🟢 Starting Calibration Pipeline

=== Processing MDC027_W031_2025-03-24 ===

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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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📁 Processing Rigs:  50%|█████     | 1/2 [01:05<01:05, 65.65s/it]

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→ 1000 (659 images):  93%|█████████▎| 615/659 [01:20<00:07,  5.86it/s]

→ 1000 (659 images):  94%|█████████▍| 618/659 [01:20<00:05,  8.02it/s]

→ 1000 (659 images):  94%|█████████▍| 621/659 [01:21<00:04,  9.41it/s]

→ 1000 (659 images):  95%|█████████▍| 623/659 [01:21<00:03,  9.99it/s]

→ 1000 (659 images):  95%|█████████▍| 626/659 [01:21<00:02, 12.65it/s]

→ 1000 (659 images):  95%|█████████▌| 628/659 [01:21<00:02, 13.74it/s]

→ 1000 (659 images):  96%|█████████▌| 631/659 [01:21<00:01, 15.70it/s]

→ 1000 (659 images):  96%|█████████▌| 634/659 [01:21<00:01, 17.89it/s]

→ 1000 (659 images):  97%|█████████▋| 637/659 [01:22<00:01, 15.73it/s]

→ 1000 (659 images):  97%|█████████▋| 639/659 [01:22<00:01, 15.64it/s]

→ 1000 (659 images):  97%|█████████▋| 641/659 [01:23<00:03,  5.07it/s]

→ 1000 (659 images):  98%|█████████▊| 644/659 [01:23<00:02,  7.04it/s]

→ 1000 (659 images):  98%|█████████▊| 648/659 [01:23<00:01, 10.11it/s]

→ 1000 (659 images):  99%|█████████▉| 651/659 [01:23<00:00, 10.76it/s]

→ 1000 (659 images):  99%|█████████▉| 653/659 [01:23<00:00, 11.41it/s]

→ 1000 (659 images): 100%|█████████▉| 656/659 [01:24<00:00, 13.43it/s]

→ 1000 (659 images): 100%|██████████| 659/659 [01:24<00:00, 15.08it/s]

                                                                      
📁 Processing Rigs: 100%|██████████| 2/2 [02:30<00:00, 76.66s/it]
📁 Processing Rigs: 100%|██████████| 2/2 [02:30<00:00, 75.01s/it]
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1159 frames from 2 rigs to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 500
[📋] 1000: 100 frames selected from 659
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized/rig_frames.json

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

[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, 32.89it/s]
Averaging bright frames:  20%|██        | 8/40 [00:00<00:00, 36.15it/s]
Averaging bright frames:  30%|███       | 12/40 [00:00<00:00, 37.76it/s]
Averaging bright frames:  42%|████▎     | 17/40 [00:00<00:00, 38.88it/s]
Averaging bright frames:  55%|█████▌    | 22/40 [00:00<00:00, 39.39it/s]
Averaging bright frames:  65%|██████▌   | 26/40 [00:00<00:00, 39.48it/s]
Averaging bright frames:  75%|███████▌  | 30/40 [00:00<00:00, 39.14it/s]
Averaging bright frames:  85%|████████▌ | 34/40 [00:00<00:00, 39.27it/s]
Averaging bright frames:  95%|█████████▌| 38/40 [00:00<00:00, 39.07it/s]
                                                                        
Total time to process (v2): 0.06
[RESULT] Combined: Center=(1020, 746), Radius=838

