2026-07-12T23:50:42 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC005_W041_2024-11-20 ===
2026-07-12T23:50:42 - INFO: Camera ID: 12131552
2026-07-12T23:50:42 - INFO: Wand ID: 
2026-07-12T23:50:42 - INFO: Recorded At: 2024-11-20T18:45:52Z

-------------------------------------------------------------------
                        Step 1: Digitization                       
-------------------------------------------------------------------

[🔄] 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 1516 stationary frames, 223 remain

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                                                                          [A[PASS2] Frame 0001170: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 0 untrusted, 223 total

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                                                                          [A
[TIMING] 500 — 223 frames
  Phase 1  radial detect   (parallel)  :   3.75s  (16.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.85s  (8.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.10s
  Phase 3  finalize        (sequential):   4.36s  (19.6 ms/frame)
    decode + undistort                 :   2.37s  (10.6 ms/frame)
    corner apply + refinement          :   0.88s  (3.9 ms/frame)
    segment                            :   0.89s  (4.0 ms/frame)
    smooth                             :   0.15s  (0.7 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.07s

Processing Rigs:  50%|█████     | 1/2 [00:16<00:16, 16.40s/it][INFO] Filtered 1603 stationary frames, 266 remain

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                                                                           [A[PASS2] Frame 0000372: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000373: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000374: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000375: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000604: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000614: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000779: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000791: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001381: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001382: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001383: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001384: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001603: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001604: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001605: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001606: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001607: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001608: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001609: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001610: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001617: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001618: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001619: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001620: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001621: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001622: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001623: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001624: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001625: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001626: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001627: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001628: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001629: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001630: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001631: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001632: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001633: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001634: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001639: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001869: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001870: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001871: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000374: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000779: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001383: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001605: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001606: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001609: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001619: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001622: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001624: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001627: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001631: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001634: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001869: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001870: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001871: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001869: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001870: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001871: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 24 frames fixed, 24 untrusted, 266 total

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                                                                           [A
[TIMING] 1000 — 266 frames
  Phase 1  radial detect   (parallel)  :   4.37s  (16.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.14s  (8.1 ms/frame)
  Phase 2b corner fix      (sequential):   1.11s
  Phase 3  finalize        (sequential):   5.25s  (19.7 ms/frame)
    decode + undistort                 :   2.78s  (10.5 ms/frame)
    corner apply + refinement          :   1.15s  (4.3 ms/frame)
    segment                            :   1.08s  (4.1 ms/frame)
    smooth                             :   0.15s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  12.86s

Processing Rigs: 100%|██████████| 2/2 [00:35<00:00, 18.21s/it]Processing Rigs: 100%|██████████| 2/2 [00:35<00:00, 17.94s/it]
[💾] Saved RigFrameDataset to /working/MDC005_W041_2024-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 489 frames from 2 rigs to /working/MDC005_W041_2024-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC005_W041_2024-11-20/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 223
[📋] 1000: 100 frames selected from 266
[💾] Saved RigFrameDataset to /working/MDC005_W041_2024-11-20/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC005_W041_2024-11-20/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                      Step 2: Casing Detection                     
-------------------------------------------------------------------

-> 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 [00:04<03:05,  4.76s/it]Computing max image from bright frames:   5%|▌         | 2/40 [00:04<01:18,  2.05s/it]Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:32,  1.10it/s]Computing max image from bright frames:  15%|█▌        | 6/40 [00:05<00:20,  1.70it/s]Computing max image from bright frames:  20%|██        | 8/40 [00:06<00:12,  2.57it/s]Computing max image from bright frames:  25%|██▌       | 10/40 [00:06<00:08,  3.64it/s]Computing max image from bright frames:  28%|██▊       | 11/40 [00:06<00:08,  3.29it/s]Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:06,  4.21it/s]Computing max image from bright frames:  35%|███▌      | 14/40 [00:06<00:05,  4.76it/s]Computing max image from bright frames:  40%|████      | 16/40 [00:07<00:03,  6.26it/s]Computing max image from bright frames:  45%|████▌     | 18/40 [00:07<00:02,  7.83it/s]Computing max image from bright frames:  50%|█████     | 20/40 [00:07<00:02,  7.93it/s]Computing max image from bright frames:  55%|█████▌    | 22/40 [00:07<00:01,  9.10it/s]Computing max image from bright frames:  60%|██████    | 24/40 [00:07<00:01, 10.70it/s]Computing max image from bright frames:  70%|███████   | 28/40 [00:07<00:00, 15.71it/s]Computing max image from bright frames:  75%|███████▌  | 30/40 [00:08<00:00, 14.55it/s]Computing max image from bright frames:  80%|████████  | 32/40 [00:08<00:00, 12.74it/s]Computing max image from bright frames:  85%|████████▌ | 34/40 [00:09<00:01,  4.54it/s]Computing max image from bright frames:  90%|█████████ | 36/40 [00:09<00:00,  5.04it/s]Computing max image from bright frames:  92%|█████████▎| 37/40 [00:09<00:00,  4.95it/s]Computing max image from bright frames:  98%|█████████▊| 39/40 [00:10<00:00,  6.52it/s]                                                                                       [✅] Casing detection complete: cx=996.0, cy=759.0

-------------------------------------------------------------------
                        Step 3: Calibration                        
-------------------------------------------------------------------

