
🟢 Starting Calibration Pipeline

=== Processing MDC027_W031_2025-03-24 ===

-------------------------------------------------------------------
                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 3 las0.zip files to process
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                                                                      [A📁 Processing Rigs: 100%|██████████| 3/3 [05:17<00:00, 96.90s/it] 📁 Processing Rigs: 100%|██████████| 3/3 [05:17<00:00, 105.83s/it]
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2508 frames from 3 rigs to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 1349
[📋] 500: 100 frames selected from 500
[📋] 1000: 100 frames selected from 659
[💾] Saved RigFrameDataset to /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC027_W031_2025-03-24/calibration/debug/1_digitized/rig_frames.json

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

[INFO] Using 60 total sampled bright frames
Averaging bright frames:   0%|          | 0/60 [00:00<?, ?it/s]Averaging bright frames:   5%|▌         | 3/60 [00:00<00:02, 28.04it/s]Averaging bright frames:  12%|█▏        | 7/60 [00:00<00:01, 34.84it/s]Averaging bright frames:  18%|█▊        | 11/60 [00:00<00:01, 37.03it/s]Averaging bright frames:  27%|██▋       | 16/60 [00:00<00:01, 38.65it/s]Averaging bright frames:  33%|███▎      | 20/60 [00:00<00:01, 38.98it/s]Averaging bright frames:  40%|████      | 24/60 [00:00<00:00, 39.04it/s]Averaging bright frames:  47%|████▋     | 28/60 [00:00<00:00, 39.30it/s]Averaging bright frames:  55%|█████▌    | 33/60 [00:00<00:00, 39.57it/s]Averaging bright frames:  63%|██████▎   | 38/60 [00:00<00:00, 40.04it/s]Averaging bright frames:  72%|███████▏  | 43/60 [00:01<00:00, 40.30it/s]Averaging bright frames:  80%|████████  | 48/60 [00:01<00:00, 40.35it/s]Averaging bright frames:  88%|████████▊ | 53/60 [00:01<00:00, 40.23it/s]Averaging bright frames:  97%|█████████▋| 58/60 [00:01<00:00, 40.06it/s]                                                                        Total time to process (v2): 0.07
[RESULT] Combined: Center=(1020, 746), Radius=838
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500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.034):  46%|████▌     | 46/100 [00:00<00:00, 67.22it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  47%|████▋     | 47/100 [00:00<00:00, 67.22it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  48%|████▊     | 48/100 [00:00<00:00, 67.22it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  49%|████▉     | 49/100 [00:00<00:00, 67.22it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  50%|█████     | 50/100 [00:00<00:00, 67.22it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  51%|█████     | 51/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  51%|█████     | 51/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  52%|█████▏    | 52/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  53%|█████▎    | 53/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  54%|█████▍    | 54/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  55%|█████▌    | 55/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.034):  56%|█████▌    | 56/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  57%|█████▋    | 57/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  58%|█████▊    | 58/100 [00:00<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  59%|█████▉    | 59/100 [00:01<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  60%|██████    | 60/100 [00:01<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  61%|██████    | 61/100 [00:01<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  62%|██████▏   | 62/100 [00:01<00:00, 91.08it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  63%|██████▎   | 63/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  63%|██████▎   | 63/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  64%|██████▍   | 64/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  65%|██████▌   | 65/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  66%|██████▌   | 66/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  67%|██████▋   | 67/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  68%|██████▊   | 68/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  69%|██████▉   | 69/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  70%|███████   | 70/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  71%|███████   | 71/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  72%|███████▏  | 72/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  73%|███████▎  | 73/100 [00:01<00:00, 91.61it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  74%|███████▍  | 74/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  74%|███████▍  | 74/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  75%|███████▌  | 75/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  76%|███████▌  | 76/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  77%|███████▋  | 77/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  78%|███████▊  | 78/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  79%|███████▉  | 79/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  80%|████████  | 80/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  81%|████████  | 81/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  82%|████████▏ | 82/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  83%|████████▎ | 83/100 [00:01<00:00, 93.56it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  84%|████████▍ | 84/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  84%|████████▍ | 84/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  85%|████████▌ | 85/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  86%|████████▌ | 86/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  87%|████████▋ | 87/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  88%|████████▊ | 88/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  89%|████████▉ | 89/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  90%|█████████ | 90/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  91%|█████████ | 91/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  92%|█████████▏| 92/100 [00:01<00:00, 81.