]

   🔄 1000:  38%|███▊      | 38/100 [00:00<00:00, 184.05frame/s]

   🔄 1000:  57%|█████▋    | 57/100 [00:00<00:00, 183.34frame/s]

   🔄 1000:  76%|███████▌  | 76/100 [00:00<00:00, 183.21frame/s]

   🔄 1000:  95%|█████████▌| 95/100 [00:00<00:00, 184.54frame/s]

                                                                
📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.07rig/s]
📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.07rig/s]
[✅] Saved PTDs: /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3240000 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 83361/3240000 [00:00<00:03, 833602.85px/s]
📤 Writing LUT:   5%|▌         | 172871/3240000 [00:00<00:03, 869774.34px/s]
📤 Writing LUT:   8%|▊         | 264462/3240000 [00:00<00:03, 890838.25px/s]
📤 Writing LUT:  11%|█         | 356331/3240000 [00:00<00:03, 901830.71px/s]
📤 Writing LUT:  14%|█▍        | 448026/3240000 [00:00<00:03, 907280.78px/s]
📤 Writing LUT:  17%|█▋        | 538755/3240000 [00:00<00:02, 906073.05px/s]
📤 Writing LUT:  19%|█▉        | 631227/3240000 [00:00<00:02, 912162.54px/s]
📤 Writing LUT:  22%|██▏       | 724689/3240000 [00:00<00:02, 919306.41px/s]
📤 Writing LUT:  25%|██▌       | 817930/3240000 [00:00<00:02, 923398.58px/s]
📤 Writing LUT:  28%|██▊       | 910271/3240000 [00:01<00:02, 920240.80px/s]
📤 Writing LUT:  31%|███       | 1003183/3240000 [00:01<00:02, 922949.53px/s]
📤 Writing LUT:  34%|███▍      | 1095481/3240000 [00:01<00:02, 922149.22px/s]
📤 Writing LUT:  37%|███▋      | 1189144/3240000 [00:01<00:02, 926526.03px/s]
📤 Writing LUT:  40%|███▉      | 1282194/3240000 [00:01<00:02, 927724.40px/s]
📤 Writing LUT:  42%|████▏     | 1375490/3240000 [00:01<00:02, 929298.06px/s]
📤 Writing LUT:  45%|████▌     | 1468422/3240000 [00:01<00:01, 928950.50px/s]
📤 Writing LUT:  48%|████▊     | 1561318/3240000 [00:01<00:01, 924421.07px/s]
📤 Writing LUT:  51%|█████     | 1654560/3240000 [00:01<00:01, 926812.87px/s]
📤 Writing LUT:  54%|█████▍    | 1748451/3240000 [00:01<00:01, 930433.78px/s]
📤 Writing LUT:  57%|█████▋    | 1842163/3240000 [00:02<00:01, 932435.65px/s]
📤 Writing LUT:  60%|█████▉    | 1935410/3240000 [00:02<00:01, 931765.64px/s]
📤 Writing LUT:  63%|██████▎   | 2028589/3240000 [00:02<00:01, 930656.09px/s]
📤 Writing LUT:  65%|██████▌   | 2121657/3240000 [00:02<00:01, 930272.23px/s]
📤 Writing LUT:  68%|██████▊   | 2215049/3240000 [00:02<00:01, 931364.63px/s]
📤 Writing LUT:  71%|███████   | 2308474/3240000 [00:02<00:00, 932228.99px/s]
📤 Writing LUT:  74%|███████▍  | 2401698/3240000 [00:02<00:00, 930988.85px/s]
📤 Writing LUT:  77%|███████▋  | 2494798/3240000 [00:02<00:00, 923518.34px/s]
📤 Writing LUT:  80%|███████▉  | 2588136/3240000 [00:02<00:00, 926456.20px/s]
📤 Writing LUT:  83%|████████▎ | 2681531/3240000 [00:02<00:00, 928690.85px/s]
📤 Writing LUT:  86%|████████▌ | 2775702/3240000 [00:03<00:00, 932583.47px/s]
📤 Writing LUT:  89%|████████▊ | 2869950/3240000 [00:03<00:00, 935541.25px/s]
📤 Writing LUT:  91%|█████████▏| 2963511/3240000 [00:03<00:00, 934610.37px/s]
📤 Writing LUT:  94%|█████████▍| 3056977/3240000 [00:03<00:00, 934035.34px/s]
📤 Writing LUT:  97%|█████████▋| 3150384/3240000 [00:03<00:00, 931758.70px/s]
📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 925238.37px/s]
[✅] LUT written to: /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-083WF-W028(6)_2025-11-20' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-083WF-W028(6)_2025-11-20/calibration/MDCP3D-083WF-W028(6)_2025-11-20_unknown.lut
[🏁] Finished processing MDCP3D-083WF-W028(6)_2025-11-20


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