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500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   6%|▌         | 6/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   6%|▌         | 6/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   7%|▋         | 7/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   8%|▊         | 8/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   9%|▉         | 9/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  10%|█         | 10/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):  11%|█         | 11/100 [00:00<00:04, 21.11it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 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[00:00<00:02, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  19%|█▉        | 19/100 [00:00<00:02, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.058 | Best: {'fx': 526.263} (0.058):  20%|██        | 20/100 [00:00<00:02, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.077 | Best: {'fx': 533.333} (0.077):  21%|██        | 21/100 [00:00<00:02, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.032 | Best: {'fx': 533.333} (0.077):  22%|██▏       | 22/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  23%|██▎       | 23/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.043 | Best: {'fx': 533.333} (0.077):  24%|██▍       | 24/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.077):  25%|██▌       | 25/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  26%|██▌       | 26/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  27%|██▋       | 27/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.077):  28%|██▊       | 28/100 [00:00<00:01, 39.23it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.077):  29%|██▉       | 29/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.077):  29%|██▉       | 29/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  30%|███       | 30/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.077):  31%|███       | 31/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  32%|███▏      | 32/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.077):  33%|███▎      | 33/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.077):  34%|███▍      | 34/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  35%|███▌      | 35/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.077):  36%|███▌      | 36/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.077):  37%|███▋      | 37/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.077):  38%|███▊      | 38/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.077):  39%|███▉      | 39/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.077):  40%|████      | 40/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.077):  41%|████      | 41/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.077):  42%|████▏     | 42/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.077):  43%|████▎     | 43/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.077):  44%|████▍     | 44/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.007 | Best: {'fx': 533.333} (0.077):  45%|████▌     | 45/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.077):  46%|████▌     | 46/100 [00:00<00:00, 74.10it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.077):  47%|████▋     | 47/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.077):  47%|████▋     | 47/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.077):  48%|████▊     | 48/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.077):  49%|████▉     | 49/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.077):  50%|█████     | 50/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  51%|█████     | 51/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.077):  52%|█████▏    | 52/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  53%|█████▎    | 53/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.077):  54%|█████▍    | 54/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  55%|█████▌    | 55/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  56%|█████▌    | 56/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  57%|█████▋    | 57/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  58%|█████▊    | 58/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  59%|█████▉    | 59/100 [00:00<00:00, 105.31it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  60%|██████    | 60/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  60%|██████    | 60/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  61%|██████    | 61/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  62%|██████▏   | 62/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.077):  63%|██████▎   | 63/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  64%|██████▍   | 64/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  65%|██████▌   | 65/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  66%|██████▌   | 66/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  67%|██████▋   | 67/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  68%|██████▊   | 68/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  69%|██████▉   | 69/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  70%|███████   | 70/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  71%|███████   | 71/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  72%|███████▏  | 72/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  73%|███████▎  | 73/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  74%|███████▍  | 74/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  75%|███████▌  | 75/100 [00:00<00:00, 111.95it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  76%|███████▌  | 76/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  76%|███████▌  | 76/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  77%|███████▋  | 77/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  78%|███████▊  | 78/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  79%|███████▉  | 79/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  80%|████████  | 80/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  81%|████████  | 81/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  82%|████████▏ | 82/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  83%|████████▎ | 83/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  84%|████████▍ | 84/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  85%|████████▌ | 85/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  86%|████████▌ | 86/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  87%|████████▋ | 87/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  88%|████████▊ | 88/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  89%|████████▉ | 89/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  90%|█████████ | 90/100 [00:00<00:00, 124.59it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  91%|█████████ | 91/100 [00:00<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  91%|█████████ | 91/100 [00:00<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  92%|█████████▏| 92/100 [00:00<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  93%|█████████▎| 93/100 [00:01<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  94%|█████████▍| 94/100 [00:01<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  95%|█████████▌| 95/100 [00:01<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  96%|█████████▌| 96/100 [00:01<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  97%|█████████▋| 97/100 [00:01<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  98%|█████████▊| 98/100 [00:01<00:00, 129.82it/s]Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.077):  99%|█████████▉| 99/100 [00:01<00:00, 129.82it/s]                                                                                                                                          0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.011 | Best: {'fx': 508.333} (0.011):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.011 | Best: {'fx': 508.333} (0.011):   2%|▏         | 1/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.012 | Best: {'fx': 509.354} (0.012):   2%|▏         | 1/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.013 | Best: {'fx': 510.374} (0.013):   4%|▍         | 2/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.014 | Best: {'fx': 511.395} (0.014):   6%|▌         | 3/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.016 | Best: {'fx': 512.415} (0.016):   8%|▊         | 4/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.018 | Best: {'fx': 513.435} (0.018):  10%|█         | 5/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.022 | Best: {'fx': 514.456} (0.022):  12%|█▏        | 6/50 [00:00<00:06,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.027 | Best: {'fx': 515.476} (0.027):  14%|█▍        | 7/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.029 | Best: {'fx': 516.497} (0.029):  16%|█▌        | 8/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.043 | Best: {'fx': 522.619} (0.043):  18%|█▊        | 9/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.030 | Best: {'fx': 522.619} (0.043):  20%|██        | 10/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.033 | Best: {'fx': 522.619} (0.043):  22%|██▏       | 11/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.035 | Best: {'fx': 522.619} (0.043):  24%|██▍       | 12/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.048 | Best: {'fx': 523.639} (0.048):  26%|██▌       | 13/50 [00:00<00:05,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.040 | Best: {'fx': 523.639} (0.048):  28%|██▊       | 14/50 [00:00<00:04,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.037 | Best: {'fx': 523.639} (0.048):  30%|███       | 15/50 [00:00<00:04,  7.21it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.052 | Best: {'fx': 524.66} (0.052):  32%|███▏      | 16/50 [00:00<00:04,  7.21it/s] Brute | Start: {'fx': 533.333} (0.077) | Current: 0.052 | Best: {'fx': 524.66} (0.052):  34%|███▍      | 17/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.055 | Best: {'fx': 525.68} (0.055):  34%|███▍      | 17/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.067 | Best: {'fx': 527.721} (0.067):  36%|███▌      | 18/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.058 | Best: {'fx': 527.721} (0.067):  38%|███▊      | 19/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.079 | Best: {'fx': 530.782} (0.079):  40%|████      | 20/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.070 | Best: {'fx': 530.782} (0.079):  42%|████▏     | 21/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.081 | Best: {'fx': 531.803} (0.081):  44%|████▍     | 22/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.073 | Best: {'fx': 531.803} (0.081):  46%|████▌     | 23/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.073 | Best: {'fx': 531.803} (0.081):  48%|████▊     | 24/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.071 | Best: {'fx': 531.803} (0.081):  50%|█████     | 25/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.068 | Best: {'fx': 531.803} (0.081):  52%|█████▏    | 26/50 [00:00<00:00, 75.65it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.068 | Best: {'fx': 531.803} (0.081):  54%|█████▍    | 27/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.057 | Best: {'fx': 531.803} (0.081):  54%|█████▍    | 27/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.060 | Best: {'fx': 531.803} (0.081):  56%|█████▌    | 28/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.041 | Best: {'fx': 531.803} (0.081):  58%|█████▊    | 29/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.052 | Best: {'fx': 531.803} (0.081):  60%|██████    | 30/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.045 | Best: {'fx': 531.803} (0.081):  62%|██████▏   | 31/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.048 | Best: {'fx': 531.803} (0.081):  64%|██████▍   | 32/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.038 | Best: {'fx': 531.803} (0.081):  66%|██████▌   | 33/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.043 | Best: {'fx': 531.803} (0.081):  68%|██████▊   | 34/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.033 | Best: {'fx': 531.803} (0.081):  70%|███████   | 35/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.034 | Best: {'fx': 531.803} (0.081):  72%|███████▏  | 36/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.036 | Best: {'fx': 531.803} (0.081):  74%|███████▍  | 37/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.031 | Best: {'fx': 531.803} (0.081):  76%|███████▌  | 38/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.029 | Best: {'fx': 531.803} (0.081):  78%|███████▊  | 39/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.028 | Best: {'fx': 531.803} (0.081):  80%|████████  | 40/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.032 | Best: {'fx': 531.803} (0.081):  82%|████████▏ | 41/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.028 | Best: {'fx': 531.803} (0.081):  84%|████████▍ | 42/50 [00:00<00:00, 81.63it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.028 | Best: {'fx': 531.803} (0.081):  86%|████████▌ | 43/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.027 | Best: {'fx': 531.803} (0.081):  86%|████████▌ | 43/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.025 | Best: {'fx': 531.803} (0.081):  88%|████████▊ | 44/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.024 | Best: {'fx': 531.803} (0.081):  90%|█████████ | 45/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.026 | Best: {'fx': 531.803} (0.081):  92%|█████████▏| 46/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.023 | Best: {'fx': 531.803} (0.081):  94%|█████████▍| 47/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.025 | Best: {'fx': 531.803} (0.081):  96%|█████████▌| 48/50 [00:00<00:00, 108.55it/s]Brute | Start: {'fx': 533.333} (0.077) | Current: 0.023 | Best: {'fx': 531.803} (0.081):  98%|█████████▊| 49/50 [00:00<00:00, 108.55it/s]                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 996.0
    py: 759.0
    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: 23.5965: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 23.5965, picklable_objective(x0) = 23.6155
