🟢 Starting Calibration Pipeline

=== Processing MDCSL001_W003(6)_2025-07-09 ===

-------------------------------------------------------------------
                        Step 1: Digitization                       
-------------------------------------------------------------------

[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process

📁 Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s]

→ 1500 (23 images):   0%|          | 0/23 [00:00<?, ?it/s]

→ 1500 (23 images):   4%|▍         | 1/23 [00:02<00:54,  2.47s/it]

→ 1500 (23 images):  17%|█▋        | 4/23 [00:02<00:09,  1.99it/s]

→ 1500 (23 images):  35%|███▍      | 8/23 [00:02<00:03,  4.68it/s]

→ 1500 (23 images):  48%|████▊     | 11/23 [00:03<00:02,  4.62it/s]

→ 1500 (23 images):  57%|█████▋    | 13/23 [00:03<00:01,  5.82it/s]

→ 1500 (23 images):  83%|████████▎ | 19/23 [00:03<00:00, 11.13it/s]

                                                                   
📁 Processing Rigs:  25%|██▌       | 1/4 [00:03<00:11,  3.70s/it]

→ 2000R (24 images):   0%|          | 0/24 [00:00<?, ?it/s]

→ 2000R (24 images):   4%|▍         | 1/24 [00:02<00:56,  2.47s/it]

→ 2000R (24 images):  12%|█▎        | 3/24 [00:02<00:14,  1.45it/s]

→ 2000R (24 images):  21%|██        | 5/24 [00:02<00:06,  2.76it/s]

→ 2000R (24 images):  33%|███▎      | 8/24 [00:02<00:03,  5.10it/s]

→ 2000R (24 images):  42%|████▏     | 10/24 [00:03<00:02,  4.90it/s]

→ 2000R (24 images):  50%|█████     | 12/24 [00:03<00:01,  6.03it/s]

→ 2000R (24 images):  71%|███████   | 17/24 [00:03<00:00, 11.14it/s]

→ 2000R (24 images):  96%|█████████▌| 23/24 [00:03<00:00, 17.86it/s]

                                                                    
📁 Processing Rigs:  50%|█████     | 2/4 [00:07<00:07,  3.74s/it]

→ 1000 (19 images):   0%|          | 0/19 [00:00<?, ?it/s]

→ 1000 (19 images):   5%|▌         | 1/19 [00:02<00:44,  2.46s/it]

→ 1000 (19 images):  26%|██▋       | 5/19 [00:02<00:05,  2.56it/s]

→ 1000 (19 images):  58%|█████▊    | 11/19 [00:02<00:01,  6.63it/s]

→ 1000 (19 images):  79%|███████▉  | 15/19 [00:03<00:00,  5.87it/s]

                                                                   
📁 Processing Rigs:  75%|███████▌  | 3/4 [00:11<00:03,  3.68s/it]

→ 2000L (28 images):   0%|          | 0/28 [00:00<?, ?it/s]

→ 2000L (28 images):   4%|▎         | 1/28 [00:02<01:09,  2.57s/it]

→ 2000L (28 images):  14%|█▍        | 4/28 [00:02<00:13,  1.84it/s]

→ 2000L (28 images):  18%|█▊        | 5/28 [00:03<00:12,  1.80it/s]

→ 2000L (28 images):  21%|██▏       | 6/28 [00:03<00:09,  2.32it/s]

→ 2000L (28 images):  36%|███▌      | 10/28 [00:03<00:03,  5.52it/s]

→ 2000L (28 images):  50%|█████     | 14/28 [00:03<00:01,  9.11it/s]

→ 2000L (28 images):  61%|██████    | 17/28 [00:03<00:01, 10.45it/s]

→ 2000L (28 images):  79%|███████▊  | 22/28 [00:04<00:00, 16.07it/s]

→ 2000L (28 images): 100%|██████████| 28/28 [00:04<00:00, 22.93it/s]

                                                                    
📁 Processing Rigs: 100%|██████████| 4/4 [00:15<00:00,  3.87s/it]
📁 Processing Rigs: 100%|██████████| 4/4 [00:15<00:00,  3.81s/it]
[💾] Saved RigFrameDataset to /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 94 frames from 4 rigs to /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 23 frames selected from 23
[📋] 2000R: 24 frames selected from 24
[📋] 1000: 19 frames selected from 19
[📋] 2000L: 28 frames selected from 28
[💾] Saved RigFrameDataset to /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/1_digitized/rig_frames.json

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

[INFO] Using 19 total sampled bright frames

Averaging bright frames:   0%|          | 0/19 [00:00<?, ?it/s]
Averaging bright frames:  21%|██        | 4/19 [00:00<00:00, 34.04it/s]
Averaging bright frames:  47%|████▋     | 9/19 [00:00<00:00, 41.49it/s]
Averaging bright frames:  74%|███████▎  | 14/19 [00:00<00:00, 40.59it/s]
Averaging bright frames: 100%|██████████| 19/19 [00:00<00:00, 42.91it/s]
                                                                        
Total time to process (v2): 0.07
[RESULT] Combined: Center=(874, 862), Radius=881