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  93%|█████████▎| 93/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  93%|█████████▎| 93/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  94%|█████████▍| 94/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  95%|█████████▌| 95/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  96%|█████████▌| 96/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  97%|█████████▋| 97/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  98%|█████████▊| 98/100 [00:01<00:00, 81.91it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.034):  99%|█████████▉| 99/100 [00:01<00:00, 81.91it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.007 | Best: {'fx': 509.354} (0.007):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.007 | Best: {'fx': 509.354} (0.007):   2%|▏         | 1/50 [00:00<00:08,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.008 | Best: {'fx': 510.374} (0.008):   2%|▏         | 1/50 [00:00<00:08,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.007 | Best: {'fx': 510.374} (0.008):   4%|▍         | 2/50 [00:00<00:08,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.008 | Best: {'fx': 511.395} (0.008):   6%|▌         | 3/50 [00:00<00:08,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.009 | Best: {'fx': 512.415} (0.009):   8%|▊         | 4/50 [00:00<00:08,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.010 | Best: {'fx': 514.456} (0.010):  10%|█         | 5/50 [00:00<00:07,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.009 | Best: {'fx': 514.456} (0.010):  12%|█▏        | 6/50 [00:00<00:07,  5.69it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.009 | Best: {'fx': 514.456} (0.010):  14%|█▍        | 7/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.016 | Best: {'fx': 517.517} (0.016):  14%|█▍        | 7/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.014 | Best: {'fx': 517.517} (0.016):  16%|█▌        | 8/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 520.578} (0.019):  18%|█▊        | 9/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.022 | Best: {'fx': 522.619} (0.022):  20%|██        | 10/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.020 | Best: {'fx': 522.619} (0.022):  22%|██▏       | 11/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.015 | Best: {'fx': 522.619} (0.022):  24%|██▍       | 12/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.018 | Best: {'fx': 522.619} (0.022):  26%|██▌       | 13/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.017 | Best: {'fx': 522.619} (0.022):  28%|██▊       | 14/50 [00:00<00:01, 29.82it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.017 | Best: {'fx': 522.619} (0.022):  30%|███       | 15/50 [00:00<00:00, 48.71it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.027 | Best: {'fx': 524.66} (0.027):  30%|███       | 15/50 [00:00<00:00, 48.71it/s] Brute | Start: {'fx': 533.333} (0.034) | Current: 0.023 | Best: {'fx': 524.66} (0.027):  32%|███▏      | 16/50 [00:00<00:00, 48.71it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.038 | Best: {'fx': 528.741} (0.038):  34%|███▍      | 17/50 [00:00<00:00, 48.71it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.036 | Best: {'fx': 528.741} (0.038):  36%|███▌      | 18/50 [00:00<00:00, 48.71it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.031 | Best: {'fx': 528.741} (0.038):  38%|███▊      | 19/50 [00:00<00:00, 48.71it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.035 | Best: {'fx': 528.741} (0.038):  40%|████      | 20/50 [00:00<00:00, 48.71it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.035 | Best: {'fx': 528.741} (0.038):  42%|████▏     | 21/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.033 | Best: {'fx': 528.741} (0.038):  42%|████▏     | 21/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.034 | Best: {'fx': 528.741} (0.038):  44%|████▍     | 22/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.033 | Best: {'fx': 528.741} (0.038):  46%|████▌     | 23/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.035 | Best: {'fx': 528.741} (0.038):  48%|████▊     | 24/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.031 | Best: {'fx': 528.741} (0.038):  50%|█████     | 25/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.029 | Best: {'fx': 528.741} (0.038):  52%|█████▏    | 26/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.031 | Best: {'fx': 528.741} (0.038):  54%|█████▍    | 27/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.028 | Best: {'fx': 528.741} (0.038):  56%|█████▌    | 28/50 [00:00<00:00, 51.46it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.028 | Best: {'fx': 528.741} (0.038):  58%|█████▊    | 29/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.021 | Best: {'fx': 528.741} (0.038):  58%|█████▊    | 29/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.020 | Best: {'fx': 528.741} (0.038):  60%|██████    | 30/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.025 | Best: {'fx': 528.741} (0.038):  62%|██████▏   | 31/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.024 | Best: {'fx': 528.741} (0.038):  64%|██████▍   | 32/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.023 | Best: {'fx': 528.741} (0.038):  66%|██████▌   | 33/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.022 | Best: {'fx': 528.741} (0.038):  68%|██████▊   | 34/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.021 | Best: {'fx': 528.741} (0.038):  70%|███████   | 35/50 [00:00<00:00, 58.26it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.021 | Best: {'fx': 