Best score: 23.6155: : 0iter [00:01, ?iter/s] Best score: 23.6155: : 1iter [00:01,  1.85s/iter]Best score: 23.6155: : 1iter [00:01,  1.85s/iter]Best score: 23.6155: : 2iter [00:02,  1.85s/iter]Best score: 23.6155: : 3iter [00:02,  1.85iter/s]Best score: 23.6155: : 3iter [00:02,  1.85iter/s]Best score: 23.6155: : 4iter [00:02,  1.85iter/s]Best score: 23.6155: : 5iter [00:02,  1.85iter/s]Best score: 23.6155: : 6iter [00:02,  1.85iter/s]Best score: 23.6155: : 7iter [00:02,  5.25iter/s]Best score: 23.6155: : 7iter [00:02,  5.25iter/s]Best score: 23.6155: : 8iter [00:02,  5.25iter/s]Best score: 23.6155: : 9iter [00:02,  5.25iter/s]Best score: 23.6155: : 10iter [00:02,  5.25iter/s]Best score: 23.6155: : 11iter [00:02,  9.09iter/s]Best score: 23.6155: : 11iter [00:02,  9.09iter/s]Best score: 23.6155: : 12iter [00:02,  9.09iter/s]Best score: 23.6155: : 13iter [00:02,  9.09iter/s]Best score: 23.6155: : 14iter [00:02,  9.09iter/s]Best score: 23.6155: : 15iter [00:02, 13.32iter/s]Best score: 23.6155: : 15iter [00:02, 13.32iter/s]Best score: 23.6155: : 16iter [00:02, 13.32iter/s]Best score: 23.6155: : 17iter [00:02, 13.32iter/s]Best score: 23.6155: : 18iter [00:02, 13.32iter/s]Best score: 23.6155: : 19iter [00:02, 17.64iter/s]Best score: 23.6155: : 19iter [00:02, 17.64iter/s]Best score: 23.6155: : 20iter [00:02, 17.64iter/s]Best score: 23.6155: : 21iter [00:02, 17.64iter/s]Best score: 23.6155: : 22iter [00:02, 17.64iter/s]Best score: 23.6155: : 23iter [00:02, 21.73iter/s]Best score: 23.6155: : 23iter [00:02, 21.73iter/s]Best score: 23.6155: : 24iter [00:02, 21.73iter/s]Best score: 23.6155: : 25iter [00:02, 21.73iter/s]Best score: 23.6155: : 26iter [00:02, 21.73iter/s]Best score: 23.6155: : 27iter [00:02, 25.44iter/s]Best score: 23.6155: : 27iter [00:02, 25.44iter/s]Best score: 23.6155: : 28iter [00:02, 25.44iter/s]Best score: 23.6155: : 29iter [00:02, 25.44iter/s]Best score: 23.6155: : 30iter [00:02, 25.44iter/s]Best score: 23.6155: : 31iter [00:02, 28.48iter/s]Best score: 23.6155: : 31iter [00:02, 28.48iter/s]Best score: 23.6155: : 32iter [00:02, 28.48iter/s]Best score: 23.6155: : 33iter [00:02, 28.48iter/s]Best score: 23.6155: : 34iter [00:02, 28.48iter/s]Best score: 23.6155: : 35iter [00:02, 30.94iter/s]Best score: 23.6155: : 35iter [00:02, 30.94iter/s]Best score: 23.6155: : 36iter [00:02, 30.94iter/s]Best score: 23.6155: : 37iter [00:02, 30.94iter/s]Best score: 23.6155: : 38iter [00:02, 30.94iter/s]Best score: 23.6155: : 39iter [00:02, 32.88iter/s]Best score: 23.6155: : 39iter [00:02, 32.88iter/s]Best score: 23.6155: : 40iter [00:03, 32.88iter/s]Best score: 23.6155: : 41iter [00:03, 32.88iter/s]Best score: 23.6155: : 42iter [00:03, 32.88iter/s]Best score: 23.6155: : 43iter [00:03, 34.29iter/s]Best score: 23.6155: : 43iter [00:03, 34.29iter/s]Best score: 23.6155: : 44iter [00:03, 34.29iter/s]Best score: 23.6155: : 45iter [00:03, 34.29iter/s]Best score: 23.6155: : 46iter [00:03, 34.29iter/s]Best score: 23.6155: : 47iter [00:03, 35.39iter/s]Best score: 23.6155: : 47iter [00:03, 35.39iter/s]Best score: 23.6155: : 48iter [00:03, 35.39iter/s]
[ITER 50] Best: 23.6155
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 996.0000
  py: 759.0000
  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: 23.6155: : 49iter [00:03, 35.39iter/s]Best score: 23.6155: : 50iter [00:03, 35.39iter/s]Best score: 23.6155: : 51iter [00:03, 36.20iter/s]Best score: 23.6155: : 51iter [00:03, 36.20iter/s]Best score: 23.6155: : 52iter [00:03, 36.20iter/s]Best score: 23.6155: : 53iter [00:03, 36.20iter/s]Best score: 23.6155: : 54iter [00:03, 36.20iter/s]Best score: 23.6155: : 55iter [00:03, 37.16iter/s]Best score: 23.6155: : 55iter [00:03, 37.16iter/s]Best score: 23.6155: : 56iter [00:03, 37.16iter/s]Best score: 23.6155: : 57iter [00:03, 37.16iter/s]Best score: 23.6155: : 58iter [00:03, 37.16iter/s]Best score: 23.6155: : 59iter [00:03, 37.69iter/s]Best score: 23.6155: : 59iter [00:03, 37.69iter/s]Best score: 23.6155: : 60iter [00:03, 37.69iter/s]Best score: 23.6155: : 61iter [00:03, 37.69iter/s]Best score: 23.6155: : 62iter [00:03, 37.69iter/s]Best score: 23.6155: : 63iter [00:03, 37.49iter/s]Best score: 23.6155: : 63iter [00:03, 37.49iter/s]Best score: 23.6155: : 64iter [00:03, 37.49iter/s]Best score: 23.6155: : 65iter [00:03, 37.49iter/s]Best score: 23.6155: : 66iter [00:03, 37.49iter/s]Best score: 23.6155: : 67iter [00:03, 38.02iter/s]Best score: 23.6155: : 67iter [00:03, 38.02iter/s]Best score: 23.6155: : 68iter [00:03, 38.02iter/s]Best score: 23.6155: : 69iter [00:03, 38.02iter/s]Best score: 23.6155: : 70iter [00:03, 38.02iter/s]Best score: 23.6155: : 71iter [00:03, 37.48iter/s]Best score: 23.6155: : 71iter [00:03, 37.48iter/s]Best score: 23.6155: : 72iter [00:03, 37.48iter/s]Best score: 23.6155: : 73iter [00:03, 37.48iter/s]Best score: 23.6155: : 74iter [00:03, 37.48iter/s]Best score: 23.6155: : 75iter [00:03, 37.97iter/s]Best score: 23.6155: : 75iter [00:03, 37.97iter/s]Best score: 23.6155: : 76iter [00:03, 37.97iter/s]Best score: 23.6155: : 77iter [00:03, 37.97iter/s]Best score: 23.6155: : 78iter [00:03, 37.97iter/s]Best score: 23.6155: : 79iter [00:03, 38.48iter/s]Best score: 23.6155: : 79iter [00:04, 38.48iter/s]Best score: 23.6155: : 80iter [00:04, 38.48iter/s]Best score: 23.6155: : 81iter [00:04, 38.48iter/s]Best score: 23.6155: : 82iter [00:04, 38.48iter/s]Best score: 23.6155: : 83iter [00:04, 38.31iter/s]Best score: 23.6155: : 83iter [00:04, 38.31iter/s]Best score: 23.6155: : 84iter [00:04, 38.31iter/s]Best score: 23.6155: : 85iter [00:04, 38.31iter/s]Best score: 23.6155: : 86iter [00:04, 38.31iter/s]Best score: 23.6155: : 87iter [00:04, 38.70iter/s]Best score: 23.6155: : 87iter [00:04, 38.70iter/s]Best score: 23.6155: : 88iter [00:04, 38.70iter/s]Best score: 23.6155: : 89iter [00:04, 38.70iter/s]Best score: 23.6155: : 90iter [00:04, 38.70iter/s]Best score: 23.6155: : 91iter [00:04, 38.77iter/s]Best score: 23.6155: : 91iter [00:04, 38.77iter/s]Best score: 23.6155: : 92iter [00:04, 38.77iter/s]Best score: 23.6155: : 93iter [00:04, 38.77iter/s]Best score: 23.6155: : 94iter [00:04, 38.77iter/s]Best score: 23.6155: : 95iter [00:04, 39.06iter/s]Best score: 23.6155: : 95iter [00:04, 39.06iter/s]Best score: 23.6155: : 96iter [00:04, 39.06iter/s]Best score: 23.6155: : 97iter [00:04, 39.06iter/s]Best score: 23.6155: : 98iter [00:04, 39.06iter/s]
[ITER 100] Best: 23.6155
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 996.0000
  py: 759.0000
  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: 23.6155: : 99iter [00:04, 39.06iter/s]Best score: 23.6155: : 100iter [00:04, 39.26iter/s]Best score: 23.6155: : 100iter [00:04, 39.26iter/s]Best score: 23.6155: : 101iter [00:04, 39.26iter/s]Best score: 23.6155: : 102iter [00:04, 39.26iter/s]Best score: 23.6155: : 103iter [00:04, 39.26iter/s]Best score: 23.6155: : 104iter [00:04, 36.64iter/s]Best score: 23.6155: : 104iter [00:04, 36.64iter/s]Best score: 23.6155: : 105iter [00:04, 36.64iter/s]Best score: 23.6155: : 106iter [00:04, 36.64iter/s]Best score: 23.6155: : 107iter [00:04, 36.64iter/s]Best score: 23.6155: : 108iter [00:04, 37.27iter/s]Best score: 23.6155: : 108iter [00:04, 37.27iter/s]Best score: 23.6155: : 109iter [00:04, 37.27iter/s]Best score: 23.6155: : 110iter [00:04, 37.27iter/s]Best score: 23.6155: : 111iter [00:04, 37.27iter/s]Best score: 23.6155: : 112iter [00:04, 37.34iter/s]Best score: 23.6155: : 112iter [00:04, 37.34iter/s]Best score: 23.6155: : 113iter [00:04, 37.34iter/s]Best score: 23.6155: : 114iter [00:04, 37.34iter/s]Best score: 23.6155: : 115iter [00:04, 37.34iter/s]Best score: 23.6155: : 116iter [00:04, 37.88iter/s]Best score: 23.6155: : 116iter [00:04, 37.88iter/s]Best score: 23.6155: : 117iter [00:05, 37.88iter/s]Best score: 23.6155: : 118iter [00:05, 37.88iter/s]Best score: 23.6155: : 119iter [00:05, 37.88iter/s]Best score: 23.6155: : 120iter [00:05, 38.08iter/s]Best score: 23.6155: : 120iter [00:05, 38.08iter/s]Best score: 23.6155: : 121iter [00:05, 38.08iter/s]Best score: 23.6155: : 122iter [00:05, 38.08iter/s]Best score: 23.6155: : 123iter [00:05, 38.08iter/s]Best score: 23.6155: : 124iter [00:05, 38.44iter/s]Best score: 23.6155: : 124iter [00:05, 38.44iter/s]Best score: 23.6155: : 125iter [00:05, 38.44iter/s]Best score: 23.6155: : 126iter [00:05, 38.44iter/s]Best score: 23.6155: : 127iter [00:05, 38.44iter/s]Best score: 23.6155: : 128iter [00:05, 38.61iter/s]Best score: 23.6155: : 128iter [00:05, 38.61iter/s]Best score: 23.6155: : 129iter [00:05, 38.61iter/s]Best score: 23.6155: : 130iter [00:05, 38.61iter/s]Best score: 23.6155: : 131iter [00:05, 38.61iter/s]Best score: 23.6155: : 132iter [00:05, 38.68iter/s]Best score: 23.6155: : 132iter [00:05, 38.68iter/s]Best score: 23.6155: : 133iter [00:05, 38.68iter/s]Best score: 23.6155: : 134iter [00:05, 38.68iter/s]Best score: 23.6155: : 135iter [00:05, 38.68iter/s]Best score: 23.6155: : 136iter [00:05, 38.95iter/s]Best score: 23.6155: : 136iter [00:05, 38.95iter/s]Best score: 23.6155: : 137iter [00:05, 38.95iter/s]Best score: 23.6155: : 138iter [00:05, 38.95iter/s]Best score: 23.6155: : 139iter [00:05, 38.95iter/s]Best score: 23.6155: : 140iter [00:05, 38.77iter/s]Best score: 23.6155: : 140iter [00:05, 38.77iter/s]Best score: 23.6155: : 141iter [00:05, 38.77iter/s]Best score: 23.6155: : 142iter [00:05, 38.77iter/s]Best score: 23.6155: : 143iter [00:05, 38.77iter/s]Best score: 23.6155: : 144iter [00:05, 38.87iter/s]Best score: 23.6155: : 144iter [00:05, 38.87iter/s]Best score: 23.6155: : 145iter [00:05, 38.87iter/s]Best score: 23.6155: : 146iter [00:05, 38.87iter/s]Best score: 23.6155: : 147iter [00:05, 38.87iter/s]Best score: 23.6155: : 148iter [00:05, 39.03iter/s]Best score: 23.6155: : 148iter [00:05, 39.03iter/s]
[ITER 150] Best: 23.6155
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 996.0000
  py: 759.0000
  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: 23.6155: : 149iter [00:05, 39.03iter/s]Best score: 23.6155: : 150iter [00:05, 39.03iter/s]Best score: 23.6155: : 151iter [00:05, 39.03iter/s]Best score: 23.6155: : 152iter [00:05, 31.82iter/s]Best score: 23.6155: : 152iter [00:05, 31.82iter/s]Best score: 23.6155: : 153iter [00:06, 31.82iter/s]Best score: 23.6155: : 154iter [00:06, 31.82iter/s]Best score: 23.6155: : 155iter [00:06, 31.82iter/s]Best score: 23.6155: : 156iter [00:06, 33.71iter/s]Best score: 23.6155: : 156iter [00:06, 33.71iter/s]Best score: 23.6155: : 157iter [00:06, 33.71iter/s]Best score: 23.6155: : 158iter [00:06, 33.71iter/s]Best score: 23.6155: : 159iter [00:06, 33.71iter/s]Best score: 23.6155: : 160iter [00:06, 35.04iter/s]Best score: 23.6155: : 160iter [00:06, 35.04iter/s]Best score: 23.6155: : 161iter [00:06, 35.04iter/s]Best score: 23.6155: : 162iter [00:06, 35.04iter/s]Best score: 23.6155: : 163iter [00:06, 35.04iter/s]Best score: 23.6155: : 164iter [00:06, 36.16iter/s]Best score: 23.6155: : 164iter [00:06, 36.16iter/s]Best score: 23.6155: : 165iter [00:06, 36.16iter/s]Best score: 23.6155: : 166iter [00:06, 36.16iter/s]Best score: 23.6155: : 167iter [00:06, 36.16iter/s]Best score: 23.6155: : 168iter [00:06, 37.10iter/s]Best score: 23.6155: : 168iter [00:06, 37.10iter/s]Best score: 23.6155: : 169iter [00:06, 37.10iter/s]Best score: 23.6155: : 170iter [00:06, 37.10iter/s]Best score: 23.6155: : 171iter [00:06, 37.10iter/s]Best score: 23.6155: : 172iter [00:06, 37.38iter/s]Best score: 23.6155: : 172iter [00:06, 37.38iter/s]Best score: 23.6155: : 173iter [00:06, 37.38iter/s]Best score: 23.6155: : 174iter [00:06, 37.38iter/s]Best score: 23.6155: : 175iter [00:06, 37.38iter/s]Best score: 23.6155: : 176iter [00:06, 37.93iter/s]Best score: 23.6155: : 176iter [00:06, 37.93iter/s]Best score: 23.6155: : 177iter [00:06, 37.93iter/s]Best score: 23.6155: : 178iter [00:06, 37.93iter/s]Best score: 23.6155: : 179iter [00:06, 37.93iter/s]Best score: 23.6155: : 180iter [00:06, 38.34iter/s]Best score: 23.6155: : 180iter [00:06, 38.34iter/s]Best score: 23.6155: : 181iter [00:06, 38.34iter/s]Best score: 23.6155: : 182iter [00:06, 38.34iter/s]Best score: 23.6155: : 183iter [00:06, 38.34iter/s]Best score: 23.6155: : 184iter [00:06, 38.76iter/s]Best score: 23.6155: : 184iter [00:06, 38.76iter/s]Best score: 23.6155: : 185iter [00:06, 38.76iter/s]Best score: 23.6155: : 186iter [00:06, 38.76iter/s]Best score: 23.6155: : 187iter [00:06, 38.76iter/s]Best score: 23.6155: : 188iter [00:06, 39.04iter/s]Best score: 23.6155: : 188iter [00:06, 39.04iter/s]Best score: 23.6155: : 189iter [00:06, 39.04iter/s]Best score: 23.6155: : 190iter [00:06, 39.04iter/s]Best score: 23.6155: : 191iter [00:06, 39.04iter/s]Best score: 23.6155: : 192iter [00:06, 39.03iter/s]Best score: 23.6155: : 192iter [00:07, 39.03iter/s]Best score: 23.6155: : 193iter [00:07, 39.03iter/s]Best score: 23.6155: : 194iter [00:07, 39.03iter/s]Best score: 23.6155: : 195iter [00:07, 39.03iter/s]Best score: 23.6155: : 196iter [00:07, 39.27iter/s]Best score: 23.6155: : 196iter [00:07, 39.27iter/s]Best score: 23.6155: : 197iter [00:07, 39.27iter/s]Best score: 23.6155: : 198iter [00:07, 39.27iter/s]
[ITER 200] Best: 23.6155
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 996.0000
  py: 759.0000
  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: 23.6155: : 199iter [00:07, 39.27iter/s]Best score: 23.6155: : 200iter [00:07, 39.22iter/s]Best score: 23.6155: : 200iter [00:07, 39.22iter/s]Best score: 23.6155: : 201iter [00:07, 39.22iter/s]Best score: 23.6155: : 202iter [00:07, 39.22iter/s]Best score: 23.6155: : 203iter [00:07, 39.22iter/s]Best score: 23.6155: : 204iter [00:07, 32.15iter/s]Best score: 23.6155: : 204iter [00:07, 32.15iter/s]Best score: 23.6155: : 205iter [00:07, 32.15iter/s]Best score: 23.6155: : 206iter [00:07, 32.15iter/s]Best score: 23.6155: : 207iter [00:07, 32.15iter/s]Best score: 23.6155: : 208iter [00:07, 32.15iter/s]Best score: 23.6155: : 209iter [00:07, 34.52iter/s]Best score: 23.6155: : 209iter [00:07, 34.52iter/s]Best score: 23.6155: : 210iter [00:07, 34.52iter/s]Best score: 23.6155: : 211iter [00:07, 34.52iter/s]Best score: 23.6155: : 212iter [00:07, 34.52iter/s]Best score: 23.6155: : 213iter [00:07, 35.85iter/s]Best score: 23.6155: : 213iter [00:07, 35.85iter/s]Best score: 23.6155: : 214iter [00:07, 35.85iter/s]Best score: 23.6155: : 215iter [00:07, 35.85iter/s]Best score: 23.6155: : 216iter [00:07, 35.85iter/s]Best score: 23.6155: : 217iter [00:07, 36.94iter/s]Best score: 23.6155: : 217iter [00:07, 36.94iter/s]Best score: 23.6155: : 218iter [00:07, 36.94iter/s]Best score: 23.6155: : 219iter [00:07, 36.94iter/s]Best score: 23.6155: : 220iter [00:07, 36.94iter/s]Best score: 23.6155: : 221iter [00:07, 37.49iter/s]Best score: 23.6155: : 221iter [00:07, 37.49iter/s]Best score: 23.6155: : 222iter [00:07, 37.49iter/s]Best score: 23.6155: : 223iter [00:07, 37.49iter/s]Best score: 23.6155: : 224iter [00:07, 37.49iter/s]Best score: 23.6155: : 225iter [00:07, 38.04iter/s]Best score: 23.6155: : 225iter [00:07, 38.04iter/s]Best score: 23.6155: : 226iter [00:07, 38.04iter/s]Best score: 23.6155: : 227iter [00:07, 38.04iter/s]Best score: 23.6155: : 228iter [00:07, 38.04iter/s]Best score: 23.6155: : 229iter [00:07, 38.20iter/s]Best score: 23.0167: : 229iter [00:08, 38.20iter/s]Best score: 23.0167: : 230iter [00:08, 38.20iter/s]Best score: 23.0167: : 231iter [00:08, 38.20iter/s]Best score: 23.0167: : 232iter [00:08, 38.20iter/s]Best score: 23.0167: : 233iter [00:08, 38.49iter/s]Best score: 23.0167: : 233iter [00:08, 38.49iter/s]Best score: 23.0167: : 234iter [00:08, 38.49iter/s]Best score: 23.0167: : 235iter [00:08, 38.49iter/s]Best score: 23.0167: : 236iter [00:08, 38.49iter/s]Best score: 23.0167: : 237iter [00:08, 38.49iter/s]Best score: 23.0167: : 238iter [00:08, 39.08iter/s]Best score: 23.0167: : 238iter [00:08, 39.08iter/s]Best score: 6.0874: : 239iter [00:08, 39.08iter/s] Best score: 6.0874: : 240iter [00:08, 39.08iter/s]Best score: 6.0874: : 241iter [00:08, 39.08iter/s]Best score: 6.0874: : 242iter [00:08, 39.07iter/s]Best score: 6.0874: : 242iter [00:08, 39.07iter/s]Best score: 6.0874: : 243iter [00:08, 39.07iter/s]Best score: 6.0874: : 244iter [00:08, 39.07iter/s]Best score: 6.0874: : 245iter [00:08, 39.07iter/s]Best score: 6.0874: : 246iter [00:08, 39.07iter/s]Best score: 6.0874: : 247iter [00:08, 39.41iter/s]Best score: 6.0874: : 247iter [00:08, 39.41iter/s]Best score: 6.0874: : 248iter [00:08, 39.41iter/s]
