
🟢 Starting Calibration Pipeline

=== Processing MDC103_W050_2025-11-03 ===

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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 [04:15<00:00, 131.17s/it]📁 Processing Rigs: 100%|██████████| 2/2 [04:15<00:00, 127.60s/it]
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-11-03/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2098 frames from 2 rigs to /working/MDC103_W050_2025-11-03/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-11-03/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 874
[📋] 1000: 100 frames selected from 1224
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-11-03/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC103_W050_2025-11-03/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames
Averaging bright frames:   0%|          | 0/40 [00:00<?, ?it/s]Averaging bright frames:  10%|█         | 4/40 [00:00<00:01, 34.38it/s]Averaging bright frames:  22%|██▎       | 9/40 [00:00<00:00, 42.63it/s]Averaging bright frames:  38%|███▊      | 15/40 [00:00<00:00, 46.44it/s]Averaging bright frames:  52%|█████▎    | 21/40 [00:00<00:00, 48.04it/s]Averaging bright frames:  65%|██████▌   | 26/40 [00:00<00:00, 47.90it/s]Averaging bright frames:  80%|████████  | 32/40 [00:00<00:00, 48.92it/s]Averaging bright frames:  92%|█████████▎| 37/40 [00:00<00:00, 48.36it/s]                                                                        Total time to process (v2): 0.06
[RESULT] Combined: Center=(820, 754), Radius=676
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   1%|          | 1/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   1%|          | 1/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   7%|▋         | 7/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 356.566} (0.000):   8%|▊         | 8/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 363.636} (0.000):   9%|▉         | 9/100 [00:00<00:10,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 370.707} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 377.778} (0.000):  11%|█         | 11/100 [00:00<00:09,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  12%|█▏        | 12/100 [00:00<00:09,  9.05it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 434.343} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 434.343} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 434.343} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 441.414} (0.000):  20%|██        | 20/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 448.485} (0.000):  21%|██        | 21/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 469.697} (0.000):  22%|██▏       | 22/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 469.697} (0.000):  23%|██▎       | 23/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 497.98} (0.000):  24%|██▍       | 24/100 [00:00<00:01, 69.63it/s] Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 497.98} (0.000):  25%|██▌       | 25/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 497.98} (0.000):  26%|██▌       | 26/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 497.98} (0.000):  27%|██▋       | 27/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 497.98} (0.000):  28%|██▊       | 28/100 [00:00<00:01, 69.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 497.98} (0.000):  29%|██▉       | 29/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 505.051} (0.000):  29%|██▉       | 29/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 526.263} (0.000):  30%|███       | 30/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 526.263} (0.000):  31%|███       | 31/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 526.263} (0.000):  32%|███▏      | 32/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  33%|███▎      | 33/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  34%|███▍      | 34/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  35%|███▌      | 35/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 554.545} (0.000):  36%|███▌      | 36/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.002 | Best: {'fx': 568.687} (0.002):  37%|███▋      | 37/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.002 | Best: {'fx': 568.687} (0.002):  38%|███▊      | 38/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.003 | Best: {'fx': 575.758} (0.003):  39%|███▉      | 39/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.005 | Best: {'fx': 582.828} (0.005):  40%|████      | 40/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 604.04} (0.008):  41%|████      | 41/100 [00:00<00:00, 108.11it/s] Brute | Start: {'fx': 500.0} (0.000) | Current: 0.005 | Best: {'fx': 604.04} (0.008):  42%|████▏     | 42/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 604.04} (0.008):  43%|████▎     | 43/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 604.04} (0.008):  44%|████▍     | 44/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 611.111} (0.009):  45%|████▌     | 45/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  46%|████▌     | 46/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  47%|████▋     | 47/100 [00:00<00:00, 108.11it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  48%|████▊     | 48/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  48%|████▊     | 48/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  49%|████▉     | 49/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  50%|█████     | 50/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  51%|█████     | 51/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  52%|█████▏    | 52/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  53%|█████▎    | 53/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  54%|█████▍    | 