
🟢 Starting Calibration Pipeline

=== Processing pipesim_calibration ===

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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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                                                                       [A📁 Processing Rigs: 100%|██████████| 4/4 [00:57<00:00, 14.18s/it]📁 Processing Rigs: 100%|██████████| 4/4 [00:57<00:00, 14.26s/it]
[💾] Saved RigFrameDataset to /working/pipesim_calibration/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 426 frames from 4 rigs to /working/pipesim_calibration/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/pipesim_calibration/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 106
[📋] 2000L: 100 frames selected from 107
[📋] 1000: 100 frames selected from 106
[📋] 2000R: 100 frames selected from 107
[💾] Saved RigFrameDataset to /working/pipesim_calibration/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/pipesim_calibration/calibration/debug/1_digitized/rig_frames.json

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                        Step 2: Calibration                        
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-> Calculating the casing center ...
[WARNING] No bright frames found across dataset. Falling back to image center.
[INFO] Using image center (900, 900) with radius 720
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[00:01<00:04, 18.88it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  18%|█▊        | 18/100 [00:01<00:04, 18.88it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.006 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:01<00:04, 18.88it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.006 | Best: {'fx': 434.343} (0.007):  20%|██        | 20/100 [00:01<00:02, 29.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:02, 29.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:02, 29.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.013 | Best: {'fx': 462.626} (0.013):  22%|██▏       | 22/100 [00:01<00:02, 29.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.011 | Best: {'fx': 462.626} (0.013):  23%|██▎       | 23/100 [00:01<00:02, 29.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.011 | Best: {'fx': 462.626} (0.013):  24%|██▍       | 24/100 [00:01<00:02, 29.47it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.015 | Best: {'fx': 469.697} (0.015):  24%|██▍       | 24/100 [00:01<00:02, 29.47it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.017 | Best: {'fx': 476.768} (0.017):  25%|██▌       | 25/100 [00:01<00:02, 29.47it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.043 | Best: {'fx': 519.192} (0.043):  26%|██▌       | 26/100 [00:01<00:02, 29.47it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.062 | Best: {'fx': 533.333} (0.062):  27%|██▋       | 27/100 [00:01<00:02, 29.47it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.062 | Best: {'fx': 533.333} (0.062):  28%|██▊       | 28/100 [00:01<00:02, 31.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.025 | Best: {'fx': 533.333} (0.062):  28%|██▊       | 28/100 [00:01<00:02, 31.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.051 | Best: {'fx': 533.333} (0.062):  29%|██▉       | 29/100 [00:01<00:02, 31.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.068 | Best: {'fx': 540.404} (0.068):  30%|███       | 30/100 [00:01<00:02, 31.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 540.404} (0.068):  31%|███       | 31/100 [00:01<00:02, 31.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 540.404} (0.068):  32%|███▏      | 32/100 [00:01<00:02, 31.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 540.404} (0.068):  33%|███▎      | 33/100 [00:01<00:01, 35.76it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.070 | Best: {'fx': 547.475} (0.070):  33%|███▎      | 33/100 [00:01<00:01, 35.76it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.029 | Best: {'fx': 547.475} (0.070):  34%|███▍      | 34/100 [00:01<00:01, 35.76it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.063 | Best: {'fx': 547.475} (0.070):  35%|███▌      | 35/100 [00:01<00:01, 35.76it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.067 | Best: {'fx': 547.475} (0.070):  36%|███▌      | 36/100 [00:01<00:01, 35.76it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.065 | Best: {'fx': 547.475} (0.070):  37%|███▋      | 37/100 [00:01<00:01, 35.76it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.065 | Best: {'fx': 547.475} (0.070):  38%|███▊      | 38/100 [00:01<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.036 | Best: {'fx': 547.475} (0.070):  38%|███▊      | 38/100 [00:01<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.055 | Best: {'fx': 547.475} (0.070):  39%|███▉      | 39/100 [00:01<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.058 | Best: {'fx': 547.475} (0.070):  40%|████      | 40/100 [00:01<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.056 | Best: {'fx': 547.475} (0.070):  41%|████      | 41/100 [00:01<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.055 | Best: {'fx': 547.475} (0.070):  42%|████▏     | 42/100 [00:01<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.055 | Best: {'fx': 547.475} (0.070):  43%|████▎     | 43/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.051 | Best: {'fx': 547.475} (0.070):  43%|████▎     | 43/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.045 | Best: {'fx': 547.475} (0.070):  44%|████▍     | 44/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.054 | Best: {'fx': 547.475} (0.070):  45%|████▌     | 45/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.044 | Best: {'fx': 547.475} (0.070):  46%|████▌     | 46/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.044 | Best: {'fx': 547.475} (0.070):  47%|████▋     | 47/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.048 | Best: {'fx': 547.475} (0.070):  48%|████▊     | 48/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.032 | Best: {'fx': 547.475} (0.070):  49%|████▉     | 49/100 [00:01<00:01, 41.86it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.032 | Best: {'fx': 547.475} (0.070):  50%|█████     | 50/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.042 | Best: {'fx': 547.475} (0.070):  50%|█████     | 50/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.044 | Best: {'fx': 547.475} (0.070):  51%|█████     | 51/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.029 | Best: {'fx': 547.475} (0.070):  52%|█████▏    | 52/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.028 | Best: {'fx': 547.475} (0.070):  53%|█████▎    | 53/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.037 | Best: {'fx': 