
🟢 Starting Calibration Pipeline

=== Processing STLC-SMD-083WF_W028(6)_2026-02-04 ===

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process
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                                                                          [A[PASS2] Summary: 0 frames fixed, 135 untrusted, 197 total

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                                                                           [AProcessing Rigs:  25%|██▌       | 1/4 [01:50<05:32, 110.88s/it][INFO] Filtered 341 stationary frames, 334 remain

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                                                                          [A[PASS2] Frame 0000720: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000721: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000722: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000723: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000721: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000722: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 3 frames fixed, 244 untrusted, 333 total

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                                                                           [AProcessing Rigs:  50%|█████     | 2/4 [04:24<04:31, 135.74s/it][INFO] Filtered 588 stationary frames, 576 remain

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                                                                           [A[PASS2] Frame 0000700: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000737: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0000940: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0001249: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000700: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000940: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0001247: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Frame 0001249: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Frame 0000700: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000725: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000940: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0001249: fixed corners {0} (was_fixed=True, trusted=False)
[PASS2] Summary: 3 frames fixed, 505 untrusted, 575 total

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                                                                            [AProcessing Rigs:  75%|███████▌  | 3/4 [07:20<02:34, 154.19s/it][INFO] Filtered 309 stationary frames, 366 remain

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                                                                           [A[PASS2] Frame 0000752: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 296 untrusted, 365 total

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                                                                            [AProcessing Rigs: 100%|██████████| 4/4 [09:41<00:00, 149.22s/it]Processing Rigs: 100%|██████████| 4/4 [09:41<00:00, 145.45s/it]
[💾] Saved RigFrameDataset to /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1470 frames from 4 rigs to /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 197
[📋] 1500: 100 frames selected from 333
[📋] 2000R: 100 frames selected from 575
[📋] 2000L: 100 frames selected from 365
[💾] Saved RigFrameDataset to /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:  12%|█▎        | 10/80 [00:00<00:00, 95.02it/s]Computing max image from bright frames:  26%|██▋       | 21/80 [00:00<00:00, 101.92it/s]Computing max image from bright frames:  40%|████      | 32/80 [00:00<00:00, 105.27it/s]Computing max image from bright frames:  55%|█████▌    | 44/80 [00:00<00:00, 109.59it/s]Computing max image from bright frames:  69%|██████▉   | 55/80 [00:00<00:00, 105.90it/s]Computing max image from bright frames:  84%|████████▍ | 67/80 [00:00<00:00, 107.85it/s]Computing max image from bright frames:  98%|█████████▊| 78/80 [00:00<00:00, 106.35it/s]                                                                                        [INFO] Computed max image from 80 bright frames
[INFO] Corner median intensity: 0, black threshold: 0
[INFO] Saved casing detection debug images to /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/2_calibration/casing_detection
[INFO] Found 3065 casing boundary candidate points
[RESULT] Detected casing: center=(1007, 593), radius=771, inliers=496/3065
  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:18,  5.35it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:18,  5.35it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.001 | Best: {'fx': 314.141} (0.001):   2%|▏         | 2/100 [00:00<00:18,  5.35it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.003 | Best: {'fx': 321.212} (0.003):   3%|▎         | 3/100 [00:00<00:18,  5.35it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   4%|▍         | 4/100 [00:00<00:17,  5.35it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   5%|▌         | 5/100 [00:00<00:17,  5.35it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   6%|▌         | 6/100 [00:00<00:03, 24.23it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.003 | Best: {'fx': 328.283} (0.003):   6%|▌         | 6/100 [00:00<00:03, 24.23it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.004 | Best: {'fx': 349.495} (0.004):   7%|▋         | 7/100 [00:00<00:03, 24.23it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.004 | Best: {'fx': 356.566} (0.004):   8%|▊         | 8/100 [00:00<00:03, 24.23it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.005 | Best: {'fx': 363.636} (0.005):   9%|▉         | 9/100 [00:00<00:03, 24.23it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.005 | Best: {'fx': 363.636} (0.005):  10%|█         | 10/100 [00:00<00:03, 29.16it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.007 | Best: {'fx': 377.778} (0.007):  10%|█         | 10/100 [00:00<00:03, 29.16it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.007 | Best: {'fx': 370.707} (0.007):  11%|█         | 11/100 [00:00<00:03, 29.16it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 398.99} (0.008):  12%|█▏        | 12/100 [00:00<00:03, 29.16it/s] Brute | Start: {'fx': 500.0} (0.013) | Current: 0.007 | Best: {'fx': 398.99} (0.008):  13%|█▎        | 13/100 [00:00<00:02, 29.16it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.007 | Best: {'fx': 398.99} (0.008):  14%|█▍        | 14/100 [00:00<00:02, 29.16it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.007 | Best: {'fx': 398.99} (0.008):  15%|█▌        | 15/100 [00:00<00:02, 34.10it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.007 | Best: {'fx': 398.99} (0.008):  15%|█▌        | 15/100 [00:00<00:02, 34.10it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 413.131} (0.008):  16%|█▌        | 16/100 [00:00<00:02, 34.10it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 420.202} (0.008):  17%|█▋        | 17/100 [00:00<00:02, 34.10it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 420.202} (0.008):  18%|█▊        | 18/100 [00:00<00:02, 34.10it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 420.202} (0.008):  19%|█▉        | 19/100 [00:00<00:02, 34.10it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 420.202} (0.008):  20%|██        | 20/100 [00:00<00:02, 37.55it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 420.202} (0.008):  20%|██        | 20/100 [00:00<00:02, 37.55it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.008 | Best: {'fx': 448.485} (0.008):  21%|██        | 21/100 [00:00<00:02, 37.55it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.009 | Best: {'fx': 455.556} (0.009):  22%|██▏       | 22/100 [00:00<00:02, 37.55it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.012 | Best: {'fx': 476.768} (0.012):  23%|██▎       | 23/100 [00:00<00:02, 37.55it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.012 | Best: {'fx': 476.768} (0.012):  24%|██▍       | 24/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.010 | Best: {'fx': 476.768} (0.012):  24%|██▍       | 24/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.012 | Best: {'fx': 490.909} (0.012):  25%|██▌       | 25/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.011 | Best: {'fx': 490.909} (0.012):  26%|██▌       | 26/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.013 | Best: {'fx': 497.98} (0.013):  27%|██▋       | 27/100 [00:00<00:02, 34.21it/s] Brute | Start: {'fx': 500.0} (0.013) | Current: 0.012 | Best: {'fx': 497.98} (0.013):  28%|██▊       | 28/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.013 | Best: {'fx': 505.051} (0.013):  29%|██▉       | 29/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.014 | Best: {'fx': 512.121} (0.014):  30%|███       | 30/100 [00:00<00:02, 34.21it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.014 | Best: {'fx': 512.121} (0.014):  31%|███       | 31/100 [00:00<00:01, 41.83it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.015 | Best: {'fx': 533.333} (0.015):  31%|███       | 31/100 [00:01<00:01, 41.83it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.018 | Best: {'fx': 540.404} (0.018):  32%|███▏      | 32/100 [00:01<00:01, 41.83it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.014 | Best: {'fx': 540.404} (0.018):  33%|███▎      | 33/100 [00:01<00:01, 41.83it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.015 | Best: {'fx': 540.404} (0.018):  34%|███▍      | 34/100 [00:01<00:01, 41.83it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.021 | Best: {'fx': 568.687} (0.021):  35%|███▌      | 35/100 [00:01<00:01, 41.83it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.021 | Best: {'fx': 568.687} (0.021):  36%|███▌      | 36/100 [00:01<00:02, 27.11it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.020 | Best: {'fx': 568.687} (0.021):  36%|███▌      | 36/100 [00:01<00:02, 27.11it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 582.828} (0.022):  37%|███▋      | 37/100 [00:01<00:02, 27.11it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.019 | Best: {'fx': 582.828} (0.022):  38%|███▊      | 38/100 [00:01<00:02, 27.11it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.019 | Best: {'fx': 582.828} (0.022):  39%|███▉      | 39/100 [00:01<00:02, 27.11it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.021 | Best: {'fx': 582.828} (0.022):  40%|████      | 40/100 [00:01<00:02, 27.11it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.021 | Best: {'fx': 582.828} (0.022):  41%|████      | 41/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 589.899} (0.022):  41%|████      | 41/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.023 | Best: {'fx': 611.111} (0.023):  42%|████▏     | 42/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.023 | Best: {'fx': 611.111} (0.023):  43%|████▎     | 43/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.023 | Best: {'fx': 625.253} (0.023):  44%|████▍     | 44/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  45%|████▌     | 45/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  46%|████▌     | 46/100 [00:01<00:01, 30.77it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  47%|████▋     | 47/100 [00:01<00:01, 36.85it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  47%|████▋     | 47/100 [00:01<00:01, 36.85it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  48%|████▊     | 48/100 [00:01<00:01, 36.85it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  49%|████▉     | 49/100 [00:01<00:01, 36.85it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  50%|█████     | 50/100 [00:01<00:01, 36.85it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  51%|█████     | 51/100 [00:01<00:01, 36.85it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  52%|█████▏    | 52/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  52%|█████▏    | 52/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  53%|█████▎    | 53/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.023 | Best: {'fx': 625.253} (0.023):  54%|█████▍    | 54/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  55%|█████▌    | 55/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  56%|█████▌    | 56/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  57%|█████▋    | 57/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  58%|█████▊    | 58/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  59%|█████▉    | 59/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  60%|██████    | 60/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  61%|██████    | 61/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  62%|██████▏   | 62/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  63%|██████▎   | 63/100 [00:01<00:01, 36.52it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  64%|██████▍   | 64/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  64%|██████▍   | 64/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  65%|██████▌   | 65/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  66%|██████▌   | 66/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  67%|██████▋   | 67/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  68%|██████▊   | 68/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  69%|██████▉   | 69/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  70%|███████   | 70/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  71%|███████   | 71/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  72%|███████▏  | 72/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  73%|███████▎  | 73/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  74%|███████▍  | 74/100 [00:01<00:00, 55.88it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  75%|███████▌  | 75/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  75%|███████▌  | 75/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  76%|███████▌  | 76/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  77%|███████▋  | 77/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  78%|███████▊  | 78/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  79%|███████▉  | 79/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  80%|████████  | 80/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  81%|████████  | 81/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  82%|████████▏ | 82/100 [00:01<00:00, 67.93it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  83%|████████▎ | 83/100 [00:01<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  83%|████████▎ | 83/100 [00:01<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  84%|████████▍ | 84/100 [00:01<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  85%|████████▌ | 85/100 [00:01<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  86%|████████▌ | 86/100 [00:02<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  87%|████████▋ | 87/100 [00:02<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  88%|████████▊ | 88/100 [00:02<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  89%|████████▉ | 89/100 [00:02<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  90%|█████████ | 90/100 [00:02<00:00, 56.90it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  91%|█████████ | 91/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  91%|█████████ | 91/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  92%|█████████▏| 92/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  93%|█████████▎| 93/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  94%|█████████▍| 