  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:45,  2.19it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:45,  2.19it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:44,  2.19it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 335.354} (0.001):   3%|▎         | 3/100 [00:00<00:44,  2.19it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 335.354} (0.001):   4%|▍         | 4/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 335.354} (0.001):   4%|▍         | 4/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 342.424} (0.001):   5%|▌         | 5/100 [00:00<00:11,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 342.424} (0.001):   6%|▌         | 6/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:10,  8.60it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   9%|▉         | 9/100 [00:00<00:05, 16.58it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.002 | Best: {'fx': 370.707} (0.002):   9%|▉         | 9/100 [00:00<00:05, 16.58it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:05, 16.58it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  11%|█         | 11/100 [00:00<00:05, 16.58it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.003 | Best: {'fx': 391.919} (0.004):  12%|█▏        | 12/100 [00:00<00:05, 16.58it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.002 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:05, 16.58it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:05, 16.58it/s] 
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 23.08it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  15%|█▌        | 15/100 [00:00<00:03, 23.08it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  16%|█▌        | 16/100 [00:00<00:03, 23.08it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  17%|█▋        | 17/100 [00:00<00:03, 23.08it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  18%|█▊        | 18/100 [00:00<00:03, 24.43it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  18%|█▊        | 18/100 [00:01<00:03, 24.43it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 448.485} (0.011):  19%|█▉        | 19/100 [00:01<00:03, 24.43it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  20%|██        | 20/100 [00:01<00:03, 24.43it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.005 | Best: {'fx': 455.556} (0.013):  21%|██        | 21/100 [00:01<00:03, 24.43it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  22%|██▏       | 22/100 [00:01<00:03, 24.43it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 30.05it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.007 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 30.05it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 30.05it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.066 | Best: {'fx': 519.192} (0.066):  25%|██▌       | 25/100 [00:01<00:02, 30.05it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.032 | Best: {'fx': 519.192} (0.066):  26%|██▌       | 26/100 [00:01<00:02, 30.05it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.032 | Best: {'fx': 519.192} (0.066):  27%|██▋       | 27/100 [00:01<00:02, 28.55it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.081 | Best: {'fx': 526.263} (0.081):  27%|██▋       | 27/100 [00:01<00:02, 28.55it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.027 | Best: {'fx': 526.263} (0.081):  28%|██▊       | 28/100 [00:01<00:02, 28.55it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  29%|██▉       | 29/100 [00:01<00:02, 28.55it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.058 | Best: {'fx': 526.263} (0.081):  30%|███       | 30/100 [00:01<00:02, 28.55it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.058 | Best: {'fx': 526.263} (0.081):  31%|███       | 31/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.076 | Best: {'fx': 526.263} (0.081):  31%|███       | 31/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.045 | Best: {'fx': 526.263} (0.081):  32%|███▏      | 32/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.039 | Best: {'fx': 526.263} (0.081):  33%|███▎      | 33/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.048 | Best: {'fx': 526.263} (0.081):  34%|███▍      | 34/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.057 | Best: {'fx': 526.263} (0.081):  35%|███▌      | 35/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.067 | Best: {'fx': 526.263} (0.081):  36%|███▌      | 36/100 [00:01<00:02, 30.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.067 | Best: {'fx': 526.263} (0.081):  37%|███▋      | 37/100 [00:01<00:01, 37.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.052 | Best: {'fx': 526.263} (0.081):  37%|███▋      | 37/100 [00:01<00:01, 37.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.041 | Best: {'fx': 526.263} (0.081):  38%|███▊      | 38/100 [00:01<00:01, 37.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.038 | Best: {'fx': 526.263} (0.081):  39%|███▉      | 39/100 [00:01<00:01, 37.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 526.263} (0.081):  40%|████      | 40/100 [00:01<00:01, 37.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.035 | Best: {'fx': 526.263} (0.081):  41%|████      | 41/100 [00:01<00:01, 37.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.035 | Best: {'fx': 526.263} (0.081):  42%|████▏     | 42/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.033 | Best: {'fx': 526.263} (0.081):  42%|████▏     | 42/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.032 | Best: {'fx': 526.263} (0.081):  43%|████▎     | 43/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.030 | Best: {'fx': 526.263} (0.081):  44%|████▍     | 44/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 526.263} (0.081):  45%|████▌     | 45/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 526.263} (0.081):  46%|████▌     | 46/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.029 | Best: {'fx': 526.263} (0.081):  47%|████▋     | 47/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.026 | Best: {'fx': 526.263} (0.081):  48%|████▊     | 48/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.027 | Best: {'fx': 526.263} (0.081):  49%|████▉     | 49/100 [00:01<00:01, 36.88it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.027 | Best: {'fx': 526.263} (0.081):  50%|█████     | 50/100 [00:01<00:01, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 526.263} (0.081):  50%|█████     | 50/100 [00:01<00:01, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 526.263} (0.081):  51%|█████     | 51/100 [00:01<00:01, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 526.263} (0.081):  52%|█████▏    | 52/100 [00:01<00:01, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.081):  53%|█████▎    | 53/100 [00:01<00:00, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 526.263} (0.081):  54%|█████▍    | 54/100 [00:01<00:00, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.081):  55%|█████▌    | 55/100 [00:01<00:00, 47.37it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.081):  56%|█████▌    | 56/100 [00:01<00:01, 39.84it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.081):  56%|█████▌    | 56/100 [00:01<00:01, 39.84it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  57%|█████▋    | 57/100 [00:01<00:01, 39.84it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.081):  58%|█████▊    | 58/100 [00:02<00:01, 39.84it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.081):  59%|█████▉    | 59/100 [00:02<00:01, 39.84it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  60%|██████    | 60/100 [00:02<00:01, 39.84it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  61%|██████    | 61/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.081):  61%|██████    | 61/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  62%|██████▏   | 62/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.081):  63%|██████▎   | 63/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.081):  64%|██████▍   | 64/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  65%|██████▌   | 65/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  66%|██████▌   | 66/100 [00:02<00:00, 39.38it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  67%|██████▋   | 67/100 [00:02<00:00, 43.78it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  67%|██████▋   | 67/100 [00:02<00:00, 43.78it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.081):  68%|██████▊   | 68/100 [00:02<00:00, 43.78it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  69%|██████▉   | 69/100 [00:02<00:00, 43.78it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  70%|███████   | 70/100 [00:02<00:00, 43.78it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  71%|███████   | 71/100 [00:02<00:00, 43.78it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  72%|███████▏  | 72/100 [00:02<00:00, 44.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  72%|███████▏  | 72/100 [00:02<00:00, 44.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  73%|███████▎  | 73/100 [00:02<00:00, 44.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  74%|███████▍  | 74/100 [00:02<00:00, 44.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.081):  75%|███████▌  | 75/100 [00:02<00:00, 44.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  76%|███████▌  | 76/100 [00:02<00:00, 44.17it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  77%|███████▋  | 77/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  77%|███████▋  | 77/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  78%|███████▊  | 78/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  79%|███████▉  | 79/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  80%|████████  | 80/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  81%|████████  | 81/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  82%|████████▏ | 82/100 [00:02<00:00, 41.33it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  83%|████████▎ | 83/100 [00:02<00:00, 43.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  83%|████████▎ | 83/100 [00:02<00:00, 43.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  84%|████████▍ | 84/100 [00:02<00:00, 43.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  85%|████████▌ | 85/100 [00:02<00:00, 43.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  86%|████████▌ | 86/100 [00:02<00:00, 43.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  87%|████████▋ | 87/100 [00:02<00:00, 43.94it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  88%|████████▊ | 88/100 [00:02<00:00, 43.96it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  88%|████████▊ | 88/100 [00:02<00:00, 43.96it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.018 | Best: {'fx': 526.263} (0.081):  89%|████████▉ | 89/100 [00:02<00:00, 43.96it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  90%|█████████ | 90/100 [00:02<00:00, 43.96it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  91%|█████████ | 91/100 [00:02<00:00, 43.96it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  92%|█████████▏| 92/100 [00:02<00:00, 43.96it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  93%|█████████▎| 93/100 [00:02<00:00, 42.83it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  93%|█████████▎| 93/100 [00:02<00:00, 42.83it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  94%|█████████▍| 94/100 [00:02<00:00, 42.83it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  95%|█████████▌| 95/100 [00:02<00:00, 42.83it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  96%|█████████▌| 96/100 [00:02<00:00, 42.83it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  97%|█████████▋| 97/100 [00:02<00:00, 42.83it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  98%|█████████▊| 98/100 [00:02<00:00, 42.59it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  98%|█████████▊| 98/100 [00:02<00:00, 42.59it/s]
Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 526.263} (0.081):  99%|█████████▉| 99/100 [00:02<00:00, 42.59it/s]
                                                                                                                                       

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.044 | Best: {'fx': 502.283} (0.044):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.044 | Best: {'fx': 502.283} (0.044):   2%|▏         | 1/50 [00:00<00:20,  2.39it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.043 | Best: {'fx': 502.283} (0.044):   2%|▏         | 1/50 [00:00<00:20,  2.39it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.045 | Best: {'fx': 503.303} (0.045):   4%|▍         | 2/50 [00:00<00:20,  2.39it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.047 | Best: {'fx': 504.324} (0.047):   6%|▌         | 3/50 [00:00<00:19,  2.39it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.049 | Best: {'fx': 505.344} (0.049):   8%|▊         | 4/50 [00:00<00:19,  2.39it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.050 | Best: {'fx': 506.365} (0.050):  10%|█         | 5/50 [00:00<00:18,  2.39it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.050 | Best: {'fx': 506.365} (0.050):  12%|█▏        | 6/50 [00:00<00:03, 13.81it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.051 | Best: {'fx': 507.385} (0.051):  12%|█▏        | 6/50 [00:00<00:03, 13.81it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.053 | Best: {'fx': 509.426} (0.053):  14%|█▍        | 7/50 [00:00<00:03, 13.81it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.054 | Best: {'fx': 510.446} (0.054):  16%|█▌        | 8/50 [00:00<00:03, 13.81it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.056 | Best: {'fx': 511.467} (0.056):  18%|█▊        | 9/50 [00:00<00:02, 13.81it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.056 | Best: {'fx': 511.467} (0.056):  20%|██        | 10/50 [00:00<00:02, 19.28it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.052 | Best: {'fx': 511.467} (0.056):  20%|██        | 10/50 [00:00<00:02, 19.28it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.058 | Best: {'fx': 512.487} (0.058):  22%|██▏       | 11/50 [00:00<00:02, 19.28it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.065 | Best: {'fx': 517.589} (0.065):  24%|██▍       | 12/50 [00:00<00:01, 19.28it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.065 | Best: {'fx': 517.589} (0.065):  26%|██▌       | 13/50 [00:00<00:02, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.067 | Best: {'fx': 519.63} (0.067):  26%|██▌       | 13/50 [00:00<00:02, 17.83it/s] 
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.059 | Best: {'fx': 519.63} (0.067):  28%|██▊       | 14/50 [00:00<00:02, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.061 | Best: {'fx': 519.63} (0.067):  30%|███       | 15/50 [00:00<00:01, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.068 | Best: {'fx': 520.65} (0.068):  32%|███▏      | 16/50 [00:00<00:01, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.063 | Best: {'fx': 520.65} (0.068):  34%|███▍      | 17/50 [00:00<00:01, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.065 | Best: {'fx': 520.65} (0.068):  36%|███▌      | 18/50 [00:00<00:01, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.064 | Best: {'fx': 520.65} (0.068):  38%|███▊      | 19/50 [00:00<00:01, 17.83it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.064 | Best: {'fx': 520.65} (0.068):  40%|████      | 20/50 [00:00<00:01, 29.18it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.074 | Best: {'fx': 523.712} (0.074):  40%|████      | 20/50 [00:01<00:01, 29.18it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.070 | Best: {'fx': 523.712} (0.074):  42%|████▏     | 21/50 [00:01<00:00, 29.18it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.080 | Best: {'fx': 525.752} (0.080):  44%|████▍     | 22/50 [00:01<00:00, 29.18it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.072 | Best: {'fx': 525.752} (0.080):  46%|████▌     | 23/50 [00:01<00:00, 29.18it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.072 | Best: {'fx': 525.752} (0.080):  48%|████▊     | 24/50 [00:01<00:00, 29.23it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.083 | Best: {'fx': 526.773} (0.083):  48%|████▊     | 24/50 [00:01<00:00, 29.23it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.077 | Best: {'fx': 526.773} (0.083):  50%|█████     | 25/50 [00:01<00:00, 29.23it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.088 | Best: {'fx': 528.814} (0.088):  52%|█████▏    | 26/50 [00:01<00:00, 29.23it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.085 | Best: {'fx': 528.814} (0.088):  54%|█████▍    | 27/50 [00:01<00:00, 29.23it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.085 | Best: {'fx': 528.814} (0.088):  56%|█████▌    | 28/50 [00:01<00:00, 30.79it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.084 | Best: {'fx': 528.814} (0.088):  56%|█████▌    | 28/50 [00:01<00:00, 30.79it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.082 | Best: {'fx': 528.814} (0.088):  58%|█████▊    | 29/50 [00:01<00:00, 30.79it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.075 | Best: {'fx': 528.814} (0.088):  60%|██████    | 30/50 [00:01<00:00, 30.79it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.073 | Best: {'fx': 528.814} (0.088):  62%|██████▏   | 31/50 [00:01<00:00, 30.79it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.073 | Best: {'fx': 528.814} (0.088):  64%|██████▍   | 32/50 [00:01<00:00, 32.14it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.072 | Best: {'fx': 528.814} (0.088):  64%|██████▍   | 32/50 [00:01<00:00, 32.14it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.080 | Best: {'fx': 528.814} (0.088):  66%|██████▌   | 33/50 [00:01<00:00, 32.14it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.068 | Best: {'fx': 528.814} (0.088):  68%|██████▊   | 34/50 [00:01<00:00, 32.14it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.077 | Best: {'fx': 528.814} (0.088):  70%|███████   | 35/50 [00:01<00:00, 32.14it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.071 | Best: {'fx': 528.814} (0.088):  72%|███████▏  | 36/50 [00:01<00:00, 32.14it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.071 | Best: {'fx': 528.814} (0.088):  74%|███████▍  | 37/50 [00:01<00:00, 34.66it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.067 | Best: {'fx': 528.814} (0.088):  74%|███████▍  | 37/50 [00:01<00:00, 34.66it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.070 | Best: {'fx': 528.814} (0.088):  76%|███████▌  | 38/50 [00:01<00:00, 34.66it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.066 | Best: {'fx': 528.814} (0.088):  78%|███████▊  | 39/50 [00:01<00:00, 34.66it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.063 | Best: {'fx': 528.814} (0.088):  80%|████████  | 40/50 [00:01<00:00, 34.66it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.065 | Best: {'fx': 528.814} (0.088):  82%|████████▏ | 41/50 [00:01<00:00, 34.66it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.065 | Best: {'fx': 528.814} (0.088):  84%|████████▍ | 42/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.061 | Best: {'fx': 528.814} (0.088):  84%|████████▍ | 42/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.065 | Best: {'fx': 528.814} (0.088):  86%|████████▌ | 43/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.059 | Best: {'fx': 528.814} (0.088):  88%|████████▊ | 44/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.057 | Best: {'fx': 528.814} (0.088):  90%|█████████ | 45/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.062 | Best: {'fx': 528.814} (0.088):  92%|█████████▏| 46/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.055 | Best: {'fx': 528.814} (0.088):  94%|█████████▍| 47/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.056 | Best: {'fx': 528.814} (0.088):  96%|█████████▌| 48/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.054 | Best: {'fx': 528.814} (0.088):  98%|█████████▊| 49/50 [00:01<00:00, 37.09it/s]
Brute | Start: {'fx': 526.263} (0.081) | Current: 0.054 | Best: {'fx': 528.814} (0.088): 100%|██████████| 50/50 [00:01<00:00, 46.58it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 528.8136466707896
    px: 874.0
    py: 862.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 7 frames per rig (excluded 0 incomplete frames): 28 frames will be used for optimization
  Rigs: 1500(1500mm):7/20, 2000R(2000mm):7/20, 1000(1000mm):7/19, 2000L(2000mm):7/20
  Total: 28/79 frames used for optimization