547.475} (0.070):  54%|█████▍    | 54/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.031 | Best: {'fx': 547.475} (0.070):  55%|█████▌    | 55/100 [00:01<00:01, 44.56it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.031 | Best: {'fx': 547.475} (0.070):  56%|█████▌    | 56/100 [00:01<00:00, 47.98it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.034 | Best: {'fx': 547.475} (0.070):  56%|█████▌    | 56/100 [00:01<00:00, 47.98it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.026 | Best: {'fx': 547.475} (0.070):  57%|█████▋    | 57/100 [00:01<00:00, 47.98it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.027 | Best: {'fx': 547.475} (0.070):  58%|█████▊    | 58/100 [00:01<00:00, 47.98it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.027 | Best: {'fx': 547.475} (0.070):  59%|█████▉    | 59/100 [00:02<00:00, 47.98it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.025 | Best: {'fx': 547.475} (0.070):  60%|██████    | 60/100 [00:02<00:00, 47.98it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.025 | Best: {'fx': 547.475} (0.070):  61%|██████    | 61/100 [00:02<00:00, 40.34it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.026 | Best: {'fx': 547.475} (0.070):  61%|██████    | 61/100 [00:02<00:00, 40.34it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.025 | Best: {'fx': 547.475} (0.070):  62%|██████▏   | 62/100 [00:02<00:00, 40.34it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.024 | Best: {'fx': 547.475} (0.070):  63%|██████▎   | 63/100 [00:02<00:00, 40.34it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.022 | Best: {'fx': 547.475} (0.070):  64%|██████▍   | 64/100 [00:02<00:00, 40.34it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.023 | Best: {'fx': 547.475} (0.070):  65%|██████▌   | 65/100 [00:02<00:00, 40.34it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.023 | Best: {'fx': 547.475} (0.070):  66%|██████▌   | 66/100 [00:02<00:00, 40.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.024 | Best: {'fx': 547.475} (0.070):  66%|██████▌   | 66/100 [00:02<00:00, 40.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.024 | Best: {'fx': 547.475} (0.070):  67%|██████▋   | 67/100 [00:02<00:00, 40.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.023 | Best: {'fx': 547.475} (0.070):  68%|██████▊   | 68/100 [00:02<00:00, 40.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.023 | Best: {'fx': 547.475} (0.070):  69%|██████▉   | 69/100 [00:02<00:00, 40.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.022 | Best: {'fx': 547.475} (0.070):  70%|███████   | 70/100 [00:02<00:00, 40.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.022 | Best: {'fx': 547.475} (0.070):  71%|███████   | 71/100 [00:02<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.022 | Best: {'fx': 547.475} (0.070):  71%|███████   | 71/100 [00:02<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  72%|███████▏  | 72/100 [00:02<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  73%|███████▎  | 73/100 [00:02<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.022 | Best: {'fx': 547.475} (0.070):  74%|███████▍  | 74/100 [00:02<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  75%|███████▌  | 75/100 [00:02<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  76%|███████▌  | 76/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  76%|███████▌  | 76/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.022 | Best: {'fx': 547.475} (0.070):  77%|███████▋  | 77/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  78%|███████▊  | 78/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  79%|███████▉  | 79/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  80%|████████  | 80/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  81%|████████  | 81/100 [00:02<00:00, 37.38it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  82%|████████▏ | 82/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.021 | Best: {'fx': 547.475} (0.070):  82%|████████▏ | 82/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  83%|████████▎ | 83/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  84%|████████▍ | 84/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  85%|████████▌ | 85/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  86%|████████▌ | 86/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  87%|████████▋ | 87/100 [00:02<00:00, 39.42it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  88%|████████▊ | 88/100 [00:02<00:00, 42.94it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  88%|████████▊ | 88/100 [00:02<00:00, 42.94it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  89%|████████▉ | 89/100 [00:02<00:00, 42.94it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  90%|█████████ | 90/100 [00:02<00:00, 42.94it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  91%|█████████ | 91/100 [00:02<00:00, 42.94it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  92%|█████████▏| 92/100 [00:02<00:00, 42.94it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  93%|█████████▎| 93/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  93%|█████████▎| 93/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  94%|█████████▍| 94/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.020 | Best: {'fx': 547.475} (0.070):  95%|█████████▌| 95/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  96%|█████████▌| 96/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  97%|█████████▋| 97/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  98%|█████████▊| 98/100 [00:02<00:00, 42.66it/s]Brute | Start: {'fx': 500.0} (0.026) | Current: 0.019 | Best: {'fx': 547.475} (0.070):  99%|█████████▉| 99/100 [00:02<00:00, 42.66it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.048 | Best: {'fx': 522.475} (0.048):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.048 | Best: {'fx': 522.475} (0.048):   2%|▏         | 1/50 [00:00<00:13,  3.61it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.047 | Best: {'fx': 522.475} (0.048):   2%|▏         | 1/50 [00:00<00:13,  3.61it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.052 | Best: {'fx': 525.536} (0.052):   4%|▍         | 2/50 [00:00<00:13,  3.61it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.053 | Best: {'fx': 527.577} (0.053):   6%|▌         | 3/50 [00:00<00:13,  3.61it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.053 | Best: {'fx': 527.577} (0.053):   8%|▊         | 4/50 [00:00<00:03, 12.01it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.049 | Best: {'fx': 527.577} (0.053):   8%|▊         | 4/50 [00:00<00:03, 12.01it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.059 | Best: {'fx': 530.638} (0.059):  10%|█         | 5/50 [00:00<00:03, 12.01it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.059 | Best: {'fx': 530.638} (0.059):  12%|█▏        | 6/50 [00:00<00:03, 14.21it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.059 | Best: {'fx': 531.658} (0.059):  12%|█▏        | 6/50 [00:00<00:03, 14.21it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.061 | Best: {'fx': 532.679} (0.061):  14%|█▍        | 7/50 [00:00<00:03, 14.21it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.052 | Best: {'fx': 532.679} (0.061):  16%|█▌        | 8/50 [00:00<00:02, 14.21it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.056 | Best: {'fx': 532.679} (0.061):  18%|█▊        | 9/50 [00:00<00:02, 14.21it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.054 | Best: {'fx': 532.679} (0.061):  20%|██        | 10/50 [00:00<00:02, 14.21it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 536.76} (0.064):  22%|██▏       | 11/50 [00:00<00:02, 14.21it/s] Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 536.76} (0.064):  24%|██▍       | 12/50 [00:00<00:01, 24.22it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.063 | Best: {'fx': 536.76} (0.064):  24%|██▍       | 12/50 [00:00<00:01, 24.22it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.063 | Best: {'fx': 536.76} (0.064):  26%|██▌       | 13/50 [00:00<00:01, 24.22it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.063 | Best: {'fx': 536.76} (0.064):  28%|██▊       | 14/50 [00:00<00:01, 24.22it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.063 | Best: {'fx': 536.76} (0.064):  30%|███       | 15/50 [00:00<00:01, 25.28it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 537.781} (0.064):  30%|███       | 15/50 [00:00<00:01, 25.28it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 540.842} (0.069):  32%|███▏      | 16/50 [00:00<00:01, 25.28it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 540.842} (0.069):  34%|███▍      | 17/50 [00:00<00:01, 25.28it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 540.842} (0.069):  36%|███▌      | 18/50 [00:00<00:01, 26.24it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 540.842} (0.069):  36%|███▌      | 18/50 [00:00<00:01, 26.24it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.067 | Best: {'fx': 540.842} (0.069):  38%|███▊      | 19/50 [00:00<00:01, 26.24it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 540.842} (0.069):  40%|████      | 20/50 [00:00<00:01, 26.24it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 541.863} (0.069):  42%|████▏     | 21/50 [00:00<00:01, 26.24it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 541.863} (0.069):  44%|████▍     | 22/50 [00:00<00:00, 30.09it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 541.863} (0.069):  44%|████▍     | 22/50 [00:00<00:00, 30.09it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.070 | Best: {'fx': 547.985} (0.070):  46%|████▌     | 23/50 [00:01<00:00, 30.09it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  48%|████▊     | 24/50 [00:01<00:00, 30.09it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.069 | Best: {'fx': 547.985} (0.070):  50%|█████     | 25/50 [00:01<00:00, 30.09it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  52%|█████▏    | 26/50 [00:01<00:00, 30.09it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  54%|█████▍    | 27/50 [00:01<00:00, 33.04it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  54%|█████▍    | 27/50 [00:01<00:00, 33.04it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.067 | Best: {'fx': 547.985} (0.070):  56%|█████▌    | 28/50 [00:01<00:00, 33.04it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  58%|█████▊    | 29/50 [00:01<00:00, 33.04it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.067 | Best: {'fx': 547.985} (0.070):  60%|██████    | 30/50 [00:01<00:00, 33.04it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  62%|██████▏   | 31/50 [00:01<00:00, 33.04it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.068 | Best: {'fx': 547.985} (0.070):  64%|██████▍   | 32/50 [00:01<00:00, 27.89it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.067 | Best: {'fx': 547.985} (0.070):  64%|██████▍   | 32/50 [00:01<00:00, 27.89it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.067 | Best: {'fx': 547.985} (0.070):  66%|██████▌   | 33/50 [00:01<00:00, 27.89it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.067 | Best: {'fx': 547.985} (0.070):  68%|██████▊   | 34/50 [00:01<00:00, 27.89it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.065 | Best: {'fx': 547.985} (0.070):  70%|███████   | 35/50 [00:01<00:00, 27.89it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.065 | Best: {'fx': 547.985} (0.070):  72%|███████▏  | 36/50 [00:01<00:00, 27.77it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 547.985} (0.070):  72%|███████▏  | 36/50 [00:01<00:00, 27.77it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 547.985} (0.070):  74%|███████▍  | 37/50 [00:01<00:00, 27.77it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 547.985} (0.070):  76%|███████▌  | 38/50 [00:01<00:00, 27.77it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 547.985} (0.070):  78%|███████▊  | 39/50 [00:01<00:00, 27.77it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 547.985} (0.070):  80%|████████  | 40/50 [00:01<00:00, 27.77it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 547.985} (0.070):  82%|████████▏ | 41/50 [00:01<00:00, 32.67it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.065 | Best: {'fx': 547.985} (0.070):  82%|████████▏ | 41/50 [00:01<00:00, 32.67it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.066 | Best: {'fx': 547.985} (0.070):  84%|████████▍ | 42/50 [00:01<00:00, 32.67it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.063 | Best: {'fx': 547.985} (0.070):  86%|████████▌ | 43/50 [00:01<00:00, 32.67it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.063 | Best: {'fx': 547.985} (0.070):  88%|████████▊ | 44/50 [00:01<00:00, 32.67it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 547.985} (0.070):  90%|█████████ | 45/50 [00:01<00:00, 32.67it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 547.985} (0.070):  92%|█████████▏| 46/50 [00:01<00:00, 36.14it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.064 | Best: {'fx': 547.985} (0.070):  92%|█████████▏| 46/50 [00:01<00:00, 36.14it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.062 | Best: {'fx': 547.985} (0.070):  94%|█████████▍| 47/50 [00:01<00:00, 36.14it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.065 | Best: {'fx': 547.985} (0.070):  96%|█████████▌| 48/50 [00:01<00:00, 36.14it/s]Brute | Start: {'fx': 547.475} (0.070) | Current: 0.062 | Best: {'fx': 547.985} (0.070):  98%|█████████▊| 49/50 [00:01<00:00, 36.14it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 547.9849515563801
