2026-09-11T00:45:11 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC051_W028_2026-09-04 ===
2026-09-11T00:45:11 - INFO: Camera ID: 6396147
2026-09-11T00:45:11 - INFO: Wand ID: 
2026-09-11T00:45:11 - INFO: Recorded At: 2026-09-04T13:19:52Z

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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 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 410 stationary frames, 420 remain


→ 500 (420 images) Phase 1:   0%|          | 0/420 [00:00<?, ?it/s]

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→ 500 (420 images) Phase 1:   7%|▋         | 29/420 [00:00<00:06, 62.85it/s]

→ 500 (420 images) Phase 1:  11%|█         | 46/420 [00:01<00:08, 43.29it/s]

→ 500 (420 images) Phase 1:  15%|█▌        | 65/420 [00:01<00:06, 51.67it/s]

→ 500 (420 images) Phase 1:  18%|█▊        | 75/420 [00:01<00:06, 53.77it/s]

→ 500 (420 images) Phase 1:  23%|██▎       | 97/420 [00:01<00:05, 63.12it/s]

→ 500 (420 images) Phase 1:  25%|██▌       | 106/420 [00:01<00:04, 63.08it/s]

→ 500 (420 images) Phase 1:  30%|███       | 126/420 [00:02<00:03, 83.75it/s]

→ 500 (420 images) Phase 1:  33%|███▎      | 137/420 [00:02<00:04, 63.46it/s]

→ 500 (420 images) Phase 1:  36%|███▌      | 152/420 [00:02<00:03, 77.31it/s]

→ 500 (420 images) Phase 1:  39%|███▉      | 163/420 [00:02<00:04, 60.62it/s]

→ 500 (420 images) Phase 1:  42%|████▏     | 178/420 [00:02<00:03, 74.15it/s]

→ 500 (420 images) Phase 1:  46%|████▌     | 193/420 [00:03<00:03, 69.82it/s]

→ 500 (420 images) Phase 1:  48%|████▊     | 202/420 [00:03<00:03, 68.66it/s]

→ 500 (420 images) Phase 1:  53%|█████▎    | 222/420 [00:03<00:02, 93.40it/s]

→ 500 (420 images) Phase 1:  56%|█████▌    | 234/420 [00:03<00:02, 65.37it/s]

→ 500 (420 images) Phase 1:  60%|██████    | 254/420 [00:03<00:01, 87.35it/s]

→ 500 (420 images) Phase 1:  64%|██████▎   | 267/420 [00:04<00:02, 65.52it/s]

→ 500 (420 images) Phase 1:  68%|██████▊   | 286/420 [00:04<00:01, 84.22it/s]

→ 500 (420 images) Phase 1:  71%|███████   | 299/420 [00:04<00:01, 64.55it/s]

→ 500 (420 images) Phase 1:  76%|███████▌  | 318/420 [00:04<00:01, 82.79it/s]

→ 500 (420 images) Phase 1:  79%|███████▉  | 331/420 [00:05<00:01, 64.63it/s]

→ 500 (420 images) Phase 1:  83%|████████▎ | 350/420 [00:05<00:00, 83.02it/s]

→ 500 (420 images) Phase 1:  86%|████████▋ | 363/420 [00:05<00:00, 66.15it/s]

→ 500 (420 images) Phase 1:  91%|█████████ | 381/420 [00:05<00:00, 81.69it/s]

→ 500 (420 images) Phase 1:  94%|█████████▎| 393/420 [00:05<00:00, 65.29it/s]

→ 500 (420 images) Phase 1:  99%|█████████▉| 416/420 [00:06<00:00, 90.14it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/420 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/420 [00:00<00:54,  7.73it/s]

→ 500 Phase 2: corners:   7%|▋         | 29/420 [00:00<00:02, 142.58it/s]

→ 500 Phase 2: corners:  11%|█         | 45/420 [00:00<00:02, 126.51it/s]

→ 500 Phase 2: corners:  14%|█▍        | 59/420 [00:00<00:02, 120.46it/s]

→ 500 Phase 2: corners:  18%|█▊        | 74/420 [00:00<00:02, 127.03it/s]

→ 500 Phase 2: corners:  21%|██        | 89/420 [00:00<00:02, 132.03it/s]

→ 500 Phase 2: corners:  25%|██▍       | 104/420 [00:00<00:02, 136.20it/s]

→ 500 Phase 2: corners:  28%|██▊       | 118/420 [00:00<00:02, 129.77it/s]

→ 500 Phase 2: corners:  33%|███▎      | 138/420 [00:01<00:01, 146.15it/s]

→ 500 Phase 2: corners:  37%|███▋      | 156/420 [00:01<00:01, 154.05it/s]

→ 500 Phase 2: corners:  41%|████      | 172/420 [00:01<00:01, 145.22it/s]

→ 500 Phase 2: corners:  45%|████▍     | 188/420 [00:01<00:01, 147.34it/s]

→ 500 Phase 2: corners:  48%|████▊     | 203/420 [00:01<00:01, 138.43it/s]

→ 500 Phase 2: corners:  52%|█████▏    | 220/420 [00:01<00:01, 146.19it/s]

→ 500 Phase 2: corners:  56%|█████▌    | 236/420 [00:01<00:01, 148.57it/s]

→ 500 Phase 2: corners:  60%|██████    | 252/420 [00:01<00:01, 149.36it/s]

→ 500 Phase 2: corners:  64%|██████▍   | 268/420 [00:01<00:01, 151.97it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 284/420 [00:02<00:00, 150.12it/s]

→ 500 Phase 2: corners:  71%|███████▏  | 300/420 [00:02<00:00, 148.58it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 315/420 [00:02<00:00, 148.00it/s]

→ 500 Phase 2: corners:  79%|███████▊  | 330/420 [00:02<00:00, 144.70it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 346/420 [00:02<00:00, 145.79it/s]

→ 500 Phase 2: corners:  86%|████████▌ | 361/420 [00:02<00:00, 142.83it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 376/420 [00:02<00:00, 141.61it/s]

→ 500 Phase 2: corners:  94%|█████████▎| 393/420 [00:02<00:00, 149.46it/s]

                                                                          [PASS2] Frame 0000925: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001162: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001165: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001168: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001169: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001168: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 3 frames fixed, 30 untrusted, 420 total


→ 500 Phase 3: finalize:   0%|          | 0/420 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   2%|▏         | 7/420 [00:00<00:06, 68.66it/s]

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→ 500 Phase 3: finalize:  10%|█         | 42/420 [00:00<00:05, 63.15it/s]

→ 500 Phase 3: finalize:  12%|█▏        | 49/420 [00:00<00:06, 59.16it/s]

→ 500 Phase 3: finalize:  13%|█▎        | 56/420 [00:00<00:06, 56.82it/s]

→ 500 Phase 3: finalize:  15%|█▍        | 62/420 [00:01<00:06, 55.50it/s]

→ 500 Phase 3: finalize:  16%|█▌        | 68/420 [00:01<00:06, 54.70it/s]

→ 500 Phase 3: finalize:  18%|█▊        | 74/420 [00:01<00:06, 54.42it/s]

→ 500 Phase 3: finalize:  19%|█▉        | 80/420 [00:01<00:06, 54.31it/s]

→ 500 Phase 3: finalize:  20%|██        | 86/420 [00:01<00:06, 54.41it/s]

→ 500 Phase 3: finalize:  22%|██▏       | 92/420 [00:01<00:06, 54.34it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 98/420 [00:01<00:05, 54.16it/s]

→ 500 Phase 3: finalize:  25%|██▍       | 104/420 [00:01<00:05, 53.90it/s]

→ 500 Phase 3: finalize:  26%|██▌       | 110/420 [00:01<00:05, 53.90it/s]

→ 500 Phase 3: finalize:  28%|██▊       | 116/420 [00:02<00:05, 53.86it/s]

→ 500 Phase 3: finalize:  29%|██▉       | 122/420 [00:02<00:05, 53.69it/s]

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→ 500 Phase 3: finalize:  32%|███▏      | 134/420 [00:02<00:05, 53.26it/s]

→ 500 Phase 3: finalize:  33%|███▎      | 140/420 [00:02<00:05, 53.03it/s]

→ 500 Phase 3: finalize:  35%|███▍      | 146/420 [00:02<00:05, 52.55it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 152/420 [00:02<00:05, 51.85it/s]

→ 500 Phase 3: finalize:  38%|███▊      | 158/420 [00:02<00:05, 51.62it/s]

→ 500 Phase 3: finalize:  39%|███▉      | 164/420 [00:02<00:04, 51.27it/s]

→ 500 Phase 3: finalize:  40%|████      | 170/420 [00:03<00:04, 51.36it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 176/420 [00:03<00:04, 51.24it/s]

→ 500 Phase 3: finalize:  43%|████▎     | 182/420 [00:03<00:04, 51.55it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 188/420 [00:03<00:04, 52.23it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 194/420 [00:03<00:04, 52.57it/s]

→ 500 Phase 3: finalize:  48%|████▊     | 200/420 [00:03<00:04, 52.75it/s]

→ 500 Phase 3: finalize:  49%|████▉     | 206/420 [00:03<00:04, 53.09it/s]

→ 500 Phase 3: finalize:  50%|█████     | 212/420 [00:03<00:03, 53.36it/s]

→ 500 Phase 3: finalize:  52%|█████▏    | 218/420 [00:03<00:03, 53.54it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 224/420 [00:04<00:03, 53.61it/s]

→ 500 Phase 3: finalize:  55%|█████▍    | 230/420 [00:04<00:03, 53.65it/s]

→ 500 Phase 3: finalize:  56%|█████▌    | 236/420 [00:04<00:03, 53.59it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 242/420 [00:04<00:03, 53.31it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 248/420 [00:04<00:03, 52.93it/s]

→ 500 Phase 3: finalize:  60%|██████    | 254/420 [00:04<00:03, 52.63it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 260/420 [00:04<00:03, 52.26it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 266/420 [00:04<00:02, 51.71it/s]

→ 500 Phase 3: finalize:  65%|██████▍   | 272/420 [00:04<00:02, 51.32it/s]

→ 500 Phase 3: finalize:  66%|██████▌   | 278/420 [00:05<00:02, 51.08it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 284/420 [00:05<00:02, 51.05it/s]

→ 500 Phase 3: finalize:  69%|██████▉   | 290/420 [00:05<00:02, 50.95it/s]

→ 500 Phase 3: finalize:  70%|███████   | 296/420 [00:05<00:02, 50.72it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 302/420 [00:05<00:02, 50.53it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 308/420 [00:05<00:02, 50.47it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 314/420 [00:05<00:02, 50.67it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 320/420 [00:05<00:01, 50.96it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 326/420 [00:06<00:01, 51.13it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 332/420 [00:06<00:01, 51.28it/s]

→ 500 Phase 3: finalize:  80%|████████  | 338/420 [00:06<00:01, 51.53it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 344/420 [00:06<00:01, 51.71it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 350/420 [00:06<00:01, 51.54it/s]

→ 500 Phase 3: finalize:  85%|████████▍ | 356/420 [00:06<00:01, 51.53it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 362/420 [00:06<00:01, 51.68it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 368/420 [00:06<00:01, 51.98it/s]

→ 500 Phase 3: finalize:  89%|████████▉ | 374/420 [00:06<00:00, 52.44it/s]

→ 500 Phase 3: finalize:  90%|█████████ | 380/420 [00:07<00:00, 52.84it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 386/420 [00:07<00:00, 53.04it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 392/420 [00:07<00:00, 53.01it/s]

→ 500 Phase 3: finalize:  95%|█████████▍| 398/420 [00:07<00:00, 52.84it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 404/420 [00:07<00:00, 52.71it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 410/420 [00:07<00:00, 52.64it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 416/420 [00:07<00:00, 52.65it/s]

                                                                          
[TIMING] 500 — 420 frames
  Phase 1  radial detect   (parallel)  :   6.25s  (14.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.86s  (6.8 ms/frame)
  Phase 2b corner fix      (sequential):   0.31s
  Phase 3  finalize        (sequential):   7.84s  (18.7 ms/frame)
    decode + undistort                 :   4.20s  (10.0 ms/frame)
    corner apply + refinement          :   1.52s  (3.6 ms/frame)
    segment                            :   1.60s  (3.8 ms/frame)
    smooth                             :   0.40s  (0.9 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  17.26s


Processing Rigs:  50%|█████     | 1/2 [00:20<00:20, 20.97s/it][INFO] Filtered 610 stationary frames, 422 remain


→ 1000 (422 images) Phase 1:   0%|          | 0/422 [00:00<?, ?it/s]

→ 1000 (422 images) Phase 1:   0%|          | 1/422 [00:00<04:21,  1.61it/s]

→ 1000 (422 images) Phase 1:   7%|▋         | 29/422 [00:00<00:07, 52.78it/s]

→ 1000 (422 images) Phase 1:  11%|█         | 45/422 [00:01<00:07, 48.04it/s]

→ 1000 (422 images) Phase 1:  15%|█▌        | 64/422 [00:01<00:05, 67.62it/s]

→ 1000 (422 images) Phase 1:  18%|█▊        | 77/422 [00:01<00:06, 56.69it/s]

→ 1000 (422 images) Phase 1:  23%|██▎       | 96/422 [00:01<00:04, 77.34it/s]

→ 1000 (422 images) Phase 1:  26%|██▌       | 109/422 [00:01<00:05, 61.70it/s]

→ 1000 (422 images) Phase 1:  30%|███       | 127/422 [00:02<00:03, 77.43it/s]

→ 1000 (422 images) Phase 1:  33%|███▎      | 139/422 [00:02<00:04, 62.37it/s]

→ 1000 (422 images) Phase 1:  37%|███▋      | 158/422 [00:02<00:03, 79.00it/s]

→ 1000 (422 images) Phase 1:  40%|████      | 169/422 [00:02<00:04, 62.17it/s]

→ 1000 (422 images) Phase 1:  45%|████▍     | 189/422 [00:02<00:02, 82.32it/s]

→ 1000 (422 images) Phase 1:  48%|████▊     | 201/422 [00:03<00:03, 63.01it/s]

→ 1000 (422 images) Phase 1:  52%|█████▏    | 220/422 [00:03<00:02, 82.57it/s]

→ 1000 (422 images) Phase 1:  55%|█████▌    | 233/422 [00:03<00:02, 64.87it/s]

→ 1000 (422 images) Phase 1:  60%|█████▉    | 252/422 [00:03<00:02, 82.92it/s]

→ 1000 (422 images) Phase 1:  63%|██████▎   | 264/422 [00:04<00:02, 64.78it/s]

→ 1000 (422 images) Phase 1:  67%|██████▋   | 284/422 [00:04<00:01, 82.74it/s]

→ 1000 (422 images) Phase 1:  70%|███████   | 296/422 [00:04<00:01, 65.41it/s]

→ 1000 (422 images) Phase 1:  75%|███████▍  | 316/422 [00:04<00:01, 83.00it/s]

→ 1000 (422 images) Phase 1:  78%|███████▊  | 328/422 [00:04<00:01, 65.10it/s]

→ 1000 (422 images) Phase 1:  82%|████████▏ | 348/422 [00:05<00:00, 83.87it/s]

→ 1000 (422 images) Phase 1:  85%|████████▌ | 360/422 [00:05<00:00, 65.48it/s]

→ 1000 (422 images) Phase 1:  90%|█████████ | 380/422 [00:05<00:00, 82.97it/s]

→ 1000 (422 images) Phase 1:  93%|█████████▎| 392/422 [00:05<00:00, 66.08it/s]

→ 1000 (422 images) Phase 1:  98%|█████████▊| 413/422 [00:05<00:00, 86.85it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/422 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/422 [00:00<01:43,  4.05it/s]

→ 1000 Phase 2: corners:   4%|▎         | 15/422 [00:00<00:07, 52.72it/s]

→ 1000 Phase 2: corners:   6%|▋         | 27/422 [00:00<00:05, 74.75it/s]

→ 1000 Phase 2: corners:   9%|▉         | 38/422 [00:00<00:04, 84.11it/s]

→ 1000 Phase 2: corners:  13%|█▎        | 54/422 [00:00<00:03, 104.65it/s]

→ 1000 Phase 2: corners:  16%|█▌        | 68/422 [00:00<00:03, 112.66it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 81/422 [00:00<00:03, 112.17it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 97/422 [00:01<00:02, 123.42it/s]

→ 1000 Phase 2: corners:  26%|██▋       | 111/422 [00:01<00:02, 125.37it/s]

→ 1000 Phase 2: corners:  30%|███       | 128/422 [00:01<00:02, 138.09it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 143/422 [00:01<00:02, 137.24it/s]

→ 1000 Phase 2: corners:  37%|███▋      | 157/422 [00:01<00:01, 134.24it/s]

→ 1000 Phase 2: corners:  41%|████      | 171/422 [00:01<00:01, 127.58it/s]

→ 1000 Phase 2: corners:  45%|████▍     | 188/422 [00:01<00:01, 135.32it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 202/422 [00:01<00:01, 132.72it/s]

→ 1000 Phase 2: corners:  51%|█████     | 216/422 [00:01<00:01, 131.35it/s]

→ 1000 Phase 2: corners:  55%|█████▍    | 231/422 [00:01<00:01, 133.23it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 245/422 [00:02<00:01, 133.27it/s]

→ 1000 Phase 2: corners:  62%|██████▏   | 262/422 [00:02<00:01, 141.34it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 279/422 [00:02<00:00, 147.37it/s]

→ 1000 Phase 2: corners:  70%|██████▉   | 294/422 [00:02<00:00, 139.85it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 309/422 [00:02<00:00, 142.58it/s]

→ 1000 Phase 2: corners:  77%|███████▋  | 324/422 [00:02<00:00, 144.17it/s]

→ 1000 Phase 2: corners:  81%|████████  | 340/422 [00:02<00:00, 148.69it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 355/422 [00:02<00:00, 145.23it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 373/422 [00:02<00:00, 154.78it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 389/422 [00:03<00:00, 155.66it/s]

                                                                           [PASS2] Frame 0000414: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000415: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000417: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000432: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000435: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000531: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000533: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000534: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000552: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000554: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000555: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000558: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000561: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000565: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000567: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000568: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000573: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000574: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000576: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000577: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000579: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000580: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000582: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000585: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000586: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000589: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000661: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000663: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000664: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000666: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000667: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000669: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000670: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000672: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000673: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000675: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000676: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000678: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000681: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000682: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000684: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000685: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001043: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001044: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001046: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001047: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001065: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001067: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001068: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001070: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001071: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001073: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001074: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001076: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001077: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001079: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001080: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001082: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001083: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001085: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001086: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001088: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001089: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001136: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001137: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001139: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001140: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001142: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001143: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001145: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001146: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001148: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001149: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001151: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001152: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001154: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001155: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001157: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001160: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001161: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001163: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001166: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001181: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001182: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001184: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001187: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001188: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001193: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001194: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001196: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001197: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001299: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001308: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001310: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001311: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001314: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001316: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001317: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001319: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001320: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001322: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001329: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001331: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001332: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001334: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000417: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000434: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000534: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000555: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000568: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000570: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000571: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000577: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000580: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000664: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000666: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000672: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000675: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000676: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000684: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001046: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001068: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001073: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001079: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001080: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001086: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001136: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001139: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001143: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001146: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001148: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001152: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001155: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001163: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001164: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001184: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001187: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001196: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001311: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001313: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001316: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001320: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001332: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 70 frames fixed, 55 untrusted, 422 total


→ 1000 Phase 3: finalize:   0%|          | 0/422 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   1%|▏         | 6/422 [00:00<00:07, 54.78it/s]

→ 1000 Phase 3: finalize:   3%|▎         | 12/422 [00:00<00:07, 55.09it/s]

→ 1000 Phase 3: finalize:   4%|▍         | 18/422 [00:00<00:07, 54.08it/s]

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→ 1000 Phase 3: finalize:   9%|▊         | 36/422 [00:00<00:07, 53.73it/s]

→ 1000 Phase 3: finalize:  10%|▉         | 42/422 [00:00<00:07, 53.61it/s]

→ 1000 Phase 3: finalize:  11%|█▏        | 48/422 [00:00<00:06, 54.22it/s]

→ 1000 Phase 3: finalize:  13%|█▎        | 54/422 [00:00<00:06, 54.14it/s]