54/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 703.03} (0.009):  55%|█████▌    | 55/100 [00:00<00:00, 138.04it/s] Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 703.03} (0.009):  56%|█████▌    | 56/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 703.03} (0.009):  57%|█████▋    | 57/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 710.101} (0.009):  58%|█████▊    | 58/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 724.242} (0.009):  59%|█████▉    | 59/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 731.313} (0.009):  60%|██████    | 60/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 731.313} (0.009):  61%|██████    | 61/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  62%|██████▏   | 62/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  63%|██████▎   | 63/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  64%|██████▍   | 64/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  65%|██████▌   | 65/100 [00:00<00:00, 138.04it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 802.02} (0.009):  66%|██████▌   | 66/100 [00:00<00:00, 138.04it/s] Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 802.02} (0.009):  67%|██████▋   | 67/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 780.808} (0.010):  67%|██████▋   | 67/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  68%|██████▊   | 68/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  69%|██████▉   | 69/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 794.949} (0.010):  70%|███████   | 70/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  71%|███████   | 71/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  72%|███████▏  | 72/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  73%|███████▎  | 73/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  74%|███████▍  | 74/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  75%|███████▌  | 75/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  76%|███████▌  | 76/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  77%|███████▋  | 77/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  78%|███████▊  | 78/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  79%|███████▉  | 79/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  80%|████████  | 80/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  81%|████████  | 81/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  82%|████████▏ | 82/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  83%|████████▎ | 83/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  84%|████████▍ | 84/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  85%|████████▌ | 85/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  86%|████████▌ | 86/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  87%|████████▋ | 87/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  88%|████████▊ | 88/100 [00:00<00:00, 153.45it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  89%|████████▉ | 89/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  89%|████████▉ | 89/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  90%|█████████ | 90/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  91%|█████████ | 91/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  92%|█████████▏| 92/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  93%|█████████▎| 93/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  94%|█████████▍| 94/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  95%|█████████▌| 95/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  96%|█████████▌| 96/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  97%|█████████▋| 97/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  98%|█████████▊| 98/100 [00:00<00:00, 171.63it/s]Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  99%|█████████▉| 99/100 [00:00<00:00, 171.63it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 763.899} (0.010):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 763.899} (0.010):   2%|▏         | 1/50 [00:00<00:05,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.010):   2%|▏         | 1/50 [00:00<00:05,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.010):   4%|▍         | 2/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.010):   6%|▌         | 3/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.010):   8%|▊         | 4/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.010):  10%|█         | 5/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.010):  12%|█▏        | 6/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  14%|█▍        | 7/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  16%|█▌        | 8/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  18%|█▊        | 9/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  20%|██        | 10/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  22%|██▏       | 11/50 [00:00<00:04,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  24%|██▍       | 12/50 [00:00<00:03,  9.64it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  26%|██▌       | 13/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  26%|██▌       | 13/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  28%|██▊       | 14/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  30%|███       | 15/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  32%|███▏      | 16/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  34%|███▍      | 17/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  36%|███▌      | 18/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  38%|███▊      | 19/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  40%|████      | 20/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 785.328} (0.010):  42%|████▏     | 