🟢 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, 65.93s/it]

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→ 1000 (659 images):  92%|█████████▏| 609/659 [01:20<00:14,  3.39it/s]

→ 1000 (659 images):  93%|█████████▎| 611/659 [01:20<00:12,  3.95it/s]

→ 1000 (659 images):  93%|█████████▎| 613/659 [01:20<00:09,  4.89it/s]

→ 1000 (659 images):  93%|█████████▎| 615/659 [01:20<00:07,  6.09it/s]

→ 1000 (659 images):  94%|█████████▎| 617/659 [01:21<00:05,  7.35it/s]

→ 1000 (659 images):  94%|█████████▍| 620/659 [01:21<00:04,  8.35it/s]

→ 1000 (659 images):  94%|█████████▍| 622/659 [01:21<00:04,  9.22it/s]

→ 1000 (659 images):  95%|█████████▍| 624/659 [01:21<00:03,  9.05it/s]

→ 1000 (659 images):  95%|█████████▌| 627/659 [01:21<00:02, 11.19it/s]

→ 1000 (659 images):  96%|█████████▌| 630/659 [01:22<00:02, 13.57it/s]

→ 1000 (659 images):  96%|█████████▌| 633/659 [01:22<00:01, 15.47it/s]

→ 1000 (659 images):  97%|█████████▋| 636/659 [01:22<00:01, 15.97it/s]

→ 1000 (659 images):  97%|█████████▋| 639/659 [01:22<00:01, 17.24it/s]

→ 1000 (659 images):  97%|█████████▋| 641/659 [01:23<00:02,  6.35it/s]

→ 1000 (659 images):  98%|█████████▊| 643/659 [01:23<00:02,  7.52it/s]

→ 1000 (659 images):  98%|█████████▊| 646/659 [01:23<00:01,  9.16it/s]

→ 1000 (659 images):  98%|█████████▊| 648/659 [01:23<00:01,  9.93it/s]

→ 1000 (659 images):  99%|█████████▊| 650/659 [01:24<00:00, 10.66it/s]

→ 1000 (659 images):  99%|█████████▉| 653/659 [01:24<00:00, 12.69it/s]

→ 1000 (659 images):  99%|█████████▉| 655/659 [01:24<00:00, 12.79it/s]

→ 1000 (659 images): 100%|█████████▉| 658/659 [01:24<00:00, 10.81it/s]

