
🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-008_W008(6)_2025-11-20 ===

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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 4 las0.zip files to process
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                                                                      [A📁 Processing Rigs: 100%|██████████| 4/4 [06:42<00:00, 101.39s/it]📁 Processing Rigs: 100%|██████████| 4/4 [06:42<00:00, 100.53s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2962 frames from 4 rigs to /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 674
[📋] 2000L: 100 frames selected from 761
[📋] 2000R: 100 frames selected from 805
[📋] 1000: 100 frames selected from 722
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/1_digitized/rig_frames.json

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

[INFO] Using 80 total sampled bright frames
Averaging bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Averaging bright frames:   4%|▍         | 3/80 [00:00<00:02, 27.10it/s]Averaging bright frames:   9%|▉         | 7/80 [00:00<00:02, 34.18it/s]Averaging bright frames:  15%|█▌        | 12/80 [00:00<00:01, 40.92it/s]Averaging bright frames:  21%|██▏       | 17/80 [00:00<00:01, 41.80it/s]Averaging bright frames:  28%|██▊       | 22/80 [00:00<00:01, 40.87it/s]Averaging bright frames:  34%|███▍      | 27/80 [00:00<00:01, 40.89it/s]Averaging bright frames:  40%|████      | 32/80 [00:00<00:01, 41.39it/s]Averaging bright frames:  46%|████▋     | 37/80 [00:00<00:00, 43.66it/s]Averaging bright frames:  52%|█████▎    | 42/80 [00:01<00:00, 44.64it/s]Averaging bright frames:  59%|█████▉    | 47/80 [00:01<00:00, 43.24it/s]Averaging bright frames:  65%|██████▌   | 52/80 [00:01<00:00, 42.31it/s]Averaging bright frames:  71%|███████▏  | 57/80 [00:01<00:00, 43.51it/s]Averaging bright frames:  78%|███████▊  | 62/80 [00:01<00:00, 44.62it/s]Averaging bright frames:  84%|████████▍ | 67/80 [00:01<00:00, 43.41it/s]Averaging bright frames:  90%|█████████ | 72/80 [00:01<00:00, 42.24it/s]Averaging bright frames:  96%|█████████▋| 77/80 [00:01<00:00, 41.59it/s]                                                                        Total time to process (v2): 0.06
[RESULT] Combined: Center=(882, 902), Radius=888
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377.778} (0.003):  11%|█         | 11/100 [00:00<00:05, 17.66it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.002 | Best: {'fx': 377.778} (0.003):  12%|█▏        | 12/100 [00:00<00:04, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 20.56it/s] Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 406.061} (0.005):  14%|█▍        | 14/100 [00:00<00:04, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 406.061} (0.005):  15%|█▌        | 15/100 [00:00<00:04, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  16%|█▌        | 16/100 [00:00<00:04, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:00<00:04, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:00<00:03, 20.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  19%|█▉        | 19/100 [00:00<00:02, 30.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:00<00:02, 30.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:00<00:02, 30.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:00<00:02, 30.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  22%|██▏       | 22/100 [00:01<00:02, 30.83it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 26.54it/s]Brute | Start: {'fx': 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505.051} (0.049):  29%|██▉       | 29/100 [00:01<00:02, 30.19it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.058 | Best: {'fx': 512.121} (0.058):  30%|███       | 30/100 [00:01<00:02, 30.19it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.064 | Best: {'fx': 519.192} (0.064):  31%|███       | 31/100 [00:01<00:02, 30.19it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.064 | Best: {'fx': 519.192} (0.064):  32%|███▏      | 32/100 [00:01<00:03, 19.87it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.072 | Best: {'fx': 540.404} (0.072):  32%|███▏      | 32/100 [00:01<00:03, 19.87it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.079 | Best: {'fx': 533.333} (0.079):  33%|███▎      | 33/100 [00:01<00:03, 19.87it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.043 | Best: {'fx': 533.333} (0.079):  34%|███▍      | 34/100 [00:01<00:03, 19.87it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.043 | Best: {'fx': 533.333} (0.079):  35%|███▌      | 35/100 [00:01<00:03, 20.97it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.076 | Best: {'fx': 533.333} (0.079):  35%|███▌      | 35/100 [00:01<00:03, 20.97it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.062 | Best: {'fx': 533.333} (0.079):  36%|███▌      | 36/100 [00:01<00:03, 20.97it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.048 | Best: {'fx': 533.333} (0.079):  37%|███▋      | 37/100 [00:01<00:03, 20.97it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.054 | Best: {'fx': 533.333} (0.079):  38%|███▊      | 38/100 [00:01<00:02, 20.97it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.054 | Best: {'fx': 533.333} (0.079):  39%|███▉      | 39/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.029 | Best: {'fx': 533.333} (0.079):  39%|███▉      | 39/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.035 | Best: {'fx': 533.333} (0.079):  40%|████      | 40/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.037 | Best: {'fx': 533.333} (0.079):  41%|████      | 41/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.033 | Best: {'fx': 533.333} (0.079):  42%|████▏     | 42/100 [00:01<00:02, 24.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.033 | Best: {'fx': 533.333} (0.079):  43%|████▎     | 43/100 [00:01<00:02, 27.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.040 | Best: {'fx': 533.333} (0.079):  43%|████▎     | 43/100 [00:01<00:02, 27.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.030 | Best: {'fx': 533.333} (0.079):  44%|████▍     | 44/100 [00:01<00:02, 27.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.032 | Best: {'fx': 533.333} (0.079):  45%|████▌     | 45/100 [00:01<00:01, 27.