
🟢 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 [05:06<00:00, 146.16s/it]📁 Processing Rigs: 100%|██████████| 2/2 [05:06<00:00, 153.04s/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...
[📋] 1000: 100 frames selected from 1498
[📋] 500: 100 frames selected from 837
[💾] 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:  10%|█         | 4/40 [00:00<00:00, 36.75it/s]Averaging bright frames:  22%|██▎       | 9/40 [00:00<00:00, 41.80it/s]Averaging bright frames:  35%|███▌      | 14/40 [00:00<00:00, 44.00it/s]Averaging bright frames:  48%|████▊     | 19/40 [00:00<00:00, 44.54it/s]Averaging bright frames:  60%|██████    | 24/40 [00:00<00:00, 44.90it/s]Averaging bright frames:  72%|███████▎  | 29/40 [00:00<00:00, 46.25it/s]Averaging bright frames:  88%|████████▊ | 35/40 [00:00<00:00, 47.74it/s]Averaging bright frames: 100%|██████████| 40/40 [00:00<00:00, 48.40it/s]                                                                        Total time to process (v2): 0.05
[RESULT] Combined: Center=(1004, 744), Radius=841
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(0.079):  47%|████▋     | 47/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  48%|████▊     | 48/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  49%|████▉     | 49/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  50%|█████     | 50/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  51%|█████     | 51/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  52%|█████▏    | 52/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  53%|█████▎    | 53/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  54%|█████▍    | 54/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  55%|█████▌    | 55/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  56%|█████▌    | 56/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  57%|█████▋    | 57/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  58%|█████▊    | 58/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  59%|█████▉    | 59/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  60%|██████    | 60/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  61%|██████    | 61/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  62%|██████▏   | 62/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  63%|██████▎   | 63/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  64%|██████▍   | 64/100 [00:00<00:00, 122.81it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  65%|██████▌   | 65/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  65%|██████▌   | 65/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  66%|██████▌   | 66/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  67%|██████▋   | 67/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  68%|██████▊   | 68/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  69%|██████▉   | 69/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  70%|███████   | 70/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  71%|███████   | 71/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  72%|███████▏  | 72/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  73%|███████▎  | 73/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  74%|███████▍  | 74/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  75%|███████▌  | 75/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  76%|███████▌  | 76/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  77%|███████▋  | 77/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  78%|███████▊  | 78/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  79%|███████▉  | 79/100 [00:00<00:00, 140.77it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  80%|████████  | 80/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  80%|████████  | 80/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  81%|████████  | 81/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  82%|████████▏ | 82/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  83%|████████▎ | 83/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  84%|████████▍ | 84/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  85%|████████▌ | 85/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  86%|████████▌ | 86/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  87%|████████▋ | 87/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  88%|████████▊ | 88/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  89%|████████▉ | 89/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  90%|█████████ | 90/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  91%|█████████ | 91/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  92%|█████████▏| 92/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  93%|█████████▎| 93/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  94%|█████████▍| 94/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  95%|█████████▌| 95/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  96%|█████████▌| 96/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  97%|█████████▋| 97/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  98%|█████████▊| 98/100 [00:00<00:00, 141.24it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  99%|█████████▉| 99/100 [00:00<00:00, 141.24it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.006 | Best: {'fx': 508.333} (0.006):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.006 | Best: {'fx': 508.333} (0.006):   2%|▏         | 1/50 [00:00<00:05,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.013 | Best: {'fx': 509.354} (0.013):   2%|▏         | 1/50 [00:00<00:05,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.026 | Best: {'fx': 515.476} (0.026):   4%|▍         | 2/50 [00:00<00:05,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.014 | Best: {'fx': 515.476} (0.026):   6%|▌         | 3/50 [00:00<00:05,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 516.497} (0.028):   8%|▊         | 4/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.015 | Best: {'fx': 516.497} (0.028):  10%|█         | 5/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.025 | Best: {'fx': 516.497} (0.028):  12%|█▏        | 6/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.021 | Best: {'fx': 516.497} (0.028):  14%|█▍        | 7/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  16%|█▌        | 8/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 518.537} (0.032):  18%|█▊        | 9/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 518.537} (0.032):  20%|██        | 10/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.033 | Best: {'fx': 519.558} (0.033):  22%|██▏       | 11/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.036 | Best: {'fx': 520.578} (0.036):  24%|██▍       | 12/50 [00:00<00:04,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  26%|██▌       | 13/50 [00:00<00:03,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.049 | Best: {'fx': 523.639} (0.049):  28%|██▊       | 14/50 [00:00<00:03,  9.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.049 | Best: {'fx': 523.639} (0.049):  30%|███       | 15/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.043 | Best: {'fx': 523.639} (0.049):  30%|███       | 15/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.051 | Best: {'fx': 524.66} (0.051):  32%|███▏      | 16/50 [00:00<00:00, 83.72it/s] Brute | Start: {'fx': 533.333} (0.079) | Current: 0.055 | Best: {'fx': 525.68} (0.055):  34%|███▍      | 17/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.060 | Best: {'fx': 526.701} (0.060):  36%|███▌      | 18/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.063 | Best: {'fx': 527.721} (0.063):  38%|███▊      | 19/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.076 | Best: {'fx': 529.762} (0.076):  40%|████      | 20/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.074 | Best: {'fx': 529.762} (0.076):  42%|████▏     | 21/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.067 | Best: {'fx': 529.762} (0.076):  44%|████▍     | 22/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.076 | Best: {'fx': 531.803} (0.076):  46%|████▌     | 23/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.079 | Best: {'fx': 532.823} (0.079):  48%|████▊     | 24/50 [00:00<00:00, 83.72it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.079 | Best: {'fx': 532.823} (0.079):  50%|█████     | 25/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.049 | Best: {'fx': 532.823} (0.079):  50%|█████     | 25/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.073 | Best: {'fx': 532.823} (0.079):  52%|█████▏    | 26/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.057 | Best: {'fx': 532.823} (0.079):  54%|█████▍    | 27/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.054 | Best: {'fx': 532.823} (0.079):  56%|█████▌    | 28/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 532.823} (0.079):  58%|█████▊    | 29/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.044 | Best: {'fx': 532.823} (0.079):  60%|██████    | 30/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 532.823} (0.079):  62%|██████▏   | 31/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.059 | Best: {'fx': 532.823} (0.079):  64%|██████▍   | 32/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.046 | Best: {'fx': 532.823} (0.079):  66%|██████▌   | 33/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.041 | Best: {'fx': 532.823} (0.079):  68%|██████▊   | 34/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.039 | Best: {'fx': 532.823} (0.079):  70%|███████   | 35/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.037 | Best: {'fx': 532.823} (0.079):  72%|███████▏  | 36/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.034 | Best: {'fx': 532.823} (0.079):  74%|███████▍  | 37/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 532.823} (0.079):  76%|███████▌  | 38/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.036 | Best: {'fx': 532.823} (0.079):  78%|███████▊  | 39/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 532.823} (0.079):  80%|████████  | 40/50 [00:00<00:00, 83.19it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 532.823} (0.079):  82%|████████▏ | 41/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.029 | Best: {'fx': 532.823} (0.079):  82%|████████▏ | 41/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.025 | Best: {'fx': 532.823} (0.079):  84%|████████▍ | 42/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 532.823} (0.079):  86%|████████▌ | 43/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 532.823} (0.079):  88%|████████▊ | 44/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 532.823} (0.079):  90%|█████████ | 45/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.027 | Best: {'fx': 532.823} (0.079):  92%|█████████▏| 46/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.027 | Best: {'fx': 532.823} (0.079):  94%|█████████▍| 