2026-08-25T20:55:36 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDCP3D-SL-009_W043(6)_2025-05-14_P3D3 ===
2026-08-25T20:55:36 - INFO: Camera ID: 06PFL
2026-08-25T20:55:36 - INFO: Wand ID: 00001f0c348e
2026-08-25T20:55:36 - INFO: Recorded At: 2025-05-13T23:14:08Z

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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 148 stationary frames, 326 remain


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→ 1500 Phase 2: corners:  94%|█████████▍| 307/326 [00:02<00:00, 128.11it/s]

→ 1500 Phase 2: corners: 100%|██████████| 326/326 [00:02<00:00, 139.00it/s]

                                                                           [PASS2] Frame 0000201: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000258: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000259: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000260: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000261: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000352: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000353: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000354: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000355: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000260: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 8 untrusted, 326 total


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→ 1500 Phase 3: finalize:  99%|█████████▉| 323/326 [00:06<00:00, 52.12it/s]

                                                                           
[TIMING] 1500 — 326 frames
  Phase 1  radial detect   (parallel)  :   6.05s  (18.5 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.81s  (8.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.33s
  Phase 3  finalize        (sequential):   6.29s  (19.3 ms/frame)
    decode + undistort                 :   3.25s  (10.0 ms/frame)
    corner apply + refinement          :   1.46s  (4.5 ms/frame)
    segment                            :   1.29s  (4.0 ms/frame)
    smooth                             :   0.18s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  15.48s


Processing Rigs:  25%|██▌       | 1/4 [00:17<00:52, 17.43s/it][INFO] Filtered 179 stationary frames, 197 remain


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→ 1000 Phase 2: corners:  46%|████▌     | 91/197 [00:01<00:01, 101.74it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 105/197 [00:01<00:00, 110.70it/s]

→ 1000 Phase 2: corners:  60%|█████▉    | 118/197 [00:01<00:00, 115.00it/s]

→ 1000 Phase 2: corners:  66%|██████▋   | 131/197 [00:01<00:00, 116.77it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 144/197 [00:01<00:00, 117.56it/s]

→ 1000 Phase 2: corners:  80%|████████  | 158/197 [00:01<00:00, 121.52it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 176/197 [00:02<00:00, 137.44it/s]

                                                                           [PASS2] Frame 0000270: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 0 untrusted, 197 total


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→ 1000 Phase 3: finalize:  76%|███████▌  | 150/197 [00:02<00:00, 54.44it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 156/197 [00:02<00:00, 54.44it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 162/197 [00:02<00:00, 54.50it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 168/197 [00:03<00:00, 54.58it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 174/197 [00:03<00:00, 54.73it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 180/197 [00:03<00:00, 54.94it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 186/197 [00:03<00:00, 55.23it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 192/197 [00:03<00:00, 55.46it/s]

                                                                           
[TIMING] 1000 — 197 frames
  Phase 1  radial detect   (parallel)  :   3.98s  (20.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.18s  (11.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.10s
  Phase 3  finalize        (sequential):   3.53s  (17.9 ms/frame)
    decode + undistort                 :   1.85s  (9.4 ms/frame)
    corner apply + refinement          :   0.74s  (3.8 ms/frame)
    segment                            :   0.77s  (3.9 ms/frame)
    smooth                             :   0.11s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.79s


Processing Rigs:  50%|█████     | 2/4 [00:28<00:27, 13.76s/it][INFO] Filtered 241 stationary frames, 464 remain


→ 2000L (464 images) Phase 1:   0%|          | 0/464 [00:00<?, ?it/s]

→ 2000L (464 images) Phase 1:   0%|          | 1/464 [00:00<06:47,  1.14it/s]

→ 2000L (464 images) Phase 1:   1%|          | 5/464 [00:00<01:11,  6.42it/s]

→ 2000L (464 images) Phase 1:   3%|▎         | 12/464 [00:01<00:27, 16.68it/s]

→ 2000L (464 images) Phase 1:   5%|▌         | 24/464 [00:01<00:12, 34.76it/s]

→ 2000L (464 images) Phase 1:   7%|▋         | 31/464 [00:01<00:10, 39.79it/s]

→ 2000L (464 images) Phase 1:   8%|▊         | 38/464 [00:01<00:12, 34.94it/s]

→ 2000L (464 images) Phase 1:  11%|█         | 49/464 [00:01<00:08, 47.57it/s]

→ 2000L (464 images) Phase 1:  12%|█▏        | 57/464 [00:01<00:07, 51.36it/s]

→ 2000L (464 images) Phase 1:  14%|█▍        | 64/464 [00:01<00:07, 51.43it/s]

→ 2000L (464 images) Phase 1:  15%|█▌        | 70/464 [00:02<00:08, 46.71it/s]

→ 2000L (464 images) Phase 1:  16%|█▋        | 76/464 [00:02<00:08, 47.17it/s]

→ 2000L (464 images) Phase 1:  18%|█▊        | 85/464 [00:02<00:06, 56.78it/s]

→ 2000L (464 images) Phase 1:  20%|█▉        | 92/464 [00:02<00:07, 49.64it/s]

→ 2000L (464 images) Phase 1:  21%|██▏       | 99/464 [00:02<00:06, 53.65it/s]

→ 2000L (464 images) Phase 1:  23%|██▎       | 106/464 [00:02<00:06, 53.09it/s]

→ 2000L (464 images) Phase 1:  25%|██▌       | 117/464 [00:02<00:06, 57.65it/s]

→ 2000L (464 images) Phase 1:  27%|██▋       | 123/464 [00:03<00:06, 49.71it/s]

→ 2000L (464 images) Phase 1:  29%|██▉       | 134/464 [00:03<00:05, 59.65it/s]

→ 2000L (464 images) Phase 1:  30%|███       | 141/464 [00:03<00:05, 61.77it/s]

→ 2000L (464 images) Phase 1:  32%|███▏      | 148/464 [00:03<00:05, 58.18it/s]

→ 2000L (464 images) Phase 1:  33%|███▎      | 155/464 [00:03<00:05, 57.86it/s]

→ 2000L (464 images) Phase 1:  35%|███▍      | 161/464 [00:03<00:05, 53.23it/s]

→ 2000L (464 images) Phase 1:  36%|███▋      | 169/464 [00:03<00:05, 58.47it/s]

→ 2000L (464 images) Phase 1:  38%|███▊      | 176/464 [00:03<00:04, 59.68it/s]

→ 2000L (464 images) Phase 1:  39%|███▉      | 183/464 [00:04<00:04, 59.63it/s]

→ 2000L (464 images) Phase 1:  41%|████      | 190/464 [00:04<00:05, 54.63it/s]

→ 2000L (464 images) Phase 1:  42%|████▏     | 197/464 [00:04<00:04, 55.65it/s]

→ 2000L (464 images) Phase 1:  44%|████▍     | 203/464 [00:04<00:05, 50.04it/s]

→ 2000L (464 images) Phase 1:  45%|████▌     | 211/464 [00:04<00:04, 55.92it/s]

→ 2000L (464 images) Phase 1:  48%|████▊     | 221/464 [00:04<00:04, 56.90it/s]

→ 2000L (464 images) Phase 1:  49%|████▉     | 227/464 [00:04<00:04, 49.75it/s]

→ 2000L (464 images) Phase 1:  51%|█████     | 235/464 [00:05<00:04, 56.26it/s]

→ 2000L (464 images) Phase 1:  52%|█████▏    | 242/464 [00:05<00:04, 54.38it/s]

→ 2000L (464 images) Phase 1:  54%|█████▎    | 249/464 [00:05<00:03, 56.13it/s]

→ 2000L (464 images) Phase 1:  55%|█████▌    | 256/464 [00:05<00:03, 53.30it/s]

→ 2000L (464 images) Phase 1:  57%|█████▋    | 264/464 [00:05<00:03, 58.76it/s]

→ 2000L (464 images) Phase 1:  58%|█████▊    | 271/464 [00:05<00:03, 53.46it/s]

→ 2000L (464 images) Phase 1:  60%|█████▉    | 277/464 [00:05<00:03, 51.58it/s]

→ 2000L (464 images) Phase 1:  61%|██████    | 284/464 [00:05<00:03, 53.74it/s]

→ 2000L (464 images) Phase 1:  62%|██████▎   | 290/464 [00:06<00:03, 50.51it/s]

→ 2000L (464 images) Phase 1:  64%|██████▍   | 299/464 [00:06<00:02, 58.87it/s]

→ 2000L (464 images) Phase 1:  66%|██████▌   | 306/464 [00:06<00:02, 61.65it/s]

→ 2000L (464 images) Phase 1:  67%|██████▋   | 313/464 [00:06<00:02, 50.76it/s]

→ 2000L (464 images) Phase 1:  69%|██████▉   | 320/464 [00:06<00:02, 54.99it/s]

→ 2000L (464 images) Phase 1:  71%|███████   | 328/464 [00:06<00:02, 56.71it/s]

→ 2000L (464 images) Phase 1:  72%|███████▏  | 334/464 [00:06<00:02, 52.03it/s]

→ 2000L (464 images) Phase 1:  74%|███████▍  | 344/464 [00:07<00:01, 63.58it/s]

→ 2000L (464 images) Phase 1:  76%|███████▌  | 352/464 [00:07<00:01, 65.46it/s]

→ 2000L (464 images) Phase 1:  77%|███████▋  | 359/464 [00:07<00:01, 54.52it/s]

→ 2000L (464 images) Phase 1:  79%|███████▉  | 366/464 [00:07<00:01, 55.07it/s]

→ 2000L (464 images) Phase 1:  81%|████████  | 376/464 [00:07<00:01, 65.40it/s]

→ 2000L (464 images) Phase 1:  83%|████████▎ | 384/464 [00:07<00:01, 61.28it/s]

→ 2000L (464 images) Phase 1:  84%|████████▍ | 391/464 [00:07<00:01, 54.66it/s]

→ 2000L (464 images) Phase 1:  86%|████████▋ | 401/464 [00:07<00:00, 64.43it/s]

→ 2000L (464 images) Phase 1:  88%|████████▊ | 408/464 [00:08<00:00, 56.30it/s]

→ 2000L (464 images) Phase 1:  90%|████████▉ | 416/464 [00:08<00:00, 58.70it/s]

→ 2000L (464 images) Phase 1:  91%|█████████ | 423/464 [00:08<00:00, 57.92it/s]

→ 2000L (464 images) Phase 1:  93%|█████████▎| 430/464 [00:08<00:00, 57.23it/s]

→ 2000L (464 images) Phase 1:  94%|█████████▍| 438/464 [00:08<00:00, 60.42it/s]

→ 2000L (464 images) Phase 1:  97%|█████████▋| 449/464 [00:08<00:00, 71.36it/s]

→ 2000L (464 images) Phase 1:  99%|█████████▊| 458/464 [00:08<00:00, 73.86it/s]

                                                                               

→ 2000L Phase 2: corners:   0%|          | 0/464 [00:00<?, ?it/s]

→ 2000L Phase 2: corners:   0%|          | 1/464 [00:00<04:29,  1.72it/s]

→ 2000L Phase 2: corners:   3%|▎         | 12/464 [00:00<00:19, 22.85it/s]

→ 2000L Phase 2: corners:   6%|▌         | 27/464 [00:00<00:08, 50.01it/s]

→ 2000L Phase 2: corners:  10%|▉         | 45/464 [00:00<00:05, 80.24it/s]

→ 2000L Phase 2: corners:  12%|█▎        | 58/464 [00:01<00:04, 86.93it/s]

→ 2000L Phase 2: corners:  15%|█▌        | 71/464 [00:01<00:04, 95.73it/s]

→ 2000L Phase 2: corners:  18%|█▊        | 84/464 [00:01<00:03, 104.59it/s]

→ 2000L Phase 2: corners:  21%|██        | 97/464 [00:01<00:03, 105.66it/s]

→ 2000L Phase 2: corners:  24%|██▍       | 113/464 [00:01<00:02, 119.58it/s]

→ 2000L Phase 2: corners:  27%|██▋       | 127/464 [00:01<00:02, 123.11it/s]

→ 2000L Phase 2: corners:  31%|███       | 143/464 [00:01<00:02, 127.38it/s]

→ 2000L Phase 2: corners:  34%|███▍      | 157/464 [00:01<00:02, 126.62it/s]

→ 2000L Phase 2: corners:  37%|███▋      | 171/464 [00:01<00:02, 130.16it/s]

→ 2000L Phase 2: corners:  40%|███▉      | 185/464 [00:01<00:02, 130.85it/s]

→ 2000L Phase 2: corners:  43%|████▎     | 199/464 [00:02<00:02, 124.22it/s]

→ 2000L Phase 2: corners:  47%|████▋     | 218/464 [00:02<00:01, 140.07it/s]

→ 2000L Phase 2: corners:  50%|█████     | 233/464 [00:02<00:01, 137.11it/s]

→ 2000L Phase 2: corners:  53%|█████▎    | 247/464 [00:02<00:01, 135.27it/s]

→ 2000L Phase 2: corners:  57%|█████▋    | 264/464 [00:02<00:01, 142.97it/s]

→ 2000L Phase 2: corners:  60%|██████    | 279/464 [00:02<00:01, 111.88it/s]

→ 2000L Phase 2: corners:  63%|██████▎   | 292/464 [00:02<00:01, 114.84it/s]

→ 2000L Phase 2: corners:  66%|██████▌   | 305/464 [00:02<00:01, 116.06it/s]

→ 2000L Phase 2: corners:  71%|███████   | 329/464 [00:03<00:00, 147.17it/s]

→ 2000L Phase 2: corners:  74%|███████▍  | 345/464 [00:03<00:00, 148.80it/s]

→ 2000L Phase 2: corners:  78%|███████▊  | 361/464 [00:03<00:00, 136.00it/s]

→ 2000L Phase 2: corners:  81%|████████▏ | 377/464 [00:03<00:00, 138.98it/s]

→ 2000L Phase 2: corners:  85%|████████▍ | 394/464 [00:03<00:00, 145.13it/s]

→ 2000L Phase 2: corners:  88%|████████▊ | 409/464 [00:03<00:00, 134.92it/s]

→ 2000L Phase 2: corners:  92%|█████████▏| 429/464 [00:03<00:00, 150.49it/s]

→ 2000L Phase 2: corners:  96%|█████████▌| 445/464 [00:03<00:00, 147.99it/s]

→ 2000L Phase 2: corners:  99%|█████████▉| 461/464 [00:03<00:00, 148.12it/s]

                                                                            [PASS2] Frame 0000449: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000450: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000451: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000731: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000732: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000733: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000734: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 3 frames fixed, 7 untrusted, 464 total


→ 2000L Phase 3: finalize:   0%|          | 0/464 [00:00<?, ?it/s]

→ 2000L Phase 3: finalize:   1%|▏         | 6/464 [00:00<00:08, 54.94it/s]

→ 2000L Phase 3: finalize:   3%|▎         | 12/464 [00:00<00:08, 54.72it/s]

→ 2000L Phase 3: finalize:   4%|▍         | 18/464 [00:00<00:08, 54.44it/s]

→ 2000L Phase 3: finalize:   5%|▌         | 24/464 [00:00<00:08, 54.10it/s]

→ 2000L Phase 3: finalize:   6%|▋         | 30/464 [00:00<00:08, 53.88it/s]

→ 2000L Phase 3: finalize:   8%|▊         | 36/464 [00:00<00:07, 53.89it/s]

→ 2000L Phase 3: finalize:   9%|▉         | 42/464 [00:00<00:07, 54.61it/s]

→ 2000L Phase 3: finalize:  10%|█         | 48/464 [00:00<00:07, 54.53it/s]

→ 2000L Phase 3: finalize:  12%|█▏        | 54/464 [00:00<00:07, 54.78it/s]

→ 2000L Phase 3: finalize:  13%|█▎        | 60/464 [00:01<00:07, 55.38it/s]

→ 2000L Phase 3: finalize:  14%|█▍        | 66/464 [00:01<00:07, 55.93it/s]

→ 2000L Phase 3: finalize:  16%|█▌        | 72/464 [00:01<00:06, 56.25it/s]

→ 2000L Phase 3: finalize:  17%|█▋        | 78/464 [00:01<00:06, 56.52it/s]

→ 2000L Phase 3: finalize:  18%|█▊        | 84/464 [00:01<00:06, 56.63it/s]

→ 2000L Phase 3: finalize:  19%|█▉        | 90/464 [00:01<00:06, 55.21it/s]

→ 2000L Phase 3: finalize:  21%|██        | 96/464 [00:01<00:06, 54.00it/s]

→ 2000L Phase 3: finalize:  22%|██▏       | 102/464 [00:01<00:06, 53.11it/s]

→ 2000L Phase 3: finalize:  23%|██▎       | 108/464 [00:01<00:06, 52.48it/s]

→ 2000L Phase 3: finalize:  25%|██▍       | 114/464 [00:02<00:06, 51.58it/s]

→ 2000L Phase 3: finalize:  26%|██▌       | 120/464 [00:02<00:06, 51.27it/s]

→ 2000L Phase 3: finalize:  27%|██▋       | 126/464 [00:02<00:06, 51.45it/s]

→ 2000L Phase 3: finalize:  28%|██▊       | 132/464 [00:02<00:06, 51.19it/s]

→ 2000L Phase 3: finalize:  30%|██▉       | 138/464 [00:02<00:06, 51.25it/s]

→ 2000L Phase 3: finalize:  31%|███       | 144/464 [00:02<00:06, 50.94it/s]

→ 2000L Phase 3: finalize:  32%|███▏      | 150/464 [00:02<00:06, 50.81it/s]

→ 2000L Phase 3: finalize:  34%|███▎      | 156/464 [00:02<00:06, 50.56it/s]

→ 2000L Phase 3: finalize:  35%|███▍      | 162/464 [00:03<00:05, 50.43it/s]

→ 2000L Phase 3: finalize:  36%|███▌      | 168/464 [00:03<00:05, 50.54it/s]

→ 2000L Phase 3: finalize:  38%|███▊      | 174/464 [00:03<00:05, 50.74it/s]

→ 2000L Phase 3: finalize:  39%|███▉      | 180/464 [00:03<00:05, 50.96it/s]

→ 2000L Phase 3: finalize:  40%|████      | 186/464 [00:03<00:05, 51.38it/s]

→ 2000L Phase 3: finalize:  41%|████▏     | 192/464 [00:03<00:05, 51.79it/s]

→ 2000L Phase 3: finalize:  43%|████▎     | 198/464 [00:03<00:05, 51.85it/s]

→ 2000L Phase 3: finalize:  44%|████▍     | 204/464 [00:03<00:04, 52.72it/s]

→ 2000L Phase 3: finalize:  45%|████▌     | 210/464 [00:03<00:04, 53.27it/s]

→ 2000L Phase 3: finalize:  47%|████▋     | 216/464 [00:04<00:04, 53.79it/s]

→ 2000L Phase 3: finalize:  48%|████▊     | 222/464 [00:04<00:04, 54.14it/s]

→ 2000L Phase 3: finalize:  49%|████▉     | 228/464 [00:04<00:04, 54.33it/s]

→ 2000L Phase 3: finalize:  50%|█████     | 234/464 [00:04<00:04, 54.65it/s]

→ 2000L Phase 3: finalize:  52%|█████▏    | 240/464 [00:04<00:04, 54.76it/s]

→ 2000L Phase 3: finalize:  53%|█████▎    | 246/464 [00:04<00:03, 54.56it/s]

→ 2000L Phase 3: finalize:  54%|█████▍    | 252/464 [00:04<00:03, 54.37it/s]

→ 2000L Phase 3: finalize:  56%|█████▌    | 258/464 [00:04<00:03, 54.26it/s]

→ 2000L Phase 3: finalize:  57%|█████▋    | 264/464 [00:04<00:03, 53.84it/s]

→ 2000L Phase 3: finalize:  58%|█████▊    | 270/464 [00:05<00:03, 53.74it/s]

→ 2000L Phase 3: finalize:  59%|█████▉    | 276/464 [00:05<00:03, 53.86it/s]

→ 2000L Phase 3: finalize:  61%|██████    | 282/464 [00:05<00:03, 53.89it/s]

→ 2000L Phase 3: finalize:  62%|██████▏   | 288/464 [00:05<00:03, 53.85it/s]

→ 2000L Phase 3: finalize:  63%|██████▎   | 294/464 [00:05<00:03, 53.84it/s]

→ 2000L Phase 3: finalize:  65%|██████▍   | 300/464 [00:05<00:03, 53.74it/s]

→ 2000L Phase 3: finalize:  66%|██████▌   | 306/464 [00:05<00:02, 53.86it/s]

→ 2000L Phase 3: finalize:  67%|██████▋   | 312/464 [00:05<00:02, 53.75it/s]

→ 2000L Phase 3: finalize:  69%|██████▊   | 318/464 [00:05<00:02, 53.47it/s]

→ 2000L Phase 3: finalize:  70%|██████▉   | 324/464 [00:06<00:02, 53.10it/s]

→ 2000L Phase 3: finalize:  71%|███████   | 330/464 [00:06<00:02, 52.05it/s]

→ 2000L Phase 3: finalize:  72%|███████▏  | 336/464 [00:06<00:02, 51.54it/s]

→ 2000L Phase 3: finalize:  74%|███████▎  | 342/464 [00:06<00:02, 51.06it/s]

→ 2000L Phase 3: finalize:  75%|███████▌  | 348/464 [00:06<00:02, 50.89it/s]

→ 2000L Phase 3: finalize:  76%|███████▋  | 354/464 [00:06<00:02, 50.28it/s]

→ 2000L Phase 3: finalize:  78%|███████▊  | 360/464 [00:06<00:02, 50.48it/s]

→ 2000L Phase 3: finalize:  79%|███████▉  | 366/464 [00:06<00:01, 50.61it/s]

→ 2000L Phase 3: finalize:  80%|████████  | 372/464 [00:07<00:01, 50.98it/s]

→ 2000L Phase 3: finalize:  81%|████████▏ | 378/464 [00:07<00:01, 51.11it/s]

→ 2000L Phase 3: finalize:  83%|████████▎ | 384/464 [00:07<00:01, 51.26it/s]

→ 2000L Phase 3: finalize:  84%|████████▍ | 390/464 [00:07<00:01, 51.31it/s]

→ 2000L Phase 3: finalize:  85%|████████▌ | 396/464 [00:07<00:01, 51.33it/s]

→ 2000L Phase 3: finalize:  87%|████████▋ | 402/464 [00:07<00:01, 51.26it/s]

→ 2000L Phase 3: finalize:  88%|████████▊ | 408/464 [00:07<00:01, 50.58it/s]

→ 2000L Phase 3: finalize:  89%|████████▉ | 414/464 [00:07<00:00, 51.22it/s]

→ 2000L Phase 3: finalize:  91%|█████████ | 420/464 [00:07<00:00, 51.26it/s]

→ 2000L Phase 3: finalize:  92%|█████████▏| 426/464 [00:08<00:00, 51.43it/s]

→ 2000L Phase 3: finalize:  93%|█████████▎| 432/464 [00:08<00:00, 51.32it/s]

→ 2000L Phase 3: finalize:  94%|█████████▍| 438/464 [00:08<00:00, 51.20it/s]

→ 2000L Phase 3: finalize:  96%|█████████▌| 444/464 [00:08<00:00, 51.06it/s]

→ 2000L Phase 3: finalize:  97%|█████████▋| 450/464 [00:08<00:00, 50.94it/s]

→ 2000L Phase 3: finalize:  98%|█████████▊| 456/464 [00:08<00:00, 50.97it/s]

→ 2000L Phase 3: finalize: 100%|█████████▉| 462/464 [00:08<00:00, 51.13it/s]

                                                                            
[TIMING] 2000L — 464 frames
  Phase 1  radial detect   (parallel)  :   9.06s  (19.5 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.09s  (8.8 ms/frame)
  Phase 2b corner fix      (sequential):   0.30s
  Phase 3  finalize        (sequential):   8.83s  (19.0 ms/frame)
    decode + undistort                 :   4.46s  (9.6 ms/frame)
    corner apply + refinement          :   2.12s  (4.6 ms/frame)
    segment                            :   1.84s  (4.0 ms/frame)
    smooth                             :   0.26s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.27s


Processing Rigs:  75%|███████▌  | 3/4 [00:53<00:18, 18.86s/it][INFO] Filtered 186 stationary frames, 427 remain


→ 2000R (427 images) Phase 1:   0%|          | 0/427 [00:00<?, ?it/s]

→ 2000R (427 images) Phase 1:   0%|          | 1/427 [00:00<06:48,  1.04it/s]

→ 2000R (427 images) Phase 1:   2%|▏         | 8/427 [00:01<00:42,  9.91it/s]

→ 2000R (427 images) Phase 1:   4%|▍         | 19/427 [00:01<00:16, 25.24it/s]

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→ 2000R (427 images) Phase 1:  99%|█████████▉| 424/427 [00:08<00:00, 77.12it/s]

                                                                               

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→ 2000R Phase 2: corners:  60%|█████▉    | 256/427 [00:02<00:01, 128.60it/s]

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→ 2000R Phase 2: corners:  76%|███████▌  | 324/427 [00:03<00:00, 145.94it/s]

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→ 2000R Phase 2: corners:  84%|████████▍ | 358/427 [00:03<00:00, 154.64it/s]

→ 2000R Phase 2: corners:  88%|████████▊ | 374/427 [00:03<00:00, 150.66it/s]

→ 2000R Phase 2: corners:  92%|█████████▏| 391/427 [00:03<00:00, 152.82it/s]

→ 2000R Phase 2: corners:  96%|█████████▌| 408/427 [00:03<00:00, 157.58it/s]

→ 2000R Phase 2: corners: 100%|█████████▉| 425/427 [00:03<00:00, 160.56it/s]

                                                                            [PASS2] Frame 0000241: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000407: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000408: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000409: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000410: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000441: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000442: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000444: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000445: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000447: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000506: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000507: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000508: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000738: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000739: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000740: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000741: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000743: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000744: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000745: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000746: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000233: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000241: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000507: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000740: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000745: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000233: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000241: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Summary: 11 frames fixed, 17 untrusted, 427 total


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→ 2000R Phase 3: finalize:  94%|█████████▍| 402/427 [00:07<00:00, 51.08it/s]

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→ 2000R Phase 3: finalize:  97%|█████████▋| 414/427 [00:07<00:00, 50.63it/s]

→ 2000R Phase 3: finalize:  98%|█████████▊| 420/427 [00:08<00:00, 50.76it/s]

→ 2000R Phase 3: finalize: 100%|█████████▉| 426/427 [00:08<00:00, 50.82it/s]

                                                                            
[TIMING] 2000R — 427 frames
  Phase 1  radial detect   (parallel)  :   8.27s  (19.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.90s  (9.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.67s
  Phase 3  finalize        (sequential):   8.16s  (19.1 ms/frame)
    decode + undistort                 :   4.13s  (9.7 ms/frame)
    corner apply + refinement          :   1.96s  (4.6 ms/frame)
    segment                            :   1.69s  (4.0 ms/frame)
    smooth                             :   0.24s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  20.99s


Processing Rigs: 100%|██████████| 4/4 [01:16<00:00, 20.63s/it]
Processing Rigs: 100%|██████████| 4/4 [01:16<00:00, 19.22s/it]
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1414 frames from 4 rigs to /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1500: 100 frames selected from 326
[📋] 1000: 100 frames selected from 197
[📋] 2000L: 100 frames selected from 464
[📋] 2000R: 100 frames selected from 427
[💾] Saved RigFrameDataset to /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/1_digitized/rig_frames.json

