2026-10-06T04:56:28 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC101_W036_2026-10-01 ===
2026-10-06T04:56:28 - INFO: Camera ID: 14409211
2026-10-06T04:56:28 - INFO: Wand ID: 
2026-10-06T04:56:28 - INFO: Recorded At: 2026-10-01T18:06:38Z

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

[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 642 stationary frames, 544 remain


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                                                                           [PASS2] Frame 0000307: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000308: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000309: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000310: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000316: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000317: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000318: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000319: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000427: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000430: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000436: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000439: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000440: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000447: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000448: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000465: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000466: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000467: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000531: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000532: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000533: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000534: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000537: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000538: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000539: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000540: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000544: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000545: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000546: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000547: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000548: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000549: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000550: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000551: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000552: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000554: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000555: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000556: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000557: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000560: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000561: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000562: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000563: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000564: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000976: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Frame 0000988: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000989: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000990: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000991: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000992: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000993: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001010: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001011: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001012: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001013: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001186: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001187: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001188: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001189: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001193: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001194: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001195: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001196: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001215: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001216: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001217: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001218: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000309: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000318: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000439: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000451: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000452: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000466: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000533: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000539: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000546: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000549: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000551: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000556: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000562: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000563: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000990: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000991: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000992: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001012: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001188: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001195: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001217: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 43 frames fixed, 35 untrusted, 544 total


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→ 1000 Phase 3: finalize:  56%|█████▋    | 306/544 [00:05<00:04, 48.71it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 311/544 [00:06<00:04, 48.69it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 316/544 [00:06<00:04, 48.58it/s]

→ 1000 Phase 3: finalize:  59%|█████▉    | 321/544 [00:06<00:04, 48.47it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 326/544 [00:06<00:04, 48.54it/s]

→ 1000 Phase 3: finalize:  61%|██████    | 331/544 [00:06<00:04, 48.60it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 336/544 [00:06<00:04, 48.68it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 341/544 [00:06<00:04, 48.66it/s]

→ 1000 Phase 3: finalize:  64%|██████▎   | 346/544 [00:06<00:04, 48.43it/s]

→ 1000 Phase 3: finalize:  65%|██████▍   | 351/544 [00:06<00:03, 48.48it/s]

→ 1000 Phase 3: finalize:  65%|██████▌   | 356/544 [00:07<00:03, 48.62it/s]

→ 1000 Phase 3: finalize:  66%|██████▋   | 361/544 [00:07<00:03, 48.78it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 366/544 [00:07<00:03, 49.02it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 371/544 [00:07<00:03, 49.25it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 377/544 [00:07<00:03, 49.61it/s]

→ 1000 Phase 3: finalize:  70%|███████   | 383/544 [00:07<00:03, 50.11it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 389/544 [00:07<00:03, 50.60it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 395/544 [00:07<00:02, 51.08it/s]

→ 1000 Phase 3: finalize:  74%|███████▎  | 401/544 [00:07<00:02, 51.24it/s]

→ 1000 Phase 3: finalize:  75%|███████▍  | 407/544 [00:08<00:02, 51.52it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 413/544 [00:08<00:02, 51.74it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 419/544 [00:08<00:02, 52.04it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 425/544 [00:08<00:02, 52.18it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 431/544 [00:08<00:02, 52.15it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 437/544 [00:08<00:02, 52.40it/s]

→ 1000 Phase 3: finalize:  81%|████████▏ | 443/544 [00:08<00:01, 52.95it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 449/544 [00:08<00:01, 53.24it/s]

→ 1000 Phase 3: finalize:  84%|████████▎ | 455/544 [00:08<00:01, 53.23it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 461/544 [00:09<00:01, 53.56it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 467/544 [00:09<00:01, 53.93it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 473/544 [00:09<00:01, 54.18it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 479/544 [00:09<00:01, 54.31it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 485/544 [00:09<00:01, 54.45it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 491/544 [00:09<00:00, 54.65it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 497/544 [00:09<00:00, 54.82it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 503/544 [00:09<00:00, 54.74it/s]

→ 1000 Phase 3: finalize:  94%|█████████▎| 509/544 [00:09<00:00, 54.70it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 515/544 [00:10<00:00, 54.52it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 521/544 [00:10<00:00, 54.44it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 527/544 [00:10<00:00, 54.08it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 533/544 [00:10<00:00, 53.75it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 539/544 [00:10<00:00, 53.49it/s]

                                                                           
[TIMING] 1000 — 544 frames
  Phase 1  radial detect   (parallel)  :   7.90s  (14.5 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.59s  (6.6 ms/frame)
  Phase 2b corner fix      (sequential):   1.79s
  Phase 3  finalize        (sequential):  10.56s  (19.4 ms/frame)
    decode + undistort                 :   5.35s  (9.8 ms/frame)
    corner apply + refinement          :   2.52s  (4.6 ms/frame)
    segment                            :   2.20s  (4.0 ms/frame)
    smooth                             :   0.30s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  23.83s


Processing Rigs:  50%|█████     | 1/2 [00:27<00:27, 27.73s/it][INFO] Filtered 492 stationary frames, 605 remain


→ 500 (605 images) Phase 1:   0%|          | 0/605 [00:00<?, ?it/s]

→ 500 (605 images) Phase 1:   0%|          | 1/605 [00:00<07:23,  1.36it/s]

→ 500 (605 images) Phase 1:   3%|▎         | 16/605 [00:00<00:23, 25.54it/s]

→ 500 (605 images) Phase 1:   5%|▍         | 29/605 [00:00<00:12, 44.69it/s]

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→ 500 (605 images) Phase 1:  10%|▉         | 60/605 [00:01<00:08, 66.79it/s]

→ 500 (605 images) Phase 1:  12%|█▏        | 72/605 [00:01<00:09, 53.99it/s]

→ 500 (605 images) Phase 1:  15%|█▌        | 91/605 [00:01<00:06, 75.22it/s]

→ 500 (605 images) Phase 1:  17%|█▋        | 103/605 [00:02<00:08, 57.79it/s]

→ 500 (605 images) Phase 1:  20%|██        | 123/605 [00:02<00:06, 79.46it/s]

→ 500 (605 images) Phase 1:  22%|██▏       | 136/605 [00:02<00:07, 62.73it/s]

→ 500 (605 images) Phase 1:  26%|██▌       | 155/605 [00:02<00:05, 81.26it/s]

→ 500 (605 images) Phase 1:  28%|██▊       | 168/605 [00:03<00:06, 63.11it/s]

→ 500 (605 images) Phase 1:  31%|███       | 188/605 [00:03<00:04, 83.42it/s]

→ 500 (605 images) Phase 1:  33%|███▎      | 201/605 [00:03<00:06, 64.84it/s]

→ 500 (605 images) Phase 1:  36%|███▌      | 219/605 [00:03<00:04, 82.21it/s]

→ 500 (605 images) Phase 1:  38%|███▊      | 232/605 [00:03<00:05, 63.60it/s]

→ 500 (605 images) Phase 1:  41%|████▏     | 251/605 [00:03<00:04, 82.59it/s]

→ 500 (605 images) Phase 1:  44%|████▎     | 264/605 [00:04<00:05, 64.58it/s]

→ 500 (605 images) Phase 1:  47%|████▋     | 285/605 [00:04<00:03, 86.61it/s]

→ 500 (605 images) Phase 1:  49%|████▉     | 299/605 [00:04<00:04, 66.72it/s]

→ 500 (605 images) Phase 1:  53%|█████▎    | 319/605 [00:04<00:03, 80.08it/s]

→ 500 (605 images) Phase 1:  55%|█████▍    | 331/605 [00:05<00:04, 60.23it/s]

→ 500 (605 images) Phase 1:  58%|█████▊    | 351/605 [00:05<00:03, 75.46it/s]

→ 500 (605 images) Phase 1:  60%|█████▉    | 362/605 [00:05<00:04, 60.63it/s]

→ 500 (605 images) Phase 1:  63%|██████▎   | 383/605 [00:05<00:02, 78.38it/s]

→ 500 (605 images) Phase 1:  65%|██████▌   | 394/605 [00:06<00:03, 62.37it/s]

→ 500 (605 images) Phase 1:  69%|██████▊   | 415/605 [00:06<00:02, 81.21it/s]

→ 500 (605 images) Phase 1:  70%|███████   | 426/605 [00:06<00:02, 63.92it/s]

→ 500 (605 images) Phase 1:  74%|███████▍  | 447/605 [00:06<00:01, 82.95it/s]

