🟢 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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→ 500 (874 images):  90%|█████████ | 787/874 [01:38<00:07, 11.32it/s]

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→ 500 (874 images):  99%|█████████▊| 863/874 [01:46<00:00, 21.16it/s]

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→ 500 (874 images): 100%|█████████▉| 870/874 [01:47<00:00,  9.54it/s]

                                                                     
📁 Processing Rigs:  50%|█████     | 1/2 [01:48<01:48, 108.10s/it]

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→ 1000 (1224 images):  95%|█████████▌| 1164/1224 [02:23<00:06,  8.63it/s]

→ 1000 (1224 images):  95%|█████████▌| 1166/1224 [02:23<00:05, 10.91it/s]

→ 1000 (1224 images):  95%|█████████▌| 1168/1224 [02:23<00:04, 12.65it/s]

→ 1000 (1224 images):  96%|█████████▌| 1170/1224 [02:23<00:03, 13.84it/s]

→ 1000 (1224 images):  96%|█████████▌| 1172/1224 [02:24<00:03, 15.17it/s]

→ 1000 (1224 images):  96%|█████████▌| 1174/1224 [02:24<00:03, 14.71it/s]

→ 1000 (1224 images):  96%|█████████▌| 1176/1224 [02:24<00:03, 15.05it/s]

→ 1000 (1224 images):  96%|█████████▌| 1178/1224 [02:24<00:03, 14.32it/s]

→ 1000 (1224 images):  96%|█████████▋| 1180/1224 [02:24<00:03, 13.58it/s]

→ 1000 (1224 images):  97%|█████████▋| 1182/1224 [02:24<00:04, 10.12it/s]

→ 1000 (1224 images):  97%|█████████▋| 1184/1224 [02:25<00:05,  7.24it/s]

→ 1000 (1224 images):  97%|█████████▋| 1185/1224 [02:25<00:07,  5.26it/s]

→ 1000 (1224 images):  97%|█████████▋| 1186/1224 [02:25<00:06,  5.80it/s]

→ 1000 (1224 images):  97%|█████████▋| 1187/1224 [02:26<00:07,  5.20it/s]

→ 1000 (1224 images):  97%|█████████▋| 1188/1224 [02:26<00:06,  5.80it/s]

→ 1000 (1224 images):  97%|█████████▋| 1189/1224 [02:26<00:07,  4.92it/s]

→ 1000 (1224 images):  97%|█████████▋| 1190/1224 [02:26<00:07,  4.60it/s]

→ 1000 (1224 images):  97%|█████████▋| 1191/1224 [02:26<00:06,  5.38it/s]

→ 1000 (1224 images):  97%|█████████▋| 1192/1224 [02:27<00:05,  5.47it/s]

→ 1000 (1224 images):  98%|█████████▊| 1194/1224 [02:27<00:03,  8.09it/s]

→ 1000 (1224 images):  98%|█████████▊| 1196/1224 [02:27<00:03,  7.93it/s]

→ 1000 (1224 images):  98%|█████████▊| 1199/1224 [02:27<00:02, 11.02it/s]

→ 1000 (1224 images):  98%|█████████▊| 1201/1224 [02:27<00:01, 12.08it/s]

→ 1000 (1224 images):  98%|█████████▊| 1204/1224 [02:27<00:01, 14.71it/s]

→ 1000 (1224 images):  99%|█████████▊| 1207/1224 [02:28<00:00, 17.50it/s]

→ 1000 (1224 images):  99%|█████████▉| 1210/1224 [02:28<00:00, 16.57it/s]

→ 1000 (1224 images):  99%|█████████▉| 1214/1224 [02:28<00:00, 20.32it/s]

→ 1000 (1224 images):  99%|█████████▉| 1217/1224 [02:28<00:00, 12.91it/s]

→ 1000 (1224 images): 100%|█████████▉| 1219/1224 [02:29<00:00, 10.31it/s]

→ 1000 (1224 images): 100%|█████████▉| 1221/1224 [02:29<00:00, 11.23it/s]

→ 1000 (1224 images): 100%|█████████▉| 1223/1224 [02:29<00:00,  8.61it/s]

                                                                         
📁 Processing Rigs: 100%|██████████| 2/2 [04:17<00:00, 132.62s/it]
📁 Processing Rigs: 100%|██████████| 2/2 [04:17<00:00, 128.94s/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, 34.74it/s]
Averaging bright frames:  22%|██▎       | 9/40 [00:00<00:00, 41.95it/s]
Averaging bright frames:  35%|███▌      | 14/40 [00:00<00:00, 44.37it/s]
Averaging bright frames:  50%|█████     | 20/40 [00:00<00:00, 47.05it/s]
Averaging bright frames:  62%|██████▎   | 25/40 [00:00<00:00, 47.07it/s]
Averaging bright frames:  78%|███████▊  | 31/40 [00:00<00:00, 48.40it/s]
Averaging bright frames:  92%|█████████▎| 37/40 [00:00<00:00, 49.17it/s]
                                                                        
Total time to process (v2): 0.06
[RESULT] Combined: Center=(820, 754), Radius=676

