🟢 Starting Calibration Pipeline

=== Processing MDC103_W050_2025-05-07 ===

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

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📁 Processing Rigs:  50%|█████     | 1/2 [02:17<02:17, 137.72s/it]

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→ 1000 (1195 images): 100%|██████████| 1195/1195 [02:21<00:00, 11.73it/s]

                                                                         
📁 Processing Rigs: 100%|██████████| 2/2 [04:39<00:00, 139.85s/it]
📁 Processing Rigs: 100%|██████████| 2/2 [04:39<00:00, 139.53s/it]
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2348 frames from 2 rigs to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 1153
[📋] 1000: 100 frames selected from 1195
[💾] Saved RigFrameDataset to /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC103_W050_2025-05-07/calibration/debug/1_digitized/rig_frames.json

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

[INFO] Using 40 total sampled bright frames

Averaging bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Averaging bright frames:  10%|█         | 4/40 [00:00<00:01, 34.97it/s]
Averaging bright frames:  22%|██▎       | 9/40 [00:00<00:00, 43.21it/s]
Averaging bright frames:  38%|███▊      | 15/40 [00:00<00:00, 46.64it/s]
Averaging bright frames:  52%|█████▎    | 21/40 [00:00<00:00, 48.00it/s]
Averaging bright frames:  65%|██████▌   | 26/40 [00:00<00:00, 46.69it/s]
Averaging bright frames:  78%|███████▊  | 31/40 [00:00<00:00, 47.07it/s]
Averaging bright frames:  90%|█████████ | 36/40 [00:00<00:00, 46.88it/s]
                                                                        
Total time to process (v2): 0.05
[RESULT] Combined: Center=(1078, 790), Radius=906

  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<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:09,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:09,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  26%|██▌       | 26/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.002 | Best: {'fx': 505.051} (0.002):  27%|██▋       | 27/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.004 | Best: {'fx': 526.263} (0.004):  28%|██▊       | 28/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.004):  29%|██▉       | 29/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.002 | Best: {'fx': 526.263} (0.004):  30%|███       | 30/100 [00:00<00:00, 88.25it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.002 | Best: {'fx': 526.263} (0.004):  31%|███       | 31/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.000 | Best: {'fx': 526.263} (0.004):  31%|███       | 31/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 540.404} (0.008):  32%|███▏      | 32/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.005 | Best: {'fx': 540.404} (0.008):  33%|███▎      | 33/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.007 | Best: {'fx': 540.404} (0.008):  34%|███▍      | 34/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.003 | Best: {'fx': 540.404} (0.008):  35%|███▌      | 35/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.006 | Best: {'fx': 540.404} (0.008):  36%|███▌      | 36/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.008 | Best: {'fx': 561.616} (0.008):  37%|███▋      | 37/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 575.758} (0.011):  38%|███▊      | 38/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 582.828} (0.012):  39%|███▉      | 39/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.013 | Best: {'fx': 596.97} (0.013):  40%|████      | 40/100 [00:00<00:00, 108.63it/s] 
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 596.97} (0.013):  41%|████      | 41/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 596.97} (0.013):  42%|████▏     | 42/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.013 | Best: {'fx': 589.899} (0.013):  43%|████▎     | 43/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 589.899} (0.013):  44%|████▍     | 44/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 589.899} (0.013):  45%|████▌     | 45/100 [00:00<00:00, 108.63it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 589.899} (0.013):  46%|████▌     | 46/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 589.899} (0.013):  46%|████▌     | 46/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 589.899} (0.013):  47%|████▋     | 47/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 589.899} (0.013):  48%|████▊     | 48/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.012 | Best: {'fx': 589.899} (0.013):  49%|████▉     | 49/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 589.899} (0.013):  50%|█████     | 50/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  51%|█████     | 51/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 589.899} (0.013):  52%|█████▏    | 52/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.011 | Best: {'fx': 589.899} (0.013):  53%|█████▎    | 53/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  54%|█████▍    | 54/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  55%|█████▌    | 55/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  56%|█████▌    | 56/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  57%|█████▋    | 57/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  58%|█████▊    | 58/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  59%|█████▉    | 59/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.010 | Best: {'fx': 589.899} (0.013):  60%|██████    | 60/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  61%|██████    | 61/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  62%|██████▏   | 62/100 [00:00<00:00, 122.88it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  63%|██████▎   | 63/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  63%|██████▎   | 63/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  64%|██████▍   | 64/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  65%|██████▌   | 65/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  66%|██████▌   | 66/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  67%|██████▋   | 67/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  68%|██████▊   | 68/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  69%|██████▉   | 69/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  70%|███████   | 70/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  71%|███████   | 71/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  72%|███████▏  | 72/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  73%|███████▎  | 73/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  74%|███████▍  | 74/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  75%|███████▌  | 75/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  76%|███████▌  | 76/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  77%|███████▋  | 77/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  78%|███████▊  | 78/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  79%|███████▉  | 79/100 [00:00<00:00, 131.78it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  80%|████████  | 80/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  80%|████████  | 80/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  81%|████████  | 81/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  82%|████████▏ | 82/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  83%|████████▎ | 83/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  84%|████████▍ | 84/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  85%|████████▌ | 85/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  86%|████████▌ | 86/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  87%|████████▋ | 87/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  88%|████████▊ | 88/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  89%|████████▉ | 89/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  90%|█████████ | 90/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  91%|█████████ | 91/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  92%|█████████▏| 92/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  93%|█████████▎| 93/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  94%|█████████▍| 94/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  95%|█████████▌| 95/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  96%|█████████▌| 96/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  97%|█████████▋| 97/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  98%|█████████▊| 98/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013):  99%|█████████▉| 99/100 [00:00<00:00, 143.71it/s]
Brute | Start: {'fx': 500.0} (0.001) | Current: 0.009 | Best: {'fx': 589.899} (0.013): 100%|██████████| 100/100 [00:00<00:00, 155.41it/s]
                                                                                                                                         

