
🟢 Starting Calibration Pipeline

=== Processing MDC028_W009(6)_2024-07-23 ===

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process
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                                                                      [A📁 Processing Rigs:  75%|███████▌  | 3/4 [04:09<01:19, 79.68s/it]
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                                                                          [A📁 Processing Rigs: 100%|██████████| 4/4 [06:29<00:00, 103.67s/it]📁 Processing Rigs: 100%|██████████| 4/4 [06:29<00:00, 97.49s/it] 
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 3015 frames from 4 rigs to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 2000L: 100 frames selected from 795
[📋] 1500: 100 frames selected from 597
[📋] 1000: 100 frames selected from 534
[📋] 2000R: 100 frames selected from 1089
[💾] Saved RigFrameDataset to /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC028_W009(6)_2024-07-23/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Averaging bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Averaging bright frames:   5%|▌         | 4/80 [00:00<00:02, 37.44it/s]Averaging bright frames:  11%|█▏        | 9/80 [00:00<00:01, 43.15it/s]Averaging bright frames:  18%|█▊        | 14/80 [00:00<00:01, 45.42it/s]Averaging bright frames:  24%|██▍       | 19/80 [00:00<00:01, 46.35it/s]Averaging bright frames:  30%|███       | 24/80 [00:00<00:01, 46.93it/s]Averaging bright frames:  36%|███▋      | 29/80 [00:00<00:01, 47.41it/s]Averaging bright frames:  42%|████▎     | 34/80 [00:00<00:00, 47.48it/s]Averaging bright frames:  49%|████▉     | 39/80 [00:00<00:00, 47.67it/s]Averaging bright frames:  55%|█████▌    | 44/80 [00:00<00:00, 47.75it/s]Averaging bright frames:  61%|██████▏   | 49/80 [00:01<00:00, 47.65it/s]Averaging bright frames:  68%|██████▊   | 54/80 [00:01<00:00, 47.62it/s]Averaging bright frames:  74%|███████▍  | 59/80 [00:01<00:00, 47.38it/s]Averaging bright frames:  80%|████████  | 64/80 [00:01<00:00, 47.53it/s]Averaging bright frames:  86%|████████▋ | 69/80 [00:01<00:00, 47.60it/s]Averaging bright frames:  92%|█████████▎| 74/80 [00:01<00:00, 47.51it/s]Averaging bright frames:  99%|█████████▉| 79/80 [00:01<00:00, 47.53it/s]                                                                        Total time to process (v2): 0.06
[RESULT] Combined: Center=(1020, 766), Radius=835
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:29,  3.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:29,  3.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 335.354} (0.001):   3%|▎         | 3/100 [00:00<00:28,  3.39it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 335.354} (0.001):   4%|▍         | 4/100 [00:00<00:08, 11.17it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 335.354} (0.001):   4%|▍         | 4/100 [00:00<00:08, 11.17it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.000 | Best: {'fx': 335.354} (0.001):   5%|▌         | 5/100 [00:00<00:08, 11.17it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 342.424} (0.001):   6%|▌         | 6/100 [00:00<00:08, 11.17it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   7%|▋         | 7/100 [00:00<00:08, 11.17it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:05, 17.42it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:05, 17.42it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.002 | Best: {'fx': 370.707} (0.002):   9%|▉         | 9/100 [00:00<00:05, 17.42it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.002 | Best: {'fx': 377.778} (0.002):  10%|█         | 10/100 [00:00<00:05, 17.42it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.001 | Best: {'fx': 377.778} (0.002):  11%|█         | 11/100 [00:00<00:05, 17.42it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:05, 17.42it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  13%|█▎        | 13/100 [00:00<00:03, 24.90it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 24.90it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:03, 24.90it/s] Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 24.90it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 413.131} (0.004):  16%|█▌        | 16/100 [00:00<00:03, 24.90it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.004 | Best: {'fx': 413.131} (0.004):  17%|█▋        | 17/100 [00:00<00:03, 26.78it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:00<00:03, 26.78it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.006 | Best: {'fx': 434.343} (0.006):  18%|█▊        | 18/100 [00:00<00:03, 26.78it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.005 | Best: {'fx': 434.343} (0.006):  19%|█▉        | 19/100 [00:00<00:03, 26.78it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:00<00:02, 26.78it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  21%|██        | 21/100 [00:00<00:02, 28.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.009 | Best: {'fx': 448.485} (0.009):  21%|██        | 21/100 [00:00<00:02, 28.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.011 | Best: {'fx': 455.556} (0.011):  22%|██▏       | 22/100 [00:00<00:02, 28.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.015 | Best: {'fx': 469.697} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 28.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.013 | Best: {'fx': 469.697} (0.015):  24%|██▍       | 24/100 [00:01<00:02, 28.12it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.013 | Best: {'fx': 469.697} (0.015):  25%|██▌       | 25/100 [00:01<00:02, 29.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 483.838} (0.021):  25%|██▌       | 25/100 [00:01<00:02, 29.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.035 | Best: {'fx': 497.98} (0.035):  26%|██▌       | 26/100 [00:01<00:02, 29.22it/s] Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 497.98} (0.035):  27%|██▋       | 27/100 [00:01<00:02, 29.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.042 | Best: {'fx': 505.051} (0.042):  28%|██▊       | 28/100 [00:01<00:02, 29.