
🟢 Starting Calibration Pipeline

=== Processing MDC015_W038_2025-05-14 ===

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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 3 las0.zip files to process
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                                                                        [A📁 Processing Rigs: 100%|██████████| 3/3 [08:58<00:00, 178.79s/it]📁 Processing Rigs: 100%|██████████| 3/3 [08:58<00:00, 179.50s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 4573 frames from 3 rigs to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 1415
[📋] 1500: 100 frames selected from 1775
[📋] 500: 100 frames selected from 1383
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized/rig_frames.json

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

[INFO] Using 60 total sampled bright frames
Averaging bright frames:   0%|          | 0/60 [00:00<?, ?it/s]Averaging bright frames:   7%|▋         | 4/60 [00:00<00:01, 37.76it/s]Averaging bright frames:  15%|█▌        | 9/60 [00:00<00:01, 41.74it/s]Averaging bright frames:  23%|██▎       | 14/60 [00:00<00:01, 43.61it/s]Averaging bright frames:  32%|███▏      | 19/60 [00:00<00:00, 43.94it/s]Averaging bright frames:  40%|████      | 24/60 [00:00<00:00, 45.13it/s]Averaging bright frames:  48%|████▊     | 29/60 [00:00<00:00, 45.40it/s]Averaging bright frames:  57%|█████▋    | 34/60 [00:00<00:00, 45.83it/s]Averaging bright frames:  65%|██████▌   | 39/60 [00:00<00:00, 45.71it/s]Averaging bright frames:  73%|███████▎  | 44/60 [00:00<00:00, 45.89it/s]Averaging bright frames:  82%|████████▏ | 49/60 [00:01<00:00, 45.92it/s]Averaging bright frames:  90%|█████████ | 54/60 [00:01<00:00, 46.44it/s]Averaging bright frames:  98%|█████████▊| 59/60 [00:01<00:00, 46.40it/s]                                                                        Total time to process (v2): 0.05
[RESULT] Combined: Center=(1000, 736), Radius=840
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:43,  2.26it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:43,  2.26it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:23,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:23,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:23,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:23,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:22,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:22,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:22,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:22,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:22,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:21,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:21,  4.14it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 26.15it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 26.15it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.018 | Best: {'fx': 519.192} (0.018):  13%|█▎        | 13/100 [00:00<00:03, 26.15it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 519.192} (0.018):  14%|█▍        | 14/100 [00:00<00:03, 26.15it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.034 | Best: {'fx': 526.263} (0.034):  15%|█▌        | 15/100 [00:00<00:03, 26.15it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.029 | Best: {'fx': 526.263} (0.034):  16%|█▌        | 16/100 [00:00<00:03, 26.15it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.029 | Best: {'fx': 526.263} (0.034):  17%|█▋        | 17/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.023 | Best: {'fx': 526.263} (0.034):  17%|█▋        | 17/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.017 | Best: {'fx': 526.263} (0.034):  18%|█▊        | 18/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.021 | Best: {'fx': 526.263} (0.034):  19%|█▉        | 19/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.019 | Best: {'fx': 526.263} (0.034):  20%|██        | 20/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  21%|██        | 21/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  22%|██▏       | 22/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.017 | Best: {'fx': 526.263} (0.034):  23%|██▎       | 23/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  24%|██▍       | 24/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.015 | Best: {'fx': 526.263} (0.034):  25%|██▌       | 25/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  26%|██▌       | 26/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  27%|██▋       | 27/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  28%|██▊       | 28/100 [00:00<00:02, 31.29it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  29%|██▉       | 29/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  29%|██▉       | 29/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.014 | Best: {'fx': 526.263} (0.034):  30%|███       | 30/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  31%|███       | 31/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  32%|███▏      | 32/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  33%|███▎      | 33/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  34%|███▍      | 34/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.014 | Best: {'fx': 526.263} (0.034):  35%|███▌      | 35/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.013 | Best: {'fx': 526.263} (0.034):  36%|███▌      | 36/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.007 | Best: {'fx': 526.263} (0.034):  37%|███▋      | 37/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.034):  38%|███▊      | 38/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 526.263} (0.034):  39%|███▉      | 39/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 526.263} (0.034):  40%|████      | 40/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.001 | Best: {'fx': 526.263} (0.034):  41%|████      | 41/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 526.263} (0.034):  42%|████▏     | 42/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 526.263} (0.034):  43%|████▎     | 43/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 526.263} (0.034):  44%|████▍     | 44/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 526.263} (0.034):  45%|████▌     | 45/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.001 | Best: {'fx': 526.263} (0.034):  46%|████▌     | 46/100 [00:00<00:01, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.004 | Best: {'fx': 526.263} (0.034):  47%|████▋     | 47/100 [00:00<00:00, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.001 | Best: {'fx': 526.263} (0.034):  48%|████▊     | 48/100 [00:01<00:00, 53.70it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.001 | Best: {'fx': 526.263} (0.034):  49%|████▉     | 49/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 526.263} (0.034):  49%|████▉     | 49/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 526.263} (0.034):  50%|█████     | 50/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 526.263} (0.034):  51%|█████     | 51/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 526.263} (0.034):  52%|█████▏    | 52/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  53%|█████▎    | 53/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 526.263} (0.034):  54%|█████▍    | 54/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  55%|█████▌    | 55/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  56%|█████▌    | 56/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  57%|█████▋    | 57/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  58%|█████▊    | 58/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  59%|█████▉    | 59/100 [00:01<00:00, 89.00it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  60%|██████    | 60/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  60%|██████    | 60/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  61%|██████    | 61/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  62%|██████▏   | 62/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  63%|██████▎   | 63/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  64%|██████▍   | 64/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  65%|██████▌   | 65/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  66%|██████▌   | 66/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  67%|██████▋   | 67/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  68%|██████▊   | 68/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  69%|██████▉   | 69/100 [00:01<00:00, 85.06it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 526.263} (0.034):  70%|███████   | 70/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  70%|███████   | 70/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  71%|███████   | 71/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  72%|███████▏  | 72/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  73%|███████▎  | 73/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  74%|███████▍  | 74/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  75%|███████▌  | 75/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  76%|███████▌  | 76/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  77%|███████▋  | 77/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  78%|███████▊  | 78/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  79%|███████▉  | 79/100 [00:01<00:00, 86.97it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  80%|████████  | 80/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  80%|████████  | 80/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  81%|████████  | 81/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  82%|████████▏ | 82/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  83%|████████▎ | 83/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  84%|████████▍ | 84/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  85%|████████▌ | 85/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  86%|████████▌ | 86/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  87%|████████▋ | 87/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  88%|████████▊ | 88/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  89%|████████▉ | 89/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  90%|█████████ | 90/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  91%|█████████ | 91/100 [00:01<00:00, 70.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  92%|█████████▏| 92/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  92%|█████████▏| 92/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  93%|█████████▎| 93/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  94%|█████████▍| 94/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  95%|█████████▌| 95/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  96%|█████████▌| 96/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  97%|█████████▋| 97/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  98%|█████████▊| 98/100 [00:01<00:00, 80.93it/s]Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 526.263} (0.034):  99%|█████████▉| 99/100 [00:01<00:00, 80.93it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.001 | Best: {'fx': 501.263} (0.001):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.001 | Best: {'fx': 501.263} (0.001):   2%|▏         | 1/50 [00:00<00:08,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.001 | Best: {'fx': 502.283} (0.001):   2%|▏         | 1/50 [00:00<00:08,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.003 | Best: {'fx': 504.324} (0.003):   4%|▍         | 2/50 [00:00<00:08,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.001 | Best: {'fx': 504.324} (0.003):   6%|▌         | 3/50 [00:00<00:07,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.004 | Best: {'fx': 506.365} (0.004):   8%|▊         | 4/50 [00:00<00:07,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.004 | Best: {'fx': 506.365} (0.004):  10%|█         | 5/50 [00:00<00:07,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.006 | Best: {'fx': 510.446} (0.006):  12%|█▏        | 6/50 [00:00<00:07,  5.96it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.006 | Best: {'fx': 510.446} (0.006):  14%|█▍        | 7/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.004 | Best: {'fx': 510.446} (0.006):  14%|█▍        | 7/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.004 | Best: {'fx': 510.446} (0.006):  16%|█▌        | 8/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.005 | Best: {'fx': 510.446} (0.006):  18%|█▊        | 9/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.006 | Best: {'fx': 511.467} (0.006):  20%|██        | 10/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.011 | Best: {'fx': 515.548} (0.011):  22%|██▏       | 11/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.010 | Best: {'fx': 