
🟢 Starting Calibration Pipeline

=== Processing MDC015_W068_2025-11-25 ===

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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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                                                                         [A📁 Processing Rigs: 100%|██████████| 2/2 [04:40<00:00, 146.96s/it]📁 Processing Rigs: 100%|██████████| 2/2 [04:40<00:00, 140.24s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2335 frames from 2 rigs to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 837
[📋] 1000: 100 frames selected from 1498
[💾] Saved RigFrameDataset to /working/MDC015_W068_2025-11-25/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W068_2025-11-25/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:   8%|▊         | 3/40 [00:00<00:01, 29.72it/s]Averaging bright frames:  20%|██        | 8/40 [00:00<00:00, 40.42it/s]Averaging bright frames:  32%|███▎      | 13/40 [00:00<00:00, 44.22it/s]Averaging bright frames:  45%|████▌     | 18/40 [00:00<00:00, 46.22it/s]Averaging bright frames:  57%|█████▊    | 23/40 [00:00<00:00, 46.16it/s]Averaging bright frames:  70%|███████   | 28/40 [00:00<00:00, 45.68it/s]Averaging bright frames:  82%|████████▎ | 33/40 [00:00<00:00, 45.80it/s]Averaging bright frames:  95%|█████████▌| 38/40 [00:00<00:00, 45.68it/s]                                                                        Total time to process (v2): 0.05
[RESULT] Combined: Center=(1004, 744), Radius=841
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(0.002):  27%|██▋       | 27/100 [00:00<00:00, 91.31it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.004 | Best: {'fx': 505.051} (0.004):  28%|██▊       | 28/100 [00:00<00:00, 91.31it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.003 | Best: {'fx': 505.051} (0.004):  29%|██▉       | 29/100 [00:00<00:00, 91.31it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.019 | Best: {'fx': 512.121} (0.019):  30%|███       | 30/100 [00:00<00:00, 91.31it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 561.616} (0.022):  31%|███       | 31/100 [00:00<00:00, 91.31it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 561.616} (0.022):  32%|███▏      | 32/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  32%|███▏      | 32/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.057 | Best: {'fx': 526.263} (0.057):  33%|███▎      | 33/100 [00:00<00:00, 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(0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.079):  41%|████      | 41/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.079):  42%|████▏     | 42/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.079):  43%|████▎     | 43/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.079):  44%|████▍     | 44/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.079):  45%|████▌     | 45/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  46%|████▌     | 46/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  47%|████▋     | 47/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  48%|████▊     | 48/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  49%|████▉     | 49/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  50%|█████     | 50/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  51%|█████     | 51/100 [00:00<00:00, 100.85it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  52%|█████▏    | 52/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  52%|█████▏    | 52/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  53%|█████▎    | 53/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  54%|█████▍    | 54/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  55%|█████▌    | 55/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  56%|█████▌    | 56/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  57%|█████▋    | 57/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  58%|█████▊    | 58/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  59%|█████▉    | 59/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  60%|██████    | 60/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  61%|██████    | 61/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  62%|██████▏   | 62/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  63%|██████▎   | 63/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  64%|██████▍   | 64/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  65%|██████▌   | 65/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  66%|██████▌   | 66/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  67%|██████▋   | 67/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  68%|██████▊   | 68/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  69%|██████▉   | 69/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  70%|███████   | 70/100 [00:00<00:00, 134.51it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  71%|███████   | 71/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  71%|███████   | 71/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  72%|███████▏  | 72/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  73%|███████▎  | 73/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  74%|███████▍  | 74/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  75%|███████▌  | 75/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  76%|███████▌  | 76/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  77%|███████▋  | 77/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  78%|███████▊  | 78/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  79%|███████▉  | 79/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  80%|████████  | 80/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  81%|████████  | 