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.042 | Best: {'fx': 505.051} (0.042):  29%|██▉       | 29/100 [00:01<00:02, 31.79it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.052 | Best: {'fx': 512.121} (0.052):  29%|██▉       | 29/100 [00:01<00:02, 31.79it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.028 | Best: {'fx': 512.121} (0.052):  30%|███       | 30/100 [00:01<00:02, 31.79it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.070 | Best: {'fx': 519.192} (0.070):  31%|███       | 31/100 [00:01<00:02, 31.79it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.101 | Best: {'fx': 526.263} (0.101):  32%|███▏      | 32/100 [00:01<00:02, 31.79it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.101 | Best: {'fx': 526.263} (0.101):  33%|███▎      | 33/100 [00:01<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.099 | Best: {'fx': 526.263} (0.101):  33%|███▎      | 33/100 [00:01<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.053 | Best: {'fx': 526.263} (0.101):  34%|███▍      | 34/100 [00:01<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.036 | Best: {'fx': 526.263} (0.101):  35%|███▌      | 35/100 [00:01<00:02, 23.23it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.036 | Best: {'fx': 526.263} (0.101):  36%|███▌      | 36/100 [00:01<00:03, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.038 | Best: {'fx': 526.263} (0.101):  36%|███▌      | 36/100 [00:01<00:03, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.076 | Best: {'fx': 526.263} (0.101):  37%|███▋      | 37/100 [00:01<00:02, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.062 | Best: {'fx': 526.263} (0.101):  38%|███▊      | 38/100 [00:01<00:02, 21.20it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.062 | Best: {'fx': 526.263} (0.101):  39%|███▉      | 39/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.028 | Best: {'fx': 526.263} (0.101):  39%|███▉      | 39/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.046 | Best: {'fx': 526.263} (0.101):  40%|████      | 40/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.042 | Best: {'fx': 526.263} (0.101):  41%|████      | 41/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.032 | Best: {'fx': 526.263} (0.101):  42%|████▏     | 42/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.029 | Best: {'fx': 526.263} (0.101):  43%|████▎     | 43/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.033 | Best: {'fx': 526.263} (0.101):  44%|████▍     | 44/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.030 | Best: {'fx': 526.263} (0.101):  45%|████▌     | 45/100 [00:01<00:02, 22.05it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.030 | Best: {'fx': 526.263} (0.101):  46%|████▌     | 46/100 [00:01<00:01, 31.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.025 | Best: {'fx': 526.263} (0.101):  46%|████▌     | 46/100 [00:01<00:01, 31.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.027 | Best: {'fx': 526.263} (0.101):  47%|████▋     | 47/100 [00:01<00:01, 31.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.026 | Best: {'fx': 526.263} (0.101):  48%|████▊     | 48/100 [00:01<00:01, 31.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.025 | Best: {'fx': 526.263} (0.101):  49%|████▉     | 49/100 [00:01<00:01, 31.16it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.025 | Best: {'fx': 526.263} (0.101):  50%|█████     | 50/100 [00:01<00:01, 31.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.023 | Best: {'fx': 526.263} (0.101):  50%|█████     | 50/100 [00:02<00:01, 31.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.023 | Best: {'fx': 526.263} (0.101):  51%|█████     | 51/100 [00:02<00:01, 31.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.024 | Best: {'fx': 526.263} (0.101):  52%|█████▏    | 52/100 [00:02<00:01, 31.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.101):  53%|█████▎    | 53/100 [00:02<00:01, 31.22it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.101):  54%|█████▍    | 54/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  54%|█████▍    | 54/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.101):  55%|█████▌    | 55/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.101):  56%|█████▌    | 56/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  57%|█████▋    | 57/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  58%|█████▊    | 58/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.101):  59%|█████▉    | 59/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.022 | Best: {'fx': 526.263} (0.101):  60%|██████    | 60/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.023 | Best: {'fx': 526.263} (0.101):  61%|██████    | 61/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  62%|██████▏   | 62/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  63%|██████▎   | 63/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  64%|██████▍   | 64/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  65%|██████▌   | 65/100 [00:02<00:01, 32.85it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  66%|██████▌   | 66/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.021 | Best: {'fx': 526.263} (0.101):  66%|██████▌   | 66/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  67%|██████▋   | 67/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.020 | Best: {'fx': 526.263} (0.101):  68%|██████▊   | 68/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  69%|██████▉   | 69/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  70%|███████   | 70/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  71%|███████   | 71/100 [00:02<00:00, 52.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  72%|███████▏  | 72/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  72%|███████▏  | 72/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  73%|███████▎  | 73/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  74%|███████▍  | 74/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  75%|███████▌  | 75/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  76%|███████▌  | 76/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  77%|███████▋  | 77/100 [00:02<00:00, 53.32it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  78%|███████▊  | 78/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.019 | Best: {'fx': 526.263} (0.101):  78%|███████▊  | 78/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  79%|███████▉  | 79/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  80%|████████  | 80/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  81%|████████  | 81/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  82%|████████▏ | 82/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  83%|████████▎ | 83/100 [00:02<00:00, 54.35it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  84%|████████▍ | 84/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  84%|████████▍ | 84/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  85%|████████▌ | 85/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  86%|████████▌ | 86/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  87%|████████▋ | 87/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  88%|████████▊ | 88/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  89%|████████▉ | 89/100 [00:02<00:00, 51.41it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.018 | Best: {'fx': 526.263} (0.101):  90%|█████████ | 90/100 [00:02<00:00, 46.82it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  90%|█████████ | 90/100 [00:02<00:00, 46.82it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  91%|█████████ | 91/100 [00:02<00:00, 46.82it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  92%|█████████▏| 92/100 [00:02<00:00, 46.82it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  93%|█████████▎| 93/100 [00:02<00:00, 