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

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

Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]
Computing max image from bright frames:   1%|▏         | 1/80 [00:04<05:58,  4.54s/it]
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Computing max image from bright frames:   6%|▋         | 5/80 [00:05<00:54,  1.37it/s]
Computing max image from bright frames:   8%|▊         | 6/80 [00:05<00:39,  1.87it/s]
Computing max image from bright frames:   9%|▉         | 7/80 [00:05<00:30,  2.42it/s]
Computing max image from bright frames:  10%|█         | 8/80 [00:05<00:23,  3.02it/s]
Computing max image from bright frames:  11%|█▏        | 9/80 [00:06<00:20,  3.47it/s]
Computing max image from bright frames:  12%|█▎        | 10/80 [00:06<00:16,  4.14it/s]
Computing max image from bright frames:  14%|█▍        | 11/80 [00:06<00:14,  4.75it/s]
Computing max image from bright frames:  15%|█▌        | 12/80 [00:06<00:12,  5.29it/s]
Computing max image from bright frames:  16%|█▋        | 13/80 [00:06<00:11,  6.01it/s]
Computing max image from bright frames:  19%|█▉        | 15/80 [00:06<00:08,  7.23it/s]
Computing max image from bright frames:  21%|██▏       | 17/80 [00:06<00:07,  8.52it/s]
Computing max image from bright frames:  22%|██▎       | 18/80 [00:07<00:07,  8.46it/s]
Computing max image from bright frames:  24%|██▍       | 19/80 [00:07<00:07,  8.69it/s]
Computing max image from bright frames:  25%|██▌       | 20/80 [00:07<00:07,  8.41it/s]
Computing max image from bright frames:  26%|██▋       | 21/80 [00:07<00:07,  8.41it/s]
Computing max image from bright frames:  28%|██▊       | 22/80 [00:07<00:07,  8.07it/s]
Computing max image from bright frames:  29%|██▉       | 23/80 [00:07<00:07,  7.84it/s]
Computing max image from bright frames:  30%|███       | 24/80 [00:07<00:07,  7.82it/s]
Computing max image from bright frames:  31%|███▏      | 25/80 [00:08<00:07,  7.69it/s]
Computing max image from bright frames:  34%|███▍      | 27/80 [00:08<00:08,  6.46it/s]
Computing max image from bright frames:  35%|███▌      | 28/80 [00:08<00:08,  6.06it/s]
Computing max image from bright frames:  36%|███▋      | 29/80 [00:08<00:08,  6.32it/s]
Computing max image from bright frames:  39%|███▉      | 31/80 [00:08<00:07,  6.61it/s]
Computing max image from bright frames:  40%|████      | 32/80 [00:09<00:09,  4.80it/s]
Computing max image from bright frames:  41%|████▏     | 33/80 [00:10<00:18,  2.50it/s]
Computing max image from bright frames:  42%|████▎     | 34/80 [00:10<00:15,  2.93it/s]
Computing max image from bright frames:  44%|████▍     | 35/80 [00:10<00:12,  3.56it/s]
Computing max image from bright frames:  45%|████▌     | 36/80 [00:10<00:12,  3.65it/s]
Computing max image from bright frames:  48%|████▊     | 38/80 [00:11<00:08,  4.69it/s]
Computing max image from bright frames:  50%|█████     | 40/80 [00:11<00:06,  5.89it/s]
Computing max image from bright frames:  51%|█████▏    | 41/80 [00:11<00:06,  5.93it/s]
Computing max image from bright frames:  52%|█████▎    | 42/80 [00:11<00:07,  4.81it/s]
Computing max image from bright frames:  54%|█████▍    | 43/80 [00:12<00:07,  4.93it/s]
Computing max image from bright frames:  55%|█████▌    | 44/80 [00:12<00:06,  5.42it/s]
Computing max image from bright frames:  57%|█████▊    | 46/80 [00:12<00:04,  6.97it/s]
Computing max image from bright frames:  60%|██████    | 48/80 [00:12<00:04,  7.73it/s]
Computing max image from bright frames:  62%|██████▎   | 50/80 [00:12<00:03,  9.61it/s]
Computing max image from bright frames:  65%|██████▌   | 52/80 [00:12<00:02, 10.68it/s]
Computing max image from bright frames:  68%|██████▊   | 54/80 [00:13<00:02,  9.92it/s]
Computing max image from bright frames:  70%|███████   | 56/80 [00:13<00:03,  6.92it/s]
Computing max image from bright frames:  72%|███████▎  | 58/80 [00:13<00:02,  8.43it/s]
Computing max image from bright frames:  75%|███████▌  | 60/80 [00:14<00:02,  6.96it/s]
Computing max image from bright frames:  79%|███████▉  | 63/80 [00:14<00:01,  9.81it/s]
Computing max image from bright frames:  81%|████████▏ | 65/80 [00:14<00:02,  5.52it/s]
Computing max image from bright frames:  84%|████████▍ | 67/80 [00:15<00:02,  6.45it/s]
Computing max image from bright frames:  86%|████████▋ | 69/80 [00:15<00:01,  6.33it/s]
Computing max image from bright frames:  89%|████████▉ | 71/80 [00:15<00:01,  6.42it/s]
Computing max image from bright frames:  92%|█████████▎| 74/80 [00:15<00:00,  8.47it/s]
Computing max image from bright frames:  95%|█████████▌| 76/80 [00:16<00:00,  9.86it/s]
Computing max image from bright frames:  99%|█████████▉| 79/80 [00:16<00:00, 12.28it/s]
                                                                                       
[✅] Casing detection complete: cx=856.0, cy=860.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:34,  2.84it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   1%|          | 1/100 [00:00<00:34,  2.84it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   2%|▏         | 2/100 [00:00<00:34,  2.84it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   3%|▎         | 3/100 [00:00<00:34,  2.84it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:09,  9.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:09,  9.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   5%|▌         | 5/100 [00:00<00:09,  9.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:09,  9.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   7%|▋         | 7/100 [00:00<00:06, 14.57it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:06, 14.57it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   8%|▊         | 8/100 [00:00<00:06, 14.57it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:06, 14.57it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:06, 14.57it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:06, 14.57it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  12%|█▏        | 12/100 [00:00<00:04, 21.34it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.003 | Best: {'fx': 384.848} (0.003):  12%|█▏        | 12/100 [00:00<00:04, 21.34it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 21.34it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 21.34it/s] 
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 21.34it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.004 | Best: {'fx': 406.061} (0.004):  16%|█▌        | 16/100 [00:00<00:03, 25.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  16%|█▌        | 16/100 [00:00<00:03, 25.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.005 | Best: {'fx': 427.273} (0.006):  17%|█▋        | 17/100 [00:01<00:03, 25.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  18%|█▊        | 18/100 [00:01<00:03, 25.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.008 | Best: {'fx': 441.414} (0.008):  19%|█▉        | 19/100 [00:01<00:03, 22.38it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.007 | Best: {'fx': 441.414} (0.008):  19%|█▉        | 19/100 [00:01<00:03, 22.38it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.005 | Best: {'fx': 441.414} (0.008):  20%|██        | 20/100 [00:01<00:03, 22.38it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:01<00:03, 22.38it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  22%|██▏       | 22/100 [00:01<00:03, 22.38it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  23%|██▎       | 23/100 [00:01<00:03, 24.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:03, 24.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:03, 24.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 476.768} (0.021):  25%|██▌       | 25/100 [00:01<00:03, 24.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 483.838} (0.026):  26%|██▌       | 26/100 [00:01<00:03, 24.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 483.838} (0.026):  27%|██▋       | 27/100 [00:01<00:02, 27.45it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.033 | Best: {'fx': 490.909} (0.033):  27%|██▋       | 27/100 [00:01<00:02, 27.45it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.038 | Best: {'fx': 497.98} (0.038):  28%|██▊       | 28/100 [00:01<00:02, 27.45it/s] 
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  29%|██▉       | 29/100 [00:01<00:02, 27.45it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.046 | Best: {'fx': 505.051} (0.046):  30%|███       | 30/100 [00:01<00:03, 23.28it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.056 | Best: {'fx': 512.121} (0.056):  30%|███       | 30/100 [00:01<00:03, 23.28it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.081 | Best: {'fx': 526.263} (0.081):  31%|███       | 31/100 [00:01<00:02, 23.28it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.068 | Best: {'fx': 526.263} (0.081):  32%|███▏      | 32/100 [00:01<00:02, 23.28it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.068 | Best: {'fx': 526.263} (0.081):  33%|███▎      | 33/100 [00:01<00:03, 19.51it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.066 | Best: {'fx': 526.263} (0.081):  33%|███▎      | 33/100 [00:01<00:03, 19.51it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.045 | Best: {'fx': 526.263} (0.081):  34%|███▍      | 34/100 [00:01<00:03, 19.51it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.078 | Best: {'fx': 526.263} (0.081):  35%|███▌      | 35/100 [00:01<00:03, 19.51it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.078 | Best: {'fx': 526.263} (0.081):  36%|███▌      | 36/100 [00:01<00:03, 18.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.085 | Best: {'fx': 533.333} (0.085):  36%|███▌      | 36/100 [00:01<00:03, 18.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.042 | Best: {'fx': 533.333} (0.085):  37%|███▋      | 37/100 [00:01<00:03, 18.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.057 | Best: {'fx': 533.333} (0.085):  38%|███▊      | 38/100 [00:01<00:03, 18.82it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.057 | Best: {'fx': 533.333} (0.085):  39%|███▉      | 39/100 [00:01<00:03, 20.14it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.050 | Best: {'fx': 533.333} (0.085):  39%|███▉      | 39/100 [00:01<00:03, 20.14it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.035 | Best: {'fx': 533.333} (0.085):  40%|████      | 40/100 [00:01<00:02, 20.14it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.036 | Best: {'fx': 533.333} (0.085):  41%|████      | 41/100 [00:02<00:02, 20.14it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.033 | Best: {'fx': 533.333} (0.085):  42%|████▏     | 42/100 [00:02<00:02, 20.14it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.039 | Best: {'fx': 533.333} (0.085):  43%|████▎     | 43/100 [00:02<00:02, 20.14it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.039 | Best: {'fx': 533.333} (0.085):  44%|████▍     | 44/100 [00:02<00:02, 26.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.032 | Best: {'fx': 533.333} (0.085):  44%|████▍     | 44/100 [00:02<00:02, 26.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.028 | Best: {'fx': 533.333} (0.085):  45%|████▌     | 45/100 [00:02<00:02, 26.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.085):  46%|████▌     | 46/100 [00:02<00:02, 26.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.085):  47%|████▋     | 47/100 [00:02<00:02, 23.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.030 | Best: {'fx': 533.333} (0.085):  47%|████▋     | 47/100 [00:02<00:02, 23.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 533.333} (0.085):  48%|████▊     | 48/100 [00:02<00:02, 23.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.029 | Best: {'fx': 533.333} (0.085):  49%|████▉     | 49/100 [00:02<00:02, 23.42it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.029 | Best: {'fx': 533.333} (0.085):  50%|█████     | 50/100 [00:02<00:02, 24.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.027 | Best: {'fx': 533.333} (0.085):  50%|█████     | 50/100 [00:02<00:02, 24.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.085):  51%|█████     | 51/100 [00:02<00:01, 24.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.025 | Best: {'fx': 533.333} (0.085):  52%|█████▏    | 52/100 [00:02<00:01, 24.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.026 | Best: {'fx': 533.333} (0.085):  53%|█████▎    | 53/100 [00:02<00:01, 24.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  54%|█████▍    | 54/100 [00:02<00:01, 24.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  55%|█████▌    | 55/100 [00:02<00:01, 27.18it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  55%|█████▌    | 55/100 [00:02<00:01, 27.18it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  56%|█████▌    | 56/100 [00:02<00:01, 27.18it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  57%|█████▋    | 57/100 [00:02<00:01, 27.18it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  58%|█████▊    | 58/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.024 | Best: {'fx': 533.333} (0.085):  58%|█████▊    | 58/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  59%|█████▉    | 59/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  60%|██████    | 60/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  61%|██████    | 61/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.085):  62%|██████▏   | 62/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.085):  63%|██████▎   | 63/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  64%|██████▍   | 64/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  65%|██████▌   | 65/100 [00:02<00:01, 26.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  66%|██████▌   | 66/100 [00:02<00:00, 38.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.023 | Best: {'fx': 533.333} (0.085):  66%|██████▌   | 66/100 [00:02<00:00, 38.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  67%|██████▋   | 67/100 [00:02<00:00, 38.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  68%|██████▊   | 68/100 [00:02<00:00, 38.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.022 | Best: {'fx': 533.333} (0.085):  69%|██████▉   | 69/100 [00:02<00:00, 38.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  70%|███████   | 70/100 [00:02<00:00, 38.49it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  71%|███████   | 71/100 [00:02<00:00, 41.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  71%|███████   | 71/100 [00:02<00:00, 41.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  72%|███████▏  | 72/100 [00:02<00:00, 41.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  73%|███████▎  | 73/100 [00:02<00:00, 41.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  74%|███████▍  | 74/100 [00:02<00:00, 41.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  75%|███████▌  | 75/100 [00:02<00:00, 41.09it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  76%|███████▌  | 76/100 [00:02<00:00, 39.00it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.020 | Best: {'fx': 533.333} (0.085):  76%|███████▌  | 76/100 [00:03<00:00, 39.00it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  77%|███████▋  | 77/100 [00:03<00:00, 39.00it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  78%|███████▊  | 78/100 [00:03<00:00, 39.00it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  79%|███████▉  | 79/100 [00:03<00:00, 39.00it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  80%|████████  | 80/100 [00:03<00:00, 39.00it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  81%|████████  | 81/100 [00:03<00:00, 37.73it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  81%|████████  | 81/100 [00:03<00:00, 37.73it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  82%|████████▏ | 82/100 [00:03<00:00, 37.73it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  83%|████████▎ | 83/100 [00:03<00:00, 37.73it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  84%|████████▍ | 84/100 [00:03<00:00, 37.73it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  85%|████████▌ | 85/100 [00:03<00:00, 36.25it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  85%|████████▌ | 85/100 [00:03<00:00, 36.25it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  86%|████████▌ | 86/100 [00:03<00:00, 36.25it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  87%|████████▋ | 87/100 [00:03<00:00, 36.25it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  88%|████████▊ | 88/100 [00:03<00:00, 36.25it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  89%|████████▉ | 89/100 [00:03<00:00, 29.95it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  89%|████████▉ | 89/100 [00:03<00:00, 29.95it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  90%|█████████ | 90/100 [00:03<00:00, 29.95it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  91%|█████████ | 91/100 [00:03<00:00, 29.95it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  92%|█████████▏| 92/100 [00:03<00:00, 29.95it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  93%|█████████▎| 93/100 [00:03<00:00, 31.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  93%|█████████▎| 93/100 [00:03<00:00, 31.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  94%|█████████▍| 94/100 [00:03<00:00, 31.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  95%|█████████▌| 95/100 [00:03<00:00, 31.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  96%|█████████▌| 96/100 [00:03<00:00, 31.78it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  97%|█████████▋| 97/100 [00:03<00:00, 33.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  97%|█████████▋| 97/100 [00:03<00:00, 33.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  98%|█████████▊| 98/100 [00:03<00:00, 33.74it/s]
Brute | Start: {'fx': 500.0} (0.041) | Current: 0.018 | Best: {'fx': 533.333} (0.085):  99%|█████████▉| 99/100 [00:03<00:00, 33.74it/s]
                                                                                                                                       

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.052 | Best: {'fx': 509.354} (0.052):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.052 | Best: {'fx': 509.354} (0.052):   2%|▏         | 1/50 [00:00<00:28,  1.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.054 | Best: {'fx': 510.374} (0.054):   2%|▏         | 1/50 [00:00<00:28,  1.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.050 | Best: {'fx': 510.374} (0.054):   4%|▍         | 2/50 [00:00<00:27,  1.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.057 | Best: {'fx': 512.415} (0.057):   6%|▌         | 3/50 [00:00<00:27,  1.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.057 | Best: {'fx': 512.415} (0.057):   8%|▊         | 4/50 [00:00<00:08,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.055 | Best: {'fx': 512.415} (0.057):   8%|▊         | 4/50 [00:00<00:08,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.060 | Best: {'fx': 514.456} (0.060):  10%|█         | 5/50 [00:00<00:07,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 514.456} (0.060):  12%|█▏        | 6/50 [00:00<00:07,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.061 | Best: {'fx': 515.476} (0.061):  14%|█▍        | 7/50 [00:00<00:07,  5.69it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.061 | Best: {'fx': 515.476} (0.061):  16%|█▌        | 8/50 [00:00<00:03, 11.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.065 | Best: {'fx': 517.517} (0.065):  16%|█▌        | 8/50 [00:01<00:03, 11.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.067 | Best: {'fx': 518.537} (0.067):  18%|█▊        | 9/50 [00:01<00:03, 11.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.068 | Best: {'fx': 519.558} (0.068):  20%|██        | 10/50 [00:01<00:03, 11.72it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.068 | Best: {'fx': 519.558} (0.068):  22%|██▏       | 11/50 [00:01<00:02, 13.76it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.063 | Best: {'fx': 519.558} (0.068):  22%|██▏       | 11/50 [00:01<00:02, 13.76it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.070 | Best: {'fx': 520.578} (0.070):  24%|██▍       | 12/50 [00:01<00:02, 13.76it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.072 | Best: {'fx': 521.599} (0.072):  26%|██▌       | 13/50 [00:01<00:02, 13.76it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.074 | Best: {'fx': 523.639} (0.074):  28%|██▊       | 14/50 [00:01<00:02, 13.76it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.074 | Best: {'fx': 523.639} (0.074):  30%|███       | 15/50 [00:01<00:01, 18.09it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.073 | Best: {'fx': 523.639} (0.074):  30%|███       | 15/50 [00:01<00:01, 18.09it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.076 | Best: {'fx': 524.66} (0.076):  32%|███▏      | 16/50 [00:01<00:01, 18.09it/s] 
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.080 | Best: {'fx': 525.68} (0.080):  34%|███▍      | 17/50 [00:01<00:01, 18.09it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 526.701} (0.082):  36%|███▌      | 18/50 [00:01<00:01, 18.09it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 526.701} (0.082):  38%|███▊      | 19/50 [00:01<00:01, 18.84it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.083 | Best: {'fx': 527.721} (0.083):  38%|███▊      | 19/50 [00:01<00:01, 18.84it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 528.741} (0.084):  40%|████      | 20/50 [00:01<00:01, 18.84it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.079 | Best: {'fx': 528.741} (0.084):  42%|████▏     | 21/50 [00:01<00:01, 18.84it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.079 | Best: {'fx': 528.741} (0.084):  44%|████▍     | 22/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.085 | Best: {'fx': 530.782} (0.085):  44%|████▍     | 22/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 530.782} (0.085):  46%|████▌     | 23/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.083 | Best: {'fx': 530.782} (0.085):  48%|████▊     | 24/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.085 | Best: {'fx': 530.782} (0.085):  50%|█████     | 25/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.085 | Best: {'fx': 530.782} (0.085):  52%|█████▏    | 26/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.085 | Best: {'fx': 530.782} (0.085):  54%|█████▍    | 27/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 530.782} (0.085):  56%|█████▌    | 28/50 [00:01<00:01, 17.07it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 530.782} (0.085):  58%|█████▊    | 29/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.078 | Best: {'fx': 530.782} (0.085):  58%|█████▊    | 29/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.083 | Best: {'fx': 530.782} (0.085):  60%|██████    | 30/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.077 | Best: {'fx': 530.782} (0.085):  62%|██████▏   | 31/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.067 | Best: {'fx': 530.782} (0.085):  64%|██████▍   | 32/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.072 | Best: {'fx': 530.782} (0.085):  66%|██████▌   | 33/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.075 | Best: {'fx': 530.782} (0.085):  68%|██████▊   | 34/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 530.782} (0.085):  70%|███████   | 35/50 [00:01<00:00, 25.94it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.082 | Best: {'fx': 530.782} (0.085):  72%|███████▏  | 36/50 [00:01<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.070 | Best: {'fx': 530.782} (0.085):  72%|███████▏  | 36/50 [00:01<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.079 | Best: {'fx': 530.782} (0.085):  74%|███████▍  | 37/50 [00:01<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.068 | Best: {'fx': 530.782} (0.085):  76%|███████▌  | 38/50 [00:01<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.065 | Best: {'fx': 530.782} (0.085):  78%|███████▊  | 39/50 [00:01<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.063 | Best: {'fx': 530.782} (0.085):  80%|████████  | 40/50 [00:01<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.059 | Best: {'fx': 530.782} (0.085):  82%|████████▏ | 41/50 [00:02<00:00, 32.32it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.059 | Best: {'fx': 530.782} (0.085):  84%|████████▍ | 42/50 [00:02<00:00, 38.01it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.063 | Best: {'fx': 530.782} (0.085):  84%|████████▍ | 42/50 [00:02<00:00, 38.01it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 530.782} (0.085):  86%|████████▌ | 43/50 [00:02<00:00, 38.01it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.058 | Best: {'fx': 530.782} (0.085):  88%|████████▊ | 44/50 [00:02<00:00, 38.01it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.061 | Best: {'fx': 530.782} (0.085):  90%|█████████ | 45/50 [00:02<00:00, 38.01it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.055 | Best: {'fx': 530.782} (0.085):  92%|█████████▏| 46/50 [00:02<00:00, 38.01it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.055 | Best: {'fx': 530.782} (0.085):  94%|█████████▍| 47/50 [00:02<00:00, 39.99it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.055 | Best: {'fx': 530.782} (0.085):  94%|█████████▍| 47/50 [00:02<00:00, 39.99it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.057 | Best: {'fx': 530.782} (0.085):  96%|█████████▌| 48/50 [00:02<00:00, 39.99it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.053 | Best: {'fx': 530.782} (0.085):  98%|█████████▊| 49/50 [00:02<00:00, 39.99it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 856.0
    py: 860.0
    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: 1500(1500mm):20/20, 1000(1000mm):20/20, 2000L(2000mm):20/20, 2000R(2000mm):20/20
  Total: 80/80 frames used for optimization

Initial score: 31.2104: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 31.2104, picklable_objective(x0) = 31.2425

Best score: 31.2425: : 0iter [00:02, ?iter/s] 
Best score: 31.2425: : 1iter [00:02,  2.04s/iter]
Best score: 31.2425: : 1iter [00:02,  2.04s/iter]
Best score: 31.2425: : 2iter [00:02,  1.11iter/s]
Best score: 31.2425: : 2iter [00:02,  1.11iter/s]
Best score: 31.2425: : 3iter [00:02,  1.87iter/s]
Best score: 31.2425: : 3iter [00:02,  1.87iter/s]
Best score: 31.2425: : 4iter [00:02,  1.87iter/s]
Best score: 31.2425: : 5iter [00:02,  3.47iter/s]
Best score: 31.2425: : 5iter [00:02,  3.47iter/s]
Best score: 31.2425: : 6iter [00:02,  4.26iter/s]
Best score: 31.2425: : 6iter [00:02,  4.26iter/s]
Best score: 31.2425: : 7iter [00:02,  5.05iter/s]
Best score: 31.2425: : 7iter [00:02,  5.05iter/s]
Best score: 31.2425: : 8iter [00:02,  5.85iter/s]
Best score: 31.2425: : 8iter [00:02,  5.85iter/s]
Best score: 31.2425: : 9iter [00:02,  6.66iter/s]
Best score: 31.2425: : 9iter [00:02,  6.66iter/s]
Best score: 31.2425: : 10iter [00:02,  7.04iter/s]
Best score: 31.2425: : 10iter [00:03,  7.04iter/s]
Best score: 31.2425: : 11iter [00:03,  7.04iter/s]
Best score: 31.2425: : 12iter [00:03,  8.12iter/s]
Best score: 31.2425: : 12iter [00:03,  8.12iter/s]
Best score: 31.2425: : 13iter [00:03,  8.12iter/s]
Best score: 31.2425: : 14iter [00:03,  8.64iter/s]
Best score: 31.2425: : 14iter [00:03,  8.64iter/s]
Best score: 31.2425: : 15iter [00:03,  8.77iter/s]
Best score: 31.2425: : 15iter [00:03,  8.77iter/s]
Best score: 31.2425: : 16iter [00:03,  8.74iter/s]
Best score: 31.2425: : 16iter [00:03,  8.74iter/s]
Best score: 31.2425: : 17iter [00:03,  9.00iter/s]
Best score: 31.2425: : 17iter [00:03,  9.00iter/s]
Best score: 31.2425: : 18iter [00:03,  9.23iter/s]
Best score: 31.2425: : 18iter [00:03,  9.23iter/s]
Best score: 31.2425: : 19iter [00:03,  9.39iter/s]
Best score: 31.2425: : 19iter [00:04,  9.39iter/s]
Best score: 31.2425: : 20iter [00:04,  9.35iter/s]
Best score: 31.2425: : 20iter [00:04,  9.35iter/s]
Best score: 31.2425: : 21iter [00:04,  9.47iter/s]
Best score: 31.2425: : 21iter [00:04,  9.47iter/s]
Best score: 31.2425: : 22iter [00:04,  9.47iter/s]
Best score: 31.2425: : 23iter [00:04,  9.80iter/s]
Best score: 31.2425: : 23iter [00:04,  9.80iter/s]
Best score: 31.2425: : 24iter [00:04,  9.85iter/s]
Best score: 31.2425: : 24iter [00:04,  9.85iter/s]
Best score: 31.2425: : 25iter [00:04,  9.85iter/s]
Best score: 31.2425: : 26iter [00:04, 10.06iter/s]
Best score: 31.2425: : 26iter [00:04, 10.06iter/s]
Best score: 31.2425: : 27iter [00:04, 10.06iter/s]
Best score: 31.2425: : 28iter [00:04, 10.21iter/s]
Best score: 31.2425: : 28iter [00:04, 10.21iter/s]
Best score: 31.2425: : 29iter [00:05, 10.21iter/s]
Best score: 31.2425: : 30iter [00:05,  9.98iter/s]
Best score: 31.2425: : 30iter [00:05,  9.98iter/s]
Best score: 31.2425: : 31iter [00:05,  9.98iter/s]
Best score: 31.2425: : 32iter [00:05,  9.95iter/s]
Best score: 31.2425: : 32iter [00:05,  9.95iter/s]
Best score: 31.2425: : 33iter [00:05,  9.94iter/s]
Best score: 31.2425: : 33iter [00:05,  9.94iter/s]
Best score: 31.2425: : 34iter [00:05,  9.94iter/s]
Best score: 31.2425: : 35iter [00:05, 10.14iter/s]
Best score: 31.2425: : 35iter [00:05, 10.14iter/s]
Best score: 31.2425: : 36iter [00:05, 10.14iter/s]
Best score: 31.2425: : 37iter [00:05,  9.96iter/s]
Best score: 31.2425: : 37iter [00:05,  9.96iter/s]
Best score: 31.2425: : 38iter [00:05,  9.96iter/s]
Best score: 31.2425: : 39iter [00:05, 10.04iter/s]
Best score: 31.2425: : 39iter [00:06, 10.04iter/s]
Best score: 31.2425: : 40iter [00:06, 10.04iter/s]
Best score: 31.2425: : 41iter [00:06,  9.94iter/s]
Best score: 31.2425: : 41iter [00:06,  9.94iter/s]
Best score: 31.2425: : 42iter [00:06,  9.94iter/s]
Best score: 31.2425: : 43iter [00:06, 10.03iter/s]
Best score: 31.2425: : 43iter [00:06, 10.03iter/s]
Best score: 31.2425: : 44iter [00:06, 10.03iter/s]
Best score: 31.2425: : 45iter [00:06, 10.05iter/s]
Best score: 31.2425: : 45iter [00:06, 10.05iter/s]
Best score: 31.2425: : 46iter [00:06, 10.05iter/s]
Best score: 31.2425: : 47iter [00:06,  9.90iter/s]
Best score: 31.2425: : 47iter [00:06,  9.90iter/s]
Best score: 31.2425: : 48iter [00:06,  9.90iter/s]
Best score: 31.2425: : 48iter [00:06,  9.90iter/s]
Best score: 31.2425: : 49iter [00:06,  9.89iter/s]
[ITER 50] Best: 31.2425
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 856.0000
  py: 860.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
Best score: 31.2425: : 49iter [00:07,  9.89iter/s]
Best score: 31.2425: : 50iter [00:07,  9.54iter/s]
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 31.2425: : 50iter [00:07,  9.54iter/s]
Best score: 31.2425: : 51iter [00:07,  9.42iter/s]
Best score: 31.2425: : 51iter [00:07,  9.42iter/s]
Best score: 31.2425: : 52iter [00:07,  9.33iter/s]
Best score: 31.2425: : 52iter [00:07,  9.33iter/s]
Best score: 31.2425: : 53iter [00:07,  9.42iter/s]
Best score: 31.2425: : 53iter [00:07,  9.42iter/s]
Best score: 31.2425: : 54iter [00:07,  9.44iter/s]
Best score: 31.2425: : 54iter [00:07,  9.44iter/s]
Best score: 31.2425: : 55iter [00:07,  9.49iter/s]
Best score: 31.2425: : 55iter [00:07,  9.49iter/s]
Best score: 31.2425: : 56iter [00:07,  9.57iter/s]
Best score: 31.2425: : 56iter [00:07,  9.57iter/s]
Best score: 31.2425: : 57iter [00:07,  9.57iter/s]
Best score: 31.2425: : 58iter [00:07,  9.77iter/s]
Best score: 31.2425: : 58iter [00:07,  9.77iter/s]
Best score: 31.2425: : 59iter [00:08,  9.77iter/s]
Best score: 31.2425: : 60iter [00:08,  9.66iter/s]
Best score: 31.2425: : 60iter [00:08,  9.66iter/s]
Best score: 31.2425: : 61iter [00:08,  9.66iter/s]
Best score: 31.2425: : 62iter [00:08,  9.71iter/s]
Best score: 31.2425: : 62iter [00:08,  9.71iter/s]
Best score: 31.2425: : 63iter [00:08,  9.62iter/s]
Best score: 31.2425: : 63iter [00:08,  9.62iter/s]
Best score: 31.2425: : 64iter [00:08,  9.61iter/s]
Best score: 31.2425: : 64iter [00:08,  9.61iter/s]
Best score: 31.2425: : 65iter [00:08,  9.65iter/s]
Best score: 31.2425: : 65iter [00:08,  9.65iter/s]
Best score: 31.2425: : 66iter [00:08,  9.65iter/s]
Best score: 31.2425: : 67iter [00:08,  9.69iter/s]
Best score: 31.2425: : 67iter [00:08,  9.69iter/s]
Best score: 31.2425: : 68iter [00:09,  9.69iter/s]
Best score: 31.2425: : 69iter [00:09,  9.91iter/s]
Best score: 31.2425: : 69iter [00:09,  9.91iter/s]
Best score: 31.2425: : 70iter [00:09,  9.60iter/s]
Best score: 31.2425: : 70iter [00:09,  9.60iter/s]
Best score: 31.2425: : 71iter [00:09,  9.60iter/s]
Best score: 31.2425: : 72iter [00:09,  9.51iter/s]
Best score: 31.2425: : 72iter [00:09,  9.51iter/s]
Best score: 31.2425: : 73iter [00:09,  9.30iter/s]
Best score: 31.2425: : 73iter [00:09,  9.30iter/s]
Best score: 31.2425: : 74iter [00:09,  9.06iter/s]
Best score: 31.2425: : 74iter [00:09,  9.06iter/s]
Best score: 31.2425: : 75iter [00:09,  9.02iter/s]
Best score: 31.2425: : 75iter [00:09,  9.02iter/s]
Best score: 31.2425: : 76iter [00:09,  9.03iter/s]
Best score: 31.2425: : 76iter [00:09,  9.03iter/s]
Best score: 31.2425: : 77iter [00:09,  9.27iter/s]
Best score: 31.2425: : 77iter [00:09,  9.27iter/s]
Best score: 31.2425: : 78iter [00:09,  9.37iter/s]
Best score: 31.2425: : 78iter [00:10,  9.37iter/s]
Best score: 31.2425: : 79iter [00:10,  9.36iter/s]
Best score: 21.1035: : 79iter [00:10,  9.36iter/s]
Best score: 21.1035: : 80iter [00:10,  9.07iter/s]
Best score: 21.1035: : 80iter [00:10,  9.07iter/s]
Best score: 21.1035: : 81iter [00:10,  9.11iter/s]
Best score: 21.1035: : 81iter [00:10,  9.11iter/s]
Best score: 21.1035: : 82iter [00:10,  9.28iter/s]
Best score: 21.1035: : 82iter [00:10,  9.28iter/s]
Best score: 21.1035: : 83iter [00:10,  9.28iter/s]
Best score: 21.1035: : 84iter [00:10,  9.63iter/s]
Best score: 21.1035: : 84iter [00:10,  9.63iter/s]
Best score: 21.1035: : 85iter [00:10,  9.57iter/s]
Best score: 21.1035: : 85iter [00:10,  9.57iter/s]
Best score: 21.1035: : 86iter [00:10,  9.57iter/s]
Best score: 21.1035: : 86iter [00:10,  9.57iter/s]
Best score: 21.1035: : 87iter [00:11,  9.57iter/s]
Best score: 21.1035: : 88iter [00:11,  9.88iter/s]
Best score: 21.1035: : 88iter [00:11,  9.88iter/s]
Best score: 20.6455: : 89iter [00:11,  9.88iter/s]
Best score: 20.6455: : 90iter [00:11,  9.64iter/s]
Best score: 20.6455: : 90iter [00:11,  9.64iter/s]
Best score: 20.6455: : 91iter [00:11,  9.71iter/s]
Best score: 20.6455: : 91iter [00:11,  9.71iter/s]
Best score: 20.6455: : 92iter [00:11,  9.70iter/s]
Best score: 20.6455: : 92iter [00:11,  9.70iter/s]
Best score: 20.6455: : 93iter [00:11,  9.70iter/s]
Best score: 20.6455: : 94iter [00:11,  9.73iter/s]
Best score: 20.6455: : 94iter [00:11,  9.73iter/s]
Best score: 20.6455: : 95iter [00:11,  9.68iter/s]
Best score: 20.6455: : 95iter [00:11,  9.68iter/s]
Best score: 20.6455: : 96iter [00:11,  9.58iter/s]
Best score: 20.6455: : 96iter [00:11,  9.58iter/s]
Best score: 20.6455: : 97iter [00:11,  9.52iter/s]
Best score: 20.6455: : 97iter [00:12,  9.52iter/s]
Best score: 20.6455: : 98iter [00:12,  9.50iter/s]
Best score: 20.6455: : 98iter [00:12,  9.50iter/s]
Best score: 20.6455: : 99iter [00:12,  9.57iter/s]
Best score: 13.1177: : 99iter [00:12,  9.57iter/s]
Best score: 13.1177: : 100iter [00:12,  9.39iter/s]
[ITER 100] Best: 13.1177
[PARAMS] Current best parameters:
  fx: 555.8953
  fy: 558.3397
  px: 843.8955
  py: 865.2437
  k1: -0.02408718
  k2: -0.00529138
  k3: 0.00373088
  k4: 0.00015081
  p1: 0.00151509
  p2: 0.01541913
  p3: 0.01378034
  p4: -0.03091276
  s1: 0.0102
  s2: -0.0184
  s3: 0.0366
  s4: 0.0141
  tilt_x: 0.0134
  tilt_y: 0.0497
  scale: 2.9980
  rotation: 0.3008

