🟢 Starting Calibration Pipeline

=== Processing MDC103_W050_2025-05-07 ===

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

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📁 Processing Rigs:  50%|█████     | 1/2 [02:19<02:19, 139.03s/it]

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📁 Processing Rigs: 100%|██████████| 2/2 [04:41<00:00, 141.31s/it]
📁 Processing Rigs: 100%|██████████| 2/2 [04:41<00:00, 140.97s/it]
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2348 frames from 2 rigs to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 1153
[📋] 1000: 100 frames selected from 1195
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized/rig_frames.json

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

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

Averaging bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Averaging bright frames:  10%|█         | 4/40 [00:00<00:01, 33.48it/s]
Averaging bright frames:  22%|██▎       | 9/40 [00:00<00:00, 40.61it/s]
Averaging bright frames:  35%|███▌      | 14/40 [00:00<00:00, 43.62it/s]
Averaging bright frames:  48%|████▊     | 19/40 [00:00<00:00, 44.85it/s]
Averaging bright frames:  60%|██████    | 24/40 [00:00<00:00, 45.41it/s]
Averaging bright frames:  72%|███████▎  | 29/40 [00:00<00:00, 45.66it/s]
Averaging bright frames:  85%|████████▌ | 34/40 [00:00<00:00, 46.08it/s]
Averaging bright frames:  98%|█████████▊| 39/40 [00:00<00:00, 46.27it/s]
                                                                        
Total time to process (v2): 0.05
[RESULT] Combined: Center=(1016, 756), Radius=835

  0%|          | 0/100 [00:00<?, ?it/s]
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Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:10,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:09,  9.33it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 476.768} (0.001):  25%|██▌       | 25/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 483.838} (0.002):  26%|██▌       | 26/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.034 | Best: {'fx': 519.192} (0.034):  27%|██▋       | 27/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 519.192} (0.034):  28%|██▊       | 28/100 [00:00<00:00, 82.34it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 519.192} (0.034):  29%|██▉       | 29/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.003 | Best: {'fx': 519.192} (0.034):  29%|██▉       | 29/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.044 | Best: {'fx': 540.404} (0.044):  30%|███       | 30/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.006 | Best: {'fx': 540.404} (0.044):  31%|███       | 31/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.020 | Best: {'fx': 540.404} (0.044):  32%|███▏      | 32/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.054 | Best: {'fx': 526.263} (0.054):  33%|███▎      | 33/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.065 | Best: {'fx': 533.333} (0.065):  34%|███▍      | 34/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.031 | Best: {'fx': 533.333} (0.065):  35%|███▌      | 35/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.065):  36%|███▌      | 36/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.021 | Best: {'fx': 533.333} (0.065):  37%|███▋      | 37/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.018 | Best: {'fx': 533.333} (0.065):  38%|███▊      | 38/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.024 | Best: {'fx': 533.333} (0.065):  39%|███▉      | 39/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.065):  40%|████      | 40/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  41%|████      | 41/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.065):  42%|████▏     | 42/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  43%|████▎     | 43/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.065):  44%|████▍     | 44/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  45%|████▌     | 45/100 [00:00<00:00, 97.08it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  46%|████▌     | 46/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  46%|████▌     | 46/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  47%|████▋     | 47/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  48%|████▊     | 48/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  49%|████▉     | 49/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  50%|█████     | 50/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  51%|█████     | 51/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  52%|█████▏    | 52/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  53%|█████▎    | 53/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  54%|█████▍    | 54/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  55%|█████▌    | 55/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  56%|█████▌    | 56/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  57%|█████▋    | 57/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  58%|█████▊    | 58/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  59%|█████▉    | 59/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  60%|██████    | 60/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  61%|██████    | 61/100 [00:00<00:00, 123.48it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  62%|██████▏   | 62/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  62%|██████▏   | 62/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  63%|██████▎   | 63/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  64%|██████▍   | 64/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  65%|██████▌   | 65/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  66%|██████▌   | 66/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  67%|██████▋   | 67/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  68%|██████▊   | 68/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  69%|██████▉   | 69/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  70%|███████   | 70/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  71%|███████   | 71/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  72%|███████▏  | 72/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  73%|███████▎  | 73/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  74%|███████▍  | 74/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  75%|███████▌  | 75/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  76%|███████▌  | 76/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  77%|███████▋  | 77/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  78%|███████▊  | 78/100 [00:00<00:00, 135.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  79%|███████▉  | 79/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  79%|███████▉  | 79/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  80%|████████  | 80/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  81%|████████  | 81/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  82%|████████▏ | 82/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  83%|████████▎ | 83/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  84%|████████▍ | 84/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  85%|████████▌ | 85/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  86%|████████▌ | 86/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  87%|████████▋ | 87/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  88%|████████▊ | 88/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  89%|████████▉ | 89/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  90%|█████████ | 90/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  91%|█████████ | 91/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  92%|█████████▏| 92/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  93%|█████████▎| 93/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  94%|█████████▍| 94/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  95%|█████████▌| 95/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  96%|█████████▌| 96/100 [00:00<00:00, 143.17it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  97%|█████████▋| 97/100 [00:00<00:00, 153.71it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  97%|█████████▋| 97/100 [00:00<00:00, 153.71it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  98%|█████████▊| 98/100 [00:00<00:00, 153.71it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  99%|█████████▉| 99/100 [00:00<00:00, 153.71it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   2%|▏         | 1/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.011 | Best: {'fx': 510.374} (0.015):   2%|▏         | 1/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.012 | Best: {'fx': 510.374} (0.015):   4%|▍         | 2/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.019 | Best: {'fx': 511.395} (0.019):   6%|▌         | 3/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.024 | Best: {'fx': 513.435} (0.024):   8%|▊         | 4/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.026 | Best: {'fx': 514.456} (0.026):  10%|█         | 5/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.020 | Best: {'fx': 514.456} (0.026):  12%|█▏        | 6/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.027 | Best: {'fx': 515.476} (0.027):  14%|█▍        | 7/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 516.497} (0.029):  16%|█▌        | 8/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  18%|█▊        | 9/50 [00:00<00:05,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.032 | Best: {'fx': 518.537} (0.033):  20%|██        | 10/50 [00:00<00:04,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.047 | Best: {'fx': 523.639} (0.047):  22%|██▏       | 11/50 [00:00<00:04,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.047 | Best: {'fx': 522.619} (0.047):  24%|██▍       | 12/50 [00:00<00:04,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.051 | Best: {'fx': 524.66} (0.051):  26%|██▌       | 13/50 [00:00<00:04,  8.20it/s] 
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.036 | Best: {'fx': 524.66} (0.051):  28%|██▊       | 14/50 [00:00<00:04,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.052 | Best: {'fx': 525.68} (0.052):  30%|███       | 15/50 [00:00<00:04,  8.20it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.052 | Best: {'fx': 525.68} (0.052):  32%|███▏      | 16/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.039 | Best: {'fx': 525.68} (0.052):  32%|███▏      | 16/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.057 | Best: {'fx': 526.701} (0.057):  34%|███▍      | 17/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.041 | Best: {'fx': 526.701} (0.057):  36%|███▌      | 18/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.063 | Best: {'fx': 528.741} (0.063):  38%|███▊      | 19/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.067 | Best: {'fx': 530.782} (0.067):  40%|████      | 20/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.066 | Best: {'fx': 530.782} (0.067):  42%|████▏     | 21/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.073 | Best: {'fx': 531.803} (0.073):  44%|████▍     | 22/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.060 | Best: {'fx': 531.803} (0.073):  46%|████▌     | 23/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.069 | Best: {'fx': 531.803} (0.073):  48%|████▊     | 24/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.066 | Best: {'fx': 531.803} (0.073):  50%|█████     | 25/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.063 | Best: {'fx': 531.803} (0.073):  52%|█████▏    | 26/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.055 | Best: {'fx': 531.803} (0.073):  54%|█████▍    | 27/50 [00:00<00:00, 84.00it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.055 | Best: {'fx': 531.803} (0.073):  56%|█████▌    | 28/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.047 | Best: {'fx': 531.803} (0.073):  56%|█████▌    | 28/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.054 | Best: {'fx': 531.803} (0.073):  58%|█████▊    | 29/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.050 | Best: {'fx': 531.803} (0.073):  60%|██████    | 30/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.042 | Best: {'fx': 531.803} (0.073):  62%|██████▏   | 31/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.042 | Best: {'fx': 531.803} (0.073):  64%|██████▍   | 32/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.039 | Best: {'fx': 531.803} (0.073):  66%|██████▌   | 33/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.037 | Best: {'fx': 531.803} (0.073):  68%|██████▊   | 34/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.036 | Best: {'fx': 531.803} (0.073):  70%|███████   | 35/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.034 | Best: {'fx': 531.803} (0.073):  72%|███████▏  | 36/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.032 | Best: {'fx': 531.803} (0.073):  74%|███████▍  | 37/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.031 | Best: {'fx': 531.803} (0.073):  76%|███████▌  | 38/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.030 | Best: {'fx': 531.803} (0.073):  78%|███████▊  | 39/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 531.803} (0.073):  80%|████████  | 40/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 531.803} (0.073):  82%|████████▏ | 41/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 531.803} (0.073):  84%|████████▍ | 42/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.027 | Best: {'fx': 531.803} (0.073):  86%|████████▌ | 43/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.027 | Best: {'fx': 531.803} (0.073):  88%|████████▊ | 44/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.026 | Best: {'fx': 531.803} (0.073):  90%|█████████ | 45/50 [00:00<00:00, 96.29it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.026 | Best: {'fx': 531.803} (0.073):  92%|█████████▏| 46/50 [00:00<00:00, 125.36it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 531.803} (0.073):  92%|█████████▏| 46/50 [00:00<00:00, 125.36it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.024 | Best: {'fx': 531.803} (0.073):  94%|█████████▍| 47/50 [00:00<00:00, 125.36it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 531.803} (0.073):  96%|█████████▌| 48/50 [00:00<00:00, 125.36it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 531.803} (0.073):  98%|█████████▊| 49/50 [00:00<00:00, 125.36it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1016.0
    py: 756.0
    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): 7 frames will be used for optimization
  Rigs: 1000(1000mm):7/20
  Total: 7/20 frames used for optimization