46.82it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  94%|█████████▍| 94/100 [00:02<00:00, 46.82it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  95%|█████████▌| 95/100 [00:02<00:00, 43.40it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  95%|█████████▌| 95/100 [00:02<00:00, 43.40it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  96%|█████████▌| 96/100 [00:02<00:00, 43.40it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  97%|█████████▋| 97/100 [00:02<00:00, 43.40it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  98%|█████████▊| 98/100 [00:02<00:00, 43.40it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101):  99%|█████████▉| 99/100 [00:02<00:00, 43.40it/s]Brute | Start: {'fx': 500.0} (0.037) | Current: 0.017 | Best: {'fx': 526.263} (0.101): 100%|██████████| 100/100 [00:02<00:00, 41.44it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.040 | Best: {'fx': 503.303} (0.040):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.040 | Best: {'fx': 503.303} (0.040):   2%|▏         | 1/50 [00:00<00:27,  1.78it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.039 | Best: {'fx': 503.303} (0.040):   2%|▏         | 1/50 [00:00<00:27,  1.78it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.041 | Best: {'fx': 504.324} (0.041):   4%|▍         | 2/50 [00:00<00:26,  1.78it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.038 | Best: {'fx': 504.324} (0.041):   6%|▌         | 3/50 [00:00<00:26,  1.78it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.043 | Best: {'fx': 506.365} (0.043):   8%|▊         | 4/50 [00:00<00:25,  1.78it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.043 | Best: {'fx': 506.365} (0.043):  10%|█         | 5/50 [00:00<00:04,  9.24it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.042 | Best: {'fx': 506.365} (0.043):  10%|█         | 5/50 [00:00<00:04,  9.24it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.048 | Best: {'fx': 509.426} (0.048):  12%|█▏        | 6/50 [00:00<00:04,  9.24it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.045 | Best: {'fx': 509.426} (0.048):  14%|█▍        | 7/50 [00:00<00:04,  9.24it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.047 | Best: {'fx': 509.426} (0.048):  16%|█▌        | 8/50 [00:00<00:04,  9.24it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.047 | Best: {'fx': 509.426} (0.048):  18%|█▊        | 9/50 [00:00<00:02, 15.45it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.051 | Best: {'fx': 511.467} (0.051):  18%|█▊        | 9/50 [00:00<00:02, 15.45it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.057 | Best: {'fx': 514.528} (0.057):  20%|██        | 10/50 [00:00<00:02, 15.45it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.050 | Best: {'fx': 514.528} (0.057):  22%|██▏       | 11/50 [00:00<00:02, 15.45it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.050 | Best: {'fx': 514.528} (0.057):  24%|██▍       | 12/50 [00:00<00:02, 17.30it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.053 | Best: {'fx': 514.528} (0.057):  24%|██▍       | 12/50 [00:00<00:02, 17.30it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.056 | Best: {'fx': 514.528} (0.057):  26%|██▌       | 13/50 [00:01<00:02, 17.30it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.060 | Best: {'fx': 515.548} (0.060):  28%|██▊       | 14/50 [00:01<00:02, 17.30it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.065 | Best: {'fx': 517.589} (0.065):  30%|███       | 15/50 [00:01<00:02, 17.30it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.065 | Best: {'fx': 517.589} (0.065):  32%|███▏      | 16/50 [00:01<00:01, 22.10it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.068 | Best: {'fx': 518.61} (0.068):  32%|███▏      | 16/50 [00:01<00:01, 22.10it/s] Brute | Start: {'fx': 526.263} (0.101) | Current: 0.077 | Best: {'fx': 520.65} (0.077):  34%|███▍      | 17/50 [00:01<00:01, 22.10it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.062 | Best: {'fx': 520.65} (0.077):  36%|███▌      | 18/50 [00:01<00:01, 22.10it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.072 | Best: {'fx': 520.65} (0.077):  38%|███▊      | 19/50 [00:01<00:01, 22.10it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.072 | Best: {'fx': 520.65} (0.077):  40%|████      | 20/50 [00:01<00:01, 23.75it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.082 | Best: {'fx': 521.671} (0.082):  40%|████      | 20/50 [00:01<00:01, 23.75it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.086 | Best: {'fx': 522.691} (0.086):  42%|████▏     | 21/50 [00:01<00:01, 23.75it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.096 | Best: {'fx': 524.732} (0.096):  44%|████▍     | 22/50 [00:01<00:01, 23.75it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.099 | Best: {'fx': 525.752} (0.099):  46%|████▌     | 23/50 [00:01<00:01, 23.75it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.102 | Best: {'fx': 532.895} (0.102):  48%|████▊     | 24/50 [00:01<00:01, 23.75it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.102 | Best: {'fx': 532.895} (0.102):  50%|█████     | 25/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.101 | Best: {'fx': 532.895} (0.102):  50%|█████     | 25/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.091 | Best: {'fx': 532.895} (0.102):  52%|█████▏    | 26/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.107 | Best: {'fx': 528.814} (0.107):  54%|█████▍    | 27/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.094 | Best: {'fx': 528.814} (0.107):  56%|█████▌    | 28/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.098 | Best: {'fx': 528.814} (0.107):  58%|█████▊    | 29/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.087 | Best: {'fx': 528.814} (0.107):  60%|██████    | 30/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.083 | Best: {'fx': 528.814} (0.107):  62%|██████▏   | 31/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.107 | Best: {'fx': 529.834} (0.107):  64%|██████▍   | 32/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.103 | Best: {'fx': 529.834} (0.107):  66%|██████▌   | 33/50 [00:01<00:00, 25.16it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.103 | Best: {'fx': 529.834} (0.107):  68%|██████▊   | 34/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.092 | Best: {'fx': 529.834} (0.107):  68%|██████▊   | 34/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.076 | Best: {'fx': 529.834} (0.107):  70%|███████   | 35/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.106 | Best: {'fx': 529.834} (0.107):  72%|███████▏  | 36/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.069 | Best: {'fx': 529.834} (0.107):  74%|███████▍  | 37/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.081 | Best: {'fx': 529.834} (0.107):  76%|███████▌  | 38/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.073 | Best: {'fx': 529.834} (0.107):  78%|███████▊  | 39/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.071 | Best: {'fx': 529.834} (0.107):  80%|████████  | 40/50 [00:01<00:00, 37.54it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.071 | Best: {'fx': 529.834} (0.107):  82%|████████▏ | 41/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.105 | Best: {'fx': 529.834} (0.107):  82%|████████▏ | 41/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.066 | Best: {'fx': 529.834} (0.107):  84%|████████▍ | 42/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.064 | Best: {'fx': 529.834} (0.107):  86%|████████▌ | 43/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.078 | Best: {'fx': 529.834} (0.107):  88%|████████▊ | 44/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.061 | Best: {'fx': 529.834} (0.107):  90%|█████████ | 45/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.063 | Best: {'fx': 529.834} (0.107):  92%|█████████▏| 46/50 [00:01<00:00, 45.02it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.063 | Best: {'fx': 529.834} (0.107):  94%|█████████▍| 47/50 [00:01<00:00, 44.49it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.058 | Best: {'fx': 529.834} (0.107):  94%|█████████▍| 47/50 [00:01<00:00, 44.49it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.059 | Best: {'fx': 529.834} (0.107):  96%|█████████▌| 48/50 [00:01<00:00, 44.49it/s]Brute | Start: {'fx': 526.263} (0.101) | Current: 0.057 | Best: {'fx': 529.834} (0.107):  98%|█████████▊| 49/50 [00:01<00:00, 44.49it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.8340548340549
    px: 1020.0
    py: 766.0
    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 3 incomplete frames): 28 frames will be used for optimization
  Rigs: 2000L(2000mm):7/20, 1500(1500mm):7/20, 1000(1000mm):7/20, 2000R(2000mm):7/20
  Total: 28/80 frames used for optimization
Initial score: 9.8544: 0iter [00:00, ?iter/s]Initial score: 9.8544: 1iter [00:03,  3.44s/iter]Initial score: 9.8544: 2iter [00:05,  2.46s/iter]Initial score: 9.8544: 3iter [00:06,  2.12s/iter]Initial score: 9.8544: 4iter [00:08,  1.96s/iter]Initial score: 9.8544: 5iter [00:10,  1.87s/iter]Initial score: 9.8544: 6iter [00:12,  1.84s/iter]Initial score: 9.8544: 7iter [00:13,  1.80s/iter]Initial score: 9.8544: 8iter [00:15,  1.77s/iter]Initial score: 9.8544: 9iter [00:17,  1.77s/iter]Initial score: 9.8544: 10iter [00:19,  1.77s/iter]Initial score: 9.8544: 11iter [00:20,  1.77s/iter]Initial score: 9.8544: 12iter [00:22,  1.75s/iter]Initial score: 9.8544: 13iter [00:24,  1.74s/iter]Initial score: 9.8544: 14iter [00:26,  1.76s/iter]Initial score: 9.8544: 15iter [00:27,  1.76s/iter]Initial score: 9.8544: 16iter [00:29,  1.75s/iter]Initial score: 9.8544: 17iter [00:31,  1.74s/iter]Initial score: 9.8544: 18iter [00:33,  1.74s/iter]Initial score: 9.8544: 19iter [00:34,  1.73s/iter]Initial score: 9.8544: 20iter [00:36,  1.75s/iter]Initial score: 9.8544: 21iter [00:38,  1.74s/iter]Initial score: 9.8544: 22iter [00:40,  1.75s/iter]Initial score: 9.8544: 23iter [00:41,  1.75s/iter]Initial score: 9.8544: 24iter [00:43,  1.75s/iter]Initial score: 9.8544: 25iter [00:45,  1.74s/iter]Initial score: 9.8544: 26iter [00:47,  1.74s/iter]Initial score: 9.8544: 27iter [00:48,  1.73s/iter]Initial score: 9.8544: 28iter [00:50,  1.73s/iter]Initial score: 9.8544: 29iter [00:52,  1.73s/iter]Initial score: 9.8544: 30iter [00:53,  1.73s/iter]Initial score: 9.8544: 31iter [00:55,  1.73s/iter]Initial score: 9.8544: 32iter [00:57,  1.72s/iter]Initial score: 9.8544: 33iter [00:59,  1.71s/iter]Initial score: 9.8544: 34iter [01:00,  1.70s/iter]Initial score: 9.8544: 35iter [01:02,  1.71s/iter]Initial score: 9.8544: 36iter [01:04,  1.70s/iter]Initial score: 9.8544: 37iter [01:05,  1.72s/iter]Initial score: 9.8544: 38iter [01:07,  1.73s/iter]Initial score: 9.8544: 39iter [01:09,  1.74s/iter]Initial score: 9.8544: 40iter [01:11,  1.73s/iter]Initial score: 9.8544: 41iter [01:12,  1.73s/iter]Initial score: 9.8544: 42iter [01:14,  1.72s/iter]Initial score: 9.8544: 43iter [01:16,  1.72s/iter]Best score: 5.6374: : 43iter [01:17,  1.72s/iter] Best score: 5.6374: : 44iter [01:17,  1.71s/iter]Best score: 5.6374: : 45iter [01:19,  1.70s/iter]Best score: 5.6374: : 46iter [01:21,  1.71s/iter]Best score: 5.6374: : 47iter [01:23,  1.70s/iter]Best score: 5.6374: : 48iter [01:24,  1.70s/iter]Best score: 5.6374: : 49iter [01:26,  1.70s/iter]
[ITER 50] Current best score: 5.6374
[PARAMS] Current best parameters:
  fx: 535.9708
  fy: 532.7172
  px: 1016.7548
  py: 769.1857
  k1: -0.00078892
  k2: 0.01190140
  k3: -0.01258847
  k4: 0.00263292
  p1: 0.00948641
  p2: -0.00226452
  p3: -0.00302903
  p4: -0.00196606
  scale: 2.9782
  rotation: -1.3535
Best score: 5.6374: : 50iter [01:28,  1.79s/iter]Best score: 5.6374: : 51iter [01:30,  1.78s/iter]Best score: 5.6374: : 52iter [01:31,  1.77s/iter]Best score: 4.7322: : 52iter [01:33,  1.77s/iter]Best score: 4.7322: : 53iter [01:33,  1.75s/iter]Best score: 4.7322: : 54iter [01:35,  1.72s/iter]Best score: 4.7322: : 55iter [01:36,  1.71s/iter]Best score: 4.7322: : 56iter [01:38,  1.72s/iter]Best score: 4.7322: : 57iter [01:40,  1.72s/iter]Best score: 4.7322: : 58iter [01:42,  1.73s/iter]Best score: 4.7322: : 59iter [01:43,  1.71s/iter]Best score: 4.7322: : 60iter [01:45,  1.71s/iter]Best score: 4.7322: : 61iter [01:47,  1.71s/iter]Best score: 4.7322: : 62iter [01:48,  1.71s/iter]Best score: 4.7322: : 63iter [01:50,  1.72s/iter]Best score: 4.7322: : 64iter [01:52,  1.71s/iter]Best score: 4.7322: : 65iter [01:54,  1.71s/iter]Best score: 4.2885: : 65iter [01:55,  1.71s/iter]Best score: 4.2885: : 66iter [01:55,  1.70s/iter]Best score: 4.2885: : 67iter [01:57,  1.71s/iter]Best score: 4.2885: : 68iter [01:59,  1.69s/iter]Best score: 4.2885: : 69iter [02:00,  1.71s/iter]Best score: 4.2885: : 70iter [02:02,  1.71s/iter]Best score: 2.5441: : 70iter [02:04,  1.71s/iter]Best score: 2.5441: : 71iter [02:04,  1.72s/iter]Best score: 2.5441: : 72iter [02:06,  1.73s/iter]Best score: 2.5441: : 73iter [02:07,  1.71s/iter]Best score: 2.5441: : 74iter [02:09,  1.73s/iter]Best score: 2.5441: : 75iter [02:11,  1.73s/iter]Best score: 2.5441: : 76iter [02:12,  1.72s/iter]Best score: 2.5441: : 77iter [02:14,  1.72s/iter]Best score: 2.5441: : 78iter [02:16,  1.72s/iter]Best score: 2.5441: : 79iter [02:18,  1.72s/iter]Best score: 2.5441: : 80iter [02:19,  1.70s/iter]Best score: 2.5441: : 81iter [02:21,  1.70s/iter]Best score: 2.5441: : 82iter [02:23,  1.72s/iter]Best score: 2.1658: : 82iter [02:24,  1.72s/iter]Best score: 2.1658: : 83iter [02:24,  1.72s/iter]Best score: 2.1658: : 84iter [02:26,  1.71s/iter]Best score: 2.1658: : 85iter [02:28,  1.69s/iter]Best score: 2.1658: : 86iter [02:29,  1.68s/iter]Best score: 2.1658: : 87iter [02:31,  1.69s/iter]Best score: 2.1658: : 88iter [02:33,  1.68s/iter]Best score: 0.6652: : 88iter [02:34,  1.68s/iter]Best score: 0.6652: : 89iter [02:34,  1.68s/iter]Best score: 0.6652: : 90iter [02:36,  1.71s/iter]Best score: 0.6652: : 91iter [02:38,  1.72s/iter]Best score: 0.6652: : 92iter [02:40,  1.73s/iter]Best score: 0.6652: : 93iter [02:41,  1.71s/iter]Best score: 0.6652: : 94iter [02:43,  1.70s/iter]Best score: 0.6652: : 95iter [02:45,  1.71s/iter]Best score: 0.6652: : 96iter [02:47,  1.72s/iter]Best score: 0.6652: : 97iter [02:48,  1.75s/iter]Best score: 0.6652: : 98iter [02:50,  1.74s/iter]Best score: 0.6652: : 99iter [02:52,  1.75s/iter]