  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:11,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:10,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:09,  8.50it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.004 | Best: {'fx': 497.98} (0.004):  25%|██▌       | 25/100 [00:00<00:00, 88.42it/s] 
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 497.98} (0.004):  26%|██▌       | 26/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  27%|██▋       | 27/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.054 | Best: {'fx': 526.263} (0.054):  28%|██▊       | 28/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.017 | Best: {'fx': 526.263} (0.054):  29%|██▉       | 29/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.001 | Best: {'fx': 526.263} (0.054):  30%|███       | 30/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 526.263} (0.054):  31%|███       | 31/100 [00:00<00:00, 88.42it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 526.263} (0.054):  32%|███▏      | 32/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.003 | Best: {'fx': 526.263} (0.054):  32%|███▏      | 32/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.037 | Best: {'fx': 526.263} (0.054):  33%|███▎      | 33/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.056 | Best: {'fx': 533.333} (0.056):  34%|███▍      | 34/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.024 | Best: {'fx': 533.333} (0.056):  35%|███▌      | 35/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.028 | Best: {'fx': 533.333} (0.056):  36%|███▌      | 36/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.018 | Best: {'fx': 533.333} (0.056):  37%|███▋      | 37/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.020 | Best: {'fx': 533.333} (0.056):  38%|███▊      | 38/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.013 | Best: {'fx': 533.333} (0.056):  39%|███▉      | 39/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.017 | Best: {'fx': 533.333} (0.056):  40%|████      | 40/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.056):  41%|████      | 41/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.014 | Best: {'fx': 533.333} (0.056):  42%|████▏     | 42/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.014 | Best: {'fx': 533.333} (0.056):  43%|████▎     | 43/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.015 | Best: {'fx': 533.333} (0.056):  44%|████▍     | 44/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.013 | Best: {'fx': 533.333} (0.056):  45%|████▌     | 45/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.056):  46%|████▌     | 46/100 [00:00<00:00, 113.80it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.056):  47%|████▋     | 47/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.056):  47%|████▋     | 47/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.056):  48%|████▊     | 48/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.056):  49%|████▉     | 49/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.056):  50%|█████     | 50/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  51%|█████     | 51/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  52%|█████▏    | 52/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.056):  53%|█████▎    | 53/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  54%|█████▍    | 54/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  55%|█████▌    | 55/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  56%|█████▌    | 56/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  57%|█████▋    | 57/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  58%|█████▊    | 58/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  59%|█████▉    | 59/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  60%|██████    | 60/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.056):  61%|██████    | 61/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  62%|██████▏   | 62/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  63%|██████▎   | 63/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  64%|██████▍   | 64/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  65%|██████▌   | 65/100 [00:00<00:00, 124.93it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  66%|██████▌   | 66/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  66%|██████▌   | 66/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  67%|██████▋   | 67/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  68%|██████▊   | 68/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  69%|██████▉   | 69/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  70%|███████   | 70/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  71%|███████   | 71/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  72%|███████▏  | 72/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  73%|███████▎  | 73/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  74%|███████▍  | 74/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  75%|███████▌  | 75/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  76%|███████▌  | 76/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  77%|███████▋  | 77/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  78%|███████▊  | 78/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  79%|███████▉  | 79/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  80%|████████  | 80/100 [00:00<00:00, 147.44it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  81%|████████  | 81/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  81%|████████  | 81/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  82%|████████▏ | 82/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  83%|████████▎ | 83/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  84%|████████▍ | 84/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  85%|████████▌ | 85/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  86%|████████▌ | 86/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  87%|████████▋ | 87/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  88%|████████▊ | 88/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  89%|████████▉ | 89/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  90%|█████████ | 90/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  91%|█████████ | 91/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  92%|█████████▏| 92/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  93%|█████████▎| 93/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  94%|█████████▍| 94/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  95%|█████████▌| 95/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  96%|█████████▌| 96/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  97%|█████████▋| 97/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  98%|█████████▊| 98/100 [00:00<00:00, 144.51it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.056):  99%|█████████▉| 99/100 [00:00<00:00, 144.51it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.014 | Best: {'fx': 508.333} (0.014):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.014 | Best: {'fx': 509.354} (0.014):   2%|▏         | 1/50 [00:00<00:05,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.014 | Best: {'fx': 509.354} (0.014):   4%|▍         | 2/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.015 | Best: {'fx': 510.374} (0.015):   4%|▍         | 2/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.017 | Best: {'fx': 512.415} (0.017):   6%|▌         | 3/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.016 | Best: {'fx': 512.415} (0.017):   8%|▊         | 4/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.019 | Best: {'fx': 514.456} (0.019):  10%|█         | 5/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.018 | Best: {'fx': 514.456} (0.019):  12%|█▏        | 6/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.022 | Best: {'fx': 515.476} (0.022):  14%|█▍        | 7/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.028 | Best: {'fx': 516.497} (0.028):  16%|█▌        | 8/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.033 | Best: {'fx': 519.558} (0.033):  20%|██        | 10/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.041 | Best: {'fx': 523.639} (0.041):  22%|██▏       | 11/50 [00:00<00:02, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.046 | Best: {'fx': 524.66} (0.046):  24%|██▍       | 12/50 [00:00<00:02, 18.77it/s] 
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.031 | Best: {'fx': 524.66} (0.046):  26%|██▌       | 13/50 [00:00<00:01, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.037 | Best: {'fx': 524.66} (0.046):  28%|██▊       | 14/50 [00:00<00:01, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.049 | Best: {'fx': 525.68} (0.049):  30%|███       | 15/50 [00:00<00:01, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.036 | Best: {'fx': 525.68} (0.049):  32%|███▏      | 16/50 [00:00<00:01, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.055 | Best: {'fx': 526.701} (0.055):  34%|███▍      | 17/50 [00:00<00:01, 18.77it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.055 | Best: {'fx': 526.701} (0.055):  36%|███▌      | 18/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.039 | Best: {'fx': 526.701} (0.055):  36%|███▌      | 18/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.058 | Best: {'fx': 529.762} (0.058):  38%|███▊      | 19/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.059 | Best: {'fx': 527.721} (0.059):  40%|████      | 20/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.063 | Best: {'fx': 528.741} (0.063):  42%|████▏     | 21/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.054 | Best: {'fx': 528.741} (0.063):  44%|████▍     | 22/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.055 | Best: {'fx': 528.741} (0.063):  46%|████▌     | 23/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.052 | Best: {'fx': 528.741} (0.063):  48%|████▊     | 24/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.056 | Best: {'fx': 528.741} (0.063):  50%|█████     | 25/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.052 | Best: {'fx': 528.741} (0.063):  52%|█████▏    | 26/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.049 | Best: {'fx': 528.741} (0.063):  54%|█████▍    | 27/50 [00:00<00:00, 95.20it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.049 | Best: {'fx': 528.741} (0.063):  56%|█████▌    | 28/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.046 | Best: {'fx': 528.741} (0.063):  56%|█████▌    | 28/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.039 | Best: {'fx': 528.741} (0.063):  58%|█████▊    | 29/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.036 | Best: {'fx': 528.741} (0.063):  60%|██████    | 30/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.043 | Best: {'fx': 528.741} (0.063):  62%|██████▏   | 31/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.040 | Best: {'fx': 528.741} (0.063):  64%|██████▍   | 32/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.034 | Best: {'fx': 528.741} (0.063):  66%|██████▌   | 33/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.032 | Best: {'fx': 528.741} (0.063):  68%|██████▊   | 34/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.033 | Best: {'fx': 528.741} (0.063):  70%|███████   | 35/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.026 | Best: {'fx': 528.741} (0.063):  72%|███████▏  | 36/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.030 | Best: {'fx': 528.741} (0.063):  74%|███████▍  | 37/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.031 | Best: {'fx': 528.741} (0.063):  76%|███████▌  | 38/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.028 | Best: {'fx': 528.741} (0.063):  78%|███████▊  | 39/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.029 | Best: {'fx': 528.741} (0.063):  80%|████████  | 40/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.027 | Best: {'fx': 528.741} (0.063):  82%|████████▏ | 41/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.023 | Best: {'fx': 528.741} (0.063):  84%|████████▍ | 42/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.026 | Best: {'fx': 528.741} (0.063):  86%|████████▌ | 43/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.024 | Best: {'fx': 528.741} (0.063):  88%|████████▊ | 44/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.021 | Best: {'fx': 528.741} (0.063):  90%|█████████ | 45/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.023 | Best: {'fx': 528.741} (0.063):  92%|█████████▏| 46/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.024 | Best: {'fx': 528.741} (0.063):  94%|█████████▍| 47/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.024 | Best: {'fx': 528.741} (0.063):  96%|█████████▌| 48/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.022 | Best: {'fx': 528.741} (0.063):  98%|█████████▊| 49/50 [00:00<00:00, 90.29it/s]
Brute | Start: {'fx': 533.333} (0.056) | Current: 0.022 | Best: {'fx': 528.741} (0.063): 100%|██████████| 50/50 [00:00<00:00, 137.16it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 528.7414965986395
    px: 1020.0
    py: 746.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 7 frames per rig (excluded 1 incomplete frames): 7 frames will be used for optimization
  Rigs: 1000(1000mm):7/20
  Total: 7/20 frames used for optimization

Initial score: 108.0093: 0iter [00:00, ?iter/s]
Initial score: 108.0093: 1iter [00:01,  1.10s/iter]
Initial score: 108.0093: 2iter [00:01,  1.39iter/s]
Initial score: 108.0093: 3iter [00:02,  1.63iter/s]
Initial score: 108.0093: 4iter [00:02,  1.76iter/s]
Initial score: 108.0093: 5iter [00:03,  1.77iter/s]
Initial score: 108.0093: 6iter [00:03,  1.83iter/s]
Initial score: 108.0093: 7iter [00:04,  1.97iter/s]
Initial score: 108.0093: 8iter [00:04,  2.01iter/s]
Initial score: 108.0093: 9iter [00:05,  2.02iter/s]
Initial score: 108.0093: 10iter [00:05,  1.98iter/s]
Initial score: 108.0093: 11iter [00:06,  2.01iter/s]
Initial score: 108.0093: 12iter [00:06,  1.97iter/s]
Initial score: 108.0093: 13iter [00:07,  1.93iter/s]
Initial score: 108.0093: 14iter [00:07,  1.96iter/s]
Initial score: 108.0093: 15iter [00:08,  1.98iter/s]
Initial score: 108.0093: 16iter [00:08,  1.96iter/s]
Initial score: 108.0093: 17iter [00:09,  1.95iter/s]
Initial score: 108.0093: 18iter [00:09,  1.97iter/s]
Initial score: 108.0093: 19iter [00:10,  1.94iter/s]
Initial score: 108.0093: 20iter [00:10,  1.93iter/s]
Best score: 74.8785: : 20iter [00:11,  1.93iter/s]  
Best score: 74.8785: : 21iter [00:11,  1.89iter/s]
Best score: 74.8785: : 22iter [00:11,  1.95iter/s]
Best score: 74.8785: : 23iter [00:12,  2.02iter/s]
Best score: 74.8785: : 24iter [00:12,  2.01iter/s]
Best score: 74.8785: : 25iter [00:13,  2.02iter/s]
Best score: 74.8785: : 26iter [00:13,  2.05iter/s]
Best score: 74.8785: : 27iter [00:14,  1.96iter/s]
Best score: 74.8785: : 28iter [00:14,  1.99iter/s]
Best score: 74.8785: : 29iter [00:15,  2.06iter/s]
Best score: 74.8785: : 30iter [00:15,  2.14iter/s]
Best score: 74.8785: : 31iter [00:15,  2.17iter/s]
Best score: 74.8785: : 32iter [00:16,  2.17iter/s]
Best score: 74.8785: : 33iter [00:16,  2.16iter/s]
Best score: 74.8785: : 34iter [00:17,  2.09iter/s]
Best score: 74.8785: : 35iter [00:17,  2.13iter/s]
Best score: 74.8785: : 36iter [00:18,  2.10iter/s]
Best score: 74.8785: : 37iter [00:18,  2.08iter/s]
Best score: 74.8785: : 38iter [00:19,  2.14iter/s]
Best score: 74.8785: : 39iter [00:19,  2.05iter/s]
Best score: 38.1174: : 39iter [00:20,  2.05iter/s]
Best score: 38.1174: : 40iter [00:20,  2.07iter/s]
Best score: 38.1174: : 41iter [00:20,  2.12iter/s]
Best score: 38.1174: : 42iter [00:21,  2.03iter/s]
Best score: 38.1174: : 43iter [00:21,  2.06iter/s]
Best score: 38.1174: : 44iter [00:22,  2.07iter/s]
Best score: 38.1174: : 45iter [00:22,  2.07iter/s]
Best score: 38.1174: : 46iter [00:23,  2.11iter/s]
Best score: 25.1410: : 46iter [00:23,  2.11iter/s]
Best score: 25.1410: : 47iter [00:23,  2.07iter/s]
Best score: 25.1410: : 48iter [00:24,  2.07iter/s]
Best score: 25.1410: : 49iter [00:24,  2.00iter/s]
[ITER 50] Current best score: 25.1410
[PARAMS] Current best parameters:
  fx: 528.7047
  fy: 530.2896
  px: 1024.2840
  py: 756.9479
  k1: -0.07223778
  k2: 0.02407132
  k3: 0.02536075
  k4: -0.01093537
  p1: 0.00138474
  p2: 0.00031497
  p3: 0.00429414
  p4: 0.00292249
  scale: 3.2752
  rotation: -0.8955