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Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   3%|▎         | 3/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   5%|▌         | 5/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   7%|▋         | 7/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 356.566} (0.000):   8%|▊         | 8/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 377.778} (0.000):   9%|▉         | 9/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 384.848} (0.000):  10%|█         | 10/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 384.848} (0.000):  11%|█         | 11/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 406.061} (0.000):  12%|█▏        | 12/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 406.061} (0.000):  13%|█▎        | 13/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  14%|█▍        | 14/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  15%|█▌        | 15/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  16%|█▌        | 16/100 [00:00<00:05, 16.79it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 413.131} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 434.343} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 434.343} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 434.343} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 455.556} (0.000):  20%|██        | 20/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 455.556} (0.000):  21%|██        | 21/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 469.697} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 469.697} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 469.697} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 512.121} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 512.121} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 512.121} (0.000):  27%|██▋       | 27/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 533.333} (0.000):  28%|██▊       | 28/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  29%|██▉       | 29/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  30%|███       | 30/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  31%|███       | 31/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  32%|███▏      | 32/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  33%|███▎      | 33/100 [00:00<00:00, 88.92it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  34%|███▍      | 34/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  34%|███▍      | 34/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 540.404} (0.000):  35%|███▌      | 35/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.000 | Best: {'fx': 554.545} (0.000):  36%|███▌      | 36/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 604.04} (0.008):  37%|███▋      | 37/100 [00:00<00:00, 119.45it/s] 
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.002 | Best: {'fx': 604.04} (0.008):  38%|███▊      | 38/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.002 | Best: {'fx': 604.04} (0.008):  39%|███▉      | 39/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 604.04} (0.008):  40%|████      | 40/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.005 | Best: {'fx': 604.04} (0.008):  41%|████      | 41/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.003 | Best: {'fx': 604.04} (0.008):  42%|████▏     | 42/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.005 | Best: {'fx': 604.04} (0.008):  43%|████▎     | 43/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 604.04} (0.008):  44%|████▍     | 44/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 611.111} (0.009):  45%|████▌     | 45/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  46%|████▌     | 46/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  47%|████▋     | 47/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  48%|████▊     | 48/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  49%|████▉     | 49/100 [00:00<00:00, 119.45it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  50%|█████     | 50/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  50%|█████     | 50/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.007 | Best: {'fx': 611.111} (0.009):  51%|█████     | 51/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  52%|█████▏    | 52/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  53%|█████▎    | 53/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  54%|█████▍    | 54/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 611.111} (0.009):  55%|█████▌    | 55/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 710.101} (0.009):  56%|█████▌    | 56/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.008 | Best: {'fx': 710.101} (0.009):  57%|█████▋    | 57/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  58%|█████▊    | 58/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  59%|█████▉    | 59/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  60%|██████    | 60/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  61%|██████    | 61/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 745.455} (0.009):  62%|██████▏   | 62/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 780.808} (0.010):  63%|██████▎   | 63/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  64%|██████▍   | 64/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  65%|██████▌   | 65/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  66%|██████▌   | 66/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  67%|██████▋   | 67/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  68%|██████▊   | 68/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  69%|██████▉   | 69/100 [00:00<00:00, 129.86it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 780.808} (0.010):  70%|███████   | 70/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  70%|███████   | 70/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  71%|███████   | 71/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  72%|███████▏  | 72/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  73%|███████▎  | 73/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  74%|███████▍  | 74/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  75%|███████▌  | 75/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  76%|███████▌  | 76/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  77%|███████▋  | 77/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  78%|███████▊  | 78/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  79%|███████▉  | 79/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.010 | Best: {'fx': 787.879} (0.010):  80%|████████  | 80/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  81%|████████  | 81/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  82%|████████▏ | 82/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  83%|████████▎ | 83/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  84%|████████▍ | 84/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  85%|████████▌ | 85/100 [00:00<00:00, 153.72it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  86%|████████▌ | 86/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  86%|████████▌ | 86/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  87%|████████▋ | 87/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  88%|████████▊ | 88/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  89%|████████▉ | 89/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  90%|█████████ | 90/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  91%|█████████ | 91/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  92%|█████████▏| 92/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  93%|█████████▎| 93/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  94%|█████████▍| 94/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  95%|█████████▌| 95/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  96%|█████████▌| 96/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  97%|█████████▋| 97/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  98%|█████████▊| 98/100 [00:00<00:00, 154.85it/s]
Brute | Start: {'fx': 500.0} (0.000) | Current: 0.009 | Best: {'fx': 787.879} (0.010):  99%|█████████▉| 99/100 [00:00<00:00, 154.85it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 762.879} (0.009):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 762.879} (0.009):   2%|▏         | 1/50 [00:00<00:05,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.009):   2%|▏         | 1/50 [00:00<00:05,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.009):   4%|▍         | 2/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.009):   6%|▌         | 3/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.009):   8%|▊         | 4/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.009):  10%|█         | 5/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 763.899} (0.009):  12%|█▏        | 6/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  14%|█▍        | 7/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  16%|█▌        | 8/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  18%|█▊        | 9/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  20%|██        | 10/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  22%|██▏       | 11/50 [00:00<00:04,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  24%|██▍       | 12/50 [00:00<00:03,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  26%|██▌       | 13/50 [00:00<00:03,  9.72it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  28%|██▊       | 14/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  28%|██▊       | 14/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  30%|███       | 15/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  32%|███▏      | 16/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  34%|███▍      | 17/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  36%|███▌      | 18/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  38%|███▊      | 19/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  40%|████      | 20/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  42%|████▏     | 21/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  44%|████▍     | 22/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  46%|████▌     | 23/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  48%|████▊     | 24/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  50%|█████     | 25/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  52%|█████▏    | 26/50 [00:00<00:00, 79.15it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  54%|█████▍    | 27/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  54%|█████▍    | 27/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  56%|█████▌    | 28/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  58%|█████▊    | 29/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  60%|██████    | 30/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  62%|██████▏   | 31/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  64%|██████▍   | 32/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  66%|██████▌   | 33/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  68%|██████▊   | 34/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  70%|███████   | 35/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  72%|███████▏  | 36/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  74%|███████▍  | 37/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  76%|███████▌  | 38/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  78%|███████▊  | 39/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  80%|████████  | 40/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  82%|████████▏ | 41/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.009 | Best: {'fx': 770.022} (0.010):  84%|████████▍ | 42/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  86%|████████▌ | 43/50 [00:00<00:00, 100.59it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 770.022} (0.010):  88%|████████▊ | 44/50 [00:00<00:00, 123.47it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 807.777} (0.010):  88%|████████▊ | 44/50 [00:00<00:00, 123.47it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 807.777} (0.010):  90%|█████████ | 45/50 [00:00<00:00, 123.47it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 807.777} (0.010):  92%|█████████▏| 46/50 [00:00<00:00, 123.47it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 810.838} (0.010):  94%|█████████▍| 47/50 [00:00<00:00, 123.47it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 811.858} (0.010):  96%|█████████▌| 48/50 [00:00<00:00, 123.47it/s]
Brute | Start: {'fx': 787.879} (0.010) | Current: 0.010 | Best: {'fx': 811.858} (0.010):  98%|█████████▊| 49/50 [00:00<00:00, 123.47it/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: 1491.7346: 0iter [00:00, ?iter/s]
Best score: 1092.2433: : 0iter [00:01, ?iter/s] 
Best score: 1092.2433: : 1iter [00:01,  1.06s/iter]
Best score: 1063.1522: : 1iter [00:01,  1.06s/iter]
Best score: 1063.1522: : 2iter [00:01,  1.39iter/s]
Best score: 1063.1522: : 3iter [00:02,  1.56iter/s]
Best score: 1042.3149: : 3iter [00:02,  1.56iter/s]
Best score: 1042.3149: : 4iter [00:02,  1.67iter/s]
Best score: 975.1469: : 4iter [00:03,  1.67iter/s] 
Best score: 975.1469: : 5iter [00:03,  1.83iter/s]
Best score: 884.1766: : 5iter [00:03,  1.83iter/s]
Best score: 884.1766: : 6iter [00:03,  1.94iter/s]
Best score: 884.1766: : 7iter [00:04,  1.89iter/s]
Best score: 884.1766: : 8iter [00:04,  1.87iter/s]
Best score: 884.1766: : 9iter [00:05,  1.89iter/s]
Best score: 884.1766: : 10iter [00:05,  1.93iter/s]
Best score: 878.4230: : 10iter [00:06,  1.93iter/s]
Best score: 878.4230: : 11iter [00:06,  1.94iter/s]
Best score: 878.4230: : 12iter [00:06,  1.98iter/s]
Best score: 878.4230: : 13iter [00:07,  1.97iter/s]
Best score: 878.4230: : 14iter [00:07,  1.95iter/s]
Best score: 878.4230: : 15iter [00:08,  1.96iter/s]
Best score: 865.9786: : 15iter [00:08,  1.96iter/s]
Best score: 865.9786: : 16iter [00:08,  1.98iter/s]
Best score: 865.9786: : 17iter [00:09,  1.97iter/s]
Best score: 865.9786: : 18iter [00:09,  1.94iter/s]
Best score: 865.9786: : 19iter [00:10,  1.98iter/s]
Best score: 865.9786: : 20iter [00:10,  1.98iter/s]
Best score: 861.6457: : 20iter [00:11,  1.98iter/s]
Best score: 861.6457: : 21iter [00:11,  1.97iter/s]
Best score: 861.6457: : 22iter [00:11,  2.03iter/s]
Best score: 861.6457: : 23iter [00:12,  1.94iter/s]
Best score: 861.6457: : 24iter [00:12,  1.95iter/s]
Best score: 861.6457: : 25iter [00:13,  2.02iter/s]
Best score: 861.6457: : 26iter [00:13,  2.03iter/s]
Best score: 821.8662: : 26iter [00:14,  2.03iter/s]
Best score: 821.8662: : 27iter [00:14,  1.99iter/s]
Best score: 821.8662: : 28iter [00:14,  1.91iter/s]
Best score: 821.8662: : 29iter [00:15,  1.92iter/s]
Best score: 821.8662: : 30iter [00:15,  1.95iter/s]
Best score: 821.8662: : 31iter [00:16,  2.05iter/s]
Best score: 821.8662: : 32iter [00:16,  2.07iter/s]
Best score: 821.8662: : 33iter [00:17,  2.03iter/s]
Best score: 821.8662: : 34iter [00:17,  2.03iter/s]
Best score: 821.8662: : 35iter [00:18,  1.95iter/s]
Best score: 821.8662: : 36iter [00:18,  1.89iter/s]
Best score: 815.6680: : 36iter [00:19,  1.89iter/s]
Best score: 815.6680: : 37iter [00:19,  2.00iter/s]
Best score: 815.6680: : 38iter [00:19,  1.97iter/s]
Best score: 815.6680: : 39iter [00:20,  1.93iter/s]
Best score: 815.6680: : 40iter [00:20,  1.93iter/s]
Best score: 815.6680: : 41iter [00:21,  1.92iter/s]
Best score: 815.6680: : 42iter [00:21,  1.90iter/s]
Best score: 815.6680: : 43iter [00:22,  1.88iter/s]
Best score: 815.6680: : 44iter [00:23,  1.85iter/s]
Best score: 815.6680: : 45iter [00:23,  1.93iter/s]
Best score: 815.6680: : 46iter [00:24,  1.90iter/s]
Best score: 815.6680: : 47iter [00:24,  1.90iter/s]
Best score: 815.6680: : 48iter [00:25,  1.91iter/s]
Best score: 815.6680: : 49iter [00:25,  1.97iter/s]
[ITER 50] Current best score: 815.6680
[PARAMS] Current best parameters:
  fx: 856.4270
  fy: 732.4574
  px: 859.9437
  py: 762.7262
  k1: 0.04496879
  k2: 0.07846036
  k3: 0.04770939
  k4: 0.01985801
  p1: -0.00047654
  p2: -0.00959735
  p3: -0.00365835
  p4: -0.00068926
  scale: 2.4238
  rotation: -0.8168