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.009 | Best: {'fx': 567.96} (0.009):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.009 | Best: {'fx': 567.96} (0.009):   2%|▏         | 1/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.008 | Best: {'fx': 567.96} (0.009):   2%|▏         | 1/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.008 | Best: {'fx': 567.96} (0.009):   4%|▍         | 2/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.008 | Best: {'fx': 567.96} (0.009):   6%|▌         | 3/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.009 | Best: {'fx': 568.981} (0.009):   8%|▊         | 4/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.009 | Best: {'fx': 570.001} (0.009):  10%|█         | 5/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.009 | Best: {'fx': 571.021} (0.009):  12%|█▏        | 6/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.010 | Best: {'fx': 572.042} (0.010):  14%|█▍        | 7/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.010 | Best: {'fx': 573.062} (0.010):  16%|█▌        | 8/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.010 | Best: {'fx': 574.083} (0.010):  18%|█▊        | 9/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.011 | Best: {'fx': 576.123} (0.011):  20%|██        | 10/50 [00:00<00:04,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.010 | Best: {'fx': 576.123} (0.011):  22%|██▏       | 11/50 [00:00<00:03,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 581.226} (0.013):  24%|██▍       | 12/50 [00:00<00:03,  9.85it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 581.226} (0.013):  26%|██▌       | 13/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.011 | Best: {'fx': 581.226} (0.013):  26%|██▌       | 13/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.011 | Best: {'fx': 581.226} (0.013):  28%|██▊       | 14/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 581.226} (0.013):  30%|███       | 15/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 581.226} (0.013):  32%|███▏      | 16/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 581.226} (0.013):  34%|███▍      | 17/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 581.226} (0.013):  36%|███▌      | 18/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  38%|███▊      | 19/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  40%|████      | 20/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  42%|████▏     | 21/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  44%|████▍     | 22/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  46%|████▌     | 23/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  48%|████▊     | 24/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  50%|█████     | 25/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  52%|█████▏    | 26/50 [00:00<00:00, 73.96it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  54%|█████▍    | 27/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  54%|█████▍    | 27/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  56%|█████▌    | 28/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  58%|█████▊    | 29/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  60%|██████    | 30/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  62%|██████▏   | 31/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  64%|██████▍   | 32/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  66%|██████▌   | 33/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  68%|██████▊   | 34/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  70%|███████   | 35/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  72%|███████▏  | 36/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  74%|███████▍  | 37/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  76%|███████▌  | 38/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  78%|███████▊  | 39/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  80%|████████  | 40/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  82%|████████▏ | 41/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  84%|████████▍ | 42/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  86%|████████▌ | 43/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  88%|████████▊ | 44/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  90%|█████████ | 45/50 [00:00<00:00, 102.18it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.013 | Best: {'fx': 583.266} (0.013):  92%|█████████▏| 46/50 [00:00<00:00, 134.47it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  92%|█████████▏| 46/50 [00:00<00:00, 134.47it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  94%|█████████▍| 47/50 [00:00<00:00, 134.47it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  96%|█████████▌| 48/50 [00:00<00:00, 134.47it/s]
Brute | Start: {'fx': 589.899} (0.013) | Current: 0.012 | Best: {'fx': 583.266} (0.013):  98%|█████████▊| 49/50 [00:00<00:00, 134.47it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 583.2663368377654
    px: 1078.0
    py: 790.0
    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 7 incomplete frames): 7 frames will be used for optimization
  Rigs: 1000(1000mm):7/20
  Total: 7/20 frames used for optimization

Initial score: 1063.4385: 0iter [00:00, ?iter/s]
Best score: 544.3082: : 0iter [00:01, ?iter/s]  
Best score: 544.3082: : 1iter [00:01,  1.11s/iter]
Best score: 544.3082: : 2iter [00:01,  1.32iter/s]
Best score: 544.3082: : 3iter [00:02,  1.56iter/s]
Best score: 430.1458: : 3iter [00:02,  1.56iter/s]
Best score: 430.1458: : 4iter [00:02,  1.79iter/s]
Best score: 430.1458: : 5iter [00:03,  1.73iter/s]
Best score: 398.6589: : 5iter [00:03,  1.73iter/s]
Best score: 398.6589: : 6iter [00:03,  1.83iter/s]
Best score: 398.6589: : 7iter [00:04,  1.89iter/s]
Best score: 398.6589: : 8iter [00:04,  1.85iter/s]
Best score: 398.6589: : 9iter [00:05,  1.91iter/s]
Best score: 398.6589: : 10iter [00:05,  1.86iter/s]
Best score: 398.6589: : 11iter [00:06,  1.87iter/s]
Best score: 395.9071: : 11iter [00:06,  1.87iter/s]
Best score: 395.9071: : 12iter [00:06,  1.89iter/s]
Best score: 395.9071: : 13iter [00:07,  1.95iter/s]
Best score: 302.0310: : 13iter [00:07,  1.95iter/s]
Best score: 302.0310: : 14iter [00:07,  1.91iter/s]
Best score: 302.0310: : 15iter [00:08,  1.83iter/s]
Best score: 302.0310: : 16iter [00:08,  1.90iter/s]
Best score: 302.0310: : 17iter [00:09,  1.94iter/s]
Best score: 302.0310: : 18iter [00:09,  1.96iter/s]
Best score: 213.6080: : 18iter [00:10,  1.96iter/s]
Best score: 213.6080: : 19iter [00:10,  2.05iter/s]
Best score: 213.6080: : 20iter [00:10,  2.09iter/s]
Best score: 213.6080: : 21iter [00:11,  2.00iter/s]
Best score: 208.0820: : 21iter [00:11,  2.00iter/s]
Best score: 208.0820: : 22iter [00:11,  1.98iter/s]
Best score: 208.0820: : 23iter [00:12,  2.01iter/s]
Best score: 208.0820: : 24iter [00:12,  2.01iter/s]
Best score: 208.0820: : 25iter [00:13,  2.04iter/s]
Best score: 208.0820: : 26iter [00:13,  1.98iter/s]
Best score: 208.0820: : 27iter [00:14,  1.90iter/s]
Best score: 208.0820: : 28iter [00:14,  1.94iter/s]
Best score: 208.0820: : 29iter [00:15,  2.00iter/s]
Best score: 208.0820: : 30iter [00:15,  1.97iter/s]
Best score: 204.9684: : 30iter [00:16,  1.97iter/s]
Best score: 204.9684: : 31iter [00:16,  1.98iter/s]
Best score: 185.9523: : 31iter [00:16,  1.98iter/s]
Best score: 185.9523: : 32iter [00:16,  1.98iter/s]
Best score: 185.9523: : 33iter [00:17,  2.00iter/s]
Best score: 185.9523: : 34iter [00:17,  2.00iter/s]
Best score: 185.9523: : 35iter [00:18,  1.96iter/s]
Best score: 185.9523: : 36iter [00:18,  1.99iter/s]
Best score: 185.9523: : 37iter [00:19,  1.93iter/s]
Best score: 185.9523: : 38iter [00:19,  1.95iter/s]
Best score: 185.9523: : 39iter [00:20,  2.01iter/s]
Best score: 185.9523: : 40iter [00:21,  1.91iter/s]
Best score: 185.9523: : 41iter [00:21,  2.03iter/s]
Best score: 185.9523: : 42iter [00:21,  1.99iter/s]
Best score: 185.9523: : 43iter [00:22,  1.99iter/s]
Best score: 185.9523: : 44iter [00:23,  1.92iter/s]
Best score: 185.9523: : 45iter [00:23,  1.96iter/s]
Best score: 185.9523: : 46iter [00:24,  1.86iter/s]
Best score: 185.9523: : 47iter [00:24,  1.90iter/s]
Best score: 185.9523: : 48iter [00:25,  1.87iter/s]
Best score: 163.5117: : 48iter [00:25,  1.87iter/s]
Best score: 163.5117: : 49iter [00:25,  1.91iter/s]
[ITER 50] Current best score: 163.5117
[PARAMS] Current best parameters:
  fx: 603.6260
  fy: 593.3174
  px: 1040.8235
  py: 764.6936
  k1: -0.05685989
  k2: 0.00936042
  k3: 0.00772397
  k4: -0.00222957
  p1: 0.00823330
  p2: 0.00830966
  p3: 0.00065846
  p4: 0.00476091
  scale: 3.0250
  rotation: -0.0967