21/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 785.328} (0.010):  44%|████▍     | 22/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 785.328} (0.010):  46%|████▌     | 23/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 785.328} (0.010):  48%|████▊     | 24/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 787.369} (0.010):  50%|█████     | 25/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 787.369} (0.010):  52%|█████▏    | 26/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 787.369} (0.010):  54%|█████▍    | 27/50 [00:00<00:00, 70.44it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 787.369} (0.010):  56%|█████▌    | 28/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  56%|█████▌    | 28/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 789.409} (0.010):  58%|█████▊    | 29/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  60%|██████    | 30/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  62%|██████▏   | 31/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 789.409} (0.010):  64%|██████▍   | 32/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 789.409} (0.010):  66%|██████▌   | 33/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  68%|██████▊   | 34/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  70%|███████   | 35/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  72%|███████▏  | 36/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 789.409} (0.010):  74%|███████▍  | 37/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  76%|███████▌  | 38/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 789.409} (0.010):  78%|███████▊  | 39/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  80%|████████  | 40/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 789.409} (0.010):  82%|████████▏ | 41/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  84%|████████▍ | 42/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 789.409} (0.010):  86%|████████▌ | 43/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 807.777} (0.010):  88%|████████▊ | 44/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 810.838} (0.010):  90%|█████████ | 45/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 810.838} (0.010):  92%|█████████▏| 46/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 810.838} (0.010):  94%|█████████▍| 47/50 [00:00<00:00, 103.63it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 810.838} (0.010):  96%|█████████▌| 48/50 [00:00<00:00, 139.09it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 810.838} (0.010):  96%|█████████▌| 48/50 [00:00<00:00, 139.09it/s]Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 810.838} (0.010):  98%|█████████▊| 49/50 [00:00<00:00, 139.09it/s]                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 787.8787878787879
    px: 820.0
    py: 754.0
    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 10 incomplete frames): 7 frames will be used for optimization
  Rigs: 1000(1000mm):7/20
  Total: 7/20 frames used for optimization
Initial score: 1528.1275: 0iter [00:00, ?iter/s]Best score: 1014.3880: : 0iter [00:01, ?iter/s] Best score: 1014.3880: : 1iter [00:01,  1.18s/iter]Best score: 1014.3880: : 2iter [00:01,  1.26iter/s]Best score: 942.0128: : 2iter [00:02,  1.26iter/s] Best score: 942.0128: : 3iter [00:02,  1.49iter/s]Best score: 942.0128: : 4iter [00:02,  1.63iter/s]Best score: 942.0128: : 5iter [00:03,  1.71iter/s]Best score: 942.0128: : 6iter [00:03,  1.80iter/s]Best score: 927.6954: : 6iter [00:04,  1.80iter/s]Best score: 927.6954: : 7iter [00:04,  1.88iter/s]Best score: 927.6954: : 8iter [00:04,  1.88iter/s]Best score: 927.6954: : 9iter [00:05,  1.92iter/s]Best score: 927.6954: : 10iter [00:05,  1.98iter/s]Best score: 858.4716: : 10iter [00:06,  1.98iter/s]Best score: 858.4716: : 11iter [00:06,  1.97iter/s]Best score: 858.4716: : 12iter [00:06,  1.99iter/s]Best score: 858.4716: : 13iter [00:07,  1.90iter/s]Best score: 858.4716: : 14iter [00:07,  1.91iter/s]Best score: 858.4716: : 15iter [00:08,  1.90iter/s]Best score: 858.4716: : 16iter [00:08,  1.92iter/s]Best score: 858.4716: : 17iter [00:09,  1.94iter/s]Best score: 858.4716: : 18iter [00:09,  1.99iter/s]Best score: 858.4716: : 19iter [00:10,  1.92iter/s]Best score: 858.4716: : 20iter [00:10,  1.90iter/s]Best score: 858.4716: : 21iter [00:11,  1.96iter/s]Best score: 858.4716: : 22iter [00:11,  1.95iter/s]Best score: 858.4716: : 23iter [00:12,  2.00iter/s]Best score: 858.4716: : 24iter [00:12,  1.96iter/s]Best score: 858.4716: : 25iter [00:13,  1.99iter/s]Best score: 858.4716: : 26iter [00:13,  2.10iter/s]Best score: 858.4716: : 27iter [00:14,  2.07iter/s]Best score: 858.4716: : 28iter [00:14,  2.04iter/s]Best score: 858.4716: : 29iter [00:15,  2.11iter/s]Best score: 851.0723: : 29iter [00:15,  2.11iter/s]Best score: 851.0723: : 30iter [00:15,  2.09iter/s]Best score: 826.2263: : 30iter [00:16,  2.09iter/s]Best score: 826.2263: : 31iter [00:16,  2.02iter/s]Best score: 826.2263: : 32iter [00:16,  2.06iter/s]Best score: 826.2263: : 33iter [00:17,  2.03iter/s]Best score: 826.2263: : 34iter [00:17,  2.04iter/s]Best score: 826.2263: : 35iter [00:18,  2.03iter/s]Best score: 826.2263: : 36iter [00:18,  2.02iter/s]Best score: 826.2263: : 37iter [00:19,  2.05iter/s]Best score: 826.2263: : 38iter [00:19,  2.08iter/s]Best score: 826.2263: : 39iter [00:20,  2.07iter/s]Best score: 826.2263: : 40iter [00:20,  2.03iter/s]Best score: 826.2263: : 41iter [00:21,  1.93iter/s]Best score: 826.2263: : 42iter [00:21,  1.95iter/s]Best score: 826.2263: : 43iter [00:22,  2.02iter/s]Best score: 826.2263: : 44iter [00:22,  1.99iter/s]Best score: 826.2263: : 45iter [00:23,  1.91iter/s]Best score: 826.2263: : 46iter [00:23,  2.00iter/s]Best score: 826.2263: : 47iter [00:24,  1.97iter/s]Best score: 826.2263: : 48iter [00:24,  1.94iter/s]Best score: 826.2263: : 49iter [00:25,  2.02iter/s]