515.548} (0.011):  24%|██▍       | 12/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.007 | Best: {'fx': 515.548} (0.011):  26%|██▌       | 13/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.009 | Best: {'fx': 515.548} (0.011):  28%|██▊       | 14/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.015 | Best: {'fx': 516.569} (0.015):  30%|███       | 15/50 [00:00<00:01, 28.69it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.015 | Best: {'fx': 516.569} (0.015):  32%|███▏      | 16/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.015 | Best: {'fx': 517.589} (0.015):  32%|███▏      | 16/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.021 | Best: {'fx': 521.671} (0.021):  34%|███▍      | 17/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.020 | Best: {'fx': 521.671} (0.021):  36%|███▌      | 18/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.018 | Best: {'fx': 521.671} (0.021):  38%|███▊      | 19/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.018 | Best: {'fx': 521.671} (0.021):  40%|████      | 20/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.033 | Best: {'fx': 526.773} (0.033):  42%|████▏     | 21/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.023 | Best: {'fx': 526.773} (0.033):  44%|████▍     | 22/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.022 | Best: {'fx': 526.773} (0.033):  46%|████▌     | 23/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.025 | Best: {'fx': 526.773} (0.033):  48%|████▊     | 24/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.038 | Best: {'fx': 528.814} (0.038):  50%|█████     | 25/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.030 | Best: {'fx': 528.814} (0.038):  52%|█████▏    | 26/50 [00:00<00:00, 44.80it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.030 | Best: {'fx': 528.814} (0.038):  54%|█████▍    | 27/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.035 | Best: {'fx': 528.814} (0.038):  54%|█████▍    | 27/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.033 | Best: {'fx': 528.814} (0.038):  56%|█████▌    | 28/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.030 | Best: {'fx': 528.814} (0.038):  58%|█████▊    | 29/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.030 | Best: {'fx': 528.814} (0.038):  60%|██████    | 30/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.026 | Best: {'fx': 528.814} (0.038):  62%|██████▏   | 31/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.033 | Best: {'fx': 528.814} (0.038):  64%|██████▍   | 32/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.037 | Best: {'fx': 528.814} (0.038):  66%|██████▌   | 33/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.028 | Best: {'fx': 528.814} (0.038):  68%|██████▊   | 34/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.027 | Best: {'fx': 528.814} (0.038):  70%|███████   | 35/50 [00:00<00:00, 62.94it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.027 | Best: {'fx': 528.814} (0.038):  72%|███████▏  | 36/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.027 | Best: {'fx': 528.814} (0.038):  72%|███████▏  | 36/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.025 | Best: {'fx': 528.814} (0.038):  74%|███████▍  | 37/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.018 | Best: {'fx': 528.814} (0.038):  76%|███████▌  | 38/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.021 | Best: {'fx': 528.814} (0.038):  78%|███████▊  | 39/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.021 | Best: {'fx': 528.814} (0.038):  80%|████████  | 40/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.021 | Best: {'fx': 528.814} (0.038):  82%|████████▏ | 41/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.024 | Best: {'fx': 528.814} (0.038):  84%|████████▍ | 42/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.022 | Best: {'fx': 528.814} (0.038):  86%|████████▌ | 43/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.020 | Best: {'fx': 528.814} (0.038):  88%|████████▊ | 44/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.019 | Best: {'fx': 528.814} (0.038):  90%|█████████ | 45/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.019 | Best: {'fx': 528.814} (0.038):  92%|█████████▏| 46/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.019 | Best: {'fx': 528.814} (0.038):  94%|█████████▍| 47/50 [00:00<00:00, 70.02it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.019 | Best: {'fx': 528.814} (0.038):  96%|█████████▌| 48/50 [00:00<00:00, 82.46it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.021 | Best: {'fx': 528.814} (0.038):  96%|█████████▌| 48/50 [00:00<00:00, 82.46it/s]Brute | Start: {'fx': 526.263} (0.034) | Current: 0.019 | Best: {'fx': 528.814} (0.038):  98%|█████████▊| 49/50 [00:00<00:00, 82.46it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 528.8136466707896
    px: 1000.0
    py: 736.0
    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): 14 frames will be used for optimization
  Rigs: 1000(1000mm):7/20, 1500(1500mm):7/20
  Total: 14/40 frames used for optimization
Initial score: 528.3570: 0iter [00:00, ?iter/s]Initial score: 528.3570: 1iter [00:02,  2.15s/iter]Initial score: 528.3570: 2iter [00:03,  1.45s/iter]Initial score: 528.3570: 3iter [00:04,  1.24s/iter]Initial score: 528.3570: 4iter [00:05,  1.11s/iter]Initial score: 528.3570: 5iter [00:05,  1.05s/iter]Initial score: 528.3570: 6iter [00:06,  1.01s/iter]Initial score: 528.3570: 7iter [00:07,  1.04iter/s]Initial score: 528.3570: 8iter [00:08,  1.03iter/s]Initial score: 528.3570: 9iter [00:09,  1.03iter/s]Initial score: 528.3570: 10iter [00:10,  1.05iter/s]Initial score: 528.3570: 11iter [00:11,  1.04iter/s]Initial score: 528.3570: 12iter [00:12,  1.04iter/s]Initial score: 528.3570: 13iter [00:13,  1.06iter/s]Initial score: 528.3570: 14iter [00:14,  1.04iter/s]Initial score: 528.3570: 15iter [00:15,  1.05iter/s]Initial score: 528.3570: 16iter [00:16,  1.03iter/s]Initial score: 528.3570: 17iter [00:17,  1.04iter/s]Initial score: 528.3570: 18iter [00:18,  1.02iter/s]Initial score: 528.3570: 19iter [00:19,  1.01iter/s]Initial score: 528.3570: 20iter [00:20,  1.03iter/s]Initial score: 528.3570: 21iter [00:21,  1.02iter/s]Initial score: 528.3570: 22iter [00:22,  1.05iter/s]Initial score: 528.3570: 23iter [00:23,  1.02iter/s]Initial score: 528.3570: 24iter [00:24,  1.02iter/s]Initial score: 528.3570: 25iter [00:25,  1.04iter/s]Initial score: 