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 533.333} (0.079):  46%|████▌     | 46/100 [00:01<00:01, 27.56it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 533.333} (0.079):  47%|████▋     | 47/100 [00:01<00:01, 29.03it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 533.333} (0.079):  47%|████▋     | 47/100 [00:01<00:01, 29.03it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.079):  48%|████▊     | 48/100 [00:02<00:01, 29.03it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.027 | Best: {'fx': 533.333} (0.079):  49%|████▉     | 49/100 [00:02<00:01, 29.03it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  50%|█████     | 50/100 [00:02<00:01, 29.03it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  51%|█████     | 51/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.026 | Best: {'fx': 533.333} (0.079):  51%|█████     | 51/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  52%|█████▏    | 52/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.026 | Best: {'fx': 533.333} (0.079):  53%|█████▎    | 53/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 533.333} (0.079):  54%|█████▍    | 54/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 533.333} (0.079):  55%|█████▌    | 55/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  56%|█████▌    | 56/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.079):  57%|█████▋    | 57/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.079):  58%|█████▊    | 58/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.079):  59%|█████▉    | 59/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.079):  60%|██████    | 60/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  60%|██████    | 60/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  61%|██████    | 61/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.079):  62%|██████▏   | 62/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.079):  63%|██████▎   | 63/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  64%|██████▍   | 64/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.079):  65%|██████▌   | 65/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.079):  66%|██████▌   | 66/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.079):  67%|██████▋   | 67/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  68%|██████▊   | 68/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.079):  69%|██████▉   | 69/100 [00:02<00:00, 43.78it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.079):  70%|███████   | 70/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.079):  70%|███████   | 70/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  71%|███████   | 71/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.079):  72%|███████▏  | 72/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.079):  73%|███████▎  | 73/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.079):  74%|███████▍  | 74/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  75%|███████▌  | 75/100 [00:02<00:00, 53.48it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  76%|███████▌  | 76/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  76%|███████▌  | 76/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  77%|███████▋  | 77/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  78%|███████▊  | 78/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  79%|███████▉  | 79/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  80%|████████  | 80/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  81%|████████  | 81/100 [00:02<00:00, 50.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  82%|████████▏ | 82/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  82%|████████▏ | 82/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  83%|████████▎ | 83/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  84%|████████▍ | 84/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  85%|████████▌ | 85/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  86%|████████▌ | 86/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  87%|████████▋ | 87/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  88%|████████▊ | 88/100 [00:02<00:00, 48.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  89%|████████▉ | 89/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  89%|████████▉ | 89/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  90%|█████████ | 90/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  91%|█████████ | 91/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  92%|█████████▏| 92/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  93%|█████████▎| 93/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  94%|█████████▍| 94/100 [00:02<00:00, 49.23it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  95%|█████████▌| 95/100 [00:02<00:00, 45.99it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  95%|█████████▌| 95/100 [00:02<00:00, 45.99it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  96%|█████████▌| 96/100 [00:02<00:00, 45.99it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  97%|█████████▋| 97/100 [00:02<00:00, 45.99it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  98%|█████████▊| 98/100 [00:03<00:00, 45.99it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079):  99%|█████████▉| 99/100 [00:03<00:00, 45.99it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.018 | Best: {'fx': 533.333} (0.079): 100%|██████████| 100/100 [00:03<00:00, 39.57it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.054 | Best: {'fx': 509.354} (0.054):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.054 | Best: {'fx': 509.354} (0.054):   2%|▏         | 1/50 [00:00<00:26,  1.88it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.053 | Best: {'fx': 509.354} (0.054):   2%|▏         | 1/50 [00:00<00:26,  1.88it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.055 | Best: {'fx': 510.374} (0.055):   4%|▍         | 2/50 [00:00<00:25,  1.88it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.057 | Best: {'fx': 511.395} (0.057):   6%|▌         | 3/50 [00:00<00:25,  1.88it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.058 | Best: {'fx': 512.415} (0.058):   8%|▊         | 4/50 [00:00<00:24,  1.88it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.059 | Best: {'fx': 513.435} (0.059):  10%|█         | 5/50 [00:00<00:23,  1.88it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.059 | Best: {'fx': 513.435} (0.059):  12%|█▏        | 6/50 [00:00<00:04, 10.43it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.060 | Best: {'fx': 514.456} (0.060):  12%|█▏        | 6/50 [00:00<00:04, 10.43it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.061 | Best: {'fx': 516.497} (0.061):  14%|█▍        | 7/50 [00:00<00:04, 10.43it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.062 | Best: {'fx': 517.517} (0.062):  16%|█▌        | 8/50 [00:00<00:04, 10.43it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.061 | Best: {'fx': 517.517} (0.062):  18%|█▊        | 9/50 [00:00<00:03, 10.43it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.061 | Best: {'fx': 517.517} (0.062):  20%|██        | 10/50 [00:00<00:02, 16.