🟢 Starting Calibration Pipeline

=== Processing MDC044_W027_2025-05-29 ===

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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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→ 500 (1371 images): 100%|█████████▉| 1370/1371 [02:47<00:00, 16.00it/s]

                                                                        
📁 Processing Rigs:  50%|█████     | 1/2 [02:47<02:47, 167.63s/it]

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📁 Processing Rigs: 100%|██████████| 2/2 [05:56<00:00, 179.93s/it]
📁 Processing Rigs: 100%|██████████| 2/2 [05:56<00:00, 178.09s/it]
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2933 frames from 2 rigs to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 1371
[📋] 1000: 100 frames selected from 1562
[💾] Saved RigFrameDataset to /working/MDC044_W027_2025-05-29/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC044_W027_2025-05-29/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<00:00, 39.14it/s]
Averaging bright frames:  22%|██▎       | 9/40 [00:00<00:00, 44.25it/s]
Averaging bright frames:  35%|███▌      | 14/40 [00:00<00:00, 46.09it/s]
Averaging bright frames:  48%|████▊     | 19/40 [00:00<00:00, 46.40it/s]
Averaging bright frames:  60%|██████    | 24/40 [00:00<00:00, 46.93it/s]
Averaging bright frames:  72%|███████▎  | 29/40 [00:00<00:00, 45.45it/s]
Averaging bright frames:  85%|████████▌ | 34/40 [00:00<00:00, 45.98it/s]
Averaging bright frames:  98%|█████████▊| 39/40 [00:00<00:00, 46.11it/s]
                                                                        
Total time to process (v2): 0.05
[RESULT] Combined: Center=(1018, 746), Radius=833