528.3570: 26iter [00:26,  1.03iter/s]Initial score: 528.3570: 27iter [00:27,  1.03iter/s]Initial score: 528.3570: 28iter [00:28,  1.01iter/s]Initial score: 528.3570: 29iter [00:29,  1.01iter/s]Initial score: 528.3570: 30iter [00:30,  1.00s/iter]Initial score: 528.3570: 31iter [00:31,  1.01s/iter]Initial score: 528.3570: 32iter [00:32,  1.01iter/s]Best score: 372.0159: : 32iter [00:33,  1.01iter/s] Best score: 372.0159: : 33iter [00:33,  1.01iter/s]Best score: 372.0159: : 34iter [00:34,  1.01iter/s]Best score: 372.0159: : 35iter [00:35,  1.01iter/s]Best score: 372.0159: : 36iter [00:36,  1.03iter/s]Best score: 372.0159: : 37iter [00:36,  1.05iter/s]Best score: 372.0159: : 38iter [00:37,  1.05iter/s]Best score: 372.0159: : 39iter [00:38,  1.05iter/s]Best score: 372.0159: : 40iter [00:39,  1.04iter/s]Best score: 372.0159: : 41iter [00:40,  1.03iter/s]Best score: 372.0159: : 42iter [00:41,  1.03iter/s]Best score: 372.0159: : 43iter [00:42,  1.01iter/s]Best score: 372.0159: : 44iter [00:43,  1.05iter/s]Best score: 372.0159: : 45iter [00:44,  1.07iter/s]Best score: 372.0159: : 46iter [00:45,  1.05iter/s]Best score: 372.0159: : 47iter [00:46,  1.05iter/s]Best score: 372.0159: : 48iter [00:47,  1.06iter/s]Best score: 372.0159: : 49iter [00:48,  1.07iter/s]
[ITER 50] Current best score: 372.0159
[PARAMS] Current best parameters:
  fx: 534.4045
  fy: 530.3111
  px: 1001.6267
  py: 752.0037
  k1: -0.01216074
  k2: 0.00570930
  k3: -0.02659194
  k4: 0.01163992
  p1: 0.00278583
  p2: -0.00376805
  p3: -0.00640140
  p4: 0.00559718
  scale: 3.4093
  rotation: 0.5613
Best score: 372.0159: : 50iter [00:49,  1.03iter/s]Best score: 372.0159: : 51iter [00:50,  1.04iter/s]Best score: 372.0159: : 52iter [00:51,  1.01iter/s]Best score: 372.0159: : 53iter [00:52,  1.03iter/s]Best score: 372.0159: : 54iter [00:53,  1.03iter/s]Best score: 372.0159: : 55iter [00:54,  1.04iter/s]Best score: 372.0159: : 56iter [00:55,  1.02iter/s]Best score: 372.0159: : 57iter [00:56,  1.05iter/s]Best score: 372.0159: : 58iter [00:57,  1.03iter/s]Best score: 372.0159: : 59iter [00:58,  1.02iter/s]Best score: 372.0159: : 60iter [00:59,  1.00iter/s]Best score: 372.0159: : 61iter [01:00,  1.02iter/s]Best score: 372.0159: : 62iter [01:01,  1.06iter/s]Best score: 372.0159: : 63iter [01:01,  1.06iter/s]Best score: 368.8292: : 63iter [01:03,  1.06iter/s]Best score: 368.8292: : 64iter [01:03,  1.03iter/s]Best score: 368.8292: : 65iter [01:04,  1.01iter/s]Best score: 368.8292: : 66iter [01:05,  1.02iter/s]Best score: 368.8292: : 67iter [01:05,  1.02iter/s]Best score: 368.8292: : 68iter [01:06,  1.04iter/s]Best score: 368.8292: : 69iter [01:07,  1.05iter/s]Best score: 368.8292: : 70iter [01:08,  1.07iter/s]Best score: 368.8292: : 71iter [01:09,  1.08iter/s]Best score: 368.8292: : 72iter [01:10,  1.07iter/s]Best score: 368.8292: : 73iter [01:11,  1.08iter/s]Best score: 368.8292: : 74iter [01:12,  1.07iter/s]Best score: 368.8292: : 75iter [01:13,  1.08iter/s]Best score: 368.8292: : 76iter [01:14,  1.02iter/s]Best score: 368.8292: : 77iter [01:15,  1.02iter/s]Best score: 368.8292: : 78iter [01:16,  1.01iter/s]Best score: 368.8292: : 79iter [01:17,  1.03iter/s]Best score: 368.8292: : 80iter [01:18,  1.04iter/s]Best score: 368.8292: : 81iter [01:19,  1.05iter/s]Best score: 368.8292: : 82iter [01:20,  1.08iter/s]Best score: 368.8292: : 83iter [01:21,  1.06iter/s]Best score: 368.8292: : 84iter [01:22,  1.06iter/s]Best score: 368.8292: : 85iter [01:23,  1.04iter/s]Best score: 264.0288: : 85iter [01:24,  1.04iter/s]Best score: 264.0288: : 86iter [01:24,  1.02iter/s]Best score: 164.4397: : 86iter [01:25,  1.02iter/s]Best score: 164.4397: : 87iter [01:25,  1.00iter/s]Best score: 164.4397: : 88iter [01:26,  1.02iter/s]Best score: 164.4397: : 89iter [01:27,  1.00iter/s]Best score: 85.9291: : 89iter [01:27,  1.00iter/s] Best score: 85.9291: : 90iter [01:27,  1.05iter/s]Best score: 85.9291: : 91iter [01:29,  1.01iter/s]Best score: 85.9291: : 92iter [01:30,  1.01iter/s]Best score: 85.9291: : 93iter [01:31,  1.01s/iter]Best score: 75.2743: : 93iter [01:31,  1.01s/iter]Best score: 75.2743: : 94iter [01:31,  1.02iter/s]Best score: 75.2743: : 95iter [01:32,  1.02iter/s]Best score: 75.2743: : 96iter [01:33,  1.05iter/s]Best score: 75.2743: : 97iter [01:34,  1.04iter/s]Best score: 75.2743: : 98iter [01:35,  1.01iter/s]Best score: 75.2743: : 99iter [01:36,  1.04iter/s]
[ITER 100] Current best score: 75.2743
[PARAMS] Current best parameters:
  fx: 515.7055
  fy: 512.8959
  px: 997.3700
  py: 745.5991
  k1: 0.04753849
  k2: 0.00480713
  k3: -0.02964701
  k4: 0.00916553
  p1: -0.00182580
  p2: -0.00117136
  p3: -0.00314915
  p4: 0.00106571
  scale: 2.9439
  rotation: 0.5803
Best score: 75.2743: : 100iter [01:37,  1.00iter/s]Best score: 75.2743: : 101iter [01:38,  1.03iter/s]Best score: 75.2743: : 102iter [01:39,  1.03iter/s]Best score: 75.2743: : 103iter [01:40,  1.04iter/s]Best score: 75.2743: : 104iter [01:41,  1.05iter/s]Best score: 18.1172: : 104iter [01:42,  1.05iter/s]Best score: 18.1172: : 105iter [01:42,  1.02iter/s]Best score: 18.1172: : 106iter [01:43,  1.01iter/s]Best score: 18.1172: : 107iter [01:44,  1.03iter/s]Best score: 18.1172: : 108iter [01:45,  1.06iter/s]Best score: 18.1172: : 109iter [01:46,  1.04iter/s]Best score: 18.1172: : 110iter [01:47,  1.03iter/s]Best score: 18.1172: : 111iter [01:48,  1.04iter/s]Best score: 18.1172: : 112iter [01:49,  1.03iter/s]Best score: 18.1172: : 113iter [01:50,  1.03iter/s]Best score: 18.1172: : 114iter [01:51,  1.06iter/s]Best score: 18.1172: : 115iter [01:52,  1.07iter/s]Best score: 18.1172: : 116iter [01:53,  1.05iter/s]Best score: 18.1172: : 117iter [01:54,  1.05iter/s]Best score: 18.1172: : 118iter [01:55,  1.04iter/s]Best score: 18.1172: : 119iter [01:56,  1.01iter/s]Best score: 18.1172: : 120iter [01:57,  1.05iter/s]Best score: 18.1172: : 121iter [01:57,  1.06iter/s]Best score: 18.1172: : 122iter [01:58,  1.08iter/s]Best score: 18.1172: : 123iter [01:59,  1.06iter/s]Best score: 18.1172: : 124iter [02:00,  1.06iter/s]Best score: 18.1172: : 125iter [02:01,  1.06iter/s]Best score: 18.1172: : 126iter [02:02,  1.03iter/s]Best score: 18.1172: : 127iter [02:03,  1.02iter/s]Best score: 18.1172: : 128iter [02:04,  1.03iter/s]Best score: 18.1172: : 129iter [02:05,  1.04iter/s]Best score: 18.1172: : 130iter [02:06,  1.08iter/s]Best score: 18.1172: : 131iter [02:07,  1.05iter/s]Best score: 18.1172: : 132iter [02:08,  1.06iter/s]Best score: 18.1172: : 133iter [02:09,  1.03iter/s]Best score: 18.1172: : 134iter [02:10,  1.01iter/s]Best score: 18.1172: : 135iter [02:11,  1.01iter/s]Best score: 18.1172: : 136iter [02:12,  1.01s/iter]Best score: 18.1172: : 137iter [02:13,  1.01iter/s]Best score: 18.1172: : 138iter [02:14,  1.02s/iter]Best score: 18.1172: : 139iter [02:15,  1.01s/iter]Best score: 18.1172: : 140iter [02:16,  1.00s/iter]Best score: 18.1172: : 141iter [02:17,  1.02iter/s]Best score: 18.1172: : 142iter [02:18,  1.00iter/s]Best score: 18.1172: : 143iter [02:19,  1.04iter/s]Best score: 18.1172: : 144iter [02:20,  1.02iter/s]Best score: 18.1172: : 145iter [02:21,  1.03iter/s]Best score: 18.1172: : 146iter [02:22,  1.02iter/s]Best score: 18.1172: : 147iter [02:23,  1.04iter/s]Best score: 18.1172: : 148iter [02:24,  1.01iter/s]Best score: 18.1172: : 149iter [02:25,  1.02iter/s]