528.741} (0.038):  72%|███████▏  | 36/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.020 | Best: {'fx': 528.741} (0.038):  72%|███████▏  | 36/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.022 | Best: {'fx': 528.741} (0.038):  74%|███████▍  | 37/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  76%|███████▌  | 38/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.021 | Best: {'fx': 528.741} (0.038):  78%|███████▊  | 39/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  80%|████████  | 40/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.020 | Best: {'fx': 528.741} (0.038):  82%|████████▏ | 41/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  84%|████████▍ | 42/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.018 | Best: {'fx': 528.741} (0.038):  86%|████████▌ | 43/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.018 | Best: {'fx': 528.741} (0.038):  88%|████████▊ | 44/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  90%|█████████ | 45/50 [00:00<00:00, 60.56it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  92%|█████████▏| 46/50 [00:00<00:00, 68.27it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  92%|█████████▏| 46/50 [00:00<00:00, 68.27it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.018 | Best: {'fx': 528.741} (0.038):  94%|█████████▍| 47/50 [00:00<00:00, 68.27it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.018 | Best: {'fx': 528.741} (0.038):  96%|█████████▌| 48/50 [00:00<00:00, 68.27it/s]Brute | Start: {'fx': 533.333} (0.034) | Current: 0.019 | Best: {'fx': 528.741} (0.038):  98%|█████████▊| 49/50 [00:00<00:00, 68.27it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 528.7414965986395
    px: 1020.0
    py: 746.0
    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 2 incomplete frames): 14 frames will be used for optimization
  Rigs: 1500(1500mm):7/20, 1000(1000mm):7/20
  Total: 14/40 frames used for optimization
Initial score: 418.4525: 0iter [00:00, ?iter/s]Initial score: 418.4525: 1iter [00:02,  2.07s/iter]Initial score: 418.4525: 2iter [00:03,  1.42s/iter]Initial score: 418.4525: 3iter [00:03,  1.17s/iter]Initial score: 418.4525: 4iter [00:04,  1.06s/iter]Initial score: 418.4525: 5iter [00:05,  1.02s/iter]Initial score: 418.4525: 6iter [00:06,  1.02iter/s]Initial score: 418.4525: 7iter [00:07,  1.05iter/s]Initial score: 418.4525: 8iter [00:08,  1.09iter/s]Initial score: 418.4525: 9iter [00:09,  1.02iter/s]Initial score: 418.4525: 10iter [00:10,  1.01iter/s]Initial score: 418.4525: 11iter [00:11,  1.02iter/s]Initial score: 418.4525: 12iter [00:12,  1.04iter/s]Initial score: 418.4525: 13iter [00:13,  1.06iter/s]Initial score: 418.4525: 14iter [00:14,  1.05iter/s]Initial score: 418.4525: 15iter [00:15,  1.06iter/s]Initial score: 418.4525: 16iter [00:16,  1.08iter/s]Best score: 302.6218: : 16iter [00:16,  1.08iter/s] Best score: 302.6218: : 17iter [00:16,  1.08iter/s]Best score: 302.6218: : 18iter [00:17,  1.09iter/s]Best score: 302.6218: : 19iter [00:18,  1.08iter/s]Best score: 302.6218: : 20iter [00:19,  1.10iter/s]Best score: 302.6218: : 21iter [00:20,  1.11iter/s]Best score: 302.6218: : 22iter [00:21,  1.08iter/s]Best score: 302.6218: : 23iter [00:22,  1.08iter/s]Best score: 302.6218: : 24iter [00:23,  1.07iter/s]Best score: 302.6218: : 25iter [00:24,  1.07iter/s]Best score: 302.6218: : 26iter [00:25,  1.07iter/s]Best score: 302.6218: : 27iter [00:26,  1.04iter/s]Best score: 302.6218: : 28iter [00:27,  1.08iter/s]Best score: 302.6218: : 29iter [00:28,  1.07iter/s]Best score: 302.6218: : 30iter [00:29,  1.10iter/s]Best score: 302.6218: : 31iter [00:30,  1.07iter/s]Best score: 302.6218: : 32iter [00:30,  1.09iter/s]Best score: 302.6218: : 33iter [00:31,  1.10iter/s]Best score: 302.6218: : 34iter [00:32,  1.10iter/s]Best score: 302.6218: : 35iter [00:33,  1.09iter/s]Best score: 302.6218: : 36iter [00:34,  1.09iter/s]Best score: 302.6218: : 37iter [00:35,  1.07iter/s]Best score: 302.6218: : 38iter [00:36,  1.08iter/s]Best score: 302.6218: : 39iter [00:37,  1.08iter/s]Best score: 302.6218: : 40iter [00:38,  1.11iter/s]Best score: 302.6218: : 41iter [00:39,  1.11iter/s]Best score: 302.6218: : 42iter [00:40,  1.10iter/s]Best score: 302.6218: : 43iter [00:40,  1.12iter/s]Best score: 302.6218: : 44iter [00:41,  1.09iter/s]Best score: 302.6218: : 45iter [00:42,  1.06iter/s]Best score: 302.6218: : 46iter [00:43,  1.09iter/s]Best score: 302.6218: : 47iter [00:44,  1.09iter/s]Best score: 302.6218: : 48iter [00:45,  1.07iter/s]Best score: 302.6218: : 49iter [00:46,  1.07iter/s]
[ITER 50] Current best score: 302.6218
[PARAMS] Current best parameters:
  fx: 534.7414
  fy: 534.9246
  px: 1028.4308
  py: 749.8026
  k1: -0.00064093
  k2: -0.00018574
  k3: 0.00131889
  k4: -0.00087935
  p1: 0.00156497
  p2: 0.00824492
  p3: -0.00401003
  p4: 0.00005255
  scale: 2.8805
  rotation: -1.1592
Best score: 302.6218: : 50iter [00:47,  1.04iter/s]Best score: 302.6218: : 51iter [00:48,  1.04iter/s]Best score: 302.6218: : 52iter [00:49,  1.05iter/s]Best score: 302.6218: : 53iter [00:50,  1.05iter/s]Best score: 302.6218: : 54iter [00:51,  1.07iter/s]Best score: 253.9907: : 54iter [00:52,  1.07iter/s]Best score: 253.9907: : 55iter [00:52,  1.08iter/s]Best score: 154.8021: : 55iter [00:53,  1.08iter/s]Best score: 154.8021: : 56iter [00:53,  1.07iter/s]Best score: 154.8021: : 57iter [00:54,  1.05iter/s]Best score: 154.8021: : 58iter [00:54,  1.09iter/s]Best score: 154.8021: : 59iter [00:55,  1.08iter/s]Best score: 154.8021: : 60iter [00:56,  1.07iter/s]Best score: 154.8021: : 61iter [00:57,  1.09iter/s]Best score: 133.7490: : 61iter [00:58,  1.09iter/s]Best score: 133.7490: : 62iter [00:58,  1.09iter/s]Best score: 133.7490: : 63iter [00:59,  1.07iter/s]Best score: 133.7490: : 64iter [01:00,  1.08iter/s]Best score: 108.6757: : 64iter [01:01,  1.08iter/s]Best score: 108.6757: : 65iter [01:01,  1.07iter/s]Best score: 108.6757: : 66iter [01:02,  