Best score: 13.1177: : 100iter [00:12,  9.39iter/s]
Best score: 13.1177: : 101iter [00:12,  7.55iter/s]
Best score: 13.1177: : 101iter [00:12,  7.55iter/s]
Best score: 13.1177: : 102iter [00:12,  7.97iter/s]
Best score: 13.1177: : 102iter [00:12,  7.97iter/s]
Best score: 13.1177: : 103iter [00:12,  8.42iter/s]
Best score: 13.1177: : 103iter [00:12,  8.42iter/s]
Best score: 13.1177: : 104iter [00:12,  8.42iter/s]
Best score: 13.1177: : 105iter [00:12,  9.00iter/s]
Best score: 13.1177: : 105iter [00:13,  9.00iter/s]
Best score: 13.1177: : 106iter [00:13,  9.19iter/s]
Best score: 13.1177: : 106iter [00:13,  9.19iter/s]
Best score: 13.1177: : 107iter [00:13,  9.34iter/s]
Best score: 13.1177: : 107iter [00:13,  9.34iter/s]
Best score: 13.1177: : 108iter [00:13,  9.33iter/s]
Best score: 13.1177: : 108iter [00:13,  9.33iter/s]
Best score: 13.1177: : 109iter [00:13,  9.48iter/s]
Best score: 13.1177: : 109iter [00:13,  9.48iter/s]
Best score: 13.1177: : 110iter [00:13,  9.40iter/s]
Best score: 13.1177: : 110iter [00:13,  9.40iter/s]
Best score: 13.1177: : 111iter [00:13,  9.45iter/s]
Best score: 13.1177: : 111iter [00:13,  9.45iter/s]
Best score: 13.1177: : 112iter [00:13,  9.39iter/s]
Best score: 13.1177: : 112iter [00:13,  9.39iter/s]
Best score: 13.1177: : 113iter [00:13,  9.49iter/s]
Best score: 13.1177: : 113iter [00:13,  9.49iter/s]
Best score: 13.1177: : 114iter [00:13,  9.61iter/s]
Best score: 13.1177: : 114iter [00:13,  9.61iter/s]
Best score: 13.1177: : 115iter [00:13,  9.62iter/s]
Best score: 13.1177: : 115iter [00:14,  9.62iter/s]
Best score: 13.1177: : 116iter [00:14,  9.51iter/s]
Best score: 13.1177: : 116iter [00:14,  9.51iter/s]
Best score: 13.1177: : 117iter [00:14,  9.45iter/s]
Best score: 13.1177: : 117iter [00:14,  9.45iter/s]
Best score: 13.1177: : 118iter [00:14,  9.16iter/s]
Best score: 13.1177: : 118iter [00:14,  9.16iter/s]
Best score: 13.1177: : 119iter [00:14,  9.32iter/s]
Best score: 13.1177: : 119iter [00:14,  9.32iter/s]
Best score: 13.1177: : 120iter [00:14,  9.36iter/s]
Best score: 13.1177: : 120iter [00:14,  9.36iter/s]
Best score: 13.1177: : 121iter [00:14,  9.50iter/s]
Best score: 13.1177: : 121iter [00:14,  9.50iter/s]
Best score: 13.1177: : 122iter [00:14,  9.40iter/s]
Best score: 13.1177: : 122iter [00:14,  9.40iter/s]
Best score: 13.1177: : 123iter [00:14,  9.50iter/s]
Best score: 13.1177: : 123iter [00:14,  9.50iter/s]
Best score: 13.1177: : 124iter [00:15,  9.50iter/s]
Best score: 13.1177: : 125iter [00:15,  9.82iter/s]
Best score: 13.1177: : 125iter [00:15,  9.82iter/s]
Best score: 13.1177: : 126iter [00:15,  9.86iter/s]
Best score: 13.1177: : 126iter [00:15,  9.86iter/s]
Best score: 13.1177: : 127iter [00:15,  9.86iter/s]
Best score: 13.1177: : 128iter [00:15, 10.06iter/s]
Best score: 13.1177: : 128iter [00:15, 10.06iter/s]
Best score: 13.1177: : 129iter [00:15, 10.06iter/s]
Best score: 13.1177: : 130iter [00:15,  9.85iter/s]
Best score: 13.1177: : 130iter [00:15,  9.85iter/s]
Best score: 13.1177: : 131iter [00:15,  9.85iter/s]
Best score: 13.1177: : 131iter [00:15,  9.85iter/s]
Best score: 13.1177: : 132iter [00:15,  9.85iter/s]
Best score: 13.1177: : 132iter [00:15,  9.85iter/s]
Best score: 13.1177: : 133iter [00:15,  9.79iter/s]
Best score: 13.1177: : 133iter [00:15,  9.79iter/s]
Best score: 13.1177: : 134iter [00:15,  9.83iter/s]
Best score: 13.1177: : 134iter [00:16,  9.83iter/s]
Best score: 13.1177: : 135iter [00:16,  9.83iter/s]
Best score: 13.1177: : 136iter [00:16, 10.05iter/s]
Best score: 13.1177: : 136iter [00:16, 10.05iter/s]
Best score: 13.1177: : 137iter [00:16, 10.05iter/s]
Best score: 13.1177: : 138iter [00:16, 10.21iter/s]
Best score: 13.1177: : 138iter [00:16, 10.21iter/s]
Best score: 13.1177: : 139iter [00:16, 10.21iter/s]
Best score: 13.1177: : 140iter [00:16, 10.03iter/s]
Best score: 13.1177: : 140iter [00:16, 10.03iter/s]
Best score: 13.1177: : 141iter [00:16, 10.03iter/s]
Best score: 13.1177: : 142iter [00:16,  9.86iter/s]
Best score: 13.1177: : 142iter [00:16,  9.86iter/s]
Best score: 13.1177: : 143iter [00:16,  9.85iter/s]
Best score: 13.1177: : 143iter [00:16,  9.85iter/s]
Best score: 13.1177: : 144iter [00:16,  9.83iter/s]
Best score: 13.1177: : 144iter [00:17,  9.83iter/s]
Best score: 13.1177: : 145iter [00:17,  9.85iter/s]
Best score: 13.1177: : 145iter [00:17,  9.85iter/s]
Best score: 13.1177: : 146iter [00:17,  9.85iter/s]
Best score: 13.1177: : 147iter [00:17,  9.79iter/s]
Best score: 13.1177: : 147iter [00:17,  9.79iter/s]
Best score: 13.1177: : 148iter [00:17,  9.79iter/s]
Best score: 13.1177: : 149iter [00:17,  9.80iter/s]
[ITER 150] Best: 13.1177
[PARAMS] Current best parameters:
  fx: 555.8953
  fy: 558.3397
  px: 843.8955
  py: 865.2437
  k1: -0.02408718
  k2: -0.00529138
  k3: 0.00373088
  k4: 0.00015081
  p1: 0.00151509
  p2: 0.01541913
  p3: 0.01378034
  p4: -0.03091276
  s1: 0.0102
  s2: -0.0184
  s3: 0.0366
  s4: 0.0141
  tilt_x: 0.0134
  tilt_y: 0.0497
  scale: 2.9980
  rotation: 0.3008

Best score: 13.1177: : 149iter [00:17,  9.80iter/s]
Best score: 13.1177: : 150iter [00:17,  9.57iter/s]
Best score: 13.1177: : 150iter [00:17,  9.57iter/s]
Best score: 13.1177: : 151iter [00:17,  5.77iter/s]
Best score: 13.1177: : 151iter [00:18,  5.77iter/s]
Best score: 13.1177: : 152iter [00:18,  6.41iter/s]
Best score: 13.1177: : 152iter [00:18,  6.41iter/s]
Best score: 13.1177: : 153iter [00:18,  6.41iter/s]
Best score: 13.1177: : 154iter [00:18,  7.54iter/s]
Best score: 13.1177: : 154iter [00:18,  7.54iter/s]
Best score: 13.1177: : 155iter [00:18,  7.96iter/s]
Best score: 13.1177: : 155iter [00:18,  7.96iter/s]
Best score: 13.1177: : 156iter [00:18,  7.96iter/s]
Best score: 13.1177: : 157iter [00:18,  8.60iter/s]
Best score: 13.1177: : 157iter [00:18,  8.60iter/s]
Best score: 13.1177: : 158iter [00:18,  8.60iter/s]
Best score: 13.1177: : 159iter [00:18,  9.20iter/s]
Best score: 13.1177: : 159iter [00:18,  9.20iter/s]
Best score: 13.1177: : 160iter [00:18,  9.17iter/s]
Best score: 13.1177: : 160iter [00:18,  9.17iter/s]
Best score: 13.1177: : 161iter [00:19,  9.17iter/s]
Best score: 13.1177: : 162iter [00:19,  9.58iter/s]
Best score: 13.1177: : 162iter [00:19,  9.58iter/s]
Best score: 13.1177: : 163iter [00:19,  9.59iter/s]
Best score: 13.1177: : 163iter [00:19,  9.59iter/s]
Best score: 13.1177: : 164iter [00:19,  9.59iter/s]
Best score: 13.1177: : 165iter [00:19,  9.76iter/s]
Best score: 13.1177: : 165iter [00:19,  9.76iter/s]
Best score: 13.1177: : 166iter [00:19,  9.76iter/s]
Best score: 13.1177: : 167iter [00:19, 10.01iter/s]
Best score: 13.1177: : 167iter [00:19, 10.01iter/s]
Best score: 13.1177: : 168iter [00:19, 10.01iter/s]
Best score: 13.1177: : 169iter [00:19, 10.10iter/s]
Best score: 11.7895: : 169iter [00:19, 10.10iter/s]
Best score: 11.7895: : 170iter [00:19, 10.10iter/s]
Best score: 11.7895: : 171iter [00:19,  9.94iter/s]
Best score: 11.7895: : 171iter [00:20,  9.94iter/s]
Best score: 11.7895: : 172iter [00:20,  9.94iter/s]
Best score: 11.7895: : 173iter [00:20, 10.05iter/s]
Best score: 11.7895: : 173iter [00:20, 10.05iter/s]
Best score: 11.7895: : 174iter [00:20, 10.05iter/s]
Best score: 11.7895: : 175iter [00:20,  9.97iter/s]
Best score: 11.7895: : 175iter [00:20,  9.97iter/s]
Best score: 11.7895: : 176iter [00:20,  9.83iter/s]
Best score: 11.7895: : 176iter [00:20,  9.83iter/s]
Best score: 11.7895: : 177iter [00:20,  9.84iter/s]
Best score: 11.7895: : 177iter [00:20,  9.84iter/s]
Best score: 11.7895: : 178iter [00:20,  9.84iter/s]
Best score: 11.7895: : 179iter [00:20,  9.96iter/s]
Best score: 5.2924: : 179iter [00:20,  9.96iter/s] 
Best score: 5.2924: : 180iter [00:20,  9.73iter/s]
Best score: 5.2924: : 180iter [00:20,  9.73iter/s]
Best score: 5.2924: : 181iter [00:20,  9.79iter/s]
Best score: 5.2924: : 181iter [00:21,  9.79iter/s]
Best score: 5.2924: : 182iter [00:21,  9.79iter/s]
Best score: 5.2924: : 183iter [00:21,  9.94iter/s]
Best score: 5.2924: : 183iter [00:21,  9.94iter/s]
Best score: 5.2924: : 184iter [00:21,  9.85iter/s]
Best score: 5.2924: : 184iter [00:21,  9.85iter/s]
Best score: 5.2924: : 185iter [00:21,  9.81iter/s]
Best score: 5.2924: : 185iter [00:21,  9.81iter/s]
Best score: 5.2924: : 186iter [00:21,  9.81iter/s]
Best score: 5.2924: : 187iter [00:21,  9.86iter/s]
Best score: 5.2924: : 187iter [00:21,  9.86iter/s]
Best score: 5.2924: : 188iter [00:21,  9.86iter/s]
Best score: 5.2924: : 189iter [00:21,  9.95iter/s]
Best score: 3.3008: : 189iter [00:21,  9.95iter/s]
Best score: 3.3008: : 190iter [00:21,  9.64iter/s]
Best score: 3.3008: : 190iter [00:21,  9.64iter/s]
Best score: 3.3008: : 191iter [00:21,  9.65iter/s]
Best score: 3.3008: : 191iter [00:22,  9.65iter/s]
Best score: 3.3008: : 192iter [00:22,  9.55iter/s]
Best score: 3.3008: : 192iter [00:22,  9.55iter/s]
Best score: 3.3008: : 193iter [00:22,  9.55iter/s]
Best score: 3.3008: : 194iter [00:22,  9.92iter/s]
Best score: 3.3008: : 194iter [00:22,  9.92iter/s]
Best score: 3.3008: : 195iter [00:22,  9.92iter/s]
Best score: 3.3008: : 196iter [00:22, 10.11iter/s]
Best score: 3.3008: : 196iter [00:22, 10.11iter/s]
Best score: 3.3008: : 197iter [00:22, 10.00iter/s]
Best score: 3.3008: : 197iter [00:22, 10.00iter/s]
Best score: 3.3008: : 198iter [00:22,  9.98iter/s]
Best score: 3.3008: : 198iter [00:22,  9.98iter/s]
Best score: 3.3008: : 199iter [00:22,  9.95iter/s]
[ITER 200] Best: 3.3008
[PARAMS] Current best parameters:
  fx: 546.7203
  fy: 547.3657
  px: 848.0133
  py: 861.5016
  k1: 0.03645451
  k2: -0.02790062
  k3: -0.02526599
  k4: 0.01503757
  p1: 0.00655821
  p2: 0.02562621
  p3: -0.00775481
  p4: -0.02027106
  s1: 0.0101
  s2: -0.0110
  s3: 0.0129
  s4: 0.0166
  tilt_x: 0.0131
  tilt_y: 0.0407
  scale: 2.9084
  rotation: -0.3847

Best score: 3.3008: : 199iter [00:22,  9.95iter/s]
Best score: 3.3008: : 200iter [00:22,  9.66iter/s]
[Periodic outlier @ iter 100] Removing 2000L frame 12 (score=23.38, threshold=21.94)
  Remaining frames: 79

Best score: 3.3008: : 200iter [00:23,  9.66iter/s]
Best score: 3.3008: : 201iter [00:23,  4.37iter/s]
Best score: 3.3008: : 201iter [00:23,  4.37iter/s]
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Best score: 3.3008: : 204iter [00:23,  6.33iter/s]
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Best score: 3.3008: : 205iter [00:23,  6.83iter/s]
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Best score: 3.3008: : 206iter [00:24,  7.43iter/s]
Best score: 3.3008: : 207iter [00:24,  7.82iter/s]
Best score: 3.3008: : 207iter [00:24,  7.82iter/s]
Best score: 3.3008: : 208iter [00:24,  8.15iter/s]
Best score: 3.3008: : 208iter [00:24,  8.15iter/s]
Best score: 3.3008: : 209iter [00:24,  8.41iter/s]
Best score: 3.0582: : 209iter [00:24,  8.41iter/s]
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Best score: 3.0582: : 214iter [00:24,  9.01iter/s]
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Best score: 3.0582: : 215iter [00:25,  9.12iter/s]
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Best score: 3.0582: : 218iter [00:25,  8.94iter/s]
Best score: 3.0582: : 218iter [00:25,  8.94iter/s]
Best score: 3.0582: : 219iter [00:25,  8.92iter/s]
Best score: 3.0582: : 219iter [00:25,  8.92iter/s]
Best score: 3.0582: : 220iter [00:25,  8.75iter/s]
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Best score: 3.0582: : 221iter [00:25,  8.83iter/s]
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Best score: 3.0582: : 224iter [00:26,  8.91iter/s]
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Best score: 3.0582: : 230iter [00:26,  8.66iter/s]
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Best score: 3.0582: : 232iter [00:26,  8.93iter/s]
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Best score: 3.0582: : 233iter [00:27,  8.90iter/s]
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Best score: 3.0582: : 235iter [00:27,  8.97iter/s]
Best score: 3.0582: : 235iter [00:27,  8.97iter/s]
Best score: 3.0582: : 236iter [00:27,  9.22iter/s]
Best score: 3.0582: : 236iter [00:27,  9.22iter/s]
Best score: 3.0582: : 237iter [00:27,  9.09iter/s]
Best score: 3.0582: : 237iter [00:27,  9.09iter/s]
Best score: 3.0582: : 238iter [00:27,  8.99iter/s]
Best score: 3.0582: : 238iter [00:27,  8.99iter/s]
Best score: 3.0582: : 239iter [00:27,  8.95iter/s]
Best score: 3.0582: : 239iter [00:27,  8.95iter/s]
Best score: 3.0582: : 240iter [00:27,  8.67iter/s]
Best score: 3.0582: : 240iter [00:27,  8.67iter/s]
Best score: 3.0582: : 241iter [00:27,  8.65iter/s]
Best score: 3.0582: : 241iter [00:28,  8.65iter/s]
Best score: 3.0582: : 242iter [00:28,  8.65iter/s]
Best score: 3.0582: : 242iter [00:28,  8.65iter/s]
Best score: 3.0582: : 243iter [00:28,  8.74iter/s]
Best score: 3.0582: : 243iter [00:28,  8.74iter/s]
Best score: 3.0582: : 244iter [00:28,  8.91iter/s]
Best score: 3.0582: : 244iter [00:28,  8.91iter/s]
Best score: 3.0582: : 245iter [00:28,  8.73iter/s]
Best score: 3.0582: : 245iter [00:28,  8.73iter/s]
Best score: 3.0582: : 246iter [00:28,  8.79iter/s]
Best score: 3.0582: : 246iter [00:28,  8.79iter/s]
Best score: 3.0582: : 247iter [00:28,  8.92iter/s]
Best score: 3.0582: : 247iter [00:28,  8.92iter/s]
Best score: 3.0582: : 248iter [00:28,  9.06iter/s]
Best score: 3.0582: : 248iter [00:28,  9.06iter/s]
Best score: 3.0582: : 249iter [00:28,  9.16iter/s]
[ITER 250] Best: 3.0582
[PARAMS] Current best parameters:
  fx: 546.7203
  fy: 547.3657
  px: 848.0133
  py: 861.5016
  k1: 0.03645451
  k2: -0.02790062
  k3: -0.02526599
  k4: 0.01503757
  p1: 0.00655821
  p2: 0.02562621
  p3: -0.00775481
  p4: -0.02027106
  s1: 0.0101
  s2: -0.0110
  s3: 0.0129
  s4: 0.0166
  tilt_x: 0.0131
  tilt_y: 0.0407
  scale: 2.9084
  rotation: -0.3847

Best score: 3.0582: : 249iter [00:28,  9.16iter/s]
Best score: 3.0582: : 250iter [00:28,  8.83iter/s]
Best score: 3.0582: : 250iter [00:29,  8.83iter/s]
Best score: 3.0582: : 251iter [00:29,  4.95iter/s]
Best score: 3.0582: : 251iter [00:29,  4.95iter/s]
Best score: 3.0582: : 252iter [00:29,  5.79iter/s]
Best score: 3.0582: : 252iter [00:29,  5.79iter/s]
Best score: 3.0582: : 253iter [00:29,  6.54iter/s]
Best score: 3.0582: : 253iter [00:29,  6.54iter/s]
Best score: 3.0582: : 254iter [00:29,  7.13iter/s]
Best score: 3.0582: : 254iter [00:29,  7.13iter/s]
Best score: 3.0582: : 255iter [00:29,  7.47iter/s]
Best score: 3.0582: : 255iter [00:29,  7.47iter/s]
Best score: 3.0582: : 256iter [00:29,  7.89iter/s]
Best score: 3.0582: : 256iter [00:29,  7.89iter/s]
Best score: 3.0582: : 257iter [00:29,  8.25iter/s]
Best score: 3.0582: : 257iter [00:30,  8.25iter/s]
Best score: 3.0582: : 258iter [00:30,  8.55iter/s]
Best score: 3.0582: : 258iter [00:30,  8.55iter/s]
Best score: 3.0582: : 259iter [00:30,  8.67iter/s]
Best score: 3.0582: : 259iter [00:30,  8.67iter/s]
Best score: 3.0582: : 260iter [00:30,  8.67iter/s]
Best score: 3.0582: : 260iter [00:30,  8.67iter/s]
Best score: 3.0582: : 261iter [00:30,  8.89iter/s]
Best score: 3.0582: : 261iter [00:30,  8.89iter/s]
Best score: 3.0582: : 262iter [00:30,  9.05iter/s]
Best score: 3.0582: : 262iter [00:30,  9.05iter/s]
Best score: 3.0582: : 263iter [00:30,  8.94iter/s]
Best score: 3.0582: : 263iter [00:30,  8.94iter/s]
Best score: 3.0582: : 264iter [00:30,  8.76iter/s]
Best score: 3.0582: : 264iter [00:30,  8.76iter/s]
Best score: 3.0582: : 265iter [00:30,  8.93iter/s]
Best score: 3.0582: : 265iter [00:30,  8.93iter/s]
Best score: 3.0582: : 266iter [00:30,  9.17iter/s]
Best score: 3.0582: : 266iter [00:31,  9.17iter/s]
Best score: 3.0582: : 267iter [00:31,  9.13iter/s]
Best score: 3.0582: : 267iter [00:31,  9.13iter/s]
Best score: 3.0582: : 268iter [00:31,  9.03iter/s]
Best score: 3.0582: : 268iter [00:31,  9.03iter/s]
Best score: 3.0582: : 269iter [00:31,  9.12iter/s]
Best score: 3.0582: : 269iter [00:31,  9.12iter/s]
Best score: 3.0582: : 270iter [00:31,  8.79iter/s]
Best score: 3.0582: : 270iter [00:31,  8.79iter/s]
Best score: 3.0582: : 271iter [00:31,  8.79iter/s]
Best score: 3.0582: : 271iter [00:31,  8.79iter/s]
Best score: 3.0582: : 272iter [00:31,  8.94iter/s]
Best score: 3.0582: : 272iter [00:31,  8.94iter/s]
Best score: 3.0582: : 273iter [00:31,  9.16iter/s]
Best score: 3.0582: : 273iter [00:31,  9.16iter/s]
Best score: 3.0582: : 274iter [00:31,  9.28iter/s]
Best score: 3.0582: : 274iter [00:32,  9.28iter/s]
Best score: 3.0582: : 275iter [00:32,  6.45iter/s]
Best score: 3.0582: : 275iter [00:32,  6.45iter/s]
Best score: 3.0582: : 276iter [00:32,  7.13iter/s]
Best score: 3.0582: : 276iter [00:32,  7.13iter/s]
Best score: 3.0582: : 277iter [00:32,  7.53iter/s]
Best score: 3.0582: : 277iter [00:32,  7.53iter/s]
Best score: 3.0582: : 278iter [00:32,  7.85iter/s]
Best score: 3.0582: : 278iter [00:32,  7.85iter/s]
Best score: 3.0582: : 279iter [00:32,  8.26iter/s]
Best score: 3.0582: : 279iter [00:32,  8.26iter/s]
Best score: 3.0582: : 280iter [00:32,  8.28iter/s]
Best score: 3.0582: : 280iter [00:32,  8.28iter/s]
Best score: 3.0582: : 281iter [00:32,  8.51iter/s]
Best score: 3.0582: : 281iter [00:32,  8.51iter/s]
Best score: 3.0582: : 282iter [00:32,  8.72iter/s]
Best score: 3.0582: : 282iter [00:33,  8.72iter/s]
Best score: 3.0582: : 283iter [00:33,  8.75iter/s]
Best score: 3.0582: : 283iter [00:33,  8.75iter/s]
Best score: 3.0582: : 284iter [00:33,  9.03iter/s]
Best score: 3.0582: : 284iter [00:33,  9.03iter/s]
Best score: 3.0582: : 285iter [00:33,  9.07iter/s]
Best score: 3.0582: : 285iter [00:33,  9.07iter/s]
Best score: 3.0582: : 286iter [00:33,  9.06iter/s]
Best score: 3.0582: : 286iter [00:33,  9.06iter/s]
Best score: 3.0582: : 287iter [00:33,  8.95iter/s]
Best score: 3.0582: : 287iter [00:33,  8.95iter/s]
Best score: 3.0582: : 288iter [00:33,  9.04iter/s]
Best score: 3.0582: : 288iter [00:33,  9.04iter/s]
Best score: 3.0582: : 289iter [00:33,  9.22iter/s]
Best score: 3.0582: : 289iter [00:33,  9.22iter/s]
Best score: 3.0582: : 290iter [00:33,  9.10iter/s]
Best score: 3.0582: : 290iter [00:33,  9.10iter/s]
Best score: 3.0582: : 291iter [00:33,  9.17iter/s]
Best score: 3.0582: : 291iter [00:33,  9.17iter/s]
Best score: 3.0582: : 292iter [00:33,  9.16iter/s]
Best score: 3.0582: : 292iter [00:34,  9.16iter/s]
Best score: 3.0582: : 293iter [00:34,  9.33iter/s]
Best score: 3.0582: : 293iter [00:34,  9.33iter/s]
Best score: 3.0582: : 294iter [00:34,  9.40iter/s]
Best score: 3.0582: : 294iter [00:34,  9.40iter/s]
Best score: 3.0582: : 295iter [00:34,  9.42iter/s]
Best score: 3.0582: : 295iter [00:34,  9.42iter/s]
Best score: 3.0582: : 296iter [00:34,  9.52iter/s]
Best score: 3.0582: : 296iter [00:34,  9.52iter/s]
Best score: 3.0582: : 297iter [00:34,  9.58iter/s]
Best score: 3.0582: : 297iter [00:34,  9.58iter/s]
Best score: 3.0582: : 298iter [00:34,  9.71iter/s]
Best score: 3.0582: : 298iter [00:34,  9.71iter/s]
Best score: 3.0582: : 299iter [00:34,  9.64iter/s]
[ITER 300] Best: 2.8101
[PARAMS] Current best parameters:
  fx: 538.3505
  fy: 538.8109
  px: 853.5193
  py: 857.6412
  k1: 0.04606550
  k2: -0.03606633
  k3: -0.01491156
  k4: 0.01106944
  p1: -0.00242704
  p2: 0.03301369
  p3: -0.00903157
  p4: -0.01690756
  s1: 0.0104
  s2: -0.0060
  s3: 0.0122
  s4: 0.0159
  tilt_x: 0.0129
  tilt_y: 0.0302
  scale: 2.9119
  rotation: -0.0186