47/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.026 | Best: {'fx': 532.823} (0.079):  96%|█████████▌| 48/50 [00:00<00:00, 111.33it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 532.823} (0.079):  98%|█████████▊| 49/50 [00:00<00:00, 111.33it/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: 19.5466: 0iter [00:00, ?iter/s]Initial score: 19.5466: 1iter [00:01,  1.12s/iter]Initial score: 19.5466: 2iter [00:01,  1.29iter/s]Initial score: 19.5466: 3iter [00:02,  1.52iter/s]Initial score: 19.5466: 4iter [00:02,  1.61iter/s]Initial score: 19.5466: 5iter [00:03,  1.78iter/s]Initial score: 19.5466: 6iter [00:03,  1.81iter/s]Initial score: 19.5466: 7iter [00:04,  1.76iter/s]Initial score: 19.5466: 8iter [00:04,  1.96iter/s]Initial score: 19.5466: 9iter [00:05,  1.93iter/s]Initial score: 19.5466: 10iter [00:05,  1.91iter/s]Initial score: 19.5466: 11iter [00:06,  1.87iter/s]Initial score: 19.5466: 12iter [00:06,  2.01iter/s]Initial score: 19.5466: 13iter [00:07,  1.95iter/s]Initial score: 19.5466: 14iter [00:07,  1.89iter/s]Initial score: 19.5466: 15iter [00:08,  1.99iter/s]Initial score: 19.5466: 16iter [00:08,  1.97iter/s]Initial score: 19.5466: 17iter [00:09,  1.92iter/s]Initial score: 19.5466: 18iter [00:09,  1.91iter/s]Initial score: 19.5466: 19iter [00:10,  1.86iter/s]Initial score: 19.5466: 20iter [00:11,  1.83iter/s]Initial score: 19.5466: 21iter [00:11,  1.87iter/s]Initial score: 19.5466: 22iter [00:12,  1.89iter/s]Initial score: 19.5466: 23iter [00:12,  1.89iter/s]Initial score: 19.5466: 24iter [00:13,  1.86iter/s]Initial score: 19.5466: 25iter [00:13,  1.99iter/s]Initial score: 19.5466: 26iter [00:14,  1.94iter/s]Initial score: 19.5466: 27iter [00:14,  1.93iter/s]Initial score: 19.5466: 28iter [00:15,  1.95iter/s]Initial score: 19.5466: 29iter [00:15,  1.86iter/s]Initial score: 19.5466: 30iter [00:16,  1.85iter/s]Initial score: 19.5466: 31iter [00:16,  1.94iter/s]Initial score: 19.5466: 32iter [00:17,  1.92iter/s]Initial score: 19.5466: 33iter [00:17,  1.88iter/s]Initial score: 19.5466: 34iter [00:18,  1.88iter/s]Initial score: 19.5466: 35iter [00:18,  1.90iter/s]Initial score: 19.5466: 36iter [00:19,  1.83iter/s]Initial score: 19.5466: 37iter [00:20,  1.83iter/s]Initial score: 19.5466: 38iter [00:20,  1.81iter/s]Initial score: 19.5466: 39iter [00:21,  1.82iter/s]Initial score: 19.5466: 40iter [00:21,  1.91iter/s]Initial score: 19.5466: 41iter [00:22,  1.89iter/s]Initial score: 19.5466: 42iter [00:22,  1.90iter/s]Initial score: 19.5466: 43iter [00:23,  1.89iter/s]Initial score: 19.5466: 44iter [00:23,  1.85iter/s]Initial score: 19.5466: 45iter [00:24,  1.89iter/s]Initial score: 19.5466: 46iter [00:24,  1.87iter/s]Initial score: 19.5466: 47iter [00:25,  1.86iter/s]Initial score: 19.5466: 48iter [00:25,  1.83iter/s]Initial score: 19.5466: 49iter [00:26,  1.82iter/s]
[ITER 50] Current best score: 18.9531
[PARAMS] Current best parameters:
  fx: 521.7534
  fy: 522.1722
  px: 999.7981
  py: 744.1917
  k1: 0.00630515
  k2: 0.00648253
  k3: 0.00195545
  k4: -0.00260234
  p1: 0.00378600
  p2: -0.00473961
  p3: 0.00198916
  p4: 0.00255178
  scale: 2.9674
  rotation: -0.5332
Best score: 18.9531: : 49iter [00:27,  1.82iter/s] Best score: 18.9531: : 50iter [00:27,  1.81iter/s]Best score: 18.9531: : 51iter [00:27,  1.83iter/s]Best score: 18.9531: : 52iter [00:28,  1.88iter/s]Best score: 18.9531: : 53iter [00:28,  1.82iter/s]Best score: 18.9531: : 54iter [00:29,  1.80iter/s]Best score: 18.9531: : 55iter [00:29,  1.83iter/s]Best score: 18.9531: : 56iter [00:30,  1.88iter/s]Best score: 18.9531: : 57iter [00:30,  1.83iter/s]Best score: 18.9531: : 58iter [00:31,  1.80iter/s]Best score: 18.9531: : 59iter [00:31,  1.81iter/s]Best score: 18.9531: : 60iter [00:32,  1.88iter/s]Best score: 18.9531: : 61iter [00:32,  1.88iter/s]Best score: 18.9531: : 62iter [00:33,  1.90iter/s]Best score: 18.9531: : 63iter [00:34,  1.86iter/s]Best score: 18.9531: : 64iter [00:34,  1.85iter/s]Best score: 18.9531: : 65iter [00:35,  1.78iter/s]Best score: 18.9531: : 66iter [00:35,  1.82iter/s]Best score: 18.9531: : 67iter [00:36,  1.90iter/s]Best score: 18.9531: : 68iter [00:36,  1.89iter/s]Best score: 18.9531: : 69iter [00:37,  1.85iter/s]Best score: 18.9531: : 70iter [00:37,  1.86iter/s]Best score: 18.9531: : 71iter [00:38,  1.90iter/s]Best score: 18.9531: : 72iter [00:38,  1.87iter/s]Best score: 18.9531: : 73iter [00:39,  1.82iter/s]Best score: 18.9531: : 74iter [00:40,  1.83iter/s]Best score: 18.9531: : 75iter [00:40,  1.83iter/s]Best score: 18.9531: : 76iter [00:41,  1.88iter/s]Best score: 18.9531: : 77iter [00:41,  1.85iter/s]Best score: 18.9531: : 78iter [00:42,  1.92iter/s]Best score: 18.9531: : 79iter [00:42,  1.94iter/s]Best score: 18.9531: : 80iter [00:43,  1.95iter/s]Best score: 18.9531: : 81iter [00:43,  2.06iter/s]Best score: 11.5436: : 81iter [00:44,  2.06iter/s]Best score: 11.5436: : 82iter [00:44,  1.97iter/s]Best score: 11.5436: : 83iter [00:44,  1.93iter/s]Best score: 11.5436: : 84iter [00:45,  1.92iter/s]Best score: 11.5436: : 85iter [00:45,  1.90iter/s]Best score: 11.5436: : 86iter [00:46,  1.85iter/s]Best score: 11.5436: : 87iter [00:46,  1.88iter/s]Best score: 11.5436: : 88iter [00:47,  1.85iter/s]Best score: 11.5436: : 89iter [00:47,  1.85iter/s]Best score: 11.5436: : 90iter [00:48,  1.88iter/s]Best score: 11.5436: : 91iter [00:48,  1.89iter/s]Best score: 11.5436: : 92iter [00:49,  1.84iter/s]Best score: 11.5436: : 93iter [00:50,  1.85iter/s]Best score: 11.5436: : 94iter [00:50,  1.83iter/s]Best score: 11.5436: : 95iter [00:51,  1.82iter/s]Best score: 11.5436: : 96iter [00:51,  1.86iter/s]Best score: 11.5436: : 97iter [00:52,  1.85iter/s]Best score: 11.5436: : 98iter [00:52,  1.85iter/s]Best score: 11.5436: : 99iter [00:53,  1.90iter/s]