→ 500 (605 images) Phase 1:  76%|███████▌  | 459/605 [00:07<00:02, 65.46it/s]

→ 500 (605 images) Phase 1:  79%|███████▉  | 479/605 [00:07<00:01, 83.61it/s]

→ 500 (605 images) Phase 1:  81%|████████  | 491/605 [00:07<00:01, 64.99it/s]

→ 500 (605 images) Phase 1:  84%|████████▍ | 508/605 [00:07<00:01, 80.97it/s]

→ 500 (605 images) Phase 1:  86%|████████▌ | 520/605 [00:07<00:01, 62.51it/s]

→ 500 (605 images) Phase 1:  89%|████████▉ | 540/605 [00:08<00:00, 80.67it/s]

→ 500 (605 images) Phase 1:  91%|█████████ | 552/605 [00:08<00:00, 64.16it/s]

→ 500 (605 images) Phase 1:  94%|█████████▍| 570/605 [00:08<00:00, 80.03it/s]

→ 500 (605 images) Phase 1:  96%|█████████▌| 581/605 [00:08<00:00, 71.49it/s]

→ 500 (605 images) Phase 1:  98%|█████████▊| 591/605 [00:08<00:00, 74.74it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/605 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/605 [00:00<04:12,  2.39it/s]

→ 500 Phase 2: corners:   3%|▎         | 19/605 [00:00<00:12, 47.54it/s]

→ 500 Phase 2: corners:   5%|▍         | 30/605 [00:00<00:09, 57.70it/s]

→ 500 Phase 2: corners:   7%|▋         | 44/605 [00:00<00:07, 77.90it/s]

→ 500 Phase 2: corners:  10%|▉         | 58/605 [00:00<00:05, 93.19it/s]

→ 500 Phase 2: corners:  12%|█▏        | 70/605 [00:00<00:05, 97.96it/s]

→ 500 Phase 2: corners:  14%|█▍        | 86/605 [00:01<00:04, 113.01it/s]

→ 500 Phase 2: corners:  17%|█▋        | 101/605 [00:01<00:04, 122.42it/s]

→ 500 Phase 2: corners:  19%|█▉        | 115/605 [00:01<00:04, 121.35it/s]

→ 500 Phase 2: corners:  22%|██▏       | 132/605 [00:01<00:03, 130.88it/s]

→ 500 Phase 2: corners:  25%|██▍       | 149/605 [00:01<00:03, 138.45it/s]

→ 500 Phase 2: corners:  27%|██▋       | 165/605 [00:01<00:03, 143.66it/s]

→ 500 Phase 2: corners:  30%|██▉       | 180/605 [00:01<00:03, 135.73it/s]

→ 500 Phase 2: corners:  32%|███▏      | 194/605 [00:01<00:03, 134.98it/s]

→ 500 Phase 2: corners:  34%|███▍      | 208/605 [00:01<00:03, 131.69it/s]

→ 500 Phase 2: corners:  37%|███▋      | 226/605 [00:02<00:02, 143.03it/s]

→ 500 Phase 2: corners:  40%|████      | 242/605 [00:02<00:02, 140.98it/s]

→ 500 Phase 2: corners:  43%|████▎     | 258/605 [00:02<00:02, 144.41it/s]

→ 500 Phase 2: corners:  45%|████▌     | 273/605 [00:02<00:02, 115.63it/s]

→ 500 Phase 2: corners:  48%|████▊     | 290/605 [00:02<00:02, 126.87it/s]

→ 500 Phase 2: corners:  51%|█████     | 309/605 [00:02<00:02, 141.10it/s]

→ 500 Phase 2: corners:  54%|█████▎    | 324/605 [00:02<00:01, 142.45it/s]

→ 500 Phase 2: corners:  56%|█████▌    | 339/605 [00:02<00:01, 140.35it/s]

→ 500 Phase 2: corners:  59%|█████▊    | 354/605 [00:03<00:01, 141.81it/s]

→ 500 Phase 2: corners:  61%|██████    | 370/605 [00:03<00:01, 145.16it/s]

→ 500 Phase 2: corners:  64%|██████▍   | 386/605 [00:03<00:01, 147.30it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 404/605 [00:03<00:01, 156.07it/s]

→ 500 Phase 2: corners:  70%|██████▉   | 421/605 [00:03<00:01, 158.40it/s]

→ 500 Phase 2: corners:  72%|███████▏  | 438/605 [00:03<00:01, 161.32it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 455/605 [00:03<00:00, 161.02it/s]

→ 500 Phase 2: corners:  78%|███████▊  | 473/605 [00:03<00:00, 166.13it/s]

→ 500 Phase 2: corners:  81%|████████  | 490/605 [00:03<00:00, 162.24it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 507/605 [00:03<00:00, 163.58it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 525/605 [00:04<00:00, 166.86it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 542/605 [00:04<00:00, 163.36it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 563/605 [00:04<00:00, 173.99it/s]

→ 500 Phase 2: corners:  99%|█████████▊| 596/605 [00:04<00:00, 218.71it/s]

                                                                          [PASS2] Frame 0000564: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000565: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000566: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000567: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000568: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000570: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000571: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000572: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000573: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000574: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000578: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000579: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000581: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000582: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000583: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000584: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000585: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000586: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000587: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000655: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000656: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000657: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000658: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000659: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000660: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000661: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000662: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000663: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000664: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000665: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000668: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000669: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000670: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000671: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000672: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000673: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000679: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000680: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000681: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000687: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000688: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000689: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000690: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000691: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000692: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000693: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000694: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000700: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000701: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000702: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000703: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000704: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000706: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000708: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000709: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000710: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000711: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000712: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000713: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000714: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000715: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000720: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000721: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000722: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000723: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000724: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000727: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000728: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000729: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000730: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000731: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000732: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000733: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000734: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000735: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000736: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000740: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000741: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000742: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000743: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001022: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001023: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001024: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001025: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001117: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001118: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001119: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001120: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000567: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000569: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000570: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000572: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000575: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000578: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000583: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000585: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000586: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000587: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000657: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000658: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000659: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000660: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000661: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000663: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000664: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000665: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000670: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000672: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000679: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000680: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000681: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000689: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000691: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000693: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000702: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000703: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000705: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000710: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000713: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000714: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000722: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000723: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000729: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000732: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000733: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000735: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000741: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000742: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001024: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000576: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000577: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000578: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000585: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000586: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000587: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000659: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000660: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000661: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000663: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000664: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000665: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000669: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000670: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000679: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000680: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000681: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Summary: 61 frames fixed, 53 untrusted, 605 total


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→ 500 Phase 3: finalize:  96%|█████████▌| 578/605 [00:10<00:00, 54.79it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 584/605 [00:10<00:00, 54.66it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 590/605 [00:10<00:00, 54.58it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 596/605 [00:10<00:00, 54.43it/s]

→ 500 Phase 3: finalize: 100%|█████████▉| 602/605 [00:11<00:00, 54.31it/s]

                                                                          
[TIMING] 500 — 605 frames
  Phase 1  radial detect   (parallel)  :   8.94s  (14.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.47s  (7.4 ms/frame)
  Phase 2b corner fix      (sequential):   2.57s
  Phase 3  finalize        (sequential):  11.11s  (18.4 ms/frame)
    decode + undistort                 :   5.83s  (9.6 ms/frame)
    corner apply + refinement          :   2.30s  (3.8 ms/frame)
    segment                            :   2.44s  (4.0 ms/frame)
    smooth                             :   0.35s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  27.09s


Processing Rigs: 100%|██████████| 2/2 [00:58<00:00, 29.35s/it]
Processing Rigs: 100%|██████████| 2/2 [00:58<00:00, 29.11s/it]
[💾] Saved RigFrameDataset to /working/MDC101_W036_2026-10-01/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1149 frames from 2 rigs to /working/MDC101_W036_2026-10-01/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC101_W036_2026-10-01/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 544
[📋] 500: 100 frames selected from 605
[💾] Saved RigFrameDataset to /working/MDC101_W036_2026-10-01/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC101_W036_2026-10-01/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:04<02:51,  4.40s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:04<01:17,  2.03s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:04<00:43,  1.17s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:19,  1.77it/s]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:05<00:15,  2.21it/s]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:05<00:13,  2.43it/s]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:05<00:06,  4.78it/s]
Computing max image from bright frames:  30%|███       | 12/40 [00:06<00:05,  5.09it/s]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:06<00:03,  7.58it/s]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:06<00:04,  5.65it/s]
Computing max image from bright frames:  50%|█████     | 20/40 [00:06<00:02,  7.58it/s]
Computing max image from bright frames:  55%|█████▌    | 22/40 [00:07<00:02,  8.90it/s]
Computing max image from bright frames:  65%|██████▌   | 26/40 [00:07<00:01, 12.18it/s]
Computing max image from bright frames:  70%|███████   | 28/40 [00:07<00:01, 10.49it/s]
Computing max image from bright frames:  78%|███████▊  | 31/40 [00:07<00:00, 12.96it/s]
Computing max image from bright frames:  82%|████████▎ | 33/40 [00:08<00:01,  5.70it/s]
Computing max image from bright frames:  88%|████████▊ | 35/40 [00:09<00:00,  5.47it/s]
Computing max image from bright frames:  92%|█████████▎| 37/40 [00:09<00:00,  6.63it/s]
Computing max image from bright frames: 100%|██████████| 40/40 [00:09<00:00,  8.33it/s]
                                                                                       