Initial score: 29.9069: 0iter [00:00, ?iter/s]
Initial score: 29.9069: 1iter [00:01,  1.10s/iter]
Initial score: 29.9069: 2iter [00:01,  1.29iter/s]
Initial score: 29.9069: 3iter [00:02,  1.54iter/s]
Initial score: 29.9069: 4iter [00:02,  1.70iter/s]
Initial score: 29.9069: 5iter [00:03,  1.70iter/s]
Initial score: 29.9069: 6iter [00:03,  1.74iter/s]
Initial score: 29.9069: 7iter [00:04,  1.90iter/s]
Initial score: 29.9069: 8iter [00:04,  1.92iter/s]
Initial score: 29.9069: 9iter [00:05,  1.93iter/s]
Initial score: 29.9069: 10iter [00:05,  1.93iter/s]
Initial score: 29.9069: 11iter [00:06,  2.00iter/s]
Initial score: 29.9069: 12iter [00:06,  1.94iter/s]
Initial score: 29.9069: 13iter [00:07,  1.96iter/s]
Initial score: 29.9069: 14iter [00:07,  2.09iter/s]
Initial score: 29.9069: 15iter [00:08,  2.06iter/s]
Initial score: 29.9069: 16iter [00:08,  1.92iter/s]
Initial score: 29.9069: 17iter [00:09,  1.93iter/s]
Initial score: 29.9069: 18iter [00:09,  1.90iter/s]
Initial score: 29.9069: 19iter [00:10,  1.83iter/s]
Initial score: 29.9069: 20iter [00:10,  1.91iter/s]
Initial score: 29.9069: 21iter [00:11,  1.94iter/s]
Initial score: 29.9069: 22iter [00:11,  1.91iter/s]
Initial score: 29.9069: 23iter [00:12,  2.03iter/s]
Initial score: 29.9069: 24iter [00:12,  1.94iter/s]
Initial score: 29.9069: 25iter [00:13,  1.98iter/s]
Initial score: 29.9069: 26iter [00:13,  1.94iter/s]
Initial score: 29.9069: 27iter [00:14,  1.91iter/s]
Initial score: 29.9069: 28iter [00:14,  1.91iter/s]
Initial score: 29.9069: 29iter [00:15,  1.87iter/s]
Initial score: 29.9069: 30iter [00:16,  1.86iter/s]
Initial score: 29.9069: 31iter [00:16,  1.88iter/s]
Initial score: 29.9069: 32iter [00:17,  1.91iter/s]
Initial score: 29.9069: 33iter [00:17,  1.88iter/s]
Initial score: 29.9069: 34iter [00:18,  1.81iter/s]
Initial score: 29.9069: 35iter [00:18,  1.94iter/s]
Initial score: 29.9069: 36iter [00:19,  1.94iter/s]
Initial score: 29.9069: 37iter [00:19,  1.86iter/s]
Initial score: 29.9069: 38iter [00:20,  1.97iter/s]
Initial score: 29.9069: 39iter [00:20,  1.99iter/s]
Initial score: 29.9069: 40iter [00:21,  1.93iter/s]
Initial score: 29.9069: 41iter [00:21,  1.95iter/s]
Initial score: 29.9069: 42iter [00:22,  1.88iter/s]
Initial score: 29.9069: 43iter [00:22,  1.93iter/s]
Initial score: 29.9069: 44iter [00:23,  1.94iter/s]
Initial score: 29.9069: 45iter [00:23,  1.93iter/s]
Initial score: 29.9069: 46iter [00:24,  1.94iter/s]
Initial score: 29.9069: 47iter [00:24,  1.95iter/s]
Initial score: 29.9069: 48iter [00:25,  1.94iter/s]
Initial score: 29.9069: 49iter [00:25,  1.99iter/s]
[ITER 50] Current best score: 29.9069
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1016.0000
  py: 756.0000
  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: 29.9069: 50iter [00:26,  2.03iter/s]
Initial score: 29.9069: 51iter [00:26,  2.02iter/s]
Initial score: 29.9069: 52iter [00:27,  1.92iter/s]
Initial score: 29.9069: 53iter [00:27,  1.91iter/s]
Initial score: 29.9069: 54iter [00:28,  1.89iter/s]
Initial score: 29.9069: 55iter [00:29,  1.90iter/s]
Initial score: 29.9069: 56iter [00:29,  1.86iter/s]
Best score: 28.4309: : 56iter [00:30,  1.86iter/s] 
Best score: 28.4309: : 57iter [00:30,  1.86iter/s]
Best score: 28.4309: : 58iter [00:30,  1.86iter/s]
Best score: 20.3441: : 58iter [00:31,  1.86iter/s]
Best score: 20.3441: : 59iter [00:31,  1.85iter/s]
Best score: 20.3441: : 60iter [00:31,  1.87iter/s]
Best score: 20.3441: : 61iter [00:32,  1.91iter/s]
Best score: 20.3441: : 62iter [00:32,  1.96iter/s]
Best score: 20.3441: : 63iter [00:33,  1.92iter/s]
Best score: 20.3441: : 64iter [00:33,  1.92iter/s]
Best score: 20.3441: : 65iter [00:34,  1.87iter/s]
Best score: 20.3441: : 66iter [00:34,  1.88iter/s]
Best score: 20.3441: : 67iter [00:35,  1.89iter/s]
Best score: 20.3441: : 68iter [00:35,  1.88iter/s]
Best score: 20.3441: : 69iter [00:36,  1.94iter/s]
Best score: 20.3441: : 70iter [00:36,  1.97iter/s]
Best score: 20.3441: : 71iter [00:37,  2.01iter/s]
Best score: 20.3441: : 72iter [00:37,  2.01iter/s]
Best score: 20.3441: : 73iter [00:38,  1.95iter/s]
Best score: 20.3441: : 74iter [00:38,  1.99iter/s]
Best score: 20.3441: : 75iter [00:39,  2.03iter/s]
Best score: 6.9154: : 75iter [00:39,  2.03iter/s] 
Best score: 6.9154: : 76iter [00:39,  2.07iter/s]
Best score: 6.9154: : 77iter [00:40,  2.04iter/s]
Best score: 6.9154: : 78iter [00:40,  1.92iter/s]
Best score: 6.9154: : 79iter [00:41,  1.91iter/s]
Best score: 6.9154: : 80iter [00:41,  1.98iter/s]
Best score: 6.9154: : 81iter [00:42,  2.01iter/s]
Best score: 6.9154: : 82iter [00:42,  1.92iter/s]
Best score: 6.9154: : 83iter [00:43,  1.89iter/s]
Best score: 6.9154: : 84iter [00:44,  1.89iter/s]
Best score: 6.9154: : 85iter [00:44,  1.90iter/s]
Best score: 6.9154: : 86iter [00:45,  1.91iter/s]
Best score: 6.9154: : 87iter [00:45,  1.99iter/s]
Best score: 6.9154: : 88iter [00:46,  1.96iter/s]
Best score: 6.9154: : 89iter [00:46,  1.91iter/s]
Best score: 6.9154: : 90iter [00:47,  1.88iter/s]
Best score: 6.9154: : 91iter [00:47,  1.83iter/s]
Best score: 6.9154: : 92iter [00:48,  1.91iter/s]
Best score: 6.9154: : 93iter [00:48,  1.93iter/s]
Best score: 6.9154: : 94iter [00:49,  1.86iter/s]
Best score: 6.9154: : 95iter [00:49,  1.96iter/s]
Best score: 5.2455: : 95iter [00:50,  1.96iter/s]
Best score: 5.2455: : 96iter [00:50,  1.94iter/s]
Best score: 5.2455: : 97iter [00:50,  1.88iter/s]
Best score: 5.2455: : 98iter [00:51,  2.05iter/s]
Best score: 5.2455: : 99iter [00:51,  2.02iter/s]
[ITER 100] Current best score: 5.2455
[PARAMS] Current best parameters:
  fx: 499.3736
  fy: 499.6541
  px: 1014.7819
  py: 759.8312
  k1: 0.03127483
  k2: -0.00266803
  k3: 0.00466533
  k4: -0.00245435
  p1: 0.00010133
  p2: 0.00284478
  p3: -0.00425831
  p4: 0.00190615
  scale: 2.9999
  rotation: -0.3436

