2026-09-30T22:05:30 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC012_W046_2026-09-28 ===
2026-09-30T22:05:30 - INFO: Camera ID: 15091914
2026-09-30T22:05:30 - INFO: Wand ID: 
2026-09-30T22:05:30 - INFO: Recorded At: 2026-09-28T15:56:26Z

-------------------------------------------------------------------
                        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 2 las0.zip files to process
Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 497 stationary frames, 697 remain

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                                                                           [A[PASS2] Frame 0000324: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000325: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000326: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000327: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000382: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000383: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000384: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000385: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000454: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000455: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000456: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000896: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000897: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000898: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000899: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000921: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000922: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000923: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000924: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000942: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000943: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000944: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000945: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000951: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000952: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000953: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000954: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001016: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001017: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001018: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001019: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001020: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001021: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001087: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001088: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001089: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001090: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001207: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001208: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001209: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001210: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000326: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000384: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000455: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000898: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000923: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000944: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000953: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001018: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001020: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001089: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001209: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 22 frames fixed, 22 untrusted, 697 total

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                                                                           [A
[TIMING] 1000 — 697 frames
  Phase 1  radial detect   (parallel)  :  11.77s  (16.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.33s  (7.6 ms/frame)
  Phase 2b corner fix      (sequential):   1.41s
  Phase 3  finalize        (sequential):  14.14s  (20.3 ms/frame)
    decode + undistort                 :   7.12s  (10.2 ms/frame)
    corner apply + refinement          :   3.39s  (4.9 ms/frame)
    segment                            :   2.98s  (4.3 ms/frame)
    smooth                             :   0.41s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  32.65s

Processing Rigs:  50%|█████     | 1/2 [00:38<00:38, 38.66s/it]
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                                                                          [A[PASS2] Frame 0000808: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000818: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000819: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000821: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000876: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000878: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 5 frames fixed, 1 untrusted, 695 total

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                                                                          [A
[TIMING] 500 — 695 frames
  Phase 1  radial detect   (parallel)  :  12.63s  (18.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.78s  (8.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.35s
  Phase 3  finalize        (sequential):  13.56s  (19.5 ms/frame)
    decode + undistort                 :   6.97s  (10.0 ms/frame)
    corner apply + refinement          :   2.95s  (4.2 ms/frame)
    segment                            :   2.98s  (4.3 ms/frame)
    smooth                             :   0.41s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  32.33s

Processing Rigs: 100%|██████████| 2/2 [01:14<00:00, 37.26s/it]Processing Rigs: 100%|██████████| 2/2 [01:14<00:00, 37.47s/it]
[💾] Saved RigFrameDataset to /working/MDC012_W046_2026-09-28/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1392 frames from 2 rigs to /working/MDC012_W046_2026-09-28/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC012_W046_2026-09-28/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 697
[📋] 500: 100 frames selected from 695
[💾] Saved RigFrameDataset to /working/MDC012_W046_2026-09-28/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC012_W046_2026-09-28/calibration/debug/1_digitized/rig_frames.json

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                      Step 2: Casing Detection                     
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-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]Computing max image from bright frames:   2%|▎         | 1/40 [00:05<03:29,  5.38s/it]Computing max image from bright frames:   5%|▌         | 2/40 [00:05<01:26,  2.28s/it]Computing max image from bright frames:   8%|▊         | 3/40 [00:05<00:47,  1.29s/it]Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:29,  1.21it/s]Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:19,  1.75it/s]Computing max image from bright frames:  18%|█▊        | 7/40 [00:06<00:10,  3.03it/s]Computing max image from bright frames:  22%|██▎       | 9/40 [00:06<00:06,  4.53it/s]Computing max image from bright frames:  28%|██▊       | 11/40 [00:06<00:04,  6.15it/s]Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:03,  7.77it/s]Computing max image from bright frames:  38%|███▊      | 15/40 [00:06<00:03,  7.36it/s]Computing max image from bright frames:  42%|████▎     | 17/40 [00:06<00:02,  8.59it/s]Computing max image from bright frames:  48%|████▊     | 19/40 [00:07<00:02,  8.45it/s]Computing max image from bright frames:  55%|█████▌    | 22/40 [00:07<00:01, 11.23it/s]Computing max image from bright frames:  60%|██████    | 24/40 [00:07<00:01, 10.14it/s]Computing max image from bright frames:  68%|██████▊   | 27/40 [00:07<00:01, 12.82it/s]Computing max image from bright frames:  75%|███████▌  | 30/40 [00:07<00:00, 14.24it/s]Computing max image from bright frames:  82%|████████▎ | 33/40 [00:09<00:01,  5.07it/s]Computing max image from bright frames:  88%|████████▊ | 35/40 [00:09<00:00,  5.41it/s]Computing max image from bright frames:  92%|█████████▎| 37/40 [00:09<00:00,  5.94it/s]Computing max image from bright frames:  98%|█████████▊| 39/40 [00:09<00:00,  7.25it/s]                                                                                       [✅] Casing detection complete: cx=995.0, cy=762.0

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                        Step 3: Calibration                        
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  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:29,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:29,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   2%|▏         | 2/100 [00:00<00:29,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   4%|▍         | 4/100 [00:00<00:28,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   5%|▌         | 5/100 [00:00<00:28,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   6%|▌         | 6/100 [00:00<00:27,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   7%|▋         | 7/100 [00:00<00:27,  3.37it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   8%|▊         | 8/100 [00:00<00:04, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.036 | Best: {'fx': 547.475} (0.036):   8%|▊         | 8/100 [00:00<00:04, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 547.475} (0.036):   9%|▉         | 9/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 547.475} (0.036):  10%|█         | 10/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.029 | Best: {'fx': 547.475} (0.036):  11%|█         | 11/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.047 | Best: {'fx': 526.263} (0.047):  12%|█▏        | 12/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 526.263} (0.047):  13%|█▎        | 13/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.076 | Best: {'fx': 533.333} (0.076):  14%|█▍        | 14/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  15%|█▌        | 15/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.060 | Best: {'fx': 533.333} (0.076):  16%|█▌        | 16/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  17%|█▋        | 17/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  18%|█▊        | 18/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.028 | Best: {'fx': 533.333} (0.076):  19%|█▉        | 19/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.020 | Best: {'fx': 533.333} (0.076):  20%|██        | 20/100 [00:00<00:03, 22.94it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.020 | Best: {'fx': 533.333} (0.076):  21%|██        | 21/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.023 | Best: {'fx': 533.333} (0.076):  21%|██        | 21/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.018 | Best: {'fx': 533.333} (0.076):  22%|██▏       | 22/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  23%|██▎       | 23/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  24%|██▍       | 24/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  25%|██▌       | 25/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  26%|██▌       | 26/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  27%|██▋       | 27/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  28%|██▊       | 28/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  29%|██▉       | 29/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.076):  30%|███       | 30/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.076):  31%|███       | 31/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  32%|███▏      | 32/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.076):  33%|███▎      | 33/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.076):  34%|███▍      | 34/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.076):  35%|███▌      | 35/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  36%|███▌      | 36/100 [00:00<00:01, 54.90it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  37%|███▋      | 37/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.076):  37%|███▋      | 37/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  38%|███▊      | 38/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  39%|███▉      | 39/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.076):  40%|████      | 40/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.076):  41%|████      | 41/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.076):  42%|████▏     | 42/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  43%|████▎     | 43/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 533.333} (0.076):  44%|████▍     | 44/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  45%|████▌     | 45/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.005 | Best: {'fx': 533.333} (0.076):  46%|████▌     | 46/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  47%|████▋     | 47/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.076):  48%|████▊     | 48/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  49%|████▉     | 49/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.076):  50%|█████     | 50/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  51%|█████     | 51/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.076):  52%|█████▏    | 52/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.076):  53%|█████▎    | 53/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  54%|█████▍    | 54/100 [00:00<00:00, 85.30it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  55%|█████▌    | 55/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  55%|█████▌    | 55/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  56%|█████▌    | 56/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  57%|█████▋    | 57/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  58%|█████▊    | 58/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  59%|█████▉    | 59/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  60%|██████    | 60/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  61%|██████    | 61/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  62%|██████▏   | 62/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.076):  63%|██████▎   | 63/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  64%|██████▍   | 64/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  65%|██████▌   | 65/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  66%|██████▌   | 66/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  67%|██████▋   | 67/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  68%|██████▊   | 68/100 [00:00<00:00, 112.93it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  69%|██████▉   | 69/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  69%|██████▉   | 69/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  70%|███████   | 70/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  71%|███████   | 71/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  72%|███████▏  | 72/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  73%|███████▎  | 73/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  74%|███████▍  | 74/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  75%|███████▌  | 75/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  76%|███████▌  | 76/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  77%|███████▋  | 77/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  78%|███████▊  | 78/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  79%|███████▉  | 79/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  80%|████████  | 80/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  81%|████████  | 81/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  82%|████████▏ | 82/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  83%|████████▎ | 83/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  84%|████████▍ | 84/100 [00:00<00:00, 113.55it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  85%|████████▌ | 85/100 [00:00<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  85%|████████▌ | 85/100 [00:00<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  86%|████████▌ | 86/100 [00:00<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  87%|████████▋ | 87/100 [00:00<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  88%|████████▊ | 88/100 [00:00<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  89%|████████▉ | 89/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  90%|█████████ | 90/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  91%|█████████ | 91/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  92%|█████████▏| 92/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  93%|█████████▎| 93/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  94%|█████████▍| 94/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  95%|█████████▌| 95/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  96%|█████████▌| 96/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  97%|█████████▋| 97/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  98%|█████████▊| 98/100 [00:01<00:00, 126.50it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  99%|█████████▉| 99/100 [00:01<00:00, 101.36it/s]Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.076):  99%|█████████▉| 99/100 [00:01<00:00, 101.36it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.012 | Best: {'fx': 510.374} (0.012):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.012 | Best: {'fx': 510.374} (0.012):   2%|▏         | 1/50 [00:00<00:05,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.007 | Best: {'fx': 510.374} (0.012):   2%|▏         | 1/50 [00:00<00:05,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.021 | Best: {'fx': 514.456} (0.021):   4%|▍         | 2/50 [00:00<00:05,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.008 | Best: {'fx': 514.456} (0.021):   6%|▌         | 3/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.023 | Best: {'fx': 515.476} (0.023):   8%|▊         | 4/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.015 | Best: {'fx': 515.476} (0.023):  10%|█         | 5/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.018 | Best: {'fx': 515.476} (0.023):  12%|█▏        | 6/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.019 | Best: {'fx': 515.476} (0.023):  14%|█▍        | 7/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.026 | Best: {'fx': 517.517} (0.026):  16%|█▌        | 8/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.025 | Best: {'fx': 517.517} (0.026):  18%|█▊        | 9/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.028 | Best: {'fx': 518.537} (0.028):  20%|██        | 10/50 [00:00<00:04,  9.41it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.028 | Best: {'fx': 518.537} (0.028):  22%|██▏       | 11/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.030 | Best: {'fx': 519.558} (0.030):  22%|██▏       | 11/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.031 | Best: {'fx': 520.578} (0.031):  24%|██▍       | 12/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.033 | Best: {'fx': 521.599} (0.033):  26%|██▌       | 13/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.036 | Best: {'fx': 522.619} (0.036):  28%|██▊       | 14/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.038 | Best: {'fx': 523.639} (0.038):  30%|███       | 15/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.060 | Best: {'fx': 529.762} (0.060):  32%|███▏      | 16/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.042 | Best: {'fx': 529.762} (0.060):  34%|███▍      | 17/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.056 | Best: {'fx': 529.762} (0.060):  36%|███▌      | 18/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.048 | Best: {'fx': 529.762} (0.060):  38%|███▊      | 19/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.046 | Best: {'fx': 529.762} (0.060):  40%|████      | 20/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.063 | Best: {'fx': 530.782} (0.063):  42%|████▏     | 21/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.052 | Best: {'fx': 530.782} (0.063):  44%|████▍     | 22/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.068 | Best: {'fx': 531.803} (0.068):  46%|████▌     | 23/50 [00:00<00:00, 58.19it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.068 | Best: {'fx': 531.803} (0.068):  48%|████▊     | 24/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.077 | Best: {'fx': 533.844} (0.077):  48%|████▊     | 24/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.077 | Best: {'fx': 533.844} (0.077):  50%|█████     | 25/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.060 | Best: {'fx': 533.844} (0.077):  52%|█████▏    | 26/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.074 | Best: {'fx': 533.844} (0.077):  54%|█████▍    | 27/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.076 | Best: {'fx': 533.844} (0.077):  56%|█████▌    | 28/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.069 | Best: {'fx': 533.844} (0.077):  58%|█████▊    | 29/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.078 | Best: {'fx': 535.884} (0.078):  60%|██████    | 30/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.056 | Best: {'fx': 535.884} (0.078):  62%|██████▏   | 31/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.062 | Best: {'fx': 535.884} (0.078):  64%|██████▍   | 32/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.040 | Best: {'fx': 535.884} (0.078):  66%|██████▌   | 33/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.050 | Best: {'fx': 535.884} (0.078):  68%|██████▊   | 34/50 [00:00<00:00, 83.18it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.050 | Best: {'fx': 535.884} (0.078):  70%|███████   | 35/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.043 | Best: {'fx': 535.884} (0.078):  70%|███████   | 35/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.047 | Best: {'fx': 535.884} (0.078):  72%|███████▏  | 36/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.041 | Best: {'fx': 535.884} (0.078):  74%|███████▍  | 37/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.037 | Best: {'fx': 535.884} (0.078):  76%|███████▌  | 38/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.036 | Best: {'fx': 535.884} (0.078):  78%|███████▊  | 39/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.034 | Best: {'fx': 535.884} (0.078):  80%|████████  | 40/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.032 | Best: {'fx': 535.884} (0.078):  82%|████████▏ | 41/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.026 | Best: {'fx': 535.884} (0.078):  84%|████████▍ | 42/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.025 | Best: {'fx': 535.884} (0.078):  86%|████████▌ | 43/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.025 | Best: {'fx': 535.884} (0.078):  88%|████████▊ | 44/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.031 | Best: {'fx': 535.884} (0.078):  90%|█████████ | 45/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.027 | Best: {'fx': 535.884} (0.078):  92%|█████████▏| 46/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.031 | Best: {'fx': 535.884} (0.078):  94%|█████████▍| 47/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.029 | Best: {'fx': 535.884} (0.078):  96%|█████████▌| 48/50 [00:00<00:00, 90.24it/s]Brute | Start: {'fx': 533.333} (0.076) | Current: 0.028 | Best: {'fx': 535.884} (0.078):  98%|█████████▊| 49/50 [00:00<00:00, 90.24it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 535.8843537414966
    px: 995.0
    py: 762.0
    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 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization
Initial score: 22.0458: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 22.0458, picklable_objective(x0) = 22.0523
Best score: 22.0523: : 0iter [00:01, ?iter/s] Best score: 22.0523: : 1iter [00:01,  1.94s/iter]Best score: 22.0523: : 1iter [00:01,  1.94s/iter]Best score: 22.0523: : 2iter [00:02,  1.94s/iter]Best score: 22.0523: : 3iter [00:02,  1.75iter/s]Best score: 22.0523: : 3iter [00:02,  1.75iter/s]Best score: 22.0523: : 4iter [00:02,  1.75iter/s]Best score: 22.0523: : 5iter [00:02,  1.75iter/s]Best score: 22.0523: : 6iter [00:02,  1.75iter/s]Best score: 22.0523: : 7iter [00:02,  4.98iter/s]Best score: 22.0523: : 7iter [00:02,  4.98iter/s]Best score: 22.0523: : 8iter [00:02,  4.98iter/s]Best score: 22.0523: : 9iter [00:02,  4.98iter/s]Best score: 22.0523: : 10iter [00:02,  4.98iter/s]Best score: 22.0523: : 11iter [00:02,  8.71iter/s]Best score: 22.0523: : 11iter [00:02,  8.71iter/s]Best score: 22.0523: : 12iter [00:02,  8.71iter/s]Best score: 22.0523: : 13iter [00:02,  8.71iter/s]Best score: 22.0523: : 14iter [00:02,  8.71iter/s]Best score: 22.0523: : 15iter [00:02, 12.82iter/s]Best score: 22.0523: : 15iter [00:02, 12.82iter/s]Best score: 22.0523: : 16iter [00:02, 12.82iter/s]Best score: 22.0523: : 17iter [00:02, 12.82iter/s]Best score: 22.0523: : 18iter [00:02, 12.82iter/s]Best score: 22.0523: : 19iter [00:02, 17.05iter/s]Best score: 22.0523: : 19iter [00:02, 17.05iter/s]Best score: 22.0523: : 20iter [00:02, 17.05iter/s]Best score: 22.0523: : 21iter [00:02, 17.05iter/s]Best score: 22.0523: : 22iter [00:02, 17.05iter/s]Best score: 22.0523: : 23iter [00:02, 21.08iter/s]Best score: 22.0523: : 23iter [00:02, 21.08iter/s]Best score: 22.0523: : 24iter [00:02, 21.08iter/s]Best score: 22.0523: : 25iter [00:02, 21.08iter/s]Best score: 22.0523: : 26iter [00:02, 21.08iter/s]Best score: 22.0523: : 27iter [00:02, 24.74iter/s]Best score: 22.0523: : 27iter [00:02, 24.74iter/s]Best score: 22.0523: : 28iter [00:02, 24.74iter/s]Best score: 22.0523: : 29iter [00:02, 24.74iter/s]Best score: 22.0523: : 30iter [00:02, 24.74iter/s]Best score: 22.0523: : 31iter [00:02, 27.79iter/s]Best score: 22.0523: : 31iter [00:02, 27.79iter/s]Best score: 22.0523: : 32iter [00:02, 27.79iter/s]Best score: 22.0523: : 33iter [00:02, 27.79iter/s]Best score: 22.0523: : 34iter [00:02, 27.79iter/s]Best score: 22.0523: : 35iter [00:02, 30.46iter/s]Best score: 22.0523: : 35iter [00:02, 30.46iter/s]Best score: 22.0523: : 36iter [00:03, 30.46iter/s]Best score: 22.0523: : 37iter [00:03, 30.46iter/s]Best score: 22.0523: : 38iter [00:03, 30.46iter/s]Best score: 22.0523: : 39iter [00:03, 32.60iter/s]Best score: 22.0523: : 39iter [00:03, 32.60iter/s]Best score: 22.0523: : 40iter [00:03, 32.60iter/s]Best score: 22.0523: : 41iter [00:03, 32.60iter/s]Best score: 22.0523: : 42iter [00:03, 32.60iter/s]Best score: 22.0523: : 43iter [00:03, 34.05iter/s]Best score: 22.0523: : 43iter [00:03, 34.05iter/s]Best score: 22.0523: : 44iter [00:03, 34.05iter/s]Best score: 22.0523: : 45iter [00:03, 34.05iter/s]Best score: 22.0523: : 46iter [00:03, 34.05iter/s]Best score: 22.0523: : 47iter [00:03, 35.25iter/s]Best score: 22.0523: : 47iter [00:03, 35.25iter/s]Best score: 22.0523: : 48iter [00:03, 35.25iter/s]