                                                                      
📁 Processing Rigs: 100%|██████████| 2/2 [02:30<00:00, 77.11s/it]
📁 Processing Rigs: 100%|██████████| 2/2 [02:30<00:00, 75.43s/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, 32.65it/s]
Averaging bright frames:  20%|██        | 8/40 [00:00<00:00, 36.48it/s]
Averaging bright frames:  30%|███       | 12/40 [00:00<00:00, 37.73it/s]
Averaging bright frames:  40%|████      | 16/40 [00:00<00:00, 38.17it/s]
Averaging bright frames:  50%|█████     | 20/40 [00:00<00:00, 38.67it/s]
Averaging bright frames:  60%|██████    | 24/40 [00:00<00:00, 38.88it/s]
Averaging bright frames:  70%|███████   | 28/40 [00:00<00:00, 39.07it/s]
Averaging bright frames:  80%|████████  | 32/40 [00:00<00:00, 39.19it/s]
Averaging bright frames:  90%|█████████ | 36/40 [00:00<00:00, 39.33it/s]
Averaging bright frames: 100%|██████████| 40/40 [00:01<00:00, 39.45it/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,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:10,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:09,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:09,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:09,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:09,  8.67it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  21%|██        | 21/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 483.838} (0.001):  22%|██▏       | 22/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.003 | Best: {'fx': 490.909} (0.003):  23%|██▎       | 23/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 490.909} (0.003):  24%|██▍       | 24/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.004 | Best: {'fx': 497.98} (0.004):  25%|██▌       | 25/100 [00:00<00:00, 89.60it/s] 
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 497.98} (0.004):  26%|██▌       | 26/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 497.98} (0.004):  27%|██▋       | 27/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.017 | Best: {'fx': 512.121} (0.017):  28%|██▊       | 28/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.000 | Best: {'fx': 512.121} (0.017):  29%|██▉       | 29/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 512.121} (0.017):  30%|███       | 30/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.032 | Best: {'fx': 519.192} (0.032):  31%|███       | 31/100 [00:00<00:00, 89.60it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.032 | Best: {'fx': 519.192} (0.032):  32%|███▏      | 32/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.024 | Best: {'fx': 519.192} (0.032):  32%|███▏      | 32/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.018 | Best: {'fx': 519.192} (0.032):  33%|███▎      | 33/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.053 | Best: {'fx': 526.263} (0.053):  34%|███▍      | 34/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.057 | Best: {'fx': 533.333} (0.057):  35%|███▌      | 35/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.028 | Best: {'fx': 533.333} (0.057):  36%|███▌      | 36/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.038 | Best: {'fx': 533.333} (0.057):  37%|███▋      | 37/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.020 | Best: {'fx': 533.333} (0.057):  38%|███▊      | 38/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.014 | Best: {'fx': 533.333} (0.057):  39%|███▉      | 39/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.017 | Best: {'fx': 533.333} (0.057):  40%|████      | 40/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.015 | Best: {'fx': 533.333} (0.057):  41%|████      | 41/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.057):  42%|████▏     | 42/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.057):  43%|████▎     | 43/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.014 | Best: {'fx': 533.333} (0.057):  44%|████▍     | 44/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.013 | Best: {'fx': 533.333} (0.057):  45%|████▌     | 45/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.057):  46%|████▌     | 46/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.013 | Best: {'fx': 533.333} (0.057):  47%|████▋     | 47/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.012 | Best: {'fx': 533.333} (0.057):  48%|████▊     | 48/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.057):  49%|████▉     | 49/100 [00:00<00:00, 109.24it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.057):  50%|█████     | 50/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.057):  50%|█████     | 50/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  51%|█████     | 51/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  52%|█████▏    | 52/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  53%|█████▎    | 53/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  54%|█████▍    | 54/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.011 | Best: {'fx': 533.333} (0.057):  55%|█████▌    | 55/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  56%|█████▌    | 56/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  57%|█████▋    | 57/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  58%|█████▊    | 58/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  59%|█████▉    | 59/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  60%|██████    | 60/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  61%|██████    | 61/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  62%|██████▏   | 62/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.010 | Best: {'fx': 533.333} (0.057):  63%|██████▎   | 63/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  64%|██████▍   | 64/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  65%|██████▌   | 65/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  66%|██████▌   | 66/100 [00:00<00:00, 130.95it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  67%|██████▋   | 67/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  67%|██████▋   | 67/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  68%|██████▊   | 68/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  69%|██████▉   | 69/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  70%|███████   | 70/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  71%|███████   | 71/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  72%|███████▏  | 72/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  73%|███████▎  | 73/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  74%|███████▍  | 74/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  75%|███████▌  | 75/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  76%|███████▌  | 76/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  77%|███████▋  | 77/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  78%|███████▊  | 78/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  79%|███████▉  | 79/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  80%|████████  | 80/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  81%|████████  | 81/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  82%|████████▏ | 82/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  83%|████████▎ | 83/100 [00:00<00:00, 144.16it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  84%|████████▍ | 84/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  84%|████████▍ | 84/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  85%|████████▌ | 85/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  86%|████████▌ | 86/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  87%|████████▋ | 87/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  88%|████████▊ | 88/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  89%|████████▉ | 89/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  90%|█████████ | 90/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  91%|█████████ | 91/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  92%|█████████▏| 92/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  93%|█████████▎| 93/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  94%|█████████▍| 94/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  95%|█████████▌| 95/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  96%|█████████▌| 96/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  97%|█████████▋| 97/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  98%|█████████▊| 98/100 [00:00<00:00, 152.00it/s]
Brute | Start: {'fx': 500.0} (0.007) | Current: 0.009 | Best: {'fx': 533.333} (0.057):  99%|█████████▉| 99/100 [00:00<00:00, 152.00it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.013 | Best: {'fx': 508.333} (0.013):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.013 | Best: {'fx': 508.333} (0.013):   2%|▏         | 1/50 [00:00<00:05,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.014 | Best: {'fx': 509.354} (0.014):   2%|▏         | 1/50 [00:00<00:05,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.025 | Best: {'fx': 515.476} (0.025):   4%|▍         | 2/50 [00:00<00:05,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.016 | Best: {'fx': 515.476} (0.025):   6%|▌         | 3/50 [00:00<00:05,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.016 | Best: {'fx': 515.476} (0.025):   8%|▊         | 4/50 [00:00<00:05,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.018 | Best: {'fx': 515.476} (0.025):  10%|█         | 5/50 [00:00<00:05,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.017 | Best: {'fx': 515.476} (0.025):  12%|█▏        | 6/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.019 | Best: {'fx': 515.476} (0.025):  14%|█▍        | 7/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.028 | Best: {'fx': 516.497} (0.028):  16%|█▌        | 8/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.035 | Best: {'fx': 520.578} (0.035):  18%|█▊        | 9/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.030 | Best: {'fx': 520.578} (0.035):  20%|██        | 10/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.033 | Best: {'fx': 520.578} (0.035):  22%|██▏       | 11/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.033 | Best: {'fx': 520.578} (0.035):  24%|██▍       | 12/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.038 | Best: {'fx': 521.599} (0.038):  26%|██▌       | 13/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.039 | Best: {'fx': 522.619} (0.039):  28%|██▊       | 14/50 [00:00<00:04,  8.95it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.039 | Best: {'fx': 522.619} (0.039):  30%|███       | 15/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.041 | Best: {'fx': 523.639} (0.041):  30%|███       | 15/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.046 | Best: {'fx': 524.66} (0.046):  32%|███▏      | 16/50 [00:00<00:00, 80.98it/s] 
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.050 | Best: {'fx': 525.68} (0.050):  34%|███▍      | 17/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.054 | Best: {'fx': 526.701} (0.054):  36%|███▌      | 18/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.058 | Best: {'fx': 529.762} (0.058):  38%|███▊      | 19/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.059 | Best: {'fx': 527.721} (0.059):  40%|████      | 20/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.059 | Best: {'fx': 528.741} (0.059):  42%|████▏     | 21/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.057 | Best: {'fx': 528.741} (0.059):  44%|████▍     | 22/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.055 | Best: {'fx': 528.741} (0.059):  46%|████▌     | 23/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.055 | Best: {'fx': 528.741} (0.059):  48%|████▊     | 24/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.052 | Best: {'fx': 528.741} (0.059):  50%|█████     | 25/50 [00:00<00:00, 80.98it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.052 | Best: {'fx': 528.741} (0.059):  52%|█████▏    | 26/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.052 | Best: {'fx': 528.741} (0.059):  52%|█████▏    | 26/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.040 | Best: {'fx': 528.741} (0.059):  54%|█████▍    | 27/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.050 | Best: {'fx': 528.741} (0.059):  56%|█████▌    | 28/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.046 | Best: {'fx': 528.741} (0.059):  58%|█████▊    | 29/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.042 | Best: {'fx': 528.741} (0.059):  60%|██████    | 30/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.039 | Best: {'fx': 528.741} (0.059):  62%|██████▏   | 31/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.036 | Best: {'fx': 528.741} (0.059):  64%|██████▍   | 32/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.029 | Best: {'fx': 528.741} (0.059):  66%|██████▌   | 33/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.034 | Best: {'fx': 528.741} (0.059):  68%|██████▊   | 34/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.027 | Best: {'fx': 528.741} (0.059):  70%|███████   | 35/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.031 | Best: {'fx': 528.741} (0.059):  72%|███████▏  | 36/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.033 | Best: {'fx': 528.741} (0.059):  74%|███████▍  | 37/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.032 | Best: {'fx': 528.741} (0.059):  76%|███████▌  | 38/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.029 | Best: {'fx': 528.741} (0.059):  78%|███████▊  | 39/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.028 | Best: {'fx': 528.741} (0.059):  80%|████████  | 40/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.026 | Best: {'fx': 528.741} (0.059):  82%|████████▏ | 41/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.026 | Best: {'fx': 528.741} (0.059):  84%|████████▍ | 42/50 [00:00<00:00, 90.01it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.026 | Best: {'fx': 528.741} (0.059):  86%|████████▌ | 43/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.025 | Best: {'fx': 528.741} (0.059):  86%|████████▌ | 43/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.024 | Best: {'fx': 528.741} (0.059):  88%|████████▊ | 44/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.024 | Best: {'fx': 528.741} (0.059):  90%|█████████ | 45/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.023 | Best: {'fx': 528.741} (0.059):  92%|█████████▏| 46/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.023 | Best: {'fx': 528.741} (0.059):  94%|█████████▍| 47/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.022 | Best: {'fx': 528.741} (0.059):  96%|█████████▌| 48/50 [00:00<00:00, 115.52it/s]
Brute | Start: {'fx': 533.333} (0.057) | Current: 0.022 | Best: {'fx': 528.741} (0.059):  98%|█████████▊| 49/50 [00:00<00:00, 115.52it/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: 108.7838: 0iter [00:00, ?iter/s]
Initial score: 108.7838: 1iter [00:01,  1.13s/iter]
Initial score: 108.7838: 2iter [00:01,  1.37iter/s]
Initial score: 108.7838: 3iter [00:02,  1.63iter/s]
Initial score: 108.7838: 4iter [00:02,  1.83iter/s]
Initial score: 108.7838: 5iter [00:03,  1.83iter/s]
Initial score: 108.7838: 6iter [00:03,  1.88iter/s]
Initial score: 108.7838: 7iter [00:04,  1.95iter/s]
Initial score: 108.7838: 8iter [00:04,  2.00iter/s]
Initial score: 108.7838: 9iter [00:05,  1.98iter/s]
Initial score: 108.7838: 10iter [00:05,  1.98iter/s]
Initial score: 108.7838: 11iter [00:06,  1.97iter/s]
Initial score: 108.7838: 12iter [00:06,  1.91iter/s]
Initial score: 108.7838: 13iter [00:07,  1.91iter/s]
Initial score: 108.7838: 14iter [00:07,  1.93iter/s]
Initial score: 108.7838: 15iter [00:08,  1.93iter/s]
Initial score: 108.7838: 16iter [00:08,  1.93iter/s]
Initial score: 108.7838: 17iter [00:09,  1.97iter/s]
Initial score: 108.7838: 18iter [00:09,  1.97iter/s]
Initial score: 108.7838: 19iter [00:10,  1.94iter/s]
Initial score: 108.7838: 20iter [00:10,  1.93iter/s]
Initial score: 108.7838: 21iter [00:11,  1.96iter/s]
Initial score: 108.7838: 22iter [00:11,  1.98iter/s]
Initial score: 108.7838: 23iter [00:12,  2.01iter/s]
Initial score: 108.7838: 24iter [00:12,  2.04iter/s]
Initial score: 108.7838: 25iter [00:13,  2.05iter/s]
Initial score: 108.7838: 26iter [00:13,  1.99iter/s]
Initial score: 108.7838: 27iter [00:14,  2.05iter/s]
Initial score: 108.7838: 28iter [00:14,  2.08iter/s]
Initial score: 108.7838: 29iter [00:15,  2.13iter/s]
Best score: 93.5547: : 29iter [00:15,  2.13iter/s]  
Best score: 93.5547: : 30iter [00:15,  2.09iter/s]
Best score: 93.5547: : 31iter [00:16,  2.04iter/s]
Best score: 93.5547: : 32iter [00:16,  1.98iter/s]
Best score: 93.5547: : 33iter [00:17,  1.95iter/s]
Best score: 93.5547: : 34iter [00:17,  1.95iter/s]
Best score: 93.5547: : 35iter [00:18,  2.00iter/s]
Best score: 93.5547: : 36iter [00:18,  1.96iter/s]
Best score: 93.5547: : 37iter [00:19,  1.93iter/s]
Best score: 93.5547: : 38iter [00:19,  1.93iter/s]
Best score: 93.5547: : 39iter [00:20,  1.91iter/s]
Best score: 93.5547: : 40iter [00:20,  1.92iter/s]
Best score: 93.5547: : 41iter [00:21,  2.01iter/s]
Best score: 93.5547: : 42iter [00:21,  2.04iter/s]
Best score: 93.5547: : 43iter [00:22,  2.10iter/s]
Best score: 93.5547: : 44iter [00:22,  2.13iter/s]
Best score: 93.5547: : 45iter [00:23,  2.10iter/s]
Best score: 93.5547: : 46iter [00:23,  2.09iter/s]
Best score: 93.5547: : 47iter [00:23,  2.14iter/s]
Best score: 93.5547: : 48iter [00:24,  2.09iter/s]
Best score: 93.5547: : 49iter [00:24,  2.05iter/s]
[ITER 50] Current best score: 38.7693
[PARAMS] Current best parameters:
  fx: 521.6599
  fy: 522.3675
  px: 1019.8333
  py: 751.7547
  k1: 0.03672046
  k2: 0.01093494
  k3: -0.02212962
  k4: 0.00557784
  p1: -0.00648790
  p2: 0.00476140
  p3: 0.00256022
  p4: 0.00123574
  scale: 2.7376
  rotation: -0.2161