    px: 900.0
    py: 900.0
    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, 1000(1000mm):7/20, 2000R(2000mm):7/20
  Total: 28/80 frames used for optimization
Initial score: 631.7694: 0iter [00:00, ?iter/s]Best score: 124.7839: : 0iter [00:03, ?iter/s] Best score: 124.7839: : 1iter [00:03,  3.30s/iter]Best score: 75.2142: : 1iter [00:04,  3.30s/iter] Best score: 75.2142: : 2iter [00:04,  2.33s/iter]Best score: 75.2142: : 3iter [00:06,  2.09s/iter]Best score: 75.2142: : 4iter [00:08,  1.92s/iter]Best score: 75.2142: : 5iter [00:10,  1.82s/iter]Best score: 57.2999: : 5iter [00:11,  1.82s/iter]Best score: 57.2999: : 6iter [00:11,  1.75s/iter]Best score: 49.9394: : 6iter [00:13,  1.75s/iter]Best score: 49.9394: : 7iter [00:13,  1.74s/iter]Best score: 49.9394: : 8iter [00:15,  1.73s/iter]Best score: 43.9494: : 8iter [00:16,  1.73s/iter]Best score: 43.9494: : 9iter [00:16,  1.71s/iter]Best score: 29.4999: : 9iter [00:18,  1.71s/iter]Best score: 29.4999: : 10iter [00:18,  1.70s/iter]Best score: 29.4999: : 11iter [00:20,  1.69s/iter]Best score: 29.4999: : 12iter [00:21,  1.69s/iter]Best score: 29.4999: : 13iter [00:23,  1.67s/iter]Best score: 29.4999: : 14iter [00:25,  1.66s/iter]Best score: 29.4999: : 15iter [00:26,  1.68s/iter]Best score: 29.4999: : 16iter [00:28,  1.68s/iter]Best score: 29.4999: : 17iter [00:30,  1.67s/iter]Best score: 29.4999: : 18iter [00:31,  1.68s/iter]Best score: 29.4999: : 19iter [00:33,  1.68s/iter]Best score: 29.4999: : 20iter [00:35,  1.68s/iter]Best score: 29.4999: : 21iter [00:36,  1.68s/iter]Best score: 29.4999: : 22iter [00:38,  1.67s/iter]Best score: 29.4999: : 23iter [00:40,  1.69s/iter]Best score: 9.2232: : 23iter [00:41,  1.69s/iter] Best score: 9.2232: : 24iter [00:41,  1.67s/iter]Best score: 9.2232: : 25iter [00:43,  1.66s/iter]Best score: 9.2232: : 26iter [00:45,  1.67s/iter]Best score: 9.2232: : 27iter [00:46,  1.69s/iter]Best score: 9.2232: : 28iter [00:48,  1.69s/iter]Best score: 9.2232: : 29iter [00:50,  1.68s/iter]Best score: 9.2232: : 30iter [00:52,  1.71s/iter]Best score: 9.2232: : 31iter [00:53,  1.69s/iter]Best score: 9.2232: : 32iter [00:55,  1.69s/iter]Best score: 9.2232: : 33iter [00:57,  1.69s/iter]Best score: 9.2232: : 34iter [00:58,  1.70s/iter]Best score: 9.2232: : 35iter [01:00,  1.70s/iter]Best score: 9.2232: : 36iter [01:02,  1.69s/iter]Best score: 9.0594: : 36iter [01:03,  1.69s/iter]Best score: 9.0594: : 37iter [01:03,  1.69s/iter]Best score: 9.0594: : 38iter [01:05,  1.69s/iter]Best score: 9.0594: : 39iter [01:07,  1.67s/iter]Best score: 9.0594: : 40iter [01:08,  1.66s/iter]Best score: 9.0594: : 41iter [01:10,  1.68s/iter]Best score: 9.0594: : 42iter [01:12,  1.67s/iter]Best score: 9.0594: : 43iter [01:13,  1.67s/iter]Best score: 9.0594: : 44iter [01:15,  1.68s/iter]Best score: 9.0594: : 45iter [01:17,  1.70s/iter]Best score: 9.0594: : 46iter [01:18,  1.67s/iter]Best score: 6.3089: : 46iter [01:20,  1.67s/iter]Best score: 6.3089: : 47iter [01:20,  1.67s/iter]Best score: 6.3089: : 48iter [01:22,  1.66s/iter]Best score: 4.8268: : 48iter [01:23,  1.66s/iter]Best score: 4.8268: : 49iter [01:23,  1.65s/iter]
[ITER 50] Current best score: 4.8268
[PARAMS] Current best parameters:
  fx: 570.8019
  fy: 568.3760
  px: 911.2614
  py: 907.2024
  k1: -0.11105746
  k2: 0.07045575
  k3: -0.00847964
  k4: 0.01270352
  p1: 0.00861457
  p2: 0.00910145
  p3: 0.00331330
  p4: 0.00159135
  scale: 2.9482
  rotation: 0.3027
Best score: 4.8268: : 50iter [01:25,  1.71s/iter]Best score: 4.8268: : 51iter [01:27,  1.71s/iter]Best score: 4.8268: : 52iter [01:29,  1.69s/iter]Best score: 4.8268: : 53iter [01:30,  1.71s/iter]Best score: 4.8268: : 54iter [01:32,  1.69s/iter]Best score: 4.8268: : 55iter [01:34,  1.68s/iter]Best score: 4.8268: : 56iter [01:35,  1.66s/iter]Best score: 4.8268: : 57iter [01:37,  1.65s/iter]Best score: 3.5776: : 57iter [01:39,  1.65s/iter]Best score: 3.5776: : 58iter [01:39,  1.67s/iter]Best score: 3.5776: : 59iter [01:40,  1.65s/iter]Best score: 3.5776: : 60iter [01:42,  1.67s/iter]Best score: 3.5776: : 61iter [01:43,  1.65s/iter]Best score: 3.5776: : 62iter [01:45,  1.66s/iter]Best score: 3.5776: : 63iter [01:47,  1.67s/iter]Best score: 3.5776: : 64iter [01:49,  1.68s/iter]Best score: 3.5776: : 65iter [01:50,  1.69s/iter]Best score: 3.5776: : 66iter [01:52,  1.68s/iter]Best score: 3.5776: : 67iter [01:54,  1.68s/iter]Best score: 3.5776: : 68iter [01:55,  1.68s/iter]Best score: 3.5776: : 69iter [01:57,  1.65s/iter]Best score: 3.1257: : 69iter [01:59,  1.65s/iter]Best score: 3.1257: : 70iter [01:59,  1.67s/iter]Best score: 3.1257: : 71iter [02:00,  1.66s/iter]Best score: 3.1257: : 72iter [02:02,  1.67s/iter]Best score: 3.1257: : 73iter [02:04,  1.66s/iter]Best score: 3.1257: : 74iter [02:05,  1.66s/iter]Best score: 3.1257: : 75iter [02:07,  1.68s/iter]Best score: 3.1257: : 76iter [02:09,  1.68s/iter]Best score: 3.1257: : 77iter [02:10,  1.67s/iter]Best score: 3.1257: : 78iter [02:12,  1.68s/iter]Best score: 3.1257: : 79iter [02:14,  1.68s/iter]Best score: 3.1257: : 80iter [02:15,  1.67s/iter]Best score: 3.1257: : 81iter [02:17,  1.68s/iter]Best score: 3.1257: : 82iter [02:19,  1.68s/iter]Best score: 3.1257: : 83iter [02:20,  1.70s/iter]Best score: 3.1257: : 84iter [02:22,  1.70s/iter]Best score: 1.1072: : 84iter [02:24,  1.70s/iter]Best score: 1.1072: : 85iter [02:24,  1.69s/iter]Best score: 1.1072: : 86iter [02:25,  1.68s/iter]Best score: 1.1072: : 87iter [02:27,  1.69s/iter]Best score: 1.1072: : 88iter [02:29,  1.71s/iter]Best score: 1.1072: : 89iter [02:31,  1.70s/iter]Best score: 1.1072: : 90iter [02:32,  1.70s/iter]Best score: 0.5178: : 90iter [02:34,  1.70s/iter]Best score: 0.5178: : 91iter [02:34,  1.68s/iter]Best score: 0.5178: : 92iter [02:36,  1.66s/iter]Best score: 0.5178: : 93iter [02:37,  1.65s/iter]Best score: 0.5178: : 94iter [02:39,  1.67s/iter]Best score: 0.5178: : 95iter [02:41,  1.67s/iter]Best score: 0.5178: : 96iter [02:42,  1.66s/iter]Best score: 0.5178: : 97iter [02:44,  1.65s/iter]Best score: 0.5178: : 98iter [02:45,  1.61s/iter]Best score: 0.5178: : 99iter [02:47,  1.62s/iter]
[ITER 100] Current best score: 0.5178
[PARAMS] Current best parameters:
  fx: 577.7543
  fy: 579.5849
  px: 900.9497
  py: 900.6142
  k1: -0.08294494
  k2: 0.05290455
  k3: -0.00335382
  k4: 0.00773437
  p1: 0.00332230
  p2: 0.00283948
  p3: -0.00066600
  p4: -0.00890601
  scale: 2.8783
  rotation: 0.3239