[ITER 50] Best: 22.0523
[PARAMS] Current best parameters:
  fx: 535.8844
  fy: 535.8844
  px: 995.0000
  py: 762.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 22.0523: : 49iter [00:03, 35.25iter/s]Best score: 22.0523: : 50iter [00:03, 35.25iter/s]Best score: 22.0523: : 51iter [00:03, 36.12iter/s]Best score: 22.0523: : 51iter [00:03, 36.12iter/s]Best score: 22.0523: : 52iter [00:03, 36.12iter/s]Best score: 22.0523: : 53iter [00:03, 36.12iter/s]Best score: 22.0523: : 54iter [00:03, 36.12iter/s]Best score: 22.0523: : 55iter [00:03, 37.03iter/s]Best score: 22.0523: : 55iter [00:03, 37.03iter/s]Best score: 22.0523: : 56iter [00:03, 37.03iter/s]Best score: 22.0523: : 57iter [00:03, 37.03iter/s]Best score: 22.0523: : 58iter [00:03, 37.03iter/s]Best score: 22.0523: : 59iter [00:03, 37.50iter/s]Best score: 22.0523: : 59iter [00:03, 37.50iter/s]Best score: 22.0523: : 60iter [00:03, 37.50iter/s]Best score: 22.0523: : 61iter [00:03, 37.50iter/s]Best score: 22.0523: : 62iter [00:03, 37.50iter/s]Best score: 22.0523: : 63iter [00:03, 37.74iter/s]Best score: 22.0523: : 63iter [00:03, 37.74iter/s]Best score: 22.0523: : 64iter [00:03, 37.74iter/s]Best score: 22.0523: : 65iter [00:03, 37.74iter/s]Best score: 22.0523: : 66iter [00:03, 37.74iter/s]Best score: 22.0523: : 67iter [00:03, 38.07iter/s]Best score: 22.0523: : 67iter [00:03, 38.07iter/s]Best score: 22.0523: : 68iter [00:03, 38.07iter/s]Best score: 22.0523: : 69iter [00:03, 38.07iter/s]Best score: 22.0523: : 70iter [00:03, 38.07iter/s]Best score: 22.0523: : 71iter [00:03, 37.70iter/s]Best score: 22.0523: : 71iter [00:03, 37.70iter/s]Best score: 22.0523: : 72iter [00:03, 37.70iter/s]Best score: 22.0523: : 73iter [00:03, 37.70iter/s]Best score: 22.0523: : 74iter [00:03, 37.70iter/s]Best score: 22.0523: : 75iter [00:03, 37.91iter/s]Best score: 22.0523: : 75iter [00:04, 37.91iter/s]Best score: 22.0523: : 76iter [00:04, 37.91iter/s]Best score: 22.0523: : 77iter [00:04, 37.91iter/s]Best score: 22.0523: : 78iter [00:04, 37.91iter/s]Best score: 22.0523: : 79iter [00:04, 38.16iter/s]Best score: 22.0523: : 79iter [00:04, 38.16iter/s]Best score: 22.0523: : 80iter [00:04, 38.16iter/s]Best score: 22.0523: : 81iter [00:04, 38.16iter/s]Best score: 22.0523: : 82iter [00:04, 38.16iter/s]Best score: 22.0523: : 83iter [00:04, 38.13iter/s]Best score: 22.0523: : 83iter [00:04, 38.13iter/s]Best score: 22.0523: : 84iter [00:04, 38.13iter/s]Best score: 22.0523: : 85iter [00:04, 38.13iter/s]Best score: 22.0523: : 86iter [00:04, 38.13iter/s]Best score: 22.0523: : 87iter [00:04, 37.99iter/s]Best score: 22.0523: : 87iter [00:04, 37.99iter/s]Best score: 22.0523: : 88iter [00:04, 37.99iter/s]Best score: 22.0523: : 89iter [00:04, 37.99iter/s]Best score: 22.0523: : 90iter [00:04, 37.99iter/s]Best score: 22.0523: : 91iter [00:04, 38.09iter/s]Best score: 22.0523: : 91iter [00:04, 38.09iter/s]Best score: 22.0523: : 92iter [00:04, 38.09iter/s]Best score: 22.0523: : 93iter [00:04, 38.09iter/s]Best score: 22.0523: : 94iter [00:04, 38.09iter/s]Best score: 22.0523: : 95iter [00:04, 38.44iter/s]Best score: 22.0523: : 95iter [00:04, 38.44iter/s]Best score: 22.0523: : 96iter [00:04, 38.44iter/s]Best score: 22.0523: : 97iter [00:04, 38.44iter/s]Best score: 22.0523: : 98iter [00:04, 38.44iter/s]Best score: 22.0523: : 99iter [00:04, 38.86iter/s]
[ITER 100] Best: 22.0523
[PARAMS] Current best parameters:
  fx: 535.8844
  fy: 535.8844
  px: 995.0000
  py: 762.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 22.0523: : 99iter [00:04, 38.86iter/s]Best score: 22.0523: : 100iter [00:04, 38.86iter/s]Best score: 22.0523: : 101iter [00:04, 38.86iter/s]Best score: 22.0523: : 102iter [00:04, 38.86iter/s]Best score: 22.0523: : 103iter [00:04, 35.80iter/s]Best score: 22.0523: : 103iter [00:04, 35.80iter/s]Best score: 22.0523: : 104iter [00:04, 35.80iter/s]Best score: 22.0523: : 105iter [00:04, 35.80iter/s]Best score: 22.0523: : 106iter [00:04, 35.80iter/s]Best score: 22.0523: : 107iter [00:04, 36.46iter/s]Best score: 22.0523: : 107iter [00:04, 36.46iter/s]Best score: 22.0523: : 108iter [00:04, 36.46iter/s]Best score: 22.0523: : 109iter [00:04, 36.46iter/s]Best score: 22.0523: : 110iter [00:04, 36.46iter/s]Best score: 22.0523: : 111iter [00:04, 37.01iter/s]Best score: 22.0523: : 111iter [00:04, 37.01iter/s]Best score: 22.0523: : 112iter [00:05, 37.01iter/s]Best score: 22.0523: : 113iter [00:05, 37.01iter/s]Best score: 22.0523: : 114iter [00:05, 37.01iter/s]Best score: 22.0523: : 115iter [00:05, 37.34iter/s]Best score: 22.0523: : 115iter [00:05, 37.34iter/s]Best score: 22.0523: : 116iter [00:05, 37.34iter/s]Best score: 22.0523: : 117iter [00:05, 37.34iter/s]Best score: 22.0523: : 118iter [00:05, 37.34iter/s]Best score: 22.0523: : 119iter [00:05, 37.54iter/s]Best score: 22.0523: : 119iter [00:05, 37.54iter/s]Best score: 22.0523: : 120iter [00:05, 37.54iter/s]Best score: 22.0523: : 121iter [00:05, 37.54iter/s]Best score: 22.0523: : 122iter [00:05, 37.54iter/s]Best score: 22.0523: : 123iter [00:05, 37.52iter/s]Best score: 22.0523: : 123iter [00:05, 37.52iter/s]Best score: 22.0523: : 124iter [00:05, 37.52iter/s]Best score: 22.0523: : 125iter [00:05, 37.52iter/s]Best score: 22.0523: : 126iter [00:05, 37.52iter/s]Best score: 22.0523: : 127iter [00:05, 38.01iter/s]Best score: 22.0523: : 127iter [00:05, 38.01iter/s]Best score: 22.0523: : 128iter [00:05, 38.01iter/s]Best score: 22.0523: : 129iter [00:05, 38.01iter/s]Best score: 22.0523: : 130iter [00:05, 38.01iter/s]Best score: 22.0523: : 131iter [00:05, 37.86iter/s]Best score: 22.0523: : 131iter [00:05, 37.86iter/s]Best score: 22.0523: : 132iter [00:05, 37.86iter/s]Best score: 22.0523: : 133iter [00:05, 37.86iter/s]Best score: 22.0523: : 134iter [00:05, 37.86iter/s]Best score: 22.0523: : 135iter [00:05, 38.21iter/s]Best score: 22.0523: : 135iter [00:05, 38.21iter/s]Best score: 22.0523: : 136iter [00:05, 38.21iter/s]Best score: 22.0523: : 137iter [00:05, 38.21iter/s]Best score: 22.0523: : 138iter [00:05, 38.21iter/s]Best score: 22.0523: : 139iter [00:05, 38.43iter/s]Best score: 22.0523: : 139iter [00:05, 38.43iter/s]Best score: 22.0523: : 140iter [00:05, 38.43iter/s]Best score: 22.0523: : 141iter [00:05, 38.43iter/s]Best score: 22.0523: : 142iter [00:05, 38.43iter/s]Best score: 22.0523: : 143iter [00:05, 37.97iter/s]Best score: 22.0523: : 143iter [00:05, 37.97iter/s]Best score: 22.0523: : 144iter [00:05, 37.97iter/s]Best score: 22.0523: : 145iter [00:05, 37.97iter/s]Best score: 22.0523: : 146iter [00:05, 37.97iter/s]Best score: 22.0523: : 147iter [00:05, 38.24iter/s]Best score: 22.0523: : 147iter [00:05, 38.24iter/s]Best score: 22.0523: : 148iter [00:05, 38.24iter/s]
[ITER 150] Best: 22.0523
[PARAMS] Current best parameters:
  fx: 535.8844
  fy: 535.8844
  px: 995.0000
  py: 762.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 22.0523: : 149iter [00:05, 38.24iter/s]Best score: 22.0523: : 150iter [00:06, 38.24iter/s]Best score: 22.0523: : 151iter [00:06, 31.15iter/s]Best score: 22.0523: : 151iter [00:06, 31.15iter/s]Best score: 22.0523: : 152iter [00:06, 31.15iter/s]Best score: 22.0523: : 153iter [00:06, 31.15iter/s]Best score: 22.0523: : 154iter [00:06, 31.15iter/s]Best score: 22.0523: : 155iter [00:06, 33.00iter/s]Best score: 22.0523: : 155iter [00:06, 33.00iter/s]Best score: 22.0523: : 156iter [00:06, 33.00iter/s]Best score: 22.0523: : 157iter [00:06, 33.00iter/s]Best score: 22.0523: : 158iter [00:06, 33.00iter/s]Best score: 22.0523: : 159iter [00:06, 34.43iter/s]Best score: 22.0523: : 159iter [00:06, 34.43iter/s]Best score: 22.0523: : 160iter [00:06, 34.43iter/s]Best score: 22.0523: : 161iter [00:06, 34.43iter/s]Best score: 22.0523: : 162iter [00:06, 34.43iter/s]Best score: 22.0523: : 163iter [00:06, 35.35iter/s]Best score: 22.0523: : 163iter [00:06, 35.35iter/s]Best score: 22.0523: : 164iter [00:06, 35.35iter/s]Best score: 22.0523: : 165iter [00:06, 35.35iter/s]Best score: 22.0523: : 166iter [00:06, 35.35iter/s]Best score: 22.0523: : 167iter [00:06, 36.30iter/s]Best score: 22.0523: : 167iter [00:06, 36.30iter/s]Best score: 22.0523: : 168iter [00:06, 36.30iter/s]Best score: 22.0523: : 169iter [00:06, 36.30iter/s]Best score: 22.0523: : 170iter [00:06, 36.30iter/s]Best score: 22.0523: : 171iter [00:06, 36.50iter/s]Best score: 22.0523: : 171iter [00:06, 36.50iter/s]Best score: 22.0523: : 172iter [00:06, 36.50iter/s]Best score: 22.0523: : 173iter [00:06, 36.50iter/s]Best score: 22.0523: : 174iter [00:06, 36.50iter/s]Best score: 22.0523: : 175iter [00:06, 37.26iter/s]Best score: 22.0523: : 175iter [00:06, 37.26iter/s]Best score: 22.0523: : 176iter [00:06, 37.26iter/s]Best score: 22.0523: : 177iter [00:06, 37.26iter/s]Best score: 22.0523: : 178iter [00:06, 37.26iter/s]Best score: 22.0523: : 179iter [00:06, 37.64iter/s]Best score: 22.0523: : 179iter [00:06, 37.64iter/s]Best score: 22.0523: : 180iter [00:06, 37.64iter/s]Best score: 22.0523: : 181iter [00:06, 37.64iter/s]Best score: 22.0523: : 182iter [00:06, 37.64iter/s]Best score: 22.0523: : 183iter [00:06, 37.76iter/s]Best score: 22.0523: : 183iter [00:06, 37.76iter/s]Best score: 22.0523: : 184iter [00:06, 37.76iter/s]Best score: 22.0523: : 185iter [00:07, 37.76iter/s]Best score: 22.0523: : 186iter [00:07, 37.76iter/s]Best score: 22.0523: : 187iter [00:07, 38.06iter/s]Best score: 22.0523: : 187iter [00:07, 38.06iter/s]Best score: 22.0523: : 188iter [00:07, 38.06iter/s]Best score: 22.0523: : 189iter [00:07, 38.06iter/s]Best score: 22.0523: : 190iter [00:07, 38.06iter/s]Best score: 22.0523: : 191iter [00:07, 38.21iter/s]Best score: 22.0523: : 191iter [00:07, 38.21iter/s]Best score: 22.0523: : 192iter [00:07, 38.21iter/s]Best score: 22.0523: : 193iter [00:07, 38.21iter/s]Best score: 22.0523: : 194iter [00:07, 38.21iter/s]Best score: 22.0523: : 195iter [00:07, 38.72iter/s]Best score: 22.0523: : 195iter [00:07, 38.72iter/s]Best score: 22.0523: : 196iter [00:07, 38.72iter/s]Best score: 22.0523: : 197iter [00:07, 38.72iter/s]Best score: 22.0523: : 198iter [00:07, 38.72iter/s]Best score: 22.0523: : 199iter [00:07, 38.45iter/s]
[ITER 200] Best: 22.0523
[PARAMS] Current best parameters:
  fx: 535.8844
  fy: 535.8844
  px: 995.0000
  py: 762.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000