[ITER 50] Current best score: 839.6458
[PARAMS] Current best parameters:
  fx: 861.3381
  fy: 726.8102
  px: 859.7589
  py: 765.9750
  k1: 0.04533238
  k2: 0.07797484
  k3: 0.04650303
  k4: 0.01943428
  p1: -0.00098877
  p2: -0.00966518
  p3: -0.00316047
  p4: 0.00177300
  scale: 2.4198
  rotation: -0.2704
Best score: 826.2263: : 50iter [00:25,  1.89iter/s]Best score: 826.2263: : 51iter [00:26,  1.86iter/s]Best score: 826.2263: : 52iter [00:27,  1.87iter/s]Best score: 825.8060: : 52iter [00:27,  1.87iter/s]Best score: 825.8060: : 53iter [00:27,  1.89iter/s]Best score: 825.8060: : 54iter [00:28,  1.91iter/s]Best score: 825.8060: : 55iter [00:28,  1.92iter/s]Best score: 825.8060: : 56iter [00:29,  1.98iter/s]Best score: 825.8060: : 57iter [00:29,  1.94iter/s]Best score: 825.8060: : 58iter [00:30,  1.95iter/s]Best score: 820.2160: : 58iter [00:30,  1.95iter/s]Best score: 820.2160: : 59iter [00:30,  1.95iter/s]Best score: 820.2160: : 60iter [00:31,  1.92iter/s]Best score: 820.2160: : 61iter [00:31,  1.97iter/s]Best score: 820.2160: : 62iter [00:32,  1.95iter/s]Best score: 820.2160: : 63iter [00:32,  1.97iter/s]Best score: 820.2160: : 64iter [00:33,  1.85iter/s]Best score: 820.2160: : 65iter [00:33,  1.87iter/s]Best score: 820.2160: : 66iter [00:34,  1.87iter/s]Best score: 820.2160: : 67iter [00:34,  1.82iter/s]Best score: 820.2160: : 68iter [00:35,  1.88iter/s]Best score: 817.0917: : 68iter [00:35,  1.88iter/s]Best score: 817.0917: : 69iter [00:35,  1.88iter/s]Best score: 817.0917: : 70iter [00:36,  1.94iter/s]Best score: 817.0917: : 71iter [00:36,  1.91iter/s]Best score: 817.0917: : 72iter [00:37,  1.88iter/s]Best score: 817.0917: : 73iter [00:37,  1.89iter/s]Best score: 817.0917: : 74iter [00:38,  1.88iter/s]Best score: 817.0917: : 75iter [00:39,  1.91iter/s]Best score: 817.0917: : 76iter [00:39,  1.98iter/s]Best score: 817.0917: : 77iter [00:40,  1.91iter/s]Best score: 817.0917: : 78iter [00:40,  1.88iter/s]Best score: 817.0917: : 79iter [00:41,  1.89iter/s]Best score: 817.0917: : 80iter [00:41,  1.87iter/s]Best score: 817.0917: : 81iter [00:42,  1.86iter/s]Best score: 817.0917: : 82iter [00:42,  1.90iter/s]Best score: 817.0917: : 83iter [00:43,  1.90iter/s]Best score: 817.0917: : 84iter [00:43,  1.90iter/s]Best score: 817.0917: : 85iter [00:44,  1.86iter/s]Best score: 817.0917: : 86iter [00:44,  1.87iter/s]Best score: 817.0917: : 87iter [00:45,  1.89iter/s]Best score: 817.0917: : 88iter [00:45,  1.92iter/s]Best score: 817.0917: : 89iter [00:46,  1.95iter/s]Best score: 817.0917: : 90iter [00:46,  1.93iter/s]Best score: 817.0917: : 91iter [00:47,  1.94iter/s]Best score: 817.0917: : 92iter [00:47,  1.92iter/s]Best score: 817.0917: : 93iter [00:48,  1.94iter/s]Best score: 817.0917: : 94iter [00:48,  2.04iter/s]Best score: 817.0917: : 95iter [00:49,  1.93iter/s]Best score: 817.0917: : 96iter [00:49,  2.02iter/s]Best score: 817.0917: : 97iter [00:50,  1.98iter/s]Best score: 817.0917: : 98iter [00:50,  2.02iter/s]Best score: 817.0917: : 99iter [00:51,  1.98iter/s]
[ITER 100] Current best score: 818.0448
[PARAMS] Current best parameters:
  fx: 863.9315
  fy: 730.2416
  px: 859.8584
  py: 785.1133
  k1: 0.04998581
  k2: 0.07996614
  k3: 0.04980802
  k4: 0.01915869
  p1: 0.00284928
  p2: -0.00984618
  p3: -0.00903013
  p4: -0.00602584
  scale: 2.4151
  rotation: -1.0048
Best score: 817.0917: : 100iter [00:51,  1.99iter/s]Best score: 817.0917: : 101iter [00:52,  1.98iter/s]Best score: 817.0917: : 102iter [00:53,  1.92iter/s]Best score: 817.0917: : 103iter [00:53,  2.03iter/s]Best score: 817.0917: : 104iter [00:54,  1.94iter/s]Best score: 817.0917: : 105iter [00:54,  1.93iter/s]Best score: 817.0917: : 106iter [00:55,  1.97iter/s]Best score: 817.0917: : 107iter [00:55,  1.93iter/s]Best score: 817.0917: : 108iter [00:56,  1.97iter/s]Best score: 817.0917: : 109iter [00:56,  2.01iter/s]Best score: 817.0917: : 110iter [00:57,  2.02iter/s]Best score: 817.0917: : 111iter [00:57,  2.04iter/s]Best score: 817.0917: : 112iter [00:57,  2.01iter/s]Best score: 815.7738: : 112iter [00:58,  2.01iter/s]Best score: 815.7738: : 113iter [00:58,  1.94iter/s]Best score: 815.6545: : 113iter [00:59,  1.94iter/s]Best score: 815.6545: : 114iter [00:59,  1.91iter/s]Best score: 815.6545: : 115iter [00:59,  1.92iter/s]Best score: 815.6545: : 116iter [01:00,  1.85iter/s]Best score: 815.6545: : 117iter [01:00,  1.89iter/s]Best score: 815.6545: : 118iter [01:01,  1.93iter/s]Best score: 815.6545: : 119iter [01:01,  1.93iter/s]Best score: 815.6545: : 120iter [01:02,  1.97iter/s]Best score: 815.6545: : 121iter [01:02,  1.92iter/s]Best score: 815.6545: : 122iter [01:03,  1.91iter/s]Best score: 815.6545: : 123iter [01:03,  1.92iter/s]Best score: 815.6545: : 124iter [01:04,  1.89iter/s]Best score: 815.6545: : 125iter [01:04,  1.88iter/s]Best score: 815.6545: : 126iter [01:05,  1.97iter/s]Best score: 815.6545: : 127iter [01:05,  2.02iter/s]Best score: 815.6545: : 128iter [01:06,  1.90iter/s]Best score: 815.6545: : 129iter [01:06,  1.98iter/s]Best score: 815.6545: : 130iter [01:07,  1.91iter/s]Best score: 815.6545: : 131iter [01:07,  1.89iter/s]Best score: 815.6545: : 132iter [01:08,  2.00iter/s]Best score: 815.6545: : 133iter [01:08,  1.99iter/s]Best score: 815.6545: : 134iter [01:09,  1.95iter/s]Best score: 815.6545: : 135iter [01:09,  2.08iter/s]Best score: 815.6545: : 136iter [01:10,  2.05iter/s]Best score: 815.6545: : 137iter [01:10,  2.02iter/s]Best score: 815.6545: : 138iter [01:11,  1.99iter/s]Best score: 815.6545: : 139iter [01:11,  1.97iter/s]Best score: 815.6545: : 140iter [01:12,  1.95iter/s]Best score: 815.6545: : 141iter [01:12,  1.99iter/s]Best score: 815.6545: : 142iter [01:13,  1.97iter/s]Best score: 815.6545: : 143iter [01:14,  1.88iter/s]Best score: 815.6545: : 144iter [01:14,  1.91iter/s]Best score: 815.6545: : 145iter [01:15,  1.89iter/s]Best score: 815.6545: : 146iter [01:15,  1.92iter/s]Best score: 815.6545: : 147iter [01:16,  1.92iter/s]Best score: 815.6545: : 148iter [01:16,  1.92iter/s]Best score: 815.6545: : 149iter [01:17,  2.00iter/s]