1.08iter/s]Best score: 108.6757: : 67iter [01:03,  1.08iter/s]Best score: 108.6757: : 68iter [01:04,  1.10iter/s]Best score: 108.6757: : 69iter [01:05,  1.11iter/s]Best score: 108.6757: : 70iter [01:05,  1.11iter/s]Best score: 108.6757: : 71iter [01:06,  1.12iter/s]Best score: 108.6757: : 72iter [01:07,  1.10iter/s]Best score: 108.6757: : 73iter [01:08,  1.11iter/s]Best score: 108.6757: : 74iter [01:09,  1.09iter/s]Best score: 108.6757: : 75iter [01:10,  1.08iter/s]Best score: 108.6757: : 76iter [01:11,  1.08iter/s]Best score: 108.6757: : 77iter [01:12,  1.07iter/s]Best score: 108.6757: : 78iter [01:13,  1.05iter/s]Best score: 108.6757: : 79iter [01:14,  1.05iter/s]Best score: 108.6757: : 80iter [01:15,  1.06iter/s]Best score: 108.6757: : 81iter [01:16,  1.09iter/s]Best score: 108.6757: : 82iter [01:17,  1.10iter/s]Best score: 108.6757: : 83iter [01:18,  1.08iter/s]Best score: 108.6757: : 84iter [01:18,  1.08iter/s]Best score: 108.6757: : 85iter [01:19,  1.09iter/s]Best score: 108.6757: : 86iter [01:20,  1.07iter/s]Best score: 108.6757: : 87iter [01:21,  1.07iter/s]Best score: 108.6757: : 88iter [01:22,  1.06iter/s]Best score: 108.6757: : 89iter [01:23,  1.06iter/s]Best score: 108.6757: : 90iter [01:24,  1.07iter/s]Best score: 108.6757: : 91iter [01:25,  1.07iter/s]Best score: 108.6757: : 92iter [01:26,  1.07iter/s]Best score: 108.6757: : 93iter [01:27,  1.08iter/s]Best score: 108.6757: : 94iter [01:28,  1.08iter/s]Best score: 108.6757: : 95iter [01:29,  1.08iter/s]Best score: 108.6757: : 96iter [01:30,  1.06iter/s]Best score: 81.2652: : 96iter [01:31,  1.06iter/s] Best score: 81.2652: : 97iter [01:31,  1.05iter/s]Best score: 81.2652: : 98iter [01:32,  1.06iter/s]Best score: 77.1524: : 98iter [01:32,  1.06iter/s]Best score: 77.1524: : 99iter [01:32,  1.07iter/s]
[ITER 100] Current best score: 77.1524
[PARAMS] Current best parameters:
  fx: 554.4349
  fy: 554.3129
  px: 1019.1419
  py: 754.6228
  k1: -0.09251880
  k2: 0.00745557
  k3: 0.03672444
  k4: -0.01186717
  p1: 0.00215507
  p2: 0.00116026
  p3: -0.00196461
  p4: 0.00071240
  scale: 3.2454
  rotation: 0.5486
Best score: 77.1524: : 100iter [01:33,  1.06iter/s]Best score: 77.1524: : 101iter [01:34,  1.09iter/s]Best score: 77.1524: : 102iter [01:35,  1.08iter/s]Best score: 77.1524: : 103iter [01:36,  1.11iter/s]Best score: 77.1524: : 104iter [01:37,  1.11iter/s]Best score: 77.1524: : 105iter [01:38,  1.10iter/s]Best score: 77.1524: : 106iter [01:39,  1.08iter/s]Best score: 77.1524: : 107iter [01:40,  1.09iter/s]Best score: 77.1524: : 108iter [01:41,  1.07iter/s]Best score: 77.1524: : 109iter [01:42,  1.10iter/s]Best score: 77.1524: : 110iter [01:43,  1.10iter/s]Best score: 77.1524: : 111iter [01:43,  1.08iter/s]Best score: 77.1524: : 112iter [01:44,  1.09iter/s]Best score: 77.1524: : 113iter [01:45,  1.08iter/s]Best score: 54.7605: : 113iter [01:46,  1.08iter/s]Best score: 54.7605: : 114iter [01:46,  1.08iter/s]Best score: 54.7605: : 115iter [01:47,  1.09iter/s]Best score: 54.7605: : 116iter [01:48,  1.09iter/s]Best score: 54.7605: : 117iter [01:49,  1.11iter/s]Best score: 54.7605: : 118iter [01:50,  1.09iter/s]Best score: 50.0791: : 118iter [01:51,  1.09iter/s]Best score: 50.0791: : 119iter [01:51,  1.08iter/s]Best score: 50.0791: : 120iter [01:52,  1.07iter/s]Best score: 50.0791: : 121iter [01:53,  1.09iter/s]Best score: 50.0791: : 122iter [01:54,  1.08iter/s]Best score: 50.0791: : 123iter [01:55,  1.09iter/s]Best score: 50.0791: : 124iter [01:55,  1.07iter/s]Best score: 50.0791: : 125iter [01:56,  1.08iter/s]Best score: 50.0791: : 126iter [01:57,  1.09iter/s]Best score: 50.0791: : 127iter [01:58,  1.07iter/s]Best score: 50.0791: : 128iter [01:59,  1.07iter/s]Best score: 50.0791: : 129iter [02:00,  1.09iter/s]Best score: 50.0791: : 130iter [02:01,  1.10iter/s]Best score: 50.0791: : 131iter [02:02,  1.11iter/s]Best score: 50.0791: : 132iter [02:03,  1.09iter/s]Best score: 50.0791: : 133iter [02:04,  1.08iter/s]Best score: 50.0791: : 134iter [02:05,  1.10iter/s]Best score: 50.0791: : 135iter [02:06,  1.08iter/s]Best score: 50.0791: : 136iter [02:06,  1.09iter/s]Best score: 50.0791: : 137iter [02:07,  1.09iter/s]Best score: 50.0791: : 138iter [02:08,  1.10iter/s]Best score: 50.0791: : 139iter [02:09,  1.08iter/s]Best score: 50.0791: : 140iter [02:10,  1.07iter/s]Best score: 50.0791: : 141iter [02:11,  1.09iter/s]Best score: 50.0791: : 142iter [02:12,  1.07iter/s]Best score: 50.0791: : 143iter [02:13,  1.08iter/s]Best score: 50.0791: : 144iter [02:14,  1.06iter/s]Best score: 50.0791: : 145iter [02:15,  1.05iter/s]Best score: 50.0791: : 146iter [02:16,  1.07iter/s]Best score: 50.0791: : 147iter [02:17,  1.06iter/s]Best score: 50.0791: : 148iter [02:18,  1.05iter/s]Best score: 50.0791: : 149iter [02:19,  1.08iter/s]
[ITER 150] Current best score: 50.0791
[PARAMS] Current best parameters:
  fx: 556.2282
  fy: 553.9061
  px: 1019.7191
  py: 754.1394
  k1: -0.09493386
  k2: 0.00628339
  k3: 0.03730074
  k4: -0.01185534
  p1: -0.00154556
  p2: -0.00336313
  p3: -0.00100459
  p4: 0.00025053
  scale: 3.3311
  rotation: 0.5184