[ITER 250] Best: 6.0874
[PARAMS] Current best parameters:
  fx: 527.1704
  fy: 528.1389
  px: 990.6792
  py: 756.2133
  k1: -0.00011218
  k2: -0.01795603
  k3: 0.02955871
  k4: -0.01050892
  p1: -0.00800784
  p2: -0.01397202
  p3: -0.00916558
  p4: 0.00614959
  s1: -0.0021
  s2: -0.0013
  s3: 0.0057
  s4: -0.0062
  tilt_x: -0.0070
  tilt_y: -0.0189
  scale: 2.9928
  rotation: 0.4441
Best score: 6.0874: : 249iter [00:08, 39.41iter/s]Best score: 6.0874: : 250iter [00:08, 39.41iter/s]Best score: 6.0874: : 251iter [00:08, 32.05iter/s]Best score: 6.0874: : 251iter [00:08, 32.05iter/s]Best score: 6.0874: : 252iter [00:08, 32.05iter/s]Best score: 6.0874: : 253iter [00:08, 32.05iter/s]Best score: 6.0874: : 254iter [00:08, 32.05iter/s]Best score: 6.0874: : 255iter [00:08, 32.05iter/s]Best score: 6.0874: : 256iter [00:08, 34.33iter/s]Best score: 6.0874: : 256iter [00:08, 34.33iter/s]Best score: 6.0874: : 257iter [00:08, 34.33iter/s]Best score: 6.0874: : 258iter [00:08, 34.33iter/s]Best score: 6.0874: : 259iter [00:08, 34.33iter/s]Best score: 6.0874: : 260iter [00:08, 35.51iter/s]Best score: 6.0874: : 260iter [00:08, 35.51iter/s]Best score: 6.0874: : 261iter [00:08, 35.51iter/s]Best score: 6.0874: : 262iter [00:08, 35.51iter/s]Best score: 6.0874: : 263iter [00:08, 35.51iter/s]Best score: 6.0874: : 264iter [00:08, 35.51iter/s]Best score: 6.0874: : 265iter [00:08, 36.95iter/s]Best score: 6.0874: : 265iter [00:09, 36.95iter/s]Best score: 6.0874: : 266iter [00:09, 36.95iter/s]Best score: 6.0874: : 267iter [00:09, 36.95iter/s]Best score: 6.0874: : 268iter [00:09, 36.95iter/s]Best score: 5.8047: : 269iter [00:09, 36.95iter/s]Best score: 5.8047: : 270iter [00:09, 37.61iter/s]Best score: 5.8047: : 270iter [00:09, 37.61iter/s]Best score: 5.8047: : 271iter [00:09, 37.61iter/s]Best score: 5.8047: : 272iter [00:09, 37.61iter/s]Best score: 5.8047: : 273iter [00:09, 37.61iter/s]Best score: 5.8047: : 274iter [00:09, 38.07iter/s]Best score: 5.8047: : 274iter [00:09, 38.07iter/s]Best score: 5.8047: : 275iter [00:09, 38.07iter/s]Best score: 5.8047: : 276iter [00:09, 38.07iter/s]Best score: 5.8047: : 277iter [00:09, 38.07iter/s]Best score: 5.8047: : 278iter [00:09, 38.37iter/s]Best score: 5.8047: : 278iter [00:09, 38.37iter/s]Best score: 5.8047: : 279iter [00:09, 38.37iter/s]Best score: 5.8047: : 280iter [00:09, 38.37iter/s]Best score: 5.8047: : 281iter [00:09, 38.37iter/s]Best score: 5.8047: : 282iter [00:09, 38.42iter/s]Best score: 5.8047: : 282iter [00:09, 38.42iter/s]Best score: 5.8047: : 283iter [00:09, 38.42iter/s]Best score: 5.8047: : 284iter [00:09, 38.42iter/s]Best score: 5.8047: : 285iter [00:09, 38.42iter/s]Best score: 5.8047: : 286iter [00:09, 38.70iter/s]Best score: 5.8047: : 286iter [00:09, 38.70iter/s]Best score: 5.8047: : 287iter [00:09, 38.70iter/s]Best score: 5.8047: : 288iter [00:09, 38.70iter/s]Best score: 4.0637: : 289iter [00:09, 38.70iter/s]Best score: 4.0637: : 290iter [00:09, 38.83iter/s]Best score: 4.0637: : 290iter [00:09, 38.83iter/s]Best score: 4.0637: : 291iter [00:09, 38.83iter/s]Best score: 4.0637: : 292iter [00:09, 38.83iter/s]Best score: 4.0637: : 293iter [00:09, 38.83iter/s]Best score: 4.0637: : 294iter [00:09, 39.03iter/s]Best score: 4.0637: : 294iter [00:09, 39.03iter/s]Best score: 4.0637: : 295iter [00:09, 39.03iter/s]Best score: 4.0637: : 296iter [00:09, 39.03iter/s]Best score: 4.0637: : 297iter [00:09, 39.03iter/s]Best score: 4.0637: : 298iter [00:09, 39.13iter/s]Best score: 4.0637: : 298iter [00:09, 39.13iter/s]
[ITER 300] Best: 3.4810
[PARAMS] Current best parameters:
  fx: 506.6368
  fy: 506.5220
  px: 993.1487
  py: 762.9758
  k1: 0.01952232
  k2: 0.01049339
  k3: -0.00310178
  k4: -0.00121798
  p1: -0.00763816
  p2: -0.01310840
  p3: 0.01583566
  p4: 0.01048835
  s1: -0.0062
  s2: -0.0064
  s3: -0.0016
  s4: -0.0169
  tilt_x: 0.0239
  tilt_y: -0.0186
  scale: 2.9889
  rotation: 0.1203
Best score: 3.4810: : 299iter [00:09, 39.13iter/s]Best score: 3.4810: : 300iter [00:09, 39.13iter/s]Best score: 3.4810: : 301iter [00:10, 39.13iter/s]Best score: 3.4810: : 302iter [00:10, 31.86iter/s]Best score: 3.4810: : 302iter [00:10, 31.86iter/s]Best score: 3.4810: : 303iter [00:10, 31.86iter/s]Best score: 3.4810: : 304iter [00:10, 31.86iter/s]Best score: 3.4810: : 305iter [00:10, 31.86iter/s]Best score: 3.4810: : 306iter [00:10, 33.91iter/s]Best score: 3.4810: : 306iter [00:10, 33.91iter/s]Best score: 3.4810: : 307iter [00:10, 33.91iter/s]Best score: 3.4810: : 308iter [00:10, 33.91iter/s]Best score: 3.4810: : 309iter [00:10, 33.91iter/s]Best score: 3.4810: : 310iter [00:10, 35.14iter/s]Best score: 3.4810: : 310iter [00:10, 35.14iter/s]Best score: 3.4810: : 311iter [00:10, 35.14iter/s]Best score: 3.4810: : 312iter [00:10, 35.14iter/s]Best score: 3.4810: : 313iter [00:10, 35.14iter/s]Best score: 3.4810: : 314iter [00:10, 36.17iter/s]Best score: 3.4810: : 314iter [00:10, 36.17iter/s]Best score: 3.4810: : 315iter [00:10, 36.17iter/s]Best score: 3.4810: : 316iter [00:10, 36.17iter/s]Best score: 3.4810: : 317iter [00:10, 36.17iter/s]Best score: 3.4810: : 318iter [00:10, 37.21iter/s]Best score: 3.4810: : 318iter [00:10, 37.21iter/s]Best score: 3.4214: : 319iter [00:10, 37.21iter/s]Best score: 3.4214: : 320iter [00:10, 37.21iter/s]Best score: 3.4214: : 321iter [00:10, 37.21iter/s]Best score: 3.4214: : 322iter [00:10, 37.73iter/s]Best score: 3.4214: : 322iter [00:10, 37.73iter/s]Best score: 3.4214: : 323iter [00:10, 37.73iter/s]Best score: 3.4214: : 324iter [00:10, 37.73iter/s]Best score: 3.4214: : 325iter [00:10, 37.73iter/s]Best score: 3.4214: : 326iter [00:10, 38.20iter/s]Best score: 3.4214: : 326iter [00:10, 38.20iter/s]Best score: 3.4214: : 327iter [00:10, 38.20iter/s]Best score: 3.4214: : 328iter [00:10, 38.20iter/s]Best score: 2.0882: : 329iter [00:10, 38.20iter/s]Best score: 2.0882: : 330iter [00:10, 38.46iter/s]Best score: 2.0882: : 330iter [00:10, 38.46iter/s]Best score: 2.0882: : 331iter [00:10, 38.46iter/s]Best score: 2.0882: : 332iter [00:10, 38.46iter/s]Best score: 2.0882: : 333iter [00:10, 38.46iter/s]Best score: 2.0882: : 334iter [00:10, 38.85iter/s]Best score: 2.0882: : 334iter [00:10, 38.85iter/s]Best score: 2.0882: : 335iter [00:10, 38.85iter/s]Best score: 2.0882: : 336iter [00:10, 38.85iter/s]Best score: 2.0882: : 337iter [00:10, 38.85iter/s]Best score: 2.0882: : 338iter [00:10, 38.96iter/s]Best score: 2.0882: : 338iter [00:10, 38.96iter/s]Best score: 1.9094: : 339iter [00:10, 38.96iter/s]Best score: 1.9094: : 340iter [00:11, 38.96iter/s]Best score: 1.9094: : 341iter [00:11, 38.96iter/s]Best score: 1.9094: : 342iter [00:11, 38.97iter/s]Best score: 1.9094: : 342iter [00:11, 38.97iter/s]Best score: 1.9094: : 343iter [00:11, 38.97iter/s]Best score: 1.9094: : 344iter [00:11, 38.97iter/s]Best score: 1.9094: : 345iter [00:11, 38.97iter/s]Best score: 1.9094: : 346iter [00:11, 39.21iter/s]Best score: 1.9094: : 346iter [00:11, 39.21iter/s]Best score: 1.9094: : 347iter [00:11, 39.21iter/s]Best score: 1.9094: : 348iter [00:11, 39.21iter/s]
[ITER 350] Best: 1.8988
[PARAMS] Current best parameters:
  fx: 506.5729
  fy: 506.6805
  px: 989.2740
  py: 757.3499
  k1: 0.01249139
  k2: -0.00093567
  k3: 0.01449174
  k4: -0.00634970
  p1: -0.01225435
  p2: -0.01021077
  p3: -0.00639010
  p4: 0.01266898
  s1: 0.0023
  s2: -0.0097
  s3: 0.0056
  s4: -0.0110
  tilt_x: 0.0054
  tilt_y: 0.0021
  scale: 3.0124
  rotation: 0.2123
Best score: 1.8988: : 349iter [00:11, 39.21iter/s]Best score: 1.8988: : 350iter [00:11, 38.94iter/s]Best score: 1.8988: : 350iter [00:11, 38.94iter/s]Best score: 1.8988: : 351iter [00:11, 38.94iter/s]Best score: 1.8988: : 352iter [00:11, 38.94iter/s]Best score: 1.8988: : 353iter [00:11, 38.94iter/s]Best score: 1.8988: : 354iter [00:11, 31.81iter/s]Best score: 1.8988: : 354iter [00:11, 31.81iter/s]Best score: 1.8988: : 355iter [00:11, 31.81iter/s]Best score: 1.8988: : 356iter [00:11, 31.81iter/s]Best score: 1.8988: : 357iter [00:11, 31.81iter/s]Best score: 1.8988: : 358iter [00:11, 33.81iter/s]Best score: 1.8988: : 358iter [00:11, 33.81iter/s]Best score: 1.3699: : 359iter [00:11, 33.81iter/s]Best score: 1.3699: : 360iter [00:11, 33.81iter/s]Best score: 1.3699: : 361iter [00:11, 33.81iter/s]Best score: 1.3699: : 362iter [00:11, 34.95iter/s]Best score: 1.3699: : 362iter [00:11, 34.95iter/s]Best score: 1.3699: : 363iter [00:11, 34.95iter/s]Best score: 1.3699: : 364iter [00:11, 34.95iter/s]Best score: 1.3699: : 365iter [00:11, 34.95iter/s]Best score: 1.3699: : 366iter [00:11, 29.37iter/s]Best score: 1.3699: : 366iter [00:11, 29.37iter/s]Best score: 1.3699: : 367iter [00:11, 29.37iter/s]Best score: 1.3699: : 368iter [00:11, 29.37iter/s]Best score: 1.2339: : 369iter [00:11, 29.37iter/s]Best score: 1.2339: : 370iter [00:11, 31.70iter/s]Best score: 1.2339: : 370iter [00:11, 31.70iter/s]Best score: 1.2339: : 371iter [00:11, 31.70iter/s]Best score: 1.2339: : 372iter [00:11, 31.70iter/s]Best score: 1.2339: : 373iter [00:12, 31.70iter/s]Best score: 1.2339: : 374iter [00:12, 31.70iter/s]Best score: 1.2339: : 375iter [00:12, 34.22iter/s]Best score: 1.2339: : 375iter [00:12, 34.22iter/s]Best score: 1.2339: : 376iter [00:12, 34.22iter/s]Best score: 1.2339: : 377iter [00:12, 34.22iter/s]Best score: 1.2339: : 378iter [00:12, 34.22iter/s]Best score: 1.2339: : 379iter [00:12, 35.69iter/s]Best score: 0.6847: : 379iter [00:12, 35.69iter/s]Best score: 0.6847: : 380iter [00:12, 35.69iter/s]Best score: 0.6847: : 381iter [00:12, 35.69iter/s]Best score: 0.6847: : 382iter [00:12, 35.69iter/s]Best score: 0.6847: : 383iter [00:12, 36.57iter/s]Best score: 0.6847: : 383iter [00:12, 36.57iter/s]Best score: 0.6847: : 384iter [00:12, 36.57iter/s]Best score: 0.6847: : 385iter [00:12, 36.57iter/s]Best score: 0.6847: : 386iter [00:12, 36.57iter/s]Best score: 0.6847: : 387iter [00:12, 37.40iter/s]Best score: 0.6847: : 387iter [00:12, 37.40iter/s]Best score: 0.6847: : 388iter [00:12, 37.40iter/s]Best score: 0.6847: : 389iter [00:12, 37.40iter/s]Best score: 0.6847: : 390iter [00:12, 37.40iter/s]Best score: 0.6847: : 391iter [00:12, 37.76iter/s]Best score: 0.6847: : 391iter [00:12, 37.76iter/s]Best score: 0.6847: : 392iter [00:12, 37.76iter/s]Best score: 0.6847: : 393iter [00:12, 37.76iter/s]Best score: 0.6847: : 394iter [00:12, 37.76iter/s]Best score: 0.6847: : 395iter [00:12, 37.76iter/s]Best score: 0.6847: : 396iter [00:12, 38.58iter/s]Best score: 0.6847: : 396iter [00:12, 38.58iter/s]Best score: 0.6847: : 397iter [00:12, 38.58iter/s]Best score: 0.6847: : 398iter [00:12, 38.58iter/s]
[ITER 400] Best: 0.6847
[PARAMS] Current best parameters:
  fx: 520.6528
  fy: 520.6764
  px: 994.2210
  py: 759.3428
  k1: -0.00803514
  k2: 0.00794859
  k3: 0.00612469
  k4: -0.00348219
  p1: -0.01067091
  p2: -0.00522341
  p3: -0.00835052
  p4: 0.00251048
  s1: 0.0082
  s2: -0.0009
  s3: 0.0106
  s4: -0.0012
  tilt_x: 0.0076
  tilt_y: -0.0110
  scale: 3.0633
  rotation: 0.1991
Best score: 0.6847: : 399iter [00:12, 38.58iter/s]Best score: 0.6847: : 400iter [00:12, 38.63iter/s]Best score: 0.6847: : 400iter [00:12, 38.63iter/s]Best score: 0.6847: : 401iter [00:12, 38.63iter/s]Best score: 0.6847: : 402iter [00:12, 38.63iter/s]Best score: 0.6847: : 403iter [00:12, 38.63iter/s]Best score: 0.6847: : 404iter [00:12, 32.01iter/s]Best score: 0.6847: : 404iter [00:12, 32.01iter/s]Best score: 0.6847: : 405iter [00:12, 32.01iter/s]Best score: 0.6847: : 406iter [00:12, 32.01iter/s]Best score: 0.6847: : 407iter [00:12, 32.01iter/s]Best score: 0.6847: : 408iter [00:12, 33.63iter/s]Best score: 0.6847: : 408iter [00:12, 33.63iter/s]Best score: 0.6847: : 409iter [00:13, 33.63iter/s]Best score: 0.6847: : 410iter [00:13, 33.63iter/s]Best score: 0.6847: : 411iter [00:13, 33.63iter/s]Best score: 0.6847: : 412iter [00:13, 34.84iter/s]Best score: 0.6847: : 412iter [00:13, 34.84iter/s]Best score: 0.6847: : 413iter [00:13, 34.84iter/s]Best score: 0.6847: : 414iter [00:13, 34.84iter/s]Best score: 0.6847: : 415iter [00:13, 34.84iter/s]Best score: 0.6847: : 416iter [00:13, 34.84iter/s]Best score: 0.6847: : 417iter [00:13, 36.59iter/s]Best score: 0.6847: : 417iter [00:13, 36.59iter/s]Best score: 0.6847: : 418iter [00:13, 36.59iter/s]Best score: 0.5147: : 419iter [00:13, 36.59iter/s]Best score: 0.5147: : 420iter [00:13, 36.59iter/s]Best score: 0.5147: : 421iter [00:13, 37.09iter/s]Best score: 0.5147: : 421iter [00:13, 37.09iter/s]Best score: 0.5147: : 422iter [00:13, 37.09iter/s]Best score: 0.5147: : 423iter [00:13, 37.09iter/s]Best score: 0.5147: : 424iter [00:13, 37.09iter/s]Best score: 0.5147: : 425iter [00:13, 37.09iter/s]Best score: 0.5147: : 426iter [00:13, 38.10iter/s]Best score: 0.5147: : 426iter [00:13, 38.10iter/s]Best score: 0.5147: : 427iter [00:13, 38.10iter/s]Best score: 0.5147: : 428iter [00:13, 38.10iter/s]Best score: 0.5044: : 429iter [00:13, 38.10iter/s]Best score: 0.5044: : 430iter [00:13, 38.32iter/s]Best score: 0.5044: : 430iter [00:13, 38.32iter/s]Best score: 0.5044: : 431iter [00:13, 38.32iter/s]Best score: 0.5044: : 432iter [00:13, 38.32iter/s]Best score: 0.5044: : 433iter [00:13, 38.32iter/s]Best score: 0.5044: : 434iter [00:13, 38.60iter/s]Best score: 0.5044: : 434iter [00:13, 38.60iter/s]Best score: 0.5044: : 435iter [00:13, 38.60iter/s]Best score: 0.5044: : 436iter [00:13, 38.60iter/s]Best score: 0.5044: : 437iter [00:13, 38.60iter/s]Best score: 0.5044: : 438iter [00:13, 38.92iter/s]Best score: 0.5044: : 438iter [00:13, 38.92iter/s]Best score: 0.5044: : 439iter [00:13, 38.92iter/s]Best score: 0.5044: : 440iter [00:13, 38.92iter/s]Best score: 0.5044: : 441iter [00:13, 38.92iter/s]Best score: 0.5044: : 442iter [00:13, 39.02iter/s]Best score: 0.5044: : 442iter [00:13, 39.02iter/s]Best score: 0.5044: : 443iter [00:13, 39.02iter/s]Best score: 0.5044: : 444iter [00:13, 39.02iter/s]Best score: 0.5044: : 445iter [00:13, 39.02iter/s]Best score: 0.5044: : 446iter [00:13, 39.13iter/s]Best score: 0.5044: : 446iter [00:13, 39.13iter/s]Best score: 0.5044: : 447iter [00:13, 39.13iter/s]Best score: 0.5044: : 448iter [00:13, 39.13iter/s]Best score: 0.4658: : 449iter [00:14, 39.13iter/s]Best score: 0.4658: : 450iter [00:14, 38.80iter/s]
[ITER 450] Best: 0.4658
[PARAMS] Current best parameters:
  fx: 520.2339
  fy: 520.3603
  px: 995.4261
  py: 761.4195
  k1: -0.00277783
  k2: 0.01802431
  k3: -0.00683462
  k4: -0.00006298
  p1: -0.00922523
  p2: -0.00893960
  p3: 0.00174120
  p4: 0.00530173
  s1: 0.0024
  s2: -0.0034
  s3: 0.0049
  s4: -0.0061
  tilt_x: 0.0147
  tilt_y: -0.0239
  scale: 3.0287
  rotation: 0.1486