94/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  95%|█████████▌| 95/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  96%|█████████▌| 96/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  97%|█████████▋| 97/100 [00:02<00:00, 54.08it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  98%|█████████▊| 98/100 [00:02<00:00, 55.33it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  98%|█████████▊| 98/100 [00:02<00:00, 55.33it/s]Brute | Start: {'fx': 500.0} (0.013) | Current: 0.022 | Best: {'fx': 625.253} (0.023):  99%|█████████▉| 99/100 [00:02<00:00, 55.33it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 600.253} (0.022):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 600.253} (0.022):   2%|▏         | 1/50 [00:00<00:18,  2.72it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 601.273} (0.022):   2%|▏         | 1/50 [00:00<00:18,  2.72it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 606.375} (0.023):   4%|▍         | 2/50 [00:00<00:17,  2.72it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 606.375} (0.023):   6%|▌         | 3/50 [00:00<00:06,  7.00it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 606.375} (0.023):   6%|▌         | 3/50 [00:00<00:06,  7.00it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 606.375} (0.023):   8%|▊         | 4/50 [00:00<00:06,  7.00it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 604.334} (0.023):  10%|█         | 5/50 [00:00<00:06,  7.00it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 604.334} (0.023):  12%|█▏        | 6/50 [00:00<00:06,  7.00it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 604.334} (0.023):  14%|█▍        | 7/50 [00:00<00:06,  7.00it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 604.334} (0.023):  16%|█▌        | 8/50 [00:00<00:02, 17.45it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 604.334} (0.023):  16%|█▌        | 8/50 [00:00<00:02, 17.45it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 604.334} (0.023):  18%|█▊        | 9/50 [00:00<00:02, 17.45it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 604.334} (0.023):  20%|██        | 10/50 [00:00<00:02, 17.45it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 604.334} (0.023):  22%|██▏       | 11/50 [00:00<00:01, 20.13it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 604.334} (0.023):  22%|██▏       | 11/50 [00:00<00:01, 20.13it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 604.334} (0.023):  24%|██▍       | 12/50 [00:00<00:01, 20.13it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 610.457} (0.023):  26%|██▌       | 13/50 [00:00<00:01, 20.13it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 610.457} (0.023):  28%|██▊       | 14/50 [00:00<00:01, 20.13it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  30%|███       | 15/50 [00:00<00:01, 20.13it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  32%|███▏      | 16/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  32%|███▏      | 16/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  34%|███▍      | 17/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  36%|███▌      | 18/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  38%|███▊      | 19/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  40%|████      | 20/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  42%|████▏     | 21/50 [00:00<00:01, 27.52it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  44%|████▍     | 22/50 [00:00<00:00, 34.47it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  44%|████▍     | 22/50 [00:01<00:00, 34.47it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  46%|████▌     | 23/50 [00:01<00:00, 34.47it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  48%|████▊     | 24/50 [00:01<00:00, 34.47it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  50%|█████     | 25/50 [00:01<00:00, 34.47it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  52%|█████▏    | 26/50 [00:01<00:00, 34.47it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  54%|█████▍    | 27/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  54%|█████▍    | 27/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  56%|█████▌    | 28/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  58%|█████▊    | 29/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  60%|██████    | 30/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  62%|██████▏   | 31/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  64%|██████▍   | 32/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 617.599} (0.023):  66%|██████▌   | 33/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  68%|██████▊   | 34/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 617.599} (0.023):  70%|███████   | 35/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 624.742} (0.023):  72%|███████▏  | 36/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 624.742} (0.023):  74%|███████▍  | 37/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  76%|███████▌  | 38/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  78%|███████▊  | 39/50 [00:01<00:00, 36.94it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  80%|████████  | 40/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  80%|████████  | 40/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 625.763} (0.023):  82%|████████▏ | 41/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 625.763} (0.023):  84%|████████▍ | 42/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 625.763} (0.023):  86%|████████▌ | 43/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 625.763} (0.023):  88%|████████▊ | 44/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 625.763} (0.023):  90%|█████████ | 45/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  92%|█████████▏| 46/50 [00:01<00:00, 61.80it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  94%|█████████▍| 47/50 [00:01<00:00, 61.33it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  94%|█████████▍| 47/50 [00:01<00:00, 61.33it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.022 | Best: {'fx': 625.763} (0.023):  96%|█████████▌| 48/50 [00:01<00:00, 61.33it/s]Brute | Start: {'fx': 625.253} (0.023) | Current: 0.023 | Best: {'fx': 625.763} (0.023):  98%|█████████▊| 49/50 [00:01<00:00, 61.33it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 625.2525252525253
    px: 1007.0
    py: 593.0
    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 20 frames per rig (excluded 47 incomplete frames): 18 frames will be used for optimization
  Rigs: 1000(1000mm):3/15, 1500(1500mm):8/19, 2000R(2000mm):1/17, 2000L(2000mm):6/14
  Total: 18/65 frames used for optimization
Initial score: 725.7301: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 725.7301, picklable_objective(x0) = 773.5824