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Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  19%|█▉        | 19/100 [00:00<00:01, 50.72it/s]
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Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.078):  26%|██▌       | 26/100 [00:00<00:01, 50.72it/s]
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Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.078):  33%|███▎      | 33/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  34%|███▍      | 34/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  35%|███▌      | 35/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  36%|███▌      | 36/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  37%|███▋      | 37/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.078):  38%|███▊      | 38/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  39%|███▉      | 39/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.078):  40%|████      | 40/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.078):  41%|████      | 41/100 [00:00<00:01, 50.72it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.078):  42%|████▏     | 42/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.078):  42%|████▏     | 42/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.078):  43%|████▎     | 43/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.078):  44%|████▍     | 44/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.078):  45%|████▌     | 45/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.078):  46%|████▌     | 46/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.078):  47%|████▋     | 47/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.004 | Best: {'fx': 533.333} (0.078):  48%|████▊     | 48/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 533.333} (0.078):  49%|████▉     | 49/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.078):  50%|█████     | 50/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 533.333} (0.078):  51%|█████     | 51/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  52%|█████▏    | 52/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  53%|█████▎    | 53/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.078):  54%|█████▍    | 54/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.078):  55%|█████▌    | 55/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  56%|█████▌    | 56/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  57%|█████▋    | 57/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  58%|█████▊    | 58/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  59%|█████▉    | 59/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  60%|██████    | 60/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  61%|██████    | 61/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  62%|██████▏   | 62/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  63%|██████▎   | 63/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  64%|██████▍   | 64/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  65%|██████▌   | 65/100 [00:00<00:00, 104.19it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  66%|██████▌   | 66/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  66%|██████▌   | 66/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  67%|██████▋   | 67/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  68%|██████▊   | 68/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.078):  69%|██████▉   | 69/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  70%|███████   | 70/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  71%|███████   | 71/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  72%|███████▏  | 72/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  73%|███████▎  | 73/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  74%|███████▍  | 74/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  75%|███████▌  | 75/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  76%|███████▌  | 76/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  77%|███████▋  | 77/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  78%|███████▊  | 78/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  79%|███████▉  | 79/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  80%|████████  | 80/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  81%|████████  | 81/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  82%|████████▏ | 82/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  83%|████████▎ | 83/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  84%|████████▍ | 84/100 [00:00<00:00, 142.78it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  85%|████████▌ | 85/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  85%|████████▌ | 85/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  86%|████████▌ | 86/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  87%|████████▋ | 87/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  88%|████████▊ | 88/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  89%|████████▉ | 89/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  90%|█████████ | 90/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  91%|█████████ | 91/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  92%|█████████▏| 92/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  93%|█████████▎| 93/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  94%|█████████▍| 94/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  95%|█████████▌| 95/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  96%|█████████▌| 96/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  97%|█████████▋| 97/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  98%|█████████▊| 98/100 [00:00<00:00, 155.98it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.078):  99%|█████████▉| 99/100 [00:00<00:00, 155.98it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.008 | Best: {'fx': 508.333} (0.008):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.008 | Best: {'fx': 508.333} (0.008):   2%|▏         | 1/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.014 | Best: {'fx': 509.354} (0.014):   2%|▏         | 1/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.016 | Best: {'fx': 510.374} (0.016):   4%|▍         | 2/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.017 | Best: {'fx': 511.395} (0.017):   6%|▌         | 3/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.017 | Best: {'fx': 512.415} (0.017):   8%|▊         | 4/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.022 | Best: {'fx': 513.435} (0.022):  10%|█         | 5/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.028 | Best: {'fx': 516.497} (0.028):  12%|█▏        | 6/50 [00:00<00:05,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  14%|█▍        | 7/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.027 | Best: {'fx': 517.517} (0.030):  16%|█▌        | 8/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.025 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  20%|██        | 10/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.035 | Best: {'fx': 519.558} (0.035):  22%|██▏       | 11/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.037 | Best: {'fx': 520.578} (0.037):  24%|██▍       | 12/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  26%|██▌       | 13/50 [00:00<00:04,  8.70it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  28%|██▊       | 14/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.043 | Best: {'fx': 522.619} (0.043):  28%|██▊       | 14/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.049 | Best: {'fx': 523.639} (0.049):  30%|███       | 15/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.057 | Best: {'fx': 525.68} (0.057):  32%|███▏      | 16/50 [00:00<00:00, 72.44it/s] 
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.063 | Best: {'fx': 526.701} (0.063):  34%|███▍      | 17/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.053 | Best: {'fx': 526.701} (0.063):  36%|███▌      | 18/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.066 | Best: {'fx': 527.721} (0.066):  38%|███▊      | 19/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.074 | Best: {'fx': 528.741} (0.074):  40%|████      | 20/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.085 | Best: {'fx': 531.803} (0.085):  42%|████▏     | 21/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.073 | Best: {'fx': 531.803} (0.085):  44%|████▍     | 22/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.070 | Best: {'fx': 531.803} (0.085):  46%|████▌     | 23/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.072 | Best: {'fx': 531.803} (0.085):  48%|████▊     | 24/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.076 | Best: {'fx': 531.803} (0.085):  50%|█████     | 25/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 531.803} (0.085):  52%|█████▏    | 26/50 [00:00<00:00, 72.44it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 531.803} (0.085):  54%|█████▍    | 27/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.069 | Best: {'fx': 531.803} (0.085):  54%|█████▍    | 27/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.046 | Best: {'fx': 531.803} (0.085):  56%|█████▌    | 28/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.061 | Best: {'fx': 531.803} (0.085):  58%|█████▊    | 29/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.052 | Best: {'fx': 531.803} (0.085):  60%|██████    | 30/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.037 | Best: {'fx': 531.803} (0.085):  62%|██████▏   | 31/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.060 | Best: {'fx': 531.803} (0.085):  64%|██████▍   | 32/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.048 | Best: {'fx': 531.803} (0.085):  66%|██████▌   | 33/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.054 | Best: {'fx': 531.803} (0.085):  68%|██████▊   | 34/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.041 | Best: {'fx': 531.803} (0.085):  70%|███████   | 35/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.033 | Best: {'fx': 531.803} (0.085):  72%|███████▏  | 36/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.035 | Best: {'fx': 531.803} (0.085):  74%|███████▍  | 37/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.041 | Best: {'fx': 531.803} (0.085):  76%|███████▌  | 38/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.036 | Best: {'fx': 531.803} (0.085):  78%|███████▊  | 39/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.032 | Best: {'fx': 531.803} (0.085):  80%|████████  | 40/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.024 | Best: {'fx': 531.803} (0.085):  82%|████████▏ | 41/50 [00:00<00:00, 95.55it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.024 | Best: {'fx': 531.803} (0.085):  84%|████████▍ | 42/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.031 | Best: {'fx': 531.803} (0.085):  84%|████████▍ | 42/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.030 | Best: {'fx': 531.803} (0.085):  86%|████████▌ | 43/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.024 | Best: {'fx': 531.803} (0.085):  88%|████████▊ | 44/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.027 | Best: {'fx': 531.803} (0.085):  90%|█████████ | 45/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.028 | Best: {'fx': 531.803} (0.085):  92%|█████████▏| 46/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.026 | Best: {'fx': 531.803} (0.085):  94%|█████████▍| 47/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.028 | Best: {'fx': 531.803} (0.085):  96%|█████████▌| 48/50 [00:00<00:00, 114.63it/s]
Brute | Start: {'fx': 533.333} (0.078) | Current: 0.027 | Best: {'fx': 531.803} (0.085):  98%|█████████▊| 49/50 [00:00<00:00, 114.63it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1018.0
    py: 746.0
    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: 16.9425: 0iter [00:00, ?iter/s]
Initial score: 16.9425: 1iter [00:01,  1.23s/iter]
Initial score: 16.9425: 2iter [00:01,  1.24iter/s]
Initial score: 16.9425: 3iter [00:02,  1.51iter/s]
Initial score: 16.9425: 4iter [00:02,  1.63iter/s]
Initial score: 16.9425: 5iter [00:03,  1.67iter/s]
Initial score: 16.9425: 6iter [00:03,  1.74iter/s]
Initial score: 16.9425: 7iter [00:04,  1.82iter/s]
Initial score: 16.9425: 8iter [00:04,  1.81iter/s]
Initial score: 16.9425: 9iter [00:05,  1.82iter/s]
Initial score: 16.9425: 10iter [00:05,  1.90iter/s]
Initial score: 16.9425: 11iter [00:06,  1.81iter/s]
Initial score: 16.9425: 12iter [00:07,  1.80iter/s]
Initial score: 16.9425: 13iter [00:07,  1.89iter/s]
Initial score: 16.9425: 14iter [00:08,  1.88iter/s]
Initial score: 16.9425: 15iter [00:08,  1.86iter/s]
Initial score: 16.9425: 16iter [00:09,  1.83iter/s]
Initial score: 16.9425: 17iter [00:09,  1.89iter/s]
Initial score: 16.9425: 18iter [00:10,  1.89iter/s]
Initial score: 16.9425: 19iter [00:10,  1.92iter/s]
Initial score: 16.9425: 20iter [00:11,  1.94iter/s]
Initial score: 16.9425: 21iter [00:11,  1.88iter/s]
Initial score: 16.9425: 22iter [00:12,  1.96iter/s]
Initial score: 16.9425: 23iter [00:12,  1.94iter/s]
Initial score: 16.9425: 24iter [00:13,  1.92iter/s]
Initial score: 16.9425: 25iter [00:13,  1.96iter/s]
Initial score: 16.9425: 26iter [00:14,  1.99iter/s]
Initial score: 16.9425: 27iter [00:14,  2.00iter/s]
Initial score: 16.9425: 28iter [00:15,  1.95iter/s]
Initial score: 16.9425: 29iter [00:15,  1.94iter/s]
Initial score: 16.9425: 30iter [00:16,  1.92iter/s]
Initial score: 16.9425: 31iter [00:16,  1.87iter/s]
Initial score: 16.9425: 32iter [00:17,  1.87iter/s]
Initial score: 16.9425: 33iter [00:18,  1.86iter/s]
Initial score: 16.9425: 34iter [00:18,  1.93iter/s]
Initial score: 16.9425: 35iter [00:19,  1.89iter/s]
Initial score: 16.9425: 36iter [00:19,  1.91iter/s]
Initial score: 16.9425: 37iter [00:20,  1.92iter/s]
Initial score: 16.9425: 38iter [00:20,  1.87iter/s]
Initial score: 16.9425: 39iter [00:21,  1.84iter/s]
Initial score: 16.9425: 40iter [00:21,  1.83iter/s]
Initial score: 16.9425: 41iter [00:22,  1.87iter/s]
Initial score: 16.9425: 42iter [00:22,  1.94iter/s]
Initial score: 16.9425: 43iter [00:23,  1.93iter/s]
Initial score: 16.9425: 44iter [00:23,  1.93iter/s]
Initial score: 16.9425: 45iter [00:24,  1.95iter/s]
Initial score: 16.9425: 46iter [00:24,  1.99iter/s]
Initial score: 16.9425: 47iter [00:25,  1.92iter/s]
Initial score: 16.9425: 48iter [00:25,  1.95iter/s]
Initial score: 16.9425: 49iter [00:26,  1.84iter/s]
[ITER 50] Current best score: 16.9425
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1018.0000
  py: 746.0000
  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: 16.9425: 50iter [00:27,  1.77iter/s]
Initial score: 16.9425: 51iter [00:27,  1.87iter/s]
Initial score: 16.9425: 52iter [00:28,  1.85iter/s]
Initial score: 16.9425: 53iter [00:28,  1.91iter/s]
Initial score: 16.9425: 54iter [00:29,  2.01iter/s]
Initial score: 16.9425: 55iter [00:29,  1.95iter/s]
Initial score: 16.9425: 56iter [00:30,  1.93iter/s]
Initial score: 16.9425: 57iter [00:30,  1.90iter/s]
Initial score: 16.9425: 58iter [00:31,  1.93iter/s]
Initial score: 16.9425: 59iter [00:31,  1.88iter/s]
Initial score: 16.9425: 60iter [00:32,  1.98iter/s]
Initial score: 16.9425: 61iter [00:32,  1.99iter/s]
Initial score: 16.9425: 62iter [00:33,  1.95iter/s]
Initial score: 16.9425: 63iter [00:33,  1.92iter/s]
Initial score: 16.9425: 64iter [00:34,  1.96iter/s]
Initial score: 16.9425: 65iter [00:34,  1.88iter/s]
Initial score: 16.9425: 66iter [00:35,  1.86iter/s]
Initial score: 16.9425: 67iter [00:35,  1.91iter/s]
Initial score: 16.9425: 68iter [00:36,  1.83iter/s]
Initial score: 16.9425: 69iter [00:36,  1.87iter/s]
Initial score: 16.9425: 70iter [00:37,  1.86iter/s]
Initial score: 16.9425: 71iter [00:38,  1.87iter/s]
Initial score: 16.9425: 72iter [00:38,  1.88iter/s]
Initial score: 16.9425: 73iter [00:38,  1.99iter/s]
Initial score: 16.9425: 74iter [00:39,  1.98iter/s]
Initial score: 16.9425: 75iter [00:39,  1.97iter/s]
Initial score: 16.9425: 76iter [00:40,  1.94iter/s]
Initial score: 16.9425: 77iter [00:41,  1.90iter/s]
Initial score: 16.9425: 78iter [00:41,  1.85iter/s]
Initial score: 16.9425: 79iter [00:42,  1.94iter/s]
Initial score: 16.9425: 80iter [00:42,  1.96iter/s]
Initial score: 16.9425: 81iter [00:43,  1.97iter/s]
Initial score: 16.9425: 82iter [00:43,  2.02iter/s]
Best score: 11.2342: : 82iter [00:44,  2.02iter/s] 
Best score: 11.2342: : 83iter [00:44,  1.93iter/s]
Best score: 8.4527: : 83iter [00:44,  1.93iter/s] 
Best score: 8.4527: : 84iter [00:44,  1.89iter/s]
Best score: 8.4527: : 85iter [00:45,  1.99iter/s]
Best score: 8.4527: : 86iter [00:45,  1.97iter/s]
Best score: 8.4527: : 87iter [00:46,  1.89iter/s]
Best score: 8.1601: : 87iter [00:46,  1.89iter/s]
Best score: 8.1601: : 88iter [00:46,  1.99iter/s]
Best score: 8.1601: : 89iter [00:47,  1.97iter/s]
Best score: 8.1601: : 90iter [00:47,  1.94iter/s]
Best score: 8.1601: : 91iter [00:48,  1.96iter/s]
Best score: 8.1601: : 92iter [00:48,  1.94iter/s]
Best score: 8.1601: : 93iter [00:49,  1.89iter/s]
Best score: 8.1601: : 94iter [00:49,  1.88iter/s]
Best score: 8.1601: : 95iter [00:50,  1.88iter/s]
Best score: 8.1601: : 96iter [00:50,  1.88iter/s]
Best score: 8.1601: : 97iter [00:51,  1.86iter/s]
Best score: 8.1601: : 98iter [00:51,  1.90iter/s]
Best score: 8.1601: : 99iter [00:52,  1.90iter/s]
[ITER 100] Current best score: 8.1601
[PARAMS] Current best parameters:
  fx: 529.9613
  fy: 531.2417
  px: 1012.2592
  py: 743.1893
  k1: 0.02977029
  k2: 0.00531678
  k3: -0.03168867
  k4: 0.01132310
  p1: 0.00063270
  p2: -0.00703059
  p3: -0.00585336
  p4: 0.00067408
  scale: 2.9315
  rotation: 0.2803

