
🟢 Starting Calibration Pipeline

=== Processing MDC048_W021(6)_2025-11-19 ===

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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
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 481 stationary frames, 202 remain

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                                                                          [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 202 total

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                                                                           [AProcessing Rigs:  25%|██▌       | 1/4 [03:29<10:29, 209.83s/it][INFO] Filtered 433 stationary frames, 174 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 0 untrusted, 173 total

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                                                                            [AProcessing Rigs:  50%|█████     | 2/4 [06:22<06:15, 187.79s/it][INFO] Filtered 469 stationary frames, 204 remain

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→ 2000L Phase 2: corners: 100%|██████████| 204/204 [02:43<00:00,  1.23it/s][A
                                                                           [A[PASS2] Summary: 0 frames fixed, 2 untrusted, 204 total

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                                                                            [AProcessing Rigs:  75%|███████▌  | 3/4 [09:40<03:12, 192.78s/it][INFO] Filtered 370 stationary frames, 140 remain

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                                                                              [A
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                                                                          [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 140 total

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                                                                           [AProcessing Rigs: 100%|██████████| 4/4 [12:14<00:00, 177.24s/it]Processing Rigs: 100%|██████████| 4/4 [12:14<00:00, 183.58s/it]
[💾] Saved RigFrameDataset to /working/MDC048_W021(6)_2025-11-19/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 720 frames from 4 rigs to /working/MDC048_W021(6)_2025-11-19/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC048_W021(6)_2025-11-19/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 202
[📋] 2000R: 100 frames selected from 174
[📋] 2000L: 100 frames selected from 204
[📋] 1000: 100 frames selected from 140
[💾] Saved RigFrameDataset to /working/MDC048_W021(6)_2025-11-19/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC048_W021(6)_2025-11-19/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 60 total sampled bright frames
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533.333} (0.105):  47%|████▋     | 47/100 [00:02<00:02, 23.51it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.029 | Best: {'fx': 533.333} (0.105):  48%|████▊     | 48/100 [00:02<00:02, 23.51it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.029 | Best: {'fx': 533.333} (0.105):  49%|████▉     | 49/100 [00:02<00:01, 26.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 533.333} (0.105):  49%|████▉     | 49/100 [00:02<00:01, 26.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.027 | Best: {'fx': 533.333} (0.105):  50%|█████     | 50/100 [00:02<00:01, 26.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 533.333} (0.105):  51%|█████     | 51/100 [00:02<00:01, 26.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 533.333} (0.105):  52%|█████▏    | 52/100 [00:02<00:02, 20.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.026 | Best: {'fx': 533.333} (0.105):  52%|█████▏    | 52/100 [00:02<00:02, 20.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.026 | Best: {'fx': 533.333} (0.105):  53%|█████▎    | 53/100 [00:02<00:02, 20.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 533.333} (0.105):  54%|█████▍    | 54/100 [00:02<00:02, 20.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 533.333} (0.105):  55%|█████▌    | 55/100 [00:02<00:02, 20.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 533.333} (0.105):  56%|█████▌    | 56/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 533.333} (0.105):  56%|█████▌    | 56/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 533.333} (0.105):  57%|█████▋    | 57/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  58%|█████▊    | 58/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 533.333} (0.105):  59%|█████▉    | 59/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 533.333} (0.105):  60%|██████    | 60/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 533.333} (0.105):  61%|██████    | 61/100 [00:02<00:01, 22.58it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 533.333} (0.105):  62%|██████▏   | 62/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 533.333} (0.105):  62%|██████▏   | 62/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 533.333} (0.105):  63%|██████▎   | 63/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  64%|██████▍   | 64/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  65%|██████▌   | 65/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  66%|██████▌   | 66/100 [00:02<00:01, 29.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  67%|██████▋   | 67/100 [00:02<00:00, 34.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  67%|██████▋   | 67/100 [00:02<00:00, 34.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 533.333} (0.105):  68%|██████▊   | 68/100 [00:02<00:00, 34.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 533.333} (0.105):  69%|██████▉   | 69/100 [00:03<00:00, 34.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 533.333} (0.105):  70%|███████   | 70/100 [00:03<00:00, 34.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 533.333} (0.105):  71%|███████   | 71/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 533.333} (0.105):  71%|███████   | 71/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 533.333} (0.105):  72%|███████▏  | 72/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 533.333} (0.105):  73%|███████▎  | 73/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  74%|███████▍  | 74/100 [00:03<00:00, 34.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  75%|███████▌  | 75/100 [00:03<00:00, 33.60it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  75%|███████▌  | 75/100 [00:03<00:00, 33.60it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  76%|███████▌  | 76/100 [00:03<00:00, 33.60it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  77%|███████▋  | 77/100 [00:03<00:00, 33.60it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  78%|███████▊  | 78/100 [00:03<00:00, 33.60it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  79%|███████▉  | 79/100 [00:03<00:00, 28.67it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  79%|███████▉  | 79/100 [00:03<00:00, 28.67it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  80%|████████  | 80/100 [00:03<00:00, 28.67it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  81%|████████  | 81/100 [00:03<00:00, 28.67it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  82%|████████▏ | 82/100 [00:03<00:00, 28.67it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  83%|████████▎ | 83/100 [00:03<00:00, 29.42it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  83%|████████▎ | 83/100 [00:03<00:00, 29.42it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 533.333} (0.105):  84%|████████▍ | 84/100 [00:03<00:00, 29.42it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  85%|████████▌ | 85/100 [00:03<00:00, 29.42it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  86%|████████▌ | 86/100 [00:03<00:00, 29.42it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  87%|████████▋ | 87/100 [00:03<00:00, 29.55it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  87%|████████▋ | 87/100 [00:03<00:00, 29.55it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  88%|████████▊ | 88/100 [00:03<00:00, 29.55it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  89%|████████▉ | 89/100 [00:03<00:00, 29.55it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  90%|█████████ | 90/100 [00:03<00:00, 29.55it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  91%|█████████ | 91/100 [00:03<00:00, 27.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  91%|█████████ | 91/100 [00:03<00:00, 27.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  92%|█████████▏| 92/100 [00:03<00:00, 27.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  93%|█████████▎| 93/100 [00:03<00:00, 27.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  94%|█████████▍| 94/100 [00:03<00:00, 27.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  95%|█████████▌| 95/100 [00:04<00:00, 27.84it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  96%|█████████▌| 96/100 [00:04<00:00, 24.89it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  96%|█████████▌| 96/100 [00:04<00:00, 24.89it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  97%|█████████▋| 97/100 [00:04<00:00, 24.89it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  98%|█████████▊| 98/100 [00:04<00:00, 24.89it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  99%|█████████▉| 99/100 [00:04<00:00, 25.42it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 533.333} (0.105):  99%|█████████▉| 99/100 [00:04<00:00, 25.42it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.051 | Best: {'fx': 508.333} (0.051):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.051 | Best: {'fx': 508.333} (0.051):   2%|▏         | 1/50 [00:00<00:24,  2.01it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.053 | Best: {'fx': 509.354} (0.053):   2%|▏         | 1/50 [00:00<00:24,  2.01it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.054 | Best: {'fx': 510.374} (0.054):   4%|▍         | 2/50 [00:00<00:23,  2.01it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.056 | Best: {'fx': 511.395} (0.056):   6%|▌         | 3/50 [00:00<00:23,  2.01it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.056 | Best: {'fx': 511.395} (0.056):   8%|▊         | 4/50 [00:00<00:06,  7.51it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.058 | Best: {'fx': 512.415} (0.058):   8%|▊         | 4/50 [00:00<00:06,  7.51it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.060 | Best: {'fx': 513.435} (0.060):  10%|█         | 5/50 [00:00<00:05,  7.51it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.062 | Best: {'fx': 514.456} (0.062):  12%|█▏        | 6/50 [00:00<00:05,  7.51it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.062 | Best: {'fx': 514.456} (0.062):  14%|█▍        | 7/50 [00:00<00:03, 11.87it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.064 | Best: {'fx': 515.476} (0.064):  14%|█▍        | 7/50 [00:00<00:03, 11.87it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.067 | Best: {'fx': 516.497} (0.067):  16%|█▌        | 8/50 [00:00<00:03, 11.87it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.070 | Best: {'fx': 517.517} (0.070):  18%|█▊        | 9/50 [00:00<00:03, 11.87it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.072 | Best: {'fx': 518.537} (0.072):  20%|██        | 10/50 [00:00<00:03, 11.87it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.072 | Best: {'fx': 518.537} (0.072):  22%|██▏       | 11/50 [00:00<00:02, 16.65it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.075 | Best: {'fx': 519.558} (0.075):  22%|██▏       | 11/50 [00:00<00:02, 16.65it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.079 | Best: {'fx': 520.578} (0.079):  24%|██▍       | 12/50 [00:01<00:02, 16.65it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.082 | Best: {'fx': 521.599} (0.082):  26%|██▌       | 13/50 [00:01<00:02, 16.65it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.082 | Best: {'fx': 521.599} (0.082):  28%|██▊       | 14/50 [00:01<00:02, 15.19it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.084 | Best: {'fx': 522.619} (0.084):  28%|██▊       | 14/50 [00:01<00:02, 15.19it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.086 | Best: {'fx': 523.639} (0.086):  30%|███       | 15/50 [00:01<00:02, 15.19it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.086 | Best: {'fx': 523.639} (0.086):  32%|███▏      | 16/50 [00:01<00:02, 16.06it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.089 | Best: {'fx': 524.66} (0.089):  32%|███▏      | 16/50 [00:01<00:02, 16.06it/s] Brute | Start: {'fx': 533.333} (0.105) | Current: 0.094 | Best: {'fx': 526.701} (0.094):  34%|███▍      | 17/50 [00:01<00:02, 16.06it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.091 | Best: {'fx': 526.701} (0.094):  36%|███▌      | 18/50 [00:01<00:01, 16.06it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.094 | Best: {'fx': 527.721} (0.094):  38%|███▊      | 19/50 [00:01<00:01, 16.06it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.094 | Best: {'fx': 527.721} (0.094):  40%|████      | 20/50 [00:01<00:01, 19.57it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.096 | Best: {'fx': 528.741} (0.096):  40%|████      | 20/50 [00:01<00:01, 19.57it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.099 | Best: {'fx': 529.762} (0.099):  42%|████▏     | 21/50 [00:01<00:01, 19.57it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.102 | Best: {'fx': 530.782} (0.102):  44%|████▍     | 22/50 [00:01<00:01, 19.57it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.102 | Best: {'fx': 530.782} (0.102):  46%|████▌     | 23/50 [00:01<00:01, 18.32it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.107 | Best: {'fx': 532.823} (0.107):  46%|████▌     | 23/50 [00:01<00:01, 18.32it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.104 | Best: {'fx': 532.823} (0.107):  48%|████▊     | 24/50 [00:01<00:01, 18.32it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.107 | Best: {'fx': 531.803} (0.107):  50%|█████     | 25/50 [00:01<00:01, 18.32it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.092 | Best: {'fx': 531.803} (0.107):  52%|█████▏    | 26/50 [00:01<00:01, 18.32it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.101 | Best: {'fx': 531.803} (0.107):  54%|█████▍    | 27/50 [00:01<00:01, 18.32it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.101 | Best: {'fx': 531.803} (0.107):  56%|█████▌    | 28/50 [00:01<00:00, 23.46it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.090 | Best: {'fx': 531.803} (0.107):  56%|█████▌    | 28/50 [00:01<00:00, 23.46it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.100 | Best: {'fx': 531.803} (0.107):  58%|█████▊    | 29/50 [00:01<00:00, 23.46it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.101 | Best: {'fx': 531.803} (0.107):  60%|██████    | 30/50 [00:01<00:00, 23.46it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.097 | Best: {'fx': 531.803} (0.107):  62%|██████▏   | 31/50 [00:01<00:00, 23.46it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.086 | Best: {'fx': 531.803} (0.107):  64%|██████▍   | 32/50 [00:01<00:00, 23.46it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.086 | Best: {'fx': 531.803} (0.107):  66%|██████▌   | 33/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.081 | Best: {'fx': 531.803} (0.107):  66%|██████▌   | 33/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.096 | Best: {'fx': 531.803} (0.107):  68%|██████▊   | 34/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.080 | Best: {'fx': 531.803} (0.107):  70%|███████   | 35/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.084 | Best: {'fx': 531.803} (0.107):  72%|███████▏  | 36/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.076 | Best: {'fx': 531.803} (0.107):  74%|███████▍  | 37/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.074 | Best: {'fx': 531.803} (0.107):  76%|███████▌  | 38/50 [00:01<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.072 | Best: {'fx': 531.803} (0.107):  78%|███████▊  | 39/50 [00:02<00:00, 27.35it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.072 | Best: {'fx': 531.803} (0.107):  80%|████████  | 40/50 [00:02<00:00, 32.08it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.069 | Best: {'fx': 531.803} (0.107):  80%|████████  | 40/50 [00:02<00:00, 32.08it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.068 | Best: {'fx': 531.803} (0.107):  82%|████████▏ | 41/50 [00:02<00:00, 32.08it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.066 | Best: {'fx': 531.803} (0.107):  84%|████████▍ | 42/50 [00:02<00:00, 32.08it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.064 | Best: {'fx': 531.803} (0.107):  86%|████████▌ | 43/50 [00:02<00:00, 32.08it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.064 | Best: {'fx': 531.803} (0.107):  88%|████████▊ | 44/50 [00:02<00:00, 32.62it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.062 | Best: {'fx': 531.803} (0.107):  88%|████████▊ | 44/50 [00:02<00:00, 32.62it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.063 | Best: {'fx': 531.803} (0.107):  90%|█████████ | 45/50 [00:02<00:00, 32.62it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.061 | Best: {'fx': 531.803} (0.107):  92%|█████████▏| 46/50 [00:02<00:00, 32.62it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.060 | Best: {'fx': 531.803} (0.107):  94%|█████████▍| 47/50 [00:02<00:00, 32.62it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.059 | Best: {'fx': 531.803} (0.107):  96%|█████████▌| 48/50 [00:02<00:00, 32.62it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.059 | Best: {'fx': 531.803} (0.107):  98%|█████████▊| 49/50 [00:02<00:00, 35.24it/s]Brute | Start: {'fx': 533.333} (0.105) | Current: 0.057 | Best: {'fx': 531.803} (0.107):  98%|█████████▊| 49/50 [00:02<00:00, 35.24it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1010.0
    py: 723.0
    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