Best score: 38.7693: : 49iter [00:25,  2.05iter/s]
Best score: 38.7693: : 50iter [00:25,  1.97iter/s]
Best score: 38.7693: : 51iter [00:26,  2.00iter/s]
Best score: 38.7693: : 52iter [00:26,  1.99iter/s]
Best score: 38.7693: : 53iter [00:27,  1.97iter/s]
Best score: 38.7693: : 54iter [00:27,  1.96iter/s]
Best score: 38.7675: : 54iter [00:28,  1.96iter/s]
Best score: 38.7675: : 55iter [00:28,  1.92iter/s]
Best score: 38.7675: : 56iter [00:28,  1.90iter/s]
Best score: 38.7675: : 57iter [00:29,  1.96iter/s]
Best score: 38.7675: : 58iter [00:29,  2.03iter/s]
Best score: 30.4733: : 58iter [00:30,  2.03iter/s]
Best score: 30.4733: : 59iter [00:30,  1.96iter/s]
Best score: 30.4733: : 60iter [00:30,  1.94iter/s]
Best score: 30.4733: : 61iter [00:31,  1.90iter/s]
Best score: 30.4733: : 62iter [00:31,  1.94iter/s]
Best score: 30.4733: : 63iter [00:32,  2.03iter/s]
Best score: 30.4733: : 64iter [00:32,  2.09iter/s]
Best score: 30.4733: : 65iter [00:33,  2.03iter/s]
Best score: 30.4733: : 66iter [00:33,  2.02iter/s]
Best score: 30.4733: : 67iter [00:34,  2.08iter/s]
Best score: 15.3755: : 67iter [00:34,  2.08iter/s]
Best score: 15.3755: : 68iter [00:34,  2.11iter/s]
Best score: 15.3755: : 69iter [00:35,  2.05iter/s]
Best score: 15.3755: : 70iter [00:35,  1.97iter/s]
Best score: 15.3755: : 71iter [00:36,  2.00iter/s]
Best score: 15.3755: : 72iter [00:36,  2.02iter/s]
Best score: 15.3755: : 73iter [00:37,  2.02iter/s]
Best score: 15.3755: : 74iter [00:37,  2.04iter/s]
Best score: 15.3755: : 75iter [00:37,  2.07iter/s]
Best score: 15.3755: : 76iter [00:38,  2.12iter/s]
Best score: 15.3755: : 77iter [00:39,  2.00iter/s]
Best score: 15.3755: : 78iter [00:39,  2.00iter/s]
Best score: 15.3755: : 79iter [00:39,  2.09iter/s]
Best score: 15.3755: : 80iter [00:40,  2.06iter/s]
Best score: 15.3755: : 81iter [00:40,  2.11iter/s]
Best score: 15.3755: : 82iter [00:41,  2.03iter/s]
Best score: 15.3755: : 83iter [00:41,  1.96iter/s]
Best score: 15.3755: : 84iter [00:42,  1.92iter/s]
Best score: 15.3755: : 85iter [00:43,  1.88iter/s]
Best score: 15.3755: : 86iter [00:43,  1.94iter/s]
Best score: 15.3755: : 87iter [00:44,  1.96iter/s]
Best score: 15.3755: : 88iter [00:44,  1.98iter/s]
Best score: 12.7816: : 88iter [00:45,  1.98iter/s]
Best score: 12.7816: : 89iter [00:45,  2.03iter/s]
Best score: 12.7816: : 90iter [00:45,  2.03iter/s]
Best score: 12.7816: : 91iter [00:45,  2.08iter/s]
Best score: 12.7816: : 92iter [00:46,  2.04iter/s]
Best score: 12.7816: : 93iter [00:46,  2.01iter/s]
Best score: 12.7816: : 94iter [00:47,  2.08iter/s]
Best score: 8.5900: : 94iter [00:47,  2.08iter/s] 
Best score: 8.5900: : 95iter [00:47,  2.03iter/s]
Best score: 8.5900: : 96iter [00:48,  2.02iter/s]
Best score: 8.5900: : 97iter [00:48,  2.03iter/s]
Best score: 8.5900: : 98iter [00:49,  1.99iter/s]
Best score: 8.5900: : 99iter [00:49,  2.03iter/s]
[ITER 100] Current best score: 8.5900
[PARAMS] Current best parameters:
  fx: 522.5093
  fy: 520.6831
  px: 1021.1167
  py: 756.1381
  k1: 0.03918211
  k2: 0.00152431
  k3: -0.02480572
  k4: 0.00786517
  p1: 0.00273384
  p2: -0.00054125
  p3: 0.00722727
  p4: -0.00081522
  scale: 2.9332
  rotation: 0.0509