81/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  82%|████████▏ | 82/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  83%|████████▎ | 83/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  84%|████████▍ | 84/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  85%|████████▌ | 85/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  86%|████████▌ | 86/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  87%|████████▋ | 87/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  88%|████████▊ | 88/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  89%|████████▉ | 89/100 [00:00<00:00, 147.48it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  90%|█████████ | 90/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  90%|█████████ | 90/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  91%|█████████ | 91/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  92%|█████████▏| 92/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  93%|█████████▎| 93/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  94%|█████████▍| 94/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  95%|█████████▌| 95/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  96%|█████████▌| 96/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  97%|█████████▋| 97/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  98%|█████████▊| 98/100 [00:00<00:00, 155.59it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  99%|█████████▉| 99/100 [00:00<00:00, 155.59it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.007 | Best: {'fx': 508.333} (0.007):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.007 | Best: {'fx': 508.333} (0.007):   2%|▏         | 1/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.013 | Best: {'fx': 509.354} (0.013):   2%|▏         | 1/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.014 | Best: {'fx': 510.374} (0.014):   4%|▍         | 2/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.015 | Best: {'fx': 511.395} (0.015):   6%|▌         | 3/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 514.456} (0.024):   8%|▊         | 4/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 516.497} (0.028):  10%|█         | 5/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.021 | Best: {'fx': 516.497} (0.028):  12%|█▏        | 6/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.026 | Best: {'fx': 516.497} (0.028):  14%|█▍        | 7/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 516.497} (0.028):  16%|█▌        | 8/50 [00:00<00:05,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  20%|██        | 10/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.049 | Best: {'fx': 523.639} (0.049):  22%|██▏       | 11/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.033 | Best: {'fx': 523.639} (0.049):  24%|██▍       | 12/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.033 | Best: {'fx': 523.639} (0.049):  26%|██▌       | 13/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.051 | Best: {'fx': 524.66} (0.051):  28%|██▊       | 14/50 [00:00<00:04,  8.33it/s] Brute | Start: {'fx': 533.333} (0.079) | Current: 0.037 | Best: {'fx': 524.66} (0.051):  30%|███       | 15/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.056 | Best: {'fx': 525.68} (0.056):  32%|███▏      | 16/50 [00:00<00:04,  8.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.056 | Best: {'fx': 525.68} (0.056):  34%|███▍      | 17/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.043 | Best: {'fx': 525.68} (0.056):  34%|███▍      | 17/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.059 | Best: {'fx': 526.701} (0.059):  36%|███▌      | 18/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.074 | Best: {'fx': 533.844} (0.074):  38%|███▊      | 19/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.063 | Best: {'fx': 533.844} (0.074):  40%|████      | 20/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.076 | Best: {'fx': 529.762} (0.076):  42%|████▏     | 21/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 529.762} (0.076):  44%|████▍     | 22/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.075 | Best: {'fx': 529.762} (0.076):  46%|████▌     | 23/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.078 | Best: {'fx': 532.823} (0.078):  48%|████▊     | 24/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.076 | Best: {'fx': 532.823} (0.078):  50%|█████     | 25/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.059 | Best: {'fx': 532.823} (0.078):  52%|█████▏    | 26/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.053 | Best: {'fx': 532.823} (0.078):  54%|█████▍    | 27/50 [00:00<00:00, 90.13it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.053 | Best: {'fx': 532.823} (0.078):  56%|█████▌    | 28/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 532.823} (0.078):  56%|█████▌    | 28/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.058 | Best: {'fx': 532.823} (0.078):  58%|█████▊    | 29/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 532.823} (0.078):  60%|██████    | 30/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.041 | Best: {'fx': 532.823} (0.078):  62%|██████▏   | 31/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.046 | Best: {'fx': 532.823} (0.078):  64%|██████▍   | 32/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.037 | Best: {'fx': 532.823} (0.078):  66%|██████▌   | 33/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.035 | Best: {'fx': 532.823} (0.078):  68%|██████▊   | 34/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.049 | Best: {'fx': 532.823} (0.078):  70%|███████   | 35/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 532.823} (0.078):  72%|███████▏  | 36/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.044 | Best: {'fx': 532.823} (0.078):  74%|███████▍  | 37/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.038 | Best: {'fx': 532.823} (0.078):  76%|███████▌  | 38/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.034 | Best: {'fx': 532.823} (0.078):  78%|███████▊  | 39/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.027 | Best: {'fx': 532.823} (0.078):  80%|████████  | 40/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.026 | Best: {'fx': 532.823} (0.078):  82%|████████▏ | 41/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.025 | Best: {'fx': 532.823} (0.078):  84%|████████▍ | 42/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 532.823} (0.078):  86%|████████▌ | 43/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 532.823} (0.078):  88%|████████▊ | 44/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 532.823} (0.078):  90%|█████████ | 45/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.029 | Best: {'fx': 532.823} (0.078):  92%|█████████▏| 46/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 532.823} (0.078):  94%|█████████▍| 47/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 532.823} (0.078):  96%|█████████▌| 48/50 [00:00<00:00, 95.58it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 532.823} (0.078):  98%|█████████▊| 49/50 [00:00<00:00, 137.86it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 532.823} (0.078):  98%|█████████▊| 49/50 [00:00<00:00, 137.86it/s]                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.3333333333334
    px: 1004.0
    py: 744.0
    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: 19.5005: 0iter [00:00, ?iter/s]Initial score: 19.5005: 1iter [00:01,  1.08s/iter]Initial score: 19.5005: 2iter [00:01,  1.34iter/s]Initial score: 19.5005: 3iter [00:02,  1.54iter/s]Initial score: 19.5005: 4iter [00:02,  1.67iter/s]Initial score: 19.5005: 5iter [00:03,  1.86iter/s]Initial score: 19.5005: 6iter [00:03,  1.89iter/s]Initial score: 19.5005: 7iter [00:04,  1.87iter/s]Initial score: 19.5005: 8iter [00:04,  1.90iter/s]Initial score: 19.5005: 9iter [00:05,  1.96iter/s]Initial score: 19.5005: 10iter [00:05,  1.95iter/s]Initial score: 19.5005: 11iter [00:06,  1.92iter/s]Initial score: 19.5005: 12iter [00:06,  1.93iter/s]Initial score: 19.5005: 13iter [00:07,  1.89iter/s]Initial score: 19.5005: 14iter [00:07,  1.90iter/s]Initial score: 19.5005: 15iter [00:08,  2.03iter/s]Initial score: 19.5005: 16iter [00:08,  2.01iter/s]Initial score: 19.5005: 17iter [00:09,  1.95iter/s]Initial score: 19.5005: 18iter [00:09,  2.03iter/s]Initial score: 19.5005: 19iter [00:10,  1.94iter/s]Initial score: 19.5005: 20iter [00:10,  1.89iter/s]Initial score: 19.5005: 21iter [00:11,  1.86iter/s]Initial score: 19.5005: 22iter [00:11,  1.85iter/s]Initial score: 19.5005: 23iter [00:12,  1.82iter/s]Initial score: 19.5005: 24iter [00:13,  1.84iter/s]Initial score: 19.5005: 25iter [00:13,  1.98iter/s]Initial score: 19.5005: 26iter [00:13,  1.97iter/s]Initial score: 19.5005: 27iter [00:14,  1.92iter/s]Initial score: 19.5005: 28iter [00:14,  1.97iter/s]Initial score: 19.5005: 29iter [00:15,  1.94iter/s]Initial score: 19.5005: 30iter [00:16,  1.86iter/s]Initial score: 19.5005: 31iter [00:16,  1.99iter/s]Initial score: 19.5005: 32iter [00:17,  1.99iter/s]Initial score: 19.5005: 33iter [00:17,  1.96iter/s]Initial score: 19.5005: 34iter [00:17,  2.07iter/s]Initial score: 19.5005: 35iter [00:18,  2.02iter/s]Initial score: 19.5005: 36iter [00:19,  1.94iter/s]Initial score: 19.5005: 37iter [00:19,  1.94iter/s]Initial score: 19.5005: 38iter [00:20,  1.92iter/s]Initial score: 19.5005: 39iter [00:20,  1.82iter/s]Initial score: 19.5005: 40iter [00:21,  1.88iter/s]Initial score: 19.5005: 41iter [00:21,  1.87iter/s]Initial score: 19.5005: 42iter [00:22,  1.87iter/s]Initial score: 19.5005: 43iter [00:22,  1.90iter/s]Initial score: 19.5005: 44iter [00:23,  1.86iter/s]Initial score: 19.5005: 45iter [00:23,  1.87iter/s]Initial score: 19.5005: 46iter [00:24,  1.90iter/s]Initial score: 19.5005: 47iter [00:24,  1.91iter/s]Initial score: 19.5005: 48iter [00:25,  1.81iter/s]Initial score: 19.5005: 49iter [00:26,  1.84iter/s]
[ITER 50] Current best score: 19.5005
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 1004.0000
  py: 744.0000
  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: 19.5005: 50iter [00:26,  1.88iter/s]Initial score: 19.5005: 51iter [00:27,  1.87iter/s]Initial score: 19.5005: 52iter [00:27,  1.90iter/s]Initial score: 19.5005: 53iter [00:28,  1.97iter/s]Initial score: 19.5005: 54iter [00:28,  1.96iter/s]Initial score: 19.5005: 55iter [00:29,  1.93iter/s]Initial score: 19.5005: 56iter [00:29,  1.89iter/s]Initial score: 19.5005: 57iter [00:30,  1.88iter/s]Initial score: 19.5005: 58iter [00:30,  1.86iter/s]Initial score: 19.5005: 59iter [00:31,  1.85iter/s]Initial score: 19.5005: 60iter [00:31,  1.81iter/s]Initial score: 19.5005: 61iter [00:32,  1.77iter/s]Initial score: 19.5005: 62iter [00:32,  1.90iter/s]Initial score: 19.5005: 63iter [00:33,  1.93iter/s]Initial score: 19.5005: 64iter [00:33,  1.95iter/s]Initial score: 19.5005: 65iter [00:34,  1.91iter/s]Initial score: 19.5005: 66iter [00:34,  1.97iter/s]Initial score: 19.5005: 67iter [00:35,  1.95iter/s]Initial score: 19.5005: 68iter [00:36,  1.92iter/s]Initial score: 19.5005: 69iter [00:36,  1.94iter/s]Initial score: 19.5005: 70iter [00:37,  1.87iter/s]Initial score: 19.5005: 71iter [00:37,  1.91iter/s]Initial score: 19.5005: 72iter [00:38,  1.88iter/s]Initial score: 19.5005: 73iter [00:38,  1.92iter/s]Initial score: 19.5005: 74iter [00:39,  1.89iter/s]Best score: 15.1846: : 74iter [00:39,  1.89iter/s] Best score: 15.1846: : 75iter [00:39,  1.85iter/s]Best score: 15.1846: : 76iter [00:40,  1.83iter/s]Best score: 15.1846: : 77iter [00:40,  1.89iter/s]Best score: 15.1846: : 78iter [00:41,  