[ITER 100] Current best score: 0.6652
[PARAMS] Current best parameters:
  fx: 550.6018
  fy: 550.0482
  px: 1017.1507
  py: 768.1611
  k1: -0.01048185
  k2: -0.00671699
  k3: -0.00288972
  k4: 0.00249511
  p1: 0.00063354
  p2: -0.00911320
  p3: 0.00663931
  p4: -0.00146382
  scale: 2.9807
  rotation: -1.2227
Best score: 0.6652: : 100iter [02:54,  1.81s/iter]Best score: 0.6652: : 101iter [02:56,  1.78s/iter]Best score: 0.6652: : 102iter [02:57,  1.78s/iter]Best score: 0.6652: : 103iter [02:59,  1.76s/iter]Best score: 0.6652: : 104iter [03:01,  1.74s/iter]Best score: 0.6652: : 105iter [03:02,  1.75s/iter]Best score: 0.6652: : 106iter [03:04,  1.74s/iter]Best score: 0.6652: : 107iter [03:06,  1.73s/iter]Best score: 0.6652: : 108iter [03:08,  1.71s/iter]Best score: 0.6652: : 109iter [03:09,  1.73s/iter]Best score: 0.6652: : 110iter [03:11,  1.72s/iter]Best score: 0.6652: : 111iter [03:13,  1.73s/iter]Best score: 0.6652: : 112iter [03:14,  1.70s/iter]Best score: 0.6652: : 113iter [03:16,  1.71s/iter]Best score: 0.6652: : 114iter [03:18,  1.70s/iter]Best score: 0.6652: : 115iter [03:20,  1.69s/iter]Best score: 0.6652: : 116iter [03:21,  1.70s/iter]Best score: 0.6652: : 117iter [03:23,  1.70s/iter]Best score: 0.6652: : 118iter [03:25,  1.69s/iter]Best score: 0.6652: : 119iter [03:26,  1.73s/iter]Best score: 0.6652: : 120iter [03:28,  1.73s/iter]Best score: 0.6652: : 121iter [03:30,  1.73s/iter]Best score: 0.6652: : 122iter [03:32,  1.71s/iter]Best score: 0.6652: : 123iter [03:33,  1.72s/iter]Best score: 0.6652: : 124iter [03:35,  1.72s/iter]Best score: 0.6652: : 125iter [03:37,  1.73s/iter]Best score: 0.6652: : 126iter [03:39,  1.74s/iter]Best score: 0.5695: : 126iter [03:40,  1.74s/iter]Best score: 0.5695: : 127iter [03:40,  1.73s/iter]Best score: 0.5695: : 128iter [03:42,  1.72s/iter]Best score: 0.5695: : 129iter [03:44,  1.71s/iter]Best score: 0.5695: : 130iter [03:45,  1.73s/iter]Best score: 0.5695: : 131iter [03:47,  1.75s/iter]Best score: 0.5695: : 132iter [03:49,  1.74s/iter]Best score: 0.5695: : 133iter [03:51,  1.75s/iter]Best score: 0.5695: : 134iter [03:52,  1.74s/iter]Best score: 0.5695: : 135iter [03:54,  1.75s/iter]Best score: 0.5695: : 136iter [03:56,  1.75s/iter]Best score: 0.5695: : 137iter [03:58,  1.74s/iter]Best score: 0.5695: : 138iter [03:59,  1.73s/iter]Best score: 0.5695: : 139iter [04:01,  1.74s/iter]Best score: 0.5695: : 140iter [04:03,  1.75s/iter]Best score: 0.5695: : 141iter [04:05,  1.75s/iter]Best score: 0.5695: : 142iter [04:06,  1.75s/iter]Best score: 0.5695: : 143iter [04:08,  1.73s/iter]Best score: 0.5695: : 144iter [04:10,  1.73s/iter]Best score: 0.5695: : 145iter [04:12,  1.74s/iter]Best score: 0.5695: : 146iter [04:13,  1.73s/iter]Best score: 0.5695: : 147iter [04:15,  1.74s/iter]Best score: 0.5695: : 148iter [04:17,  1.72s/iter]Best score: 0.5695: : 149iter [04:18,  1.73s/iter]
[ITER 150] Current best score: 0.5695
[PARAMS] Current best parameters:
  fx: 558.6214
  fy: 557.9232
  px: 1019.2009
  py: 769.2975
  k1: -0.01214276
  k2: -0.01046000
  k3: -0.00179777
  k4: 0.00226909
  p1: 0.00248470
  p2: -0.00478000
  p3: 0.00598017
  p4: -0.00270145
  scale: 2.9569
  rotation: -1.1687
Best score: 0.5695: : 150iter [04:20,  1.80s/iter]Best score: 0.5695: : 151iter [04:22,  1.77s/iter]Best score: 0.5695: : 152iter [04:24,  1.73s/iter]Best score: 0.5695: : 153iter [04:25,  1.72s/iter]Best score: 0.5695: : 154iter [04:27,  1.74s/iter]Best score: 0.5695: : 155iter [04:29,  1.75s/iter]Best score: 0.5695: : 156iter [04:31,  1.73s/iter]Best score: 0.5695: : 157iter [04:32,  1.72s/iter]Best score: 0.5695: : 158iter [04:34,  1.72s/iter]Best score: 0.5695: : 159iter [04:36,  1.72s/iter]Best score: 0.5695: : 160iter [04:38,  1.74s/iter]Best score: 0.5695: : 161iter [04:39,  1.73s/iter]Best score: 0.5695: : 162iter [04:41,  1.71s/iter]Best score: 0.5695: : 163iter [04:43,  1.72s/iter]Best score: 0.5695: : 164iter [04:44,  1.74s/iter]Best score: 0.5695: : 165iter [04:46,  1.74s/iter]Best score: 0.5695: : 166iter [04:48,  1.71s/iter]Best score: 0.5695: : 167iter [04:50,  1.73s/iter]Best score: 0.5695: : 168iter [04:51,  1.72s/iter]Best score: 0.5695: : 169iter [04:53,  1.72s/iter]Best score: 0.5695: : 170iter [04:55,  1.72s/iter]Best score: 0.5695: : 171iter [04:57,  1.73s/iter]Best score: 0.5695: : 172iter [04:58,  1.74s/iter]Best score: 0.5695: : 173iter [05:00,  1.73s/iter]Best score: 0.5695: : 174iter [05:02,  1.75s/iter]Best score: 0.5695: : 175iter [05:04,  1.74s/iter]Best score: 0.5695: : 176iter [05:05,  1.72s/iter]Best score: 0.5695: : 177iter [05:07,  1.72s/iter]Best score: 0.5668: : 177iter [05:09,  1.72s/iter]Best score: 0.5668: : 178iter [05:09,  1.73s/iter]Best score: 0.5668: : 179iter [05:10,  1.70s/iter]Best score: 0.5668: : 180iter [05:12,  1.72s/iter]Best score: 0.5668: : 181iter [05:14,  1.71s/iter]Best score: 0.5668: : 182iter [05:16,  1.72s/iter]Best score: 0.5668: : 183iter [05:17,  1.72s/iter]Best score: 0.5668: : 184iter [05:19,  1.72s/iter]Best score: 0.5668: : 185iter [05:21,  1.71s/iter]Best score: 0.5668: : 186iter [05:22,  1.71s/iter]Best score: 0.5668: : 187iter [05:24,  1.70s/iter]Best score: 0.5668: : 188iter [05:26,  1.73s/iter]Best score: 0.5668: : 189iter [05:27,  1.71s/iter]Best score: 0.5668: : 190iter [05:29,  1.71s/iter]Best score: 0.5668: : 191iter [05:31,  1.71s/iter]Best score: 0.5668: : 192iter [05:33,  1.73s/iter]Best score: 0.4820: : 192iter [05:34,  1.73s/iter]Best score: 0.4820: : 193iter [05:34,  1.73s/iter]Best score: 0.4820: : 194iter [05:36,  1.75s/iter]Best score: 0.4820: : 195iter [05:38,  1.73s/iter]Best score: 0.4820: : 196iter [05:40,  1.73s/iter]Best score: 0.4820: : 197iter [05:41,  1.72s/iter]Best score: 0.4820: : 198iter [05:43,  1.72s/iter]Best score: 0.4820: : 199iter [05:45,  1.71s/iter]
[ITER 200] Current best score: 0.4820
[PARAMS] Current best parameters:
  fx: 553.2712
  fy: 552.7235
  px: 1018.7853
  py: 767.1211
  k1: -0.01107438
  k2: -0.01437108
  k3: 0.00983577
  k4: -0.00277063
  p1: -0.00195982
  p2: -0.00454541
  p3: 0.00827179
  p4: -0.00285567
  scale: 2.9622
  rotation: -1.2078