Best score: 5.2455: : 100iter [00:52,  1.96iter/s]
Best score: 5.2455: : 101iter [00:52,  1.91iter/s]
Best score: 5.2455: : 102iter [00:53,  1.88iter/s]
Best score: 5.2455: : 103iter [00:53,  1.89iter/s]
Best score: 5.2455: : 104iter [00:54,  1.93iter/s]
Best score: 5.2455: : 105iter [00:54,  1.93iter/s]
Best score: 5.2455: : 106iter [00:55,  1.96iter/s]
Best score: 5.2455: : 107iter [00:55,  1.92iter/s]
Best score: 5.2455: : 108iter [00:56,  1.95iter/s]
Best score: 5.2455: : 109iter [00:57,  1.87iter/s]
Best score: 5.2455: : 110iter [00:57,  1.87iter/s]
Best score: 5.2455: : 111iter [00:58,  1.98iter/s]
Best score: 5.2455: : 112iter [00:58,  1.99iter/s]
Best score: 5.2455: : 113iter [00:59,  1.89iter/s]
Best score: 5.2455: : 114iter [00:59,  1.93iter/s]
Best score: 5.2455: : 115iter [01:00,  1.86iter/s]
Best score: 5.2455: : 116iter [01:00,  1.82iter/s]
Best score: 5.2455: : 117iter [01:01,  1.84iter/s]
Best score: 5.2455: : 118iter [01:01,  1.80iter/s]
Best score: 5.2455: : 119iter [01:02,  1.80iter/s]
Best score: 5.2455: : 120iter [01:02,  1.84iter/s]
Best score: 5.2455: : 121iter [01:03,  1.85iter/s]
Best score: 5.2455: : 122iter [01:04,  1.86iter/s]
Best score: 5.2455: : 123iter [01:04,  1.87iter/s]
Best score: 5.2455: : 124iter [01:05,  1.88iter/s]
Best score: 5.2455: : 125iter [01:05,  1.95iter/s]
Best score: 5.2455: : 126iter [01:06,  2.01iter/s]
Best score: 5.2455: : 127iter [01:06,  1.93iter/s]
Best score: 5.2455: : 128iter [01:07,  1.93iter/s]
Best score: 5.2455: : 129iter [01:07,  1.89iter/s]
Best score: 5.2455: : 130iter [01:08,  1.97iter/s]
Best score: 5.2455: : 131iter [01:08,  1.94iter/s]
Best score: 5.2455: : 132iter [01:09,  1.86iter/s]
Best score: 5.2455: : 133iter [01:09,  1.89iter/s]
Best score: 5.2455: : 134iter [01:10,  1.89iter/s]
Best score: 5.2455: : 135iter [01:10,  1.89iter/s]
Best score: 5.2455: : 136iter [01:11,  2.04iter/s]
Best score: 5.2455: : 137iter [01:11,  2.03iter/s]
Best score: 3.4229: : 137iter [01:12,  2.03iter/s]
Best score: 3.4229: : 138iter [01:12,  2.01iter/s]
Best score: 3.4229: : 139iter [01:12,  2.04iter/s]
Best score: 3.4229: : 140iter [01:13,  1.98iter/s]
Best score: 3.4229: : 141iter [01:13,  1.90iter/s]
Best score: 3.4229: : 142iter [01:14,  2.03iter/s]
Best score: 3.4229: : 143iter [01:14,  2.04iter/s]
Best score: 3.4229: : 144iter [01:15,  1.98iter/s]
Best score: 3.4229: : 145iter [01:15,  1.99iter/s]
Best score: 3.4229: : 146iter [01:16,  2.02iter/s]
Best score: 3.4229: : 147iter [01:16,  1.92iter/s]
Best score: 3.4229: : 148iter [01:17,  1.94iter/s]
Best score: 3.4229: : 149iter [01:17,  1.93iter/s]
[ITER 150] Current best score: 3.4229
[PARAMS] Current best parameters:
  fx: 509.0089
  fy: 507.6820
  px: 1014.4198
  py: 757.2961
  k1: 0.02787018
  k2: -0.00727607
  k3: -0.00224374
  k4: 0.00156541
  p1: -0.00309918
  p2: 0.00231365
  p3: 0.00059616
  p4: 0.00037060
  scale: 3.0660
  rotation: -0.6161