[ITER 100] Current best score: 13.8850
[PARAMS] Current best parameters:
  fx: 507.8753
  fy: 509.5573
  px: 996.5348
  py: 746.2102
  k1: -0.00450250
  k2: 0.01644724
  k3: 0.00266570
  k4: -0.00337825
  p1: 0.00595652
  p2: -0.00715232
  p3: 0.00162585
  p4: 0.00335863
  scale: 3.1476
  rotation: 0.3484
Best score: 11.5436: : 100iter [00:53,  1.87iter/s]Best score: 11.5436: : 101iter [00:54,  1.84iter/s]Best score: 11.5436: : 102iter [00:54,  1.78iter/s]Best score: 11.5436: : 103iter [00:55,  1.80iter/s]Best score: 11.5436: : 104iter [00:56,  1.82iter/s]Best score: 11.5436: : 105iter [00:56,  1.84iter/s]Best score: 11.5436: : 106iter [00:57,  1.86iter/s]Best score: 11.5436: : 107iter [00:57,  1.81iter/s]Best score: 11.5436: : 108iter [00:58,  1.84iter/s]Best score: 9.9571: : 108iter [00:58,  1.84iter/s] Best score: 9.9571: : 109iter [00:58,  1.87iter/s]Best score: 9.9571: : 110iter [00:59,  1.79iter/s]Best score: 9.9571: : 111iter [00:59,  1.79iter/s]Best score: 9.9571: : 112iter [01:00,  1.84iter/s]Best score: 9.9571: : 113iter [01:00,  1.83iter/s]Best score: 9.9571: : 114iter [01:01,  1.81iter/s]Best score: 9.9571: : 115iter [01:02,  1.78iter/s]Best score: 9.9571: : 116iter [01:02,  1.79iter/s]Best score: 9.9571: : 117iter [01:03,  1.84iter/s]Best score: 9.9571: : 118iter [01:03,  1.77iter/s]Best score: 9.9571: : 119iter [01:04,  1.85iter/s]Best score: 9.9571: : 120iter [01:04,  1.82iter/s]Best score: 9.9571: : 121iter [01:05,  1.82iter/s]Best score: 9.9571: : 122iter [01:05,  1.85iter/s]Best score: 9.9571: : 123iter [01:06,  1.87iter/s]Best score: 9.9571: : 124iter [01:06,  1.86iter/s]Best score: 9.9571: : 125iter [01:07,  1.90iter/s]Best score: 9.9571: : 126iter [01:08,  1.89iter/s]Best score: 9.9571: : 127iter [01:08,  1.90iter/s]Best score: 9.9571: : 128iter [01:09,  1.95iter/s]Best score: 9.9571: : 129iter [01:09,  1.94iter/s]Best score: 9.9571: : 130iter [01:10,  1.87iter/s]Best score: 9.9571: : 131iter [01:10,  1.85iter/s]Best score: 9.9571: : 132iter [01:11,  1.87iter/s]Best score: 8.2021: : 132iter [01:11,  1.87iter/s]Best score: 8.2021: : 133iter [01:11,  1.84iter/s]Best score: 8.2021: : 134iter [01:12,  1.80iter/s]Best score: 8.2021: : 135iter [01:12,  1.76iter/s]Best score: 7.5173: : 135iter [01:13,  1.76iter/s]Best score: 7.5173: : 136iter [01:13,  1.79iter/s]Best score: 7.5173: : 137iter [01:14,  1.77iter/s]Best score: 7.5173: : 138iter [01:14,  1.87iter/s]Best score: 7.5173: : 139iter [01:15,  1.85iter/s]Best score: 7.5173: : 140iter [01:15,  1.83iter/s]Best score: 7.5173: : 141iter [01:16,  1.86iter/s]Best score: 7.5173: : 142iter [01:16,  1.85iter/s]Best score: 2.7672: : 142iter [01:17,  1.85iter/s]Best score: 2.7672: : 143iter [01:17,  1.85iter/s]Best score: 2.7672: : 144iter [01:17,  1.83iter/s]Best score: 2.7672: : 145iter [01:18,  1.78iter/s]Best score: 2.7672: : 146iter [01:18,  1.84iter/s]Best score: 2.7672: : 147iter [01:19,  1.85iter/s]Best score: 2.7672: : 148iter [01:19,  1.96iter/s]Best score: 2.7672: : 149iter [01:20,  1.86iter/s]
[ITER 150] Current best score: 2.7672
[PARAMS] Current best parameters:
  fx: 548.6446
  fy: 547.2172
  px: 997.7352
  py: 741.6208
  k1: -0.03901775
  k2: 0.01598204
  k3: -0.00169085
  k4: -0.00057345
  p1: -0.00327151
  p2: -0.00416133
  p3: -0.00034618
  p4: -0.00081831
  scale: 3.1208
  rotation: 0.0743
Best score: 2.7672: : 150iter [01:21,  1.73iter/s]Best score: 2.7672: : 151iter [01:21,  1.88iter/s]Best score: 0.8881: : 151iter [01:22,  1.88iter/s]Best score: 0.8881: : 152iter [01:22,  1.89iter/s]Best score: 0.8881: : 153iter [01:22,  1.86iter/s]Best score: 0.8881: : 154iter [01:23,  1.87iter/s]Best score: 0.8881: : 155iter [01:23,  1.86iter/s]Best score: 0.8881: : 156iter [01:24,  1.86iter/s]Best score: 0.8881: : 157iter [01:24,  1.91iter/s]Best score: 0.8881: : 158iter [01:25,  1.86iter/s]Best score: 0.8881: : 159iter [01:25,  1.85iter/s]Best score: 0.8881: : 160iter [01:26,  1.85iter/s]Best score: 0.8881: : 161iter [01:26,  1.86iter/s]Best score: 0.8881: : 162iter [01:27,  1.84iter/s]Best score: 0.8881: : 163iter [01:27,  1.95iter/s]Best score: 0.8881: : 164iter [01:28,  1.95iter/s]Best score: 0.8881: : 165iter [01:28,  1.92iter/s]Best score: 0.8881: : 166iter [01:29,  1.86iter/s]Best score: 0.8881: : 167iter [01:30,  1.82iter/s]Best score: 0.8881: : 168iter [01:30,  1.83iter/s]Best score: 0.8881: : 169iter [01:31,  1.83iter/s]Best score: 0.8881: : 170iter [01:31,  1.88iter/s]Best score: 0.8881: : 171iter [01:32,  1.88iter/s]Best score: 0.8881: : 172iter [01:32,  1.94iter/s]Best score: 0.8881: : 173iter [01:33,  1.90iter/s]Best score: 0.8881: : 174iter [01:33,  1.85iter/s]Best score: 0.8881: : 175iter [01:34,  1.86iter/s]Best score: 0.8881: : 176iter [01:34,  1.91iter/s]Best score: 0.8881: : 177iter [01:35,  1.89iter/s]Best score: 0.8881: : 178iter [01:35,  1.86iter/s]Best score: 0.8881: : 179iter [01:36,  1.87iter/s]Best score: 0.8881: : 180iter [01:37,  1.80iter/s]Best score: 0.8881: : 181iter [01:37,  1.85iter/s]Best score: 0.8881: : 182iter [01:38,  1.92iter/s]Best score: 0.8881: : 183iter [01:38,  1.93iter/s]Best score: 0.8881: : 184iter [01:39,  1.90iter/s]Best score: 0.8881: : 185iter [01:39,  1.85iter/s]Best score: 0.8881: : 186iter [01:40,  1.82iter/s]Best score: 0.8881: : 187iter [01:40,  1.82iter/s]Best score: 0.8881: : 188iter [01:41,  1.85iter/s]Best score: 0.8881: : 189iter [01:41,  1.90iter/s]Best score: 0.8881: : 190iter [01:42,  1.86iter/s]Best score: 0.8881: : 191iter [01:42,  1.87iter/s]Best score: 0.8881: : 192iter [01:43,  1.90iter/s]Best score: 0.8881: : 193iter [01:44,  1.81iter/s]Best score: 0.8881: : 194iter [01:44,  1.80iter/s]Best score: 0.8881: : 195iter [01:45,  1.83iter/s]Best score: 0.8881: : 196iter [01:45,  1.85iter/s]Best score: 0.8881: : 197iter [01:46,  1.87iter/s]Best score: 0.8881: : 198iter [01:46,  1.86iter/s]Best score: 0.8881: : 199iter [01:47,  1.93iter/s]