Initial score: 27.3744: 0iter [00:00, ?iter/s]
Initial score: 27.3744: 1iter [00:03,  3.30s/iter]
Initial score: 27.3744: 2iter [00:04,  2.34s/iter]
Initial score: 27.3744: 3iter [00:06,  1.98s/iter]
Initial score: 27.3744: 4iter [00:08,  1.87s/iter]
Initial score: 27.3744: 5iter [00:09,  1.78s/iter]
Initial score: 27.3744: 6iter [00:11,  1.74s/iter]
Initial score: 27.3744: 7iter [00:13,  1.72s/iter]
Initial score: 27.3744: 8iter [00:14,  1.69s/iter]
Initial score: 27.3744: 9iter [00:16,  1.69s/iter]
Initial score: 27.3744: 10iter [00:18,  1.67s/iter]
Initial score: 27.3744: 11iter [00:19,  1.67s/iter]
Initial score: 27.3744: 12iter [00:21,  1.68s/iter]
Initial score: 27.3744: 13iter [00:23,  1.68s/iter]
Initial score: 27.3744: 14iter [00:24,  1.68s/iter]
Initial score: 27.3744: 15iter [00:26,  1.68s/iter]
Initial score: 27.3744: 16iter [00:28,  1.68s/iter]
Best score: 15.4694: : 16iter [00:29,  1.68s/iter] 
Best score: 15.4694: : 17iter [00:29,  1.65s/iter]
Best score: 15.4694: : 18iter [00:31,  1.65s/iter]
Best score: 15.4694: : 19iter [00:33,  1.67s/iter]
Best score: 15.4694: : 20iter [00:34,  1.66s/iter]
Best score: 15.4694: : 21iter [00:36,  1.67s/iter]
Best score: 15.4694: : 22iter [00:38,  1.67s/iter]
Best score: 15.4694: : 23iter [00:39,  1.67s/iter]
Best score: 15.1216: : 23iter [00:41,  1.67s/iter]
Best score: 15.1216: : 24iter [00:41,  1.67s/iter]
Best score: 15.1216: : 25iter [00:43,  1.68s/iter]
Best score: 15.1216: : 26iter [00:44,  1.68s/iter]
Best score: 15.1216: : 27iter [00:46,  1.68s/iter]
Best score: 15.1216: : 28iter [00:48,  1.67s/iter]
Best score: 15.1216: : 29iter [00:49,  1.68s/iter]
Best score: 15.1216: : 30iter [00:51,  1.67s/iter]
Best score: 15.1216: : 31iter [00:53,  1.67s/iter]
Best score: 15.1216: : 32iter [00:54,  1.66s/iter]
Best score: 15.1216: : 33iter [00:56,  1.68s/iter]
Best score: 15.1216: : 34iter [00:58,  1.66s/iter]
Best score: 15.1216: : 35iter [00:59,  1.66s/iter]
Best score: 15.1216: : 36iter [01:01,  1.66s/iter]
Best score: 13.9948: : 36iter [01:03,  1.66s/iter]
Best score: 13.9948: : 37iter [01:03,  1.66s/iter]
Best score: 13.9948: : 38iter [01:04,  1.65s/iter]
Best score: 13.9948: : 39iter [01:06,  1.64s/iter]
Best score: 13.9948: : 40iter [01:08,  1.63s/iter]
Best score: 13.9948: : 41iter [01:09,  1.62s/iter]
Best score: 13.9948: : 42iter [01:11,  1.64s/iter]
Best score: 13.9948: : 43iter [01:12,  1.64s/iter]
Best score: 13.9948: : 44iter [01:14,  1.64s/iter]
Best score: 13.9948: : 45iter [01:16,  1.66s/iter]
Best score: 13.9948: : 46iter [01:18,  1.67s/iter]
Best score: 13.9948: : 47iter [01:19,  1.66s/iter]
Best score: 13.9948: : 48iter [01:21,  1.67s/iter]
Best score: 13.9948: : 49iter [01:22,  1.66s/iter]
[ITER 50] Current best score: 5.6040
[PARAMS] Current best parameters:
  fx: 528.4390
  fy: 528.2367
  px: 864.8368
  py: 862.5829
  k1: 0.01056359
  k2: 0.01872402
  k3: -0.02566084
  k4: 0.00725739
  p1: 0.00285570
  p2: 0.00267128
  p3: -0.00669740
  p4: -0.00732572
  scale: 2.9300
  rotation: -0.4199