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.063 | Best: {'fx': 518.537} (0.063):  20%|██        | 10/50 [00:00<00:02, 16.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.064 | Best: {'fx': 519.558} (0.064):  22%|██▏       | 11/50 [00:00<00:02, 16.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 521.599} (0.068):  24%|██▍       | 12/50 [00:00<00:02, 16.37it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 521.599} (0.068):  26%|██▌       | 13/50 [00:00<00:02, 18.12it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.070 | Best: {'fx': 522.619} (0.070):  26%|██▌       | 13/50 [00:00<00:02, 18.12it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.066 | Best: {'fx': 522.619} (0.070):  28%|██▊       | 14/50 [00:01<00:01, 18.12it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.077 | Best: {'fx': 526.701} (0.077):  30%|███       | 15/50 [00:01<00:01, 18.12it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.077 | Best: {'fx': 526.701} (0.077):  32%|███▏      | 16/50 [00:01<00:01, 19.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.071 | Best: {'fx': 526.701} (0.077):  32%|███▏      | 16/50 [00:01<00:01, 19.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.075 | Best: {'fx': 526.701} (0.077):  34%|███▍      | 17/50 [00:01<00:01, 19.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.078 | Best: {'fx': 527.721} (0.078):  36%|███▌      | 18/50 [00:01<00:01, 19.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.078 | Best: {'fx': 527.721} (0.078):  38%|███▊      | 19/50 [00:01<00:01, 21.52it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.082 | Best: {'fx': 529.762} (0.082):  38%|███▊      | 19/50 [00:01<00:01, 21.52it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.073 | Best: {'fx': 529.762} (0.082):  40%|████      | 20/50 [00:01<00:01, 21.52it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.079 | Best: {'fx': 529.762} (0.082):  42%|████▏     | 21/50 [00:01<00:01, 21.52it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.080 | Best: {'fx': 529.762} (0.082):  44%|████▍     | 22/50 [00:01<00:01, 21.52it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.080 | Best: {'fx': 529.762} (0.082):  46%|████▌     | 23/50 [00:01<00:01, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.081 | Best: {'fx': 529.762} (0.082):  46%|████▌     | 23/50 [00:01<00:01, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.071 | Best: {'fx': 529.762} (0.082):  48%|████▊     | 24/50 [00:01<00:01, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.073 | Best: {'fx': 529.762} (0.082):  50%|█████     | 25/50 [00:01<00:01, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.080 | Best: {'fx': 529.762} (0.082):  52%|█████▏    | 26/50 [00:01<00:01, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.070 | Best: {'fx': 529.762} (0.082):  54%|█████▍    | 27/50 [00:01<00:01, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.069 | Best: {'fx': 529.762} (0.082):  56%|█████▌    | 28/50 [00:01<00:00, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.078 | Best: {'fx': 529.762} (0.082):  58%|█████▊    | 29/50 [00:01<00:00, 22.78it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.078 | Best: {'fx': 529.762} (0.082):  60%|██████    | 30/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.068 | Best: {'fx': 529.762} (0.082):  60%|██████    | 30/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.078 | Best: {'fx': 529.762} (0.082):  62%|██████▏   | 31/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.074 | Best: {'fx': 529.762} (0.082):  64%|██████▍   | 32/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.065 | Best: {'fx': 529.762} (0.082):  66%|██████▌   | 33/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.079 | Best: {'fx': 529.762} (0.082):  68%|██████▊   | 34/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.058 | Best: {'fx': 529.762} (0.082):  70%|███████   | 35/50 [00:01<00:00, 32.94it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.058 | Best: {'fx': 529.762} (0.082):  72%|███████▏  | 36/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.076 | Best: {'fx': 529.762} (0.082):  72%|███████▏  | 36/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.056 | Best: {'fx': 529.762} (0.082):  74%|███████▍  | 37/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.064 | Best: {'fx': 529.762} (0.082):  76%|███████▌  | 38/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.074 | Best: {'fx': 529.762} (0.082):  78%|███████▊  | 39/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.063 | Best: {'fx': 529.762} (0.082):  80%|████████  | 40/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.061 | Best: {'fx': 529.762} (0.082):  82%|████████▏ | 41/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.059 | Best: {'fx': 529.762} (0.082):  84%|████████▍ | 42/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.060 | Best: {'fx': 529.762} (0.082):  86%|████████▌ | 43/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.055 | Best: {'fx': 529.762} (0.082):  88%|████████▊ | 44/50 [00:01<00:00, 39.21it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.055 | Best: {'fx': 529.762} (0.082):  90%|█████████ | 45/50 [00:01<00:00, 51.14it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.052 | Best: {'fx': 529.762} (0.082):  90%|█████████ | 45/50 [00:01<00:00, 51.14it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.054 | Best: {'fx': 529.762} (0.082):  92%|█████████▏| 46/50 [00:01<00:00, 51.14it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.053 | Best: {'fx': 529.762} (0.082):  94%|█████████▍| 47/50 [00:01<00:00, 51.14it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.050 | Best: {'fx': 529.762} (0.082):  96%|█████████▌| 48/50 [00:01<00:00, 51.14it/s]Brute | Start: {'fx': 533.333} (0.079) | Current: 0.051 | Best: {'fx': 529.762} (0.082):  98%|█████████▊| 49/50 [00:01<00:00, 51.14it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.7619047619048