Best score: 8.1601: : 100iter [00:53,  1.87iter/s]
Best score: 8.1601: : 101iter [00:53,  1.85iter/s]
Best score: 8.1601: : 102iter [00:54,  1.92iter/s]
Best score: 8.1601: : 103iter [00:54,  1.97iter/s]
Best score: 8.1601: : 104iter [00:55,  1.90iter/s]
Best score: 8.1601: : 105iter [00:55,  2.02iter/s]
Best score: 8.1601: : 106iter [00:56,  2.02iter/s]
Best score: 6.0051: : 106iter [00:56,  2.02iter/s]
Best score: 6.0051: : 107iter [00:56,  2.01iter/s]
Best score: 6.0051: : 108iter [00:57,  2.02iter/s]
Best score: 6.0051: : 109iter [00:57,  1.99iter/s]
Best score: 6.0051: : 110iter [00:58,  1.92iter/s]
Best score: 6.0051: : 111iter [00:58,  1.91iter/s]
Best score: 6.0051: : 112iter [00:59,  1.97iter/s]
Best score: 6.0051: : 113iter [00:59,  1.93iter/s]
Best score: 6.0051: : 114iter [01:00,  1.89iter/s]
Best score: 6.0051: : 115iter [01:00,  1.90iter/s]
Best score: 6.0051: : 116iter [01:01,  1.88iter/s]
Best score: 6.0051: : 117iter [01:01,  1.86iter/s]
Best score: 6.0051: : 118iter [01:02,  1.82iter/s]
Best score: 6.0051: : 119iter [01:02,  1.89iter/s]
Best score: 6.0051: : 120iter [01:03,  1.88iter/s]
Best score: 6.0051: : 121iter [01:04,  1.82iter/s]
Best score: 6.0051: : 122iter [01:04,  1.83iter/s]
Best score: 4.8261: : 122iter [01:05,  1.83iter/s]
Best score: 4.8261: : 123iter [01:05,  1.85iter/s]
Best score: 4.8261: : 124iter [01:05,  1.86iter/s]
Best score: 4.8261: : 125iter [01:06,  1.83iter/s]
Best score: 4.8261: : 126iter [01:06,  1.88iter/s]
Best score: 4.8261: : 127iter [01:07,  1.88iter/s]
Best score: 4.8261: : 128iter [01:07,  1.88iter/s]
Best score: 4.8261: : 129iter [01:08,  1.95iter/s]
Best score: 4.8261: : 130iter [01:08,  1.93iter/s]
Best score: 4.8261: : 131iter [01:09,  1.91iter/s]
Best score: 4.8261: : 132iter [01:09,  1.85iter/s]
Best score: 4.8261: : 133iter [01:10,  1.84iter/s]
Best score: 4.8261: : 134iter [01:10,  1.84iter/s]
Best score: 4.8261: : 135iter [01:11,  1.87iter/s]
Best score: 3.5588: : 135iter [01:11,  1.87iter/s]
Best score: 3.5588: : 136iter [01:11,  1.94iter/s]
Best score: 3.5588: : 137iter [01:12,  1.94iter/s]
Best score: 3.5588: : 138iter [01:12,  1.94iter/s]
Best score: 3.5588: : 139iter [01:13,  1.92iter/s]
Best score: 3.5588: : 140iter [01:14,  1.90iter/s]
Best score: 3.5588: : 141iter [01:14,  1.87iter/s]
Best score: 3.5588: : 142iter [01:15,  1.97iter/s]
Best score: 3.5588: : 143iter [01:15,  2.01iter/s]
Best score: 3.3259: : 143iter [01:16,  2.01iter/s]
Best score: 3.3259: : 144iter [01:16,  1.92iter/s]
Best score: 3.3259: : 145iter [01:16,  2.01iter/s]
Best score: 3.3259: : 146iter [01:17,  1.94iter/s]
Best score: 3.3259: : 147iter [01:17,  1.83iter/s]
Best score: 3.3259: : 148iter [01:18,  1.86iter/s]
Best score: 3.3259: : 149iter [01:18,  1.91iter/s]
[ITER 150] Current best score: 3.3259
[PARAMS] Current best parameters:
  fx: 526.3851
  fy: 527.1640
  px: 1013.2159
  py: 744.2591
  k1: 0.02803417
  k2: 0.00779389
  k3: -0.01968439
  k4: 0.00484914
  p1: -0.00036775
  p2: -0.00765636
  p3: -0.00282957
  p4: 0.00263861
  scale: 2.8932
  rotation: -0.1199