[✅] Casing detection complete: cx=999.0, cy=761.0

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


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Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
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Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   2%|▏         | 2/100 [00:00<00:29,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:28,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:28,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   6%|▌         | 6/100 [00:00<00:27,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   7%|▋         | 7/100 [00:00<00:27,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   8%|▊         | 8/100 [00:00<00:27,  3.36it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   9%|▉         | 9/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   9%|▉         | 9/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):  10%|█         | 10/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):  11%|█         | 11/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 335.354} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 519.192} (0.017):  13%|█▎        | 13/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.035 | Best: {'fx': 526.263} (0.035):  14%|█▍        | 14/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.053 | Best: {'fx': 533.333} (0.053):  15%|█▌        | 15/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  16%|█▌        | 16/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.037 | Best: {'fx': 533.333} (0.053):  17%|█▋        | 17/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  18%|█▊        | 18/100 [00:00<00:03, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  19%|█▉        | 19/100 [00:00<00:02, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.027 | Best: {'fx': 533.333} (0.053):  20%|██        | 20/100 [00:00<00:02, 27.13it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.027 | Best: {'fx': 533.333} (0.053):  21%|██        | 21/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  21%|██        | 21/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.023 | Best: {'fx': 533.333} (0.053):  22%|██▏       | 22/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  23%|██▎       | 23/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.053):  24%|██▍       | 24/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  25%|██▌       | 25/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.053):  26%|██▌       | 26/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.053):  27%|██▋       | 27/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  28%|██▊       | 28/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  29%|██▉       | 29/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.053):  30%|███       | 30/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.053):  31%|███       | 31/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.053):  32%|███▏      | 32/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  33%|███▎      | 33/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.053):  34%|███▍      | 34/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.053):  35%|███▌      | 35/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.053):  36%|███▌      | 36/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.053):  37%|███▋      | 37/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.053):  38%|███▊      | 38/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  39%|███▉      | 39/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.053):  40%|████      | 40/100 [00:00<00:01, 55.18it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.053):  41%|████      | 41/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.053):  41%|████      | 41/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.053):  42%|████▏     | 42/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.007 | Best: {'fx': 533.333} (0.053):  43%|████▎     | 43/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  44%|████▍     | 44/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.053):  45%|████▌     | 45/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.053):  46%|████▌     | 46/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  47%|████▋     | 47/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  48%|████▊     | 48/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.053):  49%|████▉     | 49/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.053):  50%|█████     | 50/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.053):  51%|█████     | 51/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  52%|█████▏    | 52/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  53%|█████▎    | 53/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  54%|█████▍    | 54/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  55%|█████▌    | 55/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  56%|█████▌    | 56/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  57%|█████▋    | 57/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  58%|█████▊    | 58/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  59%|█████▉    | 59/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  60%|██████    | 60/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  61%|██████    | 61/100 [00:00<00:00, 97.78it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  62%|██████▏   | 62/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.053):  62%|██████▏   | 62/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  63%|██████▎   | 63/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  64%|██████▍   | 64/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  65%|██████▌   | 65/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  66%|██████▌   | 66/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  67%|██████▋   | 67/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  68%|██████▊   | 68/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  69%|██████▉   | 69/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  70%|███████   | 70/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  71%|███████   | 71/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  72%|███████▏  | 72/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  73%|███████▎  | 73/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  74%|███████▍  | 74/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  75%|███████▌  | 75/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  76%|███████▌  | 76/100 [00:00<00:00, 128.01it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  77%|███████▋  | 77/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  77%|███████▋  | 77/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  78%|███████▊  | 78/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  79%|███████▉  | 79/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  80%|████████  | 80/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  81%|████████  | 81/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  82%|████████▏ | 82/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  83%|████████▎ | 83/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  84%|████████▍ | 84/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  85%|████████▌ | 85/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  86%|████████▌ | 86/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  87%|████████▋ | 87/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  88%|████████▊ | 88/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  89%|████████▉ | 89/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  90%|█████████ | 90/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  91%|█████████ | 91/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  92%|█████████▏| 92/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  93%|█████████▎| 93/100 [00:00<00:00, 131.22it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  94%|█████████▍| 94/100 [00:00<00:00, 135.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  94%|█████████▍| 94/100 [00:00<00:00, 135.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  95%|█████████▌| 95/100 [00:00<00:00, 135.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  96%|█████████▌| 96/100 [00:00<00:00, 135.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  97%|█████████▋| 97/100 [00:00<00:00, 135.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  98%|█████████▊| 98/100 [00:01<00:00, 135.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.053):  99%|█████████▉| 99/100 [00:01<00:00, 135.71it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.012 | Best: {'fx': 510.374} (0.012):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.012 | Best: {'fx': 510.374} (0.012):   2%|▏         | 1/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.009 | Best: {'fx': 510.374} (0.012):   2%|▏         | 1/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.011 | Best: {'fx': 510.374} (0.012):   4%|▍         | 2/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.012 | Best: {'fx': 511.395} (0.012):   6%|▌         | 3/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.013 | Best: {'fx': 514.456} (0.013):   8%|▊         | 4/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.013 | Best: {'fx': 514.456} (0.013):  10%|█         | 5/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.012 | Best: {'fx': 514.456} (0.013):  12%|█▏        | 6/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.017 | Best: {'fx': 519.558} (0.017):  14%|█▍        | 7/50 [00:00<00:05,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.013 | Best: {'fx': 519.558} (0.017):  16%|█▌        | 8/50 [00:00<00:04,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.013 | Best: {'fx': 519.558} (0.017):  18%|█▊        | 9/50 [00:00<00:04,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.016 | Best: {'fx': 519.558} (0.017):  20%|██        | 10/50 [00:00<00:04,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.023 | Best: {'fx': 522.619} (0.023):  22%|██▏       | 11/50 [00:00<00:04,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.016 | Best: {'fx': 522.619} (0.023):  24%|██▍       | 12/50 [00:00<00:04,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.019 | Best: {'fx': 522.619} (0.023):  26%|██▌       | 13/50 [00:00<00:04,  8.60it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.019 | Best: {'fx': 522.619} (0.023):  28%|██▊       | 14/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.020 | Best: {'fx': 522.619} (0.023):  28%|██▊       | 14/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.026 | Best: {'fx': 523.639} (0.026):  30%|███       | 15/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.028 | Best: {'fx': 524.66} (0.028):  32%|███▏      | 16/50 [00:00<00:00, 71.44it/s] 
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.031 | Best: {'fx': 525.68} (0.031):  34%|███▍      | 17/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.038 | Best: {'fx': 527.721} (0.038):  36%|███▌      | 18/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.042 | Best: {'fx': 528.741} (0.042):  38%|███▊      | 19/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.036 | Best: {'fx': 528.741} (0.042):  40%|████      | 20/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.050 | Best: {'fx': 531.803} (0.050):  42%|████▏     | 21/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.054 | Best: {'fx': 532.823} (0.054):  44%|████▍     | 22/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.043 | Best: {'fx': 532.823} (0.054):  46%|████▌     | 23/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.049 | Best: {'fx': 532.823} (0.054):  48%|████▊     | 24/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.051 | Best: {'fx': 532.823} (0.054):  50%|█████     | 25/50 [00:00<00:00, 71.44it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.051 | Best: {'fx': 532.823} (0.054):  52%|█████▏    | 26/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.046 | Best: {'fx': 532.823} (0.054):  52%|█████▏    | 26/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.056 | Best: {'fx': 533.844} (0.056):  54%|█████▍    | 27/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.053 | Best: {'fx': 533.844} (0.056):  56%|█████▌    | 28/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.042 | Best: {'fx': 533.844} (0.056):  58%|█████▊    | 29/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.029 | Best: {'fx': 533.844} (0.056):  60%|██████    | 30/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.038 | Best: {'fx': 533.844} (0.056):  62%|██████▏   | 31/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.041 | Best: {'fx': 533.844} (0.056):  64%|██████▍   | 32/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.035 | Best: {'fx': 533.844} (0.056):  66%|██████▌   | 33/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.033 | Best: {'fx': 533.844} (0.056):  68%|██████▊   | 34/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.031 | Best: {'fx': 533.844} (0.056):  70%|███████   | 35/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.031 | Best: {'fx': 533.844} (0.056):  72%|███████▏  | 36/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.028 | Best: {'fx': 533.844} (0.056):  74%|███████▍  | 37/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.027 | Best: {'fx': 533.844} (0.056):  76%|███████▌  | 38/50 [00:00<00:00, 89.35it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.027 | Best: {'fx': 533.844} (0.056):  78%|███████▊  | 39/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.027 | Best: {'fx': 533.844} (0.056):  78%|███████▊  | 39/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.026 | Best: {'fx': 533.844} (0.056):  80%|████████  | 40/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.023 | Best: {'fx': 533.844} (0.056):  82%|████████▏ | 41/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.026 | Best: {'fx': 533.844} (0.056):  84%|████████▍ | 42/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.025 | Best: {'fx': 533.844} (0.056):  86%|████████▌ | 43/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.024 | Best: {'fx': 533.844} (0.056):  88%|████████▊ | 44/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.023 | Best: {'fx': 533.844} (0.056):  90%|█████████ | 45/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.022 | Best: {'fx': 533.844} (0.056):  92%|█████████▏| 46/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.022 | Best: {'fx': 533.844} (0.056):  94%|█████████▍| 47/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.024 | Best: {'fx': 533.844} (0.056):  96%|█████████▌| 48/50 [00:00<00:00, 102.85it/s]
Brute | Start: {'fx': 533.333} (0.053) | Current: 0.023 | Best: {'fx': 533.844} (0.056):  98%|█████████▊| 49/50 [00:00<00:00, 102.85it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.843537414966
    px: 999.0
    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: 380.1947: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 380.1947, picklable_objective(x0) = 379.9991