1.92iter/s]Best score: 15.1846: : 79iter [00:41,  1.89iter/s]Best score: 15.1846: : 80iter [00:42,  1.90iter/s]Best score: 15.1846: : 81iter [00:42,  1.84iter/s]Best score: 15.1846: : 82iter [00:43,  1.88iter/s]Best score: 15.1846: : 83iter [00:43,  1.96iter/s]Best score: 15.1846: : 84iter [00:44,  1.93iter/s]Best score: 15.1846: : 85iter [00:45,  1.86iter/s]Best score: 15.1846: : 86iter [00:45,  1.89iter/s]Best score: 15.1846: : 87iter [00:46,  1.93iter/s]Best score: 15.1846: : 88iter [00:46,  1.84iter/s]Best score: 15.1846: : 89iter [00:47,  1.96iter/s]Best score: 14.6121: : 89iter [00:47,  1.96iter/s]Best score: 14.6121: : 90iter [00:47,  1.93iter/s]Best score: 14.6121: : 91iter [00:48,  1.93iter/s]Best score: 14.6121: : 92iter [00:48,  1.91iter/s]Best score: 14.6121: : 93iter [00:49,  1.86iter/s]Best score: 13.9237: : 93iter [00:49,  1.86iter/s]Best score: 13.9237: : 94iter [00:49,  1.86iter/s]Best score: 13.9237: : 95iter [00:50,  1.88iter/s]Best score: 13.9237: : 96iter [00:50,  1.90iter/s]Best score: 13.9237: : 97iter [00:51,  1.87iter/s]Best score: 13.9237: : 98iter [00:51,  1.86iter/s]Best score: 13.9237: : 99iter [00:52,  1.93iter/s]
[ITER 100] Current best score: 13.9237
[PARAMS] Current best parameters:
  fx: 536.3560
  fy: 533.5426
  px: 1002.0188
  py: 741.1814
  k1: 0.01246722
  k2: -0.01785583
  k3: 0.00887136
  k4: -0.00198756
  p1: -0.00803436
  p2: -0.00077896
  p3: 0.00680091
  p4: -0.00108471
  scale: 2.9750
  rotation: -0.6568
Best score: 13.9237: : 100iter [00:52,  1.89iter/s]Best score: 13.9237: : 101iter [00:53,  1.88iter/s]Best score: 13.9237: : 102iter [00:53,  2.01iter/s]Best score: 11.7193: : 102iter [00:54,  2.01iter/s]Best score: 11.7193: : 103iter [00:54,  1.99iter/s]Best score: 11.7193: : 104iter [00:54,  1.92iter/s]Best score: 11.7193: : 105iter [00:55,  1.90iter/s]Best score: 11.7193: : 106iter [00:55,  1.94iter/s]Best score: 11.7193: : 107iter [00:56,  1.93iter/s]Best score: 11.7193: : 108iter [00:56,  2.03iter/s]Best score: 11.7193: : 109iter [00:57,  1.95iter/s]Best score: 11.7193: : 110iter [00:58,  1.89iter/s]Best score: 4.5444: : 110iter [00:58,  1.89iter/s] Best score: 4.5444: : 111iter [00:58,  1.89iter/s]Best score: 4.5444: : 112iter [00:59,  1.86iter/s]Best score: 4.5444: : 113iter [00:59,  1.87iter/s]Best score: 4.5444: : 114iter [01:00,  1.97iter/s]Best score: 4.5444: : 115iter [01:00,  1.93iter/s]Best score: 4.5444: : 116iter [01:01,  1.81iter/s]Best score: 4.5444: : 117iter [01:01,  1.89iter/s]Best score: 4.5444: : 118iter [01:02,  1.91iter/s]Best score: 4.5444: : 119iter [01:02,  1.87iter/s]Best score: 4.5444: : 120iter [01:03,  1.90iter/s]Best score: 4.5444: : 121iter [01:03,  1.85iter/s]Best score: 4.5444: : 122iter [01:04,  1.89iter/s]Best score: 4.5444: : 123iter [01:04,  1.94iter/s]Best score: 4.5444: : 124iter [01:05,  2.06iter/s]Best score: 4.5444: : 125iter [01:05,  1.95iter/s]Best score: 4.5444: : 126iter [01:06,  1.96iter/s]Best score: 4.5444: : 127iter [01:06,  2.04iter/s]Best score: 4.5444: : 128iter [01:07,  1.95iter/s]Best score: 4.5444: : 129iter [01:08,  1.88iter/s]Best score: 4.5444: : 130iter [01:08,  1.88iter/s]Best score: 4.5444: : 131iter [01:09,  1.88iter/s]Best score: 4.5444: : 132iter [01:09,  1.86iter/s]Best score: 4.5444: : 133iter [01:10,  1.94iter/s]Best score: 4.5444: : 134iter [01:10,  1.98iter/s]Best score: 4.5444: : 135iter [01:11,  1.96iter/s]Best score: 4.5444: : 136iter [01:11,  1.99iter/s]Best score: 4.5444: : 137iter [01:12,  1.98iter/s]Best score: 4.5444: : 138iter [01:12,  1.91iter/s]Best score: 4.5444: : 139iter [01:13,  1.92iter/s]Best score: 4.5444: : 140iter [01:13,  1.89iter/s]Best score: 3.4245: : 140iter [01:14,  1.89iter/s]Best score: 3.4245: : 141iter [01:14,  1.88iter/s]Best score: 3.4245: : 142iter [01:14,  1.89iter/s]Best score: 3.4204: : 142iter [01:15,  1.89iter/s]Best score: 3.4204: : 143iter [01:15,  1.83iter/s]Best score: 3.4204: : 144iter [01:15,  1.78iter/s]Best score: 3.4204: : 145iter [01:16,  1.81iter/s]Best score: 3.4204: : 146iter [01:16,  1.86iter/s]Best score: 3.4204: : 147iter [01:17,  1.84iter/s]Best score: 3.4204: : 148iter [01:18,  1.87iter/s]Best score: 3.4204: : 149iter [01:18,  1.91iter/s]
[ITER 150] Current best score: 3.4204
[PARAMS] Current best parameters:
  fx: 534.1965
  fy: 534.6197
  px: 1000.2200
  py: 740.1617
  k1: 0.00365864
  k2: -0.01288721
  k3: 0.00414798
  k4: 0.00013226
  p1: -0.00966790
  p2: -0.00350359
  p3: 0.00693438
  p4: -0.00215960
  scale: 3.0756
  rotation: 0.0759
Best score: 3.4204: : 150iter [01:19,  1.89iter/s]Best score: 3.4204: : 151iter [01:19,  1.90iter/s]Best score: 3.4204: : 152iter [01:20,  1.86iter/s]Best score: 3.4204: : 153iter [01:20,  1.93iter/s]Best score: 3.4204: : 154iter [01:21,  1.97iter/s]Best score: 3.4204: : 155iter [01:21,  1.83iter/s]Best score: 3.4204: : 156iter [01:22,  1.86iter/s]Best score: 3.4204: : 157iter [01:22,  1.85iter/s]Best score: 3.4204: : 158iter [01:23,  1.82iter/s]Best score: 2.2314: : 158iter [01:23,  1.82iter/s]Best score: 2.2314: : 159iter [01:23,  1.88iter/s]Best score: 2.2314: : 160iter [01:24,  1.90iter/s]Best score: 2.2314: : 161iter [01:24,  1.89iter/s]Best score: 2.2314: : 162iter [01:25,  1.87iter/s]Best score: 2.2314: : 163iter [01:26,  1.85iter/s]Best score: 2.2314: : 164iter [01:26,  1.87iter/s]Best score: 2.2314: : 165iter [01:27,  1.93iter/s]Best score: 2.2314: : 166iter [01:27,  1.88iter/s]Best score: 2.2314: : 167iter [01:28,  1.93iter/s]Best score: 2.2314: : 168iter [01:28,  1.83iter/s]Best score: 2.2314: : 169iter [01:29,  1.83iter/s]Best score: 2.2314: : 170iter [01:29,  1.86iter/s]Best score: 2.2314: : 171iter [01:30,  1.88iter/s]Best score: 1.8405: : 171iter [01:30,  1.88iter/s]Best score: 1.8405: : 172iter [01:30,  1.85iter/s]Best score: 1.8405: : 173iter [01:31,  1.90iter/s]Best score: 1.8405: : 174iter [01:31,  1.95iter/s]Best score: 1.8405: : 175iter [01:32,  1.88iter/s]Best score: 1.8405: : 176iter [01:32,  1.99iter/s]Best score: 1.8405: : 177iter [01:33,  2.02iter/s]Best score: 1.8405: : 178iter [01:33,  1.99iter/s]Best score: 1.8405: : 179iter [01:34,  2.00iter/s]Best score: 1.5215: : 179iter [01:34,  2.00iter/s]Best score: 1.5215: : 180iter [01:34,  2.00iter/s]Best score: 1.5215: : 181iter [01:35,  1.97iter/s]Best score: 1.5215: : 182iter [01:35,  1.92iter/s]Best score: 1.5215: : 183iter [01:36,  1.93iter/s]Best score: 1.5215: : 184iter [01:37,  1.85iter/s]Best score: 1.5215: : 185iter [01:37,  1.87iter/s]Best score: 1.5215: : 186iter [01:38,  1.84iter/s]Best score: 1.5215: : 187iter [01:38,  1.87iter/s]Best score: 1.5215: : 188iter [01:39,  1.90iter/s]Best score: 1.5215: : 189iter [01:39,  1.83iter/s]Best score: 1.5215: : 190iter [01:40,  1.84iter/s]Best score: 1.5215: : 191iter [01:40,  1.84iter/s]Best score: 1.5215: : 192iter [01:41,  1.88iter/s]Best score: 1.5215: : 193iter [01:41,  1.92iter/s]Best score: 1.5215: : 194iter [01:42,  1.91iter/s]Best score: 1.5215: : 195iter [01:42,  1.94iter/s]Best score: 1.5215: : 196iter [01:43,  1.95iter/s]Best score: 1.5215: : 197iter [01:43,  2.00iter/s]Best score: 1.5215: : 198iter [01:44,  1.93iter/s]Best score: 1.5215: : 199iter [01:44,  1.90iter/s]
[ITER 200] Current best score: 1.5215
[PARAMS] Current best parameters:
  fx: 517.8152
  fy: 517.3097
  px: 999.1475
  py: 743.5541
  k1: 0.03477230
  k2: -0.01354382
  k3: 0.00487261
  k4: -0.00229765
  p1: 0.00016059
  p2: -0.00335294
  p3: 0.00031596
  p4: 0.00036321
  scale: 2.9491
  rotation: -0.1778
Best score: 1.5215: : 200iter [01:45,  1.79iter/s]Best score: 1.5215: : 201iter [01:46,  1.87iter/s]Best score: 1.5215: : 202iter [01:46,  1.85iter/s]Best score: 1.5215: : 203iter [01:47,  1.86iter/s]Best score: 1.5215: : 204iter [01:47,  1.89iter/s]Best score: 1.5215: : 205iter [01:48,  1.88iter/s]Best score: 1.5215: : 206iter [01:48,  1.86iter/s]Best score: 1.5215: : 207iter [01:49,  1.88iter/s]Best score: 1.5215: : 208iter [01:49,  1.88iter/s]Best score: 1.5215: : 209iter [01:50,  1.89iter/s]Best score: 1.5215: : 210iter [01:50,  1.99iter/s]Best score: 1.5215: : 211iter [01:51,  1.96iter/s]Best score: 1.5215: : 212iter [01:51,  1.86iter/s]Best score: 1.5215: : 213iter [01:52,  1.86iter/s]Best score: 1.3324: : 213iter [01:52,  1.86iter/s]Best score: 1.3324: : 214iter [01:52,  1.84iter/s]Best score: 1.3324: : 215iter [01:53,  1.82iter/s]Best score: 1.3324: : 216iter [01:54,  1.81iter/s]Best score: 1.3324: : 217iter [01:54,  1.83iter/s]Best score: 1.3324: : 218iter [01:55,  1.87iter/s]Best score: 1.3324: : 219iter [01:55,  1.88iter/s]Best score: 1.3324: : 220iter [01:56,  1.94iter/s]Best score: 1.3324: : 221iter [01:56,  1.91iter/s]Best score: 1.3324: : 222iter [01:57,  1.88iter/s]Best score: 1.3324: : 223iter [01:57,  1.93iter/s]Best score: 1.3324: : 224iter [01:58,  1.90iter/s]Best score: 1.3324: : 225iter [01:58,  1.89iter/s]Best score: 1.3324: : 226iter [01:59,  1.89iter/s]Best score: 1.3324: : 227iter [01:59,  1.91iter/s]Best score: 1.3324: : 228iter [02:00,  1.92iter/s]Best score: 1.3324: : 229iter [02:00,  1.95iter/s]Best score: 1.3324: : 230iter [02:01,  1.90iter/s]Best score: 1.3324: : 231iter [02:01,  1.90iter/s]Best score: 1.3324: : 232iter [02:02,  1.85iter/s]Best score: 1.3324: : 233iter [02:03,  1.82iter/s]Best score: 1.3324: : 234iter [02:03,  1.87iter/s]Best score: 1.3324: : 235iter [02:04,  1.91iter/s]Best score: 1.3324: : 236iter [02:04,  1.90iter/s]Best score: 1.3324: : 237iter [02:05,  1.82iter/s]Best score: 1.3324: : 238iter [02:05,  1.87iter/s]Best score: 1.3324: : 239iter [02:06,  1.87iter/s]Best score: 1.3324: : 240iter [02:06,  1.86iter/s]Best score: 1.3324: : 241iter [02:07,  1.86iter/s]Best score: 1.3324: : 242iter [02:07,  1.90iter/s]Best score: 1.3324: : 243iter [02:08,  1.91iter/s]Best score: 1.3324: : 244iter [02:08,  1.92iter/s]Best score: 1.3324: : 245iter [02:09,  2.04iter/s]Best score: 1.3324: : 246iter [02:09,  1.98iter/s]Best score: 1.3324: : 247iter [02:10,  1.89iter/s]Best score: 1.3324: : 248iter [02:10,  1.96iter/s]Best score: 1.3324: : 249iter [02:11,  1.94iter/s]
[ITER 250] Current best score: 1.0503
[PARAMS] Current best parameters:
  fx: 530.2112
  fy: 529.3379
  px: 999.5630
  py: 740.6334
  k1: 0.01617741
  k2: -0.01302312
  k3: 0.00651425
  k4: -0.00228493
  p1: -0.00681726
  p2: -0.00273519
  p3: 0.00470653
  p4: -0.00066470
  scale: 2.9867
  rotation: 0.2058
Best score: 1.0503: : 249iter [02:12,  1.94iter/s]Best score: 1.0503: : 250iter [02:12,  1.77iter/s]Best score: 1.0503: : 251iter [02:12,  1.80iter/s]Best score: 1.0503: : 252iter [02:13,  1.84iter/s]Best score: 1.0503: : 253iter [02:13,  1.87iter/s]Best score: 1.0503: : 254iter [02:14,  1.87iter/s]Best score: 1.0503: : 255iter [02:14,  1.88iter/s]Best score: 1.0503: : 256iter [02:15,  1.91iter/s]Best score: 1.0503: : 257iter [02:15,  1.91iter/s]Best score: 1.0503: : 258iter [02:16,  1.88iter/s]Best score: 1.0503: : 259iter [02:16,  1.92iter/s]Best score: 1.0503: : 260iter [02:17,  1.89iter/s]Best score: 1.0503: : 261iter [02:17,  1.87iter/s]Best score: 1.0503: : 262iter [02:18,  1.91iter/s]Best score: 1.0503: : 263iter [02:18,  1.89iter/s]Best score: 1.0503: : 264iter [02:19,  1.89iter/s]Best score: 1.0503: : 265iter [02:19,  1.87iter/s]Best score: 1.0503: : 266iter [02:20,  1.85iter/s]Best score: 1.0503: : 267iter [02:21,  1.88iter/s]Best score: 1.0503: : 268iter [02:21,  1.90iter/s]Best score: 1.0503: : 269iter [02:22,  1.90iter/s]Best score: 0.8860: : 269iter [02:22,  1.90iter/s]Best score: 0.8860: : 270iter [02:22,  1.91iter/s]Best score: 0.8860: : 271iter [02:23,  1.88iter/s]Best score: 0.8860: : 272iter [02:23,  