Best score: 8.5900: : 100iter [00:50,  2.00iter/s]
Best score: 8.5900: : 101iter [00:50,  1.98iter/s]
Best score: 8.5900: : 102iter [00:51,  1.96iter/s]
Best score: 8.5900: : 103iter [00:52,  1.92iter/s]
Best score: 8.5900: : 104iter [00:52,  1.99iter/s]
Best score: 8.5900: : 105iter [00:53,  1.97iter/s]
Best score: 8.5900: : 106iter [00:53,  1.95iter/s]
Best score: 8.5900: : 107iter [00:54,  1.94iter/s]
Best score: 8.5900: : 108iter [00:54,  1.92iter/s]
Best score: 8.5900: : 109iter [00:55,  1.99iter/s]
Best score: 8.5900: : 110iter [00:55,  2.00iter/s]
Best score: 8.5900: : 111iter [00:56,  1.97iter/s]
Best score: 8.5900: : 112iter [00:56,  1.97iter/s]
Best score: 8.5900: : 113iter [00:57,  2.00iter/s]
Best score: 8.5900: : 114iter [00:57,  2.00iter/s]
Best score: 8.5900: : 115iter [00:58,  1.99iter/s]
Best score: 8.5900: : 116iter [00:58,  1.95iter/s]
Best score: 8.5900: : 117iter [00:59,  1.94iter/s]
Best score: 8.5900: : 118iter [00:59,  1.91iter/s]
Best score: 8.5900: : 119iter [01:00,  1.98iter/s]
Best score: 8.5900: : 120iter [01:00,  1.96iter/s]
Best score: 8.5900: : 121iter [01:01,  2.03iter/s]
Best score: 8.5900: : 122iter [01:01,  2.02iter/s]
Best score: 8.5900: : 123iter [01:02,  2.00iter/s]
Best score: 8.5900: : 124iter [01:02,  1.97iter/s]
Best score: 8.5900: : 125iter [01:03,  1.95iter/s]
Best score: 8.5900: : 126iter [01:03,  1.96iter/s]
Best score: 8.5900: : 127iter [01:04,  1.95iter/s]
Best score: 7.8743: : 127iter [01:04,  1.95iter/s]
Best score: 7.8743: : 128iter [01:04,  1.92iter/s]
Best score: 7.8743: : 129iter [01:05,  2.04iter/s]
Best score: 7.8743: : 130iter [01:05,  1.96iter/s]
Best score: 7.8743: : 131iter [01:06,  1.98iter/s]
Best score: 7.8743: : 132iter [01:06,  2.08iter/s]
Best score: 7.8743: : 133iter [01:07,  2.02iter/s]
Best score: 7.8743: : 134iter [01:07,  2.05iter/s]
Best score: 7.8743: : 135iter [01:08,  2.09iter/s]
Best score: 7.8743: : 136iter [01:08,  2.00iter/s]
Best score: 7.8743: : 137iter [01:09,  2.09iter/s]
Best score: 7.8743: : 138iter [01:09,  2.11iter/s]
Best score: 2.4912: : 138iter [01:10,  2.11iter/s]
Best score: 2.4912: : 139iter [01:10,  2.03iter/s]
Best score: 2.4912: : 140iter [01:10,  1.94iter/s]
Best score: 2.4912: : 141iter [01:11,  2.02iter/s]
Best score: 2.4912: : 142iter [01:11,  2.02iter/s]
Best score: 2.4912: : 143iter [01:12,  1.94iter/s]
Best score: 2.4912: : 144iter [01:12,  1.93iter/s]
Best score: 2.4912: : 145iter [01:13,  1.84iter/s]
Best score: 2.4912: : 146iter [01:13,  1.87iter/s]
Best score: 2.4912: : 147iter [01:14,  1.90iter/s]
Best score: 2.4912: : 148iter [01:14,  1.99iter/s]
Best score: 2.4912: : 149iter [01:15,  1.98iter/s]
[ITER 150] Current best score: 2.4912
[PARAMS] Current best parameters:
  fx: 519.8361
  fy: 519.2777
  px: 1019.6189
  py: 755.3329
  k1: -0.00899848
  k2: 0.04773427
  k3: -0.02940002
  k4: 0.00491459
  p1: 0.00327016
  p2: 0.00084053
  p3: -0.00247034
  p4: 0.00055269
  scale: 2.9326
  rotation: -0.3117

Best score: 2.4912: : 150iter [01:15,  1.95iter/s]
Best score: 2.4912: : 151iter [01:16,  1.99iter/s]
Best score: 2.4912: : 152iter [01:16,  1.99iter/s]
Best score: 2.4912: : 153iter [01:17,  1.94iter/s]
Best score: 2.4912: : 154iter [01:17,  2.00iter/s]
Best score: 2.4912: : 155iter [01:18,  1.95iter/s]
Best score: 2.4912: : 156iter [01:18,  1.92iter/s]
Best score: 2.4912: : 157iter [01:19,  1.88iter/s]
Best score: 2.4912: : 158iter [01:19,  1.89iter/s]
Best score: 2.4912: : 159iter [01:20,  1.90iter/s]
Best score: 2.4912: : 160iter [01:20,  1.95iter/s]
Best score: 2.4912: : 161iter [01:21,  1.97iter/s]
Best score: 2.4912: : 162iter [01:21,  1.92iter/s]
Best score: 2.4912: : 163iter [01:22,  1.98iter/s]
Best score: 2.4912: : 164iter [01:22,  1.95iter/s]
Best score: 2.4912: : 165iter [01:23,  1.89iter/s]
Best score: 2.4912: : 166iter [01:23,  1.98iter/s]
Best score: 2.4912: : 167iter [01:24,  1.93iter/s]
Best score: 2.4912: : 168iter [01:25,  1.96iter/s]
Best score: 2.4912: : 169iter [01:25,  2.03iter/s]
Best score: 2.4912: : 170iter [01:26,  1.95iter/s]
Best score: 2.4912: : 171iter [01:26,  2.04iter/s]
Best score: 2.4912: : 172iter [01:26,  2.06iter/s]
Best score: 2.4912: : 173iter [01:27,  2.05iter/s]
Best score: 2.4912: : 174iter [01:27,  1.97iter/s]
Best score: 2.4912: : 175iter [01:28,  2.03iter/s]
Best score: 2.4912: : 176iter [01:28,  2.05iter/s]
Best score: 2.4912: : 177iter [01:29,  2.06iter/s]
Best score: 2.4912: : 178iter [01:29,  2.11iter/s]
Best score: 2.4912: : 179iter [01:30,  2.05iter/s]
Best score: 1.3750: : 179iter [01:30,  2.05iter/s]
Best score: 1.3750: : 180iter [01:30,  2.05iter/s]
Best score: 1.3750: : 181iter [01:31,  1.99iter/s]
Best score: 1.3750: : 182iter [01:31,  2.03iter/s]
Best score: 1.3750: : 183iter [01:32,  2.03iter/s]
Best score: 1.3750: : 184iter [01:32,  1.97iter/s]
Best score: 1.3750: : 185iter [01:33,  2.01iter/s]
Best score: 1.3750: : 186iter [01:33,  2.03iter/s]
Best score: 1.3750: : 187iter [01:34,  2.02iter/s]
Best score: 1.3750: : 188iter [01:34,  2.00iter/s]
Best score: 1.3750: : 189iter [01:35,  2.04iter/s]
Best score: 1.3750: : 190iter [01:35,  2.04iter/s]
Best score: 1.3750: : 191iter [01:36,  2.03iter/s]
Best score: 1.3750: : 192iter [01:36,  2.02iter/s]
Best score: 1.3750: : 193iter [01:37,  1.79iter/s]
Best score: 1.3750: : 194iter [01:38,  1.84iter/s]
Best score: 1.3750: : 195iter [01:38,  1.90iter/s]
Best score: 1.3750: : 196iter [01:38,  2.03iter/s]
Best score: 1.3750: : 197iter [01:39,  2.06iter/s]
Best score: 1.3750: : 198iter [01:39,  1.97iter/s]
Best score: 1.3750: : 199iter [01:40,  1.96iter/s]
[ITER 200] Current best score: 1.3750
[PARAMS] Current best parameters:
  fx: 512.4165
  fy: 511.9189
  px: 1020.5434
  py: 754.6233
  k1: -0.00661569
  k2: 0.05194645
  k3: -0.03159658
  k4: 0.00523728
  p1: 0.00021322
  p2: 0.00069877
  p3: 0.00331997
  p4: -0.00255126
  scale: 2.9694
  rotation: -0.4320