    px: 882.0
    py: 902.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 7 frames per rig (excluded 0 incomplete frames): 28 frames will be used for optimization
  Rigs: 1500(1500mm):7/20, 2000L(2000mm):7/20, 2000R(2000mm):7/20, 1000(1000mm):7/20
  Total: 28/80 frames used for optimization
Initial score: 32.4620: 0iter [00:00, ?iter/s]Initial score: 32.4620: 1iter [00:03,  3.43s/iter]Initial score: 32.4620: 2iter [00:05,  2.44s/iter]Initial score: 32.4620: 3iter [00:06,  2.09s/iter]Initial score: 32.4620: 4iter [00:08,  1.95s/iter]Initial score: 32.4620: 5iter [00:10,  1.85s/iter]Initial score: 32.4620: 6iter [00:11,  1.81s/iter]Initial score: 32.4620: 7iter [00:13,  1.78s/iter]Initial score: 32.4620: 8iter [00:15,  1.74s/iter]Initial score: 32.4620: 9iter [00:17,  1.74s/iter]Initial score: 32.4620: 10iter [00:18,  1.74s/iter]Best score: 17.8349: : 10iter [00:20,  1.74s/iter] Best score: 17.8349: : 11iter [00:20,  1.76s/iter]Best score: 17.8349: : 12iter [00:22,  1.74s/iter]Best score: 17.8349: : 13iter [00:24,  1.73s/iter]Best score: 17.8349: : 14iter [00:25,  1.73s/iter]Best score: 17.8349: : 15iter [00:27,  1.74s/iter]Best score: 17.8349: : 16iter [00:29,  1.75s/iter]Best score: 17.8349: : 17iter [00:31,  1.74s/iter]Best score: 17.8349: : 18iter [00:32,  1.72s/iter]Best score: 17.8349: : 19iter [00:34,  1.71s/iter]Best score: 17.8349: : 20iter [00:36,  1.72s/iter]Best score: 17.8349: : 21iter [00:37,  1.72s/iter]Best score: 17.8349: : 22iter [00:39,  1.72s/iter]Best score: 17.8349: : 23iter [00:41,  1.71s/iter]Best score: 17.8349: : 24iter [00:42,  1.70s/iter]Best score: 17.8349: : 25iter [00:44,  1.71s/iter]Best score: 17.8349: : 26iter [00:46,  1.72s/iter]Best score: 17.8349: : 27iter [00:48,  1.73s/iter]Best score: 17.7445: : 27iter [00:49,  1.73s/iter]Best score: 17.7445: : 28iter [00:49,  1.72s/iter]Best score: 17.7445: : 29iter [00:51,  1.71s/iter]Best score: 15.6560: : 29iter [00:53,  1.71s/iter]Best score: 15.6560: : 30iter [00:53,  1.71s/iter]Best score: 15.6560: : 31iter [00:55,  1.73s/iter]Best score: 15.6560: : 32iter [00:56,  1.74s/iter]Best score: 15.6560: : 33iter [00:58,  1.74s/iter]Best score: 15.6560: : 34iter [01:00,  1.72s/iter]Best score: 15.6560: : 35iter [01:01,  1.71s/iter]Best score: 15.6560: : 36iter [01:03,  1.73s/iter]Best score: 15.6560: : 37iter [01:05,  1.72s/iter]Best score: 15.6560: : 38iter [01:07,  1.71s/iter]Best score: 15.6560: : 39iter [01:08,  1.69s/iter]Best score: 15.6560: : 40iter [01:10,  1.70s/iter]Best score: 15.6560: : 41iter [01:12,  1.71s/iter]Best score: 15.6560: : 42iter [01:13,  1.73s/iter]Best score: 15.6560: : 43iter [01:15,  1.72s/iter]Best score: 4.3210: : 43iter [01:17,  1.72s/iter] Best score: 4.3210: : 44iter [01:17,  1.72s/iter]Best score: 4.3210: : 45iter [01:18,  1.71s/iter]Best score: 4.3210: : 46iter [01:20,  1.69s/iter]Best score: 4.3210: : 47iter [01:22,  1.69s/iter]Best score: 4.3210: : 48iter [01:24,  1.72s/iter]Best score: 4.3210: : 49iter [01:25,  1.71s/iter]
[ITER 50] Current best score: 4.3210
[PARAMS] Current best parameters:
  fx: 571.4318
  fy: 570.2743
  px: 879.8249
  py: 911.2385
  k1: -0.08722555
  k2: 0.02630807
  k3: 0.02077422
  k4: -0.01116778
  p1: 0.00307563
  p2: -0.00299918
  p3: -0.00761370
  p4: -0.00495548
  scale: 3.0416
  rotation: -0.7969
Best score: 4.3210: : 50iter [01:27,  1.78s/iter]Best score: 4.3210: : 51iter [01:29,  1.76s/iter]Best score: 4.3210: : 52iter [01:31,  1.74s/iter]Best score: 4.3210: : 53iter [01:32,  1.74s/iter]Best score: 4.3210: : 54iter [01:34,  1.73s/iter]Best score: 4.3210: : 55iter [01:36,  1.72s/iter]Best score: 4.3210: : 56iter [01:37,  1.71s/iter]Best score: 4.3210: : 57iter [01:39,  1.71s/iter]Best score: 4.3210: : 58iter [01:41,  1.70s/iter]Best score: 4.3210: : 59iter [01:43,  1.70s/iter]Best score: 4.3210: : 60iter [01:44,  1.70s/iter]Best score: 4.3210: : 61iter [01:46,  1.71s/iter]Best score: 4.3210: : 62iter [01:48,  1.71s/iter]Best score: 4.3210: : 63iter [01:49,  1.71s/iter]Best score: 4.3210: : 64iter [01:51,  1.70s/iter]Best score: 4.3210: : 65iter [01:53,  1.70s/iter]Best score: 4.3210: : 66iter [01:55,  1.71s/iter]Best score: 4.3210: : 67iter [01:56,  1.71s/iter]Best score: 4.3210: : 68iter [01:58,  1.73s/iter]Best score: 4.3210: : 69iter [02:00,  1.72s/iter]Best score: 4.3210: : 70iter [02:01,  1.73s/iter]Best score: 4.3210: : 71iter [02:03,  1.73s/iter]Best score: 2.3774: : 71iter [02:05,  1.73s/iter]Best score: 2.3774: : 72iter [02:05,  1.72s/iter]Best score: 2.3774: : 73iter [02:07,  1.73s/iter]Best score: 2.3774: : 74iter [02:08,  1.73s/iter]Best score: 2.3774: : 75iter [02:10,  1.74s/iter]Best score: 2.3774: : 76iter [02:12,  1.73s/iter]Best score: 2.3774: : 77iter [02:14,  1.70s/iter]Best score: 2.3774: : 78iter [02:15,  1.72s/iter]Best score: 2.3774: : 79iter [02:17,  1.72s/iter]Best score: 2.3774: : 80iter [02:19,  1.71s/iter]Best score: 2.3774: : 81iter [02:20,  1.73s/iter]Best score: 2.3774: : 82iter [02:22,  1.75s/iter]Best score: 2.3774: : 83iter [02:24,  1.75s/iter]Best score: 2.3774: : 84iter [02:26,  1.74s/iter]Best score: 2.3774: : 85iter [02:27,  1.73s/iter]Best score: 2.3774: : 86iter [02:29,  1.73s/iter]Best score: 2.3774: : 87iter [02:31,  1.72s/iter]Best score: 2.3774: : 88iter [02:33,  1.72s/iter]Best score: 2.3774: : 89iter [02:34,  1.71s/iter]Best score: 2.3774: : 90iter [02:36,  1.71s/iter]Best score: 2.3774: : 91iter [02:38,  1.71s/iter]Best score: 2.3774: : 92iter [02:39,  1.71s/iter]Best score: 2.3774: : 93iter [02:41,  1.73s/iter]Best score: 2.3774: : 94iter [02:43,  1.71s/iter]Best score: 2.3774: : 95iter [02:45,  1.72s/iter]Best score: 2.3774: : 96iter [02:46,  1.71s/iter]Best score: 2.3774: : 97iter [02:48,  1.71s/iter]Best score: 2.3774: : 98iter [02:50,  1.72s/iter]Best score: 2.3774: : 99iter [02:51,  1.71s/iter]