[ITER 150] Current best score: 18.1172
[PARAMS] Current best parameters:
  fx: 523.0771
  fy: 521.0722
  px: 998.2496
  py: 744.5427
  k1: 0.03561634
  k2: 0.01319762
  k3: -0.02982223
  k4: 0.00807101
  p1: -0.00160008
  p2: 0.00018153
  p3: 0.00002881
  p4: -0.00051255
  scale: 2.8321
  rotation: -0.5329
Best score: 18.1172: : 150iter [02:26,  1.01s/iter]Best score: 18.1172: : 151iter [02:27,  1.00s/iter]Best score: 18.1172: : 152iter [02:28,  1.01iter/s]Best score: 18.1172: : 153iter [02:29,  1.01s/iter]Best score: 18.1172: : 154iter [02:30,  1.01iter/s]Best score: 18.1172: : 155iter [02:31,  1.04iter/s]Best score: 18.1172: : 156iter [02:32,  1.02iter/s]Best score: 18.1172: : 157iter [02:33,  1.04iter/s]Best score: 14.7234: : 157iter [02:34,  1.04iter/s]Best score: 14.7234: : 158iter [02:34,  1.07iter/s]Best score: 14.7234: : 159iter [02:34,  1.07iter/s]Best score: 14.7234: : 160iter [02:35,  1.04iter/s]Best score: 14.7234: : 161iter [02:36,  1.03iter/s]Best score: 14.7234: : 162iter [02:37,  1.03iter/s]Best score: 14.7234: : 163iter [02:38,  1.04iter/s]Best score: 14.7234: : 164iter [02:39,  1.07iter/s]Best score: 14.7234: : 165iter [02:40,  1.06iter/s]Best score: 14.7234: : 166iter [02:41,  1.05iter/s]Best score: 14.7234: : 167iter [02:42,  1.07iter/s]Best score: 14.7234: : 168iter [02:43,  1.04iter/s]Best score: 14.7234: : 169iter [02:44,  1.04iter/s]Best score: 14.7234: : 170iter [02:45,  1.04iter/s]Best score: 14.7234: : 171iter [02:46,  1.04iter/s]Best score: 14.7234: : 172iter [02:47,  1.04iter/s]Best score: 14.7234: : 173iter [02:48,  1.03iter/s]Best score: 14.7234: : 174iter [02:49,  1.01iter/s]Best score: 14.7234: : 175iter [02:50,  1.01iter/s]Best score: 14.7234: : 176iter [02:51,  1.01iter/s]Best score: 14.7234: : 177iter [02:52,  1.05iter/s]Best score: 14.7234: : 178iter [02:53,  1.03iter/s]Best score: 14.7234: : 179iter [02:54,  1.05iter/s]Best score: 14.7234: : 180iter [02:55,  1.05iter/s]Best score: 14.7234: : 181iter [02:56,  1.06iter/s]Best score: 14.7234: : 182iter [02:57,  1.09iter/s]Best score: 14.7234: : 183iter [02:58,  1.05iter/s]Best score: 14.7234: : 184iter [02:59,  1.02iter/s]Best score: 14.7234: : 185iter [02:59,  1.04iter/s]Best score: 14.7234: : 186iter [03:00,  1.06iter/s]Best score: 8.5047: : 186iter [03:01,  1.06iter/s] Best score: 8.5047: : 187iter [03:01,  1.03iter/s]Best score: 8.5047: : 188iter [03:02,  1.01iter/s]Best score: 8.5047: : 189iter [03:03,  1.04iter/s]Best score: 8.5047: : 190iter [03:04,  1.07iter/s]Best score: 8.5047: : 191iter [03:05,  1.06iter/s]Best score: 8.5047: : 192iter [03:06,  1.07iter/s]Best score: 8.5047: : 193iter [03:07,  1.04iter/s]Best score: 8.5047: : 194iter [03:08,  1.04iter/s]Best score: 8.5047: : 195iter [03:09,  1.01iter/s]Best score: 8.5047: : 196iter [03:10,  1.00iter/s]Best score: 8.5047: : 197iter [03:11,  1.01iter/s]Best score: 8.5047: : 198iter [03:12,  1.04iter/s]Best score: 8.5047: : 199iter [03:13,  1.05iter/s]
[ITER 200] Current best score: 8.5047
[PARAMS] Current best parameters:
  fx: 529.9930
  fy: 529.7862
  px: 997.3823
  py: 743.4960
  k1: 0.02629346
  k2: 0.00980110
  k3: -0.02493483
  k4: 0.00676841
  p1: -0.00201988
  p2: -0.00386766
  p3: -0.00038009
  p4: 0.00033231
  scale: 2.8301
  rotation: -0.2478
Best score: 8.5047: : 200iter [03:14,  1.00iter/s]Best score: 8.5047: : 201iter [03:15,  1.01s/iter]Best score: 8.5047: : 202iter [03:16,  1.00iter/s]Best score: 8.5047: : 203iter [03:17,  1.01iter/s]Best score: 8.5047: : 204iter [03:18,  1.02iter/s]Best score: 8.5047: : 205iter [03:19,  1.06iter/s]Best score: 8.5047: : 206iter [03:20,  1.04iter/s]Best score: 8.5047: : 207iter [03:21,  1.07iter/s]Best score: 8.5047: : 208iter [03:22,  1.05iter/s]Best score: 8.5047: : 209iter [03:23,  1.07iter/s]Best score: 8.5047: : 210iter [03:24,  1.06iter/s]Best score: 8.5047: : 211iter [03:25,  1.05iter/s]Best score: 8.5047: : 212iter [03:26,  1.02iter/s]Best score: 8.5047: : 213iter [03:27,  1.02iter/s]Best score: 8.5047: : 214iter [03:28,  1.02iter/s]Best score: 8.5047: : 215iter [03:29,  1.02iter/s]Best score: 8.5047: : 216iter [03:30,  1.01iter/s]Best score: 8.5047: : 217iter [03:31,  1.00iter/s]Best score: 8.5047: : 218iter [03:32,  1.01iter/s]Best score: 8.5047: : 219iter [03:32,  1.03iter/s]Best score: 8.5047: : 220iter [03:33,  1.04iter/s]Best score: 8.5047: : 221iter [03:34,  1.04iter/s]Best score: 8.5047: : 222iter [03:35,  1.08iter/s]Best score: 8.5047: : 223iter [03:36,  1.05iter/s]Best score: 8.5047: : 224iter [03:37,  1.05iter/s]Best score: 8.5047: : 225iter [03:38,  1.04iter/s]Best score: 8.5047: : 226iter [03:39,  1.01iter/s]Best score: 8.5047: : 227iter [03:40,  1.04iter/s]Best score: 8.5047: : 228iter [03:41,  1.05iter/s]Best score: 8.5047: : 229iter [03:42,  1.05iter/s]Best score: 8.5047: : 230iter [03:43,  1.04iter/s]Best score: 8.5047: : 231iter [03:44,  1.00s/iter]Best score: 8.5047: : 232iter [03:45,  1.03iter/s]Best score: 8.1044: : 232iter [03:46,  1.03iter/s]Best score: 8.1044: : 233iter [03:46,  1.02iter/s]Best score: 8.1044: : 234iter [03:47,  1.03iter/s]Best score: 8.1044: : 235iter [03:48,  1.05iter/s]Best score: 8.1044: : 236iter [03:49,  1.04iter/s]Best score: 8.1044: : 237iter [03:50,  1.02iter/s]Best score: 8.1044: : 238iter [03:51,  1.01iter/s]Best score: 8.1044: : 239iter [03:52,  1.02iter/s]Best score: 8.1044: : 240iter [03:53,  1.02iter/s]Best score: 8.1044: : 241iter [03:54,  1.03iter/s]Best score: 8.1044: : 242iter [03:55,  1.02iter/s]Best score: 8.1044: : 243iter [03:56,  1.06iter/s]Best score: 8.1044: : 244iter [03:57,  1.04iter/s]Best score: 8.1044: : 245iter [03:57,  1.07iter/s]Best score: 8.1044: : 246iter [03:58,  1.05iter/s]Best score: 8.1044: : 247iter [03:59,  1.04iter/s]Best score: 8.1044: : 248iter [04:00,  1.03iter/s]Best score: 8.1044: : 249iter [04:01,  1.03iter/s]