Best score: 1.3750: : 200iter [01:41,  1.87iter/s]
Best score: 1.3750: : 201iter [01:41,  1.88iter/s]
Best score: 1.3750: : 202iter [01:42,  1.97iter/s]
Best score: 1.3750: : 203iter [01:42,  1.93iter/s]
Best score: 1.3750: : 204iter [01:43,  1.96iter/s]
Best score: 1.3750: : 205iter [01:43,  2.05iter/s]
Best score: 1.3750: : 206iter [01:43,  2.09iter/s]
Best score: 1.3750: : 207iter [01:44,  1.99iter/s]
Best score: 1.3750: : 208iter [01:45,  1.99iter/s]
Best score: 1.3750: : 209iter [01:45,  2.04iter/s]
Best score: 1.3750: : 210iter [01:46,  1.99iter/s]
Best score: 1.3750: : 211iter [01:46,  2.00iter/s]
Best score: 1.3750: : 212iter [01:47,  1.95iter/s]
Best score: 1.3750: : 213iter [01:47,  1.97iter/s]
Best score: 1.3750: : 214iter [01:48,  1.95iter/s]
Best score: 1.3750: : 215iter [01:48,  1.95iter/s]
Best score: 1.3750: : 216iter [01:49,  1.96iter/s]
Best score: 1.3750: : 217iter [01:49,  1.91iter/s]
Best score: 1.3750: : 218iter [01:50,  1.89iter/s]
Best score: 1.3750: : 219iter [01:50,  1.88iter/s]
Best score: 1.3750: : 220iter [01:51,  1.86iter/s]
Best score: 1.3750: : 221iter [01:51,  1.88iter/s]
Best score: 1.3750: : 222iter [01:52,  1.92iter/s]
Best score: 1.3750: : 223iter [01:52,  2.00iter/s]
Best score: 1.3750: : 224iter [01:53,  2.04iter/s]
Best score: 1.3750: : 225iter [01:53,  1.99iter/s]
Best score: 1.3750: : 226iter [01:54,  2.02iter/s]
Best score: 1.3750: : 227iter [01:54,  1.97iter/s]
Best score: 1.3750: : 228iter [01:55,  1.98iter/s]
Best score: 1.3750: : 229iter [01:55,  2.00iter/s]
Best score: 1.3750: : 230iter [01:56,  1.98iter/s]
Best score: 1.3750: : 231iter [01:56,  1.99iter/s]
Best score: 1.3750: : 232iter [01:57,  1.92iter/s]
Best score: 1.3750: : 233iter [01:57,  1.93iter/s]
Best score: 1.3750: : 234iter [01:58,  1.95iter/s]
Best score: 1.3750: : 235iter [01:58,  1.97iter/s]
Best score: 1.3750: : 236iter [01:59,  1.97iter/s]
Best score: 1.3750: : 237iter [01:59,  2.03iter/s]
Best score: 1.3750: : 238iter [02:00,  1.98iter/s]
Best score: 1.3750: : 239iter [02:00,  2.00iter/s]
Best score: 1.3750: : 240iter [02:01,  2.03iter/s]
Best score: 1.3750: : 241iter [02:01,  1.97iter/s]
Best score: 1.3750: : 242iter [02:02,  2.03iter/s]
Best score: 1.3750: : 243iter [02:02,  1.98iter/s]
Best score: 1.3750: : 244iter [02:03,  1.93iter/s]
Best score: 1.3750: : 245iter [02:03,  1.88iter/s]
Best score: 1.3750: : 246iter [02:04,  1.92iter/s]
Best score: 1.3750: : 247iter [02:04,  1.93iter/s]
Best score: 1.3750: : 248iter [02:05,  1.93iter/s]
Best score: 1.3750: : 249iter [02:05,  1.99iter/s]
[ITER 250] Current best score: 1.6377
[PARAMS] Current best parameters:
  fx: 510.0414
  fy: 509.8794
  px: 1018.1070
  py: 755.8186
  k1: -0.02462217
  k2: 0.06817501
  k3: -0.03520163
  k4: 0.00548590
  p1: 0.00181625
  p2: -0.00330062
  p3: 0.00274188
  p4: -0.00080851
  scale: 3.0226
  rotation: -0.0620

Best score: 1.3750: : 250iter [02:06,  1.96iter/s]
Best score: 1.3750: : 251iter [02:06,  2.01iter/s]
Best score: 1.3750: : 252iter [02:07,  1.99iter/s]
Best score: 1.3750: : 253iter [02:07,  2.00iter/s]
Best score: 1.3750: : 254iter [02:08,  2.01iter/s]
Best score: 1.3750: : 255iter [02:09,  1.94iter/s]
Best score: 1.3750: : 256iter [02:09,  1.97iter/s]
Best score: 1.3750: : 257iter [02:10,  1.96iter/s]
Best score: 1.3750: : 258iter [02:10,  1.96iter/s]
Best score: 1.3750: : 259iter [02:10,  2.05iter/s]
Best score: 1.3750: : 260iter [02:11,  2.00iter/s]
Best score: 1.3750: : 261iter [02:11,  2.00iter/s]
Best score: 1.3750: : 262iter [02:12,  2.02iter/s]
Best score: 1.3750: : 263iter [02:12,  2.00iter/s]
Best score: 1.3750: : 264iter [02:13,  2.00iter/s]
Best score: 1.3750: : 265iter [02:14,  1.95iter/s]
Best score: 1.3750: : 266iter [02:14,  2.01iter/s]
Best score: 1.3750: : 267iter [02:15,  1.99iter/s]
Best score: 1.3750: : 268iter [02:15,  2.04iter/s]
Best score: 1.3750: : 269iter [02:15,  1.99iter/s]
Best score: 1.3750: : 270iter [02:16,  1.94iter/s]
Best score: 1.3750: : 271iter [02:17,  1.99iter/s]
Best score: 1.3750: : 272iter [02:17,  1.96iter/s]
Best score: 1.3750: : 273iter [02:18,  1.95iter/s]
Best score: 1.3750: : 274iter [02:18,  1.96iter/s]
Best score: 1.3750: : 275iter [02:19,  1.95iter/s]
Best score: 1.3750: : 276iter [02:19,  2.02iter/s]
Best score: 1.2177: : 276iter [02:20,  2.02iter/s]
Best score: 1.2177: : 277iter [02:20,  2.02iter/s]
Best score: 1.2177: : 278iter [02:20,  2.05iter/s]
Best score: 1.2177: : 279iter [02:20,  2.09iter/s]
Best score: 1.2177: : 280iter [02:21,  2.10iter/s]
Best score: 1.2177: : 281iter [02:21,  2.06iter/s]
Best score: 1.2177: : 282iter [02:22,  2.01iter/s]
Best score: 1.2177: : 283iter [02:22,  2.00iter/s]
Best score: 1.2177: : 284iter [02:23,  2.03iter/s]
Best score: 1.2177: : 285iter [02:23,  2.07iter/s]
Best score: 1.2177: : 286iter [02:24,  2.01iter/s]
Best score: 1.2177: : 287iter [02:24,  2.00iter/s]
Best score: 1.2177: : 288iter [02:25,  1.94iter/s]
Best score: 1.2177: : 289iter [02:26,  1.89iter/s]
Best score: 1.2177: : 290iter [02:26,  1.91iter/s]
Best score: 1.2177: : 291iter [02:27,  1.99iter/s]
Best score: 1.2177: : 292iter [02:27,  2.00iter/s]
Best score: 1.2177: : 293iter [02:28,  2.01iter/s]
Best score: 1.2177: : 294iter [02:28,  2.01iter/s]
Best score: 1.2177: : 295iter [02:29,  1.97iter/s]
Best score: 1.2177: : 296iter [02:29,  1.97iter/s]
Best score: 1.2177: : 297iter [02:30,  1.94iter/s]
Best score: 1.2177: : 298iter [02:30,  2.01iter/s]
Best score: 1.2177: : 299iter [02:31,  2.04iter/s]
[ITER 300] Current best score: 1.2177
[PARAMS] Current best parameters:
  fx: 518.4371
  fy: 518.6788
  px: 1020.1027
  py: 755.3523
  k1: -0.03915772
  k2: 0.07628667
  k3: -0.03581957
  k4: 0.00450396
  p1: 0.00217339
  p2: -0.00134196
  p3: 0.00094015
  p4: -0.00127262
  scale: 3.0086
  rotation: -0.2924