[ITER 100] Current best score: 3.1434
[PARAMS] Current best parameters:
  fx: 548.9475
  fy: 549.4805
  px: 879.6029
  py: 907.4882
  k1: -0.05654747
  k2: 0.02185790
  k3: 0.01899400
  k4: -0.01106671
  p1: -0.00647940
  p2: -0.00643811
  p3: -0.00402830
  p4: 0.00209818
  scale: 3.0133
  rotation: -0.2983
Best score: 2.3774: : 100iter [02:53,  1.78s/iter]Best score: 2.3774: : 101iter [02:55,  1.74s/iter]Best score: 2.3774: : 102iter [02:57,  1.72s/iter]Best score: 2.3774: : 103iter [02:58,  1.73s/iter]Best score: 2.3774: : 104iter [03:00,  1.71s/iter]Best score: 2.3774: : 105iter [03:02,  1.72s/iter]Best score: 2.3774: : 106iter [03:04,  1.72s/iter]Best score: 2.3774: : 107iter [03:05,  1.73s/iter]Best score: 2.3774: : 108iter [03:07,  1.71s/iter]Best score: 2.3774: : 109iter [03:09,  1.72s/iter]Best score: 2.3774: : 110iter [03:10,  1.70s/iter]Best score: 2.3774: : 111iter [03:12,  1.70s/iter]Best score: 2.3774: : 112iter [03:14,  1.72s/iter]Best score: 2.3774: : 113iter [03:16,  1.72s/iter]Best score: 2.2118: : 113iter [03:17,  1.72s/iter]Best score: 2.2118: : 114iter [03:17,  1.72s/iter]Best score: 2.2118: : 115iter [03:19,  1.71s/iter]Best score: 2.2118: : 116iter [03:21,  1.72s/iter]Best score: 2.2118: : 117iter [03:22,  1.73s/iter]Best score: 2.2118: : 118iter [03:24,  1.74s/iter]Best score: 2.2118: : 119iter [03:26,  1.74s/iter]Best score: 2.2118: : 120iter [03:28,  1.72s/iter]Best score: 2.2118: : 121iter [03:29,  1.71s/iter]Best score: 1.9897: : 121iter [03:31,  1.71s/iter]Best score: 1.9897: : 122iter [03:31,  1.72s/iter]Best score: 1.9897: : 123iter [03:33,  1.71s/iter]Best score: 1.9897: : 124iter [03:34,  1.72s/iter]Best score: 1.9897: : 125iter [03:36,  1.72s/iter]Best score: 1.9897: : 126iter [03:38,  1.70s/iter]Best score: 1.9897: : 127iter [03:40,  1.71s/iter]Best score: 1.9897: : 128iter [03:41,  1.71s/iter]Best score: 1.9897: : 129iter [03:43,  1.72s/iter]Best score: 1.9897: : 130iter [03:45,  1.71s/iter]Best score: 1.8851: : 130iter [03:46,  1.71s/iter]Best score: 1.8851: : 131iter [03:46,  1.71s/iter]Best score: 1.8851: : 132iter [03:48,  1.72s/iter]Best score: 1.8851: : 133iter [03:50,  1.72s/iter]Best score: 1.8851: : 134iter [03:52,  1.73s/iter]Best score: 1.8851: : 135iter [03:53,  1.72s/iter]Best score: 1.8851: : 136iter [03:55,  1.70s/iter]Best score: 1.8851: : 137iter [03:57,  1.69s/iter]Best score: 1.8851: : 138iter [03:58,  1.70s/iter]Best score: 1.8851: : 139iter [04:00,  1.72s/iter]Best score: 1.8851: : 140iter [04:02,  1.72s/iter]Best score: 1.8851: : 141iter [04:04,  1.71s/iter]Best score: 1.8851: : 142iter [04:05,  1.71s/iter]Best score: 1.8851: : 143iter [04:07,  1.70s/iter]Best score: 1.8851: : 144iter [04:09,  1.69s/iter]Best score: 1.8851: : 145iter [04:10,  1.69s/iter]Best score: 1.8851: : 146iter [04:12,  1.69s/iter]Best score: 1.8851: : 147iter [04:14,  1.70s/iter]Best score: 1.8851: : 148iter [04:15,  1.69s/iter]Best score: 1.8851: : 149iter [04:17,  1.71s/iter]
[ITER 150] Current best score: 1.8851
[PARAMS] Current best parameters:
  fx: 531.9274
  fy: 530.5277
  px: 883.8934
  py: 905.1469
  k1: -0.01039998
  k2: -0.00161749
  k3: 0.01237927
  k4: -0.00515743
  p1: -0.00844468
  p2: 0.00322712
  p3: -0.00431134
  p4: 0.00131932
  scale: 3.0039
  rotation: -0.1548
Best score: 1.8851: : 150iter [04:19,  1.75s/iter]Best score: 1.8851: : 151iter [04:21,  1.74s/iter]Best score: 1.8851: : 152iter [04:22,  1.73s/iter]Best score: 1.8851: : 153iter [04:24,  1.73s/iter]Best score: 1.8851: : 154iter [04:26,  1.73s/iter]Best score: 1.8851: : 155iter [04:28,  1.73s/iter]Best score: 1.8851: : 156iter [04:29,  1.73s/iter]Best score: 1.8851: : 157iter [04:31,  1.74s/iter]Best score: 1.5297: : 157iter [04:33,  1.74s/iter]Best score: 1.5297: : 158iter [04:33,  1.74s/iter]Best score: 1.5297: : 159iter [04:35,  1.75s/iter]Best score: 1.5297: : 160iter [04:36,  1.74s/iter]Best score: 1.5297: : 161iter [04:38,  1.74s/iter]Best score: 1.5297: : 162iter [04:40,  1.72s/iter]Best score: 1.5297: : 163iter [04:41,  1.73s/iter]Best score: 1.5297: : 164iter [04:43,  1.71s/iter]Best score: 1.5297: : 165iter [04:45,  1.72s/iter]Best score: 0.9873: : 165iter [04:47,  1.72s/iter]Best score: 0.9873: : 166iter [04:47,  1.70s/iter]Best score: 0.9873: : 167iter [04:48,  1.71s/iter]Best score: 0.9873: : 168iter [04:50,  1.71s/iter]Best score: 0.9873: : 169iter [04:52,  1.69s/iter]Best score: 0.9873: : 170iter [04:53,  1.70s/iter]Best score: 0.9873: : 171iter [04:55,  1.71s/iter]Best score: 0.9873: : 172iter [04:57,  1.70s/iter]Best score: 0.9873: : 173iter [04:58,  1.72s/iter]Best score: 0.9873: : 174iter [05:00,  1.73s/iter]Best score: 0.9873: : 175iter [05:02,  1.73s/iter]Best score: 0.9873: : 176iter [05:04,  1.73s/iter]Best score: 0.9873: : 177iter [05:05,  1.72s/iter]Best score: 0.9873: : 178iter [05:07,  1.72s/iter]Best score: 0.9873: : 179iter [05:09,  1.72s/iter]Best score: 0.9873: : 180iter [05:11,  1.72s/iter]Best score: 0.9873: : 181iter [05:12,  1.71s/iter]Best score: 0.9873: : 182iter [05:14,  1.73s/iter]Best score: 0.9873: : 183iter [05:16,  1.72s/iter]Best score: 0.9873: : 184iter [05:17,  1.72s/iter]Best score: 0.9873: : 185iter [05:19,  1.72s/iter]Best score: 0.9873: : 186iter [05:21,  1.71s/iter]Best score: 0.9873: : 187iter [05:23,  1.74s/iter]Best score: 0.9873: : 188iter [05:24,  1.72s/iter]Best score: 0.9873: : 189iter [05:26,  1.70s/iter]Best score: 0.9873: : 190iter [05:28,  1.71s/iter]Best score: 0.9873: : 191iter [05:29,  1.71s/iter]Best score: 0.9873: : 192iter [05:31,  1.70s/iter]Best score: 0.9873: : 193iter [05:33,  1.70s/iter]Best score: 0.9873: : 194iter [05:34,  1.69s/iter]Best score: 0.9873: : 195iter [05:36,  1.70s/iter]Best score: 0.9873: : 196iter [05:38,  1.71s/iter]Best score: 0.9873: : 197iter [05:40,  1.70s/iter]Best score: 0.9873: : 198iter [05:41,  1.70s/iter]Best score: 0.9873: : 199iter [05:43,  1.71s/iter]
[ITER 200] Current best score: 0.9893
[PARAMS] Current best parameters:
  fx: 531.0997
  fy: 529.1770
  px: 884.9244
  py: 905.9442
  k1: -0.01070378
  k2: -0.00332741
  k3: 0.01555228
  k4: -0.00637084
  p1: -0.00914081
  p2: 0.00435667
  p3: -0.00352782
  p4: 0.00073431