Best score: 773.5824: : 0iter [00:01, ?iter/s] Best score: 773.5824: : 1iter [00:01,  1.72s/iter]Best score: 773.5824: : 1iter [00:01,  1.72s/iter]Best score: 773.5824: : 2iter [00:01,  1.72s/iter]Best score: 773.5824: : 3iter [00:01,  1.72s/iter]Best score: 773.5824: : 4iter [00:01,  1.72s/iter]Best score: 773.5824: : 5iter [00:01,  3.56iter/s]Best score: 773.5824: : 5iter [00:01,  3.56iter/s]Best score: 773.5824: : 6iter [00:01,  3.56iter/s]Best score: 773.5824: : 7iter [00:01,  3.56iter/s]Best score: 773.5824: : 8iter [00:01,  3.56iter/s]Best score: 773.5824: : 9iter [00:01,  7.01iter/s]Best score: 554.8191: : 9iter [00:01,  7.01iter/s]Best score: 554.8191: : 10iter [00:02,  7.01iter/s]Best score: 554.8191: : 11iter [00:02,  7.01iter/s]Best score: 554.8191: : 12iter [00:02,  7.01iter/s]Best score: 554.8191: : 13iter [00:02, 10.74iter/s]Best score: 554.8191: : 13iter [00:02, 10.74iter/s]Best score: 554.8191: : 14iter [00:02, 10.74iter/s]Best score: 554.8191: : 15iter [00:02, 10.74iter/s]Best score: 554.8191: : 16iter [00:02, 10.74iter/s]Best score: 554.8191: : 17iter [00:02, 14.63iter/s]Best score: 554.8191: : 17iter [00:02, 14.63iter/s]Best score: 554.8191: : 18iter [00:02, 14.63iter/s]Best score: 526.4553: : 19iter [00:02, 14.63iter/s]Best score: 526.4553: : 20iter [00:02, 14.63iter/s]Best score: 526.4553: : 21iter [00:02, 18.21iter/s]Best score: 526.4553: : 21iter [00:02, 18.21iter/s]Best score: 526.4553: : 22iter [00:02, 18.21iter/s]Best score: 526.4553: : 23iter [00:02, 18.21iter/s]Best score: 526.4553: : 24iter [00:02, 18.21iter/s]Best score: 526.4553: : 25iter [00:02, 21.68iter/s]Best score: 526.4553: : 25iter [00:02, 21.68iter/s]Best score: 526.4553: : 26iter [00:02, 21.68iter/s]Best score: 526.4553: : 27iter [00:02, 21.68iter/s]Best score: 526.4553: : 28iter [00:02, 21.68iter/s]Best score: 526.4553: : 29iter [00:02, 24.70iter/s]Best score: 516.6329: : 29iter [00:02, 24.70iter/s]Best score: 516.6329: : 30iter [00:02, 24.70iter/s]Best score: 516.6329: : 31iter [00:02, 24.70iter/s]Best score: 516.6329: : 32iter [00:02, 24.70iter/s]Best score: 516.6329: : 33iter [00:02, 26.96iter/s]Best score: 516.6329: : 33iter [00:02, 26.96iter/s]Best score: 516.6329: : 34iter [00:02, 26.96iter/s]Best score: 516.6329: : 35iter [00:02, 26.96iter/s]Best score: 516.6329: : 36iter [00:02, 26.96iter/s]Best score: 516.6329: : 37iter [00:02, 28.87iter/s]Best score: 516.6329: : 37iter [00:02, 28.87iter/s]Best score: 516.6329: : 38iter [00:02, 28.87iter/s]Best score: 512.8237: : 39iter [00:02, 28.87iter/s]Best score: 512.8237: : 40iter [00:02, 28.87iter/s]Best score: 512.8237: : 41iter [00:02, 30.07iter/s]Best score: 512.8237: : 41iter [00:02, 30.07iter/s]Best score: 512.8237: : 42iter [00:02, 30.07iter/s]Best score: 512.8237: : 43iter [00:02, 30.07iter/s]Best score: 512.8237: : 44iter [00:03, 30.07iter/s]Best score: 512.8237: : 45iter [00:03, 31.30iter/s]Best score: 512.8237: : 45iter [00:03, 31.30iter/s]Best score: 512.8237: : 46iter [00:03, 31.30iter/s]Best score: 512.8237: : 47iter [00:03, 31.30iter/s]Best score: 512.8237: : 48iter [00:03, 31.30iter/s]Best score: 512.8237: : 49iter [00:03, 32.21iter/s]
[ITER 50] Best: 506.9914
[PARAMS] Current best parameters:
  fx: 565.6015
  fy: 666.9366
  px: 968.8054
  py: 632.8699
  k1: 0.00698755
  k2: 0.07696135
  k3: 0.04201353
  k4: 0.01536548
  p1: -0.00893724
  p2: 0.00981241
  p3: -0.00734215
  p4: -0.00834815
  scale: 2.7886
  rotation: 1.7873
Best score: 506.9914: : 49iter [00:03, 32.21iter/s]Best score: 506.9914: : 50iter [00:03, 32.21iter/s]Best score: 506.9914: : 51iter [00:03, 32.21iter/s]Best score: 506.9914: : 52iter [00:03, 32.21iter/s]Best score: 506.9914: : 53iter [00:03, 32.57iter/s]Best score: 506.9914: : 53iter [00:03, 32.57iter/s]Best score: 506.9914: : 54iter [00:03, 32.57iter/s]Best score: 506.9914: : 55iter [00:03, 32.57iter/s]Best score: 506.9914: : 56iter [00:03, 32.57iter/s]Best score: 506.9914: : 57iter [00:03, 33.18iter/s]Best score: 506.9914: : 57iter [00:03, 33.18iter/s]Best score: 506.9914: : 58iter [00:03, 33.18iter/s]Best score: 504.8457: : 59iter [00:03, 33.18iter/s]Best score: 504.8457: : 60iter [00:03, 33.18iter/s]Best score: 504.8457: : 61iter [00:03, 33.36iter/s]Best score: 504.8457: : 61iter [00:03, 33.36iter/s]Best score: 504.8457: : 62iter [00:03, 33.36iter/s]Best score: 504.8457: : 63iter [00:03, 33.36iter/s]Best score: 504.8457: : 64iter [00:03, 33.36iter/s]Best score: 504.8457: : 65iter [00:03, 33.79iter/s]Best score: 504.8457: : 65iter [00:03, 33.79iter/s]Best score: 504.8457: : 66iter [00:03, 33.79iter/s]Best score: 504.8457: : 67iter [00:03, 33.79iter/s]Best score: 504.8457: : 68iter [00:03, 33.79iter/s]Best score: 504.8457: : 69iter [00:03, 34.09iter/s]Best score: 504.8457: : 69iter [00:03, 34.09iter/s]Best score: 504.8457: : 70iter [00:03, 34.09iter/s]Best score: 504.8457: : 71iter [00:03, 34.09iter/s]Best score: 504.8457: : 72iter [00:03, 34.09iter/s]Best score: 504.8457: : 73iter [00:03, 33.96iter/s]Best score: 504.8457: : 73iter [00:03, 33.96iter/s]Best score: 504.8457: : 74iter [00:03, 33.96iter/s]Best score: 504.8457: : 75iter [00:03, 33.96iter/s]Best score: 504.8457: : 76iter [00:03, 33.96iter/s]Best score: 504.8457: : 77iter [00:03, 34.18iter/s]Best score: 504.8457: : 77iter [00:03, 34.18iter/s]Best score: 504.8457: : 78iter [00:04, 34.18iter/s]Best score: 504.1027: : 79iter [00:04, 34.18iter/s]Best score: 504.1027: : 80iter [00:04, 34.18iter/s]Best score: 504.1027: : 81iter [00:04, 34.10iter/s]Best score: 504.1027: : 81iter [00:04, 34.10iter/s]Best score: 504.1027: : 82iter [00:04, 34.10iter/s]Best score: 504.1027: : 83iter [00:04, 34.10iter/s]Best score: 504.1027: : 84iter [00:04, 34.10iter/s]Best score: 504.1027: : 85iter [00:04, 34.25iter/s]Best score: 504.1027: : 85iter [00:04, 34.25iter/s]Best score: 504.1027: : 86iter [00:04, 34.25iter/s]Best score: 504.1027: : 87iter [00:04, 34.25iter/s]Best score: 504.1027: : 88iter [00:04, 34.25iter/s]Best score: 504.1027: : 89iter [00:04, 34.36iter/s]Best score: 502.0464: : 89iter [00:04, 34.36iter/s]Best score: 502.0464: : 90iter [00:04, 34.36iter/s]Best score: 502.0464: : 91iter [00:04, 34.36iter/s]Best score: 502.0464: : 92iter [00:04, 34.36iter/s]Best score: 502.0464: : 93iter [00:04, 34.14iter/s]Best score: 502.0464: : 93iter [00:04, 34.14iter/s]Best score: 502.0464: : 94iter [00:04, 34.14iter/s]Best score: 502.0464: : 95iter [00:04, 34.14iter/s]Best score: 502.0464: : 96iter [00:04, 34.14iter/s]Best score: 502.0464: : 97iter [00:04, 34.30iter/s]Best score: 502.0464: : 97iter [00:04, 34.30iter/s]Best score: 502.0464: : 98iter [00:04, 34.30iter/s]
[ITER 100] Best: 501.1888
[PARAMS] Current best parameters:
  fx: 562.9688
  fy: 686.4771
  px: 967.1618
  py: 632.6198
  k1: -0.00202777
  k2: 0.07309462
  k3: 0.04477275
  k4: 0.01443538
  p1: -0.00965562
  p2: 0.00971780
  p3: -0.00918972
  p4: -0.00551365
  scale: 2.7738
  rotation: 2.2799
Best score: 501.1888: : 99iter [00:04, 34.30iter/s]Best score: 501.1888: : 100iter [00:04, 34.30iter/s]Best score: 501.1888: : 101iter [00:04, 34.11iter/s]Best score: 501.1888: : 101iter [00:04, 34.11iter/s]Best score: 501.1888: : 102iter [00:04, 34.11iter/s]Best score: 501.1888: : 103iter [00:04, 34.11iter/s]Best score: 501.1888: : 104iter [00:04, 34.11iter/s]Best score: 501.1888: : 105iter [00:04, 34.28iter/s]Best score: 501.1888: : 105iter [00:04, 34.28iter/s]Best score: 501.1888: : 106iter [00:04, 34.28iter/s]Best score: 501.1888: : 107iter [00:04, 34.28iter/s]Best score: 501.1888: : 108iter [00:04, 34.28iter/s]Best score: 501.1888: : 109iter [00:04, 34.29iter/s]Best score: 500.9645: : 109iter [00:04, 34.29iter/s]Best score: 500.9645: : 110iter [00:04, 34.29iter/s]Best score: 500.9645: : 111iter [00:04, 34.29iter/s]Best score: 500.9645: : 112iter [00:04, 34.29iter/s]Best score: 500.9645: : 113iter [00:04, 34.16iter/s]Best score: 500.9645: : 113iter [00:05, 34.16iter/s]Best score: 500.9645: : 114iter [00:05, 34.16iter/s]Best score: 500.9645: : 115iter [00:05, 34.16iter/s]Best score: 500.9645: : 116iter [00:05, 34.16iter/s]Best score: 500.9645: : 117iter [00:05, 34.23iter/s]Best score: 500.9645: : 117iter [00:05, 34.23iter/s]Best score: 500.9645: : 118iter [00:05, 34.23iter/s]Best score: 500.1252: : 119iter [00:05, 34.23iter/s]Best score: 500.1252: : 120iter [00:05, 34.23iter/s]Best score: 500.1252: : 121iter [00:05, 25.57iter/s]Best score: 500.1252: : 121iter [00:05, 25.57iter/s]Best score: 500.1252: : 122iter [00:05, 25.57iter/s]Best score: 500.1252: : 123iter [00:05, 25.57iter/s]Best score: 500.1252: : 124iter [00:05, 25.57iter/s]Best score: 500.1252: : 125iter [00:05, 27.75iter/s]Best score: 500.1252: : 125iter [00:05, 27.75iter/s]Best score: 500.1252: : 126iter [00:05, 27.75iter/s]Best score: 500.1252: : 127iter [00:05, 27.75iter/s]Best