Best score: 379.9991: : 0iter [00:01, ?iter/s] 
Best score: 379.9991: : 1iter [00:01,  1.83s/iter]
Best score: 379.9991: : 1iter [00:01,  1.83s/iter]
Best score: 379.9991: : 2iter [00:02,  1.83s/iter]
Best score: 379.9991: : 3iter [00:02,  1.85iter/s]
Best score: 379.9991: : 3iter [00:02,  1.85iter/s]
Best score: 379.9991: : 4iter [00:02,  1.85iter/s]
Best score: 379.9991: : 5iter [00:02,  1.85iter/s]
Best score: 379.9991: : 6iter [00:02,  1.85iter/s]
Best score: 379.9991: : 7iter [00:02,  5.25iter/s]
Best score: 379.9991: : 7iter [00:02,  5.25iter/s]
Best score: 379.9991: : 8iter [00:02,  5.25iter/s]
Best score: 379.9991: : 9iter [00:02,  5.25iter/s]
Best score: 379.9991: : 10iter [00:02,  5.25iter/s]
Best score: 379.9991: : 11iter [00:02,  9.15iter/s]
Best score: 379.9991: : 11iter [00:02,  9.15iter/s]
Best score: 379.9991: : 12iter [00:02,  9.15iter/s]
Best score: 379.9991: : 13iter [00:02,  9.15iter/s]
Best score: 379.9991: : 14iter [00:02,  9.15iter/s]
Best score: 379.9991: : 15iter [00:02,  9.15iter/s]
Best score: 379.9991: : 16iter [00:02, 14.33iter/s]
Best score: 379.9991: : 16iter [00:02, 14.33iter/s]
Best score: 379.9991: : 17iter [00:02, 14.33iter/s]
Best score: 379.9991: : 18iter [00:02, 14.33iter/s]
Best score: 379.9991: : 19iter [00:02, 14.33iter/s]
Best score: 379.9991: : 20iter [00:02, 18.42iter/s]
Best score: 379.9991: : 20iter [00:02, 18.42iter/s]
Best score: 379.9991: : 21iter [00:02, 18.42iter/s]
Best score: 379.9991: : 22iter [00:02, 18.42iter/s]
Best score: 379.9991: : 23iter [00:02, 18.42iter/s]
Best score: 379.9991: : 24iter [00:02, 22.44iter/s]
Best score: 379.9991: : 24iter [00:02, 22.44iter/s]
Best score: 379.9991: : 25iter [00:02, 22.44iter/s]
Best score: 379.9991: : 26iter [00:02, 22.44iter/s]
Best score: 379.9991: : 27iter [00:02, 22.44iter/s]
Best score: 379.9991: : 28iter [00:02, 22.44iter/s]
Best score: 379.9991: : 29iter [00:02, 26.83iter/s]
Best score: 379.9991: : 29iter [00:02, 26.83iter/s]
Best score: 379.9991: : 30iter [00:02, 26.83iter/s]
Best score: 379.9991: : 31iter [00:02, 26.83iter/s]
Best score: 379.9991: : 32iter [00:02, 26.83iter/s]
Best score: 379.9991: : 33iter [00:02, 29.65iter/s]
Best score: 379.9991: : 33iter [00:02, 29.65iter/s]
Best score: 379.9991: : 34iter [00:02, 29.65iter/s]
Best score: 379.9991: : 35iter [00:02, 29.65iter/s]
Best score: 379.9991: : 36iter [00:02, 29.65iter/s]
Best score: 379.9991: : 37iter [00:02, 29.65iter/s]
Best score: 379.9991: : 38iter [00:02, 32.62iter/s]
Best score: 379.9991: : 38iter [00:02, 32.62iter/s]
Best score: 379.9991: : 39iter [00:02, 32.62iter/s]
Best score: 379.9991: : 40iter [00:02, 32.62iter/s]
Best score: 379.9991: : 41iter [00:02, 32.62iter/s]
Best score: 379.9991: : 42iter [00:02, 33.82iter/s]
Best score: 379.9991: : 42iter [00:03, 33.82iter/s]
Best score: 379.9991: : 43iter [00:03, 33.82iter/s]
Best score: 379.9991: : 44iter [00:03, 33.82iter/s]
Best score: 379.9991: : 45iter [00:03, 33.82iter/s]
Best score: 379.9991: : 46iter [00:03, 35.30iter/s]
Best score: 379.9991: : 46iter [00:03, 35.30iter/s]
Best score: 379.9991: : 47iter [00:03, 35.30iter/s]
Best score: 379.9991: : 48iter [00:03, 35.30iter/s]
[ITER 50] Best: 379.9991
[PARAMS] Current best parameters:
  fx: 533.8435
  fy: 533.8435
  px: 999.0000
  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: 379.9991: : 49iter [00:03, 35.30iter/s]
Best score: 379.9991: : 50iter [00:03, 36.43iter/s]
Best score: 379.9991: : 50iter [00:03, 36.43iter/s]
Best score: 379.9991: : 51iter [00:03, 36.43iter/s]
Best score: 379.9991: : 52iter [00:03, 36.43iter/s]
Best score: 379.9991: : 53iter [00:03, 36.43iter/s]
Best score: 379.9991: : 54iter [00:03, 36.43iter/s]
Best score: 379.9991: : 55iter [00:03, 37.77iter/s]
Best score: 379.9991: : 55iter [00:03, 37.77iter/s]
Best score: 379.9991: : 56iter [00:03, 37.77iter/s]
Best score: 379.9991: : 57iter [00:03, 37.77iter/s]
Best score: 379.9991: : 58iter [00:03, 37.77iter/s]
Best score: 379.9991: : 59iter [00:03, 38.04iter/s]
Best score: 379.9991: : 59iter [00:03, 38.04iter/s]
Best score: 379.9991: : 60iter [00:03, 38.04iter/s]
Best score: 379.9991: : 61iter [00:03, 38.04iter/s]
Best score: 379.9991: : 62iter [00:03, 38.04iter/s]
Best score: 379.9991: : 63iter [00:03, 38.37iter/s]
Best score: 379.9991: : 63iter [00:03, 38.37iter/s]
Best score: 379.9991: : 64iter [00:03, 38.37iter/s]
Best score: 379.9991: : 65iter [00:03, 38.37iter/s]
Best score: 379.9991: : 66iter [00:03, 38.37iter/s]
Best score: 379.9991: : 67iter [00:03, 38.83iter/s]
Best score: 379.9991: : 67iter [00:03, 38.83iter/s]
Best score: 379.9991: : 68iter [00:03, 38.83iter/s]
Best score: 290.0979: : 69iter [00:03, 38.83iter/s]
Best score: 290.0979: : 70iter [00:03, 38.83iter/s]
Best score: 290.0979: : 71iter [00:03, 38.85iter/s]
Best score: 290.0979: : 71iter [00:03, 38.85iter/s]
Best score: 290.0979: : 72iter [00:03, 38.85iter/s]
Best score: 290.0979: : 73iter [00:03, 38.85iter/s]
Best score: 290.0979: : 74iter [00:03, 38.85iter/s]
Best score: 290.0979: : 75iter [00:03, 38.85iter/s]
Best score: 290.0979: : 76iter [00:03, 39.28iter/s]
Best score: 290.0979: : 76iter [00:03, 39.28iter/s]
Best score: 290.0979: : 77iter [00:03, 39.28iter/s]
Best score: 290.0979: : 78iter [00:03, 39.28iter/s]
Best score: 290.0979: : 79iter [00:03, 39.28iter/s]
Best score: 290.0979: : 80iter [00:03, 39.19iter/s]
Best score: 290.0979: : 80iter [00:03, 39.19iter/s]
Best score: 290.0979: : 81iter [00:04, 39.19iter/s]
Best score: 290.0979: : 82iter [00:04, 39.19iter/s]
Best score: 290.0979: : 83iter [00:04, 39.19iter/s]
Best score: 290.0979: : 84iter [00:04, 39.19iter/s]
Best score: 290.0979: : 85iter [00:04, 39.66iter/s]
Best score: 290.0979: : 85iter [00:04, 39.66iter/s]
Best score: 290.0979: : 86iter [00:04, 39.66iter/s]
Best score: 290.0979: : 87iter [00:04, 39.66iter/s]
Best score: 290.0979: : 88iter [00:04, 39.66iter/s]
Best score: 270.1477: : 89iter [00:04, 39.66iter/s]
Best score: 270.1477: : 90iter [00:04, 39.52iter/s]
Best score: 270.1477: : 90iter [00:04, 39.52iter/s]
Best score: 270.1477: : 91iter [00:04, 39.52iter/s]
Best score: 270.1477: : 92iter [00:04, 39.52iter/s]
Best score: 270.1477: : 93iter [00:04, 39.52iter/s]
Best score: 270.1477: : 94iter [00:04, 39.52iter/s]
Best score: 270.1477: : 95iter [00:04, 39.73iter/s]
Best score: 270.1477: : 95iter [00:04, 39.73iter/s]
Best score: 270.1477: : 96iter [00:04, 39.73iter/s]
Best score: 270.1477: : 97iter [00:04, 39.73iter/s]
Best score: 270.1477: : 98iter [00:04, 39.73iter/s]
[ITER 100] Best: 270.1477
[PARAMS] Current best parameters:
  fx: 615.7924
  fy: 637.4420
  px: 1025.8987
  py: 768.4440
  k1: -0.02788491
  k2: 0.03709808
  k3: -0.06142275
  k4: 0.01989325
  p1: -0.01936160
  p2: 0.04744530
  p3: -0.01076794
  p4: 0.03036095
  s1: 0.0197
  s2: -0.0275
  s3: -0.0057
  s4: -0.0173
  tilt_x: 0.0491
  tilt_y: -0.0595
  scale: 2.4206
  rotation: -2.2814