[ITER 200] Current best score: 0.8881
[PARAMS] Current best parameters:
  fx: 560.4995
  fy: 560.6250
  px: 1000.5034
  py: 741.1374
  k1: -0.04863691
  k2: 0.03187406
  k3: -0.01455628
  k4: 0.00206350
  p1: -0.00490120
  p2: -0.00041886
  p3: 0.00390125
  p4: -0.00108994
  scale: 3.0251
  rotation: 0.0907
Best score: 0.8881: : 200iter [01:47,  1.85iter/s]Best score: 0.8881: : 201iter [01:48,  1.89iter/s]Best score: 0.8881: : 202iter [01:48,  1.89iter/s]Best score: 0.8881: : 203iter [01:49,  1.83iter/s]Best score: 0.8881: : 204iter [01:49,  1.92iter/s]Best score: 0.8881: : 205iter [01:50,  1.86iter/s]Best score: 0.8881: : 206iter [01:50,  1.91iter/s]Best score: 0.8881: : 207iter [01:51,  1.93iter/s]Best score: 0.8881: : 208iter [01:52,  1.85iter/s]Best score: 0.8881: : 209iter [01:52,  1.87iter/s]Best score: 0.8881: : 210iter [01:53,  1.92iter/s]Best score: 0.8881: : 211iter [01:53,  1.92iter/s]Best score: 0.8881: : 212iter [01:54,  1.94iter/s]Best score: 0.8881: : 213iter [01:54,  1.92iter/s]Best score: 0.8881: : 214iter [01:55,  1.81iter/s]Best score: 0.8881: : 215iter [01:55,  1.79iter/s]Best score: 0.8881: : 216iter [01:56,  1.92iter/s]Best score: 0.8881: : 217iter [01:56,  1.84iter/s]Best score: 0.8881: : 218iter [01:57,  1.79iter/s]Best score: 0.8881: : 219iter [01:58,  1.78iter/s]Best score: 0.8881: : 220iter [01:58,  1.87iter/s]Best score: 0.8881: : 221iter [01:59,  1.81iter/s]Best score: 0.8881: : 222iter [01:59,  1.88iter/s]Best score: 0.8881: : 223iter [02:00,  1.85iter/s]Best score: 0.8881: : 224iter [02:00,  1.83iter/s]Best score: 0.8881: : 225iter [02:01,  1.90iter/s]Best score: 0.8881: : 226iter [02:01,  1.81iter/s]Best score: 0.8881: : 227iter [02:02,  1.80iter/s]Best score: 0.8881: : 228iter [02:02,  1.89iter/s]Best score: 0.8881: : 229iter [02:03,  1.93iter/s]Best score: 0.8881: : 230iter [02:03,  1.88iter/s]Best score: 0.8881: : 231iter [02:04,  1.88iter/s]Best score: 0.8881: : 232iter [02:04,  1.85iter/s]Best score: 0.8881: : 233iter [02:05,  1.77iter/s]Best score: 0.8881: : 234iter [02:06,  1.86iter/s]Best score: 0.8881: : 235iter [02:06,  1.86iter/s]Best score: 0.8881: : 236iter [02:07,  1.89iter/s]Best score: 0.8881: : 237iter [02:07,  1.97iter/s]Best score: 0.8881: : 238iter [02:08,  1.88iter/s]Best score: 0.8881: : 239iter [02:08,  1.82iter/s]Best score: 0.5361: : 239iter [02:09,  1.82iter/s]Best score: 0.5361: : 240iter [02:09,  1.90iter/s]Best score: 0.5361: : 241iter [02:09,  1.87iter/s]Best score: 0.5361: : 242iter [02:10,  1.89iter/s]Best score: 0.5361: : 243iter [02:10,  1.92iter/s]Best score: 0.5361: : 244iter [02:11,  1.91iter/s]Best score: 0.5361: : 245iter [02:11,  1.86iter/s]Best score: 0.5361: : 246iter [02:12,  1.84iter/s]Best score: 0.5361: : 247iter [02:12,  1.87iter/s]Best score: 0.5361: : 248iter [02:13,  1.88iter/s]Best score: 0.5361: : 249iter [02:13,  1.91iter/s]
[ITER 250] Current best score: 0.5361
[PARAMS] Current best parameters:
  fx: 561.1939
  fy: 560.8052
  px: 1000.5013
  py: 742.5817
  k1: -0.04087489
  k2: 0.01984944
  k3: -0.00959883
  k4: 0.00165606
  p1: -0.00306133
  p2: -0.00097567
  p3: 0.00483601
  p4: -0.00260225
  scale: 3.0268
  rotation: 0.1249
Best score: 0.5361: : 250iter [02:14,  1.94iter/s]Best score: 0.5361: : 251iter [02:15,  1.85iter/s]Best score: 0.5361: : 252iter [02:15,  1.85iter/s]Best score: 0.5361: : 253iter [02:16,  1.82iter/s]Best score: 0.5361: : 254iter [02:16,  1.85iter/s]Best score: 0.5361: : 255iter [02:17,  1.90iter/s]Best score: 0.5361: : 256iter [02:17,  1.87iter/s]Best score: 0.5361: : 257iter [02:18,  1.87iter/s]Best score: 0.5361: : 258iter [02:18,  1.93iter/s]Best score: 0.5361: : 259iter [02:19,  1.87iter/s]Best score: 0.5361: : 260iter [02:19,  1.92iter/s]Best score: 0.5361: : 261iter [02:20,  1.83iter/s]Best score: 0.5361: : 262iter [02:21,  1.79iter/s]Best score: 0.5361: : 263iter [02:21,  1.85iter/s]Best score: 0.5361: : 264iter [02:22,  1.82iter/s]Best score: 0.5361: : 265iter [02:22,  1.82iter/s]Best score: 0.5361: : 266iter [02:23,  1.84iter/s]Best score: 0.5361: : 267iter [02:23,  1.90iter/s]Best score: 0.5361: : 268iter [02:24,  1.88iter/s]Best score: 0.5361: : 269iter [02:24,  1.88iter/s]Best score: 0.5361: : 270iter [02:25,  1.80iter/s]Best score: 0.5361: : 271iter [02:25,  1.72iter/s]Best score: 0.5361: : 272iter [02:26,  1.76iter/s]Best score: 0.5361: : 273iter [02:27,  1.79iter/s]Best score: 0.5361: : 274iter [02:27,  1.87iter/s]Best score: 0.5361: : 275iter [02:28,  1.86iter/s]Best score: 0.5361: : 276iter [02:28,  1.94iter/s]Best score: 0.5361: : 277iter [02:29,  1.93iter/s]Best score: 0.5361: : 278iter [02:29,  1.93iter/s]Best score: 0.5361: : 279iter [02:30,  1.87iter/s]Best score: 0.5361: : 280iter [02:30,  1.93iter/s]Best score: 0.5361: : 