Best score: 22.0523: : 199iter [00:07, 38.45iter/s]Best score: 22.0523: : 200iter [00:07, 38.45iter/s]Best score: 22.0523: : 201iter [00:07, 38.45iter/s]Best score: 22.0523: : 202iter [00:07, 38.45iter/s]Best score: 22.0523: : 203iter [00:07, 31.28iter/s]Best score: 22.0523: : 203iter [00:07, 31.28iter/s]Best score: 22.0523: : 204iter [00:07, 31.28iter/s]Best score: 22.0523: : 205iter [00:07, 31.28iter/s]Best score: 22.0523: : 206iter [00:07, 31.28iter/s]Best score: 22.0523: : 207iter [00:07, 33.21iter/s]Best score: 22.0523: : 207iter [00:07, 33.21iter/s]Best score: 22.0523: : 208iter [00:07, 33.21iter/s]Best score: 22.0523: : 209iter [00:07, 33.21iter/s]Best score: 22.0523: : 210iter [00:07, 33.21iter/s]Best score: 22.0523: : 211iter [00:07, 34.63iter/s]Best score: 22.0523: : 211iter [00:07, 34.63iter/s]Best score: 22.0523: : 212iter [00:07, 34.63iter/s]Best score: 22.0523: : 213iter [00:07, 34.63iter/s]Best score: 22.0523: : 214iter [00:07, 34.63iter/s]Best score: 22.0523: : 215iter [00:07, 35.80iter/s]Best score: 22.0523: : 215iter [00:07, 35.80iter/s]Best score: 22.0523: : 216iter [00:07, 35.80iter/s]Best score: 22.0523: : 217iter [00:07, 35.80iter/s]Best score: 22.0523: : 218iter [00:07, 35.80iter/s]Best score: 22.0523: : 219iter [00:07, 36.82iter/s]Best score: 22.0523: : 219iter [00:07, 36.82iter/s]Best score: 22.0523: : 220iter [00:07, 36.82iter/s]Best score: 22.0523: : 221iter [00:08, 36.82iter/s]Best score: 22.0523: : 222iter [00:08, 36.82iter/s]Best score: 22.0523: : 223iter [00:08, 36.98iter/s]Best score: 22.0523: : 223iter [00:08, 36.98iter/s]Best score: 22.0523: : 224iter [00:08, 36.98iter/s]Best score: 22.0523: : 225iter [00:08, 36.98iter/s]Best score: 22.0523: : 226iter [00:08, 36.98iter/s]Best score: 22.0523: : 227iter [00:08, 37.68iter/s]Best score: 22.0523: : 227iter [00:08, 37.68iter/s]Best score: 22.0523: : 228iter [00:08, 37.68iter/s]Best score: 22.0523: : 229iter [00:08, 37.68iter/s]Best score: 22.0523: : 230iter [00:08, 37.68iter/s]Best score: 22.0523: : 231iter [00:08, 38.13iter/s]Best score: 22.0523: : 231iter [00:08, 38.13iter/s]Best score: 22.0523: : 232iter [00:08, 38.13iter/s]Best score: 22.0523: : 233iter [00:08, 38.13iter/s]Best score: 22.0523: : 234iter [00:08, 38.13iter/s]Best score: 22.0523: : 235iter [00:08, 38.48iter/s]Best score: 22.0523: : 235iter [00:08, 38.48iter/s]Best score: 22.0523: : 236iter [00:08, 38.48iter/s]Best score: 22.0523: : 237iter [00:08, 38.48iter/s]Best score: 22.0523: : 238iter [00:08, 38.48iter/s]Best score: 22.0523: : 239iter [00:08, 38.74iter/s]Best score: 18.4686: : 239iter [00:08, 38.74iter/s]Best score: 18.4686: : 240iter [00:08, 38.74iter/s]Best score: 18.4686: : 241iter [00:08, 38.74iter/s]Best score: 18.4686: : 242iter [00:08, 38.74iter/s]Best score: 18.4686: : 243iter [00:08, 38.83iter/s]Best score: 18.4686: : 243iter [00:08, 38.83iter/s]Best score: 18.4686: : 244iter [00:08, 38.83iter/s]Best score: 18.4686: : 245iter [00:08, 38.83iter/s]Best score: 18.4686: : 246iter [00:08, 38.83iter/s]Best score: 18.4686: : 247iter [00:08, 39.14iter/s]Best score: 18.4686: : 247iter [00:08, 39.14iter/s]Best score: 18.4686: : 248iter [00:08, 39.14iter/s]
[ITER 250] Best: 7.8231
[PARAMS] Current best parameters:
  fx: 544.7008
  fy: 544.3924
  px: 985.3317
  py: 758.0561
  k1: -0.02310312
  k2: 0.00755250
  k3: 0.01121425
  k4: -0.00619399
  p1: -0.00321151
  p2: -0.01248647
  p3: -0.01318909
  p4: 0.00215332
  s1: 0.0014
  s2: 0.0062
  s3: -0.0116
  s4: 0.0038
  tilt_x: 0.0029
  tilt_y: 0.0138
  scale: 3.0035
  rotation: -0.4442
Best score: 7.8231: : 249iter [00:08, 39.14iter/s] Best score: 7.8231: : 250iter [00:08, 39.14iter/s]Best score: 7.8231: : 251iter [00:08, 31.53iter/s]Best score: 7.8231: : 251iter [00:08, 31.53iter/s]Best score: 7.8231: : 252iter [00:08, 31.53iter/s]Best score: 7.8231: : 253iter [00:08, 31.53iter/s]Best score: 7.8231: : 254iter [00:08, 31.53iter/s]Best score: 7.8231: : 255iter [00:08, 33.52iter/s]Best score: 7.8231: : 255iter [00:08, 33.52iter/s]Best score: 7.8231: : 256iter [00:08, 33.52iter/s]Best score: 7.8231: : 257iter [00:09, 33.52iter/s]Best score: 7.8231: : 258iter [00:09, 33.52iter/s]Best score: 7.8231: : 259iter [00:09, 35.08iter/s]Best score: 3.4805: : 259iter [00:09, 35.08iter/s]Best score: 3.4805: : 260iter [00:09, 35.08iter/s]Best score: 3.4805: : 261iter [00:09, 35.08iter/s]Best score: 3.4805: : 262iter [00:09, 35.08iter/s]Best score: 3.4805: : 263iter [00:09, 35.98iter/s]Best score: 3.4805: : 263iter [00:09, 35.98iter/s]Best score: 3.4805: : 264iter [00:09, 35.98iter/s]Best score: 3.4805: : 265iter [00:09, 35.98iter/s]Best score: 3.4805: : 266iter [00:09, 35.98iter/s]Best score: 3.4805: : 267iter [00:09, 36.97iter/s]Best score: 3.4805: : 267iter [00:09, 36.97iter/s]Best score: 3.4805: : 268iter [00:09, 36.97iter/s]Best score: 3.4228: : 269iter [00:09, 36.97iter/s]Best score: 3.4228: : 270iter [00:09, 36.97iter/s]Best score: 3.4228: : 271iter [00:09, 37.36iter/s]Best score: 3.4228: : 271iter [00:09, 37.36iter/s]Best score: 3.4228: : 272iter [00:09, 37.36iter/s]Best score: 3.4228: : 273iter [00:09, 37.36iter/s]Best score: 3.4228: : 274iter [00:09, 37.36iter/s]Best score: 3.4228: : 275iter [00:09, 38.00iter/s]Best score: 3.4228: : 275iter [00:09, 38.00iter/s]Best score: 3.4228: : 276iter [00:09, 38.00iter/s]Best score: 3.4228: : 277iter [00:09, 38.00iter/s]Best score: 3.4228: : 278iter [00:09, 38.00iter/s]Best score: 3.4228: : 279iter [00:09, 38.40iter/s]Best score: 3.4228: : 279iter [00:09, 38.40iter/s]Best score: 3.4228: : 280iter [00:09, 38.40iter/s]Best score: 3.4228: : 281iter [00:09, 38.40iter/s]Best score: 3.4228: : 282iter [00:09, 38.40iter/s]Best score: 3.4228: : 283iter [00:09, 38.39iter/s]Best score: 3.4228: : 283iter [00:09, 38.39iter/s]Best score: 3.4228: : 284iter [00:09, 38.39iter/s]Best score: 3.4228: : 285iter [00:09, 38.39iter/s]Best score: 3.4228: : 286iter [00:09, 38.39iter/s]Best score: 3.4228: : 287iter [00:09, 38.50iter/s]Best score: 3.4228: : 287iter [00:09, 38.50iter/s]Best score: 3.4228: : 288iter [00:09, 38.50iter/s]Best score: 3.4228: : 289iter [00:09, 38.50iter/s]Best score: 3.4228: : 290iter [00:09, 38.50iter/s]Best score: 3.4228: : 291iter [00:09, 38.44iter/s]Best score: 3.4228: : 291iter [00:09, 38.44iter/s]Best score: 3.4228: : 292iter [00:09, 38.44iter/s]Best score: 3.4228: : 293iter [00:09, 38.44iter/s]Best score: 3.4228: : 294iter [00:09, 38.44iter/s]Best score: 3.4228: : 295iter [00:09, 38.47iter/s]Best score: 3.4228: : 295iter [00:09, 38.47iter/s]Best score: 3.4228: : 296iter [00:10, 38.47iter/s]Best score: 3.4228: : 297iter [00:10, 38.47iter/s]Best score: 3.4228: : 298iter [00:10, 38.47iter/s]
[ITER 300] Best: 3.1836
[PARAMS] Current best parameters:
  fx: 544.4343
  fy: 542.9647
  px: 982.3327
  py: 754.8624
  k1: -0.02628610
  k2: 0.01621970
  k3: 0.00622833
  k4: -0.00543710
  p1: -0.00551073
  p2: -0.01306980
  p3: -0.00761869
  p4: 0.00032693
  s1: 0.0100
  s2: 0.0000
  s3: 0.0082
  s4: -0.0011
  tilt_x: -0.0144
  tilt_y: 0.0268
  scale: 2.9988
  rotation: -0.4102
Best score: 3.1836: : 299iter [00:10, 38.47iter/s]Best score: 3.1836: : 300iter [00:10, 38.71iter/s]Best score: 3.1836: : 300iter [00:10, 38.71iter/s]Best score: 3.1836: : 301iter [00:10, 38.71iter/s]Best score: 3.1836: : 302iter [00:10, 38.71iter/s]Best score: 3.1836: : 303iter [00:10, 38.71iter/s]Best score: 3.1836: : 304iter [00:10, 32.01iter/s]Best score: 3.1836: : 304iter [00:10, 32.01iter/s]Best score: 3.1836: : 305iter [00:10, 32.01iter/s]Best score: 3.1836: : 306iter [00:10, 32.01iter/s]Best score: 3.1836: : 307iter [00:10, 32.01iter/s]Best score: 3.1836: : 308iter [00:10, 33.97iter/s]Best score: 3.1836: : 308iter [00:10, 33.97iter/s]Best score: 2.3523: : 309iter [00:10, 33.97iter/s]Best score: 2.3523: : 310iter [00:10, 33.97iter/s]Best score: 2.3523: : 311iter [00:10, 33.97iter/s]Best score: 2.3523: : 312iter [00:10, 35.31iter/s]Best score: 2.3523: : 312iter [00:10, 35.31iter/s]Best score: 2.3523: : 313iter [00:10, 35.31iter/s]Best score: 2.3523: : 314iter [00:10, 35.31iter/s]Best score: 2.3523: : 315iter [00:10, 35.31iter/s]Best score: 2.3523: : 316iter [00:10, 36.46iter/s]Best score: 2.3523: : 316iter [00:10, 36.46iter/s]Best score: 2.3523: : 317iter [00:10, 36.46iter/s]Best score: 2.3523: : 318iter [00:10, 36.46iter/s]Best score: 1.4012: : 319iter [00:10, 36.46iter/s]Best score: 1.4012: : 320iter [00:10, 36.98iter/s]Best score: 1.4012: : 320iter [00:10, 36.98iter/s]Best score: 1.4012: : 321iter [00:10, 36.98iter/s]Best score: 1.4012: : 322iter [00:10, 36.98iter/s]Best score: 1.4012: : 323iter [00:10, 36.98iter/s]Best score: 1.4012: : 324iter [00:10, 37.77iter/s]Best score: 1.4012: : 324iter [00:10, 37.77iter/s]Best score: 1.4012: : 325iter [00:10, 37.77iter/s]Best score: 1.4012: : 326iter [00:10, 37.77iter/s]Best score: 1.4012: : 327iter [00:10, 37.77iter/s]Best score: 1.4012: : 328iter [00:10, 38.30iter/s]Best score: 1.4012: : 328iter [00:10, 38.30iter/s]Best score: 1.4012: : 329iter [00:10, 38.30iter/s]Best score: 1.4012: : 330iter [00:10, 38.30iter/s]Best score: 1.4012: : 331iter [00:10, 38.30iter/s]Best score: 1.4012: : 332iter [00:10, 38.22iter/s]Best score: 1.4012: : 332iter [00:11, 38.22iter/s]Best score: 1.4012: : 333iter [00:11, 38.22iter/s]Best score: 1.4012: : 334iter [00:11, 38.22iter/s]Best score: 1.4012: : 335iter [00:11, 38.22iter/s]Best score: 1.4012: : 336iter [00:11, 38.58iter/s]Best score: 1.4012: : 336iter [00:11, 38.58iter/s]Best score: 1.4012: : 337iter [00:11, 38.58iter/s]Best score: 1.4012: : 338iter [00:11, 38.58iter/s]Best score: 1.4012: : 339iter [00:11, 38.58iter/s]Best score: 1.4012: : 340iter [00:11, 38.33iter/s]Best score: 1.4012: : 340iter [00:11, 38.33iter/s]Best score: 1.4012: : 341iter [00:11, 38.33iter/s]Best score: 1.4012: : 342iter [00:11, 38.33iter/s]Best score: 1.4012: : 343iter [00:11, 38.33iter/s]Best score: 1.4012: : 344iter [00:11, 38.58iter/s]Best score: 1.4012: : 344iter [00:11, 38.58iter/s]Best score: 1.4012: : 345iter [00:11, 38.58iter/s]Best score: 1.4012: : 346iter [00:11, 38.58iter/s]Best score: 1.4012: : 347iter [00:11, 38.58iter/s]Best score: 1.4012: : 348iter [00:11, 38.94iter/s]Best score: 1.4012: : 348iter [00:11, 38.94iter/s]
[ITER 350] Best: 1.2632
[PARAMS] Current best parameters:
  fx: 544.7111
  fy: 544.5859
  px: 980.8516
  py: 761.4842
  k1: -0.01277241
  k2: 0.02119205
  k3: -0.01706624
  k4: 0.00346637
  p1: 0.00339703
  p2: -0.01553296
  p3: -0.01814082
  p4: 0.00241187
  s1: 0.0145
  s2: 0.0038
  s3: 0.0150
  s4: -0.0022
  tilt_x: -0.0007
  tilt_y: 0.0317
  scale: 3.0005
  rotation: -0.5049
Best score: 1.2632: : 349iter [00:11, 38.94iter/s]Best score: 1.2632: : 350iter [00:11, 38.94iter/s]Best score: 1.2632: : 351iter [00:11, 38.94iter/s]Best score: 1.2632: : 352iter [00:11, 31.59iter/s]Best score: 1.2632: : 352iter [00:11, 31.59iter/s]Best score: 1.2632: : 353iter [00:11, 31.59iter/s]Best score: 1.2632: : 354iter [00:11, 31.59iter/s]Best score: 1.2632: : 355iter [00:11, 31.59iter/s]Best score: 1.2632: : 356iter [00:11, 33.66iter/s]Best score: 1.2632: : 356iter [00:11, 33.66iter/s]Best score: 1.2632: : 357iter [00:11, 33.66iter/s]Best score: 1.2632: : 358iter [00:11, 33.66iter/s]Best score: 1.1375: : 359iter [00:11, 33.66iter/s]Best score: 1.1375: : 360iter [00:11, 34.99iter/s]Best score: 1.1375: : 360iter [00:11, 34.99iter/s]Best score: 1.1375: : 361iter [00:11, 34.99iter/s]Best score: 1.1375: : 362iter [00:11, 34.99iter/s]Best score: 1.1375: : 363iter [00:11, 34.99iter/s]Best score: 1.1375: : 364iter [00:11, 28.51iter/s]Best score: 1.1375: : 364iter [00:12, 28.51iter/s]Best score: 1.1375: : 365iter [00:12, 28.51iter/s]Best score: 1.1375: : 366iter [00:12, 28.51iter/s]Best score: 1.1375: : 367iter [00:12, 28.51iter/s]Best score: 1.1375: : 368iter [00:12, 31.11iter/s]Best score: 1.1375: : 368iter [00:12, 31.11iter/s]Best score: 0.8248: : 369iter [00:12, 31.11iter/s]Best score: 0.8248: : 370iter [00:12, 31.11iter/s]Best score: 0.8248: : 371iter [00:12, 31.11iter/s]Best score: 0.8248: : 372iter [00:12, 33.19iter/s]Best score: 0.8248: : 372iter [00:12, 33.19iter/s]Best score: 0.8248: : 373iter [00:12, 33.19iter/s]Best score: 0.8248: : 374iter [00:12, 33.19iter/s]Best score: 0.8248: : 375iter [00:12, 33.19iter/s]Best score: 0.8248: : 376iter [00:12, 34.87iter/s]Best score: 0.8248: : 376iter [00:12, 34.87iter/s]Best score: 0.8248: : 377iter [00:12, 34.87iter/s]Best score: 0.8248: : 378iter [00:12, 34.87iter/s]Best score: 0.8248: : 379iter [00:12, 34.87iter/s]Best score: 0.8248: : 380iter [00:12, 35.34iter/s]Best score: 0.8248: : 380iter [00:12, 35.34iter/s]Best score: 0.8248: : 381iter [00:12, 35.34iter/s]Best score: 0.8248: : 382iter [00:12, 35.34iter/s]Best score: 0.8248: : 383iter [00:12, 35.34iter/s]Best score: 0.8248: : 384iter [00:12, 36.18iter/s]Best score: 0.8248: : 384iter [00:12, 36.18iter/s]Best score: 0.8248: : 385iter [00:12, 36.18iter/s]Best score: 0.8248: : 386iter [00:12, 36.18iter/s]Best score: 0.8248: : 387iter [00:12, 36.18iter/s]Best score: 0.8248: : 388iter [00:12, 37.13iter/s]Best score: 0.8248: : 388iter [00:12, 37.13iter/s]Best score: 0.7606: : 389iter [00:12, 37.13iter/s]Best score: 0.7606: : 390iter [00:12, 37.13iter/s]Best score: 0.7606: : 391iter [00:12, 37.13iter/s]Best score: 0.7606: : 392iter [00:12, 37.48iter/s]Best score: 0.7606: : 392iter [00:12, 37.48iter/s]Best score: 0.7606: : 393iter [00:12, 37.48iter/s]Best score: 0.7606: : 394iter [00:12, 37.48iter/s]Best score: 0.7606: : 395iter [00:12, 37.48iter/s]Best score: 0.7606: : 396iter [00:12, 38.19iter/s]Best score: 0.7606: : 396iter [00:12, 38.19iter/s]Best score: 0.7606: : 397iter [00:12, 38.19iter/s]Best score: 0.7606: : 398iter [00:12, 38.19iter/s]
[ITER 400] Best: 0.6299
[PARAMS] Current best parameters:
  fx: 547.7152
  fy: 547.6679
  px: 982.8262
  py: 759.5108
  k1: -0.02215655
  k2: 0.01565095
  k3: -0.00680736
  k4: 0.00050315
  p1: 0.00404268
  p2: -0.01271643
  p3: -0.01529330
  p4: 0.00245743
  s1: 0.0086
  s2: 0.0027
  s3: 0.0105
  s4: -0.0045
  tilt_x: -0.0072
  tilt_y: 0.0251
  scale: 3.0344
  rotation: -0.5454