Best score: 270.1477: : 99iter [00:04, 39.73iter/s]
Best score: 270.1477: : 100iter [00:04, 39.68iter/s]
Best score: 270.1477: : 100iter [00:04, 39.68iter/s]
Best score: 270.1477: : 101iter [00:04, 39.68iter/s]
Best score: 270.1477: : 102iter [00:04, 39.68iter/s]
Best score: 270.1477: : 103iter [00:04, 39.68iter/s]
Best score: 270.1477: : 104iter [00:04, 37.34iter/s]
Best score: 270.1477: : 104iter [00:04, 37.34iter/s]
Best score: 270.1477: : 105iter [00:04, 37.34iter/s]
Best score: 270.1477: : 106iter [00:04, 37.34iter/s]
Best score: 270.1477: : 107iter [00:04, 37.34iter/s]
Best score: 270.1477: : 108iter [00:04, 37.34iter/s]
Best score: 270.1477: : 109iter [00:04, 38.20iter/s]
Best score: 270.1477: : 109iter [00:04, 38.20iter/s]
Best score: 270.1477: : 110iter [00:04, 38.20iter/s]
Best score: 270.1477: : 111iter [00:04, 38.20iter/s]
Best score: 270.1477: : 112iter [00:04, 38.20iter/s]
Best score: 270.1477: : 113iter [00:04, 38.49iter/s]
Best score: 270.1477: : 113iter [00:04, 38.49iter/s]
Best score: 270.1477: : 114iter [00:04, 38.49iter/s]
Best score: 270.1477: : 115iter [00:04, 38.49iter/s]
Best score: 270.1477: : 116iter [00:04, 38.49iter/s]
Best score: 270.1477: : 117iter [00:04, 38.49iter/s]
Best score: 270.1477: : 118iter [00:04, 39.02iter/s]
Best score: 270.1477: : 118iter [00:04, 39.02iter/s]
Best score: 270.1477: : 119iter [00:04, 39.02iter/s]
Best score: 270.1477: : 120iter [00:05, 39.02iter/s]
Best score: 270.1477: : 121iter [00:05, 39.02iter/s]
Best score: 270.1477: : 122iter [00:05, 39.21iter/s]
Best score: 270.1477: : 122iter [00:05, 39.21iter/s]
Best score: 270.1477: : 123iter [00:05, 39.21iter/s]
Best score: 270.1477: : 124iter [00:05, 39.21iter/s]
Best score: 270.1477: : 125iter [00:05, 39.21iter/s]
Best score: 270.1477: : 126iter [00:05, 39.36iter/s]
Best score: 270.1477: : 126iter [00:05, 39.36iter/s]
Best score: 270.1477: : 127iter [00:05, 39.36iter/s]
Best score: 270.1477: : 128iter [00:05, 39.36iter/s]
Best score: 270.1477: : 129iter [00:05, 39.36iter/s]
Best score: 270.1477: : 130iter [00:05, 39.32iter/s]
Best score: 270.1477: : 130iter [00:05, 39.32iter/s]
Best score: 270.1477: : 131iter [00:05, 39.32iter/s]
Best score: 270.1477: : 132iter [00:05, 39.32iter/s]
Best score: 270.1477: : 133iter [00:05, 39.32iter/s]
Best score: 270.1477: : 134iter [00:05, 39.32iter/s]
Best score: 270.1477: : 135iter [00:05, 39.65iter/s]
Best score: 270.1477: : 135iter [00:05, 39.65iter/s]
Best score: 270.1477: : 136iter [00:05, 39.65iter/s]
Best score: 270.1477: : 137iter [00:05, 39.65iter/s]
Best score: 270.1477: : 138iter [00:05, 39.65iter/s]
Best score: 270.1477: : 139iter [00:05, 39.65iter/s]
Best score: 270.1477: : 140iter [00:05, 39.63iter/s]
Best score: 270.1477: : 140iter [00:05, 39.63iter/s]
Best score: 270.1477: : 141iter [00:05, 39.63iter/s]
Best score: 270.1477: : 142iter [00:05, 39.63iter/s]
Best score: 270.1477: : 143iter [00:05, 39.63iter/s]
Best score: 270.1477: : 144iter [00:05, 39.63iter/s]
Best score: 270.1477: : 145iter [00:05, 39.79iter/s]
Best score: 270.1477: : 145iter [00:05, 39.79iter/s]
Best score: 270.1477: : 146iter [00:05, 39.79iter/s]
Best score: 270.1477: : 147iter [00:05, 39.79iter/s]
Best score: 270.1477: : 148iter [00:05, 39.79iter/s]
[ITER 150] Best: 270.1477
[PARAMS] Current best parameters:
  fx: 615.7924
  fy: 637.4420
  px: 1025.8987
  py: 768.4440
  k1: -0.02788491
  k2: 0.03709808
  k3: -0.06142275
  k4: 0.01989325
  p1: -0.01936160
  p2: 0.04744530
  p3: -0.01076794
  p4: 0.03036095
  s1: 0.0197
  s2: -0.0275
  s3: -0.0057
  s4: -0.0173
  tilt_x: 0.0491
  tilt_y: -0.0595
  scale: 2.4206
  rotation: -2.2814