Best score: 25.1410: : 50iter [00:25,  1.91iter/s]
Best score: 25.1410: : 51iter [00:25,  1.91iter/s]
Best score: 25.1410: : 52iter [00:26,  2.04iter/s]
Best score: 25.1410: : 53iter [00:26,  1.98iter/s]
Best score: 14.2834: : 53iter [00:27,  1.98iter/s]
Best score: 14.2834: : 54iter [00:27,  2.00iter/s]
Best score: 14.2834: : 55iter [00:27,  1.97iter/s]
Best score: 14.2834: : 56iter [00:28,  2.02iter/s]
Best score: 14.2834: : 57iter [00:28,  2.00iter/s]
Best score: 14.2834: : 58iter [00:29,  2.06iter/s]
Best score: 14.2834: : 59iter [00:29,  2.08iter/s]
Best score: 14.2834: : 60iter [00:30,  2.09iter/s]
Best score: 14.2834: : 61iter [00:30,  2.09iter/s]
Best score: 14.2834: : 62iter [00:31,  2.09iter/s]
Best score: 14.2834: : 63iter [00:31,  2.02iter/s]
Best score: 14.2834: : 64iter [00:32,  2.04iter/s]
Best score: 14.2834: : 65iter [00:32,  2.04iter/s]
Best score: 14.2834: : 66iter [00:33,  1.97iter/s]
Best score: 14.2834: : 67iter [00:33,  1.97iter/s]
Best score: 14.2834: : 68iter [00:34,  1.94iter/s]
Best score: 14.2834: : 69iter [00:34,  1.93iter/s]
Best score: 14.2834: : 70iter [00:35,  1.98iter/s]
Best score: 14.2834: : 71iter [00:35,  1.98iter/s]
Best score: 14.2834: : 72iter [00:36,  1.93iter/s]
Best score: 14.2834: : 73iter [00:36,  2.03iter/s]
Best score: 14.2834: : 74iter [00:37,  2.01iter/s]
Best score: 14.2834: : 75iter [00:37,  1.98iter/s]
Best score: 14.2834: : 76iter [00:38,  1.97iter/s]
Best score: 14.2834: : 77iter [00:38,  1.94iter/s]
Best score: 14.2834: : 78iter [00:39,  2.03iter/s]
Best score: 14.2834: : 79iter [00:39,  2.01iter/s]
Best score: 14.2834: : 80iter [00:40,  1.98iter/s]
Best score: 14.2834: : 81iter [00:40,  2.05iter/s]
Best score: 14.2834: : 82iter [00:41,  2.00iter/s]
Best score: 14.2834: : 83iter [00:41,  2.09iter/s]
Best score: 14.2834: : 84iter [00:42,  2.11iter/s]
Best score: 14.2834: : 85iter [00:42,  2.07iter/s]
Best score: 14.2834: : 86iter [00:43,  2.02iter/s]
Best score: 14.2834: : 87iter [00:43,  2.03iter/s]
Best score: 14.2834: : 88iter [00:44,  1.97iter/s]
Best score: 14.2834: : 89iter [00:44,  1.96iter/s]
Best score: 14.2834: : 90iter [00:45,  1.99iter/s]
Best score: 14.2834: : 91iter [00:45,  1.97iter/s]
Best score: 14.2834: : 92iter [00:46,  2.00iter/s]
Best score: 14.2834: : 93iter [00:46,  2.09iter/s]
Best score: 14.2834: : 94iter [00:47,  2.12iter/s]
Best score: 8.6031: : 94iter [00:47,  2.12iter/s] 
Best score: 8.6031: : 95iter [00:47,  2.15iter/s]
Best score: 8.6031: : 96iter [00:47,  2.15iter/s]
Best score: 8.6031: : 97iter [00:48,  2.11iter/s]
Best score: 8.6031: : 98iter [00:48,  2.09iter/s]
Best score: 8.6031: : 99iter [00:49,  2.06iter/s]
[ITER 100] Current best score: 8.6031
[PARAMS] Current best parameters:
  fx: 518.1736
  fy: 518.3929
  px: 1019.5973
  py: 755.5416
  k1: -0.05384623
  k2: 0.02497658
  k3: 0.02116796
  k4: -0.00984982
  p1: 0.00534997
  p2: -0.00250523
  p3: -0.00587104
  p4: -0.00153962
  scale: 3.2046
  rotation: 0.0184