Best score: 3.3259: : 150iter [01:19,  1.82iter/s]
Best score: 3.3259: : 151iter [01:19,  1.92iter/s]
Best score: 3.3259: : 152iter [01:20,  1.88iter/s]
Best score: 3.3259: : 153iter [01:20,  1.85iter/s]
Best score: 3.3259: : 154iter [01:21,  1.94iter/s]
Best score: 3.3259: : 155iter [01:21,  1.94iter/s]
Best score: 3.3259: : 156iter [01:22,  1.91iter/s]
Best score: 3.3259: : 157iter [01:22,  1.91iter/s]
Best score: 3.3259: : 158iter [01:23,  1.83iter/s]
Best score: 3.3259: : 159iter [01:24,  1.82iter/s]
Best score: 2.7685: : 159iter [01:24,  1.82iter/s]
Best score: 2.7685: : 160iter [01:24,  1.82iter/s]
Best score: 2.7685: : 161iter [01:25,  1.83iter/s]
Best score: 2.7685: : 162iter [01:25,  1.83iter/s]
Best score: 2.7685: : 163iter [01:26,  1.82iter/s]
Best score: 2.7685: : 164iter [01:26,  1.87iter/s]
Best score: 2.7685: : 165iter [01:27,  1.92iter/s]
Best score: 2.7685: : 166iter [01:27,  1.92iter/s]
Best score: 2.7685: : 167iter [01:28,  1.90iter/s]
Best score: 2.7685: : 168iter [01:28,  1.90iter/s]
Best score: 2.7685: : 169iter [01:29,  1.88iter/s]
Best score: 2.7685: : 170iter [01:29,  1.85iter/s]
Best score: 2.7685: : 171iter [01:30,  1.90iter/s]
Best score: 2.7685: : 172iter [01:31,  1.88iter/s]
Best score: 2.7685: : 173iter [01:31,  1.83iter/s]
Best score: 2.7685: : 174iter [01:32,  1.89iter/s]
Best score: 2.7685: : 175iter [01:32,  1.93iter/s]
Best score: 2.7685: : 176iter [01:33,  1.90iter/s]
Best score: 2.7685: : 177iter [01:33,  1.96iter/s]
Best score: 2.7685: : 178iter [01:34,  2.01iter/s]
Best score: 2.7685: : 179iter [01:34,  2.00iter/s]
Best score: 2.7685: : 180iter [01:35,  2.02iter/s]
Best score: 2.7685: : 181iter [01:35,  2.05iter/s]
Best score: 2.7685: : 182iter [01:36,  2.00iter/s]
Best score: 2.7685: : 183iter [01:36,  1.99iter/s]
Best score: 2.4286: : 183iter [01:37,  1.99iter/s]
Best score: 2.4286: : 184iter [01:37,  1.96iter/s]
Best score: 2.1663: : 184iter [01:37,  1.96iter/s]
Best score: 2.1663: : 185iter [01:37,  1.93iter/s]
Best score: 2.1663: : 186iter [01:38,  2.00iter/s]
Best score: 2.1663: : 187iter [01:38,  1.90iter/s]
Best score: 2.1663: : 188iter [01:39,  1.87iter/s]
Best score: 2.1663: : 189iter [01:39,  1.89iter/s]
Best score: 2.1663: : 190iter [01:40,  1.83iter/s]
Best score: 2.1663: : 191iter [01:40,  1.85iter/s]
Best score: 2.1663: : 192iter [01:41,  1.85iter/s]
Best score: 2.1663: : 193iter [01:41,  1.80iter/s]
Best score: 2.1663: : 194iter [01:42,  1.82iter/s]
Best score: 2.1663: : 195iter [01:43,  1.90iter/s]
Best score: 1.8309: : 195iter [01:43,  1.90iter/s]
Best score: 1.8309: : 196iter [01:43,  1.87iter/s]
Best score: 1.8309: : 197iter [01:44,  1.86iter/s]
Best score: 1.8309: : 198iter [01:44,  1.82iter/s]
Best score: 1.8309: : 199iter [01:45,  1.85iter/s]
[ITER 200] Current best score: 1.8309
[PARAMS] Current best parameters:
  fx: 533.6254
  fy: 533.8041
  px: 1014.7091
  py: 744.3929
  k1: 0.02309302
  k2: -0.00880719
  k3: -0.00740727
  k4: 0.00258971
  p1: 0.00101434
  p2: -0.00527241
  p3: -0.00493948
  p4: 0.00181858
  scale: 2.9443
  rotation: 0.2000