Best score: 1.2177: : 300iter [02:31,  1.98iter/s]
Best score: 1.2177: : 301iter [02:32,  2.04iter/s]
Best score: 1.2177: : 302iter [02:32,  2.01iter/s]
Best score: 1.2177: : 303iter [02:33,  1.94iter/s]
Best score: 1.2177: : 304iter [02:33,  1.90iter/s]
Best score: 1.2177: : 305iter [02:34,  1.88iter/s]
Best score: 1.2177: : 306iter [02:34,  1.85iter/s]
Best score: 1.2177: : 307iter [02:35,  1.89iter/s]
Best score: 1.2177: : 308iter [02:35,  1.89iter/s]
Best score: 1.2177: : 309iter [02:36,  1.92iter/s]
Best score: 1.2177: : 310iter [02:36,  1.95iter/s]
Best score: 1.2177: : 311iter [02:37,  1.89iter/s]
Best score: 1.2177: : 312iter [02:37,  1.88iter/s]
Best score: 1.2177: : 313iter [02:38,  1.88iter/s]
Best score: 1.2177: : 314iter [02:38,  1.96iter/s]
Best score: 1.2177: : 315iter [02:39,  1.99iter/s]
Best score: 1.2177: : 316iter [02:39,  1.99iter/s]
Best score: 1.2177: : 317iter [02:40,  2.02iter/s]
Best score: 1.2177: : 318iter [02:40,  2.05iter/s]
Best score: 1.2177: : 319iter [02:41,  2.05iter/s]
Best score: 1.2177: : 320iter [02:41,  2.07iter/s]
Best score: 1.2177: : 321iter [02:42,  2.07iter/s]
Best score: 1.2177: : 322iter [02:42,  1.99iter/s]
Best score: 1.2177: : 323iter [02:43,  2.03iter/s]
Best score: 1.2177: : 324iter [02:43,  2.09iter/s]
Best score: 1.2177: : 325iter [02:44,  2.08iter/s]
Best score: 1.2177: : 326iter [02:44,  2.08iter/s]
Best score: 1.2177: : 327iter [02:45,  2.03iter/s]
Best score: 1.2177: : 328iter [02:45,  2.02iter/s]
Best score: 1.2177: : 329iter [02:46,  1.98iter/s]
Best score: 1.2177: : 330iter [02:46,  2.01iter/s]
Best score: 1.2177: : 331iter [02:47,  2.00iter/s]
Best score: 1.2177: : 332iter [02:47,  2.00iter/s]
Best score: 1.2177: : 333iter [02:48,  1.94iter/s]
Best score: 1.2177: : 334iter [02:48,  1.93iter/s]
Best score: 1.2177: : 335iter [02:49,  1.96iter/s]
Best score: 1.2177: : 336iter [02:49,  1.90iter/s]
Best score: 1.2177: : 337iter [02:50,  1.89iter/s]
Best score: 1.2177: : 338iter [02:50,  1.83iter/s]
Best score: 1.2177: : 339iter [02:51,  1.91iter/s]
Best score: 1.2177: : 340iter [02:51,  1.95iter/s]
Best score: 1.2177: : 341iter [02:52,  1.95iter/s]
Best score: 1.2177: : 342iter [02:52,  1.94iter/s]
Best score: 1.2177: : 343iter [02:53,  1.96iter/s]
Best score: 1.2177: : 344iter [02:53,  1.97iter/s]
Best score: 1.2177: : 345iter [02:54,  1.90iter/s]
Best score: 0.8533: : 345iter [02:54,  1.90iter/s]
Best score: 0.8533: : 346iter [02:54,  2.02iter/s]
Best score: 0.8533: : 347iter [02:55,  2.06iter/s]
Best score: 0.8533: : 348iter [02:55,  2.09iter/s]
Best score: 0.8533: : 349iter [02:56,  2.11iter/s]
[ITER 350] Current best score: 0.6682
[PARAMS] Current best parameters:
  fx: 510.2936
  fy: 510.0601
  px: 1020.7276
  py: 753.9588
  k1: -0.00301171
  k2: 0.04718577
  k3: -0.03054825
  k4: 0.00566862
  p1: -0.00162114
  p2: 0.00001290
  p3: 0.00221393
  p4: 0.00004373
  scale: 3.0053
  rotation: -0.2668