  [Ring preference] diameter=3500 mm  →  radius=508.1 px

[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 1 incomplete frames): 79 frames will be used for optimization
  Rigs: 1500(1500mm):20/20, 2000R(2000mm):19/20, 2000L(2000mm):20/20, 1000(1000mm):20/20
  Total: 79/80 frames used for optimization
Initial score: 11.8211: 0iter [00:00, ?iter/s]  [Ring mask] 92,765/99,256 points within ring radius 508.1 px
[DEBUG] objective(x0) = 11.8211, picklable_objective(x0) = 10.1881
Best score: 10.1881: : 0iter [00:02, ?iter/s] Best score: 10.1881: : 1iter [00:02,  2.25s/iter]Best score: 10.1881: : 1iter [00:02,  2.25s/iter]Best score: 10.1881: : 2iter [00:02,  1.02s/iter]Best score: 10.1881: : 2iter [00:02,  1.02s/iter]Best score: 10.1881: : 3iter [00:02,  1.60iter/s]Best score: 10.1881: : 3iter [00:02,  1.60iter/s]Best score: 10.1881: : 4iter [00:02,  2.27iter/s]Best score: 10.1881: : 4iter [00:02,  2.27iter/s]Best score: 10.1881: : 5iter [00:02,  2.96iter/s]Best score: 10.1881: : 5iter [00:03,  2.96iter/s]Best score: 10.1881: : 6iter [00:03,  3.62iter/s]Best score: 10.1881: : 6iter [00:03,  3.62iter/s]Best score: 10.1881: : 7iter [00:03,  4.20iter/s]Best score: 10.1881: : 7iter [00:03,  4.20iter/s]Best score: 10.1881: : 8iter [00:03,  4.71iter/s]Best score: 10.1881: : 8iter [00:03,  4.71iter/s]Best score: 10.1881: : 9iter [00:03,  5.13iter/s]Best score: 10.1881: : 9iter [00:03,  5.13iter/s]Best score: 10.1881: : 10iter [00:03,  5.32iter/s]Best score: 10.1881: : 10iter [00:03,  5.32iter/s]Best score: 10.1881: : 11iter [00:03,  5.60iter/s]Best score: 10.1881: : 11iter [00:03,  5.60iter/s]Best score: 10.1881: : 12iter [00:03,  5.80iter/s]Best score: 10.1881: : 12iter [00:04,  5.80iter/s]Best score: 10.1881: : 13iter [00:04,  5.94iter/s]Best score: 10.1881: : 13iter [00:04,  5.94iter/s]Best score: 10.1881: : 14iter [00:04,  6.04iter/s]Best score: 10.1881: : 14iter [00:04,  6.04iter/s]Best score: 10.1881: : 15iter [00:04,  6.13iter/s]Best score: 10.1881: : 15iter [00:04,  6.13iter/s]Best score: 10.1881: : 16iter [00:04,  6.18iter/s]Best score: 10.1881: : 16iter [00:04,  6.18iter/s]Best score: 10.1881: : 17iter [00:04,  6.22iter/s]Best score: 10.1881: : 17iter [00:04,  6.22iter/s]Best score: 10.1881: : 18iter [00:04,  6.26iter/s]Best score: 10.1881: : 18iter [00:05,  6.26iter/s]Best score: 10.1881: : 19iter [00:05,  6.28iter/s]Best score: 10.1881: : 19iter [00:05,  6.28iter/s]Best score: 10.1881: : 20iter [00:05,  6.12iter/s]Best score: 10.1881: : 20iter [00:05,  6.12iter/s]Best score: 10.1881: : 21iter [00:05,  6.19iter/s]Best score: 10.1881: : 21iter [00:05,  6.19iter/s]Best score: 10.1881: : 22iter [00:05,  6.24iter/s]Best score: 10.1881: : 22iter [00:05,  6.24iter/s]Best score: 10.1881: : 23iter [00:05,  6.27iter/s]Best score: 10.1881: : 23iter [00:05,  6.27iter/s]Best score: 10.1881: : 24iter [00:05,  6.32iter/s]Best score: 10.1881: : 24iter [00:06,  6.32iter/s]Best score: 10.1881: : 25iter [00:06,  6.33iter/s]Best score: 10.1881: : 25iter [00:06,  6.33iter/s]Best score: 10.1881: : 26iter [00:06,  6.34iter/s]Best score: 10.1881: : 26iter [00:06,  6.34iter/s]Best score: 10.1881: : 27iter [00:06,  6.35iter/s]Best score: 10.1881: : 27iter [00:06,  6.35iter/s]Best score: 10.1881: : 28iter [00:06,  6.36iter/s]Best score: 10.1881: : 28iter [00:06,  6.36iter/s]Best score: 10.1881: : 29iter [00:06,  6.36iter/s]Best score: 10.1881: : 29iter [00:06,  6.36iter/s]Best score: 10.1881: : 30iter [00:06,  6.19iter/s]Best score: 10.1881: : 30iter [00:07,  6.19iter/s]Best score: 10.1881: : 31iter [00:07,  6.26iter/s]Best score: 10.1881: : 31iter [00:07,  6.26iter/s]Best score: 10.1881: : 32iter [00:07,  6.31iter/s]Best score: 10.1881: : 32iter [00:07,  6.31iter/s]Best score: 10.1881: : 33iter [00:07,  6.34iter/s]Best score: 10.1881: : 33iter [00:07,  6.34iter/s]Best score: 10.1881: : 34iter [00:07,  6.37iter/s]Best score: 10.1881: : 34iter [00:07,  6.37iter/s]Best score: 10.1881: : 35iter [00:07,  6.38iter/s]Best score: 10.1881: : 35iter [00:07,  6.38iter/s]Best score: 10.1881: : 36iter [00:07,  6.37iter/s]Best score: 10.1881: : 36iter [00:07,  6.37iter/s]Best score: 10.1881: : 37iter [00:07,  6.35iter/s]Best score: 10.1881: : 37iter [00:08,  6.35iter/s]Best score: 10.1881: : 38iter [00:08,  6.36iter/s]Best score: 10.1881: : 38iter [00:08,  6.36iter/s]Best score: 10.1881: : 39iter [00:08,  6.37iter/s]Best score: 10.1881: : 39iter [00:08,  6.37iter/s]Best score: 10.1881: : 40iter [00:08,  6.19iter/s]Best score: 10.1881: : 40iter [00:08,  6.19iter/s]Best score: 10.1881: : 41iter [00:08,  6.23iter/s]Best score: 10.1881: : 41iter [00:08,  6.23iter/s]Best score: 10.1881: : 42iter [00:08,  6.27iter/s]Best score: 10.1881: : 42iter [00:08,  6.27iter/s]Best score: 10.1881: : 43iter [00:08,  6.31iter/s]Best score: 10.1881: : 43iter [00:09,  6.31iter/s]Best score: 10.1881: : 44iter [00:09,  6.33iter/s]Best score: 10.1881: : 44iter [00:09,  6.33iter/s]Best score: 10.1881: : 45iter [00:09,  6.35iter/s]Best score: 10.1881: : 45iter [00:09,  6.35iter/s]Best score: 10.1881: : 46iter [00:09,  6.36iter/s]Best score: 10.1881: : 46iter [00:09,  6.36iter/s]Best score: 10.1881: : 47iter [00:09,  6.37iter/s]Best score: 10.1881: : 47iter [00:09,  6.37iter/s]Best score: 10.1881: : 48iter [00:09,  6.39iter/s]Best score: 10.1881: : 48iter [00:09,  6.39iter/s]Best score: 10.1881: : 49iter [00:09,  6.39iter/s]
[ITER 50] Best: 10.1881
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1010.0000
  py: 723.0000
  k1: 0.00000000
  k2: 0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  scale: 3.0000
  rotation: 0.0000
Best score: 10.1881: : 49iter [00:10,  6.39iter/s]Best score: 10.1881: : 50iter [00:10,  6.20iter/s]Best score: 10.1881: : 50iter [00:10,  6.20iter/s]Best score: 10.1881: : 51iter [00:10,  6.27iter/s]Best score: 10.1881: : 51iter [00:10,  6.27iter/s]Best score: 10.1881: : 52iter [00:10,  6.31iter/s]Best score: 10.1881: : 52iter [00:10,  6.31iter/s]Best score: 10.1881: : 53iter [00:10,  6.34iter/s]Best score: 10.1881: : 53iter [00:10,  6.34iter/s]Best score: 10.1881: : 54iter [00:10,  6.36iter/s]Best score: 10.1881: : 54iter [00:10,  6.36iter/s]Best score: 10.1881: : 55iter [00:10,  6.36iter/s]Best score: 10.1881: : 55iter [00:10,  6.36iter/s]Best score: 10.1881: : 56iter [00:10,  6.37iter/s]Best score: 10.1881: : 56iter [00:11,  6.37iter/s]Best score: 10.1881: : 57iter [00:11,  6.39iter/s]Best score: 10.1881: : 57iter [00:11,  6.39iter/s]Best score: 10.1881: : 58iter [00:11,  6.40iter/s]Best score: 10.1881: : 58iter [00:11,  6.40iter/s]Best score: 10.1881: : 59iter [00:11,  6.40iter/s]Best score: 7.8351: : 59iter [00:11,  6.40iter/s] Best score: 7.8351: : 60iter [00:11,  6.20iter/s]Best score: 7.8351: : 60iter [00:11,  6.20iter/s]Best score: 7.8351: : 61iter [00:11,  6.25iter/s]Best score: 7.8351: : 61iter [00:11,  6.25iter/s]Best score: 7.8351: : 62iter [00:11,  6.28iter/s]Best score: 7.8351: : 62iter [00:12,  6.28iter/s]Best score: 7.8351: : 63iter [00:12,  6.32iter/s]Best score: 7.8351: : 63iter [00:12,  6.32iter/s]Best score: 7.8351: : 64iter [00:12,  6.34iter/s]Best score: 7.8351: : 64iter [00:12,  6.34iter/s]Best score: 7.8351: : 65iter [00:12,  6.35iter/s]Best score: 7.8351: : 65iter [00:12,  6.35iter/s]Best score: 7.8351: : 66iter [00:12,  6.38iter/s]Best score: 7.8351: : 66iter [00:12,  6.38iter/s]Best score: 7.8351: : 67iter [00:12,  6.40iter/s]Best score: 7.8351: : 67iter [00:12,  6.40iter/s]Best score: 7.8351: : 68iter [00:12,  6.40iter/s]Best score: 7.8351: : 68iter [00:13,  6.40iter/s]Best score: 7.8351: : 69iter [00:13,  6.40iter/s]Best score: 5.5573: : 69iter [00:13,  6.40iter/s]Best score: 5.5573: : 70iter [00:13,  6.20iter/s]Best score: 5.5573: : 70iter [00:13,  6.20iter/s]Best score: 5.5573: : 71iter [00:13,  6.27iter/s]Best score: 5.5573: : 71iter [00:13,  6.27iter/s]Best score: 5.5573: : 72iter [00:13,  6.30iter/s]Best score: 5.5573: : 72iter [00:13,  