281iter [02:31,  1.87iter/s]Best score: 0.5361: : 282iter [02:31,  1.89iter/s]Best score: 0.5361: : 283iter [02:32,  1.86iter/s]Best score: 0.5361: : 284iter [02:32,  1.84iter/s]Best score: 0.5361: : 285iter [02:33,  1.78iter/s]Best score: 0.5361: : 286iter [02:34,  1.80iter/s]Best score: 0.5361: : 287iter [02:34,  1.75iter/s]Best score: 0.5361: : 288iter [02:35,  1.78iter/s]Best score: 0.5361: : 289iter [02:35,  1.75iter/s]Best score: 0.5361: : 290iter [02:36,  1.77iter/s]Best score: 0.5361: : 291iter [02:36,  1.81iter/s]Best score: 0.5361: : 292iter [02:37,  1.77iter/s]Best score: 0.5361: : 293iter [02:37,  1.80iter/s]Best score: 0.5361: : 294iter [02:38,  1.87iter/s]Best score: 0.5361: : 295iter [02:39,  1.83iter/s]Best score: 0.5361: : 296iter [02:39,  1.86iter/s]Best score: 0.5361: : 297iter [02:40,  1.87iter/s]Best score: 0.5361: : 298iter [02:40,  1.81iter/s]Best score: 0.5361: : 299iter [02:41,  1.81iter/s]
[ITER 300] Current best score: 0.5361
[PARAMS] Current best parameters:
  fx: 561.1939
  fy: 560.8052
  px: 1000.5013
  py: 742.5817
  k1: -0.04087489
  k2: 0.01984944
  k3: -0.00959883
  k4: 0.00165606
  p1: -0.00306133
  p2: -0.00097567
  p3: 0.00483601
  p4: -0.00260225
  scale: 3.0268
  rotation: 0.1249
Best score: 0.5361: : 300iter [02:41,  1.76iter/s]Best score: 0.5361: : 301iter [02:42,  1.76iter/s]Best score: 0.5361: : 302iter [02:42,  1.77iter/s]Best score: 0.5361: : 303iter [02:43,  1.77iter/s]Best score: 0.5361: : 304iter [02:44,  1.75iter/s]Best score: 0.5361: : 305iter [02:44,  1.73iter/s]Best score: 0.5361: : 306iter [02:45,  1.77iter/s]Best score: 0.5361: : 307iter [02:45,  1.79iter/s]Best score: 0.5361: : 308iter [02:46,  1.75iter/s]Best score: 0.5361: : 309iter [02:46,  1.75iter/s]Best score: 0.5361: : 310iter [02:47,  1.85iter/s]Best score: 0.5361: : 311iter [02:47,  1.86iter/s]Best score: 0.5361: : 312iter [02:48,  1.86iter/s]Best score: 0.5361: : 313iter [02:49,  1.83iter/s]Best score: 0.5361: : 314iter [02:49,  1.88iter/s]Best score: 0.5361: : 315iter [02:50,  1.87iter/s]Best score: 0.5361: : 316iter [02:50,  1.94iter/s]Best score: 0.5361: : 317iter [02:51,  1.88iter/s]Best score: 0.5361: : 318iter [02:51,  1.84iter/s]Best score: 0.5361: : 319iter [02:52,  1.78iter/s]Best score: 0.5361: : 320iter [02:52,  1.79iter/s]Best score: 0.5089: : 320iter [02:53,  1.79iter/s]Best score: 0.5089: : 321iter [02:53,  1.84iter/s]Best score: 0.5089: : 322iter [02:53,  1.88iter/s]Best score: 0.5089: : 323iter [02:54,  1.89iter/s]Best score: 0.5089: : 324iter [02:54,  1.91iter/s]Best score: 0.5089: : 325iter [02:55,  1.90iter/s]Best score: 0.5089: : 326iter [02:55,  1.90iter/s]Best score: 0.5089: : 327iter [02:56,  1.94iter/s]Best score: 0.5089: : 328iter [02:56,  1.90iter/s]Best score: 0.5089: : 329iter [02:57,  1.83iter/s]Best score: 0.5089: : 330iter [02:58,  1.91iter/s]Best score: 0.5089: : 331iter [02:58,  1.91iter/s]Best score: 0.5089: : 332iter [02:59,  1.90iter/s]Best score: 0.4820: : 332iter [02:59,  1.90iter/s]Best score: 0.4820: : 333iter [02:59,  2.00iter/s]Best score: 0.4820: : 334iter [03:00,  2.02iter/s]Best score: 0.4820: : 335iter [03:00,  1.98iter/s]Best score: 0.4820: : 336iter [03:01,  1.95iter/s]Best score: 0.4820: : 337iter [03:01,  1.94iter/s]Best score: 0.4820: : 338iter [03:02,  1.93iter/s]Best score: 0.4820: : 339iter [03:02,  1.90iter/s]Best score: 0.4820: : 340iter [03:03,  1.96iter/s]Best score: 0.4820: : 341iter [03:03,  1.89iter/s]Best score: 0.4820: : 342iter [03:04,  1.83iter/s]Best score: 0.4820: : 343iter [03:04,  1.89iter/s]Best score: 0.4820: : 344iter [03:05,  1.94iter/s]Best score: 0.4820: : 345iter [03:05,  1.85iter/s]Best score: 0.4820: : 346iter [03:06,  1.81iter/s]Best score: 0.4820: : 347iter [03:07,  1.82iter/s]Best score: 0.4820: : 348iter [03:07,  1.79iter/s]Best score: 0.4820: : 349iter [03:08,  1.93iter/s]
[ITER 350] Current best score: 0.4913
[PARAMS] Current best parameters:
  fx: 558.4973
  fy: 558.1342
  px: 1001.2013
  py: 741.7346
  k1: -0.05122974
  k2: 0.04250058
  k3: -0.02129251
  k4: 0.00337179
  p1: -0.00394042
  p2: 0.00098596
  p3: 0.00592025
  p4: -0.00315790
  scale: 3.0162
  rotation: 0.0249
Best score: 0.4820: : 350iter [03:08,  1.80iter/s]Best score: 0.4820: : 351iter [03:09,  1.78iter/s]Best score: 0.4820: : 352iter [03:09,  1.81iter/s]Best score: 0.4820: : 353iter [03:10,  1.78iter/s]Best score: 0.4820: : 354iter [03:10,  1.81iter/s]Best score: 0.4820: : 355iter [03:11,  1.81iter/s]Best score: 0.4820: : 356iter [03:12,  1.76iter/s]Best score: 0.4820: : 357iter [03:12,  1.77iter/s]Best score: 0.4820: : 358iter [03:13,  1.85iter/s]Best score: 0.4820: : 359iter [03:13,  1.85iter/s]Best score: 0.4820: : 360iter [03:14,  1.87iter/s]Best score: 0.4820: : 361iter [03:14,  1.88iter/s]Best score: 0.4820: : 362iter [03:15,  1.91iter/s]Best score: 0.4820: : 363iter [03:15,  1.91iter/s]Best score: 0.4820: : 364iter [03:16,  1.87iter/s]Best score: 0.4820: : 365iter [03:16,  1.83iter/s]Best score: 0.4820: : 366iter [03:17,  1.84iter/s]Best score: 0.4820: : 367iter [03:17,  1.85iter/s]Best score: 0.4820: : 368iter [03:18,  1.83iter/s]Best score: 0.4820: : 369iter [03:19,  1.77iter/s]Best score: 0.4820: : 370iter [03:19,  1.80iter/s]Best score: 0.4820: : 371iter [03:20,  1.83iter/s]Best score: 0.4820: : 372iter [03:20,  1.86iter/s]Best score: 0.4820: : 373iter [03:21,  1.86iter/s]Best score: 0.4820: : 374iter [03:21,  1.92iter/s]Best score: 0.4717: : 374iter [03:22,  1.92iter/s]Best