2026-09-03T19:35:37 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-013_W046(6)_2026-09-04 ===
2026-09-03T19:35:37 - INFO: Camera ID: 0F7YM
2026-09-03T19:35:37 - INFO: Wand ID: 00001f0c51aa
2026-09-03T19:35:37 - INFO: Recorded At: 2026-09-03T18:46:35Z

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 219 stationary frames, 261 remain

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                                                                           [A[PASS2] Frame 0000294: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000298: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000544: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000545: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000546: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 8 untrusted, 260 total

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                                                                           [A
[TIMING] 1000 — 261 frames
  Phase 1  radial detect   (parallel)  :   4.25s  (16.3 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.94s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   0.16s
  Phase 3  finalize        (sequential):   4.74s  (18.2 ms/frame)
    decode + undistort                 :   2.56s  (9.8 ms/frame)
    corner apply + refinement          :   0.93s  (3.5 ms/frame)
    segment                            :   1.01s  (3.9 ms/frame)
    smooth                             :   0.16s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  11.10s

Processing Rigs:  25%|██▌       | 1/4 [00:13<00:39, 13.27s/it][INFO] Filtered 353 stationary frames, 602 remain

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                                                                            [A[PASS2] Frame 0000134: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000590: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000591: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000592: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000593: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000595: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000599: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000600: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000601: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000602: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000606: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000616: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000617: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000619: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000621: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000670: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000671: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000672: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000673: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000704: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000592: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000617: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000618: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000619: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000621: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000672: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 15 frames fixed, 17 untrusted, 601 total

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                                                                            [A
[TIMING] 2000R — 602 frames
  Phase 1  radial detect   (parallel)  :   9.16s  (15.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.22s  (7.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.77s
  Phase 3  finalize        (sequential):  11.58s  (19.2 ms/frame)
    decode + undistort                 :   5.98s  (9.9 ms/frame)
    corner apply + refinement          :   2.65s  (4.4 ms/frame)
    segment                            :   2.40s  (4.0 ms/frame)
    smooth                             :   0.35s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  25.74s

Processing Rigs:  50%|█████     | 2/4 [00:43<00:45, 22.99s/it][INFO] Filtered 301 stationary frames, 418 remain

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                                                                           [A[PASS2] Frame 0000321: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000322: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000649: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 12 untrusted, 417 total

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                                                                           [A
[TIMING] 1500 — 418 frames
  Phase 1  radial detect   (parallel)  :   6.69s  (16.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.36s  (8.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.19s
  Phase 3  finalize        (sequential):   7.87s  (18.8 ms/frame)
    decode + undistort                 :   4.08s  (9.8 ms/frame)
    corner apply + refinement          :   1.72s  (4.1 ms/frame)
    segment                            :   1.66s  (4.0 ms/frame)
    smooth                             :   0.26s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  18.11s

Processing Rigs:  75%|███████▌  | 3/4 [01:04<00:22, 22.14s/it][INFO] Filtered 458 stationary frames, 686 remain

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                                                                            [A[PASS2] Frame 0000189: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000251: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001184: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001185: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001186: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001188: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001189: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001187: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001187: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 6 frames fixed, 8 untrusted, 685 total

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                                                                            [A
[TIMING] 2000L — 686 frames
  Phase 1  radial detect   (parallel)  :  10.56s  (15.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.16s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   0.55s
  Phase 3  finalize        (sequential):  13.08s  (19.1 ms/frame)
    decode + undistort                 :   6.88s  (10.0 ms/frame)
    corner apply + refinement          :   2.86s  (4.2 ms/frame)
    segment                            :   2.73s  (4.0 ms/frame)
    smooth                             :   0.39s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  29.36s

Processing Rigs: 100%|██████████| 4/4 [01:38<00:00, 26.92s/it]Processing Rigs: 100%|██████████| 4/4 [01:38<00:00, 24.61s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1963 frames from 4 rigs to /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 260
[📋] 2000R: 100 frames selected from 601
[📋] 1500: 100 frames selected from 417
[📋] 2000L: 100 frames selected from 685
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
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-------------------------------------------------------------------
                        Step 3: Calibration                        
-------------------------------------------------------------------