→ 1000 Phase 3: finalize:  14%|█▍        | 60/422 [00:01<00:06, 54.30it/s]

→ 1000 Phase 3: finalize:  16%|█▌        | 66/422 [00:01<00:06, 54.19it/s]

→ 1000 Phase 3: finalize:  17%|█▋        | 72/422 [00:01<00:06, 54.17it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 78/422 [00:01<00:06, 54.08it/s]

→ 1000 Phase 3: finalize:  20%|█▉        | 84/422 [00:01<00:06, 54.01it/s]

→ 1000 Phase 3: finalize:  21%|██▏       | 90/422 [00:01<00:06, 53.82it/s]

→ 1000 Phase 3: finalize:  23%|██▎       | 96/422 [00:01<00:06, 53.86it/s]

→ 1000 Phase 3: finalize:  24%|██▍       | 102/422 [00:01<00:05, 53.79it/s]

→ 1000 Phase 3: finalize:  26%|██▌       | 108/422 [00:02<00:05, 53.81it/s]

→ 1000 Phase 3: finalize:  27%|██▋       | 114/422 [00:02<00:05, 53.47it/s]

→ 1000 Phase 3: finalize:  28%|██▊       | 120/422 [00:02<00:05, 53.05it/s]

→ 1000 Phase 3: finalize:  30%|██▉       | 126/422 [00:02<00:05, 52.55it/s]

→ 1000 Phase 3: finalize:  31%|███▏      | 132/422 [00:02<00:05, 52.19it/s]

→ 1000 Phase 3: finalize:  33%|███▎      | 138/422 [00:02<00:05, 51.77it/s]

→ 1000 Phase 3: finalize:  34%|███▍      | 144/422 [00:02<00:05, 51.19it/s]

→ 1000 Phase 3: finalize:  36%|███▌      | 150/422 [00:02<00:05, 50.59it/s]

→ 1000 Phase 3: finalize:  37%|███▋      | 156/422 [00:02<00:05, 50.05it/s]

→ 1000 Phase 3: finalize:  38%|███▊      | 162/422 [00:03<00:05, 49.55it/s]

→ 1000 Phase 3: finalize:  40%|███▉      | 167/422 [00:03<00:05, 49.25it/s]

→ 1000 Phase 3: finalize:  41%|████      | 172/422 [00:03<00:05, 48.96it/s]

→ 1000 Phase 3: finalize:  42%|████▏     | 177/422 [00:03<00:05, 48.76it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 182/422 [00:03<00:04, 48.61it/s]

→ 1000 Phase 3: finalize:  44%|████▍     | 187/422 [00:03<00:04, 48.58it/s]

→ 1000 Phase 3: finalize:  45%|████▌     | 192/422 [00:03<00:04, 48.53it/s]

→ 1000 Phase 3: finalize:  47%|████▋     | 197/422 [00:03<00:04, 48.57it/s]

→ 1000 Phase 3: finalize:  48%|████▊     | 202/422 [00:03<00:04, 48.60it/s]

→ 1000 Phase 3: finalize:  49%|████▉     | 207/422 [00:03<00:04, 48.64it/s]

→ 1000 Phase 3: finalize:  50%|█████     | 212/422 [00:04<00:04, 48.57it/s]

→ 1000 Phase 3: finalize:  51%|█████▏    | 217/422 [00:04<00:04, 48.47it/s]

→ 1000 Phase 3: finalize:  53%|█████▎    | 222/422 [00:04<00:04, 48.38it/s]

→ 1000 Phase 3: finalize:  54%|█████▍    | 227/422 [00:04<00:04, 48.33it/s]

→ 1000 Phase 3: finalize:  55%|█████▍    | 232/422 [00:04<00:03, 48.21it/s]

→ 1000 Phase 3: finalize:  56%|█████▌    | 237/422 [00:04<00:03, 48.13it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 242/422 [00:04<00:03, 48.07it/s]

→ 1000 Phase 3: finalize:  59%|█████▊    | 247/422 [00:04<00:03, 48.10it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 252/422 [00:04<00:03, 48.22it/s]

→ 1000 Phase 3: finalize:  61%|██████    | 257/422 [00:05<00:03, 48.31it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 262/422 [00:05<00:03, 48.38it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 267/422 [00:05<00:03, 48.63it/s]

→ 1000 Phase 3: finalize:  64%|██████▍   | 272/422 [00:05<00:03, 48.91it/s]

→ 1000 Phase 3: finalize:  66%|██████▌   | 277/422 [00:05<00:02, 49.15it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 283/422 [00:05<00:02, 49.53it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 289/422 [00:05<00:02, 50.02it/s]

→ 1000 Phase 3: finalize:  70%|██████▉   | 295/422 [00:05<00:02, 50.33it/s]

→ 1000 Phase 3: finalize:  71%|███████▏  | 301/422 [00:05<00:02, 50.61it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 307/422 [00:06<00:02, 50.78it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 313/422 [00:06<00:02, 51.06it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 319/422 [00:06<00:02, 51.16it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 325/422 [00:06<00:01, 51.35it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 331/422 [00:06<00:01, 51.68it/s]

→ 1000 Phase 3: finalize:  80%|███████▉  | 337/422 [00:06<00:01, 51.88it/s]

→ 1000 Phase 3: finalize:  81%|████████▏ | 343/422 [00:06<00:01, 52.21it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 349/422 [00:06<00:01, 52.64it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 355/422 [00:06<00:01, 53.17it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 361/422 [00:07<00:01, 53.35it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 367/422 [00:07<00:01, 53.70it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 373/422 [00:07<00:00, 53.89it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 379/422 [00:07<00:00, 54.11it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 385/422 [00:07<00:00, 54.18it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 391/422 [00:07<00:00, 54.18it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 397/422 [00:07<00:00, 54.08it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 403/422 [00:07<00:00, 53.97it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 409/422 [00:07<00:00, 53.29it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 415/422 [00:08<00:00, 52.72it/s]

→ 1000 Phase 3: finalize: 100%|█████████▉| 421/422 [00:08<00:00, 52.33it/s]

                                                                           
[TIMING] 1000 — 422 frames
  Phase 1  radial detect   (parallel)  :   6.20s  (14.7 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.15s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   2.56s
  Phase 3  finalize        (sequential):   8.20s  (19.4 ms/frame)
    decode + undistort                 :   4.18s  (9.9 ms/frame)
    corner apply + refinement          :   1.88s  (4.5 ms/frame)
    segment                            :   1.71s  (4.1 ms/frame)
    smooth                             :   0.28s  (0.7 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  20.11s


Processing Rigs: 100%|██████████| 2/2 [00:45<00:00, 22.83s/it]
Processing Rigs: 100%|██████████| 2/2 [00:45<00:00, 22.55s/it]
[💾] Saved RigFrameDataset to /working/MDC051_W028_2026-09-04/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 842 frames from 2 rigs to /working/MDC051_W028_2026-09-04/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC051_W028_2026-09-04/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 420
[📋] 1000: 100 frames selected from 422
[💾] Saved RigFrameDataset to /working/MDC051_W028_2026-09-04/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC051_W028_2026-09-04/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 20 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/20 [00:00<?, ?it/s]
Computing max image from bright frames:   5%|▌         | 1/20 [00:03<01:04,  3.40s/it]
Computing max image from bright frames:  10%|█         | 2/20 [00:03<00:26,  1.47s/it]
Computing max image from bright frames:  20%|██        | 4/20 [00:03<00:09,  1.70it/s]
Computing max image from bright frames:  35%|███▌      | 7/20 [00:03<00:03,  3.58it/s]
Computing max image from bright frames:  45%|████▌     | 9/20 [00:04<00:02,  4.56it/s]
Computing max image from bright frames:  55%|█████▌    | 11/20 [00:04<00:01,  5.78it/s]
Computing max image from bright frames:  65%|██████▌   | 13/20 [00:04<00:01,  4.78it/s]
Computing max image from bright frames:  70%|███████   | 14/20 [00:04<00:01,  5.07it/s]
Computing max image from bright frames:  75%|███████▌  | 15/20 [00:05<00:00,  5.03it/s]
Computing max image from bright frames:  80%|████████  | 16/20 [00:05<00:00,  5.18it/s]
Computing max image from bright frames:  90%|█████████ | 18/20 [00:05<00:00,  5.74it/s]
Computing max image from bright frames: 100%|██████████| 20/20 [00:05<00:00,  7.46it/s]
                                                                                       
[✅] Casing detection complete: cx=978.0, cy=761.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:09,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:09,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:09,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:09,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:09,  9.11it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:00, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 80.70it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.034 | Best: {'fx': 519.192} (0.034):  26%|██▌       | 26/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.001 | Best: {'fx': 519.192} (0.034):  27%|██▋       | 27/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.067 | Best: {'fx': 526.263} (0.067):  28%|██▊       | 28/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.001 | Best: {'fx': 526.263} (0.067):  29%|██▉       | 29/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.003 | Best: {'fx': 526.263} (0.067):  30%|███       | 30/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.008 | Best: {'fx': 526.263} (0.067):  31%|███       | 31/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.022 | Best: {'fx': 526.263} (0.067):  32%|███▏      | 32/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.081 | Best: {'fx': 533.333} (0.081):  33%|███▎      | 33/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.046 | Best: {'fx': 533.333} (0.081):  34%|███▍      | 34/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.031 | Best: {'fx': 533.333} (0.081):  35%|███▌      | 35/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  36%|███▌      | 36/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.025 | Best: {'fx': 533.333} (0.081):  37%|███▋      | 37/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.021 | Best: {'fx': 533.333} (0.081):  38%|███▊      | 38/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  39%|███▉      | 39/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  40%|████      | 40/100 [00:00<00:00, 83.00it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  41%|████      | 41/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.014 | Best: {'fx': 533.333} (0.081):  41%|████      | 41/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.015 | Best: {'fx': 533.333} (0.081):  42%|████▏     | 42/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  43%|████▎     | 43/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  44%|████▍     | 44/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  45%|████▌     | 45/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  46%|████▌     | 46/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  47%|████▋     | 47/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  48%|████▊     | 48/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  49%|████▉     | 49/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  50%|█████     | 50/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  51%|█████     | 51/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  52%|█████▏    | 52/100 [00:00<00:00, 112.01it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  53%|█████▎    | 53/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  53%|█████▎    | 53/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  54%|█████▍    | 54/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  55%|█████▌    | 55/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  56%|█████▌    | 56/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  57%|█████▋    | 57/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  58%|█████▊    | 58/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  59%|█████▉    | 59/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  60%|██████    | 60/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  61%|██████    | 61/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  62%|██████▏   | 62/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  63%|██████▎   | 63/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  64%|██████▍   | 64/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  65%|██████▌   | 65/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  66%|██████▌   | 66/100 [00:00<00:00, 114.71it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  67%|██████▋   | 67/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  67%|██████▋   | 67/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  68%|██████▊   | 68/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  69%|██████▉   | 69/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  70%|███████   | 70/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  71%|███████   | 71/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  72%|███████▏  | 72/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  73%|███████▎  | 73/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  74%|███████▍  | 74/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  75%|███████▌  | 75/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  76%|███████▌  | 76/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  77%|███████▋  | 77/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  78%|███████▊  | 78/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  79%|███████▉  | 79/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  80%|████████  | 80/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  81%|████████  | 81/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  82%|████████▏ | 82/100 [00:00<00:00, 121.91it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  83%|████████▎ | 83/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  83%|████████▎ | 83/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  84%|████████▍ | 84/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  85%|████████▌ | 85/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  86%|████████▌ | 86/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  87%|████████▋ | 87/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  88%|████████▊ | 88/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  89%|████████▉ | 89/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  90%|█████████ | 90/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  91%|█████████ | 91/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  92%|█████████▏| 92/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  93%|█████████▎| 93/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  94%|█████████▍| 94/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  95%|█████████▌| 95/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  96%|█████████▌| 96/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  97%|█████████▋| 97/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  98%|█████████▊| 98/100 [00:00<00:00, 131.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  99%|█████████▉| 99/100 [00:00<00:00, 131.25it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.015 | Best: {'fx': 509.354} (0.015):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.015 | Best: {'fx': 509.354} (0.015):   2%|▏         | 1/50 [00:00<00:06,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.012 | Best: {'fx': 509.354} (0.015):   2%|▏         | 1/50 [00:00<00:06,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.017 | Best: {'fx': 510.374} (0.017):   4%|▍         | 2/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.025 | Best: {'fx': 513.435} (0.025):   6%|▌         | 3/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.019 | Best: {'fx': 513.435} (0.025):   8%|▊         | 4/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.023 | Best: {'fx': 513.435} (0.025):  10%|█         | 5/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 514.456} (0.026):  12%|█▏        | 6/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.027 | Best: {'fx': 515.476} (0.027):  14%|█▍        | 7/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  16%|█▌        | 8/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 518.537} (0.033):  18%|█▊        | 9/50 [00:00<00:05,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 518.537} (0.033):  20%|██        | 10/50 [00:00<00:04,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.043 | Best: {'fx': 521.599} (0.043):  22%|██▏       | 11/50 [00:00<00:04,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 521.599} (0.043):  24%|██▍       | 12/50 [00:00<00:04,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 521.599} (0.043):  26%|██▌       | 13/50 [00:00<00:04,  8.05it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 521.599} (0.043):  28%|██▊       | 14/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.045 | Best: {'fx': 522.619} (0.045):  28%|██▊       | 14/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.050 | Best: {'fx': 523.639} (0.050):  30%|███       | 15/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.055 | Best: {'fx': 524.66} (0.055):  32%|███▏      | 16/50 [00:00<00:00, 69.97it/s] 
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.062 | Best: {'fx': 525.68} (0.062):  34%|███▍      | 17/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.074 | Best: {'fx': 527.721} (0.074):  36%|███▌      | 18/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.069 | Best: {'fx': 527.721} (0.074):  38%|███▊      | 19/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.078 | Best: {'fx': 528.741} (0.078):  40%|████      | 20/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.086 | Best: {'fx': 529.762} (0.086):  42%|████▏     | 21/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.085 | Best: {'fx': 529.762} (0.086):  44%|████▍     | 22/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.089 | Best: {'fx': 531.803} (0.089):  46%|████▌     | 23/50 [00:00<00:00, 69.97it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.089 | Best: {'fx': 531.803} (0.089):  48%|████▊     | 24/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.060 | Best: {'fx': 531.803} (0.089):  48%|████▊     | 24/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.085 | Best: {'fx': 531.803} (0.089):  50%|█████     | 25/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.078 | Best: {'fx': 531.803} (0.089):  52%|█████▏    | 26/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.068 | Best: {'fx': 531.803} (0.089):  54%|█████▍    | 27/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.049 | Best: {'fx': 531.803} (0.089):  56%|█████▌    | 28/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.066 | Best: {'fx': 531.803} (0.089):  58%|█████▊    | 29/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.056 | Best: {'fx': 531.803} (0.089):  60%|██████    | 30/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.046 | Best: {'fx': 531.803} (0.089):  62%|██████▏   | 31/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.043 | Best: {'fx': 531.803} (0.089):  64%|██████▍   | 32/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.038 | Best: {'fx': 531.803} (0.089):  66%|██████▌   | 33/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.041 | Best: {'fx': 531.803} (0.089):  68%|██████▊   | 34/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.034 | Best: {'fx': 531.803} (0.089):  70%|███████   | 35/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 531.803} (0.089):  72%|███████▏  | 36/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 531.803} (0.089):  74%|███████▍  | 37/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 531.803} (0.089):  76%|███████▌  | 38/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 531.803} (0.089):  78%|███████▊  | 39/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.037 | Best: {'fx': 531.803} (0.089):  80%|████████  | 40/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.028 | Best: {'fx': 531.803} (0.089):  82%|████████▏ | 41/50 [00:00<00:00, 74.62it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.028 | Best: {'fx': 531.803} (0.089):  84%|████████▍ | 42/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.027 | Best: {'fx': 531.803} (0.089):  84%|████████▍ | 42/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 531.803} (0.089):  86%|████████▌ | 43/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 531.803} (0.089):  88%|████████▊ | 44/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.023 | Best: {'fx': 531.803} (0.089):  90%|█████████ | 45/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.025 | Best: {'fx': 531.803} (0.089):  92%|█████████▏| 46/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 531.803} (0.089):  94%|█████████▍| 47/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 531.803} (0.089):  96%|█████████▌| 48/50 [00:00<00:00, 111.39it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.022 | Best: {'fx': 531.803} (0.089):  98%|█████████▊| 49/50 [00:00<00:00, 111.39it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 978.0
    py: 761.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization

Initial score: 16.7637: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 16.7637, picklable_objective(x0) = 16.7829