[ITER 150] Current best score: 815.7859
[PARAMS] Current best parameters:
  fx: 866.0254
  fy: 720.3163
  px: 859.9697
  py: 792.8037
  k1: 0.04986761
  k2: 0.07994832
  k3: 0.04992214
  k4: 0.01999308
  p1: 0.00350984
  p2: -0.00989203
  p3: -0.00909297
  p4: -0.00743915
  scale: 2.4151
  rotation: -0.7108
Best score: 815.6545: : 150iter [01:17,  1.90iter/s]Best score: 815.6545: : 151iter [01:18,  1.91iter/s]Best score: 815.6545: : 152iter [01:18,  2.02iter/s]Best score: 815.6545: : 153iter [01:19,  1.95iter/s]Best score: 815.6545: : 154iter [01:19,  1.92iter/s]Best score: 815.6545: : 155iter [01:20,  1.90iter/s]Best score: 815.6545: : 156iter [01:20,  1.88iter/s]Best score: 815.6545: : 157iter [01:21,  1.96iter/s]Best score: 815.6545: : 158iter [01:21,  1.94iter/s]Best score: 815.6545: : 159iter [01:22,  1.93iter/s]Best score: 815.6545: : 160iter [01:22,  1.93iter/s]Best score: 815.6545: : 161iter [01:23,  1.96iter/s]Best score: 815.6545: : 162iter [01:23,  1.90iter/s]Best score: 815.6545: : 163iter [01:24,  1.89iter/s]Best score: 815.6545: : 164iter [01:24,  1.93iter/s]Best score: 815.6545: : 165iter [01:25,  1.93iter/s]Best score: 815.6545: : 166iter [01:25,  1.92iter/s]Best score: 815.6545: : 167iter [01:26,  1.93iter/s]Best score: 815.6545: : 168iter [01:26,  1.91iter/s]Best score: 815.6545: : 169iter [01:27,  1.92iter/s]Best score: 815.6545: : 170iter [01:28,  1.91iter/s]Best score: 815.5979: : 170iter [01:28,  1.91iter/s]Best score: 815.5979: : 171iter [01:28,  1.95iter/s]Best score: 815.5979: : 172iter [01:29,  1.94iter/s]Best score: 815.5979: : 173iter [01:29,  2.03iter/s]Best score: 815.5979: : 174iter [01:29,  2.03iter/s]Best score: 815.5979: : 175iter [01:30,  1.97iter/s]Best score: 815.5979: : 176iter [01:30,  1.98iter/s]Best score: 815.5979: : 177iter [01:31,  1.94iter/s]Best score: 815.5979: : 178iter [01:32,  1.92iter/s]Best score: 815.5979: : 179iter [01:32,  1.95iter/s]Best score: 815.5979: : 180iter [01:33,  1.86iter/s]Best score: 815.5979: : 181iter [01:33,  1.90iter/s]Best score: 815.5979: : 182iter [01:34,  1.88iter/s]Best score: 815.5979: : 183iter [01:34,  1.87iter/s]Best score: 815.5979: : 184iter [01:35,  1.94iter/s]Best score: 815.5979: : 185iter [01:35,  1.90iter/s]Best score: 815.5979: : 186iter [01:36,  1.86iter/s]Best score: 815.5979: : 187iter [01:36,  1.91iter/s]Best score: 815.5979: : 188iter [01:37,  1.96iter/s]Best score: 815.5979: : 189iter [01:37,  1.93iter/s]Best score: 815.5979: : 190iter [01:38,  1.90iter/s]Best score: 815.5979: : 191iter [01:38,  1.87iter/s]Best score: 815.5979: : 192iter [01:39,  1.91iter/s]Best score: 815.5979: : 193iter [01:39,  1.93iter/s]Best score: 815.5979: : 194iter [01:40,  1.89iter/s]Best score: 815.5979: : 195iter [01:40,  1.97iter/s]Best score: 815.5979: : 196iter [01:41,  2.00iter/s]Best score: 815.5979: : 197iter [01:41,  1.99iter/s]Best score: 815.5979: : 198iter [01:42,  2.04iter/s]Best score: 813.9512: : 198iter [01:42,  2.04iter/s]Best score: 813.9512: : 199iter [01:42,  1.94iter/s]
[ITER 200] Current best score: 813.9512
[PARAMS] Current best parameters:
  fx: 865.8842
  fy: 722.6894
  px: 859.9908
  py: 792.8924
  k1: 0.04994069
  k2: 0.07998681
  k3: 0.04999361
  k4: 0.01999347
  p1: 0.00403606
  p2: -0.00999932
  p3: -0.00990558
  p4: -0.00850698
  scale: 2.4157
  rotation: -0.6920
Best score: 813.9512: : 200iter [01:43,  1.89iter/s]Best score: 813.9512: : 201iter [01:43,  1.97iter/s]Best score: 813.9512: : 202iter [01:44,  1.97iter/s]Best score: 813.9512: : 203iter [01:45,  1.91iter/s]Best score: 813.9512: : 204iter [01:45,  1.94iter/s]Best score: 813.9512: : 205iter [01:46,  1.90iter/s]Best score: 813.9512: : 206iter [01:46,  1.99iter/s]Best score: 813.9512: : 207iter [01:47,  2.01iter/s]Best score: 813.9512: : 208iter [01:47,  1.99iter/s]Best score: 813.9512: : 209iter [01:48,  1.98iter/s]Best score: 813.9512: : 210iter [01:48,  1.96iter/s]Best score: 813.9512: : 211iter [01:49,  2.01iter/s]Best score: 813.9512: : 212iter [01:49,  1.98iter/s]Best score: 813.9512: : 213iter [01:50,  1.97iter/s]Best score: 813.9512: : 214iter [01:50,  1.91iter/s]Best score: 813.9512: : 215iter [01:51,  2.01iter/s]Best score: 813.9512: : 216iter [01:51,  2.01iter/s]Best score: 813.9512: : 217iter [01:52,  1.99iter/s]Best score: 813.9512: : 218iter [01:52,  1.98iter/s]Best score: 813.9512: : 219iter [01:53,  1.89iter/s]Best score: 813.9512: : 220iter [01:53,  1.94iter/s]Best score: 813.9512: : 221iter [01:54,  1.93iter/s]Best score: 813.9512: : 222iter [01:54,  2.02iter/s]Best score: 813.9512: : 223iter [01:55,  2.04iter/s]Best score: 813.9512: : 224iter [01:55,  2.03iter/s]Best score: 813.9512: : 225iter [01:56,  2.01iter/s]Best score: 813.9512: : 226iter [01:56,  1.96iter/s]Best score: 813.9512: : 227iter [01:57,  1.95iter/s]Best score: 813.9512: : 228iter [01:57,  1.91iter/s]Best score: 813.9512: : 229iter [01:58,  1.88iter/s]Best score: 813.9512: : 230iter [01:58,  2.01iter/s]Best score: 813.9512: : 231iter [01:59,  1.96iter/s]Best score: 813.9512: : 232iter [01:59,  1.94iter/s]Best score: 813.9512: : 233iter [02:00,  1.86iter/s]Best score: 813.9512: : 234iter [02:00,  1.83iter/s]Best score: 813.9512: : 235iter [02:01,  1.82iter/s]Best score: 813.9512: : 236iter [02:02,  1.78iter/s]Best score: 813.9512: : 237iter [02:02,  1.85iter/s]Best score: 813.9512: : 238iter [02:03,  1.86iter/s]Best score: 813.9512: : 239iter [02:03,  1.85iter/s]Best score: 813.9512: : 240iter [02:04,  1.90iter/s]Best score: 813.9512: : 241iter [02:04,  1.92iter/s]Best score: 813.9512: : 242iter [02:05,  1.82iter/s]Best score: 813.9512: : 243iter [02:05,  1.85iter/s]Best score: 813.9512: : 244iter [02:06,  1.94iter/s]Best score: 813.9512: : 245iter [02:06,  2.01iter/s]Best score: 813.9512: : 246iter [02:07,  2.05iter/s]Best score: 813.9512: : 247iter [02:07,  2.10iter/s]Best score: 813.9512: : 248iter [02:08,  2.08iter/s]Best score: 813.9512: : 249iter [02:08,  2.07iter/s]