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384.848} (0.003):  11%|█         | 11/100 [00:00<00:04, 21.41it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.002 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:04, 21.41it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.002 | Best: {'fx': 384.848} (0.003):  13%|█▎        | 13/100 [00:00<00:03, 23.33it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 23.33it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  14%|█▍        | 14/100 [00:00<00:03, 23.33it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 23.33it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  16%|█▌        | 16/100 [00:00<00:03, 24.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 24.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.005 | Best: {'fx': 420.202} (0.005):  17%|█▋        | 17/100 [00:00<00:03, 24.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  18%|█▊        | 18/100 [00:00<00:03, 24.27it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  19%|█▉        | 19/100 [00:00<00:03, 22.75it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.006 | Best: {'fx': 448.485} (0.010):  19%|█▉        | 19/100 [00:00<00:03, 22.75it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.007 | Best: {'fx': 448.485} (0.010):  20%|██        | 20/100 [00:00<00:03, 22.75it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.008 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:01<00:03, 22.75it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.011 | Best: {'fx': 455.556} (0.011):  22%|██▏       | 22/100 [00:01<00:03, 22.75it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.011 | Best: {'fx': 455.556} (0.011):  23%|██▎       | 23/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.014 | Best: {'fx': 462.626} (0.014):  23%|██▎       | 23/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 476.768} (0.020):  25%|██▌       | 25/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.028 | Best: {'fx': 490.909} (0.028):  26%|██▌       | 26/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 490.909} (0.028):  27%|██▋       | 27/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.045 | Best: {'fx': 505.051} (0.045):  28%|██▊       | 28/100 [00:01<00:03, 23.77it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.045 | Best: {'fx': 505.051} (0.045):  29%|██▉       | 29/100 [00:01<00:02, 29.66it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.055 | Best: {'fx': 512.121} (0.055):  29%|██▉       | 29/100 [00:01<00:02, 29.66it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.037 | Best: {'fx': 512.121} (0.055):  30%|███       | 30/100 [00:01<00:02, 29.66it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.065 | Best: {'fx': 519.192} (0.065):  31%|███       | 31/100 [00:01<00:02, 29.66it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.078 | Best: {'fx': 533.333} (0.078):  32%|███▏      | 32/100 [00:01<00:02, 29.66it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.078 | Best: {'fx': 533.333} (0.078):  33%|███▎      | 33/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.072 | Best: {'fx': 533.333} (0.078):  33%|███▎      | 33/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.077 | Best: {'fx': 533.333} (0.078):  34%|███▍      | 34/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.062 | Best: {'fx': 533.333} (0.078):  35%|███▌      | 35/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.044 | Best: {'fx': 533.333} (0.078):  36%|███▌      | 36/100 [00:01<00:03, 19.52it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.044 | Best: {'fx': 533.333} (0.078):  37%|███▋      | 37/100 [00:01<00:02, 22.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.055 | Best: {'fx': 533.333} (0.078):  37%|███▋      | 37/100 [00:01<00:02, 22.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.041 | Best: {'fx': 533.333} (0.078):  38%|███▊      | 38/100 [00:01<00:02, 22.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.036 | Best: {'fx': 533.333} (0.078):  39%|███▉      | 39/100 [00:01<00:02, 22.62it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.036 | Best: {'fx': 533.333} (0.078):  40%|████      | 40/100 [00:01<00:02, 23.12it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.034 | Best: {'fx': 533.333} (0.078):  40%|████      | 40/100 [00:01<00:02, 23.12it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.048 | Best: {'fx': 533.333} (0.078):  41%|████      | 41/100 [00:01<00:02, 23.12it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.038 | Best: {'fx': 533.333} (0.078):  42%|████▏     | 42/100 [00:01<00:02, 23.12it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.038 | Best: {'fx': 533.333} (0.078):  43%|████▎     | 43/100 [00:01<00:02, 23.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.027 | Best: {'fx': 533.333} (0.078):  43%|████▎     | 43/100 [00:02<00:02, 23.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.029 | Best: {'fx': 533.333} (0.078):  44%|████▍     | 44/100 [00:02<00:02, 23.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.032 | Best: {'fx': 533.333} (0.078):  45%|████▌     | 45/100 [00:02<00:02, 23.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.026 | Best: {'fx': 533.333} (0.078):  46%|████▌     | 46/100 [00:02<00:02, 23.82it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.026 | Best: {'fx': 533.333} (0.078):  47%|████▋     | 47/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.031 | Best: {'fx': 533.333} (0.078):  47%|████▋     | 47/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.030 | Best: {'fx': 533.333} (0.078):  48%|████▊     | 48/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 533.333} (0.078):  49%|████▉     | 49/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.025 | Best: {'fx': 533.333} (0.078):  50%|█████     | 50/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 533.333} (0.078):  51%|█████     | 51/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.028 | Best: {'fx': 533.333} (0.078):  52%|█████▏    | 52/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.026 | Best: {'fx': 533.333} (0.078):  53%|█████▎    | 53/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 533.333} (0.078):  54%|█████▍    | 54/100 [00:02<00:02, 22.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 533.333} (0.078):  55%|█████▌    | 55/100 [00:02<00:01, 31.74it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 533.333} (0.078):  55%|█████▌    | 55/100 [00:02<00:01, 31.74it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.025 | Best: {'fx': 533.333} (0.078):  56%|█████▌    | 56/100 [00:02<00:01, 31.74it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.024 | Best: {'fx': 533.333} (0.078):  57%|█████▋    | 57/100 [00:02<00:01, 31.74it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 533.333} (0.078):  58%|█████▊    | 58/100 [00:02<00:01, 31.74it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 533.333} (0.078):  59%|█████▉    | 59/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 533.333} (0.078):  59%|█████▉    | 59/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  60%|██████    | 60/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 533.333} (0.078):  61%|██████    | 61/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 533.333} (0.078):  62%|██████▏   | 62/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 533.333} (0.078):  63%|██████▎   | 63/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  64%|██████▍   | 64/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  65%|██████▌   | 65/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.021 | Best: {'fx': 533.333} (0.078):  66%|██████▌   | 66/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 533.333} (0.078):  67%|██████▋   | 67/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.023 | Best: {'fx': 533.333} (0.078):  68%|██████▊   | 68/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  69%|██████▉   | 69/100 [00:02<00:01, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 533.333} (0.078):  70%|███████   | 70/100 [00:02<00:00, 30.21it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.022 | Best: {'fx': 533.333} (0.078):  71%|███████   | 71/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  71%|███████   | 71/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  72%|███████▏  | 72/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  73%|███████▎  | 73/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  74%|███████▍  | 74/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  75%|███████▌  | 75/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  76%|███████▌  | 76/100 [00:02<00:00, 48.22it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  77%|███████▋  | 77/100 [00:02<00:00, 40.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  77%|███████▋  | 77/100 [00:02<00:00, 40.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  78%|███████▊  | 78/100 [00:02<00:00, 40.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.020 | Best: {'fx': 533.333} (0.078):  79%|███████▉  | 79/100 [00:02<00:00, 40.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  80%|████████  | 80/100 [00:02<00:00, 40.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  81%|████████  | 81/100 [00:02<00:00, 40.13it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  82%|████████▏ | 82/100 [00:02<00:00, 37.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  82%|████████▏ | 82/100 [00:02<00:00, 37.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  83%|████████▎ | 83/100 [00:03<00:00, 37.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  84%|████████▍ | 84/100 [00:03<00:00, 37.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  85%|████████▌ | 85/100 [00:03<00:00, 37.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  86%|████████▌ | 86/100 [00:03<00:00, 37.83it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  87%|████████▋ | 87/100 [00:03<00:00, 38.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.019 | Best: {'fx': 533.333} (0.078):  87%|████████▋ | 87/100 [00:03<00:00, 38.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  88%|████████▊ | 88/100 [00:03<00:00, 38.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  89%|████████▉ | 89/100 [00:03<00:00, 38.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  90%|█████████ | 90/100 [00:03<00:00, 38.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  91%|█████████ | 91/100 [00:03<00:00, 38.73it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  92%|█████████▏| 92/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  92%|█████████▏| 92/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  93%|█████████▎| 93/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  94%|█████████▍| 94/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  95%|█████████▌| 95/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  96%|█████████▌| 96/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  97%|█████████▋| 97/100 [00:03<00:00, 34.38it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  98%|█████████▊| 98/100 [00:03<00:00, 34.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  98%|█████████▊| 98/100 [00:03<00:00, 34.64it/s]Brute | Start: {'fx': 500.0} (0.038) | Current: 0.018 | Best: {'fx': 533.333} (0.078):  99%|█████████▉| 99/100 [00:03<00:00, 34.64it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.050 | Best: {'fx': 508.333} (0.050):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.050 | Best: {'fx': 508.333} (0.050):   2%|▏         | 1/50 [00:00<00:25,  1.89it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.051 | Best: {'fx': 509.354} (0.051):   2%|▏         | 1/50 [00:00<00:25,  1.89it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.054 | Best: {'fx': 511.395} (0.054):   4%|▍         | 2/50 [00:00<00:25,  1.89it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.054 | Best: {'fx': 511.395} (0.054):   6%|▌         | 3/50 [00:00<00:08,  5.56it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.053 | Best: {'fx': 511.395} (0.054):   6%|▌         | 3/50 [00:00<00:08,  5.56it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.057 | Best: {'fx': 513.435} (0.057):   8%|▊         | 4/50 [00:00<00:08,  5.56it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.055 | Best: {'fx': 513.435} (0.057):  10%|█         | 5/50 [00:00<00:08,  5.56it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.059 | Best: {'fx': 514.456} (0.059):  12%|█▏        | 6/50 [00:00<00:07,  5.56it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.061 | Best: {'fx': 516.497} (0.061):  14%|█▍        | 7/50 [00:00<00:07,  5.56it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.061 | Best: {'fx': 516.497} (0.061):  16%|█▌        | 8/50 [00:00<00:02, 14.12it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.060 | Best: {'fx': 516.497} (0.061):  16%|█▌        | 8/50 [00:00<00:02, 14.12it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.064 | Best: {'fx': 517.517} (0.064):  18%|█▊        | 9/50 [00:00<00:02, 14.12it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.064 | Best: {'fx': 519.558} (0.064):  20%|██        | 10/50 [00:00<00:02, 14.12it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.069 | Best: {'fx': 522.619} (0.069):  22%|██▏       | 11/50 [00:00<00:02, 14.12it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.069 | Best: {'fx': 522.619} (0.069):  24%|██▍       | 12/50 [00:00<00:02, 16.58it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.065 | Best: {'fx': 522.619} (0.069):  24%|██▍       | 12/50 [00:01<00:02, 16.58it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.066 | Best: {'fx': 522.619} (0.069):  26%|██▌       | 13/50 [00:01<00:02, 16.58it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.065 | Best: {'fx': 522.619} (0.069):  28%|██▊       | 14/50 [00:01<00:02, 16.58it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.076 | Best: {'fx': 525.68} (0.076):  30%|███       | 15/50 [00:01<00:02, 16.58it/s] Brute | Start: {'fx': 533.333} (0.078) | Current: 0.076 | Best: {'fx': 525.68} (0.076):  32%|███▏      | 16/50 [00:01<00:01, 20.25it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.079 | Best: {'fx': 527.721} (0.079):  32%|███▏      | 16/50 [00:01<00:01, 20.25it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.071 | Best: {'fx': 527.721} (0.079):  34%|███▍      | 17/50 [00:01<00:01, 20.25it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 527.721} (0.079):  36%|███▌      | 18/50 [00:01<00:01, 20.25it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 527.721} (0.079):  38%|███▊      | 19/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.074 | Best: {'fx': 527.721} (0.079):  38%|███▊      | 19/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.079 | Best: {'fx': 527.721} (0.079):  40%|████      | 20/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.073 | Best: {'fx': 527.721} (0.079):  42%|████▏     | 21/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.081 | Best: {'fx': 529.762} (0.081):  44%|████▍     | 22/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.081 | Best: {'fx': 531.803} (0.081):  46%|████▌     | 23/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.080 | Best: {'fx': 531.803} (0.081):  48%|████▊     | 24/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.069 | Best: {'fx': 531.803} (0.081):  50%|█████     | 25/50 [00:01<00:01, 19.78it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.069 | Best: {'fx': 531.803} (0.081):  52%|█████▏    | 26/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.071 | Best: {'fx': 531.803} (0.081):  52%|█████▏    | 26/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 531.803} (0.081):  54%|█████▍    | 27/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.061 | Best: {'fx': 531.803} (0.081):  56%|█████▌    | 28/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.067 | Best: {'fx': 531.803} (0.081):  58%|█████▊    | 29/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.079 | Best: {'fx': 531.803} (0.081):  60%|██████    | 30/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.068 | Best: {'fx': 531.803} (0.081):  62%|██████▏   | 31/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.064 | Best: {'fx': 531.803} (0.081):  64%|██████▍   | 32/50 [00:01<00:00, 26.70it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.064 | Best: {'fx': 531.803} (0.081):  66%|██████▌   | 33/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.063 | Best: {'fx': 531.803} (0.081):  66%|██████▌   | 33/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.066 | Best: {'fx': 531.803} (0.081):  68%|██████▊   | 34/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.075 | Best: {'fx': 531.803} (0.081):  70%|███████   | 35/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.079 | Best: {'fx': 531.803} (0.081):  72%|███████▏  | 36/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.076 | Best: {'fx': 531.803} (0.081):  74%|███████▍  | 37/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 531.803} (0.081):  76%|███████▌  | 38/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.077 | Best: {'fx': 531.803} (0.081):  78%|███████▊  | 39/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.059 | Best: {'fx': 531.803} (0.081):  80%|████████  | 40/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.059 | Best: {'fx': 531.803} (0.081):  82%|████████▏ | 41/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.057 | Best: {'fx': 531.803} (0.081):  84%|████████▍ | 42/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.058 | Best: {'fx': 531.803} (0.081):  86%|████████▌ | 43/50 [00:01<00:00, 34.67it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.058 | Best: {'fx': 531.803} (0.081):  88%|████████▊ | 44/50 [00:01<00:00, 51.54it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.055 | Best: {'fx': 531.803} (0.081):  88%|████████▊ | 44/50 [00:01<00:00, 51.54it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.055 | Best: {'fx': 531.803} (0.081):  90%|█████████ | 45/50 [00:01<00:00, 51.54it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.054 | Best: {'fx': 531.803} (0.081):  92%|█████████▏| 46/50 [00:01<00:00, 51.54it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.051 | Best: {'fx': 531.803} (0.081):  94%|█████████▍| 47/50 [00:01<00:00, 51.54it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.052 | Best: {'fx': 531.803} (0.081):  96%|█████████▌| 48/50 [00:01<00:00, 51.54it/s]Brute | Start: {'fx': 533.333} (0.078) | Current: 0.051 | Best: {'fx': 531.803} (0.081):  98%|█████████▊| 49/50 [00:01<00:00, 51.54it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 861.0