Best score: 0.6682: : 349iter [02:56,  2.11iter/s]
Best score: 0.6682: : 350iter [02:56,  2.02iter/s]
Best score: 0.6682: : 351iter [02:57,  1.97iter/s]
Best score: 0.6682: : 352iter [02:57,  1.97iter/s]
Best score: 0.6682: : 353iter [02:58,  1.97iter/s]
Best score: 0.6682: : 354iter [02:58,  1.94iter/s]
Best score: 0.6682: : 355iter [02:59,  1.96iter/s]
Best score: 0.6682: : 356iter [02:59,  2.00iter/s]
Best score: 0.6682: : 357iter [03:00,  2.01iter/s]
Best score: 0.6682: : 358iter [03:00,  2.04iter/s]
Best score: 0.6682: : 359iter [03:01,  2.08iter/s]
Best score: 0.6682: : 360iter [03:01,  2.04iter/s]
Best score: 0.6682: : 361iter [03:02,  2.02iter/s]
Best score: 0.6682: : 362iter [03:02,  2.05iter/s]
Best score: 0.6682: : 363iter [03:03,  1.98iter/s]
Best score: 0.6682: : 364iter [03:03,  2.06iter/s]
Best score: 0.6682: : 365iter [03:04,  2.07iter/s]
Best score: 0.6682: : 366iter [03:04,  2.09iter/s]
Best score: 0.6682: : 367iter [03:05,  2.09iter/s]
Best score: 0.6682: : 368iter [03:05,  2.10iter/s]
Best score: 0.6682: : 369iter [03:06,  2.03iter/s]
Best score: 0.6682: : 370iter [03:06,  2.03iter/s]
Best score: 0.6682: : 371iter [03:07,  2.00iter/s]
Best score: 0.6682: : 372iter [03:07,  2.03iter/s]
Best score: 0.6682: : 373iter [03:08,  2.01iter/s]
Best score: 0.6682: : 374iter [03:08,  2.02iter/s]
Best score: 0.6682: : 375iter [03:09,  2.00iter/s]
Best score: 0.6682: : 376iter [03:09,  1.97iter/s]
Best score: 0.6682: : 377iter [03:10,  1.98iter/s]
Best score: 0.6682: : 378iter [03:10,  1.94iter/s]
Best score: 0.6682: : 379iter [03:11,  1.98iter/s]
Best score: 0.6682: : 380iter [03:11,  2.00iter/s]
Best score: 0.6682: : 381iter [03:12,  2.03iter/s]
Best score: 0.6682: : 382iter [03:12,  1.99iter/s]
Best score: 0.6682: : 383iter [03:13,  1.98iter/s]
Best score: 0.6682: : 384iter [03:13,  2.05iter/s]
Best score: 0.6682: : 385iter [03:14,  2.06iter/s]
Best score: 0.6682: : 386iter [03:14,  2.07iter/s]
Best score: 0.6682: : 387iter [03:15,  2.03iter/s]
Best score: 0.6682: : 388iter [03:15,  2.04iter/s]
Best score: 0.6682: : 389iter [03:16,  1.98iter/s]
Best score: 0.6682: : 390iter [03:16,  2.00iter/s]
Best score: 0.6682: : 391iter [03:17,  1.94iter/s]
Best score: 0.6682: : 392iter [03:17,  1.98iter/s]
Best score: 0.6682: : 393iter [03:18,  1.97iter/s]
Best score: 0.6682: : 394iter [03:18,  1.95iter/s]
Best score: 0.6682: : 395iter [03:19,  1.95iter/s]
Best score: 0.6682: : 396iter [03:19,  1.97iter/s]
Best score: 0.6682: : 397iter [03:20,  1.87iter/s]
Best score: 0.6682: : 398iter [03:20,  1.88iter/s]
Best score: 0.6682: : 399iter [03:21,  1.93iter/s]
[ITER 400] Current best score: 0.6682
[PARAMS] Current best parameters:
  fx: 510.2936
  fy: 510.0601
  px: 1020.7276
  py: 753.9588
  k1: -0.00301171
  k2: 0.04718577
  k3: -0.03054825
  k4: 0.00566862
  p1: -0.00162114
  p2: 0.00001290
  p3: 0.00221393
  p4: 0.00004373
  scale: 3.0053
  rotation: -0.2668

Best score: 0.6682: : 400iter [03:21,  1.89iter/s]
Best score: 0.6682: : 401iter [03:22,  1.96iter/s]
Best score: 0.6682: : 402iter [03:22,  1.95iter/s]
Best score: 0.6682: : 403iter [03:23,  1.95iter/s]
Best score: 0.6682: : 404iter [03:23,  1.96iter/s]
Best score: 0.6682: : 405iter [03:24,  2.01iter/s]
Best score: 0.6682: : 406iter [03:24,  2.05iter/s]
Best score: 0.5888: : 406iter [03:25,  2.05iter/s]
Best score: 0.5888: : 407iter [03:25,  2.07iter/s]
Best score: 0.5888: : 408iter [03:25,  2.06iter/s]
Best score: 0.5888: : 409iter [03:26,  2.02iter/s]
Best score: 0.5888: : 410iter [03:26,  2.03iter/s]
Best score: 0.5888: : 411iter [03:27,  2.05iter/s]
Best score: 0.5888: : 412iter [03:27,  2.04iter/s]
Best score: 0.5888: : 413iter [03:28,  1.99iter/s]
Best score: 0.5888: : 414iter [03:28,  2.05iter/s]
Best score: 0.5888: : 415iter [03:29,  1.98iter/s]
Best score: 0.5888: : 416iter [03:29,  1.99iter/s]
Best score: 0.5888: : 417iter [03:30,  2.00iter/s]
Best score: 0.5888: : 418iter [03:30,  2.02iter/s]
Best score: 0.5888: : 419iter [03:31,  2.10iter/s]
Best score: 0.5888: : 420iter [03:31,  2.09iter/s]
Best score: 0.5888: : 421iter [03:32,  2.02iter/s]
Best score: 0.5888: : 422iter [03:32,  2.00iter/s]
Best score: 0.5888: : 423iter [03:33,  2.02iter/s]
Best score: 0.5888: : 424iter [03:33,  2.04iter/s]
Best score: 0.5888: : 425iter [03:34,  1.98iter/s]
Best score: 0.5888: : 426iter [03:34,  1.93iter/s]
Best score: 0.5888: : 427iter [03:35,  1.91iter/s]
Best score: 0.5888: : 428iter [03:35,  1.88iter/s]
Best score: 0.5888: : 429iter [03:36,  1.97iter/s]
Best score: 0.5888: : 430iter [03:36,  2.00iter/s]
Best score: 0.5888: : 431iter [03:37,  1.93iter/s]
Best score: 0.5888: : 432iter [03:37,  2.01iter/s]
Best score: 0.5888: : 433iter [03:38,  1.96iter/s]
Best score: 0.5888: : 434iter [03:38,  1.97iter/s]
Best score: 0.5888: : 435iter [03:39,  2.10iter/s]
Best score: 0.5888: : 436iter [03:39,  2.06iter/s]
Best score: 0.5888: : 437iter [03:40,  2.03iter/s]
Best score: 0.5888: : 438iter [03:40,  2.07iter/s]
Best score: 0.5888: : 439iter [03:41,  2.04iter/s]
Best score: 0.5888: : 440iter [03:41,  1.95iter/s]
Best score: 0.5888: : 441iter [03:42,  1.98iter/s]
Best score: 0.5888: : 442iter [03:42,  1.95iter/s]
Best score: 0.4845: : 442iter [03:43,  1.95iter/s]
Best score: 0.4845: : 443iter [03:43,  1.93iter/s]
Best score: 0.4845: : 444iter [03:43,  1.93iter/s]
Best score: 0.4845: : 445iter [03:44,  1.88iter/s]
Best score: 0.4845: : 446iter [03:45,  1.84iter/s]
Best score: 0.4845: : 447iter [03:45,  1.88iter/s]
Best score: 0.4845: : 448iter [03:46,  1.99iter/s]
Best score: 0.4845: : 449iter [03:46,  2.01iter/s]
[ITER 450] Current best score: 0.4845
[PARAMS] Current best parameters:
  fx: 514.2866
  fy: 514.0568
  px: 1021.0369
  py: 753.5528
  k1: -0.02354043
  k2: 0.07273514
  k3: -0.04290174
  k4: 0.00763805
  p1: -0.00133935
  p2: 0.00154351
  p3: 0.00088167
  p4: -0.00086071
  scale: 2.9925
  rotation: -0.2229