6.30iter/s]Best score: 5.5573: : 73iter [00:13,  6.33iter/s]Best score: 5.5573: : 73iter [00:13,  6.33iter/s]Best score: 5.5573: : 74iter [00:13,  6.37iter/s]Best score: 5.5573: : 74iter [00:13,  6.37iter/s]Best score: 5.5573: : 75iter [00:13,  6.38iter/s]Best score: 5.5573: : 75iter [00:14,  6.38iter/s]Best score: 5.5573: : 76iter [00:14,  6.39iter/s]Best score: 5.5573: : 76iter [00:14,  6.39iter/s]Best score: 5.5573: : 77iter [00:14,  6.38iter/s]Best score: 5.5573: : 77iter [00:14,  6.38iter/s]Best score: 5.5573: : 78iter [00:14,  6.38iter/s]Best score: 5.5573: : 78iter [00:14,  6.38iter/s]Best score: 5.5573: : 79iter [00:14,  6.38iter/s]Best score: 5.4336: : 79iter [00:14,  6.38iter/s]Best score: 5.4336: : 80iter [00:14,  6.18iter/s]Best score: 5.4336: : 80iter [00:14,  6.18iter/s]Best score: 5.4336: : 81iter [00:14,  6.24iter/s]Best score: 5.4336: : 81iter [00:15,  6.24iter/s]Best score: 5.4336: : 82iter [00:15,  6.28iter/s]Best score: 5.4336: : 82iter [00:15,  6.28iter/s]Best score: 5.4336: : 83iter [00:15,  6.31iter/s]Best score: 5.4336: : 83iter [00:15,  6.31iter/s]Best score: 5.4336: : 84iter [00:15,  6.34iter/s]Best score: 5.4336: : 84iter [00:15,  6.34iter/s]Best score: 5.4336: : 85iter [00:15,  6.34iter/s]Best score: 5.4336: : 85iter [00:15,  6.34iter/s]Best score: 5.4336: : 86iter [00:15,  6.34iter/s]Best score: 5.4336: : 86iter [00:15,  6.34iter/s]Best score: 5.4336: : 87iter [00:15,  6.35iter/s]Best score: 5.4336: : 87iter [00:16,  6.35iter/s]Best score: 5.4336: : 88iter [00:16,  6.35iter/s]Best score: 5.4336: : 88iter [00:16,  6.35iter/s]Best score: 5.4336: : 89iter [00:16,  6.35iter/s]Best score: 2.8473: : 89iter [00:16,  6.35iter/s]Best score: 2.8473: : 90iter [00:16,  6.16iter/s]Best score: 2.8473: : 90iter [00:16,  6.16iter/s]Best score: 2.8473: : 91iter [00:16,  6.21iter/s]Best score: 2.8473: : 91iter [00:16,  6.21iter/s]Best score: 2.8473: : 92iter [00:16,  6.23iter/s]Best score: 2.8473: : 92iter [00:16,  6.23iter/s]Best score: 2.8473: : 93iter [00:16,  6.26iter/s]Best score: 2.8473: : 93iter [00:16,  6.26iter/s]Best score: 2.8473: : 94iter [00:16,  6.29iter/s]Best score: 2.8473: : 94iter [00:17,  6.29iter/s]Best score: 2.8473: : 95iter [00:17,  6.31iter/s]Best score: 2.8473: : 95iter [00:17,  6.31iter/s]Best score: 2.8473: : 96iter [00:17,  6.32iter/s]Best score: 2.8473: : 96iter [00:17,  6.32iter/s]Best score: 2.8473: : 97iter [00:17,  6.34iter/s]Best score: 2.8473: : 97iter [00:17,  6.34iter/s]Best score: 2.8473: : 98iter [00:17,  6.34iter/s]Best score: 2.8473: : 98iter [00:17,  6.34iter/s]Best score: 2.8473: : 99iter [00:17,  6.35iter/s]
[ITER 100] Best: 2.8473
[PARAMS] Current best parameters:
  fx: 548.0412
  fy: 547.0614
  px: 1017.1720
  py: 729.5446
  k1: -0.04872977
  k2: -0.00817575
  k3: 0.04299529
  k4: -0.01705055
  p1: 0.00711427
  p2: 0.00341404
  p3: -0.00357924
  p4: 0.00545485
  scale: 3.1186
  rotation: -0.4404
Best score: 2.8473: : 99iter [00:17,  6.35iter/s]Best score: 2.8473: : 100iter [00:17,  6.16iter/s]Best score: 2.8473: : 100iter [00:18,  6.16iter/s]Best score: 2.8473: : 101iter [00:18,  6.21iter/s]Best score: 2.8473: : 101iter [00:18,  6.21iter/s]Best score: 2.8473: : 102iter [00:18,  6.25iter/s]Best score: 2.8473: : 102iter [00:18,  6.25iter/s]Best score: 2.8473: : 103iter [00:18,  6.28iter/s]Best score: 2.8473: : 103iter [00:18,  6.28iter/s]Best score: 2.8473: : 104iter [00:18,  6.29iter/s]Best score: 2.8473: : 104iter [00:18,  6.29iter/s]Best score: 2.8473: : 105iter [00:18,  6.30iter/s]Best score: 2.8473: : 105iter [00:18,  6.30iter/s]Best score: 2.8473: : 106iter [00:18,  6.31iter/s]Best score: 2.8473: : 106iter [00:19,  6.31iter/s]Best score: 2.8473: : 107iter [00:19,  6.33iter/s]Best score: 2.8473: : 107iter [00:19,  6.33iter/s]Best score: 2.8473: : 108iter [00:19,  6.34iter/s]Best score: 2.8473: : 108iter [00:19,  6.34iter/s]Best score: 2.8473: : 109iter [00:19,  6.35iter/s]Best score: 1.4841: : 109iter [00:19,  6.35iter/s]Best score: 1.4841: : 110iter [00:19,  6.15iter/s]Best score: 1.4841: : 110iter [00:19,  6.15iter/s]Best score: 1.4841: : 111iter [00:19,  6.22iter/s]Best score: 1.4841: : 111iter [00:19,  6.22iter/s]Best score: 1.4841: : 112iter [00:19,  6.26iter/s]Best score: 1.4841: : 112iter [00:20,  6.26iter/s]Best score: 1.4841: : 113iter [00:20,  6.31iter/s]Best score: 1.4841: : 113iter [00:20,  6.31iter/s]Best score: 1.4841: : 114iter [00:20,  6.33iter/s]Best score: 1.4841: : 114iter [00:20,  6.33iter/s]Best score: 1.4841: : 115iter [00:20,  6.33iter/s]Best score: 1.4841: : 115iter [00:20,  6.33iter/s]Best score: 1.4841: : 116iter [00:20,  6.35iter/s]Best score: 1.4841: : 116iter [00:20,  6.35iter/s]Best score: 1.4841: : 117iter [00:20,  6.35iter/s]Best score: 1.4841: : 117iter [00:20,  6.35iter/s]Best score: 1.4841: : 118iter [00:20,  6.36iter/s]Best score: 1.4841: : 118iter [00:20,  6.36iter/s]Best score: 1.4841: : 119iter [00:20,  6.38iter/s]Best score: 1.4841: : 119iter [00:21,  6.38iter/s]Best score: 1.4841: : 120iter [00:21,  6.19iter/s]Best score: 1.4841: : 120iter [00:21,  6.19iter/s]Best score: 1.4841: : 121iter [00:21,  6.24iter/s]Best score: 1.4841: : 121iter [00:21,  6.24iter/s]Best score: 1.4841: : 122iter [00:21,  6.26iter/s]Best score: 1.4841: : 122iter [00:21,  6.26iter/s]Best score: 1.4841: : 123iter [00:21,  6.29iter/s]Best score: 1.4841: : 123iter [00:21,  6.29iter/s]Best score: 1.4841: : 124iter [00:21,  6.31iter/s]Best score: 1.4841: : 124iter [00:21,  6.31iter/s]Best score: 1.4841: : 125iter [00:21,  6.33iter/s]Best score: 1.4841: : 125iter [00:22,  6.33iter/s]Best score: 1.4841: : 126iter [00:22,  6.35iter/s]Best score: 1.4841: : 126iter [00:22,  6.35iter/s]Best score: 1.4841: : 127iter [00:22,  6.36iter/s]Best score: 1.4841: : 127iter [00:22,  6.36iter/s]Best score: 1.4841: : 128iter [00:22,  6.37iter/s]Best score: 1.4841: : 128iter [00:22,  6.37iter/s]Best score: 1.4841: : 129iter [00:22,  6.38iter/s]Best score: 1.3884: : 129iter [00:22,  6.38iter/s]Best score: 1.3884: : 130iter [00:22,  6.17iter/s]Best score: 1.3884: : 130iter [00:22,  6.17iter/s]Best score: 1.3884: : 131iter [00:22,  6.24iter/s]Best score: 1.3884: : 131iter [00:23,  6.24iter/s]Best score: 1.3884: : 132iter [00:23,  6.29iter/s]Best score: 1.3884: : 132iter [00:23,  6.29iter/s]Best score: 1.3884: : 133iter [00:23,  6.32iter/s]Best score: 1.3884: : 133iter [00:23,  6.32iter/s]Best score: 1.3884: : 134iter [00:23,  6.35iter/s]Best score: 1.3884: : 134iter [00:23,  6.35iter/s]Best score: 1.3884: : 135iter [00:23,  6.36iter/s]Best score: 1.3884: : 135iter [00:23,  6.36iter/s]Best score: 1.3884: : 136iter [00:23,  6.36iter/s]Best score: 1.3884: : 136iter [00:23,  6.36iter/s]Best score: 1.3884: : 137iter [00:23,  6.37iter/s]Best score: 1.3884: : 137iter [00:23,  6.37iter/s]Best score: 1.3884: : 138iter [00:23,  6.39iter/s]Best score: 1.3884: : 138iter [00:24,  6.39iter/s]Best score: 1.3884: : 139iter [00:24,  6.40iter/s]Best score: 0.8058: : 139iter [00:24,  6.40iter/s]Best score: 0.8058: : 140iter [00:24,  6.19iter/s]Best score: 0.8058: : 140iter [00:24,  6.19iter/s]Best score: 0.8058: : 141iter [00:24,  6.24iter/s]Best score: 0.8058: : 141iter [00:24,  6.24iter/s]Best score: 0.8058: : 142iter [00:24,  6.30iter/s]Best score: 0.8058: : 142iter [00:24,  6.30iter/s]Best score: 0.8058: : 143iter [00:24,  6.33iter/s]Best score: 0.8058: : 143iter [00:24,  6.33iter/s]Best score: 0.8058: : 144iter [00:24,  6.35iter/s]Best score: 0.8058: : 144iter [00:25,  6.35iter/s]Best score: 0.8058: : 145iter [00:25,  6.36iter/s]Best score: 0.8058: : 145iter [00:25,  6.36iter/s]Best score: 0.8058: : 146iter [00:25,  6.36iter/s]Best score: 0.8058: : 146iter [00:25,  6.36iter/s]Best score: 0.8058: : 147iter [00:25,  6.38iter/s]Best score: 0.8058: : 147iter [00:25,  6.38iter/s]Best score: 0.8058: : 148iter [00:25,  6.38iter/s]Best score: 0.8058: : 148iter [00:25,  6.38iter/s]Best score: 0.8058: : 149iter [00:25,  6.38iter/s]