Best score: 163.5117: : 50iter [00:26,  1.91iter/s]
Best score: 163.5117: : 51iter [00:26,  1.94iter/s]
Best score: 163.5117: : 52iter [00:27,  1.87iter/s]
Best score: 163.5117: : 53iter [00:27,  1.85iter/s]
Best score: 163.5117: : 54iter [00:28,  1.86iter/s]
Best score: 163.5117: : 55iter [00:28,  1.88iter/s]
Best score: 163.5117: : 56iter [00:29,  1.91iter/s]
Best score: 163.5117: : 57iter [00:29,  1.93iter/s]
Best score: 163.5117: : 58iter [00:30,  1.92iter/s]
Best score: 163.5117: : 59iter [00:30,  1.99iter/s]
Best score: 163.5117: : 60iter [00:31,  1.98iter/s]
Best score: 163.5117: : 61iter [00:31,  1.90iter/s]
Best score: 163.5117: : 62iter [00:32,  1.97iter/s]
Best score: 163.5117: : 63iter [00:32,  1.91iter/s]
Best score: 163.5117: : 64iter [00:33,  1.89iter/s]
Best score: 163.5117: : 65iter [00:34,  1.88iter/s]
Best score: 163.5117: : 66iter [00:34,  1.96iter/s]
Best score: 163.5117: : 67iter [00:35,  1.98iter/s]
Best score: 163.5117: : 68iter [00:35,  1.96iter/s]
Best score: 163.5117: : 69iter [00:35,  2.03iter/s]
Best score: 163.5117: : 70iter [00:36,  2.04iter/s]
Best score: 163.5117: : 71iter [00:36,  2.05iter/s]
Best score: 163.5117: : 72iter [00:37,  2.11iter/s]
Best score: 163.5117: : 73iter [00:37,  2.07iter/s]
Best score: 163.5117: : 74iter [00:38,  1.94iter/s]
Best score: 163.5117: : 75iter [00:38,  1.97iter/s]
Best score: 163.5117: : 76iter [00:39,  2.02iter/s]
Best score: 163.5117: : 77iter [00:39,  1.98iter/s]
Best score: 163.5117: : 78iter [00:40,  1.94iter/s]
Best score: 163.5117: : 79iter [00:40,  2.01iter/s]
Best score: 163.5117: : 80iter [00:41,  1.93iter/s]
Best score: 131.2658: : 80iter [00:41,  1.93iter/s]
Best score: 131.2658: : 81iter [00:41,  2.01iter/s]
Best score: 131.2658: : 82iter [00:42,  1.99iter/s]
Best score: 131.2658: : 83iter [00:43,  1.93iter/s]
Best score: 131.2658: : 84iter [00:43,  1.93iter/s]
Best score: 131.2658: : 85iter [00:44,  2.00iter/s]
Best score: 131.2658: : 86iter [00:44,  1.96iter/s]
Best score: 131.2658: : 87iter [00:45,  1.92iter/s]
Best score: 131.2658: : 88iter [00:45,  1.89iter/s]
Best score: 131.2658: : 89iter [00:46,  1.84iter/s]
Best score: 131.2658: : 90iter [00:46,  1.84iter/s]
Best score: 121.1346: : 90iter [00:47,  1.84iter/s]
Best score: 121.1346: : 91iter [00:47,  1.83iter/s]
Best score: 121.1346: : 92iter [00:47,  1.80iter/s]
Best score: 121.1346: : 93iter [00:48,  1.88iter/s]
Best score: 121.1346: : 94iter [00:48,  1.85iter/s]
Best score: 121.1346: : 95iter [00:49,  1.78iter/s]
Best score: 121.1346: : 96iter [00:50,  1.83iter/s]
Best score: 121.1346: : 97iter [00:50,  1.81iter/s]
Best score: 121.1346: : 98iter [00:51,  1.83iter/s]
Best score: 121.1346: : 99iter [00:51,  1.84iter/s]
[ITER 100] Current best score: 126.9220
[PARAMS] Current best parameters:
  fx: 568.7503
  fy: 560.7669
  px: 1038.9282
  py: 750.1530
  k1: -0.01658035
  k2: -0.00979540
  k3: 0.01561262
  k4: -0.00407851
  p1: -0.00151353
  p2: 0.00847731
  p3: 0.00252181
  p4: -0.00935934
  scale: 3.1102
  rotation: 0.2627

Best score: 121.1346: : 100iter [00:52,  1.86iter/s]
Best score: 121.1346: : 101iter [00:52,  1.86iter/s]
Best score: 121.1346: : 102iter [00:53,  1.85iter/s]
Best score: 121.1346: : 103iter [00:53,  1.95iter/s]
Best score: 121.1346: : 104iter [00:54,  1.97iter/s]
Best score: 121.1346: : 105iter [00:54,  2.00iter/s]
Best score: 121.1346: : 106iter [00:55,  1.93iter/s]
Best score: 121.1346: : 107iter [00:55,  1.94iter/s]
Best score: 121.1346: : 108iter [00:56,  1.94iter/s]
Best score: 121.1346: : 109iter [00:56,  2.03iter/s]
Best score: 121.1346: : 110iter [00:57,  2.00iter/s]
Best score: 121.1346: : 111iter [00:57,  1.97iter/s]
Best score: 121.1346: : 112iter [00:58,  2.04iter/s]
Best score: 121.1346: : 113iter [00:58,  1.98iter/s]
Best score: 121.1346: : 114iter [00:59,  2.01iter/s]
Best score: 121.1346: : 115iter [00:59,  2.10iter/s]
Best score: 121.1346: : 116iter [01:00,  2.06iter/s]
Best score: 121.1346: : 117iter [01:00,  1.97iter/s]
Best score: 121.1346: : 118iter [01:01,  1.98iter/s]
Best score: 115.7464: : 118iter [01:01,  1.98iter/s]
Best score: 115.7464: : 119iter [01:01,  2.02iter/s]
Best score: 115.7464: : 120iter [01:02,  2.00iter/s]
Best score: 115.7464: : 121iter [01:02,  2.03iter/s]
Best score: 115.7464: : 122iter [01:03,  2.07iter/s]
Best score: 115.7464: : 123iter [01:03,  2.01iter/s]
Best score: 115.7464: : 124iter [01:04,  2.01iter/s]
Best score: 115.7464: : 125iter [01:04,  1.96iter/s]
Best score: 115.7464: : 126iter [01:05,  1.89iter/s]
Best score: 115.7464: : 127iter [01:05,  1.89iter/s]
Best score: 115.7464: : 128iter [01:06,  1.95iter/s]
Best score: 115.7464: : 129iter [01:06,  2.00iter/s]
Best score: 115.7464: : 130iter [01:07,  1.95iter/s]
Best score: 115.7464: : 131iter [01:07,  1.99iter/s]
Best score: 115.7464: : 132iter [01:08,  1.97iter/s]
Best score: 115.7464: : 133iter [01:08,  1.95iter/s]
Best score: 115.7464: : 134iter [01:09,  1.97iter/s]
Best score: 115.7464: : 135iter [01:09,  1.97iter/s]
Best score: 115.7464: : 136iter [01:10,  1.95iter/s]
Best score: 115.7464: : 137iter [01:10,  1.93iter/s]
Best score: 115.7464: : 138iter [01:11,  1.97iter/s]
Best score: 115.7464: : 139iter [01:11,  1.93iter/s]
Best score: 115.7464: : 140iter [01:12,  2.00iter/s]
Best score: 115.7464: : 141iter [01:12,  2.04iter/s]
Best score: 115.7464: : 142iter [01:13,  1.99iter/s]
Best score: 115.7464: : 143iter [01:13,  2.04iter/s]
Best score: 115.7464: : 144iter [01:14,  2.07iter/s]
Best score: 115.7464: : 145iter [01:14,  2.02iter/s]
Best score: 115.7464: : 146iter [01:15,  1.96iter/s]
Best score: 115.7464: : 147iter [01:15,  2.00iter/s]
Best score: 115.7464: : 148iter [01:16,  1.94iter/s]
Best score: 115.7464: : 149iter [01:16,  1.94iter/s]
[ITER 150] Current best score: 121.2218
[PARAMS] Current best parameters:
  fx: 588.1659
  fy: 582.7094
  px: 1038.3297
  py: 758.9753
  k1: -0.04726650
  k2: -0.01763222
  k3: 0.02421191
  k4: -0.00408934
  p1: 0.00582030
  p2: 0.00945144
  p3: 0.00433597
  p4: -0.00903554
  scale: 3.2109
  rotation: -0.0303