Best score: 16.7829: : 0iter [00:01, ?iter/s] 
Best score: 16.7829: : 1iter [00:01,  1.95s/iter]
Best score: 16.7829: : 1iter [00:01,  1.95s/iter]
Best score: 16.7829: : 2iter [00:02,  1.95s/iter]
Best score: 16.7829: : 3iter [00:02,  1.95s/iter]
Best score: 16.7829: : 4iter [00:02,  1.95s/iter]
Best score: 16.7829: : 5iter [00:02,  3.21iter/s]
Best score: 16.7829: : 5iter [00:02,  3.21iter/s]
Best score: 16.7829: : 6iter [00:02,  3.21iter/s]
Best score: 16.7829: : 7iter [00:02,  3.21iter/s]
Best score: 16.7829: : 8iter [00:02,  3.21iter/s]
Best score: 16.7829: : 9iter [00:02,  6.47iter/s]
Best score: 16.7829: : 9iter [00:02,  6.47iter/s]
Best score: 16.7829: : 10iter [00:02,  6.47iter/s]
Best score: 16.7829: : 11iter [00:02,  6.47iter/s]
Best score: 16.7829: : 12iter [00:02,  6.47iter/s]
Best score: 16.7829: : 13iter [00:02, 10.22iter/s]
Best score: 16.7829: : 13iter [00:02, 10.22iter/s]
Best score: 16.7829: : 14iter [00:02, 10.22iter/s]
Best score: 16.7829: : 15iter [00:02, 10.22iter/s]
Best score: 16.7829: : 16iter [00:02, 10.22iter/s]
Best score: 16.7829: : 17iter [00:02, 10.22iter/s]
Best score: 16.7829: : 18iter [00:02, 15.19iter/s]
Best score: 16.7829: : 18iter [00:02, 15.19iter/s]
Best score: 16.7829: : 19iter [00:02, 15.19iter/s]
Best score: 16.7829: : 20iter [00:02, 15.19iter/s]
Best score: 16.7829: : 21iter [00:02, 15.19iter/s]
Best score: 16.7829: : 22iter [00:02, 19.10iter/s]
Best score: 16.7829: : 22iter [00:02, 19.10iter/s]
Best score: 16.7829: : 23iter [00:02, 19.10iter/s]
Best score: 16.7829: : 24iter [00:02, 19.10iter/s]
Best score: 16.7829: : 25iter [00:02, 19.10iter/s]
Best score: 16.7829: : 26iter [00:02, 22.99iter/s]
Best score: 16.7829: : 26iter [00:02, 22.99iter/s]
Best score: 16.7829: : 27iter [00:02, 22.99iter/s]
Best score: 16.7829: : 28iter [00:02, 22.99iter/s]
Best score: 16.7829: : 29iter [00:02, 22.99iter/s]
Best score: 16.7829: : 30iter [00:02, 26.52iter/s]
Best score: 16.7829: : 30iter [00:02, 26.52iter/s]
Best score: 16.7829: : 31iter [00:02, 26.52iter/s]
Best score: 16.7829: : 32iter [00:02, 26.52iter/s]
Best score: 16.7829: : 33iter [00:02, 26.52iter/s]
Best score: 16.7829: : 34iter [00:02, 29.61iter/s]
Best score: 16.7829: : 34iter [00:02, 29.61iter/s]
Best score: 16.7829: : 35iter [00:02, 29.61iter/s]
Best score: 16.7829: : 36iter [00:02, 29.61iter/s]
Best score: 16.7829: : 37iter [00:02, 29.61iter/s]
Best score: 16.7829: : 38iter [00:02, 32.11iter/s]
Best score: 16.7829: : 38iter [00:02, 32.11iter/s]
Best score: 16.7829: : 39iter [00:02, 32.11iter/s]
Best score: 16.7829: : 40iter [00:02, 32.11iter/s]
Best score: 16.7829: : 41iter [00:02, 32.11iter/s]
Best score: 16.7829: : 42iter [00:02, 33.99iter/s]
Best score: 16.7829: : 42iter [00:03, 33.99iter/s]
Best score: 16.7829: : 43iter [00:03, 33.99iter/s]
Best score: 16.7829: : 44iter [00:03, 33.99iter/s]
Best score: 16.7829: : 45iter [00:03, 33.99iter/s]
Best score: 16.7829: : 46iter [00:03, 33.99iter/s]
Best score: 16.7829: : 47iter [00:03, 35.93iter/s]
Best score: 16.7829: : 47iter [00:03, 35.93iter/s]
Best score: 16.7829: : 48iter [00:03, 35.93iter/s]
[ITER 50] Best: 16.7829
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 978.0000
  py: 761.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 16.7829: : 49iter [00:03, 35.93iter/s]
Best score: 16.7829: : 50iter [00:03, 35.93iter/s]
Best score: 16.7829: : 51iter [00:03, 36.81iter/s]
Best score: 16.7829: : 51iter [00:03, 36.81iter/s]
Best score: 16.7829: : 52iter [00:03, 36.81iter/s]
Best score: 16.7829: : 53iter [00:03, 36.81iter/s]
Best score: 16.7829: : 54iter [00:03, 36.81iter/s]
Best score: 16.7829: : 55iter [00:03, 36.81iter/s]
Best score: 16.7829: : 56iter [00:03, 37.84iter/s]
Best score: 16.7829: : 56iter [00:03, 37.84iter/s]
Best score: 16.7829: : 57iter [00:03, 37.84iter/s]
Best score: 16.7829: : 58iter [00:03, 37.84iter/s]
Best score: 16.7829: : 59iter [00:03, 37.84iter/s]
Best score: 16.7829: : 60iter [00:03, 38.26iter/s]
Best score: 16.7829: : 60iter [00:03, 38.26iter/s]
Best score: 16.7829: : 61iter [00:03, 38.26iter/s]
Best score: 16.7829: : 62iter [00:03, 38.26iter/s]
Best score: 16.7829: : 63iter [00:03, 38.26iter/s]
Best score: 16.7829: : 64iter [00:03, 38.26iter/s]
Best score: 16.7829: : 65iter [00:03, 38.94iter/s]
Best score: 16.7829: : 65iter [00:03, 38.94iter/s]
Best score: 16.7829: : 66iter [00:03, 38.94iter/s]
Best score: 16.7829: : 67iter [00:03, 38.94iter/s]
Best score: 16.7829: : 68iter [00:03, 38.94iter/s]
Best score: 16.7829: : 69iter [00:03, 38.94iter/s]
Best score: 16.7829: : 70iter [00:03, 39.16iter/s]
Best score: 16.7829: : 70iter [00:03, 39.16iter/s]
Best score: 16.7829: : 71iter [00:03, 39.16iter/s]
Best score: 16.7829: : 72iter [00:03, 39.16iter/s]
Best score: 16.7829: : 73iter [00:03, 39.16iter/s]
Best score: 16.7829: : 74iter [00:03, 39.16iter/s]
Best score: 16.7829: : 75iter [00:03, 39.56iter/s]
Best score: 16.7829: : 75iter [00:03, 39.56iter/s]
Best score: 16.7829: : 76iter [00:03, 39.56iter/s]
Best score: 16.7829: : 77iter [00:03, 39.56iter/s]
Best score: 16.7829: : 78iter [00:03, 39.56iter/s]
Best score: 16.7829: : 79iter [00:03, 39.56iter/s]
Best score: 16.7829: : 80iter [00:03, 39.50iter/s]
Best score: 16.7829: : 80iter [00:03, 39.50iter/s]
Best score: 16.7829: : 81iter [00:03, 39.50iter/s]
Best score: 16.7829: : 82iter [00:04, 39.50iter/s]
Best score: 16.7829: : 83iter [00:04, 39.50iter/s]
Best score: 16.7829: : 84iter [00:04, 39.56iter/s]
Best score: 16.7829: : 84iter [00:04, 39.56iter/s]
Best score: 16.7829: : 85iter [00:04, 39.56iter/s]
Best score: 16.7829: : 86iter [00:04, 39.56iter/s]
Best score: 16.7829: : 87iter [00:04, 39.56iter/s]
Best score: 16.7829: : 88iter [00:04, 39.56iter/s]
Best score: 16.7829: : 89iter [00:04, 39.79iter/s]
Best score: 16.7829: : 89iter [00:04, 39.79iter/s]
Best score: 16.7829: : 90iter [00:04, 39.79iter/s]
Best score: 16.7829: : 91iter [00:04, 39.79iter/s]
Best score: 16.7829: : 92iter [00:04, 39.79iter/s]
Best score: 16.7829: : 93iter [00:04, 39.39iter/s]
Best score: 16.7829: : 93iter [00:04, 39.39iter/s]
Best score: 16.7829: : 94iter [00:04, 39.39iter/s]
Best score: 16.7829: : 95iter [00:04, 39.39iter/s]
Best score: 16.7829: : 96iter [00:04, 39.39iter/s]
Best score: 16.7829: : 97iter [00:04, 39.39iter/s]
Best score: 16.7829: : 98iter [00:04, 39.59iter/s]
Best score: 16.7829: : 98iter [00:04, 39.59iter/s]
[ITER 100] Best: 16.7829
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 978.0000
  py: 761.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 16.7829: : 99iter [00:04, 39.59iter/s]
Best score: 16.7829: : 100iter [00:04, 39.59iter/s]
Best score: 16.7829: : 101iter [00:04, 39.59iter/s]
Best score: 16.7829: : 102iter [00:04, 37.06iter/s]
Best score: 16.7829: : 102iter [00:04, 37.06iter/s]
Best score: 16.7829: : 103iter [00:04, 37.06iter/s]
Best score: 16.7829: : 104iter [00:04, 37.06iter/s]
Best score: 16.7829: : 105iter [00:04, 37.06iter/s]
Best score: 16.7829: : 106iter [00:04, 37.79iter/s]
Best score: 16.7829: : 106iter [00:04, 37.79iter/s]
Best score: 16.7829: : 107iter [00:04, 37.79iter/s]
Best score: 16.7829: : 108iter [00:04, 37.79iter/s]
Best score: 16.7829: : 109iter [00:04, 37.79iter/s]
Best score: 16.7829: : 110iter [00:04, 38.23iter/s]
Best score: 16.7829: : 110iter [00:04, 38.23iter/s]
Best score: 16.7829: : 111iter [00:04, 38.23iter/s]
Best score: 16.7829: : 112iter [00:04, 38.23iter/s]
Best score: 16.7829: : 113iter [00:04, 38.23iter/s]
Best score: 16.7829: : 114iter [00:04, 38.23iter/s]
Best score: 16.7829: : 115iter [00:04, 38.85iter/s]
Best score: 16.7829: : 115iter [00:04, 38.85iter/s]
Best score: 16.7829: : 116iter [00:04, 38.85iter/s]
Best score: 16.7829: : 117iter [00:04, 38.85iter/s]
Best score: 16.7829: : 118iter [00:04, 38.85iter/s]
Best score: 16.7829: : 119iter [00:04, 38.85iter/s]
Best score: 16.7829: : 120iter [00:04, 39.08iter/s]
Best score: 16.7829: : 120iter [00:04, 39.08iter/s]
Best score: 16.7829: : 121iter [00:05, 39.08iter/s]
Best score: 16.7829: : 122iter [00:05, 39.08iter/s]
Best score: 16.7829: : 123iter [00:05, 39.08iter/s]
Best score: 16.7829: : 124iter [00:05, 39.29iter/s]
Best score: 16.7829: : 124iter [00:05, 39.29iter/s]
Best score: 16.7829: : 125iter [00:05, 39.29iter/s]
Best score: 16.7829: : 126iter [00:05, 39.29iter/s]
Best score: 16.7829: : 127iter [00:05, 39.29iter/s]
Best score: 16.7829: : 128iter [00:05, 39.32iter/s]
Best score: 16.7829: : 128iter [00:05, 39.32iter/s]
Best score: 16.7829: : 129iter [00:05, 39.32iter/s]
Best score: 16.7829: : 130iter [00:05, 39.32iter/s]
Best score: 16.7829: : 131iter [00:05, 39.32iter/s]
Best score: 16.7829: : 132iter [00:05, 39.30iter/s]
Best score: 16.7829: : 132iter [00:05, 39.30iter/s]
Best score: 16.7829: : 133iter [00:05, 39.30iter/s]
Best score: 16.7829: : 134iter [00:05, 39.30iter/s]
Best score: 16.7829: : 135iter [00:05, 39.30iter/s]
Best score: 16.7829: : 136iter [00:05, 39.30iter/s]
Best score: 16.7829: : 137iter [00:05, 39.54iter/s]
Best score: 16.7829: : 137iter [00:05, 39.54iter/s]
Best score: 16.7829: : 138iter [00:05, 39.54iter/s]
Best score: 16.7829: : 139iter [00:05, 39.54iter/s]
Best score: 16.7829: : 140iter [00:05, 39.54iter/s]
Best score: 16.7829: : 141iter [00:05, 39.39iter/s]
Best score: 16.7829: : 141iter [00:05, 39.39iter/s]
Best score: 16.7829: : 142iter [00:05, 39.39iter/s]
Best score: 16.7829: : 143iter [00:05, 39.39iter/s]
Best score: 16.7829: : 144iter [00:05, 39.39iter/s]
Best score: 16.7829: : 145iter [00:05, 39.54iter/s]
Best score: 16.7829: : 145iter [00:05, 39.54iter/s]
Best score: 16.7829: : 146iter [00:05, 39.54iter/s]
Best score: 16.7829: : 147iter [00:05, 39.54iter/s]
Best score: 16.7829: : 148iter [00:05, 39.54iter/s]
[ITER 150] Best: 16.7829
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 978.0000
  py: 761.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 16.7829: : 149iter [00:05, 39.54iter/s]
Best score: 16.7829: : 150iter [00:05, 39.53iter/s]
Best score: 16.7829: : 150iter [00:05, 39.53iter/s]
Best score: 16.7829: : 151iter [00:05, 39.53iter/s]
Best score: 16.7829: : 152iter [00:05, 39.53iter/s]
Best score: 16.7829: : 153iter [00:05, 39.53iter/s]
Best score: 16.7829: : 154iter [00:05, 32.81iter/s]
Best score: 16.7829: : 154iter [00:05, 32.81iter/s]
Best score: 16.7829: : 155iter [00:05, 32.81iter/s]
Best score: 16.7829: : 156iter [00:05, 32.81iter/s]
Best score: 16.7829: : 157iter [00:06, 32.81iter/s]
Best score: 16.7829: : 158iter [00:06, 34.55iter/s]
Best score: 16.7829: : 158iter [00:06, 34.55iter/s]
Best score: 16.7829: : 159iter [00:06, 34.55iter/s]
Best score: 16.7829: : 160iter [00:06, 34.55iter/s]
Best score: 16.7829: : 161iter [00:06, 34.55iter/s]
Best score: 16.7829: : 162iter [00:06, 35.80iter/s]
Best score: 16.7829: : 162iter [00:06, 35.80iter/s]
Best score: 16.7829: : 163iter [00:06, 35.80iter/s]
Best score: 16.7829: : 164iter [00:06, 35.80iter/s]
Best score: 16.7829: : 165iter [00:06, 35.80iter/s]
Best score: 16.7829: : 166iter [00:06, 35.80iter/s]
Best score: 16.7829: : 167iter [00:06, 37.15iter/s]
Best score: 16.7829: : 167iter [00:06, 37.15iter/s]
Best score: 16.7829: : 168iter [00:06, 37.15iter/s]
Best score: 16.7829: : 169iter [00:06, 37.15iter/s]
Best score: 16.7829: : 170iter [00:06, 37.15iter/s]
Best score: 16.7829: : 171iter [00:06, 37.70iter/s]
Best score: 16.7829: : 171iter [00:06, 37.70iter/s]
Best score: 16.7829: : 172iter [00:06, 37.70iter/s]
Best score: 16.7829: : 173iter [00:06, 37.70iter/s]
Best score: 16.7829: : 174iter [00:06, 37.70iter/s]
Best score: 16.7829: : 175iter [00:06, 37.70iter/s]
Best score: 16.7829: : 176iter [00:06, 38.54iter/s]
Best score: 16.7829: : 176iter [00:06, 38.54iter/s]
Best score: 16.7829: : 177iter [00:06, 38.54iter/s]
Best score: 16.7829: : 178iter [00:06, 38.54iter/s]
Best score: 16.7829: : 179iter [00:06, 38.54iter/s]
Best score: 16.7829: : 180iter [00:06, 38.77iter/s]
Best score: 16.7829: : 180iter [00:06, 38.77iter/s]
Best score: 16.7829: : 181iter [00:06, 38.77iter/s]
Best score: 16.7829: : 182iter [00:06, 38.77iter/s]
Best score: 16.7829: : 183iter [00:06, 38.77iter/s]
Best score: 16.7829: : 184iter [00:06, 39.07iter/s]
Best score: 16.7829: : 184iter [00:06, 39.07iter/s]
Best score: 16.7829: : 185iter [00:06, 39.07iter/s]
Best score: 16.7829: : 186iter [00:06, 39.07iter/s]
Best score: 16.7829: : 187iter [00:06, 39.07iter/s]
Best score: 16.7829: : 188iter [00:06, 39.07iter/s]
Best score: 16.7829: : 189iter [00:06, 39.51iter/s]
Best score: 16.7829: : 189iter [00:06, 39.51iter/s]
Best score: 16.7829: : 190iter [00:06, 39.51iter/s]
Best score: 16.7829: : 191iter [00:06, 39.51iter/s]
Best score: 16.7829: : 192iter [00:06, 39.51iter/s]
Best score: 16.7829: : 193iter [00:06, 39.61iter/s]
Best score: 16.7829: : 193iter [00:06, 39.61iter/s]
Best score: 16.7829: : 194iter [00:06, 39.61iter/s]
Best score: 16.7829: : 195iter [00:06, 39.61iter/s]
Best score: 16.7829: : 196iter [00:06, 39.61iter/s]
Best score: 16.7829: : 197iter [00:07, 39.61iter/s]
Best score: 16.7829: : 198iter [00:07, 39.95iter/s]
Best score: 16.7829: : 198iter [00:07, 39.95iter/s]
[ITER 200] Best: 11.1055
[PARAMS] Current best parameters:
  fx: 532.8832
  fy: 532.4091
  px: 979.0760
  py: 762.9541
  k1: 0.02017560
  k2: -0.00773398
  k3: -0.00955887
  k4: 0.00458440
  p1: 0.00075820
  p2: 0.00307240
  p3: 0.00351886
  p4: 0.00044173
  s1: 0.0014
  s2: -0.0040
  s3: -0.0005
  s4: 0.0012
  tilt_x: -0.0017
  tilt_y: 0.0090
  scale: 2.9404
  rotation: -0.8852

Best score: 11.1055: : 199iter [00:07, 39.95iter/s]
Best score: 11.1055: : 200iter [00:07, 39.95iter/s]
Best score: 11.1055: : 201iter [00:07, 39.95iter/s]
Best score: 11.1055: : 202iter [00:07, 32.39iter/s]
Best score: 11.1055: : 202iter [00:07, 32.39iter/s]
Best score: 11.1055: : 203iter [00:07, 32.39iter/s]
Best score: 11.1055: : 204iter [00:07, 32.39iter/s]
Best score: 11.1055: : 205iter [00:07, 32.39iter/s]
Best score: 11.1055: : 206iter [00:07, 32.39iter/s]
Best score: 11.1055: : 207iter [00:07, 34.75iter/s]
Best score: 11.1055: : 207iter [00:07, 34.75iter/s]
Best score: 11.1055: : 208iter [00:07, 34.75iter/s]
Best score: 11.1055: : 209iter [00:07, 34.75iter/s]
Best score: 11.1055: : 210iter [00:07, 34.75iter/s]
Best score: 11.1055: : 211iter [00:07, 34.75iter/s]
Best score: 11.1055: : 212iter [00:07, 36.30iter/s]
Best score: 11.1055: : 212iter [00:07, 36.30iter/s]
Best score: 11.1055: : 213iter [00:07, 36.30iter/s]
Best score: 11.1055: : 214iter [00:07, 36.30iter/s]
Best score: 11.1055: : 215iter [00:07, 36.30iter/s]
Best score: 11.1055: : 216iter [00:07, 36.30iter/s]
Best score: 11.1055: : 217iter [00:07, 37.58iter/s]
Best score: 11.1055: : 217iter [00:07, 37.58iter/s]
Best score: 11.1055: : 218iter [00:07, 37.58iter/s]
Best score: 10.1861: : 219iter [00:07, 37.58iter/s]
Best score: 10.1861: : 220iter [00:07, 37.58iter/s]
Best score: 10.1861: : 221iter [00:07, 38.15iter/s]
Best score: 10.1861: : 221iter [00:07, 38.15iter/s]
Best score: 10.1861: : 222iter [00:07, 38.15iter/s]
Best score: 10.1861: : 223iter [00:07, 38.15iter/s]
Best score: 10.1861: : 224iter [00:07, 38.15iter/s]
Best score: 10.1861: : 225iter [00:07, 38.15iter/s]
Best score: 10.1861: : 226iter [00:07, 39.00iter/s]
Best score: 10.1861: : 226iter [00:07, 39.00iter/s]
Best score: 10.1861: : 227iter [00:07, 39.00iter/s]
Best score: 10.1861: : 228iter [00:07, 39.00iter/s]
Best score: 9.8428: : 229iter [00:07, 39.00iter/s] 
Best score: 9.8428: : 230iter [00:07, 39.22iter/s]
Best score: 9.8428: : 230iter [00:07, 39.22iter/s]
Best score: 9.8428: : 231iter [00:07, 39.22iter/s]
Best score: 9.8428: : 232iter [00:07, 39.22iter/s]
Best score: 9.8428: : 233iter [00:07, 39.22iter/s]
Best score: 9.8428: : 234iter [00:08, 39.22iter/s]
Best score: 9.8428: : 235iter [00:08, 39.84iter/s]
Best score: 9.8428: : 235iter [00:08, 39.84iter/s]
Best score: 9.8428: : 236iter [00:08, 39.84iter/s]
Best score: 9.8428: : 237iter [00:08, 39.84iter/s]
Best score: 9.8428: : 238iter [00:08, 39.84iter/s]
Best score: 6.1911: : 239iter [00:08, 39.84iter/s]
Best score: 6.1911: : 240iter [00:08, 40.05iter/s]
Best score: 6.1911: : 240iter [00:08, 40.05iter/s]
Best score: 6.1911: : 241iter [00:08, 40.05iter/s]
Best score: 6.1911: : 242iter [00:08, 40.05iter/s]
Best score: 6.1911: : 243iter [00:08, 40.05iter/s]
Best score: 6.1911: : 244iter [00:08, 40.05iter/s]
Best score: 6.1911: : 245iter [00:08, 40.19iter/s]
Best score: 6.1911: : 245iter [00:08, 40.19iter/s]
Best score: 6.1911: : 246iter [00:08, 40.19iter/s]
Best score: 6.1911: : 247iter [00:08, 40.19iter/s]
Best score: 6.1911: : 248iter [00:08, 40.19iter/s]
[ITER 250] Best: 6.1911
[PARAMS] Current best parameters:
  fx: 530.8156
  fy: 530.8193
  px: 981.4635
  py: 765.5264
  k1: 0.04749178
  k2: -0.03282769
  k3: -0.00277301
  k4: 0.00377842
  p1: 0.00982116
  p2: 0.01563495
  p3: 0.01578439
  p4: -0.01640298
  s1: -0.0057
  s2: 0.0104
  s3: 0.0027
  s4: 0.0086
  tilt_x: -0.0092
  tilt_y: 0.0270
  scale: 2.9329
  rotation: -0.9176