Best score: 0.5178: : 100iter [02:49,  1.70s/iter]Best score: 0.5178: : 101iter [02:50,  1.69s/iter]Best score: 0.5178: : 102iter [02:52,  1.67s/iter]Best score: 0.5178: : 103iter [02:54,  1.69s/iter]Best score: 0.5178: : 104iter [02:56,  1.69s/iter]Best score: 0.5178: : 105iter [02:57,  1.67s/iter]Best score: 0.5178: : 106iter [02:59,  1.67s/iter]Best score: 0.5178: : 107iter [03:00,  1.67s/iter]Best score: 0.5178: : 108iter [03:02,  1.68s/iter]Best score: 0.5178: : 109iter [03:04,  1.67s/iter]Best score: 0.5178: : 110iter [03:05,  1.65s/iter]Best score: 0.5178: : 111iter [03:07,  1.68s/iter]Best score: 0.5178: : 112iter [03:09,  1.68s/iter]Best score: 0.5178: : 113iter [03:11,  1.68s/iter]Best score: 0.5178: : 114iter [03:12,  1.69s/iter]Best score: 0.5178: : 115iter [03:14,  1.69s/iter]Best score: 0.5178: : 116iter [03:16,  1.68s/iter]Best score: 0.5178: : 117iter [03:17,  1.67s/iter]Best score: 0.5178: : 118iter [03:19,  1.67s/iter]Best score: 0.5178: : 119iter [03:21,  1.68s/iter]Best score: 0.5178: : 120iter [03:22,  1.69s/iter]Best score: 0.5178: : 121iter [03:24,  1.68s/iter]Best score: 0.5178: : 122iter [03:26,  1.68s/iter]Best score: 0.5178: : 123iter [03:27,  1.67s/iter]Best score: 0.5178: : 124iter [03:29,  1.67s/iter]Best score: 0.5178: : 125iter [03:31,  1.68s/iter]Best score: 0.5178: : 126iter [03:32,  1.67s/iter]Best score: 0.5178: : 127iter [03:34,  1.67s/iter]Best score: 0.5178: : 128iter [03:36,  1.67s/iter]Best score: 0.5178: : 129iter [03:37,  1.67s/iter]Best score: 0.5178: : 130iter [03:39,  1.66s/iter]Best score: 0.5178: : 131iter [03:41,  1.68s/iter]Best score: 0.5178: : 132iter [03:42,  1.66s/iter]Best score: 0.5178: : 133iter [03:44,  1.67s/iter]Best score: 0.5178: : 134iter [03:46,  1.66s/iter]Best score: 0.5178: : 135iter [03:47,  1.66s/iter]Best score: 0.5178: : 136iter [03:49,  1.65s/iter]Best score: 0.5178: : 137iter [03:51,  1.66s/iter]Best score: 0.5178: : 138iter [03:52,  1.68s/iter]Best score: 0.5178: : 139iter [03:54,  1.69s/iter]Best score: 0.5178: : 140iter [03:56,  1.70s/iter]Best score: 0.5178: : 141iter [03:58,  1.70s/iter]Best score: 0.5178: : 142iter [03:59,  1.68s/iter]Best score: 0.5178: : 143iter [04:01,  1.67s/iter]Best score: 0.5178: : 144iter [04:03,  1.69s/iter]Best score: 0.5178: : 145iter [04:04,  1.69s/iter]Best score: 0.5178: : 146iter [04:06,  1.69s/iter]Best score: 0.3713: : 146iter [04:08,  1.69s/iter]Best score: 0.3713: : 147iter [04:08,  1.71s/iter]Best score: 0.3713: : 148iter [04:09,  1.71s/iter]Best score: 0.3713: : 149iter [04:11,  1.69s/iter]
[ITER 150] Current best score: 0.3713
[PARAMS] Current best parameters:
  fx: 584.1876
  fy: 582.9735
  px: 899.3246
  py: 903.2436
  k1: -0.07592001
  k2: 0.03602507
  k3: -0.00060925
  k4: 0.01016902
  p1: 0.00561958
  p2: -0.00165337
  p3: 0.00162260
  p4: -0.00365050
  scale: 2.8788
  rotation: 0.2334
Best score: 0.3713: : 150iter [04:13,  1.72s/iter]Best score: 0.3713: : 151iter [04:15,  1.71s/iter]Best score: 0.3713: : 152iter [04:16,  1.71s/iter]Best score: 0.3713: : 153iter [04:18,  1.70s/iter]Best score: 0.3713: : 154iter [04:20,  1.69s/iter]Best score: 0.3713: : 155iter [04:21,  1.66s/iter]Best score: 0.3713: : 156iter [04:23,  1.67s/iter]Best score: 0.3713: : 157iter [04:25,  1.68s/iter]Best score: 0.3713: : 158iter [04:26,  1.66s/iter]Best score: 0.3713: : 159iter [04:28,  1.68s/iter]Best score: 0.3713: : 160iter [04:30,  1.68s/iter]Best score: 0.3713: : 161iter [04:31,  1.67s/iter]Best score: 0.3713: : 162iter [04:33,  1.67s/iter]Best score: 0.3713: : 163iter [04:35,  1.66s/iter]Best score: 0.3713: : 164iter [04:36,  1.68s/iter]Best score: 0.3713: : 165iter [04:38,  1.67s/iter]Best score: 0.3461: : 165iter [04:40,  1.67s/iter]Best score: 0.3461: : 166iter [04:40,  1.66s/iter]Best score: 0.3461: : 167iter [04:41,  1.68s/iter]Best score: 0.3461: : 168iter [04:43,  1.67s/iter]Best score: 0.3461: : 169iter [04:45,  1.66s/iter]Best score: 0.3461: : 170iter [04:46,  1.67s/iter]Best score: 0.3461: : 171iter [04:48,  1.66s/iter]Best score: 0.3461: : 172iter [04:50,  1.66s/iter]Best score: 0.3461: : 173iter [04:51,  1.65s/iter]Best score: 0.2981: : 173iter [04:53,  1.65s/iter]Best score: 0.2981: : 174iter [04:53,  1.67s/iter]Best score: 0.2981: : 175iter [04:55,  1.66s/iter]Best score: 0.2981: : 176iter [04:56,  1.68s/iter]Best score: 0.2981: : 177iter [04:58,  1.69s/iter]Best score: 0.2981: : 178iter [05:00,  1.68s/iter]Best score: 0.2981: : 179iter [05:01,  1.70s/iter]Best score: 0.2981: : 180iter [05:03,  1.69s/iter]Best score: 0.2981: : 181iter [05:05,  1.69s/iter]Best score: 0.2981: : 182iter [05:06,  1.68s/iter]Best score: 0.2981: : 183iter [05:08,  1.69s/iter]Best score: 0.2981: : 184iter [05:10,  1.70s/iter]Best score: 0.2981: : 185iter [05:12,  1.71s/iter]Best score: 0.2981: : 186iter [05:13,  1.70s/iter]Best score: 0.1622: : 186iter [05:15,  1.70s/iter]Best score: 0.1622: : 187iter [05:15,  1.69s/iter]Best score: 0.1622: : 188iter [05:17,  1.69s/iter]Best score: 0.1622: : 189iter [05:18,  1.70s/iter]Best score: 0.1622: : 190iter [05:20,  1.70s/iter]Best score: 0.1622: : 191iter [05:22,  1.69s/iter]Best score: 0.1622: : 192iter [05:23,  1.69s/iter]Best score: 0.1622: : 193iter [05:25,  1.68s/iter]Best score: 0.1622: : 194iter [05:27,  1.67s/iter]Best score: 0.1622: : 195iter [05:28,  1.70s/iter]Best score: 0.1622: : 196iter [05:30,  1.68s/iter]Best score: 0.1622: : 197iter [05:32,  1.68s/iter]Best score: 0.1622: : 198iter [05:33,  1.67s/iter]Best score: 0.1622: : 199iter [05:35,  1.67s/iter]
[ITER 200] Current best score: 0.1622
[PARAMS] Current best parameters:
  fx: 590.0672
  fy: 589.6132
  px: 902.0345
  py: 902.1703
  k1: -0.08024479
  k2: 0.03546238
  k3: 0.00421081
  k4: 0.00769815
  p1: 0.00354484
  p2: 0.00231567
  p3: 0.00073564
  p4: -0.00075573
  scale: 2.8592
  rotation: 0.2319
Best score: 0.1622: : 200iter [05:37,  1.73s/iter]Best score: 0.1622: : 201iter [05:39,  1.71s/iter]Best score: 0.1622: : 202iter [05:40,  1.69s/iter]Best score: 0.1622: : 203iter [05:42,  1.68s/iter]Best score: 0.1622: : 204iter [05:44,  1.67s/iter]Best score: 0.1622: : 205iter [05:45,  1.67s/iter]Best score: 0.1622: : 206iter [05:47,  1.67s/iter]Best score: 0.1622: : 207iter [05:48,  1.66s/iter]Best score: 0.1622: : 208iter [05:50,  1.67s/iter]Best score: 0.1622: : 209iter [05:52,  1.65s/iter]Best score: 0.1622: : 210iter [05:53,  1.66s/iter]Best score: 0.1622: : 211iter [05:55,  1.68s/iter]Best score: 0.1622: : 212iter [05:57,  1.67s/iter]Best score: 0.1622: : 213iter [05:59,  1.69s/iter]Best score: 0.1622: : 214iter [06:00,  1.69s/iter]Best score: 0.1622: : 215iter [06:02,  1.68s/iter]Best score: 0.1622: : 216iter [06:04,  1.70s/iter]Best score: 0.1622: : 217iter [06:05,  1.70s/iter]Best score: 0.1622: : 218iter [06:07,  1.66s/iter]Best score: 0.1622: : 219iter [06:09,  1.66s/iter]Best score: 0.1622: : 220iter [06:10,  1.66s/iter]Best score: 0.1622: : 221iter [06:12,  1.67s/iter]Best