    py: 994.0
    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): 80 frames will be used for optimization
  Rigs: 1000(1000mm):20/20, 2000R(2000mm):20/20, 1500(1500mm):20/20, 2000L(2000mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 43.7855: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 43.7855, picklable_objective(x0) = 43.8152
Best score: 43.8152: : 0iter [00:02, ?iter/s] Best score: 43.8152: : 1iter [00:02,  2.01s/iter]Best score: 43.8152: : 1iter [00:02,  2.01s/iter]Best score: 43.8152: : 2iter [00:02,  2.01s/iter]Best score: 43.8152: : 3iter [00:02,  1.70iter/s]Best score: 43.8152: : 3iter [00:02,  1.70iter/s]Best score: 43.8152: : 4iter [00:02,  1.70iter/s]Best score: 43.8152: : 5iter [00:02,  3.03iter/s]Best score: 43.8152: : 5iter [00:02,  3.03iter/s]Best score: 43.8152: : 6iter [00:02,  3.03iter/s]Best score: 43.8152: : 7iter [00:02,  4.39iter/s]Best score: 43.8152: : 7iter [00:02,  4.39iter/s]Best score: 43.8152: : 8iter [00:02,  4.39iter/s]Best score: 43.8152: : 9iter [00:02,  5.71iter/s]Best score: 43.8152: : 9iter [00:02,  5.71iter/s]Best score: 43.8152: : 10iter [00:02,  5.71iter/s]Best score: 43.8152: : 11iter [00:02,  6.86iter/s]Best score: 43.8152: : 11iter [00:02,  6.86iter/s]Best score: 43.8152: : 12iter [00:03,  6.86iter/s]Best score: 43.8152: : 13iter [00:03,  7.92iter/s]Best score: 43.8152: : 13iter [00:03,  7.92iter/s]Best score: 43.8152: : 14iter [00:03,  7.92iter/s]Best score: 43.8152: : 15iter [00:03,  8.80iter/s]Best score: 43.8152: : 15iter [00:03,  8.80iter/s]Best score: 43.8152: : 16iter [00:03,  8.80iter/s]Best score: 43.8152: : 17iter [00:03,  9.46iter/s]Best score: 43.8152: : 17iter [00:03,  9.46iter/s]Best score: 43.8152: : 18iter [00:03,  9.46iter/s]Best score: 43.8152: : 19iter [00:03,  9.99iter/s]Best score: 43.8152: : 19iter [00:03,  9.99iter/s]Best score: 43.8152: : 20iter [00:03,  9.99iter/s]Best score: 43.8152: : 21iter [00:03, 10.28iter/s]Best score: 43.8152: : 21iter [00:03, 10.28iter/s]Best score: 43.8152: : 22iter [00:03, 10.28iter/s]Best score: 43.8152: : 23iter [00:03, 10.60iter/s]Best score: 43.8152: : 23iter [00:04, 10.60iter/s]Best score: 43.8152: : 24iter [00:04, 10.60iter/s]Best score: 43.8152: : 25iter [00:04, 10.84iter/s]Best score: 43.8152: : 25iter [00:04, 10.84iter/s]Best score: 43.8152: : 26iter [00:04, 10.84iter/s]Best score: 43.8152: : 27iter [00:04, 11.00iter/s]Best score: 43.8152: : 27iter [00:04, 11.00iter/s]Best score: 43.8152: : 28iter [00:04, 11.00iter/s]Best score: 43.8152: : 29iter [00:04, 11.12iter/s]Best score: 43.8152: : 29iter [00:04, 11.12iter/s]Best score: 43.8152: : 30iter [00:04, 11.12iter/s]Best score: 43.8152: : 31iter [00:04, 11.05iter/s]Best score: 43.8152: : 31iter [00:04, 11.05iter/s]Best score: 43.8152: : 32iter [00:04, 11.05iter/s]Best score: 43.8152: : 33iter [00:04, 11.17iter/s]Best score: 43.8152: : 33iter [00:04, 11.17iter/s]Best score: 43.8152: : 34iter [00:05, 11.17iter/s]Best score: 43.8152: : 35iter [00:05, 11.27iter/s]Best score: 43.8152: : 35iter [00:05, 11.27iter/s]Best score: 43.8152: : 36iter [00:05, 11.27iter/s]Best score: 43.8152: : 37iter [00:05, 11.31iter/s]Best score: 43.8152: : 37iter [00:05, 11.31iter/s]Best score: 43.8152: : 38iter [00:05, 11.31iter/s]Best score: 43.8152: : 39iter [00:05, 11.30iter/s]Best score: 43.8152: : 39iter [00:05, 11.30iter/s]Best score: 43.8152: : 40iter [00:05, 11.30iter/s]Best score: 43.8152: : 41iter [00:05, 11.12iter/s]Best score: 43.8152: : 41iter [00:05, 11.12iter/s]Best score: 43.8152: : 42iter [00:05, 11.12iter/s]Best score: 43.8152: : 43iter [00:05, 11.17iter/s]Best score: 43.8152: : 43iter [00:05, 11.17iter/s]Best score: 43.8152: : 44iter [00:05, 11.17iter/s]Best score: 43.8152: : 45iter [00:05, 11.27iter/s]Best score: 43.8152: : 45iter [00:05, 11.27iter/s]Best score: 43.8152: : 46iter [00:06, 11.27iter/s]Best score: 43.8152: : 47iter [00:06, 11.31iter/s]Best score: 43.8152: : 47iter [00:06, 11.31iter/s]Best score: 43.8152: : 48iter [00:06, 11.31iter/s]Best score: 43.8152: : 49iter [00:06, 11.36iter/s]Best score: 20.1731: : 49iter [00:06, 11.36iter/s]
[ITER 50] Best: 20.1731
[PARAMS] Current best parameters:
  fx: 533.7223
  fy: 522.9757
  px: 849.9343
  py: 988.7662
  k1: -0.07789403
  k2: 0.03268721
  k3: 0.02762836
  k4: -0.01303020
  p1: -0.00911410
  p2: 0.00205465
  p3: -0.00833333
  p4: -0.00148833
  s1: -0.0019
  s2: -0.0014
  s3: -0.0121
  s4: -0.0064
  tilt_x: -0.0187
  tilt_y: 0.0179
  scale: 3.1995
  rotation: -0.8850
Best score: 20.1731: : 50iter [00:06, 11.36iter/s]Best score: 20.1731: : 51iter [00:06, 11.23iter/s]Best score: 20.1731: : 51iter [00:06, 11.23iter/s]Best score: 20.1731: : 52iter [00:06, 11.23iter/s]Best score: 20.1731: : 53iter [00:06, 11.32iter/s]Best score: 20.1731: : 53iter [00:06, 11.32iter/s]Best score: 20.1731: : 54iter [00:06, 11.32iter/s]Best score: 20.1731: : 55iter [00:06, 11.35iter/s]Best score: 20.1731: : 55iter [00:06, 11.35iter/s]Best score: 20.1731: : 56iter [00:06, 11.35iter/s]Best score: 20.1731: : 57iter [00:06, 11.41iter/s]Best score: 20.1731: : 57iter [00:07, 11.41iter/s]Best score: 20.1731: : 58iter [00:07, 11.41iter/s]Best score: 20.1731: : 59iter [00:07, 11.48iter/s]Best score: 20.1731: : 59iter [00:07, 11.48iter/s]Best score: 20.1731: : 60iter [00:07, 11.48iter/s]Best score: 20.1731: : 61iter [00:07, 11.27iter/s]Best score: 20.1731: : 61iter [00:07, 11.27iter/s]Best score: 20.1731: : 62iter [00:07, 11.27iter/s]Best score: 20.1731: : 63iter [00:07, 11.36iter/s]Best score: 20.1731: : 63iter [00:07, 11.36iter/s]Best score: 20.1731: : 64iter [00:07, 11.36iter/s]Best score: 20.1731: : 65iter [00:07, 11.36iter/s]Best score: 20.1731: : 65iter [00:07, 11.36iter/s]Best score: 20.1731: : 66iter [00:07, 11.36iter/s]Best score: 20.1731: : 67iter [00:07, 11.42iter/s]Best score: 20.1731: : 67iter [00:07, 11.42iter/s]Best score: 20.1731: : 68iter [00:08, 11.42iter/s]Best score: 20.1731: : 69iter [00:08, 11.43iter/s]Best score: 11.6339: : 69iter [00:08, 11.43iter/s]Best score: 11.6339: : 70iter [00:08, 11.43iter/s]Best score: 11.6339: : 71iter [00:08, 11.24iter/s]Best score: 11.6339: : 71iter [00:08, 11.24iter/s]Best score: 11.6339: : 72iter [00:08, 11.24iter/s]Best score: 11.6339: : 73iter [00:08, 11.37iter/s]Best score: 11.6339: : 73iter [00:08, 11.37iter/s]Best score: 11.6339: : 74iter [00:08, 11.37iter/s]Best score: 11.6339: : 75iter [00:08, 11.47iter/s]Best score: 11.6339: : 75iter [00:08, 11.47iter/s]Best score: 11.6339: : 76iter [00:08, 11.47iter/s]Best score: 11.6339: : 77iter [00:08, 11.54iter/s]Best score: 11.6339: : 77iter [00:08, 11.54iter/s]Best score: 11.6339: : 78iter [00:08, 11.54iter/s]Best score: 11.6339: : 79iter [00:08, 11.55iter/s]Best score: 10.8703: : 79iter [00:08, 11.55iter/s]Best score: 10.8703: : 80iter [00:09, 11.55iter/s]Best score: 10.8703: : 81iter [00:09, 11.34iter/s]Best score: 10.8703: : 81iter [00:09, 11.34iter/s]Best score: 10.8703: : 82iter [00:09, 11.34iter/s]Best score: 10.8703: : 83iter [00:09, 11.36iter/s]Best score: 10.8703: : 83iter [00:09, 11.36iter/s]Best score: 10.8703: : 84iter [00:09, 11.36iter/s]Best score: 10.8703: : 85iter [00:09, 11.38iter/s]Best score: 10.8703: : 85iter [00:09, 11.38iter/s]Best score: 10.8703: : 86iter [00:09, 11.38iter/s]Best score: 10.8703: : 87iter [00:09, 11.42iter/s]Best score: 10.8703: : 87iter [00:09, 11.42iter/s]Best score: 10.8703: : 88iter [00:09, 11.42iter/s]Best score: 10.8703: : 89iter [00:09, 11.45iter/s]Best score: 9.9751: : 89iter [00:09, 11.45iter/s] Best score: 9.9751: : 90iter [00:09, 11.45iter/s]Best score: 9.9751: : 91iter [00:09, 11.28iter/s]Best score: 9.9751: : 91iter [00:10, 11.28iter/s]Best score: 9.9751: : 92iter [00:10, 11.28iter/s]Best score: 9.9751: : 93iter [00:10, 11.32iter/s]Best score: 9.9751: : 93iter [00:10, 11.32iter/s]Best score: 9.9751: : 94iter [00:10, 11.32iter/s]Best score: 9.9751: : 95iter [00:10, 11.36iter/s]Best score: 9.9751: : 95iter [00:10, 11.36iter/s]Best score: 9.9751: : 96iter [00:10, 11.36iter/s]Best score: 9.9751: : 97iter [00:10, 11.40iter/s]Best score: 9.9751: : 97iter [00:10, 11.40iter/s]Best score: 9.9751: : 98iter [00:10, 11.40iter/s]Best score: 9.9751: : 99iter [00:10, 11.48iter/s]
[ITER 100] Best: 5.1154
[PARAMS] Current best parameters:
  fx: 518.3063
  fy: 516.7328
  px: 843.6033
  py: 996.6237
  k1: -0.03771310
  k2: 0.03946451
  k3: -0.00136362
  k4: -0.00423561
  p1: -0.01517135
  p2: -0.01272734
  p3: 0.02285849
  p4: 0.00551475
  s1: 0.0031
  s2: -0.0214
  s3: -0.0160
  s4: -0.0079
  tilt_x: 0.0035
  tilt_y: 0.0173
  scale: 3.1215
  rotation: -0.2808
Best score: 5.1154: : 99iter [00:10, 11.48iter/s]Best score: 5.1154: : 100iter [00:10, 11.48iter/s]Best score: 5.1154: : 101iter [00:10,  9.79iter/s]Best score: 5.1154: : 101iter [00:11,  9.79iter/s]Best score: 5.1154: : 102iter [00:11,  9.79iter/s]Best score: 5.1154: : 103iter [00:11, 10.19iter/s]Best score: 5.1154: : 103iter [00:11, 10.19iter/s]Best score: 5.1154: : 104iter [00:11, 10.19iter/s]Best score: 5.1154: : 105iter [00:11, 10.49iter/s]Best score: 5.1154: : 105iter [00:11, 10.49iter/s]Best score: 5.1154: : 106iter [00:11, 10.49iter/s]Best score: 5.1154: : 107iter [00:11, 10.73iter/s]Best score: 5.1154: : 107iter [00:11, 10.73iter/s]Best score: 5.1154: : 108iter [00:11, 10.73iter/s]Best score: 5.1154: : 109iter [00:11, 10.89iter/s]Best score: 5.1154: : 109iter [00:11, 10.89iter/s]Best score: 5.1154: : 110iter [00:11, 10.89iter/s]Best score: 5.1154: : 111iter [00:11, 10.81iter/s]Best score: 5.1154: : 111iter [00:11, 10.81iter/s]Best score: 5.1154: : 112iter [00:11, 10.81iter/s]Best score: 5.1154: : 113iter [00:11, 10.97iter/s]Best score: 5.1154: : 113iter [00:12, 10.97iter/s]Best score: 5.1154: : 114iter [00:12, 10.97iter/s]Best score: 5.1154: : 115iter [00:12, 11.02iter/s]Best score: 5.1154: : 115iter [00:12, 11.02iter/s]Best score: 5.1154: : 116iter [00:12, 11.02iter/s]Best score: 5.1154: : 117iter [00:12, 11.15iter/s]Best score: 5.1154: : 117iter [00:12, 11.15iter/s]Best score: 5.1154: : 118iter [00:12, 11.15iter/s]Best score: 5.1154: : 119iter [00:12, 11.26iter/s]Best score: 5.1154: : 119iter [00:12, 11.26iter/s]Best score: 5.1154: : 120iter [00:12, 11.26iter/s]Best score: 5.1154: : 121iter [00:12, 11.12iter/s]Best score: 5.1154: : 121iter [00:12, 11.12iter/s]Best score: 5.1154: : 122iter [00:12, 11.12iter/s]Best score: 5.1154: : 123iter [00:12, 11.24iter/s]Best score: 5.1154: : 123iter [00:12, 11.24iter/s]Best score: 5.1154: : 124iter [00:13, 11.24iter/s]Best score: 5.1154: : 125iter [00:13, 11.32iter/s]Best score: 5.1154: : 125iter [00:13, 11.32iter/s]Best score: 5.1154: : 126iter [00:13, 11.32iter/s]Best score: 5.1154: : 127iter [00:13, 11.40iter/s]Best score: 5.1154: : 127iter [00:13, 11.40iter/s]Best score: 5.1154: : 128iter [00:13, 11.40iter/s]Best score: 5.1154: : 129iter [00:13, 11.41iter/s]Best score: 5.1154: : 129iter [00:13, 11.41iter/s]Best score: 5.1154: : 130iter [00:13, 11.41iter/s]Best score: 5.1154: : 131iter [00:13, 11.23iter/s]Best score: 5.1154: : 131iter [00:13, 11.23iter/s]Best score: 5.1154: : 132iter [00:13, 11.23iter/s]Best score: 5.1154: : 133iter [00:13, 11.32iter/s]Best score: 5.1154: : 133iter [00:13, 11.32iter/s]Best score: 5.1154: : 134iter [00:13, 11.32iter/s]Best score: 5.1154: : 135iter [00:13, 11.36iter/s]Best score: 5.1154: : 135iter [00:14, 11.36iter/s]Best score: 5.1154: : 136iter [00:14, 11.36iter/s]Best score: 5.1154: : 137iter [00:14, 11.46iter/s]Best score: 5.1154: : 137iter [00:14, 11.46iter/s]Best score: 5.1154: : 138iter [00:14, 11.46iter/s]Best score: 5.1154: : 139iter [00:14, 11.48iter/s]Best score: 5.1154: : 139iter [00:14, 11.48iter/s]Best score: 5.1154: : 140iter [00:14, 11.48iter/s]Best score: 5.1154: : 141iter [00:14, 11.34iter/s]Best score: 5.1154: : 141iter [00:14, 11.34iter/s]Best score: 5.1154: : 142iter [00:14, 11.34iter/s]Best score: 5.1154: : 143iter [00:14, 11.41iter/s]Best score: 5.1154: : 143iter [00:14, 11.41iter/s]Best score: 5.1154: : 144iter [00:14, 11.41iter/s]Best score: 5.1154: : 145iter [00:14, 11.50iter/s]Best score: 5.1154: : 145iter [00:14, 11.50iter/s]Best score: 5.1154: : 146iter [00:14, 11.50iter/s]Best score: 5.1154: : 147iter [00:14, 11.52iter/s]Best score: 5.1154: : 147iter [00:15, 11.52iter/s]Best score: 5.1154: : 148iter [00:15, 11.52iter/s]Best score: 5.1154: : 149iter [00:15, 11.53iter/s]
[ITER 150] Best: 5.1154
[PARAMS] Current best parameters:
  fx: 518.3063
  fy: 516.7328
  px: 843.6033
  py: 996.6237
  k1: -0.03771310
  k2: 0.03946451
  k3: -0.00136362
  k4: -0.00423561
  p1: -0.01517135
  p2: -0.01272734
  p3: 0.02285849
  p4: 0.00551475
  s1: 0.0031
  s2: -0.0214
  s3: -0.0160
  s4: -0.0079
  tilt_x: 0.0035
  tilt_y: 0.0173
  scale: 3.1215
  rotation: -0.2808
Best score: 5.1154: : 149iter [00:15, 11.53iter/s]Best score: 5.1154: : 150iter [00:15, 11.53iter/s]Best score: 5.1154: : 151iter [00:15,  7.48iter/s]Best score: 5.1154: : 151iter [00:15,  7.48iter/s]Best score: 5.1154: : 152iter [00:15,  7.48iter/s]Best score: 5.1154: : 153iter [00:15,  8.36iter/s]Best score: 5.1154: : 153iter [00:15,  8.36iter/s]Best score: 5.1154: : 154iter [00:15,  8.36iter/s]Best score: 5.1154: : 155iter [00:15,  9.10iter/s]Best score: 5.1154: : 155iter [00:16,  9.10iter/s]Best score: 5.1154: : 156iter [00:16,  9.10iter/s]Best score: 5.1154: : 157iter [00:16,  9.74iter/s]Best score: 5.1154: : 157iter [00:16,  9.74iter/s]Best score: 5.1154: : 158iter [00:16,  9.74iter/s]Best score: 5.1154: : 159iter [00:16, 10.25iter/s]Best score: 5.1154: : 159iter [00:16, 10.25iter/s]Best score: 5.1154: : 160iter [00:16, 10.25iter/s]Best score: 5.1154: : 161iter [00:16, 10.47iter/s]Best score: 5.1154: : 161iter [00:16, 10.47iter/s]Best score: 5.1154: : 162iter [00:16, 10.47iter/s]Best score: 5.1154: : 163iter [00:16, 10.81iter/s]Best score: 5.1154: : 163iter [00:16, 10.81iter/s]Best score: 5.1154: : 164iter [00:16, 10.81iter/s]Best score: 5.1154: : 165iter [00:16, 11.03iter/s]Best score: 5.1154: : 165iter [00:16, 11.03iter/s]Best score: 5.1154: : 166iter [00:17, 11.03iter/s]Best score: 5.1154: : 167iter [00:17, 11.19iter/s]Best score: 5.1154: : 167iter [00:17, 11.19iter/s]Best score: 5.1154: : 168iter [00:17, 11.19iter/s]Best score: 5.1154: : 169iter [00:17, 11.29iter/s]Best score: 4.4977: : 169iter [00:17, 11.29iter/s]Best score: 4.4977: : 170iter [00:17, 11.29iter/s]Best score: 4.4977: : 171iter [00:17, 11.20iter/s]Best score: 4.4977: : 171iter [00:17, 11.20iter/s]Best score: 4.4977: : 172iter [00:17, 11.20iter/s]Best score: 4.4977: : 173iter [00:17, 11.35iter/s]Best score: 4.4977: : 173iter [00:17, 11.35iter/s]Best score: 4.4977: : 174iter [00:17, 11.35iter/s]Best score: 4.4977: : 175iter [00:17, 11.44iter/s]Best score: 4.4977: : 175iter [00:17, 11.44iter/s]Best score: 4.4977: : 176iter [00:17, 11.44iter/s]Best score: 4.4977: : 177iter [00:17, 11.49iter/s]Best score: 4.4977: : 177iter [00:17, 11.49iter/s]Best score: 4.4977: : 178iter [00:18, 11.49iter/s]Best score: 4.4977: : 179iter [00:18, 11.57iter/s]Best score: 3.8654: : 179iter [00:18, 11.57iter/s]Best score: 3.8654: : 180iter [00:18, 11.57iter/s]Best score: 3.8654: : 181iter [00:18, 11.40iter/s]Best score: 3.8654: : 181iter [00:18, 11.40iter/s]Best score: 3.8654: : 182iter [00:18, 11.40iter/s]Best score: 3.8654: : 183iter [00:18, 11.48iter/s]Best score: 3.8654: : 183iter [00:18, 11.48iter/s]Best score: 3.8654: : 184iter [00:18, 11.48iter/s]Best score: 3.8654: : 185iter [00:18, 11.51iter/s]Best score: 3.8654: : 185iter [00:18, 11.51iter/s]Best score: 3.8654: : 186iter [00:18, 11.51iter/s]Best score: 3.8654: : 187iter [00:18, 11.57iter/s]Best score: 3.8654: : 187iter [00:18, 11.57iter/s]Best score: 3.8654: : 188iter [00:18, 11.57iter/s]Best score: 3.8654: : 189iter [00:18, 11.59iter/s]Best score: 3.8166: : 189iter [00:19, 11.59iter/s]Best score: 3.8166: : 190iter [00:19, 11.59iter/s]Best score: 3.8166: : 191iter [00:19, 11.42iter/s]Best score: 3.8166: : 191iter [00:19, 11.42iter/s]Best score: 3.8166: : 192iter [00:19, 11.42iter/s]Best score: 3.8166: : 193iter [00:19, 11.48iter/s]Best score: 3.8166: : 193iter [00:19, 11.48iter/s]Best score: 3.8166: : 194iter [00:19, 11.48iter/s]Best score: 3.8166: : 195iter [00:19, 11.55iter/s]Best score: 3.8166: : 195iter [00:19, 11.55iter/s]Best score: 3.8166: : 196iter [00:19, 11.55iter/s]Best score: 3.8166: : 197iter [00:19, 11.58iter/s]Best score: 3.8166: : 197iter [00:19, 11.58iter/s]Best score: 3.8166: : 198iter [00:19, 11.58iter/s]Best score: 3.8166: : 199iter [00:19, 11.63iter/s]
[ITER 200] Best: 3.8166
[PARAMS] Current best parameters:
  fx: 515.7026
  fy: 514.3647
  px: 838.7863
  py: 996.1662
  k1: -0.03821305
  k2: 0.05734316
  k3: -0.00928600
  k4: -0.00449945
  p1: -0.01759753
  p2: -0.02341277
  p3: 0.01937967
  p4: -0.00287030
  s1: -0.0002
  s2: -0.0142
  s3: -0.0122
  s4: 0.0117
  tilt_x: -0.0029
  tilt_y: 0.0161
  scale: 3.0617
  rotation: -0.1426