Best score: 815.6680: : 50iter [00:26,  1.93iter/s]
Best score: 815.6680: : 51iter [00:26,  1.89iter/s]
Best score: 815.6680: : 52iter [00:27,  1.86iter/s]
Best score: 815.6680: : 53iter [00:27,  1.86iter/s]
Best score: 810.5724: : 53iter [00:28,  1.86iter/s]
Best score: 810.5724: : 54iter [00:28,  1.88iter/s]
Best score: 810.5724: : 55iter [00:28,  1.94iter/s]
Best score: 810.5724: : 56iter [00:29,  1.88iter/s]
Best score: 810.5724: : 57iter [00:29,  1.98iter/s]
Best score: 810.5724: : 58iter [00:30,  2.02iter/s]
Best score: 810.5724: : 59iter [00:30,  1.96iter/s]
Best score: 810.5724: : 60iter [00:31,  2.03iter/s]
Best score: 810.5724: : 61iter [00:31,  1.97iter/s]
Best score: 810.5724: : 62iter [00:32,  2.00iter/s]
Best score: 810.5724: : 63iter [00:32,  1.97iter/s]
Best score: 807.4885: : 63iter [00:33,  1.97iter/s]
Best score: 807.4885: : 64iter [00:33,  1.95iter/s]
Best score: 805.0727: : 64iter [00:33,  1.95iter/s]
Best score: 805.0727: : 65iter [00:33,  1.98iter/s]
Best score: 805.0727: : 66iter [00:34,  2.05iter/s]
Best score: 797.5579: : 66iter [00:34,  2.05iter/s]
Best score: 797.5579: : 67iter [00:34,  2.03iter/s]
Best score: 797.5579: : 68iter [00:35,  1.96iter/s]
Best score: 797.5579: : 69iter [00:35,  1.92iter/s]
Best score: 797.5579: : 70iter [00:36,  1.91iter/s]
Best score: 797.5579: : 71iter [00:36,  1.89iter/s]
Best score: 797.5579: : 72iter [00:37,  1.98iter/s]
Best score: 797.5579: : 73iter [00:37,  1.96iter/s]
Best score: 797.5579: : 74iter [00:38,  2.01iter/s]
Best score: 797.5579: : 75iter [00:38,  2.06iter/s]
Best score: 797.5579: : 76iter [00:39,  2.06iter/s]
Best score: 797.5579: : 77iter [00:39,  2.04iter/s]
Best score: 795.9032: : 77iter [00:40,  2.04iter/s]
Best score: 795.9032: : 78iter [00:40,  2.00iter/s]
Best score: 795.9032: : 79iter [00:40,  1.97iter/s]
Best score: 795.9032: : 80iter [00:41,  1.92iter/s]
Best score: 795.9032: : 81iter [00:41,  1.92iter/s]
Best score: 795.9032: : 82iter [00:42,  1.92iter/s]
Best score: 795.9032: : 83iter [00:42,  1.99iter/s]
Best score: 795.9032: : 84iter [00:43,  2.00iter/s]
Best score: 793.3585: : 84iter [00:43,  2.00iter/s]
Best score: 793.3585: : 85iter [00:43,  1.95iter/s]
Best score: 793.3585: : 86iter [00:44,  1.95iter/s]
Best score: 793.3585: : 87iter [00:44,  2.06iter/s]
Best score: 793.3585: : 88iter [00:45,  2.01iter/s]
Best score: 792.9054: : 88iter [00:45,  2.01iter/s]
Best score: 792.9054: : 89iter [00:45,  1.98iter/s]
Best score: 792.9054: : 90iter [00:46,  2.08iter/s]
Best score: 790.8174: : 90iter [00:46,  2.08iter/s]
Best score: 790.8174: : 91iter [00:46,  1.95iter/s]
Best score: 790.8174: : 92iter [00:47,  2.01iter/s]
Best score: 790.8174: : 93iter [00:47,  2.04iter/s]
Best score: 790.0260: : 93iter [00:48,  2.04iter/s]
Best score: 790.0260: : 94iter [00:48,  2.01iter/s]
Best score: 790.0260: : 95iter [00:48,  2.05iter/s]
Best score: 790.0260: : 96iter [00:49,  2.11iter/s]
Best score: 790.0260: : 97iter [00:49,  1.99iter/s]
Best score: 790.0260: : 98iter [00:50,  2.04iter/s]
Best score: 790.0260: : 99iter [00:50,  1.99iter/s]
[ITER 100] Current best score: 790.2872
[PARAMS] Current best parameters:
  fx: 866.1558
  fy: 722.2120
  px: 859.9783
  py: 763.5296
  k1: 0.04997039
  k2: 0.07982273
  k3: 0.04977728
  k4: 0.01970267
  p1: -0.00866884
  p2: -0.00997457
  p3: -0.00868014
  p4: -0.00351346
  scale: 2.4188
  rotation: -0.4742