Best score: 5.6040: : 49iter [01:24,  1.66s/iter] 
Best score: 5.6040: : 50iter [01:24,  1.71s/iter]
Best score: 3.7196: : 50iter [01:26,  1.71s/iter]
Best score: 3.7196: : 51iter [01:26,  1.69s/iter]
Best score: 3.7196: : 52iter [01:28,  1.67s/iter]
Best score: 3.7196: : 53iter [01:29,  1.67s/iter]
Best score: 3.7196: : 54iter [01:31,  1.68s/iter]
Best score: 3.7196: : 55iter [01:33,  1.68s/iter]
Best score: 3.7196: : 56iter [01:34,  1.67s/iter]
Best score: 3.7196: : 57iter [01:36,  1.66s/iter]
Best score: 3.7196: : 58iter [01:38,  1.67s/iter]
Best score: 3.7196: : 59iter [01:39,  1.67s/iter]
Best score: 3.7196: : 60iter [01:41,  1.65s/iter]
Best score: 3.7196: : 61iter [01:42,  1.63s/iter]
Best score: 3.7196: : 62iter [01:44,  1.64s/iter]
Best score: 3.7196: : 63iter [01:46,  1.64s/iter]
Best score: 3.7196: : 64iter [01:47,  1.65s/iter]
Best score: 3.7196: : 65iter [01:49,  1.64s/iter]
Best score: 3.7196: : 66iter [01:51,  1.65s/iter]
Best score: 2.9076: : 66iter [01:52,  1.65s/iter]
Best score: 2.9076: : 67iter [01:52,  1.65s/iter]
Best score: 2.9076: : 68iter [01:54,  1.66s/iter]
Best score: 2.9076: : 69iter [01:56,  1.65s/iter]
Best score: 2.9076: : 70iter [01:57,  1.66s/iter]
Best score: 2.9076: : 71iter [01:59,  1.66s/iter]
Best score: 2.9076: : 72iter [02:01,  1.66s/iter]
Best score: 2.9076: : 73iter [02:02,  1.65s/iter]
Best score: 2.9076: : 74iter [02:04,  1.63s/iter]
Best score: 2.9076: : 75iter [02:06,  1.64s/iter]
Best score: 2.9076: : 76iter [02:07,  1.65s/iter]
Best score: 2.9076: : 77iter [02:09,  1.63s/iter]
Best score: 2.5412: : 77iter [02:10,  1.63s/iter]
Best score: 2.5412: : 78iter [02:10,  1.64s/iter]
Best score: 2.5412: : 79iter [02:12,  1.64s/iter]
Best score: 2.5412: : 80iter [02:14,  1.64s/iter]
Best score: 2.5412: : 81iter [02:15,  1.64s/iter]
Best score: 2.5412: : 82iter [02:17,  1.65s/iter]
Best score: 2.5412: : 83iter [02:19,  1.67s/iter]
Best score: 2.5412: : 84iter [02:20,  1.67s/iter]
Best score: 2.5412: : 85iter [02:22,  1.66s/iter]
Best score: 2.5412: : 86iter [02:24,  1.66s/iter]
Best score: 2.5412: : 87iter [02:25,  1.65s/iter]
Best score: 1.7818: : 87iter [02:27,  1.65s/iter]
Best score: 1.7818: : 88iter [02:27,  1.64s/iter]
Best score: 1.7818: : 89iter [02:29,  1.64s/iter]
Best score: 1.7818: : 90iter [02:30,  1.64s/iter]
Best score: 1.7818: : 91iter [02:32,  1.64s/iter]
Best score: 1.7818: : 92iter [02:34,  1.66s/iter]
Best score: 1.7818: : 93iter [02:35,  1.66s/iter]
Best score: 1.7818: : 94iter [02:37,  1.67s/iter]
Best score: 1.7818: : 95iter [02:39,  1.66s/iter]
Best score: 1.7818: : 96iter [02:40,  1.67s/iter]
Best score: 1.7818: : 97iter [02:42,  1.68s/iter]
Best score: 1.2324: : 97iter [02:44,  1.68s/iter]
Best score: 1.2324: : 98iter [02:44,  1.67s/iter]
Best score: 1.2324: : 99iter [02:45,  1.66s/iter]
[ITER 100] Current best score: 1.2324
[PARAMS] Current best parameters:
  fx: 515.6077
  fy: 515.3027
  px: 863.1322
  py: 861.9700
  k1: 0.00091779
  k2: 0.02511951
  k3: -0.01762862
  k4: 0.00344606
  p1: 0.00086486
  p2: 0.00170812
  p3: -0.00014521
  p4: -0.00571477
  scale: 3.0192
  rotation: -0.7763

Best score: 1.2324: : 100iter [02:47,  1.72s/iter]
Best score: 1.2324: : 101iter [02:49,  1.71s/iter]
Best score: 1.2324: : 102iter [02:50,  1.68s/iter]
Best score: 1.2324: : 103iter [02:52,  1.69s/iter]
Best score: 1.2324: : 104iter [02:54,  1.68s/iter]
Best score: 1.2324: : 105iter [02:55,  1.68s/iter]
Best score: 1.2324: : 106iter [02:57,  1.67s/iter]
Best score: 1.2324: : 107iter [02:59,  1.67s/iter]
Best score: 1.2324: : 108iter [03:00,  1.66s/iter]
Best score: 1.2324: : 109iter [03:02,  1.65s/iter]
Best score: 1.2324: : 110iter [03:04,  1.64s/iter]
Best score: 1.2324: : 111iter [03:05,  1.63s/iter]
Best score: 1.2324: : 112iter [03:07,  1.64s/iter]
Best score: 1.2324: : 113iter [03:09,  1.66s/iter]
Best score: 1.2324: : 114iter [03:10,  1.68s/iter]
Best score: 1.2324: : 115iter [03:12,  1.68s/iter]
Best score: 1.2324: : 116iter [03:14,  1.67s/iter]
Best score: 1.2324: : 117iter [03:15,  1.68s/iter]
Best score: 1.2324: : 118iter [03:17,  1.68s/iter]
Best score: 1.2324: : 119iter [03:19,  1.68s/iter]
Best score: 1.2128: : 119iter [03:20,  1.68s/iter]
Best score: 1.2128: : 120iter [03:20,  1.67s/iter]
Best score: 1.2128: : 121iter [03:22,  1.67s/iter]
Best score: 1.2128: : 122iter [03:24,  1.67s/iter]
Best score: 1.2128: : 123iter [03:26,  1.68s/iter]
Best score: 1.2128: : 124iter [03:27,  1.67s/iter]
Best score: 1.2128: : 125iter [03:29,  1.66s/iter]
Best score: 1.2128: : 126iter [03:30,  1.67s/iter]
Best score: 1.2128: : 127iter [03:32,  1.66s/iter]
Best score: 1.2128: : 128iter [03:34,  1.66s/iter]
Best score: 1.2128: : 129iter [03:35,  1.63s/iter]
Best score: 0.3849: : 129iter [03:37,  1.63s/iter]
Best score: 0.3849: : 130iter [03:37,  1.64s/iter]
Best score: 0.3849: : 131iter [03:39,  1.66s/iter]
Best score: 0.3849: : 132iter [03:40,  1.67s/iter]
Best score: 0.3849: : 133iter [03:42,  1.64s/iter]
Best score: 0.3849: : 134iter [03:44,  1.66s/iter]
Best score: 0.3849: : 135iter [03:45,  1.67s/iter]
Best score: 0.3849: : 136iter [03:47,  1.67s/iter]
Best score: 0.3849: : 137iter [03:49,  1.68s/iter]
Best score: 0.3849: : 138iter [03:50,  1.66s/iter]
Best score: 0.3849: : 139iter [03:52,  1.65s/iter]
Best score: 0.3849: : 140iter [03:54,  1.66s/iter]
Best score: 0.3849: : 141iter [03:55,  1.66s/iter]
Best score: 0.3849: : 142iter [03:57,  1.66s/iter]
Best score: 0.3849: : 143iter [03:59,  1.67s/iter]
Best score: 0.3849: : 144iter [04:00,  1.68s/iter]
Best score: 0.3849: : 145iter [04:02,  1.67s/iter]
Best score: 0.3849: : 146iter [04:04,  1.66s/iter]
Best score: 0.3849: : 147iter [04:05,  1.67s/iter]
Best score: 0.3849: : 148iter [04:07,  1.68s/iter]
Best score: 0.3849: : 149iter [04:09,  1.67s/iter]
[ITER 150] Current best score: 0.3849
[PARAMS] Current best parameters:
  fx: 502.8797
  fy: 502.5106
  px: 865.3679
  py: 858.0210
  k1: 0.01051446
  k2: 0.02125113
  k3: -0.00807094
  k4: -0.00016153
  p1: -0.00893363
  p2: 0.00489305
  p3: -0.00686037
  p4: -0.00023967
  scale: 3.0151
  rotation: -0.9040