Best score: 50.0791: : 150iter [02:20,  1.06iter/s]Best score: 50.0791: : 151iter [02:21,  1.05iter/s]Best score: 50.0791: : 152iter [02:21,  1.08iter/s]Best score: 33.0664: : 152iter [02:22,  1.08iter/s]Best score: 33.0664: : 153iter [02:22,  1.08iter/s]Best score: 33.0664: : 154iter [02:23,  1.07iter/s]Best score: 33.0664: : 155iter [02:24,  1.08iter/s]Best score: 33.0664: : 156iter [02:25,  1.08iter/s]Best score: 33.0664: : 157iter [02:26,  1.09iter/s]Best score: 33.0664: : 158iter [02:27,  1.11iter/s]Best score: 33.0664: : 159iter [02:28,  1.08iter/s]Best score: 33.0664: : 160iter [02:29,  1.07iter/s]Best score: 33.0664: : 161iter [02:30,  1.09iter/s]Best score: 33.0664: : 162iter [02:31,  1.06iter/s]Best score: 33.0664: : 163iter [02:32,  1.07iter/s]Best score: 33.0664: : 164iter [02:33,  1.07iter/s]Best score: 33.0664: : 165iter [02:34,  1.06iter/s]Best score: 33.0664: : 166iter [02:34,  1.06iter/s]Best score: 33.0664: : 167iter [02:35,  1.06iter/s]Best score: 33.0664: : 168iter [02:36,  1.07iter/s]Best score: 33.0664: : 169iter [02:37,  1.08iter/s]Best score: 33.0664: : 170iter [02:38,  1.09iter/s]Best score: 33.0664: : 171iter [02:39,  1.08iter/s]Best score: 33.0664: : 172iter [02:40,  1.08iter/s]Best score: 33.0664: : 173iter [02:41,  1.09iter/s]Best score: 33.0664: : 174iter [02:42,  1.07iter/s]Best score: 16.6336: : 174iter [02:43,  1.07iter/s]Best score: 16.6336: : 175iter [02:43,  1.06iter/s]Best score: 16.6336: : 176iter [02:44,  1.08iter/s]Best score: 16.6336: : 177iter [02:45,  1.05iter/s]Best score: 16.6336: : 178iter [02:46,  1.06iter/s]Best score: 16.6336: : 179iter [02:47,  1.07iter/s]Best score: 16.6336: : 180iter [02:48,  1.08iter/s]Best score: 16.6336: : 181iter [02:48,  1.08iter/s]Best score: 16.6336: : 182iter [02:49,  1.08iter/s]Best score: 16.6336: : 183iter [02:50,  1.08iter/s]Best score: 16.6336: : 184iter [02:51,  1.10iter/s]Best score: 16.6336: : 185iter [02:52,  1.08iter/s]Best score: 16.6336: : 186iter [02:53,  1.08iter/s]Best score: 16.6336: : 187iter [02:54,  1.06iter/s]Best score: 16.6336: : 188iter [02:55,  1.05iter/s]Best score: 16.6336: : 189iter [02:56,  1.05iter/s]Best score: 16.6336: : 190iter [02:57,  1.08iter/s]Best score: 16.6336: : 191iter [02:58,  1.09iter/s]Best score: 16.6336: : 192iter [02:59,  1.10iter/s]Best score: 16.6336: : 193iter [03:00,  1.09iter/s]Best score: 16.6336: : 194iter [03:01,  1.07iter/s]Best score: 16.6336: : 195iter [03:01,  1.07iter/s]Best score: 16.6336: : 196iter [03:02,  1.09iter/s]Best score: 16.6336: : 197iter [03:03,  1.07iter/s]Best score: 16.6336: : 198iter [03:04,  1.06iter/s]Best score: 16.6336: : 199iter [03:05,  1.08iter/s]
[ITER 200] Current best score: 16.6336
[PARAMS] Current best parameters:
  fx: 550.6701
  fy: 549.5655
  px: 1019.0907
  py: 754.9795
  k1: -0.07593385
  k2: 0.00560233
  k3: 0.02954701
  k4: -0.00958053
  p1: 0.00368856
  p2: -0.00000502
  p3: -0.00264849
  p4: -0.00131199
  scale: 3.2790
  rotation: -0.0331
Best score: 16.6336: : 200iter [03:06,  1.05iter/s]Best score: 16.6336: : 201iter [03:07,  1.04iter/s]Best score: 16.6336: : 202iter [03:08,  1.06iter/s]Best score: 16.6336: : 203iter [03:09,  1.06iter/s]Best score: 16.6336: : 204iter [03:10,  1.05iter/s]Best score: 16.6336: : 205iter [03:11,  1.07iter/s]Best score: 16.6336: : 206iter [03:12,  1.05iter/s]Best score: 16.6336: : 207iter [03:13,  1.07iter/s]Best score: 16.6336: : 208iter [03:14,  1.08iter/s]Best score: 16.6336: : 209iter [03:15,  1.07iter/s]Best score: 16.6336: : 210iter [03:15,  1.09iter/s]Best score: 16.6336: : 211iter [03:17,  1.05iter/s]Best score: 16.6336: : 212iter [03:17,  1.08iter/s]Best score: 16.6336: : 213iter [03:18,  1.08iter/s]Best score: 14.9744: : 213iter [03:19,  1.08iter/s]Best score: 14.9744: : 214iter [03:19,  1.08iter/s]Best score: 14.9744: : 215iter [03:20,  1.09iter/s]Best score: 14.9744: : 216iter [03:21,  1.09iter/s]Best score: 14.9744: : 217iter [03:22,  1.08iter/s]Best score: 14.9744: : 218iter [03:23,  1.08iter/s]Best score: 14.9744: : 219iter [03:24,  1.09iter/s]Best score: 14.9744: : 220iter [03:25,  1.08iter/s]Best score: 14.9744: : 221iter [03:26,  1.08iter/s]Best score: 14.9744: : 222iter [03:27,  1.08iter/s]Best score: 14.9744: : 223iter [03:28,  1.07iter/s]Best score: 12.7253: : 223iter [03:29,  1.07iter/s]Best score: 12.7253: : 224iter [03:29,  1.06iter/s]Best score: 12.7253: : 225iter [03:30,  1.05iter/s]Best score: 12.7253: : 226iter [03:30,  1.05iter/s]Best score: 12.7253: : 227iter [03:31,  1.06iter/s]Best score: 12.7253: : 228iter [03:32,  1.09iter/s]Best score: 12.7253: : 229iter [03:33,  1.12iter/s]Best score: 12.7253: : 230iter [03:34,  1.14iter/s]Best score: 12.7253: : 231iter [03:35,  1.11iter/s]Best score: 12.7253: : 232iter [03:36,  1.13iter/s]Best score: 12.7253: : 233iter [03:37,  1.10iter/s]Best score: 12.7253: : 234iter [03:38,  1.11iter/s]Best score: 12.7253: : 235iter [03:39,  1.08iter/s]Best score: 12.7253: : 236iter [03:39,  1.10iter/s]Best score: 12.7253: : 237iter [03:40,  1.10iter/s]Best score: 12.7253: : 238iter [03:41,  1.11iter/s]Best score: 12.7253: : 239iter [03:42,  1.11iter/s]Best score: 12.7253: : 240iter [03:43,  1.10iter/s]Best score: 12.7253: : 241iter [03:44,  1.13iter/s]Best score: 12.7253: : 242iter [03:45,  1.12iter/s]Best score: 12.7253: : 243iter [03:46,  1.09iter/s]Best score: 12.7253: : 244iter [03:47,  1.11iter/s]Best score: 12.7253: : 245iter [03:48,  1.08iter/s]Best score: 12.7253: : 246iter [03:49,  1.07iter/s]Best score: 9.6451: : 246iter [03:50,  1.07iter/s] Best score: 9.6451: : 247iter [03:50,  1.08iter/s]Best score: 9.6451: : 248iter [03:50,  1.08iter/s]Best score: 9.6451: : 249iter [03:51,  1.08iter/s]
[ITER 250] Current best score: 9.6451
[PARAMS] Current best parameters:
  fx: 541.2531
  fy: 540.5297
  px: 1020.7001
  py: 753.1042
  k1: -0.03570927
  k2: -0.00611914
  k3: 0.01864093
  k4: -0.00517125
  p1: -0.00117929
  p2: 0.00263818
  p3: -0.00191554
  p4: -0.00057413
  scale: 3.1842
  rotation: -0.0504