[ITER 150] Best: 0.5465
[PARAMS] Current best parameters:
  fx: 545.7148
  fy: 545.6223
  px: 1013.6227
  py: 726.3828
  k1: -0.01913874
  k2: -0.00900996
  k3: 0.01632424
  k4: -0.00564763
  p1: 0.00147364
  p2: -0.00166667
  p3: -0.00532794
  p4: 0.00443681
  scale: 3.0239
  rotation: -0.2848
Best score: 0.5465: : 149iter [00:25,  6.38iter/s]Best score: 0.5465: : 150iter [00:25,  6.19iter/s]Best score: 0.5465: : 150iter [00:26,  6.19iter/s]Best score: 0.5465: : 151iter [00:26,  6.25iter/s]Best score: 0.5465: : 151iter [00:26,  6.25iter/s]Best score: 0.5465: : 152iter [00:26,  6.30iter/s]Best score: 0.5465: : 152iter [00:26,  6.30iter/s]Best score: 0.5465: : 153iter [00:26,  6.34iter/s]Best score: 0.5465: : 153iter [00:26,  6.34iter/s]Best score: 0.5465: : 154iter [00:26,  6.37iter/s]Best score: 0.5465: : 154iter [00:26,  6.37iter/s]Best score: 0.5465: : 155iter [00:26,  6.38iter/s]Best score: 0.5465: : 155iter [00:26,  6.38iter/s]Best score: 0.5465: : 156iter [00:26,  6.39iter/s]Best score: 0.5465: : 156iter [00:26,  6.39iter/s]Best score: 0.5465: : 157iter [00:26,  6.39iter/s]Best score: 0.5465: : 157iter [00:27,  6.39iter/s]Best score: 0.5465: : 158iter [00:27,  6.39iter/s]Best score: 0.5465: : 158iter [00:27,  6.39iter/s]Best score: 0.5465: : 159iter [00:27,  6.40iter/s]Best score: 0.5398: : 159iter [00:27,  6.40iter/s]Best score: 0.5398: : 160iter [00:27,  6.21iter/s]Best score: 0.5398: : 160iter [00:27,  6.21iter/s]Best score: 0.5398: : 161iter [00:27,  6.28iter/s]Best score: 0.5398: : 161iter [00:27,  6.28iter/s]Best score: 0.5398: : 162iter [00:27,  6.32iter/s]Best score: 0.5398: : 162iter [00:27,  6.32iter/s]Best score: 0.5398: : 163iter [00:27,  6.33iter/s]Best score: 0.5398: : 163iter [00:28,  6.33iter/s]Best score: 0.5398: : 164iter [00:28,  6.35iter/s]Best score: 0.5398: : 164iter [00:28,  6.35iter/s]Best score: 0.5398: : 165iter [00:28,  6.36iter/s]Best score: 0.5398: : 165iter [00:28,  6.36iter/s]Best score: 0.5398: : 166iter [00:28,  6.36iter/s]Best score: 0.5398: : 166iter [00:28,  6.36iter/s]Best score: 0.5398: : 167iter [00:28,  6.36iter/s]Best score: 0.5398: : 167iter [00:28,  6.36iter/s]Best score: 0.5398: : 168iter [00:28,  6.37iter/s]Best score: 0.5398: : 168iter [00:28,  6.37iter/s]Best score: 0.5398: : 169iter [00:28,  6.38iter/s]Best score: 0.4440: : 169iter [00:29,  6.38iter/s]Best score: 0.4440: : 170iter [00:29,  6.18iter/s]Best score: 0.4440: : 170iter [00:29,  6.18iter/s]Best score: 0.4440: : 171iter [00:29,  6.24iter/s]Best score: 0.4440: : 171iter [00:29,  6.24iter/s]Best score: 0.4440: : 172iter [00:29,  6.29iter/s]Best score: 0.4440: : 172iter [00:29,  6.29iter/s]Best score: 0.4440: : 173iter [00:29,  6.33iter/s]Best score: 0.4440: : 173iter [00:29,  6.33iter/s]Best score: 0.4440: : 174iter [00:29,  6.35iter/s]Best score: 0.4440: : 174iter [00:29,  6.35iter/s]Best score: 0.4440: : 175iter [00:29,  6.37iter/s]Best score: 0.4440: : 175iter [00:29,  6.37iter/s]Best score: 0.4440: : 176iter [00:29,  6.37iter/s]Best score: 0.4440: : 176iter [00:30,  6.37iter/s]Best score: 0.4440: : 177iter [00:30,  6.39iter/s]Best score: 0.4440: : 177iter [00:30,  6.39iter/s]Best score: 0.4440: : 178iter [00:30,  6.40iter/s]Best score: 0.4440: : 178iter [00:30,  6.40iter/s]Best score: 0.4440: : 179iter [00:30,  6.41iter/s]Best score: 0.3625: : 179iter [00:30,  6.41iter/s]Best score: 0.3625: : 180iter [00:30,  6.22iter/s]Best score: 0.3625: : 180iter [00:30,  6.22iter/s]Best score: 0.3625: : 181iter [00:30,  6.28iter/s]Best score: 0.3625: : 181iter [00:30,  6.28iter/s]Best score: 0.3625: : 182iter [00:30,  6.31iter/s]Best score: 0.3625: : 182iter [00:31,  6.31iter/s]Best score: 0.3625: : 183iter [00:31,  6.35iter/s]Best score: 0.3625: : 183iter [00:31,  6.35iter/s]Best score: 0.3625: : 184iter [00:31,  6.37iter/s]Best score: 0.3625: : 184iter [00:31,  6.37iter/s]Best score: 0.3625: : 185iter [00:31,  6.39iter/s]Best score: 0.3625: : 185iter [00:31,  6.39iter/s]Best score: 0.3625: : 186iter [00:31,  6.41iter/s]Best score: 0.3625: : 186iter [00:31,  6.41iter/s]Best score: 0.3625: : 187iter [00:31,  6.41iter/s]Best score: 0.3625: : 187iter [00:31,  6.41iter/s]Best score: 0.3625: : 188iter [00:31,  6.42iter/s]Best score: 0.3625: : 188iter [00:31,  6.42iter/s]Best score: 0.3625: : 189iter [00:31,  6.42iter/s]Best score: 0.3625: : 189iter [00:32,  6.42iter/s]Best score: 0.3625: : 190iter [00:32,  6.22iter/s]Best score: 0.3625: : 190iter [00:32,  6.22iter/s]Best score: 0.3625: : 191iter [00:32,  6.28iter/s]Best score: 0.3625: : 191iter [00:32,  6.28iter/s]Best score: 0.3625: : 192iter [00:32,  6.33iter/s]Best score: 0.3625: : 192iter [00:32,  6.33iter/s]Best score: 0.3625: : 193iter [00:32,  6.34iter/s]Best score: 0.3625: : 193iter [00:32,  6.34iter/s]Best score: 0.3625: : 194iter [00:32,  6.36iter/s]Best score: 0.3625: : 194iter [00:32,  6.36iter/s]Best score: 0.3625: : 195iter [00:32,  6.39iter/s]Best score: 0.3625: : 195iter [00:33,  6.39iter/s]Best score: 0.3625: : 196iter [00:33,  6.39iter/s]Best score: 0.3625: : 196iter [00:33,  6.39iter/s]Best score: 0.3625: : 197iter [00:33,  6.40iter/s]Best score: 0.3625: : 197iter [00:33,  6.40iter/s]Best score: 0.3625: : 198iter [00:33,  6.42iter/s]Best score: 0.3625: : 198iter [00:33,  6.42iter/s]Best score: 0.3625: : 199iter [00:33,  6.43iter/s]
[ITER 200] Best: 0.3088
[PARAMS] Current best parameters:
  fx: 574.8579
  fy: 574.2631
  px: 1017.1172
  py: 727.0742
  k1: -0.04191560
  k2: -0.00306182
  k3: 0.00481565
  k4: -0.00092877
  p1: 0.00253537
  p2: 0.00539724
  p3: -0.00422672
  p4: 0.00373360
  scale: 2.9936
  rotation: -0.2147
Best score: 0.3088: : 199iter [00:33,  6.43iter/s]Best score: 0.3088: : 200iter [00:33,  6.22iter/s]Best score: 0.3088: : 200iter [00:33,  6.22iter/s]Best score: 0.3088: : 201iter [00:33,  6.27iter/s]Best score: 0.3088: : 201iter [00:34,  6.27iter/s]Best score: 0.3088: : 202iter [00:34,  6.33iter/s]Best score: 0.3088: : 202iter [00:34,  6.33iter/s]Best score: 0.3088: : 203iter [00:34,  6.36iter/s]Best score: 0.3088: : 203iter [00:34,  6.36iter/s]Best score: 0.3088: : 204iter [00:34,  6.38iter/s]Best score: 0.3088: : 204iter [00:34,  6.38iter/s]Best score: 0.3088: : 205iter [00:34,  6.37iter/s]Best score: 0.3088: : 205iter [00:34,  6.37iter/s]Best score: 0.3088: : 206iter [00:34,  6.38iter/s]Best score: 0.3088: : 206iter [00:34,  6.38iter/s]Best score: 0.3088: : 207iter [00:34,  6.40iter/s]Best score: 0.3088: : 207iter [00:34,  6.40iter/s]Best score: 0.3088: : 208iter [00:34,  6.41iter/s]Best score: 0.3088: : 208iter [00:35,  6.41iter/s]Best score: 0.3088: : 209iter [00:35,  6.43iter/s]Best score: 0.2937: : 209iter [00:35,  6.43iter/s]Best score: 0.2937: : 210iter [00:35,  6.23iter/s]Best score: 0.2937: : 210iter [00:35,  6.23iter/s]Best score: 0.2937: : 211iter [00:35,  6.29iter/s]Best score: 0.2937: : 211iter [00:35,  6.29iter/s]Best score: 0.2937: : 212iter [00:35,  6.35iter/s]Best score: 0.2937: : 212iter [00:35,  6.35iter/s]Best score: 0.2937: : 213iter [00:35,  6.36iter/s]Best score: 0.2937: : 213iter [00:35,  6.36iter/s]Best score: 0.2937: : 214iter [00:35,  6.38iter/s]Best score: 0.2937: : 214iter [00:36,  6.38iter/s]Best score: 0.2937: : 215iter [00:36,  6.40iter/s]Best score: 0.2937: : 215iter [00:36,  6.40iter/s]Best score: 0.2937: : 216iter [00:36,  6.41iter/s]Best score: 0.2937: : 216iter [00:36,  6.41iter/s]Best score: 0.2937: : 217iter [00:36,  6.40iter/s]Best score: 0.2937: : 217iter [00:36,  6.40iter/s]Best score: 0.2937: : 218iter [00:36,  6.42iter/s]Best score: 0.2937: : 218iter [00:36,  6.42iter/s]Best score: 0.2937: : 219iter [00:36,  6.43iter/s]Best score: 0.2888: : 219iter [00:36,  6.43iter/s]Best score: 0.2888: : 220iter [00:36,  6.24iter/s]Best score: 0.2888: : 220iter [00:37,  6.24iter/s]Best score: 0.2888: : 221iter [00:37,  6.31iter/s]Best score: 0.2888: : 221iter [00:37,  6.31iter/s]Best score: 0.2888: : 222iter [00:37,  6.35iter/s]Best