Best score: 3.4229: : 150iter [01:18,  1.90iter/s]
Best score: 3.4229: : 151iter [01:18,  1.86iter/s]
Best score: 3.4229: : 152iter [01:19,  1.90iter/s]
Best score: 3.4229: : 153iter [01:19,  1.86iter/s]
Best score: 3.4229: : 154iter [01:20,  1.84iter/s]
Best score: 3.4229: : 155iter [01:21,  1.88iter/s]
Best score: 3.4229: : 156iter [01:21,  1.87iter/s]
Best score: 3.4229: : 157iter [01:22,  1.86iter/s]
Best score: 3.4229: : 158iter [01:22,  1.85iter/s]
Best score: 3.4229: : 159iter [01:23,  1.92iter/s]
Best score: 3.4229: : 160iter [01:23,  1.88iter/s]
Best score: 3.4229: : 161iter [01:24,  1.93iter/s]
Best score: 3.4229: : 162iter [01:24,  2.01iter/s]
Best score: 3.4229: : 163iter [01:25,  1.94iter/s]
Best score: 3.4229: : 164iter [01:25,  1.86iter/s]
Best score: 3.3565: : 164iter [01:26,  1.86iter/s]
Best score: 3.3565: : 165iter [01:26,  1.90iter/s]
Best score: 3.3565: : 166iter [01:26,  1.97iter/s]
Best score: 3.3565: : 167iter [01:27,  2.06iter/s]
Best score: 3.0233: : 167iter [01:27,  2.06iter/s]
Best score: 3.0233: : 168iter [01:27,  1.98iter/s]
Best score: 2.9359: : 168iter [01:28,  1.98iter/s]
Best score: 2.9359: : 169iter [01:28,  2.07iter/s]
Best score: 2.9359: : 170iter [01:28,  2.04iter/s]
Best score: 2.9359: : 171iter [01:29,  1.93iter/s]
Best score: 2.9359: : 172iter [01:29,  1.92iter/s]
Best score: 2.9359: : 173iter [01:30,  1.93iter/s]
Best score: 2.9359: : 174iter [01:30,  1.94iter/s]
Best score: 2.9359: : 175iter [01:31,  1.92iter/s]
Best score: 2.9359: : 176iter [01:31,  1.95iter/s]
Best score: 2.9359: : 177iter [01:32,  1.98iter/s]
Best score: 2.9359: : 178iter [01:32,  2.04iter/s]
Best score: 2.9359: : 179iter [01:33,  2.03iter/s]
Best score: 2.9359: : 180iter [01:33,  1.99iter/s]
Best score: 2.9359: : 181iter [01:34,  1.94iter/s]
Best score: 2.9359: : 182iter [01:34,  1.97iter/s]
Best score: 2.9359: : 183iter [01:35,  1.95iter/s]
Best score: 2.9359: : 184iter [01:35,  1.94iter/s]
Best score: 2.9359: : 185iter [01:36,  1.93iter/s]
Best score: 2.6774: : 185iter [01:36,  1.93iter/s]
Best score: 2.6774: : 186iter [01:36,  1.88iter/s]
Best score: 2.6774: : 187iter [01:37,  1.91iter/s]
Best score: 2.6774: : 188iter [01:37,  2.01iter/s]
Best score: 1.5695: : 188iter [01:38,  2.01iter/s]
Best score: 1.5695: : 189iter [01:38,  2.00iter/s]
Best score: 1.5695: : 190iter [01:38,  2.03iter/s]
Best score: 1.5695: : 191iter [01:39,  2.02iter/s]
Best score: 1.5695: : 192iter [01:39,  2.02iter/s]
Best score: 1.5695: : 193iter [01:40,  2.00iter/s]
Best score: 1.5695: : 194iter [01:40,  1.96iter/s]
Best score: 1.5695: : 195iter [01:41,  1.94iter/s]
Best score: 1.5695: : 196iter [01:42,  1.88iter/s]
Best score: 1.5695: : 197iter [01:42,  1.83iter/s]
Best score: 1.5695: : 198iter [01:43,  1.92iter/s]
Best score: 1.5695: : 199iter [01:43,  1.86iter/s]
[ITER 200] Current best score: 2.1483
[PARAMS] Current best parameters:
  fx: 517.1728
  fy: 517.0757
  px: 1012.2125
  py: 759.2695
  k1: 0.04948658
  k2: -0.01589695
  k3: -0.00841389
  k4: 0.00344122
  p1: -0.00113155
  p2: -0.00057580
  p3: 0.00975410
  p4: -0.00259906
  scale: 2.9139
  rotation: -0.4373