Best score: 115.7464: : 150iter [01:17,  1.96iter/s]
Best score: 115.7464: : 151iter [01:17,  1.97iter/s]
Best score: 115.7464: : 152iter [01:18,  1.92iter/s]
Best score: 115.7464: : 153iter [01:18,  1.95iter/s]
Best score: 115.7464: : 154iter [01:19,  1.89iter/s]
Best score: 115.7464: : 155iter [01:20,  1.82iter/s]
Best score: 115.7464: : 156iter [01:20,  1.84iter/s]
Best score: 115.7464: : 157iter [01:21,  1.82iter/s]
Best score: 115.7464: : 158iter [01:21,  1.82iter/s]
Best score: 115.7464: : 159iter [01:22,  1.83iter/s]
Best score: 115.7464: : 160iter [01:22,  1.84iter/s]
Best score: 115.7464: : 161iter [01:23,  1.90iter/s]
Best score: 115.7464: : 162iter [01:23,  1.89iter/s]
Best score: 115.7464: : 163iter [01:24,  1.87iter/s]
Best score: 115.7464: : 164iter [01:24,  1.88iter/s]
Best score: 114.6379: : 164iter [01:25,  1.88iter/s]
Best score: 114.6379: : 165iter [01:25,  1.92iter/s]
Best score: 114.6379: : 166iter [01:26,  1.89iter/s]
Best score: 114.6379: : 167iter [01:26,  1.87iter/s]
Best score: 114.6379: : 168iter [01:27,  1.94iter/s]
Best score: 114.6379: : 169iter [01:27,  1.90iter/s]
Best score: 114.6379: : 170iter [01:28,  1.90iter/s]
Best score: 114.6379: : 171iter [01:28,  2.01iter/s]
Best score: 114.6379: : 172iter [01:29,  1.90iter/s]
Best score: 114.6379: : 173iter [01:29,  1.92iter/s]
Best score: 114.6379: : 174iter [01:30,  1.94iter/s]
Best score: 114.6379: : 175iter [01:30,  1.97iter/s]
Best score: 114.6379: : 176iter [01:31,  1.90iter/s]
Best score: 114.6379: : 177iter [01:31,  1.93iter/s]
Best score: 114.6379: : 178iter [01:32,  1.95iter/s]
Best score: 114.6379: : 179iter [01:32,  1.91iter/s]
Best score: 114.6379: : 180iter [01:33,  1.94iter/s]
Best score: 114.6379: : 181iter [01:33,  1.94iter/s]
Best score: 114.6379: : 182iter [01:34,  1.87iter/s]
Best score: 114.6379: : 183iter [01:34,  1.84iter/s]
Best score: 114.6379: : 184iter [01:35,  1.92iter/s]
Best score: 114.6379: : 185iter [01:35,  1.89iter/s]
Best score: 114.6379: : 186iter [01:36,  1.86iter/s]
Best score: 114.6379: : 187iter [01:36,  1.99iter/s]
Best score: 114.6379: : 188iter [01:37,  1.98iter/s]
Best score: 106.0742: : 188iter [01:37,  1.98iter/s]
Best score: 106.0742: : 189iter [01:37,  1.99iter/s]
Best score: 106.0742: : 190iter [01:38,  2.06iter/s]
Best score: 106.0742: : 191iter [01:38,  2.07iter/s]
Best score: 106.0742: : 192iter [01:39,  2.06iter/s]
Best score: 106.0742: : 193iter [01:39,  1.99iter/s]
Best score: 106.0742: : 194iter [01:40,  2.01iter/s]
Best score: 106.0742: : 195iter [01:40,  1.92iter/s]
Best score: 106.0742: : 196iter [01:41,  1.87iter/s]
Best score: 106.0742: : 197iter [01:42,  1.84iter/s]
Best score: 106.0742: : 198iter [01:42,  1.85iter/s]
Best score: 106.0742: : 199iter [01:43,  1.83iter/s]
[ITER 200] Current best score: 108.8858
[PARAMS] Current best parameters:
  fx: 582.2835
  fy: 574.7491
  px: 1038.7578
  py: 761.1689
  k1: -0.08544549
  k2: -0.00548043
  k3: 0.02630094
  k4: -0.00411810
  p1: 0.00896514
  p2: 0.00976811
  p3: -0.00132182
  p4: 0.00067665
  scale: 3.5636
  rotation: -0.3224