1.84iter/s]Best score: 0.8860: : 273iter [02:24,  1.87iter/s]Best score: 0.8860: : 274iter [02:24,  1.95iter/s]Best score: 0.8860: : 275iter [02:25,  1.98iter/s]Best score: 0.8860: : 276iter [02:25,  1.93iter/s]Best score: 0.8860: : 277iter [02:26,  1.90iter/s]Best score: 0.8860: : 278iter [02:26,  1.85iter/s]Best score: 0.8860: : 279iter [02:27,  1.88iter/s]Best score: 0.8860: : 280iter [02:27,  1.97iter/s]Best score: 0.8860: : 281iter [02:28,  1.95iter/s]Best score: 0.8860: : 282iter [02:28,  1.94iter/s]Best score: 0.8860: : 283iter [02:29,  2.00iter/s]Best score: 0.8860: : 284iter [02:29,  1.99iter/s]Best score: 0.8860: : 285iter [02:30,  1.99iter/s]Best score: 0.8860: : 286iter [02:30,  2.00iter/s]Best score: 0.8860: : 287iter [02:31,  1.88iter/s]Best score: 0.8860: : 288iter [02:31,  1.92iter/s]Best score: 0.8860: : 289iter [02:32,  1.91iter/s]Best score: 0.8860: : 290iter [02:32,  1.90iter/s]Best score: 0.8860: : 291iter [02:33,  1.89iter/s]Best score: 0.8860: : 292iter [02:34,  1.85iter/s]Best score: 0.8860: : 293iter [02:34,  1.81iter/s]Best score: 0.8860: : 294iter [02:35,  1.85iter/s]Best score: 0.8860: : 295iter [02:35,  1.91iter/s]Best score: 0.8860: : 296iter [02:36,  1.99iter/s]Best score: 0.7082: : 296iter [02:36,  1.99iter/s]Best score: 0.7082: : 297iter [02:36,  1.93iter/s]Best score: 0.7082: : 298iter [02:37,  1.98iter/s]Best score: 0.7082: : 299iter [02:37,  2.01iter/s]
[ITER 300] Current best score: 0.7082
[PARAMS] Current best parameters:
  fx: 527.6562
  fy: 527.0932
  px: 1000.8728
  py: 742.5892
  k1: 0.01560132
  k2: -0.01197300
  k3: 0.00815726
  k4: -0.00294289
  p1: -0.00189245
  p2: 0.00133258
  p3: -0.00576918
  p4: 0.00169430
  scale: 2.9794
  rotation: 0.0902
Best score: 0.7082: : 300iter [02:38,  1.85iter/s]Best score: 0.7082: : 301iter [02:38,  1.86iter/s]Best score: 0.6498: : 301iter [02:39,  1.86iter/s]Best score: 0.6498: : 302iter [02:39,  1.93iter/s]Best score: 0.6498: : 303iter [02:39,  1.92iter/s]Best score: 0.6498: : 304iter [02:40,  1.87iter/s]Best score: 0.6498: : 305iter [02:40,  1.87iter/s]Best score: 0.6498: : 306iter [02:41,  1.84iter/s]Best score: 0.6498: : 307iter [02:41,  1.91iter/s]Best score: 0.6498: : 308iter [02:42,  1.99iter/s]Best score: 0.6498: : 309iter [02:42,  1.93iter/s]Best score: 0.6498: : 310iter [02:43,  1.92iter/s]Best score: 0.6498: : 311iter [02:44,  1.86iter/s]Best score: 0.6498: : 312iter [02:44,  1.87iter/s]Best score: 0.6498: : 313iter [02:45,  1.84iter/s]Best score: 0.6498: : 314iter [02:45,  1.92iter/s]Best score: 0.6498: : 315iter [02:46,  1.87iter/s]Best score: 0.6498: : 316iter [02:46,  1.84iter/s]Best score: 0.6498: : 317iter [02:47,  1.92iter/s]Best score: 0.6498: : 318iter [02:47,  1.91iter/s]Best score: 0.6498: : 319iter [02:48,  1.93iter/s]Best score: 0.6498: : 320iter [02:48,  1.95iter/s]Best score: 0.6498: : 321iter [02:49,  1.94iter/s]Best score: 0.6498: : 322iter [02:49,  1.97iter/s]Best score: 0.6498: : 323iter [02:50,  1.94iter/s]Best score: 0.6498: : 324iter [02:50,  1.91iter/s]Best score: 0.6498: : 325iter [02:51,  1.89iter/s]Best score: 0.6498: : 326iter [02:51,  1.87iter/s]Best score: 0.6498: : 327iter [02:52,  1.86iter/s]Best score: 0.3441: : 327iter [02:52,  1.86iter/s]Best score: 0.3441: : 328iter [02:52,  1.93iter/s]Best score: 0.3441: : 329iter [02:53,  1.92iter/s]Best score: 0.3441: : 330iter [02:53,  1.95iter/s]Best score: 0.3441: : 331iter [02:54,  1.94iter/s]Best score: 0.3441: : 332iter [02:55,  1.93iter/s]Best score: 0.3441: : 333iter [02:55,  1.87iter/s]Best score: 0.3441: : 334iter [02:56,  1.86iter/s]Best score: 0.3441: : 335iter [02:56,  1.81iter/s]Best score: 0.3441: : 336iter [02:57,  1.87iter/s]Best score: 0.3441: : 337iter [02:57,  1.83iter/s]Best score: 0.3441: : 338iter [02:58,  1.85iter/s]Best score: 0.3441: : 339iter [02:58,  1.84iter/s]Best score: 0.3441: : 340iter [02:59,  1.89iter/s]Best score: 0.3441: : 341iter [02:59,  1.87iter/s]Best score: 0.3441: : 342iter [03:00,  1.91iter/s]Best score: 0.3441: : 343iter [03:00,  1.96iter/s]Best score: 0.3441: : 344iter [03:01,  1.95iter/s]Best score: 0.3441: : 345iter [03:01,  1.94iter/s]Best score: 0.3441: : 346iter [03:02,  1.99iter/s]Best score: 0.3441: : 347iter [03:02,  1.89iter/s]Best score: 0.3441: : 348iter [03:03,  1.93iter/s]Best score: 0.3441: : 349iter [03:04,  1.85iter/s]
[ITER 350] Current best score: 0.5164
[PARAMS] Current best parameters:
  fx: 527.9907
  fy: 527.5811
  px: 1000.1999
  py: 741.9212
  k1: 0.02020987
  k2: -0.01110541
  k3: 0.00285736
  k4: -0.00109083
  p1: -0.00396410
  p2: -0.00004713
  p3: 0.00361972
  p4: -0.00209518
  scale: 2.9720
  rotation: 0.0122
Best score: 0.3441: : 350iter [03:04,  1.77iter/s]Best score: 0.3441: : 351iter [03:05,  1.90iter/s]Best score: 0.3441: : 352iter [03:05,  1.93iter/s]Best score: 0.3441: : 353iter [03:06,  1.92iter/s]Best score: 0.3441: : 354iter [03:06,  1.91iter/s]Best score: 0.3441: : 355iter [03:07,  1.85iter/s]Best score: 0.3441: : 356iter [03:07,  1.89iter/s]Best score: 0.3441: : 357iter [03:08,  1.86iter/s]Best score: 0.3441: : 358iter [03:08,  1.91iter/s]Best score: 0.3441: : 359iter [03:09,  1.89iter/s]Best score: 0.3441: : 360iter [03:09,  1.87iter/s]Best score: 0.3441: : 361iter [03:10,  1.88iter/s]Best score: 0.3441: : 362iter [03:10,  1.86iter/s]Best score: 0.3441: : 363iter [03:11,  1.86iter/s]Best score: 0.3441: : 364iter [03:12,  1.86iter/s]Best score: 0.3441: : 365iter [03:12,  1.81iter/s]Best score: 0.3441: : 366iter [03:13,  1.82iter/s]Best score: 0.3441: : 367iter [03:13,  1.88iter/s]Best score: 0.3441: : 368iter [03:14,  1.92iter/s]Best score: 0.3441: : 369iter [03:14,  1.85iter/s]Best score: 0.3441: : 370iter [03:15,  1.93iter/s]Best score: 0.3441: : 371iter [03:15,  1.89iter/s]Best score: 0.3441: : 372iter [03:16,  