Best score: 8.6031: : 100iter [00:49,  2.08iter/s]
Best score: 8.6031: : 101iter [00:50,  2.11iter/s]
Best score: 8.6031: : 102iter [00:50,  2.10iter/s]
Best score: 8.6031: : 103iter [00:51,  2.05iter/s]
Best score: 8.6031: : 104iter [00:51,  2.01iter/s]
Best score: 8.6031: : 105iter [00:52,  2.02iter/s]
Best score: 6.8021: : 105iter [00:52,  2.02iter/s]
Best score: 6.8021: : 106iter [00:52,  2.01iter/s]
Best score: 6.8021: : 107iter [00:53,  2.01iter/s]
Best score: 6.8021: : 108iter [00:53,  2.02iter/s]
Best score: 5.6105: : 108iter [00:54,  2.02iter/s]
Best score: 5.6105: : 109iter [00:54,  2.02iter/s]
Best score: 5.6105: : 110iter [00:54,  2.04iter/s]
Best score: 5.6105: : 111iter [00:55,  2.04iter/s]
Best score: 5.6105: : 112iter [00:55,  1.99iter/s]
Best score: 5.6105: : 113iter [00:56,  1.97iter/s]
Best score: 5.6105: : 114iter [00:56,  1.98iter/s]
Best score: 5.6105: : 115iter [00:57,  2.03iter/s]
Best score: 5.6105: : 116iter [00:57,  2.06iter/s]
Best score: 5.6105: : 117iter [00:58,  2.13iter/s]
Best score: 5.6105: : 118iter [00:58,  2.09iter/s]
Best score: 5.6105: : 119iter [00:59,  2.04iter/s]
Best score: 5.6105: : 120iter [00:59,  2.01iter/s]
Best score: 5.6105: : 121iter [01:00,  2.08iter/s]
Best score: 5.6105: : 122iter [01:00,  2.05iter/s]
Best score: 5.6105: : 123iter [01:01,  2.04iter/s]
Best score: 5.6105: : 124iter [01:01,  2.01iter/s]
Best score: 4.3537: : 124iter [01:02,  2.01iter/s]
Best score: 4.3537: : 125iter [01:02,  2.07iter/s]
Best score: 4.3537: : 126iter [01:02,  1.99iter/s]
Best score: 4.3537: : 127iter [01:03,  2.04iter/s]
Best score: 4.3537: : 128iter [01:03,  2.12iter/s]
Best score: 4.3537: : 129iter [01:04,  2.06iter/s]
Best score: 4.3537: : 130iter [01:04,  2.03iter/s]
Best score: 4.3537: : 131iter [01:05,  2.08iter/s]
Best score: 4.3537: : 132iter [01:05,  1.95iter/s]
Best score: 4.3537: : 133iter [01:06,  2.00iter/s]
Best score: 4.3537: : 134iter [01:06,  2.06iter/s]
Best score: 4.3537: : 135iter [01:07,  2.06iter/s]
Best score: 4.3537: : 136iter [01:07,  2.08iter/s]
Best score: 4.3537: : 137iter [01:08,  2.04iter/s]
Best score: 4.3537: : 138iter [01:08,  2.04iter/s]
Best score: 4.3537: : 139iter [01:09,  2.03iter/s]
Best score: 4.3537: : 140iter [01:09,  1.96iter/s]
Best score: 4.3537: : 141iter [01:10,  1.95iter/s]
Best score: 4.3537: : 142iter [01:10,  2.04iter/s]
Best score: 4.3537: : 143iter [01:11,  2.01iter/s]
Best score: 4.3537: : 144iter [01:11,  1.95iter/s]
Best score: 4.3537: : 145iter [01:12,  1.92iter/s]
Best score: 4.3537: : 146iter [01:12,  1.89iter/s]
Best score: 4.3537: : 147iter [01:13,  1.87iter/s]
Best score: 4.3537: : 148iter [01:13,  1.89iter/s]
Best score: 4.3537: : 149iter [01:14,  1.96iter/s]
[ITER 150] Current best score: 4.5228
[PARAMS] Current best parameters:
  fx: 495.7580
  fy: 496.5904
  px: 1021.5782
  py: 755.7536
  k1: -0.02899421
  k2: 0.03754657
  k3: 0.01279234
  k4: -0.00925057
  p1: 0.00471894
  p2: 0.00589335
  p3: -0.00685522
  p4: 0.00118968
  scale: 3.1134
  rotation: -0.1724

Best score: 4.3537: : 150iter [01:14,  1.94iter/s]
Best score: 4.3537: : 151iter [01:15,  1.90iter/s]
Best score: 4.3537: : 152iter [01:15,  1.94iter/s]
Best score: 4.3537: : 153iter [01:16,  2.00iter/s]
Best score: 4.3537: : 154iter [01:16,  2.05iter/s]
Best score: 4.3537: : 155iter [01:17,  1.95iter/s]
Best score: 2.3359: : 155iter [01:17,  1.95iter/s]
Best score: 2.3359: : 156iter [01:17,  1.95iter/s]
Best score: 2.3359: : 157iter [01:18,  1.94iter/s]
Best score: 2.3359: : 158iter [01:18,  1.93iter/s]
Best score: 2.3359: : 159iter [01:19,  1.95iter/s]
Best score: 2.3359: : 160iter [01:19,  2.03iter/s]
Best score: 2.3359: : 161iter [01:20,  2.05iter/s]
Best score: 2.3359: : 162iter [01:20,  2.02iter/s]
Best score: 2.3359: : 163iter [01:21,  2.13iter/s]
Best score: 2.3359: : 164iter [01:21,  2.10iter/s]
Best score: 2.3359: : 165iter [01:22,  2.06iter/s]
Best score: 2.3359: : 166iter [01:22,  2.04iter/s]
Best score: 2.3359: : 167iter [01:23,  2.02iter/s]
Best score: 2.3359: : 168iter [01:23,  2.02iter/s]
Best score: 2.3359: : 169iter [01:24,  2.01iter/s]
Best score: 2.3359: : 170iter [01:24,  2.03iter/s]
Best score: 2.3359: : 171iter [01:25,  2.04iter/s]
Best score: 2.3359: : 172iter [01:25,  2.07iter/s]
Best score: 2.3359: : 173iter [01:26,  2.03iter/s]
Best score: 2.3359: : 174iter [01:26,  2.01iter/s]
Best score: 2.3359: : 175iter [01:27,  1.97iter/s]
Best score: 2.3359: : 176iter [01:27,  1.98iter/s]
Best score: 2.3359: : 177iter [01:28,  1.94iter/s]
Best score: 2.3359: : 178iter [01:28,  1.95iter/s]
Best score: 2.3359: : 179iter [01:29,  2.02iter/s]
Best score: 2.3359: : 180iter [01:29,  2.08iter/s]
Best score: 2.3359: : 181iter [01:30,  2.10iter/s]
Best score: 2.3359: : 182iter [01:30,  2.17iter/s]
Best score: 2.3359: : 183iter [01:31,  2.07iter/s]
Best score: 2.3359: : 184iter [01:31,  2.12iter/s]
Best score: 2.3359: : 185iter [01:32,  2.04iter/s]
Best score: 2.3359: : 186iter [01:32,  2.11iter/s]
Best score: 2.3359: : 187iter [01:32,  2.06iter/s]
Best score: 2.3359: : 188iter [01:33,  2.04iter/s]
Best score: 2.3359: : 189iter [01:33,  2.03iter/s]
Best score: 2.3359: : 190iter [01:34,  1.99iter/s]
Best score: 2.3359: : 191iter [01:34,  2.00iter/s]
Best score: 2.3359: : 192iter [01:35,  2.05iter/s]
Best score: 2.3359: : 193iter [01:35,  2.08iter/s]
Best score: 2.3359: : 194iter [01:36,  2.07iter/s]
Best score: 2.3359: : 195iter [01:36,  2.02iter/s]
Best score: 2.3359: : 196iter [01:37,  2.05iter/s]
Best score: 2.3359: : 197iter [01:37,  2.05iter/s]
Best score: 2.3359: : 198iter [01:38,  2.03iter/s]
Best score: 2.3359: : 199iter [01:38,  1.96iter/s]
[ITER 200] Current best score: 2.5553
[PARAMS] Current best parameters:
  fx: 494.0591
  fy: 494.8043
  px: 1022.1107
  py: 754.8638
  k1: -0.00244529
  k2: 0.02336914
  k3: 0.00735974
  k4: -0.00612328
  p1: 0.00105954
  p2: 0.00291495
  p3: -0.00532512
  p4: 0.00125895
  scale: 3.0874
  rotation: -0.1803