[ITER 250] Current best score: 8.1044
[PARAMS] Current best parameters:
  fx: 526.4801
  fy: 525.6705
  px: 998.7105
  py: 743.4262
  k1: 0.01773254
  k2: 0.00564246
  k3: -0.01505117
  k4: 0.00418911
  p1: -0.00167171
  p2: 0.00026116
  p3: -0.00100254
  p4: 0.00028991
  scale: 2.9220
  rotation: -0.4203
Best score: 8.1044: : 250iter [04:02,  1.01iter/s]Best score: 8.1044: : 251iter [04:03,  1.02iter/s]Best score: 7.8512: : 251iter [04:04,  1.02iter/s]Best score: 7.8512: : 252iter [04:04,  1.05iter/s]Best score: 7.8512: : 253iter [04:05,  1.04iter/s]Best score: 7.8512: : 254iter [04:06,  1.03iter/s]Best score: 7.8512: : 255iter [04:07,  1.06iter/s]Best score: 7.8512: : 256iter [04:08,  1.08iter/s]Best score: 7.8512: : 257iter [04:09,  1.05iter/s]Best score: 7.8512: : 258iter [04:10,  1.05iter/s]Best score: 7.8512: : 259iter [04:11,  1.06iter/s]Best score: 7.8512: : 260iter [04:12,  1.02iter/s]Best score: 7.8512: : 261iter [04:13,  1.03iter/s]Best score: 7.8512: : 262iter [04:14,  1.03iter/s]Best score: 7.8512: : 263iter [04:15,  1.05iter/s]Best score: 7.8512: : 264iter [04:16,  1.06iter/s]Best score: 7.8512: : 265iter [04:17,  1.05iter/s]Best score: 7.8512: : 266iter [04:18,  1.04iter/s]Best score: 7.8512: : 267iter [04:19,  1.03iter/s]Best score: 7.8512: : 268iter [04:20,  1.04iter/s]Best score: 7.8512: : 269iter [04:21,  1.02iter/s]Best score: 7.8512: : 270iter [04:22,  1.04iter/s]Best score: 7.8512: : 271iter [04:23,  1.03iter/s]Best score: 7.8512: : 272iter [04:24,  1.03iter/s]Best score: 7.8512: : 273iter [04:25,  1.00iter/s]Best score: 7.8512: : 274iter [04:26,  1.00iter/s]Best score: 7.8512: : 275iter [04:27,  1.02iter/s]Best score: 7.8512: : 276iter [04:28,  1.01iter/s]Best score: 7.8512: : 277iter [04:29,  1.02iter/s]Best score: 7.8512: : 278iter [04:30,  1.00iter/s]Best score: 6.2209: : 278iter [04:31,  1.00iter/s]Best score: 6.2209: : 279iter [04:31,  1.00iter/s]Best score: 6.2209: : 280iter [04:32,  1.02iter/s]Best score: 6.2209: : 281iter [04:32,  1.05iter/s]Best score: 6.2209: : 282iter [04:33,  1.03iter/s]Best score: 6.2209: : 283iter [04:34,  1.07iter/s]Best score: 6.2209: : 284iter [04:35,  1.04iter/s]Best score: 6.2209: : 285iter [04:36,  1.03iter/s]Best score: 6.2209: : 286iter [04:37,  1.04iter/s]Best score: 6.2209: : 287iter [04:38,  1.06iter/s]Best score: 6.2209: : 288iter [04:39,  1.08iter/s]Best score: 6.2209: : 289iter [04:40,  1.05iter/s]Best score: 6.2209: : 290iter [04:41,  1.04iter/s]Best score: 6.2209: : 291iter [04:42,  1.02iter/s]Best score: 6.2209: : 292iter [04:43,  1.05iter/s]Best score: 6.2209: : 293iter [04:44,  1.06iter/s]Best score: 6.2209: : 294iter [04:45,  1.04iter/s]Best score: 6.2209: : 295iter [04:46,  1.02iter/s]Best score: 6.2209: : 296iter [04:47,  1.01iter/s]Best score: 6.2209: : 297iter [04:48,  1.02iter/s]Best score: 6.2209: : 298iter [04:49,  1.03iter/s]Best score: 6.2209: : 299iter [04:50,  1.05iter/s]
[ITER 300] Current best score: 6.2209
[PARAMS] Current best parameters:
  fx: 520.0360
  fy: 519.5211
  px: 998.4469
  py: 742.8008
  k1: 0.02084562
  k2: 0.01758200
  k3: -0.02037535
  k4: 0.00424650
  p1: -0.00447488
  p2: -0.00012769
  p3: 0.00138815
  p4: -0.00047298
  scale: 2.8633
  rotation: -0.2540
Best score: 6.2209: : 300iter [04:51,  1.02iter/s]Best score: 6.2209: : 301iter [04:52,  1.03iter/s]Best score: 6.2209: : 302iter [04:53,  1.02iter/s]Best score: 5.0802: : 302iter [04:54,  1.02iter/s]Best score: 5.0802: : 303iter [04:54,  1.01s/iter]Best score: 5.0802: : 304iter [04:55,  1.01iter/s]Best score: 5.0802: : 305iter [04:56,  1.00iter/s]Best score: 5.0802: : 306iter [04:57,  1.01iter/s]Best score: 5.0802: : 307iter [04:58,  1.05iter/s]Best score: 5.0802: : 308iter [04:59,  1.03iter/s]Best score: 5.0802: : 309iter [05:00,  1.02iter/s]Best score: 5.0802: : 310iter [05:01,  1.03iter/s]Best score: 5.0802: : 311iter [05:02,  1.01iter/s]Best score: 5.0802: : 312iter [05:03,  1.02s/iter]Best score: 5.0802: : 313iter [05:04,  1.01s/iter]Best score: 5.0802: : 314iter [05:05,  1.01iter/s]Best score: 5.0802: : 315iter [05:06,  1.03iter/s]Best score: 5.0802: : 316iter [05:06,  1.05iter/s]Best score: 5.0802: : 317iter [05:07,  1.04iter/s]Best score: 5.0802: : 318iter [05:08,  1.03iter/s]Best score: 5.0802: : 319iter [05:09,  1.02iter/s]Best score: 5.0802: : 320iter [05:10,  1.03iter/s]Best score: 5.0802: : 321iter [05:11,  1.03iter/s]Best score: 5.0802: : 322iter [05:12,  1.03iter/s]Best score: 5.0802: : 323iter [05:13,  1.06iter/s]Best score: 5.0802: : 324iter [05:14,  1.05iter/s]Best score: 5.0802: : 325iter [05:15,  1.06iter/s]Best score: 5.0802: : 326iter [05:16,  1.05iter/s]Best score: 5.0802: : 327iter [05:17,  1.04iter/s]Best score: 5.0802: : 328iter [05:18,  1.04iter/s]Best score: 5.0802: : 329iter [05:19,  1.05iter/s]Best score: 5.0802: : 330iter [05:20,  1.05iter/s]Best score: 5.0802: : 331iter [05:21,  1.06iter/s]Best score: 5.0802: : 332iter [05:22,  1.04iter/s]Best score: 5.0802: : 333iter [05:23,  1.03iter/s]Best score: 5.0802: : 334iter [05:24,  1.04iter/s]Best score: 5.0802: : 335iter [05:25,  1.03iter/s]Best score: 5.0802: : 336iter [05:26,  1.05iter/s]Best score: 5.0802: : 337iter [05:27,  1.03iter/s]Best score: 5.0802: : 338iter [05:28,  1.05iter/s]Best score: 5.0802: : 339iter [05:28,  1.05iter/s]Best score: 5.0802: : 340iter [05:29,  1.04iter/s]Best score: 5.0802: : 341iter [05:30,  1.05iter/s]Best score: 5.0802: : 342iter [05:31,  1.05iter/s]Best score: 5.0802: : 343iter [05:32,  1.04iter/s]Best score: 5.0802: : 344iter [05:33,  1.03iter/s]Best score: 5.0802: : 345iter [05:34,  1.05iter/s]Best score: 5.0802: : 346iter [05:35,  1.04iter/s]Best score: 5.0802: : 347iter [05:36,  1.01iter/s]Best score: 5.0802: : 348iter [05:37,  1.00s/iter]Best score: 5.0802: : 349iter [05:38,  1.02s/iter]
[ITER 350] Current best score: 5.0802
[PARAMS] Current best parameters:
  fx: 517.7071
  fy: 517.5839
  px: 997.8227
  py: 743.3972
  k1: 0.01620495
  k2: 0.03043013
  k3: -0.02798150
  k4: 0.00567950
  p1: -0.00383873
  p2: -0.00207580
  p3: 0.00415077
  p4: -0.00049398
  scale: 2.8603
  rotation: -0.3029