[ITER 250] Current best score: 817.9899
[PARAMS] Current best parameters:
  fx: 866.5663
  fy: 715.2733
  px: 859.9951
  py: 793.8091
  k1: 0.04992550
  k2: 0.07999290
  k3: 0.04991248
  k4: 0.01996128
  p1: 0.00691291
  p2: -0.00999864
  p3: -0.00998960
  p4: -0.00973889
  scale: 2.4161
  rotation: -0.7538
Best score: 813.9512: : 250iter [02:09,  1.90iter/s]Best score: 813.9512: : 251iter [02:09,  1.95iter/s]Best score: 813.9512: : 252iter [02:10,  1.96iter/s]Best score: 813.9512: : 253iter [02:10,  1.93iter/s]Best score: 813.9512: : 254iter [02:11,  1.92iter/s]Best score: 813.9512: : 255iter [02:11,  1.91iter/s]Best score: 813.9512: : 256iter [02:12,  1.91iter/s]Best score: 813.9512: : 257iter [02:12,  1.95iter/s]Best score: 813.9512: : 258iter [02:13,  1.95iter/s]Best score: 813.9512: : 259iter [02:13,  1.98iter/s]Best score: 813.9512: : 260iter [02:14,  1.99iter/s]Best score: 813.9512: : 261iter [02:14,  2.04iter/s]Best score: 813.9512: : 262iter [02:15,  2.09iter/s]Best score: 813.9512: : 263iter [02:15,  2.07iter/s]Best score: 813.9512: : 264iter [02:16,  2.06iter/s]Best score: 813.9512: : 265iter [02:16,  2.08iter/s]Best score: 813.9512: : 266iter [02:17,  2.01iter/s]Best score: 813.9512: : 267iter [02:17,  2.00iter/s]Best score: 813.9512: : 268iter [02:18,  2.02iter/s]Best score: 813.9512: : 269iter [02:18,  2.01iter/s]Best score: 813.9512: : 270iter [02:19,  1.98iter/s]Best score: 813.9512: : 271iter [02:19,  1.93iter/s]Best score: 813.9512: : 272iter [02:20,  2.04iter/s]Best score: 813.9512: : 273iter [02:20,  2.08iter/s]Best score: 813.9512: : 274iter [02:21,  2.01iter/s]Best score: 813.9512: : 275iter [02:21,  1.95iter/s]Best score: 813.9512: : 276iter [02:22,  2.04iter/s]Best score: 813.9512: : 277iter [02:22,  1.98iter/s]Best score: 813.9512: : 278iter [02:23,  2.01iter/s]Best score: 813.9512: : 279iter [02:23,  1.95iter/s]Best score: 813.9512: : 280iter [02:24,  1.95iter/s]Best score: 813.9512: : 281iter [02:24,  1.92iter/s]Best score: 813.9512: : 282iter [02:25,  2.02iter/s]Best score: 813.9512: : 283iter [02:25,  1.97iter/s]Best score: 813.9512: : 284iter [02:26,  1.92iter/s]Best score: 813.9512: : 285iter [02:26,  2.03iter/s]Best score: 813.9512: : 286iter [02:27,  2.00iter/s]Best score: 813.9512: : 287iter [02:27,  2.03iter/s]Best score: 813.9512: : 288iter [02:28,  2.00iter/s]Best score: 813.9512: : 289iter [02:28,  2.00iter/s]Best score: 813.9512: : 290iter [02:29,  2.00iter/s]Best score: 813.9512: : 291iter [02:29,  1.97iter/s]Best score: 813.9512: : 292iter [02:30,  2.04iter/s]Best score: 813.9512: : 293iter [02:30,  2.02iter/s]Best score: 813.9512: : 294iter [02:31,  2.01iter/s]Best score: 813.9512: : 295iter [02:31,  2.03iter/s]Best score: 813.9512: : 296iter [02:32,  2.00iter/s]Best score: 813.9512: : 297iter [02:32,  1.99iter/s]Best score: 813.9512: : 298iter [02:33,  1.96iter/s]Best score: 813.9512: : 299iter [02:33,  2.02iter/s]
[ITER 300] Current best score: 817.9091
[PARAMS] Current best parameters:
  fx: 866.5673
  fy: 718.9594
  px: 859.9881
  py: 793.8183
  k1: 0.04999915
  k2: 0.07999300
  k3: 0.04999658
  k4: 0.01999925
  p1: 0.00687692
  p2: -0.00998831
  p3: -0.00998777
  p4: -0.00998042
  scale: 2.4150
  rotation: -0.7751
Best score: 813.9512: : 300iter [02:34,  1.98iter/s]Best score: 813.9512: : 301iter [02:34,  1.99iter/s]Best score: 813.9512: : 302iter [02:35,  1.98iter/s]Best score: 813.9512: : 303iter [02:35,  1.96iter/s]Best score: 813.9512: : 304iter [02:36,  1.95iter/s]Best score: 813.9512: : 305iter [02:36,  1.94iter/s]Best score: 813.9512: : 306iter [02:37,  1.98iter/s]Best score: 813.9512: : 307iter [02:37,  1.96iter/s]Best score: 813.9512: : 308iter [02:38,  1.89iter/s]Best score: 813.9512: : 309iter [02:38,  1.98iter/s]Best score: 813.9512: : 310iter [02:39,  1.95iter/s]Best score: 813.9512: : 311iter [02:39,  2.00iter/s]Best score: 813.9512: : 312iter [02:40,  1.95iter/s]Best score: 813.9512: : 313iter [02:40,  1.92iter/s]Best score: 813.9512: : 314iter [02:41,  1.93iter/s]Best score: 813.9512: : 315iter [02:41,  1.94iter/s]Best score: 813.9512: : 316iter [02:42,  1.86iter/s]Best score: 813.9512: : 317iter [02:43,  1.89iter/s]Best score: 813.9512: : 318iter [02:43,  1.92iter/s]Best score: 813.9512: : 319iter [02:44,  1.94iter/s]Best score: 813.9512: : 320iter [02:44,  1.92iter/s]Best score: 813.9512: : 321iter [02:45,  1.99iter/s]Best score: 813.9512: : 322iter [02:45,  2.07iter/s]Best score: 813.9512: : 323iter [02:45,  2.11iter/s]Best score: 813.9512: : 324iter [02:46,  2.09iter/s]Best score: 813.9512: : 325iter [02:46,  2.10iter/s]Best score: 813.9512: : 326iter [02:47,  2.02iter/s]Best score: 813.9512: : 327iter [02:47,  1.97iter/s]Best score: 813.9512: : 328iter [02:48,  1.98iter/s]Best score: 813.9512: : 329iter [02:49,  1.94iter/s]Best score: 813.9512: : 330iter [02:49,  2.01iter/s]Best score: 813.9512: : 331iter [02:50,  1.96iter/s]Best score: 813.9512: : 332iter [02:50,  1.98iter/s]Best score: 813.9512: : 333iter [02:51,  1.98iter/s]Best score: 813.9512: : 334iter [02:51,  1.99iter/s]Best score: 813.9512: : 335iter [02:52,  1.97iter/s]Best score: 813.9512: : 336iter [02:52,  2.02iter/s]Best score: 813.9512: : 337iter [02:53,  1.94iter/s]Best score: 813.9512: : 338iter [02:53,  2.00iter/s]Best score: 813.9512: : 339iter [02:53,  2.05iter/s]Best score: 813.9512: : 340iter [02:54,  1.97iter/s]Best score: 813.9512: : 341iter [02:55,  1.99iter/s]Best score: 813.9512: : 342iter [02:55,  2.06iter/s]Best score: 813.9512: : 343iter [02:55,  2.07iter/s]Best score: 813.9512: : 344iter [02:56,  2.05iter/s]Best score: 813.9512: : 345iter [02:56,  2.05iter/s]Best score: 813.9512: : 346iter [02:57,  1.94iter/s]Best score: 813.9512: : 347iter [02:58,  1.96iter/s]Best score: 813.9512: : 348iter [02:58,  2.03iter/s]Best score: 813.9512: : 349iter [02:58,  2.03iter/s]