Best score: 2.8101: : 299iter [00:34,  9.64iter/s]
Best score: 2.8101: : 300iter [00:34,  9.22iter/s]
Best score: 2.8101: : 300iter [00:35,  9.22iter/s]
Best score: 2.8101: : 301iter [00:35,  5.09iter/s]
Best score: 2.8101: : 301iter [00:35,  5.09iter/s]
Best score: 2.8101: : 302iter [00:35,  5.09iter/s]
Best score: 2.8101: : 303iter [00:35,  6.52iter/s]
Best score: 2.8101: : 303iter [00:35,  6.52iter/s]
Best score: 2.8101: : 304iter [00:35,  7.09iter/s]
Best score: 2.8101: : 304iter [00:35,  7.09iter/s]
Best score: 2.8101: : 305iter [00:35,  7.66iter/s]
Best score: 2.8101: : 305iter [00:35,  7.66iter/s]
Best score: 2.8101: : 306iter [00:35,  8.08iter/s]
Best score: 2.8101: : 306iter [00:35,  8.08iter/s]
Best score: 2.8101: : 307iter [00:35,  8.36iter/s]
Best score: 2.8101: : 307iter [00:35,  8.36iter/s]
Best score: 2.8101: : 308iter [00:35,  8.53iter/s]
Best score: 2.8101: : 308iter [00:36,  8.53iter/s]
Best score: 2.8101: : 309iter [00:36,  8.86iter/s]
Best score: 2.3104: : 309iter [00:36,  8.86iter/s]
Best score: 2.3104: : 310iter [00:36,  8.93iter/s]
Best score: 2.3104: : 310iter [00:36,  8.93iter/s]
Best score: 2.3104: : 311iter [00:36,  9.09iter/s]
Best score: 2.3104: : 311iter [00:36,  9.09iter/s]
Best score: 2.3104: : 312iter [00:36,  9.28iter/s]
Best score: 2.3104: : 312iter [00:36,  9.28iter/s]
Best score: 2.3104: : 313iter [00:36,  9.19iter/s]
Best score: 2.3104: : 313iter [00:36,  9.19iter/s]
Best score: 2.3104: : 314iter [00:36,  9.25iter/s]
Best score: 2.3104: : 314iter [00:36,  9.25iter/s]
Best score: 2.3104: : 315iter [00:36,  9.23iter/s]
Best score: 2.3104: : 315iter [00:36,  9.23iter/s]
Best score: 2.3104: : 316iter [00:36,  9.17iter/s]
Best score: 2.3104: : 316iter [00:36,  9.17iter/s]
Best score: 2.3104: : 317iter [00:37,  9.17iter/s]
Best score: 2.3104: : 318iter [00:37,  9.34iter/s]
Best score: 2.3104: : 318iter [00:37,  9.34iter/s]
Best score: 2.3104: : 319iter [00:37,  9.33iter/s]
Best score: 1.9165: : 319iter [00:37,  9.33iter/s]
Best score: 1.9165: : 320iter [00:37,  9.14iter/s]
Best score: 1.9165: : 320iter [00:37,  9.14iter/s]
Best score: 1.9165: : 321iter [00:37,  9.34iter/s]
Best score: 1.9165: : 321iter [00:37,  9.34iter/s]
Best score: 1.9165: : 322iter [00:37,  9.50iter/s]
Best score: 1.9165: : 322iter [00:37,  9.50iter/s]
Best score: 1.9165: : 323iter [00:37,  9.34iter/s]
Best score: 1.9165: : 323iter [00:37,  9.34iter/s]
Best score: 1.9165: : 324iter [00:37,  9.18iter/s]
Best score: 1.9165: : 324iter [00:37,  9.18iter/s]
Best score: 1.9165: : 325iter [00:37,  9.16iter/s]
Best score: 1.9165: : 325iter [00:37,  9.16iter/s]
Best score: 1.9165: : 326iter [00:38,  9.16iter/s]
Best score: 1.9165: : 327iter [00:38,  9.35iter/s]
Best score: 1.9165: : 327iter [00:38,  9.35iter/s]
Best score: 1.9165: : 328iter [00:38,  9.47iter/s]
Best score: 1.9165: : 328iter [00:38,  9.47iter/s]
Best score: 1.9165: : 329iter [00:38,  9.58iter/s]
Best score: 1.1153: : 329iter [00:38,  9.58iter/s]
Best score: 1.1153: : 330iter [00:38,  9.26iter/s]
Best score: 1.1153: : 330iter [00:38,  9.26iter/s]
Best score: 1.1153: : 331iter [00:38,  9.30iter/s]
Best score: 1.1153: : 331iter [00:38,  9.30iter/s]
Best score: 1.1153: : 332iter [00:38,  9.13iter/s]
Best score: 1.1153: : 332iter [00:38,  9.13iter/s]
Best score: 1.1153: : 333iter [00:38,  9.00iter/s]
Best score: 1.1153: : 333iter [00:38,  9.00iter/s]
Best score: 1.1153: : 334iter [00:38,  9.00iter/s]
Best score: 1.1153: : 334iter [00:38,  9.00iter/s]
Best score: 1.1153: : 335iter [00:38,  8.95iter/s]
Best score: 1.1153: : 335iter [00:38,  8.95iter/s]
Best score: 1.1153: : 336iter [00:38,  8.97iter/s]
Best score: 1.1153: : 336iter [00:39,  8.97iter/s]
Best score: 1.1153: : 337iter [00:39,  8.99iter/s]
Best score: 1.1153: : 337iter [00:39,  8.99iter/s]
Best score: 1.1153: : 338iter [00:39,  8.87iter/s]
Best score: 1.1153: : 338iter [00:39,  8.87iter/s]
Best score: 1.1153: : 339iter [00:39,  8.79iter/s]
Best score: 0.9158: : 339iter [00:39,  8.79iter/s]
Best score: 0.9158: : 340iter [00:39,  8.60iter/s]
Best score: 0.9158: : 340iter [00:39,  8.60iter/s]
Best score: 0.9158: : 341iter [00:39,  8.73iter/s]
Best score: 0.9158: : 341iter [00:39,  8.73iter/s]
Best score: 0.9158: : 342iter [00:39,  8.73iter/s]
Best score: 0.9158: : 342iter [00:39,  8.73iter/s]
Best score: 0.9158: : 343iter [00:39,  8.90iter/s]
Best score: 0.9158: : 343iter [00:39,  8.90iter/s]
Best score: 0.9158: : 344iter [00:39,  9.10iter/s]
Best score: 0.9158: : 344iter [00:40,  9.10iter/s]
Best score: 0.9158: : 345iter [00:40,  9.27iter/s]
Best score: 0.9158: : 345iter [00:40,  9.27iter/s]
Best score: 0.9158: : 346iter [00:40,  9.45iter/s]
Best score: 0.9158: : 346iter [00:40,  9.45iter/s]
Best score: 0.9158: : 347iter [00:40,  9.28iter/s]
Best score: 0.9158: : 347iter [00:40,  9.28iter/s]
Best score: 0.9158: : 348iter [00:40,  9.20iter/s]
Best score: 0.9158: : 348iter [00:40,  9.20iter/s]
Best score: 0.9158: : 349iter [00:40,  9.30iter/s]
[ITER 350] Best: 0.6459
[PARAMS] Current best parameters:
  fx: 563.3452
  fy: 564.5390
  px: 850.1694
  py: 857.1101
  k1: -0.01849645
  k2: -0.00314906
  k3: -0.01041668
  k4: 0.00539697
  p1: 0.01365925
  p2: 0.01518592
  p3: -0.02996637
  p4: -0.01378674
  s1: 0.0245
  s2: -0.0004
  s3: 0.0106
  s4: 0.0121
  tilt_x: -0.0179
  tilt_y: 0.0097
  scale: 2.9494
  rotation: -0.0967

Best score: 0.6459: : 349iter [00:40,  9.30iter/s]
Best score: 0.6459: : 350iter [00:40,  9.01iter/s]
Best score: 0.6459: : 350iter [00:40,  9.01iter/s]
Best score: 0.6459: : 351iter [00:40,  5.05iter/s]
Best score: 0.6459: : 351iter [00:41,  5.05iter/s]
Best score: 0.6459: : 352iter [00:41,  5.84iter/s]
Best score: 0.6459: : 352iter [00:41,  5.84iter/s]
Best score: 0.6459: : 353iter [00:41,  6.53iter/s]
Best score: 0.6459: : 353iter [00:41,  6.53iter/s]
Best score: 0.6459: : 354iter [00:41,  7.07iter/s]
Best score: 0.6459: : 354iter [00:41,  7.07iter/s]
Best score: 0.6459: : 355iter [00:41,  7.64iter/s]
Best score: 0.6459: : 355iter [00:41,  7.64iter/s]
Best score: 0.6459: : 356iter [00:41,  8.04iter/s]
Best score: 0.6459: : 356iter [00:41,  8.04iter/s]
Best score: 0.6459: : 357iter [00:41,  8.31iter/s]
Best score: 0.6459: : 357iter [00:41,  8.31iter/s]
Best score: 0.6459: : 358iter [00:41,  8.54iter/s]
Best score: 0.6459: : 358iter [00:41,  8.54iter/s]
Best score: 0.6459: : 359iter [00:41,  8.83iter/s]
Best score: 0.4608: : 359iter [00:41,  8.83iter/s]
Best score: 0.4608: : 360iter [00:41,  8.71iter/s]
Best score: 0.4608: : 360iter [00:42,  8.71iter/s]
Best score: 0.4608: : 361iter [00:42,  8.91iter/s]
Best score: 0.4608: : 361iter [00:42,  8.91iter/s]
Best score: 0.4608: : 362iter [00:42,  9.07iter/s]
Best score: 0.4608: : 362iter [00:42,  9.07iter/s]
Best score: 0.4608: : 363iter [00:42,  9.02iter/s]
Best score: 0.4608: : 363iter [00:42,  9.02iter/s]
Best score: 0.4608: : 364iter [00:42,  9.03iter/s]
Best score: 0.4608: : 364iter [00:42,  9.03iter/s]
Best score: 0.4608: : 365iter [00:42,  9.13iter/s]
Best score: 0.4608: : 365iter [00:42,  9.13iter/s]
Best score: 0.4608: : 366iter [00:42,  9.33iter/s]
Best score: 0.4608: : 366iter [00:42,  9.33iter/s]
Best score: 0.4608: : 367iter [00:42,  9.33iter/s]
Best score: 0.4608: : 368iter [00:42,  9.82iter/s]
Best score: 0.4608: : 368iter [00:42,  9.82iter/s]
Best score: 0.4608: : 369iter [00:42,  9.86iter/s]
Best score: 0.4433: : 369iter [00:42,  9.86iter/s]
Best score: 0.4433: : 370iter [00:42,  9.43iter/s]
Best score: 0.4433: : 370iter [00:43,  9.43iter/s]
Best score: 0.4433: : 371iter [00:43,  9.41iter/s]
Best score: 0.4433: : 371iter [00:43,  9.41iter/s]
Best score: 0.4433: : 372iter [00:43,  9.41iter/s]
Best score: 0.4433: : 372iter [00:43,  9.41iter/s]
Best score: 0.4433: : 373iter [00:43,  9.56iter/s]
Best score: 0.4433: : 373iter [00:43,  9.56iter/s]
Best score: 0.4433: : 374iter [00:43,  9.56iter/s]
Best score: 0.4433: : 375iter [00:43,  9.68iter/s]
Best score: 0.4433: : 375iter [00:43,  9.68iter/s]
Best score: 0.4433: : 376iter [00:43,  9.74iter/s]
Best score: 0.4433: : 376iter [00:43,  9.74iter/s]
Best score: 0.4433: : 377iter [00:43,  9.74iter/s]
Best score: 0.4433: : 378iter [00:43,  9.96iter/s]
Best score: 0.4433: : 378iter [00:43,  9.96iter/s]
Best score: 0.3386: : 379iter [00:44,  9.96iter/s]
Best score: 0.3386: : 380iter [00:44,  9.90iter/s]
Best score: 0.3386: : 380iter [00:44,  9.90iter/s]
Best score: 0.3386: : 381iter [00:44,  9.90iter/s]
Best score: 0.3386: : 382iter [00:44, 10.01iter/s]
Best score: 0.3386: : 382iter [00:44, 10.01iter/s]
Best score: 0.3386: : 383iter [00:44,  9.85iter/s]
Best score: 0.3386: : 383iter [00:44,  9.85iter/s]
Best score: 0.3386: : 384iter [00:44,  9.74iter/s]
Best score: 0.3386: : 384iter [00:44,  9.74iter/s]
Best score: 0.3386: : 385iter [00:44,  9.74iter/s]
Best score: 0.3386: : 386iter [00:44,  9.81iter/s]
Best score: 0.3386: : 386iter [00:44,  9.81iter/s]
Best score: 0.3386: : 387iter [00:44,  9.71iter/s]
Best score: 0.3386: : 387iter [00:44,  9.71iter/s]
Best score: 0.3386: : 388iter [00:44,  9.71iter/s]
Best score: 0.3386: : 389iter [00:44,  9.94iter/s]
Best score: 0.3386: : 389iter [00:45,  9.94iter/s]
Best score: 0.3386: : 390iter [00:45,  9.83iter/s]
Best score: 0.3386: : 390iter [00:45,  9.83iter/s]
Best score: 0.3386: : 391iter [00:45,  9.83iter/s]
Best score: 0.3386: : 392iter [00:45,  9.96iter/s]
Best score: 0.3386: : 392iter [00:45,  9.96iter/s]
Best score: 0.3386: : 393iter [00:45,  9.96iter/s]
Best score: 0.3386: : 394iter [00:45, 10.19iter/s]
Best score: 0.3386: : 394iter [00:45, 10.19iter/s]
Best score: 0.3386: : 395iter [00:45, 10.19iter/s]
Best score: 0.3386: : 396iter [00:45, 10.31iter/s]
Best score: 0.3386: : 396iter [00:45, 10.31iter/s]
Best score: 0.3386: : 397iter [00:45, 10.31iter/s]
Best score: 0.3386: : 398iter [00:45, 10.13iter/s]
Best score: 0.3386: : 398iter [00:45, 10.13iter/s]
Best score: 0.3386: : 399iter [00:46, 10.13iter/s]
Best score: 0.3386: : 400iter [00:46,  9.70iter/s]
[ITER 400] Best: 0.3386
[PARAMS] Current best parameters:
  fx: 570.8119
  fy: 571.7669
  px: 848.0310
  py: 855.0640
  k1: -0.05218421
  k2: 0.02785175
  k3: -0.01846715
  k4: 0.00469764
  p1: 0.01853442
  p2: 0.00478708
  p3: -0.03581157
  p4: -0.00827537
  s1: 0.0290
  s2: 0.0076
  s3: 0.0062
  s4: 0.0109
  tilt_x: -0.0371
  tilt_y: -0.0022
  scale: 2.9795
  rotation: -0.1837

Best score: 0.3386: : 400iter [00:46,  9.70iter/s]
Best score: 0.3386: : 401iter [00:46,  6.31iter/s]
Best score: 0.3386: : 401iter [00:46,  6.31iter/s]
Best score: 0.3386: : 402iter [00:46,  6.82iter/s]
Best score: 0.3386: : 402iter [00:46,  6.82iter/s]
Best score: 0.3386: : 403iter [00:46,  7.20iter/s]
Best score: 0.3386: : 403iter [00:46,  7.20iter/s]
Best score: 0.3386: : 404iter [00:46,  7.69iter/s]
Best score: 0.3386: : 404iter [00:46,  7.69iter/s]
Best score: 0.3386: : 405iter [00:46,  8.01iter/s]
Best score: 0.3386: : 405iter [00:46,  8.01iter/s]
Best score: 0.3386: : 406iter [00:46,  8.14iter/s]
Best score: 0.3386: : 406iter [00:47,  8.14iter/s]
Best score: 0.3386: : 407iter [00:47,  8.43iter/s]
Best score: 0.3386: : 407iter [00:47,  8.43iter/s]
Best score: 0.3386: : 408iter [00:47,  8.75iter/s]
Best score: 0.3386: : 408iter [00:47,  8.75iter/s]
Best score: 0.3386: : 409iter [00:47,  9.00iter/s]
Best score: 0.3386: : 409iter [00:47,  9.00iter/s]
Best score: 0.3386: : 410iter [00:47,  8.79iter/s]
Best score: 0.3386: : 410iter [00:47,  8.79iter/s]
Best score: 0.3386: : 411iter [00:47,  8.79iter/s]
Best score: 0.3386: : 412iter [00:47,  9.37iter/s]
Best score: 0.3386: : 412iter [00:47,  9.37iter/s]
Best score: 0.3386: : 413iter [00:47,  9.25iter/s]
Best score: 0.3386: : 413iter [00:47,  9.25iter/s]
Best score: 0.3386: : 414iter [00:47,  9.10iter/s]
Best score: 0.3386: : 414iter [00:47,  9.10iter/s]
Best score: 0.3386: : 415iter [00:47,  9.20iter/s]
Best score: 0.3386: : 415iter [00:48,  9.20iter/s]
Best score: 0.3386: : 416iter [00:48,  9.18iter/s]
Best score: 0.3386: : 416iter [00:48,  9.18iter/s]
Best score: 0.3386: : 417iter [00:48,  9.01iter/s]
Best score: 0.3386: : 417iter [00:48,  9.01iter/s]
Best score: 0.3386: : 418iter [00:48,  9.16iter/s]
Best score: 0.3386: : 418iter [00:48,  9.16iter/s]
Best score: 0.3386: : 419iter [00:48,  9.13iter/s]
Best score: 0.3386: : 419iter [00:48,  9.13iter/s]
Best score: 0.3386: : 420iter [00:48,  9.12iter/s]
Best score: 0.3386: : 420iter [00:48,  9.12iter/s]
Best score: 0.3386: : 421iter [00:48,  9.12iter/s]
Best score: 0.3386: : 422iter [00:48,  9.52iter/s]
Best score: 0.3386: : 422iter [00:48,  9.52iter/s]
Best score: 0.3386: : 423iter [00:48,  9.45iter/s]
Best score: 0.3386: : 423iter [00:48,  9.45iter/s]
Best score: 0.3386: : 424iter [00:48,  9.40iter/s]
Best score: 0.3386: : 424iter [00:49,  9.40iter/s]
Best score: 0.3386: : 425iter [00:49,  9.16iter/s]
Best score: 0.3386: : 425iter [00:49,  9.16iter/s]
Best score: 0.3386: : 426iter [00:49,  9.05iter/s]
Best score: 0.3386: : 426iter [00:49,  9.05iter/s]
Best score: 0.3386: : 427iter [00:49,  9.06iter/s]
Best score: 0.3386: : 427iter [00:49,  9.06iter/s]
Best score: 0.3386: : 428iter [00:49,  9.12iter/s]
Best score: 0.3386: : 428iter [00:49,  9.12iter/s]
Best score: 0.3386: : 429iter [00:49,  9.17iter/s]
Best score: 0.3386: : 429iter [00:49,  9.17iter/s]
Best score: 0.3386: : 430iter [00:49,  8.95iter/s]
Best score: 0.3386: : 430iter [00:49,  8.95iter/s]
Best score: 0.3386: : 431iter [00:49,  8.94iter/s]
Best score: 0.3386: : 431iter [00:49,  8.94iter/s]
Best score: 0.3386: : 432iter [00:49,  8.87iter/s]
Best score: 0.3386: : 432iter [00:49,  8.87iter/s]
Best score: 0.3386: : 433iter [00:49,  8.84iter/s]
Best score: 0.3386: : 433iter [00:50,  8.84iter/s]
Best score: 0.3386: : 434iter [00:50,  8.80iter/s]
Best score: 0.3386: : 434iter [00:50,  8.80iter/s]
Best score: 0.3386: : 435iter [00:50,  8.72iter/s]
Best score: 0.3386: : 435iter [00:50,  8.72iter/s]
Best score: 0.3386: : 436iter [00:50,  8.77iter/s]
Best score: 0.3386: : 436iter [00:50,  8.77iter/s]
Best score: 0.3386: : 437iter [00:50,  8.78iter/s]
Best score: 0.3386: : 437iter [00:50,  8.78iter/s]
Best score: 0.3386: : 438iter [00:50,  8.82iter/s]
Best score: 0.3386: : 438iter [00:50,  8.82iter/s]
Best score: 0.3386: : 439iter [00:50,  8.88iter/s]
Best score: 0.2022: : 439iter [00:50,  8.88iter/s]
Best score: 0.2022: : 440iter [00:50,  8.67iter/s]
Best score: 0.2022: : 440iter [00:50,  8.67iter/s]
Best score: 0.2022: : 441iter [00:50,  8.99iter/s]
Best score: 0.2022: : 441iter [00:50,  8.99iter/s]
Best score: 0.2022: : 442iter [00:50,  9.18iter/s]
Best score: 0.2022: : 442iter [00:51,  9.18iter/s]
Best score: 0.2022: : 443iter [00:51,  9.04iter/s]
Best score: 0.2022: : 443iter [00:51,  9.04iter/s]
Best score: 0.2022: : 444iter [00:51,  9.16iter/s]
Best score: 0.2022: : 444iter [00:51,  9.16iter/s]
Best score: 0.2022: : 445iter [00:51,  9.16iter/s]
Best score: 0.2022: : 446iter [00:51,  9.63iter/s]
Best score: 0.2022: : 446iter [00:51,  9.63iter/s]
Best score: 0.2022: : 447iter [00:51,  9.67iter/s]
Best score: 0.2022: : 447iter [00:51,  9.67iter/s]
Best score: 0.2022: : 448iter [00:51,  9.52iter/s]
Best score: 0.2022: : 448iter [00:51,  9.52iter/s]
Best score: 0.2022: : 449iter [00:51,  9.30iter/s]
[ITER 450] Best: 0.1920
[PARAMS] Current best parameters:
  fx: 559.9498
  fy: 560.5678
  px: 849.8513
  py: 856.7172
  k1: -0.04394345
  k2: 0.02591689
  k3: -0.01355194
  k4: 0.00257347
  p1: 0.01151037
  p2: 0.00761931
  p3: -0.01795435
  p4: -0.00684322
  s1: 0.0081
  s2: 0.0069
  s3: -0.0033
  s4: 0.0118
  tilt_x: -0.0150
  tilt_y: -0.0029
  scale: 2.9856
  rotation: -0.1073

Best score: 0.1920: : 449iter [00:51,  9.30iter/s]
Best score: 0.1920: : 450iter [00:51,  9.07iter/s]
Best score: 0.1920: : 450iter [00:52,  9.07iter/s]
Best score: 0.1920: : 451iter [00:52,  5.01iter/s]
Best score: 0.1920: : 451iter [00:52,  5.01iter/s]
Best score: 0.1920: : 452iter [00:52,  5.76iter/s]
Best score: 0.1920: : 452iter [00:52,  5.76iter/s]
Best score: 0.1920: : 453iter [00:52,  6.44iter/s]
Best score: 0.1920: : 453iter [00:52,  6.44iter/s]
Best score: 0.1920: : 454iter [00:52,  7.05iter/s]
Best score: 0.1920: : 454iter [00:52,  7.05iter/s]
Best score: 0.1920: : 455iter [00:52,  7.57iter/s]
Best score: 0.1920: : 455iter [00:52,  7.57iter/s]
Best score: 0.1920: : 456iter [00:52,  7.97iter/s]
Best score: 0.1920: : 456iter [00:52,  7.97iter/s]
Best score: 0.1920: : 457iter [00:52,  8.30iter/s]
Best score: 0.1920: : 457iter [00:52,  8.30iter/s]
Best score: 0.1920: : 458iter [00:53,  8.30iter/s]
Best score: 0.1920: : 459iter [00:53,  9.06iter/s]
Best score: 0.1920: : 459iter [00:53,  9.06iter/s]
Best score: 0.1920: : 460iter [00:53,  9.04iter/s]
Best score: 0.1920: : 460iter [00:53,  9.04iter/s]
Best score: 0.1920: : 461iter [00:53,  9.21iter/s]
Best score: 0.1920: : 461iter [00:53,  9.21iter/s]
Best score: 0.1920: : 462iter [00:53,  9.26iter/s]
Best score: 0.1920: : 462iter [00:53,  9.26iter/s]
Best score: 0.1920: : 463iter [00:53,  9.26iter/s]
Best score: 0.1920: : 464iter [00:53,  9.64iter/s]
Best score: 0.1920: : 464iter [00:53,  9.64iter/s]
Best score: 0.1920: : 465iter [00:53,  9.70iter/s]
Best score: 0.1920: : 465iter [00:53,  9.70iter/s]
Best score: 0.1920: : 466iter [00:53,  9.76iter/s]
Best score: 0.1920: : 466iter [00:53,  9.76iter/s]
Best score: 0.1920: : 467iter [00:53,  9.76iter/s]
Best score: 0.1920: : 468iter [00:53,  9.85iter/s]
Best score: 0.1920: : 468iter [00:54,  9.85iter/s]
Best score: 0.1920: : 469iter [00:54,  9.82iter/s]
Best score: 0.1920: : 469iter [00:54,  9.82iter/s]
Best score: 0.1920: : 470iter [00:54,  9.48iter/s]
Best score: 0.1920: : 470iter [00:54,  9.48iter/s]
Best score: 0.1920: : 471iter [00:54,  9.48iter/s]
Best score: 0.1920: : 472iter [00:54,  9.74iter/s]
Best score: 0.1920: : 472iter [00:54,  9.74iter/s]
Best score: 0.1920: : 473iter [00:54,  9.72iter/s]
Best score: 0.1920: : 473iter [00:54,  9.72iter/s]
Best score: 0.1920: : 474iter [00:54,  9.72iter/s]
Best score: 0.1920: : 475iter [00:54, 10.06iter/s]
Best score: 0.1920: : 475iter [00:54, 10.06iter/s]
Best score: 0.1920: : 476iter [00:54, 10.06iter/s]
Best score: 0.1920: : 477iter [00:54, 10.37iter/s]
Best score: 0.1920: : 477iter [00:54, 10.37iter/s]
Best score: 0.1920: : 478iter [00:55, 10.37iter/s]
Best score: 0.1920: : 479iter [00:55, 10.36iter/s]
Best score: 0.1920: : 479iter [00:55, 10.36iter/s]
Best score: 0.1920: : 480iter [00:55, 10.36iter/s]
Best score: 0.1920: : 481iter [00:55, 10.06iter/s]
Best score: 0.1920: : 481iter [00:55, 10.06iter/s]
Best score: 0.1920: : 482iter [00:55, 10.06iter/s]
Best score: 0.1920: : 483iter [00:55,  9.92iter/s]
Best score: 0.1920: : 483iter [00:55,  9.92iter/s]
Best score: 0.1920: : 484iter [00:55,  9.83iter/s]
Best score: 0.1920: : 484iter [00:55,  9.83iter/s]
Best score: 0.1920: : 485iter [00:55,  9.57iter/s]
Best score: 0.1920: : 485iter [00:55,  9.57iter/s]
Best score: 0.1920: : 486iter [00:55,  9.47iter/s]
Best score: 0.1920: : 486iter [00:55,  9.47iter/s]
Best score: 0.1920: : 487iter [00:56,  9.47iter/s]
Best score: 0.1920: : 488iter [00:56,  9.59iter/s]
Best score: 0.1920: : 488iter [00:56,  9.59iter/s]
Best score: 0.1920: : 489iter [00:56,  9.31iter/s]
Best score: 0.1920: : 489iter [00:56,  9.31iter/s]
Best score: 0.1920: : 490iter [00:56,  9.14iter/s]
Best score: 0.1920: : 490iter [00:56,  9.14iter/s]
Best score: 0.1920: : 491iter [00:56,  9.14iter/s]
Best score: 0.1920: : 492iter [00:56,  9.42iter/s]
Best score: 0.1920: : 492iter [00:56,  9.42iter/s]
Best score: 0.1920: : 493iter [00:56,  9.43iter/s]
Best score: 0.1920: : 493iter [00:56,  9.43iter/s]
Best score: 0.1920: : 494iter [00:56,  9.40iter/s]
Best score: 0.1920: : 494iter [00:56,  9.40iter/s]
Best score: 0.1920: : 495iter [00:56,  9.44iter/s]
Best score: 0.1920: : 495iter [00:56,  9.44iter/s]
Best score: 0.1920: : 496iter [00:56,  9.44iter/s]
Best score: 0.1920: : 497iter [00:56,  9.57iter/s]
Best score: 0.1920: : 497iter [00:57,  9.57iter/s]
Best score: 0.1920: : 498iter [00:57,  9.57iter/s]
Best score: 0.1920: : 498iter [00:57,  9.57iter/s]
Best score: 0.1920: : 499iter [00:57,  9.65iter/s]
[ITER 500] Best: 0.1769
[PARAMS] Current best parameters:
  fx: 557.3162
  fy: 557.4479
  px: 848.9718
  py: 857.7634
  k1: -0.04075946
  k2: 0.02697033
  k3: -0.01603156
  k4: 0.00353647
  p1: 0.01240234
  p2: 0.01101627
  p3: -0.01840017
  p4: -0.01045179
  s1: 0.0051
  s2: 0.0080
  s3: 0.0012
  s4: 0.0108
  tilt_x: -0.0111
  tilt_y: 0.0077
  scale: 2.9866
  rotation: -0.1157