  scale: 3.0143
  rotation: -0.2776
Best score: 0.9873: : 200iter [05:45,  1.77s/iter]Best score: 0.9873: : 201iter [05:47,  1.75s/iter]Best score: 0.9873: : 202iter [05:48,  1.76s/iter]Best score: 0.9873: : 203iter [05:50,  1.76s/iter]Best score: 0.9873: : 204iter [05:52,  1.76s/iter]Best score: 0.9873: : 205iter [05:54,  1.76s/iter]Best score: 0.9873: : 206iter [05:55,  1.74s/iter]Best score: 0.9873: : 207iter [05:57,  1.74s/iter]Best score: 0.9873: : 208iter [05:59,  1.73s/iter]Best score: 0.9873: : 209iter [06:01,  1.75s/iter]Best score: 0.9873: : 210iter [06:02,  1.73s/iter]Best score: 0.9873: : 211iter [06:04,  1.72s/iter]Best score: 0.9873: : 212iter [06:06,  1.71s/iter]Best score: 0.9873: : 213iter [06:07,  1.70s/iter]Best score: 0.9873: : 214iter [06:09,  1.70s/iter]Best score: 0.9873: : 215iter [06:11,  1.70s/iter]Best score: 0.9873: : 216iter [06:12,  1.71s/iter]Best score: 0.9873: : 217iter [06:14,  1.69s/iter]Best score: 0.9771: : 217iter [06:16,  1.69s/iter]Best score: 0.9771: : 218iter [06:16,  1.71s/iter]Best score: 0.9771: : 219iter [06:18,  1.71s/iter]Best score: 0.9771: : 220iter [06:19,  1.71s/iter]Best score: 0.9771: : 221iter [06:21,  1.70s/iter]Best score: 0.9771: : 222iter [06:23,  1.70s/iter]Best score: 0.9771: : 223iter [06:24,  1.70s/iter]Best score: 0.9771: : 224iter [06:26,  1.70s/iter]Best score: 0.9771: : 225iter [06:28,  1.70s/iter]Best score: 0.9771: : 226iter [06:29,  1.70s/iter]Best score: 0.9771: : 227iter [06:31,  1.72s/iter]Best score: 0.9771: : 228iter [06:33,  1.72s/iter]Best score: 0.9771: : 229iter [06:35,  1.71s/iter]Best score: 0.9771: : 230iter [06:36,  1.72s/iter]Best score: 0.9771: : 231iter [06:38,  1.71s/iter]Best score: 0.9771: : 232iter [06:40,  1.72s/iter]Best score: 0.9771: : 233iter [06:42,  1.72s/iter]Best score: 0.9190: : 233iter [06:43,  1.72s/iter]Best score: 0.9190: : 234iter [06:43,  1.74s/iter]Best score: 0.9190: : 235iter [06:45,  1.73s/iter]Best score: 0.9190: : 236iter [06:47,  1.73s/iter]Best score: 0.9190: : 237iter [06:48,  1.72s/iter]Best score: 0.9190: : 238iter [06:50,  1.73s/iter]Best score: 0.9190: : 239iter [06:52,  1.73s/iter]Best score: 0.9190: : 240iter [06:54,  1.72s/iter]Best score: 0.9190: : 241iter [06:55,  1.73s/iter]Best score: 0.9190: : 242iter [06:57,  1.73s/iter]Best score: 0.9190: : 243iter [06:59,  1.73s/iter]Best score: 0.9190: : 244iter [07:01,  1.73s/iter]Best score: 0.9190: : 245iter [07:02,  1.73s/iter]Best score: 0.9190: : 246iter [07:04,  1.74s/iter]Best score: 0.9190: : 247iter [07:06,  1.73s/iter]Best score: 0.9190: : 248iter [07:07,  1.71s/iter]Best score: 0.9190: : 249iter [07:09,  1.72s/iter]
[ITER 250] Current best score: 0.9190
[PARAMS] Current best parameters:
  fx: 530.9182
  fy: 530.3263
  px: 881.7944
  py: 907.8597
  k1: -0.00827318
  k2: -0.00365310
  k3: 0.01040795
  k4: -0.00390962
  p1: -0.00632189
  p2: -0.00315174
  p3: -0.00365404
  p4: 0.00246205
  scale: 3.0264
  rotation: -0.4669
Best score: 0.9190: : 250iter [07:11,  1.77s/iter]Best score: 0.9190: : 251iter [07:13,  1.73s/iter]Best score: 0.7991: : 251iter [07:14,  1.73s/iter]Best score: 0.7991: : 252iter [07:14,  1.72s/iter]Best score: 0.7991: : 253iter [07:16,  1.72s/iter]Best score: 0.7991: : 254iter [07:18,  1.71s/iter]Best score: 0.7991: : 255iter [07:20,  1.72s/iter]Best score: 0.7991: : 256iter [07:21,  1.72s/iter]Best score: 0.7991: : 257iter [07:23,  1.72s/iter]Best score: 0.7991: : 258iter [07:25,  1.73s/iter]Best score: 0.7991: : 259iter [07:27,  1.73s/iter]Best score: 0.7991: : 260iter [07:28,  1.72s/iter]Best score: 0.7991: : 261iter [07:30,  1.71s/iter]Best score: 0.7991: : 262iter [07:32,  1.73s/iter]Best score: 0.7991: : 263iter [07:33,  1.72s/iter]Best score: 0.7991: : 264iter [07:35,  1.71s/iter]Best score: 0.7991: : 265iter [07:37,  1.70s/iter]Best score: 0.7991: : 266iter [07:38,  1.71s/iter]Best score: 0.7991: : 267iter [07:40,  1.71s/iter]Best score: 0.7991: : 268iter [07:42,  1.70s/iter]Best score: 0.7991: : 269iter [07:44,  1.70s/iter]Best score: 0.7991: : 270iter [07:45,  1.70s/iter]Best score: 0.7991: : 271iter [07:47,  1.72s/iter]Best score: 0.7991: : 272iter [07:49,  1.72s/iter]Best score: 0.7991: : 273iter [07:50,  1.73s/iter]Best score: 0.7991: : 274iter [07:52,  1.74s/iter]Best score: 0.7991: : 275iter [07:54,  1.75s/iter]Best score: 0.7991: : 276iter [07:56,  1.76s/iter]Best score: 0.7991: : 277iter [07:58,  1.75s/iter]Best score: 0.7991: : 278iter [07:59,  1.74s/iter]Best score: 0.7991: : 279iter [08:01,  1.74s/iter]Best score: 0.7991: : 280iter [08:03,  1.73s/iter]Best score: 0.7991: : 281iter [08:05,  1.76s/iter]Best score: 0.7991: : 282iter [08:06,  1.74s/iter]Best score: 0.7991: : 283iter [08:08,  1.74s/iter]Best score: 0.7991: : 284iter [08:10,  1.73s/iter]Best score: 0.7991: : 285iter [08:11,  1.72s/iter]Best score: 0.7991: : 286iter [08:13,  1.74s/iter]Best score: 0.6465: : 286iter [08:15,  1.74s/iter]Best score: 0.6465: : 287iter [08:15,  1.73s/iter]Best score: 0.5365: : 287iter [08:17,  1.73s/iter]Best score: 0.5365: : 288iter [08:17,  1.72s/iter]Best score: 0.5365: : 289iter [08:18,  1.74s/iter]Best score: 0.5365: : 290iter [08:20,  1.75s/iter]Best score: 0.5365: : 291iter [08:22,  1.73s/iter]Best score: 0.5365: : 292iter [08:24,  1.74s/iter]Best score: 0.5365: : 293iter [08:25,  1.73s/iter]Best score: 0.5365: : 294iter [08:27,  1.73s/iter]Best score: 0.5365: : 295iter [08:29,  1.72s/iter]Best score: 0.5365: : 296iter [08:30,  1.73s/iter]Best score: 0.5365: : 297iter [08:32,  1.72s/iter]Best score: 0.5365: : 298iter [08:34,  1.72s/iter]Best score: 0.5365: : 299iter [08:36,  1.70s/iter]
[ITER 300] Current best score: 0.5365
[PARAMS] Current best parameters:
  fx: 527.0586
  fy: 526.6070
  px: 883.0042
  py: 909.2896
  k1: 0.00031677
  k2: 0.00779692
  k3: -0.00668565
  k4: 0.00146422
  p1: -0.00336671
  p2: -0.00191447
  p3: 0.00166674
  p4: -0.00035085
  scale: 2.9940
  rotation: -0.4098