Best score: 6.1911: : 249iter [00:08, 40.19iter/s]
Best score: 6.1911: : 250iter [00:08, 40.09iter/s]
Best score: 6.1911: : 250iter [00:08, 40.09iter/s]
Best score: 6.1911: : 251iter [00:08, 40.09iter/s]
Best score: 6.1911: : 252iter [00:08, 40.09iter/s]
Best score: 6.1911: : 253iter [00:08, 40.09iter/s]
Best score: 6.1911: : 254iter [00:08, 40.09iter/s]
Best score: 6.1911: : 255iter [00:08, 33.80iter/s]
Best score: 6.1911: : 255iter [00:08, 33.80iter/s]
Best score: 6.1911: : 256iter [00:08, 33.80iter/s]
Best score: 6.1911: : 257iter [00:08, 33.80iter/s]
Best score: 6.1911: : 258iter [00:08, 33.80iter/s]
Best score: 5.4745: : 259iter [00:08, 33.80iter/s]
Best score: 5.4745: : 260iter [00:08, 35.40iter/s]
Best score: 5.4745: : 260iter [00:08, 35.40iter/s]
Best score: 5.4745: : 261iter [00:08, 35.40iter/s]
Best score: 5.4745: : 262iter [00:08, 35.40iter/s]
Best score: 5.4745: : 263iter [00:08, 35.40iter/s]
Best score: 5.4745: : 264iter [00:08, 35.40iter/s]
Best score: 5.4745: : 265iter [00:08, 36.87iter/s]
Best score: 5.4745: : 265iter [00:08, 36.87iter/s]
Best score: 5.4745: : 266iter [00:08, 36.87iter/s]
Best score: 5.4745: : 267iter [00:08, 36.87iter/s]
Best score: 5.4745: : 268iter [00:08, 36.87iter/s]
Best score: 3.6479: : 269iter [00:08, 36.87iter/s]
Best score: 3.6479: : 270iter [00:08, 37.72iter/s]
Best score: 3.6479: : 270iter [00:08, 37.72iter/s]
Best score: 3.6479: : 271iter [00:09, 37.72iter/s]
Best score: 3.6479: : 272iter [00:09, 37.72iter/s]
Best score: 3.6479: : 273iter [00:09, 37.72iter/s]
Best score: 3.6479: : 274iter [00:09, 37.72iter/s]
Best score: 3.6479: : 275iter [00:09, 38.60iter/s]
Best score: 3.6479: : 275iter [00:09, 38.60iter/s]
Best score: 3.6479: : 276iter [00:09, 38.60iter/s]
Best score: 3.6479: : 277iter [00:09, 38.60iter/s]
Best score: 3.6479: : 278iter [00:09, 38.60iter/s]
Best score: 2.9636: : 279iter [00:09, 38.60iter/s]
Best score: 2.9636: : 280iter [00:09, 39.05iter/s]
Best score: 2.9636: : 280iter [00:09, 39.05iter/s]
Best score: 2.9636: : 281iter [00:09, 39.05iter/s]
Best score: 2.9636: : 282iter [00:09, 39.05iter/s]
Best score: 2.9636: : 283iter [00:09, 39.05iter/s]
Best score: 2.9636: : 284iter [00:09, 39.05iter/s]
Best score: 2.9636: : 285iter [00:09, 39.50iter/s]
Best score: 2.9636: : 285iter [00:09, 39.50iter/s]
Best score: 2.9636: : 286iter [00:09, 39.50iter/s]
Best score: 2.9636: : 287iter [00:09, 39.50iter/s]
Best score: 2.9636: : 288iter [00:09, 39.50iter/s]
Best score: 2.9636: : 289iter [00:09, 39.50iter/s]
Best score: 2.9636: : 290iter [00:09, 39.58iter/s]
Best score: 2.9636: : 290iter [00:09, 39.58iter/s]
Best score: 2.9636: : 291iter [00:09, 39.58iter/s]
Best score: 2.9636: : 292iter [00:09, 39.58iter/s]
Best score: 2.9636: : 293iter [00:09, 39.58iter/s]
Best score: 2.9636: : 294iter [00:09, 31.74iter/s]
Best score: 2.9636: : 294iter [00:09, 31.74iter/s]
Best score: 2.9636: : 295iter [00:09, 31.74iter/s]
Best score: 2.9636: : 296iter [00:09, 31.74iter/s]
Best score: 2.9636: : 297iter [00:09, 31.74iter/s]
Best score: 2.9636: : 298iter [00:09, 31.74iter/s]
Best score: 2.9636: : 299iter [00:09, 34.08iter/s]
[ITER 300] Best: 2.1998
[PARAMS] Current best parameters:
  fx: 508.1917
  fy: 507.1580
  px: 974.2241
  py: 754.2045
  k1: -0.02266148
  k2: 0.03678659
  k3: 0.00043077
  k4: -0.00482856
  p1: -0.01101236
  p2: 0.00336016
  p3: -0.00233386
  p4: 0.00362475
  s1: -0.0048
  s2: -0.0020
  s3: -0.0023
  s4: 0.0004
  tilt_x: -0.0149
  tilt_y: 0.0303
  scale: 3.1078
  rotation: -1.0259

Best score: 2.1998: : 299iter [00:09, 34.08iter/s]
Best score: 2.1998: : 300iter [00:09, 34.08iter/s]
Best score: 2.1998: : 301iter [00:09, 34.08iter/s]
Best score: 2.1998: : 302iter [00:09, 34.08iter/s]
Best score: 2.1998: : 303iter [00:09, 29.88iter/s]
Best score: 2.1998: : 303iter [00:09, 29.88iter/s]
Best score: 2.1998: : 304iter [00:10, 29.88iter/s]
Best score: 2.1998: : 305iter [00:10, 29.88iter/s]
Best score: 2.1998: : 306iter [00:10, 29.88iter/s]
Best score: 2.1998: : 307iter [00:10, 29.88iter/s]
Best score: 2.1998: : 308iter [00:10, 32.65iter/s]
Best score: 2.1998: : 308iter [00:10, 32.65iter/s]
Best score: 2.1998: : 309iter [00:10, 32.65iter/s]
Best score: 2.1998: : 310iter [00:10, 32.65iter/s]
Best score: 2.1998: : 311iter [00:10, 32.65iter/s]
Best score: 2.1998: : 312iter [00:10, 34.28iter/s]
Best score: 2.1998: : 312iter [00:10, 34.28iter/s]
Best score: 2.1998: : 313iter [00:10, 34.28iter/s]
Best score: 2.1998: : 314iter [00:10, 34.28iter/s]
Best score: 2.1998: : 315iter [00:10, 34.28iter/s]
Best score: 2.1998: : 316iter [00:10, 34.28iter/s]
Best score: 2.1998: : 317iter [00:10, 36.14iter/s]
Best score: 2.1998: : 317iter [00:10, 36.14iter/s]
Best score: 2.1998: : 318iter [00:10, 36.14iter/s]
Best score: 1.8826: : 319iter [00:10, 36.14iter/s]
Best score: 1.8826: : 320iter [00:10, 36.14iter/s]
Best score: 1.8826: : 321iter [00:10, 37.09iter/s]
Best score: 1.8826: : 321iter [00:10, 37.09iter/s]
Best score: 1.8826: : 322iter [00:10, 37.09iter/s]
Best score: 1.8826: : 323iter [00:10, 37.09iter/s]
Best score: 1.8826: : 324iter [00:10, 37.09iter/s]
Best score: 1.8826: : 325iter [00:10, 37.09iter/s]
Best score: 1.8826: : 326iter [00:10, 38.29iter/s]
Best score: 1.8826: : 326iter [00:10, 38.29iter/s]
Best score: 1.8826: : 327iter [00:10, 38.29iter/s]
Best score: 1.8826: : 328iter [00:10, 38.29iter/s]
Best score: 1.8526: : 329iter [00:10, 38.29iter/s]
Best score: 1.8526: : 330iter [00:10, 38.59iter/s]
Best score: 1.8526: : 330iter [00:10, 38.59iter/s]
Best score: 1.8526: : 331iter [00:10, 38.59iter/s]
Best score: 1.8526: : 332iter [00:10, 38.59iter/s]
Best score: 1.8526: : 333iter [00:10, 38.59iter/s]
Best score: 1.8526: : 334iter [00:10, 38.59iter/s]
Best score: 1.8526: : 335iter [00:10, 39.34iter/s]
Best score: 1.8526: : 335iter [00:10, 39.34iter/s]
Best score: 1.8526: : 336iter [00:10, 39.34iter/s]
Best score: 1.8526: : 337iter [00:10, 39.34iter/s]
Best score: 1.8526: : 338iter [00:10, 39.34iter/s]
Best score: 1.3049: : 339iter [00:10, 39.34iter/s]
Best score: 1.3049: : 340iter [00:10, 39.51iter/s]
Best score: 1.3049: : 340iter [00:10, 39.51iter/s]
Best score: 1.3049: : 341iter [00:10, 39.51iter/s]
Best score: 1.3049: : 342iter [00:10, 39.51iter/s]
Best score: 1.3049: : 343iter [00:10, 39.51iter/s]
Best score: 1.3049: : 344iter [00:10, 39.51iter/s]
Best score: 1.3049: : 345iter [00:10, 39.90iter/s]
Best score: 1.3049: : 345iter [00:11, 39.90iter/s]
Best score: 1.3049: : 346iter [00:11, 39.90iter/s]
Best score: 1.3049: : 347iter [00:11, 39.90iter/s]
Best score: 1.3049: : 348iter [00:11, 39.90iter/s]
[ITER 350] Best: 0.9388
[PARAMS] Current best parameters:
  fx: 527.9204
  fy: 527.6258
  px: 977.4947
  py: 763.3529
  k1: -0.00671669
  k2: 0.00251840
  k3: 0.00873554
  k4: -0.00438342
  p1: 0.00669266
  p2: 0.00959026
  p3: 0.00086725
  p4: -0.00631796
  s1: -0.0148
  s2: 0.0097
  s3: -0.0005
  s4: 0.0037
  tilt_x: -0.0053
  tilt_y: 0.0252
  scale: 3.0379
  rotation: -1.0016

Best score: 0.9388: : 349iter [00:11, 39.90iter/s]
Best score: 0.9388: : 350iter [00:11, 39.81iter/s]
Best score: 0.9388: : 350iter [00:11, 39.81iter/s]
Best score: 0.9388: : 351iter [00:11, 39.81iter/s]
Best score: 0.9388: : 352iter [00:11, 39.81iter/s]
Best score: 0.9388: : 353iter [00:11, 39.81iter/s]
Best score: 0.9388: : 354iter [00:11, 33.33iter/s]
Best score: 0.9388: : 354iter [00:11, 33.33iter/s]
Best score: 0.9388: : 355iter [00:11, 33.33iter/s]
Best score: 0.9388: : 356iter [00:11, 33.33iter/s]
Best score: 0.9388: : 357iter [00:11, 33.33iter/s]
Best score: 0.9388: : 358iter [00:11, 33.33iter/s]
Best score: 0.9388: : 359iter [00:11, 35.38iter/s]
Best score: 0.9388: : 359iter [00:11, 35.38iter/s]
Best score: 0.9388: : 360iter [00:11, 35.38iter/s]
Best score: 0.9388: : 361iter [00:11, 35.38iter/s]
Best score: 0.9388: : 362iter [00:11, 35.38iter/s]
Best score: 0.9388: : 363iter [00:11, 36.42iter/s]
Best score: 0.9388: : 363iter [00:11, 36.42iter/s]
Best score: 0.9388: : 364iter [00:11, 36.42iter/s]
Best score: 0.9388: : 365iter [00:11, 36.42iter/s]
Best score: 0.9388: : 366iter [00:11, 36.42iter/s]
Best score: 0.9388: : 367iter [00:11, 36.42iter/s]
Best score: 0.9388: : 368iter [00:11, 37.71iter/s]
Best score: 0.9388: : 368iter [00:11, 37.71iter/s]
Best score: 0.8411: : 369iter [00:11, 37.71iter/s]
Best score: 0.8411: : 370iter [00:11, 37.71iter/s]
Best score: 0.8411: : 371iter [00:11, 37.71iter/s]
Best score: 0.8411: : 372iter [00:11, 38.23iter/s]
Best score: 0.8411: : 372iter [00:11, 38.23iter/s]
Best score: 0.8411: : 373iter [00:11, 38.23iter/s]
Best score: 0.8411: : 374iter [00:11, 38.23iter/s]
Best score: 0.8411: : 375iter [00:11, 38.23iter/s]
Best score: 0.8411: : 376iter [00:11, 38.23iter/s]
Best score: 0.8411: : 377iter [00:11, 39.06iter/s]
Best score: 0.8411: : 377iter [00:11, 39.06iter/s]
Best score: 0.8411: : 378iter [00:11, 39.06iter/s]
Best score: 0.8308: : 379iter [00:11, 39.06iter/s]
Best score: 0.8308: : 380iter [00:11, 39.06iter/s]
Best score: 0.8308: : 381iter [00:11, 39.31iter/s]
Best score: 0.8308: : 381iter [00:11, 39.31iter/s]
Best score: 0.8308: : 382iter [00:12, 39.31iter/s]
Best score: 0.8308: : 383iter [00:12, 39.31iter/s]
Best score: 0.8308: : 384iter [00:12, 39.31iter/s]
Best score: 0.8308: : 385iter [00:12, 39.31iter/s]
Best score: 0.8308: : 386iter [00:12, 39.88iter/s]
Best score: 0.8308: : 386iter [00:12, 39.88iter/s]
Best score: 0.8308: : 387iter [00:12, 39.88iter/s]
Best score: 0.8308: : 388iter [00:12, 39.88iter/s]
Best score: 0.7658: : 389iter [00:12, 39.88iter/s]
Best score: 0.7658: : 390iter [00:12, 39.88iter/s]
Best score: 0.7658: : 391iter [00:12, 40.04iter/s]
Best score: 0.7658: : 391iter [00:12, 40.04iter/s]
Best score: 0.7658: : 392iter [00:12, 40.04iter/s]
Best score: 0.7658: : 393iter [00:12, 40.04iter/s]
Best score: 0.7658: : 394iter [00:12, 40.04iter/s]
Best score: 0.7658: : 395iter [00:12, 40.04iter/s]
Best score: 0.7658: : 396iter [00:12, 40.28iter/s]
Best score: 0.7658: : 396iter [00:12, 40.28iter/s]
Best score: 0.7658: : 397iter [00:12, 40.28iter/s]
Best score: 0.7658: : 398iter [00:12, 40.28iter/s]
[ITER 400] Best: 0.7358
[PARAMS] Current best parameters:
  fx: 524.9502
  fy: 524.9701
  px: 975.3353
  py: 765.3608
  k1: 0.00389728
  k2: 0.01878964
  k3: -0.01549949
  k4: 0.00289974
  p1: 0.00546149
  p2: 0.01088332
  p3: -0.00487054
  p4: -0.00155439
  s1: 0.0014
  s2: -0.0032
  s3: 0.0047
  s4: -0.0008
  tilt_x: 0.0078
  tilt_y: 0.0380
  scale: 2.9944
  rotation: -0.9264

Best score: 0.7358: : 399iter [00:12, 40.28iter/s]
Best score: 0.7358: : 400iter [00:12, 40.28iter/s]
Best score: 0.7358: : 401iter [00:12, 33.84iter/s]
Best score: 0.7358: : 401iter [00:12, 33.84iter/s]
Best score: 0.7358: : 402iter [00:12, 33.84iter/s]
Best score: 0.7358: : 403iter [00:12, 33.84iter/s]
Best score: 0.7358: : 404iter [00:12, 33.84iter/s]
Best score: 0.7358: : 405iter [00:12, 33.84iter/s]
Best score: 0.7358: : 406iter [00:12, 35.78iter/s]
Best score: 0.7358: : 406iter [00:12, 35.78iter/s]
Best score: 0.7358: : 407iter [00:12, 35.78iter/s]
Best score: 0.7358: : 408iter [00:12, 35.78iter/s]
Best score: 0.5738: : 409iter [00:12, 35.78iter/s]
Best score: 0.5738: : 410iter [00:12, 36.71iter/s]
Best score: 0.5738: : 410iter [00:12, 36.71iter/s]
Best score: 0.5738: : 411iter [00:12, 36.71iter/s]
Best score: 0.5738: : 412iter [00:12, 36.71iter/s]
Best score: 0.5738: : 413iter [00:12, 36.71iter/s]
Best score: 0.5738: : 414iter [00:12, 36.71iter/s]
Best score: 0.5738: : 415iter [00:12, 37.84iter/s]
Best score: 0.5738: : 415iter [00:12, 37.84iter/s]
Best score: 0.5738: : 416iter [00:12, 37.84iter/s]
Best score: 0.5738: : 417iter [00:12, 37.84iter/s]
Best score: 0.5738: : 418iter [00:12, 37.84iter/s]
Best score: 0.5738: : 419iter [00:13, 37.84iter/s]
Best score: 0.5738: : 420iter [00:13, 38.34iter/s]
Best score: 0.5738: : 420iter [00:13, 38.34iter/s]
Best score: 0.5738: : 421iter [00:13, 38.34iter/s]
Best score: 0.5738: : 422iter [00:13, 38.34iter/s]
Best score: 0.5738: : 423iter [00:13, 38.34iter/s]
Best score: 0.5738: : 424iter [00:13, 38.34iter/s]
Best score: 0.5738: : 425iter [00:13, 39.02iter/s]
Best score: 0.5738: : 425iter [00:13, 39.02iter/s]
Best score: 0.5738: : 426iter [00:13, 39.02iter/s]
Best score: 0.5738: : 427iter [00:13, 39.02iter/s]
Best score: 0.5738: : 428iter [00:13, 39.02iter/s]
Best score: 0.5290: : 429iter [00:13, 39.02iter/s]
Best score: 0.5290: : 430iter [00:13, 39.27iter/s]
Best score: 0.5290: : 430iter [00:13, 39.27iter/s]
Best score: 0.5290: : 431iter [00:13, 39.27iter/s]
Best score: 0.5290: : 432iter [00:13, 39.27iter/s]
Best score: 0.5290: : 433iter [00:13, 39.27iter/s]
Best score: 0.5290: : 434iter [00:13, 39.27iter/s]
Best score: 0.5290: : 435iter [00:13, 39.82iter/s]
Best score: 0.5290: : 435iter [00:13, 39.82iter/s]
Best score: 0.5290: : 436iter [00:13, 39.82iter/s]
Best score: 0.5290: : 437iter [00:13, 39.82iter/s]
Best score: 0.5290: : 438iter [00:13, 39.82iter/s]
Best score: 0.5290: : 439iter [00:13, 39.82iter/s]
Best score: 0.5290: : 440iter [00:13, 39.88iter/s]
Best score: 0.5290: : 440iter [00:13, 39.88iter/s]
Best score: 0.5290: : 441iter [00:13, 39.88iter/s]
Best score: 0.5290: : 442iter [00:13, 39.88iter/s]
Best score: 0.5290: : 443iter [00:13, 39.88iter/s]
Best score: 0.5290: : 444iter [00:13, 39.88iter/s]
Best score: 0.5290: : 445iter [00:13, 40.13iter/s]
Best score: 0.5290: : 445iter [00:13, 40.13iter/s]
Best score: 0.5290: : 446iter [00:13, 40.13iter/s]
Best score: 0.5290: : 447iter [00:13, 40.13iter/s]
Best score: 0.5290: : 448iter [00:13, 40.13iter/s]
[ITER 450] Best: 0.5111
[PARAMS] Current best parameters:
  fx: 518.6351
  fy: 518.2235
  px: 976.1253
  py: 760.8600
  k1: 0.00092913
  k2: 0.01550808
  k3: -0.00634328
  k4: 0.00004356
  p1: -0.00362030
  p2: 0.00979535
  p3: -0.00581715
  p4: 0.00239157
  s1: -0.0016
  s2: -0.0030
  s3: 0.0073
  s4: -0.0027
  tilt_x: 0.0037
  tilt_y: 0.0327
  scale: 3.0483
  rotation: -0.9753