Best score: 1.8309: : 200iter [01:45,  1.80iter/s]
Best score: 1.8309: : 201iter [01:46,  1.88iter/s]
Best score: 1.8309: : 202iter [01:46,  1.92iter/s]
Best score: 1.8309: : 203iter [01:47,  1.94iter/s]
Best score: 1.8309: : 204iter [01:47,  1.91iter/s]
Best score: 1.8309: : 205iter [01:48,  2.01iter/s]
Best score: 1.8309: : 206iter [01:48,  2.00iter/s]
Best score: 1.8309: : 207iter [01:49,  2.07iter/s]
Best score: 1.8309: : 208iter [01:49,  1.99iter/s]
Best score: 1.8309: : 209iter [01:50,  1.95iter/s]
Best score: 1.8309: : 210iter [01:50,  2.03iter/s]
Best score: 1.8309: : 211iter [01:51,  1.96iter/s]
Best score: 1.8309: : 212iter [01:51,  1.90iter/s]
Best score: 1.8309: : 213iter [01:52,  2.02iter/s]
Best score: 1.8309: : 214iter [01:52,  1.98iter/s]
Best score: 1.8309: : 215iter [01:53,  1.92iter/s]
Best score: 1.8309: : 216iter [01:53,  1.97iter/s]
Best score: 1.8309: : 217iter [01:54,  1.96iter/s]
Best score: 1.8309: : 218iter [01:54,  1.95iter/s]
Best score: 1.8309: : 219iter [01:55,  1.93iter/s]
Best score: 1.8309: : 220iter [01:55,  1.94iter/s]
Best score: 1.8309: : 221iter [01:56,  1.95iter/s]
Best score: 1.8309: : 222iter [01:56,  1.91iter/s]
Best score: 1.8309: : 223iter [01:57,  1.87iter/s]
Best score: 1.8309: : 224iter [01:58,  1.88iter/s]
Best score: 1.6758: : 224iter [01:58,  1.88iter/s]
Best score: 1.6758: : 225iter [01:58,  1.84iter/s]
Best score: 1.6758: : 226iter [01:59,  1.85iter/s]
Best score: 1.6758: : 227iter [01:59,  1.80iter/s]
Best score: 1.6758: : 228iter [02:00,  1.78iter/s]
Best score: 1.6758: : 229iter [02:00,  1.80iter/s]
Best score: 1.6758: : 230iter [02:01,  1.81iter/s]
Best score: 1.6758: : 231iter [02:01,  1.85iter/s]
Best score: 1.4561: : 231iter [02:02,  1.85iter/s]
Best score: 1.4561: : 232iter [02:02,  1.80iter/s]
Best score: 1.4561: : 233iter [02:03,  1.86iter/s]
Best score: 1.4561: : 234iter [02:03,  1.88iter/s]
Best score: 1.4561: : 235iter [02:04,  1.86iter/s]
Best score: 1.4561: : 236iter [02:04,  1.90iter/s]
Best score: 1.4561: : 237iter [02:05,  1.84iter/s]
Best score: 1.4561: : 238iter [02:05,  1.90iter/s]
Best score: 1.4561: : 239iter [02:06,  1.87iter/s]
Best score: 1.4561: : 240iter [02:06,  1.99iter/s]
Best score: 1.4561: : 241iter [02:07,  1.87iter/s]
Best score: 1.4561: : 242iter [02:07,  1.92iter/s]
Best score: 1.4561: : 243iter [02:08,  1.90iter/s]
Best score: 1.4561: : 244iter [02:08,  1.88iter/s]
Best score: 1.4561: : 245iter [02:09,  1.89iter/s]
Best score: 1.0049: : 245iter [02:09,  1.89iter/s]
Best score: 1.0049: : 246iter [02:09,  1.90iter/s]
Best score: 1.0049: : 247iter [02:10,  1.91iter/s]
Best score: 1.0049: : 248iter [02:10,  2.01iter/s]
Best score: 1.0049: : 249iter [02:11,  1.92iter/s]
[ITER 250] Current best score: 1.0049
[PARAMS] Current best parameters:
  fx: 533.6055
  fy: 533.0825
  px: 1015.1588
  py: 744.4936
  k1: 0.01454408
  k2: -0.00186958
  k3: -0.01183634
  k4: 0.00424263
  p1: 0.00091521
  p2: -0.00408595
  p3: -0.00456895
  p4: 0.00282917
  scale: 2.9916
  rotation: -0.0075

Best score: 1.0049: : 250iter [02:11,  1.85iter/s]
Best score: 1.0049: : 251iter [02:12,  1.96iter/s]
Best score: 1.0049: : 252iter [02:12,  1.95iter/s]
Best score: 1.0049: : 253iter [02:13,  1.99iter/s]
Best score: 1.0049: : 254iter [02:13,  1.95iter/s]
Best score: 1.0049: : 255iter [02:14,  1.95iter/s]
Best score: 1.0049: : 256iter [02:14,  1.91iter/s]
Best score: 1.0049: : 257iter [02:15,  1.89iter/s]
Best score: 1.0049: : 258iter [02:16,  1.95iter/s]
Best score: 1.0049: : 259iter [02:16,  1.94iter/s]
Best score: 1.0049: : 260iter [02:17,  1.91iter/s]
Best score: 1.0049: : 261iter [02:17,  1.96iter/s]
Best score: 1.0049: : 262iter [02:18,  1.92iter/s]
Best score: 1.0049: : 263iter [02:18,  1.93iter/s]
Best score: 1.0049: : 264iter [02:19,  1.86iter/s]
Best score: 1.0049: : 265iter [02:19,  1.84iter/s]
Best score: 1.0049: : 266iter [02:20,  1.87iter/s]
Best score: 1.0049: : 267iter [02:20,  1.83iter/s]
Best score: 1.0049: : 268iter [02:21,  1.84iter/s]
Best score: 1.0049: : 269iter [02:21,  1.93iter/s]
Best score: 1.0049: : 270iter [02:22,  1.87iter/s]
Best score: 1.0049: : 271iter [02:22,  1.86iter/s]
Best score: 1.0049: : 272iter [02:23,  1.94iter/s]
Best score: 1.0049: : 273iter [02:23,  1.91iter/s]
Best score: 1.0049: : 274iter [02:24,  1.89iter/s]
Best score: 1.0049: : 275iter [02:24,  1.99iter/s]
Best score: 1.0049: : 276iter [02:25,  1.90iter/s]
Best score: 1.0049: : 277iter [02:26,  1.90iter/s]
Best score: 1.0049: : 278iter [02:26,  1.95iter/s]
Best score: 1.0049: : 279iter [02:27,  1.96iter/s]
Best score: 1.0049: : 280iter [02:27,  1.91iter/s]
Best score: 1.0049: : 281iter [02:28,  1.87iter/s]
Best score: 1.0049: : 282iter [02:28,  1.92iter/s]
Best score: 1.0049: : 283iter [02:29,  1.92iter/s]
Best score: 1.0049: : 284iter [02:29,  1.91iter/s]
Best score: 1.0049: : 285iter [02:30,  1.88iter/s]
Best score: 1.0049: : 286iter [02:30,  1.88iter/s]
Best score: 1.0049: : 287iter [02:31,  1.87iter/s]
Best score: 1.0049: : 288iter [02:31,  1.86iter/s]
Best score: 1.0049: : 289iter [02:32,  1.91iter/s]
Best score: 1.0049: : 290iter [02:32,  1.95iter/s]
Best score: 1.0049: : 291iter [02:33,  1.93iter/s]
Best score: 1.0049: : 292iter [02:33,  1.85iter/s]
Best score: 1.0049: : 293iter [02:34,  1.91iter/s]
Best score: 1.0049: : 294iter [02:34,  1.89iter/s]
Best score: 1.0049: : 295iter [02:35,  1.89iter/s]
Best score: 1.0049: : 296iter [02:36,  1.88iter/s]
Best score: 1.0049: : 297iter [02:36,  1.83iter/s]
Best score: 1.0049: : 298iter [02:37,  1.87iter/s]
Best score: 1.0049: : 299iter [02:37,  1.86iter/s]
[ITER 300] Current best score: 1.0049
[PARAMS] Current best parameters:
  fx: 533.6055
  fy: 533.0825
  px: 1015.1588
  py: 744.4936
  k1: 0.01454408
  k2: -0.00186958
  k3: -0.01183634
  k4: 0.00424263
  p1: 0.00091521
  p2: -0.00408595
  p3: -0.00456895
  p4: 0.00282917
  scale: 2.9916
  rotation: -0.0075