Best score: 1.5695: : 200iter [01:44,  1.83iter/s]
Best score: 1.5695: : 201iter [01:44,  1.81iter/s]
Best score: 1.5695: : 202iter [01:45,  1.84iter/s]
Best score: 1.5695: : 203iter [01:45,  1.85iter/s]
Best score: 1.5695: : 204iter [01:46,  1.94iter/s]
Best score: 1.5695: : 205iter [01:46,  1.89iter/s]
Best score: 1.5695: : 206iter [01:47,  1.88iter/s]
Best score: 1.5695: : 207iter [01:47,  1.88iter/s]
Best score: 1.5695: : 208iter [01:48,  1.90iter/s]
Best score: 1.5695: : 209iter [01:49,  1.88iter/s]
Best score: 1.5695: : 210iter [01:49,  1.96iter/s]
Best score: 1.5695: : 211iter [01:50,  1.92iter/s]
Best score: 1.5695: : 212iter [01:50,  1.89iter/s]
Best score: 1.5695: : 213iter [01:51,  1.93iter/s]
Best score: 1.5695: : 214iter [01:51,  1.93iter/s]
Best score: 1.5695: : 215iter [01:52,  2.00iter/s]
Best score: 1.5695: : 216iter [01:52,  1.96iter/s]
Best score: 1.5695: : 217iter [01:53,  1.90iter/s]
Best score: 1.5695: : 218iter [01:53,  1.94iter/s]
Best score: 1.5695: : 219iter [01:54,  1.92iter/s]
Best score: 1.5695: : 220iter [01:54,  1.92iter/s]
Best score: 1.5695: : 221iter [01:55,  1.94iter/s]
Best score: 1.5695: : 222iter [01:55,  1.95iter/s]
Best score: 1.5695: : 223iter [01:56,  1.98iter/s]
Best score: 1.5695: : 224iter [01:56,  1.96iter/s]
Best score: 1.5695: : 225iter [01:57,  1.90iter/s]
Best score: 1.5695: : 226iter [01:57,  1.98iter/s]
Best score: 1.5695: : 227iter [01:58,  1.94iter/s]
Best score: 1.5695: : 228iter [01:58,  1.94iter/s]
Best score: 1.5695: : 229iter [01:59,  1.94iter/s]
Best score: 1.5695: : 230iter [01:59,  1.90iter/s]
Best score: 1.5695: : 231iter [02:00,  1.90iter/s]
Best score: 1.5695: : 232iter [02:00,  1.96iter/s]
Best score: 1.5695: : 233iter [02:01,  1.97iter/s]
Best score: 1.5695: : 234iter [02:01,  1.99iter/s]
Best score: 1.5695: : 235iter [02:02,  2.00iter/s]
Best score: 1.5695: : 236iter [02:02,  1.94iter/s]
Best score: 1.5695: : 237iter [02:03,  2.02iter/s]
Best score: 1.5695: : 238iter [02:03,  2.02iter/s]
Best score: 1.5695: : 239iter [02:04,  1.91iter/s]
Best score: 1.5695: : 240iter [02:04,  1.92iter/s]
Best score: 1.5695: : 241iter [02:05,  1.92iter/s]
Best score: 1.5695: : 242iter [02:05,  1.89iter/s]
Best score: 1.5695: : 243iter [02:06,  1.94iter/s]
Best score: 1.5695: : 244iter [02:07,  1.88iter/s]
Best score: 1.5695: : 245iter [02:07,  1.87iter/s]
Best score: 1.5695: : 246iter [02:08,  1.90iter/s]
Best score: 1.5695: : 247iter [02:08,  1.94iter/s]
Best score: 1.5695: : 248iter [02:09,  1.93iter/s]
Best score: 1.5695: : 249iter [02:09,  1.94iter/s]
[ITER 250] Current best score: 2.1483
[PARAMS] Current best parameters:
  fx: 517.1728
  fy: 517.0757
  px: 1012.2125
  py: 759.2695
  k1: 0.04948658
  k2: -0.01589695
  k3: -0.00841389
  k4: 0.00344122
  p1: -0.00113155
  p2: -0.00057580
  p3: 0.00975410
  p4: -0.00259906
  scale: 2.9139
  rotation: -0.4373

Best score: 1.5695: : 250iter [02:10,  1.90iter/s]
Best score: 1.5695: : 251iter [02:10,  1.91iter/s]
Best score: 1.5695: : 252iter [02:11,  1.93iter/s]
Best score: 1.5695: : 253iter [02:11,  1.90iter/s]
Best score: 1.5695: : 254iter [02:12,  1.93iter/s]
Best score: 1.5695: : 255iter [02:12,  1.95iter/s]
Best score: 1.5695: : 256iter [02:13,  2.00iter/s]
Best score: 1.5695: : 257iter [02:13,  1.99iter/s]
Best score: 1.5695: : 258iter [02:14,  2.00iter/s]
Best score: 1.5695: : 259iter [02:14,  2.01iter/s]
Best score: 1.5695: : 260iter [02:15,  2.00iter/s]
Best score: 1.5695: : 261iter [02:15,  1.97iter/s]
Best score: 1.5695: : 262iter [02:16,  1.98iter/s]
Best score: 1.5695: : 263iter [02:16,  1.92iter/s]
Best score: 1.5695: : 264iter [02:17,  1.89iter/s]
Best score: 1.5695: : 265iter [02:17,  2.03iter/s]
Best score: 1.5695: : 266iter [02:18,  2.05iter/s]
Best score: 1.5695: : 267iter [02:18,  2.07iter/s]
Best score: 1.5695: : 268iter [02:19,  1.97iter/s]
Best score: 1.5695: : 269iter [02:19,  1.96iter/s]
Best score: 1.5695: : 270iter [02:20,  1.98iter/s]
Best score: 1.5695: : 271iter [02:20,  1.94iter/s]
Best score: 1.5695: : 272iter [02:21,  1.99iter/s]
Best score: 1.4615: : 272iter [02:21,  1.99iter/s]
Best score: 1.4615: : 273iter [02:21,  1.96iter/s]
Best score: 0.9455: : 273iter [02:22,  1.96iter/s]
Best score: 0.9455: : 274iter [02:22,  2.01iter/s]
Best score: 0.9455: : 275iter [02:22,  1.99iter/s]
Best score: 0.9455: : 276iter [02:23,  1.97iter/s]
Best score: 0.9455: : 277iter [02:23,  1.96iter/s]
Best score: 0.9455: : 278iter [02:24,  1.98iter/s]
Best score: 0.9455: : 279iter [02:24,  1.95iter/s]
Best score: 0.9455: : 280iter [02:25,  2.01iter/s]
Best score: 0.9455: : 281iter [02:25,  1.98iter/s]
Best score: 0.9455: : 282iter [02:26,  2.02iter/s]
Best score: 0.9455: : 283iter [02:26,  2.04iter/s]
Best score: 0.9455: : 284iter [02:27,  2.01iter/s]
Best score: 0.9455: : 285iter [02:27,  1.95iter/s]
Best score: 0.9455: : 286iter [02:28,  2.05iter/s]
Best score: 0.9455: : 287iter [02:28,  2.04iter/s]
Best score: 0.9455: : 288iter [02:29,  1.97iter/s]
Best score: 0.9455: : 289iter [02:29,  1.96iter/s]
Best score: 0.9455: : 290iter [02:30,  1.92iter/s]
Best score: 0.9455: : 291iter [02:30,  1.90iter/s]
Best score: 0.9455: : 292iter [02:31,  1.98iter/s]
Best score: 0.9455: : 293iter [02:31,  1.93iter/s]
Best score: 0.9455: : 294iter [02:32,  1.88iter/s]
Best score: 0.9455: : 295iter [02:32,  1.99iter/s]
Best score: 0.9455: : 296iter [02:33,  1.95iter/s]
Best score: 0.9455: : 297iter [02:33,  1.99iter/s]
Best score: 0.9455: : 298iter [02:34,  2.05iter/s]
Best score: 0.9455: : 299iter [02:34,  2.02iter/s]
[ITER 300] Current best score: 0.9455
[PARAMS] Current best parameters:
  fx: 516.3060
  fy: 514.9335
  px: 1012.1794
  py: 757.8697
  k1: 0.03569914
  k2: -0.00916565
  k3: -0.00421858
  k4: 0.00124194
  p1: -0.00319654
  p2: -0.00182070
  p3: 0.00684410
  p4: -0.00132401
  scale: 2.9679
  rotation: -0.2687