Best score: 0.1769: : 499iter [00:57,  9.65iter/s]
Best score: 0.1769: : 500iter [00:57,  9.49iter/s]
Best score: 0.1769: : 500iter [00:57,  9.49iter/s]
Best score: 0.1769: : 501iter [00:57,  5.38iter/s]
Best score: 0.1769: : 501iter [00:57,  5.38iter/s]
Best score: 0.1769: : 502iter [00:57,  5.38iter/s]
Best score: 0.1769: : 503iter [00:57,  6.93iter/s]
Best score: 0.1769: : 503iter [00:57,  6.93iter/s]
Best score: 0.1769: : 504iter [00:58,  6.93iter/s]
Best score: 0.1769: : 505iter [00:58,  8.04iter/s]
Best score: 0.1769: : 505iter [00:58,  8.04iter/s]
Best score: 0.1769: : 506iter [00:58,  8.04iter/s]
Best score: 0.1769: : 507iter [00:58,  8.73iter/s]
Best score: 0.1769: : 507iter [00:58,  8.73iter/s]
Best score: 0.1769: : 508iter [00:58,  8.88iter/s]
Best score: 0.1769: : 508iter [00:58,  8.88iter/s]
Best score: 0.1769: : 509iter [00:58,  9.09iter/s]
Best score: 0.1723: : 509iter [00:58,  9.09iter/s]
Best score: 0.1723: : 510iter [00:58,  9.09iter/s]
Best score: 0.1723: : 510iter [00:58,  9.09iter/s]
Best score: 0.1723: : 511iter [00:58,  9.28iter/s]
Best score: 0.1723: : 511iter [00:58,  9.28iter/s]
Best score: 0.1723: : 512iter [00:58,  9.40iter/s]
Best score: 0.1723: : 512iter [00:58,  9.40iter/s]
Best score: 0.1723: : 513iter [00:58,  9.40iter/s]
Best score: 0.1723: : 514iter [00:58,  9.66iter/s]
Best score: 0.1723: : 514iter [00:59,  9.66iter/s]
Best score: 0.1723: : 515iter [00:59,  9.56iter/s]
Best score: 0.1723: : 515iter [00:59,  9.56iter/s]
Best score: 0.1723: : 516iter [00:59,  9.52iter/s]
Best score: 0.1723: : 516iter [00:59,  9.52iter/s]
Best score: 0.1723: : 517iter [00:59,  9.61iter/s]
Best score: 0.1723: : 517iter [00:59,  9.61iter/s]
Best score: 0.1723: : 518iter [00:59,  9.61iter/s]
Best score: 0.1723: : 519iter [00:59,  9.97iter/s]
Best score: 0.1723: : 519iter [00:59,  9.97iter/s]
Best score: 0.1723: : 520iter [00:59,  9.72iter/s]
Best score: 0.1723: : 520iter [00:59,  9.72iter/s]
Best score: 0.1723: : 521iter [00:59,  9.72iter/s]
Best score: 0.1723: : 522iter [00:59,  9.82iter/s]
Best score: 0.1723: : 522iter [00:59,  9.82iter/s]
Best score: 0.1723: : 523iter [00:59,  9.79iter/s]
Best score: 0.1723: : 523iter [01:00,  9.79iter/s]
Best score: 0.1723: : 524iter [01:00,  9.79iter/s]
Best score: 0.1723: : 525iter [01:00,  9.77iter/s]
Best score: 0.1723: : 525iter [01:00,  9.77iter/s]
Best score: 0.1723: : 526iter [01:00,  9.77iter/s]
Best score: 0.1723: : 527iter [01:00,  9.86iter/s]
Best score: 0.1723: : 527iter [01:00,  9.86iter/s]
Best score: 0.1723: : 528iter [01:00,  9.79iter/s]
Best score: 0.1723: : 528iter [01:00,  9.79iter/s]
Best score: 0.1723: : 529iter [01:00,  9.67iter/s]
Best score: 0.1716: : 529iter [01:00,  9.67iter/s]
Best score: 0.1716: : 530iter [01:00,  9.44iter/s]
Best score: 0.1716: : 530iter [01:00,  9.44iter/s]
Best score: 0.1716: : 531iter [01:00,  9.34iter/s]
Best score: 0.1716: : 531iter [01:00,  9.34iter/s]
Best score: 0.1716: : 532iter [01:00,  9.23iter/s]
Best score: 0.1716: : 532iter [01:00,  9.23iter/s]
Best score: 0.1716: : 533iter [01:00,  9.33iter/s]
Best score: 0.1716: : 533iter [01:01,  9.33iter/s]
Best score: 0.1716: : 534iter [01:01,  9.18iter/s]
Best score: 0.1716: : 534iter [01:01,  9.18iter/s]
Best score: 0.1716: : 535iter [01:01,  9.26iter/s]
Best score: 0.1716: : 535iter [01:01,  9.26iter/s]
Best score: 0.1716: : 536iter [01:01,  9.20iter/s]
Best score: 0.1716: : 536iter [01:01,  9.20iter/s]
Best score: 0.1716: : 537iter [01:01,  8.98iter/s]
Best score: 0.1716: : 537iter [01:01,  8.98iter/s]
Best score: 0.1716: : 538iter [01:01,  8.95iter/s]
Best score: 0.1716: : 538iter [01:01,  8.95iter/s]
Best score: 0.1716: : 539iter [01:01,  8.80iter/s]
Best score: 0.1642: : 539iter [01:01,  8.80iter/s]
Best score: 0.1642: : 540iter [01:01,  8.69iter/s]
Best score: 0.1642: : 540iter [01:01,  8.69iter/s]
Best score: 0.1642: : 541iter [01:01,  8.69iter/s]
Best score: 0.1642: : 542iter [01:01,  9.25iter/s]
Best score: 0.1642: : 542iter [01:02,  9.25iter/s]
Best score: 0.1642: : 543iter [01:02,  9.41iter/s]
Best score: 0.1642: : 543iter [01:02,  9.41iter/s]
Best score: 0.1642: : 544iter [01:02,  9.45iter/s]
Best score: 0.1642: : 544iter [01:02,  9.45iter/s]
Best score: 0.1642: : 545iter [01:02,  9.35iter/s]
Best score: 0.1642: : 545iter [01:02,  9.35iter/s]
Best score: 0.1642: : 546iter [01:02,  9.14iter/s]
Best score: 0.1642: : 546iter [01:02,  9.14iter/s]
Best score: 0.1642: : 547iter [01:02,  9.03iter/s]
Best score: 0.1642: : 547iter [01:02,  9.03iter/s]
Best score: 0.1642: : 548iter [01:02,  9.08iter/s]
Best score: 0.1642: : 548iter [01:02,  9.08iter/s]
Best score: 0.1642: : 549iter [01:02,  9.00iter/s]
[ITER 550] Best: 0.1642
[PARAMS] Current best parameters:
  fx: 560.9240
  fy: 561.2205
  px: 849.1007
  py: 857.1579
  k1: -0.04447916
  k2: 0.02769091
  k3: -0.01646944
  k4: 0.00363547
  p1: 0.01370113
  p2: 0.00822815
  p3: -0.01747371
  p4: -0.00879047
  s1: 0.0072
  s2: 0.0075
  s3: -0.0031
  s4: 0.0099
  tilt_x: -0.0167
  tilt_y: 0.0016
  scale: 2.9866
  rotation: -0.1240

Best score: 0.1642: : 549iter [01:02,  9.00iter/s]
Best score: 0.1642: : 550iter [01:02,  8.78iter/s]
Best score: 0.1642: : 550iter [01:03,  8.78iter/s]
Best score: 0.1642: : 551iter [01:03,  5.02iter/s]
Best score: 0.1642: : 551iter [01:03,  5.02iter/s]
Best score: 0.1642: : 552iter [01:03,  5.87iter/s]
Best score: 0.1642: : 552iter [01:03,  5.87iter/s]
Best score: 0.1642: : 553iter [01:03,  6.54iter/s]
Best score: 0.1642: : 553iter [01:03,  6.54iter/s]
Best score: 0.1642: : 554iter [01:03,  7.18iter/s]
Best score: 0.1642: : 554iter [01:03,  7.18iter/s]
Best score: 0.1642: : 555iter [01:03,  7.65iter/s]
Best score: 0.1642: : 555iter [01:03,  7.65iter/s]
Best score: 0.1642: : 556iter [01:03,  8.06iter/s]
Best score: 0.1642: : 556iter [01:03,  8.06iter/s]
Best score: 0.1642: : 557iter [01:03,  8.33iter/s]
Best score: 0.1642: : 557iter [01:04,  8.33iter/s]
Best score: 0.1642: : 558iter [01:04,  8.59iter/s]
Best score: 0.1642: : 558iter [01:04,  8.59iter/s]
Best score: 0.1642: : 559iter [01:04,  8.66iter/s]
Best score: 0.1634: : 559iter [01:04,  8.66iter/s]
Best score: 0.1634: : 560iter [01:04,  8.47iter/s]
Best score: 0.1634: : 560iter [01:04,  8.47iter/s]
Best score: 0.1634: : 561iter [01:04,  8.78iter/s]
Best score: 0.1634: : 561iter [01:04,  8.78iter/s]
Best score: 0.1634: : 562iter [01:04,  8.88iter/s]
Best score: 0.1634: : 562iter [01:04,  8.88iter/s]
Best score: 0.1634: : 563iter [01:04,  9.01iter/s]
Best score: 0.1634: : 563iter [01:04,  9.01iter/s]
Best score: 0.1634: : 564iter [01:04,  9.21iter/s]
Best score: 0.1634: : 564iter [01:04,  9.21iter/s]
Best score: 0.1634: : 565iter [01:04,  9.16iter/s]
Best score: 0.1634: : 565iter [01:04,  9.16iter/s]
Best score: 0.1634: : 566iter [01:04,  9.12iter/s]
Best score: 0.1634: : 566iter [01:04,  9.12iter/s]
Best score: 0.1634: : 567iter [01:04,  9.24iter/s]
Best score: 0.1634: : 567iter [01:05,  9.24iter/s]
Best score: 0.1634: : 568iter [01:05,  9.05iter/s]
Best score: 0.1634: : 568iter [01:05,  9.05iter/s]
Best score: 0.1634: : 569iter [01:05,  9.18iter/s]
Best score: 0.1634: : 569iter [01:05,  9.18iter/s]
Best score: 0.1634: : 570iter [01:05,  8.95iter/s]
Best score: 0.1634: : 570iter [01:05,  8.95iter/s]
Best score: 0.1634: : 571iter [01:05,  9.12iter/s]
Best score: 0.1634: : 571iter [01:05,  9.12iter/s]
Best score: 0.1634: : 572iter [01:05,  9.13iter/s]
Best score: 0.1634: : 572iter [01:05,  9.13iter/s]
Best score: 0.1634: : 573iter [01:05,  9.04iter/s]
Best score: 0.1634: : 573iter [01:05,  9.04iter/s]
Best score: 0.1634: : 574iter [01:05,  8.95iter/s]
Best score: 0.1634: : 574iter [01:05,  8.95iter/s]
Best score: 0.1634: : 575iter [01:05,  9.22iter/s]
Best score: 0.1634: : 575iter [01:05,  9.22iter/s]
Best score: 0.1634: : 576iter [01:05,  9.21iter/s]
Best score: 0.1634: : 576iter [01:06,  9.21iter/s]
Best score: 0.1634: : 577iter [01:06,  9.10iter/s]
Best score: 0.1634: : 577iter [01:06,  9.10iter/s]
Best score: 0.1634: : 578iter [01:06,  9.19iter/s]
Best score: 0.1634: : 578iter [01:06,  9.19iter/s]
Best score: 0.1634: : 579iter [01:06,  9.17iter/s]
Best score: 0.1573: : 579iter [01:06,  9.17iter/s]
Best score: 0.1573: : 580iter [01:06,  9.01iter/s]
Best score: 0.1573: : 580iter [01:06,  9.01iter/s]
Best score: 0.1573: : 581iter [01:06,  9.22iter/s]
Best score: 0.1573: : 581iter [01:06,  9.22iter/s]
Best score: 0.1573: : 582iter [01:06,  9.15iter/s]
Best score: 0.1573: : 582iter [01:06,  9.15iter/s]
Best score: 0.1573: : 583iter [01:06,  9.24iter/s]
Best score: 0.1573: : 583iter [01:06,  9.24iter/s]
Best score: 0.1573: : 584iter [01:06,  9.23iter/s]
Best score: 0.1573: : 584iter [01:06,  9.23iter/s]
Best score: 0.1573: : 585iter [01:06,  9.19iter/s]
Best score: 0.1573: : 585iter [01:07,  9.19iter/s]
Best score: 0.1573: : 586iter [01:07,  8.96iter/s]
Best score: 0.1573: : 586iter [01:07,  8.96iter/s]
Best score: 0.1573: : 587iter [01:07,  9.10iter/s]
Best score: 0.1573: : 587iter [01:07,  9.10iter/s]
Best score: 0.1573: : 588iter [01:07,  9.10iter/s]
Best score: 0.1573: : 588iter [01:07,  9.10iter/s]
Best score: 0.1573: : 589iter [01:07,  9.17iter/s]
Best score: 0.1517: : 589iter [01:07,  9.17iter/s]
Best score: 0.1517: : 590iter [01:07,  8.82iter/s]
Best score: 0.1517: : 590iter [01:07,  8.82iter/s]
Best score: 0.1517: : 591iter [01:07,  8.80iter/s]
Best score: 0.1517: : 591iter [01:07,  8.80iter/s]
Best score: 0.1517: : 592iter [01:07,  8.71iter/s]
Best score: 0.1517: : 592iter [01:07,  8.71iter/s]
Best score: 0.1517: : 593iter [01:07,  8.85iter/s]
Best score: 0.1517: : 593iter [01:07,  8.85iter/s]
Best score: 0.1517: : 594iter [01:07,  8.90iter/s]
Best score: 0.1517: : 594iter [01:08,  8.90iter/s]
Best score: 0.1517: : 595iter [01:08,  8.89iter/s]
Best score: 0.1517: : 595iter [01:08,  8.89iter/s]
Best score: 0.1517: : 596iter [01:08,  9.00iter/s]
Best score: 0.1517: : 596iter [01:08,  9.00iter/s]
Best score: 0.1517: : 597iter [01:08,  8.98iter/s]
Best score: 0.1517: : 597iter [01:08,  8.98iter/s]
Best score: 0.1517: : 598iter [01:08,  9.08iter/s]
Best score: 0.1517: : 598iter [01:08,  9.08iter/s]
Best score: 0.1517: : 599iter [01:08,  9.06iter/s]
[ITER 600] Best: 0.1517
[PARAMS] Current best parameters:
  fx: 558.0543
  fy: 558.0981
  px: 848.8255
  py: 857.5582
  k1: -0.04656889
  k2: 0.03210211
  k3: -0.01649828
  k4: 0.00300266
  p1: 0.01024350
  p2: 0.01091769
  p3: -0.00924932
  p4: -0.00646413
  s1: -0.0044
  s2: 0.0056
  s3: -0.0067
  s4: 0.0066
  tilt_x: -0.0078
  tilt_y: 0.0080
  scale: 2.9931
  rotation: -0.1332

Best score: 0.1517: : 599iter [01:08,  9.06iter/s]
Best score: 0.1517: : 600iter [01:08,  8.77iter/s]
Best score: 0.1517: : 600iter [01:09,  8.77iter/s]
Best score: 0.1517: : 601iter [01:09,  4.85iter/s]
Best score: 0.1517: : 601iter [01:09,  4.85iter/s]
Best score: 0.1517: : 602iter [01:09,  5.68iter/s]
Best score: 0.1517: : 602iter [01:09,  5.68iter/s]
Best score: 0.1517: : 603iter [01:09,  6.44iter/s]
Best score: 0.1517: : 603iter [01:09,  6.44iter/s]
Best score: 0.1517: : 604iter [01:09,  7.10iter/s]
Best score: 0.1517: : 604iter [01:09,  7.10iter/s]
Best score: 0.1517: : 605iter [01:09,  7.59iter/s]
Best score: 0.1517: : 605iter [01:09,  7.59iter/s]
Best score: 0.1517: : 606iter [01:09,  7.90iter/s]
Best score: 0.1517: : 606iter [01:09,  7.90iter/s]
Best score: 0.1517: : 607iter [01:09,  8.22iter/s]
Best score: 0.1517: : 607iter [01:09,  8.22iter/s]
Best score: 0.1517: : 608iter [01:09,  8.47iter/s]
Best score: 0.1517: : 608iter [01:09,  8.47iter/s]
Best score: 0.1517: : 609iter [01:09,  8.68iter/s]
Best score: 0.1517: : 609iter [01:10,  8.68iter/s]
Best score: 0.1517: : 610iter [01:10,  8.58iter/s]
Best score: 0.1517: : 610iter [01:10,  8.58iter/s]
Best score: 0.1517: : 611iter [01:10,  8.74iter/s]
Best score: 0.1517: : 611iter [01:10,  8.74iter/s]
Best score: 0.1517: : 612iter [01:10,  8.81iter/s]
Best score: 0.1517: : 612iter [01:10,  8.81iter/s]
Best score: 0.1517: : 613iter [01:10,  8.80iter/s]
Best score: 0.1517: : 613iter [01:10,  8.80iter/s]
Best score: 0.1517: : 614iter [01:10,  8.86iter/s]
Best score: 0.1517: : 614iter [01:10,  8.86iter/s]
Best score: 0.1517: : 615iter [01:10,  8.86iter/s]
Best score: 0.1517: : 615iter [01:10,  8.86iter/s]
Best score: 0.1517: : 616iter [01:10,  8.89iter/s]
Best score: 0.1517: : 616iter [01:10,  8.89iter/s]
Best score: 0.1517: : 617iter [01:10,  8.87iter/s]
Best score: 0.1517: : 617iter [01:10,  8.87iter/s]
Best score: 0.1517: : 618iter [01:10,  8.92iter/s]
Best score: 0.1517: : 618iter [01:11,  8.92iter/s]
Best score: 0.1517: : 619iter [01:11,  9.14iter/s]
Best score: 0.1517: : 619iter [01:11,  9.14iter/s]
Best score: 0.1517: : 620iter [01:11,  9.13iter/s]
Best score: 0.1517: : 620iter [01:11,  9.13iter/s]
Best score: 0.1517: : 621iter [01:11,  9.08iter/s]
Best score: 0.1517: : 621iter [01:11,  9.08iter/s]
Best score: 0.1517: : 622iter [01:11,  8.92iter/s]
Best score: 0.1517: : 622iter [01:11,  8.92iter/s]
Best score: 0.1517: : 623iter [01:11,  8.96iter/s]
Best score: 0.1517: : 623iter [01:11,  8.96iter/s]
Best score: 0.1517: : 624iter [01:11,  8.93iter/s]
Best score: 0.1517: : 624iter [01:11,  8.93iter/s]
Best score: 0.1517: : 625iter [01:11,  9.18iter/s]
Best score: 0.1517: : 625iter [01:11,  9.18iter/s]
Best score: 0.1517: : 626iter [01:11,  9.28iter/s]
Best score: 0.1517: : 626iter [01:11,  9.28iter/s]
Best score: 0.1517: : 627iter [01:11,  9.28iter/s]
Best score: 0.1517: : 627iter [01:12,  9.28iter/s]
Best score: 0.1517: : 628iter [01:12,  9.28iter/s]
Best score: 0.1517: : 629iter [01:12,  9.54iter/s]
Best score: 0.1513: : 629iter [01:12,  9.54iter/s]
Best score: 0.1513: : 630iter [01:12,  9.33iter/s]
Best score: 0.1513: : 630iter [01:12,  9.33iter/s]
Best score: 0.1513: : 631iter [01:12,  9.34iter/s]
Best score: 0.1513: : 631iter [01:12,  9.34iter/s]
Best score: 0.1513: : 632iter [01:12,  9.51iter/s]
Best score: 0.1513: : 632iter [01:12,  9.51iter/s]
Best score: 0.1513: : 633iter [01:12,  9.43iter/s]
Best score: 0.1513: : 633iter [01:12,  9.43iter/s]
Best score: 0.1513: : 634iter [01:12,  9.47iter/s]
Best score: 0.1513: : 634iter [01:12,  9.47iter/s]
Best score: 0.1513: : 635iter [01:12,  9.47iter/s]
Best score: 0.1513: : 635iter [01:12,  9.47iter/s]
Best score: 0.1513: : 636iter [01:12,  9.41iter/s]
Best score: 0.1513: : 636iter [01:13,  9.41iter/s]
Best score: 0.1513: : 637iter [01:13,  9.43iter/s]
Best score: 0.1513: : 637iter [01:13,  9.43iter/s]
Best score: 0.1513: : 638iter [01:13,  9.33iter/s]
Best score: 0.1513: : 638iter [01:13,  9.33iter/s]
Best score: 0.1513: : 639iter [01:13,  9.10iter/s]
Best score: 0.1477: : 639iter [01:13,  9.10iter/s]
Best score: 0.1477: : 640iter [01:13,  8.84iter/s]
Best score: 0.1477: : 640iter [01:13,  8.84iter/s]
Best score: 0.1477: : 641iter [01:13,  8.92iter/s]
Best score: 0.1477: : 641iter [01:13,  8.92iter/s]
Best score: 0.1477: : 642iter [01:13,  9.04iter/s]
Best score: 0.1477: : 642iter [01:13,  9.04iter/s]
Best score: 0.1477: : 643iter [01:13,  9.02iter/s]
Best score: 0.1477: : 643iter [01:13,  9.02iter/s]
Best score: 0.1477: : 644iter [01:13,  9.16iter/s]
Best score: 0.1477: : 644iter [01:13,  9.16iter/s]
Best score: 0.1477: : 645iter [01:13,  9.19iter/s]
Best score: 0.1477: : 645iter [01:14,  9.19iter/s]
Best score: 0.1477: : 646iter [01:14,  9.03iter/s]
Best score: 0.1477: : 646iter [01:14,  9.03iter/s]
Best score: 0.1477: : 647iter [01:14,  9.07iter/s]
Best score: 0.1477: : 647iter [01:14,  9.07iter/s]
Best score: 0.1477: : 648iter [01:14,  9.07iter/s]
Best score: 0.1477: : 648iter [01:14,  9.07iter/s]
Best score: 0.1477: : 649iter [01:14,  9.00iter/s]
[ITER 650] Best: 0.1477
[PARAMS] Current best parameters:
  fx: 559.6511
  fy: 559.6405
  px: 849.3188
  py: 856.9677
  k1: -0.04468230
  k2: 0.02500780
  k3: -0.01127564
  k4: 0.00175968
  p1: 0.00887569
  p2: 0.01250818
  p3: -0.01248290
  p4: -0.00616882
  s1: -0.0034
  s2: 0.0070
  s3: -0.0034
  s4: 0.0074
  tilt_x: -0.0081
  tilt_y: 0.0094
  scale: 2.9906
  rotation: -0.1250

Best score: 0.1477: : 649iter [01:14,  9.00iter/s]
Best score: 0.1477: : 650iter [01:14,  8.92iter/s]
Best score: 0.1477: : 650iter [01:14,  8.92iter/s]
Best score: 0.1477: : 651iter [01:14,  4.97iter/s]
Best score: 0.1477: : 651iter [01:14,  4.97iter/s]
Best score: 0.1477: : 652iter [01:14,  5.75iter/s]
Best score: 0.1477: : 652iter [01:15,  5.75iter/s]
Best score: 0.1477: : 653iter [01:15,  6.53iter/s]
Best score: 0.1477: : 653iter [01:15,  6.53iter/s]
Best score: 0.1477: : 654iter [01:15,  7.01iter/s]
Best score: 0.1477: : 654iter [01:15,  7.01iter/s]
Best score: 0.1477: : 655iter [01:15,  7.53iter/s]
Best score: 0.1477: : 655iter [01:15,  7.53iter/s]
Best score: 0.1477: : 656iter [01:15,  8.00iter/s]
Best score: 0.1477: : 656iter [01:15,  8.00iter/s]
Best score: 0.1477: : 657iter [01:15,  8.47iter/s]
Best score: 0.1477: : 657iter [01:15,  8.47iter/s]
Best score: 0.1477: : 658iter [01:15,  8.58iter/s]
Best score: 0.1477: : 658iter [01:15,  8.58iter/s]
Best score: 0.1477: : 659iter [01:15,  8.70iter/s]
Best score: 0.1470: : 659iter [01:15,  8.70iter/s]
Best score: 0.1470: : 660iter [01:15,  8.83iter/s]
Best score: 0.1470: : 660iter [01:15,  8.83iter/s]
Best score: 0.1470: : 661iter [01:15,  8.88iter/s]
Best score: 0.1470: : 661iter [01:16,  8.88iter/s]
Best score: 0.1470: : 662iter [01:16,  8.93iter/s]
Best score: 0.1470: : 662iter [01:16,  8.93iter/s]
Best score: 0.1470: : 663iter [01:16,  9.06iter/s]
Best score: 0.1470: : 663iter [01:16,  9.06iter/s]
Best score: 0.1470: : 664iter [01:16,  9.02iter/s]
Best score: 0.1470: : 664iter [01:16,  9.02iter/s]
Best score: 0.1470: : 665iter [01:16,  8.99iter/s]
Best score: 0.1470: : 665iter [01:16,  8.99iter/s]
Best score: 0.1470: : 666iter [01:16,  8.87iter/s]
Best score: 0.1470: : 666iter [01:16,  8.87iter/s]
Best score: 0.1470: : 667iter [01:16,  9.13iter/s]
Best score: 0.1470: : 667iter [01:16,  9.13iter/s]
Best score: 0.1470: : 668iter [01:16,  9.29iter/s]
Best score: 0.1470: : 668iter [01:16,  9.29iter/s]
Best score: 0.1470: : 669iter [01:16,  9.35iter/s]
Best score: 0.1442: : 669iter [01:16,  9.35iter/s]
Best score: 0.1442: : 670iter [01:16,  9.14iter/s]
Best score: 0.1442: : 670iter [01:17,  9.14iter/s]
Best score: 0.1442: : 671iter [01:17,  9.30iter/s]
Best score: 0.1442: : 671iter [01:17,  9.30iter/s]
Best score: 0.1442: : 672iter [01:17,  9.38iter/s]
Best score: 0.1442: : 672iter [01:17,  9.38iter/s]
Best score: 0.1442: : 673iter [01:17,  9.38iter/s]
Best score: 0.1442: : 674iter [01:17,  9.65iter/s]
Best score: 0.1442: : 674iter [01:17,  9.65iter/s]
Best score: 0.1442: : 675iter [01:17,  9.65iter/s]
Best score: 0.1442: : 676iter [01:17,  9.89iter/s]
Best score: 0.1442: : 676iter [01:17,  9.89iter/s]
Best score: 0.1442: : 677iter [01:17,  9.67iter/s]
Best score: 0.1442: : 677iter [01:17,  9.67iter/s]
Best score: 0.1442: : 678iter [01:17,  9.57iter/s]
Best score: 0.1442: : 678iter [01:17,  9.57iter/s]
Best score: 0.1442: : 679iter [01:17,  9.50iter/s]
Best score: 0.1431: : 679iter [01:17,  9.50iter/s]
Best score: 0.1431: : 680iter [01:17,  9.25iter/s]
Best score: 0.1431: : 680iter [01:18,  9.25iter/s]
Best score: 0.1431: : 681iter [01:18,  9.25iter/s]
Best score: 0.1431: : 682iter [01:18,  9.55iter/s]
Best score: 0.1431: : 682iter [01:18,  9.55iter/s]
Best score: 0.1431: : 683iter [01:18,  9.42iter/s]
Best score: 0.1431: : 683iter [01:18,  9.42iter/s]
Best score: 0.1431: : 684iter [01:18,  9.39iter/s]
Best score: 0.1431: : 684iter [01:18,  9.39iter/s]
Best score: 0.1431: : 685iter [01:18,  9.39iter/s]
Best score: 0.1431: : 686iter [01:18,  9.74iter/s]
Best score: 0.1431: : 686iter [01:18,  9.74iter/s]
Best score: 0.1431: : 687iter [01:18,  9.69iter/s]
Best score: 0.1431: : 687iter [01:18,  9.69iter/s]
Best score: 0.1431: : 688iter [01:18,  9.59iter/s]
Best score: 0.1431: : 688iter [01:18,  9.59iter/s]
Best score: 0.1431: : 689iter [01:18,  9.51iter/s]
Best score: 0.1431: : 689iter [01:19,  9.51iter/s]
Best score: 0.1431: : 690iter [01:19,  9.11iter/s]
Best score: 0.1431: : 690iter [01:19,  9.11iter/s]
Best score: 0.1431: : 691iter [01:19,  9.12iter/s]
Best score: 0.1431: : 691iter [01:19,  9.12iter/s]
Best score: 0.1431: : 692iter [01:19,  9.10iter/s]
Best score: 0.1431: : 692iter [01:19,  9.10iter/s]
Best score: 0.1431: : 693iter [01:19,  9.19iter/s]
Best score: 0.1431: : 693iter [01:19,  9.19iter/s]
Best score: 0.1431: : 694iter [01:19,  9.37iter/s]
Best score: 0.1431: : 694iter [01:19,  9.37iter/s]
Best score: 0.1431: : 695iter [01:19,  9.54iter/s]
Best score: 0.1431: : 695iter [01:19,  9.54iter/s]
Best score: 0.1431: : 696iter [01:19,  9.49iter/s]
Best score: 0.1431: : 696iter [01:19,  9.49iter/s]
Best score: 0.1431: : 697iter [01:19,  9.60iter/s]
Best score: 0.1431: : 697iter [01:19,  9.60iter/s]
Best score: 0.1431: : 698iter [01:19,  9.69iter/s]
Best score: 0.1431: : 698iter [01:19,  9.69iter/s]
Best score: 0.1431: : 699iter [01:19,  9.71iter/s]
[ITER 700] Best: 0.1423
[PARAMS] Current best parameters:
  fx: 559.2560
  fy: 559.4170
  px: 849.3828
  py: 854.5336
  k1: -0.04372893
  k2: 0.02780342
  k3: -0.01575533
  k4: 0.00333652
  p1: 0.00501528
  p2: 0.01161350
  p3: -0.01429758
  p4: -0.00186349
  s1: -0.0003
  s2: 0.0021
  s3: -0.0022
  s4: 0.0050
  tilt_x: -0.0123
  tilt_y: 0.0072
  scale: 2.9877
  rotation: -0.1194