Best score: 1.0049: : 300iter [02:38,  1.85iter/s]
Best score: 1.0049: : 301iter [02:38,  1.84iter/s]
Best score: 1.0049: : 302iter [02:39,  1.81iter/s]
Best score: 1.0049: : 303iter [02:39,  1.81iter/s]
Best score: 1.0049: : 304iter [02:40,  1.84iter/s]
Best score: 1.0049: : 305iter [02:40,  1.90iter/s]
Best score: 1.0049: : 306iter [02:41,  1.87iter/s]
Best score: 1.0049: : 307iter [02:41,  1.90iter/s]
Best score: 1.0049: : 308iter [02:42,  1.88iter/s]
Best score: 1.0049: : 309iter [02:43,  1.90iter/s]
Best score: 1.0049: : 310iter [02:43,  1.95iter/s]
Best score: 1.0049: : 311iter [02:44,  1.97iter/s]
Best score: 0.9712: : 311iter [02:44,  1.97iter/s]
Best score: 0.9712: : 312iter [02:44,  1.98iter/s]
Best score: 0.9712: : 313iter [02:44,  2.01iter/s]
Best score: 0.9712: : 314iter [02:45,  2.07iter/s]
Best score: 0.9361: : 314iter [02:45,  2.07iter/s]
Best score: 0.9361: : 315iter [02:45,  1.99iter/s]
Best score: 0.9361: : 316iter [02:46,  2.07iter/s]
Best score: 0.9003: : 316iter [02:46,  2.07iter/s]
Best score: 0.9003: : 317iter [02:46,  2.00iter/s]
Best score: 0.9003: : 318iter [02:47,  1.93iter/s]
Best score: 0.9003: : 319iter [02:47,  2.03iter/s]
Best score: 0.9003: : 320iter [02:48,  2.03iter/s]
Best score: 0.9003: : 321iter [02:48,  1.97iter/s]
Best score: 0.9003: : 322iter [02:49,  1.90iter/s]
Best score: 0.9003: : 323iter [02:50,  1.90iter/s]
Best score: 0.9003: : 324iter [02:50,  2.00iter/s]
Best score: 0.9003: : 325iter [02:51,  1.99iter/s]
Best score: 0.9003: : 326iter [02:51,  1.93iter/s]
Best score: 0.8567: : 326iter [02:52,  1.93iter/s]
Best score: 0.8567: : 327iter [02:52,  1.91iter/s]
Best score: 0.8567: : 328iter [02:52,  1.89iter/s]
Best score: 0.8567: : 329iter [02:53,  1.89iter/s]
Best score: 0.8567: : 330iter [02:53,  1.88iter/s]
Best score: 0.8567: : 331iter [02:54,  1.90iter/s]
Best score: 0.8567: : 332iter [02:54,  1.86iter/s]
Best score: 0.8567: : 333iter [02:55,  1.88iter/s]
Best score: 0.8567: : 334iter [02:55,  1.89iter/s]
Best score: 0.8567: : 335iter [02:56,  1.86iter/s]
Best score: 0.8567: : 336iter [02:56,  1.87iter/s]
Best score: 0.8567: : 337iter [02:57,  1.82iter/s]
Best score: 0.8368: : 337iter [02:58,  1.82iter/s]
Best score: 0.8368: : 338iter [02:58,  1.85iter/s]
Best score: 0.7953: : 338iter [02:58,  1.85iter/s]
Best score: 0.7953: : 339iter [02:58,  1.87iter/s]
Best score: 0.7953: : 340iter [02:59,  1.82iter/s]
Best score: 0.7953: : 341iter [02:59,  1.85iter/s]
Best score: 0.7953: : 342iter [03:00,  1.87iter/s]
Best score: 0.7004: : 342iter [03:00,  1.87iter/s]
Best score: 0.7004: : 343iter [03:00,  1.85iter/s]
Best score: 0.7004: : 344iter [03:01,  1.88iter/s]
Best score: 0.7004: : 345iter [03:01,  1.83iter/s]
Best score: 0.7004: : 346iter [03:02,  1.82iter/s]
Best score: 0.7004: : 347iter [03:02,  1.89iter/s]
Best score: 0.7004: : 348iter [03:03,  1.85iter/s]
Best score: 0.7004: : 349iter [03:03,  1.86iter/s]
[ITER 350] Current best score: 0.7004
[PARAMS] Current best parameters:
  fx: 529.7405
  fy: 529.5932
  px: 1015.7025
  py: 743.8094
  k1: 0.00171627
  k2: -0.00258204
  k3: 0.00466801
  k4: -0.00214039
  p1: -0.00029490
  p2: -0.00425848
  p3: 0.00318388
  p4: -0.00004963
  scale: 3.0284
  rotation: 0.0086

Best score: 0.7004: : 350iter [03:04,  1.81iter/s]
Best score: 0.7004: : 351iter [03:05,  1.76iter/s]
Best score: 0.7004: : 352iter [03:05,  1.81iter/s]
Best score: 0.7004: : 353iter [03:06,  1.84iter/s]
Best score: 0.7004: : 354iter [03:06,  1.88iter/s]
Best score: 0.7004: : 355iter [03:07,  1.92iter/s]
Best score: 0.7004: : 356iter [03:07,  1.96iter/s]
Best score: 0.7004: : 357iter [03:08,  1.96iter/s]
Best score: 0.7004: : 358iter [03:08,  1.95iter/s]
Best score: 0.7004: : 359iter [03:09,  1.93iter/s]
Best score: 0.7004: : 360iter [03:09,  1.98iter/s]
Best score: 0.7004: : 361iter [03:10,  1.97iter/s]
Best score: 0.7004: : 362iter [03:10,  1.91iter/s]
Best score: 0.7004: : 363iter [03:11,  1.97iter/s]
Best score: 0.7004: : 364iter [03:11,  1.93iter/s]
Best score: 0.6476: : 364iter [03:12,  1.93iter/s]
Best score: 0.6476: : 365iter [03:12,  1.86iter/s]
Best score: 0.6476: : 366iter [03:12,  1.90iter/s]
Best score: 0.6476: : 367iter [03:13,  1.90iter/s]
Best score: 0.6476: : 368iter [03:13,  1.86iter/s]
Best score: 0.6476: : 369iter [03:14,  1.98iter/s]
Best score: 0.6476: : 370iter [03:14,  1.98iter/s]
Best score: 0.6476: : 371iter [03:15,  1.96iter/s]
Best score: 0.6476: : 372iter [03:15,  1.99iter/s]
Best score: 0.6476: : 373iter [03:16,  1.96iter/s]
Best score: 0.6476: : 374iter [03:17,  1.91iter/s]
Best score: 0.6476: : 375iter [03:17,  2.02iter/s]
Best score: 0.6476: : 376iter [03:17,  2.01iter/s]
Best score: 0.6476: : 377iter [03:18,  1.91iter/s]
Best score: 0.6476: : 378iter [03:19,  1.94iter/s]
Best score: 0.6476: : 379iter [03:19,  1.94iter/s]
Best score: 0.6476: : 380iter [03:20,  1.91iter/s]
Best score: 0.6476: : 381iter [03:20,  1.90iter/s]
Best score: 0.6476: : 382iter [03:21,  1.88iter/s]
Best score: 0.6476: : 383iter [03:21,  1.89iter/s]
Best score: 0.6476: : 384iter [03:22,  1.90iter/s]
Best score: 0.6476: : 385iter [03:22,  1.96iter/s]
Best score: 0.6476: : 386iter [03:23,  1.99iter/s]
Best score: 0.6476: : 387iter [03:23,  2.03iter/s]
Best score: 0.6476: : 388iter [03:24,  2.07iter/s]
Best score: 0.6476: : 389iter [03:24,  1.99iter/s]
Best score: 0.6476: : 390iter [03:25,  2.02iter/s]
Best score: 0.6476: : 391iter [03:25,  2.05iter/s]
Best score: 0.6476: : 392iter [03:26,  1.98iter/s]
Best score: 0.6476: : 393iter [03:26,  2.02iter/s]
Best score: 0.6476: : 394iter [03:27,  2.01iter/s]
Best score: 0.6049: : 394iter [03:27,  2.01iter/s]
Best score: 0.6049: : 395iter [03:27,  1.97iter/s]
Best score: 0.6049: : 396iter [03:28,  2.05iter/s]
Best score: 0.6049: : 397iter [03:28,  1.96iter/s]
Best score: 0.6049: : 398iter [03:29,  1.96iter/s]
Best score: 0.6049: : 399iter [03:29,  1.89iter/s]
[ITER 400] Current best score: 0.6049
[PARAMS] Current best parameters:
  fx: 541.1236
  fy: 540.9612
  px: 1016.5093
  py: 742.6880
  k1: 0.01142491
  k2: -0.01516492
  k3: 0.00167361
  k4: 0.00037613
  p1: -0.00198941
  p2: -0.00165885
  p3: -0.00345361
  p4: 0.00192350
  scale: 2.9704
  rotation: 0.0832