Best score: 9.6451: : 250iter [03:52,  1.05iter/s]Best score: 9.6451: : 251iter [03:53,  1.05iter/s]Best score: 9.6451: : 252iter [03:54,  1.06iter/s]Best score: 9.6451: : 253iter [03:55,  1.08iter/s]Best score: 5.6601: : 253iter [03:56,  1.08iter/s]Best score: 5.6601: : 254iter [03:56,  1.08iter/s]Best score: 5.6601: : 255iter [03:57,  1.10iter/s]Best score: 5.6601: : 256iter [03:58,  1.09iter/s]Best score: 5.6601: : 257iter [03:59,  1.08iter/s]Best score: 5.6601: : 258iter [04:00,  1.07iter/s]Best score: 5.6601: : 259iter [04:01,  1.08iter/s]Best score: 5.6601: : 260iter [04:02,  1.08iter/s]Best score: 5.6601: : 261iter [04:02,  1.10iter/s]Best score: 5.6601: : 262iter [04:03,  1.08iter/s]Best score: 5.6601: : 263iter [04:04,  1.10iter/s]Best score: 5.6601: : 264iter [04:05,  1.08iter/s]Best score: 5.6601: : 265iter [04:06,  1.09iter/s]Best score: 5.6601: : 266iter [04:07,  1.09iter/s]Best score: 5.6601: : 267iter [04:08,  1.06iter/s]Best score: 5.6601: : 268iter [04:09,  1.09iter/s]Best score: 5.6601: : 269iter [04:10,  1.08iter/s]Best score: 5.6601: : 270iter [04:11,  1.07iter/s]Best score: 5.6601: : 271iter [04:12,  1.08iter/s]Best score: 5.6601: : 272iter [04:13,  1.08iter/s]Best score: 5.6601: : 273iter [04:14,  1.11iter/s]Best score: 5.6601: : 274iter [04:14,  1.08iter/s]Best score: 5.6601: : 275iter [04:15,  1.09iter/s]Best score: 5.6601: : 276iter [04:16,  1.08iter/s]Best score: 5.6601: : 277iter [04:17,  1.09iter/s]Best score: 5.6601: : 278iter [04:18,  1.12iter/s]Best score: 5.6601: : 279iter [04:19,  1.09iter/s]Best score: 5.6601: : 280iter [04:20,  1.09iter/s]Best score: 5.6601: : 281iter [04:21,  1.09iter/s]Best score: 5.6601: : 282iter [04:22,  1.09iter/s]Best score: 5.6601: : 283iter [04:23,  1.08iter/s]Best score: 5.6601: : 284iter [04:24,  1.09iter/s]Best score: 5.6601: : 285iter [04:25,  1.09iter/s]Best score: 5.6601: : 286iter [04:26,  1.07iter/s]Best score: 5.6601: : 287iter [04:26,  1.09iter/s]Best score: 5.6601: : 288iter [04:27,  1.10iter/s]Best score: 5.6601: : 289iter [04:28,  1.11iter/s]Best score: 5.6601: : 290iter [04:29,  1.09iter/s]Best score: 5.6601: : 291iter [04:30,  1.09iter/s]Best score: 5.6601: : 292iter [04:31,  1.10iter/s]Best score: 5.6601: : 293iter [04:32,  1.08iter/s]Best score: 5.6601: : 294iter [04:33,  1.08iter/s]Best score: 5.6601: : 295iter [04:34,  1.08iter/s]Best score: 5.6601: : 296iter [04:35,  1.09iter/s]Best score: 5.6601: : 297iter [04:36,  1.11iter/s]Best score: 5.6601: : 298iter [04:36,  1.11iter/s]Best score: 5.6601: : 299iter [04:37,  1.10iter/s]
[ITER 300] Current best score: 5.6601
[PARAMS] Current best parameters:
  fx: 538.6157
  fy: 538.3378
  px: 1021.2244
  py: 753.5233
  k1: -0.02468085
  k2: -0.00803628
  k3: 0.01758135
  k4: -0.00517638
  p1: -0.00118827
  p2: 0.00275144
  p3: 0.00122167
  p4: -0.00102602
  scale: 3.1214
  rotation: -0.0772
Best score: 5.6601: : 300iter [04:38,  1.06iter/s]Best score: 5.6601: : 301iter [04:39,  1.08iter/s]Best score: 5.6601: : 302iter [04:40,  1.10iter/s]Best score: 5.6601: : 303iter [04:41,  1.07iter/s]Best score: 5.6601: : 304iter [04:42,  1.08iter/s]Best score: 5.6601: : 305iter [04:43,  1.12iter/s]Best score: 5.6601: : 306iter [04:44,  1.10iter/s]Best score: 5.6601: : 307iter [04:45,  1.08iter/s]Best score: 5.6601: : 308iter [04:46,  1.07iter/s]Best score: 5.6601: : 309iter [04:47,  1.08iter/s]Best score: 5.6601: : 310iter [04:47,  1.11iter/s]Best score: 5.6601: : 311iter [04:48,  1.07iter/s]Best score: 5.6601: : 312iter [04:49,  1.09iter/s]Best score: 5.6601: : 313iter [04:50,  1.12iter/s]Best score: 5.6601: : 314iter [04:51,  1.11iter/s]Best score: 5.6601: : 315iter [04:52,  1.11iter/s]Best score: 5.6601: : 316iter [04:53,  1.11iter/s]Best score: 5.6601: : 317iter [04:54,  1.09iter/s]Best score: 5.6601: : 318iter [04:55,  1.09iter/s]Best score: 5.6601: : 319iter [04:56,  1.10iter/s]Best score: 5.6601: : 320iter [04:57,  1.10iter/s]Best score: 5.6601: : 321iter [04:58,  1.09iter/s]Best score: 5.6601: : 322iter [04:58,  1.07iter/s]Best score: 5.6601: : 323iter [04:59,  1.08iter/s]Best score: 5.6601: : 324iter [05:00,  1.06iter/s]Best score: 5.6601: : 325iter [05:01,  1.07iter/s]Best score: 5.6601: : 326iter [05:02,  1.08iter/s]Best score: 5.6601: : 327iter [05:03,  1.09iter/s]Best score: 5.6601: : 328iter [05:04,  1.09iter/s]Best score: 5.6601: : 329iter [05:05,  1.10iter/s]Best score: 5.6601: : 330iter [05:06,  1.09iter/s]Best score: 5.6601: : 331iter [05:07,  1.10iter/s]Best score: 5.6601: : 332iter [05:08,  1.07iter/s]Best score: 5.6601: : 333iter [05:09,  1.09iter/s]Best score: 5.6601: : 334iter [05:10,  1.06iter/s]Best score: 5.6601: : 335iter [05:11,  1.05iter/s]Best score: 5.6601: : 336iter [05:12,  1.06iter/s]Best score: 5.6601: : 337iter [05:12,  1.08iter/s]Best score: 5.6601: : 338iter [05:13,  1.07iter/s]Best score: 5.6601: : 339iter [05:14,  1.09iter/s]Best score: 5.6601: : 340iter [05:15,  1.08iter/s]Best score: 5.6601: : 341iter [05:16,  1.09iter/s]Best score: 5.6601: : 342iter [05:17,  1.09iter/s]Best score: 5.6601: : 343iter [05:18,  1.10iter/s]Best score: 5.6601: : 344iter [05:19,  1.07iter/s]Best score: 5.6601: : 345iter [05:20,  1.06iter/s]Best score: 5.6601: : 346iter [05:21,  1.07iter/s]Best score: 5.6601: : 347iter [05:22,  1.07iter/s]Best score: 5.6601: : 348iter [05:23,  1.06iter/s]Best score: 5.6601: : 349iter [05:24,  1.06iter/s]
[ITER 350] Current best score: 5.6601
[PARAMS] Current best parameters:
  fx: 538.6157
  fy: 538.3378
  px: 1021.2244
  py: 753.5233
  k1: -0.02468085
  k2: -0.00803628
  k3: 0.01758135
  k4: -0.00517638
  p1: -0.00118827
  p2: 0.00275144
  p3: 0.00122167
  p4: -0.00102602
  scale: 3.1214
  rotation: -0.0772
Best score: 5.6601: : 350iter [05:25,  1.04iter/s]Best score: 5.6601: : 351iter [05:26,  1.05iter/s]Best score: 5.6601: : 352iter [05:26,  1.07iter/s]Best score: 5.6601: : 353iter [05:27,  1.09iter/s]Best score: 5.6601: : 354iter [05:28,  1.08iter/s]Best score: 5.6601: : 355iter [05:29,  1.07iter/s]Best score: 5.6601: : 356iter [05:30,  1.08iter/s]Best score: 5.6601: : 357iter [05:31,  1.10iter/s]Best score: 4.7712: : 357iter [05:32,  1.10iter/s]Best score: 4.7712: : 358iter [05:32,  1.11iter/s]Best score: 4.3351: : 358iter [05:33,  1.11iter/s]Best score: 4.3351: : 359iter [05:33,  1.10iter/s]Best score: 4.3351: : 360iter [05:34,  1.07iter/s]Best score: 4.3351: : 361iter [05:35,  1.07iter/s]Best score: 4.3351: : 362iter [05:36,  1.09iter/s]Best score: 4.3351: : 363iter [05:36,  1.11iter/s]Best score: 4.3351: : 364iter [05:37,  1.11iter/s]Best score: 4.3351: : 365iter [05:38,  1.09iter/s]Best score: 4.3351: : 366iter [05:39,  1.09iter/s]Best score: 4.3351: : 367iter [05:40,  1.12iter/s]Best