Best score: 0.6299: : 399iter [00:12, 38.19iter/s]Best score: 0.6299: : 400iter [00:12, 38.35iter/s]Best score: 0.6299: : 400iter [00:13, 38.35iter/s]Best score: 0.6299: : 401iter [00:13, 38.35iter/s]Best score: 0.6299: : 402iter [00:13, 38.35iter/s]Best score: 0.6299: : 403iter [00:13, 38.35iter/s]Best score: 0.6299: : 404iter [00:13, 31.35iter/s]Best score: 0.6299: : 404iter [00:13, 31.35iter/s]Best score: 0.6299: : 405iter [00:13, 31.35iter/s]Best score: 0.6299: : 406iter [00:13, 31.35iter/s]Best score: 0.6299: : 407iter [00:13, 31.35iter/s]Best score: 0.6299: : 408iter [00:13, 33.51iter/s]Best score: 0.6299: : 408iter [00:13, 33.51iter/s]Best score: 0.5241: : 409iter [00:13, 33.51iter/s]Best score: 0.5241: : 410iter [00:13, 33.51iter/s]Best score: 0.5241: : 411iter [00:13, 33.51iter/s]Best score: 0.5241: : 412iter [00:13, 34.98iter/s]Best score: 0.5241: : 412iter [00:13, 34.98iter/s]Best score: 0.5241: : 413iter [00:13, 34.98iter/s]Best score: 0.5241: : 414iter [00:13, 34.98iter/s]Best score: 0.5241: : 415iter [00:13, 34.98iter/s]Best score: 0.5241: : 416iter [00:13, 36.30iter/s]Best score: 0.5241: : 416iter [00:13, 36.30iter/s]Best score: 0.5241: : 417iter [00:13, 36.30iter/s]Best score: 0.5241: : 418iter [00:13, 36.30iter/s]Best score: 0.5216: : 419iter [00:13, 36.30iter/s]Best score: 0.5216: : 420iter [00:13, 37.16iter/s]Best score: 0.5216: : 420iter [00:13, 37.16iter/s]Best score: 0.5216: : 421iter [00:13, 37.16iter/s]Best score: 0.5216: : 422iter [00:13, 37.16iter/s]Best score: 0.5216: : 423iter [00:13, 37.16iter/s]Best score: 0.5216: : 424iter [00:13, 37.77iter/s]Best score: 0.5216: : 424iter [00:13, 37.77iter/s]Best score: 0.5216: : 425iter [00:13, 37.77iter/s]Best score: 0.5216: : 426iter [00:13, 37.77iter/s]Best score: 0.5216: : 427iter [00:13, 37.77iter/s]Best score: 0.5216: : 428iter [00:13, 38.34iter/s]Best score: 0.5216: : 428iter [00:13, 38.34iter/s]Best score: 0.4993: : 429iter [00:13, 38.34iter/s]Best score: 0.4993: : 430iter [00:13, 38.34iter/s]Best score: 0.4993: : 431iter [00:13, 38.34iter/s]Best score: 0.4993: : 432iter [00:13, 38.43iter/s]Best score: 0.4993: : 432iter [00:13, 38.43iter/s]Best score: 0.4993: : 433iter [00:13, 38.43iter/s]Best score: 0.4993: : 434iter [00:13, 38.43iter/s]Best score: 0.4993: : 435iter [00:13, 38.43iter/s]Best score: 0.4993: : 436iter [00:13, 38.88iter/s]Best score: 0.4993: : 436iter [00:13, 38.88iter/s]Best score: 0.4993: : 437iter [00:13, 38.88iter/s]Best score: 0.4993: : 438iter [00:13, 38.88iter/s]Best score: 0.4007: : 439iter [00:14, 38.88iter/s]Best score: 0.4007: : 440iter [00:14, 38.64iter/s]Best score: 0.4007: : 440iter [00:14, 38.64iter/s]Best score: 0.4007: : 441iter [00:14, 38.64iter/s]Best score: 0.4007: : 442iter [00:14, 38.64iter/s]Best score: 0.4007: : 443iter [00:14, 38.64iter/s]Best score: 0.4007: : 444iter [00:14, 38.79iter/s]Best score: 0.4007: : 444iter [00:14, 38.79iter/s]Best score: 0.4007: : 445iter [00:14, 38.79iter/s]Best score: 0.4007: : 446iter [00:14, 38.79iter/s]Best score: 0.4007: : 447iter [00:14, 38.79iter/s]Best score: 0.4007: : 448iter [00:14, 38.98iter/s]Best score: 0.4007: : 448iter [00:14, 38.98iter/s]
[ITER 450] Best: 0.4007
[PARAMS] Current best parameters:
  fx: 542.1092
  fy: 541.6490
  px: 987.1248
  py: 759.0632
  k1: -0.01913417
  k2: 0.01561016
  k3: -0.00550196
  k4: 0.00004000
  p1: 0.01034734
  p2: 0.00052065
  p3: -0.01863102
  p4: 0.00317066
  s1: 0.0088
  s2: -0.0017
  s3: 0.0078
  s4: -0.0037
  tilt_x: -0.0239
  tilt_y: 0.0295
  scale: 3.0550
  rotation: -0.5436
Best score: 0.4007: : 449iter [00:14, 38.98iter/s]Best score: 0.4007: : 450iter [00:14, 38.98iter/s]Best score: 0.4007: : 451iter [00:14, 38.98iter/s]Best score: 0.4007: : 452iter [00:14, 31.56iter/s]Best score: 0.4007: : 452iter [00:14, 31.56iter/s]Best score: 0.4007: : 453iter [00:14, 31.56iter/s]Best score: 0.4007: : 454iter [00:14, 31.56iter/s]Best score: 0.4007: : 455iter [00:14, 31.56iter/s]Best score: 0.4007: : 456iter [00:14, 33.59iter/s]Best score: 0.4007: : 456iter [00:14, 33.59iter/s]Best score: 0.4007: : 457iter [00:14, 33.59iter/s]Best score: 0.4007: : 458iter [00:14, 33.59iter/s]Best score: 0.3631: : 459iter [00:14, 33.59iter/s]Best score: 0.3631: : 460iter [00:14, 34.94iter/s]Best score: 0.3631: : 460iter [00:14, 34.94iter/s]Best score: 0.3631: : 461iter [00:14, 34.94iter/s]Best score: 0.3631: : 462iter [00:14, 34.94iter/s]Best score: 0.3631: : 463iter [00:14, 34.94iter/s]Best score: 0.3631: : 464iter [00:14, 36.24iter/s]Best score: 0.3631: : 464iter [00:14, 36.24iter/s]Best score: 0.3631: : 465iter [00:14, 36.24iter/s]Best score: 0.3631: : 466iter [00:14, 36.24iter/s]Best score: 0.3631: : 467iter [00:14, 36.24iter/s]Best score: 0.3631: : 468iter [00:14, 37.19iter/s]Best score: 0.3631: : 468iter [00:14, 37.19iter/s]Best score: 0.3451: : 469iter [00:14, 37.19iter/s]Best score: 0.3451: : 470iter [00:14, 37.19iter/s]Best score: 0.3451: : 471iter [00:14, 37.19iter/s]Best score: 0.3451: : 472iter [00:14, 37.38iter/s]Best score: 0.3451: : 472iter [00:14, 37.38iter/s]Best score: 0.3451: : 473iter [00:14, 37.38iter/s]Best score: 0.3451: : 474iter [00:14, 37.38iter/s]Best score: 0.3451: : 475iter [00:15, 37.38iter/s]Best score: 0.3451: : 476iter [00:15, 38.04iter/s]Best score: 0.3451: : 476iter [00:15, 38.04iter/s]Best score: 0.3451: : 477iter [00:15, 38.04iter/s]Best score: 0.3451: : 478iter [00:15, 38.04iter/s]Best score: 0.3261: : 479iter [00:15, 38.04iter/s]Best score: 0.3261: : 480iter [00:15, 38.00iter/s]Best score: 0.3261: : 480iter [00:15, 38.00iter/s]Best score: 0.3261: : 481iter [00:15, 38.00iter/s]Best score: 0.3261: : 482iter [00:15, 38.00iter/s]Best score: 0.3261: : 483iter [00:15, 38.00iter/s]Best score: 0.3261: : 484iter [00:15, 38.28iter/s]Best score: 0.3261: : 484iter [00:15, 38.28iter/s]Best score: 0.3261: : 485iter [00:15, 38.28iter/s]Best score: 0.3261: : 486iter [00:15, 38.28iter/s]Best score: 0.3261: : 487iter [00:15, 38.28iter/s]Best score: 0.3261: : 488iter [00:15, 38.73iter/s]Best score: 0.3261: : 488iter [00:15, 38.73iter/s]Best score: 0.3219: : 489iter [00:15, 38.73iter/s]Best score: 0.3219: : 490iter [00:15, 38.73iter/s]Best score: 0.3219: : 491iter [00:15, 38.73iter/s]Best score: 0.3219: : 492iter [00:15, 38.57iter/s]Best score: 0.3219: : 492iter [00:15, 38.57iter/s]Best score: 0.3219: : 493iter [00:15, 38.57iter/s]Best score: 0.3219: : 494iter [00:15, 38.57iter/s]Best score: 0.3219: : 495iter [00:15, 38.57iter/s]Best score: 0.3219: : 496iter [00:15, 38.96iter/s]Best score: 0.3219: : 496iter [00:15, 38.96iter/s]Best score: 0.3219: : 497iter [00:15, 38.96iter/s]Best score: 0.3219: : 498iter [00:15, 38.96iter/s]
[ITER 500] Best: 0.3207
[PARAMS] Current best parameters:
  fx: 547.2541
  fy: 546.7464
  px: 988.8503
  py: 759.1784
  k1: -0.01700030
  k2: 0.00877762
  k3: -0.00321795
  k4: -0.00015127
  p1: 0.00597184
  p2: -0.00133827
  p3: -0.01407528
  p4: 0.00288975
  s1: 0.0074
  s2: -0.0012
  s3: 0.0040
  s4: -0.0017
  tilt_x: -0.0147
  tilt_y: 0.0167
  scale: 3.0356
  rotation: -0.5416
Best score: 0.3207: : 499iter [00:15, 38.96iter/s]Best score: 0.3207: : 500iter [00:15, 38.65iter/s]Best score: 0.3207: : 500iter [00:15, 38.65iter/s]Best score: 0.3207: : 501iter [00:15, 38.65iter/s]Best score: 0.3207: : 502iter [00:15, 38.65iter/s]Best score: 0.3207: : 503iter [00:15, 38.65iter/s]Best score: 0.3207: : 504iter [00:15, 31.77iter/s]Best score: 0.3207: : 504iter [00:15, 31.77iter/s]Best score: 0.3207: : 505iter [00:15, 31.77iter/s]Best score: 0.3207: : 506iter [00:15, 31.77iter/s]Best score: 0.3207: : 507iter [00:15, 31.77iter/s]Best score: 0.3207: : 508iter [00:15, 33.59iter/s]Best score: 0.3207: : 508iter [00:15, 33.59iter/s]Best score: 0.3114: : 509iter [00:15, 33.59iter/s]Best score: 0.3114: : 510iter [00:15, 33.59iter/s]Best score: 0.3114: : 511iter [00:16, 33.59iter/s]Best score: 0.3114: : 512iter [00:16, 34.56iter/s]Best score: 0.3114: : 512iter [00:16, 34.56iter/s]Best score: 0.3114: : 513iter [00:16, 34.56iter/s]Best score: 0.3114: : 514iter [00:16, 34.56iter/s]Best score: 0.3114: : 515iter [00:16, 34.56iter/s]Best score: 0.3114: : 516iter [00:16, 35.73iter/s]Best score: 0.3114: : 516iter [00:16, 35.73iter/s]Best score: 0.3114: : 517iter [00:16, 35.73iter/s]Best score: 0.3114: : 518iter [00:16, 35.73iter/s]Best score: 0.3114: : 519iter [00:16, 35.73iter/s]Best score: 0.3114: : 520iter [00:16, 36.23iter/s]Best score: 0.3114: : 520iter [00:16, 36.23iter/s]Best score: 0.3114: : 521iter [00:16, 36.23iter/s]Best score: 0.3114: : 522iter [00:16, 36.23iter/s]Best score: 0.3114: : 523iter [00:16, 36.23iter/s]Best score: 0.3114: : 524iter [00:16, 37.05iter/s]Best score: 0.3114: : 524iter [00:16, 37.05iter/s]Best score: 0.3114: : 525iter [00:16, 37.05iter/s]Best score: 0.3114: : 526iter [00:16, 37.05iter/s]Best score: 0.3114: : 527iter [00:16, 37.05iter/s]Best score: 0.3114: : 528iter [00:16, 37.71iter/s]Best score: 0.3114: : 528iter [00:16, 37.71iter/s]Best score: 0.2968: : 529iter [00:16, 37.71iter/s]Best score: 0.2968: : 530iter [00:16, 37.71iter/s]Best score: 0.2968: : 531iter [00:16, 37.71iter/s]Best score: 0.2968: : 532iter [00:16, 38.01iter/s]Best score: 0.2968: : 532iter [00:16, 38.01iter/s]Best score: 0.2968: : 533iter [00:16, 38.01iter/s]Best score: 0.2968: : 534iter [00:16, 38.01iter/s]Best score: 0.2968: : 535iter [00:16, 38.01iter/s]Best score: 0.2968: : 536iter [00:16, 38.53iter/s]Best score: 0.2968: : 536iter [00:16, 38.53iter/s]Best score: 0.2968: : 537iter [00:16, 38.53iter/s]Best score: 0.2968: : 538iter [00:16, 38.53iter/s]Best score: 0.2937: : 539iter [00:16, 38.53iter/s]Best score: 0.2937: : 540iter [00:16, 38.51iter/s]Best score: 0.2937: : 540iter [00:16, 38.51iter/s]Best score: 0.2937: : 541iter [00:16, 38.51iter/s]Best score: 0.2937: : 542iter [00:16, 38.51iter/s]Best score: 0.2937: : 543iter [00:16, 38.51iter/s]Best score: 0.2937: : 544iter [00:16, 38.51iter/s]Best score: 0.2937: : 545iter [00:16, 39.02iter/s]Best score: 0.2937: : 545iter [00:16, 39.02iter/s]Best score: 0.2937: : 546iter [00:16, 39.02iter/s]Best score: 0.2937: : 547iter [00:16, 39.02iter/s]Best score: 0.2937: : 548iter [00:16, 39.02iter/s]Best score: 0.2937: : 549iter [00:16, 38.95iter/s]Best score: 0.2937: : 549iter [00:16, 38.95iter/s]
[ITER 550] Best: 0.2937
[PARAMS] Current best parameters:
  fx: 550.0710
  fy: 549.4764
  px: 989.9138
  py: 759.1043
  k1: -0.02046222
  k2: 0.00758832
  k3: -0.00091375
  k4: -0.00090304
  p1: 0.00650971
  p2: -0.00052363
  p3: -0.01117104
  p4: 0.00305536
  s1: 0.0043
  s2: -0.0011
  s3: -0.0008
  s4: -0.0017
  tilt_x: -0.0159
  tilt_y: 0.0137
  scale: 3.0378
  rotation: -0.5535
Best score: 0.2937: : 550iter [00:17, 38.95iter/s]Best score: 0.2937: : 551iter [00:17, 38.95iter/s]Best score: 0.2937: : 552iter [00:17, 38.95iter/s]Best score: 0.2937: : 553iter [00:17, 31.55iter/s]Best score: 0.2937: : 553iter [00:17, 31.55iter/s]Best score: 0.2937: : 554iter [00:17, 31.55iter/s]Best score: 0.2937: : 555iter [00:17, 31.55iter/s]Best score: 0.2937: : 556iter [00:17, 31.55iter/s]Best score: 0.2937: : 557iter [00:17, 33.50iter/s]Best score: 0.2937: : 557iter [00:17, 33.50iter/s]Best score: 0.2937: : 558iter [00:17, 33.50iter/s]Best score: 0.2937: : 559iter [00:17, 33.50iter/s]Best score: 0.2937: : 560iter [00:17, 33.50iter/s]Best score: 0.2937: : 561iter [00:17, 34.75iter/s]Best score: 0.2937: : 561iter [00:17, 34.75iter/s]Best score: 0.2937: : 562iter [00:17, 34.75iter/s]Best score: 0.2937: : 563iter [00:17, 34.75iter/s]Best score: 0.2937: : 564iter [00:17, 34.75iter/s]Best score: 0.2937: : 565iter [00:17, 36.05iter/s]Best score: 0.2937: : 565iter [00:17, 36.05iter/s]Best score: 0.2937: : 566iter [00:17, 36.05iter/s]Best score: 0.2937: : 567iter [00:17, 36.05iter/s]Best score: 0.2937: : 568iter [00:17, 36.05iter/s]Best score: 0.2937: : 569iter [00:17, 36.48iter/s]Best score: 0.2927: : 569iter [00:17, 36.48iter/s]Best score: 0.2927: : 570iter [00:17, 36.48iter/s]Best score: 0.2927: : 571iter [00:17, 36.48iter/s]Best score: 0.2927: : 572iter [00:17, 36.48iter/s]Best score: 0.2927: : 573iter [00:17, 36.98iter/s]Best score: 0.2927: : 573iter [00:17, 36.98iter/s]Best score: 0.2927: : 574iter [00:17, 36.98iter/s]Best score: 0.2927: : 575iter [00:17, 36.98iter/s]Best score: 0.2927: : 576iter [00:17, 36.98iter/s]Best score: 0.2927: : 577iter [00:17, 37.69iter/s]Best score: 0.2927: : 577iter [00:17, 37.69iter/s]Best score: 0.2927: : 578iter [00:17, 37.69iter/s]Best score: 0.2864: : 579iter [00:17, 37.69iter/s]Best score: 0.2864: : 580iter [00:17, 37.69iter/s]Best score: 0.2864: : 581iter [00:17, 37.88iter/s]Best score: 0.2864: : 581iter [00:17, 37.88iter/s]Best score: 0.2864: : 582iter [00:17, 37.88iter/s]Best score: 0.2864: : 583iter [00:17, 37.88iter/s]Best score: 0.2864: : 584iter [00:17, 37.88iter/s]Best score: 0.2864: : 585iter [00:17, 38.44iter/s]Best score: 0.2864: : 585iter [00:18, 38.44iter/s]Best score: 0.2864: : 586iter [00:18, 38.44iter/s]Best score: 0.2864: : 587iter [00:18, 38.44iter/s]Best score: 0.2864: : 588iter [00:18, 38.44iter/s]Best score: 0.2864: : 589iter [00:18, 38.67iter/s]Best score: 0.2733: : 589iter [00:18, 38.67iter/s]Best score: 0.2733: : 590iter [00:18, 38.67iter/s]Best score: 0.2733: : 591iter [00:18, 38.67iter/s]Best score: 0.2733: : 592iter [00:18, 38.67iter/s]Best score: 0.2733: : 593iter [00:18, 38.61iter/s]Best score: 0.2733: : 593iter [00:18, 38.61iter/s]Best score: 0.2733: : 594iter [00:18, 38.61iter/s]Best score: 0.2733: : 595iter [00:18, 38.61iter/s]Best score: 0.2733: : 596iter [00:18, 38.61iter/s]Best score: 0.2733: : 597iter [00:18, 38.96iter/s]Best score: 0.2733: : 597iter [00:18, 38.96iter/s]Best score: 0.2733: : 598iter [00:18, 38.96iter/s]