Best score: 270.1477: : 149iter [00:05, 39.79iter/s]
Best score: 270.1477: : 150iter [00:05, 39.69iter/s]
Best score: 270.1477: : 150iter [00:05, 39.69iter/s]
Best score: 270.1477: : 151iter [00:05, 39.69iter/s]
Best score: 270.1477: : 152iter [00:05, 39.69iter/s]
Best score: 270.1477: : 153iter [00:05, 39.69iter/s]
Best score: 270.1477: : 154iter [00:05, 33.10iter/s]
Best score: 270.1477: : 154iter [00:05, 33.10iter/s]
Best score: 270.1477: : 155iter [00:05, 33.10iter/s]
Best score: 270.1477: : 156iter [00:05, 33.10iter/s]
Best score: 270.1477: : 157iter [00:06, 33.10iter/s]
Best score: 270.1477: : 158iter [00:06, 33.10iter/s]
Best score: 270.1477: : 159iter [00:06, 35.15iter/s]
Best score: 209.9833: : 159iter [00:06, 35.15iter/s]
Best score: 209.9833: : 160iter [00:06, 35.15iter/s]
Best score: 209.9833: : 161iter [00:06, 35.15iter/s]
Best score: 209.9833: : 162iter [00:06, 35.15iter/s]
Best score: 209.9833: : 163iter [00:06, 36.29iter/s]
Best score: 209.9833: : 163iter [00:06, 36.29iter/s]
Best score: 209.9833: : 164iter [00:06, 36.29iter/s]
Best score: 209.9833: : 165iter [00:06, 36.29iter/s]
Best score: 209.9833: : 166iter [00:06, 36.29iter/s]
Best score: 209.9833: : 167iter [00:06, 36.29iter/s]
Best score: 209.9833: : 168iter [00:06, 37.57iter/s]
Best score: 209.9833: : 168iter [00:06, 37.57iter/s]
Best score: 160.9971: : 169iter [00:06, 37.57iter/s]
Best score: 160.9971: : 170iter [00:06, 37.57iter/s]
Best score: 160.9971: : 171iter [00:06, 37.57iter/s]
Best score: 160.9971: : 172iter [00:06, 38.04iter/s]
Best score: 160.9971: : 172iter [00:06, 38.04iter/s]
Best score: 160.9971: : 173iter [00:06, 38.04iter/s]
Best score: 160.9971: : 174iter [00:06, 38.04iter/s]
Best score: 160.9971: : 175iter [00:06, 38.04iter/s]
Best score: 160.9971: : 176iter [00:06, 38.04iter/s]
Best score: 160.9971: : 177iter [00:06, 38.96iter/s]
Best score: 160.9971: : 177iter [00:06, 38.96iter/s]
Best score: 160.9971: : 178iter [00:06, 38.96iter/s]
Best score: 160.9971: : 179iter [00:06, 38.96iter/s]
Best score: 160.9971: : 180iter [00:06, 38.96iter/s]
Best score: 160.9971: : 181iter [00:06, 39.20iter/s]
Best score: 160.9971: : 181iter [00:06, 39.20iter/s]
Best score: 160.9971: : 182iter [00:06, 39.20iter/s]
Best score: 160.9971: : 183iter [00:06, 39.20iter/s]
Best score: 160.9971: : 184iter [00:06, 39.20iter/s]
Best score: 160.9971: : 185iter [00:06, 39.20iter/s]
Best score: 160.9971: : 186iter [00:06, 39.75iter/s]
Best score: 160.9971: : 186iter [00:06, 39.75iter/s]
Best score: 160.9971: : 187iter [00:06, 39.75iter/s]
Best score: 160.9971: : 188iter [00:06, 39.75iter/s]
Best score: 112.6676: : 189iter [00:06, 39.75iter/s]
Best score: 112.6676: : 190iter [00:06, 39.69iter/s]
Best score: 112.6676: : 190iter [00:06, 39.69iter/s]
Best score: 112.6676: : 191iter [00:06, 39.69iter/s]
Best score: 112.6676: : 192iter [00:06, 39.69iter/s]
Best score: 112.6676: : 193iter [00:06, 39.69iter/s]
Best score: 112.6676: : 194iter [00:06, 39.69iter/s]
Best score: 112.6676: : 195iter [00:06, 40.00iter/s]
Best score: 112.6676: : 195iter [00:06, 40.00iter/s]
Best score: 112.6676: : 196iter [00:06, 40.00iter/s]
Best score: 112.6676: : 197iter [00:07, 40.00iter/s]
Best score: 112.6676: : 198iter [00:07, 40.00iter/s]
[ITER 200] Best: 91.8626
[PARAMS] Current best parameters:
  fx: 667.0534
  fy: 663.9487
  px: 988.7646
  py: 770.2263
  k1: -0.08449496
  k2: 0.03389885
  k3: -0.06484996
  k4: 0.02678728
  p1: -0.01761476
  p2: -0.00156535
  p3: 0.00078781
  p4: 0.02018133
  s1: 0.0083
  s2: -0.0332
  s3: -0.0134
  s4: -0.0220
  tilt_x: 0.0461
  tilt_y: -0.0525
  scale: 2.6487
  rotation: 1.4658

Best score: 91.8626: : 199iter [00:07, 40.00iter/s] 
Best score: 91.8626: : 200iter [00:07, 39.84iter/s]
Best score: 91.8626: : 200iter [00:07, 39.84iter/s]
Best score: 91.8626: : 201iter [00:07, 39.84iter/s]
Best score: 91.8626: : 202iter [00:07, 39.84iter/s]
Best score: 91.8626: : 203iter [00:07, 39.84iter/s]
Best score: 91.8626: : 204iter [00:07, 33.12iter/s]
Best score: 91.8626: : 204iter [00:07, 33.12iter/s]
Best score: 91.8626: : 205iter [00:07, 33.12iter/s]
Best score: 91.8626: : 206iter [00:07, 33.12iter/s]
Best score: 91.8626: : 207iter [00:07, 33.12iter/s]
Best score: 91.8626: : 208iter [00:07, 33.12iter/s]
Best score: 91.8626: : 209iter [00:07, 35.18iter/s]
Best score: 54.2284: : 209iter [00:07, 35.18iter/s]
Best score: 54.2284: : 210iter [00:07, 35.18iter/s]
Best score: 54.2284: : 211iter [00:07, 35.18iter/s]
Best score: 54.2284: : 212iter [00:07, 35.18iter/s]
Best score: 54.2284: : 213iter [00:07, 36.19iter/s]
Best score: 54.2284: : 213iter [00:07, 36.19iter/s]
Best score: 54.2284: : 214iter [00:07, 36.19iter/s]
Best score: 54.2284: : 215iter [00:07, 36.19iter/s]
Best score: 54.2284: : 216iter [00:07, 36.19iter/s]
Best score: 54.2284: : 217iter [00:07, 36.90iter/s]
Best score: 54.2284: : 217iter [00:07, 36.90iter/s]
Best score: 54.2284: : 218iter [00:07, 36.90iter/s]
Best score: 39.0282: : 219iter [00:07, 36.90iter/s]
Best score: 39.0282: : 220iter [00:07, 36.90iter/s]
Best score: 39.0282: : 221iter [00:07, 37.59iter/s]
Best score: 39.0282: : 221iter [00:07, 37.59iter/s]
Best score: 39.0282: : 222iter [00:07, 37.59iter/s]
Best score: 39.0282: : 223iter [00:07, 37.59iter/s]
Best score: 39.0282: : 224iter [00:07, 37.59iter/s]
Best score: 39.0282: : 225iter [00:07, 37.59iter/s]
Best score: 39.0282: : 226iter [00:07, 38.55iter/s]
Best score: 39.0282: : 226iter [00:07, 38.55iter/s]
Best score: 39.0282: : 227iter [00:07, 38.55iter/s]
Best score: 39.0282: : 228iter [00:07, 38.55iter/s]
Best score: 20.9733: : 229iter [00:07, 38.55iter/s]
Best score: 20.9733: : 230iter [00:07, 38.78iter/s]
Best score: 20.9733: : 230iter [00:07, 38.78iter/s]
Best score: 20.9733: : 231iter [00:07, 38.78iter/s]
Best score: 20.9733: : 232iter [00:07, 38.78iter/s]
Best score: 20.9733: : 233iter [00:07, 38.78iter/s]
Best score: 20.9733: : 234iter [00:08, 38.78iter/s]
Best score: 20.9733: : 235iter [00:08, 39.38iter/s]
Best score: 20.9733: : 235iter [00:08, 39.38iter/s]
Best score: 20.9733: : 236iter [00:08, 39.38iter/s]
Best score: 20.9733: : 237iter [00:08, 39.38iter/s]
Best score: 20.9733: : 238iter [00:08, 39.38iter/s]
Best score: 19.7445: : 239iter [00:08, 39.38iter/s]
Best score: 19.7445: : 240iter [00:08, 39.49iter/s]
Best score: 19.7445: : 240iter [00:08, 39.49iter/s]
Best score: 19.7445: : 241iter [00:08, 39.49iter/s]
Best score: 19.7445: : 242iter [00:08, 39.49iter/s]
Best score: 19.7445: : 243iter [00:08, 39.49iter/s]
Best score: 19.7445: : 244iter [00:08, 39.49iter/s]
Best score: 19.7445: : 245iter [00:08, 39.97iter/s]
Best score: 19.7445: : 245iter [00:08, 39.97iter/s]
Best score: 19.7445: : 246iter [00:08, 39.97iter/s]
Best score: 19.7445: : 247iter [00:08, 39.97iter/s]
Best score: 19.7445: : 248iter [00:08, 39.97iter/s]
[ITER 250] Best: 13.1044
[PARAMS] Current best parameters:
  fx: 680.4337
  fy: 681.5006
  px: 977.5573
  py: 773.6146
  k1: -0.07471264
  k2: -0.05134960
  k3: 0.01399852
  k4: 0.00411240
  p1: 0.01017588
  p2: 0.01281895
  p3: -0.01294078
  p4: 0.02891663
  s1: -0.0082
  s2: -0.0266
  s3: -0.0101
  s4: -0.0048
  tilt_x: -0.0283
  tilt_y: 0.0546
  scale: 2.7387
  rotation: -0.4107