Best score: 106.0742: : 200iter [01:43,  1.84iter/s]
Best score: 106.0742: : 201iter [01:44,  1.83iter/s]
Best score: 106.0742: : 202iter [01:44,  1.85iter/s]
Best score: 106.0742: : 203iter [01:45,  1.83iter/s]
Best score: 106.0742: : 204iter [01:45,  1.88iter/s]
Best score: 106.0742: : 205iter [01:46,  1.90iter/s]
Best score: 106.0742: : 206iter [01:46,  1.90iter/s]
Best score: 106.0742: : 207iter [01:47,  1.83iter/s]
Best score: 106.0742: : 208iter [01:47,  1.86iter/s]
Best score: 106.0742: : 209iter [01:48,  1.83iter/s]
Best score: 106.0742: : 210iter [01:49,  1.84iter/s]
Best score: 106.0742: : 211iter [01:49,  1.87iter/s]
Best score: 106.0742: : 212iter [01:50,  1.92iter/s]
Best score: 106.0742: : 213iter [01:50,  1.96iter/s]
Best score: 106.0742: : 214iter [01:51,  1.96iter/s]
Best score: 106.0742: : 215iter [01:51,  2.01iter/s]
Best score: 106.0742: : 216iter [01:52,  1.98iter/s]
Best score: 106.0742: : 217iter [01:52,  1.94iter/s]
Best score: 106.0742: : 218iter [01:53,  2.02iter/s]
Best score: 106.0742: : 219iter [01:53,  1.89iter/s]
Best score: 106.0742: : 220iter [01:54,  1.92iter/s]
Best score: 106.0742: : 221iter [01:54,  1.87iter/s]
Best score: 106.0742: : 222iter [01:55,  1.89iter/s]
Best score: 106.0742: : 223iter [01:55,  1.90iter/s]
Best score: 106.0742: : 224iter [01:56,  1.88iter/s]
Best score: 106.0742: : 225iter [01:56,  1.82iter/s]
Best score: 106.0742: : 226iter [01:57,  1.86iter/s]
Best score: 106.0742: : 227iter [01:57,  1.87iter/s]
Best score: 106.0742: : 228iter [01:58,  1.86iter/s]
Best score: 106.0742: : 229iter [01:58,  1.92iter/s]
Best score: 106.0742: : 230iter [01:59,  1.95iter/s]
Best score: 106.0742: : 231iter [01:59,  1.94iter/s]
Best score: 106.0742: : 232iter [02:00,  1.96iter/s]
Best score: 106.0742: : 233iter [02:00,  2.05iter/s]
Best score: 106.0742: : 234iter [02:01,  1.91iter/s]
Best score: 106.0742: : 235iter [02:02,  1.93iter/s]
Best score: 106.0742: : 236iter [02:02,  1.96iter/s]
Best score: 106.0742: : 237iter [02:03,  1.87iter/s]
Best score: 106.0742: : 238iter [02:03,  1.90iter/s]
Best score: 106.0742: : 239iter [02:04,  1.92iter/s]
Best score: 106.0742: : 240iter [02:04,  1.91iter/s]
Best score: 106.0742: : 241iter [02:05,  1.85iter/s]
Best score: 106.0742: : 242iter [02:05,  1.86iter/s]
Best score: 106.0742: : 243iter [02:06,  1.86iter/s]
Best score: 106.0742: : 244iter [02:06,  1.84iter/s]
Best score: 106.0742: : 245iter [02:07,  1.88iter/s]
Best score: 106.0742: : 246iter [02:07,  1.90iter/s]
Best score: 106.0742: : 247iter [02:08,  1.90iter/s]
Best score: 106.0742: : 248iter [02:08,  1.93iter/s]
Best score: 106.0742: : 249iter [02:09,  1.89iter/s]
[ITER 250] Current best score: 110.6809
[PARAMS] Current best parameters:
  fx: 587.0980
  fy: 577.4717
  px: 1038.1395
  py: 754.8201
  k1: -0.10962870
  k2: 0.03324511
  k3: 0.02622116
  k4: -0.01139605
  p1: -0.00494521
  p2: 0.00997791
  p3: -0.00122640
  p4: 0.00579051
  scale: 3.3600
  rotation: -0.1277

Best score: 106.0742: : 250iter [02:10,  1.84iter/s]
Best score: 106.0742: : 251iter [02:10,  1.86iter/s]
Best score: 106.0742: : 252iter [02:11,  1.87iter/s]
Best score: 106.0742: : 253iter [02:11,  1.91iter/s]
Best score: 106.0742: : 254iter [02:12,  1.92iter/s]
Best score: 106.0742: : 255iter [02:12,  1.98iter/s]
Best score: 106.0742: : 256iter [02:13,  1.97iter/s]
Best score: 106.0742: : 257iter [02:13,  1.94iter/s]
Best score: 106.0742: : 258iter [02:14,  1.97iter/s]
Best score: 106.0742: : 259iter [02:14,  2.01iter/s]
Best score: 106.0742: : 260iter [02:15,  1.98iter/s]
Best score: 106.0742: : 261iter [02:15,  1.99iter/s]
Best score: 106.0742: : 262iter [02:16,  1.98iter/s]
Best score: 106.0742: : 263iter [02:16,  2.00iter/s]
Best score: 106.0742: : 264iter [02:17,  2.07iter/s]
Best score: 106.0742: : 265iter [02:17,  2.01iter/s]
Best score: 106.0742: : 266iter [02:18,  1.96iter/s]
Best score: 106.0742: : 267iter [02:18,  1.93iter/s]
Best score: 106.0742: : 268iter [02:19,  1.89iter/s]
Best score: 106.0742: : 269iter [02:19,  1.83iter/s]
Best score: 106.0742: : 270iter [02:20,  1.86iter/s]
Best score: 106.0742: : 271iter [02:20,  1.92iter/s]
Best score: 106.0742: : 272iter [02:21,  1.94iter/s]
Best score: 106.0742: : 273iter [02:21,  1.98iter/s]
Best score: 106.0742: : 274iter [02:22,  2.03iter/s]
Best score: 106.0742: : 275iter [02:22,  1.97iter/s]
Best score: 106.0742: : 276iter [02:23,  2.01iter/s]
Best score: 106.0742: : 277iter [02:23,  2.01iter/s]
Best score: 106.0742: : 278iter [02:24,  1.96iter/s]
Best score: 106.0742: : 279iter [02:24,  1.98iter/s]
Best score: 106.0742: : 280iter [02:25,  1.95iter/s]
Best score: 106.0742: : 281iter [02:25,  1.98iter/s]
Best score: 106.0742: : 282iter [02:26,  1.92iter/s]
Best score: 106.0742: : 283iter [02:26,  1.98iter/s]
Best score: 106.0742: : 284iter [02:27,  1.98iter/s]
Best score: 106.0742: : 285iter [02:27,  1.99iter/s]
Best score: 106.0742: : 286iter [02:28,  2.08iter/s]
Best score: 106.0742: : 287iter [02:28,  2.04iter/s]
Best score: 106.0742: : 288iter [02:29,  1.97iter/s]
Best score: 106.0742: : 289iter [02:29,  1.96iter/s]
Best score: 106.0742: : 290iter [02:30,  1.96iter/s]
Best score: 106.0742: : 291iter [02:30,  1.96iter/s]
Best score: 106.0742: : 292iter [02:31,  1.96iter/s]
Best score: 106.0742: : 293iter [02:31,  1.92iter/s]
Best score: 106.0742: : 294iter [02:32,  1.93iter/s]
Best score: 106.0742: : 295iter [02:32,  1.89iter/s]
Best score: 106.0742: : 296iter [02:33,  1.89iter/s]
Best score: 106.0742: : 297iter [02:34,  1.89iter/s]
Best score: 106.0742: : 298iter [02:34,  1.92iter/s]
Best score: 106.0742: : 299iter [02:35,  1.94iter/s]
[ITER 300] Current best score: 108.8189
[PARAMS] Current best parameters:
  fx: 569.9834
  fy: 561.8788
  px: 1038.3876
  py: 759.7409
  k1: -0.07470512
  k2: -0.01544128
  k3: 0.03974759
  k4: -0.00837661
  p1: 0.00933531
  p2: 0.00999879
  p3: 0.00398287
  p4: -0.00238050
  scale: 3.5350
  rotation: -0.4665