Best score: 0.9455: : 300iter [02:35,  1.86iter/s]
Best score: 0.9455: : 301iter [02:36,  1.89iter/s]
Best score: 0.9455: : 302iter [02:36,  1.97iter/s]
Best score: 0.9455: : 303iter [02:37,  1.95iter/s]
Best score: 0.9455: : 304iter [02:37,  1.97iter/s]
Best score: 0.9455: : 305iter [02:37,  2.05iter/s]
Best score: 0.9455: : 306iter [02:38,  2.05iter/s]
Best score: 0.9455: : 307iter [02:39,  1.96iter/s]
Best score: 0.9455: : 308iter [02:39,  1.92iter/s]
Best score: 0.9455: : 309iter [02:40,  1.91iter/s]
Best score: 0.9455: : 310iter [02:40,  1.94iter/s]
Best score: 0.9455: : 311iter [02:41,  1.93iter/s]
Best score: 0.9455: : 312iter [02:41,  1.90iter/s]
Best score: 0.9455: : 313iter [02:42,  1.89iter/s]
Best score: 0.9455: : 314iter [02:42,  2.02iter/s]
Best score: 0.9455: : 315iter [02:43,  2.02iter/s]
Best score: 0.9455: : 316iter [02:43,  1.95iter/s]
Best score: 0.9455: : 317iter [02:44,  1.97iter/s]
Best score: 0.9455: : 318iter [02:44,  1.89iter/s]
Best score: 0.9455: : 319iter [02:45,  1.89iter/s]
Best score: 0.9455: : 320iter [02:45,  1.95iter/s]
Best score: 0.9455: : 321iter [02:46,  1.98iter/s]
Best score: 0.9455: : 322iter [02:46,  1.96iter/s]
Best score: 0.9455: : 323iter [02:47,  1.98iter/s]
Best score: 0.9455: : 324iter [02:47,  2.04iter/s]
Best score: 0.9455: : 325iter [02:48,  2.02iter/s]
Best score: 0.9455: : 326iter [02:48,  1.97iter/s]
Best score: 0.9455: : 327iter [02:49,  1.98iter/s]
Best score: 0.9455: : 328iter [02:49,  1.95iter/s]
Best score: 0.9455: : 329iter [02:50,  1.97iter/s]
Best score: 0.9455: : 330iter [02:50,  2.02iter/s]
Best score: 0.9455: : 331iter [02:51,  1.95iter/s]
Best score: 0.9455: : 332iter [02:51,  1.96iter/s]
Best score: 0.9455: : 333iter [02:52,  2.06iter/s]
Best score: 0.9455: : 334iter [02:52,  2.02iter/s]
Best score: 0.9455: : 335iter [02:53,  1.98iter/s]
Best score: 0.9455: : 336iter [02:53,  2.06iter/s]
Best score: 0.9455: : 337iter [02:54,  2.01iter/s]
Best score: 0.9455: : 338iter [02:54,  1.95iter/s]
Best score: 0.9455: : 339iter [02:55,  1.96iter/s]
Best score: 0.9455: : 340iter [02:55,  1.89iter/s]
Best score: 0.9455: : 341iter [02:56,  1.89iter/s]
Best score: 0.9455: : 342iter [02:56,  1.98iter/s]
Best score: 0.9455: : 343iter [02:57,  1.99iter/s]
Best score: 0.9455: : 344iter [02:57,  1.94iter/s]
Best score: 0.9455: : 345iter [02:58,  1.93iter/s]
Best score: 0.9455: : 346iter [02:58,  1.94iter/s]
Best score: 0.9455: : 347iter [02:59,  1.97iter/s]
Best score: 0.9455: : 348iter [02:59,  1.97iter/s]
Best score: 0.9455: : 349iter [03:00,  1.93iter/s]
[ITER 350] Current best score: 0.9455
[PARAMS] Current best parameters:
  fx: 516.3060
  fy: 514.9335
  px: 1012.1794
  py: 757.8697
  k1: 0.03569914
  k2: -0.00916565
  k3: -0.00421858
  k4: 0.00124194
  p1: -0.00319654
  p2: -0.00182070
  p3: 0.00684410
  p4: -0.00132401
  scale: 2.9679
  rotation: -0.2687

Best score: 0.9455: : 350iter [03:00,  1.95iter/s]
Best score: 0.9455: : 351iter [03:01,  1.90iter/s]
Best score: 0.9455: : 352iter [03:02,  1.91iter/s]
Best score: 0.9455: : 353iter [03:02,  1.88iter/s]
Best score: 0.9455: : 354iter [03:03,  1.90iter/s]
Best score: 0.9455: : 355iter [03:03,  1.89iter/s]
Best score: 0.9455: : 356iter [03:04,  1.89iter/s]
Best score: 0.9455: : 357iter [03:04,  1.76iter/s]
Best score: 0.9455: : 358iter [03:05,  1.86iter/s]
Best score: 0.9455: : 359iter [03:05,  1.95iter/s]
Best score: 0.9455: : 360iter [03:06,  1.95iter/s]
Best score: 0.9455: : 361iter [03:06,  1.93iter/s]
Best score: 0.9455: : 362iter [03:07,  1.94iter/s]
Best score: 0.6457: : 362iter [03:07,  1.94iter/s]
Best score: 0.6457: : 363iter [03:07,  1.89iter/s]
Best score: 0.6457: : 364iter [03:08,  1.91iter/s]
Best score: 0.6024: : 364iter [03:08,  1.91iter/s]
Best score: 0.6024: : 365iter [03:08,  1.91iter/s]
Best score: 0.6024: : 366iter [03:09,  1.92iter/s]
Best score: 0.6024: : 367iter [03:09,  1.93iter/s]
Best score: 0.6024: : 368iter [03:10,  1.99iter/s]
Best score: 0.6024: : 369iter [03:10,  1.97iter/s]
Best score: 0.6024: : 370iter [03:11,  1.98iter/s]
Best score: 0.6024: : 371iter [03:11,  1.95iter/s]
Best score: 0.6024: : 372iter [03:12,  1.96iter/s]
Best score: 0.6024: : 373iter [03:12,  2.02iter/s]
Best score: 0.6024: : 374iter [03:13,  1.94iter/s]
Best score: 0.6024: : 375iter [03:13,  1.90iter/s]
Best score: 0.6024: : 376iter [03:14,  1.97iter/s]
Best score: 0.6024: : 377iter [03:14,  1.93iter/s]
Best score: 0.6024: : 378iter [03:15,  1.90iter/s]
Best score: 0.6024: : 379iter [03:15,  1.99iter/s]
Best score: 0.6024: : 380iter [03:16,  1.95iter/s]
Best score: 0.6024: : 381iter [03:17,  1.98iter/s]
Best score: 0.6024: : 382iter [03:17,  1.94iter/s]
Best score: 0.6024: : 383iter [03:18,  1.91iter/s]
Best score: 0.6024: : 384iter [03:18,  1.92iter/s]
Best score: 0.6024: : 385iter [03:19,  1.89iter/s]
Best score: 0.6024: : 386iter [03:19,  1.90iter/s]
Best score: 0.6024: : 387iter [03:20,  1.92iter/s]
Best score: 0.6024: : 388iter [03:20,  1.89iter/s]
Best score: 0.6024: : 389iter [03:21,  1.91iter/s]
Best score: 0.6024: : 390iter [03:21,  1.92iter/s]
Best score: 0.6024: : 391iter [03:22,  1.98iter/s]
Best score: 0.6024: : 392iter [03:22,  1.91iter/s]
Best score: 0.6024: : 393iter [03:23,  1.95iter/s]
Best score: 0.6024: : 394iter [03:23,  1.90iter/s]
Best score: 0.6024: : 395iter [03:24,  1.88iter/s]
Best score: 0.6024: : 396iter [03:24,  1.87iter/s]
Best score: 0.6024: : 397iter [03:25,  1.96iter/s]
Best score: 0.6024: : 398iter [03:25,  1.91iter/s]
Best score: 0.6024: : 399iter [03:26,  1.92iter/s]
[ITER 400] Current best score: 0.6024
[PARAMS] Current best parameters:
  fx: 515.6197
  fy: 515.0146
  px: 1012.1568
  py: 757.1109
  k1: 0.04105690
  k2: -0.00983322
  k3: -0.00740110
  k4: 0.00248455
  p1: -0.00454924
  p2: -0.00190318
  p3: 0.00800945
  p4: -0.00153601
  scale: 2.9481
  rotation: -0.3228