score: 500.1252: : 128iter [00:05, 27.75iter/s]Best score: 500.1252: : 129iter [00:05, 29.57iter/s]Best score: 500.1252: : 129iter [00:05, 29.57iter/s]Best score: 500.1252: : 130iter [00:05, 29.57iter/s]Best score: 500.1252: : 131iter [00:05, 29.57iter/s]Best score: 500.1252: : 132iter [00:05, 29.57iter/s]Best score: 500.1252: : 133iter [00:05, 30.67iter/s]Best score: 500.1252: : 133iter [00:05, 30.67iter/s]Best score: 500.1252: : 134iter [00:05, 30.67iter/s]Best score: 500.1252: : 135iter [00:05, 30.67iter/s]Best score: 500.1252: : 136iter [00:05, 30.67iter/s]Best score: 500.1252: : 137iter [00:05, 31.76iter/s]Best score: 500.1252: : 137iter [00:05, 31.76iter/s]Best score: 500.1252: : 138iter [00:05, 31.76iter/s]Best score: 499.0905: : 139iter [00:05, 31.76iter/s]Best score: 499.0905: : 140iter [00:05, 31.76iter/s]Best score: 499.0905: : 141iter [00:05, 32.26iter/s]Best score: 499.0905: : 141iter [00:05, 32.26iter/s]Best score: 499.0905: : 142iter [00:06, 32.26iter/s]Best score: 499.0905: : 143iter [00:06, 32.26iter/s]Best score: 499.0905: : 144iter [00:06, 32.26iter/s]Best score: 499.0905: : 145iter [00:06, 32.79iter/s]Best score: 499.0905: : 145iter [00:06, 32.79iter/s]Best score: 499.0905: : 146iter [00:06, 32.79iter/s]Best score: 499.0905: : 147iter [00:06, 32.79iter/s]Best score: 499.0905: : 148iter [00:06, 32.79iter/s]Best score: 499.0905: : 149iter [00:06, 33.34iter/s]
[ITER 150] Best: 499.0796
[PARAMS] Current best parameters:
  fx: 563.2155
  fy: 686.5164
  px: 967.2969
  py: 632.4843
  k1: -0.01164824
  k2: 0.07801856
  k3: 0.04874747
  k4: 0.01964366
  p1: -0.00997701
  p2: 0.00996000
  p3: -0.00810985
  p4: -0.00656141
  scale: 2.7798
  rotation: 1.9224
Best score: 499.0796: : 149iter [00:06, 33.34iter/s]Best score: 499.0796: : 150iter [00:06, 33.34iter/s]Best score: 499.0796: : 151iter [00:06, 33.34iter/s]Best score: 499.0796: : 152iter [00:06, 33.34iter/s]Best score: 499.0796: : 153iter [00:06, 33.33iter/s]Best score: 499.0796: : 153iter [00:06, 33.33iter/s]Best score: 499.0796: : 154iter [00:06, 33.33iter/s]Best score: 499.0796: : 155iter [00:06, 33.33iter/s]Best score: 499.0796: : 156iter [00:06, 33.33iter/s]Best score: 499.0796: : 157iter [00:06, 33.56iter/s]Best score: 499.0796: : 157iter [00:06, 33.56iter/s]Best score: 499.0796: : 158iter [00:06, 33.56iter/s]Best score: 499.0741: : 159iter [00:06, 33.56iter/s]Best score: 499.0741: : 160iter [00:06, 33.56iter/s]Best score: 499.0741: : 161iter [00:06, 33.65iter/s]Best score: 499.0741: : 161iter [00:06, 33.65iter/s]Best score: 499.0741: : 162iter [00:06, 33.65iter/s]Best score: 499.0741: : 163iter [00:06, 33.65iter/s]Best score: 499.0741: : 164iter [00:06, 33.65iter/s]Best score: 499.0741: : 165iter [00:06, 33.88iter/s]Best score: 499.0741: : 165iter [00:06, 33.88iter/s]Best score: 499.0741: : 166iter [00:06, 33.88iter/s]Best score: 499.0741: : 167iter [00:06, 33.88iter/s]Best score: 499.0741: : 168iter [00:06, 33.88iter/s]Best score: 499.0741: : 169iter [00:06, 34.02iter/s]Best score: 498.9177: : 169iter [00:06, 34.02iter/s]Best score: 498.9177: : 170iter [00:06, 34.02iter/s]Best score: 498.9177: : 171iter [00:06, 34.02iter/s]Best score: 498.9177: : 172iter [00:06, 34.02iter/s]Best score: 498.9177: : 173iter [00:06, 33.75iter/s]Best score: 498.9177: : 173iter [00:06, 33.75iter/s]Best score: 498.9177: : 174iter [00:06, 33.75iter/s]Best score: 498.9177: : 175iter [00:06, 33.75iter/s]Best score: 498.9177: : 176iter [00:07, 33.75iter/s]Best score: 498.9177: : 177iter [00:07, 33.93iter/s]Best score: 498.9177: : 177iter [00:07, 33.93iter/s]Best score: 498.9177: : 178iter [00:07, 33.93iter/s]Best score: 498.7593: : 179iter [00:07, 33.93iter/s]Best score: 498.7593: : 180iter [00:07, 33.93iter/s]Best score: 498.7593: : 181iter [00:07, 33.84iter/s]Best score: 498.7593: : 181iter [00:07, 33.84iter/s]Best score: 498.7593: : 182iter [00:07, 33.84iter/s]Best score: 498.7593: : 183iter [00:07, 33.84iter/s]Best score: 498.7593: : 184iter [00:07, 33.84iter/s]Best score: 498.7593: : 185iter [00:07, 34.05iter/s]Best score: 498.7593: : 185iter [00:07, 34.05iter/s]Best score: 498.7593: : 186iter [00:07, 34.05iter/s]Best score: 498.7593: : 187iter [00:07, 34.05iter/s]Best score: 498.7593: : 188iter [00:07, 34.05iter/s]Best score: 498.7593: : 189iter [00:07, 34.17iter/s]Best score: 498.6190: : 189iter [00:07, 34.17iter/s]Best score: 498.6190: : 190iter [00:07, 34.17iter/s]Best score: 498.6190: : 191iter [00:07, 34.17iter/s]Best score: 498.6190: : 192iter [00:07, 34.17iter/s]Best score: 498.6190: : 193iter [00:07, 34.05iter/s]Best score: 498.6190: : 193iter [00:07, 34.05iter/s]Best score: 498.6190: : 194iter [00:07, 34.05iter/s]Best score: 498.6190: : 195iter [00:07, 34.05iter/s]Best score: 498.6190: : 196iter [00:07, 34.05iter/s]Best score: 498.6190: : 197iter [00:07, 34.20iter/s]Best score: 498.6190: : 197iter [00:07, 34.20iter/s]Best score: 498.6190: : 198iter [00:07, 34.20iter/s]
[ITER 200] Best: 498.4351
[PARAMS] Current best parameters:
  fx: 562.8574
  fy: 686.7807
  px: 967.3635
  py: 632.9882
  k1: -0.01827011
  k2: 0.07702402
  k3: 0.04899488
  k4: 0.01920966
  p1: -0.00995888
  p2: 0.00940769
  p3: -0.00975644
  p4: -0.00862185
  scale: 2.7999
  rotation: 1.6420
Best score: 498.4351: : 199iter [00:07, 34.20iter/s]Best score: 498.4351: : 200iter [00:07, 34.20iter/s]Best score: 498.4351: : 201iter [00:07, 34.11iter/s]Best score: 498.4351: : 201iter [00:07, 34.11iter/s]Best score: 498.4351: : 202iter [00:07, 34.11iter/s]Best score: 498.4351: : 203iter [00:07, 34.11iter/s]Best score: 498.4351: : 204iter [00:07, 34.11iter/s]Best score: 498.4351: : 205iter [00:07, 34.23iter/s]Best score: 498.4351: : 205iter [00:07, 34.23iter/s]Best score: 498.4351: : 206iter [00:07, 34.23iter/s]Best score: 498.4351: : 207iter [00:07, 34.23iter/s]Best score: 498.4351: : 208iter [00:07, 34.23iter/s]Best score: 498.4351: : 209iter [00:07, 34.31iter/s]Best score: 498.3316: : 209iter [00:07, 34.31iter/s]Best score: 498.3316: : 210iter [00:07, 34.31iter/s]Best score: 498.3316: : 211iter [00:08, 34.31iter/s]Best score: 498.3316: : 212iter [00:08, 34.31iter/s]Best score: 498.3316: : 213iter [00:08, 34.06iter/s]Best score: 498.3316: : 213iter [00:08, 34.06iter/s]Best score: 498.3316: : 214iter [00:08, 34.06iter/s]Best score: 498.3316: : 215iter [00:08, 34.06iter/s]Best score: 498.3316: : 216iter [00:08, 34.06iter/s]Best score: 498.3316: : 217iter [00:08, 34.11iter/s]Best score: 498.3316: : 217iter [00:08, 34.11iter/s]Best score: 498.3316: : 218iter [00:08, 34.11iter/s]Best score: 498.2055: : 219iter [00:08, 34.11iter/s]Best score: 498.2055: : 220iter [00:08, 34.11iter/s]Best score: 498.2055: : 221iter [00:08, 34.04iter/s]Best score: 498.2055: : 221iter [00:08, 34.04iter/s]Best score: 498.2055: : 222iter [00:08, 34.04iter/s]Best score: 498.2055: : 223iter [00:08, 34.04iter/s]Best score: 498.2055: : 224iter [00:08, 34.04iter/s]Best score: 498.2055: : 225iter [00:08, 34.21iter/s]Best score: 498.2055: : 225iter [00:08, 34.21iter/s]Best score: 498.2055: : 226iter [00:08, 34.21iter/s]Best score: 498.2055: : 227iter [00:08, 34.21iter/s]Best score: 498.2055: : 228iter [00:08, 34.21iter/s]Best score: 498.2055: : 229iter [00:08, 34.40iter/s]Best score: 498.2055: : 229iter [00:08, 34.40iter/s]Best score: 498.2055: : 230iter [00:08, 34.40iter/s]Best score: 498.2055: : 231iter [00:08, 34.40iter/s]Best score: 498.2055: : 232iter [00:08, 34.40iter/s]Best score: 498.2055: : 233iter [00:08, 34.06iter/s]Best score: 498.2055: : 233iter [00:08, 34.06iter/s]Best score: 498.2055: : 234iter [00:08, 34.06iter/s]Best score: 498.2055: : 235iter [00:08, 34.06iter/s]Best score: 498.2055: : 236iter [00:08, 34.06iter/s]Best score: 498.2055: : 237iter [00:08, 34.09iter/s]Best score: 498.2055: : 237iter [00:08, 34.09iter/s]Best score: 498.2055: : 238iter [00:08, 34.09iter/s]Best score: 498.1193: : 239iter [00:08, 34.09iter/s]Best score: 498.1193: : 240iter [00:08, 34.09iter/s]Best score: 498.1193: : 241iter [00:08, 33.99iter/s]Best score: 498.1193: : 241iter [00:08, 33.99iter/s]Best score: 498.1193: : 242iter [00:08, 33.99iter/s]Best score: 498.1193: : 243iter [00:08, 33.99iter/s]Best score: 498.1193: : 244iter [00:08, 33.99iter/s]Best score: 498.1193: : 245iter [00:08, 33.91iter/s]Best score: 498.1193: : 245iter [00:09, 33.91iter/s]Best score: 498.1193: : 246iter [00:09, 33.91iter/s]Best score: 498.1193: : 247iter [00:09, 33.91iter/s]Best score: 498.1193: : 248iter [00:09, 33.91iter/s]Best score: 498.1193: : 249iter [00:09, 34.00iter/s]