Best score: 3.8166: : 199iter [00:19, 11.63iter/s]Best score: 3.8166: : 200iter [00:20, 11.63iter/s]Best score: 3.8166: : 201iter [00:20,  7.50iter/s]Best score: 3.8166: : 201iter [00:20,  7.50iter/s]Best score: 3.8166: : 202iter [00:20,  7.50iter/s]Best score: 3.8166: : 203iter [00:20,  8.40iter/s]Best score: 3.8166: : 203iter [00:20,  8.40iter/s]Best score: 3.8166: : 204iter [00:20,  8.40iter/s]Best score: 3.8166: : 205iter [00:20,  9.15iter/s]Best score: 3.8166: : 205iter [00:20,  9.15iter/s]Best score: 3.8166: : 206iter [00:20,  9.15iter/s]Best score: 3.8166: : 207iter [00:20,  9.76iter/s]Best score: 3.8166: : 207iter [00:20,  9.76iter/s]Best score: 3.8166: : 208iter [00:20,  9.76iter/s]Best score: 3.8166: : 209iter [00:20, 10.24iter/s]Best score: 2.8141: : 209iter [00:21, 10.24iter/s]Best score: 2.8141: : 210iter [00:21, 10.24iter/s]Best score: 2.8141: : 211iter [00:21, 10.44iter/s]Best score: 2.8141: : 211iter [00:21, 10.44iter/s]Best score: 2.8141: : 212iter [00:21, 10.44iter/s]Best score: 2.8141: : 213iter [00:21, 10.71iter/s]Best score: 2.8141: : 213iter [00:21, 10.71iter/s]Best score: 2.8141: : 214iter [00:21, 10.71iter/s]Best score: 2.8141: : 215iter [00:21, 10.94iter/s]Best score: 2.8141: : 215iter [00:21, 10.94iter/s]Best score: 2.8141: : 216iter [00:21, 10.94iter/s]Best score: 2.8141: : 217iter [00:21, 11.12iter/s]Best score: 2.8141: : 217iter [00:21, 11.12iter/s]Best score: 2.8141: : 218iter [00:21, 11.12iter/s]Best score: 2.8141: : 219iter [00:21, 11.27iter/s]Best score: 1.8435: : 219iter [00:21, 11.27iter/s]Best score: 1.8435: : 220iter [00:22, 11.27iter/s]Best score: 1.8435: : 221iter [00:22, 11.20iter/s]Best score: 1.8435: : 221iter [00:22, 11.20iter/s]Best score: 1.8435: : 222iter [00:22, 11.20iter/s]Best score: 1.8435: : 223iter [00:22, 11.30iter/s]Best score: 1.8435: : 223iter [00:22, 11.30iter/s]Best score: 1.8435: : 224iter [00:22, 11.30iter/s]Best score: 1.8435: : 225iter [00:22, 11.40iter/s]Best score: 1.8435: : 225iter [00:22, 11.40iter/s]Best score: 1.8435: : 226iter [00:22, 11.40iter/s]Best score: 1.8435: : 227iter [00:22, 11.46iter/s]Best score: 1.8435: : 227iter [00:22, 11.46iter/s]Best score: 1.8435: : 228iter [00:22, 11.46iter/s]Best score: 1.8435: : 229iter [00:22, 11.53iter/s]Best score: 1.4701: : 229iter [00:22, 11.53iter/s]Best score: 1.4701: : 230iter [00:22, 11.53iter/s]Best score: 1.4701: : 231iter [00:22, 11.36iter/s]Best score: 1.4701: : 231iter [00:22, 11.36iter/s]Best score: 1.4701: : 232iter [00:23, 11.36iter/s]Best score: 1.4701: : 233iter [00:23, 11.45iter/s]Best score: 1.4701: : 233iter [00:23, 11.45iter/s]Best score: 1.4701: : 234iter [00:23, 11.45iter/s]Best score: 1.4701: : 235iter [00:23, 11.52iter/s]Best score: 1.4701: : 235iter [00:23, 11.52iter/s]Best score: 1.4701: : 236iter [00:23, 11.52iter/s]Best score: 1.4701: : 237iter [00:23, 11.55iter/s]Best score: 1.4701: : 237iter [00:23, 11.55iter/s]Best score: 1.4701: : 238iter [00:23, 11.55iter/s]Best score: 1.4701: : 239iter [00:23, 11.58iter/s]Best score: 1.2085: : 239iter [00:23, 11.58iter/s]Best score: 1.2085: : 240iter [00:23, 11.58iter/s]Best score: 1.2085: : 241iter [00:23, 11.38iter/s]Best score: 1.2085: : 241iter [00:23, 11.38iter/s]Best score: 1.2085: : 242iter [00:23, 11.38iter/s]Best score: 1.2085: : 243iter [00:23, 11.44iter/s]Best score: 1.2085: : 243iter [00:24, 11.44iter/s]Best score: 1.2085: : 244iter [00:24, 11.44iter/s]Best score: 1.2085: : 245iter [00:24, 11.52iter/s]Best score: 1.2085: : 245iter [00:24, 11.52iter/s]Best score: 1.2085: : 246iter [00:24, 11.52iter/s]Best score: 1.2085: : 247iter [00:24, 11.56iter/s]Best score: 1.2085: : 247iter [00:24, 11.56iter/s]Best score: 1.2085: : 248iter [00:24, 11.56iter/s]Best score: 1.2085: : 249iter [00:24, 11.53iter/s]
[ITER 250] Best: 1.1882
[PARAMS] Current best parameters:
  fx: 507.3289
  fy: 508.8683
  px: 836.7845
  py: 997.2559
  k1: 0.00670001
  k2: 0.04489574
  k3: -0.03626297
  k4: 0.00828782
  p1: -0.02064828
  p2: -0.03425719
  p3: 0.01993577
  p4: 0.00363030
  s1: -0.0042
  s2: 0.0021
  s3: -0.0201
  s4: 0.0088
  tilt_x: 0.0168
  tilt_y: 0.0096
  scale: 3.0067
  rotation: -0.6591
Best score: 1.1882: : 249iter [00:24, 11.53iter/s]Best score: 1.1882: : 250iter [00:24, 11.53iter/s]Best score: 1.1882: : 251iter [00:24,  7.48iter/s]Best score: 1.1882: : 251iter [00:25,  7.48iter/s]Best score: 1.1882: : 252iter [00:25,  7.48iter/s]Best score: 1.1882: : 253iter [00:25,  8.37iter/s]Best score: 1.1882: : 253iter [00:25,  8.37iter/s]Best score: 1.1882: : 254iter [00:25,  8.37iter/s]Best score: 1.1882: : 255iter [00:25,  9.13iter/s]Best score: 1.1882: : 255iter [00:25,  9.13iter/s]Best score: 1.1882: : 256iter [00:25,  9.13iter/s]Best score: 1.1882: : 257iter [00:25,  9.77iter/s]Best score: 1.1882: : 257iter [00:25,  9.77iter/s]Best score: 1.1882: : 258iter [00:25,  9.77iter/s]Best score: 1.1882: : 259iter [00:25, 10.28iter/s]Best score: 0.8226: : 259iter [00:25, 10.28iter/s]Best score: 0.8226: : 260iter [00:25, 10.28iter/s]Best score: 0.8226: : 261iter [00:25, 10.47iter/s]Best score: 0.8226: : 261iter [00:25, 10.47iter/s]Best score: 0.8226: : 262iter [00:25, 10.47iter/s]Best score: 0.8226: : 263iter [00:25, 10.80iter/s]Best score: 0.8226: : 263iter [00:26, 10.80iter/s]Best score: 0.8226: : 264iter [00:26, 10.80iter/s]Best score: 0.8226: : 265iter [00:26, 10.99iter/s]Best score: 0.8226: : 265iter [00:26, 10.99iter/s]Best score: 0.8226: : 266iter [00:26, 10.99iter/s]Best score: 0.8226: : 267iter [00:26, 11.18iter/s]Best score: 0.8226: : 267iter [00:26, 11.18iter/s]Best score: 0.8226: : 268iter [00:26, 11.18iter/s]Best score: 0.8226: : 269iter [00:26, 11.32iter/s]Best score: 0.8226: : 269iter [00:26, 11.32iter/s]Best score: 0.8226: : 270iter [00:26, 11.32iter/s]Best score: 0.8226: : 271iter [00:26, 11.24iter/s]Best score: 0.8226: : 271iter [00:26, 11.24iter/s]Best score: 0.8226: : 272iter [00:26, 11.24iter/s]Best score: 0.8226: : 273iter [00:26, 11.33iter/s]Best score: 0.8226: : 273iter [00:26, 11.33iter/s]Best score: 0.8226: : 274iter [00:27, 11.33iter/s]Best score: 0.8226: : 275iter [00:27,  8.82iter/s]Best score: 0.8226: : 275iter [00:27,  8.82iter/s]Best score: 0.8226: : 276iter [00:27,  8.82iter/s]Best score: 0.8226: : 277iter [00:27,  9.49iter/s]Best score: 0.8226: : 277iter [00:27,  9.49iter/s]Best score: 0.8226: : 278iter [00:27,  9.49iter/s]Best score: 0.8226: : 279iter [00:27, 10.02iter/s]Best score: 0.8226: : 279iter [00:27, 10.02iter/s]Best score: 0.8226: : 280iter [00:27, 10.02iter/s]Best score: 0.8226: : 281iter [00:27, 10.31iter/s]Best score: 0.8226: : 281iter [00:27, 10.31iter/s]Best score: 0.8226: : 282iter [00:27, 10.31iter/s]Best score: 0.8226: : 283iter [00:27, 10.68iter/s]Best score: 0.8226: : 283iter [00:27, 10.68iter/s]Best score: 0.8226: : 284iter [00:28, 10.68iter/s]Best score: 0.8226: : 285iter [00:28, 10.97iter/s]Best score: 0.8226: : 285iter [00:28, 10.97iter/s]Best score: 0.8226: : 286iter [00:28, 10.97iter/s]Best score: 0.8226: : 287iter [00:28, 11.17iter/s]Best score: 0.8226: : 287iter [00:28, 11.17iter/s]Best score: 0.8226: : 288iter [00:28, 11.17iter/s]Best score: 0.8226: : 289iter [00:28, 11.31iter/s]Best score: 0.6320: : 289iter [00:28, 11.31iter/s]Best score: 0.6320: : 290iter [00:28, 11.31iter/s]Best score: 0.6320: : 291iter [00:28, 11.23iter/s]Best score: 0.6320: : 291iter [00:28, 11.23iter/s]Best score: 0.6320: : 292iter [00:28, 11.23iter/s]Best score: 0.6320: : 293iter [00:28, 11.37iter/s]Best score: 0.6320: : 293iter [00:28, 11.37iter/s]Best score: 0.6320: : 294iter [00:28, 11.37iter/s]Best score: 0.6320: : 295iter [00:28, 11.45iter/s]Best score: 0.6320: : 295iter [00:29, 11.45iter/s]Best score: 0.6320: : 296iter [00:29, 11.45iter/s]Best score: 0.6320: : 297iter [00:29, 11.52iter/s]Best score: 0.6320: : 297iter [00:29, 11.52iter/s]Best score: 0.6320: : 298iter [00:29, 11.52iter/s]Best score: 0.6320: : 299iter [00:29, 11.57iter/s]
[ITER 300] Best: 0.6320
[PARAMS] Current best parameters:
  fx: 520.7974
  fy: 521.3118
  px: 841.3699
  py: 999.5536
  k1: -0.01035820
  k2: 0.03763973
  k3: -0.02286183
  k4: 0.00386529
  p1: -0.01595546
  p2: -0.02475867
  p3: 0.01842583
  p4: 0.01337096
  s1: -0.0168
  s2: -0.0079
  s3: -0.0093
  s4: -0.0091
  tilt_x: 0.0158
  tilt_y: 0.0058
  scale: 3.0205
  rotation: -0.6929
Best score: 0.6320: : 299iter [00:29, 11.57iter/s]
[Periodic outlier @ iter 200] Removing 1500 frame 19 (score=7.00, threshold=6.11)
  Remaining frames: 79
Best score: 0.6320: : 300iter [00:29, 11.57iter/s]Best score: 0.6320: : 301iter [00:29,  6.41iter/s]Best score: 0.6320: : 301iter [00:30,  6.41iter/s]Best score: 0.6320: : 302iter [00:30,  6.41iter/s]Best score: 0.6320: : 303iter [00:30,  7.38iter/s]Best score: 0.6320: : 303iter [00:30,  7.38iter/s]Best score: 0.6320: : 304iter [00:30,  7.38iter/s]Best score: 0.6320: : 305iter [00:30,  8.27iter/s]Best score: 0.6320: : 305iter [00:30,  8.27iter/s]Best score: 0.6320: : 306iter [00:30,  8.27iter/s]Best score: 0.6320: : 307iter [00:30,  9.00iter/s]Best score: 0.6320: : 307iter [00:30,  9.00iter/s]Best score: 0.6320: : 308iter [00:30,  9.00iter/s]Best score: 0.6320: : 309iter [00:30,  9.62iter/s]Best score: 0.5432: : 309iter [00:30,  9.62iter/s]Best score: 0.5432: : 310iter [00:30,  9.62iter/s]Best score: 0.5432: : 311iter [00:30,  9.98iter/s]Best score: 0.5432: : 311iter [00:30,  9.98iter/s]Best score: 0.5432: : 312iter [00:30,  9.98iter/s]Best score: 0.5432: : 313iter [00:30, 10.38iter/s]Best score: 0.5432: : 313iter [00:31, 10.38iter/s]Best score: 0.5432: : 314iter [00:31, 10.38iter/s]Best score: 0.5432: : 315iter [00:31, 10.69iter/s]Best score: 0.5432: : 315iter [00:31, 10.69iter/s]Best score: 0.5432: : 316iter [00:31, 10.69iter/s]Best score: 0.5432: : 317iter [00:31, 10.94iter/s]Best score: 0.5432: : 317iter [00:31, 10.94iter/s]Best score: 0.5432: : 318iter [00:31, 10.94iter/s]Best score: 0.5432: : 319iter [00:31, 11.08iter/s]Best score: 0.5432: : 319iter [00:31, 11.08iter/s]Best score: 0.5432: : 320iter [00:31, 11.08iter/s]Best score: 0.5432: : 321iter [00:31, 11.05iter/s]Best score: 0.5432: : 321iter [00:31, 11.05iter/s]Best score: 0.5432: : 322iter [00:31, 11.05iter/s]Best score: 0.5432: : 323iter [00:31, 11.16iter/s]Best score: 0.5432: : 323iter [00:31, 11.16iter/s]Best score: 0.5432: : 324iter [00:32, 11.16iter/s]Best score: 0.5432: : 325iter [00:32, 11.31iter/s]Best score: 0.5432: : 325iter [00:32, 11.31iter/s]Best score: 0.5432: : 326iter [00:32, 11.31iter/s]Best score: 0.5432: : 327iter [00:32, 11.41iter/s]Best score: 0.5432: : 327iter [00:32, 11.41iter/s]Best score: 0.5432: : 328iter [00:32, 11.41iter/s]Best score: 0.5432: : 329iter [00:32, 11.42iter/s]Best score: 0.5432: : 329iter [00:32, 11.42iter/s]Best score: 0.5432: : 330iter [00:32, 11.42iter/s]Best score: 0.5432: : 331iter [00:32, 11.22iter/s]Best score: 0.5432: : 331iter [00:32, 11.22iter/s]Best score: 0.5432: : 332iter [00:32, 11.22iter/s]Best score: 0.5432: : 333iter [00:32, 11.30iter/s]Best score: 0.5432: : 333iter [00:32, 11.30iter/s]Best score: 0.5432: : 334iter [00:32, 11.30iter/s]Best score: 0.5432: : 335iter [00:32, 11.37iter/s]Best score: 0.5432: : 335iter [00:32, 11.37iter/s]Best score: 0.5432: : 336iter [00:33, 11.37iter/s]Best score: 0.5432: : 337iter [00:33, 11.46iter/s]Best score: 0.5432: : 337iter [00:33, 11.46iter/s]Best score: 0.5432: : 338iter [00:33, 11.46iter/s]Best score: 0.5432: : 339iter [00:33, 11.53iter/s]Best score: 0.5432: : 339iter [00:33, 11.53iter/s]Best score: 0.5432: : 340iter [00:33, 11.53iter/s]Best score: 0.5432: : 341iter [00:33, 11.36iter/s]Best score: 0.5432: : 341iter [00:33, 11.36iter/s]Best score: 0.5432: : 342iter [00:33, 11.36iter/s]Best score: 0.5432: : 343iter [00:33, 11.42iter/s]Best score: 0.5432: : 343iter [00:33, 11.42iter/s]Best score: 0.5432: : 344iter [00:33, 11.42iter/s]Best score: 0.5432: : 345iter [00:33, 11.52iter/s]Best score: 0.5432: : 345iter [00:33, 11.52iter/s]Best score: 0.5432: : 346iter [00:33, 11.52iter/s]Best score: 0.5432: : 347iter [00:33, 11.60iter/s]Best score: 0.5432: : 347iter [00:34, 11.60iter/s]Best score: 0.5432: : 348iter [00:34, 11.60iter/s]Best score: 0.5432: : 349iter [00:34, 11.60iter/s]
[ITER 350] Best: 0.5432
[PARAMS] Current best parameters:
  fx: 520.7974
  fy: 521.3118
  px: 841.3699
  py: 999.5536
  k1: -0.01035820
  k2: 0.03763973
  k3: -0.02286183
  k4: 0.00386529
  p1: -0.01595546
  p2: -0.02475867
  p3: 0.01842583
  p4: 0.01337096
  s1: -0.0168
  s2: -0.0079
  s3: -0.0093
  s4: -0.0091
  tilt_x: 0.0158
  tilt_y: 0.0058
  scale: 3.0205
  rotation: -0.6929
Best score: 0.5432: : 349iter [00:34, 11.60iter/s]Best score: 0.5432: : 350iter [00:34, 11.60iter/s]Best score: 0.5432: : 351iter [00:34,  7.51iter/s]Best score: 0.5432: : 351iter [00:34,  7.51iter/s]Best score: 0.5432: : 352iter [00:34,  7.51iter/s]Best score: 0.5432: : 353iter [00:34,  8.42iter/s]Best score: 0.5432: : 353iter [00:34,  8.42iter/s]Best score: 0.5432: : 354iter [00:34,  8.42iter/s]Best score: 0.5432: : 355iter [00:34,  9.19iter/s]Best score: 0.5432: : 355iter [00:35,  9.19iter/s]Best score: 0.5432: : 356iter [00:35,  9.19iter/s]Best score: 0.5432: : 357iter [00:35,  9.74iter/s]Best score: 0.5432: : 357iter [00:35,  9.74iter/s]Best score: 0.5432: : 358iter [00:35,  9.74iter/s]Best score: 0.5432: : 359iter [00:35, 10.28iter/s]Best score: 0.5432: : 359iter [00:35, 10.28iter/s]Best score: 0.5432: : 360iter [00:35, 10.28iter/s]Best score: 0.5432: : 361iter [00:35, 10.48iter/s]Best score: 0.5432: : 361iter [00:35, 10.48iter/s]Best score: 0.5432: : 362iter [00:35, 10.48iter/s]Best score: 0.5432: : 363iter [00:35, 10.85iter/s]Best score: 0.5432: : 363iter [00:35, 10.85iter/s]Best score: 0.5432: : 364iter [00:35, 10.85iter/s]Best score: 0.5432: : 365iter [00:35, 11.08iter/s]Best score: 0.5432: : 365iter [00:35, 11.08iter/s]Best score: 0.5432: : 366iter [00:35, 11.08iter/s]Best score: 0.5432: : 367iter [00:35, 11.28iter/s]Best score: 0.5432: : 367iter [00:36, 11.28iter/s]Best score: 0.5432: : 368iter [00:36, 11.28iter/s]Best score: 0.5432: : 369iter [00:36, 11.44iter/s]Best score: 0.5432: : 369iter [00:36, 11.44iter/s]Best score: 0.5432: : 370iter [00:36, 11.44iter/s]Best score: 0.5432: : 371iter [00:36, 11.34iter/s]Best score: 0.5432: : 371iter [00:36, 11.34iter/s]Best score: 0.5432: : 372iter [00:36, 11.34iter/s]Best score: 0.5432: : 373iter [00:36, 11.45iter/s]Best score: 0.5432: : 373iter [00:36, 11.45iter/s]Best score: 0.5432: : 374iter [00:36, 11.45iter/s]Best score: 0.5432: : 375iter [00:36, 11.54iter/s]Best score: 0.5432: : 375iter [00:36, 11.54iter/s]Best score: 0.5432: : 376iter [00:36, 11.54iter/s]Best score: 0.5432: : 377iter [00:36, 11.58iter/s]Best score: 0.5432: : 377iter [00:36, 11.58iter/s]Best score: 0.5432: : 378iter [00:37, 11.58iter/s]Best score: 0.5432: : 379iter [00:37, 11.64iter/s]Best score: 0.5432: : 379iter [00:37, 11.64iter/s]Best score: 0.5432: : 380iter [00:37, 11.64iter/s]Best score: 0.5432: : 381iter [00:37, 11.47iter/s]Best score: 0.5432: : 381iter [00:37, 11.47iter/s]Best score: 0.5432: : 382iter [00:37, 11.47iter/s]Best score: 0.5432: : 383iter [00:37, 11.57iter/s]Best score: 0.5432: : 383iter [00:37, 11.57iter/s]Best score: 0.5432: : 384iter [00:37, 11.57iter/s]Best score: 0.5432: : 385iter [00:37, 11.62iter/s]Best score: 0.5432: : 385iter [00:37, 11.62iter/s]Best score: 0.5432: : 386iter [00:37, 11.62iter/s]Best score: 0.5432: : 387iter [00:37, 11.67iter/s]Best score: 0.5432: : 387iter [00:37, 11.67iter/s]Best score: 0.5432: : 388iter [00:37, 11.67iter/s]Best score: 0.5432: : 389iter [00:37, 11.73iter/s]Best score: 0.5432: : 389iter [00:37, 11.73iter/s]Best score: 0.5432: : 390iter [00:38, 11.73iter/s]Best score: 0.5432: : 391iter [00:38, 11.55iter/s]Best score: 0.5432: : 391iter [00:38, 11.55iter/s]Best score: 0.5432: : 392iter [00:38, 11.55iter/s]Best score: 0.5432: : 393iter [00:38, 11.60iter/s]Best score: 0.5432: : 393iter [00:38, 11.60iter/s]Best score: 0.5432: : 394iter [00:38, 11.60iter/s]Best score: 0.5432: : 395iter [00:38, 11.66iter/s]Best score: 0.5432: : 395iter [00:38, 11.66iter/s]Best score: 0.5432: : 396iter [00:38, 11.66iter/s]Best score: 0.5432: : 397iter [00:38, 11.70iter/s]Best score: 0.5432: : 397iter [00:38, 11.70iter/s]Best score: 0.5432: : 398iter [00:38, 11.70iter/s]Best score: 0.5432: : 399iter [00:38, 11.71iter/s]
[ITER 400] Best: 0.5432
[PARAMS] Current best parameters:
  fx: 520.7974
  fy: 521.3118
  px: 841.3699
  py: 999.5536
  k1: -0.01035820
  k2: 0.03763973
  k3: -0.02286183
  k4: 0.00386529
  p1: -0.01595546
  p2: -0.02475867
  p3: 0.01842583
  p4: 0.01337096
  s1: -0.0168
  s2: -0.0079
  s3: -0.0093
  s4: -0.0091
  tilt_x: 0.0158
  tilt_y: 0.0058
  scale: 3.0205
  rotation: -0.6929
Best score: 0.5432: : 399iter [00:38, 11.71iter/s]Best score: 0.5432: : 400iter [00:39, 11.71iter/s]Best score: 0.5432: : 401iter [00:39,  7.53iter/s]Best score: 0.5432: : 401iter [00:39,  7.53iter/s]Best score: 0.5432: : 402iter [00:39,  7.53iter/s]Best score: 0.5432: : 403iter [00:39,  8.45iter/s]Best score: 0.5432: : 403iter [00:39,  8.45iter/s]Best score: 0.5432: : 404iter [00:39,  8.45iter/s]Best score: 0.5432: : 405iter [00:39,  9.26iter/s]Best score: 0.5432: : 405iter [00:39,  9.26iter/s]Best score: 0.5432: : 406iter [00:39,  9.26iter/s]Best score: 0.5432: : 407iter [00:39,  9.90iter/s]Best score: 0.5432: : 407iter [00:39,  9.90iter/s]Best score: 0.5432: : 408iter [00:39,  9.90iter/s]Best score: 0.5432: : 409iter [00:39, 10.44iter/s]Best score: 0.5432: : 409iter [00:39, 10.44iter/s]Best score: 0.5432: : 410iter [00:40, 10.44iter/s]Best score: 0.5432: : 411iter [00:40, 10.64iter/s]Best score: 0.5432: : 411iter [00:40, 10.64iter/s]Best score: 0.5432: : 412iter [00:40, 10.64iter/s]Best score: 0.5432: : 413iter [00:40, 10.98iter/s]Best score: 0.5432: : 413iter [00:40, 10.98iter/s]Best score: 0.5432: : 414iter [00:40, 10.98iter/s]Best score: 0.5432: : 415iter [00:40, 11.23iter/s]Best score: 0.5432: : 415iter [00:40, 11.23iter/s]Best score: 0.5432: : 416iter [00:40, 11.23iter/s]Best score: 0.5432: : 417iter [00:40, 11.43iter/s]Best score: 0.5432: : 417iter [00:40, 11.43iter/s]Best score: 0.5432: : 418iter [00:40, 11.43iter/s]Best score: 0.5432: : 419iter [00:40, 11.57iter/s]Best score: 0.5432: : 419iter [00:40, 11.57iter/s]Best score: 0.5432: : 420iter [00:40, 11.57iter/s]Best score: 0.5432: : 421iter [00:40, 11.41iter/s]Best score: 0.5432: : 421iter [00:41, 11.41iter/s]Best score: 0.5432: : 422iter [00:41, 11.41iter/s]Best score: 0.5432: : 423iter [00:41, 11.52iter/s]Best score: 0.5432: : 423iter [00:41, 11.52iter/s]Best score: 0.5432: : 424iter [00:41, 11.52iter/s]Best score: 0.5432: : 425iter [00:41, 11.61iter/s]Best score: 0.5432: : 425iter [00:41, 11.61iter/s]Best score: 0.5432: : 426iter [00:41, 11.61iter/s]Best score: 0.5432: : 427iter [00:41, 11.69iter/s]Best score: 0.5432: : 427iter [00:41, 11.69iter/s]Best score: 0.5432: : 428iter [00:41, 11.69iter/s]Best score: 0.5432: : 429iter [00:41, 11.74iter/s]Best score: 0.5432: : 429iter [00:41, 11.74iter/s]Best score: 0.5432: : 430iter [00:41, 11.74iter/s]Best score: 0.5432: : 431iter [00:41, 11.56iter/s]Best score: 0.5432: : 431iter [00:41, 11.56iter/s]Best score: 0.5432: : 432iter [00:41, 11.56iter/s]Best score: 0.5432: : 433iter [00:41, 11.66iter/s]Best score: 0.5432: : 433iter [00:42, 11.66iter/s]Best score: 0.5432: : 434iter [00:42, 11.66iter/s]Best score: 0.5432: : 435iter [00:42, 11.69iter/s]Best score: 0.5432: : 435iter [00:42, 11.69iter/s]Best score: 0.5432: : 436iter [00:42, 11.69iter/s]Best score: 0.5432: : 437iter [00:42, 11.69iter/s]Best score: 0.5432: : 437iter [00:42, 11.69iter/s]Best score: 0.5432: : 438iter [00:42, 11.69iter/s]Best score: 0.5432: : 439iter [00:42, 11.73iter/s]Best score: 0.5432: : 439iter [00:42, 11.73iter/s]Best score: 0.5432: : 440iter [00:42, 11.73iter/s]Best score: 0.5432: : 441iter [00:42, 11.51iter/s]Best score: 0.5432: : 441iter [00:42, 11.51iter/s]Best score: 0.5432: : 442iter [00:42, 11.51iter/s]Best score: 0.5432: : 443iter [00:42, 11.58iter/s]Best score: 0.5432: : 443iter [00:42, 11.58iter/s]Best score: 0.5432: : 444iter [00:42, 11.58iter/s]Best score: 0.5432: : 445iter [00:42, 11.58iter/s]Best score: 0.5432: : 445iter [00:43, 11.58iter/s]Best score: 0.5432: : 446iter [00:43, 11.58iter/s]Best score: 0.5432: : 447iter [00:43, 11.61iter/s]Best score: 0.5432: : 447iter [00:43, 11.61iter/s]Best score: 0.5432: : 448iter [00:43, 11.61iter/s]Best score: 0.5432: : 449iter [00:43, 11.64iter/s]