score: 0.2888: : 222iter [00:37,  6.35iter/s]Best score: 0.2888: : 223iter [00:37,  6.38iter/s]Best score: 0.2888: : 223iter [00:37,  6.38iter/s]Best score: 0.2888: : 224iter [00:37,  6.40iter/s]Best score: 0.2888: : 224iter [00:37,  6.40iter/s]Best score: 0.2888: : 225iter [00:37,  6.41iter/s]Best score: 0.2888: : 225iter [00:37,  6.41iter/s]Best score: 0.2888: : 226iter [00:37,  6.43iter/s]Best score: 0.2888: : 226iter [00:37,  6.43iter/s]Best score: 0.2888: : 227iter [00:37,  6.43iter/s]Best score: 0.2888: : 227iter [00:38,  6.43iter/s]Best score: 0.2888: : 228iter [00:38,  6.43iter/s]Best score: 0.2888: : 228iter [00:38,  6.43iter/s]Best score: 0.2888: : 229iter [00:38,  6.44iter/s]Best score: 0.2772: : 229iter [00:38,  6.44iter/s]Best score: 0.2772: : 230iter [00:38,  6.25iter/s]Best score: 0.2772: : 230iter [00:38,  6.25iter/s]Best score: 0.2772: : 231iter [00:38,  6.31iter/s]Best score: 0.2772: : 231iter [00:38,  6.31iter/s]Best score: 0.2772: : 232iter [00:38,  6.34iter/s]Best score: 0.2772: : 232iter [00:38,  6.34iter/s]Best score: 0.2772: : 233iter [00:38,  6.37iter/s]Best score: 0.2772: : 233iter [00:39,  6.37iter/s]Best score: 0.2772: : 234iter [00:39,  6.39iter/s]Best score: 0.2772: : 234iter [00:39,  6.39iter/s]Best score: 0.2772: : 235iter [00:39,  6.40iter/s]Best score: 0.2772: : 235iter [00:39,  6.40iter/s]Best score: 0.2772: : 236iter [00:39,  6.42iter/s]Best score: 0.2772: : 236iter [00:39,  6.42iter/s]Best score: 0.2772: : 237iter [00:39,  6.42iter/s]Best score: 0.2772: : 237iter [00:39,  6.42iter/s]Best score: 0.2772: : 238iter [00:39,  6.43iter/s]Best score: 0.2772: : 238iter [00:39,  6.43iter/s]Best score: 0.2772: : 239iter [00:39,  6.43iter/s]Best score: 0.2772: : 239iter [00:40,  6.43iter/s]Best score: 0.2772: : 240iter [00:40,  6.23iter/s]Best score: 0.2772: : 240iter [00:40,  6.23iter/s]Best score: 0.2772: : 241iter [00:40,  6.29iter/s]Best score: 0.2772: : 241iter [00:40,  6.29iter/s]Best score: 0.2772: : 242iter [00:40,  6.35iter/s]Best score: 0.2772: : 242iter [00:40,  6.35iter/s]Best score: 0.2772: : 243iter [00:40,  6.38iter/s]Best score: 0.2772: : 243iter [00:40,  6.38iter/s]Best score: 0.2772: : 244iter [00:40,  6.41iter/s]Best score: 0.2772: : 244iter [00:40,  6.41iter/s]Best score: 0.2772: : 245iter [00:40,  6.42iter/s]Best score: 0.2772: : 245iter [00:40,  6.42iter/s]Best score: 0.2772: : 246iter [00:40,  6.42iter/s]Best score: 0.2772: : 246iter [00:41,  6.42iter/s]Best score: 0.2772: : 247iter [00:41,  6.43iter/s]Best score: 0.2772: : 247iter [00:41,  6.43iter/s]Best score: 0.2772: : 248iter [00:41,  6.43iter/s]Best score: 0.2772: : 248iter [00:41,  6.43iter/s]Best score: 0.2772: : 249iter [00:41,  6.44iter/s]
[ITER 250] Best: 0.2772
[PARAMS] Current best parameters:
  fx: 571.4325
  fy: 570.9690
  px: 1016.6775
  py: 728.1625
  k1: -0.04380405
  k2: 0.00054552
  k3: 0.00540395
  k4: -0.00169871
  p1: 0.00474799
  p2: 0.00487684
  p3: -0.00326463
  p4: 0.00216206
  scale: 3.0055
  rotation: -0.2194
Best score: 0.2772: : 249iter [00:41,  6.44iter/s]Best score: 0.2772: : 250iter [00:41,  6.24iter/s]Best score: 0.2772: : 250iter [00:41,  6.24iter/s]Best score: 0.2772: : 251iter [00:41,  6.31iter/s]Best score: 0.2772: : 251iter [00:41,  6.31iter/s]Best score: 0.2772: : 252iter [00:41,  6.35iter/s]Best score: 0.2772: : 252iter [00:42,  6.35iter/s]Best score: 0.2772: : 253iter [00:42,  6.38iter/s]Best score: 0.2772: : 253iter [00:42,  6.38iter/s]Best score: 0.2772: : 254iter [00:42,  6.40iter/s]Best score: 0.2772: : 254iter [00:42,  6.40iter/s]Best score: 0.2772: : 255iter [00:42,  6.41iter/s]Best score: 0.2772: : 255iter [00:42,  6.41iter/s]Best score: 0.2772: : 256iter [00:42,  6.42iter/s]Best score: 0.2772: : 256iter [00:42,  6.42iter/s]Best score: 0.2772: : 257iter [00:42,  6.42iter/s]Best score: 0.2772: : 257iter [00:42,  6.42iter/s]Best score: 0.2772: : 258iter [00:42,  6.42iter/s]Best score: 0.2772: : 258iter [00:42,  6.42iter/s]Best score: 0.2772: : 259iter [00:42,  6.42iter/s]Best score: 0.2747: : 259iter [00:43,  6.42iter/s]Best score: 0.2747: : 260iter [00:43,  6.22iter/s]Best score: 0.2747: : 260iter [00:43,  6.22iter/s]Best score: 0.2747: : 261iter [00:43,  6.29iter/s]Best score: 0.2747: : 261iter [00:43,  6.29iter/s]Best score: 0.2747: : 262iter [00:43,  6.33iter/s]Best score: 0.2747: : 262iter [00:43,  6.33iter/s]Best score: 0.2747: : 263iter [00:43,  6.36iter/s]Best score: 0.2747: : 263iter [00:43,  6.36iter/s]Best score: 0.2747: : 264iter [00:43,  6.38iter/s]Best score: 0.2747: : 264iter [00:43,  6.38iter/s]Best score: 0.2747: : 265iter [00:43,  6.39iter/s]Best score: 0.2747: : 265iter [00:44,  6.39iter/s]Best score: 0.2747: : 266iter [00:44,  6.40iter/s]Best score: 0.2747: : 266iter [00:44,  6.40iter/s]Best score: 0.2747: : 267iter [00:44,  6.41iter/s]Best score: 0.2747: : 267iter [00:44,  6.41iter/s]Best score: 0.2747: : 268iter [00:44,  6.41iter/s]Best score: 0.2747: : 268iter [00:44,  6.41iter/s]Best score: 0.2747: : 269iter [00:44,  6.41iter/s]Best score: 0.2730: : 269iter [00:44,  6.41iter/s]Best score: 0.2730: : 270iter [00:44,  6.22iter/s]Best score: 0.2730: : 270iter [00:44,  6.22iter/s]Best score: 0.2730: : 271iter [00:44,  6.27iter/s]Best score: 0.2730: : 271iter [00:45,  6.27iter/s]Best score: 0.2730: : 272iter [00:45,  6.32iter/s]Best score: 0.2730: : 272iter [00:45,  6.32iter/s]Best score: 0.2730: : 273iter [00:45,  6.35iter/s]Best score: 0.2730: : 273iter [00:45,  6.35iter/s]Best score: 0.2730: : 274iter [00:45,  6.37iter/s]Best score: 0.2730: : 274iter [00:45,  6.37iter/s]Best score: 0.2730: : 275iter [00:45,  6.38iter/s]Best score: 0.2730: : 275iter [00:45,  6.38iter/s]Best score: 0.2730: : 276iter [00:45,  6.40iter/s]Best score: 0.2730: : 276iter [00:45,  6.40iter/s]Best score: 0.2730: : 277iter [00:45,  6.40iter/s]Best score: 0.2730: : 277iter [00:45,  6.40iter/s]Best score: 0.2730: : 278iter [00:45,  6.41iter/s]Best score: 0.2730: : 278iter [00:46,  6.41iter/s]Best score: 0.2730: : 279iter [00:46,  6.41iter/s]Best score: 0.2724: : 279iter [00:46,  6.41iter/s]Best score: 0.2724: : 280iter [00:46,  6.22iter/s]Best score: 0.2724: : 280iter [00:46,  6.22iter/s]Best score: 0.2724: : 281iter [00:46,  6.28iter/s]Best score: 0.2724: : 281iter [00:46,  6.28iter/s]Best score: 0.2724: : 282iter [00:46,  6.33iter/s]Best score: 0.2724: : 282iter [00:46,  6.33iter/s]Best score: 0.2724: : 283iter [00:46,  6.36iter/s]Best score: 0.2724: : 283iter [00:46,  6.36iter/s]Best score: 0.2724: : 284iter [00:46,  6.38iter/s]Best score: 0.2724: : 284iter [00:47,  6.38iter/s]Best score: 0.2724: : 285iter [00:47,  6.39iter/s]Best score: 0.2724: : 285iter [00:47,  6.39iter/s]Best score: 0.2724: : 286iter [00:47,  6.40iter/s]Best score: 0.2724: : 286iter [00:47,  6.40iter/s]Best score: 0.2724: : 287iter [00:47,  6.41iter/s]Best score: 0.2724: : 287iter [00:47,  6.41iter/s]Best score: 0.2724: : 288iter [00:47,  6.42iter/s]Best score: 0.2724: : 288iter [00:47,  6.42iter/s]Best score: 0.2724: : 289iter [00:47,  6.43iter/s]Best score: 0.2723: : 289iter [00:47,  6.43iter/s]Best score: 0.2723: : 290iter [00:47,  6.22iter/s]Best score: 0.2723: : 290iter [00:48,  6.22iter/s]Best score: 0.2723: : 291iter [00:48,  6.28iter/s]Best score: 0.2723: : 291iter [00:48,  6.28iter/s]Best score: 0.2723: : 292iter [00:48,  6.33iter/s]Best score: 0.2723: : 292iter [00:48,  6.33iter/s]Best score: 0.2723: : 293iter [00:48,  6.36iter/s]Best score: 0.2723: : 293iter [00:48,  6.36iter/s]Best score: 0.2723: : 294iter [00:48,  6.39iter/s]Best score: 0.2723: : 294iter [00:48,  6.39iter/s]Best score: 0.2723: : 295iter [00:48,  6.40iter/s]Best score: 0.2723: : 295iter [00:48,  6.40iter/s]Best score: 0.2723: : 296iter [00:48,  6.40iter/s]Best score: 0.2723: : 296iter [00:48,  6.40iter/s]Best score: 0.2723: : 297iter [00:48,  6.42iter/s]Best score: 0.2723: : 297iter [00:49,  6.42iter/s]Best score: 0.2723: : 298iter [00:49,  6.42iter/s]Best score: 0.2723: : 298iter [00:49,  6.42iter/s]Best score: 0.2723: : 299iter [00:49,  6.42iter/s]