Best score: 0.4658: : 450iter [00:14, 38.80iter/s]Best score: 0.4658: : 451iter [00:14, 38.80iter/s]Best score: 0.4658: : 452iter [00:14, 38.80iter/s]Best score: 0.4658: : 453iter [00:14, 38.80iter/s]Best score: 0.4658: : 454iter [00:14, 31.38iter/s]Best score: 0.4658: : 454iter [00:14, 31.38iter/s]Best score: 0.4658: : 455iter [00:14, 31.38iter/s]Best score: 0.4658: : 456iter [00:14, 31.38iter/s]Best score: 0.4658: : 457iter [00:14, 31.38iter/s]Best score: 0.4658: : 458iter [00:14, 33.43iter/s]Best score: 0.4658: : 458iter [00:14, 33.43iter/s]Best score: 0.4658: : 459iter [00:14, 33.43iter/s]Best score: 0.4658: : 460iter [00:14, 33.43iter/s]Best score: 0.4658: : 461iter [00:14, 33.43iter/s]Best score: 0.4658: : 462iter [00:14, 34.90iter/s]Best score: 0.4658: : 462iter [00:14, 34.90iter/s]Best score: 0.4658: : 463iter [00:14, 34.90iter/s]Best score: 0.4658: : 464iter [00:14, 34.90iter/s]Best score: 0.4658: : 465iter [00:14, 34.90iter/s]Best score: 0.4658: : 466iter [00:14, 34.90iter/s]Best score: 0.4658: : 467iter [00:14, 36.52iter/s]Best score: 0.4658: : 467iter [00:14, 36.52iter/s]Best score: 0.4658: : 468iter [00:14, 36.52iter/s]Best score: 0.4226: : 469iter [00:14, 36.52iter/s]Best score: 0.4226: : 470iter [00:14, 36.52iter/s]Best score: 0.4226: : 471iter [00:14, 37.03iter/s]Best score: 0.4226: : 471iter [00:14, 37.03iter/s]Best score: 0.4226: : 472iter [00:14, 37.03iter/s]Best score: 0.4226: : 473iter [00:14, 37.03iter/s]Best score: 0.4226: : 474iter [00:14, 37.03iter/s]Best score: 0.4226: : 475iter [00:14, 37.83iter/s]Best score: 0.4226: : 475iter [00:14, 37.83iter/s]Best score: 0.4226: : 476iter [00:14, 37.83iter/s]Best score: 0.4226: : 477iter [00:14, 37.83iter/s]Best score: 0.4226: : 478iter [00:14, 37.83iter/s]Best score: 0.4226: : 479iter [00:14, 38.42iter/s]Best score: 0.4131: : 479iter [00:14, 38.42iter/s]Best score: 0.4131: : 480iter [00:14, 38.42iter/s]Best score: 0.4131: : 481iter [00:14, 38.42iter/s]Best score: 0.4131: : 482iter [00:14, 38.42iter/s]Best score: 0.4131: : 483iter [00:14, 38.56iter/s]Best score: 0.4131: : 483iter [00:14, 38.56iter/s]Best score: 0.4131: : 484iter [00:14, 38.56iter/s]Best score: 0.4131: : 485iter [00:15, 38.56iter/s]Best score: 0.4131: : 486iter [00:15, 38.56iter/s]Best score: 0.4131: : 487iter [00:15, 38.56iter/s]Best score: 0.4131: : 488iter [00:15, 38.90iter/s]Best score: 0.4131: : 488iter [00:15, 38.90iter/s]Best score: 0.4056: : 489iter [00:15, 38.90iter/s]Best score: 0.4056: : 490iter [00:15, 38.90iter/s]Best score: 0.4056: : 491iter [00:15, 38.90iter/s]Best score: 0.4056: : 492iter [00:15, 38.93iter/s]Best score: 0.4056: : 492iter [00:15, 38.93iter/s]Best score: 0.4056: : 493iter [00:15, 38.93iter/s]Best score: 0.4056: : 494iter [00:15, 38.93iter/s]Best score: 0.4056: : 495iter [00:15, 38.93iter/s]Best score: 0.4056: : 496iter [00:15, 38.93iter/s]Best score: 0.4056: : 497iter [00:15, 39.30iter/s]Best score: 0.4056: : 497iter [00:15, 39.30iter/s]Best score: 0.4056: : 498iter [00:15, 39.30iter/s]
[ITER 500] Best: 0.3621
[PARAMS] Current best parameters:
  fx: 514.9259
  fy: 514.9941
  px: 992.5856
  py: 759.4517
  k1: -0.01097840
  k2: 0.03965872
  k3: -0.01851652
  k4: 0.00201989
  p1: -0.00749921
  p2: -0.00424498
  p3: -0.00541931
  p4: 0.00938191
  s1: 0.0023
  s2: -0.0073
  s3: 0.0061
  s4: -0.0089
  tilt_x: 0.0022
  tilt_y: -0.0008
  scale: 3.0299
  rotation: 0.1782
Best score: 0.3621: : 499iter [00:15, 39.30iter/s]Best score: 0.3621: : 500iter [00:15, 39.30iter/s]Best score: 0.3621: : 501iter [00:15, 32.42iter/s]Best score: 0.3621: : 501iter [00:15, 32.42iter/s]Best score: 0.3621: : 502iter [00:15, 32.42iter/s]Best score: 0.3621: : 503iter [00:15, 32.42iter/s]Best score: 0.3621: : 504iter [00:15, 32.42iter/s]Best score: 0.3621: : 505iter [00:15, 34.19iter/s]Best score: 0.3621: : 505iter [00:15, 34.19iter/s]Best score: 0.3621: : 506iter [00:15, 34.19iter/s]Best score: 0.3621: : 507iter [00:15, 34.19iter/s]Best score: 0.3621: : 508iter [00:15, 34.19iter/s]Best score: 0.3621: : 509iter [00:15, 35.58iter/s]Best score: 0.3370: : 509iter [00:15, 35.58iter/s]Best score: 0.3370: : 510iter [00:15, 35.58iter/s]Best score: 0.3370: : 511iter [00:15, 35.58iter/s]Best score: 0.3370: : 512iter [00:15, 35.58iter/s]Best score: 0.3370: : 513iter [00:15, 36.56iter/s]Best score: 0.3370: : 513iter [00:15, 36.56iter/s]Best score: 0.3370: : 514iter [00:15, 36.56iter/s]Best score: 0.3370: : 515iter [00:15, 36.56iter/s]Best score: 0.3370: : 516iter [00:15, 36.56iter/s]Best score: 0.3370: : 517iter [00:15, 37.46iter/s]Best score: 0.3370: : 517iter [00:15, 37.46iter/s]Best score: 0.3370: : 518iter [00:15, 37.46iter/s]Best score: 0.3370: : 519iter [00:15, 37.46iter/s]Best score: 0.3370: : 520iter [00:15, 37.46iter/s]Best score: 0.3370: : 521iter [00:15, 37.63iter/s]Best score: 0.3370: : 521iter [00:16, 37.63iter/s]Best score: 0.3370: : 522iter [00:16, 37.63iter/s]Best score: 0.3370: : 523iter [00:16, 37.63iter/s]Best score: 0.3370: : 524iter [00:16, 37.63iter/s]Best score: 0.3370: : 525iter [00:16, 38.13iter/s]Best score: 0.3370: : 525iter [00:16, 38.13iter/s]Best score: 0.3370: : 526iter [00:16, 38.13iter/s]Best score: 0.3370: : 527iter [00:16, 38.13iter/s]Best score: 0.3370: : 528iter [00:16, 38.13iter/s]Best score: 0.3370: : 529iter [00:16, 38.47iter/s]Best score: 0.3370: : 529iter [00:16, 38.47iter/s]Best score: 0.3370: : 530iter [00:16, 38.47iter/s]Best score: 0.3370: : 531iter [00:16, 38.47iter/s]Best score: 0.3370: : 532iter [00:16, 38.47iter/s]Best score: 0.3370: : 533iter [00:16, 38.38iter/s]Best score: 0.3370: : 533iter [00:16, 38.38iter/s]Best score: 0.3370: : 534iter [00:16, 38.38iter/s]Best score: 0.3370: : 535iter [00:16, 38.38iter/s]Best score: 0.3370: : 536iter [00:16, 38.38iter/s]Best score: 0.3370: : 537iter [00:16, 38.70iter/s]Best score: 0.3370: : 537iter [00:16, 38.70iter/s]Best score: 0.3370: : 538iter [00:16, 38.70iter/s]Best score: 0.3370: : 539iter [00:16, 38.70iter/s]Best score: 0.3370: : 540iter [00:16, 38.70iter/s]Best score: 0.3370: : 541iter [00:16, 38.84iter/s]Best score: 0.3370: : 541iter [00:16, 38.84iter/s]Best score: 0.3370: : 542iter [00:16, 38.84iter/s]Best score: 0.3370: : 543iter [00:16, 38.84iter/s]Best score: 0.3370: : 544iter [00:16, 38.84iter/s]Best score: 0.3370: : 545iter [00:16, 39.14iter/s]Best score: 0.3370: : 545iter [00:16, 39.14iter/s]Best score: 0.3370: : 546iter [00:16, 39.14iter/s]Best score: 0.3370: : 547iter [00:16, 39.14iter/s]Best score: 0.3370: : 548iter [00:16, 39.14iter/s]Best score: 0.3370: : 549iter [00:16, 39.28iter/s]
[ITER 550] Best: 0.3370
[PARAMS] Current best parameters:
  fx: 518.1285
  fy: 518.2378
  px: 992.0845
  py: 759.5996
  k1: -0.00808115
  k2: 0.03363594
  k3: -0.01727884
  k4: 0.00222059
  p1: -0.00614387
  p2: -0.00331461
  p3: -0.00642708
  p4: 0.00840311
  s1: 0.0034
  s2: -0.0071
  s3: 0.0079
  s4: -0.0087
  tilt_x: -0.0007
  tilt_y: 0.0038
  scale: 3.0225
  rotation: 0.1600
Best score: 0.3370: : 549iter [00:16, 39.28iter/s]Best score: 0.3370: : 550iter [00:16, 39.28iter/s]Best score: 0.3370: : 551iter [00:16, 39.28iter/s]Best score: 0.3370: : 552iter [00:16, 39.28iter/s]Best score: 0.3370: : 553iter [00:16, 31.70iter/s]Best score: 0.3370: : 553iter [00:16, 31.70iter/s]Best score: 0.3370: : 554iter [00:16, 31.70iter/s]Best score: 0.3370: : 555iter [00:16, 31.70iter/s]Best score: 0.3370: : 556iter [00:16, 31.70iter/s]Best score: 0.3370: : 557iter [00:16, 33.61iter/s]Best score: 0.3370: : 557iter [00:17, 33.61iter/s]Best score: 0.3370: : 558iter [00:17, 33.61iter/s]Best score: 0.3370: : 559iter [00:17, 33.61iter/s]Best score: 0.3370: : 560iter [00:17, 33.61iter/s]Best score: 0.3370: : 561iter [00:17, 35.06iter/s]Best score: 0.3370: : 561iter [00:17, 35.06iter/s]Best score: 0.3370: : 562iter [00:17, 35.06iter/s]Best score: 0.3370: : 563iter [00:17, 35.06iter/s]Best score: 0.3370: : 564iter [00:17, 35.06iter/s]Best score: 0.3370: : 565iter [00:17, 35.06iter/s]Best score: 0.3370: : 566iter [00:17, 36.68iter/s]Best score: 0.3370: : 566iter [00:17, 36.68iter/s]Best score: 0.3370: : 567iter [00:17, 36.68iter/s]Best score: 0.3370: : 568iter [00:17, 36.68iter/s]Best score: 0.3347: : 569iter [00:17, 36.68iter/s]Best score: 0.3347: : 570iter [00:17, 37.18iter/s]Best score: 0.3347: : 570iter [00:17, 37.18iter/s]Best score: 0.3347: : 571iter [00:17, 37.18iter/s]Best score: 0.3347: : 572iter [00:17, 37.18iter/s]Best score: 0.3347: : 573iter [00:17, 37.18iter/s]Best score: 0.3347: : 574iter [00:17, 37.79iter/s]Best score: 0.3347: : 574iter [00:17, 37.79iter/s]Best score: 0.3347: : 575iter [00:17, 37.79iter/s]Best score: 0.3347: : 576iter [00:17, 37.79iter/s]Best score: 0.3347: : 577iter [00:17, 37.79iter/s]Best score: 0.3347: : 578iter [00:17, 37.79iter/s]Best score: 0.3347: : 579iter [00:17, 38.50iter/s]Best score: 0.3345: : 579iter [00:17, 38.50iter/s]Best score: 0.3345: : 580iter [00:17, 38.50iter/s]Best score: 0.3345: : 581iter [00:17, 38.50iter/s]Best score: 0.3345: : 582iter [00:17, 38.50iter/s]Best score: 0.3345: : 583iter [00:17, 38.68iter/s]Best score: 0.3345: : 583iter [00:17, 38.68iter/s]Best score: 0.3345: : 584iter [00:17, 38.68iter/s]Best score: 0.3345: : 585iter [00:17, 38.68iter/s]Best score: 0.3345: : 586iter [00:17, 38.68iter/s]Best score: 0.3345: : 587iter [00:17, 38.90iter/s]Best score: 0.3345: : 587iter [00:17, 38.90iter/s]Best score: 0.3345: : 588iter [00:17, 38.90iter/s]Best score: 0.3345: : 589iter [00:17, 38.90iter/s]Best score: 0.3345: : 590iter [00:17, 38.90iter/s]Best score: 0.3345: : 591iter [00:17, 38.92iter/s]Best score: 0.3345: : 591iter [00:17, 38.92iter/s]Best score: 0.3345: : 592iter [00:17, 38.92iter/s]Best score: 0.3345: : 593iter [00:17, 38.92iter/s]Best score: 0.3345: : 594iter [00:17, 38.92iter/s]Best score: 0.3345: : 595iter [00:17, 38.92iter/s]Best score: 0.3345: : 596iter [00:17, 39.42iter/s]Best score: 0.3345: : 596iter [00:17, 39.42iter/s]Best score: 0.3345: : 597iter [00:18, 39.42iter/s]Best score: 0.3345: : 598iter [00:18, 39.42iter/s]
[ITER 600] Best: 0.3345
[PARAMS] Current best parameters:
  fx: 517.4618
  fy: 517.6511
  px: 991.8908
  py: 759.5584
  k1: -0.00813631
  k2: 0.03174267
  k3: -0.01487855
  k4: 0.00154234
  p1: -0.00724463
  p2: -0.00635507
  p3: -0.00484884
  p4: 0.00917329
  s1: 0.0054
  s2: -0.0079
  s3: 0.0075
  s4: -0.0096
  tilt_x: 0.0017
  tilt_y: -0.0008
  scale: 3.0291
  rotation: 0.1797
Best score: 0.3345: : 599iter [00:18, 39.42iter/s]Best score: 0.3345: : 600iter [00:18, 39.26iter/s]Best score: 0.3345: : 600iter [00:18, 39.26iter/s]Best score: 0.3345: : 601iter [00:18, 39.26iter/s]Best score: 0.3345: : 602iter [00:18, 39.26iter/s]Best score: 0.3345: : 603iter [00:18, 39.26iter/s]Best score: 0.3345: : 604iter [00:18, 32.31iter/s]Best score: 0.3345: : 604iter [00:18, 32.31iter/s]Best score: 0.3345: : 605iter [00:18, 32.31iter/s]Best score: 0.3345: : 606iter [00:18, 32.31iter/s]Best score: 0.3345: : 607iter [00:18, 32.31iter/s]Best score: 0.3345: : 608iter [00:18, 34.18iter/s]Best score: 0.3345: : 608iter [00:18, 34.18iter/s]Best score: 0.3284: : 609iter [00:18, 34.18iter/s]Best score: 0.3284: : 610iter [00:18, 34.18iter/s]Best score: 0.3284: : 611iter [00:18, 34.18iter/s]Best score: 0.3284: : 612iter [00:18, 35.45iter/s]Best score: 0.3284: : 612iter [00:18, 35.45iter/s]Best score: 0.3284: : 613iter [00:18, 35.45iter/s]Best score: 0.3284: : 614iter [00:18, 35.45iter/s]Best score: 0.3284: : 615iter [00:18, 35.45iter/s]Best score: 0.3284: : 616iter [00:18, 36.66iter/s]Best score: 0.3284: : 616iter [00:18, 36.66iter/s]Best score: 0.3284: : 617iter [00:18, 36.66iter/s]Best score: 0.3284: : 618iter [00:18, 36.66iter/s]Best score: 0.3250: : 619iter [00:18, 36.66iter/s]Best score: 0.3250: : 620iter [00:18, 37.30iter/s]Best score: 0.3250: : 620iter [00:18, 37.30iter/s]Best score: 0.3250: : 621iter [00:18, 37.30iter/s]Best score: 0.3250: : 622iter [00:18, 37.30iter/s]Best score: 0.3250: : 623iter [00:18, 37.30iter/s]Best score: 0.3250: : 624iter [00:18, 37.30iter/s]Best score: 0.3250: : 625iter [00:18, 38.40iter/s]Best score: 0.3250: : 625iter [00:18, 38.40iter/s]Best score: 0.3250: : 626iter [00:18, 38.40iter/s]Best score: 0.3250: : 627iter [00:18, 38.40iter/s]Best score: 0.3250: : 628iter [00:18, 38.40iter/s]Best score: 0.3250: : 629iter [00:18, 38.83iter/s]Best score: 0.3250: : 629iter [00:18, 38.83iter/s]Best score: 0.3250: : 630iter [00:18, 38.83iter/s]Best score: 0.3250: : 631iter [00:18, 38.83iter/s]Best score: 0.3250: : 632iter [00:18, 38.83iter/s]Best score: 0.3250: : 633iter [00:18, 39.01iter/s]Best score: 0.3250: : 633iter [00:19, 39.01iter/s]Best score: 0.3250: : 634iter [00:19, 39.01iter/s]Best score: 0.3250: : 635iter [00:19, 39.01iter/s]Best score: 0.3250: : 636iter [00:19, 39.01iter/s]Best score: 0.3250: : 637iter [00:19, 39.01iter/s]Best score: 0.3250: : 638iter [00:19, 39.52iter/s]Best score: 0.3250: : 638iter [00:19, 39.52iter/s]Best score: 0.3199: : 639iter [00:19, 39.52iter/s]Best score: 0.3199: : 640iter [00:19, 39.52iter/s]Best score: 0.3199: : 641iter [00:19, 39.52iter/s]Best score: 0.3199: : 642iter [00:19, 39.25iter/s]Best score: 0.3199: : 642iter [00:19, 39.25iter/s]Best score: 0.3199: : 643iter [00:19, 39.25iter/s]Best score: 0.3199: : 644iter [00:19, 39.25iter/s]Best score: 0.3199: : 645iter [00:19, 39.25iter/s]Best score: 0.3199: : 646iter [00:19, 39.25iter/s]Best score: 0.3199: : 647iter [00:19, 39.59iter/s]Best score: 0.3199: : 647iter [00:19, 39.59iter/s]Best score: 0.3199: : 648iter [00:19, 39.59iter/s]
[ITER 650] Best: 0.3199
[PARAMS] Current best parameters:
  fx: 516.1445
  fy: 516.2961
  px: 991.5060
  py: 759.5594
  k1: -0.01177298
  k2: 0.03640247
  k3: -0.01561650
  k4: 0.00132061
  p1: -0.00695445
  p2: -0.00601795
  p3: -0.00555143
  p4: 0.00965899
  s1: 0.0055
  s2: -0.0085
  s3: 0.0071
  s4: -0.0095
  tilt_x: 0.0009
  tilt_y: 0.0016
  scale: 3.0347
  rotation: 0.1683