[ITER 450] Best: 0.5432
[PARAMS] Current best parameters:
  fx: 520.7974
  fy: 521.3118
  px: 841.3699
  py: 999.5536
  k1: -0.01035820
  k2: 0.03763973
  k3: -0.02286183
  k4: 0.00386529
  p1: -0.01595546
  p2: -0.02475867
  p3: 0.01842583
  p4: 0.01337096
  s1: -0.0168
  s2: -0.0079
  s3: -0.0093
  s4: -0.0091
  tilt_x: 0.0158
  tilt_y: 0.0058
  scale: 3.0205
  rotation: -0.6929
Best score: 0.5432: : 449iter [00:43, 11.64iter/s]Best score: 0.5432: : 450iter [00:43, 11.64iter/s]Best score: 0.5432: : 451iter [00:43,  7.50iter/s]Best score: 0.5432: : 451iter [00:43,  7.50iter/s]Best score: 0.5432: : 452iter [00:43,  7.50iter/s]Best score: 0.5432: : 453iter [00:43,  8.43iter/s]Best score: 0.5432: : 453iter [00:44,  8.43iter/s]Best score: 0.5432: : 454iter [00:44,  8.43iter/s]Best score: 0.5432: : 455iter [00:44,  9.20iter/s]Best score: 0.5432: : 455iter [00:44,  9.20iter/s]Best score: 0.5432: : 456iter [00:44,  9.20iter/s]Best score: 0.5432: : 457iter [00:44,  9.87iter/s]Best score: 0.5432: : 457iter [00:44,  9.87iter/s]Best score: 0.5432: : 458iter [00:44,  9.87iter/s]Best score: 0.5432: : 459iter [00:44, 10.39iter/s]Best score: 0.5432: : 459iter [00:44, 10.39iter/s]Best score: 0.5432: : 460iter [00:44, 10.39iter/s]Best score: 0.5432: : 461iter [00:44, 10.62iter/s]Best score: 0.5432: : 461iter [00:44, 10.62iter/s]Best score: 0.5432: : 462iter [00:44, 10.62iter/s]Best score: 0.5432: : 463iter [00:44, 10.97iter/s]Best score: 0.5432: : 463iter [00:44, 10.97iter/s]Best score: 0.5432: : 464iter [00:45, 10.97iter/s]Best score: 0.5432: : 465iter [00:45, 11.22iter/s]Best score: 0.5432: : 465iter [00:45, 11.22iter/s]Best score: 0.5432: : 466iter [00:45, 11.22iter/s]Best score: 0.5432: : 467iter [00:45, 11.41iter/s]Best score: 0.5432: : 467iter [00:45, 11.41iter/s]Best score: 0.5432: : 468iter [00:45, 11.41iter/s]Best score: 0.5432: : 469iter [00:45, 11.54iter/s]Best score: 0.5076: : 469iter [00:45, 11.54iter/s]Best score: 0.5076: : 470iter [00:45, 11.54iter/s]Best score: 0.5076: : 471iter [00:45, 11.45iter/s]Best score: 0.5076: : 471iter [00:45, 11.45iter/s]Best score: 0.5076: : 472iter [00:45, 11.45iter/s]Best score: 0.5076: : 473iter [00:45, 11.58iter/s]Best score: 0.5076: : 473iter [00:45, 11.58iter/s]Best score: 0.5076: : 474iter [00:45, 11.58iter/s]Best score: 0.5076: : 475iter [00:45, 11.64iter/s]Best score: 0.5076: : 475iter [00:45, 11.64iter/s]Best score: 0.5076: : 476iter [00:46, 11.64iter/s]Best score: 0.5076: : 477iter [00:46, 11.70iter/s]Best score: 0.5076: : 477iter [00:46, 11.70iter/s]Best score: 0.5076: : 478iter [00:46, 11.70iter/s]Best score: 0.5076: : 479iter [00:46, 11.75iter/s]Best score: 0.5076: : 479iter [00:46, 11.75iter/s]Best score: 0.5076: : 480iter [00:46, 11.75iter/s]Best score: 0.5076: : 481iter [00:46, 11.60iter/s]Best score: 0.5076: : 481iter [00:46, 11.60iter/s]Best score: 0.5076: : 482iter [00:46, 11.60iter/s]Best score: 0.5076: : 483iter [00:46, 11.67iter/s]Best score: 0.5076: : 483iter [00:46, 11.67iter/s]Best score: 0.5076: : 484iter [00:46, 11.67iter/s]Best score: 0.5076: : 485iter [00:46, 11.73iter/s]Best score: 0.5076: : 485iter [00:46, 11.73iter/s]Best score: 0.5076: : 486iter [00:46, 11.73iter/s]Best score: 0.5076: : 487iter [00:46, 11.71iter/s]Best score: 0.5076: : 487iter [00:46, 11.71iter/s]Best score: 0.5076: : 488iter [00:47, 11.71iter/s]Best score: 0.5076: : 489iter [00:47, 11.75iter/s]Best score: 0.5076: : 489iter [00:47, 11.75iter/s]Best score: 0.5076: : 490iter [00:47, 11.75iter/s]Best score: 0.5076: : 491iter [00:47, 11.55iter/s]Best score: 0.5076: : 491iter [00:47, 11.55iter/s]Best score: 0.5076: : 492iter [00:47, 11.55iter/s]Best score: 0.5076: : 493iter [00:47, 11.65iter/s]Best score: 0.5076: : 493iter [00:47, 11.65iter/s]Best score: 0.5076: : 494iter [00:47, 11.65iter/s]Best score: 0.5076: : 495iter [00:47, 11.71iter/s]Best score: 0.5076: : 495iter [00:47, 11.71iter/s]Best score: 0.5076: : 496iter [00:47, 11.71iter/s]Best score: 0.5076: : 497iter [00:47, 11.74iter/s]Best score: 0.5076: : 497iter [00:47, 11.74iter/s]Best score: 0.5076: : 498iter [00:47, 11.74iter/s]Best score: 0.5076: : 499iter [00:47, 11.75iter/s]
[ITER 500] Best: 0.4745
[PARAMS] Current best parameters:
  fx: 530.2015
  fy: 531.0702
  px: 836.8338
  py: 999.5529
  k1: -0.00764259
  k2: 0.03049614
  k3: -0.02787845
  k4: 0.00731483
  p1: -0.01275421
  p2: -0.02815749
  p3: 0.01481133
  p4: 0.01108117
  s1: -0.0112
  s2: -0.0056
  s3: -0.0027
  s4: -0.0114
  tilt_x: 0.0093
  tilt_y: 0.0229
  scale: 2.9952
  rotation: -0.6400
Best score: 0.4745: : 499iter [00:47, 11.75iter/s]Best score: 0.4745: : 500iter [00:48, 11.75iter/s]Best score: 0.4745: : 501iter [00:48,  7.54iter/s]Best score: 0.4745: : 501iter [00:48,  7.54iter/s]Best score: 0.4745: : 502iter [00:48,  7.54iter/s]Best score: 0.4745: : 503iter [00:48,  8.45iter/s]Best score: 0.4745: : 503iter [00:48,  8.45iter/s]Best score: 0.4745: : 504iter [00:48,  8.45iter/s]Best score: 0.4745: : 505iter [00:48,  9.24iter/s]Best score: 0.4745: : 505iter [00:48,  9.24iter/s]Best score: 0.4745: : 506iter [00:48,  9.24iter/s]Best score: 0.4745: : 507iter [00:48,  9.85iter/s]Best score: 0.4745: : 507iter [00:48,  9.85iter/s]Best score: 0.4745: : 508iter [00:49,  9.85iter/s]Best score: 0.4745: : 509iter [00:49, 10.37iter/s]Best score: 0.4448: : 509iter [00:49, 10.37iter/s]Best score: 0.4448: : 510iter [00:49, 10.37iter/s]Best score: 0.4448: : 511iter [00:49, 10.61iter/s]Best score: 0.4448: : 511iter [00:49, 10.61iter/s]Best score: 0.4448: : 512iter [00:49, 10.61iter/s]Best score: 0.4448: : 513iter [00:49, 10.94iter/s]Best score: 0.4448: : 513iter [00:49, 10.94iter/s]Best score: 0.4448: : 514iter [00:49, 10.94iter/s]Best score: 0.4448: : 515iter [00:49, 11.18iter/s]Best score: 0.4448: : 515iter [00:49, 11.18iter/s]Best score: 0.4448: : 516iter [00:49, 11.18iter/s]Best score: 0.4448: : 517iter [00:49, 11.37iter/s]Best score: 0.4448: : 517iter [00:49, 11.37iter/s]Best score: 0.4448: : 518iter [00:49, 11.37iter/s]Best score: 0.4448: : 519iter [00:49, 11.52iter/s]Best score: 0.4394: : 519iter [00:50, 11.52iter/s]Best score: 0.4394: : 520iter [00:50, 11.52iter/s]Best score: 0.4394: : 521iter [00:50, 11.44iter/s]Best score: 0.4394: : 521iter [00:50, 11.44iter/s]Best score: 0.4394: : 522iter [00:50, 11.44iter/s]Best score: 0.4394: : 523iter [00:50, 11.55iter/s]Best score: 0.4394: : 523iter [00:50, 11.55iter/s]Best score: 0.4394: : 524iter [00:50, 11.55iter/s]Best score: 0.4394: : 525iter [00:50, 11.65iter/s]Best score: 0.4394: : 525iter [00:50, 11.65iter/s]Best score: 0.4394: : 526iter [00:50, 11.65iter/s]Best score: 0.4394: : 527iter [00:50, 11.69iter/s]Best score: 0.4394: : 527iter [00:50, 11.69iter/s]Best score: 0.4394: : 528iter [00:50, 11.69iter/s]Best score: 0.4394: : 529iter [00:50, 11.72iter/s]Best score: 0.4353: : 529iter [00:50, 11.72iter/s]Best score: 0.4353: : 530iter [00:50, 11.72iter/s]Best score: 0.4353: : 531iter [00:50, 11.50iter/s]Best score: 0.4353: : 531iter [00:51, 11.50iter/s]Best score: 0.4353: : 532iter [00:51, 11.50iter/s]Best score: 0.4353: : 533iter [00:51, 11.61iter/s]Best score: 0.4353: : 533iter [00:51, 11.61iter/s]Best score: 0.4353: : 534iter [00:51, 11.61iter/s]Best score: 0.4353: : 535iter [00:51, 11.68iter/s]Best score: 0.4353: : 535iter [00:51, 11.68iter/s]Best score: 0.4353: : 536iter [00:51, 11.68iter/s]Best score: 0.4353: : 537iter [00:51, 11.67iter/s]Best score: 0.4353: : 537iter [00:51, 11.67iter/s]Best score: 0.4353: : 538iter [00:51, 11.67iter/s]Best score: 0.4353: : 539iter [00:51, 11.73iter/s]Best score: 0.4075: : 539iter [00:51, 11.73iter/s]Best score: 0.4075: : 540iter [00:51, 11.73iter/s]Best score: 0.4075: : 541iter [00:51, 11.57iter/s]Best score: 0.4075: : 541iter [00:51, 11.57iter/s]Best score: 0.4075: : 542iter [00:51, 11.57iter/s]Best score: 0.4075: : 543iter [00:51, 11.64iter/s]Best score: 0.4075: : 543iter [00:52, 11.64iter/s]Best score: 0.4075: : 544iter [00:52, 11.64iter/s]Best score: 0.4075: : 545iter [00:52, 11.71iter/s]Best score: 0.4075: : 545iter [00:52, 11.71iter/s]Best score: 0.4075: : 546iter [00:52, 11.71iter/s]Best score: 0.4075: : 547iter [00:52, 11.76iter/s]Best score: 0.4075: : 547iter [00:52, 11.76iter/s]Best score: 0.4075: : 548iter [00:52, 11.76iter/s]Best score: 0.4075: : 549iter [00:52, 11.78iter/s]
[ITER 550] Best: 0.3675
[PARAMS] Current best parameters:
  fx: 519.8028
  fy: 519.2312
  px: 849.5984
  py: 996.5714
  k1: 0.00364926
  k2: 0.01270973
  k3: -0.00612329
  k4: 0.00016809
  p1: -0.01547431
  p2: -0.01600700
  p3: 0.02765114
  p4: 0.00901076
  s1: -0.0249
  s2: -0.0028
  s3: -0.0118
  s4: -0.0096
  tilt_x: -0.0038
  tilt_y: -0.0143
  scale: 3.0161
  rotation: -0.6849
Best score: 0.3675: : 549iter [00:52, 11.78iter/s]Best score: 0.3675: : 550iter [00:52, 11.78iter/s]Best score: 0.3675: : 551iter [00:52,  7.55iter/s]Best score: 0.3675: : 551iter [00:53,  7.55iter/s]Best score: 0.3675: : 552iter [00:53,  7.55iter/s]Best score: 0.3675: : 553iter [00:53,  8.47iter/s]Best score: 0.3675: : 553iter [00:53,  8.47iter/s]Best score: 0.3675: : 554iter [00:53,  8.47iter/s]Best score: 0.3675: : 555iter [00:53,  9.27iter/s]Best score: 0.3675: : 555iter [00:53,  9.27iter/s]Best score: 0.3675: : 556iter [00:53,  9.27iter/s]Best score: 0.3675: : 557iter [00:53,  9.93iter/s]Best score: 0.3675: : 557iter [00:53,  9.93iter/s]Best score: 0.3675: : 558iter [00:53,  9.93iter/s]Best score: 0.3675: : 559iter [00:53, 10.43iter/s]Best score: 0.3525: : 559iter [00:53, 10.43iter/s]Best score: 0.3525: : 560iter [00:53, 10.43iter/s]Best score: 0.3525: : 561iter [00:53, 10.63iter/s]Best score: 0.3525: : 561iter [00:53, 10.63iter/s]Best score: 0.3525: : 562iter [00:54, 10.63iter/s]Best score: 0.3525: : 563iter [00:54, 10.97iter/s]Best score: 0.3525: : 563iter [00:54, 10.97iter/s]Best score: 0.3525: : 564iter [00:54, 10.97iter/s]Best score: 0.3525: : 565iter [00:54, 11.22iter/s]Best score: 0.3525: : 565iter [00:54, 11.22iter/s]Best score: 0.3525: : 566iter [00:54, 11.22iter/s]Best score: 0.3525: : 567iter [00:54, 11.41iter/s]Best score: 0.3525: : 567iter [00:54, 11.41iter/s]Best score: 0.3525: : 568iter [00:54, 11.41iter/s]Best score: 0.3525: : 569iter [00:54, 11.52iter/s]Best score: 0.3525: : 569iter [00:54, 11.52iter/s]Best score: 0.3525: : 570iter [00:54, 11.52iter/s]Best score: 0.3525: : 571iter [00:54, 11.42iter/s]Best score: 0.3525: : 571iter [00:54, 11.42iter/s]Best score: 0.3525: : 572iter [00:54, 11.42iter/s]Best score: 0.3525: : 573iter [00:54, 11.55iter/s]Best score: 0.3525: : 573iter [00:54, 11.55iter/s]Best score: 0.3525: : 574iter [00:55, 11.55iter/s]Best score: 0.3525: : 575iter [00:55, 11.65iter/s]Best score: 0.3525: : 575iter [00:55, 11.65iter/s]Best score: 0.3525: : 576iter [00:55, 11.65iter/s]Best score: 0.3525: : 577iter [00:55, 11.71iter/s]Best score: 0.3525: : 577iter [00:55, 11.71iter/s]Best score: 0.3525: : 578iter [00:55, 11.71iter/s]Best score: 0.3525: : 579iter [00:55, 11.68iter/s]Best score: 0.3467: : 579iter [00:55, 11.68iter/s]Best score: 0.3467: : 580iter [00:55, 11.68iter/s]Best score: 0.3467: : 581iter [00:55, 11.51iter/s]Best score: 0.3467: : 581iter [00:55, 11.51iter/s]Best score: 0.3467: : 582iter [00:55, 11.51iter/s]Best score: 0.3467: : 583iter [00:55, 11.61iter/s]Best score: 0.3467: : 583iter [00:55, 11.61iter/s]Best score: 0.3467: : 584iter [00:55, 11.61iter/s]Best score: 0.3467: : 585iter [00:55, 11.64iter/s]Best score: 0.3467: : 585iter [00:55, 11.64iter/s]Best score: 0.3467: : 586iter [00:56, 11.64iter/s]Best score: 0.3467: : 587iter [00:56, 11.72iter/s]Best score: 0.3467: : 587iter [00:56, 11.72iter/s]Best score: 0.3467: : 588iter [00:56, 11.72iter/s]Best score: 0.3467: : 589iter [00:56, 11.77iter/s]Best score: 0.3372: : 589iter [00:56, 11.77iter/s]Best score: 0.3372: : 590iter [00:56, 11.77iter/s]Best score: 0.3372: : 591iter [00:56, 11.60iter/s]Best score: 0.3372: : 591iter [00:56, 11.60iter/s]Best score: 0.3372: : 592iter [00:56, 11.60iter/s]Best score: 0.3372: : 593iter [00:56, 11.63iter/s]Best score: 0.3372: : 593iter [00:56, 11.63iter/s]Best score: 0.3372: : 594iter [00:56, 11.63iter/s]Best score: 0.3372: : 595iter [00:56, 11.69iter/s]Best score: 0.3372: : 595iter [00:56, 11.69iter/s]Best score: 0.3372: : 596iter [00:56, 11.69iter/s]Best score: 0.3372: : 597iter [00:56, 11.72iter/s]Best score: 0.3372: : 597iter [00:56, 11.72iter/s]Best score: 0.3372: : 598iter [00:57, 11.72iter/s]Best score: 0.3372: : 599iter [00:57, 11.71iter/s]
[ITER 600] Best: 0.3372
[PARAMS] Current best parameters:
  fx: 527.1646
  fy: 527.3289
  px: 841.8812
  py: 999.5716
  k1: -0.00719349
  k2: 0.02773065
  k3: -0.02169062
  k4: 0.00498506
  p1: -0.01505667
  p2: -0.01649390
  p3: 0.02062475
  p4: 0.01077566
  s1: -0.0244
  s2: -0.0056
  s3: -0.0078
  s4: -0.0093
  tilt_x: 0.0120
  tilt_y: 0.0210
  scale: 3.0101
  rotation: -0.6858
Best score: 0.3372: : 599iter [00:57, 11.71iter/s]Best score: 0.3372: : 600iter [00:57, 11.71iter/s]Best score: 0.3372: : 601iter [00:57,  7.53iter/s]Best score: 0.3372: : 601iter [00:57,  7.53iter/s]Best score: 0.3372: : 602iter [00:57,  7.53iter/s]Best score: 0.3372: : 603iter [00:57,  8.45iter/s]Best score: 0.3372: : 603iter [00:57,  8.45iter/s]Best score: 0.3372: : 604iter [00:57,  8.45iter/s]Best score: 0.3372: : 605iter [00:57,  9.22iter/s]Best score: 0.3372: : 605iter [00:57,  9.22iter/s]Best score: 0.3372: : 606iter [00:58,  9.22iter/s]Best score: 0.3372: : 607iter [00:58,  9.88iter/s]Best score: 0.3372: : 607iter [00:58,  9.88iter/s]Best score: 0.3372: : 608iter [00:58,  9.88iter/s]Best score: 0.3372: : 609iter [00:58, 10.38iter/s]Best score: 0.2967: : 609iter [00:58, 10.38iter/s]Best score: 0.2967: : 610iter [00:58, 10.38iter/s]Best score: 0.2967: : 611iter [00:58, 10.60iter/s]Best score: 0.2967: : 611iter [00:58, 10.60iter/s]Best score: 0.2967: : 612iter [00:58, 10.60iter/s]Best score: 0.2967: : 613iter [00:58, 10.92iter/s]Best score: 0.2967: : 613iter [00:58, 10.92iter/s]Best score: 0.2967: : 614iter [00:58, 10.92iter/s]Best score: 0.2967: : 615iter [00:58, 11.15iter/s]Best score: 0.2967: : 615iter [00:58, 11.15iter/s]Best score: 0.2967: : 616iter [00:58, 11.15iter/s]Best score: 0.2967: : 617iter [00:58, 11.36iter/s]Best score: 0.2967: : 617iter [00:59, 11.36iter/s]Best score: 0.2967: : 618iter [00:59, 11.36iter/s]Best score: 0.2967: : 619iter [00:59, 11.50iter/s]Best score: 0.2967: : 619iter [00:59, 11.50iter/s]Best score: 0.2967: : 620iter [00:59, 11.50iter/s]Best score: 0.2967: : 621iter [00:59, 11.42iter/s]Best score: 0.2967: : 621iter [00:59, 11.42iter/s]Best score: 0.2967: : 622iter [00:59, 11.42iter/s]Best score: 0.2967: : 623iter [00:59, 11.55iter/s]Best score: 0.2967: : 623iter [00:59, 11.55iter/s]Best score: 0.2967: : 624iter [00:59, 11.55iter/s]Best score: 0.2967: : 625iter [00:59, 11.63iter/s]Best score: 0.2967: : 625iter [00:59, 11.63iter/s]Best score: 0.2967: : 626iter [00:59, 11.63iter/s]Best score: 0.2967: : 627iter [00:59, 11.71iter/s]Best score: 0.2967: : 627iter [00:59, 11.71iter/s]Best score: 0.2967: : 628iter [00:59, 11.71iter/s]Best score: 0.2967: : 629iter [00:59, 11.75iter/s]Best score: 0.2967: : 629iter [01:00, 11.75iter/s]Best score: 0.2967: : 630iter [01:00, 11.75iter/s]Best score: 0.2967: : 631iter [01:00, 11.60iter/s]Best score: 0.2967: : 631iter [01:00, 11.60iter/s]Best score: 0.2967: : 632iter [01:00, 11.60iter/s]Best score: 0.2967: : 633iter [01:00, 11.70iter/s]Best score: 0.2967: : 633iter [01:00, 11.70iter/s]Best score: 0.2967: : 634iter [01:00, 11.70iter/s]Best score: 0.2967: : 635iter [01:00, 11.69iter/s]Best score: 0.2967: : 635iter [01:00, 11.69iter/s]Best score: 0.2967: : 636iter [01:00, 11.69iter/s]Best score: 0.2967: : 637iter [01:00, 11.74iter/s]Best score: 0.2967: : 637iter [01:00, 11.74iter/s]Best score: 0.2967: : 638iter [01:00, 11.74iter/s]Best score: 0.2967: : 639iter [01:00, 11.78iter/s]Best score: 0.2853: : 639iter [01:00, 11.78iter/s]Best score: 0.2853: : 640iter [01:00, 11.78iter/s]Best score: 0.2853: : 641iter [01:00, 11.60iter/s]Best score: 0.2853: : 641iter [01:01, 11.60iter/s]Best score: 0.2853: : 642iter [01:01, 11.60iter/s]Best score: 0.2853: : 643iter [01:01, 11.68iter/s]Best score: 0.2853: : 643iter [01:01, 11.68iter/s]Best score: 0.2853: : 644iter [01:01, 11.68iter/s]Best score: 0.2853: : 645iter [01:01, 11.72iter/s]Best score: 0.2853: : 645iter [01:01, 11.72iter/s]Best score: 0.2853: : 646iter [01:01, 11.72iter/s]Best score: 0.2853: : 647iter [01:01, 11.75iter/s]Best score: 0.2853: : 647iter [01:01, 11.75iter/s]Best score: 0.2853: : 648iter [01:01, 11.75iter/s]Best score: 0.2853: : 649iter [01:01, 11.79iter/s]
[ITER 650] Best: 0.2822
[PARAMS] Current best parameters:
  fx: 525.2282
  fy: 525.2178
  px: 846.7597
  py: 995.2764
  k1: 0.00483851
  k2: 0.00516633
  k3: -0.00322365
  k4: -0.00015210
  p1: -0.01727355
  p2: -0.01634938
  p3: 0.02377933
  p4: 0.01251577
  s1: -0.0211
  s2: -0.0087
  s3: -0.0115
  s4: -0.0117
  tilt_x: -0.0044
  tilt_y: -0.0015
  scale: 3.0047
  rotation: -0.7061