Best score: 0.6049: : 400iter [03:30,  1.84iter/s]
Best score: 0.6049: : 401iter [03:30,  1.84iter/s]
Best score: 0.6049: : 402iter [03:31,  1.85iter/s]
Best score: 0.6049: : 403iter [03:31,  1.97iter/s]
Best score: 0.6049: : 404iter [03:32,  1.96iter/s]
Best score: 0.6049: : 405iter [03:32,  2.00iter/s]
Best score: 0.6049: : 406iter [03:33,  2.01iter/s]
Best score: 0.6049: : 407iter [03:33,  2.00iter/s]
Best score: 0.6049: : 408iter [03:34,  1.99iter/s]
Best score: 0.6049: : 409iter [03:34,  1.95iter/s]
Best score: 0.6049: : 410iter [03:35,  1.90iter/s]
Best score: 0.6049: : 411iter [03:35,  1.89iter/s]
Best score: 0.6049: : 412iter [03:36,  1.96iter/s]
Best score: 0.6049: : 413iter [03:36,  1.97iter/s]
Best score: 0.6049: : 414iter [03:37,  1.94iter/s]
Best score: 0.6049: : 415iter [03:38,  1.89iter/s]
Best score: 0.6049: : 416iter [03:38,  1.90iter/s]
Best score: 0.6049: : 417iter [03:39,  1.85iter/s]
Best score: 0.6049: : 418iter [03:39,  1.91iter/s]
Best score: 0.6049: : 419iter [03:40,  1.97iter/s]
Best score: 0.6049: : 420iter [03:40,  1.97iter/s]
Best score: 0.6049: : 421iter [03:41,  2.01iter/s]
Best score: 0.6049: : 422iter [03:41,  2.00iter/s]
Best score: 0.6049: : 423iter [03:42,  1.94iter/s]
Best score: 0.6049: : 424iter [03:42,  1.99iter/s]
Best score: 0.6049: : 425iter [03:43,  1.98iter/s]
Best score: 0.6049: : 426iter [03:43,  2.01iter/s]
Best score: 0.6049: : 427iter [03:44,  2.05iter/s]
Best score: 0.6049: : 428iter [03:44,  2.02iter/s]
Best score: 0.6049: : 429iter [03:45,  1.95iter/s]
Best score: 0.6049: : 430iter [03:45,  1.98iter/s]
Best score: 0.6049: : 431iter [03:46,  2.01iter/s]
Best score: 0.6049: : 432iter [03:46,  1.99iter/s]
Best score: 0.6049: : 433iter [03:47,  2.02iter/s]
Best score: 0.6049: : 434iter [03:47,  1.92iter/s]
Best score: 0.6049: : 435iter [03:48,  1.90iter/s]
Best score: 0.6049: : 436iter [03:48,  1.97iter/s]
Best score: 0.6049: : 437iter [03:49,  1.92iter/s]
Best score: 0.6049: : 438iter [03:49,  1.91iter/s]
Best score: 0.6049: : 439iter [03:50,  1.93iter/s]
Best score: 0.6049: : 440iter [03:50,  1.88iter/s]
Best score: 0.6049: : 441iter [03:51,  1.92iter/s]
Best score: 0.6049: : 442iter [03:51,  1.88iter/s]
Best score: 0.6049: : 443iter [03:52,  1.91iter/s]
Best score: 0.6049: : 444iter [03:52,  1.94iter/s]
Best score: 0.6049: : 445iter [03:53,  1.91iter/s]
Best score: 0.6049: : 446iter [03:53,  1.96iter/s]
Best score: 0.6049: : 447iter [03:54,  1.97iter/s]
Best score: 0.6049: : 448iter [03:54,  1.92iter/s]
Best score: 0.6049: : 449iter [03:55,  1.91iter/s]
[ITER 450] Current best score: 0.6049
[PARAMS] Current best parameters:
  fx: 541.1236
  fy: 540.9612
  px: 1016.5093
  py: 742.6880
  k1: 0.01142491
  k2: -0.01516492
  k3: 0.00167361
  k4: 0.00037613
  p1: -0.00198941
  p2: -0.00165885
  p3: -0.00345361
  p4: 0.00192350
  scale: 2.9704
  rotation: 0.0832