1.88iter/s]Best score: 0.3441: : 373iter [03:16,  1.90iter/s]Best score: 0.3441: : 374iter [03:17,  1.97iter/s]Best score: 0.3441: : 375iter [03:17,  1.90iter/s]Best score: 0.3441: : 376iter [03:18,  2.01iter/s]Best score: 0.3441: : 377iter [03:18,  2.02iter/s]Best score: 0.3441: : 378iter [03:19,  1.92iter/s]Best score: 0.3441: : 379iter [03:19,  1.89iter/s]Best score: 0.3441: : 380iter [03:20,  1.81iter/s]Best score: 0.3441: : 381iter [03:21,  1.82iter/s]Best score: 0.3441: : 382iter [03:21,  1.81iter/s]Best score: 0.3441: : 383iter [03:22,  1.77iter/s]Best score: 0.3441: : 384iter [03:22,  1.81iter/s]Best score: 0.3441: : 385iter [03:23,  1.82iter/s]Best score: 0.3441: : 386iter [03:23,  1.83iter/s]Best score: 0.3441: : 387iter [03:24,  1.82iter/s]Best score: 0.3441: : 388iter [03:24,  1.84iter/s]Best score: 0.3441: : 389iter [03:25,  1.90iter/s]Best score: 0.3441: : 390iter [03:25,  1.94iter/s]Best score: 0.3441: : 391iter [03:26,  1.86iter/s]Best score: 0.3441: : 392iter [03:26,  1.91iter/s]Best score: 0.3441: : 393iter [03:27,  1.90iter/s]Best score: 0.3441: : 394iter [03:27,  1.96iter/s]Best score: 0.3441: : 395iter [03:28,  1.91iter/s]Best score: 0.3441: : 396iter [03:29,  1.93iter/s]Best score: 0.3441: : 397iter [03:29,  1.88iter/s]Best score: 0.3441: : 398iter [03:30,  1.89iter/s]Best score: 0.3441: : 399iter [03:30,  1.93iter/s]
[ITER 400] Current best score: 0.4328
[PARAMS] Current best parameters:
  fx: 528.9504
  fy: 528.6008
  px: 999.9807
  py: 741.8287
  k1: 0.02105509
  k2: -0.01151549
  k3: 0.00218763
  k4: -0.00088970
  p1: -0.00409174
  p2: -0.00069090
  p3: -0.00175857
  p4: 0.00064262
  scale: 2.9668
  rotation: 0.0069
Best score: 0.3441: : 400iter [03:31,  1.87iter/s]Best score: 0.3441: : 401iter [03:31,  1.88iter/s]Best score: 0.3441: : 402iter [03:32,  1.96iter/s]Best score: 0.3441: : 403iter [03:32,  1.89iter/s]Best score: 0.3441: : 404iter [03:33,  1.92iter/s]Best score: 0.3441: : 405iter [03:33,  2.00iter/s]Best score: 0.3441: : 406iter [03:34,  1.99iter/s]Best score: 0.3441: : 407iter [03:34,  1.94iter/s]Best score: 0.3441: : 408iter [03:35,  1.95iter/s]Best score: 0.3441: : 409iter [03:35,  1.91iter/s]Best score: 0.2950: : 409iter [03:36,  1.91iter/s]Best score: 0.2950: : 410iter [03:36,  1.92iter/s]Best score: 0.2950: : 411iter [03:36,  1.94iter/s]Best score: 0.2950: : 412iter [03:37,  1.88iter/s]Best score: 0.2950: : 413iter [03:37,  1.87iter/s]Best score: 0.2950: : 414iter [03:38,  1.96iter/s]Best score: 0.2950: : 415iter [03:38,  2.01iter/s]Best score: 0.2950: : 416iter [03:39,  1.91iter/s]Best score: 0.2950: : 417iter [03:39,  1.93iter/s]Best score: 0.2950: : 418iter [03:40,  1.90iter/s]Best score: 0.2950: : 419iter [03:41,  1.89iter/s]Best score: 0.2950: : 420iter [03:41,  1.84iter/s]Best score: 0.2950: : 421iter [03:42,  1.86iter/s]Best score: 0.2950: : 422iter [03:42,  1.86iter/s]Best score: 0.2950: : 423iter [03:43,  1.85iter/s]Best score: 0.2950: : 424iter [03:43,  1.89iter/s]Best score: 0.2950: : 425iter [03:44,  1.83iter/s]Best score: 0.2950: : 426iter [03:44,  1.82iter/s]Best score: 0.2950: : 427iter [03:45,  1.89iter/s]Best score: 0.2950: : 428iter [03:45,  1.89iter/s]Best score: 0.2950: : 429iter [03:46,  1.97iter/s]Best score: 0.2950: : 430iter [03:46,  1.91iter/s]Best score: 0.2950: : 431iter [03:47,  1.94iter/s]Best score: 0.2950: : 432iter [03:47,  2.02iter/s]Best score: 0.2950: : 433iter [03:48,  1.92iter/s]Best score: 0.2950: : 434iter [03:48,  1.87iter/s]Best score: 0.2950: : 435iter [03:49,  1.95iter/s]Best score: 0.2950: : 436iter [03:49,  1.99iter/s]Best score: 0.2950: : 437iter [03:50,  2.02iter/s]Best score: 0.2950: : 438iter [03:50,  1.95iter/s]Best score: 0.2950: : 439iter [03:51,  2.03iter/s]Best score: 0.2950: : 440iter [03:51,  2.00iter/s]Best score: 0.2950: : 441iter [03:52,  1.93iter/s]Best score: 0.2950: : 442iter [03:52,  1.92iter/s]Best score: 0.2950: : 443iter [03:53,  1.96iter/s]Best score: 0.2950: : 444iter [03:53,  1.96iter/s]Best score: 0.2950: : 445iter [03:54,  1.97iter/s]Best score: 0.2950: : 446iter [03:54,  2.00iter/s]Best score: 0.2950: : 447iter [03:55,  2.00iter/s]Best score: 0.2950: : 448iter [03:56,  1.92iter/s]Best score: 0.2950: : 449iter [03:56,  1.86iter/s]
[ITER 450] Current best score: 0.2955
[PARAMS] Current best parameters:
  fx: 540.4825
  fy: 539.7912
  px: 1000.0208
  py: 741.6677
  k1: 0.00634179
  k2: -0.01232582
  k3: 0.00297117
  k4: -0.00040110
  p1: -0.00521915
  p2: -0.00215365
  p3: 0.00455886
  p4: -0.00109419
  scale: 2.9961
  rotation: 0.0074
Best score: 0.2950: : 450iter [03:57,  1.79iter/s]Best score: 0.2950: : 451iter [03:57,  1.85iter/s]Best score: 0.2950: : 452iter [03:58,  1.82iter/s]Best score: 0.2950: : 453iter [03:58,  1.81iter/s]Best score: 0.2950: : 454iter [03:59,  1.85iter/s]Best score: 0.2950: : 455iter [03:59,  1.85iter/s]Best score: 0.2950: : 456iter [04:00,  1.87iter/s]Best score: 0.2950: : 457iter [04:00,  1.87iter/s]Best score: 0.2950: : 458iter [04:01,  1.89iter/s]Best score: 0.2950: : 459iter [04:02,  1.89iter/s]Best score: 0.2950: : 460iter [04:02,  1.91iter/s]Best score: 0.2950: : 461iter [04:03,  1.85iter/s]Best score: 0.2950: : 462iter [04:03,  1.86iter/s]Best score: 0.2950: : 463iter [04:04,  1.93iter/s]Best score: 0.2950: : 464iter [04:04,  1.98iter/s]Best score: 0.2950: : 465iter [04:05,  1.94iter/s]Best score: 0.2950: : 466iter [04:05,  1.92iter/s]Best score: 0.2950: : 467iter [04:06,  1.85iter/s]Best score: 0.2950: : 468iter [04:06,  1.82iter/s]Best score: 0.2950: : 469iter [04:07,  1.87iter/s]Best score: 0.2950: : 470iter [04:07,  1.88iter/s]Best score: 0.2950: : 471iter [04:08,  1.92iter/s]Best score: 0.2950: : 472iter [04:08,  1.94iter/s]Best score: 0.2950: : 473iter [04:09,  1.89iter/s]Best score: 0.2950: : 474iter [04:09,  