Best score: 0.2822: : 649iter [01:01, 11.79iter/s]Best score: 0.2822: : 650iter [01:02, 11.79iter/s]Best score: 0.2822: : 651iter [01:02,  7.57iter/s]Best score: 0.2822: : 651iter [01:02,  7.57iter/s]Best score: 0.2822: : 652iter [01:02,  7.57iter/s]Best score: 0.2822: : 653iter [01:02,  8.49iter/s]Best score: 0.2822: : 653iter [01:02,  8.49iter/s]Best score: 0.2822: : 654iter [01:02,  8.49iter/s]Best score: 0.2822: : 655iter [01:02,  9.28iter/s]Best score: 0.2822: : 655iter [01:02,  9.28iter/s]Best score: 0.2822: : 656iter [01:02,  9.28iter/s]Best score: 0.2822: : 657iter [01:02,  9.93iter/s]Best score: 0.2822: : 657iter [01:02,  9.93iter/s]Best score: 0.2822: : 658iter [01:02,  9.93iter/s]Best score: 0.2822: : 659iter [01:02, 10.45iter/s]Best score: 0.2791: : 659iter [01:02, 10.45iter/s]Best score: 0.2791: : 660iter [01:03, 10.45iter/s]Best score: 0.2791: : 661iter [01:03, 10.67iter/s]Best score: 0.2791: : 661iter [01:03, 10.67iter/s]Best score: 0.2791: : 662iter [01:03, 10.67iter/s]Best score: 0.2791: : 663iter [01:03, 11.01iter/s]Best score: 0.2791: : 663iter [01:03, 11.01iter/s]Best score: 0.2791: : 664iter [01:03, 11.01iter/s]Best score: 0.2791: : 665iter [01:03, 11.25iter/s]Best score: 0.2791: : 665iter [01:03, 11.25iter/s]Best score: 0.2791: : 666iter [01:03, 11.25iter/s]Best score: 0.2791: : 667iter [01:03, 11.44iter/s]Best score: 0.2791: : 667iter [01:03, 11.44iter/s]Best score: 0.2791: : 668iter [01:03, 11.44iter/s]Best score: 0.2791: : 669iter [01:03, 11.51iter/s]Best score: 0.2668: : 669iter [01:03, 11.51iter/s]Best score: 0.2668: : 670iter [01:03, 11.51iter/s]Best score: 0.2668: : 671iter [01:03, 11.42iter/s]Best score: 0.2668: : 671iter [01:03, 11.42iter/s]Best score: 0.2668: : 672iter [01:04, 11.42iter/s]Best score: 0.2668: : 673iter [01:04, 11.53iter/s]Best score: 0.2668: : 673iter [01:04, 11.53iter/s]Best score: 0.2668: : 674iter [01:04, 11.53iter/s]Best score: 0.2668: : 675iter [01:04, 11.60iter/s]Best score: 0.2668: : 675iter [01:04, 11.60iter/s]Best score: 0.2668: : 676iter [01:04, 11.60iter/s]Best score: 0.2668: : 677iter [01:04, 11.68iter/s]Best score: 0.2668: : 677iter [01:04, 11.68iter/s]Best score: 0.2668: : 678iter [01:04, 11.68iter/s]Best score: 0.2668: : 679iter [01:04, 11.75iter/s]Best score: 0.2668: : 679iter [01:04, 11.75iter/s]Best score: 0.2668: : 680iter [01:04, 11.75iter/s]Best score: 0.2668: : 681iter [01:04, 11.58iter/s]Best score: 0.2668: : 681iter [01:04, 11.58iter/s]Best score: 0.2668: : 682iter [01:04, 11.58iter/s]Best score: 0.2668: : 683iter [01:04, 11.59iter/s]Best score: 0.2668: : 683iter [01:04, 11.59iter/s]Best score: 0.2668: : 684iter [01:05, 11.59iter/s]Best score: 0.2668: : 685iter [01:05, 11.63iter/s]Best score: 0.2668: : 685iter [01:05, 11.63iter/s]Best score: 0.2668: : 686iter [01:05, 11.63iter/s]Best score: 0.2668: : 687iter [01:05, 11.61iter/s]Best score: 0.2668: : 687iter [01:05, 11.61iter/s]Best score: 0.2668: : 688iter [01:05, 11.61iter/s]Best score: 0.2668: : 689iter [01:05, 11.69iter/s]Best score: 0.2668: : 689iter [01:05, 11.69iter/s]Best score: 0.2668: : 690iter [01:05, 11.69iter/s]Best score: 0.2668: : 691iter [01:05, 11.55iter/s]Best score: 0.2668: : 691iter [01:05, 11.55iter/s]Best score: 0.2668: : 692iter [01:05, 11.55iter/s]Best score: 0.2668: : 693iter [01:05, 11.63iter/s]Best score: 0.2668: : 693iter [01:05, 11.63iter/s]Best score: 0.2668: : 694iter [01:05, 11.63iter/s]Best score: 0.2668: : 695iter [01:05, 11.67iter/s]Best score: 0.2668: : 695iter [01:05, 11.67iter/s]Best score: 0.2668: : 696iter [01:06, 11.67iter/s]Best score: 0.2668: : 697iter [01:06, 11.73iter/s]Best score: 0.2668: : 697iter [01:06, 11.73iter/s]Best score: 0.2668: : 698iter [01:06, 11.73iter/s]Best score: 0.2668: : 699iter [01:06, 11.77iter/s]
[ITER 700] Best: 0.2638
[PARAMS] Current best parameters:
  fx: 522.7723
  fy: 522.6365
  px: 849.4920
  py: 995.1521
  k1: 0.01304683
  k2: -0.00276333
  k3: 0.00033570
  k4: -0.00065113
  p1: -0.01622324
  p2: -0.01478933
  p3: 0.02773612
  p4: 0.01148609
  s1: -0.0240
  s2: -0.0069
  s3: -0.0157
  s4: -0.0112
  tilt_x: -0.0065
  tilt_y: -0.0113
  scale: 3.0004
  rotation: -0.6978
Best score: 0.2638: : 699iter [01:06, 11.77iter/s]Best score: 0.2638: : 700iter [01:06, 11.77iter/s]Best score: 0.2638: : 701iter [01:06,  7.56iter/s]Best score: 0.2638: : 701iter [01:06,  7.56iter/s]Best score: 0.2638: : 702iter [01:06,  7.56iter/s]Best score: 0.2638: : 703iter [01:06,  8.48iter/s]Best score: 0.2638: : 703iter [01:06,  8.48iter/s]Best score: 0.2638: : 704iter [01:07,  8.48iter/s]Best score: 0.2638: : 705iter [01:07,  9.29iter/s]Best score: 0.2638: : 705iter [01:07,  9.29iter/s]Best score: 0.2638: : 706iter [01:07,  9.29iter/s]Best score: 0.2638: : 707iter [01:07,  9.87iter/s]Best score: 0.2638: : 707iter [01:07,  9.87iter/s]Best score: 0.2638: : 708iter [01:07,  9.87iter/s]Best score: 0.2638: : 709iter [01:07, 10.40iter/s]Best score: 0.2638: : 709iter [01:07, 10.40iter/s]Best score: 0.2638: : 710iter [01:07, 10.40iter/s]Best score: 0.2638: : 711iter [01:07, 10.63iter/s]Best score: 0.2638: : 711iter [01:07, 10.63iter/s]Best score: 0.2638: : 712iter [01:07, 10.63iter/s]Best score: 0.2638: : 713iter [01:07, 10.97iter/s]Best score: 0.2638: : 713iter [01:07, 10.97iter/s]Best score: 0.2638: : 714iter [01:07, 10.97iter/s]Best score: 0.2638: : 715iter [01:07, 11.23iter/s]Best score: 0.2638: : 715iter [01:08, 11.23iter/s]Best score: 0.2638: : 716iter [01:08, 11.23iter/s]Best score: 0.2638: : 717iter [01:08, 11.41iter/s]Best score: 0.2638: : 717iter [01:08, 11.41iter/s]Best score: 0.2638: : 718iter [01:08, 11.41iter/s]Best score: 0.2638: : 719iter [01:08, 11.55iter/s]Best score: 0.2497: : 719iter [01:08, 11.55iter/s]Best score: 0.2497: : 720iter [01:08, 11.55iter/s]Best score: 0.2497: : 721iter [01:08, 11.44iter/s]Best score: 0.2497: : 721iter [01:08, 11.44iter/s]Best score: 0.2497: : 722iter [01:08, 11.44iter/s]Best score: 0.2497: : 723iter [01:08, 11.55iter/s]Best score: 0.2497: : 723iter [01:08, 11.55iter/s]Best score: 0.2497: : 724iter [01:08, 11.55iter/s]Best score: 0.2497: : 725iter [01:08, 11.64iter/s]Best score: 0.2497: : 725iter [01:08, 11.64iter/s]Best score: 0.2497: : 726iter [01:08, 11.64iter/s]Best score: 0.2497: : 727iter [01:08, 11.66iter/s]Best score: 0.2497: : 727iter [01:09, 11.66iter/s]Best score: 0.2497: : 728iter [01:09, 11.66iter/s]Best score: 0.2497: : 729iter [01:09, 11.66iter/s]Best score: 0.2497: : 729iter [01:09, 11.66iter/s]Best score: 0.2497: : 730iter [01:09, 11.66iter/s]Best score: 0.2497: : 731iter [01:09, 11.52iter/s]Best score: 0.2497: : 731iter [01:09, 11.52iter/s]Best score: 0.2497: : 732iter [01:09, 11.52iter/s]Best score: 0.2497: : 733iter [01:09, 11.63iter/s]Best score: 0.2497: : 733iter [01:09, 11.63iter/s]Best score: 0.2497: : 734iter [01:09, 11.63iter/s]Best score: 0.2497: : 735iter [01:09, 11.69iter/s]Best score: 0.2497: : 735iter [01:09, 11.69iter/s]Best score: 0.2497: : 736iter [01:09, 11.69iter/s]Best score: 0.2497: : 737iter [01:09, 11.74iter/s]Best score: 0.2497: : 737iter [01:09, 11.74iter/s]Best score: 0.2497: : 738iter [01:09, 11.74iter/s]Best score: 0.2497: : 739iter [01:09, 11.78iter/s]Best score: 0.2497: : 739iter [01:10, 11.78iter/s]Best score: 0.2497: : 740iter [01:10, 11.78iter/s]Best score: 0.2497: : 741iter [01:10, 11.59iter/s]Best score: 0.2497: : 741iter [01:10, 11.59iter/s]Best score: 0.2497: : 742iter [01:10, 11.59iter/s]Best score: 0.2497: : 743iter [01:10,  9.17iter/s]Best score: 0.2497: : 743iter [01:10,  9.17iter/s]Best score: 0.2497: : 744iter [01:10,  9.17iter/s]Best score: 0.2497: : 745iter [01:10,  9.84iter/s]Best score: 0.2497: : 745iter [01:10,  9.84iter/s]Best score: 0.2497: : 746iter [01:10,  9.84iter/s]Best score: 0.2497: : 747iter [01:10, 10.33iter/s]Best score: 0.2497: : 747iter [01:10, 10.33iter/s]Best score: 0.2497: : 748iter [01:10, 10.33iter/s]Best score: 0.2497: : 749iter [01:10, 10.74iter/s]
[ITER 750] Best: 0.2457
[PARAMS] Current best parameters:
  fx: 517.9826
  fy: 517.6976
  px: 851.9602
  py: 997.5435
  k1: 0.03162576
  k2: -0.02484349
  k3: 0.01381270
  k4: -0.00373943
  p1: -0.01375805
  p2: -0.01358442
  p3: 0.03066284
  p4: 0.00708105
  s1: -0.0233
  s2: -0.0052
  s3: -0.0173
  s4: -0.0082
  tilt_x: 0.0001
  tilt_y: -0.0209
  scale: 2.9892
  rotation: -0.6968
Best score: 0.2457: : 749iter [01:11, 10.74iter/s]Best score: 0.2457: : 750iter [01:11, 10.74iter/s]Best score: 0.2457: : 751iter [01:11,  7.22iter/s]Best score: 0.2457: : 751iter [01:11,  7.22iter/s]Best score: 0.2457: : 752iter [01:11,  7.22iter/s]Best score: 0.2457: : 753iter [01:11,  8.18iter/s]Best score: 0.2457: : 753iter [01:11,  8.18iter/s]Best score: 0.2457: : 754iter [01:11,  8.18iter/s]Best score: 0.2457: : 755iter [01:11,  9.01iter/s]Best score: 0.2457: : 755iter [01:11,  9.01iter/s]Best score: 0.2457: : 756iter [01:11,  9.01iter/s]Best score: 0.2457: : 757iter [01:11,  9.70iter/s]Best score: 0.2457: : 757iter [01:12,  9.70iter/s]Best score: 0.2457: : 758iter [01:12,  9.70iter/s]Best score: 0.2457: : 759iter [01:12, 10.24iter/s]Best score: 0.2457: : 759iter [01:12, 10.24iter/s]Best score: 0.2457: : 760iter [01:12, 10.24iter/s]Best score: 0.2457: : 761iter [01:12, 10.51iter/s]Best score: 0.2457: : 761iter [01:12, 10.51iter/s]Best score: 0.2457: : 762iter [01:12, 10.51iter/s]Best score: 0.2457: : 763iter [01:12, 10.87iter/s]Best score: 0.2457: : 763iter [01:12, 10.87iter/s]Best score: 0.2457: : 764iter [01:12, 10.87iter/s]Best score: 0.2457: : 765iter [01:12, 11.14iter/s]Best score: 0.2457: : 765iter [01:12, 11.14iter/s]Best score: 0.2457: : 766iter [01:12, 11.14iter/s]Best score: 0.2457: : 767iter [01:12, 11.32iter/s]Best score: 0.2457: : 767iter [01:12, 11.32iter/s]Best score: 0.2457: : 768iter [01:13, 11.32iter/s]Best score: 0.2457: : 769iter [01:13, 11.44iter/s]Best score: 0.2457: : 769iter [01:13, 11.44iter/s]Best score: 0.2457: : 770iter [01:13, 11.44iter/s]Best score: 0.2457: : 771iter [01:13, 11.37iter/s]Best score: 0.2457: : 771iter [01:13, 11.37iter/s]Best score: 0.2457: : 772iter [01:13, 11.37iter/s]Best score: 0.2457: : 773iter [01:13, 11.49iter/s]Best score: 0.2457: : 773iter [01:13, 11.49iter/s]Best score: 0.2457: : 774iter [01:13, 11.49iter/s]Best score: 0.2457: : 775iter [01:13, 11.57iter/s]Best score: 0.2457: : 775iter [01:13, 11.57iter/s]Best score: 0.2457: : 776iter [01:13, 11.57iter/s]Best score: 0.2457: : 777iter [01:13, 11.65iter/s]Best score: 0.2457: : 777iter [01:13, 11.65iter/s]Best score: 0.2457: : 778iter [01:13, 11.65iter/s]Best score: 0.2457: : 779iter [01:13, 11.70iter/s]Best score: 0.2428: : 779iter [01:13, 11.70iter/s]Best score: 0.2428: : 780iter [01:14, 11.70iter/s]Best score: 0.2428: : 781iter [01:14, 11.54iter/s]Best score: 0.2428: : 781iter [01:14, 11.54iter/s]Best score: 0.2428: : 782iter [01:14, 11.54iter/s]Best score: 0.2428: : 783iter [01:14, 11.63iter/s]Best score: 0.2428: : 783iter [01:14, 11.63iter/s]Best score: 0.2428: : 784iter [01:14, 11.63iter/s]Best score: 0.2428: : 785iter [01:14, 11.70iter/s]Best score: 0.2428: : 785iter [01:14, 11.70iter/s]Best score: 0.2428: : 786iter [01:14, 11.70iter/s]Best score: 0.2428: : 787iter [01:14, 11.73iter/s]Best score: 0.2428: : 787iter [01:14, 11.73iter/s]Best score: 0.2428: : 788iter [01:14, 11.73iter/s]Best score: 0.2428: : 789iter [01:14, 11.77iter/s]Best score: 0.2395: : 789iter [01:14, 11.77iter/s]Best score: 0.2395: : 790iter [01:14, 11.77iter/s]Best score: 0.2395: : 791iter [01:14, 11.58iter/s]Best score: 0.2395: : 791iter [01:14, 11.58iter/s]Best score: 0.2395: : 792iter [01:15, 11.58iter/s]Best score: 0.2395: : 793iter [01:15, 11.67iter/s]Best score: 0.2395: : 793iter [01:15, 11.67iter/s]Best score: 0.2395: : 794iter [01:15, 11.67iter/s]Best score: 0.2395: : 795iter [01:15, 11.73iter/s]Best score: 0.2395: : 795iter [01:15, 11.73iter/s]Best score: 0.2395: : 796iter [01:15, 11.73iter/s]Best score: 0.2395: : 797iter [01:15, 11.77iter/s]Best score: 0.2395: : 797iter [01:15, 11.77iter/s]Best score: 0.2395: : 798iter [01:15, 11.77iter/s]Best score: 0.2395: : 799iter [01:15, 11.76iter/s]
[ITER 800] Best: 0.2313
[PARAMS] Current best parameters:
  fx: 517.8224
  fy: 517.4656
  px: 853.1445
  py: 995.3725
  k1: 0.03625350
  k2: -0.03606308
  k3: 0.02212702
  k4: -0.00565830
  p1: -0.01542673
  p2: -0.01478043
  p3: 0.02818867
  p4: 0.00739490
  s1: -0.0188
  s2: -0.0054
  s3: -0.0157
  s4: -0.0084
  tilt_x: -0.0060
  tilt_y: -0.0292
  scale: 2.9931
  rotation: -0.6776
Best score: 0.2313: : 799iter [01:15, 11.76iter/s]Best score: 0.2313: : 800iter [01:16, 11.76iter/s]Best score: 0.2313: : 801iter [01:16,  7.55iter/s]Best score: 0.2313: : 801iter [01:16,  7.55iter/s]Best score: 0.2313: : 802iter [01:16,  7.55iter/s]Best score: 0.2313: : 803iter [01:16,  8.47iter/s]Best score: 0.2313: : 803iter [01:16,  8.47iter/s]Best score: 0.2313: : 804iter [01:16,  8.47iter/s]Best score: 0.2313: : 805iter [01:16,  9.26iter/s]Best score: 0.2313: : 805iter [01:16,  9.26iter/s]Best score: 0.2313: : 806iter [01:16,  9.26iter/s]Best score: 0.2313: : 807iter [01:16,  9.92iter/s]Best score: 0.2313: : 807iter [01:16,  9.92iter/s]Best score: 0.2313: : 808iter [01:16,  9.92iter/s]Best score: 0.2313: : 809iter [01:16, 10.40iter/s]Best score: 0.2313: : 809iter [01:16, 10.40iter/s]Best score: 0.2313: : 810iter [01:16, 10.40iter/s]Best score: 0.2313: : 811iter [01:16, 10.63iter/s]Best score: 0.2313: : 811iter [01:17, 10.63iter/s]Best score: 0.2313: : 812iter [01:17, 10.63iter/s]Best score: 0.2313: : 813iter [01:17, 10.88iter/s]Best score: 0.2313: : 813iter [01:17, 10.88iter/s]Best score: 0.2313: : 814iter [01:17, 10.88iter/s]Best score: 0.2313: : 815iter [01:17, 11.16iter/s]Best score: 0.2313: : 815iter [01:17, 11.16iter/s]Best score: 0.2313: : 816iter [01:17, 11.16iter/s]Best score: 0.2313: : 817iter [01:17, 11.32iter/s]Best score: 0.2313: : 817iter [01:17, 11.32iter/s]Best score: 0.2313: : 818iter [01:17, 11.32iter/s]Best score: 0.2313: : 819iter [01:17, 11.46iter/s]Best score: 0.2274: : 819iter [01:17, 11.46iter/s]Best score: 0.2274: : 820iter [01:17, 11.46iter/s]Best score: 0.2274: : 821iter [01:17, 11.36iter/s]Best score: 0.2274: : 821iter [01:17, 11.36iter/s]Best score: 0.2274: : 822iter [01:17, 11.36iter/s]Best score: 0.2274: : 823iter [01:17, 11.46iter/s]Best score: 0.2274: : 823iter [01:18, 11.46iter/s]Best score: 0.2274: : 824iter [01:18, 11.46iter/s]Best score: 0.2274: : 825iter [01:18, 11.58iter/s]Best score: 0.2274: : 825iter [01:18, 11.58iter/s]Best score: 0.2274: : 826iter [01:18, 11.58iter/s]Best score: 0.2274: : 827iter [01:18, 11.65iter/s]Best score: 0.2274: : 827iter [01:18, 11.65iter/s]Best score: 0.2274: : 828iter [01:18, 11.65iter/s]Best score: 0.2274: : 829iter [01:18, 11.71iter/s]Best score: 0.2274: : 829iter [01:18, 11.71iter/s]Best score: 0.2274: : 830iter [01:18, 11.71iter/s]Best score: 0.2274: : 831iter [01:18, 11.56iter/s]Best score: 0.2274: : 831iter [01:18, 11.56iter/s]Best score: 0.2274: : 832iter [01:18, 11.56iter/s]Best score: 0.2274: : 833iter [01:18, 11.64iter/s]Best score: 0.2274: : 833iter [01:18, 11.64iter/s]Best score: 0.2274: : 834iter [01:18, 11.64iter/s]Best score: 0.2274: : 835iter [01:18, 11.69iter/s]Best score: 0.2274: : 835iter [01:19, 11.69iter/s]Best score: 0.2274: : 836iter [01:19, 11.69iter/s]Best score: 0.2274: : 837iter [01:19, 11.71iter/s]Best score: 0.2274: : 837iter [01:19, 11.71iter/s]Best score: 0.2274: : 838iter [01:19, 11.71iter/s]Best score: 0.2274: : 839iter [01:19, 11.75iter/s]Best score: 0.2231: : 839iter [01:19, 11.75iter/s]Best score: 0.2231: : 840iter [01:19, 11.75iter/s]Best score: 0.2231: : 841iter [01:19, 11.56iter/s]Best score: 0.2231: : 841iter [01:19, 11.56iter/s]Best score: 0.2231: : 842iter [01:19, 11.56iter/s]Best score: 0.2231: : 843iter [01:19, 11.65iter/s]Best score: 0.2231: : 843iter [01:19, 11.65iter/s]Best score: 0.2231: : 844iter [01:19, 11.65iter/s]Best score: 0.2231: : 845iter [01:19, 11.71iter/s]Best score: 0.2231: : 845iter [01:19, 11.71iter/s]Best score: 0.2231: : 846iter [01:19, 11.71iter/s]Best score: 0.2231: : 847iter [01:19, 11.75iter/s]Best score: 0.2231: : 847iter [01:20, 11.75iter/s]Best score: 0.2231: : 848iter [01:20, 11.75iter/s]Best score: 0.2231: : 849iter [01:20, 11.75iter/s]
[ITER 850] Best: 0.2187
[PARAMS] Current best parameters:
  fx: 516.3442
  fy: 515.8710
  px: 860.2432
  py: 997.7609
  k1: 0.03773589
  k2: -0.03299487
  k3: 0.01885902
  k4: -0.00476172
  p1: -0.01361100
  p2: -0.00848287
  p3: 0.02985014
  p4: -0.00105521
  s1: -0.0184
  s2: 0.0011
  s3: -0.0166
  s4: -0.0004
  tilt_x: 0.0020
  tilt_y: -0.0496
  scale: 2.9932
  rotation: -0.6748
Best score: 0.2187: : 849iter [01:20, 11.75iter/s]Best score: 0.2187: : 850iter [01:20, 11.75iter/s]Best score: 0.2187: : 851iter [01:20,  7.51iter/s]Best score: 0.2187: : 851iter [01:20,  7.51iter/s]Best score: 0.2187: : 852iter [01:20,  7.51iter/s]Best score: 0.2187: : 853iter [01:20,  8.41iter/s]Best score: 0.2187: : 853iter [01:20,  8.41iter/s]Best score: 0.2187: : 854iter [01:20,  8.41iter/s]Best score: 0.2187: : 855iter [01:20,  9.22iter/s]Best score: 0.2187: : 855iter [01:21,  9.22iter/s]Best score: 0.2187: : 856iter [01:21,  9.22iter/s]Best score: 0.2187: : 857iter [01:21,  9.88iter/s]Best score: 0.2187: : 857iter [01:21,  9.88iter/s]Best score: 0.2187: : 858iter [01:21,  9.88iter/s]Best score: 0.2187: : 859iter [01:21, 10.28iter/s]Best score: 0.2184: : 859iter [01:21, 10.28iter/s]Best score: 0.2184: : 860iter [01:21, 10.28iter/s]Best score: 0.2184: : 861iter [01:21, 10.47iter/s]Best score: 0.2184: : 861iter [01:21, 10.47iter/s]Best score: 0.2184: : 862iter [01:21, 10.47iter/s]Best score: 0.2184: : 863iter [01:21, 10.84iter/s]Best score: 0.2184: : 863iter [01:21, 10.84iter/s]Best score: 0.2184: : 864iter [01:21, 10.84iter/s]Best score: 0.2184: : 865iter [01:21, 11.11iter/s]Best score: 0.2184: : 865iter [01:21, 11.11iter/s]Best score: 0.2184: : 866iter [01:22, 11.11iter/s]Best score: 0.2184: : 867iter [01:22, 11.27iter/s]Best score: 0.2184: : 867iter [01:22, 11.27iter/s]Best score: 0.2184: : 868iter [01:22, 11.27iter/s]Best score: 0.2184: : 869iter [01:22, 11.43iter/s]Best score: 0.2182: : 869iter [01:22, 11.43iter/s]Best score: 0.2182: : 870iter [01:22, 11.43iter/s]Best score: 0.2182: : 871iter [01:22, 11.36iter/s]Best score: 0.2182: : 871iter [01:22, 11.36iter/s]Best score: 0.2182: : 872iter [01:22, 11.36iter/s]Best score: 0.2182: : 873iter [01:22, 11.50iter/s]Best score: 0.2182: : 873iter [01:22, 11.50iter/s]Best score: 0.2182: : 874iter [01:22, 11.50iter/s]Best score: 0.2182: : 875iter [01:22, 11.59iter/s]Best score: 0.2182: : 875iter [01:22, 11.59iter/s]Best score: 0.2182: : 876iter [01:22, 11.59iter/s]Best score: 0.2182: : 877iter [01:22, 11.57iter/s]Best score: 0.2182: : 877iter [01:22, 11.57iter/s]Best score: 0.2182: : 878iter [01:23, 11.57iter/s]Best score: 0.2182: : 879iter [01:23, 11.66iter/s]Best score: 0.2158: : 879iter [01:23, 11.66iter/s]Best score: 0.2158: : 880iter [01:23, 11.66iter/s]Best score: 0.2158: : 881iter [01:23, 11.51iter/s]Best score: 0.2158: : 881iter [01:23, 11.51iter/s]Best score: 0.2158: : 882iter [01:23, 11.51iter/s]Best score: 0.2158: : 883iter [01:23, 11.58iter/s]Best score: 0.2158: : 883iter [01:23, 11.58iter/s]Best score: 0.2158: : 884iter [01:23, 11.58iter/s]Best score: 0.2158: : 885iter [01:23, 11.66iter/s]Best score: 0.2158: : 885iter [01:23, 11.66iter/s]Best score: 0.2158: : 886iter [01:23, 11.66iter/s]Best score: 0.2158: : 887iter [01:23, 11.72iter/s]Best score: 0.2158: : 887iter [01:23, 11.72iter/s]Best score: 0.2158: : 888iter [01:23, 11.72iter/s]Best score: 0.2158: : 889iter [01:23, 11.77iter/s]Best score: 0.2158: : 889iter [01:24, 11.77iter/s]Best score: 0.2158: : 890iter [01:24, 11.77iter/s]Best score: 0.2158: : 891iter [01:24, 11.59iter/s]Best score: 0.2158: : 891iter [01:24, 11.59iter/s]Best score: 0.2158: : 892iter [01:24, 11.59iter/s]Best score: 0.2158: : 893iter [01:24, 11.66iter/s]Best score: 0.2158: : 893iter [01:24, 11.66iter/s]Best score: 0.2158: : 894iter [01:24, 11.66iter/s]Best score: 0.2158: : 895iter [01:24, 11.74iter/s]Best score: 0.2158: : 895iter [01:24, 11.74iter/s]Best score: 0.2158: : 896iter [01:24, 11.74iter/s]Best score: 0.2158: : 897iter [01:24, 11.78iter/s]Best score: 0.2158: : 897iter [01:24, 11.78iter/s]Best score: 0.2158: : 898iter [01:24, 11.78iter/s]Best score: 0.2158: : 899iter [01:24, 11.80iter/s]