score: 0.1622: : 222iter [06:14,  1.69s/iter]Best score: 0.1622: : 223iter [06:15,  1.69s/iter]Best score: 0.1622: : 224iter [06:17,  1.67s/iter]Best score: 0.1622: : 225iter [06:19,  1.68s/iter]Best score: 0.1622: : 226iter [06:20,  1.69s/iter]Best score: 0.1622: : 227iter [06:22,  1.67s/iter]Best score: 0.1622: : 228iter [06:24,  1.67s/iter]Best score: 0.1622: : 229iter [06:25,  1.67s/iter]Best score: 0.1622: : 230iter [06:27,  1.66s/iter]Best score: 0.1622: : 231iter [06:29,  1.67s/iter]Best score: 0.1622: : 232iter [06:30,  1.67s/iter]Best score: 0.1622: : 233iter [06:32,  1.71s/iter]Best score: 0.1622: : 234iter [06:34,  1.69s/iter]Best score: 0.1622: : 235iter [06:35,  1.67s/iter]Best score: 0.1622: : 236iter [06:37,  1.69s/iter]Best score: 0.1622: : 237iter [06:39,  1.68s/iter]Best score: 0.1622: : 238iter [06:41,  1.68s/iter]Best score: 0.1622: : 239iter [06:42,  1.65s/iter]Best score: 0.1622: : 240iter [06:44,  1.68s/iter]Best score: 0.1622: : 241iter [06:45,  1.66s/iter]Best score: 0.1622: : 242iter [06:47,  1.65s/iter]Best score: 0.1622: : 243iter [06:49,  1.65s/iter]Best score: 0.1622: : 244iter [06:50,  1.66s/iter]Best score: 0.1622: : 245iter [06:52,  1.66s/iter]Best score: 0.1622: : 246iter [06:54,  1.66s/iter]Best score: 0.1622: : 247iter [06:55,  1.66s/iter]Best score: 0.1622: : 248iter [06:57,  1.65s/iter]Best score: 0.1622: : 249iter [06:59,  1.64s/iter]
[ITER 250] Current best score: 0.0651
[PARAMS] Current best parameters:
  fx: 588.5293
  fy: 588.6777
  px: 902.5229
  py: 901.6043
  k1: -0.09350128
  k2: 0.04414276
  k3: 0.00917769
  k4: 0.00401447
  p1: 0.00254641
  p2: 0.00302241
  p3: -0.00052468
  p4: -0.00084926
  scale: 2.8828
  rotation: 0.2041
Best score: 0.0651: : 249iter [07:00,  1.64s/iter]Best score: 0.0651: : 250iter [07:00,  1.69s/iter]Best score: 0.0651: : 251iter [07:02,  1.69s/iter]Best score: 0.0651: : 252iter [07:04,  1.67s/iter]Best score: 0.0651: : 253iter [07:05,  1.66s/iter]Best score: 0.0651: : 254iter [07:07,  1.66s/iter]Best score: 0.0651: : 255iter [07:09,  1.68s/iter]Best score: 0.0651: : 256iter [07:10,  1.68s/iter]Best score: 0.0651: : 257iter [07:12,  1.67s/iter]Best score: 0.0651: : 258iter [07:14,  1.65s/iter]Best score: 0.0651: : 259iter [07:15,  1.67s/iter]Best score: 0.0651: : 260iter [07:17,  1.67s/iter]Best score: 0.0651: : 261iter [07:19,  1.68s/iter]Best score: 0.0651: : 262iter [07:21,  1.69s/iter]Best score: 0.0651: : 263iter [07:22,  1.69s/iter]Best score: 0.0651: : 264iter [07:24,  1.69s/iter]Best score: 0.0651: : 265iter [07:26,  1.71s/iter]Best score: 0.0651: : 266iter [07:27,  1.71s/iter]Best score: 0.0651: : 267iter [07:29,  1.68s/iter]Best score: 0.0651: : 268iter [07:31,  1.68s/iter]Best score: 0.0651: : 269iter [07:32,  1.69s/iter]Best score: 0.0651: : 270iter [07:34,  1.69s/iter]Best score: 0.0651: : 271iter [07:36,  1.68s/iter]Best score: 0.0651: : 272iter [07:37,  1.68s/iter]Best score: 0.0651: : 273iter [07:39,  1.67s/iter]Best score: 0.0651: : 274iter [07:41,  1.66s/iter]Best score: 0.0651: : 275iter [07:42,  1.64s/iter]Best score: 0.0651: : 276iter [07:44,  1.64s/iter]Best score: 0.0651: : 277iter [07:46,  1.67s/iter]Best score: 0.0651: : 278iter [07:47,  1.67s/iter]Best score: 0.0651: : 279iter [07:49,  1.68s/iter]Best score: 0.0651: : 280iter [07:51,  1.66s/iter]Best score: 0.0651: : 281iter [07:52,  1.68s/iter]Best score: 0.0562: : 281iter [07:54,  1.68s/iter]Best score: 0.0562: : 282iter [07:54,  1.69s/iter]Best score: 0.0562: : 283iter [07:56,  1.70s/iter]Best score: 0.0562: : 284iter [07:57,  1.69s/iter]Best score: 0.0562: : 285iter [07:59,  1.67s/iter]Best score: 0.0562: : 286iter [08:01,  1.65s/iter]Best score: 0.0562: : 287iter [08:02,  1.63s/iter]Best score: 0.0562: : 288iter [08:04,  1.62s/iter]Best score: 0.0562: : 289iter [08:06,  1.63s/iter]Best score: 0.0562: : 290iter [08:07,  1.64s/iter]Best score: 0.0562: : 291iter [08:09,  1.64s/iter]Best score: 0.0562: : 292iter [08:11,  1.68s/iter]Best score: 0.0562: : 293iter [08:12,  1.68s/iter]Best score: 0.0562: : 294iter [08:14,  1.68s/iter]Best score: 0.0562: : 295iter [08:16,  1.67s/iter]Best score: 0.0562: : 296iter [08:17,  1.69s/iter]Best score: 0.0562: : 297iter [08:19,  1.69s/iter]Best score: 0.0562: : 298iter [08:21,  1.67s/iter]Best score: 0.0562: : 299iter [08:22,  1.68s/iter]
[ITER 300] Current best score: 0.0562
[PARAMS] Current best parameters:
  fx: 590.7282
  fy: 591.2167
  px: 900.0830
  py: 900.4719
  k1: -0.09560401
  k2: 0.04857445
  k3: 0.00562676
  k4: 0.00468713
  p1: 0.00110835
  p2: -0.00084050
  p3: -0.00152716
  p4: 0.00293701
  scale: 2.8740
  rotation: 0.2176
Best score: 0.0562: : 300iter [08:24,  1.73s/iter]Best score: 0.0562: : 301iter [08:26,  1.72s/iter]Best score: 0.0562: : 302iter [08:28,  1.71s/iter]Best score: 0.0562: : 303iter [08:29,  1.70s/iter]Best score: 0.0397: : 303iter [08:31,  1.70s/iter]Best score: 0.0397: : 304iter [08:31,  1.68s/iter]Best score: 0.0397: : 305iter [08:33,  1.69s/iter]Best score: 0.0397: : 306iter [08:34,  1.68s/iter]Best score: 0.0397: : 307iter [08:36,  1.70s/iter]Best score: 0.0397: : 308iter [08:38,  1.69s/iter]Best score: 0.0397: : 309iter [08:39,  1.69s/iter]Best score: 0.0397: : 310iter [08:41,  1.70s/iter]Best score: 0.0397: : 311iter [08:43,  1.70s/iter]Best score: 0.0397: : 312iter [08:45,  1.70s/iter]Best score: 0.0397: : 313iter [08:46,  1.68s/iter]Best score: 0.0397: : 314iter [08:48,  1.66s/iter]Best score: 0.0397: : 315iter [08:49,  1.67s/iter]Best score: 0.0397: : 316iter [08:51,  1.66s/iter]Best score: 0.0397: : 317iter [08:53,  1.69s/iter]Best score: 0.0397: : 318iter [08:55,  1.68s/iter]Best score: 0.0397: : 319iter [08:56,  1.68s/iter]Best score: 0.0397: : 320iter [08:58,  1.68s/iter]Best score: 0.0397: : 321iter [09:00,  1.68s/iter]Best score: 0.0397: : 322iter [09:01,  1.68s/iter]Best score: 0.0397: : 323iter [09:03,  1.68s/iter]Best score: 0.0397: : 324iter [09:05,  1.69s/iter]Best score: 0.0397: : 325iter [09:06,  1.67s/iter]Best score: 0.0397: : 326iter [09:08,  1.69s/iter]Best score: 0.0397: : 327iter [09:10,  1.70s/iter]Best score: 0.0397: : 328iter [09:11,  1.68s/iter]Best score: 0.0397: : 329iter [09:13,  1.67s/iter]Best score: 0.0397: : 330iter [09:15,  1.67s/iter]Best score: 0.0397: : 331iter [09:16,  1.66s/iter]Best score: 0.0397: : 332iter [09:18,  1.66s/iter]Best score: 0.0397: : 333iter [09:20,  1.68s/iter]Best score: 0.0397: : 334iter [09:21,  1.70s/iter]Best score: 0.0397: : 335iter [09:23,  1.69s/iter]Best score: 0.0397: : 336iter [09:25,  1.67s/iter]Best score: 0.0397: : 337iter [09:26,  1.66s/iter]Best score: 0.0397: : 338iter [09:28,  1.69s/iter]Best score: 0.0397: : 339iter [09:30,  1.69s/iter]Best score: 0.0397: : 340iter [09:31,  1.68s/iter]Best score: 0.0397: : 341iter [09:33,  1.69s/iter]Best score: 0.0397: : 342iter [09:35,  1.66s/iter]Best score: 0.0397: : 343iter [09:36,  1.67s/iter]Best score: 0.0397: : 344iter [09:38,  1.65s/iter]Best score: 0.0397: : 345iter [09:40,  1.66s/iter]Best score: 0.0397: : 346iter [09:41,  1.67s/iter]Best score: 0.0397: : 347iter [09:43,  1.69s/iter]Best score: 0.0397: : 348iter [09:45,  1.69s/iter]Best score: 0.0397: : 349iter [09:47,  1.69s/iter]