Best score: 0.4820: : 200iter [05:47,  1.78s/iter]Best score: 0.4820: : 201iter [05:48,  1.77s/iter]Best score: 0.4820: : 202iter [05:50,  1.77s/iter]Best score: 0.4820: : 203iter [05:52,  1.75s/iter]Best score: 0.4820: : 204iter [05:54,  1.73s/iter]Best score: 0.4820: : 205iter [05:55,  1.74s/iter]Best score: 0.4820: : 206iter [05:57,  1.72s/iter]Best score: 0.4820: : 207iter [05:59,  1.72s/iter]Best score: 0.4820: : 208iter [06:00,  1.72s/iter]Best score: 0.4820: : 209iter [06:02,  1.72s/iter]Best score: 0.4820: : 210iter [06:04,  1.73s/iter]Best score: 0.4820: : 211iter [06:06,  1.74s/iter]Best score: 0.4820: : 212iter [06:07,  1.74s/iter]Best score: 0.4820: : 213iter [06:09,  1.73s/iter]Best score: 0.4820: : 214iter [06:11,  1.70s/iter]Best score: 0.4820: : 215iter [06:13,  1.72s/iter]Best score: 0.4820: : 216iter [06:14,  1.73s/iter]Best score: 0.4820: : 217iter [06:16,  1.75s/iter]Best score: 0.4820: : 218iter [06:18,  1.74s/iter]Best score: 0.4820: : 219iter [06:20,  1.75s/iter]Best score: 0.4820: : 220iter [06:21,  1.75s/iter]Best score: 0.4820: : 221iter [06:23,  1.73s/iter]Best score: 0.4820: : 222iter [06:25,  1.72s/iter]Best score: 0.4820: : 223iter [06:26,  1.73s/iter]Best score: 0.4820: : 224iter [06:28,  1.73s/iter]Best score: 0.4820: : 225iter [06:30,  1.74s/iter]Best score: 0.4820: : 226iter [06:32,  1.73s/iter]Best score: 0.4820: : 227iter [06:33,  1.73s/iter]Best score: 0.4820: : 228iter [06:35,  1.73s/iter]Best score: 0.4820: : 229iter [06:37,  1.74s/iter]Best score: 0.4820: : 230iter [06:39,  1.76s/iter]Best score: 0.4820: : 231iter [06:40,  1.75s/iter]Best score: 0.4820: : 232iter [06:42,  1.76s/iter]Best score: 0.4820: : 233iter [06:44,  1.76s/iter]Best score: 0.4820: : 234iter [06:46,  1.73s/iter]Best score: 0.4820: : 235iter [06:47,  1.72s/iter]Best score: 0.3788: : 235iter [06:49,  1.72s/iter]Best score: 0.3788: : 236iter [06:49,  1.72s/iter]Best score: 0.3788: : 237iter [06:51,  1.72s/iter]Best score: 0.3788: : 238iter [06:52,  1.70s/iter]Best score: 0.3788: : 239iter [06:54,  1.71s/iter]Best score: 0.3788: : 240iter [06:56,  1.70s/iter]Best score: 0.3788: : 241iter [06:58,  1.70s/iter]Best score: 0.3788: : 242iter [06:59,  1.72s/iter]Best score: 0.2928: : 242iter [07:01,  1.72s/iter]Best score: 0.2928: : 243iter [07:01,  1.71s/iter]Best score: 0.2928: : 244iter [07:03,  1.72s/iter]Best score: 0.2928: : 245iter [07:04,  1.73s/iter]Best score: 0.2928: : 246iter [07:06,  1.73s/iter]Best score: 0.2928: : 247iter [07:08,  1.74s/iter]Best score: 0.2928: : 248iter [07:10,  1.74s/iter]Best score: 0.2928: : 249iter [07:11,  1.73s/iter]
[ITER 250] Current best score: 0.2928
[PARAMS] Current best parameters:
  fx: 563.0904
  fy: 562.6723
  px: 1018.7303
  py: 766.0898
  k1: -0.03285759
  k2: -0.01033794
  k3: 0.01554554
  k4: -0.00497315
  p1: -0.00480171
  p2: -0.00534484
  p3: 0.00731725
  p4: -0.00123039
  scale: 2.9912
  rotation: -1.1822
Best score: 0.2928: : 250iter [07:13,  1.78s/iter]Best score: 0.2928: : 251iter [07:15,  1.77s/iter]Best score: 0.2928: : 252iter [07:17,  1.76s/iter]Best score: 0.2928: : 253iter [07:19,  1.76s/iter]Best score: 0.2928: : 254iter [07:20,  1.75s/iter]Best score: 0.2928: : 255iter [07:22,  1.73s/iter]Best score: 0.2928: : 256iter [07:24,  1.72s/iter]Best score: 0.2928: : 257iter [07:25,  1.73s/iter]Best score: 0.2928: : 258iter [07:27,  1.74s/iter]Best score: 0.2928: : 259iter [07:29,  1.74s/iter]Best score: 0.2928: : 260iter [07:31,  1.73s/iter]Best score: 0.2928: : 261iter [07:32,  1.73s/iter]Best score: 0.2928: : 262iter [07:34,  1.74s/iter]Best score: 0.2928: : 263iter [07:36,  1.74s/iter]Best score: 0.2928: : 264iter [07:38,  1.74s/iter]Best score: 0.2928: : 265iter [07:39,  1.74s/iter]Best score: 0.2928: : 266iter [07:41,  1.73s/iter]Best score: 0.2928: : 267iter [07:43,  1.71s/iter]Best score: 0.2928: : 268iter [07:44,  1.71s/iter]Best score: 0.2928: : 269iter [07:46,  1.73s/iter]Best score: 0.2928: : 270iter [07:48,  1.74s/iter]Best score: 0.2928: : 271iter [07:50,  1.72s/iter]Best score: 0.2928: : 272iter [07:51,  1.73s/iter]Best score: 0.2928: : 273iter [07:53,  1.74s/iter]Best score: 0.2928: : 274iter [07:55,  1.72s/iter]Best score: 0.2928: : 275iter [07:57,  1.72s/iter]Best score: 0.2928: : 276iter [07:58,  1.73s/iter]Best score: 0.2928: : 277iter [08:00,  1.75s/iter]Best score: 0.2928: : 278iter [08:02,  1.75s/iter]Best score: 0.2928: : 279iter [08:03,  1.72s/iter]Best score: 0.2928: : 280iter [08:05,  1.73s/iter]Best score: 0.2928: : 281iter [08:07,  1.74s/iter]Best score: 0.2928: : 282iter [08:09,  1.74s/iter]Best score: 0.2928: : 283iter [08:11,  1.75s/iter]Best score: 0.2928: : 284iter [08:12,  1.74s/iter]Best score: 0.2928: : 285iter [08:14,  1.74s/iter]Best score: 0.2928: : 286iter [08:16,  1.73s/iter]Best score: 0.2928: : 287iter [08:17,  1.74s/iter]Best score: 0.2928: : 288iter [08:19,  1.74s/iter]Best score: 0.2928: : 289iter [08:21,  1.73s/iter]Best score: 0.2928: : 290iter [08:23,  1.73s/iter]Best score: 0.2928: : 291iter [08:24,  1.71s/iter]Best score: 0.2928: : 292iter [08:26,  1.73s/iter]Best score: 0.2928: : 293iter [08:28,  1.72s/iter]Best score: 0.2928: : 294iter [08:29,  1.72s/iter]Best score: 0.2928: : 295iter [08:31,  1.71s/iter]Best score: 0.2928: : 296iter [08:33,  1.71s/iter]Best score: 0.2928: : 297iter [08:35,  1.71s/iter]Best score: 0.2928: : 298iter [08:36,  1.74s/iter]Best score: 0.2928: : 299iter [08:38,  1.73s/iter]
[ITER 300] Current best score: 0.2982
[PARAMS] Current best parameters:
  fx: 562.3383
  fy: 561.6743
  px: 1019.4676
  py: 765.0554
  k1: -0.03104215
  k2: -0.01084380
  k3: 0.01642105
  k4: -0.00555670
  p1: -0.00649117
  p2: -0.00358254
  p3: 0.00548733
  p4: -0.00156673
  scale: 2.9889
  rotation: -1.2081
Best score: 0.2928: : 300iter [08:40,  1.78s/iter]Best score: 0.2928: : 301iter [08:42,  1.76s/iter]Best score: 0.2928: : 302iter [08:43,  1.74s/iter]Best score: 0.2928: : 303iter [08:45,  1.74s/iter]Best score: 0.2928: : 304iter [08:47,  1.74s/iter]Best score: 0.2928: : 305iter [08:49,  1.71s/iter]Best score: 0.2928: : 306iter [08:50,  1.72s/iter]Best score: 0.2928: : 307iter [08:52,  1.71s/iter]Best score: 0.2928: : 308iter [08:54,  1.72s/iter]Best score: 0.2928: : 309iter [08:55,  1.71s/iter]Best score: 0.2928: : 310iter [08:57,  1.72s/iter]Best score: 0.2928: : 311iter [08:59,  1.74s/iter]Best score: 0.2928: : 312iter [09:01,  1.72s/iter]Best score: 0.2928: : 313iter [09:02,  1.72s/iter]Best score: 0.2928: : 314iter [09:04,  1.73s/iter]Best score: 0.2928: : 315iter [09:06,  1.71s/iter]Best score: 0.2928: : 316iter [09:07,  1.71s/iter]Best score: 0.2928: : 317iter [09:09,  1.70s/iter]Best score: 0.2928: : 318iter [09:11,  1.71s/iter]Best score: 0.2928: : 319iter [09:13,  1.70s/iter]Best score: 0.2928: : 320iter [09:14,  1.70s/iter]Best score: 0.2928: : 321iter [09:16,  1.72s/iter]Best score: 0.2928: : 322iter [09:18,  1.72s/iter]Best score: 0.2928: : 323iter [09:19,  1.73s/iter]Best score: 0.2928: : 324iter [09:21,  1.73s/iter]Best score: 0.2928: : 325iter [09:23,  1.73s/iter]Best score: 0.2928: : 326iter [09:25,  1.72s/iter]Best score: 0.2928: : 327iter [09:26,  1.75s/iter]Best score: 0.2928: : 328iter [09:28,  1.73s/iter]Best score: 0.2928: : 329iter [09:30,  1.74s/iter]Best score: 0.2928: : 330iter [09:32,  1.74s/iter]Best score: 0.2928: : 331iter [09:33,  1.75s/iter]Best score: 0.2928: : 332iter [09:35,  1.73s/iter]Best score: 0.2928: : 333iter [09:37,  1.72s/iter]Best score: 0.2928: : 334iter [09:39,  1.73s/iter]Best score: 0.2928: : 335iter [09:40,  1.73s/iter]Best score: 0.2928: : 336iter [09:42,  1.72s/iter]Best score: 0.2928: : 337iter [09:44,  1.71s/iter]Best score: 0.2928: : 338iter [09:45,  1.71s/iter]Best score: 0.2928: : 339iter [09:47,  1.72s/iter]Best score: 0.2928: : 340iter [09:49,  1.73s/iter]Best score: 0.2928: : 341iter [09:51,  1.74s/iter]Best score: 0.2859: : 341iter [09:52,  1.74s/iter]Best score: 0.2859: : 342iter [09:52,  1.75s/iter]Best score: 0.2859: : 343iter [09:54,  1.73s/iter]Best score: 0.2859: : 344iter [09:56,  1.72s/iter]Best score: 0.2859: : 345iter [09:58,  1.75s/iter]Best score: 0.2859: : 346iter [09:59,  1.75s/iter]Best score: 0.2859: : 347iter [10:01,  1.73s/iter]Best score: 0.2859: : 348iter [10:03,  1.72s/iter]Best score: 0.2859: : 349iter [10:05,  1.75s/iter]