Best score: 0.1423: : 699iter [01:20,  9.71iter/s]
Best score: 0.1423: : 700iter [01:20,  9.43iter/s]
Best score: 0.1423: : 700iter [01:20,  9.43iter/s]
Best score: 0.1423: : 701iter [01:20,  5.14iter/s]
Best score: 0.1423: : 701iter [01:20,  5.14iter/s]
Best score: 0.1423: : 702iter [01:20,  5.14iter/s]
Best score: 0.1423: : 703iter [01:20,  6.66iter/s]
Best score: 0.1423: : 703iter [01:20,  6.66iter/s]
Best score: 0.1423: : 704iter [01:20,  7.25iter/s]
Best score: 0.1423: : 704iter [01:20,  7.25iter/s]
Best score: 0.1423: : 705iter [01:20,  7.67iter/s]
Best score: 0.1423: : 705iter [01:21,  7.67iter/s]
Best score: 0.1423: : 706iter [01:21,  8.12iter/s]
Best score: 0.1423: : 706iter [01:21,  8.12iter/s]
Best score: 0.1423: : 707iter [01:21,  8.57iter/s]
Best score: 0.1423: : 707iter [01:21,  8.57iter/s]
Best score: 0.1423: : 708iter [01:21,  8.57iter/s]
Best score: 0.1423: : 709iter [01:21,  9.31iter/s]
Best score: 0.1423: : 709iter [01:21,  9.31iter/s]
Best score: 0.1423: : 710iter [01:21,  9.39iter/s]
Best score: 0.1423: : 710iter [01:21,  9.39iter/s]
Best score: 0.1423: : 711iter [01:21,  9.39iter/s]
Best score: 0.1423: : 712iter [01:21,  9.72iter/s]
Best score: 0.1423: : 712iter [01:21,  9.72iter/s]
Best score: 0.1423: : 713iter [01:21,  9.72iter/s]
Best score: 0.1423: : 714iter [01:21,  9.99iter/s]
Best score: 0.1423: : 714iter [01:21,  9.99iter/s]
Best score: 0.1423: : 715iter [01:21,  9.99iter/s]
Best score: 0.1423: : 716iter [01:21, 10.12iter/s]
Best score: 0.1423: : 716iter [01:22, 10.12iter/s]
Best score: 0.1423: : 717iter [01:22, 10.12iter/s]
Best score: 0.1423: : 718iter [01:22, 10.12iter/s]
Best score: 0.1423: : 718iter [01:22, 10.12iter/s]
Best score: 0.1420: : 719iter [01:22, 10.12iter/s]
Best score: 0.1420: : 720iter [01:22,  9.90iter/s]
Best score: 0.1420: : 720iter [01:22,  9.90iter/s]
Best score: 0.1420: : 721iter [01:22,  9.90iter/s]
Best score: 0.1420: : 722iter [01:22,  9.89iter/s]
Best score: 0.1420: : 722iter [01:22,  9.89iter/s]
Best score: 0.1420: : 723iter [01:22,  9.82iter/s]
Best score: 0.1420: : 723iter [01:22,  9.82iter/s]
Best score: 0.1420: : 724iter [01:22,  9.82iter/s]
Best score: 0.1420: : 725iter [01:22, 10.22iter/s]
Best score: 0.1420: : 725iter [01:22, 10.22iter/s]
Best score: 0.1420: : 726iter [01:23, 10.22iter/s]
Best score: 0.1420: : 727iter [01:23, 10.36iter/s]
Best score: 0.1420: : 727iter [01:23, 10.36iter/s]
Best score: 0.1420: : 728iter [01:23, 10.36iter/s]
Best score: 0.1420: : 729iter [01:23, 10.51iter/s]
Best score: 0.1411: : 729iter [01:23, 10.51iter/s]
Best score: 0.1411: : 730iter [01:23, 10.51iter/s]
Best score: 0.1411: : 731iter [01:23, 10.40iter/s]
Best score: 0.1411: : 731iter [01:23, 10.40iter/s]
Best score: 0.1411: : 732iter [01:23, 10.40iter/s]
Best score: 0.1411: : 733iter [01:23, 10.26iter/s]
Best score: 0.1411: : 733iter [01:23, 10.26iter/s]
Best score: 0.1411: : 734iter [01:23, 10.26iter/s]
Best score: 0.1411: : 735iter [01:23, 10.12iter/s]
Best score: 0.1411: : 735iter [01:23, 10.12iter/s]
Best score: 0.1411: : 736iter [01:24, 10.12iter/s]
Best score: 0.1411: : 737iter [01:24, 10.24iter/s]
Best score: 0.1411: : 737iter [01:24, 10.24iter/s]
Best score: 0.1411: : 738iter [01:24, 10.24iter/s]
Best score: 0.1411: : 739iter [01:24, 10.23iter/s]
Best score: 0.1399: : 739iter [01:24, 10.23iter/s]
Best score: 0.1399: : 740iter [01:24, 10.23iter/s]
Best score: 0.1399: : 741iter [01:24, 10.17iter/s]
Best score: 0.1399: : 741iter [01:24, 10.17iter/s]
Best score: 0.1399: : 742iter [01:24, 10.17iter/s]
Best score: 0.1399: : 743iter [01:24, 10.27iter/s]
Best score: 0.1399: : 743iter [01:24, 10.27iter/s]
Best score: 0.1399: : 744iter [01:24, 10.27iter/s]
Best score: 0.1399: : 745iter [01:24, 10.35iter/s]
Best score: 0.1399: : 745iter [01:24, 10.35iter/s]
Best score: 0.1399: : 746iter [01:25, 10.35iter/s]
Best score: 0.1399: : 747iter [01:25, 10.32iter/s]
Best score: 0.1399: : 747iter [01:25, 10.32iter/s]
Best score: 0.1399: : 748iter [01:25, 10.32iter/s]
Best score: 0.1399: : 749iter [01:25, 10.09iter/s]
[ITER 750] Best: 0.1371
[PARAMS] Current best parameters:
  fx: 566.4118
  fy: 566.4458
  px: 849.8272
  py: 853.2545
  k1: -0.04368207
  k2: 0.01382997
  k3: -0.00552095
  k4: 0.00098164
  p1: 0.00141886
  p2: 0.01136875
  p3: -0.02514460
  p4: -0.00016891
  s1: 0.0106
  s2: 0.0011
  s3: 0.0112
  s4: 0.0032
  tilt_x: -0.0117
  tilt_y: 0.0036
  scale: 2.9815
  rotation: -0.1150

Best score: 0.1371: : 749iter [01:25, 10.09iter/s]
Best score: 0.1371: : 750iter [01:25, 10.09iter/s]
Best score: 0.1371: : 751iter [01:25,  6.71iter/s]
Best score: 0.1371: : 751iter [01:25,  6.71iter/s]
Best score: 0.1371: : 752iter [01:25,  6.71iter/s]
Best score: 0.1371: : 753iter [01:25,  7.38iter/s]
Best score: 0.1371: : 753iter [01:26,  7.38iter/s]
Best score: 0.1371: : 754iter [01:26,  7.65iter/s]
Best score: 0.1371: : 754iter [01:26,  7.65iter/s]
Best score: 0.1371: : 755iter [01:26,  7.96iter/s]
Best score: 0.1371: : 755iter [01:26,  7.96iter/s]
Best score: 0.1371: : 756iter [01:26,  8.26iter/s]
Best score: 0.1371: : 756iter [01:26,  8.26iter/s]
Best score: 0.1371: : 757iter [01:26,  8.61iter/s]
Best score: 0.1371: : 757iter [01:26,  8.61iter/s]
Best score: 0.1371: : 758iter [01:26,  8.90iter/s]
Best score: 0.1371: : 758iter [01:26,  8.90iter/s]
Best score: 0.1368: : 759iter [01:26,  8.90iter/s]
Best score: 0.1368: : 760iter [01:26,  9.26iter/s]
Best score: 0.1368: : 760iter [01:26,  9.26iter/s]
Best score: 0.1368: : 761iter [01:26,  9.26iter/s]
Best score: 0.1368: : 762iter [01:26,  9.57iter/s]
Best score: 0.1368: : 762iter [01:26,  9.57iter/s]
Best score: 0.1368: : 763iter [01:27,  9.57iter/s]
Best score: 0.1368: : 764iter [01:27,  9.99iter/s]
Best score: 0.1368: : 764iter [01:27,  9.99iter/s]
Best score: 0.1368: : 765iter [01:27,  9.99iter/s]
Best score: 0.1368: : 766iter [01:27, 10.08iter/s]
Best score: 0.1368: : 766iter [01:27, 10.08iter/s]
Best score: 0.1368: : 767iter [01:27, 10.08iter/s]
Best score: 0.1368: : 768iter [01:27, 10.03iter/s]
Best score: 0.1368: : 768iter [01:27, 10.03iter/s]
Best score: 0.1359: : 769iter [01:27, 10.03iter/s]
Best score: 0.1359: : 770iter [01:27, 10.07iter/s]
Best score: 0.1359: : 770iter [01:27, 10.07iter/s]
Best score: 0.1359: : 771iter [01:27, 10.07iter/s]
Best score: 0.1359: : 772iter [01:27, 10.30iter/s]
Best score: 0.1359: : 772iter [01:27, 10.30iter/s]
Best score: 0.1359: : 773iter [01:28, 10.30iter/s]
Best score: 0.1359: : 774iter [01:28, 10.33iter/s]
Best score: 0.1359: : 774iter [01:28, 10.33iter/s]
Best score: 0.1359: : 775iter [01:28, 10.33iter/s]
Best score: 0.1359: : 776iter [01:28, 10.42iter/s]
Best score: 0.1359: : 776iter [01:28, 10.42iter/s]
Best score: 0.1359: : 777iter [01:28, 10.42iter/s]
Best score: 0.1359: : 778iter [01:28, 10.06iter/s]
Best score: 0.1359: : 778iter [01:28, 10.06iter/s]
Best score: 0.1354: : 779iter [01:28, 10.06iter/s]
Best score: 0.1354: : 780iter [01:28,  9.77iter/s]
Best score: 0.1354: : 780iter [01:28,  9.77iter/s]
Best score: 0.1354: : 781iter [01:28,  9.77iter/s]
Best score: 0.1354: : 782iter [01:28, 10.09iter/s]
Best score: 0.1354: : 782iter [01:28, 10.09iter/s]
Best score: 0.1354: : 783iter [01:29, 10.09iter/s]
Best score: 0.1354: : 784iter [01:29, 10.00iter/s]
Best score: 0.1354: : 784iter [01:29, 10.00iter/s]
Best score: 0.1354: : 785iter [01:29, 10.00iter/s]
Best score: 0.1354: : 786iter [01:29,  9.85iter/s]
Best score: 0.1354: : 786iter [01:29,  9.85iter/s]
Best score: 0.1354: : 787iter [01:29,  9.77iter/s]
Best score: 0.1354: : 787iter [01:29,  9.77iter/s]
Best score: 0.1354: : 788iter [01:29,  9.70iter/s]
Best score: 0.1354: : 788iter [01:29,  9.70iter/s]
Best score: 0.1354: : 789iter [01:29,  9.70iter/s]
Best score: 0.1354: : 790iter [01:29,  9.65iter/s]
Best score: 0.1354: : 790iter [01:29,  9.65iter/s]
Best score: 0.1354: : 791iter [01:29,  9.62iter/s]
Best score: 0.1354: : 791iter [01:29,  9.62iter/s]
Best score: 0.1354: : 792iter [01:29,  9.62iter/s]
Best score: 0.1354: : 793iter [01:29,  9.72iter/s]
Best score: 0.1354: : 793iter [01:30,  9.72iter/s]
Best score: 0.1354: : 794iter [01:30,  9.62iter/s]
Best score: 0.1354: : 794iter [01:30,  9.62iter/s]
Best score: 0.1354: : 795iter [01:30,  9.62iter/s]
Best score: 0.1354: : 796iter [01:30,  9.98iter/s]
Best score: 0.1354: : 796iter [01:30,  9.98iter/s]
Best score: 0.1354: : 797iter [01:30,  9.87iter/s]
Best score: 0.1354: : 797iter [01:30,  9.87iter/s]
Best score: 0.1354: : 798iter [01:30,  9.82iter/s]
Best score: 0.1354: : 798iter [01:30,  9.82iter/s]
[ITER 800] Best: 0.1354
[PARAMS] Current best parameters:
  fx: 566.7865
  fy: 566.8983
  px: 850.3141
  py: 853.2379
  k1: -0.04597101
  k2: 0.01574278
  k3: -0.00568182
  k4: 0.00079291
  p1: 0.00149806
  p2: 0.01198970
  p3: -0.02642448
  p4: 0.00088147
  s1: 0.0113
  s2: 0.0002
  s3: 0.0133
  s4: 0.0012
  tilt_x: -0.0120
  tilt_y: 0.0023
  scale: 2.9830
  rotation: -0.1201

Best score: 0.1354: : 799iter [01:30,  9.82iter/s]
Best score: 0.1354: : 800iter [01:30,  9.80iter/s]
Best score: 0.1354: : 800iter [01:31,  9.80iter/s]
Best score: 0.1354: : 801iter [01:31,  5.89iter/s]
Best score: 0.1354: : 801iter [01:31,  5.89iter/s]
Best score: 0.1354: : 802iter [01:31,  6.51iter/s]
Best score: 0.1354: : 802iter [01:31,  6.51iter/s]
Best score: 0.1354: : 803iter [01:31,  7.10iter/s]
Best score: 0.1354: : 803iter [01:31,  7.10iter/s]
Best score: 0.1354: : 804iter [01:31,  7.67iter/s]
Best score: 0.1354: : 804iter [01:31,  7.67iter/s]
Best score: 0.1354: : 805iter [01:31,  7.67iter/s]
Best score: 0.1354: : 806iter [01:31,  8.39iter/s]
Best score: 0.1354: : 806iter [01:31,  8.39iter/s]
Best score: 0.1354: : 807iter [01:31,  8.53iter/s]
Best score: 0.1354: : 807iter [01:31,  8.53iter/s]
Best score: 0.1354: : 808iter [01:31,  8.72iter/s]
Best score: 0.1354: : 808iter [01:31,  8.72iter/s]
Best score: 0.1354: : 809iter [01:31,  8.90iter/s]
Best score: 0.1351: : 809iter [01:32,  8.90iter/s]
Best score: 0.1351: : 810iter [01:32,  8.90iter/s]
Best score: 0.1351: : 810iter [01:32,  8.90iter/s]
Best score: 0.1351: : 811iter [01:32,  8.90iter/s]
Best score: 0.1351: : 812iter [01:32,  9.39iter/s]
Best score: 0.1351: : 812iter [01:32,  9.39iter/s]
Best score: 0.1351: : 813iter [01:32,  9.39iter/s]
Best score: 0.1351: : 814iter [01:32,  9.76iter/s]
Best score: 0.1351: : 814iter [01:32,  9.76iter/s]
Best score: 0.1351: : 815iter [01:32,  9.76iter/s]
Best score: 0.1351: : 816iter [01:32, 10.00iter/s]
Best score: 0.1351: : 816iter [01:32, 10.00iter/s]
Best score: 0.1351: : 817iter [01:32,  9.95iter/s]
Best score: 0.1351: : 817iter [01:32,  9.95iter/s]
Best score: 0.1351: : 818iter [01:32,  9.82iter/s]
Best score: 0.1351: : 818iter [01:32,  9.82iter/s]
Best score: 0.1351: : 819iter [01:32,  9.80iter/s]
Best score: 0.1344: : 819iter [01:33,  9.80iter/s]
Best score: 0.1344: : 820iter [01:33,  9.67iter/s]
Best score: 0.1344: : 820iter [01:33,  9.67iter/s]
Best score: 0.1344: : 821iter [01:33,  9.67iter/s]
Best score: 0.1344: : 822iter [01:33,  9.87iter/s]
Best score: 0.1344: : 822iter [01:33,  9.87iter/s]
Best score: 0.1344: : 823iter [01:33,  9.87iter/s]
Best score: 0.1344: : 824iter [01:33, 10.08iter/s]
Best score: 0.1344: : 824iter [01:33, 10.08iter/s]
Best score: 0.1344: : 825iter [01:33, 10.08iter/s]
Best score: 0.1344: : 826iter [01:33, 10.22iter/s]
Best score: 0.1344: : 826iter [01:33, 10.22iter/s]
Best score: 0.1344: : 827iter [01:33, 10.22iter/s]
Best score: 0.1344: : 828iter [01:33, 10.06iter/s]
Best score: 0.1344: : 828iter [01:33, 10.06iter/s]
Best score: 0.1344: : 829iter [01:34, 10.06iter/s]
Best score: 0.1344: : 830iter [01:34,  9.86iter/s]
Best score: 0.1344: : 830iter [01:34,  9.86iter/s]
Best score: 0.1344: : 831iter [01:34,  9.86iter/s]
Best score: 0.1344: : 832iter [01:34, 10.09iter/s]
Best score: 0.1344: : 832iter [01:34, 10.09iter/s]
Best score: 0.1344: : 833iter [01:34, 10.09iter/s]
Best score: 0.1344: : 834iter [01:34, 10.18iter/s]
Best score: 0.1344: : 834iter [01:34, 10.18iter/s]
Best score: 0.1344: : 835iter [01:34, 10.18iter/s]
Best score: 0.1344: : 836iter [01:34, 10.20iter/s]
Best score: 0.1344: : 836iter [01:34, 10.20iter/s]
Best score: 0.1344: : 837iter [01:34, 10.20iter/s]
Best score: 0.1344: : 838iter [01:34, 10.23iter/s]
Best score: 0.1344: : 838iter [01:34, 10.23iter/s]
Best score: 0.1344: : 839iter [01:35, 10.23iter/s]
Best score: 0.1344: : 840iter [01:35, 10.13iter/s]
Best score: 0.1344: : 840iter [01:35, 10.13iter/s]
Best score: 0.1344: : 841iter [01:35, 10.13iter/s]
Best score: 0.1344: : 842iter [01:35, 10.27iter/s]
Best score: 0.1344: : 842iter [01:35, 10.27iter/s]
Best score: 0.1344: : 843iter [01:35, 10.27iter/s]
Best score: 0.1344: : 844iter [01:35, 10.30iter/s]
Best score: 0.1344: : 844iter [01:35, 10.30iter/s]
Best score: 0.1344: : 845iter [01:35, 10.30iter/s]
Best score: 0.1344: : 846iter [01:35, 10.37iter/s]
Best score: 0.1344: : 846iter [01:35, 10.37iter/s]
Best score: 0.1344: : 847iter [01:35, 10.37iter/s]
Best score: 0.1344: : 848iter [01:35, 10.32iter/s]
Best score: 0.1344: : 848iter [01:35, 10.32iter/s]
Best score: 0.1344: : 849iter [01:36, 10.32iter/s]
Best score: 0.1344: : 850iter [01:36, 10.05iter/s]
[ITER 850] Best: 0.1344
[PARAMS] Current best parameters:
  fx: 567.1329
  fy: 567.2137
  px: 850.1445
  py: 852.8508
  k1: -0.04518008
  k2: 0.01513914
  k3: -0.00605241
  k4: 0.00102243
  p1: 0.00054105
  p2: 0.01017648
  p3: -0.02599269
  p4: 0.00076858
  s1: 0.0133
  s2: -0.0001
  s3: 0.0135
  s4: 0.0013
  tilt_x: -0.0119
  tilt_y: -0.0001
  scale: 2.9805
  rotation: -0.1164

Best score: 0.1344: : 850iter [01:36, 10.05iter/s]
Best score: 0.1344: : 851iter [01:36, 10.05iter/s]
Best score: 0.1344: : 852iter [01:36,  6.80iter/s]
Best score: 0.1344: : 852iter [01:36,  6.80iter/s]
Best score: 0.1344: : 853iter [01:36,  7.16iter/s]
Best score: 0.1344: : 853iter [01:36,  7.16iter/s]
Best score: 0.1344: : 854iter [01:36,  7.56iter/s]
Best score: 0.1344: : 854iter [01:36,  7.56iter/s]
Best score: 0.1344: : 855iter [01:36,  7.56iter/s]
Best score: 0.1344: : 856iter [01:36,  8.29iter/s]
Best score: 0.1344: : 856iter [01:37,  8.29iter/s]
Best score: 0.1344: : 857iter [01:37,  8.29iter/s]
Best score: 0.1344: : 858iter [01:37,  8.78iter/s]
Best score: 0.1344: : 858iter [01:37,  8.78iter/s]
Best score: 0.1344: : 859iter [01:37,  8.96iter/s]
Best score: 0.1344: : 859iter [01:37,  8.96iter/s]
Best score: 0.1344: : 860iter [01:37,  8.89iter/s]
Best score: 0.1344: : 860iter [01:37,  8.89iter/s]
Best score: 0.1344: : 861iter [01:37,  9.12iter/s]
Best score: 0.1344: : 861iter [01:37,  9.12iter/s]
Best score: 0.1344: : 862iter [01:37,  9.16iter/s]
Best score: 0.1344: : 862iter [01:37,  9.16iter/s]
Best score: 0.1344: : 863iter [01:37,  9.16iter/s]
Best score: 0.1344: : 864iter [01:37,  9.64iter/s]
Best score: 0.1344: : 864iter [01:37,  9.64iter/s]
Best score: 0.1344: : 865iter [01:37,  9.64iter/s]
Best score: 0.1344: : 866iter [01:37,  9.91iter/s]
Best score: 0.1344: : 866iter [01:38,  9.91iter/s]
Best score: 0.1344: : 867iter [01:38,  9.91iter/s]
Best score: 0.1344: : 868iter [01:38,  9.92iter/s]
Best score: 0.1344: : 868iter [01:38,  9.92iter/s]
Best score: 0.1344: : 869iter [01:38,  9.86iter/s]
Best score: 0.1342: : 869iter [01:38,  9.86iter/s]
Best score: 0.1342: : 870iter [01:38,  9.68iter/s]
Best score: 0.1342: : 870iter [01:38,  9.68iter/s]
Best score: 0.1342: : 871iter [01:38,  9.68iter/s]
Best score: 0.1342: : 872iter [01:38, 10.00iter/s]
Best score: 0.1342: : 872iter [01:38, 10.00iter/s]
Best score: 0.1342: : 873iter [01:38,  9.94iter/s]
Best score: 0.1342: : 873iter [01:38,  9.94iter/s]
Best score: 0.1342: : 874iter [01:38,  9.90iter/s]
Best score: 0.1342: : 874iter [01:38,  9.90iter/s]
Best score: 0.1342: : 875iter [01:38,  9.90iter/s]
Best score: 0.1342: : 876iter [01:38, 10.00iter/s]
Best score: 0.1342: : 876iter [01:39, 10.00iter/s]
Best score: 0.1342: : 877iter [01:39, 10.00iter/s]
Best score: 0.1342: : 878iter [01:39, 10.09iter/s]
Best score: 0.1342: : 878iter [01:39, 10.09iter/s]
Best score: 0.1342: : 879iter [01:39, 10.09iter/s]
Best score: 0.1342: : 880iter [01:39,  9.89iter/s]
Best score: 0.1342: : 880iter [01:39,  9.89iter/s]
Best score: 0.1342: : 881iter [01:39,  9.89iter/s]
Best score: 0.1342: : 881iter [01:39,  9.89iter/s]
Best score: 0.1342: : 882iter [01:39,  9.89iter/s]
Best score: 0.1342: : 883iter [01:39,  9.86iter/s]
Best score: 0.1342: : 883iter [01:39,  9.86iter/s]
Best score: 0.1342: : 884iter [01:39,  9.86iter/s]
Best score: 0.1342: : 885iter [01:39,  9.93iter/s]
Best score: 0.1342: : 885iter [01:39,  9.93iter/s]
Best score: 0.1342: : 886iter [01:39,  9.81iter/s]
Best score: 0.1342: : 886iter [01:40,  9.81iter/s]
Best score: 0.1342: : 887iter [01:40,  9.85iter/s]
Best score: 0.1342: : 887iter [01:40,  9.85iter/s]
Best score: 0.1342: : 888iter [01:40,  9.85iter/s]
Best score: 0.1342: : 889iter [01:40,  9.81iter/s]
Best score: 0.1338: : 889iter [01:40,  9.81iter/s]
Best score: 0.1338: : 890iter [01:40,  9.32iter/s]
Best score: 0.1338: : 890iter [01:40,  9.32iter/s]
Best score: 0.1338: : 891iter [01:40,  9.32iter/s]
Best score: 0.1338: : 892iter [01:40,  9.66iter/s]
Best score: 0.1338: : 892iter [01:40,  9.66iter/s]
Best score: 0.1338: : 893iter [01:40,  9.66iter/s]
Best score: 0.1338: : 894iter [01:40,  9.59iter/s]
Best score: 0.1338: : 894iter [01:40,  9.59iter/s]
Best score: 0.1338: : 895iter [01:40,  9.56iter/s]
Best score: 0.1338: : 895iter [01:41,  9.56iter/s]
Best score: 0.1338: : 896iter [01:41,  9.53iter/s]
Best score: 0.1338: : 896iter [01:41,  9.53iter/s]
Best score: 0.1338: : 897iter [01:41,  9.53iter/s]
Best score: 0.1338: : 897iter [01:41,  9.53iter/s]
Best score: 0.1338: : 898iter [01:41,  9.52iter/s]
Best score: 0.1338: : 898iter [01:41,  9.52iter/s]
Best score: 0.1338: : 899iter [01:41,  9.55iter/s]
[ITER 900] Best: 0.1338
[PARAMS] Current best parameters:
  fx: 567.3784
  fy: 567.4569
  px: 850.5179
  py: 852.5663
  k1: -0.04571573
  k2: 0.01519005
  k3: -0.00585171
  k4: 0.00095283
  p1: 0.00058164
  p2: 0.01038504
  p3: -0.02726257
  p4: 0.00097489
  s1: 0.0146
  s2: -0.0000
  s3: 0.0141
  s4: 0.0012
  tilt_x: -0.0134
  tilt_y: -0.0014
  scale: 2.9812
  rotation: -0.1099