[ITER 350] Current best score: 0.0212
[PARAMS] Current best parameters:
  fx: 592.0812
  fy: 592.3894
  px: 901.8260
  py: 899.2297
  k1: -0.10512704
  k2: 0.05586202
  k3: 0.00604076
  k4: 0.00334747
  p1: 0.00028688
  p2: 0.00184794
  p3: -0.00216545
  p4: 0.00145484
  scale: 2.8860
  rotation: 0.1738
Best score: 0.0212: : 349iter [09:48,  1.69s/iter]Best score: 0.0212: : 350iter [09:48,  1.73s/iter]Best score: 0.0212: : 351iter [09:50,  1.72s/iter]Best score: 0.0212: : 352iter [09:52,  1.71s/iter]Best score: 0.0212: : 353iter [09:54,  1.73s/iter]Best score: 0.0212: : 354iter [09:55,  1.70s/iter]Best score: 0.0212: : 355iter [09:57,  1.71s/iter]Best score: 0.0212: : 356iter [09:59,  1.72s/iter]Best score: 0.0212: : 357iter [10:00,  1.71s/iter]Best score: 0.0212: : 358iter [10:02,  1.70s/iter]Best score: 0.0212: : 359iter [10:04,  1.69s/iter]Best score: 0.0212: : 360iter [10:05,  1.69s/iter]Best score: 0.0212: : 361iter [10:07,  1.68s/iter]Best score: 0.0212: : 362iter [10:09,  1.67s/iter]Best score: 0.0212: : 363iter [10:10,  1.66s/iter]Best score: 0.0212: : 364iter [10:12,  1.69s/iter]Best score: 0.0212: : 365iter [10:14,  1.69s/iter]Best score: 0.0212: : 366iter [10:15,  1.67s/iter]Best score: 0.0212: : 367iter [10:17,  1.68s/iter]Best score: 0.0212: : 368iter [10:19,  1.66s/iter]Best score: 0.0212: : 369iter [10:20,  1.65s/iter]Best score: 0.0212: : 370iter [10:22,  1.67s/iter]Best score: 0.0212: : 371iter [10:24,  1.68s/iter]Best score: 0.0212: : 372iter [10:25,  1.69s/iter]Best score: 0.0212: : 373iter [10:27,  1.70s/iter]Best score: 0.0212: : 374iter [10:29,  1.68s/iter]Best score: 0.0212: : 375iter [10:30,  1.67s/iter]Best score: 0.0212: : 376iter [10:32,  1.66s/iter]Best score: 0.0212: : 377iter [10:34,  1.63s/iter]Best score: 0.0212: : 378iter [10:35,  1.64s/iter]Best score: 0.0212: : 379iter [10:37,  1.64s/iter]Best score: 0.0212: : 380iter [10:39,  1.63s/iter]Best score: 0.0212: : 381iter [10:40,  1.65s/iter]Best score: 0.0212: : 382iter [10:42,  1.68s/iter]Best score: 0.0212: : 383iter [10:44,  1.68s/iter]Best score: 0.0212: : 384iter [10:45,  1.67s/iter]Best score: 0.0212: : 385iter [10:47,  1.67s/iter]Best score: 0.0212: : 386iter [10:49,  1.70s/iter]Best score: 0.0212: : 387iter [10:50,  1.69s/iter]Best score: 0.0212: : 388iter [10:52,  1.66s/iter]Best score: 0.0212: : 389iter [10:54,  1.66s/iter]Best score: 0.0212: : 390iter [10:55,  1.69s/iter]Best score: 0.0212: : 391iter [10:57,  1.68s/iter]Best score: 0.0212: : 392iter [10:59,  1.68s/iter]Best score: 0.0212: : 393iter [11:01,  1.69s/iter]Best score: 0.0212: : 394iter [11:02,  1.70s/iter]Best score: 0.0212: : 395iter [11:04,  1.70s/iter]Best score: 0.0212: : 396iter [11:06,  1.68s/iter]Best score: 0.0212: : 397iter [11:07,  1.68s/iter]Best score: 0.0212: : 398iter [11:09,  1.68s/iter]Best score: 0.0212: : 399iter [11:11,  1.66s/iter]
[ITER 400] Current best score: 0.0212
[PARAMS] Current best parameters:
  fx: 592.0812
  fy: 592.3894
  px: 901.8260
  py: 899.2297
  k1: -0.10512704
  k2: 0.05586202
  k3: 0.00604076
  k4: 0.00334747
  p1: 0.00028688
  p2: 0.00184794
  p3: -0.00216545
  p4: 0.00145484
  scale: 2.8860
  rotation: 0.1738
Best score: 0.0212: : 400iter [11:12,  1.72s/iter]Best score: 0.0212: : 401iter [11:14,  1.72s/iter]Best score: 0.0212: : 402iter [11:16,  1.68s/iter]Best score: 0.0212: : 403iter [11:17,  1.68s/iter]Best score: 0.0212: : 404iter [11:19,  1.70s/iter]Best score: 0.0212: : 405iter [11:21,  1.69s/iter]Best score: 0.0212: : 406iter [11:22,  1.68s/iter]Best score: 0.0212: : 407iter [11:24,  1.67s/iter]Best score: 0.0212: : 408iter [11:26,  1.68s/iter]Best score: 0.0212: : 409iter [11:28,  1.68s/iter]Best score: 0.0212: : 410iter [11:29,  1.67s/iter]Best score: 0.0212: : 411iter [11:31,  1.68s/iter]Best score: 0.0212: : 412iter [11:33,  1.69s/iter]Best score: 0.0212: : 413iter [11:34,  1.69s/iter]Best score: 0.0212: : 414iter [11:36,  1.66s/iter]Best score: 0.0212: : 415iter [11:38,  1.67s/iter]Best score: 0.0212: : 416iter [11:39,  1.65s/iter]Best score: 0.0212: : 417iter [11:41,  1.64s/iter]Best score: 0.0194: : 417iter [11:42,  1.64s/iter]Best score: 0.0194: : 418iter [11:42,  1.65s/iter]Best score: 0.0194: : 419iter [11:44,  1.69s/iter]Best score: 0.0194: : 420iter [11:46,  1.66s/iter]Best score: 0.0194: : 421iter [11:47,  1.65s/iter]Best score: 0.0194: : 422iter [11:49,  1.65s/iter]Best score: 0.0194: : 423iter [11:51,  1.65s/iter]Best score: 0.0194: : 424iter [11:52,  1.68s/iter]Best score: 0.0194: : 425iter [11:54,  1.67s/iter]Best score: 0.0194: : 426iter [11:56,  1.66s/iter]Best score: 0.0194: : 427iter [11:57,  1.68s/iter]Best score: 0.0194: : 428iter [11:59,  1.67s/iter]Best score: 0.0194: : 429iter [12:01,  1.66s/iter]Best score: 0.0194: : 430iter [12:02,  1.66s/iter]Best score: 0.0194: : 431iter [12:04,  1.67s/iter]Best score: 0.0194: : 432iter [12:06,  1.67s/iter]Best score: 0.0194: : 433iter [12:08,  1.69s/iter]Best score: 0.0194: : 434iter [12:09,  1.69s/iter]Best score: 0.0194: : 435iter [12:11,  1.68s/iter]Best score: 0.0194: : 436iter [12:13,  1.70s/iter]Best score: 0.0194: : 437iter [12:14,  1.69s/iter]Best score: 0.0194: : 438iter [12:16,  1.68s/iter]Best score: 0.0194: : 439iter [12:18,  1.67s/iter]Best score: 0.0194: : 440iter [12:19,  1.66s/iter]Best score: 0.0194: : 441iter [12:21,  1.67s/iter]Best score: 0.0194: : 442iter [12:23,  1.69s/iter]Best score: 0.0194: : 443iter [12:24,  1.69s/iter]Best score: 0.0194: : 444iter [12:26,  1.68s/iter]Best score: 0.0194: : 445iter [12:28,  1.70s/iter]Best score: 0.0194: : 446iter [12:29,  1.69s/iter]Best score: 0.0194: : 447iter [12:31,  1.69s/iter]Best score: 0.0194: : 448iter [12:33,  1.70s/iter]Best score: 0.0194: : 449iter [12:35,  1.69s/iter]
[ITER 450] Current best score: 0.0194
[PARAMS] Current best parameters:
  fx: 590.4535
  fy: 590.8799
  px: 901.6568
  py: 899.7337
  k1: -0.10345008
  k2: 0.05647454
  k3: 0.00436349
  k4: 0.00406837
  p1: 0.00113030
  p2: 0.00157260
  p3: 0.00097933
  p4: -0.00198755
  scale: 2.8859
  rotation: 0.1623