[ITER 600] Best: 0.2733
[PARAMS] Current best parameters:
  fx: 552.4988
  fy: 552.0499
  px: 989.2318
  py: 757.6717
  k1: -0.01920230
  k2: 0.00336937
  k3: 0.00067627
  k4: -0.00103955
  p1: 0.00417337
  p2: -0.00271473
  p3: -0.00867074
  p4: 0.00313178
  s1: 0.0051
  s2: -0.0021
  s3: -0.0015
  s4: -0.0021
  tilt_x: -0.0187
  tilt_y: 0.0129
  scale: 3.0327
  rotation: -0.5527
Best score: 0.2733: : 599iter [00:18, 38.96iter/s]Best score: 0.2733: : 600iter [00:18, 38.96iter/s]Best score: 0.2733: : 601iter [00:18, 31.63iter/s]Best score: 0.2733: : 601iter [00:18, 31.63iter/s]Best score: 0.2733: : 602iter [00:18, 31.63iter/s]Best score: 0.2733: : 603iter [00:18, 31.63iter/s]Best score: 0.2733: : 604iter [00:18, 31.63iter/s]Best score: 0.2733: : 605iter [00:18, 33.57iter/s]Best score: 0.2733: : 605iter [00:18, 33.57iter/s]Best score: 0.2733: : 606iter [00:18, 33.57iter/s]Best score: 0.2733: : 607iter [00:18, 33.57iter/s]Best score: 0.2733: : 608iter [00:18, 33.57iter/s]Best score: 0.2733: : 609iter [00:18, 35.24iter/s]Best score: 0.2733: : 609iter [00:18, 35.24iter/s]Best score: 0.2733: : 610iter [00:18, 35.24iter/s]Best score: 0.2733: : 611iter [00:18, 35.24iter/s]Best score: 0.2733: : 612iter [00:18, 35.24iter/s]Best score: 0.2733: : 613iter [00:18, 35.82iter/s]Best score: 0.2733: : 613iter [00:18, 35.82iter/s]Best score: 0.2733: : 614iter [00:18, 35.82iter/s]Best score: 0.2733: : 615iter [00:18, 35.82iter/s]Best score: 0.2733: : 616iter [00:18, 35.82iter/s]Best score: 0.2733: : 617iter [00:18, 36.80iter/s]Best score: 0.2733: : 617iter [00:18, 36.80iter/s]Best score: 0.2733: : 618iter [00:18, 36.80iter/s]Best score: 0.2696: : 619iter [00:18, 36.80iter/s]Best score: 0.2696: : 620iter [00:18, 36.80iter/s]Best score: 0.2696: : 621iter [00:18, 37.46iter/s]Best score: 0.2696: : 621iter [00:19, 37.46iter/s]Best score: 0.2696: : 622iter [00:19, 37.46iter/s]Best score: 0.2696: : 623iter [00:19, 37.46iter/s]Best score: 0.2696: : 624iter [00:19, 37.46iter/s]Best score: 0.2696: : 625iter [00:19, 38.07iter/s]Best score: 0.2696: : 625iter [00:19, 38.07iter/s]Best score: 0.2696: : 626iter [00:19, 38.07iter/s]Best score: 0.2696: : 627iter [00:19, 38.07iter/s]Best score: 0.2696: : 628iter [00:19, 38.07iter/s]Best score: 0.2696: : 629iter [00:19, 38.59iter/s]Best score: 0.2676: : 629iter [00:19, 38.59iter/s]Best score: 0.2676: : 630iter [00:19, 38.59iter/s]Best score: 0.2676: : 631iter [00:19, 38.59iter/s]Best score: 0.2676: : 632iter [00:19, 38.59iter/s]Best score: 0.2676: : 633iter [00:19, 38.38iter/s]Best score: 0.2676: : 633iter [00:19, 38.38iter/s]Best score: 0.2676: : 634iter [00:19, 38.38iter/s]Best score: 0.2676: : 635iter [00:19, 38.38iter/s]Best score: 0.2676: : 636iter [00:19, 38.38iter/s]Best score: 0.2676: : 637iter [00:19, 38.60iter/s]Best score: 0.2676: : 637iter [00:19, 38.60iter/s]Best score: 0.2676: : 638iter [00:19, 38.60iter/s]Best score: 0.2599: : 639iter [00:19, 38.60iter/s]Best score: 0.2599: : 640iter [00:19, 38.60iter/s]Best score: 0.2599: : 641iter [00:19, 38.46iter/s]Best score: 0.2599: : 641iter [00:19, 38.46iter/s]Best score: 0.2599: : 642iter [00:19, 38.46iter/s]Best score: 0.2599: : 643iter [00:19, 38.46iter/s]Best score: 0.2599: : 644iter [00:19, 38.46iter/s]Best score: 0.2599: : 645iter [00:19, 38.81iter/s]Best score: 0.2599: : 645iter [00:19, 38.81iter/s]Best score: 0.2599: : 646iter [00:19, 38.81iter/s]Best score: 0.2599: : 647iter [00:19, 38.81iter/s]Best score: 0.2599: : 648iter [00:19, 38.81iter/s]Best score: 0.2599: : 649iter [00:19, 38.85iter/s]
[ITER 650] Best: 0.2584
[PARAMS] Current best parameters:
  fx: 549.2827
  fy: 548.8193
  px: 990.4398
  py: 756.7710
  k1: -0.01843353
  k2: 0.00634334
  k3: -0.00051292
  k4: -0.00099212
  p1: 0.00757784
  p2: -0.00296381
  p3: -0.00816277
  p4: 0.00312740
  s1: 0.0063
  s2: -0.0024
  s3: -0.0050
  s4: -0.0028
  tilt_x: -0.0301
  tilt_y: 0.0059
  scale: 3.0366
  rotation: -0.5457
Best score: 0.2584: : 649iter [00:19, 38.85iter/s]Best score: 0.2584: : 650iter [00:19, 38.85iter/s]Best score: 0.2584: : 651iter [00:19, 38.85iter/s]Best score: 0.2584: : 652iter [00:19, 38.85iter/s]Best score: 0.2584: : 653iter [00:19, 31.29iter/s]Best score: 0.2584: : 653iter [00:19, 31.29iter/s]Best score: 0.2584: : 654iter [00:19, 31.29iter/s]Best score: 0.2584: : 655iter [00:19, 31.29iter/s]Best score: 0.2584: : 656iter [00:19, 31.29iter/s]Best score: 0.2584: : 657iter [00:19, 33.28iter/s]Best score: 0.2584: : 657iter [00:20, 33.28iter/s]Best score: 0.2584: : 658iter [00:20, 33.28iter/s]Best score: 0.2584: : 659iter [00:20, 33.28iter/s]Best score: 0.2584: : 660iter [00:20, 33.28iter/s]Best score: 0.2584: : 661iter [00:20, 34.54iter/s]Best score: 0.2584: : 661iter [00:20, 34.54iter/s]Best score: 0.2584: : 662iter [00:20, 34.54iter/s]Best score: 0.2584: : 663iter [00:20, 34.54iter/s]Best score: 0.2584: : 664iter [00:20, 34.54iter/s]Best score: 0.2584: : 665iter [00:20, 35.94iter/s]Best score: 0.2584: : 665iter [00:20, 35.94iter/s]Best score: 0.2584: : 666iter [00:20, 35.94iter/s]Best score: 0.2584: : 667iter [00:20, 35.94iter/s]Best score: 0.2584: : 668iter [00:20, 35.94iter/s]Best score: 0.2584: : 669iter [00:20, 37.03iter/s]Best score: 0.2583: : 669iter [00:20, 37.03iter/s]Best score: 0.2583: : 670iter [00:20, 37.03iter/s]Best score: 0.2583: : 671iter [00:20, 37.03iter/s]Best score: 0.2583: : 672iter [00:20, 37.03iter/s]Best score: 0.2583: : 673iter [00:20, 37.45iter/s]Best score: 0.2583: : 673iter [00:20, 37.45iter/s]Best score: 0.2583: : 674iter [00:20, 37.45iter/s]Best score: 0.2583: : 675iter [00:20, 37.45iter/s]Best score: 0.2583: : 676iter [00:20, 37.45iter/s]Best score: 0.2583: : 677iter [00:20, 38.07iter/s]Best score: 0.2583: : 677iter [00:20, 38.07iter/s]Best score: 0.2583: : 678iter [00:20, 38.07iter/s]Best score: 0.2579: : 679iter [00:20, 38.07iter/s]Best score: 0.2579: : 680iter [00:20, 38.07iter/s]Best score: 0.2579: : 681iter [00:20, 38.20iter/s]Best score: 0.2579: : 681iter [00:20, 38.20iter/s]Best score: 0.2579: : 682iter [00:20, 38.20iter/s]Best score: 0.2579: : 683iter [00:20, 38.20iter/s]Best score: 0.2579: : 684iter [00:20, 38.20iter/s]Best score: 0.2579: : 685iter [00:20, 29.96iter/s]Best score: 0.2579: : 685iter [00:20, 29.96iter/s]Best score: 0.2579: : 686iter [00:20, 29.96iter/s]Best score: 0.2579: : 687iter [00:20, 29.96iter/s]Best score: 0.2579: : 688iter [00:20, 29.96iter/s]Best score: 0.2579: : 689iter [00:20, 32.32iter/s]Best score: 0.2544: : 689iter [00:20, 32.32iter/s]Best score: 0.2544: : 690iter [00:20, 32.32iter/s]Best score: 0.2544: : 691iter [00:20, 32.32iter/s]Best score: 0.2544: : 692iter [00:21, 32.32iter/s]Best score: 0.2544: : 693iter [00:21, 33.91iter/s]Best score: 0.2544: : 693iter [00:21, 33.91iter/s]Best score: 0.2544: : 694iter [00:21, 33.91iter/s]Best score: 0.2544: : 695iter [00:21, 33.91iter/s]Best score: 0.2544: : 696iter [00:21, 33.91iter/s]Best score: 0.2544: : 697iter [00:21, 35.33iter/s]Best score: 0.2544: : 697iter [00:21, 35.33iter/s]Best score: 0.2544: : 698iter [00:21, 35.33iter/s]
[ITER 700] Best: 0.2544
[PARAMS] Current best parameters:
  fx: 546.5712
  fy: 546.0829
  px: 991.4125
  py: 757.0426
  k1: -0.01330566
  k2: 0.00595680
  k3: -0.00210395
  k4: -0.00043438
  p1: 0.00845493
  p2: -0.00231762
  p3: -0.00943520
  p4: 0.00224964
  s1: 0.0081
  s2: -0.0018
  s3: -0.0045
  s4: -0.0020
  tilt_x: -0.0301
  tilt_y: 0.0020
  scale: 3.0295
  rotation: -0.5445
Best score: 0.2544: : 699iter [00:21, 35.33iter/s]Best score: 0.2544: : 700iter [00:21, 35.33iter/s]Best score: 0.2544: : 701iter [00:21, 29.43iter/s]Best score: 0.2544: : 701iter [00:21, 29.43iter/s]Best score: 0.2544: : 702iter [00:21, 29.43iter/s]Best score: 0.2544: : 703iter [00:21, 29.43iter/s]Best score: 0.2544: : 704iter [00:21, 29.43iter/s]Best score: 0.2544: : 705iter [00:21, 29.43iter/s]Best score: 0.2544: : 706iter [00:21, 32.42iter/s]Best score: 0.2544: : 706iter [00:21, 32.42iter/s]Best score: 0.2544: : 707iter [00:21, 32.42iter/s]Best score: 0.2544: : 708iter [00:21, 32.42iter/s]Best score: 0.2540: : 709iter [00:21, 32.42iter/s]Best score: 0.2540: : 710iter [00:21, 34.01iter/s]Best score: 0.2540: : 710iter [00:21, 34.01iter/s]Best score: 0.2540: : 711iter [00:21, 34.01iter/s]Best score: 0.2540: : 712iter [00:21, 34.01iter/s]Best score: 0.2540: : 713iter [00:21, 34.01iter/s]Best score: 0.2540: : 714iter [00:21, 34.01iter/s]Best score: 0.2540: : 715iter [00:21, 35.79iter/s]Best score: 0.2540: : 715iter [00:21, 35.79iter/s]Best score: 0.2540: : 716iter [00:21, 35.79iter/s]Best score: 0.2540: : 717iter [00:21, 35.79iter/s]Best score: 0.2540: : 718iter [00:21, 35.79iter/s]Best score: 0.2540: : 719iter [00:21, 36.66iter/s]Best score: 0.2527: : 719iter [00:21, 36.66iter/s]Best score: 0.2527: : 720iter [00:21, 36.66iter/s]Best score: 0.2527: : 721iter [00:21, 36.66iter/s]Best score: 0.2527: : 722iter [00:21, 36.66iter/s]Best score: 0.2527: : 723iter [00:21, 36.87iter/s]Best score: 0.2527: : 723iter [00:21, 36.87iter/s]Best score: 0.2527: : 724iter [00:21, 36.87iter/s]Best score: 0.2527: : 725iter [00:21, 36.87iter/s]Best score: 0.2527: : 726iter [00:21, 36.87iter/s]Best score: 0.2527: : 727iter [00:21, 37.25iter/s]Best score: 0.2527: : 727iter [00:21, 37.25iter/s]Best score: 0.2527: : 728iter [00:22, 37.25iter/s]Best score: 0.2514: : 729iter [00:22, 37.25iter/s]Best score: 0.2514: : 730iter [00:22, 37.25iter/s]Best score: 0.2514: : 731iter [00:22, 37.62iter/s]Best score: 0.2514: : 731iter [00:22, 37.62iter/s]Best score: 0.2514: : 732iter [00:22, 37.62iter/s]Best score: 0.2514: : 733iter [00:22, 37.62iter/s]Best score: 0.2514: : 734iter [00:22, 37.62iter/s]Best score: 0.2514: : 735iter [00:22, 38.14iter/s]Best score: 0.2514: : 735iter [00:22, 38.14iter/s]Best score: 0.2514: : 736iter [00:22, 38.14iter/s]Best score: 0.2514: : 737iter [00:22, 38.14iter/s]Best score: 0.2514: : 738iter [00:22, 38.14iter/s]Best score: 0.2514: : 739iter [00:22, 38.16iter/s]Best score: 0.2514: : 739iter [00:22, 38.16iter/s]Best score: 0.2514: : 740iter [00:22, 38.16iter/s]Best score: 0.2514: : 741iter [00:22, 38.16iter/s]Best score: 0.2514: : 742iter [00:22, 38.16iter/s]Best score: 0.2514: : 743iter [00:22, 38.24iter/s]Best score: 0.2514: : 743iter [00:22, 38.24iter/s]Best score: 0.2514: : 744iter [00:22, 38.24iter/s]Best score: 0.2514: : 745iter [00:22, 38.24iter/s]Best score: 0.2514: : 746iter [00:22, 38.24iter/s]Best score: 0.2514: : 747iter [00:22, 38.38iter/s]Best score: 0.2514: : 747iter [00:22, 38.38iter/s]Best score: 0.2514: : 748iter [00:22, 38.38iter/s]
[ITER 750] Best: 0.2506
[PARAMS] Current best parameters:
  fx: 545.3562
  fy: 544.9027
  px: 991.6808
  py: 757.0431
  k1: -0.01391418
  k2: 0.00703108
  k3: -0.00208168
  k4: -0.00052717
  p1: 0.00841723
  p2: -0.00351248
  p3: -0.01056858
  p4: 0.00301216
  s1: 0.0106
  s2: -0.0025
  s3: -0.0034
  s4: -0.0026
  tilt_x: -0.0300
  tilt_y: -0.0018
  scale: 3.0353
  rotation: -0.5398
Best score: 0.2506: : 749iter [00:22, 38.38iter/s]Best score: 0.2506: : 750iter [00:22, 38.38iter/s]Best score: 0.2506: : 751iter [00:22, 31.23iter/s]Best score: 0.2506: : 751iter [00:22, 31.23iter/s]Best score: 0.2506: : 752iter [00:22, 31.23iter/s]Best score: 0.2506: : 753iter [00:22, 31.23iter/s]Best score: 0.2506: : 754iter [00:22, 31.23iter/s]Best score: 0.2506: : 755iter [00:22, 33.32iter/s]Best score: 0.2506: : 755iter [00:22, 33.32iter/s]Best score: 0.2506: : 756iter [00:22, 33.32iter/s]Best score: 0.2506: : 757iter [00:22, 33.32iter/s]Best score: 0.2506: : 758iter [00:22, 33.32iter/s]Best score: 0.2506: : 759iter [00:22, 34.91iter/s]Best score: 0.2501: : 759iter [00:22, 34.91iter/s]Best score: 0.2501: : 760iter [00:22, 34.91iter/s]Best score: 0.2501: : 761iter [00:22, 34.91iter/s]Best score: 0.2501: : 762iter [00:22, 34.91iter/s]Best score: 0.2501: : 763iter [00:22, 35.95iter/s]Best score: 0.2501: : 763iter [00:23, 35.95iter/s]Best score: 0.2501: : 764iter [00:23, 35.95iter/s]Best score: 0.2501: : 765iter [00:23, 35.95iter/s]Best score: 0.2501: : 766iter [00:23, 35.95iter/s]Best score: 0.2501: : 767iter [00:23, 36.91iter/s]Best score: 0.2501: : 767iter [00:23, 36.91iter/s]Best score: 0.2501: : 768iter [00:23, 36.91iter/s]Best score: 0.2477: : 769iter [00:23, 36.91iter/s]Best score: 0.2477: : 770iter [00:23, 36.91iter/s]Best score: 0.2477: : 771iter [00:23, 37.37iter/s]Best score: 0.2477: : 771iter [00:23, 37.37iter/s]Best score: 0.2477: : 772iter [00:23, 37.37iter/s]Best score: 0.2477: : 773iter [00:23, 37.37iter/s]Best score: 0.2477: : 774iter [00:23, 37.37iter/s]Best score: 0.2477: : 775iter [00:23, 37.76iter/s]Best score: 0.2477: : 775iter [00:23, 37.76iter/s]Best score: 0.2477: : 776iter [00:23, 37.76iter/s]Best score: 0.2477: : 777iter [00:23, 37.76iter/s]Best score: 0.2477: : 778iter [00:23, 37.76iter/s]Best score: 0.2477: : 779iter [00:23, 38.24iter/s]Best score: 0.2477: : 779iter [00:23, 38.24iter/s]Best score: 0.2477: : 780iter [00:23, 38.24iter/s]Best score: 0.2477: : 781iter [00:23, 38.24iter/s]Best score: 0.2477: : 782iter [00:23, 38.24iter/s]Best score: 0.2477: : 783iter [00:23, 38.27iter/s]Best score: 0.2477: : 783iter [00:23, 38.27iter/s]Best score: 0.2477: : 784iter [00:23, 38.27iter/s]Best score: 0.2477: : 785iter [00:23, 38.27iter/s]Best score: 0.2477: : 786iter [00:23, 38.27iter/s]Best score: 0.2477: : 787iter [00:23, 38.61iter/s]Best score: 0.2477: : 787iter [00:23, 38.61iter/s]Best score: 0.2477: : 788iter [00:23, 38.61iter/s]Best score: 0.2472: : 789iter [00:23, 38.61iter/s]Best score: 0.2472: : 790iter [00:23, 38.61iter/s]Best score: 0.2472: : 791iter [00:23, 38.70iter/s]Best score: 0.2472: : 791iter [00:23, 38.70iter/s]Best score: 0.2472: : 792iter [00:23, 38.70iter/s]Best score: 0.2472: : 793iter [00:23, 38.70iter/s]Best score: 0.2472: : 794iter [00:23, 38.70iter/s]Best score: 0.2472: : 795iter [00:23, 39.06iter/s]Best score: 0.2472: : 795iter [00:23, 39.06iter/s]Best score: 0.2472: : 796iter [00:23, 39.06iter/s]Best score: 0.2472: : 797iter [00:23, 39.06iter/s]Best score: 0.2472: : 798iter [00:23, 39.06iter/s]Best score: 0.2472: : 799iter [00:23, 38.91iter/s]