[ITER 350] Current best score: 0.2859
[PARAMS] Current best parameters:
  fx: 569.1207
  fy: 568.4849
  px: 1019.6008
  py: 764.8033
  k1: -0.03539508
  k2: -0.01037821
  k3: 0.01410166
  k4: -0.00448817
  p1: -0.00722560
  p2: -0.00319904
  p3: 0.00460459
  p4: 0.00006160
  scale: 2.9788
  rotation: -1.1736
Best score: 0.2859: : 350iter [10:06,  1.77s/iter]Best score: 0.2859: : 351iter [10:08,  1.76s/iter]Best score: 0.2859: : 352iter [10:10,  1.76s/iter]Best score: 0.2859: : 353iter [10:12,  1.74s/iter]Best score: 0.2859: : 354iter [10:13,  1.72s/iter]Best score: 0.2859: : 355iter [10:15,  1.74s/iter]Best score: 0.2859: : 356iter [10:17,  1.73s/iter]Best score: 0.2859: : 357iter [10:18,  1.72s/iter]Best score: 0.2859: : 358iter [10:20,  1.72s/iter]Best score: 0.2859: : 359iter [10:22,  1.73s/iter]Best score: 0.2859: : 360iter [10:24,  1.71s/iter]Best score: 0.2859: : 361iter [10:25,  1.73s/iter]Best score: 0.2859: : 362iter [10:27,  1.75s/iter]Best score: 0.2859: : 363iter [10:29,  1.74s/iter]Best score: 0.2859: : 364iter [10:31,  1.73s/iter]Best score: 0.2859: : 365iter [10:32,  1.71s/iter]Best score: 0.2859: : 366iter [10:34,  1.73s/iter]Best score: 0.2859: : 367iter [10:36,  1.72s/iter]Best score: 0.2859: : 368iter [10:37,  1.72s/iter]Best score: 0.2859: : 369iter [10:39,  1.73s/iter]Best score: 0.2859: : 370iter [10:41,  1.71s/iter]Best score: 0.2859: : 371iter [10:43,  1.71s/iter]Best score: 0.2859: : 372iter [10:44,  1.70s/iter]Best score: 0.2859: : 373iter [10:46,  1.70s/iter]Best score: 0.2859: : 374iter [10:48,  1.71s/iter]Best score: 0.2719: : 374iter [10:49,  1.71s/iter]Best score: 0.2719: : 375iter [10:49,  1.73s/iter]Best score: 0.2719: : 376iter [10:51,  1.72s/iter]Best score: 0.2719: : 377iter [10:53,  1.73s/iter]Best score: 0.2719: : 378iter [10:55,  1.73s/iter]Best score: 0.2719: : 379iter [10:56,  1.74s/iter]Best score: 0.2719: : 380iter [10:58,  1.74s/iter]Best score: 0.2719: : 381iter [11:00,  1.72s/iter]Best score: 0.2719: : 382iter [11:02,  1.72s/iter]Best score: 0.2719: : 383iter [11:03,  1.70s/iter]Best score: 0.2719: : 384iter [11:05,  1.70s/iter]Best score: 0.2719: : 385iter [11:07,  1.71s/iter]Best score: 0.2719: : 386iter [11:08,  1.71s/iter]Best score: 0.2719: : 387iter [11:10,  1.71s/iter]Best score: 0.2719: : 388iter [11:12,  1.73s/iter]Best score: 0.2719: : 389iter [11:14,  1.73s/iter]Best score: 0.2719: : 390iter [11:15,  1.74s/iter]Best score: 0.2719: : 391iter [11:17,  1.73s/iter]Best score: 0.2719: : 392iter [11:19,  1.73s/iter]Best score: 0.2719: : 393iter [11:20,  1.72s/iter]Best score: 0.2719: : 394iter [11:22,  1.73s/iter]Best score: 0.2719: : 395iter [11:24,  1.72s/iter]Best score: 0.2719: : 396iter [11:26,  1.73s/iter]Best score: 0.2719: : 397iter [11:27,  1.72s/iter]Best score: 0.2719: : 398iter [11:29,  1.71s/iter]Best score: 0.2719: : 399iter [11:31,  1.75s/iter]
[ITER 400] Current best score: 0.2719
[PARAMS] Current best parameters:
  fx: 555.2947
  fy: 554.9503
  px: 1019.6749
  py: 765.1594
  k1: -0.02057816
  k2: -0.01336287
  k3: 0.01412620
  k4: -0.00428300
  p1: -0.00640551
  p2: -0.00314761
  p3: 0.00469660
  p4: 0.00023984
  scale: 2.9885
  rotation: -1.1813
Best score: 0.2719: : 400iter [11:33,  1.81s/iter]Best score: 0.2719: : 401iter [11:34,  1.77s/iter]Best score: 0.2719: : 402iter [11:36,  1.77s/iter]Best score: 0.2719: : 403iter [11:38,  1.75s/iter]Best score: 0.2719: : 404iter [11:40,  1.74s/iter]Best score: 0.2719: : 405iter [11:41,  1.73s/iter]Best score: 0.2719: : 406iter [11:43,  1.71s/iter]Best score: 0.2719: : 407iter [11:45,  1.71s/iter]Best score: 0.2719: : 408iter [11:46,  1.71s/iter]Best score: 0.2719: : 409iter [11:48,  1.73s/iter]Best score: 0.2719: : 410iter [11:50,  1.72s/iter]Best score: 0.2719: : 411iter [11:52,  1.72s/iter]Best score: 0.2719: : 412iter [11:53,  1.72s/iter]Best score: 0.2719: : 413iter [11:55,  1.71s/iter]Best score: 0.2719: : 414iter [11:57,  1.72s/iter]Best score: 0.2719: : 415iter [11:58,  1.70s/iter]Best score: 0.2719: : 416iter [12:00,  1.72s/iter]Best score: 0.2719: : 417iter [12:02,  1.72s/iter]Best score: 0.2719: : 418iter [12:04,  1.73s/iter]Best score: 0.2719: : 419iter [12:05,  1.75s/iter]Best score: 0.2719: : 420iter [12:07,  1.75s/iter]Best score: 0.2719: : 421iter [12:09,  1.74s/iter]Best score: 0.2719: : 422iter [12:11,  1.74s/iter]Best score: 0.2719: : 423iter [12:12,  1.76s/iter]Best score: 0.2719: : 424iter [12:14,  1.75s/iter]Best score: 0.2719: : 425iter [12:16,  1.75s/iter]Best score: 0.2719: : 426iter [12:18,  1.74s/iter]Best score: 0.2719: : 427iter [12:19,  1.74s/iter]Best score: 0.2719: : 428iter [12:21,  1.75s/iter]Best score: 0.2719: : 429iter [12:23,  1.73s/iter]Best score: 0.2719: : 430iter [12:25,  1.74s/iter]Best score: 0.2719: : 431iter [12:26,  1.76s/iter]Best score: 0.2719: : 432iter [12:28,  1.75s/iter]Best score: 0.2719: : 433iter [12:30,  1.74s/iter]Best score: 0.2719: : 434iter [12:32,  1.74s/iter]Best score: 0.2719: : 435iter [12:33,  1.75s/iter]Best score: 0.2719: : 436iter [12:35,  1.71s/iter]Best score: 0.2719: : 437iter [12:37,  1.74s/iter]Best score: 0.2719: : 438iter [12:39,  1.73s/iter]Best score: 0.2719: : 439iter [12:40,  1.74s/iter]Best score: 0.2719: : 440iter [12:42,  1.73s/iter]Best score: 0.2719: : 441iter [12:44,  1.71s/iter]Best score: 0.2719: : 442iter [12:45,  1.73s/iter]Best score: 0.2719: : 443iter [12:47,  1.72s/iter]Best score: 0.2719: : 444iter [12:49,  1.72s/iter]Best score: 0.2719: : 445iter [12:51,  1.71s/iter]Best score: 0.2719: : 446iter [12:52,  1.72s/iter]Best score: 0.2719: : 447iter [12:54,  1.73s/iter]Best score: 0.2719: : 448iter [12:56,  1.73s/iter]Best score: 0.2719: : 449iter [12:58,  1.73s/iter]
[ITER 450] Current best score: 0.2719
[PARAMS] Current best parameters:
  fx: 555.2947
  fy: 554.9503
  px: 1019.6749
  py: 765.1594
  k1: -0.02057816
  k2: -0.01336287
  k3: 0.01412620
  k4: -0.00428300
  p1: -0.00640551
  p2: -0.00314761
  p3: 0.00469660
  p4: 0.00023984
  scale: 2.9885
  rotation: -1.1813