Best score: 0.6024: : 400iter [03:27,  1.88iter/s]
Best score: 0.6024: : 401iter [03:27,  1.86iter/s]
Best score: 0.6024: : 402iter [03:28,  1.89iter/s]
Best score: 0.6024: : 403iter [03:28,  1.88iter/s]
Best score: 0.6024: : 404iter [03:29,  1.94iter/s]
Best score: 0.6024: : 405iter [03:29,  1.82iter/s]
Best score: 0.6024: : 406iter [03:30,  1.83iter/s]
Best score: 0.6024: : 407iter [03:30,  1.92iter/s]
Best score: 0.6024: : 408iter [03:31,  1.94iter/s]
Best score: 0.6024: : 409iter [03:31,  1.92iter/s]
Best score: 0.6024: : 410iter [03:32,  1.94iter/s]
Best score: 0.6024: : 411iter [03:32,  1.94iter/s]
Best score: 0.6024: : 412iter [03:33,  1.92iter/s]
Best score: 0.6024: : 413iter [03:33,  1.88iter/s]
Best score: 0.6024: : 414iter [03:34,  1.91iter/s]
Best score: 0.6024: : 415iter [03:34,  1.97iter/s]
Best score: 0.6024: : 416iter [03:35,  1.89iter/s]
Best score: 0.6024: : 417iter [03:35,  1.95iter/s]
Best score: 0.6024: : 418iter [03:36,  1.93iter/s]
Best score: 0.6024: : 419iter [03:36,  1.89iter/s]
Best score: 0.6024: : 420iter [03:37,  1.90iter/s]
Best score: 0.6024: : 421iter [03:38,  1.89iter/s]
Best score: 0.6024: : 422iter [03:38,  1.87iter/s]
Best score: 0.6024: : 423iter [03:38,  2.02iter/s]
Best score: 0.6024: : 424iter [03:39,  2.01iter/s]
Best score: 0.6024: : 425iter [03:40,  1.94iter/s]
Best score: 0.6024: : 426iter [03:40,  1.97iter/s]
Best score: 0.6024: : 427iter [03:41,  1.99iter/s]
Best score: 0.6024: : 428iter [03:41,  1.92iter/s]
Best score: 0.6024: : 429iter [03:42,  1.96iter/s]
Best score: 0.6024: : 430iter [03:42,  1.96iter/s]
Best score: 0.6024: : 431iter [03:43,  1.87iter/s]
Best score: 0.6024: : 432iter [03:43,  1.89iter/s]
Best score: 0.6024: : 433iter [03:44,  1.89iter/s]
Best score: 0.6024: : 434iter [03:44,  1.91iter/s]
Best score: 0.6024: : 435iter [03:45,  1.91iter/s]
Best score: 0.6024: : 436iter [03:45,  1.95iter/s]
Best score: 0.6024: : 437iter [03:46,  2.01iter/s]
Best score: 0.6024: : 438iter [03:46,  2.00iter/s]
Best score: 0.6024: : 439iter [03:47,  1.95iter/s]
Best score: 0.6024: : 440iter [03:47,  1.93iter/s]
Best score: 0.6024: : 441iter [03:48,  1.94iter/s]
Best score: 0.6024: : 442iter [03:48,  1.93iter/s]
Best score: 0.6024: : 443iter [03:49,  1.89iter/s]
Best score: 0.6024: : 444iter [03:49,  1.95iter/s]
Best score: 0.6024: : 445iter [03:50,  1.94iter/s]
Best score: 0.6024: : 446iter [03:50,  1.92iter/s]
Best score: 0.6024: : 447iter [03:51,  1.93iter/s]
Best score: 0.6024: : 448iter [03:51,  1.96iter/s]
Best score: 0.6024: : 449iter [03:52,  1.98iter/s]
[ITER 450] Current best score: 0.6024
[PARAMS] Current best parameters:
  fx: 515.6197
  fy: 515.0146
  px: 1012.1568
  py: 757.1109
  k1: 0.04105690
  k2: -0.00983322
  k3: -0.00740110
  k4: 0.00248455
  p1: -0.00454924
  p2: -0.00190318
  p3: 0.00800945
  p4: -0.00153601
  scale: 2.9481
  rotation: -0.3228