Best score: 790.0260: : 100iter [00:51,  1.91iter/s]
Best score: 790.0260: : 101iter [00:51,  1.93iter/s]
Best score: 790.0260: : 102iter [00:52,  2.00iter/s]
Best score: 790.0260: : 103iter [00:52,  1.97iter/s]
Best score: 789.8540: : 103iter [00:53,  1.97iter/s]
Best score: 789.8540: : 104iter [00:53,  1.95iter/s]
Best score: 789.8540: : 105iter [00:53,  1.98iter/s]
Best score: 789.8540: : 106iter [00:54,  1.98iter/s]
Best score: 789.8540: : 107iter [00:54,  1.98iter/s]
Best score: 789.8540: : 108iter [00:55,  2.03iter/s]
Best score: 789.8540: : 109iter [00:55,  1.97iter/s]
Best score: 789.8540: : 110iter [00:56,  2.00iter/s]
Best score: 789.8540: : 111iter [00:56,  1.99iter/s]
Best score: 789.8540: : 112iter [00:57,  2.01iter/s]
Best score: 789.8540: : 113iter [00:57,  1.98iter/s]
Best score: 789.8540: : 114iter [00:58,  2.00iter/s]
Best score: 789.8540: : 115iter [00:58,  2.03iter/s]
Best score: 789.8540: : 116iter [00:59,  1.94iter/s]
Best score: 789.8540: : 117iter [00:59,  1.98iter/s]
Best score: 789.8540: : 118iter [01:00,  2.04iter/s]
Best score: 789.8540: : 119iter [01:00,  1.98iter/s]
Best score: 789.8540: : 120iter [01:01,  2.02iter/s]
Best score: 789.8540: : 121iter [01:01,  1.95iter/s]
Best score: 789.8540: : 122iter [01:02,  2.04iter/s]
Best score: 789.8540: : 123iter [01:02,  2.06iter/s]
Best score: 789.8540: : 124iter [01:03,  1.96iter/s]
Best score: 789.8540: : 125iter [01:03,  1.96iter/s]
Best score: 789.8540: : 126iter [01:04,  2.01iter/s]
Best score: 789.8540: : 127iter [01:04,  2.00iter/s]
Best score: 789.8540: : 128iter [01:05,  1.96iter/s]
Best score: 789.8540: : 129iter [01:05,  1.95iter/s]
Best score: 789.8540: : 130iter [01:06,  2.00iter/s]
Best score: 789.8540: : 131iter [01:06,  2.03iter/s]
Best score: 789.8540: : 132iter [01:07,  1.96iter/s]
Best score: 789.8540: : 133iter [01:08,  1.89iter/s]
Best score: 789.8540: : 134iter [01:08,  1.99iter/s]
Best score: 789.8540: : 135iter [01:09,  1.95iter/s]
Best score: 789.8540: : 136iter [01:09,  1.88iter/s]
Best score: 789.8540: : 137iter [01:10,  2.01iter/s]
Best score: 789.8540: : 138iter [01:10,  1.98iter/s]
Best score: 789.8540: : 139iter [01:11,  1.93iter/s]
Best score: 789.8540: : 140iter [01:11,  2.05iter/s]
Best score: 789.8540: : 141iter [01:11,  2.05iter/s]
Best score: 789.8540: : 142iter [01:12,  2.06iter/s]
Best score: 789.8540: : 143iter [01:12,  2.00iter/s]
Best score: 789.8540: : 144iter [01:13,  1.97iter/s]
Best score: 789.8540: : 145iter [01:14,  1.90iter/s]
Best score: 789.8540: : 146iter [01:14,  2.02iter/s]
Best score: 789.8540: : 147iter [01:14,  2.03iter/s]
Best score: 789.8540: : 148iter [01:15,  2.00iter/s]
Best score: 789.8540: : 149iter [01:15,  2.02iter/s]
[ITER 150] Current best score: 798.6961
[PARAMS] Current best parameters:
  fx: 865.8910
  fy: 715.3042
  px: 859.8760
  py: 792.0123
  k1: 0.04978625
  k2: 0.07985002
  k3: 0.04994421
  k4: 0.01982324
  p1: 0.00496753
  p2: -0.00999257
  p3: -0.00973555
  p4: -0.00654668
  scale: 2.4151
  rotation: -0.7162

Best score: 789.8540: : 150iter [01:16,  1.91iter/s]
Best score: 789.8540: : 151iter [01:17,  1.92iter/s]
Best score: 789.8540: : 152iter [01:17,  2.01iter/s]
Best score: 789.8540: : 153iter [01:18,  2.00iter/s]
Best score: 789.8540: : 154iter [01:18,  1.96iter/s]
Best score: 789.8540: : 155iter [01:19,  1.98iter/s]
Best score: 789.8540: : 156iter [01:19,  1.99iter/s]
Best score: 789.8540: : 157iter [01:20,  2.06iter/s]
Best score: 789.8540: : 158iter [01:20,  2.15iter/s]
Best score: 789.8540: : 159iter [01:20,  2.15iter/s]
Best score: 789.8540: : 160iter [01:21,  2.01iter/s]
Best score: 789.8540: : 161iter [01:21,  2.12iter/s]
Best score: 789.8540: : 162iter [01:22,  2.00iter/s]
Best score: 789.8540: : 163iter [01:22,  1.99iter/s]
Best score: 789.8540: : 164iter [01:23,  2.12iter/s]
Best score: 789.8540: : 165iter [01:23,  2.11iter/s]
Best score: 789.8540: : 166iter [01:24,  2.07iter/s]
Best score: 789.8540: : 167iter [01:24,  2.03iter/s]
Best score: 789.8540: : 168iter [01:25,  2.09iter/s]
Best score: 789.8540: : 169iter [01:25,  1.99iter/s]
Best score: 789.8540: : 170iter [01:26,  1.93iter/s]
Best score: 789.8540: : 171iter [01:26,  2.01iter/s]
Best score: 789.8540: : 172iter [01:27,  1.95iter/s]
Best score: 789.8540: : 173iter [01:27,  1.98iter/s]
Best score: 789.8540: : 174iter [01:28,  2.04iter/s]
Best score: 789.8540: : 175iter [01:28,  2.02iter/s]
Best score: 789.8540: : 176iter [01:29,  2.02iter/s]
Best score: 789.8540: : 177iter [01:29,  2.07iter/s]
Best score: 789.8540: : 178iter [01:30,  2.03iter/s]
Best score: 789.8540: : 179iter [01:30,  1.98iter/s]
Best score: 789.8540: : 180iter [01:31,  1.94iter/s]
Best score: 789.8540: : 181iter [01:31,  1.98iter/s]
Best score: 789.8540: : 182iter [01:32,  1.97iter/s]
Best score: 789.8540: : 183iter [01:32,  1.95iter/s]
Best score: 789.8540: : 184iter [01:33,  1.94iter/s]
Best score: 789.8540: : 185iter [01:33,  1.91iter/s]
Best score: 789.8540: : 186iter [01:34,  1.98iter/s]
Best score: 789.8540: : 187iter [01:34,  1.93iter/s]
Best score: 789.8540: : 188iter [01:35,  1.92iter/s]
Best score: 789.8540: : 189iter [01:36,  1.96iter/s]
Best score: 789.8540: : 190iter [01:36,  1.91iter/s]
Best score: 789.8540: : 191iter [01:37,  1.97iter/s]
Best score: 789.8540: : 192iter [01:37,  2.04iter/s]
Best score: 789.8540: : 193iter [01:37,  2.02iter/s]
Best score: 789.8540: : 194iter [01:38,  1.99iter/s]
Best score: 789.8540: : 195iter [01:39,  1.99iter/s]
Best score: 789.8540: : 196iter [01:39,  2.03iter/s]
Best score: 789.8540: : 197iter [01:40,  1.98iter/s]
Best score: 789.8540: : 198iter [01:40,  1.99iter/s]
Best score: 789.8540: : 199iter [01:41,  1.96iter/s]
[ITER 200] Current best score: 798.4382
[PARAMS] Current best parameters:
  fx: 864.8893
  fy: 712.1807
  px: 859.9475
  py: 793.7636
  k1: 0.04998962
  k2: 0.07999330
  k3: 0.04999290
  k4: 0.01991320
  p1: 0.00620240
  p2: -0.00997515
  p3: -0.00985950
  p4: -0.00842935
  scale: 2.4173
  rotation: -0.6845