[ITER 800] Best: 0.2456
[PARAMS] Current best parameters:
  fx: 542.6397
  fy: 542.1139
  px: 991.7559
  py: 757.1561
  k1: -0.01747591
  k2: 0.01371735
  k3: -0.00448564
  k4: -0.00031525
  p1: 0.01374638
  p2: -0.00115224
  p3: -0.01252102
  p4: 0.00235736
  s1: 0.0105
  s2: -0.0020
  s3: -0.0065
  s4: -0.0019
  tilt_x: -0.0401
  tilt_y: 0.0027
  scale: 3.0494
  rotation: -0.5508
Best score: 0.2456: : 799iter [00:23, 38.91iter/s]Best score: 0.2456: : 800iter [00:24, 38.91iter/s]Best score: 0.2456: : 801iter [00:24, 38.91iter/s]Best score: 0.2456: : 802iter [00:24, 38.91iter/s]Best score: 0.2456: : 803iter [00:24, 31.53iter/s]Best score: 0.2456: : 803iter [00:24, 31.53iter/s]Best score: 0.2456: : 804iter [00:24, 31.53iter/s]Best score: 0.2456: : 805iter [00:24, 31.53iter/s]Best score: 0.2456: : 806iter [00:24, 31.53iter/s]Best score: 0.2456: : 807iter [00:24, 33.52iter/s]Best score: 0.2456: : 807iter [00:24, 33.52iter/s]Best score: 0.2456: : 808iter [00:24, 33.52iter/s]Best score: 0.2439: : 809iter [00:24, 33.52iter/s]Best score: 0.2439: : 810iter [00:24, 33.52iter/s]Best score: 0.2439: : 811iter [00:24, 34.87iter/s]Best score: 0.2439: : 811iter [00:24, 34.87iter/s]Best score: 0.2439: : 812iter [00:24, 34.87iter/s]Best score: 0.2439: : 813iter [00:24, 34.87iter/s]Best score: 0.2439: : 814iter [00:24, 34.87iter/s]Best score: 0.2439: : 815iter [00:24, 36.10iter/s]Best score: 0.2439: : 815iter [00:24, 36.10iter/s]Best score: 0.2439: : 816iter [00:24, 36.10iter/s]Best score: 0.2439: : 817iter [00:24, 36.10iter/s]Best score: 0.2439: : 818iter [00:24, 36.10iter/s]Best score: 0.2439: : 819iter [00:24, 37.17iter/s]Best score: 0.2430: : 819iter [00:24, 37.17iter/s]Best score: 0.2430: : 820iter [00:24, 37.17iter/s]Best score: 0.2430: : 821iter [00:24, 37.17iter/s]Best score: 0.2430: : 822iter [00:24, 37.17iter/s]Best score: 0.2430: : 823iter [00:24, 37.43iter/s]Best score: 0.2430: : 823iter [00:24, 37.43iter/s]Best score: 0.2430: : 824iter [00:24, 37.43iter/s]Best score: 0.2430: : 825iter [00:24, 37.43iter/s]Best score: 0.2430: : 826iter [00:24, 37.43iter/s]Best score: 0.2430: : 827iter [00:24, 38.12iter/s]Best score: 0.2430: : 827iter [00:24, 38.12iter/s]Best score: 0.2430: : 828iter [00:24, 38.12iter/s]Best score: 0.2428: : 829iter [00:24, 38.12iter/s]Best score: 0.2428: : 830iter [00:24, 38.12iter/s]Best score: 0.2428: : 831iter [00:24, 38.19iter/s]Best score: 0.2428: : 831iter [00:24, 38.19iter/s]Best score: 0.2428: : 832iter [00:24, 38.19iter/s]Best score: 0.2428: : 833iter [00:24, 38.19iter/s]Best score: 0.2428: : 834iter [00:24, 38.19iter/s]Best score: 0.2428: : 835iter [00:24, 38.50iter/s]Best score: 0.2428: : 835iter [00:24, 38.50iter/s]Best score: 0.2428: : 836iter [00:24, 38.50iter/s]Best score: 0.2428: : 837iter [00:24, 38.50iter/s]Best score: 0.2428: : 838iter [00:25, 38.50iter/s]Best score: 0.2428: : 839iter [00:25, 38.69iter/s]Best score: 0.2426: : 839iter [00:25, 38.69iter/s]Best score: 0.2426: : 840iter [00:25, 38.69iter/s]Best score: 0.2426: : 841iter [00:25, 38.69iter/s]Best score: 0.2426: : 842iter [00:25, 38.69iter/s]Best score: 0.2426: : 843iter [00:25, 38.55iter/s]Best score: 0.2426: : 843iter [00:25, 38.55iter/s]Best score: 0.2426: : 844iter [00:25, 38.55iter/s]Best score: 0.2426: : 845iter [00:25, 38.55iter/s]Best score: 0.2426: : 846iter [00:25, 38.55iter/s]Best score: 0.2426: : 847iter [00:25, 38.88iter/s]Best score: 0.2426: : 847iter [00:25, 38.88iter/s]Best score: 0.2426: : 848iter [00:25, 38.88iter/s]
[ITER 850] Best: 0.2426
[PARAMS] Current best parameters:
  fx: 540.7891
  fy: 540.1905
  px: 992.0096
  py: 756.8073
  k1: -0.01827927
  k2: 0.01835129
  k3: -0.00749820
  k4: 0.00031869
  p1: 0.01825335
  p2: 0.00049157
  p3: -0.01384263
  p4: 0.00169877
  s1: 0.0109
  s2: -0.0016
  s3: -0.0101
  s4: -0.0012
  tilt_x: -0.0509
  tilt_y: 0.0045
  scale: 3.0544
  rotation: -0.5575
Best score: 0.2426: : 849iter [00:25, 38.88iter/s]Best score: 0.2426: : 850iter [00:25, 38.88iter/s]Best score: 0.2426: : 851iter [00:25, 31.74iter/s]Best score: 0.2426: : 851iter [00:25, 31.74iter/s]Best score: 0.2426: : 852iter [00:25, 31.74iter/s]Best score: 0.2426: : 853iter [00:25, 31.74iter/s]Best score: 0.2426: : 854iter [00:25, 31.74iter/s]Best score: 0.2426: : 855iter [00:25, 33.74iter/s]Best score: 0.2426: : 855iter [00:25, 33.74iter/s]Best score: 0.2426: : 856iter [00:25, 33.74iter/s]Best score: 0.2426: : 857iter [00:25, 33.74iter/s]Best score: 0.2426: : 858iter [00:25, 33.74iter/s]Best score: 0.2426: : 859iter [00:25, 35.33iter/s]Best score: 0.2423: : 859iter [00:25, 35.33iter/s]Best score: 0.2423: : 860iter [00:25, 35.33iter/s]Best score: 0.2423: : 861iter [00:25, 35.33iter/s]Best score: 0.2423: : 862iter [00:25, 35.33iter/s]Best score: 0.2423: : 863iter [00:25, 36.21iter/s]Best score: 0.2423: : 863iter [00:25, 36.21iter/s]Best score: 0.2423: : 864iter [00:25, 36.21iter/s]Best score: 0.2423: : 865iter [00:25, 36.21iter/s]Best score: 0.2423: : 866iter [00:25, 36.21iter/s]Best score: 0.2423: : 867iter [00:25, 37.15iter/s]Best score: 0.2423: : 867iter [00:25, 37.15iter/s]Best score: 0.2423: : 868iter [00:25, 37.15iter/s]Best score: 0.2419: : 869iter [00:25, 37.15iter/s]Best score: 0.2419: : 870iter [00:25, 37.15iter/s]Best score: 0.2419: : 871iter [00:25, 37.58iter/s]Best score: 0.2419: : 871iter [00:25, 37.58iter/s]Best score: 0.2419: : 872iter [00:25, 37.58iter/s]Best score: 0.2419: : 873iter [00:25, 37.58iter/s]Best score: 0.2419: : 874iter [00:26, 37.58iter/s]Best score: 0.2419: : 875iter [00:26, 38.21iter/s]Best score: 0.2419: : 875iter [00:26, 38.21iter/s]Best score: 0.2419: : 876iter [00:26, 38.21iter/s]Best score: 0.2419: : 877iter [00:26, 38.21iter/s]Best score: 0.2419: : 878iter [00:26, 38.21iter/s]Best score: 0.2419: : 879iter [00:26, 38.68iter/s]Best score: 0.2417: : 879iter [00:26, 38.68iter/s]Best score: 0.2417: : 880iter [00:26, 38.68iter/s]Best score: 0.2417: : 881iter [00:26, 38.68iter/s]Best score: 0.2417: : 882iter [00:26, 38.68iter/s]Best score: 0.2417: : 883iter [00:26, 38.42iter/s]Best score: 0.2417: : 883iter [00:26, 38.42iter/s]Best score: 0.2417: : 884iter [00:26, 38.42iter/s]Best score: 0.2417: : 885iter [00:26, 38.42iter/s]Best score: 0.2417: : 886iter [00:26, 38.42iter/s]Best score: 0.2417: : 887iter [00:26, 38.69iter/s]Best score: 0.2417: : 887iter [00:26, 38.69iter/s]Best score: 0.2417: : 888iter [00:26, 38.69iter/s]Best score: 0.2412: : 889iter [00:26, 38.69iter/s]Best score: 0.2412: : 890iter [00:26, 38.69iter/s]Best score: 0.2412: : 891iter [00:26, 38.62iter/s]Best score: 0.2412: : 891iter [00:26, 38.62iter/s]Best score: 0.2412: : 892iter [00:26, 38.62iter/s]Best score: 0.2412: : 893iter [00:26, 38.62iter/s]Best score: 0.2412: : 894iter [00:26, 38.62iter/s]Best score: 0.2412: : 895iter [00:26, 38.45iter/s]Best score: 0.2412: : 895iter [00:26, 38.45iter/s]Best score: 0.2412: : 896iter [00:26, 38.45iter/s]Best score: 0.2412: : 897iter [00:26, 38.45iter/s]Best score: 0.2412: : 898iter [00:26, 38.45iter/s]Best score: 0.2412: : 899iter [00:26, 38.69iter/s]
[ITER 900] Best: 0.2412
[PARAMS] Current best parameters:
  fx: 541.1773
  fy: 540.6182
  px: 992.1179
  py: 756.5230
  k1: -0.01815802
  k2: 0.01834991
  k3: -0.00798994
  k4: 0.00052001
  p1: 0.01838688
  p2: -0.00028186
  p3: -0.01340763
  p4: 0.00166636
  s1: 0.0109
  s2: -0.0014
  s3: -0.0115
  s4: -0.0008
  tilt_x: -0.0526
  tilt_y: 0.0023
  scale: 3.0535
  rotation: -0.5572
Best score: 0.2412: : 899iter [00:26, 38.69iter/s]Best score: 0.2412: : 900iter [00:26, 38.69iter/s]Best score: 0.2412: : 901iter [00:26, 38.69iter/s]Best score: 0.2412: : 902iter [00:26, 38.69iter/s]Best score: 0.2412: : 903iter [00:26, 31.37iter/s]Best score: 0.2412: : 903iter [00:26, 31.37iter/s]Best score: 0.2412: : 904iter [00:26, 31.37iter/s]Best score: 0.2412: : 905iter [00:26, 31.37iter/s]Best score: 0.2412: : 906iter [00:26, 31.37iter/s]Best score: 0.2412: : 907iter [00:26, 33.39iter/s]Best score: 0.2412: : 907iter [00:26, 33.39iter/s]Best score: 0.2412: : 908iter [00:26, 33.39iter/s]Best score: 0.2407: : 909iter [00:26, 33.39iter/s]Best score: 0.2407: : 910iter [00:27, 33.39iter/s]Best score: 0.2407: : 911iter [00:27, 34.73iter/s]Best score: 0.2407: : 911iter [00:27, 34.73iter/s]Best score: 0.2407: : 912iter [00:27, 34.73iter/s]Best score: 0.2407: : 913iter [00:27, 34.73iter/s]Best score: 0.2407: : 914iter [00:27, 34.73iter/s]Best score: 0.2407: : 915iter [00:27, 36.03iter/s]Best score: 0.2407: : 915iter [00:27, 36.03iter/s]Best score: 0.2407: : 916iter [00:27, 36.03iter/s]Best score: 0.2407: : 917iter [00:27, 36.03iter/s]Best score: 0.2407: : 918iter [00:27, 36.03iter/s]Best score: 0.2407: : 919iter [00:27, 37.09iter/s]Best score: 0.2405: : 919iter [00:27, 37.09iter/s]Best score: 0.2405: : 920iter [00:27, 37.09iter/s]Best score: 0.2405: : 921iter [00:27, 37.09iter/s]Best score: 0.2405: : 922iter [00:27, 37.09iter/s]Best score: 0.2405: : 923iter [00:27, 37.57iter/s]Best score: 0.2405: : 923iter [00:27, 37.57iter/s]Best score: 0.2405: : 924iter [00:27, 37.57iter/s]Best score: 0.2405: : 925iter [00:27, 37.57iter/s]Best score: 0.2405: : 926iter [00:27, 37.57iter/s]Best score: 0.2405: : 927iter [00:27, 37.92iter/s]Best score: 0.2405: : 927iter [00:27, 37.92iter/s]Best score: 0.2405: : 928iter [00:27, 37.92iter/s]Best score: 0.2405: : 929iter [00:27, 37.92iter/s]Best score: 0.2405: : 930iter [00:27, 37.92iter/s]Best score: 0.2405: : 931iter [00:27, 38.23iter/s]Best score: 0.2405: : 931iter [00:27, 38.23iter/s]Best score: 0.2405: : 932iter [00:27, 38.23iter/s]Best score: 0.2405: : 933iter [00:27, 38.23iter/s]Best score: 0.2405: : 934iter [00:27, 38.23iter/s]Best score: 0.2405: : 935iter [00:27, 38.49iter/s]Best score: 0.2405: : 935iter [00:27, 38.49iter/s]Best score: 0.2405: : 936iter [00:27, 38.49iter/s]Best score: 0.2405: : 937iter [00:27, 38.49iter/s]Best score: 0.2405: : 938iter [00:27, 38.49iter/s]Best score: 0.2405: : 939iter [00:27, 38.70iter/s]Best score: 0.2405: : 939iter [00:27, 38.70iter/s]Best score: 0.2405: : 940iter [00:27, 38.70iter/s]Best score: 0.2405: : 941iter [00:27, 38.70iter/s]Best score: 0.2405: : 942iter [00:27, 38.70iter/s]Best score: 0.2405: : 943iter [00:27, 38.52iter/s]Best score: 0.2405: : 943iter [00:27, 38.52iter/s]Best score: 0.2405: : 944iter [00:27, 38.52iter/s]Best score: 0.2405: : 945iter [00:27, 38.52iter/s]Best score: 0.2405: : 946iter [00:27, 38.52iter/s]Best score: 0.2405: : 947iter [00:27, 38.89iter/s]Best score: 0.2405: : 947iter [00:27, 38.89iter/s]Best score: 0.2405: : 948iter [00:27, 38.89iter/s]
[ITER 950] Best: 0.2400
[PARAMS] Current best parameters:
  fx: 540.9407
  fy: 540.4004
  px: 992.1217
  py: 755.9800
  k1: -0.01826557
  k2: 0.01884037
  k3: -0.00830153
  k4: 0.00058910
  p1: 0.01920458
  p2: -0.00044582
  p3: -0.01305238
  p4: 0.00131288
  s1: 0.0108
  s2: -0.0011
  s3: -0.0130
  s4: -0.0006
  tilt_x: -0.0570
  tilt_y: 0.0020
  scale: 3.0550
  rotation: -0.5599