Best score: 2.3359: : 200iter [01:39,  1.90iter/s]
Best score: 2.3359: : 201iter [01:39,  1.99iter/s]
Best score: 2.3359: : 202iter [01:40,  1.99iter/s]
Best score: 2.3359: : 203iter [01:40,  1.99iter/s]
Best score: 2.3359: : 204iter [01:41,  2.02iter/s]
Best score: 2.3359: : 205iter [01:41,  2.11iter/s]
Best score: 1.8830: : 205iter [01:42,  2.11iter/s]
Best score: 1.8830: : 206iter [01:42,  2.08iter/s]
Best score: 1.8830: : 207iter [01:42,  2.09iter/s]
Best score: 1.8830: : 208iter [01:43,  2.11iter/s]
Best score: 1.8830: : 209iter [01:43,  2.10iter/s]
Best score: 1.8830: : 210iter [01:44,  2.06iter/s]
Best score: 1.8830: : 211iter [01:44,  2.09iter/s]
Best score: 1.8830: : 212iter [01:45,  2.08iter/s]
Best score: 1.8830: : 213iter [01:45,  2.03iter/s]
Best score: 1.8830: : 214iter [01:46,  2.00iter/s]
Best score: 1.8830: : 215iter [01:46,  1.95iter/s]
Best score: 1.8830: : 216iter [01:47,  2.01iter/s]
Best score: 1.8830: : 217iter [01:47,  2.03iter/s]
Best score: 1.8830: : 218iter [01:48,  2.02iter/s]
Best score: 1.8830: : 219iter [01:48,  1.98iter/s]
Best score: 1.8830: : 220iter [01:49,  1.95iter/s]
Best score: 1.8830: : 221iter [01:49,  1.96iter/s]
Best score: 1.8830: : 222iter [01:50,  1.97iter/s]
Best score: 1.8830: : 223iter [01:50,  2.03iter/s]
Best score: 1.8830: : 224iter [01:51,  1.96iter/s]
Best score: 1.8830: : 225iter [01:51,  2.02iter/s]
Best score: 1.8830: : 226iter [01:52,  2.06iter/s]
Best score: 1.8830: : 227iter [01:52,  2.00iter/s]
Best score: 1.8830: : 228iter [01:53,  2.06iter/s]
Best score: 1.8830: : 229iter [01:53,  2.02iter/s]
Best score: 1.8830: : 230iter [01:54,  2.05iter/s]
Best score: 1.8830: : 231iter [01:54,  2.01iter/s]
Best score: 1.8830: : 232iter [01:55,  2.06iter/s]
Best score: 1.8830: : 233iter [01:55,  2.04iter/s]
Best score: 1.1333: : 233iter [01:56,  2.04iter/s]
Best score: 1.1333: : 234iter [01:56,  2.01iter/s]
Best score: 1.1333: : 235iter [01:56,  2.05iter/s]
Best score: 1.1333: : 236iter [01:57,  2.04iter/s]
Best score: 1.1333: : 237iter [01:57,  1.95iter/s]
Best score: 1.1333: : 238iter [01:58,  2.01iter/s]
Best score: 1.1333: : 239iter [01:58,  2.03iter/s]
Best score: 1.1333: : 240iter [01:59,  2.01iter/s]
Best score: 1.1333: : 241iter [01:59,  1.99iter/s]
Best score: 1.1333: : 242iter [02:00,  1.97iter/s]
Best score: 1.1333: : 243iter [02:00,  2.02iter/s]
Best score: 1.1333: : 244iter [02:01,  1.99iter/s]
Best score: 1.1333: : 245iter [02:01,  1.98iter/s]
Best score: 1.1333: : 246iter [02:02,  1.97iter/s]
Best score: 1.0360: : 246iter [02:02,  1.97iter/s]
Best score: 1.0360: : 247iter [02:02,  2.02iter/s]
Best score: 1.0360: : 248iter [02:03,  2.06iter/s]
Best score: 1.0360: : 249iter [02:03,  2.01iter/s]
[ITER 250] Current best score: 1.0360
[PARAMS] Current best parameters:
  fx: 489.4343
  fy: 489.3340
  px: 1022.4771
  py: 755.2450
  k1: 0.00002833
  k2: 0.02289993
  k3: 0.00741106
  k4: -0.00563846
  p1: 0.00162546
  p2: 0.00477206
  p3: 0.00304443
  p4: -0.00249428
  scale: 3.1156
  rotation: -0.2612

Best score: 1.0360: : 250iter [02:04,  1.91iter/s]
Best score: 1.0360: : 251iter [02:04,  1.91iter/s]
Best score: 1.0360: : 252iter [02:05,  1.97iter/s]
Best score: 1.0360: : 253iter [02:05,  1.96iter/s]
Best score: 1.0360: : 254iter [02:06,  2.03iter/s]
Best score: 1.0360: : 255iter [02:06,  1.95iter/s]
Best score: 1.0360: : 256iter [02:07,  2.00iter/s]
Best score: 1.0360: : 257iter [02:07,  2.00iter/s]
Best score: 1.0360: : 258iter [02:08,  1.99iter/s]
Best score: 1.0360: : 259iter [02:08,  2.10iter/s]
Best score: 1.0360: : 260iter [02:09,  2.05iter/s]
Best score: 1.0360: : 261iter [02:09,  2.02iter/s]
Best score: 1.0360: : 262iter [02:10,  2.02iter/s]
Best score: 1.0360: : 263iter [02:10,  2.00iter/s]
Best score: 1.0360: : 264iter [02:11,  1.99iter/s]
Best score: 1.0360: : 265iter [02:11,  1.94iter/s]
Best score: 1.0360: : 266iter [02:12,  1.99iter/s]
Best score: 1.0360: : 267iter [02:12,  1.92iter/s]
Best score: 1.0360: : 268iter [02:13,  1.89iter/s]
Best score: 1.0360: : 269iter [02:13,  1.96iter/s]
Best score: 1.0360: : 270iter [02:14,  2.03iter/s]
Best score: 0.9132: : 270iter [02:14,  2.03iter/s]
Best score: 0.9132: : 271iter [02:14,  2.04iter/s]
Best score: 0.9132: : 272iter [02:15,  2.14iter/s]
Best score: 0.9132: : 273iter [02:15,  2.13iter/s]
Best score: 0.9132: : 274iter [02:16,  2.00iter/s]
Best score: 0.9132: : 275iter [02:16,  2.06iter/s]
Best score: 0.9132: : 276iter [02:17,  2.06iter/s]
Best score: 0.9132: : 277iter [02:17,  2.05iter/s]
Best score: 0.9132: : 278iter [02:18,  2.00iter/s]
Best score: 0.9132: : 279iter [02:18,  1.96iter/s]
Best score: 0.9132: : 280iter [02:19,  1.93iter/s]
Best score: 0.9132: : 281iter [02:19,  1.97iter/s]
Best score: 0.9132: : 282iter [02:20,  1.94iter/s]
Best score: 0.9132: : 283iter [02:20,  1.88iter/s]
Best score: 0.9132: : 284iter [02:21,  1.86iter/s]
Best score: 0.9132: : 285iter [02:21,  1.91iter/s]
Best score: 0.9132: : 286iter [02:22,  1.97iter/s]
Best score: 0.9132: : 287iter [02:22,  1.96iter/s]
Best score: 0.9132: : 288iter [02:23,  1.96iter/s]
Best score: 0.9132: : 289iter [02:23,  2.04iter/s]
Best score: 0.9132: : 290iter [02:24,  2.02iter/s]
Best score: 0.9132: : 291iter [02:24,  1.95iter/s]
Best score: 0.8954: : 291iter [02:25,  1.95iter/s]
Best score: 0.8954: : 292iter [02:25,  2.02iter/s]
Best score: 0.8954: : 293iter [02:25,  2.06iter/s]
Best score: 0.8954: : 294iter [02:26,  2.03iter/s]
Best score: 0.8954: : 295iter [02:26,  2.07iter/s]
Best score: 0.8954: : 296iter [02:27,  2.04iter/s]
Best score: 0.8954: : 297iter [02:27,  2.01iter/s]
Best score: 0.8954: : 298iter [02:28,  1.97iter/s]
Best score: 0.8954: : 299iter [02:28,  1.95iter/s]
[ITER 300] Current best score: 0.8954
[PARAMS] Current best parameters:
  fx: 498.4486
  fy: 498.3075
  px: 1022.2424
  py: 755.4976
  k1: 0.02190329
  k2: 0.00308583
  k3: 0.00405079
  k4: -0.00261353
  p1: 0.00282624
  p2: 0.00485696
  p3: -0.00194438
  p4: -0.00016640
  scale: 3.0499
  rotation: -0.1984