score: 4.3351: : 368iter [05:41,  1.10iter/s]Best score: 4.3351: : 369iter [05:42,  1.08iter/s]Best score: 4.3351: : 370iter [05:43,  1.08iter/s]Best score: 4.3351: : 371iter [05:44,  1.09iter/s]Best score: 4.3351: : 372iter [05:45,  1.10iter/s]Best score: 4.3351: : 373iter [05:46,  1.08iter/s]Best score: 4.3351: : 374iter [05:47,  1.10iter/s]Best score: 4.3351: : 375iter [05:47,  1.08iter/s]Best score: 4.3351: : 376iter [05:48,  1.07iter/s]Best score: 4.3351: : 377iter [05:49,  1.08iter/s]Best score: 4.3351: : 378iter [05:50,  1.08iter/s]Best score: 4.3351: : 379iter [05:51,  1.07iter/s]Best score: 4.3351: : 380iter [05:52,  1.08iter/s]Best score: 4.3351: : 381iter [05:53,  1.11iter/s]Best score: 4.3351: : 382iter [05:54,  1.08iter/s]Best score: 4.3351: : 383iter [05:55,  1.06iter/s]Best score: 4.3351: : 384iter [05:56,  1.08iter/s]Best score: 4.3351: : 385iter [05:57,  1.07iter/s]Best score: 4.3351: : 386iter [05:58,  1.09iter/s]Best score: 4.3351: : 387iter [05:59,  1.09iter/s]Best score: 4.3351: : 388iter [06:00,  1.08iter/s]Best score: 4.3351: : 389iter [06:00,  1.10iter/s]Best score: 4.3351: : 390iter [06:01,  1.08iter/s]Best score: 4.3351: : 391iter [06:02,  1.09iter/s]Best score: 4.3351: : 392iter [06:03,  1.09iter/s]Best score: 4.3351: : 393iter [06:04,  1.08iter/s]Best score: 4.3351: : 394iter [06:05,  1.09iter/s]Best score: 4.3351: : 395iter [06:06,  1.07iter/s]Best score: 4.3351: : 396iter [06:07,  1.10iter/s]Best score: 4.3351: : 397iter [06:08,  1.08iter/s]Best score: 4.3351: : 398iter [06:09,  1.09iter/s]Best score: 4.3351: : 399iter [06:10,  1.08iter/s]
[ITER 400] Current best score: 4.7167
[PARAMS] Current best parameters:
  fx: 532.2994
  fy: 532.0524
  px: 1019.1178
  py: 750.9109
  k1: 0.00408182
  k2: -0.01531018
  k3: 0.00964949
  k4: -0.00218941
  p1: -0.00683423
  p2: -0.00255076
  p3: 0.00208728
  p4: -0.00035468
  scale: 3.0527
  rotation: -0.1066
Best score: 4.3351: : 400iter [06:11,  1.07iter/s]Best score: 4.3351: : 401iter [06:11,  1.08iter/s]Best score: 4.3351: : 402iter [06:12,  1.06iter/s]Best score: 4.3351: : 403iter [06:13,  1.05iter/s]Best score: 4.3351: : 404iter [06:14,  1.07iter/s]Best score: 3.3446: : 404iter [06:15,  1.07iter/s]Best score: 3.3446: : 405iter [06:15,  1.08iter/s]Best score: 3.3446: : 406iter [06:16,  1.06iter/s]Best score: 3.3446: : 407iter [06:17,  1.08iter/s]Best score: 3.3446: : 408iter [06:18,  1.09iter/s]Best score: 3.3446: : 409iter [06:19,  1.06iter/s]Best score: 3.3446: : 410iter [06:20,  1.07iter/s]Best score: 3.3446: : 411iter [06:21,  1.08iter/s]Best score: 3.2611: : 411iter [06:22,  1.08iter/s]Best score: 3.2611: : 412iter [06:22,  1.07iter/s]Best score: 3.2611: : 413iter [06:23,  1.11iter/s]Best score: 3.2611: : 414iter [06:24,  1.08iter/s]Best score: 3.2611: : 415iter [06:24,  1.11iter/s]Best score: 3.2611: : 416iter [06:25,  1.09iter/s]Best score: 3.2611: : 417iter [06:26,  1.08iter/s]Best score: 3.2611: : 418iter [06:27,  1.06iter/s]Best score: 3.2611: : 419iter [06:28,  1.06iter/s]Best score: 3.2611: : 420iter [06:29,  1.07iter/s]Best score: 3.2611: : 421iter [06:30,  1.07iter/s]Best score: 3.2611: : 422iter [06:31,  1.10iter/s]Best score: 3.2611: : 423iter [06:32,  1.11iter/s]Best score: 3.2611: : 424iter [06:33,  1.11iter/s]Best score: 3.2611: : 425iter [06:34,  1.11iter/s]Best score: 3.2611: : 426iter [06:35,  1.10iter/s]Best score: 3.2611: : 427iter [06:35,  1.10iter/s]Best score: 3.2611: : 428iter [06:36,  1.10iter/s]Best score: 3.2611: : 429iter [06:37,  1.08iter/s]Best score: 3.2611: : 430iter [06:38,  1.08iter/s]Best score: 3.2611: : 431iter [06:39,  1.09iter/s]Best score: 3.2611: : 432iter [06:40,  1.11iter/s]Best score: 3.2611: : 433iter [06:41,  1.12iter/s]Best score: 3.2611: : 434iter [06:42,  1.13iter/s]Best score: 3.2611: : 435iter [06:43,  1.12iter/s]Best score: 3.2611: : 436iter [06:44,  1.11iter/s]Best score: 3.2611: : 437iter [06:45,  1.11iter/s]Best score: 3.2611: : 438iter [06:45,  1.10iter/s]Best score: 3.2611: : 439iter [06:46,  1.10iter/s]Best score: 3.2611: : 440iter [06:47,  1.10iter/s]Best score: 3.2611: : 441iter [06:48,  1.10iter/s]Best score: 3.2611: : 442iter [06:49,  1.08iter/s]Best score: 3.2611: : 443iter [06:50,  1.08iter/s]Best score: 3.2611: : 444iter [06:51,  1.09iter/s]Best score: 3.2611: : 445iter [06:52,  1.10iter/s]Best score: 3.2611: : 446iter [06:53,  1.08iter/s]Best score: 3.2611: : 447iter [06:54,  1.06iter/s]Best score: 3.2611: : 448iter [06:55,  1.06iter/s]Best score: 3.2611: : 449iter [06:56,  1.07iter/s]
[ITER 450] Current best score: 3.3615
[PARAMS] Current best parameters:
  fx: 529.7324
  fy: 529.7209
  px: 1020.1851
  py: 753.3622
  k1: 0.01220283
  k2: -0.01810642
  k3: 0.00977498
  k4: -0.00223047
  p1: -0.00263468
  p2: -0.00056482
  p3: 0.00522423
  p4: -0.00099970
  scale: 3.0159
  rotation: -0.1390
Best score: 3.2611: : 450iter [06:57,  1.05iter/s]Best score: 3.2611: : 451iter [06:58,  1.05iter/s]Best score: 3.2611: : 452iter [06:58,  1.08iter/s]Best score: 3.2611: : 453iter [06:59,  1.08iter/s]Best score: 3.2611: : 454iter [07:00,  1.08iter/s]Best score: 3.2611: : 455iter [07:01,  1.10iter/s]Best score: 3.2611: : 456iter [07:02,  1.08iter/s]Best score: 3.2611: : 457iter [07:03,  1.09iter/s]Best score: 3.2611: : 458iter [07:04,  1.08iter/s]Best score: 3.2611: : 459iter [07:05,  1.07iter/s]Best score: 3.2611: : 460iter [07:06,  1.10iter/s]Best score: 3.2611: : 461iter [07:07,  1.12iter/s]Best score: 3.2611: : 462iter [07:08,  1.12iter/s]Best score: 3.2611: : 463iter [07:08,  1.12iter/s]Best score: 3.2611: : 464iter [07:09,  1.09iter/s]Best score: 3.2611: : 465iter [07:10,  1.08iter/s]Best score: 3.2611: : 466iter [07:11,  1.10iter/s]Best score: 3.2611: : 467iter [07:12,  1.12iter/s]Best score: 3.2611: : 468iter [07:13,  1.12iter/s]Best score: 3.2611: : 469iter [07:14,  1.13iter/s]Best score: 3.2611: : 470iter [07:15,  1.11iter/s]Best score: 3.2611: : 471iter [07:16,  1.09iter/s]Best score: 3.2611: : 472iter [07:17,  1.10iter/s]Best score: 3.2611: : 473iter [07:18,  1.10iter/s]Best score: 3.2611: : 474iter [07:18,  1.12iter/s]Best score: 3.2611: : 475iter [07:19,  1.11iter/s]Best score: 3.2611: : 476iter [07:20,  1.11iter/s]Best score: 3.2611: : 477iter [07:21,  1.11iter/s]Best score: 3.2611: : 478iter [07:22,  1.10iter/s]Best score: 3.2611: : 479iter [07:23,  1.11iter/s]Best score: 3.2611: : 480iter [07:24,  1.08iter/s]Best score: 3.2138: : 480iter [07:25,  1.08iter/s]Best score: 3.2138: : 481iter [07:25,  1.07iter/s]Best score: 3.2138: : 482iter [07:26,  1.05iter/s]Best score: 3.2138: : 483iter [07:27,  1.07iter/s]Best score: 3.2138: : 484iter [07:28,  1.08iter/s]Best score: 3.2138: : 485iter [07:29,  1.08iter/s]Best score: 3.2138: : 486iter [07:30,  1.09iter/s]Best score: 3.2138: : 487iter [07:30,  1.08iter/s]Best score: 3.2138: : 488iter [07:31,  1.10iter/s]Best score: 3.2138: : 489iter [07:32,  1.11iter/s]Best score: 3.2138: : 490iter [07:33,  1.11iter/s]Best score: 3.2138: : 491iter [07:34,  1.09iter/s]Best score: 3.2138: : 492iter [07:35,  1.08iter/s]Best score: 3.2138: : 493iter [07:36,  1.10iter/s]Best score: 3.2138: : 494iter [07:37,  1.07iter/s]Best score: 3.2138: : 495iter [07:38,  1.05iter/s]Best score: 3.2138: : 496iter [07:39,  1.06iter/s]Best score: 3.2138: : 497iter [07:40,  1.08iter/s]Best score: 3.2138: : 498iter [07:41,  1.10iter/s]Best score: 3.2138: : 499iter [07:41,  1.09iter/s]
[ITER 500] Current best score: 3.2138
[PARAMS] Current best parameters:
  fx: 530.7726
  fy: 530.3661
  px: 1019.0383
  py: 752.7272
  k1: 0.00846388
  k2: -0.01654635
  k3: 0.01086989
  k4: -0.00275602
  p1: -0.00400457
  p2: -0.00327090
  p3: 0.00514310
  p4: -0.00071021
  scale: 3.0109
  rotation: -0.1426
Best score: 3.2138: : 500iter [07:43,  1.05iter/s]Best score: 3.2138: : 500iter [08:17,  1.01iter/s]

[📈] Forcing polish step after 500 iterations...
  Polish improved score: 3.032762 -> 2.529842
  Success: True
  Tangential distortion: p1=-0.004164, p2=-0.003535, p3=0.001995, p4=0.000047
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 2.5298
[✅] Parameters copied to: /working/MDC027_W031_2025-03-24/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 530.772617773898
    fy: 530.3660677262122
    px: 1019.0383527715102
    py: 752.7272240684823
    k1: 0.008646555309427852
    k2: -0.01650986372438292
    k3: 0.010747074611170996
    k4: -0.0027369026414261043
    p1: -0.00416432566282325
    p2: -0.003534808733714556
    p3: 0.0019947358687777673
    p4: 4.653432358961315e-05
    scale: 3.0109123628374306
    rotation: -0.14149762890357212
    mmpp: 1.2857399293671472
    nmpp: 1285739.9293671472

[🔄] Converting 2508 frames to PTD, creating debug images for 300 calibration frames...
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[✅] Saved PTDs: /working/MDC027_W031_2025-03-24/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC027_W031_2025-03-24/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%|▎         | 82232/3080448 [00:00<00:03, 822314.90px/s]📤 Writing LUT:   6%|▌         | 173346/3080448 [00:00<00:03, 874561.64px/s]📤 Writing LUT:   9%|▊         | 265481/3080448 [00:00<00:03, 895923.58px/s]📤 Writing LUT:  12%|█▏        | 357149/3080448 [00:00<00:03, 904114.04px/s]📤 Writing LUT:  15%|█▍        | 447561/3080448 [00:00<00:02, 902220.72px/s]📤 Writing LUT:  17%|█▋        | 538621/3080448 [00:00<00:02, 905063.09px/s]📤 Writing LUT:  20%|██        | 630768/3080448 [00:00<00:02, 910420.03px/s]📤 Writing LUT:  23%|██▎       | 722271/3080448 [00:00<00:02, 911883.50px/s]📤 Writing LUT:  26%|██▋       | 813461/3080448 [00:00<00:02, 910748.35px/s]📤 Writing LUT:  29%|██▉       | 906947/3080448 [00:01<00:02, 918182.32px/s]📤 Writing LUT:  32%|███▏      | 999223/3080448 [00:01<00:02, 919580.45px/s]📤 Writing LUT:  35%|███▌      | 1091183/3080448 [00:01<00:02, 916517.28px/s]📤 Writing LUT:  38%|███▊      | 1185360/3080448 [00:01<00:02, 924144.64px/s]📤 Writing LUT:  41%|████▏     | 1277779/3080448 [00:01<00:01, 921443.13px/s]📤 Writing LUT:  44%|████▍     | 1369928/3080448 [00:01<00:01, 917460.23px/s]📤 Writing LUT:  47%|████▋     | 1461680/3080448 [00:01<00:01, 916418.73px/s]📤 Writing LUT:  50%|█████     | 1553993/3080448 [00:01<00:01, 918425.19px/s]📤 Writing LUT:  53%|█████▎    | 1646481/3080448 [00:01<00:01, 920355.53px/s]📤 Writing LUT:  56%|█████▋    | 1738520/3080448 [00:01<00:01, 919064.67px/s]📤 Writing LUT:  59%|█████▉    | 1830429/3080448 [00:02<00:01, 916419.93px/s]📤 Writing LUT:  62%|██████▏   | 1923944/3080448 [00:02<00:01, 922017.61px/s]📤 Writing LUT:  65%|██████▌   | 2016150/3080448 [00:02<00:01, 921315.89px/s]📤 Writing LUT:  68%|██████▊   | 2108285/3080448 [00:02<00:01, 917537.96px/s]📤 Writing LUT:  72%|███████▏  | 2203437/3080448 [00:02<00:00, 927686.19px/s]📤 Writing LUT:  75%|███████▍  | 2296214/3080448 [00:02<00:00, 922382.73px/s]📤 Writing LUT:  78%|███████▊  | 2388464/3080448 [00:02<00:00, 918412.76px/s]📤 Writing LUT:  81%|████████  | 2480788/3080448 [00:02<00:00, 919849.22px/s]📤 Writing LUT:  84%|████████▎ | 2573198/3080448 [00:02<00:00, 921117.47px/s]📤 Writing LUT:  87%|████████▋ | 2665316/3080448 [00:02<00:00, 920220.91px/s]📤 Writing LUT:  90%|████████▉ | 2758174/3080448 [00:03<00:00, 922716.85px/s]📤 Writing LUT:  93%|█████████▎| 2852387/3080448 [00:03<00:00, 928525.19px/s]📤 Writing LUT:  96%|█████████▌| 2948519/3080448 [00:03<00:00, 938345.57px/s]📤 Writing LUT:  99%|█████████▉| 3042358/3080448 [00:03<00:00, 932951.40px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 918109.36px/s]
[✅] LUT written to: /working/MDC027_W031_2025-03-24/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC027_W031_2025-03-24' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC027_W031_2025-03-24/calibration/MDC027_W031_2025-03-24_unknown.lut
[🏁] Finished processing MDC027_W031_2025-03-24


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