Best score: 0.6049: : 450iter [03:55,  1.93iter/s]
Best score: 0.6049: : 451iter [03:56,  1.89iter/s]
Best score: 0.6049: : 452iter [03:56,  1.94iter/s]
Best score: 0.6049: : 453iter [03:57,  1.96iter/s]
Best score: 0.6049: : 454iter [03:57,  1.96iter/s]
Best score: 0.5057: : 454iter [03:58,  1.96iter/s]
Best score: 0.5057: : 455iter [03:58,  1.96iter/s]
Best score: 0.5057: : 456iter [03:58,  2.01iter/s]
Best score: 0.5057: : 457iter [03:59,  2.00iter/s]
Best score: 0.5057: : 458iter [04:00,  1.90iter/s]
Best score: 0.5057: : 459iter [04:00,  2.00iter/s]
Best score: 0.5057: : 460iter [04:00,  2.02iter/s]
Best score: 0.5057: : 461iter [04:01,  1.88iter/s]
Best score: 0.5057: : 462iter [04:02,  1.97iter/s]
Best score: 0.5057: : 463iter [04:02,  1.88iter/s]
Best score: 0.5057: : 464iter [04:03,  1.81iter/s]
Best score: 0.5057: : 465iter [04:03,  1.95iter/s]
Best score: 0.5057: : 466iter [04:04,  1.93iter/s]
Best score: 0.5057: : 467iter [04:04,  1.81iter/s]
Best score: 0.5057: : 468iter [04:05,  1.91iter/s]
Best score: 0.5057: : 469iter [04:05,  1.82iter/s]
Best score: 0.5057: : 470iter [04:06,  1.83iter/s]
Best score: 0.5057: : 471iter [04:06,  2.01iter/s]
Best score: 0.5057: : 472iter [04:07,  1.97iter/s]
Best score: 0.5057: : 473iter [04:07,  1.90iter/s]
Best score: 0.5057: : 474iter [04:08,  1.93iter/s]
Best score: 0.5057: : 475iter [04:08,  1.94iter/s]
Best score: 0.5057: : 476iter [04:09,  1.89iter/s]
Best score: 0.5057: : 477iter [04:09,  1.96iter/s]
Best score: 0.5057: : 478iter [04:10,  1.97iter/s]
Best score: 0.5057: : 479iter [04:10,  1.95iter/s]
Best score: 0.5057: : 480iter [04:11,  1.94iter/s]
Best score: 0.5057: : 481iter [04:12,  1.89iter/s]
Best score: 0.5057: : 482iter [04:12,  1.88iter/s]
Best score: 0.5057: : 483iter [04:13,  1.97iter/s]
Best score: 0.5057: : 484iter [04:13,  1.92iter/s]
Best score: 0.5057: : 485iter [04:14,  1.94iter/s]
Best score: 0.5057: : 486iter [04:14,  2.01iter/s]
Best score: 0.5057: : 487iter [04:15,  2.02iter/s]
Best score: 0.5057: : 488iter [04:15,  1.92iter/s]
Best score: 0.5057: : 489iter [04:16,  1.98iter/s]
Best score: 0.5057: : 490iter [04:16,  1.85iter/s]
Best score: 0.5057: : 491iter [04:17,  1.84iter/s]
Best score: 0.5057: : 492iter [04:17,  1.86iter/s]
Best score: 0.5057: : 493iter [04:18,  1.89iter/s]
Best score: 0.5057: : 494iter [04:18,  1.93iter/s]
Best score: 0.5057: : 495iter [04:19,  1.90iter/s]
Best score: 0.5057: : 496iter [04:19,  1.96iter/s]
Best score: 0.5057: : 497iter [04:20,  1.96iter/s]
Best score: 0.5057: : 498iter [04:20,  1.91iter/s]
Best score: 0.5057: : 499iter [04:21,  1.93iter/s]
[ITER 500] Current best score: 0.5057
[PARAMS] Current best parameters:
  fx: 537.6052
  fy: 537.4925
  px: 1015.5197
  py: 742.5453
  k1: 0.01306546
  k2: -0.01866168
  k3: 0.00629886
  k4: -0.00098069
  p1: -0.00273298
  p2: -0.00401509
  p3: -0.00080436
  p4: 0.00110737
  scale: 2.9863
  rotation: 0.0404

Best score: 0.5057: : 500iter [04:22,  1.77iter/s]
Best score: 0.5057: : 500iter [05:03,  1.65iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.450752 -> 0.450752
  Success: False
  Tangential distortion: p1=-0.002702, p2=-0.003986, p3=-0.000778, p4=0.001134
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.4508
[✅] Parameters copied to: /working/MDC044_W027_2025-05-29/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 537.6052466560019
    fy: 537.492498407399
    px: 1015.5196985032484
    py: 742.5452812914054
    k1: 0.013174012685244126
    k2: -0.018499661191443213
    k3: 0.006348265878942551
    k4: -0.0010922735630724564
    p1: -0.0027023542227552944
    p2: -0.00398630903496711
    p3: -0.0007776531187042378
    p4: 0.0011336523241192507
    scale: 2.986337061329766
    rotation: 0.04613095519327296
    mmpp: 1.281209241315964
    nmpp: 1281209.241315964

[🔄] Converting 2933 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:29<00:00, 14.72s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:29<00:00, 14.57s/rig]
[✅] Saved PTDs: /working/MDC044_W027_2025-05-29/calibration/debug/3_undistorted/output/ptd

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📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
[✅] Saved PTDs: /working/MDC044_W027_2025-05-29/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:  51%|█████     | 1560584/3080448 [00:01<00:01, 862815.51px/s]
📤 Writing LUT:  53%|█████▎    | 1647940/3080448 [00:01<00:01, 866020.13px/s]
📤 Writing LUT:  56%|█████▋    | 1734552/3080448 [00:02<00:01, 861599.58px/s]
📤 Writing LUT:  59%|█████▉    | 1821637/3080448 [00:02<00:01, 864354.65px/s]
📤 Writing LUT:  62%|██████▏   | 1909695/3080448 [00:02<00:01, 869200.67px/s]
📤 Writing LUT:  65%|██████▍   | 1999550/3080448 [00:02<00:01, 877976.33px/s]
📤 Writing LUT:  68%|██████▊   | 2087357/3080448 [00:02<00:01, 876671.25px/s]
📤 Writing LUT:  71%|███████   | 2175031/3080448 [00:02<00:01, 875145.56px/s]
📤 Writing LUT:  73%|███████▎  | 2262551/3080448 [00:02<00:00, 872166.24px/s]
📤 Writing LUT:  76%|███████▋  | 2349773/3080448 [00:02<00:00, 866430.39px/s]
📤 Writing LUT:  79%|███████▉  | 2436430/3080448 [00:02<00:00, 866471.58px/s]
📤 Writing LUT:  82%|████████▏ | 2523084/3080448 [00:02<00:00, 864520.62px/s]
📤 Writing LUT:  85%|████████▍ | 2611611/3080448 [00:03<00:00, 870711.33px/s]
📤 Writing LUT:  88%|████████▊ | 2700587/3080448 [00:03<00:00, 876402.59px/s]
📤 Writing LUT:  91%|█████████ | 2790165/3080448 [00:03<00:00, 882198.69px/s]
📤 Writing LUT:  93%|█████████▎| 2879951/3080448 [00:03<00:00, 886887.29px/s]
📤 Writing LUT:  96%|█████████▋| 2968646/3080448 [00:03<00:00, 883859.40px/s]
📤 Writing LUT:  99%|█████████▉| 3057038/3080448 [00:03<00:00, 882219.95px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 868506.39px/s]
[✅] LUT written to: /working/MDC044_W027_2025-05-29/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC044_W027_2025-05-29' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC044_W027_2025-05-29/calibration/MDC044_W027_2025-05-29_unknown.lut
[🏁] Finished processing MDC044_W027_2025-05-29


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