Best score: 13.1044: : 249iter [00:08, 39.97iter/s]
Best score: 13.1044: : 250iter [00:08, 39.94iter/s]
Best score: 13.1044: : 250iter [00:08, 39.94iter/s]
Best score: 13.1044: : 251iter [00:08, 39.94iter/s]
Best score: 13.1044: : 252iter [00:08, 39.94iter/s]
Best score: 13.1044: : 253iter [00:08, 39.94iter/s]
Best score: 13.1044: : 254iter [00:08, 33.30iter/s]
Best score: 13.1044: : 254iter [00:08, 33.30iter/s]
Best score: 13.1044: : 255iter [00:08, 33.30iter/s]
Best score: 13.1044: : 256iter [00:08, 33.30iter/s]
Best score: 13.1044: : 257iter [00:08, 33.30iter/s]
Best score: 13.1044: : 258iter [00:08, 33.30iter/s]
Best score: 13.1044: : 259iter [00:08, 35.35iter/s]
Best score: 13.1044: : 259iter [00:08, 35.35iter/s]
Best score: 13.1044: : 260iter [00:08, 35.35iter/s]
Best score: 13.1044: : 261iter [00:08, 35.35iter/s]
Best score: 13.1044: : 262iter [00:08, 35.35iter/s]
Best score: 13.1044: : 263iter [00:08, 36.39iter/s]
Best score: 13.1044: : 263iter [00:08, 36.39iter/s]
Best score: 13.1044: : 264iter [00:08, 36.39iter/s]
Best score: 13.1044: : 265iter [00:08, 36.39iter/s]
Best score: 13.1044: : 266iter [00:08, 36.39iter/s]
Best score: 13.1044: : 267iter [00:08, 36.39iter/s]
Best score: 13.1044: : 268iter [00:08, 37.65iter/s]
Best score: 13.1044: : 268iter [00:08, 37.65iter/s]
Best score: 11.7984: : 269iter [00:08, 37.65iter/s]
Best score: 11.7984: : 270iter [00:08, 37.65iter/s]
Best score: 11.7984: : 271iter [00:09, 37.65iter/s]
Best score: 11.7984: : 272iter [00:09, 38.15iter/s]
Best score: 11.7984: : 272iter [00:09, 38.15iter/s]
Best score: 11.7984: : 273iter [00:09, 38.15iter/s]
Best score: 11.7984: : 274iter [00:09, 38.15iter/s]
Best score: 11.7984: : 275iter [00:09, 38.15iter/s]
Best score: 11.7984: : 276iter [00:09, 38.15iter/s]
Best score: 11.7984: : 277iter [00:09, 38.95iter/s]
Best score: 11.7984: : 277iter [00:09, 38.95iter/s]
Best score: 11.7984: : 278iter [00:09, 38.95iter/s]
Best score: 8.2210: : 279iter [00:09, 38.95iter/s] 
Best score: 8.2210: : 280iter [00:09, 38.95iter/s]
Best score: 8.2210: : 281iter [00:09, 39.11iter/s]
Best score: 8.2210: : 281iter [00:09, 39.11iter/s]
Best score: 8.2210: : 282iter [00:09, 39.11iter/s]
Best score: 8.2210: : 283iter [00:09, 39.11iter/s]
Best score: 8.2210: : 284iter [00:09, 39.11iter/s]
Best score: 8.2210: : 285iter [00:09, 39.11iter/s]
Best score: 8.2210: : 286iter [00:09, 39.75iter/s]
Best score: 8.2210: : 286iter [00:09, 39.75iter/s]
Best score: 8.2210: : 287iter [00:09, 39.75iter/s]
Best score: 8.2210: : 288iter [00:09, 39.75iter/s]
Best score: 6.5141: : 289iter [00:09, 39.75iter/s]
Best score: 6.5141: : 290iter [00:09, 39.75iter/s]
Best score: 6.5141: : 291iter [00:09, 39.96iter/s]
Best score: 6.5141: : 291iter [00:09, 39.96iter/s]
Best score: 6.5141: : 292iter [00:09, 39.96iter/s]
Best score: 6.5141: : 293iter [00:09, 39.96iter/s]
Best score: 6.5141: : 294iter [00:09, 39.96iter/s]
Best score: 6.5141: : 295iter [00:09, 39.96iter/s]
Best score: 6.5141: : 296iter [00:09, 40.20iter/s]
Best score: 6.5141: : 296iter [00:09, 40.20iter/s]
Best score: 6.5141: : 297iter [00:09, 40.20iter/s]
Best score: 6.5141: : 298iter [00:09, 40.20iter/s]
[ITER 300] Best: 5.8449
[PARAMS] Current best parameters:
  fx: 681.7173
  fy: 682.9306
  px: 981.3485
  py: 766.9436
  k1: -0.13661302
  k2: -0.00696542
  k3: 0.02969574
  k4: -0.00934751
  p1: 0.02867026
  p2: 0.04565431
  p3: -0.04640356
  p4: -0.00496454
  s1: -0.0046
  s2: -0.0152
  s3: 0.0039
  s4: 0.0143
  tilt_x: -0.0897
  tilt_y: 0.0944
  scale: 2.8711
  rotation: -0.3324