Best score: 0.3849: : 150iter [04:11,  1.73s/iter]
Best score: 0.3849: : 151iter [04:12,  1.71s/iter]
Best score: 0.3849: : 152iter [04:14,  1.71s/iter]
Best score: 0.3849: : 153iter [04:16,  1.68s/iter]
Best score: 0.3849: : 154iter [04:17,  1.67s/iter]
Best score: 0.3849: : 155iter [04:19,  1.66s/iter]
Best score: 0.3849: : 156iter [04:21,  1.67s/iter]
Best score: 0.3849: : 157iter [04:22,  1.66s/iter]
Best score: 0.3849: : 158iter [04:24,  1.67s/iter]
Best score: 0.3849: : 159iter [04:26,  1.67s/iter]
Best score: 0.3849: : 160iter [04:27,  1.68s/iter]
Best score: 0.3849: : 161iter [04:29,  1.68s/iter]
Best score: 0.3849: : 162iter [04:31,  1.67s/iter]
Best score: 0.3849: : 163iter [04:32,  1.67s/iter]
Best score: 0.3849: : 164iter [04:34,  1.67s/iter]
Best score: 0.3849: : 165iter [04:36,  1.67s/iter]
Best score: 0.3849: : 166iter [04:37,  1.66s/iter]
Best score: 0.3849: : 167iter [04:39,  1.66s/iter]
Best score: 0.3849: : 168iter [04:41,  1.67s/iter]
Best score: 0.3849: : 169iter [04:42,  1.64s/iter]
Best score: 0.3849: : 170iter [04:44,  1.66s/iter]
Best score: 0.3849: : 171iter [04:45,  1.65s/iter]
Best score: 0.3849: : 172iter [04:47,  1.66s/iter]
Best score: 0.3849: : 173iter [04:49,  1.68s/iter]
Best score: 0.3849: : 174iter [04:51,  1.67s/iter]
Best score: 0.3716: : 174iter [04:52,  1.67s/iter]
Best score: 0.3716: : 175iter [04:52,  1.66s/iter]
Best score: 0.3716: : 176iter [04:54,  1.67s/iter]
Best score: 0.3716: : 177iter [04:55,  1.64s/iter]
Best score: 0.3716: : 178iter [04:57,  1.65s/iter]
Best score: 0.3716: : 179iter [04:59,  1.64s/iter]
Best score: 0.3716: : 180iter [05:00,  1.66s/iter]
Best score: 0.3716: : 181iter [05:02,  1.66s/iter]
Best score: 0.3716: : 182iter [05:04,  1.66s/iter]
Best score: 0.3716: : 183iter [05:05,  1.65s/iter]
Best score: 0.3716: : 184iter [05:07,  1.65s/iter]
Best score: 0.3716: : 185iter [05:09,  1.66s/iter]
Best score: 0.3716: : 186iter [05:10,  1.65s/iter]
Best score: 0.3716: : 187iter [05:12,  1.65s/iter]
Best score: 0.3716: : 188iter [05:14,  1.66s/iter]
Best score: 0.3716: : 189iter [05:15,  1.66s/iter]
Best score: 0.3716: : 190iter [05:17,  1.67s/iter]
Best score: 0.3716: : 191iter [05:19,  1.65s/iter]
Best score: 0.3716: : 192iter [05:20,  1.65s/iter]
Best score: 0.3716: : 193iter [05:22,  1.64s/iter]
Best score: 0.3716: : 194iter [05:24,  1.66s/iter]
Best score: 0.3716: : 195iter [05:25,  1.65s/iter]
Best score: 0.3716: : 196iter [05:27,  1.66s/iter]
Best score: 0.3716: : 197iter [05:29,  1.70s/iter]
Best score: 0.3716: : 198iter [05:30,  1.69s/iter]
Best score: 0.3716: : 199iter [05:32,  1.69s/iter]
[ITER 200] Current best score: 0.4033
[PARAMS] Current best parameters:
  fx: 513.9673
  fy: 514.3787
  px: 866.0889
  py: 860.6653
  k1: 0.00385072
  k2: 0.02290464
  k3: -0.01491280
  k4: 0.00259792
  p1: -0.00495607
  p2: 0.00608615
  p3: -0.00375942
  p4: -0.00129864
  scale: 3.0036
  rotation: -0.8678

Best score: 0.3716: : 200iter [05:34,  1.74s/iter]
Best score: 0.3716: : 201iter [05:36,  1.72s/iter]
Best score: 0.3716: : 202iter [05:37,  1.70s/iter]
Best score: 0.3716: : 203iter [05:39,  1.69s/iter]
Best score: 0.3716: : 204iter [05:41,  1.66s/iter]
Best score: 0.3716: : 205iter [05:42,  1.68s/iter]
Best score: 0.3716: : 206iter [05:44,  1.68s/iter]
Best score: 0.3716: : 207iter [05:46,  1.67s/iter]
Best score: 0.3716: : 208iter [05:47,  1.69s/iter]
Best score: 0.3716: : 209iter [05:49,  1.68s/iter]
Best score: 0.3716: : 210iter [05:51,  1.68s/iter]
Best score: 0.3716: : 211iter [05:52,  1.68s/iter]
Best score: 0.3716: : 212iter [05:54,  1.68s/iter]
Best score: 0.3716: : 213iter [05:56,  1.65s/iter]
Best score: 0.3716: : 214iter [05:57,  1.66s/iter]
Best score: 0.3716: : 215iter [05:59,  1.66s/iter]
Best score: 0.3716: : 216iter [06:01,  1.67s/iter]
Best score: 0.3716: : 217iter [06:02,  1.67s/iter]
Best score: 0.3716: : 218iter [06:04,  1.68s/iter]
Best score: 0.3716: : 219iter [06:06,  1.67s/iter]
Best score: 0.3716: : 220iter [06:07,  1.66s/iter]
Best score: 0.3716: : 221iter [06:09,  1.66s/iter]
Best score: 0.3716: : 222iter [06:11,  1.65s/iter]
Best score: 0.3716: : 223iter [06:12,  1.65s/iter]
Best score: 0.3716: : 224iter [06:14,  1.64s/iter]
Best score: 0.3716: : 225iter [06:15,  1.64s/iter]
Best score: 0.3716: : 226iter [06:17,  1.65s/iter]
Best score: 0.3716: : 227iter [06:19,  1.64s/iter]
Best score: 0.3716: : 228iter [06:20,  1.65s/iter]
Best score: 0.3716: : 229iter [06:22,  1.66s/iter]
Best score: 0.3716: : 230iter [06:24,  1.64s/iter]
Best score: 0.3716: : 231iter [06:25,  1.64s/iter]
Best score: 0.3716: : 232iter [06:27,  1.64s/iter]
Best score: 0.3716: : 233iter [06:29,  1.66s/iter]
Best score: 0.3716: : 234iter [06:30,  1.63s/iter]
Best score: 0.3716: : 235iter [06:32,  1.64s/iter]
Best score: 0.3716: : 236iter [06:33,  1.63s/iter]
Best score: 0.3716: : 237iter [06:35,  1.65s/iter]
Best score: 0.3716: : 238iter [06:37,  1.64s/iter]
Best score: 0.3716: : 239iter [06:38,  1.64s/iter]
Best score: 0.3716: : 240iter [06:40,  1.64s/iter]
Best score: 0.3716: : 241iter [06:42,  1.65s/iter]
Best score: 0.3716: : 242iter [06:43,  1.67s/iter]
Best score: 0.3716: : 243iter [06:45,  1.68s/iter]
Best score: 0.3716: : 244iter [06:47,  1.65s/iter]
Best score: 0.3716: : 245iter [06:48,  1.65s/iter]
Best score: 0.3566: : 245iter [06:50,  1.65s/iter]
Best score: 0.3566: : 246iter [06:50,  1.66s/iter]
Best score: 0.3566: : 247iter [06:52,  1.68s/iter]
Best score: 0.3566: : 248iter [06:53,  1.64s/iter]
Best score: 0.2920: : 248iter [06:55,  1.64s/iter]
Best score: 0.2920: : 249iter [06:55,  1.65s/iter]
[ITER 250] Current best score: 0.2920
[PARAMS] Current best parameters:
  fx: 515.1208
  fy: 515.4741
  px: 864.6611
  py: 860.4579
  k1: -0.00278060
  k2: 0.03098748
  k3: -0.01964311
  k4: 0.00350128
  p1: -0.00372978
  p2: 0.00489794
  p3: -0.00401676
  p4: -0.00284027
  scale: 3.0148
  rotation: -0.8061

Best score: 0.2920: : 250iter [06:57,  1.70s/iter]
Best score: 0.2920: : 251iter [06:58,  1.68s/iter]
Best score: 0.2920: : 252iter [07:00,  1.66s/iter]
Best score: 0.2920: : 253iter [07:02,  1.67s/iter]
Best score: 0.2920: : 254iter [07:03,  1.67s/iter]
Best score: 0.2920: : 255iter [07:05,  1.68s/iter]
Best score: 0.2920: : 256iter [07:07,  1.68s/iter]
Best score: 0.2920: : 257iter [07:09,  1.68s/iter]
Best score: 0.2920: : 258iter [07:10,  1.66s/iter]
Best score: 0.2920: : 259iter [07:12,  1.68s/iter]
Best score: 0.2920: : 260iter [07:14,  1.68s/iter]
Best score: 0.2920: : 261iter [07:15,  1.67s/iter]
Best score: 0.2920: : 262iter [07:17,  1.67s/iter]
Best score: 0.2920: : 263iter [07:19,  1.69s/iter]
Best score: 0.2920: : 264iter [07:20,  1.66s/iter]
Best score: 0.2920: : 265iter [07:22,  1.66s/iter]
Best score: 0.2920: : 266iter [07:24,  1.67s/iter]
Best score: 0.2920: : 267iter [07:25,  1.67s/iter]
Best score: 0.2920: : 268iter [07:27,  1.64s/iter]
Best score: 0.2920: : 269iter [07:29,  1.67s/iter]
Best score: 0.2920: : 270iter [07:30,  1.66s/iter]
Best score: 0.2920: : 271iter [07:32,  1.66s/iter]
Best score: 0.2920: : 272iter [07:33,  1.63s/iter]
Best score: 0.2920: : 273iter [07:35,  1.64s/iter]
Best score: 0.2920: : 274iter [07:37,  1.66s/iter]
Best score: 0.2920: : 275iter [07:38,  1.66s/iter]
Best score: 0.2920: : 276iter [07:40,  1.67s/iter]
Best score: 0.2920: : 277iter [07:42,  1.68s/iter]
Best score: 0.2920: : 278iter [07:44,  1.69s/iter]
Best score: 0.2920: : 279iter [07:45,  1.68s/iter]
Best score: 0.2920: : 280iter [07:47,  1.66s/iter]
Best score: 0.2920: : 281iter [07:48,  1.66s/iter]
Best score: 0.2920: : 282iter [07:50,  1.66s/iter]
Best score: 0.2920: : 283iter [07:52,  1.67s/iter]
Best score: 0.2920: : 284iter [07:53,  1.64s/iter]
Best score: 0.2920: : 285iter [07:55,  1.66s/iter]
Best score: 0.2920: : 286iter [07:57,  1.65s/iter]
Best score: 0.2920: : 287iter [07:58,  1.65s/iter]
Best score: 0.2920: : 288iter [08:00,  1.66s/iter]
Best score: 0.2819: : 288iter [08:02,  1.66s/iter]
Best score: 0.2819: : 289iter [08:02,  1.66s/iter]
Best score: 0.2819: : 290iter [08:03,  1.69s/iter]
Best score: 0.2819: : 291iter [08:05,  1.68s/iter]
Best score: 0.2575: : 291iter [08:07,  1.68s/iter]
Best score: 0.2575: : 292iter [08:07,  1.69s/iter]
Best score: 0.2575: : 293iter [08:08,  1.67s/iter]
Best score: 0.2575: : 294iter [08:10,  1.66s/iter]
Best score: 0.2575: : 295iter [08:12,  1.68s/iter]
Best score: 0.2575: : 296iter [08:13,  1.67s/iter]
Best score: 0.2575: : 297iter [08:15,  1.67s/iter]
Best score: 0.2575: : 298iter [08:17,  1.68s/iter]
Best score: 0.2575: : 299iter [08:19,  1.67s/iter]
[ITER 300] Current best score: 0.2575
[PARAMS] Current best parameters:
  fx: 530.1562
  fy: 530.6719
  px: 864.2042
  py: 860.3516
  k1: -0.01903488
  k2: 0.03888885
  k3: -0.02808934
  k4: 0.00644361
  p1: -0.00459899
  p2: 0.00268516
  p3: -0.00353483
  p4: -0.00163496
  scale: 3.0005
  rotation: -0.8581