Best score: 789.8540: : 200iter [01:41,  1.92iter/s]
Best score: 789.8540: : 201iter [01:42,  1.98iter/s]
Best score: 789.8540: : 202iter [01:42,  1.94iter/s]
Best score: 789.8540: : 203iter [01:43,  1.94iter/s]
Best score: 789.8540: : 204iter [01:43,  1.96iter/s]
Best score: 789.8540: : 205iter [01:44,  1.99iter/s]
Best score: 789.8540: : 206iter [01:44,  1.97iter/s]
Best score: 789.8540: : 207iter [01:45,  1.94iter/s]
Best score: 789.8540: : 208iter [01:45,  1.96iter/s]
Best score: 789.8540: : 209iter [01:46,  1.96iter/s]
Best score: 789.8540: : 210iter [01:46,  1.95iter/s]
Best score: 789.8540: : 211iter [01:47,  1.98iter/s]
Best score: 789.8540: : 212iter [01:47,  1.94iter/s]
Best score: 789.8540: : 213iter [01:48,  1.89iter/s]
Best score: 789.8540: : 214iter [01:48,  1.98iter/s]
Best score: 789.8540: : 215iter [01:49,  1.97iter/s]
Best score: 789.8540: : 216iter [01:49,  1.93iter/s]
Best score: 789.8540: : 217iter [01:50,  2.03iter/s]
Best score: 789.8540: : 218iter [01:50,  2.03iter/s]
Best score: 789.8540: : 219iter [01:51,  1.94iter/s]
Best score: 789.8540: : 220iter [01:51,  2.03iter/s]
Best score: 789.8540: : 221iter [01:52,  2.04iter/s]
Best score: 789.8540: : 222iter [01:52,  2.03iter/s]
Best score: 789.8540: : 223iter [01:53,  2.04iter/s]
Best score: 789.8540: : 224iter [01:53,  2.00iter/s]
Best score: 789.8540: : 225iter [01:54,  1.95iter/s]
Best score: 789.8540: : 226iter [01:54,  1.99iter/s]
Best score: 789.8540: : 227iter [01:55,  1.88iter/s]
Best score: 789.8540: : 228iter [01:55,  1.93iter/s]
Best score: 789.8540: : 229iter [01:56,  2.01iter/s]
Best score: 789.8540: : 230iter [01:56,  2.00iter/s]
Best score: 789.8540: : 231iter [01:57,  1.95iter/s]
Best score: 789.8540: : 232iter [01:57,  1.93iter/s]
Best score: 789.8540: : 233iter [01:58,  1.94iter/s]
Best score: 789.8540: : 234iter [01:58,  2.00iter/s]
Best score: 789.8540: : 235iter [01:59,  1.94iter/s]
Best score: 789.8540: : 236iter [01:59,  1.95iter/s]
Best score: 789.8540: : 237iter [02:00,  2.05iter/s]
Best score: 789.8540: : 238iter [02:00,  1.97iter/s]
Best score: 789.8540: : 239iter [02:01,  1.94iter/s]
Best score: 789.8540: : 240iter [02:01,  2.04iter/s]
Best score: 789.8540: : 241iter [02:02,  1.93iter/s]
Best score: 789.8540: : 242iter [02:02,  2.00iter/s]
Best score: 789.8540: : 243iter [02:03,  1.97iter/s]
Best score: 789.8540: : 244iter [02:03,  1.97iter/s]
Best score: 789.8540: : 245iter [02:04,  2.08iter/s]
Best score: 789.8540: : 246iter [02:04,  1.97iter/s]
Best score: 789.8540: : 247iter [02:05,  1.94iter/s]
Best score: 789.8540: : 248iter [02:05,  1.95iter/s]
Best score: 789.8540: : 249iter [02:06,  1.99iter/s]
[ITER 250] Current best score: 796.9130
[PARAMS] Current best parameters:
  fx: 866.2889
  fy: 713.8242
  px: 859.9643
  py: 793.8108
  k1: 0.04999027
  k2: 0.07997221
  k3: 0.04998618
  k4: 0.01996223
  p1: 0.00615532
  p2: -0.00997858
  p3: -0.00992327
  p4: -0.00981305
  scale: 2.4169
  rotation: -0.7280

Best score: 789.8540: : 250iter [02:06,  1.94iter/s]
Best score: 789.8540: : 251iter [02:07,  1.94iter/s]
Best score: 789.8540: : 252iter [02:07,  1.99iter/s]
Best score: 789.8540: : 253iter [02:08,  1.98iter/s]
Best score: 789.8540: : 254iter [02:08,  2.05iter/s]
Best score: 789.8540: : 255iter [02:09,  2.00iter/s]
Best score: 789.8540: : 256iter [02:09,  1.95iter/s]
Best score: 789.8540: : 257iter [02:10,  2.08iter/s]
Best score: 789.8540: : 258iter [02:10,  2.06iter/s]
Best score: 789.8540: : 259iter [02:11,  2.09iter/s]
Best score: 789.8540: : 260iter [02:11,  2.07iter/s]
Best score: 789.8540: : 261iter [02:12,  2.06iter/s]
Best score: 789.8540: : 262iter [02:12,  1.97iter/s]
Best score: 789.8540: : 263iter [02:13,  1.94iter/s]
Best score: 789.8540: : 264iter [02:13,  1.92iter/s]
Best score: 789.8540: : 265iter [02:14,  1.95iter/s]
Best score: 789.8540: : 266iter [02:14,  1.95iter/s]
Best score: 789.8540: : 267iter [02:15,  1.94iter/s]
Best score: 789.8540: : 268iter [02:15,  1.98iter/s]
Best score: 789.8540: : 269iter [02:16,  1.99iter/s]
Best score: 789.8540: : 270iter [02:16,  1.94iter/s]
Best score: 789.8540: : 271iter [02:17,  1.93iter/s]
Best score: 789.8540: : 272iter [02:17,  1.97iter/s]
Best score: 789.8540: : 273iter [02:18,  2.06iter/s]
Best score: 789.8540: : 274iter [02:18,  2.05iter/s]
Best score: 789.8540: : 275iter [02:19,  2.04iter/s]
Best score: 789.8540: : 276iter [02:19,  2.16iter/s]
Best score: 789.8540: : 277iter [02:20,  2.06iter/s]
Best score: 789.8540: : 278iter [02:20,  2.02iter/s]
Best score: 789.8540: : 279iter [02:21,  2.11iter/s]
Best score: 789.8540: : 280iter [02:21,  2.14iter/s]
Best score: 789.8540: : 281iter [02:22,  2.12iter/s]
Best score: 789.8540: : 282iter [02:22,  2.18iter/s]
Best score: 789.8540: : 283iter [02:23,  2.16iter/s]
Best score: 789.8540: : 284iter [02:23,  2.12iter/s]
Best score: 789.8540: : 285iter [02:24,  2.08iter/s]
Best score: 789.8540: : 286iter [02:24,  1.99iter/s]
Best score: 789.8540: : 287iter [02:25,  2.01iter/s]
Best score: 789.8540: : 288iter [02:25,  2.10iter/s]
Best score: 789.8540: : 289iter [02:26,  2.09iter/s]
Best score: 789.8540: : 290iter [02:26,  2.06iter/s]
Best score: 789.8540: : 291iter [02:26,  2.18iter/s]
Best score: 789.8540: : 292iter [02:27,  2.21iter/s]
Best score: 789.8540: : 293iter [02:27,  2.16iter/s]
Best score: 789.8540: : 294iter [02:28,  2.15iter/s]
Best score: 789.8540: : 295iter [02:28,  2.10iter/s]
Best score: 789.8540: : 296iter [02:29,  2.09iter/s]
Best score: 789.8540: : 297iter [02:29,  2.02iter/s]
Best score: 789.8540: : 298iter [02:30,  2.03iter/s]
Best score: 789.8540: : 299iter [02:30,  2.04iter/s]
[ITER 300] Current best score: 798.5670
[PARAMS] Current best parameters:
  fx: 866.6131
  fy: 712.1045
  px: 859.9977
  py: 793.9456
  k1: 0.04998281
  k2: 0.07999620
  k3: 0.04999355
  k4: 0.01999025
  p1: 0.00777206
  p2: -0.00999603
  p3: -0.00996232
  p4: -0.00948628
  scale: 2.4169
  rotation: -0.7054