Best score: 0.6024: : 450iter [03:52,  1.91iter/s]
Best score: 0.6024: : 451iter [03:53,  1.92iter/s]
Best score: 0.6024: : 452iter [03:54,  1.86iter/s]
Best score: 0.6024: : 453iter [03:54,  1.90iter/s]
Best score: 0.6024: : 454iter [03:54,  2.02iter/s]
Best score: 0.6024: : 455iter [03:55,  2.06iter/s]
Best score: 0.6024: : 456iter [03:56,  1.93iter/s]
Best score: 0.6024: : 457iter [03:56,  2.05iter/s]
Best score: 0.6024: : 458iter [03:56,  2.05iter/s]
Best score: 0.6024: : 459iter [03:57,  2.01iter/s]
Best score: 0.6024: : 460iter [03:57,  2.11iter/s]
Best score: 0.6024: : 461iter [03:58,  2.07iter/s]
Best score: 0.6024: : 462iter [03:58,  2.05iter/s]
Best score: 0.6024: : 463iter [03:59,  2.03iter/s]
Best score: 0.6024: : 464iter [03:59,  2.06iter/s]
Best score: 0.6024: : 465iter [04:00,  2.02iter/s]
Best score: 0.6024: : 466iter [04:00,  2.05iter/s]
Best score: 0.6024: : 467iter [04:01,  1.91iter/s]
Best score: 0.6024: : 468iter [04:02,  1.84iter/s]
Best score: 0.6024: : 469iter [04:02,  1.94iter/s]
Best score: 0.6024: : 470iter [04:02,  2.01iter/s]
Best score: 0.6024: : 471iter [04:03,  1.96iter/s]
Best score: 0.6024: : 472iter [04:03,  1.96iter/s]
Best score: 0.6024: : 473iter [04:04,  2.07iter/s]
Best score: 0.6024: : 474iter [04:04,  2.05iter/s]
Best score: 0.6024: : 475iter [04:05,  1.90iter/s]
Best score: 0.6024: : 476iter [04:05,  1.98iter/s]
Best score: 0.6024: : 477iter [04:06,  1.99iter/s]
Best score: 0.6024: : 478iter [04:07,  1.89iter/s]
Best score: 0.6024: : 479iter [04:07,  1.91iter/s]
Best score: 0.6024: : 480iter [04:08,  1.88iter/s]
Best score: 0.6024: : 481iter [04:08,  1.93iter/s]
Best score: 0.6024: : 482iter [04:09,  1.96iter/s]
Best score: 0.5808: : 482iter [04:09,  1.96iter/s]
Best score: 0.5808: : 483iter [04:09,  1.93iter/s]
Best score: 0.5808: : 484iter [04:10,  1.95iter/s]
Best score: 0.5808: : 485iter [04:10,  1.94iter/s]
Best score: 0.5808: : 486iter [04:11,  1.94iter/s]
Best score: 0.5808: : 487iter [04:11,  1.90iter/s]
Best score: 0.5808: : 488iter [04:12,  1.87iter/s]
Best score: 0.5808: : 489iter [04:12,  1.93iter/s]
Best score: 0.5808: : 490iter [04:13,  1.94iter/s]
Best score: 0.5119: : 490iter [04:13,  1.94iter/s]
Best score: 0.5119: : 491iter [04:13,  1.96iter/s]
Best score: 0.5119: : 492iter [04:14,  2.03iter/s]
Best score: 0.4987: : 492iter [04:14,  2.03iter/s]
Best score: 0.4987: : 493iter [04:14,  2.03iter/s]
Best score: 0.4987: : 494iter [04:15,  1.98iter/s]
Best score: 0.4987: : 495iter [04:15,  1.98iter/s]
Best score: 0.4987: : 496iter [04:16,  2.02iter/s]
Best score: 0.4987: : 497iter [04:16,  2.01iter/s]
Best score: 0.4987: : 498iter [04:17,  1.98iter/s]
Best score: 0.4987: : 499iter [04:17,  1.91iter/s]
[ITER 500] Current best score: 0.4987
[PARAMS] Current best parameters:
  fx: 508.8531
  fy: 508.1818
  px: 1013.3515
  py: 759.5893
  k1: 0.03923955
  k2: -0.00828451
  k3: -0.00262956
  k4: 0.00061712
  p1: 0.00078152
  p2: 0.00066024
  p3: 0.00622408
  p4: -0.00163793
  scale: 2.9765
  rotation: -0.3140

Best score: 0.4987: : 500iter [04:18,  1.84iter/s]
Best score: 0.4987: : 500iter [05:01,  1.66iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.495131 -> 0.495131
  Success: True
  Tangential distortion: p1=0.000788, p2=0.000653, p3=0.006228, p4=-0.001625
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.4951
[✅] Parameters copied to: /working/MDC103_W050_2025-05-07/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 508.85314459318397
    fy: 508.1818405614216
    px: 1013.3515174804359
    py: 759.5893083102552
    k1: 0.03924817779615247
    k2: -0.008247166492236136
    k3: -0.002608632767906244
    k4: 0.0005929188127093268
    p1: 0.0007875822708325238
    p2: 0.000653122859727391
    p3: 0.006228451163271321
    p4: -0.0016250321217634166
    scale: 2.976496762375504
    rotation: -0.31433590410869716
    mmpp: 1.2859673405580827
    nmpp: 1285967.3405580828

[🔄] Converting 2348 frames to PTD, creating debug images for 200 calibration frames...

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[✅] Saved PTDs: /working/MDC103_W050_2025-05-07/calibration/debug/3_undistorted/output/ptd

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[✅] Saved PTDs: /working/MDC103_W050_2025-05-07/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

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📤 Writing LUT:  53%|█████▎    | 1622597/3080448 [00:01<00:01, 909313.56px/s]
📤 Writing LUT:  56%|█████▌    | 1713541/3080448 [00:01<00:01, 906203.07px/s]
📤 Writing LUT:  59%|█████▊    | 1804171/3080448 [00:02<00:01, 904221.14px/s]
📤 Writing LUT:  62%|██████▏   | 1894600/3080448 [00:02<00:01, 902697.03px/s]
📤 Writing LUT:  64%|██████▍   | 1984874/3080448 [00:02<00:01, 901246.30px/s]
📤 Writing LUT:  67%|██████▋   | 2075340/3080448 [00:02<00:01, 902264.38px/s]
📤 Writing LUT:  70%|███████   | 2167763/3080448 [00:02<00:01, 908829.84px/s]
📤 Writing LUT:  73%|███████▎  | 2258650/3080448 [00:02<00:00, 907063.54px/s]
📤 Writing LUT:  76%|███████▋  | 2350069/3080448 [00:02<00:00, 909193.28px/s]
📤 Writing LUT:  79%|███████▉  | 2443129/3080448 [00:02<00:00, 915599.21px/s]
📤 Writing LUT:  82%|████████▏ | 2534693/3080448 [00:02<00:00, 915600.58px/s]
📤 Writing LUT:  85%|████████▌ | 2626728/3080448 [00:02<00:00, 917020.23px/s]
📤 Writing LUT:  88%|████████▊ | 2719346/3080448 [00:03<00:00, 919762.43px/s]
📤 Writing LUT:  91%|█████████▏| 2812096/3080448 [00:03<00:00, 922082.09px/s]
📤 Writing LUT:  94%|█████████▍| 2905772/3080448 [00:03<00:00, 926482.79px/s]
📤 Writing LUT:  97%|█████████▋| 2998837/3080448 [00:03<00:00, 927730.24px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 906748.17px/s]
[✅] LUT written to: /working/MDC103_W050_2025-05-07/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC103_W050_2025-05-07' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC103_W050_2025-05-07/calibration/MDC103_W050_2025-05-07_unknown.lut
[🏁] Finished processing MDC103_W050_2025-05-07


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