Best score: 0.2575: : 300iter [08:20,  1.71s/iter]
Best score: 0.2575: : 301iter [08:22,  1.70s/iter]
Best score: 0.2575: : 302iter [08:24,  1.69s/iter]
Best score: 0.2575: : 303iter [08:25,  1.68s/iter]
Best score: 0.2575: : 304iter [08:27,  1.66s/iter]
Best score: 0.2575: : 305iter [08:29,  1.67s/iter]
Best score: 0.2575: : 306iter [08:30,  1.68s/iter]
Best score: 0.2575: : 307iter [08:32,  1.68s/iter]
Best score: 0.2575: : 308iter [08:34,  1.67s/iter]
Best score: 0.2575: : 309iter [08:35,  1.67s/iter]
Best score: 0.2575: : 310iter [08:37,  1.67s/iter]
Best score: 0.2575: : 311iter [08:39,  1.66s/iter]
Best score: 0.2575: : 312iter [08:40,  1.67s/iter]
Best score: 0.2575: : 313iter [08:42,  1.68s/iter]
Best score: 0.2575: : 314iter [08:44,  1.71s/iter]
Best score: 0.2575: : 315iter [08:45,  1.69s/iter]
Best score: 0.2575: : 316iter [08:47,  1.67s/iter]
Best score: 0.2575: : 317iter [08:49,  1.68s/iter]
Best score: 0.2575: : 318iter [08:50,  1.67s/iter]
Best score: 0.2575: : 319iter [08:52,  1.68s/iter]
Best score: 0.2575: : 320iter [08:54,  1.68s/iter]
Best score: 0.2575: : 321iter [08:55,  1.68s/iter]
Best score: 0.2575: : 322iter [08:57,  1.67s/iter]
Best score: 0.2575: : 323iter [08:59,  1.66s/iter]
Best score: 0.2575: : 324iter [09:00,  1.64s/iter]
Best score: 0.2575: : 325iter [09:02,  1.65s/iter]
Best score: 0.2575: : 326iter [09:04,  1.64s/iter]
Best score: 0.2575: : 327iter [09:05,  1.63s/iter]
Best score: 0.2575: : 328iter [09:07,  1.63s/iter]
Best score: 0.2575: : 329iter [09:09,  1.65s/iter]
Best score: 0.2575: : 330iter [09:10,  1.67s/iter]
Best score: 0.2575: : 331iter [09:12,  1.66s/iter]
Best score: 0.2575: : 332iter [09:14,  1.66s/iter]
Best score: 0.2575: : 333iter [09:15,  1.67s/iter]
Best score: 0.2575: : 334iter [09:17,  1.67s/iter]
Best score: 0.2575: : 335iter [09:19,  1.65s/iter]
Best score: 0.2575: : 336iter [09:20,  1.65s/iter]
Best score: 0.2575: : 337iter [09:22,  1.66s/iter]
Best score: 0.2575: : 338iter [09:24,  1.66s/iter]
Best score: 0.2575: : 339iter [09:25,  1.67s/iter]
Best score: 0.2575: : 340iter [09:27,  1.66s/iter]
Best score: 0.2575: : 341iter [09:29,  1.67s/iter]
Best score: 0.2575: : 342iter [09:30,  1.65s/iter]
Best score: 0.2575: : 343iter [09:32,  1.64s/iter]
Best score: 0.2575: : 344iter [09:33,  1.64s/iter]
Best score: 0.2575: : 345iter [09:35,  1.66s/iter]
Best score: 0.2575: : 346iter [09:37,  1.65s/iter]
Best score: 0.2575: : 347iter [09:38,  1.64s/iter]
Best score: 0.2575: : 348iter [09:40,  1.64s/iter]
Best score: 0.2575: : 349iter [09:42,  1.65s/iter]
[ITER 350] Current best score: 0.2575
[PARAMS] Current best parameters:
  fx: 530.1562
  fy: 530.6719
  px: 864.2042
  py: 860.3516
  k1: -0.01903488
  k2: 0.03888885
  k3: -0.02808934
  k4: 0.00644361
  p1: -0.00459899
  p2: 0.00268516
  p3: -0.00353483
  p4: -0.00163496
  scale: 3.0005
  rotation: -0.8581

Best score: 0.2575: : 350iter [09:43,  1.70s/iter]
Best score: 0.2575: : 351iter [09:45,  1.68s/iter]
Best score: 0.2575: : 352iter [09:47,  1.64s/iter]
Best score: 0.2575: : 353iter [09:48,  1.66s/iter]
Best score: 0.2575: : 354iter [09:50,  1.67s/iter]
Best score: 0.2575: : 355iter [09:52,  1.67s/iter]
Best score: 0.2575: : 356iter [09:53,  1.66s/iter]
Best score: 0.2575: : 357iter [09:55,  1.67s/iter]
Best score: 0.2495: : 357iter [09:57,  1.67s/iter]
Best score: 0.2495: : 358iter [09:57,  1.68s/iter]
Best score: 0.2495: : 359iter [09:58,  1.67s/iter]
Best score: 0.2495: : 360iter [10:00,  1.66s/iter]
Best score: 0.2495: : 361iter [10:02,  1.68s/iter]
Best score: 0.2495: : 362iter [10:03,  1.68s/iter]
Best score: 0.2495: : 363iter [10:05,  1.68s/iter]
Best score: 0.2495: : 364iter [10:07,  1.66s/iter]
Best score: 0.2495: : 365iter [10:08,  1.66s/iter]
Best score: 0.2495: : 366iter [10:10,  1.65s/iter]
Best score: 0.2495: : 367iter [10:12,  1.67s/iter]
Best score: 0.2495: : 368iter [10:13,  1.66s/iter]
Best score: 0.2495: : 369iter [10:15,  1.66s/iter]
Best score: 0.2495: : 370iter [10:17,  1.68s/iter]
Best score: 0.2495: : 371iter [10:18,  1.68s/iter]
Best score: 0.2495: : 372iter [10:20,  1.67s/iter]
Best score: 0.2495: : 373iter [10:22,  1.68s/iter]
Best score: 0.2495: : 374iter [10:23,  1.67s/iter]
Best score: 0.2495: : 375iter [10:25,  1.66s/iter]
Best score: 0.2495: : 376iter [10:27,  1.66s/iter]
Best score: 0.2495: : 377iter [10:28,  1.67s/iter]
Best score: 0.2495: : 378iter [10:30,  1.66s/iter]
Best score: 0.2317: : 378iter [10:32,  1.66s/iter]
Best score: 0.2317: : 379iter [10:32,  1.66s/iter]
Best score: 0.2317: : 380iter [10:33,  1.64s/iter]
Best score: 0.2317: : 381iter [10:35,  1.67s/iter]
Best score: 0.2317: : 382iter [10:37,  1.68s/iter]
Best score: 0.2317: : 383iter [10:39,  1.69s/iter]
Best score: 0.2317: : 384iter [10:40,  1.65s/iter]
Best score: 0.2317: : 385iter [10:42,  1.66s/iter]
Best score: 0.2317: : 386iter [10:43,  1.67s/iter]
Best score: 0.2317: : 387iter [10:45,  1.67s/iter]
Best score: 0.2317: : 388iter [10:47,  1.66s/iter]
Best score: 0.2317: : 389iter [10:48,  1.68s/iter]
Best score: 0.2317: : 390iter [10:50,  1.67s/iter]
Best score: 0.2317: : 391iter [10:52,  1.67s/iter]
Best score: 0.2317: : 392iter [10:53,  1.65s/iter]
Best score: 0.2317: : 393iter [10:55,  1.65s/iter]
Best score: 0.2317: : 394iter [10:57,  1.66s/iter]
Best score: 0.2317: : 395iter [10:58,  1.65s/iter]
Best score: 0.2317: : 396iter [11:00,  1.64s/iter]
Best score: 0.2317: : 397iter [11:02,  1.64s/iter]
Best score: 0.2317: : 398iter [11:03,  1.64s/iter]
Best score: 0.2317: : 399iter [11:05,  1.66s/iter]
[ITER 400] Current best score: 0.2579
[PARAMS] Current best parameters:
  fx: 527.3552
  fy: 527.3741
  px: 864.6685
  py: 858.0175
  k1: -0.00066530
  k2: 0.01507238
  k3: -0.01329871
  k4: 0.00287743
  p1: -0.00952303
  p2: 0.00406052
  p3: -0.00397656
  p4: -0.00106032
  scale: 2.9835
  rotation: -0.8337