[ITER 250] Best: 497.9797
[PARAMS] Current best parameters:
  fx: 562.7430
  fy: 687.2117
  px: 967.1518
  py: 632.8049
  k1: -0.01570373
  k2: 0.07965764
  k3: 0.04905587
  k4: 0.01990350
  p1: -0.00989594
  p2: 0.00990698
  p3: -0.00990439
  p4: -0.00522357
  scale: 2.7894
  rotation: 1.7462
Best score: 497.9797: : 249iter [00:09, 34.00iter/s]Best score: 497.9797: : 250iter [00:09, 34.00iter/s]Best score: 497.9797: : 251iter [00:09, 34.00iter/s]Best score: 497.9797: : 252iter [00:09, 34.00iter/s]Best score: 497.9797: : 253iter [00:09, 33.84iter/s]Best score: 497.9797: : 253iter [00:09, 33.84iter/s]Best score: 497.9797: : 254iter [00:09, 33.84iter/s]Best score: 497.9797: : 255iter [00:09, 33.84iter/s]Best score: 497.9797: : 256iter [00:09, 33.84iter/s]Best score: 497.9797: : 257iter [00:09, 33.97iter/s]Best score: 497.9797: : 257iter [00:09, 33.97iter/s]Best score: 497.9797: : 258iter [00:09, 33.97iter/s]Best score: 497.9797: : 259iter [00:09, 33.97iter/s]Best score: 497.9797: : 260iter [00:09, 33.97iter/s]Best score: 497.9797: : 261iter [00:09, 33.82iter/s]Best score: 497.9797: : 261iter [00:09, 33.82iter/s]Best score: 497.9797: : 262iter [00:09, 33.82iter/s]Best score: 497.9797: : 263iter [00:09, 33.82iter/s]Best score: 497.9797: : 264iter [00:09, 33.82iter/s]Best score: 497.9797: : 265iter [00:09, 33.96iter/s]Best score: 497.9797: : 265iter [00:09, 33.96iter/s]Best score: 497.9797: : 266iter [00:09, 33.96iter/s]Best score: 497.9797: : 267iter [00:09, 33.96iter/s]Best score: 497.9797: : 268iter [00:09, 33.96iter/s]Best score: 497.9797: : 269iter [00:09, 34.02iter/s]Best score: 497.9797: : 269iter [00:09, 34.02iter/s]Best score: 497.9797: : 270iter [00:09, 34.02iter/s]Best score: 497.9797: : 271iter [00:09, 34.02iter/s]Best score: 497.9797: : 272iter [00:09, 34.02iter/s]Best score: 497.9797: : 273iter [00:09, 33.90iter/s]Best score: 497.9797: : 273iter [00:09, 33.90iter/s]Best score: 497.9797: : 274iter [00:09, 33.90iter/s]Best score: 497.9797: : 275iter [00:09, 33.90iter/s]Best score: 497.9797: : 276iter [00:09, 33.90iter/s]Best score: 497.9797: : 277iter [00:09, 34.06iter/s]Best score: 497.9797: : 277iter [00:09, 34.06iter/s]Best score: 497.9797: : 278iter [00:09, 34.06iter/s]Best score: 497.8485: : 279iter [00:10, 34.06iter/s]Best score: 497.8485: : 280iter [00:10, 34.06iter/s]Best score: 497.8485: : 281iter [00:10, 33.85iter/s]Best score: 497.8485: : 281iter [00:10, 33.85iter/s]Best score: 497.8485: : 282iter [00:10, 33.85iter/s]Best score: 497.8485: : 283iter [00:10, 33.85iter/s]Best score: 497.8485: : 284iter [00:10, 33.85iter/s]Best score: 497.8485: : 285iter [00:10, 34.08iter/s]Best score: 497.8485: : 285iter [00:10, 34.08iter/s]Best score: 497.8485: : 286iter [00:10, 34.08iter/s]Best score: 497.8485: : 287iter [00:10, 34.08iter/s]Best score: 497.8485: : 288iter [00:10, 34.08iter/s]Best score: 497.8485: : 289iter [00:10, 34.20iter/s]Best score: 497.7010: : 289iter [00:10, 34.20iter/s]Best score: 497.7010: : 290iter [00:10, 34.20iter/s]Best score: 497.7010: : 291iter [00:10, 34.20iter/s]Best score: 497.7010: : 292iter [00:10, 34.20iter/s]Best score: 497.7010: : 293iter [00:10, 34.04iter/s]Best score: 497.7010: : 293iter [00:10, 34.04iter/s]Best score: 497.7010: : 294iter [00:10, 34.04iter/s]Best score: 497.7010: : 295iter [00:10, 34.04iter/s]Best score: 497.7010: : 296iter [00:10, 34.04iter/s]Best score: 497.7010: : 297iter [00:10, 34.19iter/s]Best score: 497.7010: : 297iter [00:10, 34.19iter/s]Best score: 497.7010: : 298iter [00:10, 34.19iter/s]
[ITER 300] Best: 497.6368
[PARAMS] Current best parameters:
  fx: 562.9324
  fy: 687.6280
  px: 967.0360
  py: 632.9984
  k1: -0.02144860
  k2: 0.07975293
  k3: 0.04979647
  k4: 0.01890423
  p1: -0.00999056
  p2: 0.00983333
  p3: -0.00978325
  p4: -0.00703881
  scale: 2.7976
  rotation: 1.4817
Best score: 497.6368: : 299iter [00:10, 34.19iter/s]Best score: 497.6368: : 300iter [00:10, 34.19iter/s]Best score: 497.6368: : 301iter [00:10, 34.05iter/s]Best score: 497.6368: : 301iter [00:10, 34.05iter/s]Best score: 497.6368: : 302iter [00:10, 34.05iter/s]Best score: 497.6368: : 303iter [00:10, 34.05iter/s]Best score: 497.6368: : 304iter [00:10, 34.05iter/s]Best score: 497.6368: : 305iter [00:10, 26.62iter/s]Best score: 497.6368: : 305iter [00:10, 26.62iter/s]Best score: 497.6368: : 306iter [00:10, 26.62iter/s]Best score: 497.6368: : 307iter [00:10, 26.62iter/s]Best score: 497.6368: : 308iter [00:10, 26.62iter/s]Best score: 497.6368: : 309iter [00:10, 28.58iter/s]Best score: 497.6368: : 309iter [00:11, 28.58iter/s]Best score: 497.6368: : 310iter [00:11, 28.58iter/s]Best score: 497.6368: : 311iter [00:11, 28.58iter/s]Best score: 497.6368: : 312iter [00:11, 28.58iter/s]Best score: 497.6368: : 313iter [00:11, 29.84iter/s]Best score: 497.6368: : 313iter [00:11, 29.84iter/s]Best score: 497.6368: : 314iter [00:11, 29.84iter/s]Best score: 497.6368: : 315iter [00:11, 29.84iter/s]Best score: 497.6368: : 316iter [00:11, 29.84iter/s]Best score: 497.6368: : 317iter [00:11, 30.95iter/s]Best score: 497.6368: : 317iter [00:11, 30.95iter/s]Best score: 497.6368: : 318iter [00:11, 30.95iter/s]Best score: 497.6087: : 319iter [00:11, 30.95iter/s]Best score: 497.6087: : 320iter [00:11, 30.95iter/s]Best score: 497.6087: : 321iter [00:11, 31.56iter/s]Best score: 497.6087: : 321iter [00:11, 31.56iter/s]Best score: 497.6087: : 322iter [00:11, 31.56iter/s]Best score: 497.6087: : 323iter [00:11, 31.56iter/s]Best score: 497.6087: : 324iter [00:11, 31.56iter/s]Best score: 497.6087: : 325iter [00:11, 32.04iter/s]Best score: 497.6087: : 325iter [00:11, 32.04iter/s]Best score: 497.6087: : 326iter [00:11, 32.04iter/s]Best score: 497.6087: : 327iter [00:11, 32.04iter/s]Best score: 497.6087: : 328iter [00:11, 32.04iter/s]Best score: 497.6087: : 329iter [00:11, 32.72iter/s]Best score: 497.5891: : 329iter [00:11, 32.72iter/s]Best score: 497.5891: : 330iter [00:11, 32.72iter/s]Best score: 497.5891: : 331iter [00:11, 32.72iter/s]Best score: 497.5891: : 332iter [00:11, 32.72iter/s]Best score: 497.5891: : 333iter [00:11, 33.01iter/s]Best score: 497.5891: : 333iter [00:11, 33.01iter/s]Best score: 497.5891: : 334iter [00:11, 33.01iter/s]Best score: 497.5891: : 335iter [00:11, 33.01iter/s]Best score: 497.5891: : 336iter [00:11, 33.01iter/s]Best score: 497.5891: : 337iter [00:11, 33.50iter/s]Best score: 497.5891: : 337iter [00:11, 33.50iter/s]Best score: 497.5891: : 338iter [00:11, 33.50iter/s]Best score: 497.5891: : 339iter [00:11, 33.50iter/s]Best score: 497.5891: : 340iter [00:11, 33.50iter/s]Best score: 497.5891: : 341iter [00:11, 33.52iter/s]Best score: 497.5891: : 341iter [00:11, 33.52iter/s]Best score: 497.5891: : 342iter [00:11, 33.52iter/s]Best score: 497.5891: : 343iter [00:12, 33.52iter/s]Best score: 497.5891: : 344iter [00:12, 33.52iter/s]Best score: 497.5891: : 345iter [00:12, 33.75iter/s]Best score: 497.5891: : 345iter [00:12, 33.75iter/s]Best score: 497.5891: : 346iter [00:12, 33.75iter/s]Best score: 497.5891: : 347iter [00:12, 33.75iter/s]Best score: 497.5891: : 348iter [00:12, 33.75iter/s]Best score: 497.5891: : 349iter [00:12, 33.86iter/s]