Best score: 106.0742: : 300iter [02:35,  1.83iter/s]
Best score: 106.0742: : 301iter [02:36,  1.90iter/s]
Best score: 106.0742: : 302iter [02:36,  1.94iter/s]
Best score: 106.0742: : 303iter [02:37,  1.93iter/s]
Best score: 106.0742: : 304iter [02:37,  1.92iter/s]
Best score: 106.0742: : 305iter [02:38,  1.92iter/s]
Best score: 106.0742: : 306iter [02:38,  1.89iter/s]
Best score: 106.0742: : 307iter [02:39,  1.99iter/s]
Best score: 106.0742: : 308iter [02:39,  1.99iter/s]
Best score: 106.0742: : 309iter [02:40,  1.96iter/s]
Best score: 106.0742: : 310iter [02:40,  2.04iter/s]
Best score: 106.0742: : 311iter [02:41,  2.02iter/s]
Best score: 106.0742: : 312iter [02:41,  2.03iter/s]
Best score: 106.0742: : 313iter [02:42,  1.97iter/s]
Best score: 106.0742: : 314iter [02:42,  1.94iter/s]
Best score: 105.1466: : 314iter [02:43,  1.94iter/s]
Best score: 105.1466: : 315iter [02:43,  1.95iter/s]
Best score: 105.1466: : 316iter [02:43,  1.96iter/s]
Best score: 105.1466: : 317iter [02:44,  1.92iter/s]
Best score: 105.1466: : 318iter [02:44,  1.97iter/s]
Best score: 105.1466: : 319iter [02:45,  1.90iter/s]
Best score: 105.1466: : 320iter [02:45,  1.91iter/s]
Best score: 105.1466: : 321iter [02:46,  1.95iter/s]
Best score: 105.1466: : 322iter [02:46,  1.91iter/s]
Best score: 105.1466: : 323iter [02:47,  1.96iter/s]
Best score: 105.1466: : 324iter [02:47,  1.91iter/s]
Best score: 105.1466: : 325iter [02:48,  1.87iter/s]
Best score: 105.1466: : 326iter [02:48,  1.95iter/s]
Best score: 105.1466: : 327iter [02:49,  1.91iter/s]
Best score: 105.1466: : 328iter [02:50,  1.87iter/s]
Best score: 105.1466: : 329iter [02:50,  1.97iter/s]
Best score: 105.1466: : 330iter [02:51,  1.97iter/s]
Best score: 105.1466: : 331iter [02:51,  1.91iter/s]
Best score: 105.1466: : 332iter [02:52,  1.90iter/s]
Best score: 105.1466: : 333iter [02:52,  1.92iter/s]
Best score: 105.1466: : 334iter [02:53,  1.89iter/s]
Best score: 105.1466: : 335iter [02:53,  1.94iter/s]
Best score: 105.1466: : 336iter [02:54,  1.92iter/s]
Best score: 105.1466: : 337iter [02:54,  1.92iter/s]
Best score: 105.1466: : 338iter [02:55,  2.02iter/s]
Best score: 105.1466: : 339iter [02:55,  2.04iter/s]
Best score: 105.1466: : 340iter [02:56,  1.99iter/s]
Best score: 105.1466: : 341iter [02:56,  1.92iter/s]
Best score: 102.4221: : 341iter [02:57,  1.92iter/s]
Best score: 102.4221: : 342iter [02:57,  1.93iter/s]
Best score: 102.4221: : 343iter [02:57,  1.94iter/s]
Best score: 102.4221: : 344iter [02:58,  1.95iter/s]
Best score: 102.4221: : 345iter [02:58,  1.92iter/s]
Best score: 102.4221: : 346iter [02:59,  1.97iter/s]
Best score: 102.4221: : 347iter [02:59,  1.99iter/s]
Best score: 102.4221: : 348iter [03:00,  1.97iter/s]
Best score: 102.4221: : 349iter [03:00,  1.89iter/s]
[ITER 350] Current best score: 102.4221
[PARAMS] Current best parameters:
  fx: 567.9078
  fy: 559.4275
  px: 1038.0016
  py: 755.0483
  k1: -0.11793360
  k2: 0.01886555
  k3: 0.03930423
  k4: -0.01217537
  p1: -0.00190194
  p2: 0.00946432
  p3: 0.00066466
  p4: 0.00028191
  scale: 3.6868
  rotation: -0.5724

Best score: 102.4221: : 350iter [03:01,  1.91iter/s]
Best score: 102.4221: : 351iter [03:01,  1.91iter/s]
Best score: 102.4221: : 352iter [03:02,  1.92iter/s]
Best score: 102.4221: : 353iter [03:02,  1.87iter/s]
Best score: 102.4221: : 354iter [03:03,  1.85iter/s]
Best score: 102.4221: : 355iter [03:04,  1.81iter/s]
Best score: 102.4221: : 356iter [03:04,  1.89iter/s]
Best score: 102.4221: : 357iter [03:05,  1.86iter/s]
Best score: 102.4221: : 358iter [03:05,  1.93iter/s]
Best score: 102.4221: : 359iter [03:06,  1.91iter/s]
Best score: 102.4221: : 360iter [03:06,  1.94iter/s]
Best score: 102.4221: : 361iter [03:07,  1.96iter/s]
Best score: 102.4221: : 362iter [03:07,  1.97iter/s]
Best score: 102.4221: : 363iter [03:08,  1.92iter/s]
Best score: 102.4221: : 364iter [03:08,  2.03iter/s]
Best score: 102.4221: : 365iter [03:09,  1.94iter/s]
Best score: 102.4221: : 366iter [03:09,  1.90iter/s]
Best score: 102.4221: : 367iter [03:10,  2.01iter/s]
Best score: 102.4221: : 368iter [03:10,  1.91iter/s]
Best score: 102.4221: : 369iter [03:11,  1.87iter/s]
Best score: 102.4221: : 370iter [03:11,  1.89iter/s]
Best score: 102.4221: : 371iter [03:12,  1.83iter/s]
Best score: 102.4221: : 372iter [03:12,  1.81iter/s]
Best score: 102.4221: : 373iter [03:13,  1.91iter/s]
Best score: 102.4221: : 374iter [03:13,  1.94iter/s]
Best score: 102.4221: : 375iter [03:14,  1.94iter/s]
Best score: 102.4221: : 376iter [03:15,  1.89iter/s]
Best score: 102.4221: : 377iter [03:15,  1.92iter/s]
Best score: 102.4221: : 378iter [03:16,  1.91iter/s]
Best score: 102.4221: : 379iter [03:16,  1.85iter/s]
Best score: 102.4221: : 380iter [03:17,  1.95iter/s]
Best score: 102.4221: : 381iter [03:17,  1.90iter/s]
Best score: 102.4221: : 382iter [03:18,  1.92iter/s]
Best score: 102.4221: : 383iter [03:18,  1.93iter/s]
Best score: 102.4221: : 384iter [03:19,  1.88iter/s]
Best score: 102.4221: : 385iter [03:19,  1.95iter/s]
Best score: 102.4221: : 386iter [03:20,  1.99iter/s]
Best score: 102.4221: : 387iter [03:20,  1.94iter/s]
Best score: 102.4221: : 388iter [03:21,  2.01iter/s]
Best score: 102.4221: : 389iter [03:21,  1.95iter/s]
Best score: 102.4221: : 390iter [03:22,  2.00iter/s]
Best score: 102.4221: : 391iter [03:22,  2.09iter/s]
Best score: 102.4221: : 392iter [03:23,  1.93iter/s]
Best score: 102.4221: : 393iter [03:23,  1.90iter/s]
Best score: 102.4221: : 394iter [03:24,  2.06iter/s]
Best score: 102.4221: : 395iter [03:24,  2.04iter/s]
Best score: 102.4221: : 396iter [03:25,  1.97iter/s]
Best score: 102.4221: : 397iter [03:25,  2.07iter/s]
Best score: 102.4221: : 398iter [03:26,  2.10iter/s]
Best score: 102.4221: : 399iter [03:26,  1.98iter/s]
[ITER 400] Current best score: 104.2703
[PARAMS] Current best parameters:
  fx: 548.4431
  fy: 540.2451
  px: 1038.1203
  py: 759.9688
  k1: -0.07990981
  k2: -0.00504035
  k3: 0.04487688
  k4: -0.01158511
  p1: 0.00744929
  p2: 0.00951382
  p3: 0.00592230
  p4: -0.00404179
  scale: 3.6558
  rotation: -0.7017