1.82iter/s]Best score: 0.2950: : 475iter [04:10,  1.82iter/s]Best score: 0.2950: : 476iter [04:11,  1.85iter/s]Best score: 0.2950: : 477iter [04:11,  1.86iter/s]Best score: 0.2409: : 477iter [04:12,  1.86iter/s]Best score: 0.2409: : 478iter [04:12,  1.94iter/s]Best score: 0.2409: : 479iter [04:12,  1.91iter/s]Best score: 0.2409: : 480iter [04:13,  1.94iter/s]Best score: 0.2409: : 481iter [04:13,  1.90iter/s]Best score: 0.2409: : 482iter [04:14,  1.82iter/s]Best score: 0.2409: : 483iter [04:14,  1.82iter/s]Best score: 0.2409: : 484iter [04:15,  1.85iter/s]Best score: 0.2409: : 485iter [04:15,  1.83iter/s]Best score: 0.2409: : 486iter [04:16,  1.83iter/s]Best score: 0.2409: : 487iter [04:16,  1.83iter/s]Best score: 0.2409: : 488iter [04:17,  1.82iter/s]Best score: 0.2409: : 489iter [04:18,  1.88iter/s]Best score: 0.2409: : 490iter [04:18,  1.88iter/s]Best score: 0.2409: : 491iter [04:19,  1.82iter/s]Best score: 0.2409: : 492iter [04:19,  1.84iter/s]Best score: 0.2409: : 493iter [04:20,  1.93iter/s]Best score: 0.2409: : 494iter [04:20,  1.90iter/s]Best score: 0.2409: : 495iter [04:21,  1.93iter/s]Best score: 0.2409: : 496iter [04:21,  1.90iter/s]Best score: 0.2409: : 497iter [04:22,  1.89iter/s]Best score: 0.2409: : 498iter [04:22,  1.89iter/s]Best score: 0.2409: : 499iter [04:23,  1.94iter/s]
[ITER 500] Current best score: 0.2409
[PARAMS] Current best parameters:
  fx: 539.8206
  fy: 539.2271
  px: 998.8443
  py: 740.9942
  k1: 0.00563984
  k2: -0.01061664
  k3: 0.00242651
  k4: -0.00039785
  p1: -0.00672016
  p2: -0.00434351
  p3: 0.00327410
  p4: 0.00040394
  scale: 2.9941
  rotation: 0.0022
Best score: 0.2409: : 500iter [04:23,  1.88iter/s]Best score: 0.2409: : 500iter [05:47,  1.44iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.206167 -> 0.206165
  Success: True
  Tangential distortion: p1=-0.006829, p2=-0.004436, p3=0.003190, p4=0.000236
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2062
[✅] Parameters copied to: /working/MDC015_W068_2025-11-25/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 539.8205867480463
    fy: 539.2270993299861
    px: 998.8442562833258
    py: 740.9942145550348
    k1: 0.005640901959611515
    k2: -0.010615893501476204
    k3: 0.0024824618416202657
    k4: -0.00042367457350255797
    p1: -0.006828567686533007
    p2: -0.004435976690181286
    p3: 0.003189927212898172
    p4: 0.00023608921613911524
    scale: 2.9940898393493995
    rotation: 0.0015360449790043805
    mmpp: 1.2818238450607946
    nmpp: 1281823.8450607946

[🔄] Converting 2335 frames to PTD, creating debug images for 200 calibration frames...
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[✅] Saved PTDs: /working/MDC015_W068_2025-11-25/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC015_W068_2025-11-25/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 81850/3080448 [00:00<00:03, 818494.93px/s]📤 Writing LUT:   5%|▌         | 169204/3080448 [00:00<00:03, 850870.00px/s]📤 Writing LUT:   8%|▊         | 254291/3080448 [00:00<00:03, 850435.23px/s]📤 Writing LUT:  11%|█         | 342848/3080448 [00:00<00:03, 864298.79px/s]📤 Writing LUT:  14%|█▍        | 431150/3080448 [00:00<00:03, 871045.74px/s]📤 Writing LUT:  17%|█▋        | 520646/3080448 [00:00<00:02, 879174.37px/s]📤 Writing LUT:  20%|█▉        | 609890/3080448 [00:00<00:02, 883509.71px/s]📤 Writing LUT:  23%|██▎       | 698242/3080448 [00:00<00:02, 880886.48px/s]📤 Writing LUT:  26%|██▌       | 786333/3080448 [00:00<00:02, 878540.40px/s]📤 Writing LUT:  28%|██▊       | 874763/3080448 [00:01<00:02, 880310.33px/s]📤 Writing LUT:  31%|███▏      | 963982/3080448 [00:01<00:02, 883936.13px/s]📤 Writing LUT:  34%|███▍      | 1052378/3080448 [00:01<00:02, 877169.31px/s]📤 Writing LUT:  37%|███▋      | 1140108/3080448 [00:01<00:02, 873470.72px/s]📤 Writing LUT:  40%|███▉      | 1230459/3080448 [00:01<00:02, 882473.18px/s]📤 Writing LUT:  43%|████▎     | 1318720/3080448 [00:01<00:02, 873966.62px/s]📤 Writing LUT:  46%|████▌     | 1409330/3080448 [00:01<00:01, 883537.34px/s]📤 Writing LUT:  49%|████▊     | 1497711/3080448 [00:01<00:01, 878807.62px/s]📤 Writing LUT:  51%|█████▏    | 1586109/3080448 [00:01<00:01, 880345.72px/s]📤 Writing LUT:  54%|█████▍    | 1674160/3080448 [00:01<00:01, 876833.57px/s]📤 Writing LUT:  57%|█████▋    | 1761856/3080448 [00:02<00:01, 876567.51px/s]📤 Writing LUT:  60%|██████    | 1851783/3080448 [00:02<00:01, 883343.62px/s]📤 Writing LUT:  63%|██████▎   | 1940127/3080448 [00:02<00:01, 879058.79px/s]📤 Writing LUT:  66%|██████▌   | 2028043/3080448 [00:02<00:01, 878403.34px/s]📤 Writing LUT:  69%|██████▊   | 2117729/3080448 [00:02<00:01, 883912.71px/s]📤 Writing LUT:  72%|███████▏  | 2206128/3080448 [00:02<00:00, 875582.93px/s]📤 Writing LUT:  74%|███████▍  | 2293707/3080448 [00:02<00:00, 870627.58px/s]📤 Writing LUT:  77%|███████▋  | 2380787/3080448 [00:02<00:00, 869995.37px/s]📤 Writing LUT:  80%|████████  | 2469924/3080448 [00:02<00:00, 876357.80px/s]📤 Writing LUT:  83%|████████▎ | 2558184/3080448 [00:02<00:00, 878217.43px/s]📤 Writing LUT:  86%|████████▌ | 2648210/3080448 [00:03<00:00, 884804.65px/s]📤 Writing LUT:  89%|████████▉ | 2738757/3080448 [00:03<00:00, 890987.34px/s]📤 Writing LUT:  92%|█████████▏| 2828517/3080448 [00:03<00:00, 892965.13px/s]📤 Writing LUT:  95%|█████████▍| 2919053/3080448 [00:03<00:00, 896675.77px/s]📤 Writing LUT:  98%|█████████▊| 3008726/3080448 [00:03<00:00, 896553.90px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 879916.59px/s]
[✅] LUT written to: /working/MDC015_W068_2025-11-25/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W068_2025-11-25' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W068_2025-11-25/calibration/MDC015_W068_2025-11-25_unknown.lut
[🏁] Finished processing MDC015_W068_2025-11-25


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