[ITER 350] Best: 497.5738
[PARAMS] Current best parameters:
  fx: 562.8918
  fy: 687.7365
  px: 967.0348
  py: 632.9546
  k1: -0.01544989
  k2: 0.07998116
  k3: 0.04976654
  k4: 0.01993493
  p1: -0.00993502
  p2: 0.00996597
  p3: -0.00976643
  p4: -0.00954546
  scale: 2.7862
  rotation: 1.2425
Best score: 497.5738: : 349iter [00:12, 33.86iter/s]Best score: 497.5738: : 350iter [00:12, 33.86iter/s]Best score: 497.5738: : 351iter [00:12, 33.86iter/s]Best score: 497.5738: : 352iter [00:12, 33.86iter/s]Best score: 497.5738: : 353iter [00:12, 33.82iter/s]Best score: 497.5738: : 353iter [00:12, 33.82iter/s]Best score: 497.5738: : 354iter [00:12, 33.82iter/s]Best score: 497.5738: : 355iter [00:12, 33.82iter/s]Best score: 497.5738: : 356iter [00:12, 33.82iter/s]Best score: 497.5738: : 357iter [00:12, 34.01iter/s]Best score: 497.5738: : 357iter [00:12, 34.01iter/s]Best score: 497.5738: : 358iter [00:12, 34.01iter/s]Best score: 497.4596: : 359iter [00:12, 34.01iter/s]Best score: 497.4596: : 360iter [00:12, 34.01iter/s]Best score: 497.4596: : 361iter [00:12, 33.93iter/s]Best score: 497.4596: : 361iter [00:12, 33.93iter/s]Best score: 497.4596: : 362iter [00:12, 33.93iter/s]Best score: 497.4596: : 363iter [00:12, 33.93iter/s]Best score: 497.4596: : 364iter [00:12, 33.93iter/s]Best score: 497.4596: : 365iter [00:12, 34.05iter/s]Best score: 497.4596: : 365iter [00:12, 34.05iter/s]Best score: 497.4596: : 366iter [00:12, 34.05iter/s]Best score: 497.4596: : 367iter [00:12, 34.05iter/s]Best score: 497.4596: : 368iter [00:12, 34.05iter/s]Best score: 497.4596: : 369iter [00:12, 34.13iter/s]Best score: 497.4077: : 369iter [00:12, 34.13iter/s]Best score: 497.4077: : 370iter [00:12, 34.13iter/s]Best score: 497.4077: : 371iter [00:12, 34.13iter/s]Best score: 497.4077: : 372iter [00:12, 34.13iter/s]Best score: 497.4077: : 373iter [00:12, 33.97iter/s]Best score: 497.4077: : 373iter [00:12, 33.97iter/s]Best score: 497.4077: : 374iter [00:12, 33.97iter/s]Best score: 497.4077: : 375iter [00:12, 33.97iter/s]Best score: 497.4077: : 376iter [00:12, 33.97iter/s]Best score: 497.4077: : 377iter [00:12, 34.05iter/s]Best score: 497.4077: : 377iter [00:13, 34.05iter/s]Best score: 497.4077: : 378iter [00:13, 34.05iter/s]Best score: 497.3737: : 379iter [00:13, 34.05iter/s]Best score: 497.3737: : 380iter [00:13, 34.05iter/s]Best score: 497.3737: : 381iter [00:13, 33.77iter/s]Best score: 497.3737: : 381iter [00:13, 33.77iter/s]Best score: 497.3737: : 382iter [00:13, 33.77iter/s]Best score: 497.3737: : 383iter [00:13, 33.77iter/s]Best score: 497.3737: : 384iter [00:13, 33.77iter/s]Best score: 497.3737: : 385iter [00:13, 33.89iter/s]Best score: 497.3737: : 385iter [00:13, 33.89iter/s]Best score: 497.3737: : 386iter [00:13, 33.89iter/s]Best score: 497.3737: : 387iter [00:13, 33.89iter/s]Best score: 497.3737: : 388iter [00:13, 33.89iter/s]Best score: 497.3737: : 389iter [00:13, 34.00iter/s]Best score: 497.3737: : 389iter [00:13, 34.00iter/s]Best score: 497.3737: : 390iter [00:13, 34.00iter/s]Best score: 497.3737: : 391iter [00:13, 34.00iter/s]Best score: 497.3737: : 392iter [00:13, 34.00iter/s]Best score: 497.3737: : 393iter [00:13, 33.91iter/s]Best score: 497.3737: : 393iter [00:13, 33.91iter/s]Best score: 497.3737: : 394iter [00:13, 33.91iter/s]Best score: 497.3737: : 395iter [00:13, 33.91iter/s]Best score: 497.3737: : 396iter [00:13, 33.91iter/s]Best score: 497.3737: : 397iter [00:13, 34.02iter/s]Best score: 497.3737: : 397iter [00:13, 34.02iter/s]Best score: 497.3737: : 398iter [00:13, 34.02iter/s]
[ITER 400] Best: 497.3474
[PARAMS] Current best parameters:
  fx: 562.7837
  fy: 687.5147
  px: 967.0099
  py: 632.9569
  k1: -0.02027917
  k2: 0.07983497
  k3: 0.04997380
  k4: 0.01991059
  p1: -0.00994648
  p2: 0.00986010
  p3: -0.00995401
  p4: -0.00959144
  scale: 2.7905
  rotation: 1.5395
Best score: 497.3474: : 399iter [00:13, 34.02iter/s]Best score: 497.3474: : 400iter [00:13, 34.02iter/s]Best score: 497.3474: : 401iter [00:13, 33.90iter/s]Best score: 497.3474: : 401iter [00:13, 33.90iter/s]Best score: 497.3474: : 402iter [00:13, 33.90iter/s]Best score: 497.3474: : 403iter [00:13, 33.90iter/s]Best score: 497.3474: : 404iter [00:13, 33.90iter/s]Best score: 497.3474: : 405iter [00:13, 34.04iter/s]Best score: 497.3474: : 405iter [00:13, 34.04iter/s]Best score: 497.3474: : 406iter [00:13, 34.04iter/s]Best score: 497.3474: : 407iter [00:13, 34.04iter/s]Best score: 497.3474: : 408iter [00:13, 34.04iter/s]Best score: 497.3474: : 409iter [00:13, 34.16iter/s]Best score: 497.2821: : 409iter [00:13, 34.16iter/s]Best score: 497.2821: : 410iter [00:13, 34.16iter/s]Best score: 497.2821: : 411iter [00:14, 34.16iter/s]Best score: 497.2821: : 412iter [00:14, 34.16iter/s]Best score: 497.2821: : 413iter [00:14, 33.95iter/s]Best score: 497.2821: : 413iter [00:14, 33.95iter/s]Best score: 497.2821: : 414iter [00:14, 33.95iter/s]Best score: 497.2821: : 415iter [00:14, 33.95iter/s]Best score: 497.2821: : 416iter [00:14, 33.95iter/s]Best score: 497.2821: : 417iter [00:14, 34.15iter/s]Best score: 497.2821: : 417iter [00:14, 34.15iter/s]Best score: 497.2821: : 418iter [00:14, 34.15iter/s]Best score: 497.2821: : 419iter [00:14, 34.15iter/s]Best score: 497.2821: : 420iter [00:14, 34.15iter/s]Best score: 497.2821: : 421iter [00:14, 33.93iter/s]Best score: 497.2821: : 421iter [00:14, 33.93iter/s]Best score: 497.2821: : 422iter [00:14, 33.93iter/s]Best score: 497.2821: : 423iter [00:14, 33.93iter/s]Best score: 497.2821: : 424iter [00:14, 33.93iter/s]Best score: 497.2821: : 425iter [00:14, 34.16iter/s]Best score: 497.2821: : 425iter [00:14, 34.16iter/s]Best score: 497.2821: : 426iter [00:14, 34.16iter/s]Best score: 497.2821: : 427iter [00:14, 34.16iter/s]Best score: 497.2821: : 428iter [00:14, 34.16iter/s]Best score: 497.2821: : 429iter [00:14, 34.15iter/s]Best score: 497.2654: : 429iter [00:14, 34.15iter/s]Best score: 497.2654: : 430iter [00:14, 34.15iter/s]Best score: 497.2654: : 431iter [00:14, 34.15iter/s]Best score: 497.2654: : 432iter [00:14, 34.15iter/s]Best score: 497.2654: : 433iter [00:14, 34.01iter/s]Best score: 497.2654: : 433iter [00:14, 34.01iter/s]Best score: 497.2654: : 434iter [00:14, 34.01iter/s]Best score: 497.2654: : 435iter [00:14, 34.01iter/s]Best score: 497.2654: : 436iter [00:14, 34.01iter/s]Best score: 497.2654: : 437iter [00:14, 34.21iter/s]Best score: 497.2654: : 437iter [00:14, 34.21iter/s]Best score: 497.2654: : 438iter [00:14, 34.21iter/s]Best score: 497.2462: : 439iter [00:14, 34.21iter/s]Best score: 497.2462: : 440iter [00:14, 34.21iter/s]Best score: 497.2462: : 441iter [00:14, 33.56iter/s]Best score: 497.2462: : 441iter [00:14, 33.56iter/s]Best score: 497.2462: : 442iter [00:14, 33.56iter/s]Best score: 497.2462: : 443iter [00:14, 33.56iter/s]Best score: 497.2462: : 444iter [00:14, 33.56iter/s]Best score: 497.2462: : 445iter [00:14, 33.78iter/s]Best score: 497.2462: : 445iter [00:15, 33.78iter/s]Best score: 497.2462: : 446iter [00:15, 33.78iter/s]Best score: 497.2462: : 447iter [00:15, 33.78iter/s]Best score: 497.2462: : 448iter [00:15, 33.78iter/s]Best score: 497.2462: : 449iter [00:15, 26.24iter/s]
[ITER 450] Best: 497.2061
[PARAMS] Current best parameters:
  fx: 562.7541
  fy: 687.6655
  px: 967.0058
  py: 632.9924
  k1: -0.02302747
  k2: 0.07983374
  k3: 0.04994015
  k4: 0.01994072
  p1: -0.00999015
  p2: 0.00998841
  p3: -0.00996988
  p4: -0.00967690
  scale: 2.7961
  rotation: 1.4989