Best score: 0.8954: : 300iter [02:29,  1.94iter/s]
Best score: 0.8954: : 301iter [02:29,  1.93iter/s]
Best score: 0.8954: : 302iter [02:30,  1.90iter/s]
Best score: 0.8954: : 303iter [02:30,  1.94iter/s]
Best score: 0.8954: : 304iter [02:31,  1.99iter/s]
Best score: 0.8954: : 305iter [02:31,  1.99iter/s]
Best score: 0.8954: : 306iter [02:32,  1.99iter/s]
Best score: 0.8954: : 307iter [02:32,  2.08iter/s]
Best score: 0.8954: : 308iter [02:33,  2.06iter/s]
Best score: 0.8954: : 309iter [02:33,  2.01iter/s]
Best score: 0.8954: : 310iter [02:34,  2.00iter/s]
Best score: 0.8954: : 311iter [02:34,  1.96iter/s]
Best score: 0.8954: : 312iter [02:35,  1.98iter/s]
Best score: 0.8954: : 313iter [02:35,  1.95iter/s]
Best score: 0.8954: : 314iter [02:36,  1.99iter/s]
Best score: 0.8954: : 315iter [02:36,  2.02iter/s]
Best score: 0.8954: : 316iter [02:37,  2.06iter/s]
Best score: 0.8954: : 317iter [02:37,  2.08iter/s]
Best score: 0.8954: : 318iter [02:38,  2.04iter/s]
Best score: 0.8954: : 319iter [02:38,  1.98iter/s]
Best score: 0.8954: : 320iter [02:39,  2.06iter/s]
Best score: 0.8954: : 321iter [02:39,  1.96iter/s]
Best score: 0.8954: : 322iter [02:40,  1.98iter/s]
Best score: 0.8954: : 323iter [02:40,  1.93iter/s]
Best score: 0.8954: : 324iter [02:41,  1.91iter/s]
Best score: 0.8954: : 325iter [02:42,  1.87iter/s]
Best score: 0.8954: : 326iter [02:42,  1.95iter/s]
Best score: 0.8954: : 327iter [02:42,  1.95iter/s]
Best score: 0.8954: : 328iter [02:43,  1.94iter/s]
Best score: 0.8954: : 329iter [02:44,  1.89iter/s]
Best score: 0.8954: : 330iter [02:44,  1.96iter/s]
Best score: 0.8954: : 331iter [02:45,  1.98iter/s]
Best score: 0.8954: : 332iter [02:45,  1.98iter/s]
Best score: 0.8954: : 333iter [02:45,  2.03iter/s]
Best score: 0.8954: : 334iter [02:46,  2.01iter/s]
Best score: 0.8954: : 335iter [02:47,  1.95iter/s]
Best score: 0.8954: : 336iter [02:47,  1.96iter/s]
Best score: 0.8954: : 337iter [02:48,  1.97iter/s]
Best score: 0.8954: : 338iter [02:48,  1.97iter/s]
Best score: 0.8954: : 339iter [02:49,  1.93iter/s]
Best score: 0.8954: : 340iter [02:49,  1.95iter/s]
Best score: 0.8954: : 341iter [02:50,  1.91iter/s]
Best score: 0.8954: : 342iter [02:50,  1.87iter/s]
Best score: 0.8954: : 343iter [02:51,  1.97iter/s]
Best score: 0.8954: : 344iter [02:51,  2.00iter/s]
Best score: 0.8954: : 345iter [02:52,  2.04iter/s]
Best score: 0.8954: : 346iter [02:52,  2.03iter/s]
Best score: 0.8954: : 347iter [02:53,  2.01iter/s]
Best score: 0.8954: : 348iter [02:53,  2.01iter/s]
Best score: 0.8954: : 349iter [02:54,  2.09iter/s]
[ITER 350] Current best score: 0.8954
[PARAMS] Current best parameters:
  fx: 498.4486
  fy: 498.3075
  px: 1022.2424
  py: 755.4976
  k1: 0.02190329
  k2: 0.00308583
  k3: 0.00405079
  k4: -0.00261353
  p1: 0.00282624
  p2: 0.00485696
  p3: -0.00194438
  p4: -0.00016640
  scale: 3.0499
  rotation: -0.1984