Best score: 0.3199: : 649iter [00:19, 39.59iter/s]Best score: 0.3199: : 650iter [00:19, 39.59iter/s]Best score: 0.3199: : 651iter [00:19, 32.44iter/s]Best score: 0.3199: : 651iter [00:19, 32.44iter/s]Best score: 0.3199: : 652iter [00:19, 32.44iter/s]Best score: 0.3199: : 653iter [00:19, 32.44iter/s]Best score: 0.3199: : 654iter [00:19, 32.44iter/s]Best score: 0.3199: : 655iter [00:19, 32.44iter/s]Best score: 0.3199: : 656iter [00:19, 34.72iter/s]Best score: 0.3199: : 656iter [00:19, 34.72iter/s]Best score: 0.3199: : 657iter [00:19, 34.72iter/s]Best score: 0.3199: : 658iter [00:19, 34.72iter/s]Best score: 0.3199: : 659iter [00:19, 34.72iter/s]Best score: 0.3199: : 660iter [00:19, 35.93iter/s]Best score: 0.3199: : 660iter [00:19, 35.93iter/s]Best score: 0.3199: : 661iter [00:19, 35.93iter/s]Best score: 0.3199: : 662iter [00:19, 35.93iter/s]Best score: 0.3199: : 663iter [00:19, 35.93iter/s]Best score: 0.3199: : 664iter [00:19, 35.93iter/s]Best score: 0.3199: : 665iter [00:19, 37.18iter/s]Best score: 0.3199: : 665iter [00:19, 37.18iter/s]Best score: 0.3199: : 666iter [00:19, 37.18iter/s]Best score: 0.3199: : 667iter [00:19, 37.18iter/s]Best score: 0.3199: : 668iter [00:19, 37.18iter/s]Best score: 0.3196: : 669iter [00:19, 37.18iter/s]Best score: 0.3196: : 670iter [00:19, 37.81iter/s]Best score: 0.3196: : 670iter [00:20, 37.81iter/s]Best score: 0.3196: : 671iter [00:20, 37.81iter/s]Best score: 0.3196: : 672iter [00:20, 37.81iter/s]Best score: 0.3196: : 673iter [00:20, 37.81iter/s]Best score: 0.3196: : 674iter [00:20, 37.81iter/s]Best score: 0.3196: : 675iter [00:20, 38.51iter/s]Best score: 0.3196: : 675iter [00:20, 38.51iter/s]Best score: 0.3196: : 676iter [00:20, 38.51iter/s]Best score: 0.3196: : 677iter [00:20, 38.51iter/s]Best score: 0.3196: : 678iter [00:20, 38.51iter/s]Best score: 0.3196: : 679iter [00:20, 38.66iter/s]Best score: 0.3181: : 679iter [00:20, 38.66iter/s]Best score: 0.3181: : 680iter [00:20, 38.66iter/s]Best score: 0.3181: : 681iter [00:20, 38.66iter/s]Best score: 0.3181: : 682iter [00:20, 38.66iter/s]Best score: 0.3181: : 683iter [00:20, 38.67iter/s]Best score: 0.3181: : 683iter [00:20, 38.67iter/s]Best score: 0.3181: : 684iter [00:20, 38.67iter/s]Best score: 0.3181: : 685iter [00:20, 38.67iter/s]Best score: 0.3181: : 686iter [00:20, 38.67iter/s]Best score: 0.3181: : 687iter [00:20, 31.63iter/s]Best score: 0.3181: : 687iter [00:20, 31.63iter/s]Best score: 0.3181: : 688iter [00:20, 31.63iter/s]Best score: 0.3176: : 689iter [00:20, 31.63iter/s]Best score: 0.3176: : 690iter [00:20, 31.63iter/s]Best score: 0.3176: : 691iter [00:20, 33.38iter/s]Best score: 0.3176: : 691iter [00:20, 33.38iter/s]Best score: 0.3176: : 692iter [00:20, 33.38iter/s]Best score: 0.3176: : 693iter [00:20, 33.38iter/s]Best score: 0.3176: : 694iter [00:20, 33.38iter/s]Best score: 0.3176: : 695iter [00:20, 35.04iter/s]Best score: 0.3176: : 695iter [00:20, 35.04iter/s]Best score: 0.3176: : 696iter [00:20, 35.04iter/s]Best score: 0.3176: : 697iter [00:20, 35.04iter/s]Best score: 0.3176: : 698iter [00:20, 35.04iter/s]Best score: 0.3176: : 699iter [00:20, 36.27iter/s]
[ITER 700] Best: 0.3169
[PARAMS] Current best parameters:
  fx: 513.6774
  fy: 513.8048
  px: 990.6576
  py: 758.8029
  k1: -0.01215029
  k2: 0.03959804
  k3: -0.01673547
  k4: 0.00137866
  p1: -0.00605653
  p2: -0.00714594
  p3: -0.00405444
  p4: 0.00957943
  s1: 0.0040
  s2: -0.0083
  s3: 0.0037
  s4: -0.0092
  tilt_x: -0.0045
  tilt_y: 0.0032
  scale: 3.0397
  rotation: 0.1753
Best score: 0.3169: : 699iter [00:20, 36.27iter/s]Best score: 0.3169: : 700iter [00:20, 36.27iter/s]Best score: 0.3169: : 701iter [00:20, 36.27iter/s]Best score: 0.3169: : 702iter [00:20, 36.27iter/s]Best score: 0.3169: : 703iter [00:20, 30.28iter/s]Best score: 0.3169: : 703iter [00:21, 30.28iter/s]Best score: 0.3169: : 704iter [00:21, 30.28iter/s]Best score: 0.3169: : 705iter [00:21, 30.28iter/s]Best score: 0.3169: : 706iter [00:21, 30.28iter/s]Best score: 0.3169: : 707iter [00:21, 32.59iter/s]Best score: 0.3169: : 707iter [00:21, 32.59iter/s]Best score: 0.3169: : 708iter [00:21, 32.59iter/s]Best score: 0.3148: : 709iter [00:21, 32.59iter/s]Best score: 0.3148: : 710iter [00:21, 32.59iter/s]Best score: 0.3148: : 711iter [00:21, 34.19iter/s]Best score: 0.3148: : 711iter [00:21, 34.19iter/s]Best score: 0.3148: : 712iter [00:21, 34.19iter/s]Best score: 0.3148: : 713iter [00:21, 34.19iter/s]Best score: 0.3148: : 714iter [00:21, 34.19iter/s]Best score: 0.3148: : 715iter [00:21, 35.74iter/s]Best score: 0.3148: : 715iter [00:21, 35.74iter/s]Best score: 0.3148: : 716iter [00:21, 35.74iter/s]Best score: 0.3148: : 717iter [00:21, 35.74iter/s]Best score: 0.3148: : 718iter [00:21, 35.74iter/s]Best score: 0.3136: : 719iter [00:21, 35.74iter/s]Best score: 0.3136: : 720iter [00:21, 36.87iter/s]Best score: 0.3136: : 720iter [00:21, 36.87iter/s]Best score: 0.3136: : 721iter [00:21, 36.87iter/s]Best score: 0.3136: : 722iter [00:21, 36.87iter/s]Best score: 0.3136: : 723iter [00:21, 36.87iter/s]Best score: 0.3136: : 724iter [00:21, 37.61iter/s]Best score: 0.3136: : 724iter [00:21, 37.61iter/s]Best score: 0.3136: : 725iter [00:21, 37.61iter/s]Best score: 0.3136: : 726iter [00:21, 37.61iter/s]Best score: 0.3136: : 727iter [00:21, 37.61iter/s]Best score: 0.3136: : 728iter [00:21, 38.19iter/s]Best score: 0.3136: : 728iter [00:21, 38.19iter/s]Best score: 0.3136: : 729iter [00:21, 38.19iter/s]Best score: 0.3136: : 730iter [00:21, 38.19iter/s]Best score: 0.3136: : 731iter [00:21, 38.19iter/s]Best score: 0.3136: : 732iter [00:21, 38.50iter/s]Best score: 0.3136: : 732iter [00:21, 38.50iter/s]Best score: 0.3136: : 733iter [00:21, 38.50iter/s]Best score: 0.3136: : 734iter [00:21, 38.50iter/s]Best score: 0.3136: : 735iter [00:21, 38.50iter/s]Best score: 0.3136: : 736iter [00:21, 38.50iter/s]Best score: 0.3136: : 737iter [00:21, 39.10iter/s]Best score: 0.3136: : 737iter [00:21, 39.10iter/s]Best score: 0.3136: : 738iter [00:21, 39.10iter/s]Best score: 0.3136: : 739iter [00:21, 39.10iter/s]Best score: 0.3136: : 740iter [00:21, 39.10iter/s]Best score: 0.3136: : 741iter [00:21, 39.09iter/s]Best score: 0.3136: : 741iter [00:21, 39.09iter/s]Best score: 0.3136: : 742iter [00:22, 39.09iter/s]Best score: 0.3136: : 743iter [00:22, 39.09iter/s]Best score: 0.3136: : 744iter [00:22, 39.09iter/s]Best score: 0.3136: : 745iter [00:22, 39.28iter/s]Best score: 0.3136: : 745iter [00:22, 39.28iter/s]Best score: 0.3136: : 746iter [00:22, 39.28iter/s]Best score: 0.3136: : 747iter [00:22, 39.28iter/s]Best score: 0.3136: : 748iter [00:22, 39.28iter/s]Best score: 0.3136: : 749iter [00:22, 39.35iter/s]
[ITER 750] Best: 0.3135
[PARAMS] Current best parameters:
  fx: 513.5332
  fy: 513.6392
  px: 990.1588
  py: 757.6918
  k1: -0.01274561
  k2: 0.04255079
  k3: -0.01947109
  k4: 0.00213571
  p1: -0.00790368
  p2: -0.00799233
  p3: -0.00605054
  p4: 0.01078357
  s1: 0.0062
  s2: -0.0093
  s3: 0.0061
  s4: -0.0103
  tilt_x: -0.0059
  tilt_y: 0.0039
  scale: 3.0378
  rotation: 0.1740
Best score: 0.3135: : 749iter [00:22, 39.35iter/s]Best score: 0.3135: : 750iter [00:22, 39.35iter/s]Best score: 0.3135: : 751iter [00:22, 39.35iter/s]Best score: 0.3135: : 752iter [00:22, 39.35iter/s]Best score: 0.3135: : 753iter [00:22, 30.60iter/s]Best score: 0.3135: : 753iter [00:22, 30.60iter/s]Best score: 0.3135: : 754iter [00:22, 30.60iter/s]Best score: 0.3135: : 755iter [00:22, 30.60iter/s]Best score: 0.3135: : 756iter [00:22, 30.60iter/s]Best score: 0.3135: : 757iter [00:22, 32.85iter/s]Best score: 0.3135: : 757iter [00:22, 32.85iter/s]Best score: 0.3135: : 758iter [00:22, 32.85iter/s]Best score: 0.3125: : 759iter [00:22, 32.85iter/s]Best score: 0.3125: : 760iter [00:22, 32.85iter/s]Best score: 0.3125: : 761iter [00:22, 34.57iter/s]Best score: 0.3125: : 761iter [00:22, 34.57iter/s]Best score: 0.3125: : 762iter [00:22, 34.57iter/s]Best score: 0.3125: : 763iter [00:22, 34.57iter/s]Best score: 0.3125: : 764iter [00:22, 34.57iter/s]Best score: 0.3125: : 765iter [00:22, 34.57iter/s]Best score: 0.3125: : 766iter [00:22, 36.35iter/s]Best score: 0.3125: : 766iter [00:22, 36.35iter/s]Best score: 0.3125: : 767iter [00:22, 36.35iter/s]Best score: 0.3125: : 768iter [00:22, 36.35iter/s]Best score: 0.3125: : 769iter [00:22, 36.35iter/s]Best score: 0.3125: : 770iter [00:22, 37.12iter/s]Best score: 0.3125: : 770iter [00:22, 37.12iter/s]Best score: 0.3125: : 771iter [00:22, 37.12iter/s]Best score: 0.3125: : 772iter [00:22, 37.12iter/s]Best score: 0.3125: : 773iter [00:22, 37.12iter/s]Best score: 0.3125: : 774iter [00:22, 37.12iter/s]Best score: 0.3125: : 775iter [00:22, 38.06iter/s]Best score: 0.3125: : 775iter [00:22, 38.06iter/s]Best score: 0.3125: : 776iter [00:22, 38.06iter/s]Best score: 0.3125: : 777iter [00:22, 38.06iter/s]Best score: 0.3125: : 778iter [00:23, 38.06iter/s]Best score: 0.3121: : 779iter [00:23, 38.06iter/s]Best score: 0.3121: : 780iter [00:23, 38.30iter/s]Best score: 0.3121: : 780iter [00:23, 38.30iter/s]Best score: 0.3121: : 781iter [00:23, 38.30iter/s]Best score: 0.3121: : 782iter [00:23, 38.30iter/s]Best score: 0.3121: : 783iter [00:23, 38.30iter/s]Best score: 0.3121: : 784iter [00:23, 38.73iter/s]Best score: 0.3121: : 784iter [00:23, 38.73iter/s]Best score: 0.3121: : 785iter [00:23, 38.73iter/s]Best score: 0.3121: : 786iter [00:23, 38.73iter/s]Best score: 0.3121: : 787iter [00:23, 38.73iter/s]Best score: 0.3121: : 788iter [00:23, 38.73iter/s]Best score: 0.3121: : 789iter [00:23, 39.11iter/s]Best score: 0.3117: : 789iter [00:23, 39.11iter/s]Best score: 0.3117: : 790iter [00:23, 39.11iter/s]Best score: 0.3117: : 791iter [00:23, 39.11iter/s]Best score: 0.3117: : 792iter [00:23, 39.11iter/s]Best score: 0.3117: : 793iter [00:23, 39.14iter/s]Best score: 0.3117: : 793iter [00:23, 39.14iter/s]Best score: 0.3117: : 794iter [00:23, 39.14iter/s]Best score: 0.3117: : 795iter [00:23, 39.14iter/s]Best score: 0.3117: : 796iter [00:23, 39.14iter/s]Best score: 0.3117: : 797iter [00:23, 39.32iter/s]Best score: 0.3117: : 797iter [00:23, 39.32iter/s]Best score: 0.3117: : 798iter [00:23, 39.32iter/s]
[ITER 800] Best: 0.3099
[PARAMS] Current best parameters:
  fx: 513.1101
  fy: 513.2136
  px: 989.3802
  py: 757.1870
  k1: -0.01876870
  k2: 0.05054296
  k3: -0.02290427
  k4: 0.00262696
  p1: -0.00437203
  p2: -0.00590501
  p3: -0.00876994
  p4: 0.00969266
  s1: 0.0064
  s2: -0.0083
  s3: 0.0053
  s4: -0.0093
  tilt_x: -0.0155
  tilt_y: 0.0119
  scale: 3.0473
  rotation: 0.1750
Best score: 0.3099: : 799iter [00:23, 39.32iter/s]Best score: 0.3099: : 800iter [00:23, 39.32iter/s]Best score: 0.3099: : 801iter [00:23, 32.18iter/s]Best score: 0.3099: : 801iter [00:23, 32.18iter/s]Best score: 0.3099: : 802iter [00:23, 32.18iter/s]Best score: 0.3099: : 803iter [00:23, 32.18iter/s]Best score: 0.3099: : 804iter [00:23, 32.18iter/s]Best score: 0.3099: : 805iter [00:23, 34.01iter/s]Best score: 0.3099: : 805iter [00:23, 34.01iter/s]Best score: 0.3099: : 806iter [00:23, 34.01iter/s]Best score: 0.3099: : 807iter [00:23, 34.01iter/s]Best score: 0.3099: : 808iter [00:23, 34.01iter/s]Best score: 0.3099: : 809iter [00:23, 35.55iter/s]Best score: 0.3099: : 809iter [00:23, 35.55iter/s]Best score: 0.3099: : 810iter [00:23, 35.55iter/s]Best score: 0.3099: : 811iter [00:23, 35.55iter/s]Best score: 0.3099: : 812iter [00:23, 35.55iter/s]Best score: 0.3099: : 813iter [00:23, 36.47iter/s]Best score: 0.3099: : 813iter [00:23, 36.47iter/s]Best score: 0.3099: : 814iter [00:24, 36.47iter/s]Best score: 0.3099: : 815iter [00:24, 36.47iter/s]Best score: 0.3099: : 816iter [00:24, 36.47iter/s]Best score: 0.3099: : 817iter [00:24, 36.47iter/s]Best score: 0.3099: : 818iter [00:24, 37.67iter/s]Best score: 0.3099: : 818iter [00:24, 37.67iter/s]Best score: 0.3099: : 819iter [00:24, 37.67iter/s]Best score: 0.3099: : 820iter [00:24, 37.67iter/s]Best score: 0.3099: : 821iter [00:24, 37.67iter/s]Best score: 0.3099: : 822iter [00:24, 38.03iter/s]Best score: 0.3099: : 822iter [00:24, 38.03iter/s]Best score: 0.3099: : 823iter [00:24, 38.03iter/s]Best score: 0.3099: : 824iter [00:24, 38.03iter/s]Best score: 0.3099: : 825iter [00:24, 38.03iter/s]Best score: 0.3099: : 826iter [00:24, 38.56iter/s]Best score: 0.3099: : 826iter [00:24, 38.56iter/s]Best score: 0.3099: : 827iter [00:24, 38.56iter/s]Best score: 0.3099: : 828iter [00:24, 38.56iter/s]Best score: 0.3096: : 829iter [00:24, 38.56iter/s]Best score: 0.3096: : 830iter [00:24, 38.59iter/s]Best score: 0.3096: : 830iter [00:24, 38.59iter/s]Best score: 0.3096: : 831iter [00:24, 38.59iter/s]Best score: 0.3096: : 832iter [00:24, 38.59iter/s]Best score: 0.3096: : 833iter [00:24, 38.59iter/s]Best score: 0.3096: : 834iter [00:24, 38.59iter/s]Best score: 0.3096: : 835iter [00:24, 39.13iter/s]Best score: 0.3096: : 835iter [00:24, 39.13iter/s]Best score: 0.3096: : 836iter [00:24, 39.13iter/s]Best score: 0.3096: : 837iter [00:24, 39.13iter/s]Best score: 0.3096: : 838iter [00:24, 39.13iter/s]Best score: 0.3096: : 839iter [00:24, 39.15iter/s]Best score: 0.3096: : 839iter [00:24, 39.15iter/s]Best score: 0.3096: : 840iter [00:24, 39.15iter/s]Best score: 0.3096: : 841iter [00:24, 39.15iter/s]Best score: 0.3096: : 842iter [00:24, 39.15iter/s]Best score: 0.3096: : 843iter [00:24, 38.86iter/s]Best score: 0.3096: : 843iter [00:24, 38.86iter/s]Best score: 0.3096: : 844iter [00:24, 38.86iter/s]Best score: 0.3096: : 845iter [00:24, 38.86iter/s]Best score: 0.3096: : 846iter [00:24, 38.86iter/s]Best score: 0.3096: : 847iter [00:24, 39.10iter/s]Best score: 0.3096: : 847iter [00:24, 39.10iter/s]Best score: 0.3096: : 848iter [00:24, 39.10iter/s]
[ITER 850] Best: 0.3093
[PARAMS] Current best parameters:
  fx: 512.2545
  fy: 512.3534
  px: 989.9764
  py: 757.8810
  k1: -0.01945415
  k2: 0.05277286
  k3: -0.02398858
  k4: 0.00278592
  p1: -0.00395331
  p2: -0.00590328
  p3: -0.00912076
  p4: 0.00989953
  s1: 0.0069
  s2: -0.0084
  s3: 0.0056
  s4: -0.0094
  tilt_x: -0.0129
  tilt_y: 0.0089
  scale: 3.0485
  rotation: 0.1771
Best score: 0.3093: : 849iter [00:24, 39.10iter/s]Best score: 0.3093: : 850iter [00:25, 39.10iter/s]Best score: 0.3093: : 851iter [00:25, 31.82iter/s]Best score: 0.3093: : 851iter [00:25, 31.82iter/s]Best score: 0.3093: : 852iter [00:25, 31.82iter/s]Best score: 0.3093: : 853iter [00:25, 31.82iter/s]Best score: 0.3093: : 854iter [00:25, 31.82iter/s]Best score: 0.3093: : 855iter [00:25, 33.84iter/s]Best score: 0.3093: : 855iter [00:25, 33.84iter/s]Best score: 0.3093: : 856iter [00:25, 33.84iter/s]Best score: 0.3093: : 857iter [00:25, 33.84iter/s]Best score: 0.3093: : 858iter [00:25, 33.84iter/s]Best score: 0.3093: : 859iter [00:25, 35.40iter/s]Best score: 0.3093: : 859iter [00:25, 35.40iter/s]Best score: 0.3093: : 860iter [00:25, 35.40iter/s]Best score: 0.3093: : 861iter [00:25, 35.40iter/s]Best score: 0.3093: : 862iter [00:25, 35.40iter/s]Best score: 0.3093: : 863iter [00:25, 36.34iter/s]Best score: 0.3093: : 863iter [00:25, 36.34iter/s]Best score: 0.3093: : 864iter [00:25, 36.34iter/s]Best score: 0.3093: : 865iter [00:25, 36.34iter/s]Best score: 0.3093: : 866iter [00:25, 36.34iter/s]Best score: 0.3093: : 867iter [00:25, 37.27iter/s]Best score: 0.3093: : 867iter [00:25, 37.27iter/s]Best score: 0.3093: : 868iter [00:25, 37.27iter/s]Best score: 0.3090: : 869iter [00:25, 37.27iter/s]Best score: 0.3090: : 870iter [00:25, 37.27iter/s]Best score: 0.3090: : 871iter [00:25, 37.81iter/s]Best score: 0.3090: : 871iter [00:25, 37.81iter/s]Best score: 0.3090: : 872iter [00:25, 37.81iter/s]Best score: 0.3090: : 873iter [00:25, 37.81iter/s]Best score: 0.3090: : 874iter [00:25, 37.81iter/s]Best score: 0.3090: : 875iter [00:25, 37.81iter/s]Best score: 0.3090: : 876iter [00:25, 38.68iter/s]Best score: 0.3090: : 876iter [00:25, 38.68iter/s]Best score: 0.3090: : 877iter [00:25, 38.68iter/s]Best score: 0.3090: : 878iter [00:25, 38.68iter/s]Best score: 0.3089: : 879iter [00:25, 38.68iter/s]Best score: 0.3089: : 880iter [00:25, 38.86iter/s]Best score: 0.3089: : 880iter [00:25, 38.86iter/s]Best score: 0.3089: : 881iter [00:25, 38.86iter/s]Best score: 0.3089: : 882iter [00:25, 38.86iter/s]Best score: 0.3089: : 883iter [00:25, 38.86iter/s]Best score: 0.3089: : 884iter [00:25, 38.86iter/s]Best score: 0.3089: : 885iter [00:25, 39.28iter/s]Best score: 0.3089: : 885iter [00:25, 39.28iter/s]Best score: 0.3089: : 886iter [00:25, 39.28iter/s]Best score: 0.3089: : 887iter [00:25, 39.28iter/s]Best score: 0.3089: : 888iter [00:25, 39.28iter/s]Best score: 0.3087: : 889iter [00:25, 39.28iter/s]Best score: 0.3087: : 890iter [00:25, 39.49iter/s]Best score: 0.3087: : 890iter [00:26, 39.49iter/s]Best score: 0.3087: : 891iter [00:26, 39.49iter/s]Best score: 0.3087: : 892iter [00:26, 39.49iter/s]Best score: 0.3087: : 893iter [00:26, 39.49iter/s]Best score: 0.3087: : 894iter [00:26, 39.49iter/s]Best score: 0.3087: : 895iter [00:26, 39.90iter/s]Best score: 0.3087: : 895iter [00:26, 39.90iter/s]Best score: 0.3087: : 896iter [00:26, 39.90iter/s]Best score: 0.3087: : 897iter [00:26, 39.90iter/s]Best score: 0.3087: : 898iter [00:26, 39.90iter/s]