Best score: 789.8540: : 300iter [02:31,  1.98iter/s]
Best score: 789.8540: : 301iter [02:31,  1.97iter/s]
Best score: 789.8540: : 302iter [02:32,  1.99iter/s]
Best score: 789.8540: : 303iter [02:32,  2.01iter/s]
Best score: 789.8540: : 304iter [02:33,  2.06iter/s]
Best score: 789.8540: : 305iter [02:33,  2.07iter/s]
Best score: 789.8540: : 306iter [02:34,  2.02iter/s]
Best score: 789.8540: : 307iter [02:34,  2.12iter/s]
Best score: 789.8540: : 308iter [02:35,  2.12iter/s]
Best score: 789.8540: : 309iter [02:35,  2.14iter/s]
Best score: 789.8540: : 310iter [02:36,  2.15iter/s]
Best score: 789.8540: : 311iter [02:36,  2.11iter/s]
Best score: 789.8540: : 312iter [02:37,  2.01iter/s]
Best score: 789.8540: : 313iter [02:37,  2.11iter/s]
Best score: 789.8540: : 314iter [02:38,  2.03iter/s]
Best score: 789.8540: : 315iter [02:38,  2.00iter/s]
Best score: 789.8540: : 316iter [02:39,  2.12iter/s]
Best score: 789.8540: : 317iter [02:39,  2.12iter/s]
Best score: 789.8540: : 318iter [02:40,  2.12iter/s]
Best score: 789.8540: : 319iter [02:40,  2.15iter/s]
Best score: 789.8540: : 320iter [02:40,  2.17iter/s]
Best score: 789.8540: : 321iter [02:41,  2.25iter/s]
Best score: 789.8540: : 322iter [02:41,  2.23iter/s]
Best score: 789.8540: : 323iter [02:42,  2.15iter/s]
Best score: 789.8540: : 324iter [02:42,  2.11iter/s]
Best score: 789.8540: : 325iter [02:43,  2.17iter/s]
Best score: 789.8540: : 326iter [02:43,  2.12iter/s]
Best score: 789.8540: : 327iter [02:44,  2.14iter/s]
Best score: 789.8540: : 328iter [02:44,  2.20iter/s]
Best score: 789.8540: : 329iter [02:45,  2.05iter/s]
Best score: 789.8540: : 330iter [02:45,  2.08iter/s]
Best score: 789.8540: : 331iter [02:46,  2.19iter/s]
Best score: 789.8540: : 332iter [02:46,  2.13iter/s]
Best score: 789.8540: : 333iter [02:47,  2.12iter/s]
Best score: 789.8540: : 334iter [02:47,  2.11iter/s]
Best score: 789.8540: : 335iter [02:48,  2.05iter/s]
Best score: 789.8540: : 336iter [02:48,  2.05iter/s]
Best score: 789.8540: : 337iter [02:49,  2.01iter/s]
Best score: 789.8540: : 338iter [02:49,  2.06iter/s]
Best score: 789.8540: : 339iter [02:49,  2.11iter/s]
Best score: 789.8540: : 340iter [02:50,  2.02iter/s]
Best score: 789.8540: : 341iter [02:50,  2.11iter/s]
Best score: 789.8540: : 342iter [02:51,  2.01iter/s]
Best score: 789.8540: : 343iter [02:51,  2.03iter/s]
Best score: 789.8540: : 344iter [02:52,  2.07iter/s]
Best score: 789.8540: : 345iter [02:52,  2.08iter/s]
Best score: 789.8540: : 346iter [02:53,  2.02iter/s]
Best score: 789.8540: : 347iter [02:53,  2.00iter/s]
Best score: 789.8540: : 348iter [02:54,  1.99iter/s]
Best score: 789.8540: : 349iter [02:54,  2.09iter/s]
[ITER 350] Current best score: 798.3077
[PARAMS] Current best parameters:
  fx: 866.6649
  fy: 712.9279
  px: 859.9964
  py: 793.9771
  k1: 0.04999930
  k2: 0.07999585
  k3: 0.04999564
  k4: 0.01999789
  p1: 0.00740504
  p2: -0.00999594
  p3: -0.00998053
  p4: -0.00998315
  scale: 2.4165
  rotation: -0.7113