Best score: 0.5365: : 300iter [08:37,  1.78s/iter]Best score: 0.5365: : 301iter [08:39,  1.77s/iter]Best score: 0.5365: : 302iter [08:41,  1.74s/iter]Best score: 0.5365: : 303iter [08:43,  1.71s/iter]Best score: 0.5365: : 304iter [08:44,  1.73s/iter]Best score: 0.5365: : 305iter [08:46,  1.73s/iter]Best score: 0.5365: : 306iter [08:48,  1.73s/iter]Best score: 0.5365: : 307iter [08:49,  1.73s/iter]Best score: 0.5365: : 308iter [08:51,  1.71s/iter]Best score: 0.5365: : 309iter [08:53,  1.71s/iter]Best score: 0.5365: : 310iter [08:55,  1.71s/iter]Best score: 0.5365: : 311iter [08:56,  1.71s/iter]Best score: 0.5365: : 312iter [08:58,  1.72s/iter]Best score: 0.5365: : 313iter [09:00,  1.73s/iter]Best score: 0.5365: : 314iter [09:02,  1.74s/iter]Best score: 0.5365: : 315iter [09:03,  1.74s/iter]Best score: 0.5365: : 316iter [09:05,  1.76s/iter]Best score: 0.5365: : 317iter [09:07,  1.77s/iter]Best score: 0.5365: : 318iter [09:09,  1.76s/iter]Best score: 0.5365: : 319iter [09:10,  1.74s/iter]Best score: 0.5365: : 320iter [09:12,  1.73s/iter]Best score: 0.5365: : 321iter [09:14,  1.72s/iter]Best score: 0.5365: : 322iter [09:15,  1.71s/iter]Best score: 0.5129: : 322iter [09:17,  1.71s/iter]Best score: 0.5129: : 323iter [09:17,  1.73s/iter]Best score: 0.5129: : 324iter [09:19,  1.73s/iter]Best score: 0.5129: : 325iter [09:21,  1.73s/iter]Best score: 0.5129: : 326iter [09:22,  1.72s/iter]Best score: 0.5129: : 327iter [09:24,  1.73s/iter]Best score: 0.5129: : 328iter [09:26,  1.73s/iter]Best score: 0.5129: : 329iter [09:28,  1.72s/iter]Best score: 0.5129: : 330iter [09:29,  1.71s/iter]Best score: 0.5129: : 331iter [09:31,  1.70s/iter]Best score: 0.5129: : 332iter [09:33,  1.70s/iter]Best score: 0.5129: : 333iter [09:34,  1.72s/iter]Best score: 0.5129: : 334iter [09:36,  1.72s/iter]Best score: 0.5129: : 335iter [09:38,  1.72s/iter]Best score: 0.5129: : 336iter [09:40,  1.73s/iter]Best score: 0.5129: : 337iter [09:41,  1.72s/iter]Best score: 0.5129: : 338iter [09:43,  1.72s/iter]Best score: 0.5129: : 339iter [09:45,  1.71s/iter]Best score: 0.5129: : 340iter [09:46,  1.73s/iter]Best score: 0.5129: : 341iter [09:48,  1.72s/iter]Best score: 0.5129: : 342iter [09:50,  1.70s/iter]Best score: 0.5129: : 343iter [09:52,  1.71s/iter]Best score: 0.5129: : 344iter [09:53,  1.73s/iter]Best score: 0.5129: : 345iter [09:55,  1.73s/iter]Best score: 0.5129: : 346iter [09:57,  1.73s/iter]Best score: 0.5129: : 347iter [09:58,  1.73s/iter]Best score: 0.5129: : 348iter [10:00,  1.73s/iter]Best score: 0.5129: : 349iter [10:02,  1.71s/iter]
[ITER 350] Current best score: 0.4771
[PARAMS] Current best parameters:
  fx: 530.3755
  fy: 529.7056
  px: 881.4322
  py: 910.7770
  k1: -0.00836468
  k2: 0.01471802
  k3: -0.00887757
  k4: 0.00152409
  p1: -0.00119828
  p2: -0.00519927
  p3: 0.00178090
  p4: 0.00012501
  scale: 2.9966
  rotation: -0.3937
Best score: 0.4771: : 349iter [10:04,  1.71s/iter]Best score: 0.4771: : 350iter [10:04,  1.78s/iter]Best score: 0.4771: : 351iter [10:06,  1.76s/iter]Best score: 0.4771: : 352iter [10:07,  1.76s/iter]Best score: 0.4771: : 353iter [10:09,  1.75s/iter]Best score: 0.4771: : 354iter [10:11,  1.75s/iter]Best score: 0.4771: : 355iter [10:12,  1.75s/iter]Best score: 0.4771: : 356iter [10:14,  1.75s/iter]Best score: 0.4771: : 357iter [10:16,  1.75s/iter]Best score: 0.4771: : 358iter [10:18,  1.74s/iter]Best score: 0.4771: : 359iter [10:19,  1.72s/iter]Best score: 0.4771: : 360iter [10:21,  1.72s/iter]Best score: 0.4771: : 361iter [10:23,  1.72s/iter]Best score: 0.4771: : 362iter [10:25,  1.71s/iter]Best score: 0.4771: : 363iter [10:26,  1.72s/iter]Best score: 0.4771: : 364iter [10:28,  1.73s/iter]Best score: 0.4771: : 365iter [10:30,  1.73s/iter]Best score: 0.4771: : 366iter [10:32,  1.74s/iter]Best score: 0.4771: : 367iter [10:33,  1.72s/iter]Best score: 0.4771: : 368iter [10:35,  1.76s/iter]Best score: 0.4771: : 369iter [10:37,  1.77s/iter]Best score: 0.4771: : 370iter [10:39,  1.77s/iter]Best score: 0.4771: : 371iter [10:40,  1.76s/iter]Best score: 0.4771: : 372iter [10:42,  1.75s/iter]Best score: 0.4771: : 373iter [10:44,  1.73s/iter]Best score: 0.4771: : 374iter [10:45,  1.73s/iter]Best score: 0.4771: : 375iter [10:47,  1.74s/iter]Best score: 0.4771: : 376iter [10:49,  1.74s/iter]Best score: 0.4771: : 377iter [10:51,  1.72s/iter]Best score: 0.4771: : 378iter [10:52,  1.73s/iter]Best score: 0.4771: : 379iter [10:54,  1.71s/iter]Best score: 0.4771: : 380iter [10:56,  1.71s/iter]Best score: 0.4771: : 381iter [10:57,  1.68s/iter]Best score: 0.4771: : 382iter [10:59,  1.71s/iter]Best score: 0.4771: : 383iter [11:01,  1.71s/iter]Best score: 0.4771: : 384iter [11:03,  1.71s/iter]Best score: 0.4771: : 385iter [11:04,  1.72s/iter]Best score: 0.4771: : 386iter [11:06,  1.71s/iter]Best score: 0.4771: : 387iter [11:08,  1.72s/iter]Best score: 0.4771: : 388iter [11:10,  1.73s/iter]Best score: 0.4771: : 389iter [11:11,  1.75s/iter]Best score: 0.4771: : 390iter [11:13,  1.74s/iter]Best score: 0.4571: : 390iter [11:15,  1.74s/iter]Best score: 0.4571: : 391iter [11:15,  1.74s/iter]Best score: 0.4571: : 392iter [11:16,  1.73s/iter]Best score: 0.4571: : 393iter [11:18,  1.73s/iter]Best score: 0.4571: : 394iter [11:20,  1.72s/iter]Best score: 0.4571: : 395iter [11:22,  1.73s/iter]Best score: 0.4571: : 396iter [11:23,  1.71s/iter]Best score: 0.4571: : 397iter [11:25,  1.73s/iter]Best score: 0.4571: : 398iter [11:27,  1.72s/iter]Best score: 0.4571: : 399iter [11:29,  1.72s/iter]
[ITER 400] Current best score: 0.4632
[PARAMS] Current best parameters:
  fx: 526.5933
  fy: 525.6497
  px: 882.1963
  py: 909.0247
  k1: 0.00044785
  k2: 0.00343036
  k3: -0.00005277
  k4: -0.00085734
  p1: -0.00444982
  p2: -0.00225743
  p3: 0.00209806
  p4: -0.00032395
  scale: 2.9945
  rotation: -0.3637