Best score: 0.5111: : 449iter [00:13, 40.13iter/s]
Best score: 0.5111: : 450iter [00:13, 40.10iter/s]
Best score: 0.5111: : 450iter [00:13, 40.10iter/s]
Best score: 0.5111: : 451iter [00:13, 40.10iter/s]
Best score: 0.5111: : 452iter [00:13, 40.10iter/s]
Best score: 0.5111: : 453iter [00:13, 40.10iter/s]
Best score: 0.5111: : 454iter [00:13, 40.10iter/s]
Best score: 0.5111: : 455iter [00:13, 34.03iter/s]
Best score: 0.5111: : 455iter [00:13, 34.03iter/s]
Best score: 0.5111: : 456iter [00:13, 34.03iter/s]
Best score: 0.5111: : 457iter [00:14, 34.03iter/s]
Best score: 0.5111: : 458iter [00:14, 34.03iter/s]
Best score: 0.5111: : 459iter [00:14, 34.03iter/s]
Best score: 0.5111: : 460iter [00:14, 35.61iter/s]
Best score: 0.5111: : 460iter [00:14, 35.61iter/s]
Best score: 0.5111: : 461iter [00:14, 35.61iter/s]
Best score: 0.5111: : 462iter [00:14, 35.61iter/s]
Best score: 0.5111: : 463iter [00:14, 35.61iter/s]
Best score: 0.5111: : 464iter [00:14, 35.61iter/s]
Best score: 0.5111: : 465iter [00:14, 37.16iter/s]
Best score: 0.5111: : 465iter [00:14, 37.16iter/s]
Best score: 0.5111: : 466iter [00:14, 37.16iter/s]
Best score: 0.5111: : 467iter [00:14, 37.16iter/s]
Best score: 0.5111: : 468iter [00:14, 37.16iter/s]
Best score: 0.4961: : 469iter [00:14, 37.16iter/s]
Best score: 0.4961: : 470iter [00:14, 37.96iter/s]
Best score: 0.4961: : 470iter [00:14, 37.96iter/s]
Best score: 0.4961: : 471iter [00:14, 37.96iter/s]
Best score: 0.4961: : 472iter [00:14, 37.96iter/s]
Best score: 0.4961: : 473iter [00:14, 37.96iter/s]
Best score: 0.4961: : 474iter [00:14, 37.96iter/s]
Best score: 0.4961: : 475iter [00:14, 38.68iter/s]
Best score: 0.4961: : 475iter [00:14, 38.68iter/s]
Best score: 0.4961: : 476iter [00:14, 38.68iter/s]
Best score: 0.4961: : 477iter [00:14, 38.68iter/s]
Best score: 0.4961: : 478iter [00:14, 38.68iter/s]
Best score: 0.4961: : 479iter [00:14, 38.68iter/s]
Best score: 0.4961: : 480iter [00:14, 38.96iter/s]
Best score: 0.4961: : 480iter [00:14, 38.96iter/s]
Best score: 0.4961: : 481iter [00:14, 38.96iter/s]
Best score: 0.4961: : 482iter [00:14, 38.96iter/s]
Best score: 0.4961: : 483iter [00:14, 38.96iter/s]
Best score: 0.4961: : 484iter [00:14, 38.96iter/s]
Best score: 0.4961: : 485iter [00:14, 39.46iter/s]
Best score: 0.4961: : 485iter [00:14, 39.46iter/s]
Best score: 0.4961: : 486iter [00:14, 39.46iter/s]
Best score: 0.4961: : 487iter [00:14, 39.46iter/s]
Best score: 0.4961: : 488iter [00:14, 39.46iter/s]
Best score: 0.4961: : 489iter [00:14, 39.46iter/s]
Best score: 0.4961: : 490iter [00:14, 39.74iter/s]
Best score: 0.4961: : 490iter [00:14, 39.74iter/s]
Best score: 0.4961: : 491iter [00:14, 39.74iter/s]
Best score: 0.4961: : 492iter [00:14, 39.74iter/s]
Best score: 0.4961: : 493iter [00:14, 39.74iter/s]
Best score: 0.4961: : 494iter [00:14, 39.74iter/s]
Best score: 0.4961: : 495iter [00:14, 40.04iter/s]
Best score: 0.4961: : 495iter [00:14, 40.04iter/s]
Best score: 0.4961: : 496iter [00:14, 40.04iter/s]
Best score: 0.4961: : 497iter [00:15, 40.04iter/s]
Best score: 0.4961: : 498iter [00:15, 40.04iter/s]
[ITER 500] Best: 0.4804
[PARAMS] Current best parameters:
  fx: 521.8919
  fy: 521.6289
  px: 975.7547
  py: 761.4947
  k1: -0.00058024
  k2: 0.01239348
  k3: -0.00425415
  k4: -0.00037309
  p1: -0.00019060
  p2: 0.01100600
  p3: -0.00513354
  p4: 0.00032784
  s1: -0.0048
  s2: -0.0006
  s3: 0.0053
  s4: -0.0018
  tilt_x: 0.0003
  tilt_y: 0.0364
  scale: 3.0436
  rotation: -0.9645

Best score: 0.4804: : 499iter [00:15, 40.04iter/s]
Best score: 0.4804: : 500iter [00:15, 40.02iter/s]
Best score: 0.4804: : 500iter [00:15, 40.02iter/s]
Best score: 0.4804: : 501iter [00:15, 40.02iter/s]
Best score: 0.4804: : 502iter [00:15, 40.02iter/s]
Best score: 0.4804: : 503iter [00:15, 40.02iter/s]
Best score: 0.4804: : 504iter [00:15, 40.02iter/s]
Best score: 0.4804: : 505iter [00:15, 33.87iter/s]
Best score: 0.4804: : 505iter [00:15, 33.87iter/s]
Best score: 0.4804: : 506iter [00:15, 33.87iter/s]
Best score: 0.4804: : 507iter [00:15, 33.87iter/s]
Best score: 0.4804: : 508iter [00:15, 33.87iter/s]
Best score: 0.4653: : 509iter [00:15, 33.87iter/s]
Best score: 0.4653: : 510iter [00:15, 35.45iter/s]
Best score: 0.4653: : 510iter [00:15, 35.45iter/s]
Best score: 0.4653: : 511iter [00:15, 35.45iter/s]
Best score: 0.4653: : 512iter [00:15, 35.45iter/s]
Best score: 0.4653: : 513iter [00:15, 35.45iter/s]
Best score: 0.4653: : 514iter [00:15, 35.45iter/s]
Best score: 0.4653: : 515iter [00:15, 36.81iter/s]
Best score: 0.4653: : 515iter [00:15, 36.81iter/s]
Best score: 0.4653: : 516iter [00:15, 36.81iter/s]
Best score: 0.4653: : 517iter [00:15, 36.81iter/s]
Best score: 0.4653: : 518iter [00:15, 36.81iter/s]
Best score: 0.4619: : 519iter [00:15, 36.81iter/s]
Best score: 0.4619: : 520iter [00:15, 37.77iter/s]
Best score: 0.4619: : 520iter [00:15, 37.77iter/s]
Best score: 0.4619: : 521iter [00:15, 37.77iter/s]
Best score: 0.4619: : 522iter [00:15, 37.77iter/s]
Best score: 0.4619: : 523iter [00:15, 37.77iter/s]
Best score: 0.4619: : 524iter [00:15, 37.77iter/s]
Best score: 0.4619: : 525iter [00:15, 38.66iter/s]
Best score: 0.4619: : 525iter [00:15, 38.66iter/s]
Best score: 0.4619: : 526iter [00:15, 38.66iter/s]
Best score: 0.4619: : 527iter [00:15, 38.66iter/s]
Best score: 0.4619: : 528iter [00:15, 38.66iter/s]
Best score: 0.4602: : 529iter [00:15, 38.66iter/s]
Best score: 0.4602: : 530iter [00:15, 39.09iter/s]
Best score: 0.4602: : 530iter [00:15, 39.09iter/s]
Best score: 0.4602: : 531iter [00:15, 39.09iter/s]
Best score: 0.4602: : 532iter [00:15, 39.09iter/s]
Best score: 0.4602: : 533iter [00:15, 39.09iter/s]
Best score: 0.4602: : 534iter [00:16, 39.09iter/s]
Best score: 0.4602: : 535iter [00:16, 39.61iter/s]
Best score: 0.4602: : 535iter [00:16, 39.61iter/s]
Best score: 0.4602: : 536iter [00:16, 39.61iter/s]
Best score: 0.4602: : 537iter [00:16, 39.61iter/s]
Best score: 0.4602: : 538iter [00:16, 39.61iter/s]
Best score: 0.4601: : 539iter [00:16, 39.61iter/s]
Best score: 0.4601: : 540iter [00:16, 39.73iter/s]
Best score: 0.4601: : 540iter [00:16, 39.73iter/s]
Best score: 0.4601: : 541iter [00:16, 39.73iter/s]
Best score: 0.4601: : 542iter [00:16, 39.73iter/s]
Best score: 0.4601: : 543iter [00:16, 39.73iter/s]
Best score: 0.4601: : 544iter [00:16, 39.73iter/s]
Best score: 0.4601: : 545iter [00:16, 39.99iter/s]
Best score: 0.4601: : 545iter [00:16, 39.99iter/s]
Best score: 0.4601: : 546iter [00:16, 39.99iter/s]
Best score: 0.4601: : 547iter [00:16, 39.99iter/s]
Best score: 0.4601: : 548iter [00:16, 39.99iter/s]
[ITER 550] Best: 0.4601
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4601: : 549iter [00:16, 39.99iter/s]
Best score: 0.4601: : 550iter [00:16, 39.93iter/s]
[Periodic outlier @ iter 450] Removing 1000 frame 16 (score=1.58, threshold=1.28)
  Remaining frames: 19