score: 0.4717: : 375iter [03:22,  1.88iter/s]Best score: 0.4717: : 376iter [03:22,  1.83iter/s]Best score: 0.4717: : 377iter [03:23,  1.92iter/s]Best score: 0.4717: : 378iter [03:23,  1.87iter/s]Best score: 0.4717: : 379iter [03:24,  1.81iter/s]Best score: 0.4717: : 380iter [03:24,  1.88iter/s]Best score: 0.4717: : 381iter [03:25,  1.89iter/s]Best score: 0.4717: : 382iter [03:25,  1.90iter/s]Best score: 0.4717: : 383iter [03:26,  1.88iter/s]Best score: 0.4717: : 384iter [03:27,  1.88iter/s]Best score: 0.4169: : 384iter [03:27,  1.88iter/s]Best score: 0.4169: : 385iter [03:27,  1.88iter/s]Best score: 0.4169: : 386iter [03:28,  1.87iter/s]Best score: 0.4169: : 387iter [03:28,  1.79iter/s]Best score: 0.4169: : 388iter [03:29,  1.83iter/s]Best score: 0.4169: : 389iter [03:29,  1.80iter/s]Best score: 0.4169: : 390iter [03:30,  1.79iter/s]Best score: 0.4169: : 391iter [03:30,  1.80iter/s]Best score: 0.4169: : 392iter [03:31,  1.83iter/s]Best score: 0.4169: : 393iter [03:31,  1.83iter/s]Best score: 0.4169: : 394iter [03:32,  1.77iter/s]Best score: 0.4169: : 395iter [03:33,  1.72iter/s]Best score: 0.4169: : 396iter [03:33,  1.77iter/s]Best score: 0.4169: : 397iter [03:34,  1.79iter/s]Best score: 0.4169: : 398iter [03:34,  1.80iter/s]Best score: 0.4169: : 399iter [03:35,  1.83iter/s]
[ITER 400] Current best score: 0.4169
[PARAMS] Current best parameters:
  fx: 555.5799
  fy: 555.1246
  px: 1000.7074
  py: 740.2343
  k1: -0.04529258
  k2: 0.04423940
  k3: -0.02703400
  k4: 0.00543299
  p1: -0.00792370
  p2: -0.00025487
  p3: 0.00665730
  p4: -0.00177234
  scale: 3.0145
  rotation: 0.0023
Best score: 0.4169: : 400iter [03:35,  1.74iter/s]Best score: 0.4169: : 401iter [03:36,  1.72iter/s]Best score: 0.4169: : 402iter [03:37,  1.81iter/s]Best score: 0.4169: : 403iter [03:37,  1.78iter/s]Best score: 0.4169: : 404iter [03:38,  1.80iter/s]Best score: 0.4169: : 405iter [03:38,  1.83iter/s]Best score: 0.4169: : 406iter [03:39,  1.88iter/s]Best score: 0.4169: : 407iter [03:39,  1.88iter/s]Best score: 0.4169: : 408iter [03:40,  1.87iter/s]Best score: 0.4169: : 409iter [03:40,  1.87iter/s]Best score: 0.4169: : 410iter [03:41,  1.85iter/s]Best score: 0.4169: : 411iter [03:41,  1.84iter/s]Best score: 0.4169: : 412iter [03:42,  1.79iter/s]Best score: 0.4169: : 413iter [03:43,  1.75iter/s]Best score: 0.4169: : 414iter [03:43,  1.82iter/s]Best score: 0.4169: : 415iter [03:44,  1.92iter/s]Best score: 0.4169: : 416iter [03:44,  1.83iter/s]Best score: 0.4169: : 417iter [03:45,  1.80iter/s]Best score: 0.4169: : 418iter [03:45,  1.79iter/s]Best score: 0.4169: : 419iter [03:46,  1.81iter/s]Best score: 0.4169: : 420iter [03:46,  1.92iter/s]Best score: 0.4169: : 421iter [03:47,  1.97iter/s]Best score: 0.4169: : 422iter [03:47,  1.87iter/s]Best score: 0.4169: : 423iter [03:48,  1.82iter/s]Best score: 0.4169: : 424iter [03:49,  1.82iter/s]Best score: 0.4169: : 425iter [03:49,  1.79iter/s]Best score: 0.4169: : 426iter [03:50,  1.80iter/s]Best score: 0.2869: : 426iter [03:50,  1.80iter/s]Best score: 0.2869: : 427iter [03:50,  1.83iter/s]Best score: 0.2869: : 428iter [03:51,  1.83iter/s]Best score: 0.2869: : 429iter [03:51,  1.78iter/s]Best score: 0.2869: : 430iter [03:52,  1.77iter/s]Best score: 0.2869: : 431iter [03:52,  1.80iter/s]Best score: 0.2869: : 432iter [03:53,  1.81iter/s]Best score: 0.2869: : 433iter [03:53,  1.90iter/s]Best score: 0.2869: : 434iter [03:54,  1.83iter/s]Best score: 0.2869: : 435iter [03:55,  1.84iter/s]Best score: 0.2869: : 436iter [03:55,  1.87iter/s]Best score: 0.2869: : 437iter [03:56,  1.93iter/s]Best score: 0.2869: : 438iter [03:56,  1.90iter/s]Best score: 0.2869: : 439iter [03:57,  1.89iter/s]Best score: 0.2869: : 440iter [03:57,  1.94iter/s]Best score: 0.2869: : 441iter [03:58,  1.90iter/s]Best score: 0.2869: : 442iter [03:58,  1.81iter/s]Best score: 0.2869: : 443iter [03:59,  1.84iter/s]Best score: 0.2869: : 444iter [03:59,  1.76iter/s]Best score: 0.2869: : 445iter [04:00,  1.78iter/s]Best score: 0.2869: : 446iter [04:01,  1.78iter/s]Best score: 0.2869: : 447iter [04:01,  1.77iter/s]Best score: 0.2869: : 448iter [04:02,  1.77iter/s]Best score: 0.2869: : 449iter [04:02,  1.80iter/s]
[ITER 450] Current best score: 0.2869
[PARAMS] Current best parameters:
  fx: 554.8257
  fy: 554.1446
  px: 1001.0960
  py: 740.9092
  k1: -0.04484199
  k2: 0.04455775
  k3: -0.02765503
  k4: 0.00568394
  p1: -0.00682522
  p2: 0.00046898
  p3: 0.00722704
  p4: -0.00257463
  scale: 3.0210
  rotation: 0.0190
Best score: 0.2869: : 450iter [04:03,  1.77iter/s]Best score: 0.2869: : 451iter [04:03,  1.79iter/s]Best score: 0.2869: : 452iter [04:04,  1.82iter/s]Best score: 0.2869: : 453iter [04:04,  1.79iter/s]Best score: 0.2869: : 454iter [04:05,  1.81iter/s]Best score: 0.2869: : 455iter [04:05,  1.90iter/s]Best score: 0.2869: : 456iter [04:06,  1.78iter/s]Best score: 0.2869: : 457iter [04:07,  1.82iter/s]Best score: 0.2869: : 458iter [04:07,  1.83iter/s]Best score: 0.2869: : 459iter [04:08,  1.82iter/s]Best score: 0.2869: : 460iter [04:08,  1.86iter/s]Best score: 0.2869: : 461iter [04:09,  1.86iter/s]Best score: 0.2869: : 462iter [04:09,  1.88iter/s]Best score: 0.2869: : 463iter [04:10,  1.85iter/s]Best score: 0.2869: : 464iter [04:10,  1.86iter/s]Best score: 0.2869: : 465iter [04:11,  1.95iter/s]Best score: 0.2869: : 466iter [04:11,  1.91iter/s]Best score: 0.2869: : 467iter [04:12,  1.88iter/s]Best score: 0.2869: : 468iter [04:12,  1.98iter/s]Best score: 0.2869: : 469iter [04:13,  1.98iter/s]Best score: 0.2869: : 470iter [04:13,  1.97iter/s]Best score: 0.2869: : 471iter [04:14,  2.00iter/s]Best score: 0.2869: : 472iter [04:14,  1.89iter/s]Best score: 0.2869: : 473iter [04:15,  1.90iter/s]Best score: 0.2869: : 474iter [04:16,  1.85iter/s]Best score: 0.2869: : 475iter [04:16,  1.87iter/s]Best score: 0.2869: : 476iter [04:17,  1.92iter/s]Best score: 0.2869: : 477iter [04:17,  1.93iter/s]Best score: 0.2869: : 478iter [04:18,  1.87iter/s]Best score: 0.2869: : 479iter [04:18,  1.88iter/s]Best score: 0.2869: : 480iter [04:19,  1.95iter/s]Best score: 0.2869: : 481iter [04:19,  1.92iter/s]Best score: 0.2869: : 482iter [04:20,  1.84iter/s]Best score: 0.2869: : 483iter [04:20,  1.91iter/s]Best score: 0.2869: : 484iter [04:21,  1.92iter/s]Best score: 0.2869: : 485iter [04:21,  1.89iter/s]Best score: 0.2869: : 486iter [04:22,  1.90iter/s]Best score: 0.2869: : 487iter [04:22,  1.93iter/s]Best score: 0.2869: : 488iter [04:23,  1.89iter/s]Best score: 0.2869: : 489iter [04:23,  1.91iter/s]Best score: 0.2869: : 490iter [04:24,  1.91iter/s]Best score: 0.2869: : 491iter [04:24,  1.89iter/s]Best score: 0.2869: : 492iter [04:25,  1.90iter/s]Best score: 0.2869: : 493iter [04:26,  1.88iter/s]Best score: 0.2869: : 494iter [04:26,  1.86iter/s]Best score: 0.2869: : 495iter [04:27,  1.94iter/s]Best score: 0.2869: : 496iter [04:27,  1.90iter/s]Best score: 0.2869: : 497iter [04:28,  1.86iter/s]Best score: 0.2869: : 498iter [04:28,  1.98iter/s]Best score: 0.2869: : 499iter [04:29,  2.01iter/s]
[ITER 500] Current best score: 0.2869
[PARAMS] Current best parameters:
  fx: 554.8257
  fy: 554.1446
  px: 1001.0960
  py: 740.9092
  k1: -0.04484199
  k2: 0.04455775
  k3: -0.02765503
  k4: 0.00568394
  p1: -0.00682522
  p2: 0.00046898
  p3: 0.00722704
  p4: -0.00257463
  scale: 3.0210
  rotation: 0.0190
Best score: 0.2869: : 500iter [04:29,  1.82iter/s]Best score: 0.2869: : 500iter [04:46,  1.74iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.266092 -> 0.257397
  Success: True
  Tangential distortion: p1=-0.006737, p2=0.000661, p3=0.006448, p4=-0.002650
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2574
[✅] 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: 554.8257486166854
    fy: 554.144648197294
    px: 1001.0960025742058
    py: 740.9091783624312
    k1: -0.04481203126617009
    k2: 0.04455769737572664
    k3: -0.02766803584877575
    k4: 0.005691115046091378
    p1: -0.006736757722027329
    p2: 0.0006611309243124361
    p3: 0.0064479137547657096
    p4: -0.0026499036767538646
    scale: 3.0209966504711003
    rotation: 0.020181317102569367
    mmpp: 1.2818629073282908
    nmpp: 1281862.9073282909

[🔄] 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%|▎         | 81080/3080448 [00:00<00:03, 810796.91px/s]📤 Writing LUT:   5%|▌         | 169386/3080448 [00:00<00:03, 853301.43px/s]📤 Writing LUT:   8%|▊         | 257574/3080448 [00:00<00:03, 866348.52px/s]📤 Writing LUT:  11%|█         | 346349/3080448 [00:00<00:03, 874796.24px/s]📤 Writing LUT:  14%|█▍        | 437648/3080448 [00:00<00:02, 888568.68px/s]📤 Writing LUT:  17%|█▋        | 526505/3080448 [00:00<00:02, 888312.35px/s]📤 Writing LUT:  20%|█▉        | 615358/3080448 [00:00<00:02, 888382.38px/s]📤 Writing LUT:  23%|██▎       | 704197/3080448 [00:00<00:02, 884603.45px/s]📤 Writing LUT:  26%|██▌       | 794037/3080448 [00:00<00:02, 888901.71px/s]📤 Writing LUT:  29%|██▊       | 883884/3080448 [00:01<00:02, 891849.07px/s]📤 Writing LUT:  32%|███▏      | 973073/3080448 [00:01<00:02, 890839.52px/s]📤 Writing LUT:  34%|███▍      | 1062160/3080448 [00:01<00:02, 885659.15px/s]📤 Writing LUT:  37%|███▋      | 1153278/3080448 [00:01<00:02, 893348.74px/s]📤 Writing LUT:  40%|████      | 1242623/3080448 [00:01<00:02, 893027.08px/s]📤 Writing LUT:  43%|████▎     | 1331933/3080448 [00:01<00:01, 891468.80px/s]📤 Writing LUT:  46%|████▌     | 1421803/3080448 [00:01<00:01, 893605.33px/s]📤 Writing LUT:  49%|████▉     | 1511168/3080448 [00:01<00:01, 892608.95px/s]📤 Writing LUT:  52%|█████▏    | 1600563/3080448 [00:01<00:01, 893009.29px/s]📤 Writing LUT:  55%|█████▍    | 1689866/3080448 [00:01<00:01, 884217.58px/s]📤 Writing LUT:  58%|█████▊    | 1778308/3080448 [00:02<00:01, 881866.15px/s]📤 Writing LUT:  61%|██████    | 1866509/3080448 [00:02<00:01, 880426.02px/s]📤 Writing LUT:  63%|██████▎   | 1955139/3080448 [00:02<00:01, 882175.21px/s]📤 Writing LUT:  66%|██████▋   | 2043364/3080448 [00:02<00:01, 877934.78px/s]📤 Writing LUT:  69%|██████▉   | 2133209/3080448 [00:02<00:01, 884048.43px/s]📤 Writing LUT:  72%|███████▏  | 2221624/3080448 [00:02<00:00, 882940.44px/s]📤 Writing LUT:  75%|███████▌  | 2311712/3080448 [00:02<00:00, 888298.30px/s]📤 Writing LUT:  78%|███████▊  | 2400549/3080448 [00:02<00:00, 884034.36px/s]📤 Writing LUT:  81%|████████  | 2488961/3080448 [00:02<00:00, 869021.48px/s]📤 Writing LUT:  84%|████████▎ | 2577814/3080448 [00:02<00:00, 874779.92px/s]📤 Writing LUT:  87%|████████▋ | 2666479/3080448 [00:03<00:00, 878300.62px/s]📤 Writing LUT:  90%|████████▉ | 2757636/3080448 [00:03<00:00, 888201.73px/s]📤 Writing LUT:  92%|█████████▏| 2848836/3080448 [00:03<00:00, 895300.04px/s]📤 Writing LUT:  95%|█████████▌| 2939936/3080448 [00:03<00:00, 899991.01px/s]📤 Writing LUT:  98%|█████████▊| 3031230/3080448 [00:03<00:00, 903862.99px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 886606.42px/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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