Best score: 5.0802: : 350iter [05:39,  1.01s/iter]Best score: 5.0802: : 351iter [05:40,  1.00iter/s]Best score: 5.0802: : 352iter [05:41,  1.01iter/s]Best score: 5.0802: : 353iter [05:42,  1.00iter/s]Best score: 5.0802: : 354iter [05:43,  1.03iter/s]Best score: 5.0802: : 355iter [05:44,  1.03iter/s]Best score: 5.0802: : 356iter [05:45,  1.04iter/s]Best score: 4.9276: : 356iter [05:46,  1.04iter/s]Best score: 4.9276: : 357iter [05:46,  1.07iter/s]Best score: 4.9276: : 358iter [05:47,  1.04iter/s]Best score: 4.9276: : 359iter [05:48,  1.02iter/s]Best score: 4.9276: : 360iter [05:49,  1.05iter/s]Best score: 4.9276: : 361iter [05:50,  1.01iter/s]Best score: 4.9276: : 362iter [05:51,  1.03iter/s]Best score: 4.9276: : 363iter [05:52,  1.04iter/s]Best score: 4.9276: : 364iter [05:53,  1.02iter/s]Best score: 4.9276: : 365iter [05:54,  1.04iter/s]Best score: 4.9276: : 366iter [05:55,  1.03iter/s]Best score: 4.9276: : 367iter [05:56,  1.03iter/s]Best score: 4.9276: : 368iter [05:57,  1.06iter/s]Best score: 4.9276: : 369iter [05:58,  1.06iter/s]Best score: 4.9276: : 370iter [05:59,  1.06iter/s]Best score: 4.8846: : 370iter [06:00,  1.06iter/s]Best score: 4.8846: : 371iter [06:00,  1.03iter/s]Best score: 4.8846: : 372iter [06:01,  1.03iter/s]Best score: 3.2243: : 372iter [06:02,  1.03iter/s]Best score: 3.2243: : 373iter [06:02,  1.03iter/s]Best score: 3.2243: : 374iter [06:02,  1.04iter/s]Best score: 3.2243: : 375iter [06:03,  1.06iter/s]Best score: 3.2243: : 376iter [06:04,  1.06iter/s]Best score: 3.2243: : 377iter [06:05,  1.05iter/s]Best score: 3.2243: : 378iter [06:06,  1.05iter/s]Best score: 3.2243: : 379iter [06:07,  1.02iter/s]Best score: 3.2243: : 380iter [06:08,  1.03iter/s]Best score: 3.2243: : 381iter [06:09,  1.04iter/s]Best score: 3.2243: : 382iter [06:10,  1.04iter/s]Best score: 3.2243: : 383iter [06:11,  1.03iter/s]Best score: 3.2243: : 384iter [06:12,  1.04iter/s]Best score: 3.2243: : 385iter [06:13,  1.06iter/s]Best score: 3.2243: : 386iter [06:14,  1.07iter/s]Best score: 3.2243: : 387iter [06:15,  1.05iter/s]Best score: 3.2243: : 388iter [06:16,  1.04iter/s]Best score: 3.2243: : 389iter [06:17,  1.04iter/s]Best score: 3.2243: : 390iter [06:18,  1.04iter/s]Best score: 3.2243: : 391iter [06:19,  1.08iter/s]Best score: 3.2243: : 392iter [06:20,  1.07iter/s]Best score: 3.2243: : 393iter [06:21,  1.05iter/s]Best score: 3.2243: : 394iter [06:22,  1.04iter/s]Best score: 3.2243: : 395iter [06:23,  1.03iter/s]Best score: 3.2243: : 396iter [06:24,  1.03iter/s]Best score: 3.2243: : 397iter [06:25,  1.02iter/s]Best score: 3.2243: : 398iter [06:25,  1.03iter/s]Best score: 3.2243: : 399iter [06:26,  1.03iter/s]
[ITER 400] Current best score: 3.7078
[PARAMS] Current best parameters:
  fx: 523.7879
  fy: 523.3414
  px: 999.1272
  py: 743.1027
  k1: 0.00998677
  k2: 0.02672878
  k3: -0.02499901
  k4: 0.00521700
  p1: -0.00362101
  p2: 0.00136664
  p3: 0.00018978
  p4: -0.00013148
  scale: 2.8664
  rotation: -0.5755
Best score: 3.2243: : 400iter [06:27,  1.01iter/s]Best score: 3.2243: : 401iter [06:28,  1.02iter/s]Best score: 3.2243: : 402iter [06:29,  1.05iter/s]Best score: 3.2243: : 403iter [06:30,  1.08iter/s]Best score: 3.2243: : 404iter [06:31,  1.04iter/s]Best score: 3.2243: : 405iter [06:32,  1.03iter/s]Best score: 3.2243: : 406iter [06:33,  1.02iter/s]Best score: 3.2243: : 407iter [06:34,  1.04iter/s]Best score: 3.2243: : 408iter [06:35,  1.03iter/s]Best score: 3.2243: : 409iter [06:36,  1.03iter/s]Best score: 3.2243: : 410iter [06:37,  1.04iter/s]Best score: 3.2243: : 411iter [06:38,  1.03iter/s]Best score: 3.2243: : 412iter [06:39,  1.03iter/s]Best score: 3.2243: : 413iter [06:40,  1.04iter/s]Best score: 3.2243: : 414iter [06:41,  1.05iter/s]Best score: 3.2243: : 415iter [06:42,  1.06iter/s]Best score: 3.2243: : 416iter [06:43,  1.04iter/s]Best score: 3.2243: : 417iter [06:44,  1.03iter/s]Best score: 3.2243: : 418iter [06:45,  1.02iter/s]Best score: 3.2243: : 419iter [06:46,  1.01iter/s]Best score: 3.2243: : 420iter [06:47,  1.01iter/s]Best score: 3.2243: : 421iter [06:48,  1.03iter/s]Best score: 3.2243: : 422iter [06:49,  1.03iter/s]Best score: 3.2243: : 423iter [06:50,  1.04iter/s]Best score: 3.2243: : 424iter [06:51,  1.02iter/s]Best score: 3.2243: : 425iter [06:52,  1.03iter/s]Best score: 3.2243: : 426iter [06:52,  1.06iter/s]Best score: 3.2243: : 427iter [06:53,  1.08iter/s]Best score: 3.2243: : 428iter [06:54,  1.04iter/s]Best score: 3.2243: : 429iter [06:55,  1.04iter/s]Best score: 3.2243: : 430iter [06:56,  1.05iter/s]Best score: 3.2243: : 431iter [06:57,  1.02iter/s]Best score: 3.2243: : 432iter [06:58,  1.02iter/s]Best score: 3.2243: : 433iter [06:59,  1.05iter/s]Best score: 3.2243: : 434iter [07:00,  1.03iter/s]Best score: 3.2243: : 435iter [07:01,  1.03iter/s]Best score: 3.2243: : 436iter [07:02,  1.04iter/s]Best score: 3.2243: : 437iter [07:03,  1.03iter/s]Best score: 3.2243: : 438iter [07:04,  1.03iter/s]Best score: 3.2243: : 439iter [07:05,  1.05iter/s]Best score: 3.2243: : 440iter [07:06,  1.04iter/s]Best score: 3.2243: : 441iter [07:07,  1.04iter/s]Best score: 3.2243: : 442iter [07:08,  1.05iter/s]Best score: 3.2243: : 443iter [07:09,  1.05iter/s]Best score: 3.2243: : 444iter [07:10,  1.07iter/s]Best score: 3.2243: : 445iter [07:11,  1.06iter/s]Best score: 3.2243: : 446iter [07:12,  1.04iter/s]Best score: 3.2243: : 447iter [07:13,  1.03iter/s]Best score: 3.2243: : 448iter [07:14,  1.03iter/s]Best score: 3.2243: : 449iter [07:15,  1.01iter/s]
[ITER 450] Current best score: 3.5825
[PARAMS] Current best parameters:
  fx: 523.7560
  fy: 523.3230
  px: 998.5625
  py: 742.8904
  k1: 0.01251053
  k2: 0.01760217
  k3: -0.01836161
  k4: 0.00383571
  p1: -0.00407981
  p2: -0.00036993
  p3: 0.00352171
  p4: -0.00049635
  scale: 2.8834
  rotation: -0.3475