Best score: 0.2317: : 400iter [11:07,  1.71s/iter]
Best score: 0.2317: : 401iter [11:08,  1.69s/iter]
Best score: 0.2317: : 402iter [11:10,  1.68s/iter]
Best score: 0.2317: : 403iter [11:12,  1.68s/iter]
Best score: 0.2317: : 404iter [11:13,  1.67s/iter]
Best score: 0.2317: : 405iter [11:15,  1.68s/iter]
Best score: 0.2317: : 406iter [11:17,  1.68s/iter]
Best score: 0.2317: : 407iter [11:18,  1.67s/iter]
Best score: 0.2317: : 408iter [11:20,  1.68s/iter]
Best score: 0.2317: : 409iter [11:22,  1.66s/iter]
Best score: 0.2317: : 410iter [11:23,  1.66s/iter]
Best score: 0.2317: : 411iter [11:25,  1.66s/iter]
Best score: 0.2317: : 412iter [11:27,  1.65s/iter]
Best score: 0.2317: : 413iter [11:28,  1.65s/iter]
Best score: 0.2317: : 414iter [11:30,  1.64s/iter]
Best score: 0.2317: : 415iter [11:32,  1.65s/iter]
Best score: 0.2317: : 416iter [11:33,  1.63s/iter]
Best score: 0.2317: : 417iter [11:35,  1.64s/iter]
Best score: 0.2317: : 418iter [11:37,  1.66s/iter]
Best score: 0.2317: : 419iter [11:38,  1.65s/iter]
Best score: 0.2317: : 420iter [11:40,  1.67s/iter]
Best score: 0.2317: : 421iter [11:42,  1.67s/iter]
Best score: 0.2317: : 422iter [11:43,  1.67s/iter]
Best score: 0.2317: : 423iter [11:45,  1.66s/iter]
Best score: 0.2317: : 424iter [11:47,  1.66s/iter]
Best score: 0.2317: : 425iter [11:48,  1.66s/iter]
Best score: 0.2317: : 426iter [11:50,  1.65s/iter]
Best score: 0.2317: : 427iter [11:51,  1.64s/iter]
Best score: 0.2317: : 428iter [11:53,  1.64s/iter]
Best score: 0.2317: : 429iter [11:55,  1.65s/iter]
Best score: 0.2317: : 430iter [11:56,  1.65s/iter]
Best score: 0.2317: : 431iter [11:58,  1.66s/iter]
Best score: 0.2317: : 432iter [12:00,  1.64s/iter]
Best score: 0.2317: : 433iter [12:01,  1.64s/iter]
Best score: 0.2317: : 434iter [12:03,  1.63s/iter]
Best score: 0.2317: : 435iter [12:05,  1.65s/iter]
Best score: 0.2317: : 436iter [12:06,  1.65s/iter]
Best score: 0.2317: : 437iter [12:08,  1.67s/iter]
Best score: 0.2317: : 438iter [12:10,  1.67s/iter]
Best score: 0.2317: : 439iter [12:11,  1.67s/iter]
Best score: 0.2317: : 440iter [12:13,  1.67s/iter]
Best score: 0.2317: : 441iter [12:15,  1.67s/iter]
Best score: 0.2317: : 442iter [12:16,  1.65s/iter]
Best score: 0.2317: : 443iter [12:18,  1.66s/iter]
Best score: 0.2317: : 444iter [12:20,  1.65s/iter]
Best score: 0.2317: : 445iter [12:21,  1.63s/iter]
Best score: 0.2317: : 446iter [12:23,  1.65s/iter]
Best score: 0.2317: : 447iter [12:25,  1.65s/iter]
Best score: 0.2317: : 448iter [12:26,  1.64s/iter]
Best score: 0.2317: : 449iter [12:28,  1.66s/iter]
[ITER 450] Current best score: 0.2318
[PARAMS] Current best parameters:
  fx: 525.3428
  fy: 525.3368
  px: 864.2370
  py: 858.1969
  k1: -0.00112101
  k2: 0.01812505
  k3: -0.01563047
  k4: 0.00363635
  p1: -0.00904033
  p2: 0.00281464
  p3: -0.00317621
  p4: -0.00154306
  scale: 2.9904
  rotation: -0.8701

Best score: 0.2317: : 450iter [12:30,  1.71s/iter]
Best score: 0.2317: : 451iter [12:31,  1.68s/iter]
Best score: 0.2317: : 452iter [12:33,  1.67s/iter]
Best score: 0.2317: : 453iter [12:35,  1.67s/iter]
Best score: 0.2317: : 454iter [12:36,  1.65s/iter]
Best score: 0.2317: : 455iter [12:38,  1.65s/iter]
Best score: 0.2317: : 456iter [12:40,  1.67s/iter]
Best score: 0.2317: : 457iter [12:41,  1.65s/iter]
Best score: 0.2317: : 458iter [12:43,  1.63s/iter]
Best score: 0.2317: : 459iter [12:44,  1.64s/iter]
Best score: 0.2317: : 460iter [12:46,  1.65s/iter]
Best score: 0.2317: : 461iter [12:48,  1.67s/iter]
Best score: 0.2317: : 462iter [12:49,  1.65s/iter]
Best score: 0.2317: : 463iter [12:51,  1.66s/iter]
Best score: 0.2317: : 464iter [12:53,  1.66s/iter]
Best score: 0.2317: : 465iter [12:55,  1.68s/iter]
Best score: 0.2317: : 466iter [12:56,  1.66s/iter]
Best score: 0.2317: : 467iter [12:58,  1.65s/iter]
Best score: 0.2317: : 468iter [12:59,  1.65s/iter]
Best score: 0.2317: : 469iter [13:01,  1.66s/iter]
Best score: 0.2317: : 470iter [13:03,  1.64s/iter]
Best score: 0.2317: : 471iter [13:04,  1.65s/iter]
Best score: 0.2317: : 472iter [13:06,  1.64s/iter]
Best score: 0.2317: : 473iter [13:08,  1.64s/iter]
Best score: 0.2317: : 474iter [13:09,  1.62s/iter]
Best score: 0.2317: : 475iter [13:11,  1.66s/iter]
Best score: 0.2317: : 476iter [13:13,  1.66s/iter]
Best score: 0.2317: : 477iter [13:14,  1.66s/iter]
Best score: 0.2317: : 478iter [13:16,  1.68s/iter]
Best score: 0.2317: : 479iter [13:18,  1.68s/iter]
Best score: 0.2317: : 480iter [13:19,  1.68s/iter]
Best score: 0.2317: : 481iter [13:21,  1.66s/iter]
Best score: 0.2317: : 482iter [13:23,  1.67s/iter]
Best score: 0.2317: : 483iter [13:24,  1.65s/iter]
Best score: 0.2317: : 484iter [13:26,  1.66s/iter]
Best score: 0.2317: : 485iter [13:28,  1.66s/iter]
Best score: 0.2317: : 486iter [13:29,  1.67s/iter]
Best score: 0.2317: : 487iter [13:31,  1.66s/iter]
Best score: 0.2317: : 488iter [13:33,  1.65s/iter]
Best score: 0.2317: : 489iter [13:34,  1.67s/iter]
Best score: 0.2300: : 489iter [13:36,  1.67s/iter]
Best score: 0.2300: : 490iter [13:36,  1.67s/iter]
Best score: 0.2300: : 491iter [13:38,  1.67s/iter]
Best score: 0.2300: : 492iter [13:39,  1.66s/iter]
Best score: 0.2300: : 493iter [13:41,  1.68s/iter]
Best score: 0.2300: : 494iter [13:43,  1.65s/iter]
Best score: 0.2300: : 495iter [13:44,  1.66s/iter]
Best score: 0.2300: : 496iter [13:46,  1.64s/iter]
Best score: 0.2300: : 497iter [13:48,  1.65s/iter]
Best score: 0.2300: : 498iter [13:49,  1.65s/iter]
Best score: 0.2300: : 499iter [13:51,  1.65s/iter]
[ITER 500] Current best score: 0.2300
[PARAMS] Current best parameters:
  fx: 530.6760
  fy: 530.6488
  px: 863.8273
  py: 858.2224
  k1: -0.01039639
  k2: 0.02059372
  k3: -0.01441137
  k4: 0.00289303
  p1: -0.00893799
  p2: 0.00224022
  p3: -0.00557763
  p4: -0.00008672
  scale: 2.9965
  rotation: -0.8877