Best score: 102.4221: : 400iter [03:27,  2.02iter/s]
Best score: 102.4221: : 401iter [03:27,  2.02iter/s]
Best score: 102.4221: : 402iter [03:28,  2.00iter/s]
Best score: 102.4221: : 403iter [03:28,  1.98iter/s]
Best score: 102.4221: : 404iter [03:29,  1.95iter/s]
Best score: 102.4221: : 405iter [03:29,  1.91iter/s]
Best score: 102.4221: : 406iter [03:30,  1.90iter/s]
Best score: 102.4221: : 407iter [03:30,  1.95iter/s]
Best score: 102.4221: : 408iter [03:31,  1.90iter/s]
Best score: 102.4221: : 409iter [03:31,  1.91iter/s]
Best score: 102.4221: : 410iter [03:32,  1.90iter/s]
Best score: 102.4221: : 411iter [03:32,  1.89iter/s]
Best score: 102.4221: : 412iter [03:33,  1.93iter/s]
Best score: 102.4221: : 413iter [03:33,  1.89iter/s]
Best score: 102.4221: : 414iter [03:34,  1.87iter/s]
Best score: 102.4221: : 415iter [03:35,  1.81iter/s]
Best score: 102.4221: : 416iter [03:35,  1.78iter/s]
Best score: 102.4221: : 417iter [03:36,  1.81iter/s]
Best score: 102.4221: : 418iter [03:36,  1.82iter/s]
Best score: 102.4221: : 419iter [03:37,  1.85iter/s]
Best score: 102.4221: : 420iter [03:37,  1.92iter/s]
Best score: 102.4221: : 421iter [03:38,  1.90iter/s]
Best score: 102.4221: : 422iter [03:38,  1.92iter/s]
Best score: 102.4221: : 423iter [03:39,  2.02iter/s]
Best score: 102.4221: : 424iter [03:39,  1.96iter/s]
Best score: 102.4221: : 425iter [03:40,  1.96iter/s]
Best score: 102.4221: : 426iter [03:40,  2.05iter/s]
Best score: 102.4221: : 427iter [03:41,  2.04iter/s]
Best score: 102.4221: : 428iter [03:41,  1.99iter/s]
Best score: 102.4221: : 429iter [03:42,  2.08iter/s]
Best score: 102.4221: : 430iter [03:42,  1.99iter/s]
Best score: 102.4221: : 431iter [03:43,  2.02iter/s]
Best score: 102.4221: : 432iter [03:43,  2.07iter/s]
Best score: 102.4221: : 433iter [03:44,  1.94iter/s]
Best score: 102.4221: : 434iter [03:44,  2.02iter/s]
Best score: 102.4221: : 435iter [03:45,  1.97iter/s]
Best score: 102.4221: : 436iter [03:45,  1.88iter/s]
Best score: 102.4221: : 437iter [03:46,  1.93iter/s]
Best score: 102.4221: : 438iter [03:46,  2.03iter/s]
Best score: 102.4221: : 439iter [03:47,  1.95iter/s]
Best score: 102.4221: : 440iter [03:47,  1.89iter/s]
Best score: 102.4221: : 441iter [03:48,  2.00iter/s]
Best score: 102.4221: : 442iter [03:48,  2.00iter/s]
Best score: 102.4221: : 443iter [03:49,  1.94iter/s]
Best score: 102.4221: : 444iter [03:49,  1.95iter/s]
Best score: 102.4221: : 445iter [03:50,  1.88iter/s]
Best score: 102.4221: : 446iter [03:50,  1.84iter/s]
Best score: 102.4221: : 447iter [03:51,  1.87iter/s]
Best score: 102.4221: : 448iter [03:52,  1.86iter/s]
Best score: 102.4221: : 449iter [03:52,  1.83iter/s]
[ITER 450] Current best score: 104.7559
[PARAMS] Current best parameters:
  fx: 545.9172
  fy: 538.2046
  px: 1038.0223
  py: 759.3539
  k1: -0.08991716
  k2: 0.00611898
  k3: 0.04372181
  k4: -0.01252513
  p1: 0.00260444
  p2: 0.00987545
  p3: 0.00716933
  p4: -0.00027023
  scale: 3.6682
  rotation: -0.6532

Best score: 102.4221: : 450iter [03:53,  1.92iter/s]
Best score: 101.0734: : 450iter [03:53,  1.92iter/s]
Best score: 101.0734: : 451iter [03:53,  1.84iter/s]
Best score: 101.0734: : 452iter [03:54,  1.83iter/s]
Best score: 101.0734: : 453iter [03:54,  1.96iter/s]
Best score: 101.0734: : 454iter [03:55,  1.99iter/s]
Best score: 101.0734: : 455iter [03:55,  1.97iter/s]
Best score: 101.0734: : 456iter [03:56,  1.96iter/s]
Best score: 101.0734: : 457iter [03:56,  1.91iter/s]
Best score: 101.0734: : 458iter [03:57,  1.92iter/s]
Best score: 101.0734: : 459iter [03:57,  1.93iter/s]
Best score: 101.0734: : 460iter [03:58,  1.98iter/s]
Best score: 101.0734: : 461iter [03:58,  1.98iter/s]
Best score: 101.0734: : 462iter [03:59,  1.97iter/s]
Best score: 101.0734: : 463iter [03:59,  1.95iter/s]
Best score: 101.0734: : 464iter [04:00,  1.86iter/s]
Best score: 101.0734: : 465iter [04:00,  1.90iter/s]
Best score: 101.0734: : 466iter [04:01,  1.90iter/s]
Best score: 101.0734: : 467iter [04:01,  1.89iter/s]
Best score: 101.0734: : 468iter [04:02,  1.86iter/s]
Best score: 101.0734: : 469iter [04:02,  1.91iter/s]
Best score: 101.0734: : 470iter [04:03,  1.92iter/s]
Best score: 101.0734: : 471iter [04:04,  1.87iter/s]
Best score: 101.0734: : 472iter [04:04,  1.88iter/s]
Best score: 101.0734: : 473iter [04:05,  1.87iter/s]
Best score: 101.0734: : 474iter [04:05,  1.86iter/s]
Best score: 101.0734: : 475iter [04:06,  1.99iter/s]
Best score: 101.0734: : 476iter [04:06,  1.98iter/s]
Best score: 101.0734: : 477iter [04:07,  1.96iter/s]
Best score: 101.0734: : 478iter [04:07,  1.97iter/s]
Best score: 101.0734: : 479iter [04:08,  1.93iter/s]
Best score: 99.5311: : 479iter [04:08,  1.93iter/s] 
Best score: 99.5311: : 480iter [04:08,  1.91iter/s]
Best score: 99.5311: : 481iter [04:09,  1.90iter/s]
Best score: 99.5311: : 482iter [04:09,  1.89iter/s]
Best score: 99.5311: : 483iter [04:10,  1.87iter/s]
Best score: 99.5311: : 484iter [04:10,  1.88iter/s]
Best score: 99.5311: : 485iter [04:11,  1.88iter/s]
Best score: 99.5311: : 486iter [04:11,  1.84iter/s]
Best score: 99.5311: : 487iter [04:12,  1.89iter/s]
Best score: 99.5311: : 488iter [04:12,  1.96iter/s]
Best score: 99.5311: : 489iter [04:13,  1.96iter/s]
Best score: 99.5311: : 490iter [04:13,  1.92iter/s]
Best score: 99.5311: : 491iter [04:14,  1.96iter/s]
Best score: 99.5311: : 492iter [04:14,  1.94iter/s]
Best score: 99.5311: : 493iter [04:15,  1.89iter/s]
Best score: 99.5311: : 494iter [04:16,  1.97iter/s]
Best score: 99.5311: : 495iter [04:16,  1.93iter/s]
Best score: 99.5311: : 496iter [04:17,  1.89iter/s]
Best score: 99.5311: : 497iter [04:17,  1.87iter/s]
Best score: 99.5311: : 498iter [04:18,  1.84iter/s]
Best score: 99.5311: : 499iter [04:18,  1.83iter/s]
[ITER 500] Current best score: 100.1951
[PARAMS] Current best parameters:
  fx: 542.8608
  fy: 536.1339
  px: 1038.0746
  py: 755.6756
  k1: -0.07061074
  k2: -0.00275034
  k3: 0.03976618
  k4: -0.01037176
  p1: -0.00358236
  p2: 0.00994216
  p3: 0.00888002
  p4: 0.00082275
  scale: 3.6260
  rotation: -0.4916