Best score: 0.4601: : 550iter [00:16, 39.93iter/s]
Best score: 0.4601: : 551iter [00:16, 39.93iter/s]
Best score: 0.4601: : 552iter [00:16, 39.93iter/s]
Best score: 0.4601: : 553iter [00:16, 39.93iter/s]
Best score: 0.4601: : 554iter [00:16, 39.93iter/s]
Best score: 0.4601: : 555iter [00:16, 31.75iter/s]
Best score: 0.4601: : 555iter [00:16, 31.75iter/s]
Best score: 0.4601: : 556iter [00:16, 31.75iter/s]
Best score: 0.4601: : 557iter [00:16, 31.75iter/s]
Best score: 0.4601: : 558iter [00:16, 31.75iter/s]
Best score: 0.4043: : 559iter [00:16, 31.75iter/s]
Best score: 0.4043: : 560iter [00:16, 34.02iter/s]
Best score: 0.4043: : 560iter [00:16, 34.02iter/s]
Best score: 0.4043: : 561iter [00:16, 34.02iter/s]
Best score: 0.4043: : 562iter [00:16, 34.02iter/s]
Best score: 0.4043: : 563iter [00:16, 34.02iter/s]
Best score: 0.4043: : 564iter [00:16, 34.02iter/s]
Best score: 0.4043: : 565iter [00:16, 36.13iter/s]
Best score: 0.4043: : 565iter [00:16, 36.13iter/s]
Best score: 0.4043: : 566iter [00:16, 36.13iter/s]
Best score: 0.4043: : 567iter [00:16, 36.13iter/s]
Best score: 0.4043: : 568iter [00:16, 36.13iter/s]
Best score: 0.4043: : 569iter [00:16, 36.13iter/s]
Best score: 0.4043: : 570iter [00:16, 37.37iter/s]
Best score: 0.4043: : 570iter [00:16, 37.37iter/s]
Best score: 0.4043: : 571iter [00:17, 37.37iter/s]
Best score: 0.4043: : 572iter [00:17, 37.37iter/s]
Best score: 0.4043: : 573iter [00:17, 37.37iter/s]
Best score: 0.4043: : 574iter [00:17, 37.37iter/s]
Best score: 0.4043: : 575iter [00:17, 38.60iter/s]
Best score: 0.4043: : 575iter [00:17, 38.60iter/s]
Best score: 0.4043: : 576iter [00:17, 38.60iter/s]
Best score: 0.4043: : 577iter [00:17, 38.60iter/s]
Best score: 0.4043: : 578iter [00:17, 38.60iter/s]
Best score: 0.4043: : 579iter [00:17, 38.60iter/s]
Best score: 0.4043: : 580iter [00:17, 39.21iter/s]
Best score: 0.4043: : 580iter [00:17, 39.21iter/s]
Best score: 0.4043: : 581iter [00:17, 39.21iter/s]
Best score: 0.4043: : 582iter [00:17, 39.21iter/s]
Best score: 0.4043: : 583iter [00:17, 39.21iter/s]
Best score: 0.4043: : 584iter [00:17, 39.21iter/s]
Best score: 0.4043: : 585iter [00:17, 39.74iter/s]
Best score: 0.4043: : 585iter [00:17, 39.74iter/s]
Best score: 0.4043: : 586iter [00:17, 39.74iter/s]
Best score: 0.4043: : 587iter [00:17, 39.74iter/s]
Best score: 0.4043: : 588iter [00:17, 39.74iter/s]
Best score: 0.4043: : 589iter [00:17, 39.74iter/s]
Best score: 0.4043: : 590iter [00:17, 40.05iter/s]
Best score: 0.4043: : 590iter [00:17, 40.05iter/s]
Best score: 0.4043: : 591iter [00:17, 40.05iter/s]
Best score: 0.4043: : 592iter [00:17, 40.05iter/s]
Best score: 0.4043: : 593iter [00:17, 40.05iter/s]
Best score: 0.4043: : 594iter [00:17, 40.05iter/s]
Best score: 0.4043: : 595iter [00:17, 40.60iter/s]
Best score: 0.4043: : 595iter [00:17, 40.60iter/s]
Best score: 0.4043: : 596iter [00:17, 40.60iter/s]
Best score: 0.4043: : 597iter [00:17, 40.60iter/s]
Best score: 0.4043: : 598iter [00:17, 40.60iter/s]
[ITER 600] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 599iter [00:17, 40.60iter/s]
Best score: 0.4043: : 600iter [00:17, 40.70iter/s]
Best score: 0.4043: : 600iter [00:17, 40.70iter/s]
Best score: 0.4043: : 601iter [00:17, 40.70iter/s]
Best score: 0.4043: : 602iter [00:17, 40.70iter/s]
Best score: 0.4043: : 603iter [00:17, 40.70iter/s]
Best score: 0.4043: : 604iter [00:17, 40.70iter/s]
Best score: 0.4043: : 605iter [00:17, 34.61iter/s]
Best score: 0.4043: : 605iter [00:17, 34.61iter/s]
Best score: 0.4043: : 606iter [00:17, 34.61iter/s]
Best score: 0.4043: : 607iter [00:17, 34.61iter/s]
Best score: 0.4043: : 608iter [00:18, 34.61iter/s]
Best score: 0.4043: : 609iter [00:18, 29.98iter/s]
Best score: 0.4043: : 609iter [00:18, 29.98iter/s]
Best score: 0.4043: : 610iter [00:18, 29.98iter/s]
Best score: 0.4043: : 611iter [00:18, 29.98iter/s]
Best score: 0.4043: : 612iter [00:18, 29.98iter/s]
Best score: 0.4043: : 613iter [00:18, 29.98iter/s]
Best score: 0.4043: : 614iter [00:18, 32.62iter/s]
Best score: 0.4043: : 614iter [00:18, 32.62iter/s]
Best score: 0.4043: : 615iter [00:18, 32.62iter/s]
Best score: 0.4043: : 616iter [00:18, 32.62iter/s]
Best score: 0.4043: : 617iter [00:18, 32.62iter/s]
Best score: 0.4043: : 618iter [00:18, 32.62iter/s]
Best score: 0.4043: : 619iter [00:18, 34.88iter/s]
Best score: 0.4043: : 619iter [00:18, 34.88iter/s]
Best score: 0.4043: : 620iter [00:18, 34.88iter/s]
Best score: 0.4043: : 621iter [00:18, 34.88iter/s]
Best score: 0.4043: : 622iter [00:18, 34.88iter/s]
Best score: 0.4043: : 623iter [00:18, 34.88iter/s]
Best score: 0.4043: : 624iter [00:18, 36.49iter/s]
Best score: 0.4043: : 624iter [00:18, 36.49iter/s]
Best score: 0.4043: : 625iter [00:18, 36.49iter/s]
Best score: 0.4043: : 626iter [00:18, 36.49iter/s]
Best score: 0.4043: : 627iter [00:18, 36.49iter/s]
Best score: 0.4043: : 628iter [00:18, 36.49iter/s]
Best score: 0.4043: : 629iter [00:18, 37.95iter/s]
Best score: 0.4043: : 629iter [00:18, 37.95iter/s]
Best score: 0.4043: : 630iter [00:18, 37.95iter/s]
Best score: 0.4043: : 631iter [00:18, 37.95iter/s]
Best score: 0.4043: : 632iter [00:18, 37.95iter/s]
Best score: 0.4043: : 633iter [00:18, 37.95iter/s]
Best score: 0.4043: : 634iter [00:18, 38.83iter/s]
Best score: 0.4043: : 634iter [00:18, 38.83iter/s]
Best score: 0.4043: : 635iter [00:18, 38.83iter/s]
Best score: 0.4043: : 636iter [00:18, 38.83iter/s]
Best score: 0.4043: : 637iter [00:18, 38.83iter/s]
Best score: 0.4043: : 638iter [00:18, 38.83iter/s]
Best score: 0.4043: : 639iter [00:18, 39.64iter/s]
Best score: 0.4043: : 639iter [00:18, 39.64iter/s]
Best score: 0.4043: : 640iter [00:18, 39.64iter/s]
Best score: 0.4043: : 641iter [00:18, 39.64iter/s]
Best score: 0.4043: : 642iter [00:18, 39.64iter/s]
Best score: 0.4043: : 643iter [00:18, 39.64iter/s]
Best score: 0.4043: : 644iter [00:18, 40.02iter/s]
Best score: 0.4043: : 644iter [00:18, 40.02iter/s]
Best score: 0.4043: : 645iter [00:18, 40.02iter/s]
Best score: 0.4043: : 646iter [00:19, 40.02iter/s]
Best score: 0.4043: : 647iter [00:19, 40.02iter/s]
Best score: 0.4043: : 648iter [00:19, 40.02iter/s]
Best score: 0.4043: : 649iter [00:19, 40.59iter/s]
[ITER 650] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 649iter [00:19, 40.59iter/s]
Best score: 0.4043: : 650iter [00:19, 40.59iter/s]
Best score: 0.4043: : 651iter [00:19, 40.59iter/s]
Best score: 0.4043: : 652iter [00:19, 40.59iter/s]
Best score: 0.4043: : 653iter [00:19, 40.59iter/s]
Best score: 0.4043: : 654iter [00:19, 34.33iter/s]
Best score: 0.4043: : 654iter [00:19, 34.33iter/s]
Best score: 0.4043: : 655iter [00:19, 34.33iter/s]
Best score: 0.4043: : 656iter [00:19, 34.33iter/s]
Best score: 0.4043: : 657iter [00:19, 34.33iter/s]
Best score: 0.4043: : 658iter [00:19, 34.33iter/s]
Best score: 0.4043: : 659iter [00:19, 36.31iter/s]
Best score: 0.4043: : 659iter [00:19, 36.31iter/s]
Best score: 0.4043: : 660iter [00:19, 36.31iter/s]
Best score: 0.4043: : 661iter [00:19, 36.31iter/s]
Best score: 0.4043: : 662iter [00:19, 36.31iter/s]
Best score: 0.4043: : 663iter [00:19, 36.31iter/s]
Best score: 0.4043: : 664iter [00:19, 37.65iter/s]
Best score: 0.4043: : 664iter [00:19, 37.65iter/s]
Best score: 0.4043: : 665iter [00:19, 37.65iter/s]
Best score: 0.4043: : 666iter [00:19, 37.65iter/s]
Best score: 0.4043: : 667iter [00:19, 37.65iter/s]
Best score: 0.4043: : 668iter [00:19, 37.65iter/s]
Best score: 0.4043: : 669iter [00:19, 38.80iter/s]
Best score: 0.4043: : 669iter [00:19, 38.80iter/s]
Best score: 0.4043: : 670iter [00:19, 38.80iter/s]
Best score: 0.4043: : 671iter [00:19, 38.80iter/s]
Best score: 0.4043: : 672iter [00:19, 38.80iter/s]
Best score: 0.4043: : 673iter [00:19, 38.80iter/s]
Best score: 0.4043: : 674iter [00:19, 39.62iter/s]
Best score: 0.4043: : 674iter [00:19, 39.62iter/s]
Best score: 0.4043: : 675iter [00:19, 39.62iter/s]
Best score: 0.4043: : 676iter [00:19, 39.62iter/s]
Best score: 0.4043: : 677iter [00:19, 39.62iter/s]
Best score: 0.4043: : 678iter [00:19, 39.62iter/s]
Best score: 0.4043: : 679iter [00:19, 40.40iter/s]
Best score: 0.4043: : 679iter [00:19, 40.40iter/s]
Best score: 0.4043: : 680iter [00:19, 40.40iter/s]
Best score: 0.4043: : 681iter [00:19, 40.40iter/s]
Best score: 0.4043: : 682iter [00:19, 40.40iter/s]
Best score: 0.4043: : 683iter [00:19, 40.40iter/s]
Best score: 0.4043: : 684iter [00:19, 40.78iter/s]
Best score: 0.4043: : 684iter [00:19, 40.78iter/s]
Best score: 0.4043: : 685iter [00:20, 40.78iter/s]
Best score: 0.4043: : 686iter [00:20, 40.78iter/s]
Best score: 0.4043: : 687iter [00:20, 40.78iter/s]
Best score: 0.4043: : 688iter [00:20, 40.78iter/s]
Best score: 0.4043: : 689iter [00:20, 41.33iter/s]
Best score: 0.4043: : 689iter [00:20, 41.33iter/s]
Best score: 0.4043: : 690iter [00:20, 41.33iter/s]
Best score: 0.4043: : 691iter [00:20, 41.33iter/s]
Best score: 0.4043: : 692iter [00:20, 41.33iter/s]
Best score: 0.4043: : 693iter [00:20, 41.33iter/s]
Best score: 0.4043: : 694iter [00:20, 41.38iter/s]
Best score: 0.4043: : 694iter [00:20, 41.38iter/s]
Best score: 0.4043: : 695iter [00:20, 41.38iter/s]
Best score: 0.4043: : 696iter [00:20, 41.38iter/s]
Best score: 0.4043: : 697iter [00:20, 41.38iter/s]
Best score: 0.4043: : 698iter [00:20, 41.38iter/s]
Best score: 0.4043: : 699iter [00:20, 41.63iter/s]
[ITER 700] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 699iter [00:20, 41.63iter/s]
Best score: 0.4043: : 700iter [00:20, 41.63iter/s]
Best score: 0.4043: : 701iter [00:20, 41.63iter/s]
Best score: 0.4043: : 702iter [00:20, 41.63iter/s]
Best score: 0.4043: : 703iter [00:20, 41.63iter/s]
Best score: 0.4043: : 704iter [00:20, 34.89iter/s]
Best score: 0.4043: : 704iter [00:20, 34.89iter/s]
Best score: 0.4043: : 705iter [00:20, 34.89iter/s]
Best score: 0.4043: : 706iter [00:20, 34.89iter/s]
Best score: 0.4043: : 707iter [00:20, 34.89iter/s]
Best score: 0.4043: : 708iter [00:20, 34.89iter/s]
Best score: 0.4043: : 709iter [00:20, 36.75iter/s]
Best score: 0.4043: : 709iter [00:20, 36.75iter/s]
Best score: 0.4043: : 710iter [00:20, 36.75iter/s]
Best score: 0.4043: : 711iter [00:20, 36.75iter/s]
Best score: 0.4043: : 712iter [00:20, 36.75iter/s]
Best score: 0.4043: : 713iter [00:20, 36.75iter/s]
Best score: 0.4043: : 714iter [00:20, 38.07iter/s]
Best score: 0.4043: : 714iter [00:20, 38.07iter/s]
Best score: 0.4043: : 715iter [00:20, 38.07iter/s]
Best score: 0.4043: : 716iter [00:20, 38.07iter/s]
Best score: 0.4043: : 717iter [00:20, 38.07iter/s]
Best score: 0.4043: : 718iter [00:20, 38.07iter/s]
Best score: 0.4043: : 719iter [00:20, 39.35iter/s]
Best score: 0.4043: : 719iter [00:20, 39.35iter/s]
Best score: 0.4043: : 720iter [00:20, 39.35iter/s]
Best score: 0.4043: : 721iter [00:20, 39.35iter/s]
Best score: 0.4043: : 722iter [00:20, 39.35iter/s]
Best score: 0.4043: : 723iter [00:20, 39.35iter/s]
Best score: 0.4043: : 724iter [00:20, 40.05iter/s]
Best score: 0.4043: : 724iter [00:21, 40.05iter/s]
Best score: 0.4043: : 725iter [00:21, 40.05iter/s]
Best score: 0.4043: : 726iter [00:21, 40.05iter/s]
Best score: 0.4043: : 727iter [00:21, 40.05iter/s]
Best score: 0.4043: : 728iter [00:21, 40.05iter/s]
Best score: 0.4043: : 729iter [00:21, 40.73iter/s]
Best score: 0.4043: : 729iter [00:21, 40.73iter/s]
Best score: 0.4043: : 730iter [00:21, 40.73iter/s]
Best score: 0.4043: : 731iter [00:21, 40.73iter/s]
Best score: 0.4043: : 732iter [00:21, 40.73iter/s]
Best score: 0.4043: : 733iter [00:21, 40.73iter/s]
Best score: 0.4043: : 734iter [00:21, 40.96iter/s]
Best score: 0.4043: : 734iter [00:21, 40.96iter/s]
Best score: 0.4043: : 735iter [00:21, 40.96iter/s]
Best score: 0.4043: : 736iter [00:21, 40.96iter/s]
Best score: 0.4043: : 737iter [00:21, 40.96iter/s]
Best score: 0.4043: : 738iter [00:21, 40.96iter/s]
Best score: 0.4043: : 739iter [00:21, 41.44iter/s]
Best score: 0.4043: : 739iter [00:21, 41.44iter/s]
Best score: 0.4043: : 740iter [00:21, 41.44iter/s]
Best score: 0.4043: : 741iter [00:21, 41.44iter/s]
Best score: 0.4043: : 742iter [00:21, 41.44iter/s]
Best score: 0.4043: : 743iter [00:21, 41.44iter/s]
Best score: 0.4043: : 744iter [00:21, 41.46iter/s]
Best score: 0.4043: : 744iter [00:21, 41.46iter/s]
Best score: 0.4043: : 745iter [00:21, 41.46iter/s]
Best score: 0.4043: : 746iter [00:21, 41.46iter/s]
Best score: 0.4043: : 747iter [00:21, 41.46iter/s]
Best score: 0.4043: : 748iter [00:21, 41.46iter/s]
Best score: 0.4043: : 749iter [00:21, 41.79iter/s]
[ITER 750] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 749iter [00:21, 41.79iter/s]
Best score: 0.4043: : 750iter [00:21, 41.79iter/s]
Best score: 0.4043: : 751iter [00:21, 41.79iter/s]
Best score: 0.4043: : 752iter [00:21, 41.79iter/s]
Best score: 0.4043: : 753iter [00:21, 41.79iter/s]
Best score: 0.4043: : 754iter [00:21, 34.96iter/s]
Best score: 0.4043: : 754iter [00:21, 34.96iter/s]
Best score: 0.4043: : 755iter [00:21, 34.96iter/s]
Best score: 0.4043: : 756iter [00:21, 34.96iter/s]
Best score: 0.4043: : 757iter [00:21, 34.96iter/s]
Best score: 0.4043: : 758iter [00:21, 34.96iter/s]
Best score: 0.4043: : 759iter [00:21, 36.89iter/s]
Best score: 0.4043: : 759iter [00:21, 36.89iter/s]
Best score: 0.4043: : 760iter [00:21, 36.89iter/s]
Best score: 0.4043: : 761iter [00:21, 36.89iter/s]
Best score: 0.4043: : 762iter [00:22, 36.89iter/s]
Best score: 0.4043: : 763iter [00:22, 36.89iter/s]
Best score: 0.4043: : 764iter [00:22, 38.19iter/s]
Best score: 0.4043: : 764iter [00:22, 38.19iter/s]
Best score: 0.4043: : 765iter [00:22, 38.19iter/s]
Best score: 0.4043: : 766iter [00:22, 38.19iter/s]
Best score: 0.4043: : 767iter [00:22, 38.19iter/s]
Best score: 0.4043: : 768iter [00:22, 38.19iter/s]
Best score: 0.4043: : 769iter [00:22, 39.54iter/s]
Best score: 0.4043: : 769iter [00:22, 39.54iter/s]
Best score: 0.4043: : 770iter [00:22, 39.54iter/s]
Best score: 0.4043: : 771iter [00:22, 39.54iter/s]
Best score: 0.4043: : 772iter [00:22, 39.54iter/s]
Best score: 0.4043: : 773iter [00:22, 39.54iter/s]
Best score: 0.4043: : 774iter [00:22, 40.15iter/s]
Best score: 0.4043: : 774iter [00:22, 40.15iter/s]
Best score: 0.4043: : 775iter [00:22, 40.15iter/s]
Best score: 0.4043: : 776iter [00:22, 40.15iter/s]
Best score: 0.4043: : 777iter [00:22, 40.15iter/s]
Best score: 0.4043: : 778iter [00:22, 40.15iter/s]
Best score: 0.4043: : 779iter [00:22, 40.78iter/s]
Best score: 0.4043: : 779iter [00:22, 40.78iter/s]
Best score: 0.4043: : 780iter [00:22, 40.78iter/s]
Best score: 0.4043: : 781iter [00:22, 40.78iter/s]
Best score: 0.4043: : 782iter [00:22, 40.78iter/s]
Best score: 0.4043: : 783iter [00:22, 40.78iter/s]
Best score: 0.4043: : 784iter [00:22, 41.12iter/s]
Best score: 0.4043: : 784iter [00:22, 41.12iter/s]
Best score: 0.4043: : 785iter [00:22, 41.12iter/s]
Best score: 0.4043: : 786iter [00:22, 41.12iter/s]
Best score: 0.4043: : 787iter [00:22, 41.12iter/s]
Best score: 0.4043: : 788iter [00:22, 41.12iter/s]
Best score: 0.4043: : 789iter [00:22, 41.54iter/s]
Best score: 0.4043: : 789iter [00:22, 41.54iter/s]
Best score: 0.4043: : 790iter [00:22, 41.54iter/s]
Best score: 0.4043: : 791iter [00:22, 41.54iter/s]
Best score: 0.4043: : 792iter [00:22, 41.54iter/s]
Best score: 0.4043: : 793iter [00:22, 41.54iter/s]
Best score: 0.4043: : 794iter [00:22, 41.61iter/s]
Best score: 0.4043: : 794iter [00:22, 41.61iter/s]
Best score: 0.4043: : 795iter [00:22, 41.61iter/s]
Best score: 0.4043: : 796iter [00:22, 41.61iter/s]
Best score: 0.4043: : 797iter [00:22, 41.61iter/s]
Best score: 0.4043: : 798iter [00:22, 41.61iter/s]
Best score: 0.4043: : 799iter [00:22, 41.93iter/s]
[ITER 800] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 799iter [00:22, 41.93iter/s]
Best score: 0.4043: : 800iter [00:22, 41.93iter/s]
Best score: 0.4043: : 801iter [00:23, 41.93iter/s]
Best score: 0.4043: : 802iter [00:23, 41.93iter/s]
Best score: 0.4043: : 803iter [00:23, 41.93iter/s]
Best score: 0.4043: : 804iter [00:23, 35.26iter/s]
Best score: 0.4043: : 804iter [00:23, 35.26iter/s]
Best score: 0.4043: : 805iter [00:23, 35.26iter/s]
Best score: 0.4043: : 806iter [00:23, 35.26iter/s]
Best score: 0.4043: : 807iter [00:23, 35.26iter/s]
Best score: 0.4043: : 808iter [00:23, 35.26iter/s]
Best score: 0.4043: : 809iter [00:23, 37.28iter/s]
Best score: 0.4043: : 809iter [00:23, 37.28iter/s]
Best score: 0.4043: : 810iter [00:23, 37.28iter/s]
Best score: 0.4043: : 811iter [00:23, 37.28iter/s]
Best score: 0.4043: : 812iter [00:23, 37.28iter/s]
Best score: 0.4043: : 813iter [00:23, 37.28iter/s]
Best score: 0.4043: : 814iter [00:23, 38.48iter/s]
Best score: 0.4043: : 814iter [00:23, 38.48iter/s]
Best score: 0.4043: : 815iter [00:23, 38.48iter/s]
Best score: 0.4043: : 816iter [00:23, 38.48iter/s]
Best score: 0.4043: : 817iter [00:23, 38.48iter/s]
Best score: 0.4043: : 818iter [00:23, 38.48iter/s]
Best score: 0.4043: : 819iter [00:23, 39.63iter/s]
Best score: 0.4043: : 819iter [00:23, 39.63iter/s]
Best score: 0.4043: : 820iter [00:23, 39.63iter/s]
Best score: 0.4043: : 821iter [00:23, 39.63iter/s]
Best score: 0.4043: : 822iter [00:23, 39.63iter/s]
Best score: 0.4043: : 823iter [00:23, 39.63iter/s]
Best score: 0.4043: : 824iter [00:23, 40.22iter/s]
Best score: 0.4043: : 824iter [00:23, 40.22iter/s]
Best score: 0.4043: : 825iter [00:23, 40.22iter/s]
Best score: 0.4043: : 826iter [00:23, 40.22iter/s]
Best score: 0.4043: : 827iter [00:23, 40.22iter/s]
Best score: 0.4043: : 828iter [00:23, 40.22iter/s]
Best score: 0.4043: : 829iter [00:23, 40.95iter/s]
Best score: 0.4043: : 829iter [00:23, 40.95iter/s]
Best score: 0.4043: : 830iter [00:23, 40.95iter/s]
Best score: 0.4043: : 831iter [00:23, 40.95iter/s]
Best score: 0.4043: : 832iter [00:23, 40.95iter/s]
Best score: 0.4043: : 833iter [00:23, 40.95iter/s]
Best score: 0.4043: : 834iter [00:23, 41.12iter/s]
Best score: 0.4043: : 834iter [00:23, 41.12iter/s]
Best score: 0.4043: : 835iter [00:23, 41.12iter/s]
Best score: 0.4043: : 836iter [00:23, 41.12iter/s]
Best score: 0.4043: : 837iter [00:23, 41.12iter/s]
Best score: 0.4043: : 838iter [00:23, 41.12iter/s]
Best score: 0.4043: : 839iter [00:23, 41.56iter/s]
Best score: 0.4043: : 839iter [00:23, 41.56iter/s]
Best score: 0.4043: : 840iter [00:23, 41.56iter/s]
Best score: 0.4043: : 841iter [00:23, 41.56iter/s]
Best score: 0.4043: : 842iter [00:23, 41.56iter/s]
Best score: 0.4043: : 843iter [00:24, 41.56iter/s]
Best score: 0.4043: : 844iter [00:24, 41.46iter/s]
Best score: 0.4043: : 844iter [00:24, 41.46iter/s]
Best score: 0.4043: : 845iter [00:24, 41.46iter/s]
Best score: 0.4043: : 846iter [00:24, 41.46iter/s]
Best score: 0.4043: : 847iter [00:24, 41.46iter/s]
Best score: 0.4043: : 848iter [00:24, 41.46iter/s]
Best score: 0.4043: : 849iter [00:24, 41.74iter/s]
[ITER 850] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 849iter [00:24, 41.74iter/s]
Best score: 0.4043: : 850iter [00:24, 41.74iter/s]
Best score: 0.4043: : 851iter [00:24, 41.74iter/s]
Best score: 0.4043: : 852iter [00:24, 41.74iter/s]
Best score: 0.4043: : 853iter [00:24, 41.74iter/s]
Best score: 0.4043: : 854iter [00:24, 35.01iter/s]
Best score: 0.4043: : 854iter [00:24, 35.01iter/s]
Best score: 0.4043: : 855iter [00:24, 35.01iter/s]
Best score: 0.4043: : 856iter [00:24, 35.01iter/s]
Best score: 0.4043: : 857iter [00:24, 35.01iter/s]
Best score: 0.4043: : 858iter [00:24, 35.01iter/s]
Best score: 0.4043: : 859iter [00:24, 37.00iter/s]
Best score: 0.4043: : 859iter [00:24, 37.00iter/s]
Best score: 0.4043: : 860iter [00:24, 37.00iter/s]
Best score: 0.4043: : 861iter [00:24, 37.00iter/s]
Best score: 0.4043: : 862iter [00:24, 37.00iter/s]
Best score: 0.4043: : 863iter [00:24, 37.00iter/s]
Best score: 0.4043: : 864iter [00:24, 38.22iter/s]
Best score: 0.4043: : 864iter [00:24, 38.22iter/s]
Best score: 0.4043: : 865iter [00:24, 38.22iter/s]
Best score: 0.4043: : 866iter [00:24, 38.22iter/s]
Best score: 0.4043: : 867iter [00:24, 38.22iter/s]
Best score: 0.4043: : 868iter [00:24, 38.22iter/s]
Best score: 0.4043: : 869iter [00:24, 39.52iter/s]
Best score: 0.4043: : 869iter [00:24, 39.52iter/s]
Best score: 0.4043: : 870iter [00:24, 39.52iter/s]
Best score: 0.4043: : 871iter [00:24, 39.52iter/s]
Best score: 0.4043: : 872iter [00:24, 39.52iter/s]
Best score: 0.4043: : 873iter [00:24, 39.52iter/s]
Best score: 0.4043: : 874iter [00:24, 40.24iter/s]
Best score: 0.4043: : 874iter [00:24, 40.24iter/s]
Best score: 0.4043: : 875iter [00:24, 40.24iter/s]
Best score: 0.4043: : 876iter [00:24, 40.24iter/s]
Best score: 0.4043: : 877iter [00:24, 40.24iter/s]
Best score: 0.4043: : 878iter [00:24, 40.24iter/s]
Best score: 0.4043: : 879iter [00:24, 40.86iter/s]
Best score: 0.4043: : 879iter [00:24, 40.86iter/s]
Best score: 0.4043: : 880iter [00:24, 40.86iter/s]
Best score: 0.4043: : 881iter [00:24, 40.86iter/s]
Best score: 0.4043: : 882iter [00:25, 40.86iter/s]
Best score: 0.4043: : 883iter [00:25, 40.86iter/s]
Best score: 0.4043: : 884iter [00:25, 40.94iter/s]
Best score: 0.4043: : 884iter [00:25, 40.94iter/s]
Best score: 0.4043: : 885iter [00:25, 40.94iter/s]
Best score: 0.4043: : 886iter [00:25, 40.94iter/s]
Best score: 0.4043: : 887iter [00:25, 40.94iter/s]
Best score: 0.4043: : 888iter [00:25, 40.94iter/s]
Best score: 0.4043: : 889iter [00:25, 41.42iter/s]
Best score: 0.4043: : 889iter [00:25, 41.42iter/s]
Best score: 0.4043: : 890iter [00:25, 41.42iter/s]
Best score: 0.4043: : 891iter [00:25, 41.42iter/s]
Best score: 0.4043: : 892iter [00:25, 41.42iter/s]
Best score: 0.4043: : 893iter [00:25, 41.42iter/s]
Best score: 0.4043: : 894iter [00:25, 41.37iter/s]
Best score: 0.4043: : 894iter [00:25, 41.37iter/s]
Best score: 0.4043: : 895iter [00:25, 41.37iter/s]
Best score: 0.4043: : 896iter [00:25, 41.37iter/s]
Best score: 0.4043: : 897iter [00:25, 41.37iter/s]
Best score: 0.4043: : 898iter [00:25, 41.37iter/s]
Best score: 0.4043: : 899iter [00:25, 41.68iter/s]
[ITER 900] Best: 0.4043
[PARAMS] Current best parameters:
  fx: 522.7310
  fy: 522.6053
  px: 976.6839
  py: 761.2535
  k1: 0.00424295
  k2: 0.00608700
  k3: -0.00212566
  k4: -0.00053456
  p1: -0.00340762
  p2: 0.00927826
  p3: -0.00096568
  p4: 0.00089784
  s1: -0.0052
  s2: -0.0018
  s3: 0.0051
  s4: -0.0027
  tilt_x: 0.0050
  tilt_y: 0.0287
  scale: 3.0333
  rotation: -0.9770