[ITER 300] Best: 0.2716
[PARAMS] Current best parameters:
  fx: 570.7527
  fy: 570.2622
  px: 1016.7695
  py: 728.3953
  k1: -0.03885263
  k2: -0.00409383
  k3: 0.00656705
  k4: -0.00161547
  p1: 0.00508293
  p2: 0.00513837
  p3: -0.00313278
  p4: 0.00236292
  scale: 2.9999
  rotation: -0.2404
Best score: 0.2716: : 299iter [00:49,  6.42iter/s]Best score: 0.2716: : 300iter [00:49,  6.23iter/s]Best score: 0.2716: : 300iter [00:49,  6.23iter/s]Best score: 0.2716: : 301iter [00:49,  6.29iter/s]Best score: 0.2716: : 301iter [00:49,  6.29iter/s]Best score: 0.2716: : 302iter [00:49,  6.33iter/s]Best score: 0.2716: : 302iter [00:49,  6.33iter/s]Best score: 0.2716: : 303iter [00:49,  6.36iter/s]Best score: 0.2716: : 303iter [00:50,  6.36iter/s]Best score: 0.2716: : 304iter [00:50,  6.38iter/s]Best score: 0.2716: : 304iter [00:50,  6.38iter/s]Best score: 0.2716: : 305iter [00:50,  6.38iter/s]Best score: 0.2716: : 305iter [00:50,  6.38iter/s]Best score: 0.2716: : 306iter [00:50,  6.39iter/s]Best score: 0.2716: : 306iter [00:50,  6.39iter/s]Best score: 0.2716: : 307iter [00:50,  6.40iter/s]Best score: 0.2716: : 307iter [00:50,  6.40iter/s]Best score: 0.2716: : 308iter [00:50,  6.40iter/s]Best score: 0.2716: : 308iter [00:50,  6.40iter/s]Best score: 0.2716: : 309iter [00:50,  6.41iter/s]Best score: 0.2712: : 309iter [00:51,  6.41iter/s]Best score: 0.2712: : 310iter [00:51,  6.21iter/s]Best score: 0.2712: : 310iter [00:51,  6.21iter/s]Best score: 0.2712: : 311iter [00:51,  6.28iter/s]Best score: 0.2712: : 311iter [00:51,  6.28iter/s]Best score: 0.2712: : 312iter [00:51,  6.32iter/s]Best score: 0.2712: : 312iter [00:51,  6.32iter/s]Best score: 0.2712: : 313iter [00:51,  6.35iter/s]Best score: 0.2712: : 313iter [00:51,  6.35iter/s]Best score: 0.2712: : 314iter [00:51,  6.37iter/s]Best score: 0.2712: : 314iter [00:51,  6.37iter/s]Best score: 0.2712: : 315iter [00:51,  6.39iter/s]Best score: 0.2712: : 315iter [00:51,  6.39iter/s]Best score: 0.2712: : 316iter [00:51,  6.39iter/s]Best score: 0.2712: : 316iter [00:52,  6.39iter/s]Best score: 0.2712: : 317iter [00:52,  6.40iter/s]Best score: 0.2712: : 317iter [00:52,  6.40iter/s]Best score: 0.2712: : 318iter [00:52,  6.41iter/s]Best score: 0.2712: : 318iter [00:52,  6.41iter/s]Best score: 0.2712: : 319iter [00:52,  6.42iter/s]Best score: 0.2711: : 319iter [00:52,  6.42iter/s]Best score: 0.2711: : 320iter [00:52,  6.23iter/s]Best score: 0.2711: : 320iter [00:52,  6.23iter/s]Best score: 0.2711: : 321iter [00:52,  6.29iter/s]Best score: 0.2711: : 321iter [00:52,  6.29iter/s]Best score: 0.2711: : 322iter [00:52,  6.32iter/s]Best score: 0.2711: : 322iter [00:53,  6.32iter/s]Best score: 0.2711: : 323iter [00:53,  6.36iter/s]Best score: 0.2711: : 323iter [00:53,  6.36iter/s]Best score: 0.2711: : 324iter [00:53,  6.37iter/s]Best score: 0.2711: : 324iter [00:53,  6.37iter/s]Best score: 0.2711: : 325iter [00:53,  6.38iter/s]Best score: 0.2711: : 325iter [00:53,  6.38iter/s]Best score: 0.2711: : 326iter [00:53,  6.39iter/s]Best score: 0.2711: : 326iter [00:53,  6.39iter/s]Best score: 0.2711: : 327iter [00:53,  6.40iter/s]Best score: 0.2711: : 327iter [00:53,  6.40iter/s]Best score: 0.2711: : 328iter [00:53,  6.41iter/s]Best score: 0.2711: : 328iter [00:53,  6.41iter/s]Best score: 0.2711: : 329iter [00:53,  6.42iter/s]Best score: 0.2568: : 329iter [00:54,  6.42iter/s]Best score: 0.2568: : 330iter [00:54,  6.23iter/s]Best score: 0.2568: : 330iter [00:54,  6.23iter/s]Best score: 0.2568: : 331iter [00:54,  6.29iter/s]Best score: 0.2568: : 331iter [00:54,  6.29iter/s]Best score: 0.2568: : 332iter [00:54,  6.33iter/s]Best score: 0.2568: : 332iter [00:54,  6.33iter/s]Best score: 0.2568: : 333iter [00:54,  6.37iter/s]Best score: 0.2568: : 333iter [00:54,  6.37iter/s]Best score: 0.2568: : 334iter [00:54,  6.38iter/s]Best score: 0.2568: : 334iter [00:54,  6.38iter/s]Best score: 0.2568: : 335iter [00:54,  6.41iter/s]Best score: 0.2568: : 335iter [00:55,  6.41iter/s]Best score: 0.2568: : 336iter [00:55,  6.42iter/s]Best score: 0.2568: : 336iter [00:55,  6.42iter/s]Best score: 0.2568: : 337iter [00:55,  6.42iter/s]Best score: 0.2568: : 337iter [00:55,  6.42iter/s]Best score: 0.2568: : 338iter [00:55,  6.43iter/s]Best score: 0.2568: : 338iter [00:55,  6.43iter/s]Best score: 0.2568: : 339iter [00:55,  6.42iter/s]Best score: 0.2565: : 339iter [00:55,  6.42iter/s]Best score: 0.2565: : 340iter [00:55,  6.22iter/s]Best score: 0.2565: : 340iter [00:55,  6.22iter/s]Best score: 0.2565: : 341iter [00:55,  6.28iter/s]Best score: 0.2565: : 341iter [00:56,  6.28iter/s]Best score: 0.2565: : 342iter [00:56,  6.30iter/s]Best score: 0.2565: : 342iter [00:56,  6.30iter/s]Best score: 0.2565: : 343iter [00:56,  6.33iter/s]Best score: 0.2565: : 343iter [00:56,  6.33iter/s]Best score: 0.2565: : 344iter [00:56,  6.38iter/s]Best score: 0.2565: : 344iter [00:56,  6.38iter/s]Best score: 0.2565: : 345iter [00:56,  6.40iter/s]Best score: 0.2565: : 345iter [00:56,  6.40iter/s]Best score: 0.2565: : 346iter [00:56,  6.41iter/s]Best score: 0.2565: : 346iter [00:56,  6.41iter/s]Best score: 0.2565: : 347iter [00:56,  6.41iter/s]Best score: 0.2565: : 347iter [00:56,  6.41iter/s]Best score: 0.2565: : 348iter [00:56,  6.42iter/s]Best score: 0.2565: : 348iter [00:57,  6.42iter/s]Best score: 0.2565: : 349iter [00:57,  6.41iter/s]
[ITER 350] Best: 0.2559
[PARAMS] Current best parameters:
  fx: 557.0930
  fy: 556.6219
  px: 1010.7878
  py: 722.7745
  k1: -0.02161812
  k2: -0.00890703
  k3: 0.00721277
  k4: -0.00157364
  p1: -0.00904595
  p2: -0.00983832
  p3: 0.00459905
  p4: 0.00286025
  scale: 3.0012
  rotation: -0.2461
Best score: 0.2559: : 349iter [00:57,  6.41iter/s]Best score: 0.2559: : 350iter [00:57,  6.23iter/s]Best score: 0.2559: : 350iter [00:57,  6.23iter/s]Best score: 0.2559: : 351iter [00:57,  6.30iter/s]Best score: 0.2559: : 351iter [00:57,  6.30iter/s]Best score: 0.2559: : 352iter [00:57,  6.35iter/s]Best score: 0.2559: : 352iter [00:57,  6.35iter/s]Best score: 0.2559: : 353iter [00:57,  6.39iter/s]Best score: 0.2559: : 353iter [00:57,  6.39iter/s]Best score: 0.2559: : 354iter [00:57,  6.42iter/s]Best score: 0.2559: : 354iter [00:58,  6.42iter/s]Best score: 0.2559: : 355iter [00:58,  6.44iter/s]Best score: 0.2559: : 355iter [00:58,  6.44iter/s]Best score: 0.2559: : 356iter [00:58,  6.45iter/s]Best score: 0.2559: : 356iter [00:58,  6.45iter/s]Best score: 0.2559: : 357iter [00:58,  6.45iter/s]Best score: 0.2559: : 357iter [00:58,  6.45iter/s]Best score: 0.2559: : 358iter [00:58,  6.46iter/s]Best score: 0.2559: : 358iter [00:58,  6.46iter/s]Best score: 0.2559: : 359iter [00:58,  6.45iter/s]Best score: 0.2532: : 359iter [00:58,  6.45iter/s]Best score: 0.2532: : 360iter [00:58,  6.26iter/s]Best score: 0.2532: : 360iter [00:59,  6.26iter/s]Best score: 0.2532: : 361iter [00:59,  6.32iter/s]Best score: 0.2532: : 361iter [00:59,  6.32iter/s]Best score: 0.2532: : 362iter [00:59,  6.34iter/s]Best score: 0.2532: : 362iter [00:59,  6.34iter/s]Best score: 0.2532: : 363iter [00:59,  6.37iter/s]Best score: 0.2532: : 363iter [00:59,  6.37iter/s]Best score: 0.2532: : 364iter [00:59,  6.41iter/s]Best score: 0.2532: : 364iter [00:59,  6.41iter/s]Best score: 0.2532: : 365iter [00:59,  6.43iter/s]Best score: 0.2532: : 365iter [00:59,  6.43iter/s]Best score: 0.2532: : 366iter [00:59,  6.43iter/s]Best score: 0.2532: : 366iter [00:59,  6.43iter/s]Best score: 0.2532: : 367iter [00:59,  6.43iter/s]Best score: 0.2532: : 367iter [01:00,  6.43iter/s]Best score: 0.2532: : 368iter [01:00,  6.43iter/s]Best score: 0.2532: : 368iter [01:00,  6.43iter/s]Best score: 0.2532: : 369iter [01:00,  6.43iter/s]Best score: 0.2529: : 369iter [01:00,  6.43iter/s]Best score: 0.2529: : 370iter [01:00,  6.24iter/s]Best score: 0.2529: : 370iter [01:00,  6.24iter/s]Best score: 0.2529: : 371iter [01:00,  6.30iter/s]Best score: 