[ITER 900] Best: 0.3079
[PARAMS] Current best parameters:
  fx: 513.3947
  fy: 513.5147
  px: 990.0704
  py: 757.0963
  k1: -0.02103341
  k2: 0.05396732
  k3: -0.02502569
  k4: 0.00308551
  p1: -0.00204007
  p2: -0.00423002
  p3: -0.01195417
  p4: 0.00860610
  s1: 0.0086
  s2: -0.0073
  s3: 0.0060
  s4: -0.0082
  tilt_x: -0.0205
  tilt_y: 0.0122
  scale: 3.0490
  rotation: 0.1720
Best score: 0.3079: : 899iter [00:26, 39.90iter/s]Best score: 0.3079: : 900iter [00:26, 39.68iter/s]Best score: 0.3079: : 900iter [00:26, 39.68iter/s]Best score: 0.3079: : 901iter [00:26, 39.68iter/s]Best score: 0.3079: : 902iter [00:26, 39.68iter/s]Best score: 0.3079: : 903iter [00:26, 39.68iter/s]Best score: 0.3079: : 904iter [00:26, 32.78iter/s]Best score: 0.3079: : 904iter [00:26, 32.78iter/s]Best score: 0.3079: : 905iter [00:26, 32.78iter/s]Best score: 0.3079: : 906iter [00:26, 32.78iter/s]Best score: 0.3079: : 907iter [00:26, 32.78iter/s]Best score: 0.3079: : 908iter [00:26, 34.48iter/s]Best score: 0.3079: : 908iter [00:26, 34.48iter/s]Best score: 0.3069: : 909iter [00:26, 34.48iter/s]Best score: 0.3069: : 910iter [00:26, 34.48iter/s]Best score: 0.3069: : 911iter [00:26, 34.48iter/s]Best score: 0.3069: : 912iter [00:26, 35.59iter/s]Best score: 0.3069: : 912iter [00:26, 35.59iter/s]Best score: 0.3069: : 913iter [00:26, 35.59iter/s]Best score: 0.3069: : 914iter [00:26, 35.59iter/s]Best score: 0.3069: : 915iter [00:26, 35.59iter/s]Best score: 0.3069: : 916iter [00:26, 36.68iter/s]Best score: 0.3069: : 916iter [00:26, 36.68iter/s]Best score: 0.3069: : 917iter [00:26, 36.68iter/s]Best score: 0.3069: : 918iter [00:26, 36.68iter/s]Best score: 0.3064: : 919iter [00:26, 36.68iter/s]Best score: 0.3064: : 920iter [00:26, 37.38iter/s]Best score: 0.3064: : 920iter [00:26, 37.38iter/s]Best score: 0.3064: : 921iter [00:26, 37.38iter/s]Best score: 0.3064: : 922iter [00:26, 37.38iter/s]Best score: 0.3064: : 923iter [00:26, 37.38iter/s]Best score: 0.3064: : 924iter [00:26, 37.38iter/s]Best score: 0.3064: : 925iter [00:26, 38.30iter/s]Best score: 0.3064: : 925iter [00:26, 38.30iter/s]Best score: 0.3064: : 926iter [00:26, 38.30iter/s]Best score: 0.3064: : 927iter [00:27, 38.30iter/s]Best score: 0.3064: : 928iter [00:27, 38.30iter/s]Best score: 0.3064: : 929iter [00:27, 38.64iter/s]Best score: 0.3038: : 929iter [00:27, 38.64iter/s]Best score: 0.3038: : 930iter [00:27, 38.64iter/s]Best score: 0.3038: : 931iter [00:27, 38.64iter/s]Best score: 0.3038: : 932iter [00:27, 38.64iter/s]Best score: 0.3038: : 933iter [00:27, 38.73iter/s]Best score: 0.3038: : 933iter [00:27, 38.73iter/s]Best score: 0.3038: : 934iter [00:27, 38.73iter/s]Best score: 0.3038: : 935iter [00:27, 38.73iter/s]Best score: 0.3038: : 936iter [00:27, 38.73iter/s]Best score: 0.3038: : 937iter [00:27, 38.91iter/s]Best score: 0.3038: : 937iter [00:27, 38.91iter/s]Best score: 0.3038: : 938iter [00:27, 38.91iter/s]Best score: 0.3014: : 939iter [00:27, 38.91iter/s]Best score: 0.3014: : 940iter [00:27, 38.91iter/s]Best score: 0.3014: : 941iter [00:27, 38.96iter/s]Best score: 0.3014: : 941iter [00:27, 38.96iter/s]Best score: 0.3014: : 942iter [00:27, 38.96iter/s]Best score: 0.3014: : 943iter [00:27, 38.96iter/s]Best score: 0.3014: : 944iter [00:27, 38.96iter/s]Best score: 0.3014: : 945iter [00:27, 38.96iter/s]Best score: 0.3014: : 946iter [00:27, 39.50iter/s]Best score: 0.3014: : 946iter [00:27, 39.50iter/s]Best score: 0.3014: : 947iter [00:27, 39.50iter/s]Best score: 0.3014: : 948iter [00:27, 39.50iter/s]
[ITER 950] Best: 0.3007
[PARAMS] Current best parameters:
  fx: 514.0054
  fy: 514.2188
  px: 991.5578
  py: 757.2792
  k1: -0.03946763
  k2: 0.07965279
  k3: -0.03879683
  k4: 0.00574529
  p1: 0.00828894
  p2: 0.00016467
  p3: -0.01915768
  p4: 0.00465708
  s1: 0.0127
  s2: -0.0033
  s3: 0.0039
  s4: -0.0046
  tilt_x: -0.0406
  tilt_y: 0.0138
  scale: 3.0729
  rotation: 0.1575
Best score: 0.3007: : 949iter [00:27, 39.50iter/s]Best score: 0.3007: : 950iter [00:27, 39.30iter/s]Best score: 0.3007: : 950iter [00:27, 39.30iter/s]Best score: 0.3007: : 951iter [00:27, 39.30iter/s]Best score: 0.3007: : 952iter [00:27, 39.30iter/s]Best score: 0.3007: : 953iter [00:27, 39.30iter/s]Best score: 0.3007: : 954iter [00:27, 32.29iter/s]Best score: 0.3007: : 954iter [00:27, 32.29iter/s]Best score: 0.3007: : 955iter [00:27, 32.29iter/s]Best score: 0.3007: : 956iter [00:27, 32.29iter/s]Best score: 0.3007: : 957iter [00:27, 32.29iter/s]Best score: 0.3007: : 958iter [00:27, 34.13iter/s]Best score: 0.3007: : 958iter [00:27, 34.13iter/s]Best score: 0.2999: : 959iter [00:27, 34.13iter/s]Best score: 0.2999: : 960iter [00:27, 34.13iter/s]Best score: 0.2999: : 961iter [00:27, 34.13iter/s]Best score: 0.2999: : 962iter [00:27, 35.34iter/s]Best score: 0.2999: : 962iter [00:27, 35.34iter/s]Best score: 0.2999: : 963iter [00:28, 35.34iter/s]Best score: 0.2999: : 964iter [00:28, 35.34iter/s]Best score: 0.2999: : 965iter [00:28, 35.34iter/s]Best score: 0.2999: : 966iter [00:28, 36.30iter/s]Best score: 0.2999: : 966iter [00:28, 36.30iter/s]Best score: 0.2999: : 967iter [00:28, 36.30iter/s]Best score: 0.2999: : 968iter [00:28, 36.30iter/s]Best score: 0.2992: : 969iter [00:28, 36.30iter/s]Best score: 0.2992: : 970iter [00:28, 37.10iter/s]Best score: 0.2992: : 970iter [00:28, 37.10iter/s]Best score: 0.2992: : 971iter [00:28, 37.10iter/s]Best score: 0.2992: : 972iter [00:28, 37.10iter/s]Best score: 0.2992: : 973iter [00:28, 37.10iter/s]Best score: 0.2992: : 974iter [00:28, 37.10iter/s]Best score: 0.2992: : 975iter [00:28, 38.15iter/s]Best score: 0.2992: : 975iter [00:28, 38.15iter/s]Best score: 0.2992: : 976iter [00:28, 38.15iter/s]Best score: 0.2992: : 977iter [00:28, 38.15iter/s]Best score: 0.2992: : 978iter [00:28, 38.15iter/s]Best score: 0.2992: : 979iter [00:28, 38.51iter/s]Best score: 0.2992: : 979iter [00:28, 38.51iter/s]Best score: 0.2992: : 980iter [00:28, 38.51iter/s]Best score: 0.2992: : 981iter [00:28, 38.51iter/s]Best score: 0.2992: : 982iter [00:28, 38.51iter/s]Best score: 0.2992: : 983iter [00:28, 38.44iter/s]Best score: 0.2992: : 983iter [00:28, 38.44iter/s]Best score: 0.2992: : 984iter [00:28, 38.44iter/s]Best score: 0.2992: : 985iter [00:28, 38.44iter/s]Best score: 0.2992: : 986iter [00:28, 38.44iter/s]Best score: 0.2992: : 987iter [00:28, 38.44iter/s]Best score: 0.2992: : 988iter [00:28, 39.01iter/s]Best score: 0.2992: : 988iter [00:28, 39.01iter/s]Best score: 0.2992: : 989iter [00:28, 39.01iter/s]Best score: 0.2992: : 990iter [00:28, 39.01iter/s]Best score: 0.2992: : 991iter [00:28, 39.01iter/s]Best score: 0.2992: : 992iter [00:28, 38.99iter/s]Best score: 0.2992: : 992iter [00:28, 38.99iter/s]Best score: 0.2992: : 993iter [00:28, 38.99iter/s]Best score: 0.2992: : 994iter [00:28, 38.99iter/s]Best score: 0.2992: : 995iter [00:28, 38.99iter/s]Best score: 0.2992: : 996iter [00:28, 39.08iter/s]Best score: 0.2992: : 996iter [00:28, 39.08iter/s]Best score: 0.2992: : 997iter [00:28, 39.08iter/s]Best score: 0.2992: : 998iter [00:28, 39.08iter/s]
[ITER 1000] Best: 0.2992
[PARAMS] Current best parameters:
  fx: 514.9742
  fy: 515.2078
  px: 991.7109
  py: 757.4811
  k1: -0.03920588
  k2: 0.07639393
  k3: -0.03627203
  k4: 0.00514791
  p1: 0.00873931
  p2: -0.00004747
  p3: -0.01907325
  p4: 0.00438433
  s1: 0.0132
  s2: -0.0032
  s3: 0.0035
  s4: -0.0043
  tilt_x: -0.0406
  tilt_y: 0.0126
  scale: 3.0730
  rotation: 0.1600
Best score: 0.2992: : 999iter [00:28, 39.08iter/s]Best score: 0.2992: : 1000iter [00:28, 39.01iter/s]Best score: 0.2992: : 1000iter [00:29, 39.01iter/s]Best score: 0.2992: : 1001iter [00:29, 39.01iter/s]Best score: 0.2992: : 1002iter [00:29, 39.01iter/s]Best score: 0.2992: : 1003iter [00:29, 39.01iter/s]Best score: 0.2992: : 1004iter [00:29, 26.88iter/s]Best score: 0.2992: : 1004iter [00:29, 26.88iter/s]Best score: 0.2992: : 1005iter [00:29, 26.88iter/s]Best score: 0.2992: : 1006iter [00:29, 26.88iter/s]Best score: 0.2992: : 1007iter [00:29, 26.88iter/s]Best score: 0.2992: : 1008iter [00:29, 26.88iter/s]Best score: 0.2992: : 1009iter [00:29, 30.35iter/s]Best score: 0.2989: : 1009iter [00:29, 30.35iter/s]Best score: 0.2989: : 1010iter [00:29, 30.35iter/s]Best score: 0.2989: : 1011iter [00:29, 30.35iter/s]Best score: 0.2989: : 1012iter [00:29, 30.35iter/s]Best score: 0.2989: : 1013iter [00:29, 32.22iter/s]Best score: 0.2989: : 1013iter [00:29, 32.22iter/s]Best score: 0.2989: : 1014iter [00:29, 32.22iter/s]Best score: 0.2989: : 1015iter [00:29, 32.22iter/s]Best score: 0.2989: : 1016iter [00:29, 32.22iter/s]Best score: 0.2989: : 1017iter [00:29, 34.01iter/s]Best score: 0.2989: : 1017iter [00:29, 34.01iter/s]Best score: 0.2989: : 1018iter [00:29, 34.01iter/s]Best score: 0.2982: : 1019iter [00:29, 34.01iter/s]Best score: 0.2982: : 1020iter [00:29, 34.01iter/s]Best score: 0.2982: : 1021iter [00:29, 35.21iter/s]Best score: 0.2982: : 1021iter [00:29, 35.21iter/s]Best score: 0.2982: : 1022iter [00:29, 35.21iter/s]Best score: 0.2982: : 1023iter [00:29, 35.21iter/s]Best score: 0.2982: : 1024iter [00:29, 35.21iter/s]Best score: 0.2982: : 1025iter [00:29, 35.21iter/s]Best score: 0.2982: : 1026iter [00:29, 36.80iter/s]Best score: 0.2982: : 1026iter [00:29, 36.80iter/s]Best score: 0.2982: : 1027iter [00:29, 36.80iter/s]Best score: 0.2982: : 1028iter [00:29, 36.80iter/s]Best score: 0.2976: : 1029iter [00:29, 36.80iter/s]Best score: 0.2976: : 1030iter [00:29, 37.45iter/s]Best score: 0.2976: : 1030iter [00:29, 37.45iter/s]Best score: 0.2976: : 1031iter [00:29, 37.45iter/s]Best score: 0.2976: : 1032iter [00:29, 37.45iter/s]Best score: 0.2976: : 1033iter [00:29, 37.45iter/s]Best score: 0.2976: : 1034iter [00:29, 37.72iter/s]Best score: 0.2976: : 1034iter [00:29, 37.72iter/s]Best score: 0.2976: : 1035iter [00:30, 37.72iter/s]Best score: 0.2976: : 1036iter [00:30, 37.72iter/s]Best score: 0.2976: : 1037iter [00:30, 37.72iter/s]Best score: 0.2976: : 1038iter [00:30, 37.72iter/s]Best score: 0.2976: : 1039iter [00:30, 38.53iter/s]Best score: 0.2968: : 1039iter [00:30, 38.53iter/s]Best score: 0.2968: : 1040iter [00:30, 38.53iter/s]Best score: 0.2968: : 1041iter [00:30, 38.53iter/s]Best score: 0.2968: : 1042iter [00:30, 38.53iter/s]Best score: 0.2968: : 1043iter [00:30, 38.65iter/s]Best score: 0.2968: : 1043iter [00:30, 38.65iter/s]Best score: 0.2968: : 1044iter [00:30, 38.65iter/s]Best score: 0.2968: : 1045iter [00:30, 38.65iter/s]Best score: 0.2968: : 1046iter [00:30, 38.65iter/s]Best score: 0.2968: : 1047iter [00:30, 39.01iter/s]Best score: 0.2968: : 1047iter [00:30, 39.01iter/s]Best score: 0.2968: : 1048iter [00:30, 39.01iter/s]
[ITER 1050] Best: 0.2968
[PARAMS] Current best parameters:
  fx: 513.9975
  fy: 514.2252
  px: 991.8028
  py: 757.7695
  k1: -0.04211976
  k2: 0.08135527
  k3: -0.03852458
  k4: 0.00549123
  p1: 0.01002029
  p2: -0.00072999
  p3: -0.01975764
  p4: 0.00458810
  s1: 0.0147
  s2: -0.0035
  s3: 0.0032
  s4: -0.0045
  tilt_x: -0.0416
  tilt_y: 0.0108
  scale: 3.0784
  rotation: 0.1576
Best score: 0.2968: : 1049iter [00:30, 39.01iter/s]Best score: 0.2968: : 1050iter [00:30, 39.01iter/s]Best score: 0.2968: : 1051iter [00:30, 31.97iter/s]Best score: 0.2968: : 1051iter [00:30, 31.97iter/s]Best score: 0.2968: : 1052iter [00:30, 31.97iter/s]Best score: 0.2968: : 1053iter [00:30, 31.97iter/s]Best score: 0.2968: : 1054iter [00:30, 31.97iter/s]Best score: 0.2968: : 1055iter [00:30, 33.91iter/s]Best score: 0.2968: : 1055iter [00:30, 33.91iter/s]Best score: 0.2968: : 1056iter [00:30, 33.91iter/s]Best score: 0.2968: : 1057iter [00:30, 33.91iter/s]Best score: 0.2968: : 1058iter [00:30, 33.91iter/s]Best score: 0.2968: : 1059iter [00:30, 35.36iter/s]Best score: 0.2968: : 1059iter [00:30, 35.36iter/s]Best score: 0.2968: : 1060iter [00:30, 35.36iter/s]Best score: 0.2968: : 1061iter [00:30, 35.36iter/s]Best score: 0.2968: : 1062iter [00:30, 35.36iter/s]Best score: 0.2968: : 1063iter [00:30, 36.36iter/s]Best score: 0.2968: : 1063iter [00:30, 36.36iter/s]Best score: 0.2968: : 1064iter [00:30, 36.36iter/s]Best score: 0.2968: : 1065iter [00:30, 36.36iter/s]Best score: 0.2968: : 1066iter [00:30, 36.36iter/s]Best score: 0.2968: : 1067iter [00:30, 37.28iter/s]Best score: 0.2968: : 1067iter [00:30, 37.28iter/s]Best score: 0.2968: : 1068iter [00:30, 37.28iter/s]Best score: 0.2968: : 1069iter [00:30, 37.28iter/s]Best score: 0.2968: : 1070iter [00:30, 37.28iter/s]Best score: 0.2968: : 1071iter [00:30, 37.71iter/s]Best score: 0.2968: : 1071iter [00:30, 37.71iter/s]Best score: 0.2968: : 1072iter [00:31, 37.71iter/s]Best score: 0.2968: : 1073iter [00:31, 37.71iter/s]Best score: 0.2968: : 1074iter [00:31, 37.71iter/s]Best score: 0.2968: : 1075iter [00:31, 37.71iter/s]Best score: 0.2968: : 1076iter [00:31, 38.48iter/s]Best score: 0.2968: : 1076iter [00:31, 38.48iter/s]Best score: 0.2968: : 1077iter [00:31, 38.48iter/s]Best score: 0.2968: : 1078iter [00:31, 38.48iter/s]Best score: 0.2968: : 1079iter [00:31, 38.48iter/s]Best score: 0.2968: : 1080iter [00:31, 38.38iter/s]Best score: 0.2968: : 1080iter [00:31, 38.38iter/s]Best score: 0.2968: : 1081iter [00:31, 38.38iter/s]Best score: 0.2968: : 1082iter [00:31, 38.38iter/s]Best score: 0.2968: : 1083iter [00:31, 38.38iter/s]Best score: 0.2968: : 1084iter [00:31, 38.38iter/s]Best score: 0.2968: : 1085iter [00:31, 38.94iter/s]Best score: 0.2968: : 1085iter [00:31, 38.94iter/s]Best score: 0.2968: : 1086iter [00:31, 38.94iter/s]Best score: 0.2968: : 1087iter [00:31, 38.94iter/s]Best score: 0.2968: : 1088iter [00:31, 38.94iter/s]Best score: 0.2968: : 1089iter [00:31, 39.00iter/s]Best score: 0.2968: : 1089iter [00:31, 39.00iter/s]Best score: 0.2968: : 1090iter [00:31, 39.00iter/s]Best score: 0.2968: : 1091iter [00:31, 39.00iter/s]Best score: 0.2968: : 1092iter [00:31, 39.00iter/s]Best score: 0.2968: : 1093iter [00:31, 39.03iter/s]Best score: 0.2968: : 1093iter [00:31, 39.03iter/s]Best score: 0.2968: : 1094iter [00:31, 39.03iter/s]Best score: 0.2968: : 1095iter [00:31, 39.03iter/s]Best score: 0.2968: : 1096iter [00:31, 39.03iter/s]Best score: 0.2968: : 1097iter [00:31, 39.27iter/s]Best score: 0.2968: : 1097iter [00:31, 39.27iter/s]Best score: 0.2968: : 1098iter [00:31, 39.27iter/s]
[ITER 1100] Best: 0.2967
[PARAMS] Current best parameters:
  fx: 514.4333
  fy: 514.6575
  px: 991.7819
  py: 757.8206
  k1: -0.04082050
  k2: 0.07948351
  k3: -0.03785242
  k4: 0.00542195
  p1: 0.00956710
  p2: -0.00046125
  p3: -0.01967199
  p4: 0.00457179
  s1: 0.0143
  s2: -0.0034
  s3: 0.0035
  s4: -0.0045
  tilt_x: -0.0404
  tilt_y: 0.0114
  scale: 3.0752
  rotation: 0.1564
Best score: 0.2967: : 1099iter [00:31, 39.27iter/s]Best score: 0.2967: : 1100iter [00:31, 34.60iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.296715 -> 0.296407