[ITER 350] Current best score: 817.8372
[PARAMS] Current best parameters:
  fx: 866.6171
  fy: 718.4783
  px: 859.9873
  py: 793.7964
  k1: 0.04999840
  k2: 0.07999824
  k3: 0.04999284
  k4: 0.01999443
  p1: 0.00665911
  p2: -0.00999814
  p3: -0.00999567
  p4: -0.00991430
  scale: 2.4150
  rotation: -0.7652
Best score: 813.9512: : 350iter [02:59,  1.89iter/s]Best score: 813.9512: : 351iter [03:00,  2.00iter/s]Best score: 813.9512: : 352iter [03:00,  1.93iter/s]Best score: 813.9512: : 353iter [03:01,  1.91iter/s]Best score: 813.9512: : 354iter [03:01,  1.99iter/s]Best score: 813.9512: : 355iter [03:02,  2.01iter/s]Best score: 813.9512: : 356iter [03:02,  1.98iter/s]Best score: 813.9512: : 357iter [03:03,  2.01iter/s]Best score: 813.9512: : 358iter [03:03,  2.08iter/s]Best score: 813.9512: : 359iter [03:04,  2.04iter/s]Best score: 813.9512: : 360iter [03:04,  2.02iter/s]Best score: 813.9512: : 361iter [03:05,  1.99iter/s]Best score: 813.9512: : 362iter [03:05,  2.08iter/s]Best score: 813.9512: : 363iter [03:05,  2.09iter/s]Best score: 813.9512: : 364iter [03:06,  2.02iter/s]Best score: 813.9512: : 365iter [03:06,  2.08iter/s]Best score: 813.9512: : 366iter [03:07,  2.10iter/s]Best score: 813.9512: : 367iter [03:07,  2.08iter/s]Best score: 813.9512: : 368iter [03:08,  2.20iter/s]Best score: 813.9512: : 369iter [03:08,  2.10iter/s]Best score: 813.9512: : 370iter [03:09,  2.05iter/s]Best score: 813.9512: : 371iter [03:09,  2.14iter/s]Best score: 813.9512: : 372iter [03:10,  2.11iter/s]Best score: 813.9512: : 373iter [03:10,  2.03iter/s]Best score: 813.9512: : 374iter [03:11,  2.13iter/s]Best score: 813.9512: : 375iter [03:11,  2.16iter/s]Best score: 813.9512: : 376iter [03:12,  2.02iter/s]Best score: 813.9512: : 377iter [03:12,  2.08iter/s]Best score: 813.9512: : 378iter [03:13,  1.98iter/s]Best score: 813.9512: : 379iter [03:13,  1.95iter/s]Best score: 813.9512: : 380iter [03:14,  2.09iter/s]Best score: 813.9512: : 381iter [03:14,  2.10iter/s]Best score: 813.9512: : 382iter [03:15,  2.00iter/s]Best score: 813.9512: : 383iter [03:15,  1.97iter/s]Best score: 813.9512: : 384iter [03:16,  1.99iter/s]Best score: 813.9512: : 385iter [03:16,  2.11iter/s]Best score: 813.9512: : 386iter [03:17,  2.10iter/s]Best score: 813.9512: : 387iter [03:17,  2.14iter/s]Best score: 813.9512: : 388iter [03:17,  2.11iter/s]Best score: 813.9512: : 389iter [03:18,  2.04iter/s]Best score: 813.9512: : 390iter [03:18,  2.08iter/s]Best score: 813.9512: : 391iter [03:19,  2.02iter/s]Best score: 813.9512: : 392iter [03:19,  2.05iter/s]Best score: 813.9512: : 393iter [03:20,  2.10iter/s]Best score: 813.9512: : 394iter [03:20,  2.09iter/s]Best score: 813.9512: : 395iter [03:21,  2.00iter/s]Best score: 813.9512: : 396iter [03:21,  2.07iter/s]Best score: 813.9512: : 397iter [03:22,  2.00iter/s]Best score: 813.9512: : 398iter [03:22,  1.98iter/s]Best score: 813.9512: : 399iter [03:23,  1.99iter/s]
[ITER 400] Current best score: 818.0434
[PARAMS] Current best parameters:
  fx: 866.6483
  fy: 719.1395
  px: 859.9964
  py: 793.9622
  k1: 0.04999969
  k2: 0.07999920
  k3: 0.04999746
  k4: 0.01999791
  p1: 0.00665969
  p2: -0.00999948
  p3: -0.00999818
  p4: -0.00996883
  scale: 2.4150
  rotation: -0.7484
Best score: 813.9512: : 400iter [03:23,  1.96iter/s]Best score: 813.9512: : 401iter [03:24,  1.89iter/s]Best score: 813.9512: : 402iter [03:25,  1.96iter/s]Best score: 813.9512: : 403iter [03:25,  1.91iter/s]Best score: 813.9512: : 404iter [03:26,  1.93iter/s]Best score: 813.9512: : 405iter [03:26,  1.94iter/s]Best score: 813.9512: : 406iter [03:27,  1.95iter/s]Best score: 813.9512: : 407iter [03:27,  2.04iter/s]Best score: 813.9512: : 408iter [03:28,  2.01iter/s]Best score: 813.9512: : 409iter [03:28,  1.95iter/s]Best score: 813.9512: : 410iter [03:28,  2.10iter/s]Best score: 813.9512: : 411iter [03:29,  2.06iter/s]Best score: 813.9512: : 412iter [03:29,  2.04iter/s]Best score: 813.9512: : 413iter [03:30,  2.11iter/s]Best score: 813.9512: : 414iter [03:30,  2.03iter/s]Best score: 813.9512: : 415iter [03:31,  1.93iter/s]Best score: 813.9512: : 416iter [03:31,  2.03iter/s]Best score: 813.9512: : 417iter [03:32,  1.98iter/s]Best score: 813.9512: : 418iter [03:32,  2.05iter/s]Best score: 813.9512: : 419iter [03:33,  2.12iter/s]Best score: 813.9512: : 420iter [03:33,  2.15iter/s]Best score: 813.9512: : 421iter [03:34,  2.08iter/s]Best score: 813.9512: : 422iter [03:34,  2.05iter/s]Best score: 813.9512: : 423iter [03:35,  2.01iter/s]Best score: 813.9512: : 424iter [03:35,  2.05iter/s]Best score: 813.9512: : 425iter [03:36,  2.08iter/s]Best score: 813.9512: : 426iter [03:36,  2.00iter/s]Best score: 813.9512: : 427iter [03:37,  1.98iter/s]Best score: 813.9512: : 428iter [03:37,  1.99iter/s]Best score: 813.9512: : 429iter [03:38,  2.00iter/s]Best score: 813.9512: : 430iter [03:38,  2.01iter/s]Best score: 813.9512: : 431iter [03:39,  1.95iter/s]Best score: 813.9512: : 432iter [03:39,  1.94iter/s]Best score: 813.9512: : 433iter [03:40,  1.96iter/s]Best score: 813.9512: : 434iter [03:40,  1.95iter/s]Best score: 813.9512: : 435iter [03:41,  1.97iter/s]Best score: 813.9512: : 436iter [03:41,  2.08iter/s]Best score: 813.9512: : 437iter [03:42,  1.94iter/s]Best score: 813.9512: : 438iter [03:42,  1.98iter/s]Best score: 813.9512: : 439iter [03:43,  1.96iter/s]Best score: 813.9512: : 440iter [03:44,  1.92iter/s]Best score: 813.9512: : 441iter [03:44,  1.89iter/s]Best score: 813.9512: : 442iter [03:45,  1.88iter/s]Best score: 813.9512: : 443iter [03:45,  1.95iter/s]Best score: 813.9512: : 444iter [03:46,  1.95iter/s]Best score: 813.9512: : 445iter [03:46,  1.98iter/s]Best score: 813.9512: : 446iter [03:47,  1.99iter/s]Best score: 813.9512: : 447iter [03:47,  2.02iter/s]Best score: 813.9512: : 448iter [03:48,  2.02iter/s]Best score: 813.9512: : 449iter [03:48,  2.04iter/s]