Best score: 0.8954: : 350iter [02:54,  2.04iter/s]
Best score: 0.8954: : 351iter [02:55,  1.98iter/s]
Best score: 0.8954: : 352iter [02:55,  1.91iter/s]
Best score: 0.8954: : 353iter [02:56,  1.93iter/s]
Best score: 0.8954: : 354iter [02:56,  2.00iter/s]
Best score: 0.8954: : 355iter [02:57,  1.98iter/s]
Best score: 0.8954: : 356iter [02:57,  2.00iter/s]
Best score: 0.8154: : 356iter [02:58,  2.00iter/s]
Best score: 0.8154: : 357iter [02:58,  2.03iter/s]
Best score: 0.8154: : 358iter [02:58,  2.04iter/s]
Best score: 0.8154: : 359iter [02:59,  2.01iter/s]
Best score: 0.8154: : 360iter [02:59,  2.00iter/s]
Best score: 0.8154: : 361iter [03:00,  1.98iter/s]
Best score: 0.8154: : 362iter [03:00,  2.02iter/s]
Best score: 0.8154: : 363iter [03:01,  1.98iter/s]
Best score: 0.8154: : 364iter [03:01,  1.94iter/s]
Best score: 0.8154: : 365iter [03:02,  2.05iter/s]
Best score: 0.8154: : 366iter [03:02,  2.02iter/s]
Best score: 0.8154: : 367iter [03:03,  2.07iter/s]
Best score: 0.8154: : 368iter [03:03,  1.95iter/s]
Best score: 0.8154: : 369iter [03:04,  1.98iter/s]
Best score: 0.8154: : 370iter [03:04,  1.98iter/s]
Best score: 0.8154: : 371iter [03:05,  1.98iter/s]
Best score: 0.8154: : 372iter [03:05,  2.02iter/s]
Best score: 0.8154: : 373iter [03:06,  2.06iter/s]
Best score: 0.8154: : 374iter [03:06,  2.03iter/s]
Best score: 0.8154: : 375iter [03:07,  2.07iter/s]
Best score: 0.8154: : 376iter [03:07,  2.01iter/s]
Best score: 0.8154: : 377iter [03:08,  2.08iter/s]
Best score: 0.8154: : 378iter [03:08,  2.13iter/s]
Best score: 0.8154: : 379iter [03:08,  2.14iter/s]
Best score: 0.8154: : 380iter [03:09,  2.10iter/s]
Best score: 0.8154: : 381iter [03:09,  2.12iter/s]
Best score: 0.8154: : 382iter [03:10,  2.17iter/s]
Best score: 0.8154: : 383iter [03:10,  2.18iter/s]
Best score: 0.8154: : 384iter [03:11,  2.06iter/s]
Best score: 0.8154: : 385iter [03:11,  2.06iter/s]
Best score: 0.8154: : 386iter [03:12,  2.07iter/s]
Best score: 0.8154: : 387iter [03:12,  1.99iter/s]
Best score: 0.8154: : 388iter [03:13,  2.01iter/s]
Best score: 0.8154: : 389iter [03:13,  2.08iter/s]
Best score: 0.8154: : 390iter [03:14,  2.05iter/s]
Best score: 0.8154: : 391iter [03:14,  1.99iter/s]
Best score: 0.8154: : 392iter [03:15,  1.93iter/s]
Best score: 0.8154: : 393iter [03:15,  1.96iter/s]
Best score: 0.8154: : 394iter [03:16,  1.99iter/s]
Best score: 0.8154: : 395iter [03:16,  1.98iter/s]
Best score: 0.8154: : 396iter [03:17,  2.01iter/s]
Best score: 0.8154: : 397iter [03:17,  2.01iter/s]
Best score: 0.8154: : 398iter [03:18,  2.03iter/s]
Best score: 0.8154: : 399iter [03:18,  2.01iter/s]
[ITER 400] Current best score: 0.9031
[PARAMS] Current best parameters:
  fx: 497.2112
  fy: 496.7137
  px: 1022.8462
  py: 758.6635
  k1: 0.02994182
  k2: 0.01224048
  k3: -0.01120577
  k4: 0.00224371
  p1: 0.00929579
  p2: 0.00593952
  p3: -0.00346296
  p4: -0.00083422
  scale: 3.0067
  rotation: -0.2202

Best score: 0.8154: : 400iter [03:19,  1.94iter/s]
Best score: 0.8154: : 401iter [03:19,  1.96iter/s]
Best score: 0.8154: : 402iter [03:20,  2.05iter/s]
Best score: 0.8154: : 403iter [03:20,  1.98iter/s]
Best score: 0.8154: : 404iter [03:21,  2.01iter/s]
Best score: 0.8154: : 405iter [03:21,  1.99iter/s]
Best score: 0.8154: : 406iter [03:22,  2.01iter/s]
Best score: 0.8154: : 407iter [03:22,  2.01iter/s]
Best score: 0.7305: : 407iter [03:23,  2.01iter/s]
Best score: 0.7305: : 408iter [03:23,  1.99iter/s]
Best score: 0.7305: : 409iter [03:23,  2.02iter/s]
Best score: 0.7305: : 410iter [03:24,  2.03iter/s]
Best score: 0.7305: : 411iter [03:24,  2.00iter/s]
Best score: 0.7305: : 412iter [03:25,  1.98iter/s]
Best score: 0.7305: : 413iter [03:25,  1.96iter/s]
Best score: 0.7305: : 414iter [03:26,  2.00iter/s]
Best score: 0.7305: : 415iter [03:26,  2.03iter/s]
Best score: 0.7305: : 416iter [03:27,  2.04iter/s]
Best score: 0.7305: : 417iter [03:27,  2.07iter/s]
Best score: 0.7305: : 418iter [03:28,  2.03iter/s]
Best score: 0.7305: : 419iter [03:28,  2.03iter/s]
Best score: 0.7305: : 420iter [03:29,  2.09iter/s]
Best score: 0.5552: : 420iter [03:29,  2.09iter/s]
Best score: 0.5552: : 421iter [03:29,  2.03iter/s]
Best score: 0.5552: : 422iter [03:30,  1.96iter/s]
Best score: 0.5552: : 423iter [03:30,  2.03iter/s]
Best score: 0.5552: : 424iter [03:31,  2.03iter/s]
Best score: 0.5552: : 425iter [03:31,  1.96iter/s]
Best score: 0.5552: : 426iter [03:32,  1.98iter/s]
Best score: 0.5552: : 427iter [03:32,  1.95iter/s]
Best score: 0.5552: : 428iter [03:33,  1.93iter/s]
Best score: 0.5552: : 429iter [03:33,  1.92iter/s]
Best score: 0.5552: : 430iter [03:34,  2.01iter/s]
Best score: 0.5552: : 431iter [03:34,  1.96iter/s]
Best score: 0.5552: : 432iter [03:35,  1.98iter/s]
Best score: 0.5552: : 433iter [03:35,  2.03iter/s]
Best score: 0.5552: : 434iter [03:36,  2.08iter/s]
Best score: 0.5552: : 435iter [03:36,  1.98iter/s]
Best score: 0.5552: : 436iter [03:37,  2.04iter/s]
Best score: 0.5552: : 437iter [03:37,  2.11iter/s]
Best score: 0.5552: : 438iter [03:38,  2.08iter/s]
Best score: 0.5552: : 439iter [03:38,  2.05iter/s]
Best score: 0.5552: : 440iter [03:39,  1.98iter/s]
Best score: 0.5552: : 441iter [03:39,  2.03iter/s]
Best score: 0.5552: : 442iter [03:40,  1.98iter/s]
Best score: 0.5552: : 443iter [03:40,  1.97iter/s]
Best score: 0.5552: : 444iter [03:41,  1.94iter/s]
Best score: 0.5552: : 445iter [03:41,  1.96iter/s]
Best score: 0.5552: : 446iter [03:42,  1.99iter/s]
Best score: 0.5552: : 447iter [03:42,  2.03iter/s]
Best score: 0.5552: : 448iter [03:43,  1.99iter/s]
Best score: 0.5552: : 449iter [03:43,  2.02iter/s]
[ITER 450] Current best score: 0.5552
[PARAMS] Current best parameters:
  fx: 498.0807
  fy: 497.7327
  px: 1022.6200
  py: 758.2399
  k1: 0.03229602
  k2: 0.01110129
  k3: -0.01050253
  k4: 0.00175251
  p1: 0.00811671
  p2: 0.00545440
  p3: -0.00249623
  p4: -0.00091671
  scale: 2.9859
  rotation: -0.2877