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,839 → 25,839 within 3500mm ring
  Ring polish did not improve score: 0.296407
  Success: True
  Tangential distortion: p1=0.009575, p2=-0.000454, p3=-0.019669, p4=0.004574
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2964
[✅] Parameters copied to: /working/MDC005_W041_2024-11-20/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 514.4332607048967
    fy: 514.6574531199396
    px: 991.7819479021721
    py: 757.8206460600734
    k1: -0.04082759549058593
    k2: 0.07947837928018081
    k3: -0.037853840861267196
    k4: 0.005425774418858465
    p1: 0.009575285008132208
    p2: -0.00045423598668275345
    p3: -0.019669190813914487
    p4: 0.004573529916650005
    scale: 3.075165131016211
    rotation: 0.15635839291948847
    mmpp: 1.2702556998208392

[🔄] Converting 489 frames to PTD, creating debug images for 200 calibration frames...
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[✅] Saved PTDs: /working/MDC005_W041_2024-11-20/calibration/debug/4_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC005_W041_2024-11-20/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/MDC005_W041_2024-11-20/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC005_W041_2024-11-20' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC005_W041_2024-11-20/calibration/MDC005_W041_2024-11-20_unknown.lut

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

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)
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                                                               [A[✅] 500: /working/MDC005_W041_2024-11-20/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC005_W041_2024-11-20/calibration/debug/5_heatmap/500_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 1/2 [00:04<00:04,  4.09s/rig]
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                                                                [A[✅] 1000: /working/MDC005_W041_2024-11-20/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC005_W041_2024-11-20/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:10<00:00,  5.33s/rig]📁 Rigs (heatmap): 100%|██████████| 2/2 [00:10<00:00,  5.14s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC005_W041_2024-11-20/calibration/debug/5_heatmap
[🏁] Finished processing MDC005_W041_2024-11-20


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