Best score: 0.2719: : 450iter [12:59,  1.79s/iter]Best score: 0.2719: : 451iter [13:01,  1.77s/iter]Best score: 0.2719: : 452iter [13:03,  1.76s/iter]Best score: 0.2719: : 453iter [13:05,  1.74s/iter]Best score: 0.2719: : 454iter [13:06,  1.74s/iter]Best score: 0.2719: : 455iter [13:08,  1.74s/iter]Best score: 0.2719: : 456iter [13:10,  1.73s/iter]Best score: 0.2719: : 457iter [13:12,  1.74s/iter]Best score: 0.2719: : 458iter [13:13,  1.73s/iter]Best score: 0.2719: : 459iter [13:15,  1.75s/iter]Best score: 0.2719: : 460iter [13:17,  1.74s/iter]Best score: 0.2719: : 461iter [13:19,  1.73s/iter]Best score: 0.2719: : 462iter [13:20,  1.74s/iter]Best score: 0.2719: : 463iter [13:22,  1.74s/iter]Best score: 0.2719: : 464iter [13:24,  1.73s/iter]Best score: 0.2719: : 465iter [13:25,  1.72s/iter]Best score: 0.2719: : 466iter [13:27,  1.72s/iter]Best score: 0.2719: : 467iter [13:29,  1.72s/iter]Best score: 0.2719: : 468iter [13:31,  1.71s/iter]Best score: 0.2719: : 469iter [13:32,  1.71s/iter]Best score: 0.2719: : 470iter [13:34,  1.71s/iter]Best score: 0.2719: : 471iter [13:36,  1.71s/iter]Best score: 0.2719: : 472iter [13:37,  1.72s/iter]Best score: 0.2637: : 472iter [13:39,  1.72s/iter]Best score: 0.2637: : 473iter [13:39,  1.72s/iter]Best score: 0.2637: : 474iter [13:41,  1.75s/iter]Best score: 0.2637: : 475iter [13:43,  1.73s/iter]Best score: 0.2637: : 476iter [13:44,  1.75s/iter]Best score: 0.2637: : 477iter [13:46,  1.74s/iter]Best score: 0.2637: : 478iter [13:48,  1.71s/iter]Best score: 0.2637: : 479iter [13:50,  1.74s/iter]Best score: 0.2637: : 480iter [13:51,  1.77s/iter]Best score: 0.2637: : 481iter [13:53,  1.73s/iter]Best score: 0.2637: : 482iter [13:55,  1.73s/iter]Best score: 0.2637: : 483iter [13:56,  1.72s/iter]Best score: 0.2637: : 484iter [13:58,  1.74s/iter]Best score: 0.2637: : 485iter [14:00,  1.73s/iter]Best score: 0.2637: : 486iter [14:02,  1.74s/iter]Best score: 0.2637: : 487iter [14:03,  1.74s/iter]Best score: 0.2637: : 488iter [14:05,  1.73s/iter]Best score: 0.2637: : 489iter [14:07,  1.73s/iter]Best score: 0.2637: : 490iter [14:09,  1.74s/iter]Best score: 0.2637: : 491iter [14:10,  1.74s/iter]Best score: 0.2637: : 492iter [14:12,  1.73s/iter]Best score: 0.2637: : 493iter [14:14,  1.73s/iter]Best score: 0.2637: : 494iter [14:16,  1.73s/iter]Best score: 0.2637: : 495iter [14:17,  1.72s/iter]Best score: 0.2637: : 496iter [14:19,  1.72s/iter]Best score: 0.2637: : 497iter [14:21,  1.71s/iter]Best score: 0.2637: : 498iter [14:22,  1.72s/iter]Best score: 0.2637: : 499iter [14:24,  1.73s/iter]
[ITER 500] Current best score: 0.2650
[PARAMS] Current best parameters:
  fx: 563.3818
  fy: 562.7904
  px: 1019.7324
  py: 765.9314
  k1: -0.02812032
  k2: -0.01080872
  k3: 0.01159888
  k4: -0.00338088
  p1: -0.00453430
  p2: -0.00284614
  p3: 0.00284837
  p4: 0.00126506
  scale: 2.9802
  rotation: -1.1585
Best score: 0.2637: : 500iter [14:26,  1.78s/iter]Best score: 0.2637: : 500iter [14:31,  1.74s/iter]

[📈] Forcing polish step after 500 iterations...
  Polish did not improve score: 0.262739
  Success: False
  Tangential distortion: p1=-0.004534, p2=-0.002846, p3=0.002848, p4=0.001265
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2627
[✅] Parameters copied to: /working/MDC028_W009(6)_2024-07-23/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 563.3817821154548
    fy: 562.7903952431923
    px: 1019.7324040652841
    py: 765.9314018612798
    k1: -0.028120316660921026
    k2: -0.01080871652281784
    k3: 0.011598884887379547
    k4: -0.003380882095323261
    p1: -0.004534304539043433
    p2: -0.0028461403929108987
    p3: 0.0028483739334411217
    p4: 0.0012650582404322263
    scale: 2.9802015137093703
    rotation: -1.1585479138708044
    mmpp: 3.4288977644167002
    nmpp: 3428897.7644167

[🔄] Converting 3015 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDC028_W009(6)_2024-07-23/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC028_W009(6)_2024-07-23/calibration/debug/3_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 81990/3080448 [00:00<00:03, 819892.96px/s]📤 Writing LUT:   6%|▌         | 172714/3080448 [00:00<00:03, 871271.44px/s]📤 Writing LUT:   8%|▊         | 260901/3080448 [00:00<00:03, 876105.61px/s]📤 Writing LUT:  11%|█▏        | 348512/3080448 [00:00<00:03, 875823.27px/s]📤 Writing LUT:  14%|█▍        | 436637/3080448 [00:00<00:03, 877777.81px/s]📤 Writing LUT:  17%|█▋        | 527472/3080448 [00:00<00:02, 888170.48px/s]📤 Writing LUT:  20%|██        | 616290/3080448 [00:00<00:02, 883542.97px/s]📤 Writing LUT:  23%|██▎       | 704650/3080448 [00:00<00:02, 880653.56px/s]📤 Writing LUT:  26%|██▌       | 792720/3080448 [00:00<00:02, 879038.12px/s]📤 Writing LUT:  29%|██▊       | 880627/3080448 [00:01<00:02, 878322.27px/s]📤 Writing LUT:  32%|███▏      | 971044/3080448 [00:01<00:02, 886201.69px/s]📤 Writing LUT:  34%|███▍      | 1061961/3080448 [00:01<00:02, 893169.61px/s]📤 Writing LUT:  37%|███▋      | 1151283/3080448 [00:01<00:02, 884858.87px/s]📤 Writing LUT:  40%|████      | 1239789/3080448 [00:01<00:02, 882179.93px/s]📤 Writing LUT:  43%|████▎     | 1329091/3080448 [00:01<00:01, 885417.16px/s]📤 Writing LUT:  46%|████▌     | 1417964/3080448 [00:01<00:01, 886407.35px/s]📤 Writing LUT:  49%|████▉     | 1506614/3080448 [00:01<00:01, 883165.33px/s]📤 Writing LUT:  52%|█████▏    | 1594939/3080448 [00:01<00:01, 878873.31px/s]📤 Writing LUT:  55%|█████▍    | 1683973/3080448 [00:01<00:01, 882292.55px/s]📤 Writing LUT:  58%|█████▊    | 1772475/3080448 [00:02<00:01, 883106.38px/s]📤 Writing LUT:  60%|██████    | 1861675/3080448 [00:02<00:01, 885763.15px/s]📤 Writing LUT:  63%|██████▎   | 1953320/3080448 [00:02<00:01, 894949.36px/s]📤 Writing LUT:  66%|██████▋   | 2042821/3080448 [00:02<00:01, 894381.37px/s]📤 Writing LUT:  69%|██████▉   | 2132264/3080448 [00:02<00:01, 893722.14px/s]📤 Writing LUT:  72%|███████▏  | 2222654/3080448 [00:02<00:00, 896767.82px/s]📤 Writing LUT:  75%|███████▌  | 2313196/3080448 [00:02<00:00, 899359.62px/s]📤 Writing LUT:  78%|███████▊  | 2403135/3080448 [00:02<00:00, 897476.11px/s]📤 Writing LUT:  81%|████████  | 2492885/3080448 [00:02<00:00, 895467.47px/s]📤 Writing LUT:  84%|████████▍ | 2584466/3080448 [00:02<00:00, 901549.10px/s]📤 Writing LUT:  87%|████████▋ | 2675448/3080448 [00:03<00:00, 904024.15px/s]📤 Writing LUT:  90%|████████▉ | 2765854/3080448 [00:03<00:00, 900778.54px/s]📤 Writing LUT:  93%|█████████▎| 2857156/3080448 [00:03<00:00, 904435.63px/s]📤 Writing LUT:  96%|█████████▌| 2947604/3080448 [00:03<00:00, 903232.47px/s]📤 Writing LUT:  99%|█████████▊| 3037931/3080448 [00:03<00:00, 902798.31px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 889824.19px/s]
[✅] LUT written to: /working/MDC028_W009(6)_2024-07-23/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC028_W009(6)_2024-07-23' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC028_W009(6)_2024-07-23/calibration/MDC028_W009(6)_2024-07-23_unknown.lut
[🏁] Finished processing MDC028_W009(6)_2024-07-23


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