Best score: 0.0194: : 450iter [12:36,  1.74s/iter]Best score: 0.0194: : 451iter [12:38,  1.71s/iter]Best score: 0.0194: : 452iter [12:40,  1.70s/iter]Best score: 0.0194: : 453iter [12:41,  1.73s/iter]Best score: 0.0194: : 454iter [12:43,  1.73s/iter]Best score: 0.0194: : 455iter [12:45,  1.72s/iter]Best score: 0.0194: : 456iter [12:47,  1.72s/iter]Best score: 0.0194: : 457iter [12:48,  1.70s/iter]Best score: 0.0194: : 458iter [12:50,  1.70s/iter]Best score: 0.0194: : 459iter [12:52,  1.68s/iter]Best score: 0.0194: : 460iter [12:53,  1.70s/iter]Best score: 0.0194: : 461iter [12:55,  1.70s/iter]Best score: 0.0194: : 462iter [12:57,  1.69s/iter]Best score: 0.0194: : 463iter [12:58,  1.69s/iter]Best score: 0.0194: : 464iter [13:00,  1.72s/iter]Best score: 0.0194: : 465iter [13:02,  1.69s/iter]Best score: 0.0194: : 466iter [13:04,  1.70s/iter]Best score: 0.0194: : 467iter [13:05,  1.70s/iter]Best score: 0.0194: : 468iter [13:07,  1.71s/iter]Best score: 0.0194: : 469iter [13:09,  1.70s/iter]Best score: 0.0194: : 470iter [13:10,  1.69s/iter]Best score: 0.0194: : 471iter [13:12,  1.70s/iter]Best score: 0.0194: : 472iter [13:14,  1.69s/iter]Best score: 0.0194: : 473iter [13:15,  1.69s/iter]Best score: 0.0194: : 474iter [13:17,  1.67s/iter]Best score: 0.0194: : 475iter [13:19,  1.66s/iter]Best score: 0.0194: : 476iter [13:20,  1.65s/iter]Best score: 0.0194: : 477iter [13:22,  1.66s/iter]Best score: 0.0194: : 478iter [13:24,  1.67s/iter]Best score: 0.0194: : 479iter [13:25,  1.66s/iter]Best score: 0.0171: : 479iter [13:27,  1.66s/iter]Best score: 0.0171: : 480iter [13:27,  1.67s/iter]Best score: 0.0171: : 481iter [13:29,  1.66s/iter]Best score: 0.0171: : 482iter [13:30,  1.67s/iter]Best score: 0.0171: : 483iter [13:32,  1.66s/iter]Best score: 0.0171: : 484iter [13:34,  1.65s/iter]Best score: 0.0171: : 485iter [13:35,  1.64s/iter]Best score: 0.0171: : 486iter [13:37,  1.65s/iter]Best score: 0.0171: : 487iter [13:39,  1.66s/iter]Best score: 0.0171: : 488iter [13:40,  1.67s/iter]Best score: 0.0171: : 489iter [13:42,  1.67s/iter]Best score: 0.0171: : 490iter [13:44,  1.68s/iter]Best score: 0.0171: : 491iter [13:45,  1.68s/iter]Best score: 0.0171: : 492iter [13:47,  1.66s/iter]Best score: 0.0171: : 493iter [13:49,  1.67s/iter]Best score: 0.0171: : 494iter [13:50,  1.70s/iter]Best score: 0.0171: : 495iter [13:52,  1.69s/iter]Best score: 0.0171: : 496iter [13:54,  1.66s/iter]Best score: 0.0171: : 497iter [13:55,  1.67s/iter]Best score: 0.0171: : 498iter [13:57,  1.68s/iter]Best score: 0.0125: : 498iter [13:59,  1.68s/iter]Best score: 0.0125: : 499iter [13:59,  1.69s/iter]
[ITER 500] Current best score: 0.0125
[PARAMS] Current best parameters:
  fx: 593.2656
  fy: 593.2181
  px: 901.1917
  py: 899.0914
  k1: -0.10903234
  k2: 0.05758765
  k3: 0.00753323
  k4: 0.00240819
  p1: -0.00014081
  p2: 0.00081458
  p3: -0.00051304
  p4: 0.00063965
  scale: 2.8916
  rotation: 0.1701
Best score: 0.0125: : 500iter [14:01,  1.73s/iter]Best score: 0.0125: : 500iter [15:06,  1.81s/iter]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.008701 -> 0.008662
  Success: True
  Tangential distortion: p1=0.000051, p2=0.000750, p3=-0.000556, p4=0.000532
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.0087
[✅] Parameters copied to: /working/pipesim_calibration/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 593.2656419392304
    fy: 593.2181101829879
    px: 901.1916917224495
    py: 899.0913970659016
    k1: -0.10916554945948313
    k2: 0.05751877958728517
    k3: 0.007583415938160078
    k4: 0.0024885722021409074
    p1: 5.062698927038981e-05
    p2: 0.0007500891935947298
    p3: -0.0005557279570768815
    p4: 0.0005318478950931846
    scale: 2.891639087416622
    rotation: 0.1699612609475247
    mmpp: 3.2921823522515097
    nmpp: 3292182.3522515097

[🔄] Converting 426 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/pipesim_calibration/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/pipesim_calibration/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%|▎         | 87702/3240000 [00:00<00:03, 877016.65px/s]📤 Writing LUT:   6%|▌         | 182549/3240000 [00:00<00:03, 919045.91px/s]📤 Writing LUT:   9%|▊         | 279710/3240000 [00:00<00:03, 943043.00px/s]📤 Writing LUT:  12%|█▏        | 375012/3240000 [00:00<00:03, 946978.58px/s]📤 Writing LUT:  15%|█▍        | 472351/3240000 [00:00<00:02, 956500.55px/s]📤 Writing LUT:  18%|█▊        | 568909/3240000 [00:00<00:02, 959584.75px/s]📤 Writing LUT:  21%|██        | 664868/3240000 [00:00<00:02, 956263.98px/s]📤 Writing LUT:  23%|██▎       | 760497/3240000 [00:00<00:02, 955817.29px/s]📤 Writing LUT:  26%|██▋       | 856202/3240000 [00:00<00:02, 956197.92px/s]📤 Writing LUT:  29%|██▉       | 951823/3240000 [00:01<00:02, 955557.84px/s]📤 Writing LUT:  32%|███▏      | 1049558/3240000 [00:01<00:02, 962216.86px/s]📤 Writing LUT:  35%|███▌      | 1146994/3240000 [00:01<00:02, 965903.39px/s]📤 Writing LUT:  38%|███▊      | 1243586/3240000 [00:01<00:02, 963092.77px/s]📤 Writing LUT:  41%|████▏     | 1339898/3240000 [00:01<00:01, 959405.24px/s]📤 Writing LUT:  44%|████▍     | 1437890/3240000 [00:01<00:01, 965560.47px/s]📤 Writing LUT:  47%|████▋     | 1535693/3240000 [00:01<00:01, 969301.72px/s]📤 Writing LUT:  50%|█████     | 1632915/3240000 [00:01<00:01, 970176.92px/s]📤 Writing LUT:  53%|█████▎    | 1729937/3240000 [00:01<00:01, 967286.78px/s]📤 Writing LUT:  56%|█████▋    | 1826670/3240000 [00:01<00:01, 963485.76px/s]📤 Writing LUT:  59%|█████▉    | 1923914/3240000 [00:02<00:01, 966158.33px/s]📤 Writing LUT:  62%|██████▏   | 2021678/3240000 [00:02<00:01, 969589.56px/s]📤 Writing LUT:  65%|██████▌   | 2118642/3240000 [00:02<00:01, 968608.66px/s]📤 Writing LUT:  68%|██████▊   | 2215699/3240000 [00:02<00:01, 969193.99px/s]📤 Writing LUT:  71%|███████▏  | 2312621/3240000 [00:02<00:00, 967027.97px/s]📤 Writing LUT:  74%|███████▍  | 2410846/3240000 [00:02<00:00, 971580.77px/s]📤 Writing LUT:  77%|███████▋  | 2509120/3240000 [00:02<00:00, 974919.16px/s]📤 Writing LUT:  80%|████████  | 2606615/3240000 [00:02<00:00, 967632.27px/s]📤 Writing LUT:  83%|████████▎ | 2704010/3240000 [00:02<00:00, 969512.76px/s]📤 Writing LUT:  86%|████████▋ | 2801754/3240000 [00:02<00:00, 971878.86px/s]📤 Writing LUT:  90%|████████▉ | 2899899/3240000 [00:03<00:00, 974738.31px/s]📤 Writing LUT:  93%|█████████▎| 2998303/3240000 [00:03<00:00, 977520.38px/s]📤 Writing LUT:  96%|█████████▌| 3096061/3240000 [00:03<00:00, 969027.44px/s]📤 Writing LUT:  99%|█████████▊| 3192984/3240000 [00:03<00:00, 967101.88px/s]📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 963592.97px/s]
[✅] LUT written to: /working/pipesim_calibration/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'pipesim_calibration' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/pipesim_calibration/calibration/pipesim_calibration_unknown.lut
[🏁] Finished processing pipesim_calibration


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