Best score: 497.2061: : 449iter [00:15, 26.24iter/s]Best score: 497.2061: : 450iter [00:15, 26.24iter/s]Best score: 497.2061: : 451iter [00:15, 26.24iter/s]Best score: 497.2061: : 452iter [00:15, 26.24iter/s]Best score: 497.2061: : 453iter [00:15, 28.16iter/s]Best score: 497.2061: : 453iter [00:15, 28.16iter/s]Best score: 497.2061: : 454iter [00:15, 28.16iter/s]Best score: 497.2061: : 455iter [00:15, 28.16iter/s]Best score: 497.2061: : 456iter [00:15, 28.16iter/s]Best score: 497.2061: : 457iter [00:15, 29.78iter/s]Best score: 497.2061: : 457iter [00:15, 29.78iter/s]Best score: 497.2061: : 458iter [00:15, 29.78iter/s]Best score: 497.2061: : 459iter [00:15, 29.78iter/s]Best score: 497.2061: : 460iter [00:15, 29.78iter/s]Best score: 497.2061: : 461iter [00:15, 30.90iter/s]Best score: 497.2061: : 461iter [00:15, 30.90iter/s]Best score: 497.2061: : 462iter [00:15, 30.90iter/s]Best score: 497.2061: : 463iter [00:15, 30.90iter/s]Best score: 497.2061: : 464iter [00:15, 30.90iter/s]Best score: 497.2061: : 465iter [00:15, 31.89iter/s]Best score: 497.2061: : 465iter [00:15, 31.89iter/s]Best score: 497.2061: : 466iter [00:15, 31.89iter/s]Best score: 497.2061: : 467iter [00:15, 31.89iter/s]Best score: 497.2061: : 468iter [00:15, 31.89iter/s]Best score: 497.2061: : 469iter [00:15, 32.63iter/s]Best score: 497.2030: : 469iter [00:15, 32.63iter/s]Best score: 497.2030: : 470iter [00:15, 32.63iter/s]Best score: 497.2030: : 471iter [00:15, 32.63iter/s]Best score: 497.2030: : 472iter [00:15, 32.63iter/s]Best score: 497.2030: : 473iter [00:15, 32.93iter/s]Best score: 497.2030: : 473iter [00:15, 32.93iter/s]Best score: 497.2030: : 474iter [00:15, 32.93iter/s]Best score: 497.2030: : 475iter [00:16, 32.93iter/s]Best score: 497.2030: : 476iter [00:16, 32.93iter/s]Best score: 497.2030: : 477iter [00:16, 33.39iter/s]Best score: 497.2030: : 477iter [00:16, 33.39iter/s]Best score: 497.2030: : 478iter [00:16, 33.39iter/s]Best score: 497.1820: : 479iter [00:16, 33.39iter/s]Best score: 497.1820: : 480iter [00:16, 33.39iter/s]Best score: 497.1820: : 481iter [00:16, 33.44iter/s]Best score: 497.1820: : 481iter [00:16, 33.44iter/s]Best score: 497.1820: : 482iter [00:16, 33.44iter/s]Best score: 497.1820: : 483iter [00:16, 33.44iter/s]Best score: 497.1820: : 484iter [00:16, 33.44iter/s]Best score: 497.1820: : 485iter [00:16, 33.79iter/s]Best score: 497.1820: : 485iter [00:16, 33.79iter/s]Best score: 497.1820: : 486iter [00:16, 33.79iter/s]Best score: 497.1820: : 487iter [00:16, 33.79iter/s]Best score: 497.1820: : 488iter [00:16, 33.79iter/s]Best score: 497.1820: : 489iter [00:16, 34.11iter/s]Best score: 497.1820: : 489iter [00:16, 34.11iter/s]Best score: 497.1820: : 490iter [00:16, 34.11iter/s]Best score: 497.1820: : 491iter [00:16, 34.11iter/s]Best score: 497.1820: : 492iter [00:16, 34.11iter/s]Best score: 497.1820: : 493iter [00:16, 33.98iter/s]Best score: 497.1820: : 493iter [00:16, 33.98iter/s]Best score: 497.1820: : 494iter [00:16, 33.98iter/s]Best score: 497.1820: : 495iter [00:16, 33.98iter/s]Best score: 497.1820: : 496iter [00:16, 33.98iter/s]Best score: 497.1820: : 497iter [00:16, 34.12iter/s]Best score: 497.1820: : 497iter [00:16, 34.12iter/s]Best score: 497.1820: : 498iter [00:16, 34.12iter/s]
[ITER 500] Best: 497.1678
[PARAMS] Current best parameters:
  fx: 562.7557
  fy: 687.7266
  px: 967.0004
  py: 632.9958
  k1: -0.02393204
  k2: 0.07993687
  k3: 0.04999095
  k4: 0.01998072
  p1: -0.00999749
  p2: 0.00993914
  p3: -0.00998984
  p4: -0.00984743
  scale: 2.7988
  rotation: 1.5230
Best score: 497.1678: : 499iter [00:16, 34.12iter/s]Best score: 497.1678: : 500iter [00:16, 29.91iter/s]

[📈] Running L-BFGS-B polish after 500 DE iterations...
  Polish improved score: 497.167767 -> 497.123399
  Success: True
  Tangential distortion: p1=-0.010000, p2=0.010000, p3=-0.010000, p4=-0.010000
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 497.1234
[✅] Parameters copied to: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 562.7552589072903
    fy: 687.7270577617845
    px: 967.0001026618112
    py: 632.9965906037112
    k1: -0.022230997737674136
    k2: 0.08
    k3: 0.05
    k4: 0.02
    p1: -0.01
    p2: 0.01
    p3: -0.01
    p4: -0.01
    scale: 2.7956083785464303
    rotation: 1.5230426204920933
    mmpp: 5.6209382265402565
    nmpp: 5620938.226540256

[🔄] Converting 1470 frames to PTD, creating debug images for 400 calibration frames...
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                                                                  [A📁 Rigs: 100%|██████████| 4/4 [00:09<00:00,  2.47s/rig]📁 Rigs: 100%|██████████| 4/4 [00:09<00:00,  2.32s/rig]
[✅] Saved PTDs: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/3_undistorted/output/ptd
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[✅] Saved PTDs: /working/STLC-SMD-083WF_W028(6)_2026-02-04/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:   6%|▌         | 172565/3080448 [00:00<00:01, 1725647.53px/s]📤 Writing LUT:  12%|█▏        | 378194/3080448 [00:00<00:01, 1920138.68px/s]📤 Writing LUT:  19%|█▉        | 584594/3080448 [00:00<00:01, 1985827.38px/s]📤 Writing LUT:  25%|██▌       | 783177/3080448 [00:00<00:01, 1776255.06px/s]📤 Writing LUT:  31%|███▏      | 963721/3080448 [00:00<00:01, 1556218.40px/s]📤 Writing LUT:  36%|███▋      | 1124359/3080448 [00:00<00:01, 1448302.80px/s]📤 Writing LUT:  41%|████▏     | 1272795/3080448 [00:00<00:01, 1385659.84px/s]📤 Writing LUT:  46%|████▌     | 1413559/3080448 [00:00<00:01, 1341966.75px/s]📤 Writing LUT:  50%|█████     | 1549001/3080448 [00:01<00:01, 1308191.33px/s]📤 Writing LUT:  55%|█████▍    | 1680472/3080448 [00:01<00:01, 1282691.64px/s]📤 Writing LUT:  59%|█████▊    | 1809039/3080448 [00:01<00:01, 1266227.98px/s]📤 Writing LUT:  63%|██████▎   | 1935768/3080448 [00:01<00:00, 1254993.06px/s]📤 Writing LUT:  67%|██████▋   | 2061280/3080448 [00:01<00:00, 1233725.60px/s]📤 Writing LUT:  71%|███████   | 2184623/3080448 [00:01<00:00, 1214932.84px/s]📤 Writing LUT:  75%|███████▍  | 2306063/3080448 [00:01<00:00, 1198497.27px/s]📤 Writing LUT:  79%|███████▉  | 2431656/3080448 [00:01<00:00, 1215034.39px/s]📤 Writing LUT:  83%|████████▎ | 2567243/3080448 [00:01<00:00, 1256083.88px/s]📤 Writing LUT:  88%|████████▊ | 2711901/3080448 [00:01<00:00, 1312084.52px/s]📤 Writing LUT:  93%|█████████▎| 2863405/3080448 [00:02<00:00, 1372109.85px/s]📤 Writing LUT:  98%|█████████▊| 3024884/3080448 [00:02<00:00, 1444189.50px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:02<00:00, 1394077.20px/s]
[✅] LUT written to: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/3_undistorted/output/LUT.lut
[WARN] Folder name 'STLC-SMD-083WF_W028(6)_2026-02-04' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/STLC-SMD-083WF_W028(6)_2026-02-04_unknown.lut

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

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                                                                 [A[✅] 1000: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/4_heatmap/1000_heatmap.png
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                                                                 [A[✅] 1500: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/4_heatmap/1500_heatmap.png
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                                                                  [A[✅] 2000R: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/4_heatmap/2000R_heatmap.png
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                                                                  [A[✅] 2000L: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/4_heatmap/2000L_heatmap.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:06<00:00,  1.72s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:06<00:00,  1.62s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] Heatmaps saved to: /working/STLC-SMD-083WF_W028(6)_2026-02-04/calibration/debug/4_heatmap
[🏁] Finished processing STLC-SMD-083WF_W028(6)_2026-02-04


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