Best score: 0.4571: : 400iter [11:30,  1.78s/iter]Best score: 0.4571: : 401iter [11:32,  1.77s/iter]Best score: 0.4571: : 402iter [11:34,  1.75s/iter]Best score: 0.4571: : 403iter [11:36,  1.76s/iter]Best score: 0.4571: : 404iter [11:37,  1.75s/iter]Best score: 0.4571: : 405iter [11:39,  1.73s/iter]Best score: 0.4571: : 406iter [11:41,  1.74s/iter]Best score: 0.4456: : 406iter [11:43,  1.74s/iter]Best score: 0.4456: : 407iter [11:43,  1.74s/iter]Best score: 0.4456: : 408iter [11:44,  1.73s/iter]Best score: 0.4456: : 409iter [11:46,  1.73s/iter]Best score: 0.4456: : 410iter [11:48,  1.72s/iter]Best score: 0.4456: : 411iter [11:49,  1.72s/iter]Best score: 0.4456: : 412iter [11:51,  1.73s/iter]Best score: 0.4456: : 413iter [11:53,  1.74s/iter]Best score: 0.4456: : 414iter [11:55,  1.73s/iter]Best score: 0.4456: : 415iter [11:56,  1.72s/iter]Best score: 0.4456: : 416iter [11:58,  1.70s/iter]Best score: 0.4456: : 417iter [12:00,  1.70s/iter]Best score: 0.4456: : 418iter [12:01,  1.70s/iter]Best score: 0.4456: : 419iter [12:03,  1.71s/iter]Best score: 0.4456: : 420iter [12:05,  1.70s/iter]Best score: 0.4456: : 421iter [12:07,  1.70s/iter]Best score: 0.4456: : 422iter [12:08,  1.71s/iter]Best score: 0.4456: : 423iter [12:10,  1.71s/iter]Best score: 0.4096: : 423iter [12:12,  1.71s/iter]Best score: 0.4096: : 424iter [12:12,  1.71s/iter]Best score: 0.4096: : 425iter [12:13,  1.72s/iter]Best score: 0.4096: : 426iter [12:15,  1.73s/iter]Best score: 0.4096: : 427iter [12:17,  1.72s/iter]Best score: 0.4096: : 428iter [12:19,  1.72s/iter]Best score: 0.4096: : 429iter [12:20,  1.73s/iter]Best score: 0.4096: : 430iter [12:22,  1.73s/iter]Best score: 0.4096: : 431iter [12:24,  1.73s/iter]Best score: 0.4096: : 432iter [12:26,  1.73s/iter]Best score: 0.4096: : 433iter [12:27,  1.74s/iter]Best score: 0.4096: : 434iter [12:29,  1.74s/iter]Best score: 0.4096: : 435iter [12:31,  1.74s/iter]Best score: 0.4096: : 436iter [12:33,  1.74s/iter]Best score: 0.4096: : 437iter [12:34,  1.75s/iter]Best score: 0.4096: : 438iter [12:36,  1.74s/iter]Best score: 0.4096: : 439iter [12:38,  1.75s/iter]Best score: 0.4096: : 440iter [12:40,  1.74s/iter]Best score: 0.4096: : 441iter [12:41,  1.74s/iter]Best score: 0.4096: : 442iter [12:43,  1.74s/iter]Best score: 0.4096: : 443iter [12:45,  1.73s/iter]Best score: 0.4096: : 444iter [12:46,  1.75s/iter]Best score: 0.4096: : 445iter [12:48,  1.75s/iter]Best score: 0.4096: : 446iter [12:50,  1.74s/iter]Best score: 0.4096: : 447iter [12:52,  1.73s/iter]Best score: 0.4096: : 448iter [12:53,  1.72s/iter]Best score: 0.4096: : 449iter [12:55,  1.74s/iter]
[ITER 450] Current best score: 0.4096
[PARAMS] Current best parameters:
  fx: 530.7814
  fy: 530.1309
  px: 880.1759
  py: 911.1916
  k1: -0.00386353
  k2: 0.00208332
  k3: 0.00111427
  k4: -0.00097929
  p1: 0.00019591
  p2: -0.00668864
  p3: 0.00114515
  p4: 0.00027611
  scale: 2.9944
  rotation: -0.3988
Best score: 0.4096: : 450iter [12:57,  1.80s/iter]Best score: 0.4096: : 451iter [12:59,  1.77s/iter]Best score: 0.4096: : 452iter [13:01,  1.76s/iter]Best score: 0.4096: : 453iter [13:02,  1.76s/iter]Best score: 0.4096: : 454iter [13:04,  1.74s/iter]Best score: 0.4096: : 455iter [13:06,  1.74s/iter]Best score: 0.4096: : 456iter [13:07,  1.73s/iter]Best score: 0.4096: : 457iter [13:09,  1.73s/iter]Best score: 0.4096: : 458iter [13:11,  1.71s/iter]Best score: 0.4096: : 459iter [13:13,  1.72s/iter]Best score: 0.4096: : 460iter [13:14,  1.71s/iter]Best score: 0.4096: : 461iter [13:16,  1.69s/iter]Best score: 0.4096: : 462iter [13:18,  1.70s/iter]Best score: 0.4096: : 463iter [13:19,  1.72s/iter]Best score: 0.4096: : 464iter [13:21,  1.72s/iter]Best score: 0.4096: : 465iter [13:23,  1.73s/iter]Best score: 0.4096: : 466iter [13:25,  1.72s/iter]Best score: 0.4096: : 467iter [13:26,  1.72s/iter]Best score: 0.4096: : 468iter [13:28,  1.72s/iter]Best score: 0.4096: : 469iter [13:30,  1.71s/iter]Best score: 0.4096: : 470iter [13:31,  1.71s/iter]Best score: 0.4096: : 471iter [13:33,  1.72s/iter]Best score: 0.4096: : 472iter [13:35,  1.73s/iter]Best score: 0.4096: : 473iter [13:37,  1.72s/iter]Best score: 0.4096: : 474iter [13:38,  1.73s/iter]Best score: 0.4096: : 475iter [13:40,  1.71s/iter]Best score: 0.4096: : 476iter [13:42,  1.72s/iter]Best score: 0.4096: : 477iter [13:44,  1.74s/iter]Best score: 0.4096: : 478iter [13:45,  1.72s/iter]Best score: 0.4096: : 479iter [13:47,  1.72s/iter]Best score: 0.4096: : 480iter [13:49,  1.73s/iter]Best score: 0.4096: : 481iter [13:50,  1.73s/iter]Best score: 0.4096: : 482iter [13:52,  1.72s/iter]Best score: 0.4096: : 483iter [13:54,  1.71s/iter]Best score: 0.4096: : 484iter [13:56,  1.71s/iter]Best score: 0.4096: : 485iter [13:57,  1.71s/iter]Best score: 0.4096: : 486iter [13:59,  1.72s/iter]Best score: 0.4096: : 487iter [14:01,  1.71s/iter]Best score: 0.4096: : 488iter [14:02,  1.73s/iter]Best score: 0.4096: : 489iter [14:04,  1.72s/iter]Best score: 0.4096: : 490iter [14:06,  1.72s/iter]Best score: 0.4096: : 491iter [14:08,  1.73s/iter]Best score: 0.4096: : 492iter [14:09,  1.72s/iter]Best score: 0.4096: : 493iter [14:11,  1.72s/iter]Best score: 0.4096: : 494iter [14:13,  1.73s/iter]Best score: 0.4096: : 495iter [14:15,  1.74s/iter]Best score: 0.4096: : 496iter [14:16,  1.73s/iter]Best score: 0.4096: : 497iter [14:18,  1.72s/iter]Best score: 0.4096: : 498iter [14:20,  1.73s/iter]Best score: 0.4096: : 499iter [14:21,  1.73s/iter]
[ITER 500] Current best score: 0.4096
[PARAMS] Current best parameters:
  fx: 530.7814
  fy: 530.1309
  px: 880.1759
  py: 911.1916
  k1: -0.00386353
  k2: 0.00208332
  k3: 0.00111427
  k4: -0.00097929
  p1: 0.00019591
  p2: -0.00668864
  p3: 0.00114515
  p4: 0.00027611
  scale: 2.9944
  rotation: -0.3988
Best score: 0.4096: : 500iter [14:23,  1.78s/iter]Best score: 0.4096: : 500iter [15:49,  1.90s/iter]

[📈] Forcing polish step after 500 iterations...
  Polish did not improve score: 0.399006
  Success: False
  Tangential distortion: p1=0.000113, p2=-0.006642, p3=0.001098, p4=0.000227
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3990
[✅] Parameters copied to: /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 530.7814166272213
    fy: 530.1309372287769
    px: 880.1758527360987
    py: 911.1916018649962
    k1: -0.003760642774091031
    k2: 0.002234640648114821
    k3: 0.0011426349240056938
    k4: -0.0010577086715615452
    p1: 0.00011287323655953505
    p2: -0.006642214376400105
    p3: 0.0010980653427352099
    p4: 0.00022679111158130976
    scale: 2.9943673568402636
    rotation: -0.39878528510289113
    mmpp: 3.4093858784498225
    nmpp: 3409385.878449823

[🔄] Converting 2962 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
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[✅] LUT written to: /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-SL-008_W008(6)_2025-11-20' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-SL-008_W008(6)_2025-11-20/calibration/MDCP3D-SL-008_W008(6)_2025-11-20_unknown.lut
[🏁] Finished processing MDCP3D-SL-008_W008(6)_2025-11-20


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