Best score: 0.5552: : 450iter [03:44,  2.05iter/s]
Best score: 0.5552: : 451iter [03:44,  1.99iter/s]
Best score: 0.5552: : 452iter [03:45,  1.96iter/s]
Best score: 0.5552: : 453iter [03:45,  1.95iter/s]
Best score: 0.4982: : 453iter [03:46,  1.95iter/s]
Best score: 0.4982: : 454iter [03:46,  2.02iter/s]
Best score: 0.4982: : 455iter [03:46,  1.98iter/s]
Best score: 0.4982: : 456iter [03:47,  1.97iter/s]
Best score: 0.4982: : 457iter [03:47,  2.06iter/s]
Best score: 0.4982: : 458iter [03:48,  2.05iter/s]
Best score: 0.4982: : 459iter [03:48,  2.00iter/s]
Best score: 0.4982: : 460iter [03:49,  2.07iter/s]
Best score: 0.4982: : 461iter [03:49,  1.97iter/s]
Best score: 0.4982: : 462iter [03:50,  1.94iter/s]
Best score: 0.4982: : 463iter [03:50,  2.06iter/s]
Best score: 0.4982: : 464iter [03:51,  2.06iter/s]
Best score: 0.4982: : 465iter [03:51,  2.07iter/s]
Best score: 0.4982: : 466iter [03:52,  2.07iter/s]
Best score: 0.4982: : 467iter [03:52,  2.12iter/s]
Best score: 0.4982: : 468iter [03:53,  2.08iter/s]
Best score: 0.4982: : 469iter [03:53,  2.06iter/s]
Best score: 0.4982: : 470iter [03:54,  2.09iter/s]
Best score: 0.4982: : 471iter [03:54,  2.09iter/s]
Best score: 0.4982: : 472iter [03:55,  2.12iter/s]
Best score: 0.4982: : 473iter [03:55,  2.09iter/s]
Best score: 0.4982: : 474iter [03:55,  2.11iter/s]
Best score: 0.4982: : 475iter [03:56,  2.11iter/s]
Best score: 0.4982: : 476iter [03:56,  2.10iter/s]
Best score: 0.4982: : 477iter [03:57,  1.99iter/s]
Best score: 0.4982: : 478iter [03:57,  1.99iter/s]
Best score: 0.4982: : 479iter [03:58,  1.97iter/s]
Best score: 0.4982: : 480iter [03:59,  1.92iter/s]
Best score: 0.4982: : 481iter [03:59,  1.96iter/s]
Best score: 0.4982: : 482iter [04:00,  1.96iter/s]
Best score: 0.4982: : 483iter [04:00,  1.90iter/s]
Best score: 0.4982: : 484iter [04:01,  1.95iter/s]
Best score: 0.4982: : 485iter [04:01,  1.93iter/s]
Best score: 0.4982: : 486iter [04:02,  1.90iter/s]
Best score: 0.3504: : 486iter [04:02,  1.90iter/s]
Best score: 0.3504: : 487iter [04:02,  1.93iter/s]
Best score: 0.3504: : 488iter [04:03,  1.94iter/s]
Best score: 0.3504: : 489iter [04:03,  1.95iter/s]
Best score: 0.3504: : 490iter [04:04,  1.92iter/s]
Best score: 0.3504: : 491iter [04:04,  2.00iter/s]
Best score: 0.3504: : 492iter [04:05,  2.00iter/s]
Best score: 0.3504: : 493iter [04:05,  1.99iter/s]
Best score: 0.3504: : 494iter [04:06,  2.06iter/s]
Best score: 0.3504: : 495iter [04:06,  1.99iter/s]
Best score: 0.3504: : 496iter [04:07,  2.00iter/s]
Best score: 0.2658: : 496iter [04:07,  2.00iter/s]
Best score: 0.2658: : 497iter [04:07,  1.99iter/s]
Best score: 0.2658: : 498iter [04:08,  1.98iter/s]
Best score: 0.2658: : 499iter [04:08,  1.97iter/s]
[ITER 500] Current best score: 0.2658
[PARAMS] Current best parameters:
  fx: 493.7456
  fy: 493.5492
  px: 1022.0350
  py: 757.5450
  k1: 0.03890927
  k2: 0.01317156
  k3: -0.01355888
  k4: 0.00240371
  p1: 0.00701120
  p2: 0.00393519
  p3: -0.00398882
  p4: -0.00025003
  scale: 2.9749
  rotation: -0.1942

Best score: 0.2658: : 500iter [04:09,  1.96iter/s]
Best score: 0.2658: : 500iter [05:06,  1.63iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 0.207351 -> 0.207351
  Success: False
  Tangential distortion: p1=0.006978, p2=0.004265, p3=-0.003929, p4=-0.000165
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2074
[✅] Parameters copied to: /working/MDC027_W031_2025-03-24/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 493.74556196699507
    fy: 493.54918736054736
    px: 1022.0350303367828
    py: 757.5450222455528
    k1: 0.03890600479575618
    k2: 0.013222869209410437
    k3: -0.013451815001048672
    k4: 0.0023490238233571954
    p1: 0.006978130172925375
    p2: 0.00426452585383921
    p3: -0.0039288812200282885
    p4: -0.00016548727208621307
    scale: 2.974937956813948
    rotation: -0.19751321746851067
    mmpp: 1.2780595461546898
    nmpp: 1278059.5461546897

[🔄] Converting 1159 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.94s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.79s/rig]
[✅] Saved PTDs: /working/MDC027_W031_2025-03-24/calibration/debug/3_undistorted/output/ptd

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📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.11rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.11rig/s]
[✅] Saved PTDs: /working/MDC027_W031_2025-03-24/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

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📤 Writing LUT:  39%|███▉      | 1207294/3080448 [00:01<00:02, 870545.97px/s]
📤 Writing LUT:  42%|████▏     | 1294352/3080448 [00:01<00:02, 870312.63px/s]
📤 Writing LUT:  45%|████▍     | 1381691/3080448 [00:01<00:01, 871233.91px/s]
📤 Writing LUT:  48%|████▊     | 1468816/3080448 [00:01<00:01, 870543.61px/s]
📤 Writing LUT:  51%|█████     | 1556454/3080448 [00:01<00:01, 872291.55px/s]
📤 Writing LUT:  53%|█████▎    | 1645689/3080448 [00:01<00:01, 878305.30px/s]
📤 Writing LUT:  56%|█████▋    | 1733521/3080448 [00:02<00:01, 876046.14px/s]
📤 Writing LUT:  59%|█████▉    | 1821128/3080448 [00:02<00:01, 875656.55px/s]
📤 Writing LUT:  62%|██████▏   | 1908696/3080448 [00:02<00:01, 874797.98px/s]
📤 Writing LUT:  65%|██████▍   | 1996177/3080448 [00:02<00:01, 870702.69px/s]
📤 Writing LUT:  68%|██████▊   | 2084118/3080448 [00:02<00:01, 873301.38px/s]
📤 Writing LUT:  71%|███████   | 2173228/3080448 [00:02<00:01, 878622.93px/s]
📤 Writing LUT:  73%|███████▎  | 2261096/3080448 [00:02<00:00, 877025.45px/s]
📤 Writing LUT:  76%|███████▌  | 2348803/3080448 [00:02<00:00, 875314.12px/s]
📤 Writing LUT:  79%|███████▉  | 2436338/3080448 [00:02<00:00, 873623.11px/s]
📤 Writing LUT:  82%|████████▏ | 2524068/3080448 [00:02<00:00, 874720.45px/s]
📤 Writing LUT:  85%|████████▍ | 2612111/3080448 [00:03<00:00, 876428.46px/s]
📤 Writing LUT:  88%|████████▊ | 2699997/3080448 [00:03<00:00, 877154.96px/s]
📤 Writing LUT:  90%|█████████ | 2787714/3080448 [00:03<00:00, 876634.33px/s]
📤 Writing LUT:  93%|█████████▎| 2875379/3080448 [00:03<00:00, 874813.96px/s]
📤 Writing LUT:  96%|█████████▌| 2963781/3080448 [00:03<00:00, 877566.95px/s]
📤 Writing LUT:  99%|█████████▉| 3052215/3080448 [00:03<00:00, 879595.10px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 869878.70px/s]
[✅] LUT written to: /working/MDC027_W031_2025-03-24/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC027_W031_2025-03-24' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC027_W031_2025-03-24/calibration/MDC027_W031_2025-03-24_unknown.lut
[🏁] Finished processing MDC027_W031_2025-03-24


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