Best score: 99.5311: : 500iter [04:19,  1.81iter/s]
Best score: 99.5311: : 500iter [04:44,  1.76iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 85.520418 -> 85.520418
  Success: True
  Tangential distortion: p1=-0.003593, p2=0.010000, p3=0.008883, p4=0.000800
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 85.5204
[✅] Parameters copied to: /working/MDC103_W050_2025-05-07/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 542.8606838820263
    fy: 536.1339466009179
    px: 1038.074574743342
    py: 755.6754861021649
    k1: -0.07042017917802182
    k2: -0.0025970601707383566
    k3: 0.03975086543423779
    k4: -0.010358336418616965
    p1: -0.003592949556183375
    p2: 0.01
    p3: 0.008882590456283476
    p4: 0.0008004095563144425
    scale: 3.6221912589699663
    rotation: -0.49157683343933667
    mmpp: 1.4452041037657253
    nmpp: 1445204.1037657254

[🔄] Converting 2348 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:23<00:00, 11.71s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:23<00:00, 11.69s/rig]
[✅] Saved PTDs: /working/MDC103_W050_2025-05-07/calibration/debug/3_undistorted/output/ptd

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📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.12rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.12rig/s]
[✅] Saved PTDs: /working/MDC103_W050_2025-05-07/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

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📤 Writing LUT:   8%|▊         | 259026/3080448 [00:00<00:03, 875039.06px/s]
📤 Writing LUT:  11%|█▏        | 348847/3080448 [00:00<00:03, 884182.83px/s]
📤 Writing LUT:  14%|█▍        | 439371/3080448 [00:00<00:02, 891771.86px/s]
📤 Writing LUT:  17%|█▋        | 528718/3080448 [00:00<00:02, 892345.90px/s]
📤 Writing LUT:  20%|██        | 618565/3080448 [00:00<00:02, 894345.49px/s]
📤 Writing LUT:  23%|██▎       | 708000/3080448 [00:00<00:02, 893748.20px/s]
📤 Writing LUT:  26%|██▌       | 800753/3080448 [00:00<00:02, 904301.57px/s]
📤 Writing LUT:  29%|██▉       | 892756/3080448 [00:01<00:02, 909153.95px/s]
📤 Writing LUT:  32%|███▏      | 985375/3080448 [00:01<00:02, 914365.62px/s]
📤 Writing LUT:  35%|███▍      | 1078109/3080448 [00:01<00:02, 918309.54px/s]
📤 Writing LUT:  38%|███▊      | 1171541/3080448 [00:01<00:02, 923158.06px/s]
📤 Writing LUT:  41%|████      | 1265888/3080448 [00:01<00:01, 929291.03px/s]
📤 Writing LUT:  44%|████▍     | 1359263/3080448 [00:01<00:01, 930632.62px/s]
📤 Writing LUT:  47%|████▋     | 1453138/3080448 [00:01<00:01, 933074.11px/s]
📤 Writing LUT:  50%|█████     | 1546446/3080448 [00:01<00:01, 929910.87px/s]
📤 Writing LUT:  53%|█████▎    | 1639440/3080448 [00:01<00:01, 924405.19px/s]
📤 Writing LUT:  56%|█████▋    | 1734253/3080448 [00:01<00:01, 931486.83px/s]
📤 Writing LUT:  59%|█████▉    | 1827754/3080448 [00:02<00:01, 932539.23px/s]
📤 Writing LUT:  62%|██████▏   | 1921016/3080448 [00:02<00:01, 932088.10px/s]
📤 Writing LUT:  65%|██████▌   | 2014686/3080448 [00:02<00:01, 933465.87px/s]
📤 Writing LUT:  69%|██████▊   | 2110628/3080448 [00:02<00:01, 941240.93px/s]
📤 Writing LUT:  72%|███████▏  | 2204806/3080448 [00:02<00:00, 941401.42px/s]
📤 Writing LUT:  75%|███████▍  | 2298949/3080448 [00:02<00:00, 939397.24px/s]
📤 Writing LUT:  78%|███████▊  | 2393171/3080448 [00:02<00:00, 940239.98px/s]
📤 Writing LUT:  81%|████████  | 2487198/3080448 [00:02<00:00, 937057.31px/s]
📤 Writing LUT:  84%|████████▍ | 2580908/3080448 [00:02<00:00, 932049.96px/s]
📤 Writing LUT:  87%|████████▋ | 2674338/3080448 [00:02<00:00, 932719.13px/s]
📤 Writing LUT:  90%|████████▉ | 2769753/3080448 [00:03<00:00, 939119.83px/s]
📤 Writing LUT:  93%|█████████▎| 2864449/3080448 [00:03<00:00, 941463.76px/s]
📤 Writing LUT:  96%|█████████▌| 2958601/3080448 [00:03<00:00, 939189.61px/s]
📤 Writing LUT:  99%|█████████▉| 3054066/3080448 [00:03<00:00, 943812.60px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 923469.81px/s]
[✅] LUT written to: /working/MDC103_W050_2025-05-07/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC103_W050_2025-05-07' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC103_W050_2025-05-07/calibration/MDC103_W050_2025-05-07_unknown.lut
[🏁] Finished processing MDC103_W050_2025-05-07


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