[ITER 900] Best: 0.2158
[PARAMS] Current best parameters:
  fx: 515.2103
  fy: 514.7843
  px: 859.3417
  py: 998.2592
  k1: 0.04282038
  k2: -0.03896662
  k3: 0.02188917
  k4: -0.00528045
  p1: -0.01351263
  p2: -0.00853053
  p3: 0.02997387
  p4: -0.00073755
  s1: -0.0193
  s2: 0.0009
  s3: -0.0168
  s4: -0.0003
  tilt_x: 0.0041
  tilt_y: -0.0455
  scale: 2.9900
  rotation: -0.6877
Best score: 0.2158: : 899iter [01:24, 11.80iter/s]Best score: 0.2158: : 900iter [01:25, 11.80iter/s]Best score: 0.2158: : 901iter [01:25,  7.56iter/s]Best score: 0.2158: : 901iter [01:25,  7.56iter/s]Best score: 0.2158: : 902iter [01:25,  7.56iter/s]Best score: 0.2158: : 903iter [01:25,  8.49iter/s]Best score: 0.2158: : 903iter [01:25,  8.49iter/s]Best score: 0.2158: : 904iter [01:25,  8.49iter/s]Best score: 0.2158: : 905iter [01:25,  9.28iter/s]Best score: 0.2158: : 905iter [01:25,  9.28iter/s]Best score: 0.2158: : 906iter [01:25,  9.28iter/s]Best score: 0.2158: : 907iter [01:25,  9.91iter/s]Best score: 0.2158: : 907iter [01:25,  9.91iter/s]Best score: 0.2158: : 908iter [01:25,  9.91iter/s]Best score: 0.2158: : 909iter [01:25, 10.40iter/s]Best score: 0.2144: : 909iter [01:26, 10.40iter/s]Best score: 0.2144: : 910iter [01:26, 10.40iter/s]Best score: 0.2144: : 911iter [01:26, 10.63iter/s]Best score: 0.2144: : 911iter [01:26, 10.63iter/s]Best score: 0.2144: : 912iter [01:26, 10.63iter/s]Best score: 0.2144: : 913iter [01:26, 10.97iter/s]Best score: 0.2144: : 913iter [01:26, 10.97iter/s]Best score: 0.2144: : 914iter [01:26, 10.97iter/s]Best score: 0.2144: : 915iter [01:26, 11.24iter/s]Best score: 0.2144: : 915iter [01:26, 11.24iter/s]Best score: 0.2144: : 916iter [01:26, 11.24iter/s]Best score: 0.2144: : 917iter [01:26, 11.42iter/s]Best score: 0.2144: : 917iter [01:26, 11.42iter/s]Best score: 0.2144: : 918iter [01:26, 11.42iter/s]Best score: 0.2144: : 919iter [01:26, 11.55iter/s]Best score: 0.2086: : 919iter [01:26, 11.55iter/s]Best score: 0.2086: : 920iter [01:26, 11.55iter/s]Best score: 0.2086: : 921iter [01:26, 11.45iter/s]Best score: 0.2086: : 921iter [01:27, 11.45iter/s]Best score: 0.2086: : 922iter [01:27, 11.45iter/s]Best score: 0.2086: : 923iter [01:27, 11.53iter/s]Best score: 0.2086: : 923iter [01:27, 11.53iter/s]Best score: 0.2086: : 924iter [01:27, 11.53iter/s]Best score: 0.2086: : 925iter [01:27, 11.63iter/s]Best score: 0.2086: : 925iter [01:27, 11.63iter/s]Best score: 0.2086: : 926iter [01:27, 11.63iter/s]Best score: 0.2086: : 927iter [01:27, 11.68iter/s]Best score: 0.2086: : 927iter [01:27, 11.68iter/s]Best score: 0.2086: : 928iter [01:27, 11.68iter/s]Best score: 0.2086: : 929iter [01:27, 11.71iter/s]Best score: 0.1964: : 929iter [01:27, 11.71iter/s]Best score: 0.1964: : 930iter [01:27, 11.71iter/s]Best score: 0.1964: : 931iter [01:27, 11.54iter/s]Best score: 0.1964: : 931iter [01:27, 11.54iter/s]Best score: 0.1964: : 932iter [01:27, 11.54iter/s]Best score: 0.1964: : 933iter [01:27, 11.61iter/s]Best score: 0.1964: : 933iter [01:28, 11.61iter/s]Best score: 0.1964: : 934iter [01:28, 11.61iter/s]Best score: 0.1964: : 935iter [01:28, 11.67iter/s]Best score: 0.1964: : 935iter [01:28, 11.67iter/s]Best score: 0.1964: : 936iter [01:28, 11.67iter/s]Best score: 0.1964: : 937iter [01:28, 11.71iter/s]Best score: 0.1964: : 937iter [01:28, 11.71iter/s]Best score: 0.1964: : 938iter [01:28, 11.71iter/s]Best score: 0.1964: : 939iter [01:28, 11.76iter/s]Best score: 0.1951: : 939iter [01:28, 11.76iter/s]Best score: 0.1951: : 940iter [01:28, 11.76iter/s]Best score: 0.1951: : 941iter [01:28, 11.59iter/s]Best score: 0.1951: : 941iter [01:28, 11.59iter/s]Best score: 0.1951: : 942iter [01:28, 11.59iter/s]Best score: 0.1951: : 943iter [01:28, 11.63iter/s]Best score: 0.1951: : 943iter [01:28, 11.63iter/s]Best score: 0.1951: : 944iter [01:29, 11.63iter/s]Best score: 0.1951: : 945iter [01:29, 11.65iter/s]Best score: 0.1951: : 945iter [01:29, 11.65iter/s]Best score: 0.1951: : 946iter [01:29, 11.65iter/s]Best score: 0.1951: : 947iter [01:29, 11.71iter/s]Best score: 0.1951: : 947iter [01:29, 11.71iter/s]Best score: 0.1951: : 948iter [01:29, 11.71iter/s]Best score: 0.1951: : 949iter [01:29, 11.76iter/s]
[ITER 950] Best: 0.1942
[PARAMS] Current best parameters:
  fx: 512.8034
  fy: 512.4508
  px: 864.6624
  py: 996.8175
  k1: 0.03322835
  k2: -0.01832247
  k3: 0.00852399
  k4: -0.00222643
  p1: -0.01663355
  p2: 0.00241880
  p3: 0.03398737
  p4: -0.01077109
  s1: -0.0294
  s2: 0.0114
  s3: -0.0199
  s4: 0.0114
  tilt_x: 0.0036
  tilt_y: -0.0477
  scale: 2.9998
  rotation: -0.6944
Best score: 0.1942: : 949iter [01:29, 11.76iter/s]Best score: 0.1942: : 950iter [01:29, 11.76iter/s]Best score: 0.1942: : 951iter [01:29,  7.54iter/s]Best score: 0.1942: : 951iter [01:29,  7.54iter/s]Best score: 0.1942: : 952iter [01:30,  7.54iter/s]Best score: 0.1942: : 953iter [01:30,  8.46iter/s]Best score: 0.1942: : 953iter [01:30,  8.46iter/s]Best score: 0.1942: : 954iter [01:30,  8.46iter/s]Best score: 0.1942: : 955iter [01:30,  9.24iter/s]Best score: 0.1942: : 955iter [01:30,  9.24iter/s]Best score: 0.1942: : 956iter [01:30,  9.24iter/s]Best score: 0.1942: : 957iter [01:30,  9.90iter/s]Best score: 0.1942: : 957iter [01:30,  9.90iter/s]Best score: 0.1942: : 958iter [01:30,  9.90iter/s]Best score: 0.1942: : 959iter [01:30, 10.42iter/s]Best score: 0.1942: : 959iter [01:30, 10.42iter/s]Best score: 0.1942: : 960iter [01:30, 10.42iter/s]Best score: 0.1942: : 961iter [01:30, 10.64iter/s]Best score: 0.1942: : 961iter [01:30, 10.64iter/s]Best score: 0.1942: : 962iter [01:30, 10.64iter/s]Best score: 0.1942: : 963iter [01:30, 10.98iter/s]Best score: 0.1942: : 963iter [01:30, 10.98iter/s]Best score: 0.1942: : 964iter [01:31, 10.98iter/s]Best score: 0.1942: : 965iter [01:31, 11.21iter/s]Best score: 0.1942: : 965iter [01:31, 11.21iter/s]Best score: 0.1942: : 966iter [01:31, 11.21iter/s]Best score: 0.1942: : 967iter [01:31, 11.40iter/s]Best score: 0.1942: : 967iter [01:31, 11.40iter/s]Best score: 0.1942: : 968iter [01:31, 11.40iter/s]Best score: 0.1942: : 969iter [01:31, 11.53iter/s]Best score: 0.1919: : 969iter [01:31, 11.53iter/s]Best score: 0.1919: : 970iter [01:31, 11.53iter/s]Best score: 0.1919: : 971iter [01:31, 11.41iter/s]Best score: 0.1919: : 971iter [01:31, 11.41iter/s]Best score: 0.1919: : 972iter [01:31, 11.41iter/s]Best score: 0.1919: : 973iter [01:31, 11.53iter/s]Best score: 0.1919: : 973iter [01:31, 11.53iter/s]Best score: 0.1919: : 974iter [01:31, 11.53iter/s]Best score: 0.1919: : 975iter [01:31, 11.64iter/s]Best score: 0.1919: : 975iter [01:31, 11.64iter/s]Best score: 0.1919: : 976iter [01:32, 11.64iter/s]Best score: 0.1919: : 977iter [01:32, 11.70iter/s]Best score: 0.1919: : 977iter [01:32, 11.70iter/s]Best score: 0.1919: : 978iter [01:32, 11.70iter/s]Best score: 0.1919: : 979iter [01:32, 11.74iter/s]Best score: 0.1919: : 979iter [01:32, 11.74iter/s]Best score: 0.1919: : 980iter [01:32, 11.74iter/s]Best score: 0.1919: : 981iter [01:32, 11.57iter/s]Best score: 0.1919: : 981iter [01:32, 11.57iter/s]Best score: 0.1919: : 982iter [01:32, 11.57iter/s]Best score: 0.1919: : 983iter [01:32, 11.65iter/s]Best score: 0.1919: : 983iter [01:32, 11.65iter/s]Best score: 0.1919: : 984iter [01:32, 11.65iter/s]Best score: 0.1919: : 985iter [01:32, 11.70iter/s]Best score: 0.1919: : 985iter [01:32, 11.70iter/s]Best score: 0.1919: : 986iter [01:32, 11.70iter/s]Best score: 0.1919: : 987iter [01:32, 11.74iter/s]Best score: 0.1919: : 987iter [01:32, 11.74iter/s]Best score: 0.1919: : 988iter [01:33, 11.74iter/s]Best score: 0.1919: : 989iter [01:33, 11.76iter/s]Best score: 0.1919: : 989iter [01:33, 11.76iter/s]Best score: 0.1919: : 990iter [01:33, 11.76iter/s]Best score: 0.1919: : 991iter [01:33, 11.58iter/s]Best score: 0.1919: : 991iter [01:33, 11.58iter/s]Best score: 0.1919: : 992iter [01:33, 11.58iter/s]Best score: 0.1919: : 993iter [01:33, 11.65iter/s]Best score: 0.1919: : 993iter [01:33, 11.65iter/s]Best score: 0.1919: : 994iter [01:33, 11.65iter/s]Best score: 0.1919: : 995iter [01:33, 11.68iter/s]Best score: 0.1919: : 995iter [01:33, 11.68iter/s]Best score: 0.1919: : 996iter [01:33, 11.68iter/s]Best score: 0.1919: : 997iter [01:33, 11.66iter/s]Best score: 0.1919: : 997iter [01:33, 11.66iter/s]Best score: 0.1919: : 998iter [01:33, 11.66iter/s]Best score: 0.1919: : 999iter [01:33, 11.71iter/s]
[ITER 1000] Best: 0.1915
[PARAMS] Current best parameters:
  fx: 512.2671
  fy: 511.8904
  px: 863.7473
  py: 994.6347
  k1: 0.02912964
  k2: -0.01245771
  k3: 0.00598961
  k4: -0.00189192
  p1: -0.01865120
  p2: 0.00273777
  p3: 0.03277487
  p4: -0.01038566
  s1: -0.0304
  s2: 0.0122
  s3: -0.0193
  s4: 0.0116
  tilt_x: -0.0024
  tilt_y: -0.0428
  scale: 3.0040
  rotation: -0.6928
Best score: 0.1915: : 999iter [01:34, 11.71iter/s]Best score: 0.1915: : 1000iter [01:34, 11.71iter/s]Best score: 0.1915: : 1001iter [01:34,  7.50iter/s]Best score: 0.1915: : 1001iter [01:34,  7.50iter/s]Best score: 0.1915: : 1002iter [01:34,  7.50iter/s]Best score: 0.1915: : 1003iter [01:34,  8.40iter/s]Best score: 0.1915: : 1003iter [01:34,  8.40iter/s]Best score: 0.1915: : 1004iter [01:34,  8.40iter/s]Best score: 0.1915: : 1005iter [01:34,  9.19iter/s]Best score: 0.1915: : 1005iter [01:34,  9.19iter/s]Best score: 0.1915: : 1006iter [01:34,  9.19iter/s]Best score: 0.1915: : 1007iter [01:34,  9.85iter/s]Best score: 0.1915: : 1007iter [01:35,  9.85iter/s]Best score: 0.1915: : 1008iter [01:35,  9.85iter/s]Best score: 0.1915: : 1009iter [01:35, 10.35iter/s]Best score: 0.1909: : 1009iter [01:35, 10.35iter/s]Best score: 0.1909: : 1010iter [01:35, 10.35iter/s]Best score: 0.1909: : 1011iter [01:35, 10.51iter/s]Best score: 0.1909: : 1011iter [01:35, 10.51iter/s]Best score: 0.1909: : 1012iter [01:35, 10.51iter/s]Best score: 0.1909: : 1013iter [01:35, 10.87iter/s]Best score: 0.1909: : 1013iter [01:35, 10.87iter/s]Best score: 0.1909: : 1014iter [01:35, 10.87iter/s]Best score: 0.1909: : 1015iter [01:35, 11.06iter/s]Best score: 0.1909: : 1015iter [01:35, 11.06iter/s]Best score: 0.1909: : 1016iter [01:35, 11.06iter/s]Best score: 0.1909: : 1017iter [01:35, 11.19iter/s]Best score: 0.1909: : 1017iter [01:35, 11.19iter/s]Best score: 0.1909: : 1018iter [01:35, 11.19iter/s]Best score: 0.1909: : 1019iter [01:35, 11.37iter/s]Best score: 0.1902: : 1019iter [01:36, 11.37iter/s]Best score: 0.1902: : 1020iter [01:36, 11.37iter/s]Best score: 0.1902: : 1021iter [01:36, 11.30iter/s]Best score: 0.1902: : 1021iter [01:36, 11.30iter/s]Best score: 0.1902: : 1022iter [01:36, 11.30iter/s]Best score: 0.1902: : 1023iter [01:36, 11.41iter/s]Best score: 0.1902: : 1023iter [01:36, 11.41iter/s]Best score: 0.1902: : 1024iter [01:36, 11.41iter/s]Best score: 0.1902: : 1025iter [01:36, 11.53iter/s]Best score: 0.1902: : 1025iter [01:36, 11.53iter/s]Best score: 0.1902: : 1026iter [01:36, 11.53iter/s]Best score: 0.1902: : 1027iter [01:36, 11.61iter/s]Best score: 0.1902: : 1027iter [01:36, 11.61iter/s]Best score: 0.1902: : 1028iter [01:36, 11.61iter/s]Best score: 0.1902: : 1029iter [01:36, 11.65iter/s]Best score: 0.1893: : 1029iter [01:36, 11.65iter/s]Best score: 0.1893: : 1030iter [01:37, 11.65iter/s]Best score: 0.1893: : 1031iter [01:37, 11.51iter/s]Best score: 0.1893: : 1031iter [01:37, 11.51iter/s]Best score: 0.1893: : 1032iter [01:37, 11.51iter/s]Best score: 0.1893: : 1033iter [01:37, 11.58iter/s]Best score: 0.1893: : 1033iter [01:37, 11.58iter/s]Best score: 0.1893: : 1034iter [01:37, 11.58iter/s]Best score: 0.1893: : 1035iter [01:37, 11.61iter/s]Best score: 0.1893: : 1035iter [01:37, 11.61iter/s]Best score: 0.1893: : 1036iter [01:37, 11.61iter/s]Best score: 0.1893: : 1037iter [01:37, 11.68iter/s]Best score: 0.1893: : 1037iter [01:37, 11.68iter/s]Best score: 0.1893: : 1038iter [01:37, 11.68iter/s]Best score: 0.1893: : 1039iter [01:37, 11.68iter/s]Best score: 0.1888: : 1039iter [01:37, 11.68iter/s]Best score: 0.1888: : 1040iter [01:37, 11.68iter/s]Best score: 0.1888: : 1041iter [01:37, 11.47iter/s]Best score: 0.1888: : 1041iter [01:37, 11.47iter/s]Best score: 0.1888: : 1042iter [01:38, 11.47iter/s]Best score: 0.1888: : 1043iter [01:38, 11.57iter/s]Best score: 0.1888: : 1043iter [01:38, 11.57iter/s]Best score: 0.1888: : 1044iter [01:38, 11.57iter/s]Best score: 0.1888: : 1045iter [01:38, 11.65iter/s]Best score: 0.1888: : 1045iter [01:38, 11.65iter/s]Best score: 0.1888: : 1046iter [01:38, 11.65iter/s]Best score: 0.1888: : 1047iter [01:38, 11.71iter/s]Best score: 0.1888: : 1047iter [01:38, 11.71iter/s]Best score: 0.1888: : 1048iter [01:38, 11.71iter/s]Best score: 0.1888: : 1049iter [01:38, 11.72iter/s]
[ITER 1050] Best: 0.1883
[PARAMS] Current best parameters:
  fx: 511.1355
  fy: 510.8060
  px: 864.5141
  py: 995.0654
  k1: 0.03143418
  k2: -0.01354354
  k3: 0.00634524
  k4: -0.00197089
  p1: -0.01790210
  p2: 0.00450484
  p3: 0.03395632
  p4: -0.01087357
  s1: -0.0321
  s2: 0.0123
  s3: -0.0214
  s4: 0.0125
  tilt_x: -0.0015
  tilt_y: -0.0426
  scale: 3.0022
  rotation: -0.6816
Best score: 0.1883: : 1049iter [01:38, 11.72iter/s]Best score: 0.1883: : 1050iter [01:39, 11.72iter/s]Best score: 0.1883: : 1051iter [01:39,  7.52iter/s]Best score: 0.1883: : 1051iter [01:39,  7.52iter/s]Best score: 0.1883: : 1052iter [01:39,  7.52iter/s]Best score: 0.1883: : 1053iter [01:39,  8.44iter/s]Best score: 0.1883: : 1053iter [01:39,  8.44iter/s]Best score: 0.1883: : 1054iter [01:39,  8.44iter/s]Best score: 0.1883: : 1055iter [01:39,  9.23iter/s]Best score: 0.1883: : 1055iter [01:39,  9.23iter/s]Best score: 0.1883: : 1056iter [01:39,  9.23iter/s]Best score: 0.1883: : 1057iter [01:39,  9.88iter/s]Best score: 0.1883: : 1057iter [01:39,  9.88iter/s]Best score: 0.1883: : 1058iter [01:39,  9.88iter/s]Best score: 0.1883: : 1059iter [01:39, 10.39iter/s]Best score: 0.1883: : 1059iter [01:39, 10.39iter/s]Best score: 0.1883: : 1060iter [01:39, 10.39iter/s]Best score: 0.1883: : 1061iter [01:39, 10.61iter/s]Best score: 0.1883: : 1061iter [01:39, 10.61iter/s]Best score: 0.1883: : 1062iter [01:40, 10.61iter/s]Best score: 0.1883: : 1063iter [01:40, 10.95iter/s]Best score: 0.1883: : 1063iter [01:40, 10.95iter/s]Best score: 0.1883: : 1064iter [01:40, 10.95iter/s]Best score: 0.1883: : 1065iter [01:40, 11.21iter/s]Best score: 0.1883: : 1065iter [01:40, 11.21iter/s]Best score: 0.1883: : 1066iter [01:40, 11.21iter/s]Best score: 0.1883: : 1067iter [01:40, 11.38iter/s]Best score: 0.1883: : 1067iter [01:40, 11.38iter/s]Best score: 0.1883: : 1068iter [01:40, 11.38iter/s]Best score: 0.1883: : 1069iter [01:40, 11.51iter/s]Best score: 0.1880: : 1069iter [01:40, 11.51iter/s]Best score: 0.1880: : 1070iter [01:40, 11.51iter/s]Best score: 0.1880: : 1071iter [01:40, 11.41iter/s]Best score: 0.1880: : 1071iter [01:40, 11.41iter/s]Best score: 0.1880: : 1072iter [01:40, 11.41iter/s]Best score: 0.1880: : 1073iter [01:40, 11.49iter/s]Best score: 0.1880: : 1073iter [01:41, 11.49iter/s]Best score: 0.1880: : 1074iter [01:41, 11.49iter/s]Best score: 0.1880: : 1075iter [01:41, 11.58iter/s]Best score: 0.1880: : 1075iter [01:41, 11.58iter/s]Best score: 0.1880: : 1076iter [01:41, 11.58iter/s]Best score: 0.1880: : 1077iter [01:41, 11.62iter/s]Best score: 0.1880: : 1077iter [01:41, 11.62iter/s]Best score: 0.1880: : 1078iter [01:41, 11.62iter/s]Best score: 0.1880: : 1079iter [01:41, 11.69iter/s]Best score: 0.1867: : 1079iter [01:41, 11.69iter/s]Best score: 0.1867: : 1080iter [01:41, 11.69iter/s]Best score: 0.1867: : 1081iter [01:41, 11.46iter/s]Best score: 0.1867: : 1081iter [01:41, 11.46iter/s]Best score: 0.1867: : 1082iter [01:41, 11.46iter/s]Best score: 0.1867: : 1083iter [01:41, 11.55iter/s]Best score: 0.1867: : 1083iter [01:41, 11.55iter/s]Best score: 0.1867: : 1084iter [01:41, 11.55iter/s]Best score: 0.1867: : 1085iter [01:41, 11.64iter/s]Best score: 0.1867: : 1085iter [01:42, 11.64iter/s]Best score: 0.1867: : 1086iter [01:42, 11.64iter/s]Best score: 0.1867: : 1087iter [01:42, 11.68iter/s]Best score: 0.1867: : 1087iter [01:42, 11.68iter/s]Best score: 0.1867: : 1088iter [01:42, 11.68iter/s]Best score: 0.1867: : 1089iter [01:42, 11.71iter/s]Best score: 0.1867: : 1089iter [01:42, 11.71iter/s]Best score: 0.1867: : 1090iter [01:42, 11.71iter/s]Best score: 0.1867: : 1091iter [01:42, 11.56iter/s]Best score: 0.1867: : 1091iter [01:42, 11.56iter/s]Best score: 0.1867: : 1092iter [01:42, 11.56iter/s]Best score: 0.1867: : 1093iter [01:42, 11.63iter/s]Best score: 0.1867: : 1093iter [01:42, 11.63iter/s]Best score: 0.1867: : 1094iter [01:42, 11.63iter/s]Best score: 0.1867: : 1095iter [01:42, 11.69iter/s]Best score: 0.1867: : 1095iter [01:42, 11.69iter/s]Best score: 0.1867: : 1096iter [01:42, 11.69iter/s]Best score: 0.1867: : 1097iter [01:42, 11.65iter/s]Best score: 0.1867: : 1097iter [01:43, 11.65iter/s]Best score: 0.1867: : 1098iter [01:43, 11.65iter/s]Best score: 0.1867: : 1099iter [01:43, 11.62iter/s]
[ITER 1100] Best: 0.1862
[PARAMS] Current best parameters:
  fx: 512.8571
  fy: 512.4746
  px: 862.9100
  py: 995.3319
  k1: 0.03118998
  k2: -0.01734801
  k3: 0.00950067
  k4: -0.00278252
  p1: -0.01808530
  p2: 0.00235350
  p3: 0.03137968
  p4: -0.01022759
  s1: -0.0286
  s2: 0.0113
  s3: -0.0179
  s4: 0.0113
  tilt_x: -0.0001
  tilt_y: -0.0397
  scale: 3.0012
  rotation: -0.6845
Best score: 0.1862: : 1099iter [01:43, 11.62iter/s]Best score: 0.1862: : 1100iter [01:43, 10.62iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish did not improve score: 0.186221