Best score: 0.4845: : 450iter [03:47,  1.94iter/s]
Best score: 0.4845: : 451iter [03:47,  1.99iter/s]
Best score: 0.4845: : 452iter [03:48,  2.01iter/s]
Best score: 0.4845: : 453iter [03:48,  2.04iter/s]
Best score: 0.4845: : 454iter [03:48,  2.12iter/s]
Best score: 0.4845: : 455iter [03:49,  2.03iter/s]
Best score: 0.4845: : 456iter [03:49,  2.00iter/s]
Best score: 0.4845: : 457iter [03:50,  1.97iter/s]
Best score: 0.4845: : 458iter [03:50,  2.04iter/s]
Best score: 0.4845: : 459iter [03:51,  2.05iter/s]
Best score: 0.4845: : 460iter [03:51,  2.08iter/s]
Best score: 0.4845: : 461iter [03:52,  1.96iter/s]
Best score: 0.4845: : 462iter [03:52,  1.98iter/s]
Best score: 0.4845: : 463iter [03:53,  2.10iter/s]
Best score: 0.4845: : 464iter [03:53,  2.05iter/s]
Best score: 0.4845: : 465iter [03:54,  1.99iter/s]
Best score: 0.4845: : 466iter [03:54,  2.08iter/s]
Best score: 0.4845: : 467iter [03:55,  2.06iter/s]
Best score: 0.4845: : 468iter [03:55,  2.00iter/s]
Best score: 0.4845: : 469iter [03:56,  2.01iter/s]
Best score: 0.4845: : 470iter [03:56,  2.00iter/s]
Best score: 0.4845: : 471iter [03:57,  1.98iter/s]
Best score: 0.4845: : 472iter [03:57,  2.02iter/s]
Best score: 0.4845: : 473iter [03:58,  1.97iter/s]
Best score: 0.4845: : 474iter [03:58,  1.97iter/s]
Best score: 0.4845: : 475iter [03:59,  2.05iter/s]
Best score: 0.4845: : 476iter [03:59,  2.07iter/s]
Best score: 0.4845: : 477iter [04:00,  2.04iter/s]
Best score: 0.4845: : 478iter [04:00,  2.01iter/s]
Best score: 0.4845: : 479iter [04:01,  2.00iter/s]
Best score: 0.4845: : 480iter [04:01,  2.02iter/s]
Best score: 0.4845: : 481iter [04:02,  1.98iter/s]
Best score: 0.4845: : 482iter [04:02,  1.97iter/s]
Best score: 0.4845: : 483iter [04:03,  1.95iter/s]
Best score: 0.4845: : 484iter [04:03,  2.03iter/s]
Best score: 0.4845: : 485iter [04:04,  2.06iter/s]
Best score: 0.4845: : 486iter [04:04,  1.95iter/s]
Best score: 0.4845: : 487iter [04:05,  2.00iter/s]
Best score: 0.4845: : 488iter [04:05,  1.98iter/s]
Best score: 0.4845: : 489iter [04:06,  1.98iter/s]
Best score: 0.4845: : 490iter [04:06,  2.08iter/s]
Best score: 0.4845: : 491iter [04:07,  2.04iter/s]
Best score: 0.4845: : 492iter [04:07,  2.04iter/s]
Best score: 0.4845: : 493iter [04:08,  2.04iter/s]
Best score: 0.4845: : 494iter [04:08,  2.04iter/s]
Best score: 0.4845: : 495iter [04:09,  2.09iter/s]
Best score: 0.4845: : 496iter [04:09,  2.00iter/s]
Best score: 0.4845: : 497iter [04:10,  2.06iter/s]
Best score: 0.4845: : 498iter [04:10,  2.01iter/s]
Best score: 0.4845: : 499iter [04:11,  2.03iter/s]
[ITER 500] Current best score: 0.5393
[PARAMS] Current best parameters:
  fx: 509.4275
  fy: 509.3358
  px: 1019.6480
  py: 753.8142
  k1: -0.01207591
  k2: 0.06572997
  k3: -0.04199121
  k4: 0.00805533
  p1: -0.00117467
  p2: -0.00084676
  p3: 0.00158007
  p4: -0.00151021
  scale: 2.9981
  rotation: -0.2359

Best score: 0.4845: : 500iter [04:11,  1.93iter/s]
Best score: 0.4845: : 500iter [04:42,  1.77iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish did not improve score: 0.308839
  Success: False
  Tangential distortion: p1=-0.001069, p2=-0.001392, p3=0.001711, p4=-0.000921
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3088
[✅] 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: 509.42745756491206
    fy: 509.3358021363257
    px: 1019.6480057366343
    py: 753.8142036323254
    k1: -0.012051774008361186
    k2: 0.06577743958426881
    k3: -0.041949338550667674
    k4: 0.008003858322613609
    p1: -0.0010688254597374615
    p2: -0.001392396945020797
    p3: 0.0017110187565962782
    p4: -0.0009208037480032436
    scale: 2.998128285510857
    rotation: -0.2358505907818309
    mmpp: 1.2790285378558566
    nmpp: 1279028.5378558566

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

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📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.96s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.81s/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,  1.05rig/s]
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[✅] 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:  36%|███▌      | 1101879/3080448 [00:01<00:02, 858708.95px/s]
📤 Writing LUT:  39%|███▊      | 1187752/3080448 [00:01<00:02, 857077.77px/s]
📤 Writing LUT:  41%|████▏     | 1274970/3080448 [00:01<00:02, 861621.21px/s]
📤 Writing LUT:  44%|████▍     | 1361317/3080448 [00:01<00:01, 862176.04px/s]
📤 Writing LUT:  47%|████▋     | 1447537/3080448 [00:01<00:01, 861802.59px/s]
📤 Writing LUT:  50%|████▉     | 1535827/3080448 [00:01<00:01, 868135.44px/s]
📤 Writing LUT:  53%|█████▎    | 1623998/3080448 [00:01<00:01, 872208.26px/s]
📤 Writing LUT:  56%|█████▌    | 1711220/3080448 [00:02<00:01, 865456.71px/s]
📤 Writing LUT:  58%|█████▊    | 1797779/3080448 [00:02<00:01, 858696.88px/s]
📤 Writing LUT:  61%|██████    | 1885041/3080448 [00:02<00:01, 862832.84px/s]
📤 Writing LUT:  64%|██████▍   | 1971460/3080448 [00:02<00:01, 863233.33px/s]
📤 Writing LUT:  67%|██████▋   | 2059068/3080448 [00:02<00:01, 867068.37px/s]
📤 Writing LUT:  70%|██████▉   | 2145785/3080448 [00:02<00:01, 864176.58px/s]
📤 Writing LUT:  72%|███████▏  | 2232211/3080448 [00:02<00:00, 859715.01px/s]
📤 Writing LUT:  75%|███████▌  | 2318192/3080448 [00:02<00:00, 855557.31px/s]
📤 Writing LUT:  78%|███████▊  | 2403756/3080448 [00:02<00:00, 855366.75px/s]
📤 Writing LUT:  81%|████████  | 2492369/3080448 [00:02<00:00, 864538.17px/s]
📤 Writing LUT:  84%|████████▎ | 2578833/3080448 [00:03<00:00, 864374.29px/s]
📤 Writing LUT:  87%|████████▋ | 2667725/3080448 [00:03<00:00, 871712.51px/s]
📤 Writing LUT:  89%|████████▉ | 2756862/3080448 [00:03<00:00, 877594.17px/s]
📤 Writing LUT:  92%|█████████▏| 2845066/3080448 [00:03<00:00, 878924.27px/s]
📤 Writing LUT:  95%|█████████▌| 2934154/3080448 [00:03<00:00, 882504.29px/s]
📤 Writing LUT:  98%|█████████▊| 3023212/3080448 [00:03<00:00, 884924.18px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 861160.04px/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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