0.2529: : 371iter [01:00,  6.30iter/s]Best score: 0.2529: : 372iter [01:00,  6.34iter/s]Best score: 0.2529: : 372iter [01:00,  6.34iter/s]Best score: 0.2529: : 373iter [01:00,  6.36iter/s]Best score: 0.2529: : 373iter [01:01,  6.36iter/s]Best score: 0.2529: : 374iter [01:01,  6.38iter/s]Best score: 0.2529: : 374iter [01:01,  6.38iter/s]Best score: 0.2529: : 375iter [01:01,  6.39iter/s]Best score: 0.2529: : 375iter [01:01,  6.39iter/s]Best score: 0.2529: : 376iter [01:01,  6.40iter/s]Best score: 0.2529: : 376iter [01:01,  6.40iter/s]Best score: 0.2529: : 377iter [01:01,  6.41iter/s]Best score: 0.2529: : 377iter [01:01,  6.41iter/s]Best score: 0.2529: : 378iter [01:01,  6.41iter/s]Best score: 0.2529: : 378iter [01:01,  6.41iter/s]Best score: 0.2529: : 379iter [01:01,  6.41iter/s]Best score: 0.2518: : 379iter [01:02,  6.41iter/s]Best score: 0.2518: : 380iter [01:02,  6.21iter/s]Best score: 0.2518: : 380iter [01:02,  6.21iter/s]Best score: 0.2518: : 381iter [01:02,  6.28iter/s]Best score: 0.2518: : 381iter [01:02,  6.28iter/s]Best score: 0.2518: : 382iter [01:02,  6.32iter/s]Best score: 0.2518: : 382iter [01:02,  6.32iter/s]Best score: 0.2518: : 383iter [01:02,  6.36iter/s]Best score: 0.2518: : 383iter [01:02,  6.36iter/s]Best score: 0.2518: : 384iter [01:02,  6.38iter/s]Best score: 0.2518: : 384iter [01:02,  6.38iter/s]Best score: 0.2518: : 385iter [01:02,  6.39iter/s]Best score: 0.2518: : 385iter [01:02,  6.39iter/s]Best score: 0.2518: : 386iter [01:02,  6.40iter/s]Best score: 0.2518: : 386iter [01:03,  6.40iter/s]Best score: 0.2518: : 387iter [01:03,  6.41iter/s]Best score: 0.2518: : 387iter [01:03,  6.41iter/s]Best score: 0.2518: : 388iter [01:03,  6.42iter/s]Best score: 0.2518: : 388iter [01:03,  6.42iter/s]Best score: 0.2518: : 389iter [01:03,  6.43iter/s]Best score: 0.2510: : 389iter [01:03,  6.43iter/s]Best score: 0.2510: : 390iter [01:03,  6.23iter/s]Best score: 0.2510: : 390iter [01:03,  6.23iter/s]Best score: 0.2510: : 391iter [01:03,  6.29iter/s]Best score: 0.2510: : 391iter [01:03,  6.29iter/s]Best score: 0.2510: : 392iter [01:03,  6.33iter/s]Best score: 0.2510: : 392iter [01:04,  6.33iter/s]Best score: 0.2510: : 393iter [01:04,  6.38iter/s]Best score: 0.2510: : 393iter [01:04,  6.38iter/s]Best score: 0.2510: : 394iter [01:04,  6.40iter/s]Best score: 0.2510: : 394iter [01:04,  6.40iter/s]Best score: 0.2510: : 395iter [01:04,  6.42iter/s]Best score: 0.2510: : 395iter [01:04,  6.42iter/s]Best score: 0.2510: : 396iter [01:04,  6.44iter/s]Best score: 0.2510: : 396iter [01:04,  6.44iter/s]Best score: 0.2510: : 397iter [01:04,  6.44iter/s]Best score: 0.2510: : 397iter [01:04,  6.44iter/s]Best score: 0.2510: : 398iter [01:04,  6.40iter/s]Best score: 0.2510: : 398iter [01:04,  6.40iter/s]Best score: 0.2510: : 399iter [01:04,  6.41iter/s]
[ITER 400] Best: 0.2503
[PARAMS] Current best parameters:
  fx: 553.4260
  fy: 552.9313
  px: 1010.8187
  py: 722.3269
  k1: -0.01972191
  k2: -0.00740855
  k3: 0.00768127
  k4: -0.00220369
  p1: -0.00993200
  p2: -0.00961739
  p3: 0.00548460
  p4: 0.00148030
  scale: 3.0042
  rotation: -0.2457
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[01:12,  6.42iter/s]Best score: 0.2494: : 448iter [01:12,  6.42iter/s]Best score: 0.2494: : 449iter [01:12,  6.42iter/s]
[ITER 450] Best: 0.2493
[PARAMS] Current best parameters:
  fx: 553.9498
  fy: 553.4781
  px: 1010.6557
  py: 722.3608
  k1: -0.02490519
  k2: 0.00209962
  k3: 0.00073343
  k4: -0.00043660
  p1: -0.00994773
  p2: -0.00999875
  p3: 0.00506745
  p4: 0.00204016
  scale: 3.0064
  rotation: -0.2366
Best score: 0.2493: : 449iter [01:12,  6.42iter/s]Best score: 0.2493: : 450iter [01:12,  6.23iter/s]Best score: 0.2493: : 450iter [01:13,  6.23iter/s]Best score: 0.2493: : 451iter [01:13,  6.29iter/s]Best score: 0.2493: : 451iter [01:13,  6.29iter/s]Best score: 0.2493: : 452iter [01:13,  6.33iter/s]Best score: 0.2493: : 452iter [01:13,  6.33iter/s]Best score: 0.2493: : 453iter [01:13,  6.38iter/s]Best score: 0.2493: : 453iter [01:13,  6.38iter/s]Best score: 0.2493: : 454iter [01:13,  6.41iter/s]Best score: 0.2493: : 454iter [01:13,  6.41iter/s]Best score: 0.2493: : 455iter [01:13,  6.43iter/s]Best score: 0.2493: : 455iter [01:13,  6.43iter/s]Best score: 0.2493: : 456iter [01:13,  6.44iter/s]Best score: 0.2493: : 456iter [01:14,  6.44iter/s]Best score: 0.2493: : 457iter [01:14,  6.44iter/s]Best score: 0.2493: : 457iter [01:14,  6.44iter/s]Best score: 0.2493: : 458iter [01:14,  6.44iter/s]Best score: 0.2493: : 458iter [01:14,  6.44iter/s]Best score: 0.2493: : 459iter [01:14,  6.43iter/s]Best score: 0.2490: : 459iter [01:14,  6.43iter/s]Best score: 0.2490: : 460iter [01:14,  6.23iter/s]Best score: 0.2490: : 460iter [01:14,  6.23iter/s]Best score: 0.2490: : 461iter [01:14,  6.29iter/s]Best score: 0.2490: : 461iter [01:14,  6.29iter/s]Best score: 0.2490: : 462iter [01:14,  6.33iter/s]Best score: 0.2490: : 462iter [01:15,  6.33iter/s]Best score: 0.2490: : 463iter [01:15,  6.38iter/s]Best score: 0.2490: : 463iter [01:15,  6.38iter/s]Best score: 0.2490: : 464iter [01:15,  6.40iter/s]Best score: 0.2490: : 464iter [01:15,  6.40iter/s]Best score: 0.2490: : 465iter [01:15,  6.41iter/s]Best score: 0.2490: : 465iter [01:15,  6.41iter/s]Best score: 0.2490: : 466iter [01:15,  6.41iter/s]Best score: 0.2490: : 466iter [01:15,  6.41iter/s]Best score: 0.2490: : 467iter [01:15,  6.35iter/s]Best score: 0.2490: : 467iter [01:15,  6.35iter/s]Best score: 0.2490: : 468iter [01:15,  6.38iter/s]Best score: 0.2490: : 468iter [01:15,  6.38iter/s]Best 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score: 0.2485: : 488iter [01:19,  6.42iter/s]Best score: 0.2485: : 489iter [01:19,  6.42iter/s]Best score: 0.2485: : 489iter [01:19,  6.42iter/s]Best score: 0.2485: : 490iter [01:19,  6.22iter/s]Best score: 0.2485: : 490iter [01:19,  6.22iter/s]Best score: 0.2485: : 491iter [01:19,  6.30iter/s]Best score: 0.2485: : 491iter [01:19,  6.30iter/s]Best score: 0.2485: : 492iter [01:19,  6.35iter/s]Best score: 0.2485: : 492iter [01:19,  6.35iter/s]Best score: 0.2485: : 493iter [01:19,  6.39iter/s]Best score: 0.2485: : 493iter [01:19,  6.39iter/s]Best score: 0.2485: : 494iter [01:19,  6.40iter/s]Best score: 0.2485: : 494iter [01:20,  6.40iter/s]Best score: 0.2485: : 495iter [01:20,  6.42iter/s]Best score: 0.2485: : 495iter [01:20,  6.42iter/s]Best score: 0.2485: : 496iter [01:20,  6.42iter/s]Best score: 0.2485: : 496iter [01:20,  6.42iter/s]Best score: 0.2485: : 497iter [01:20,  6.43iter/s]Best score: 0.2485: : 497iter [01:20,  6.43iter/s]Best score: 0.2485: : 498iter [01:20,  6.44iter/s]Best score: 0.2485: : 498iter [01:20,  6.44iter/s]Best score: 0.2485: : 499iter [01:20,  6.44iter/s]
[ITER 500] Best: 0.2485
[PARAMS] Current best parameters:
  fx: 550.5223
  fy: 550.0452
  px: 1010.6260
  py: 722.3068
  k1: -0.01871319
  k2: -0.00190319
  k3: 0.00260744
  k4: -0.00081663
  p1: -0.00996413
  p2: -0.00998750
  p3: 0.00512901
  p4: 0.00194217
  scale: 3.0048
  rotation: -0.2416
Best score: 0.2485: : 499iter [01:20,  6.44iter/s]Best score: 0.2485: : 500iter [01:20,  6.25iter/s]Best score: 0.2485: : 500iter [01:20,  6.18iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 0.248537 -> 0.248456
  Success: True
  Tangential distortion: p1=-0.009964, p2=-0.009995, p3=0.005134, p4=0.001931
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2485
[✅] Parameters copied to: /working/MDC048_W021(6)_2025-11-19/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 550.5223367978716
    fy: 550.0451774975531
    px: 1010.6259555354975
    py: 722.3068240506868
    k1: -0.018702597243452582
    k2: -0.0019278930402080359
    k3: 0.002588059992166993
    k4: -0.0008006214697854765
    p1: -0.009963637742765052
    p2: -0.009994808684500008
    p3: 0.005134184484213973
    p4: 0.0019312129804565363
    scale: 3.0049174259743356
    rotation: -0.24155571178177107
    mmpp: 3.4444505400934537
    nmpp: 3444450.5400934536