Best score: 0.4043: : 899iter [00:25, 41.68iter/s]
Best score: 0.4043: : 900iter [00:25, 41.68iter/s]
Best score: 0.4043: : 901iter [00:25, 41.68iter/s]
Best score: 0.4043: : 902iter [00:25, 41.68iter/s]
Best score: 0.4043: : 903iter [00:25, 41.68iter/s]
Best score: 0.4043: : 904iter [00:25, 34.53iter/s]
Best score: 0.4043: : 904iter [00:25, 34.53iter/s]
Best score: 0.4043: : 905iter [00:25, 34.53iter/s]
Best score: 0.4043: : 906iter [00:25, 34.53iter/s]
Best score: 0.4043: : 907iter [00:25, 34.53iter/s]
Best score: 0.4043: : 908iter [00:25, 34.53iter/s]
Best score: 0.4043: : 909iter [00:25, 36.41iter/s]
Best score: 0.4043: : 909iter [00:25, 36.41iter/s]
Best score: 0.4043: : 910iter [00:25, 36.41iter/s]
Best score: 0.4043: : 911iter [00:25, 36.41iter/s]
Best score: 0.4043: : 912iter [00:25, 36.41iter/s]
Best score: 0.4043: : 913iter [00:25, 36.41iter/s]
Best score: 0.4043: : 914iter [00:25, 37.78iter/s]
Best score: 0.4043: : 914iter [00:25, 37.78iter/s]
Best score: 0.4043: : 915iter [00:25, 37.78iter/s]
Best score: 0.4043: : 916iter [00:25, 37.78iter/s]
Best score: 0.4043: : 917iter [00:25, 37.78iter/s]
Best score: 0.4043: : 918iter [00:25, 37.78iter/s]
Best score: 0.4043: : 919iter [00:25, 38.94iter/s]
Best score: 0.4043: : 919iter [00:25, 38.94iter/s]
Best score: 0.4043: : 920iter [00:25, 38.94iter/s]
Best score: 0.4043: : 921iter [00:26, 38.94iter/s]
Best score: 0.4043: : 922iter [00:26, 38.94iter/s]
Best score: 0.4043: : 923iter [00:26, 38.94iter/s]
Best score: 0.4043: : 924iter [00:26, 39.70iter/s]
Best score: 0.4043: : 924iter [00:26, 39.70iter/s]
Best score: 0.4043: : 925iter [00:26, 39.70iter/s]
Best score: 0.4043: : 926iter [00:26, 39.70iter/s]
Best score: 0.4043: : 927iter [00:26, 39.70iter/s]
Best score: 0.4043: : 928iter [00:26, 39.70iter/s]
Best score: 0.4043: : 929iter [00:26, 33.24iter/s]
Best score: 0.4043: : 929iter [00:26, 33.24iter/s]
Best score: 0.4043: : 930iter [00:26, 33.24iter/s]
Best score: 0.4043: : 931iter [00:26, 33.24iter/s]
Best score: 0.4043: : 932iter [00:26, 33.24iter/s]
Best score: 0.4043: : 933iter [00:26, 33.24iter/s]
Best score: 0.4043: : 934iter [00:26, 35.37iter/s]
Best score: 0.4043: : 934iter [00:26, 35.37iter/s]
Best score: 0.4043: : 935iter [00:26, 35.37iter/s]
Best score: 0.4043: : 936iter [00:26, 35.37iter/s]
Best score: 0.4043: : 937iter [00:26, 35.37iter/s]
Best score: 0.4043: : 938iter [00:26, 35.37iter/s]
Best score: 0.4043: : 939iter [00:26, 37.18iter/s]
Best score: 0.4043: : 939iter [00:26, 37.18iter/s]
Best score: 0.4043: : 940iter [00:26, 37.18iter/s]
Best score: 0.4043: : 941iter [00:26, 37.18iter/s]
Best score: 0.4043: : 942iter [00:26, 37.18iter/s]
Best score: 0.4043: : 943iter [00:26, 37.18iter/s]
Best score: 0.4043: : 944iter [00:26, 38.18iter/s]
Best score: 0.4043: : 944iter [00:26, 38.18iter/s]
Best score: 0.4043: : 945iter [00:26, 38.18iter/s]
Best score: 0.4043: : 946iter [00:26, 38.18iter/s]
Best score: 0.4043: : 947iter [00:26, 38.18iter/s]
Best score: 0.4043: : 948iter [00:26, 38.18iter/s]
Best score: 0.4043: : 949iter [00:26, 39.28iter/s]
[ITER 950] Best: 0.4025
[PARAMS] Current best parameters:
  fx: 523.4399
  fy: 523.2546
  px: 976.2988
  py: 760.8659
  k1: 0.00737998
  k2: 0.00328080
  k3: -0.00129839
  k4: -0.00070144
  p1: -0.00319766
  p2: 0.01017198
  p3: -0.00211524
  p4: 0.00122122
  s1: -0.0050
  s2: -0.0023
  s3: 0.0068
  s4: -0.0033
  tilt_x: 0.0022
  tilt_y: 0.0328
  scale: 3.0197
  rotation: -0.9816

Best score: 0.4025: : 949iter [00:26, 39.28iter/s]
Best score: 0.4025: : 950iter [00:26, 39.28iter/s]
Best score: 0.4025: : 951iter [00:26, 39.28iter/s]
Best score: 0.4025: : 952iter [00:26, 39.28iter/s]
Best score: 0.4025: : 953iter [00:26, 39.28iter/s]
Best score: 0.4025: : 954iter [00:26, 33.81iter/s]
Best score: 0.4025: : 954iter [00:26, 33.81iter/s]
Best score: 0.4025: : 955iter [00:26, 33.81iter/s]
Best score: 0.4025: : 956iter [00:27, 33.81iter/s]
Best score: 0.4025: : 957iter [00:27, 33.81iter/s]
Best score: 0.4025: : 958iter [00:27, 33.81iter/s]
Best score: 0.4025: : 959iter [00:27, 36.04iter/s]
Best score: 0.4025: : 959iter [00:27, 36.04iter/s]
Best score: 0.4025: : 960iter [00:27, 36.04iter/s]
Best score: 0.4025: : 961iter [00:27, 36.04iter/s]
Best score: 0.4025: : 962iter [00:27, 36.04iter/s]
Best score: 0.4025: : 963iter [00:27, 36.04iter/s]
Best score: 0.4025: : 964iter [00:27, 37.57iter/s]
Best score: 0.4025: : 964iter [00:27, 37.57iter/s]
Best score: 0.4025: : 965iter [00:27, 37.57iter/s]
Best score: 0.4025: : 966iter [00:27, 37.57iter/s]
Best score: 0.4025: : 967iter [00:27, 37.57iter/s]
Best score: 0.4025: : 968iter [00:27, 37.57iter/s]
Best score: 0.4025: : 969iter [00:27, 38.74iter/s]
Best score: 0.3957: : 969iter [00:27, 38.74iter/s]
Best score: 0.3957: : 970iter [00:27, 38.74iter/s]
Best score: 0.3957: : 971iter [00:27, 38.74iter/s]
Best score: 0.3957: : 972iter [00:27, 38.74iter/s]
Best score: 0.3957: : 973iter [00:27, 38.74iter/s]
Best score: 0.3957: : 974iter [00:27, 39.50iter/s]
Best score: 0.3957: : 974iter [00:27, 39.50iter/s]
Best score: 0.3957: : 975iter [00:27, 39.50iter/s]
Best score: 0.3957: : 976iter [00:27, 39.50iter/s]
Best score: 0.3957: : 977iter [00:27, 39.50iter/s]
Best score: 0.3957: : 978iter [00:27, 39.50iter/s]
Best score: 0.3957: : 979iter [00:27, 40.29iter/s]
Best score: 0.3873: : 979iter [00:27, 40.29iter/s]
Best score: 0.3873: : 980iter [00:27, 40.29iter/s]
Best score: 0.3873: : 981iter [00:27, 40.29iter/s]
Best score: 0.3873: : 982iter [00:27, 40.29iter/s]
Best score: 0.3873: : 983iter [00:27, 40.29iter/s]
Best score: 0.3873: : 984iter [00:27, 40.75iter/s]
Best score: 0.3873: : 984iter [00:27, 40.75iter/s]
Best score: 0.3873: : 985iter [00:27, 40.75iter/s]
Best score: 0.3873: : 986iter [00:27, 40.75iter/s]
Best score: 0.3873: : 987iter [00:27, 40.75iter/s]
Best score: 0.3873: : 988iter [00:27, 40.75iter/s]
Best score: 0.3873: : 989iter [00:27, 41.40iter/s]
Best score: 0.3851: : 989iter [00:27, 41.40iter/s]
Best score: 0.3851: : 990iter [00:27, 41.40iter/s]
Best score: 0.3851: : 991iter [00:27, 41.40iter/s]
Best score: 0.3851: : 992iter [00:27, 41.40iter/s]
Best score: 0.3851: : 993iter [00:27, 41.40iter/s]
Best score: 0.3851: : 994iter [00:27, 41.58iter/s]
Best score: 0.3851: : 994iter [00:27, 41.58iter/s]
Best score: 0.3851: : 995iter [00:27, 41.58iter/s]
Best score: 0.3851: : 996iter [00:27, 41.58iter/s]
Best score: 0.3851: : 997iter [00:27, 41.58iter/s]
Best score: 0.3851: : 998iter [00:28, 41.58iter/s]
Best score: 0.3851: : 999iter [00:28, 41.84iter/s]
[ITER 1000] Best: 0.3851
[PARAMS] Current best parameters:
  fx: 526.2677
  fy: 526.2619
  px: 977.6364
  py: 760.6042
  k1: 0.01443689
  k2: -0.01069156
  k3: 0.00581214
  k4: -0.00196662
  p1: 0.00097816
  p2: 0.01795570
  p3: -0.00837154
  p4: 0.00173154
  s1: -0.0051
  s2: -0.0031
  s3: 0.0059
  s4: -0.0028
  tilt_x: -0.0068
  tilt_y: 0.0411
  scale: 3.0093
  rotation: -0.9640

Best score: 0.3851: : 999iter [00:28, 41.84iter/s]
Best score: 0.3851: : 1000iter [00:28, 41.84iter/s]
Best score: 0.3851: : 1001iter [00:28, 41.84iter/s]
Best score: 0.3851: : 1002iter [00:28, 41.84iter/s]
Best score: 0.3851: : 1003iter [00:28, 41.84iter/s]
Best score: 0.3851: : 1004iter [00:28, 35.09iter/s]
Best score: 0.3851: : 1004iter [00:28, 35.09iter/s]
Best score: 0.3851: : 1005iter [00:28, 35.09iter/s]
Best score: 0.3851: : 1006iter [00:28, 35.09iter/s]
Best score: 0.3851: : 1007iter [00:28, 35.09iter/s]
Best score: 0.3851: : 1008iter [00:28, 35.09iter/s]
Best score: 0.3851: : 1009iter [00:28, 36.93iter/s]
Best score: 0.3851: : 1009iter [00:28, 36.93iter/s]
Best score: 0.3851: : 1010iter [00:28, 36.93iter/s]
Best score: 0.3851: : 1011iter [00:28, 36.93iter/s]
Best score: 0.3851: : 1012iter [00:28, 36.93iter/s]
Best score: 0.3851: : 1013iter [00:28, 36.93iter/s]
Best score: 0.3851: : 1014iter [00:28, 38.22iter/s]
Best score: 0.3851: : 1014iter [00:28, 38.22iter/s]
Best score: 0.3851: : 1015iter [00:28, 38.22iter/s]
Best score: 0.3851: : 1016iter [00:28, 38.22iter/s]
Best score: 0.3851: : 1017iter [00:28, 38.22iter/s]
Best score: 0.3851: : 1018iter [00:28, 38.22iter/s]
Best score: 0.3851: : 1019iter [00:28, 39.44iter/s]
Best score: 0.3851: : 1019iter [00:28, 39.44iter/s]
Best score: 0.3851: : 1020iter [00:28, 39.44iter/s]
Best score: 0.3851: : 1021iter [00:28, 39.44iter/s]
Best score: 0.3851: : 1022iter [00:28, 39.44iter/s]
Best score: 0.3851: : 1023iter [00:28, 39.44iter/s]
Best score: 0.3851: : 1024iter [00:28, 40.00iter/s]
Best score: 0.3851: : 1024iter [00:28, 40.00iter/s]
Best score: 0.3851: : 1025iter [00:28, 40.00iter/s]
Best score: 0.3851: : 1026iter [00:28, 40.00iter/s]
Best score: 0.3851: : 1027iter [00:28, 40.00iter/s]
Best score: 0.3851: : 1028iter [00:28, 40.00iter/s]
Best score: 0.3851: : 1029iter [00:28, 40.67iter/s]
Best score: 0.3851: : 1029iter [00:28, 40.67iter/s]
Best score: 0.3851: : 1030iter [00:28, 40.67iter/s]
Best score: 0.3851: : 1031iter [00:28, 40.67iter/s]
Best score: 0.3851: : 1032iter [00:28, 40.67iter/s]
Best score: 0.3851: : 1033iter [00:28, 40.67iter/s]
Best score: 0.3851: : 1034iter [00:28, 40.89iter/s]
Best score: 0.3851: : 1034iter [00:28, 40.89iter/s]
Best score: 0.3851: : 1035iter [00:28, 40.89iter/s]
Best score: 0.3851: : 1036iter [00:29, 40.89iter/s]
Best score: 0.3851: : 1037iter [00:29, 40.89iter/s]
Best score: 0.3851: : 1038iter [00:29, 40.89iter/s]
Best score: 0.3851: : 1039iter [00:29, 41.34iter/s]
Best score: 0.3808: : 1039iter [00:29, 41.34iter/s]
Best score: 0.3808: : 1040iter [00:29, 41.34iter/s]
Best score: 0.3808: : 1041iter [00:29, 41.34iter/s]
Best score: 0.3808: : 1042iter [00:29, 41.34iter/s]
Best score: 0.3808: : 1043iter [00:29, 41.34iter/s]
Best score: 0.3808: : 1044iter [00:29, 41.55iter/s]
Best score: 0.3808: : 1044iter [00:29, 41.55iter/s]
Best score: 0.3808: : 1045iter [00:29, 41.55iter/s]
Best score: 0.3808: : 1046iter [00:29, 41.55iter/s]
Best score: 0.3808: : 1047iter [00:29, 41.55iter/s]
Best score: 0.3808: : 1048iter [00:29, 41.55iter/s]
Best score: 0.3808: : 1049iter [00:29, 41.57iter/s]
[ITER 1050] Best: 0.3808
[PARAMS] Current best parameters:
  fx: 526.3291
  fy: 526.3706
  px: 977.6497
  py: 760.8691
  k1: 0.00555376
  k2: -0.00012209
  k3: 0.00119825
  k4: -0.00123469
  p1: 0.00032176
  p2: 0.01741816
  p3: -0.00764671
  p4: 0.00177919
  s1: -0.0058
  s2: -0.0030
  s3: 0.0078
  s4: -0.0036
  tilt_x: -0.0044
  tilt_y: 0.0397
  scale: 3.0242
  rotation: -0.9735

Best score: 0.3808: : 1049iter [00:29, 41.57iter/s]
Best score: 0.3808: : 1050iter [00:29, 41.57iter/s]
Best score: 0.3808: : 1051iter [00:29, 41.57iter/s]
Best score: 0.3808: : 1052iter [00:29, 41.57iter/s]
Best score: 0.3808: : 1053iter [00:29, 41.57iter/s]
Best score: 0.3808: : 1054iter [00:29, 34.96iter/s]
Best score: 0.3808: : 1054iter [00:29, 34.96iter/s]
Best score: 0.3808: : 1055iter [00:29, 34.96iter/s]
Best score: 0.3808: : 1056iter [00:29, 34.96iter/s]
Best score: 0.3808: : 1057iter [00:29, 34.96iter/s]
Best score: 0.3808: : 1058iter [00:29, 34.96iter/s]
Best score: 0.3808: : 1059iter [00:29, 36.90iter/s]
Best score: 0.3808: : 1059iter [00:29, 36.90iter/s]
Best score: 0.3808: : 1060iter [00:29, 36.90iter/s]
Best score: 0.3808: : 1061iter [00:29, 36.90iter/s]
Best score: 0.3808: : 1062iter [00:29, 36.90iter/s]
Best score: 0.3808: : 1063iter [00:29, 36.90iter/s]
Best score: 0.3808: : 1064iter [00:29, 38.27iter/s]
Best score: 0.3808: : 1064iter [00:29, 38.27iter/s]
Best score: 0.3808: : 1065iter [00:29, 38.27iter/s]
Best score: 0.3808: : 1066iter [00:29, 38.27iter/s]
Best score: 0.3808: : 1067iter [00:29, 38.27iter/s]
Best score: 0.3808: : 1068iter [00:29, 38.27iter/s]
Best score: 0.3808: : 1069iter [00:29, 39.46iter/s]
Best score: 0.3804: : 1069iter [00:29, 39.46iter/s]
Best score: 0.3804: : 1070iter [00:29, 39.46iter/s]
Best score: 0.3804: : 1071iter [00:29, 39.46iter/s]
Best score: 0.3804: : 1072iter [00:29, 39.46iter/s]
Best score: 0.3804: : 1073iter [00:29, 39.46iter/s]
Best score: 0.3804: : 1074iter [00:29, 40.13iter/s]
Best score: 0.3804: : 1074iter [00:29, 40.13iter/s]
Best score: 0.3804: : 1075iter [00:30, 40.13iter/s]
Best score: 0.3804: : 1076iter [00:30, 40.13iter/s]
Best score: 0.3804: : 1077iter [00:30, 40.13iter/s]
Best score: 0.3804: : 1078iter [00:30, 40.13iter/s]
Best score: 0.3804: : 1079iter [00:30, 40.79iter/s]
Best score: 0.3800: : 1079iter [00:30, 40.79iter/s]
Best score: 0.3800: : 1080iter [00:30, 40.79iter/s]
Best score: 0.3800: : 1081iter [00:30, 40.79iter/s]
Best score: 0.3800: : 1082iter [00:30, 40.79iter/s]
Best score: 0.3800: : 1083iter [00:30, 40.79iter/s]
Best score: 0.3800: : 1084iter [00:30, 40.97iter/s]
Best score: 0.3800: : 1084iter [00:30, 40.97iter/s]
Best score: 0.3800: : 1085iter [00:30, 40.97iter/s]
Best score: 0.3800: : 1086iter [00:30, 40.97iter/s]
Best score: 0.3800: : 1087iter [00:30, 40.97iter/s]
Best score: 0.3800: : 1088iter [00:30, 40.97iter/s]
Best score: 0.3800: : 1089iter [00:30, 41.28iter/s]
Best score: 0.3800: : 1089iter [00:30, 41.28iter/s]
Best score: 0.3800: : 1090iter [00:30, 41.28iter/s]
Best score: 0.3800: : 1091iter [00:30, 41.28iter/s]
Best score: 0.3800: : 1092iter [00:30, 41.28iter/s]
Best score: 0.3800: : 1093iter [00:30, 41.28iter/s]
Best score: 0.3800: : 1094iter [00:30, 41.27iter/s]
Best score: 0.3800: : 1094iter [00:30, 41.27iter/s]
Best score: 0.3800: : 1095iter [00:30, 41.27iter/s]
Best score: 0.3800: : 1096iter [00:30, 41.27iter/s]
Best score: 0.3800: : 1097iter [00:30, 41.27iter/s]
Best score: 0.3800: : 1098iter [00:30, 41.27iter/s]
Best score: 0.3800: : 1099iter [00:30, 41.54iter/s]
[ITER 1100] Best: 0.3777
[PARAMS] Current best parameters:
  fx: 524.5385
  fy: 524.5581
  px: 977.1181
  py: 760.1384
  k1: 0.01018290
  k2: -0.00342197
  k3: 0.00239742
  k4: -0.00138385
  p1: 0.00002726
  p2: 0.01810440
  p3: -0.00782534
  p4: 0.00207328
  s1: -0.0072
  s2: -0.0029
  s3: 0.0080
  s4: -0.0040
  tilt_x: -0.0076
  tilt_y: 0.0438
  scale: 3.0214
  rotation: -0.9793

Best score: 0.3777: : 1099iter [00:30, 41.54iter/s]
Best score: 0.3777: : 1100iter [00:30, 35.88iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.377736 -> 0.376190

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 24,544 → 24,544 within 3500mm ring
  Ring polish did not improve score: 0.376190
  Success: True
  Tangential distortion: p1=0.000003, p2=0.018073, p3=-0.007872, p4=0.002058
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3762
[✅] Parameters copied to: /working/MDC051_W028_2026-09-04/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 524.5385131055273
    fy: 524.5580944377756
    px: 977.1181488311621
    py: 760.1384000537425
    k1: 0.010182065517164792
    k2: -0.0034039708410731786
    k3: 0.002425032705461081
    k4: -0.0014057092173367647
    p1: 2.911871310024222e-06
    p2: 0.018072647335312157
    p3: -0.00787219726204517
    p4: 0.002057661921495696
    scale: 3.021400548944648
    rotation: -0.9792676020121545
    mmpp: 1.2806823635718407

[🔄] Converting 842 frames to PTD, creating debug images for 200 calibration frames...

📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]

   🔄 500:   0%|          | 0/420 [00:00<?, ?frame/s]

   🔄 500:   9%|▉         | 39/420 [00:00<00:01, 378.53frame/s]

   🔄 500:  18%|█▊        | 77/420 [00:00<00:01, 227.68frame/s]

   🔄 500:  25%|██▍       | 103/420 [00:00<00:01, 207.00frame/s]

   🔄 500:  30%|███       | 126/420 [00:00<00:01, 196.71frame/s]

   🔄 500:  35%|███▌      | 147/420 [00:00<00:01, 190.70frame/s]

   🔄 500:  40%|███▉      | 167/420 [00:00<00:01, 186.35frame/s]

   🔄 500:  44%|████▍     | 186/420 [00:00<00:01, 183.55frame/s]

   🔄 500:  49%|████▉     | 205/420 [00:01<00:01, 182.96frame/s]

   🔄 500:  53%|█████▎    | 224/420 [00:01<00:01, 182.93frame/s]

   🔄 500:  58%|█████▊    | 243/420 [00:01<00:00, 182.91frame/s]

   🔄 500:  62%|██████▏   | 262/420 [00:01<00:00, 182.37frame/s]

   🔄 500:  67%|██████▋   | 281/420 [00:01<00:00, 180.35frame/s]

   🔄 500:  71%|███████▏  | 300/420 [00:01<00:00, 179.15frame/s]

   🔄 500:  76%|███████▌  | 318/420 [00:01<00:00, 178.77frame/s]

   🔄 500:  80%|████████  | 336/420 [00:01<00:00, 178.87frame/s]

   🔄 500:  84%|████████▍ | 354/420 [00:01<00:00, 178.90frame/s]

   🔄 500:  89%|████████▊ | 372/420 [00:01<00:00, 178.85frame/s]

   🔄 500:  93%|█████████▎| 390/420 [00:02<00:00, 178.50frame/s]

   🔄 500:  97%|█████████▋| 408/420 [00:02<00:00, 178.40frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:04<00:04,  4.03s/rig]

   🔄 1000:   0%|          | 0/422 [00:00<?, ?frame/s]

   🔄 1000:   4%|▍         | 18/422 [00:00<00:02, 177.67frame/s]

   🔄 1000:   9%|▊         | 36/422 [00:00<00:02, 178.51frame/s]

   🔄 1000:  13%|█▎        | 54/422 [00:00<00:03, 112.77frame/s]

   🔄 1000:  17%|█▋        | 73/422 [00:00<00:02, 133.38frame/s]

   🔄 1000:  22%|██▏       | 92/422 [00:00<00:02, 147.51frame/s]

   🔄 1000:  26%|██▋       | 111/422 [00:00<00:01, 157.41frame/s]

   🔄 1000:  31%|███       | 129/422 [00:00<00:01, 163.86frame/s]

   🔄 1000:  35%|███▍      | 147/422 [00:00<00:01, 168.02frame/s]

   🔄 1000:  39%|███▉      | 165/422 [00:01<00:01, 170.43frame/s]

   🔄 1000:  43%|████▎     | 183/422 [00:01<00:01, 171.99frame/s]

   🔄 1000:  48%|████▊     | 201/422 [00:01<00:01, 173.11frame/s]

   🔄 1000:  52%|█████▏    | 219/422 [00:01<00:01, 173.41frame/s]

   🔄 1000:  56%|█████▌    | 237/422 [00:01<00:01, 173.58frame/s]

   🔄 1000:  60%|██████    | 255/422 [00:01<00:00, 173.65frame/s]

   🔄 1000:  65%|██████▍   | 273/422 [00:01<00:00, 173.79frame/s]

   🔄 1000:  69%|██████▉   | 291/422 [00:01<00:00, 174.34frame/s]

   🔄 1000:  73%|███████▎  | 309/422 [00:01<00:00, 175.12frame/s]

   🔄 1000:  77%|███████▋  | 327/422 [00:01<00:00, 175.78frame/s]

   🔄 1000:  82%|████████▏ | 345/422 [00:02<00:00, 176.34frame/s]

   🔄 1000:  86%|████████▌ | 363/422 [00:02<00:00, 176.86frame/s]

   🔄 1000:  90%|█████████ | 381/422 [00:02<00:00, 177.05frame/s]

   🔄 1000:  95%|█████████▍| 399/422 [00:02<00:00, 177.05frame/s]

   🔄 1000:  99%|█████████▉| 417/422 [00:02<00:00, 176.84frame/s]

                                                                 
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.21s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.19s/rig]
[✅] Saved PTDs: /working/MDC051_W028_2026-09-04/calibration/debug/4_undistorted/output/ptd

📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]

   🔄 500:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 500:  23%|██▎       | 23/100 [00:00<00:00, 226.58frame/s]

   🔄 500:  46%|████▌     | 46/100 [00:00<00:00, 194.04frame/s]

   🔄 500:  66%|██████▌   | 66/100 [00:00<00:00, 188.20frame/s]

   🔄 500:  85%|████████▌ | 85/100 [00:00<00:00, 184.36frame/s]

                                                               
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.17rig/s]

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 175.58frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 175.92frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 174.79frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 174.82frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 176.19frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.04rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.06rig/s]
[✅] Saved PTDs: /working/MDC051_W028_2026-09-04/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 80789/3080448 [00:00<00:03, 807883.07px/s]
📤 Writing LUT:   5%|▌         | 167425/3080448 [00:00<00:03, 842275.71px/s]
📤 Writing LUT:   8%|▊         | 255955/3080448 [00:00<00:03, 861920.83px/s]
📤 Writing LUT:  11%|█         | 343415/3080448 [00:00<00:03, 866923.47px/s]
📤 Writing LUT:  14%|█▍        | 430180/3080448 [00:00<00:03, 867184.27px/s]
📤 Writing LUT:  17%|█▋        | 520149/3080448 [00:00<00:02, 878233.64px/s]
📤 Writing LUT:  20%|█▉        | 607973/3080448 [00:00<00:02, 874675.84px/s]
📤 Writing LUT:  23%|██▎       | 696044/3080448 [00:00<00:02, 876590.45px/s]
📤 Writing LUT:  25%|██▌       | 785199/3080448 [00:00<00:02, 881257.07px/s]
📤 Writing LUT:  28%|██▊       | 873328/3080448 [00:01<00:02, 878669.07px/s]
📤 Writing LUT:  31%|███       | 961199/3080448 [00:01<00:02, 875564.37px/s]
📤 Writing LUT:  34%|███▍      | 1048760/3080448 [00:01<00:02, 875051.53px/s]
📤 Writing LUT:  37%|███▋      | 1136507/3080448 [00:01<00:02, 875781.16px/s]
📤 Writing LUT:  40%|███▉      | 1225506/3080448 [00:01<00:02, 880059.27px/s]
📤 Writing LUT:  43%|████▎     | 1313515/3080448 [00:01<00:02, 879166.84px/s]
📤 Writing LUT:  45%|████▌     | 1401434/3080448 [00:01<00:01, 877974.21px/s]
📤 Writing LUT:  48%|████▊     | 1491993/3080448 [00:01<00:01, 886258.68px/s]
📤 Writing LUT:  51%|█████▏    | 1580622/3080448 [00:01<00:01, 882084.69px/s]
📤 Writing LUT:  54%|█████▍    | 1668837/3080448 [00:01<00:01, 881599.79px/s]
📤 Writing LUT:  57%|█████▋    | 1757875/3080448 [00:02<00:01, 884225.70px/s]
📤 Writing LUT:  60%|█████▉    | 1846302/3080448 [00:02<00:01, 880023.69px/s]
📤 Writing LUT:  63%|██████▎   | 1934311/3080448 [00:02<00:01, 879280.05px/s]
📤 Writing LUT:  66%|██████▌   | 2023423/3080448 [00:02<00:01, 882817.25px/s]
📤 Writing LUT:  69%|██████▊   | 2111982/3080448 [00:02<00:01, 883643.28px/s]
📤 Writing LUT:  72%|███████▏  | 2203417/3080448 [00:02<00:00, 892835.42px/s]
📤 Writing LUT:  74%|███████▍  | 2292705/3080448 [00:02<00:00, 889773.54px/s]
📤 Writing LUT:  77%|███████▋  | 2381688/3080448 [00:02<00:00, 887072.16px/s]
📤 Writing LUT:  80%|████████  | 2470400/3080448 [00:02<00:00, 885908.35px/s]
📤 Writing LUT:  83%|████████▎ | 2560521/3080448 [00:02<00:00, 890478.50px/s]
📤 Writing LUT:  86%|████████▌ | 2649573/3080448 [00:03<00:00, 887319.97px/s]
📤 Writing LUT:  89%|████████▉ | 2739415/3080448 [00:03<00:00, 890633.37px/s]
📤 Writing LUT:  92%|█████████▏| 2829656/3080448 [00:03<00:00, 894152.93px/s]
📤 Writing LUT:  95%|█████████▍| 2919711/3080448 [00:03<00:00, 896067.43px/s]
📤 Writing LUT:  98%|█████████▊| 3010315/3080448 [00:03<00:00, 899054.18px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 882816.07px/s]
[✅] LUT written to: /working/MDC051_W028_2026-09-04/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC051_W028_2026-09-04' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC051_W028_2026-09-04/calibration/MDC051_W028_2026-09-04_unknown.lut

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

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)

📁 Rigs (heatmap):   0%|          | 0/2 [00:00<?, ?rig/s]

   🔥 500:   0%|          | 0/420 [00:00<?, ?frame/s]

   🔥 500:   3%|▎         | 12/420 [00:00<00:03, 116.59frame/s]

   🔥 500:   7%|▋         | 28/420 [00:00<00:02, 140.32frame/s]

   🔥 500:  10%|█         | 43/420 [00:00<00:03, 111.14frame/s]

   🔥 500:  13%|█▎        | 55/420 [00:00<00:03, 98.21frame/s] 

   🔥 500:  16%|█▌        | 66/420 [00:00<00:03, 91.46frame/s]

   🔥 500:  18%|█▊        | 76/420 [00:00<00:03, 87.27frame/s]

   🔥 500:  20%|██        | 85/420 [00:00<00:03, 85.34frame/s]

   🔥 500:  22%|██▏       | 94/420 [00:01<00:03, 83.92frame/s]

   🔥 500:  25%|██▍       | 103/420 [00:01<00:03, 83.44frame/s]

   🔥 500:  27%|██▋       | 112/420 [00:01<00:03, 82.77frame/s]

   🔥 500:  29%|██▉       | 121/420 [00:01<00:03, 82.98frame/s]

   🔥 500:  31%|███       | 130/420 [00:01<00:03, 82.94frame/s]

   🔥 500:  33%|███▎      | 139/420 [00:01<00:03, 82.92frame/s]

   🔥 500:  35%|███▌      | 148/420 [00:01<00:03, 81.96frame/s]

   🔥 500:  37%|███▋      | 157/420 [00:01<00:03, 81.55frame/s]

   🔥 500:  40%|███▉      | 166/420 [00:01<00:03, 80.80frame/s]

   🔥 500:  42%|████▏     | 175/420 [00:02<00:03, 80.02frame/s]

   🔥 500:  44%|████▍     | 184/420 [00:02<00:02, 80.50frame/s]

   🔥 500:  46%|████▌     | 193/420 [00:02<00:02, 81.17frame/s]

   🔥 500:  48%|████▊     | 202/420 [00:02<00:02, 81.72frame/s]

   🔥 500:  50%|█████     | 211/420 [00:02<00:02, 81.32frame/s]

   🔥 500:  52%|█████▏    | 220/420 [00:02<00:02, 80.58frame/s]

   🔥 500:  55%|█████▍    | 229/420 [00:02<00:02, 80.39frame/s]

   🔥 500:  57%|█████▋    | 238/420 [00:02<00:02, 80.38frame/s]

   🔥 500:  59%|█████▉    | 247/420 [00:02<00:02, 79.63frame/s]

   🔥 500:  61%|██████    | 255/420 [00:03<00:02, 79.65frame/s]

   🔥 500:  63%|██████▎   | 263/420 [00:03<00:01, 79.53frame/s]

   🔥 500:  65%|██████▍   | 271/420 [00:03<00:01, 79.21frame/s]

   🔥 500:  66%|██████▋   | 279/420 [00:03<00:01, 78.87frame/s]

   🔥 500:  68%|██████▊   | 287/420 [00:03<00:01, 78.63frame/s]

   🔥 500:  70%|███████   | 295/420 [00:03<00:01, 78.75frame/s]

   🔥 500:  72%|███████▏  | 303/420 [00:03<00:01, 78.84frame/s]

   🔥 500:  74%|███████▍  | 311/420 [00:03<00:01, 78.93frame/s]

   🔥 500:  76%|███████▌  | 319/420 [00:03<00:01, 78.67frame/s]

   🔥 500:  78%|███████▊  | 327/420 [00:03<00:01, 78.84frame/s]

   🔥 500:  80%|███████▉  | 335/420 [00:04<00:01, 78.62frame/s]

   🔥 500:  82%|████████▏ | 343/420 [00:04<00:00, 78.69frame/s]

   🔥 500:  84%|████████▎ | 351/420 [00:04<00:00, 78.30frame/s]

   🔥 500:  85%|████████▌ | 359/420 [00:04<00:00, 78.21frame/s]

   🔥 500:  87%|████████▋ | 367/420 [00:04<00:00, 78.70frame/s]

   🔥 500:  90%|████████▉ | 376/420 [00:04<00:00, 79.09frame/s]

   🔥 500:  91%|█████████▏| 384/420 [00:04<00:00, 79.26frame/s]

   🔥 500:  93%|█████████▎| 392/420 [00:04<00:00, 79.46frame/s]

   🔥 500:  95%|█████████▌| 401/420 [00:04<00:00, 79.94frame/s]

   🔥 500:  98%|█████████▊| 410/420 [00:04<00:00, 80.11frame/s]

   🔥 500: 100%|█████████▉| 419/420 [00:05<00:00, 80.03frame/s]

                                                               [✅] 500: /working/MDC051_W028_2026-09-04/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC051_W028_2026-09-04/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:06<00:06,  6.27s/rig]

   🔥 1000:   0%|          | 0/422 [00:00<?, ?frame/s]

   🔥 1000:   2%|▏         | 9/422 [00:00<00:04, 85.23frame/s]

   🔥 1000:   5%|▍         | 19/422 [00:00<00:04, 88.92frame/s]

   🔥 1000:   7%|▋         | 29/422 [00:00<00:04, 89.73frame/s]

   🔥 1000:   9%|▉         | 38/422 [00:00<00:04, 89.73frame/s]

   🔥 1000:  11%|█         | 47/422 [00:00<00:04, 88.89frame/s]

   🔥 1000:  13%|█▎        | 56/422 [00:00<00:04, 87.53frame/s]

   🔥 1000:  15%|█▌        | 65/422 [00:00<00:04, 86.51frame/s]

   🔥 1000:  18%|█▊        | 74/422 [00:00<00:04, 86.30frame/s]

   🔥 1000:  20%|█▉        | 83/422 [00:00<00:03, 85.20frame/s]

   🔥 1000:  22%|██▏       | 92/422 [00:01<00:03, 84.51frame/s]

   🔥 1000:  24%|██▍       | 101/422 [00:01<00:03, 83.84frame/s]

   🔥 1000:  26%|██▌       | 110/422 [00:01<00:03, 83.28frame/s]

   🔥 1000:  28%|██▊       | 119/422 [00:01<00:03, 82.93frame/s]

   🔥 1000:  30%|███       | 128/422 [00:01<00:03, 82.05frame/s]

   🔥 1000:  32%|███▏      | 137/422 [00:01<00:03, 80.93frame/s]

   🔥 1000:  35%|███▍      | 146/422 [00:01<00:03, 81.13frame/s]

   🔥 1000:  37%|███▋      | 155/422 [00:01<00:03, 81.72frame/s]

   🔥 1000:  39%|███▉      | 164/422 [00:01<00:03, 82.63frame/s]

   🔥 1000:  41%|████      | 173/422 [00:02<00:03, 81.21frame/s]

   🔥 1000:  43%|████▎     | 182/422 [00:02<00:02, 81.02frame/s]

   🔥 1000:  45%|████▌     | 191/422 [00:02<00:02, 82.03frame/s]

   🔥 1000:  47%|████▋     | 200/422 [00:02<00:02, 83.32frame/s]

   🔥 1000:  50%|████▉     | 209/422 [00:02<00:02, 83.96frame/s]

   🔥 1000:  52%|█████▏    | 218/422 [00:02<00:02, 85.33frame/s]

   🔥 1000:  54%|█████▍    | 227/422 [00:02<00:02, 86.28frame/s]

   🔥 1000:  56%|█████▌    | 236/422 [00:02<00:02, 86.19frame/s]

   🔥 1000:  58%|█████▊    | 245/422 [00:02<00:02, 85.23frame/s]

   🔥 1000:  60%|██████    | 254/422 [00:03<00:01, 84.18frame/s]

   🔥 1000:  62%|██████▏   | 263/422 [00:03<00:01, 82.70frame/s]

   🔥 1000:  64%|██████▍   | 272/422 [00:03<00:01, 81.04frame/s]

   🔥 1000:  67%|██████▋   | 281/422 [00:03<00:01, 81.33frame/s]

   🔥 1000:  69%|██████▊   | 290/422 [00:03<00:01, 82.05frame/s]

   🔥 1000:  71%|███████   | 299/422 [00:03<00:01, 82.30frame/s]

   🔥 1000:  73%|███████▎  | 308/422 [00:03<00:01, 83.66frame/s]

   🔥 1000:  75%|███████▌  | 317/422 [00:03<00:01, 85.21frame/s]

   🔥 1000:  77%|███████▋  | 326/422 [00:03<00:01, 86.41frame/s]

   🔥 1000:  79%|███████▉  | 335/422 [00:03<00:00, 87.40frame/s]

   🔥 1000:  82%|████████▏ | 344/422 [00:04<00:00, 87.73frame/s]

   🔥 1000:  84%|████████▍ | 354/422 [00:04<00:00, 88.63frame/s]

   🔥 1000:  86%|████████▌ | 363/422 [00:04<00:00, 88.38frame/s]

   🔥 1000:  88%|████████▊ | 372/422 [00:04<00:00, 87.72frame/s]

   🔥 1000:  91%|█████████ | 382/422 [00:04<00:00, 88.67frame/s]

   🔥 1000:  93%|█████████▎| 392/422 [00:04<00:00, 89.75frame/s]

   🔥 1000:  95%|█████████▌| 402/422 [00:04<00:00, 90.27frame/s]

   🔥 1000:  98%|█████████▊| 412/422 [00:04<00:00, 90.33frame/s]

   🔥 1000: 100%|██████████| 422/422 [00:04<00:00, 89.19frame/s]

                                                                [✅] 1000: /working/MDC051_W028_2026-09-04/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC051_W028_2026-09-04/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:14<00:00,  7.23s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:14<00:00,  7.09s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC051_W028_2026-09-04/calibration/debug/5_heatmap
[🏁] Finished processing MDC051_W028_2026-09-04


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