Best score: 3.2243: : 450iter [07:16,  1.01s/iter]Best score: 3.2243: : 451iter [07:17,  1.01s/iter]Best score: 3.2243: : 452iter [07:18,  1.01s/iter]Best score: 3.2243: : 453iter [07:19,  1.00s/iter]Best score: 3.2243: : 454iter [07:20,  1.01iter/s]Best score: 3.2243: : 455iter [07:21,  1.04iter/s]Best score: 3.2243: : 456iter [07:22,  1.02iter/s]Best score: 3.2243: : 457iter [07:23,  1.02iter/s]Best score: 3.2243: : 458iter [07:24,  1.04iter/s]Best score: 3.2243: : 459iter [07:25,  1.00iter/s]Best score: 3.2243: : 460iter [07:26,  1.00iter/s]Best score: 3.2243: : 461iter [07:27,  1.02iter/s]Best score: 3.2243: : 462iter [07:28,  1.03iter/s]Best score: 3.2243: : 463iter [07:28,  1.06iter/s]Best score: 3.2243: : 464iter [07:29,  1.07iter/s]Best score: 3.2243: : 465iter [07:30,  1.07iter/s]Best score: 3.2243: : 466iter [07:31,  1.06iter/s]Best score: 3.2243: : 467iter [07:32,  1.08iter/s]Best score: 3.2243: : 468iter [07:33,  1.05iter/s]Best score: 3.2243: : 469iter [07:34,  1.07iter/s]Best score: 3.2243: : 470iter [07:35,  1.09iter/s]Best score: 3.2243: : 471iter [07:36,  1.06iter/s]Best score: 3.2243: : 472iter [07:37,  1.04iter/s]Best score: 3.2243: : 473iter [07:38,  1.06iter/s]Best score: 3.2243: : 474iter [07:39,  1.05iter/s]Best score: 3.2243: : 475iter [07:40,  1.05iter/s]Best score: 3.2243: : 476iter [07:41,  1.06iter/s]Best score: 3.2243: : 477iter [07:42,  1.05iter/s]Best score: 3.2243: : 478iter [07:43,  1.04iter/s]Best score: 3.2243: : 479iter [07:44,  1.03iter/s]Best score: 3.2243: : 480iter [07:45,  1.04iter/s]Best score: 3.2243: : 481iter [07:45,  1.07iter/s]Best score: 3.0773: : 481iter [07:46,  1.07iter/s]Best score: 3.0773: : 482iter [07:46,  1.06iter/s]Best score: 3.0773: : 483iter [07:47,  1.05iter/s]Best score: 3.0773: : 484iter [07:48,  1.01iter/s]Best score: 3.0773: : 485iter [07:49,  1.02iter/s]Best score: 3.0773: : 486iter [07:50,  1.04iter/s]Best score: 3.0773: : 487iter [07:51,  1.03iter/s]Best score: 3.0773: : 488iter [07:52,  1.06iter/s]Best score: 3.0773: : 489iter [07:53,  1.08iter/s]Best score: 3.0773: : 490iter [07:54,  1.05iter/s]Best score: 3.0773: : 491iter [07:55,  1.04iter/s]Best score: 3.0773: : 492iter [07:56,  1.07iter/s]Best score: 3.0773: : 493iter [07:57,  1.05iter/s]Best score: 3.0773: : 494iter [07:58,  1.05iter/s]Best score: 3.0773: : 495iter [07:59,  1.07iter/s]Best score: 3.0773: : 496iter [08:00,  1.06iter/s]Best score: 3.0773: : 497iter [08:01,  1.06iter/s]Best score: 3.0773: : 498iter [08:02,  1.05iter/s]Best score: 3.0773: : 499iter [08:03,  1.05iter/s]
[ITER 500] Current best score: 3.0773
[PARAMS] Current best parameters:
  fx: 522.7677
  fy: 522.5579
  px: 998.3882
  py: 742.7570
  k1: 0.01100682
  k2: 0.02025070
  k3: -0.01921102
  k4: 0.00386657
  p1: -0.00459573
  p2: -0.00136616
  p3: 0.00268872
  p4: -0.00007104
  scale: 2.8865
  rotation: -0.5421
Best score: 3.0773: : 500iter [08:04,  1.02iter/s]Best score: 3.0773: : 500iter [10:18,  1.24s/iter]

[📈] Forcing polish step after 500 iterations...
  Polish did not improve score: 2.962722
  Success: False
  Tangential distortion: p1=-0.004629, p2=-0.001028, p3=0.002655, p4=-0.000133
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 2.9627
[✅] Parameters copied to: /working/MDC015_W038_2025-05-14/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 522.7676642979013
    fy: 522.5579111704515
    px: 998.3882329017059
    py: 742.757039516145
    k1: 0.011030518924799023
    k2: 0.02027439617473152
    k3: -0.019185894493807577
    k4: 0.003849280416083624
    p1: -0.004629225636151388
    p2: -0.001028452737940368
    p3: 0.0026554578408299148
    p4: -0.00013310819049070147
    scale: 2.8864897011648614
    rotation: -0.542085621211969
    mmpp: 1.2551023122055454
    nmpp: 1255102.3122055454

[🔄] Converting 4573 frames to PTD, creating debug images for 300 calibration frames...
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                                                                  [A📁 Rigs: 100%|██████████| 3/3 [00:45<00:00, 15.06s/rig]📁 Rigs: 100%|██████████| 3/3 [00:45<00:00, 15.20s/rig]
[✅] Saved PTDs: /working/MDC015_W038_2025-05-14/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC015_W038_2025-05-14/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 82620/3080448 [00:00<00:03, 826196.85px/s]📤 Writing LUT:   6%|▌         | 178067/3080448 [00:00<00:03, 901645.71px/s]📤 Writing LUT:   9%|▉         | 272949/3080448 [00:00<00:03, 923182.77px/s]📤 Writing LUT:  12%|█▏        | 365938/3080448 [00:00<00:02, 925804.90px/s]📤 Writing LUT:  15%|█▍        | 459486/3080448 [00:00<00:02, 929290.06px/s]📤 Writing LUT:  18%|█▊        | 553498/3080448 [00:00<00:02, 932970.22px/s]📤 Writing LUT:  21%|██        | 647314/3080448 [00:00<00:02, 934664.97px/s]📤 Writing LUT:  24%|██▍       | 742631/3080448 [00:00<00:02, 940554.17px/s]📤 Writing LUT:  27%|██▋       | 838229/3080448 [00:00<00:02, 945374.65px/s]📤 Writing LUT:  30%|███       | 935310/3080448 [00:01<00:02, 953224.49px/s]📤 Writing LUT:  33%|███▎      | 1030633/3080448 [00:01<00:02, 952123.20px/s]📤 Writing LUT:  37%|███▋      | 1126397/3080448 [00:01<00:02, 953799.00px/s]📤 Writing LUT:  40%|███▉      | 1221778/3080448 [00:01<00:01, 948314.68px/s]📤 Writing LUT:  43%|████▎     | 1316617/3080448 [00:01<00:01, 944448.83px/s]📤 Writing LUT:  46%|████▌     | 1411069/3080448 [00:01<00:01, 939452.35px/s]📤 Writing LUT:  49%|████▉     | 1505848/3080448 [00:01<00:01, 941941.14px/s]📤 Writing LUT:  52%|█████▏    | 1600806/3080448 [00:01<00:01, 944223.70px/s]📤 Writing LUT:  55%|█████▌    | 1695235/3080448 [00:01<00:01, 935576.75px/s]📤 Writing LUT:  58%|█████▊    | 1792621/3080448 [00:01<00:01, 946967.97px/s]📤 Writing LUT:  61%|██████▏   | 1887342/3080448 [00:02<00:01, 941282.31px/s]📤 Writing LUT:  64%|██████▍   | 1981492/3080448 [00:02<00:01, 934216.41px/s]📤 Writing LUT:  67%|██████▋   | 2074935/3080448 [00:02<00:01, 931613.15px/s]📤 Writing LUT:  70%|███████   | 2168110/3080448 [00:02<00:00, 926676.32px/s]📤 Writing LUT:  73%|███████▎  | 2260789/3080448 [00:02<00:00, 923335.98px/s]📤 Writing LUT:  76%|███████▋  | 2353130/3080448 [00:02<00:00, 921927.20px/s]📤 Writing LUT:  79%|███████▉  | 2446883/3080448 [00:02<00:00, 926569.17px/s]📤 Writing LUT:  82%|████████▏ | 2539547/3080448 [00:02<00:00, 923931.28px/s]📤 Writing LUT:  86%|████████▌ | 2634489/3080448 [00:02<00:00, 931521.16px/s]📤 Writing LUT:  89%|████████▊ | 2727650/3080448 [00:02<00:00, 927678.02px/s]📤 Writing LUT:  92%|█████████▏| 2822333/3080448 [00:03<00:00, 933387.60px/s]📤 Writing LUT:  95%|█████████▍| 2915681/3080448 [00:03<00:00, 931376.07px/s]📤 Writing LUT:  98%|█████████▊| 3011777/3080448 [00:03<00:00, 940211.75px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 935122.72px/s]
[✅] LUT written to: /working/MDC015_W038_2025-05-14/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W038_2025-05-14' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W038_2025-05-14/calibration/MDC015_W038_2025-05-14_unknown.lut
[🏁] Finished processing MDC015_W038_2025-05-14


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