Best score: 0.2400: : 949iter [00:28, 38.89iter/s]Best score: 0.2400: : 950iter [00:28, 38.89iter/s]Best score: 0.2400: : 951iter [00:28, 31.52iter/s]Best score: 0.2400: : 951iter [00:28, 31.52iter/s]Best score: 0.2400: : 952iter [00:28, 31.52iter/s]Best score: 0.2400: : 953iter [00:28, 31.52iter/s]Best score: 0.2400: : 954iter [00:28, 31.52iter/s]Best score: 0.2400: : 955iter [00:28, 33.57iter/s]Best score: 0.2400: : 955iter [00:28, 33.57iter/s]Best score: 0.2400: : 956iter [00:28, 33.57iter/s]Best score: 0.2400: : 957iter [00:28, 33.57iter/s]Best score: 0.2400: : 958iter [00:28, 33.57iter/s]Best score: 0.2400: : 959iter [00:28, 35.25iter/s]Best score: 0.2399: : 959iter [00:28, 35.25iter/s]Best score: 0.2399: : 960iter [00:28, 35.25iter/s]Best score: 0.2399: : 961iter [00:28, 35.25iter/s]Best score: 0.2399: : 962iter [00:28, 35.25iter/s]Best score: 0.2399: : 963iter [00:28, 36.18iter/s]Best score: 0.2399: : 963iter [00:28, 36.18iter/s]Best score: 0.2399: : 964iter [00:28, 36.18iter/s]Best score: 0.2399: : 965iter [00:28, 36.18iter/s]Best score: 0.2399: : 966iter [00:28, 36.18iter/s]Best score: 0.2399: : 967iter [00:28, 37.03iter/s]Best score: 0.2399: : 967iter [00:28, 37.03iter/s]Best score: 0.2399: : 968iter [00:28, 37.03iter/s]Best score: 0.2399: : 969iter [00:28, 37.03iter/s]Best score: 0.2399: : 970iter [00:28, 37.03iter/s]Best score: 0.2399: : 971iter [00:28, 37.18iter/s]Best score: 0.2399: : 971iter [00:28, 37.18iter/s]Best score: 0.2399: : 972iter [00:28, 37.18iter/s]Best score: 0.2399: : 973iter [00:28, 37.18iter/s]Best score: 0.2399: : 974iter [00:28, 37.18iter/s]Best score: 0.2399: : 975iter [00:28, 37.18iter/s]Best score: 0.2399: : 976iter [00:28, 38.21iter/s]Best score: 0.2399: : 976iter [00:28, 38.21iter/s]Best score: 0.2399: : 977iter [00:28, 38.21iter/s]Best score: 0.2399: : 978iter [00:28, 38.21iter/s]Best score: 0.2393: : 979iter [00:28, 38.21iter/s]Best score: 0.2393: : 980iter [00:28, 38.25iter/s]Best score: 0.2393: : 980iter [00:28, 38.25iter/s]Best score: 0.2393: : 981iter [00:28, 38.25iter/s]Best score: 0.2393: : 982iter [00:28, 38.25iter/s]Best score: 0.2393: : 983iter [00:28, 38.25iter/s]Best score: 0.2393: : 984iter [00:28, 38.70iter/s]Best score: 0.2393: : 984iter [00:28, 38.70iter/s]Best score: 0.2393: : 985iter [00:29, 38.70iter/s]Best score: 0.2393: : 986iter [00:29, 38.70iter/s]Best score: 0.2393: : 987iter [00:29, 38.70iter/s]Best score: 0.2393: : 988iter [00:29, 38.93iter/s]Best score: 0.2393: : 988iter [00:29, 38.93iter/s]Best score: 0.2391: : 989iter [00:29, 38.93iter/s]Best score: 0.2391: : 990iter [00:29, 38.93iter/s]Best score: 0.2391: : 991iter [00:29, 38.93iter/s]Best score: 0.2391: : 992iter [00:29, 38.88iter/s]Best score: 0.2391: : 992iter [00:29, 38.88iter/s]Best score: 0.2391: : 993iter [00:29, 38.88iter/s]Best score: 0.2391: : 994iter [00:29, 38.88iter/s]Best score: 0.2391: : 995iter [00:29, 38.88iter/s]Best score: 0.2391: : 996iter [00:29, 39.01iter/s]Best score: 0.2391: : 996iter [00:29, 39.01iter/s]Best score: 0.2391: : 997iter [00:29, 39.01iter/s]Best score: 0.2391: : 998iter [00:29, 39.01iter/s]
[ITER 1000] Best: 0.2391
[PARAMS] Current best parameters:
  fx: 540.6070
  fy: 540.0479
  px: 991.9711
  py: 755.9872
  k1: -0.01850782
  k2: 0.01994969
  k3: -0.00905879
  k4: 0.00075783
  p1: 0.02014236
  p2: 0.00049290
  p3: -0.01356445
  p4: 0.00121273
  s1: 0.0103
  s2: -0.0011
  s3: -0.0136
  s4: -0.0004
  tilt_x: -0.0589
  tilt_y: 0.0046
  scale: 3.0560
  rotation: -0.5606
Best score: 0.2391: : 999iter [00:29, 39.01iter/s]Best score: 0.2391: : 1000iter [00:29, 38.74iter/s]Best score: 0.2391: : 1000iter [00:29, 38.74iter/s]Best score: 0.2391: : 1001iter [00:29, 38.74iter/s]Best score: 0.2391: : 1002iter [00:29, 38.74iter/s]Best score: 0.2391: : 1003iter [00:29, 38.74iter/s]Best score: 0.2391: : 1004iter [00:29, 31.74iter/s]Best score: 0.2391: : 1004iter [00:29, 31.74iter/s]Best score: 0.2391: : 1005iter [00:29, 31.74iter/s]Best score: 0.2391: : 1006iter [00:29, 31.74iter/s]Best score: 0.2391: : 1007iter [00:29, 31.74iter/s]Best score: 0.2391: : 1008iter [00:29, 27.11iter/s]Best score: 0.2391: : 1008iter [00:29, 27.11iter/s]Best score: 0.2391: : 1009iter [00:29, 27.11iter/s]Best score: 0.2391: : 1010iter [00:29, 27.11iter/s]Best score: 0.2391: : 1011iter [00:29, 27.11iter/s]Best score: 0.2391: : 1012iter [00:29, 29.75iter/s]Best score: 0.2391: : 1012iter [00:29, 29.75iter/s]Best score: 0.2391: : 1013iter [00:29, 29.75iter/s]Best score: 0.2391: : 1014iter [00:29, 29.75iter/s]Best score: 0.2391: : 1015iter [00:29, 29.75iter/s]Best score: 0.2391: : 1016iter [00:29, 32.11iter/s]Best score: 0.2391: : 1016iter [00:29, 32.11iter/s]Best score: 0.2391: : 1017iter [00:30, 32.11iter/s]Best score: 0.2391: : 1018iter [00:30, 32.11iter/s]Best score: 0.2391: : 1019iter [00:30, 32.11iter/s]Best score: 0.2391: : 1020iter [00:30, 33.62iter/s]Best score: 0.2391: : 1020iter [00:30, 33.62iter/s]Best score: 0.2391: : 1021iter [00:30, 33.62iter/s]Best score: 0.2391: : 1022iter [00:30, 33.62iter/s]Best score: 0.2391: : 1023iter [00:30, 33.62iter/s]Best score: 0.2391: : 1024iter [00:30, 35.15iter/s]Best score: 0.2391: : 1024iter [00:30, 35.15iter/s]Best score: 0.2391: : 1025iter [00:30, 35.15iter/s]Best score: 0.2391: : 1026iter [00:30, 35.15iter/s]Best score: 0.2391: : 1027iter [00:30, 35.15iter/s]Best score: 0.2391: : 1028iter [00:30, 36.14iter/s]Best score: 0.2391: : 1028iter [00:30, 36.14iter/s]Best score: 0.2389: : 1029iter [00:30, 36.14iter/s]Best score: 0.2389: : 1030iter [00:30, 36.14iter/s]Best score: 0.2389: : 1031iter [00:30, 36.14iter/s]Best score: 0.2389: : 1032iter [00:30, 36.63iter/s]Best score: 0.2389: : 1032iter [00:30, 36.63iter/s]Best score: 0.2389: : 1033iter [00:30, 36.63iter/s]Best score: 0.2389: : 1034iter [00:30, 36.63iter/s]Best score: 0.2389: : 1035iter [00:30, 36.63iter/s]Best score: 0.2389: : 1036iter [00:30, 37.50iter/s]Best score: 0.2389: : 1036iter [00:30, 37.50iter/s]Best score: 0.2389: : 1037iter [00:30, 37.50iter/s]Best score: 0.2389: : 1038iter [00:30, 37.50iter/s]Best score: 0.2382: : 1039iter [00:30, 37.50iter/s]Best score: 0.2382: : 1040iter [00:30, 37.81iter/s]Best score: 0.2382: : 1040iter [00:30, 37.81iter/s]Best score: 0.2382: : 1041iter [00:30, 37.81iter/s]Best score: 0.2382: : 1042iter [00:30, 37.81iter/s]Best score: 0.2382: : 1043iter [00:30, 37.81iter/s]Best score: 0.2382: : 1044iter [00:30, 38.43iter/s]Best score: 0.2382: : 1044iter [00:30, 38.43iter/s]Best score: 0.2382: : 1045iter [00:30, 38.43iter/s]Best score: 0.2382: : 1046iter [00:30, 38.43iter/s]Best score: 0.2382: : 1047iter [00:30, 38.43iter/s]Best score: 0.2382: : 1048iter [00:30, 38.87iter/s]Best score: 0.2382: : 1048iter [00:30, 38.87iter/s]
[ITER 1050] Best: 0.2382
[PARAMS] Current best parameters:
  fx: 538.2059
  fy: 537.6646
  px: 991.8940
  py: 755.8759
  k1: -0.01744697
  k2: 0.02193490
  k3: -0.01015527
  k4: 0.00091422
  p1: 0.02086346
  p2: 0.00164492
  p3: -0.01557594
  p4: 0.00126399
  s1: 0.0114
  s2: -0.0013
  s3: -0.0119
  s4: -0.0008
  tilt_x: -0.0607
  tilt_y: 0.0072
  scale: 3.0582
  rotation: -0.5691
Best score: 0.2382: : 1049iter [00:30, 38.87iter/s]Best score: 0.2382: : 1050iter [00:30, 38.87iter/s]Best score: 0.2382: : 1051iter [00:30, 38.87iter/s]Best score: 0.2382: : 1052iter [00:30, 31.54iter/s]Best score: 0.2382: : 1052iter [00:30, 31.54iter/s]Best score: 0.2382: : 1053iter [00:31, 31.54iter/s]Best score: 0.2382: : 1054iter [00:31, 31.54iter/s]Best score: 0.2382: : 1055iter [00:31, 31.54iter/s]Best score: 0.2382: : 1056iter [00:31, 33.60iter/s]Best score: 0.2382: : 1056iter [00:31, 33.60iter/s]Best score: 0.2382: : 1057iter [00:31, 33.60iter/s]Best score: 0.2382: : 1058iter [00:31, 33.60iter/s]Best score: 0.2382: : 1059iter [00:31, 33.60iter/s]Best score: 0.2382: : 1060iter [00:31, 34.77iter/s]Best score: 0.2382: : 1060iter [00:31, 34.77iter/s]Best score: 0.2382: : 1061iter [00:31, 34.77iter/s]Best score: 0.2382: : 1062iter [00:31, 34.77iter/s]Best score: 0.2382: : 1063iter [00:31, 34.77iter/s]Best score: 0.2382: : 1064iter [00:31, 36.04iter/s]Best score: 0.2382: : 1064iter [00:31, 36.04iter/s]Best score: 0.2382: : 1065iter [00:31, 36.04iter/s]Best score: 0.2382: : 1066iter [00:31, 36.04iter/s]Best score: 0.2382: : 1067iter [00:31, 36.04iter/s]Best score: 0.2382: : 1068iter [00:31, 36.91iter/s]Best score: 0.2382: : 1068iter [00:31, 36.91iter/s]Best score: 0.2379: : 1069iter [00:31, 36.91iter/s]Best score: 0.2379: : 1070iter [00:31, 36.91iter/s]Best score: 0.2379: : 1071iter [00:31, 36.91iter/s]Best score: 0.2379: : 1072iter [00:31, 37.21iter/s]Best score: 0.2379: : 1072iter [00:31, 37.21iter/s]Best score: 0.2379: : 1073iter [00:31, 37.21iter/s]Best score: 0.2379: : 1074iter [00:31, 37.21iter/s]Best score: 0.2379: : 1075iter [00:31, 37.21iter/s]Best score: 0.2379: : 1076iter [00:31, 37.78iter/s]Best score: 0.2379: : 1076iter [00:31, 37.78iter/s]Best score: 0.2379: : 1077iter [00:31, 37.78iter/s]Best score: 0.2379: : 1078iter [00:31, 37.78iter/s]Best score: 0.2377: : 1079iter [00:31, 37.78iter/s]Best score: 0.2377: : 1080iter [00:31, 37.94iter/s]Best score: 0.2377: : 1080iter [00:31, 37.94iter/s]Best score: 0.2377: : 1081iter [00:31, 37.94iter/s]Best score: 0.2377: : 1082iter [00:31, 37.94iter/s]Best score: 0.2377: : 1083iter [00:31, 37.94iter/s]Best score: 0.2377: : 1084iter [00:31, 38.53iter/s]Best score: 0.2377: : 1084iter [00:31, 38.53iter/s]Best score: 0.2377: : 1085iter [00:31, 38.53iter/s]Best score: 0.2377: : 1086iter [00:31, 38.53iter/s]Best score: 0.2377: : 1087iter [00:31, 38.53iter/s]Best score: 0.2377: : 1088iter [00:31, 38.85iter/s]Best score: 0.2377: : 1088iter [00:31, 38.85iter/s]Best score: 0.2377: : 1089iter [00:31, 38.85iter/s]Best score: 0.2377: : 1090iter [00:31, 38.85iter/s]Best score: 0.2377: : 1091iter [00:31, 38.85iter/s]Best score: 0.2377: : 1092iter [00:31, 38.70iter/s]Best score: 0.2377: : 1092iter [00:32, 38.70iter/s]Best score: 0.2377: : 1093iter [00:32, 38.70iter/s]Best score: 0.2377: : 1094iter [00:32, 38.70iter/s]Best score: 0.2377: : 1095iter [00:32, 38.70iter/s]Best score: 0.2377: : 1096iter [00:32, 38.76iter/s]Best score: 0.2377: : 1096iter [00:32, 38.76iter/s]Best score: 0.2377: : 1097iter [00:32, 38.76iter/s]Best score: 0.2377: : 1098iter [00:32, 38.76iter/s]Best score: 0.2372: : 1099iter [00:32, 38.76iter/s]Best score: 0.2372: : 1100iter [00:32, 38.49iter/s]
[ITER 1100] Best: 0.2372
[PARAMS] Current best parameters:
  fx: 538.1245
  fy: 537.6320
  px: 991.7587
  py: 755.2866
  k1: -0.01716045
  k2: 0.02104201
  k3: -0.00939313
  k4: 0.00072686
  p1: 0.01963969
  p2: 0.00152911
  p3: -0.01587517
  p4: 0.00160882
  s1: 0.0114
  s2: -0.0015
  s3: -0.0107
  s4: -0.0011
  tilt_x: -0.0613
  tilt_y: 0.0077
  scale: 3.0592
  rotation: -0.5635
Best score: 0.2372: : 1100iter [00:32, 34.09iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.237222 -> 0.236405

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,891 → 25,891 within 3500mm ring
  Ring polish did not improve score: 0.236405
  Success: True
  Tangential distortion: p1=0.019610, p2=0.001494, p3=-0.015866, p4=0.001662
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2364
[✅] Parameters copied to: /working/MDC012_W046_2026-09-28/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 4: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 538.1244698460989
    fy: 537.6320246311976
    px: 991.758657968207
    py: 755.2866021886706
    k1: -0.01717435837121864
    k2: 0.021044376702990007
    k3: -0.009388200199956362
    k4: 0.0007266831715012225
    p1: 0.019610268095652243
    p2: 0.0014939356437060776
    p3: -0.015865801675071706
    p4: 0.0016624649003579729
    scale: 3.059156960624014
    rotation: -0.5634727676895135
    mmpp: 1.290814555273457

[🔄] Converting 1392 frames to PTD, creating debug images for 200 calibration frames...
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[✅] Saved PTDs: /working/MDC012_W046_2026-09-28/calibration/debug/4_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC012_W046_2026-09-28/calibration/debug/4_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.
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[✅] LUT written to: /working/MDC012_W046_2026-09-28/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC012_W046_2026-09-28' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC012_W046_2026-09-28/calibration/MDC012_W046_2026-09-28_unknown.lut

-------------------------------------------------------------------
                          Step 5: Heatmap                          
-------------------------------------------------------------------

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)
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                                                                [A[✅] 1000: /working/MDC012_W046_2026-09-28/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC012_W046_2026-09-28/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 1/2 [00:11<00:11, 11.14s/rig]
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                                                               [A[✅] 500: /working/MDC012_W046_2026-09-28/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC012_W046_2026-09-28/calibration/debug/5_heatmap/500_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:20<00:00, 10.32s/rig]📁 Rigs (heatmap): 100%|██████████| 2/2 [00:20<00:00, 10.44s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC012_W046_2026-09-28/calibration/debug/5_heatmap
[🏁] Finished processing MDC012_W046_2026-09-28


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