[🔄] Converting 720 frames to PTD, creating debug images for 400 calibration frames...
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                                                                 [A📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.02rig/s]
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                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.08rig/s]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.06rig/s]
[✅] Saved PTDs: /working/MDC048_W021(6)_2025-11-19/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%|▎         | 82805/3080448 [00:00<00:03, 828038.94px/s]📤 Writing LUT:   6%|▌         | 175614/3080448 [00:00<00:03, 886891.44px/s]📤 Writing LUT:   9%|▊         | 269079/3080448 [00:00<00:03, 908695.36px/s]📤 Writing LUT:  12%|█▏        | 362427/3080448 [00:00<00:02, 918476.90px/s]📤 Writing LUT:  15%|█▍        | 455280/3080448 [00:00<00:02, 922098.46px/s]📤 Writing LUT:  18%|█▊        | 548781/3080448 [00:00<00:02, 926484.91px/s]📤 Writing LUT:  21%|██        | 642310/3080448 [00:00<00:02, 929361.60px/s]📤 Writing LUT:  24%|██▍       | 737281/3080448 [00:00<00:02, 935836.97px/s]📤 Writing LUT:  27%|██▋       | 830865/3080448 [00:00<00:02, 933149.38px/s]📤 Writing LUT:  30%|███       | 925070/3080448 [00:01<00:02, 935888.55px/s]📤 Writing LUT:  33%|███▎      | 1018661/3080448 [00:01<00:02, 934572.20px/s]📤 Writing LUT:  36%|███▌      | 1114407/3080448 [00:01<00:02, 941520.26px/s]📤 Writing LUT:  39%|███▉      | 1208562/3080448 [00:01<00:01, 939866.64px/s]📤 Writing LUT:  42%|████▏     | 1302551/3080448 [00:01<00:01, 933237.74px/s]📤 Writing LUT:  45%|████▌     | 1398316/3080448 [00:01<00:01, 940547.04px/s]📤 Writing LUT:  48%|████▊     | 1492383/3080448 [00:01<00:01, 938297.05px/s]📤 Writing LUT:  52%|█████▏    | 1586560/3080448 [00:01<00:01, 939335.21px/s]📤 Writing LUT:  55%|█████▍    | 1681363/3080448 [00:01<00:01, 941936.57px/s]📤 Writing LUT:  58%|█████▊    | 1775563/3080448 [00:01<00:01, 940420.11px/s]📤 Writing LUT:  61%|██████    | 1869610/3080448 [00:02<00:01, 937544.24px/s]📤 Writing LUT:  64%|██████▎   | 1963369/3080448 [00:02<00:01, 933392.49px/s]📤 Writing LUT:  67%|██████▋   | 2057892/3080448 [00:02<00:01, 936920.09px/s]📤 Writing LUT:  70%|██████▉   | 2151590/3080448 [00:02<00:00, 933578.08px/s]📤 Writing LUT:  73%|███████▎  | 2244954/3080448 [00:02<00:00, 928638.81px/s]📤 Writing LUT:  76%|███████▌  | 2340408/3080448 [00:02<00:00, 936351.21px/s]📤 Writing LUT:  79%|███████▉  | 2434500/3080448 [00:02<00:00, 937713.65px/s]📤 Writing LUT:  82%|████████▏ | 2528280/3080448 [00:02<00:00, 930695.25px/s]📤 Writing LUT:  85%|████████▌ | 2623279/3080448 [00:02<00:00, 936438.03px/s]📤 Writing LUT:  88%|████████▊ | 2716939/3080448 [00:02<00:00, 931518.96px/s]📤 Writing LUT:  91%|█████████ | 2810106/3080448 [00:03<00:00, 928300.39px/s]📤 Writing LUT:  94%|█████████▍| 2902947/3080448 [00:03<00:00, 926336.66px/s]📤 Writing LUT:  97%|█████████▋| 2998056/3080448 [00:03<00:00, 933708.75px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 931558.30px/s]
[✅] LUT written to: /working/MDC048_W021(6)_2025-11-19/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'MDC048_W021(6)_2025-11-19' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC048_W021(6)_2025-11-19/calibration/MDC048_W021(6)_2025-11-19_unknown.lut

-------------------------------------------------------------------
                          Step 4: Heatmap                          
-------------------------------------------------------------------

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                                                                 [A[✅] 1500: /working/MDC048_W021(6)_2025-11-19/calibration/debug/4_heatmap/1500_heatmap.png
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                                                                  [A[✅] 2000R: /working/MDC048_W021(6)_2025-11-19/calibration/debug/4_heatmap/2000R_heatmap.png
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                                                                  [A[✅] 2000L: /working/MDC048_W021(6)_2025-11-19/calibration/debug/4_heatmap/2000L_heatmap.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:07<00:02,  2.62s/rig]
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                                                                 [A[✅] 1000: /working/MDC048_W021(6)_2025-11-19/calibration/debug/4_heatmap/1000_heatmap.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:09<00:00,  2.18s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:09<00:00,  2.35s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/MDC048_W021(6)_2025-11-19/calibration/debug/4_heatmap
[🏁] Finished processing MDC048_W021(6)_2025-11-19


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