[ITER 450] Current best score: 817.9694
[PARAMS] Current best parameters:
  fx: 866.6592
  fy: 718.5604
  px: 859.9991
  py: 793.9934
  k1: 0.04999971
  k2: 0.07999989
  k3: 0.04999917
  k4: 0.01999625
  p1: 0.00675131
  p2: -0.00999962
  p3: -0.00999907
  p4: -0.00999482
  scale: 2.4152
  rotation: -0.7502
Best score: 813.9512: : 450iter [03:49,  1.93iter/s]Best score: 813.9512: : 451iter [03:49,  1.95iter/s]Best score: 813.9512: : 452iter [03:50,  2.04iter/s]Best score: 813.9512: : 453iter [03:50,  2.07iter/s]Best score: 813.9512: : 454iter [03:50,  2.06iter/s]Best score: 813.9512: : 455iter [03:51,  2.14iter/s]Best score: 813.9512: : 456iter [03:51,  2.14iter/s]Best score: 813.9512: : 457iter [03:52,  2.08iter/s]Best score: 813.9512: : 458iter [03:52,  2.14iter/s]Best score: 813.9512: : 459iter [03:53,  2.14iter/s]Best score: 813.9512: : 460iter [03:53,  2.08iter/s]Best score: 813.9512: : 461iter [03:54,  2.06iter/s]Best score: 813.9512: : 462iter [03:54,  2.06iter/s]Best score: 813.9512: : 463iter [03:55,  1.99iter/s]Best score: 813.9512: : 464iter [03:55,  2.03iter/s]Best score: 813.9512: : 465iter [03:56,  2.10iter/s]Best score: 813.9512: : 466iter [03:56,  2.08iter/s]Best score: 813.9512: : 467iter [03:57,  2.21iter/s]Best score: 813.9512: : 468iter [03:57,  2.19iter/s]Best score: 813.9512: : 469iter [03:58,  2.17iter/s]Best score: 813.9512: : 470iter [03:58,  2.22iter/s]Best score: 813.9512: : 471iter [03:58,  2.21iter/s]Best score: 813.9512: : 472iter [03:59,  2.12iter/s]Best score: 813.9512: : 473iter [03:59,  2.11iter/s]Best score: 813.9512: : 474iter [04:00,  2.11iter/s]Best score: 813.9512: : 475iter [04:00,  1.98iter/s]Best score: 813.9512: : 476iter [04:01,  2.05iter/s]Best score: 813.9512: : 477iter [04:01,  2.01iter/s]Best score: 813.9512: : 478iter [04:02,  1.97iter/s]Best score: 813.9512: : 479iter [04:02,  2.10iter/s]Best score: 813.9512: : 480iter [04:03,  2.05iter/s]Best score: 813.9512: : 481iter [04:03,  2.00iter/s]Best score: 813.9512: : 482iter [04:04,  2.10iter/s]Best score: 813.9512: : 483iter [04:04,  2.11iter/s]Best score: 813.9512: : 484iter [04:05,  1.99iter/s]Best score: 813.9512: : 485iter [04:05,  2.00iter/s]Best score: 813.9512: : 486iter [04:06,  1.93iter/s]Best score: 813.9512: : 487iter [04:06,  1.97iter/s]Best score: 813.9512: : 488iter [04:07,  2.00iter/s]Best score: 813.9512: : 489iter [04:07,  2.02iter/s]Best score: 813.9512: : 490iter [04:08,  2.09iter/s]Best score: 813.9512: : 491iter [04:08,  2.00iter/s]Best score: 813.9512: : 492iter [04:09,  1.97iter/s]Best score: 813.9512: : 493iter [04:09,  1.98iter/s]Best score: 813.9512: : 494iter [04:10,  1.98iter/s]Best score: 813.9512: : 495iter [04:10,  1.97iter/s]Best score: 813.9512: : 496iter [04:11,  2.07iter/s]Best score: 813.9512: : 497iter [04:11,  2.11iter/s]Best score: 813.9512: : 498iter [04:12,  2.08iter/s]Best score: 813.9512: : 499iter [04:12,  2.20iter/s]
[ITER 500] Current best score: 818.0871
[PARAMS] Current best parameters:
  fx: 866.6645
  fy: 718.5497
  px: 859.9999
  py: 793.9999
  k1: 0.04999962
  k2: 0.07999949
  k3: 0.04999959
  k4: 0.01999975
  p1: 0.00675504
  p2: -0.00999978
  p3: -0.00999962
  p4: -0.00999496
  scale: 2.4150
  rotation: -0.7496
Best score: 813.9512: : 500iter [04:13,  2.03iter/s]Best score: 813.9512: : 500iter [04:14,  1.96iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish did not improve score: 527.997627
  Success: False
  Tangential distortion: p1=0.006755, p2=-0.010000, p3=-0.010000, p4=-0.009995
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 527.9976
[✅] Parameters copied to: /working/MDC103_W050_2025-11-03/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 866.6644577232871
    fy: 718.5497472207696
    px: 859.999936732019
    py: 793.9998604693967
    k1: 0.04999962062667911
    k2: 0.07999949035506712
    k3: 0.04999958720076946
    k4: 0.019999752066962598
    p1: 0.006755044328279622
    p2: -0.009999775591790085
    p3: -0.009999623490890014
    p4: -0.009994956220791686
    scale: 2.4150235955619674
    rotation: -0.7496125363469686
    mmpp: 1.906383691715975
    nmpp: 1906383.691715975

[🔄] Converting 2098 frames to PTD, creating debug images for 200 calibration frames...
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                                                                   [A📁 Rigs: 100%|██████████| 2/2 [00:20<00:00, 10.72s/rig]📁 Rigs: 100%|██████████| 2/2 [00:20<00:00, 10.40s/rig]
[✅] Saved PTDs: /working/MDC103_W050_2025-11-03/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC103_W050_2025-11-03/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
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[✅] LUT written to: /working/MDC103_W050_2025-11-03/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC103_W050_2025-11-03' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC103_W050_2025-11-03/calibration/MDC103_W050_2025-11-03_unknown.lut
[🏁] Finished processing MDC103_W050_2025-11-03


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