Best score: 0.2300: : 500iter [13:53,  1.71s/iter]
Best score: 0.2300: : 500iter [15:27,  1.86s/iter]

[📈] Forcing polish step after 500 iterations...
  Polish did not improve score: 0.226122
  Success: False
  Tangential distortion: p1=-0.008936, p2=0.002243, p3=-0.005575, p4=-0.000085
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2261
[✅] Parameters copied to: /working/MDCSL001_W003(6)_2025-07-09/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 530.6760273077296
    fy: 530.6488193158563
    px: 863.8272965114671
    py: 858.2223946079324
    k1: -0.010389910533265306
    k2: 0.02059596007167255
    k3: -0.014407533466721278
    k4: 0.0029038019203555094
    p1: -0.008935508889179457
    p2: 0.0022427938581802043
    p3: -0.005574547600415472
    p4: -8.46055414428855e-05
    scale: 2.9965286705636016
    rotation: -0.8877372859684407
    mmpp: 3.419296614407213
    nmpp: 3419296.6144072134

[🔄] Converting 94 frames to PTD, creating debug images for 94 calibration frames...

📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]

   🔄 1500:   0%|          | 0/23 [00:00<?, ?frame/s]

   🔄 1500:  78%|███████▊  | 18/23 [00:00<00:00, 179.64frame/s]

                                                               
📁 Rigs:  25%|██▌       | 1/4 [00:00<00:00,  4.82rig/s]

   🔄 2000R:   0%|          | 0/24 [00:00<?, ?frame/s]

   🔄 2000R:  79%|███████▉  | 19/24 [00:00<00:00, 183.10frame/s]

                                                                
📁 Rigs:  50%|█████     | 2/4 [00:00<00:00,  4.01rig/s]

   🔄 1000:   0%|          | 0/19 [00:00<?, ?frame/s]

   🔄 1000: 100%|██████████| 19/19 [00:00<00:00, 189.71frame/s]

                                                               
📁 Rigs:  75%|███████▌  | 3/4 [00:00<00:00,  4.75rig/s]

   🔄 2000L:   0%|          | 0/28 [00:00<?, ?frame/s]

   🔄 2000L:  68%|██████▊   | 19/28 [00:00<00:00, 181.59frame/s]

                                                                
📁 Rigs: 100%|██████████| 4/4 [00:00<00:00,  4.43rig/s]
📁 Rigs: 100%|██████████| 4/4 [00:00<00:00,  4.45rig/s]
[✅] Saved PTDs: /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/3_undistorted/output/ptd

📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]

   🔄 1500:   0%|          | 0/23 [00:00<?, ?frame/s]

   🔄 1500:  83%|████████▎ | 19/23 [00:00<00:00, 183.73frame/s]

                                                               
📁 Rigs:  25%|██▌       | 1/4 [00:00<00:00,  3.98rig/s]

   🔄 2000R:   0%|          | 0/24 [00:00<?, ?frame/s]

   🔄 2000R:  79%|███████▉  | 19/24 [00:00<00:00, 184.12frame/s]

                                                                
📁 Rigs:  50%|█████     | 2/4 [00:00<00:00,  4.36rig/s]

   🔄 1000:   0%|          | 0/19 [00:00<?, ?frame/s]

                                                     
📁 Rigs:  75%|███████▌  | 3/4 [00:00<00:00,  5.02rig/s]

   🔄 2000L:   0%|          | 0/28 [00:00<?, ?frame/s]

   🔄 2000L:  68%|██████▊   | 19/28 [00:00<00:00, 181.74frame/s]

                                                                
📁 Rigs: 100%|██████████| 4/4 [00:00<00:00,  4.17rig/s]
📁 Rigs: 100%|██████████| 4/4 [00:00<00:00,  4.30rig/s]
[✅] Saved PTDs: /working/MDCSL001_W003(6)_2025-07-09/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%|          | 0/3240000 [00:00<?, ?px/s]
📤 Writing LUT:   2%|▏         | 78134/3240000 [00:00<00:04, 781333.29px/s]
📤 Writing LUT:   5%|▌         | 163358/3240000 [00:00<00:03, 823037.66px/s]
📤 Writing LUT:   8%|▊         | 251355/3240000 [00:00<00:03, 849029.77px/s]
📤 Writing LUT:  10%|█         | 338516/3240000 [00:00<00:03, 857943.65px/s]
📤 Writing LUT:  13%|█▎        | 427146/3240000 [00:00<00:03, 868167.92px/s]
📤 Writing LUT:  16%|█▌        | 517632/3240000 [00:00<00:03, 880632.78px/s]
📤 Writing LUT:  19%|█▉        | 609959/3240000 [00:00<00:02, 894571.33px/s]
📤 Writing LUT:  22%|██▏       | 699417/3240000 [00:00<00:02, 891480.09px/s]
📤 Writing LUT:  24%|██▍       | 788568/3240000 [00:00<00:02, 891239.19px/s]
📤 Writing LUT:  27%|██▋       | 877694/3240000 [00:01<00:02, 887464.85px/s]
📤 Writing LUT:  30%|██▉       | 966445/3240000 [00:01<00:02, 883059.95px/s]
📤 Writing LUT:  33%|███▎      | 1054757/3240000 [00:01<00:02, 879547.21px/s]
📤 Writing LUT:  35%|███▌      | 1142717/3240000 [00:01<00:02, 879463.71px/s]
📤 Writing LUT:  38%|███▊      | 1230667/3240000 [00:01<00:02, 877440.02px/s]
📤 Writing LUT:  41%|████      | 1318414/3240000 [00:01<00:02, 876496.79px/s]
📤 Writing LUT:  43%|████▎     | 1406159/3240000 [00:01<00:02, 876782.07px/s]
📤 Writing LUT:  46%|████▌     | 1497327/3240000 [00:01<00:01, 887241.15px/s]
📤 Writing LUT:  49%|████▉     | 1586170/3240000 [00:01<00:01, 887595.96px/s]
📤 Writing LUT:  52%|█████▏    | 1674933/3240000 [00:01<00:01, 887492.21px/s]
📤 Writing LUT:  54%|█████▍    | 1763685/3240000 [00:02<00:01, 887094.22px/s]
📤 Writing LUT:  57%|█████▋    | 1852396/3240000 [00:02<00:01, 886399.67px/s]
📤 Writing LUT:  60%|█████▉    | 1941038/3240000 [00:02<00:01, 885264.82px/s]
📤 Writing LUT:  63%|██████▎   | 2030068/3240000 [00:02<00:01, 886770.10px/s]
📤 Writing LUT:  65%|██████▌   | 2118746/3240000 [00:02<00:01, 884086.90px/s]
📤 Writing LUT:  68%|██████▊   | 2207157/3240000 [00:02<00:01, 883935.83px/s]
📤 Writing LUT:  71%|███████   | 2296172/3240000 [00:02<00:01, 885795.69px/s]
📤 Writing LUT:  74%|███████▎  | 2384754/3240000 [00:02<00:00, 882890.13px/s]
📤 Writing LUT:  76%|███████▋  | 2473046/3240000 [00:02<00:00, 882263.47px/s]
📤 Writing LUT:  79%|███████▉  | 2561888/3240000 [00:02<00:00, 884104.14px/s]
📤 Writing LUT:  82%|████████▏ | 2650301/3240000 [00:03<00:00, 882117.32px/s]
📤 Writing LUT:  85%|████████▍ | 2738515/3240000 [00:03<00:00, 880987.22px/s]
📤 Writing LUT:  87%|████████▋ | 2827578/3240000 [00:03<00:00, 883868.50px/s]
📤 Writing LUT:  90%|█████████ | 2917684/3240000 [00:03<00:00, 889012.85px/s]
📤 Writing LUT:  93%|█████████▎| 3007269/3240000 [00:03<00:00, 891060.60px/s]
📤 Writing LUT:  96%|█████████▌| 3097746/3240000 [00:03<00:00, 895166.88px/s]
📤 Writing LUT:  98%|█████████▊| 3189048/3240000 [00:03<00:00, 900517.27px/s]
📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 883527.25px/s]
[✅] LUT written to: /working/MDCSL001_W003(6)_2025-07-09/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDCSL001_W003(6)_2025-07-09' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCSL001_W003(6)_2025-07-09/calibration/MDCSL001_W003(6)_2025-07-09_unknown.lut
[🏁] Finished processing MDCSL001_W003(6)_2025-07-09


================================================================================================================================
================================================================================================================================