Best score: 789.8540: : 350iter [02:55,  1.96iter/s]
Best score: 789.8540: : 351iter [02:55,  1.97iter/s]
Best score: 789.8540: : 352iter [02:56,  2.00iter/s]
Best score: 789.8540: : 353iter [02:56,  2.00iter/s]
Best score: 789.8540: : 354iter [02:57,  2.01iter/s]
Best score: 789.8540: : 355iter [02:57,  2.10iter/s]
Best score: 789.8540: : 356iter [02:58,  2.14iter/s]
Best score: 789.8540: : 357iter [02:58,  2.15iter/s]
Best score: 789.8540: : 358iter [02:59,  2.11iter/s]
Best score: 789.8540: : 359iter [02:59,  2.14iter/s]
Best score: 789.8540: : 360iter [03:00,  2.20iter/s]
Best score: 789.8540: : 361iter [03:00,  2.09iter/s]
Best score: 789.8540: : 362iter [03:01,  2.02iter/s]
Best score: 789.8540: : 363iter [03:01,  2.04iter/s]
Best score: 789.8540: : 364iter [03:02,  2.04iter/s]
Best score: 789.8540: : 365iter [03:02,  2.08iter/s]
Best score: 789.8540: : 366iter [03:03,  2.14iter/s]
Best score: 789.8540: : 367iter [03:03,  2.08iter/s]
Best score: 789.8540: : 368iter [03:04,  2.03iter/s]
Best score: 789.8540: : 369iter [03:04,  2.02iter/s]
Best score: 789.8540: : 370iter [03:04,  2.11iter/s]
Best score: 789.8540: : 371iter [03:05,  2.02iter/s]
Best score: 789.8540: : 372iter [03:06,  2.05iter/s]
Best score: 789.8540: : 373iter [03:06,  2.10iter/s]
Best score: 789.8540: : 374iter [03:06,  2.01iter/s]
Best score: 789.8540: : 375iter [03:07,  2.02iter/s]
Best score: 789.8540: : 376iter [03:07,  2.08iter/s]
Best score: 789.8540: : 377iter [03:08,  2.09iter/s]
Best score: 789.8540: : 378iter [03:08,  2.12iter/s]
Best score: 789.8540: : 379iter [03:09,  2.19iter/s]
Best score: 789.8540: : 380iter [03:09,  2.13iter/s]
Best score: 789.8540: : 381iter [03:10,  2.16iter/s]
Best score: 789.8540: : 382iter [03:10,  2.11iter/s]
Best score: 789.8540: : 383iter [03:11,  2.10iter/s]
Best score: 789.8540: : 384iter [03:11,  2.23iter/s]
Best score: 789.8540: : 385iter [03:12,  2.18iter/s]
Best score: 789.8540: : 386iter [03:12,  2.15iter/s]
Best score: 789.8540: : 387iter [03:12,  2.24iter/s]
Best score: 789.8540: : 388iter [03:13,  2.17iter/s]
Best score: 789.8540: : 389iter [03:13,  2.19iter/s]
Best score: 789.8540: : 390iter [03:14,  2.24iter/s]
Best score: 789.8540: : 391iter [03:14,  2.16iter/s]
Best score: 789.8540: : 392iter [03:15,  2.05iter/s]
Best score: 789.8540: : 393iter [03:15,  2.08iter/s]
Best score: 789.8540: : 394iter [03:16,  2.13iter/s]
Best score: 789.8540: : 395iter [03:16,  2.05iter/s]
Best score: 789.8540: : 396iter [03:17,  2.12iter/s]
Best score: 789.8540: : 397iter [03:17,  2.15iter/s]
Best score: 789.8540: : 398iter [03:18,  2.10iter/s]
Best score: 789.8540: : 399iter [03:18,  2.12iter/s]
[ITER 400] Current best score: 798.4392
[PARAMS] Current best parameters:
  fx: 866.6365
  fy: 713.4204
  px: 859.9985
  py: 793.9734
  k1: 0.04999636
  k2: 0.07999964
  k3: 0.04999686
  k4: 0.01999969
  p1: 0.00805471
  p2: -0.00999800
  p3: -0.00999413
  p4: -0.00993057
  scale: 2.4162
  rotation: -0.7077

Best score: 789.8540: : 400iter [03:19,  2.01iter/s]
Best score: 789.8540: : 401iter [03:19,  1.99iter/s]
Best score: 789.8540: : 402iter [03:20,  2.00iter/s]
Best score: 789.8540: : 403iter [03:20,  2.05iter/s]
Best score: 789.8540: : 404iter [03:21,  2.07iter/s]
Best score: 789.8540: : 405iter [03:21,  2.06iter/s]
Best score: 789.8540: : 406iter [03:22,  2.12iter/s]
Best score: 789.8540: : 407iter [03:22,  2.12iter/s]
Best score: 789.8540: : 408iter [03:23,  2.08iter/s]
Best score: 789.8540: : 409iter [03:23,  2.18iter/s]
Best score: 789.8540: : 410iter [03:23,  2.21iter/s]
Best score: 789.8540: : 411iter [03:24,  2.16iter/s]
Best score: 789.8540: : 412iter [03:24,  2.22iter/s]
Best score: 789.8540: : 413iter [03:25,  2.14iter/s]
Best score: 789.8540: : 414iter [03:25,  2.21iter/s]
Best score: 789.8540: : 415iter [03:26,  2.18iter/s]
Best score: 789.8540: : 416iter [03:26,  2.11iter/s]
Best score: 789.8540: : 417iter [03:27,  2.07iter/s]
Best score: 789.8540: : 418iter [03:27,  2.03iter/s]
Best score: 789.8540: : 419iter [03:28,  2.06iter/s]
Best score: 789.8540: : 420iter [03:28,  2.08iter/s]
Best score: 789.8540: : 421iter [03:29,  2.07iter/s]
Best score: 789.8540: : 422iter [03:29,  2.06iter/s]
Best score: 789.8540: : 423iter [03:30,  2.10iter/s]
Best score: 789.8540: : 424iter [03:30,  2.06iter/s]
Best score: 789.8540: : 425iter [03:31,  2.08iter/s]
Best score: 789.8540: : 426iter [03:31,  2.09iter/s]
Best score: 789.8540: : 427iter [03:32,  2.04iter/s]
Best score: 789.8540: : 428iter [03:32,  2.10iter/s]
Best score: 789.8540: : 429iter [03:33,  2.03iter/s]
Best score: 789.8540: : 430iter [03:33,  2.09iter/s]
Best score: 789.8540: : 431iter [03:34,  2.06iter/s]
Best score: 789.8540: : 432iter [03:34,  2.13iter/s]
Best score: 789.8540: : 433iter [03:34,  2.20iter/s]
Best score: 789.8540: : 434iter [03:35,  2.13iter/s]
Best score: 789.8540: : 435iter [03:35,  2.06iter/s]
Best score: 789.8540: : 436iter [03:36,  2.07iter/s]
Best score: 789.8540: : 437iter [03:36,  2.10iter/s]
Best score: 789.8540: : 438iter [03:37,  2.08iter/s]
Best score: 789.8540: : 439iter [03:37,  2.02iter/s]
Best score: 789.8540: : 440iter [03:38,  2.08iter/s]
Best score: 789.8540: : 441iter [03:38,  2.07iter/s]
Best score: 789.8540: : 442iter [03:39,  2.05iter/s]
Best score: 789.8540: : 443iter [03:39,  2.09iter/s]
Best score: 789.8540: : 444iter [03:40,  2.10iter/s]
Best score: 789.8540: : 445iter [03:40,  2.06iter/s]
Best score: 789.8540: : 446iter [03:41,  2.03iter/s]
Best score: 789.8540: : 447iter [03:41,  2.01iter/s]
Best score: 789.8540: : 448iter [03:42,  2.06iter/s]
Best score: 789.8540: : 449iter [03:42,  2.05iter/s]
[ITER 450] Current best score: 798.5491
[PARAMS] Current best parameters:
  fx: 866.6585
  fy: 712.7976
  px: 859.9994
  py: 793.9800
  k1: 0.04999924
  k2: 0.07999989
  k3: 0.04999890
  k4: 0.01999919
  p1: 0.00823158
  p2: -0.00999966
  p3: -0.00999411
  p4: -0.00999399
  scale: 2.4161
  rotation: -0.7097