Best score: 0.1338: : 899iter [01:41,  9.55iter/s]
Best score: 0.1338: : 900iter [01:41,  9.38iter/s]
Best score: 0.1338: : 900iter [01:41,  9.38iter/s]
Best score: 0.1338: : 901iter [01:41,  5.23iter/s]
Best score: 0.1338: : 901iter [01:41,  5.23iter/s]
Best score: 0.1338: : 902iter [01:41,  6.05iter/s]
Best score: 0.1338: : 902iter [01:42,  6.05iter/s]
Best score: 0.1338: : 903iter [01:42,  6.77iter/s]
Best score: 0.1338: : 903iter [01:42,  6.77iter/s]
Best score: 0.1338: : 904iter [01:42,  7.34iter/s]
Best score: 0.1338: : 904iter [01:42,  7.34iter/s]
Best score: 0.1338: : 905iter [01:42,  7.34iter/s]
Best score: 0.1338: : 906iter [01:42,  8.36iter/s]
Best score: 0.1338: : 906iter [01:42,  8.36iter/s]
Best score: 0.1338: : 907iter [01:42,  8.68iter/s]
Best score: 0.1338: : 907iter [01:42,  8.68iter/s]
Best score: 0.1338: : 908iter [01:42,  8.68iter/s]
Best score: 0.1338: : 909iter [01:42,  9.05iter/s]
Best score: 0.1338: : 909iter [01:42,  9.05iter/s]
Best score: 0.1338: : 910iter [01:42,  8.93iter/s]
Best score: 0.1338: : 910iter [01:42,  8.93iter/s]
Best score: 0.1338: : 911iter [01:42,  9.05iter/s]
Best score: 0.1338: : 911iter [01:42,  9.05iter/s]
Best score: 0.1338: : 912iter [01:43,  9.05iter/s]
Best score: 0.1338: : 913iter [01:43,  9.54iter/s]
Best score: 0.1338: : 913iter [01:43,  9.54iter/s]
Best score: 0.1338: : 914iter [01:43,  9.54iter/s]
Best score: 0.1338: : 914iter [01:43,  9.54iter/s]
Best score: 0.1338: : 915iter [01:43,  9.54iter/s]
Best score: 0.1338: : 915iter [01:43,  9.54iter/s]
Best score: 0.1338: : 916iter [01:43,  9.59iter/s]
Best score: 0.1338: : 916iter [01:43,  9.59iter/s]
Best score: 0.1338: : 917iter [01:43,  9.61iter/s]
Best score: 0.1338: : 917iter [01:43,  9.61iter/s]
Best score: 0.1338: : 918iter [01:43,  9.71iter/s]
Best score: 0.1338: : 918iter [01:43,  9.71iter/s]
Best score: 0.1337: : 919iter [01:43,  9.71iter/s]
Best score: 0.1337: : 920iter [01:43,  9.69iter/s]
Best score: 0.1337: : 920iter [01:43,  9.69iter/s]
Best score: 0.1337: : 921iter [01:44,  9.69iter/s]
Best score: 0.1337: : 922iter [01:44,  9.67iter/s]
Best score: 0.1337: : 922iter [01:44,  9.67iter/s]
Best score: 0.1337: : 923iter [01:44,  9.55iter/s]
Best score: 0.1337: : 923iter [01:44,  9.55iter/s]
Best score: 0.1337: : 924iter [01:44,  9.52iter/s]
Best score: 0.1337: : 924iter [01:44,  9.52iter/s]
Best score: 0.1337: : 925iter [01:44,  9.60iter/s]
Best score: 0.1337: : 925iter [01:44,  9.60iter/s]
Best score: 0.1337: : 926iter [01:44,  9.60iter/s]
Best score: 0.1337: : 927iter [01:44,  9.75iter/s]
Best score: 0.1337: : 927iter [01:44,  9.75iter/s]
Best score: 0.1337: : 928iter [01:44,  9.67iter/s]
Best score: 0.1337: : 928iter [01:44,  9.67iter/s]
Best score: 0.1337: : 929iter [01:44,  9.71iter/s]
Best score: 0.1334: : 929iter [01:44,  9.71iter/s]
Best score: 0.1334: : 930iter [01:44,  9.39iter/s]
Best score: 0.1334: : 930iter [01:44,  9.39iter/s]
Best score: 0.1334: : 931iter [01:44,  9.25iter/s]
Best score: 0.1334: : 931iter [01:45,  9.25iter/s]
Best score: 0.1334: : 932iter [01:45,  9.18iter/s]
Best score: 0.1334: : 932iter [01:45,  9.18iter/s]
Best score: 0.1334: : 933iter [01:45,  9.27iter/s]
Best score: 0.1334: : 933iter [01:45,  9.27iter/s]
Best score: 0.1334: : 934iter [01:45,  9.43iter/s]
Best score: 0.1334: : 934iter [01:45,  9.43iter/s]
Best score: 0.1334: : 935iter [01:45,  9.37iter/s]
Best score: 0.1334: : 935iter [01:45,  9.37iter/s]
Best score: 0.1334: : 936iter [01:45,  9.19iter/s]
Best score: 0.1334: : 936iter [01:45,  9.19iter/s]
Best score: 0.1334: : 937iter [01:45,  9.22iter/s]
Best score: 0.1334: : 937iter [01:45,  9.22iter/s]
Best score: 0.1334: : 938iter [01:45,  9.05iter/s]
Best score: 0.1334: : 938iter [01:45,  9.05iter/s]
Best score: 0.1334: : 939iter [01:45,  8.94iter/s]
Best score: 0.1334: : 939iter [01:45,  8.94iter/s]
Best score: 0.1334: : 940iter [01:45,  8.59iter/s]
Best score: 0.1334: : 940iter [01:46,  8.59iter/s]
Best score: 0.1334: : 941iter [01:46,  8.64iter/s]
Best score: 0.1334: : 941iter [01:46,  8.64iter/s]
Best score: 0.1334: : 942iter [01:46,  8.80iter/s]
Best score: 0.1334: : 942iter [01:46,  8.80iter/s]
Best score: 0.1334: : 943iter [01:46,  8.82iter/s]
Best score: 0.1334: : 943iter [01:46,  8.82iter/s]
Best score: 0.1334: : 944iter [01:46,  9.01iter/s]
Best score: 0.1334: : 944iter [01:46,  9.01iter/s]
Best score: 0.1334: : 945iter [01:46,  9.24iter/s]
Best score: 0.1334: : 945iter [01:46,  9.24iter/s]
Best score: 0.1334: : 946iter [01:46,  9.21iter/s]
Best score: 0.1334: : 946iter [01:46,  9.21iter/s]
Best score: 0.1334: : 947iter [01:46,  9.28iter/s]
Best score: 0.1334: : 947iter [01:46,  9.28iter/s]
Best score: 0.1334: : 948iter [01:46,  9.32iter/s]
Best score: 0.1334: : 948iter [01:46,  9.32iter/s]
Best score: 0.1334: : 949iter [01:46,  9.47iter/s]
[ITER 950] Best: 0.1334
[PARAMS] Current best parameters:
  fx: 567.6869
  fy: 567.7794
  px: 850.3984
  py: 852.5290
  k1: -0.04751319
  k2: 0.01670161
  k3: -0.00604659
  k4: 0.00083887
  p1: -0.00003083
  p2: 0.00983737
  p3: -0.02691074
Best score: 0.1334: : 949iter [01:47,  9.47iter/s]
Best score: 0.1334: : 950iter [01:47,  9.26iter/s]
  p4: 0.00132514
  s1: 0.0145
  s2: -0.0004
  s3: 0.0144
  s4: 0.0008
  tilt_x: -0.0124
  tilt_y: -0.0021
  scale: 2.9831
  rotation: -0.1099

Best score: 0.1334: : 950iter [01:47,  9.26iter/s]
Best score: 0.1334: : 951iter [01:47,  4.99iter/s]
Best score: 0.1334: : 951iter [01:47,  4.99iter/s]
Best score: 0.1334: : 952iter [01:47,  4.99iter/s]
Best score: 0.1334: : 953iter [01:47,  6.48iter/s]
Best score: 0.1334: : 953iter [01:47,  6.48iter/s]
Best score: 0.1334: : 954iter [01:47,  6.98iter/s]
Best score: 0.1334: : 954iter [01:47,  6.98iter/s]
Best score: 0.1334: : 955iter [01:47,  6.98iter/s]
Best score: 0.1334: : 956iter [01:47,  8.02iter/s]
Best score: 0.1334: : 956iter [01:48,  8.02iter/s]
Best score: 0.1334: : 957iter [01:48,  8.36iter/s]
Best score: 0.1334: : 957iter [01:48,  8.36iter/s]
Best score: 0.1334: : 958iter [01:48,  8.36iter/s]
Best score: 0.1334: : 959iter [01:48,  9.04iter/s]
Best score: 0.1333: : 959iter [01:48,  9.04iter/s]
Best score: 0.1333: : 960iter [01:48,  9.00iter/s]
Best score: 0.1333: : 960iter [01:48,  9.00iter/s]
Best score: 0.1333: : 961iter [01:48,  8.94iter/s]
Best score: 0.1333: : 961iter [01:48,  8.94iter/s]
Best score: 0.1333: : 962iter [01:48,  9.19iter/s]
Best score: 0.1333: : 962iter [01:48,  9.19iter/s]
Best score: 0.1333: : 963iter [01:48,  9.30iter/s]
Best score: 0.1333: : 963iter [01:48,  9.30iter/s]
Best score: 0.1333: : 964iter [01:48,  9.24iter/s]
Best score: 0.1333: : 964iter [01:48,  9.24iter/s]
Best score: 0.1333: : 965iter [01:48,  9.14iter/s]
Best score: 0.1333: : 965iter [01:49,  9.14iter/s]
Best score: 0.1333: : 966iter [01:49,  8.99iter/s]
Best score: 0.1333: : 966iter [01:49,  8.99iter/s]
Best score: 0.1333: : 967iter [01:49,  8.94iter/s]
Best score: 0.1333: : 967iter [01:49,  8.94iter/s]
Best score: 0.1333: : 968iter [01:49,  9.15iter/s]
Best score: 0.1333: : 968iter [01:49,  9.15iter/s]
Best score: 0.1333: : 969iter [01:49,  9.33iter/s]
Best score: 0.1332: : 969iter [01:49,  9.33iter/s]
Best score: 0.1332: : 970iter [01:49,  9.16iter/s]
Best score: 0.1332: : 970iter [01:49,  9.16iter/s]
Best score: 0.1332: : 971iter [01:49,  9.32iter/s]
Best score: 0.1332: : 971iter [01:49,  9.32iter/s]
Best score: 0.1332: : 972iter [01:49,  9.50iter/s]
Best score: 0.1332: : 972iter [01:49,  9.50iter/s]
Best score: 0.1332: : 973iter [01:49,  9.24iter/s]
Best score: 0.1332: : 973iter [01:49,  9.24iter/s]
Best score: 0.1332: : 974iter [01:49,  9.16iter/s]
Best score: 0.1332: : 974iter [01:50,  9.16iter/s]
Best score: 0.1332: : 975iter [01:50,  9.38iter/s]
Best score: 0.1332: : 975iter [01:50,  9.38iter/s]
Best score: 0.1332: : 976iter [01:50,  9.56iter/s]
Best score: 0.1332: : 976iter [01:50,  9.56iter/s]
Best score: 0.1332: : 977iter [01:50,  9.56iter/s]
Best score: 0.1332: : 978iter [01:50,  9.86iter/s]
Best score: 0.1332: : 978iter [01:50,  9.86iter/s]
Best score: 0.1332: : 979iter [01:50,  9.86iter/s]
Best score: 0.1332: : 980iter [01:50,  9.72iter/s]
Best score: 0.1332: : 980iter [01:50,  9.72iter/s]
Best score: 0.1332: : 981iter [01:50,  9.72iter/s]
Best score: 0.1332: : 982iter [01:50,  9.93iter/s]
Best score: 0.1332: : 982iter [01:50,  9.93iter/s]
Best score: 0.1332: : 983iter [01:50,  9.85iter/s]
Best score: 0.1332: : 983iter [01:50,  9.85iter/s]
Best score: 0.1332: : 984iter [01:50,  9.84iter/s]
Best score: 0.1332: : 984iter [01:51,  9.84iter/s]
Best score: 0.1332: : 985iter [01:51,  9.67iter/s]
Best score: 0.1332: : 985iter [01:51,  9.67iter/s]
Best score: 0.1332: : 986iter [01:51,  9.67iter/s]
Best score: 0.1332: : 987iter [01:51,  9.80iter/s]
Best score: 0.1332: : 987iter [01:51,  9.80iter/s]
Best score: 0.1332: : 988iter [01:51,  9.80iter/s]
Best score: 0.1332: : 989iter [01:51,  9.97iter/s]
Best score: 0.1331: : 989iter [01:51,  9.97iter/s]
Best score: 0.1331: : 990iter [01:51,  9.81iter/s]
Best score: 0.1331: : 990iter [01:51,  9.81iter/s]
Best score: 0.1331: : 991iter [01:51,  9.81iter/s]
Best score: 0.1331: : 992iter [01:51,  9.88iter/s]
Best score: 0.1331: : 992iter [01:51,  9.88iter/s]
Best score: 0.1331: : 993iter [01:51,  9.88iter/s]
Best score: 0.1331: : 993iter [01:51,  9.88iter/s]
Best score: 0.1331: : 994iter [01:51,  9.73iter/s]
Best score: 0.1331: : 994iter [01:52,  9.73iter/s]
Best score: 0.1331: : 995iter [01:52,  9.72iter/s]
Best score: 0.1331: : 995iter [01:52,  9.72iter/s]
Best score: 0.1331: : 996iter [01:52,  9.57iter/s]
Best score: 0.1331: : 996iter [01:52,  9.57iter/s]
Best score: 0.1331: : 997iter [01:52,  9.57iter/s]
Best score: 0.1331: : 997iter [01:52,  9.57iter/s]
Best score: 0.1331: : 998iter [01:52,  9.57iter/s]
Best score: 0.1331: : 998iter [01:52,  9.57iter/s]
Best score: 0.1331: : 999iter [01:52,  9.67iter/s]
[ITER 1000] Best: 0.1329
[PARAMS] Current best parameters:
  fx: 567.9922
  fy: 568.0393
  px: 850.0790
  py: 852.4145
  k1: -0.04801887
  k2: 0.01730976
  k3: -0.00662526
  k4: 0.00103059
  p1: -0.00130538
  p2: 0.00766941
  p3: -0.02473475
  p4: 0.00168813
  s1: 0.0143
  s2: -0.0009
  s3: 0.0130
  s4: 0.0006
  tilt_x: -0.0103
  tilt_y: -0.0048
  scale: 2.9831
  rotation: -0.1148

Best score: 0.1329: : 999iter [01:52,  9.67iter/s]
Best score: 0.1329: : 1000iter [01:52,  9.53iter/s]
Best score: 0.1329: : 1000iter [01:52,  9.53iter/s]
Best score: 0.1329: : 1001iter [01:52,  5.21iter/s]
Best score: 0.1329: : 1001iter [01:53,  5.21iter/s]
Best score: 0.1329: : 1002iter [01:53,  5.21iter/s]
Best score: 0.1329: : 1003iter [01:53,  6.72iter/s]
Best score: 0.1329: : 1003iter [01:53,  6.72iter/s]
Best score: 0.1329: : 1004iter [01:53,  6.72iter/s]
Best score: 0.1329: : 1005iter [01:53,  7.73iter/s]
Best score: 0.1329: : 1005iter [01:53,  7.73iter/s]
Best score: 0.1329: : 1006iter [01:53,  7.73iter/s]
Best score: 0.1329: : 1007iter [01:53,  8.59iter/s]
Best score: 0.1329: : 1007iter [01:53,  8.59iter/s]
Best score: 0.1329: : 1008iter [01:53,  8.59iter/s]
Best score: 0.1329: : 1009iter [01:53,  9.18iter/s]
Best score: 0.1329: : 1009iter [01:53,  9.18iter/s]
Best score: 0.1329: : 1010iter [01:53,  9.26iter/s]
Best score: 0.1329: : 1010iter [01:53,  9.26iter/s]
Best score: 0.1329: : 1011iter [01:54,  9.26iter/s]
Best score: 0.1329: : 1012iter [01:54,  9.66iter/s]
Best score: 0.1329: : 1012iter [01:54,  9.66iter/s]
Best score: 0.1329: : 1013iter [01:54,  9.66iter/s]
Best score: 0.1329: : 1014iter [01:54, 10.05iter/s]
Best score: 0.1329: : 1014iter [01:54, 10.05iter/s]
Best score: 0.1329: : 1015iter [01:54, 10.05iter/s]
Best score: 0.1329: : 1016iter [01:54, 10.15iter/s]
Best score: 0.1329: : 1016iter [01:54, 10.15iter/s]
Best score: 0.1329: : 1017iter [01:54, 10.15iter/s]
Best score: 0.1329: : 1018iter [01:54, 10.21iter/s]
Best score: 0.1329: : 1018iter [01:54, 10.21iter/s]
Best score: 0.1329: : 1019iter [01:54, 10.21iter/s]
Best score: 0.1329: : 1020iter [01:54, 10.27iter/s]
Best score: 0.1329: : 1020iter [01:54, 10.27iter/s]
Best score: 0.1329: : 1021iter [01:54, 10.27iter/s]
Best score: 0.1329: : 1022iter [01:54, 10.49iter/s]
Best score: 0.1329: : 1022iter [01:55, 10.49iter/s]
Best score: 0.1329: : 1023iter [01:55, 10.49iter/s]
Best score: 0.1329: : 1024iter [01:55, 10.61iter/s]
Best score: 0.1329: : 1024iter [01:55, 10.61iter/s]
Best score: 0.1329: : 1025iter [01:55, 10.61iter/s]
Best score: 0.1329: : 1026iter [01:55, 10.65iter/s]
Best score: 0.1329: : 1026iter [01:55, 10.65iter/s]
Best score: 0.1329: : 1027iter [01:55, 10.65iter/s]
Best score: 0.1329: : 1028iter [01:55, 10.57iter/s]
Best score: 0.1329: : 1028iter [01:55, 10.57iter/s]
Best score: 0.1329: : 1029iter [01:55, 10.57iter/s]
Best score: 0.1329: : 1030iter [01:55, 10.40iter/s]
Best score: 0.1329: : 1030iter [01:55, 10.40iter/s]
Best score: 0.1329: : 1031iter [01:55, 10.40iter/s]
Best score: 0.1329: : 1032iter [01:55, 10.50iter/s]
Best score: 0.1329: : 1032iter [01:56, 10.50iter/s]
Best score: 0.1329: : 1033iter [01:56, 10.50iter/s]
Best score: 0.1329: : 1034iter [01:56, 10.43iter/s]
Best score: 0.1329: : 1034iter [01:56, 10.43iter/s]
Best score: 0.1329: : 1035iter [01:56, 10.43iter/s]
Best score: 0.1329: : 1036iter [01:56, 10.15iter/s]
Best score: 0.1329: : 1036iter [01:56, 10.15iter/s]
Best score: 0.1329: : 1037iter [01:56, 10.15iter/s]
Best score: 0.1329: : 1038iter [01:56,  9.99iter/s]
Best score: 0.1329: : 1038iter [01:56,  9.99iter/s]
Best score: 0.1329: : 1039iter [01:56,  9.99iter/s]
Best score: 0.1329: : 1040iter [01:56,  9.67iter/s]
Best score: 0.1329: : 1040iter [01:56,  9.67iter/s]
Best score: 0.1329: : 1041iter [01:56,  9.64iter/s]
Best score: 0.1329: : 1041iter [01:56,  9.64iter/s]
Best score: 0.1329: : 1042iter [01:57,  9.64iter/s]
Best score: 0.1329: : 1043iter [01:57,  9.74iter/s]
Best score: 0.1329: : 1043iter [01:57,  9.74iter/s]
Best score: 0.1329: : 1044iter [01:57,  9.70iter/s]
Best score: 0.1329: : 1044iter [01:57,  9.70iter/s]
Best score: 0.1329: : 1045iter [01:57,  9.74iter/s]
Best score: 0.1329: : 1045iter [01:57,  9.74iter/s]
Best score: 0.1329: : 1046iter [01:57,  9.80iter/s]
Best score: 0.1329: : 1046iter [01:57,  9.80iter/s]
Best score: 0.1329: : 1047iter [01:57,  9.80iter/s]
Best score: 0.1329: : 1048iter [01:57, 10.00iter/s]
Best score: 0.1329: : 1048iter [01:57, 10.00iter/s]
[ITER 1050] Best: 0.1329
[PARAMS] Current best parameters:
  fx: 568.8889
  fy: 568.9469
  px: 850.0303
  py: 852.5218
  k1: -0.04793614
  k2: 0.01536347
  k3: -0.00514790
  k4: 0.00067311
  p1: -0.00092836
  p2: 0.00752945
  p3: -0.02573576
  p4: 0.00151520
  s1: 0.0153
  s2: -0.0006
  s3: 0.0138
  s4: 0.0008
  tilt_x: -0.0104
  tilt_y: -0.0048
  scale: 2.9821
  rotation: -0.1116

Best score: 0.1329: : 1049iter [01:57, 10.00iter/s]
Best score: 0.1329: : 1050iter [01:57,  9.93iter/s]
Best score: 0.1329: : 1050iter [01:58,  9.93iter/s]
Best score: 0.1329: : 1051iter [01:58,  6.13iter/s]
Best score: 0.1329: : 1051iter [01:58,  6.13iter/s]
Best score: 0.1329: : 1052iter [01:58,  6.13iter/s]
Best score: 0.1329: : 1053iter [01:58,  7.14iter/s]
Best score: 0.1329: : 1053iter [01:58,  7.14iter/s]
Best score: 0.1329: : 1054iter [01:58,  7.53iter/s]
Best score: 0.1329: : 1054iter [01:58,  7.53iter/s]
Best score: 0.1329: : 1055iter [01:58,  7.93iter/s]
Best score: 0.1329: : 1055iter [01:58,  7.93iter/s]
Best score: 0.1329: : 1056iter [01:58,  8.20iter/s]
Best score: 0.1329: : 1056iter [01:58,  8.20iter/s]
Best score: 0.1329: : 1057iter [01:58,  8.51iter/s]
Best score: 0.1329: : 1057iter [01:58,  8.51iter/s]
Best score: 0.1329: : 1058iter [01:58,  8.75iter/s]
Best score: 0.1329: : 1058iter [01:58,  8.75iter/s]
Best score: 0.1329: : 1059iter [01:59,  8.75iter/s]
Best score: 0.1329: : 1060iter [01:59,  9.01iter/s]
Best score: 0.1329: : 1060iter [01:59,  9.01iter/s]
Best score: 0.1329: : 1061iter [01:59,  9.10iter/s]
Best score: 0.1329: : 1061iter [01:59,  9.10iter/s]
Best score: 0.1329: : 1062iter [01:59,  8.94iter/s]
Best score: 0.1329: : 1062iter [01:59,  8.94iter/s]
Best score: 0.1329: : 1063iter [01:59,  8.98iter/s]
Best score: 0.1329: : 1063iter [01:59,  8.98iter/s]
Best score: 0.1329: : 1064iter [01:59,  9.18iter/s]
Best score: 0.1329: : 1064iter [01:59,  9.18iter/s]
Best score: 0.1329: : 1065iter [01:59,  9.18iter/s]
Best score: 0.1329: : 1066iter [01:59,  9.56iter/s]
Best score: 0.1329: : 1066iter [01:59,  9.56iter/s]
Best score: 0.1329: : 1067iter [01:59,  9.56iter/s]
Best score: 0.1329: : 1068iter [01:59,  9.99iter/s]
Best score: 0.1329: : 1068iter [02:00,  9.99iter/s]
Best score: 0.1327: : 1069iter [02:00,  9.99iter/s]
Best score: 0.1327: : 1070iter [02:00,  9.97iter/s]
Best score: 0.1327: : 1070iter [02:00,  9.97iter/s]
Best score: 0.1327: : 1071iter [02:00,  9.97iter/s]
Best score: 0.1327: : 1072iter [02:00, 10.26iter/s]
Best score: 0.1327: : 1072iter [02:00, 10.26iter/s]
Best score: 0.1327: : 1073iter [02:00, 10.26iter/s]
Best score: 0.1327: : 1074iter [02:00, 10.49iter/s]
Best score: 0.1327: : 1074iter [02:00, 10.49iter/s]
Best score: 0.1327: : 1075iter [02:00, 10.49iter/s]
Best score: 0.1327: : 1076iter [02:00, 10.54iter/s]
Best score: 0.1327: : 1076iter [02:00, 10.54iter/s]
Best score: 0.1327: : 1077iter [02:00, 10.54iter/s]
Best score: 0.1327: : 1078iter [02:00, 10.54iter/s]
Best score: 0.1327: : 1078iter [02:00, 10.54iter/s]
Best score: 0.1325: : 1079iter [02:01, 10.54iter/s]
Best score: 0.1325: : 1080iter [02:01, 10.36iter/s]
Best score: 0.1325: : 1080iter [02:01, 10.36iter/s]
Best score: 0.1325: : 1081iter [02:01, 10.36iter/s]
Best score: 0.1325: : 1082iter [02:01, 10.38iter/s]
Best score: 0.1325: : 1082iter [02:01, 10.38iter/s]
Best score: 0.1325: : 1083iter [02:01, 10.38iter/s]
Best score: 0.1325: : 1084iter [02:01, 10.02iter/s]
Best score: 0.1325: : 1084iter [02:01, 10.02iter/s]
Best score: 0.1325: : 1085iter [02:01, 10.02iter/s]
Best score: 0.1325: : 1086iter [02:01, 10.06iter/s]
Best score: 0.1325: : 1086iter [02:01, 10.06iter/s]
Best score: 0.1325: : 1087iter [02:01, 10.06iter/s]
Best score: 0.1325: : 1088iter [02:01, 10.01iter/s]
Best score: 0.1325: : 1088iter [02:01, 10.01iter/s]
Best score: 0.1324: : 1089iter [02:02, 10.01iter/s]
Best score: 0.1324: : 1090iter [02:02,  9.75iter/s]
Best score: 0.1324: : 1090iter [02:02,  9.75iter/s]
Best score: 0.1324: : 1091iter [02:02,  9.75iter/s]
Best score: 0.1324: : 1092iter [02:02,  9.80iter/s]
Best score: 0.1324: : 1092iter [02:02,  9.80iter/s]
Best score: 0.1324: : 1093iter [02:02,  9.80iter/s]
Best score: 0.1324: : 1094iter [02:02,  9.81iter/s]
Best score: 0.1324: : 1094iter [02:02,  9.81iter/s]
Best score: 0.1324: : 1095iter [02:02,  9.72iter/s]
Best score: 0.1324: : 1095iter [02:02,  9.72iter/s]
Best score: 0.1324: : 1096iter [02:02,  9.61iter/s]
Best score: 0.1324: : 1096iter [02:02,  9.61iter/s]
Best score: 0.1324: : 1097iter [02:02,  9.58iter/s]
Best score: 0.1324: : 1097iter [02:02,  9.58iter/s]
Best score: 0.1324: : 1098iter [02:02,  9.59iter/s]
Best score: 0.1324: : 1098iter [02:03,  9.59iter/s]
Best score: 0.1324: : 1099iter [02:03,  9.68iter/s]
[ITER 1100] Best: 0.1322
[PARAMS] Current best parameters:
  fx: 569.6167
  fy: 569.5869
  px: 850.0998
  py: 851.9991
  k1: -0.04939068
  k2: 0.01722607
  k3: -0.00659289
  k4: 0.00107005
  p1: -0.00396842
  p2: 0.00461568
  p3: -0.02042027
  p4: 0.00231935
  s1: 0.0129
  s2: -0.0013
  s3: 0.0108
  s4: -0.0000
  tilt_x: -0.0069
  tilt_y: -0.0111
  scale: 2.9821
  rotation: -0.1130

Best score: 0.1322: : 1099iter [02:03,  9.68iter/s]
Best score: 0.1322: : 1100iter [02:03,  9.38iter/s]
Best score: 0.1322: : 1100iter [02:03,  8.91iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.132230 -> 0.132223

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 100,936 → 95,075 within 3500mm ring
  Ring polish did not improve score: 0.132223
  Success: True
  Tangential distortion: p1=-0.003969, p2=0.004615, p3=-0.020420, p4=0.002320
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1322
[✅] Parameters copied to: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 569.6167394100272
    fy: 569.5868990282127
    px: 850.0997686814623
    py: 851.999139652521
    k1: -0.04938802975941721
    k2: 0.017228190234973084
    k3: -0.006594079791183041
    k4: 0.001069491864064087
    p1: -0.00396877882120261
    p2: 0.004615243369529096
    p3: -0.020419731604197792
    p4: 0.0023197176489816117
    scale: 2.982128584868127
    rotation: -0.11299045831492341
    mmpp: 3.4143674107559225

[🔄] Converting 1414 frames to PTD, creating debug images for 400 calibration frames...

📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]

   🔄 1500:   0%|          | 0/326 [00:00<?, ?frame/s]

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   🔄 1500:  60%|██████    | 197/326 [00:01<00:00, 177.55frame/s]

   🔄 1500:  66%|██████▌   | 215/326 [00:01<00:00, 177.17frame/s]

   🔄 1500:  71%|███████▏  | 233/326 [00:01<00:00, 177.41frame/s]

   🔄 1500:  77%|███████▋  | 252/326 [00:01<00:00, 178.21frame/s]

   🔄 1500:  83%|████████▎ | 270/326 [00:01<00:00, 178.57frame/s]

   🔄 1500:  89%|████████▊ | 289/326 [00:01<00:00, 179.17frame/s]

   🔄 1500:  94%|█████████▍| 308/326 [00:01<00:00, 179.72frame/s]

   🔄 1500: 100%|██████████| 326/326 [00:01<00:00, 178.62frame/s]

                                                                 
📁 Rigs:  25%|██▌       | 1/4 [00:03<00:10,  3.34s/rig]

   🔄 1000:   0%|          | 0/197 [00:00<?, ?frame/s]

   🔄 1000:  10%|▉         | 19/197 [00:00<00:00, 183.60frame/s]

   🔄 1000:  19%|█▉        | 38/197 [00:00<00:00, 183.89frame/s]

   🔄 1000:  29%|██▉       | 57/197 [00:00<00:01, 103.62frame/s]

   🔄 1000:  39%|███▊      | 76/197 [00:00<00:00, 124.79frame/s]

   🔄 1000:  48%|████▊     | 94/197 [00:00<00:00, 139.68frame/s]

   🔄 1000:  57%|█████▋    | 113/197 [00:00<00:00, 151.66frame/s]

   🔄 1000:  67%|██████▋   | 132/197 [00:00<00:00, 160.52frame/s]

   🔄 1000:  77%|███████▋  | 151/197 [00:01<00:00, 167.64frame/s]

   🔄 1000:  86%|████████▋ | 170/197 [00:01<00:00, 172.39frame/s]

   🔄 1000:  96%|█████████▌| 189/197 [00:01<00:00, 176.52frame/s]

                                                                 
📁 Rigs:  50%|█████     | 2/4 [00:05<00:05,  2.51s/rig]

   🔄 2000L:   0%|          | 0/464 [00:00<?, ?frame/s]

   🔄 2000L:   4%|▍         | 18/464 [00:00<00:02, 170.80frame/s]

   🔄 2000L:   8%|▊         | 36/464 [00:00<00:02, 171.77frame/s]

   🔄 2000L:  12%|█▏        | 54/464 [00:00<00:02, 174.29frame/s]

   🔄 2000L:  16%|█▌        | 72/464 [00:00<00:02, 176.14frame/s]

   🔄 2000L:  20%|█▉        | 91/464 [00:00<00:02, 177.76frame/s]

   🔄 2000L:  24%|██▎       | 110/464 [00:00<00:01, 178.61frame/s]

   🔄 2000L:  28%|██▊       | 129/464 [00:00<00:01, 179.51frame/s]

   🔄 2000L:  32%|███▏      | 148/464 [00:00<00:01, 180.02frame/s]

   🔄 2000L:  36%|███▌      | 167/464 [00:00<00:01, 179.09frame/s]

   🔄 2000L:  40%|███▉      | 185/464 [00:01<00:01, 177.89frame/s]

   🔄 2000L:  44%|████▍     | 203/464 [00:01<00:01, 178.06frame/s]

   🔄 2000L:  48%|████▊     | 221/464 [00:01<00:01, 178.54frame/s]

   🔄 2000L:  52%|█████▏    | 239/464 [00:01<00:01, 178.76frame/s]

   🔄 2000L:  55%|█████▌    | 257/464 [00:01<00:01, 178.75frame/s]

   🔄 2000L:  59%|█████▉    | 276/464 [00:01<00:01, 179.28frame/s]

   🔄 2000L:  63%|██████▎   | 294/464 [00:01<00:00, 179.35frame/s]

   🔄 2000L:  67%|██████▋   | 312/464 [00:01<00:00, 179.34frame/s]

   🔄 2000L:  71%|███████   | 330/464 [00:01<00:00, 178.51frame/s]

   🔄 2000L:  75%|███████▌  | 348/464 [00:01<00:00, 177.93frame/s]

   🔄 2000L:  79%|███████▉  | 367/464 [00:02<00:00, 178.74frame/s]

   🔄 2000L:  83%|████████▎ | 386/464 [00:02<00:00, 179.77frame/s]

   🔄 2000L:  87%|████████▋ | 405/464 [00:02<00:00, 180.72frame/s]

   🔄 2000L:  91%|█████████▏| 424/464 [00:02<00:00, 181.16frame/s]

   🔄 2000L:  95%|█████████▌| 443/464 [00:02<00:00, 180.07frame/s]

   🔄 2000L: 100%|█████████▉| 462/464 [00:02<00:00, 178.34frame/s]

                                                                  
📁 Rigs:  75%|███████▌  | 3/4 [00:10<00:03,  3.57s/rig]

   🔄 2000R:   0%|          | 0/427 [00:00<?, ?frame/s]

   🔄 2000R:   4%|▍         | 18/427 [00:00<00:02, 174.52frame/s]

   🔄 2000R:   8%|▊         | 36/427 [00:00<00:02, 177.16frame/s]

   🔄 2000R:  13%|█▎        | 55/427 [00:00<00:02, 179.50frame/s]

   🔄 2000R:  17%|█▋        | 74/427 [00:00<00:01, 180.67frame/s]

   🔄 2000R:  22%|██▏       | 93/427 [00:00<00:01, 180.20frame/s]

   🔄 2000R:  26%|██▌       | 112/427 [00:00<00:01, 178.92frame/s]

   🔄 2000R:  30%|███       | 130/427 [00:00<00:01, 178.35frame/s]

   🔄 2000R:  35%|███▍      | 148/427 [00:00<00:01, 178.56frame/s]

   🔄 2000R:  39%|███▉      | 166/427 [00:00<00:01, 178.93frame/s]

   🔄 2000R:  43%|████▎     | 185/427 [00:01<00:01, 179.69frame/s]

   🔄 2000R:  48%|████▊     | 204/427 [00:01<00:01, 180.14frame/s]

   🔄 2000R:  52%|█████▏    | 223/427 [00:01<00:01, 180.16frame/s]

   🔄 2000R:  57%|█████▋    | 242/427 [00:01<00:01, 180.65frame/s]

   🔄 2000R:  61%|██████    | 261/427 [00:01<00:00, 180.21frame/s]

   🔄 2000R:  66%|██████▌   | 280/427 [00:01<00:00, 179.52frame/s]

   🔄 2000R:  70%|██████▉   | 298/427 [00:01<00:00, 179.09frame/s]

   🔄 2000R:  74%|███████▍  | 317/427 [00:01<00:00, 179.89frame/s]

   🔄 2000R:  79%|███████▊  | 336/427 [00:01<00:00, 181.71frame/s]

   🔄 2000R:  83%|████████▎ | 355/427 [00:01<00:00, 182.52frame/s]

   🔄 2000R:  88%|████████▊ | 374/427 [00:02<00:00, 182.55frame/s]

   🔄 2000R:  92%|█████████▏| 393/427 [00:02<00:00, 181.79frame/s]

   🔄 2000R:  96%|█████████▋| 412/427 [00:02<00:00, 179.16frame/s]

                                                                  
📁 Rigs: 100%|██████████| 4/4 [00:14<00:00,  3.79s/rig]
📁 Rigs: 100%|██████████| 4/4 [00:14<00:00,  3.55s/rig]
[✅] Saved PTDs: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/4_undistorted/output/ptd

📁 Rigs:   0%|          | 0/4 [00:00<?, ?rig/s]

   🔄 1500:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 1500:  18%|█▊        | 18/100 [00:00<00:00, 175.71frame/s]

   🔄 1500:  36%|███▌      | 36/100 [00:00<00:00, 176.29frame/s]

   🔄 1500:  54%|█████▍    | 54/100 [00:00<00:00, 176.26frame/s]

   🔄 1500:  72%|███████▏  | 72/100 [00:00<00:00, 176.49frame/s]

   🔄 1500:  90%|█████████ | 90/100 [00:00<00:00, 177.02frame/s]

                                                                
📁 Rigs:  25%|██▌       | 1/4 [00:00<00:02,  1.10rig/s]

   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 1000:  19%|█▉        | 19/100 [00:00<00:00, 181.21frame/s]

   🔄 1000:  38%|███▊      | 38/100 [00:00<00:00, 179.96frame/s]

   🔄 1000:  56%|█████▌    | 56/100 [00:00<00:00, 178.88frame/s]

   🔄 1000:  75%|███████▌  | 75/100 [00:00<00:00, 179.77frame/s]

   🔄 1000:  94%|█████████▍| 94/100 [00:00<00:00, 180.28frame/s]

                                                                
📁 Rigs:  50%|█████     | 2/4 [00:01<00:01,  1.00rig/s]

   🔄 2000L:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 2000L:  18%|█▊        | 18/100 [00:00<00:00, 175.93frame/s]

   🔄 2000L:  36%|███▌      | 36/100 [00:00<00:00, 176.87frame/s]

   🔄 2000L:  54%|█████▍    | 54/100 [00:00<00:00, 176.75frame/s]

   🔄 2000L:  72%|███████▏  | 72/100 [00:00<00:00, 177.01frame/s]

   🔄 2000L:  90%|█████████ | 90/100 [00:00<00:00, 177.84frame/s]

                                                                 
📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.04rig/s]

   🔄 2000R:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 2000R:  18%|█▊        | 18/100 [00:00<00:00, 173.72frame/s]

   🔄 2000R:  36%|███▌      | 36/100 [00:00<00:00, 174.57frame/s]

   🔄 2000R:  54%|█████▍    | 54/100 [00:00<00:00, 175.94frame/s]

   🔄 2000R:  72%|███████▏  | 72/100 [00:00<00:00, 176.37frame/s]

   🔄 2000R:  91%|█████████ | 91/100 [00:00<00:00, 178.97frame/s]

                                                                 
📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.01s/rig]
📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.01rig/s]
[✅] Saved PTDs: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/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%|▎         | 87075/3240000 [00:00<00:03, 870746.68px/s]
📤 Writing LUT:   6%|▌         | 179777/3240000 [00:00<00:03, 903842.75px/s]
📤 Writing LUT:   9%|▊         | 275986/3240000 [00:00<00:03, 930438.94px/s]
📤 Writing LUT:  11%|█▏        | 370389/3240000 [00:00<00:03, 935803.11px/s]
📤 Writing LUT:  14%|█▍        | 463970/3240000 [00:00<00:02, 927328.52px/s]
📤 Writing LUT:  17%|█▋        | 558522/3240000 [00:00<00:02, 933473.07px/s]
📤 Writing LUT:  20%|██        | 651882/3240000 [00:00<00:02, 927800.99px/s]
📤 Writing LUT:  23%|██▎       | 744676/3240000 [00:00<00:02, 926541.35px/s]
📤 Writing LUT:  26%|██▌       | 837339/3240000 [00:00<00:02, 922251.22px/s]
📤 Writing LUT:  29%|██▊       | 929572/3240000 [00:01<00:02, 917316.53px/s]
📤 Writing LUT:  32%|███▏      | 1021312/3240000 [00:01<00:02, 915486.68px/s]
📤 Writing LUT:  34%|███▍      | 1112866/3240000 [00:01<00:02, 911437.15px/s]
📤 Writing LUT:  37%|███▋      | 1209968/3240000 [00:01<00:02, 929334.20px/s]
📤 Writing LUT:  40%|████      | 1302920/3240000 [00:01<00:02, 923167.55px/s]
📤 Writing LUT:  43%|████▎     | 1400082/3240000 [00:01<00:01, 937641.03px/s]
📤 Writing LUT:  46%|████▌     | 1495729/3240000 [00:01<00:01, 943271.35px/s]
📤 Writing LUT:  49%|████▉     | 1590703/3240000 [00:01<00:01, 945207.70px/s]
📤 Writing LUT:  52%|█████▏    | 1685240/3240000 [00:01<00:01, 943958.01px/s]
📤 Writing LUT:  55%|█████▍    | 1779647/3240000 [00:01<00:01, 942388.66px/s]
📤 Writing LUT:  58%|█████▊    | 1876593/3240000 [00:02<00:01, 950487.65px/s]
📤 Writing LUT:  61%|██████    | 1972705/3240000 [00:02<00:01, 953670.58px/s]
📤 Writing LUT:  64%|██████▍   | 2068079/3240000 [00:02<00:01, 948318.18px/s]
📤 Writing LUT:  67%|██████▋   | 2165740/3240000 [00:02<00:01, 956763.02px/s]
📤 Writing LUT:  70%|██████▉   | 2261429/3240000 [00:02<00:01, 947585.77px/s]
📤 Writing LUT:  73%|███████▎  | 2356213/3240000 [00:02<00:00, 939479.12px/s]
📤 Writing LUT:  76%|███████▌  | 2450188/3240000 [00:02<00:00, 933428.49px/s]
📤 Writing LUT:  79%|███████▊  | 2543552/3240000 [00:02<00:00, 928558.06px/s]
📤 Writing LUT:  81%|████████▏ | 2640565/3240000 [00:02<00:00, 940866.76px/s]
📤 Writing LUT:  84%|████████▍ | 2734858/3240000 [00:02<00:00, 941477.66px/s]
📤 Writing LUT:  87%|████████▋ | 2829708/3240000 [00:03<00:00, 943568.41px/s]
📤 Writing LUT:  90%|█████████ | 2924531/3240000 [00:03<00:00, 944959.50px/s]
📤 Writing LUT:  93%|█████████▎| 3019038/3240000 [00:03<00:00, 944585.36px/s]
📤 Writing LUT:  96%|█████████▌| 3117613/3240000 [00:03<00:00, 956901.89px/s]
📤 Writing LUT:  99%|█████████▉| 3213649/3240000 [00:03<00:00, 957935.03px/s]
📤 Writing LUT: 100%|██████████| 3240000/3240000 [00:03<00:00, 938236.17px/s]
[✅] LUT written to: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/4_undistorted/output/LUT.lut
[✅] LUT copied to: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/MDCP3D-SL-009_W043(6)_unknown_2025-05-14_P3D3.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)

📁 Rigs (heatmap):   0%|          | 0/4 [00:00<?, ?rig/s]

   🔥 1500:   0%|          | 0/326 [00:00<?, ?frame/s]

   🔥 1500:   3%|▎         | 9/326 [00:00<00:03, 82.80frame/s]

   🔥 1500:   6%|▌         | 19/326 [00:00<00:03, 89.12frame/s]

   🔥 1500:   9%|▉         | 29/326 [00:00<00:03, 90.81frame/s]

   🔥 1500:  12%|█▏        | 39/326 [00:00<00:03, 91.57frame/s]

   🔥 1500:  15%|█▌        | 49/326 [00:00<00:03, 89.83frame/s]

   🔥 1500:  18%|█▊        | 58/326 [00:00<00:03, 87.78frame/s]

   🔥 1500:  21%|██        | 67/326 [00:00<00:03, 85.64frame/s]

   🔥 1500:  23%|██▎       | 76/326 [00:00<00:02, 83.94frame/s]

   🔥 1500:  26%|██▌       | 85/326 [00:00<00:02, 82.64frame/s]

   🔥 1500:  29%|██▉       | 94/326 [00:01<00:02, 81.94frame/s]

   🔥 1500:  32%|███▏      | 103/326 [00:01<00:02, 81.40frame/s]

   🔥 1500:  34%|███▍      | 112/326 [00:01<00:02, 81.64frame/s]

   🔥 1500:  37%|███▋      | 121/326 [00:01<00:02, 80.55frame/s]

   🔥 1500:  40%|███▉      | 130/326 [00:01<00:02, 80.25frame/s]

   🔥 1500:  43%|████▎     | 139/326 [00:01<00:02, 79.65frame/s]

   🔥 1500:  45%|████▌     | 148/326 [00:01<00:02, 80.48frame/s]

   🔥 1500:  48%|████▊     | 157/326 [00:01<00:02, 82.54frame/s]

   🔥 1500:  51%|█████     | 166/326 [00:01<00:01, 83.85frame/s]

   🔥 1500:  54%|█████▎    | 175/326 [00:02<00:01, 84.46frame/s]

   🔥 1500:  56%|█████▋    | 184/326 [00:02<00:01, 83.93frame/s]

   🔥 1500:  59%|█████▉    | 193/326 [00:02<00:01, 83.30frame/s]

   🔥 1500:  62%|██████▏   | 202/326 [00:02<00:01, 82.46frame/s]

   🔥 1500:  65%|██████▍   | 211/326 [00:02<00:01, 81.46frame/s]

   🔥 1500:  67%|██████▋   | 220/326 [00:02<00:01, 79.57frame/s]

   🔥 1500:  70%|███████   | 229/326 [00:02<00:01, 79.77frame/s]

   🔥 1500:  73%|███████▎  | 238/326 [00:02<00:01, 81.42frame/s]

   🔥 1500:  76%|███████▌  | 247/326 [00:02<00:00, 81.49frame/s]

   🔥 1500:  79%|███████▊  | 256/326 [00:03<00:00, 82.46frame/s]

   🔥 1500:  82%|████████▏ | 266/326 [00:03<00:00, 85.38frame/s]

   🔥 1500:  85%|████████▍ | 276/326 [00:03<00:00, 87.38frame/s]

   🔥 1500:  88%|████████▊ | 286/326 [00:03<00:00, 88.66frame/s]

   🔥 1500:  91%|█████████ | 296/326 [00:03<00:00, 89.80frame/s]

   🔥 1500:  94%|█████████▍| 306/326 [00:03<00:00, 90.91frame/s]

   🔥 1500:  97%|█████████▋| 316/326 [00:03<00:00, 91.63frame/s]

   🔥 1500: 100%|██████████| 326/326 [00:03<00:00, 91.74frame/s]

                                                                [✅] 1500: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/1500_heatmap_fitted.png

📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:05<00:16,  5.36s/rig]

   🔥 1000:   0%|          | 0/197 [00:00<?, ?frame/s]

   🔥 1000:   5%|▍         | 9/197 [00:00<00:02, 88.55frame/s]

   🔥 1000:  10%|▉         | 19/197 [00:00<00:01, 91.96frame/s]

   🔥 1000:  15%|█▍        | 29/197 [00:00<00:01, 93.10frame/s]

   🔥 1000:  20%|█▉        | 39/197 [00:00<00:01, 90.41frame/s]

   🔥 1000:  25%|██▍       | 49/197 [00:00<00:01, 89.45frame/s]

   🔥 1000:  29%|██▉       | 58/197 [00:00<00:01, 88.59frame/s]

   🔥 1000:  34%|███▍      | 67/197 [00:00<00:01, 88.58frame/s]

   🔥 1000:  39%|███▊      | 76/197 [00:00<00:01, 86.92frame/s]

   🔥 1000:  43%|████▎     | 85/197 [00:00<00:01, 85.61frame/s]

   🔥 1000:  48%|████▊     | 94/197 [00:01<00:01, 86.03frame/s]

   🔥 1000:  53%|█████▎    | 104/197 [00:01<00:01, 87.61frame/s]

   🔥 1000:  57%|█████▋    | 113/197 [00:01<00:00, 87.89frame/s]

   🔥 1000:  62%|██████▏   | 122/197 [00:01<00:00, 87.78frame/s]

   🔥 1000:  66%|██████▋   | 131/197 [00:01<00:00, 86.97frame/s]

   🔥 1000:  71%|███████   | 140/197 [00:01<00:00, 86.39frame/s]

   🔥 1000:  76%|███████▌  | 149/197 [00:01<00:00, 85.91frame/s]

   🔥 1000:  80%|████████  | 158/197 [00:01<00:00, 86.50frame/s]

   🔥 1000:  85%|████████▌ | 168/197 [00:01<00:00, 89.34frame/s]

   🔥 1000:  90%|█████████ | 178/197 [00:02<00:00, 91.79frame/s]

   🔥 1000:  95%|█████████▌| 188/197 [00:02<00:00, 93.86frame/s]

                                                                [✅] 1000: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 2/4 [00:08<00:07,  3.95s/rig]

   🔥 2000L:   0%|          | 0/464 [00:00<?, ?frame/s]

   🔥 2000L:   2%|▏         | 8/464 [00:00<00:05, 77.47frame/s]

   🔥 2000L:   4%|▎         | 17/464 [00:00<00:05, 80.41frame/s]

   🔥 2000L:   6%|▌         | 26/464 [00:00<00:05, 80.79frame/s]

   🔥 2000L:   8%|▊         | 35/464 [00:00<00:05, 81.51frame/s]

   🔥 2000L:   9%|▉         | 44/464 [00:00<00:05, 81.64frame/s]

   🔥 2000L:  11%|█▏        | 53/464 [00:00<00:04, 82.32frame/s]

   🔥 2000L:  13%|█▎        | 62/464 [00:00<00:04, 83.34frame/s]

   🔥 2000L:  15%|█▌        | 71/464 [00:00<00:04, 84.56frame/s]

   🔥 2000L:  17%|█▋        | 80/464 [00:00<00:04, 85.75frame/s]

   🔥 2000L:  19%|█▉        | 89/464 [00:01<00:04, 86.12frame/s]

   🔥 2000L:  21%|██        | 98/464 [00:01<00:04, 86.91frame/s]

   🔥 2000L:  23%|██▎       | 107/464 [00:01<00:04, 85.63frame/s]

   🔥 2000L:  25%|██▌       | 116/464 [00:01<00:04, 85.29frame/s]

   🔥 2000L:  27%|██▋       | 125/464 [00:01<00:04, 84.67frame/s]

   🔥 2000L:  29%|██▉       | 134/464 [00:01<00:03, 84.59frame/s]

   🔥 2000L:  31%|███       | 143/464 [00:01<00:03, 84.22frame/s]

   🔥 2000L:  33%|███▎      | 152/464 [00:01<00:03, 84.39frame/s]

   🔥 2000L:  35%|███▍      | 161/464 [00:01<00:03, 83.77frame/s]

   🔥 2000L:  37%|███▋      | 170/464 [00:02<00:03, 82.35frame/s]

   🔥 2000L:  39%|███▊      | 179/464 [00:02<00:03, 82.41frame/s]

   🔥 2000L:  41%|████      | 188/464 [00:02<00:03, 83.11frame/s]

   🔥 2000L:  42%|████▏     | 197/464 [00:02<00:03, 84.40frame/s]

   🔥 2000L:  44%|████▍     | 206/464 [00:02<00:03, 85.85frame/s]

   🔥 2000L:  46%|████▋     | 215/464 [00:02<00:02, 87.02frame/s]

   🔥 2000L:  48%|████▊     | 224/464 [00:02<00:02, 87.04frame/s]

   🔥 2000L:  50%|█████     | 233/464 [00:02<00:02, 87.17frame/s]

   🔥 2000L:  52%|█████▏    | 243/464 [00:02<00:02, 89.68frame/s]

   🔥 2000L:  55%|█████▍    | 253/464 [00:02<00:02, 91.69frame/s]

   🔥 2000L:  57%|█████▋    | 263/464 [00:03<00:02, 93.22frame/s]

   🔥 2000L:  59%|█████▉    | 273/464 [00:03<00:02, 95.07frame/s]

   🔥 2000L:  61%|██████    | 284/464 [00:03<00:01, 97.13frame/s]

   🔥 2000L:  64%|██████▎   | 295/464 [00:03<00:01, 98.39frame/s]

   🔥 2000L:  66%|██████▌   | 305/464 [00:03<00:01, 98.16frame/s]

   🔥 2000L:  68%|██████▊   | 315/464 [00:03<00:01, 98.24frame/s]

   🔥 2000L:  70%|███████   | 325/464 [00:03<00:01, 96.03frame/s]

   🔥 2000L:  72%|███████▏  | 335/464 [00:03<00:01, 94.81frame/s]

   🔥 2000L:  74%|███████▍  | 345/464 [00:03<00:01, 94.41frame/s]

   🔥 2000L:  77%|███████▋  | 355/464 [00:04<00:01, 94.68frame/s]

   🔥 2000L:  79%|███████▉  | 366/464 [00:04<00:01, 96.66frame/s]

   🔥 2000L:  81%|████████▏ | 377/464 [00:04<00:00, 98.42frame/s]

   🔥 2000L:  84%|████████▎ | 388/464 [00:04<00:00, 99.32frame/s]

   🔥 2000L:  86%|████████▌ | 398/464 [00:04<00:00, 99.01frame/s]

   🔥 2000L:  88%|████████▊ | 408/464 [00:04<00:00, 97.76frame/s]

   🔥 2000L:  90%|█████████ | 418/464 [00:04<00:00, 96.48frame/s]

   🔥 2000L:  92%|█████████▏| 428/464 [00:04<00:00, 93.26frame/s]

   🔥 2000L:  94%|█████████▍| 438/464 [00:04<00:00, 90.66frame/s]

   🔥 2000L:  97%|█████████▋| 448/464 [00:05<00:00, 88.23frame/s]

   🔥 2000L:  98%|█████████▊| 457/464 [00:05<00:00, 86.62frame/s]

                                                                 [✅] 2000L: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/2000L_heatmap_fitted.png

📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:15<00:05,  5.37s/rig]

   🔥 2000R:   0%|          | 0/427 [00:00<?, ?frame/s]

   🔥 2000R:   2%|▏         | 9/427 [00:00<00:05, 83.31frame/s]

   🔥 2000R:   4%|▍         | 18/427 [00:00<00:04, 86.07frame/s]

   🔥 2000R:   6%|▋         | 27/427 [00:00<00:04, 87.62frame/s]

   🔥 2000R:   9%|▊         | 37/427 [00:00<00:04, 90.37frame/s]

   🔥 2000R:  11%|█         | 47/427 [00:00<00:04, 92.42frame/s]

   🔥 2000R:  13%|█▎        | 57/427 [00:00<00:03, 92.66frame/s]

   🔥 2000R:  16%|█▌        | 67/427 [00:00<00:03, 92.27frame/s]

   🔥 2000R:  18%|█▊        | 77/427 [00:00<00:03, 92.49frame/s]

   🔥 2000R:  20%|██        | 87/427 [00:00<00:03, 92.44frame/s]

   🔥 2000R:  23%|██▎       | 97/427 [00:01<00:03, 91.86frame/s]

   🔥 2000R:  25%|██▌       | 107/427 [00:01<00:03, 90.26frame/s]

   🔥 2000R:  27%|██▋       | 117/427 [00:01<00:03, 89.68frame/s]

   🔥 2000R:  30%|██▉       | 127/427 [00:01<00:03, 90.33frame/s]

   🔥 2000R:  32%|███▏      | 137/427 [00:01<00:03, 91.09frame/s]

   🔥 2000R:  34%|███▍      | 147/427 [00:01<00:03, 92.30frame/s]

   🔥 2000R:  37%|███▋      | 157/427 [00:01<00:02, 92.82frame/s]

   🔥 2000R:  39%|███▉      | 167/427 [00:01<00:02, 92.32frame/s]

   🔥 2000R:  41%|████▏     | 177/427 [00:01<00:02, 91.97frame/s]

   🔥 2000R:  44%|████▍     | 187/427 [00:02<00:02, 92.47frame/s]

   🔥 2000R:  46%|████▌     | 197/427 [00:02<00:02, 93.45frame/s]

   🔥 2000R:  48%|████▊     | 207/427 [00:02<00:02, 95.09frame/s]

   🔥 2000R:  51%|█████     | 217/427 [00:02<00:02, 95.83frame/s]

   🔥 2000R:  53%|█████▎    | 227/427 [00:02<00:02, 96.91frame/s]

   🔥 2000R:  56%|█████▌    | 238/427 [00:02<00:01, 97.89frame/s]

   🔥 2000R:  58%|█████▊    | 249/427 [00:02<00:01, 98.92frame/s]

   🔥 2000R:  61%|██████    | 259/427 [00:02<00:01, 97.96frame/s]

   🔥 2000R:  63%|██████▎   | 269/427 [00:02<00:01, 97.31frame/s]

   🔥 2000R:  65%|██████▌   | 279/427 [00:02<00:01, 94.95frame/s]

   🔥 2000R:  68%|██████▊   | 289/427 [00:03<00:01, 94.55frame/s]

   🔥 2000R:  70%|███████   | 300/427 [00:03<00:01, 96.88frame/s]

   🔥 2000R:  73%|███████▎  | 311/427 [00:03<00:01, 98.53frame/s]

   🔥 2000R:  75%|███████▌  | 322/427 [00:03<00:01, 101.26frame/s]

   🔥 2000R:  78%|███████▊  | 333/427 [00:03<00:00, 101.94frame/s]

   🔥 2000R:  81%|████████  | 344/427 [00:03<00:00, 100.94frame/s]

   🔥 2000R:  83%|████████▎ | 355/427 [00:03<00:00, 99.49frame/s] 

   🔥 2000R:  85%|████████▌ | 365/427 [00:03<00:00, 98.93frame/s]

   🔥 2000R:  88%|████████▊ | 375/427 [00:03<00:00, 98.20frame/s]

   🔥 2000R:  90%|█████████ | 385/427 [00:04<00:00, 96.69frame/s]

   🔥 2000R:  93%|█████████▎| 395/427 [00:04<00:00, 94.49frame/s]

   🔥 2000R:  95%|█████████▍| 405/427 [00:04<00:00, 91.96frame/s]

   🔥 2000R:  97%|█████████▋| 415/427 [00:04<00:00, 90.19frame/s]

   🔥 2000R: 100%|█████████▉| 425/427 [00:04<00:00, 88.51frame/s]

                                                                 [✅] 2000R: /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDCP3D-SL-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap/2000R_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 4/4 [00:21<00:00,  5.78s/rig]
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:21<00:00,  5.45s/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-009_W043(6)_2025-05-14_P3D3/calibration/debug/5_heatmap
[🏁] Finished processing MDCP3D-SL-009_W043(6)_2025-05-14_P3D3


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