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 101,964 → 95,784 within 3500mm ring
  Ring polish did not improve score: 0.186221
  Success: False
  Tangential distortion: p1=-0.018085, p2=0.002354, p3=0.031380, p4=-0.010228
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1862
[✅] Parameters copied to: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 512.8570901578477
    fy: 512.4746411210821
    px: 862.910033217019
    py: 995.3319059939938
    k1: 0.031189978110480698
    k2: -0.017348007166265292
    k3: 0.00950067467886393
    k4: -0.002782517880239333
    p1: -0.01808530190953731
    p2: 0.0023535044117818597
    p3: 0.031379677340851085
    p4: -0.010227589767534401
    scale: 3.0011993628431397
    rotation: -0.6844902716640278
    mmpp: 3.436763496817062

[🔄] Converting 1963 frames to PTD, creating debug images for 400 calibration frames...
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   🔄 2000R:  99%|█████████▉| 597/601 [00:03<00:00, 176.18frame/s][A
                                                                  [A📁 Rigs:  50%|█████     | 2/4 [00:08<00:09,  4.65s/rig]
   🔄 1500:   0%|          | 0/417 [00:00<?, ?frame/s][A
   🔄 1500:   4%|▍         | 18/417 [00:00<00:02, 178.55frame/s][A
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                                                                 [A📁 Rigs:  75%|███████▌  | 3/4 [00:13<00:04,  4.49s/rig]
   🔄 2000L:   0%|          | 0/685 [00:00<?, ?frame/s][A
   🔄 2000L:   3%|▎         | 18/685 [00:00<00:03, 172.57frame/s][A
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   🔄 2000L:  13%|█▎        | 92/685 [00:00<00:03, 178.42frame/s][A
   🔄 2000L:  16%|█▌        | 111/685 [00:00<00:03, 179.60frame/s][A
   🔄 2000L:  19%|█▉        | 130/685 [00:00<00:03, 180.98frame/s][A
   🔄 2000L:  22%|██▏       | 149/685 [00:00<00:02, 181.72frame/s][A
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   🔄 2000L:  98%|█████████▊| 673/685 [00:03<00:00, 178.64frame/s][A
                                                                  [A📁 Rigs: 100%|██████████| 4/4 [00:19<00:00,  5.41s/rig]📁 Rigs: 100%|██████████| 4/4 [00:19<00:00,  4.96s/rig]
[✅] Saved PTDs: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/4_undistorted/output/ptd
📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]
   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s][A
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   🔄 1000:  95%|█████████▌| 95/100 [00:00<00:00, 179.92frame/s][A
                                                                [A📁 Rigs:  25%|██▌       | 1/4 [00:00<00:02,  1.10rig/s]
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   🔄 2000R:  90%|█████████ | 90/100 [00:00<00:00, 175.17frame/s][A
                                                                 [A📁 Rigs:  50%|█████     | 2/4 [00:01<00:01,  1.08rig/s]
   🔄 1500:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 1500:  18%|█▊        | 18/100 [00:00<00:00, 176.97frame/s][A
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   🔄 1500:  90%|█████████ | 90/100 [00:00<00:00, 177.46frame/s][A
                                                                [A📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.06rig/s]
   🔄 2000L:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 2000L:  18%|█▊        | 18/100 [00:00<00:00, 174.83frame/s][A
   🔄 2000L:  36%|███▌      | 36/100 [00:00<00:00, 175.54frame/s][A
   🔄 2000L:  54%|█████▍    | 54/100 [00:00<00:00, 174.98frame/s][A
   🔄 2000L:  72%|███████▏  | 72/100 [00:00<00:00, 174.81frame/s][A
   🔄 2000L:  90%|█████████ | 90/100 [00:00<00:00, 175.54frame/s][A
                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.01s/rig]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.02rig/s]
[✅] Saved PTDs: /working/MDCP3D-SL-013_W046(6)_2026-09-04/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.
📤 Writing LUT:   0%|          | 0/3240000 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 82548/3240000 [00:00<00:03, 825472.91px/s]📤 Writing LUT:   5%|▌         | 171462/3240000 [00:00<00:03, 862917.23px/s]📤 Writing LUT:   8%|▊         | 259644/3240000 [00:00<00:03, 871548.01px/s]📤 Writing LUT:  11%|█         | 347677/3240000 [00:00<00:03, 875012.06px/s]📤 Writing LUT:  13%|█▎        | 435909/3240000 [00:00<00:03, 877644.63px/s]📤 Writing LUT:  16%|█▌        | 524046/3240000 [00:00<00:03, 878910.11px/s]📤 Writing LUT:  19%|█▉        | 612070/3240000 [00:00<00:02, 879341.73px/s]📤 Writing LUT:  22%|██▏       | 703312/3240000 [00:00<00:02, 889869.79px/s]📤 Writing LUT:  25%|██▍       | 796714/3240000 [00:00<00:02, 903670.66px/s]📤 Writing LUT:  27%|██▋       | 889842/3240000 [00:01<00:02, 912191.77px/s]📤 Writing LUT:  30%|███       | 981744/3240000 [00:01<00:02, 914279.15px/s]📤 Writing LUT:  33%|███▎      | 1073759/3240000 [00:01<00:02, 916062.72px/s]📤 Writing LUT:  36%|███▌      | 1167800/3240000 [00:01<00:02, 923434.59px/s]📤 Writing LUT:  39%|███▉      | 1261745/3240000 [00:01<00:02, 928270.25px/s]📤 Writing LUT:  42%|████▏     | 1354573/3240000 [00:01<00:02, 925614.63px/s]📤 Writing LUT:  45%|████▍     | 1447137/3240000 [00:01<00:01, 922722.69px/s]📤 Writing LUT:  48%|████▊     | 1539463/3240000 [00:01<00:01, 922882.62px/s]📤 Writing LUT:  50%|█████     | 1633320/3240000 [00:01<00:01, 927582.92px/s]📤 Writing LUT:  53%|█████▎    | 1726445/3240000 [00:01<00:01, 928679.96px/s]📤 Writing LUT:  56%|█████▌    | 1819600/3240000 [00:02<00:01, 929537.54px/s]📤 Writing LUT:  59%|█████▉    | 1912556/3240000 [00:02<00:01, 928888.99px/s]📤 Writing LUT:  62%|██████▏   | 2005446/3240000 [00:02<00:01, 928616.44px/s]📤 Writing LUT:  65%|██████▍   | 2098309/3240000 [00:02<00:01, 927134.40px/s]📤 Writing LUT:  68%|██████▊   | 2191024/3240000 [00:02<00:01, 926173.06px/s]📤 Writing LUT:  71%|███████   | 2285561/3240000 [00:02<00:01, 931919.04px/s]📤 Writing LUT:  73%|███████▎  | 2378755/3240000 [00:02<00:00, 929374.32px/s]📤 Writing LUT:  76%|███████▋  | 2472333/3240000 [00:02<00:00, 931288.86px/s]📤 Writing LUT:  79%|███████▉  | 2567200/3240000 [00:02<00:00, 936492.45px/s]📤 Writing LUT:  82%|████████▏ | 2661250/3240000 [00:02<00:00, 937690.64px/s]📤 Writing LUT:  85%|████████▌ | 2756155/3240000 [00:03<00:00, 941093.37px/s]📤 Writing LUT:  88%|████████▊ | 2851558/3240000 [00:03<00:00, 944970.63px/s]📤 Writing LUT:  91%|█████████ | 2946513/3240000 [00:03<00:00, 946341.34px/s]📤 Writing LUT:  94%|█████████▍| 3041149/3240000 [00:03<00:00, 942803.37px/s]📤 Writing LUT:  97%|█████████▋| 3136576/3240000 [00:03<00:00, 946233.12px/s]📤 Writing LUT: 100%|█████████▉| 3231753/3240000 [00:03<00:00, 947889.77px/s]📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 921984.03px/s]
[✅] LUT written to: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDCP3D-SL-013_W046(6)_2026-09-04' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/MDCP3D-SL-013_W046(6)_2026-09-04_unknown.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)
📁 Rigs (heatmap):   0%|          | 0/4 [00:00<?, ?rig/s]
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   🔥 1000:  22%|██▏       | 56/260 [00:00<00:02, 90.99frame/s][A
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   🔥 1000:  92%|█████████▏| 238/260 [00:02<00:00, 87.72frame/s][A
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   🔥 1000:  98%|█████████▊| 256/260 [00:02<00:00, 87.11frame/s][A
                                                                [A[✅] 1000: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:03<00:11,  3.71s/rig]
   🔥 2000R:   0%|          | 0/601 [00:00<?, ?frame/s][A
   🔥 2000R:   1%|▏         | 8/601 [00:00<00:07, 75.03frame/s][A
   🔥 2000R:   3%|▎         | 16/601 [00:00<00:07, 76.81frame/s][A
   🔥 2000R:   4%|▍         | 25/601 [00:00<00:07, 79.40frame/s][A
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                                                                 [A[✅] 2000R: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
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                                                                [A[✅] 1500: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/1500_heatmap_fitted.png
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                                                                 [A[✅] 2000L: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:28<00:00,  7.68s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:28<00:00,  7.06s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDCP3D-SL-013_W046(6)_2026-09-04/calibration/debug/5_heatmap
[🏁] Finished processing MDCP3D-SL-013_W046(6)_2026-09-04


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