Best score: 789.8540: : 450iter [03:43,  1.94iter/s]
Best score: 789.8540: : 451iter [03:43,  1.91iter/s]
Best score: 789.8540: : 452iter [03:44,  1.97iter/s]
Best score: 789.8540: : 453iter [03:44,  1.94iter/s]
Best score: 789.8540: : 454iter [03:45,  1.96iter/s]
Best score: 789.8540: : 455iter [03:45,  2.02iter/s]
Best score: 789.8540: : 456iter [03:46,  2.08iter/s]
Best score: 789.8540: : 457iter [03:46,  2.09iter/s]
Best score: 789.8540: : 458iter [03:47,  2.13iter/s]
Best score: 789.8540: : 459iter [03:47,  2.09iter/s]
Best score: 789.8540: : 460iter [03:48,  2.17iter/s]
Best score: 789.8540: : 461iter [03:48,  2.19iter/s]
Best score: 789.8540: : 462iter [03:48,  2.16iter/s]
Best score: 789.8540: : 463iter [03:49,  2.11iter/s]
Best score: 789.8540: : 464iter [03:49,  2.18iter/s]
Best score: 789.8540: : 465iter [03:50,  2.12iter/s]
Best score: 789.8540: : 466iter [03:50,  2.10iter/s]
Best score: 789.8540: : 467iter [03:51,  2.04iter/s]
Best score: 789.8540: : 468iter [03:51,  2.02iter/s]
Best score: 789.8540: : 469iter [03:52,  2.05iter/s]
Best score: 789.8540: : 470iter [03:52,  2.09iter/s]
Best score: 789.8540: : 471iter [03:53,  2.06iter/s]
Best score: 789.8540: : 472iter [03:53,  2.02iter/s]
Best score: 789.8540: : 473iter [03:54,  1.99iter/s]
Best score: 789.8540: : 474iter [03:54,  2.01iter/s]
Best score: 789.8540: : 475iter [03:55,  2.04iter/s]
Best score: 789.8540: : 476iter [03:55,  2.04iter/s]
Best score: 789.8540: : 477iter [03:56,  2.04iter/s]
Best score: 789.8540: : 478iter [03:56,  1.98iter/s]
Best score: 789.8540: : 479iter [03:57,  1.94iter/s]
Best score: 789.8540: : 480iter [03:57,  1.97iter/s]
Best score: 789.8540: : 481iter [03:58,  2.06iter/s]
Best score: 789.8540: : 482iter [03:58,  1.98iter/s]
Best score: 789.8540: : 483iter [03:59,  2.09iter/s]
Best score: 789.8540: : 484iter [03:59,  2.18iter/s]
Best score: 789.8540: : 485iter [04:00,  2.11iter/s]
Best score: 789.8540: : 486iter [04:00,  2.15iter/s]
Best score: 789.8540: : 487iter [04:01,  2.18iter/s]
Best score: 789.8540: : 488iter [04:01,  2.06iter/s]
Best score: 789.8540: : 489iter [04:02,  2.05iter/s]
Best score: 789.8540: : 490iter [04:02,  2.12iter/s]
Best score: 789.8540: : 491iter [04:03,  2.02iter/s]
Best score: 789.8540: : 492iter [04:03,  2.03iter/s]
Best score: 789.8540: : 493iter [04:04,  2.01iter/s]
Best score: 789.8540: : 494iter [04:04,  2.04iter/s]
Best score: 789.8540: : 495iter [04:05,  2.01iter/s]
Best score: 789.8540: : 496iter [04:05,  1.95iter/s]
Best score: 789.8540: : 497iter [04:06,  1.97iter/s]
Best score: 789.8540: : 498iter [04:06,  1.97iter/s]
Best score: 789.8540: : 499iter [04:07,  2.01iter/s]
[ITER 500] Current best score: 798.4028
[PARAMS] Current best parameters:
  fx: 866.6650
  fy: 713.2872
  px: 859.9997
  py: 793.9943
  k1: 0.04999981
  k2: 0.07999984
  k3: 0.04999968
  k4: 0.01999952
  p1: 0.00813431
  p2: -0.00999973
  p3: -0.00999964
  p4: -0.00998180
  scale: 2.4162
  rotation: -0.7085

Best score: 789.8540: : 500iter [04:07,  1.93iter/s]
Best score: 789.8540: : 500iter [04:18,  1.94iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 527.186886 -> 527.186886
  Success: False
  Tangential distortion: p1=0.008134, p2=-0.010000, p3=-0.010000, p4=-0.009982
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 527.1869
[✅] 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: 866.665044962441
    fy: 713.287169857852
    px: 859.9997106133751
    py: 793.9942901496937
    k1: 0.049999812701501216
    k2: 0.07999983949247078
    k3: 0.04999967943707145
    k4: 0.019999515556661615
    p1: 0.008134312908581118
    p2: -0.009999731319238743
    p3: -0.009999640477781931
    p4: -0.009981796640873716
    scale: 2.416192846710438
    rotation: -0.7085391579070061
    mmpp: 1.9067103776296694
    nmpp: 1906710.3776296694

[🔄] Converting 2098 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:20<00:00, 10.63s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:20<00:00, 10.31s/rig]
[✅] Saved PTDs: /working/MDC103_W050_2025-11-03/calibration/debug/3_undistorted/output/ptd

📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]

   🔄 500:   0%|          | 0/100 [00:00<?, ?frame/s]

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   🔄 500: 100%|██████████| 100/100 [00:00<00:00, 183.79frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.16rig/s]

   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s]

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   🔄 1000:  99%|█████████▉| 99/100 [00:00<00:00, 180.95frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.14rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.14rig/s]
[✅] 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.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   6%|▌         | 173446/3080448 [00:00<00:01, 1734453.38px/s]
📤 Writing LUT:  12%|█▏        | 375438/3080448 [00:00<00:01, 1902370.39px/s]
📤 Writing LUT:  19%|█▉        | 582847/3080448 [00:00<00:01, 1980777.56px/s]
📤 Writing LUT:  25%|██▌       | 780925/3080448 [00:00<00:01, 1881689.50px/s]
📤 Writing LUT:  31%|███▏      | 969800/3080448 [00:00<00:01, 1563888.69px/s]
📤 Writing LUT:  37%|███▋      | 1133482/3080448 [00:00<00:01, 1416062.75px/s]
📤 Writing LUT:  42%|████▏     | 1281031/3080448 [00:00<00:01, 1351689.39px/s]
📤 Writing LUT:  46%|████▌     | 1419910/3080448 [00:00<00:01, 1305832.47px/s]
📤 Writing LUT:  50%|█████     | 1552713/3080448 [00:01<00:01, 1281295.73px/s]
📤 Writing LUT:  55%|█████▍    | 1682138/3080448 [00:01<00:01, 1255730.87px/s]
📤 Writing LUT:  59%|█████▊    | 1808434/3080448 [00:01<00:01, 1241238.04px/s]
📤 Writing LUT:  63%|██████▎   | 1932954/3080448 [00:01<00:00, 1228513.76px/s]
📤 Writing LUT:  67%|██████▋   | 2056009/3080448 [00:01<00:00, 1220973.36px/s]
📤 Writing LUT:  71%|███████   | 2178210/3080448 [00:01<00:00, 1218834.73px/s]
📤 Writing LUT:  75%|███████▍  | 2300156/3080448 [00:01<00:00, 1210983.56px/s]
📤 Writing LUT:  79%|███████▉  | 2431373/3080448 [00:01<00:00, 1240500.91px/s]
📤 Writing LUT:  84%|████████▍ | 2595891/3080448 [00:01<00:00, 1359541.15px/s]
📤 Writing LUT:  90%|█████████ | 2781458/3080448 [00:01<00:00, 1506339.83px/s]
📤 Writing LUT:  97%|█████████▋| 2976801/3080448 [00:02<00:00, 1639173.86px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:02<00:00, 1443692.90px/s]
[✅] 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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