Best score: 5.8449: : 299iter [00:09, 40.20iter/s]
Best score: 5.8449: : 300iter [00:09, 40.20iter/s]
Best score: 5.8449: : 301iter [00:09, 33.44iter/s]
Best score: 5.8449: : 301iter [00:09, 33.44iter/s]
Best score: 5.8449: : 302iter [00:09, 33.44iter/s]
Best score: 5.8449: : 303iter [00:09, 33.44iter/s]
Best score: 5.8449: : 304iter [00:09, 33.44iter/s]
Best score: 5.8449: : 305iter [00:09, 33.44iter/s]
Best score: 5.8449: : 306iter [00:09, 35.33iter/s]
Best score: 5.8449: : 306iter [00:09, 35.33iter/s]
Best score: 5.8449: : 307iter [00:09, 35.33iter/s]
Best score: 5.8449: : 308iter [00:10, 35.33iter/s]
Best score: 5.8449: : 309iter [00:10, 35.33iter/s]
Best score: 5.8449: : 310iter [00:10, 35.33iter/s]
Best score: 5.8449: : 311iter [00:10, 36.74iter/s]
Best score: 5.8449: : 311iter [00:10, 36.74iter/s]
Best score: 5.8449: : 312iter [00:10, 36.74iter/s]
Best score: 5.8449: : 313iter [00:10, 36.74iter/s]
Best score: 5.8449: : 314iter [00:10, 36.74iter/s]
Best score: 5.8449: : 315iter [00:10, 36.74iter/s]
Best score: 5.8449: : 316iter [00:10, 37.87iter/s]
Best score: 5.8449: : 316iter [00:10, 37.87iter/s]
Best score: 5.8449: : 317iter [00:10, 37.87iter/s]
Best score: 5.8449: : 318iter [00:10, 37.87iter/s]
Best score: 4.6928: : 319iter [00:10, 37.87iter/s]
Best score: 4.6928: : 320iter [00:10, 37.87iter/s]
Best score: 4.6928: : 321iter [00:10, 38.58iter/s]
Best score: 4.6928: : 321iter [00:10, 38.58iter/s]
Best score: 4.6928: : 322iter [00:10, 38.58iter/s]
Best score: 4.6928: : 323iter [00:10, 38.58iter/s]
Best score: 4.6928: : 324iter [00:10, 38.58iter/s]
Best score: 4.6928: : 325iter [00:10, 38.58iter/s]
Best score: 4.6928: : 326iter [00:10, 39.36iter/s]
Best score: 4.6928: : 326iter [00:10, 39.36iter/s]
Best score: 4.6928: : 327iter [00:10, 39.36iter/s]
Best score: 4.6928: : 328iter [00:10, 39.36iter/s]
Best score: 4.6928: : 329iter [00:10, 39.36iter/s]
Best score: 4.6928: : 330iter [00:10, 39.40iter/s]
Best score: 4.6928: : 330iter [00:10, 39.40iter/s]
Best score: 4.6928: : 331iter [00:10, 39.40iter/s]
Best score: 4.6928: : 332iter [00:10, 39.40iter/s]
Best score: 4.6928: : 333iter [00:10, 39.40iter/s]
Best score: 4.6928: : 334iter [00:10, 39.40iter/s]
Best score: 4.6928: : 335iter [00:10, 39.77iter/s]
Best score: 4.6928: : 335iter [00:10, 39.77iter/s]
Best score: 4.6928: : 336iter [00:10, 39.77iter/s]
Best score: 4.6928: : 337iter [00:10, 39.77iter/s]
Best score: 4.6928: : 338iter [00:10, 39.77iter/s]
Best score: 4.6928: : 339iter [00:10, 39.77iter/s]
Best score: 4.6928: : 340iter [00:10, 39.74iter/s]
Best score: 4.6928: : 340iter [00:10, 39.74iter/s]
Best score: 4.6928: : 341iter [00:10, 39.74iter/s]
Best score: 4.6928: : 342iter [00:10, 39.74iter/s]
Best score: 4.6928: : 343iter [00:10, 39.74iter/s]
Best score: 4.6928: : 344iter [00:10, 39.74iter/s]
Best score: 4.6928: : 345iter [00:10, 39.96iter/s]
Best score: 4.6928: : 345iter [00:10, 39.96iter/s]
Best score: 4.6928: : 346iter [00:10, 39.96iter/s]
Best score: 4.6928: : 347iter [00:10, 39.96iter/s]
Best score: 4.6928: : 348iter [00:10, 39.96iter/s]
[ITER 350] Best: 4.3565
[PARAMS] Current best parameters:
  fx: 677.4938
  fy: 679.0527
  px: 984.3539
  py: 769.7794
  k1: -0.11624682
  k2: -0.02571290
  k3: 0.03047106
  k4: -0.00680666
  p1: 0.02288010
  p2: 0.03861064
  p3: -0.03246034
  p4: 0.00101852
  s1: -0.0015
  s2: -0.0136
  s3: 0.0117
  s4: 0.0006
  tilt_x: -0.0654
  tilt_y: 0.0745
  scale: 2.8493
  rotation: -0.3399

Best score: 4.3565: : 349iter [00:11, 39.96iter/s]
Best score: 4.3565: : 350iter [00:11, 39.94iter/s]
Best score: 4.3565: : 350iter [00:11, 39.94iter/s]
Best score: 4.3565: : 351iter [00:11, 39.94iter/s]
Best score: 4.3565: : 352iter [00:11, 39.94iter/s]
Best score: 4.3565: : 353iter [00:11, 39.94iter/s]
Best score: 4.3565: : 354iter [00:11, 39.94iter/s]
Best score: 4.3565: : 355iter [00:11, 33.87iter/s]
Best score: 4.3565: : 355iter [00:11, 33.87iter/s]
Best score: 4.3565: : 356iter [00:11, 33.87iter/s]
Best score: 4.3565: : 357iter [00:11, 33.87iter/s]
Best score: 4.3565: : 358iter [00:11, 33.87iter/s]
Best score: 2.9276: : 359iter [00:11, 33.87iter/s]
Best score: 2.9276: : 360iter [00:11, 35.57iter/s]
Best score: 2.9276: : 360iter [00:11, 35.57iter/s]
Best score: 2.9276: : 361iter [00:11, 35.57iter/s]
Best score: 2.9276: : 362iter [00:11, 35.57iter/s]
Best score: 2.9276: : 363iter [00:11, 35.57iter/s]
Best score: 2.9276: : 364iter [00:11, 29.56iter/s]
Best score: 2.9276: : 364iter [00:11, 29.56iter/s]
Best score: 2.9276: : 365iter [00:11, 29.56iter/s]
Best score: 2.9276: : 366iter [00:11, 29.56iter/s]
Best score: 2.9276: : 367iter [00:11, 29.56iter/s]
Best score: 2.9276: : 368iter [00:11, 29.56iter/s]
Best score: 2.9276: : 369iter [00:11, 32.46iter/s]
Best score: 2.7995: : 369iter [00:11, 32.46iter/s]
Best score: 2.7995: : 370iter [00:11, 32.46iter/s]
Best score: 2.7995: : 371iter [00:11, 32.46iter/s]
Best score: 2.7995: : 372iter [00:11, 32.46iter/s]
Best score: 2.7995: : 373iter [00:11, 32.46iter/s]
Best score: 2.7995: : 374iter [00:11, 34.53iter/s]
Best score: 2.7995: : 374iter [00:11, 34.53iter/s]
Best score: 2.7995: : 375iter [00:11, 34.53iter/s]
Best score: 2.7995: : 376iter [00:11, 34.53iter/s]
Best score: 2.7995: : 377iter [00:11, 34.53iter/s]
Best score: 2.7995: : 378iter [00:11, 34.53iter/s]
Best score: 2.7995: : 379iter [00:11, 36.30iter/s]
Best score: 1.5055: : 379iter [00:11, 36.30iter/s]
Best score: 1.5055: : 380iter [00:11, 36.30iter/s]
Best score: 1.5055: : 381iter [00:11, 36.30iter/s]
Best score: 1.5055: : 382iter [00:12, 36.30iter/s]
Best score: 1.5055: : 383iter [00:12, 36.30iter/s]
Best score: 1.5055: : 384iter [00:12, 37.41iter/s]
Best score: 1.5055: : 384iter [00:12, 37.41iter/s]
Best score: 1.5055: : 385iter [00:12, 37.41iter/s]
Best score: 1.5055: : 386iter [00:12, 37.41iter/s]
Best score: 1.5055: : 387iter [00:12, 37.41iter/s]
Best score: 1.5055: : 388iter [00:12, 37.41iter/s]
Best score: 1.5055: : 389iter [00:12, 38.43iter/s]
Best score: 1.5055: : 389iter [00:12, 38.43iter/s]
Best score: 1.5055: : 390iter [00:12, 38.43iter/s]
Best score: 1.5055: : 391iter [00:12, 38.43iter/s]
Best score: 1.5055: : 392iter [00:12, 38.43iter/s]
Best score: 1.5055: : 393iter [00:12, 38.82iter/s]
Best score: 1.5055: : 393iter [00:12, 38.82iter/s]
Best score: 1.5055: : 394iter [00:12, 38.82iter/s]
Best score: 1.5055: : 395iter [00:12, 38.82iter/s]
Best score: 1.5055: : 396iter [00:12, 38.82iter/s]
Best score: 1.5055: : 397iter [00:12, 38.82iter/s]
Best score: 1.5055: : 398iter [00:12, 39.41iter/s]
Best score: 1.5055: : 398iter [00:12, 39.41iter/s]
[ITER 400] Best: 1.5055
[PARAMS] Current best parameters:
  fx: 681.1320
  fy: 682.5204
  px: 987.5430
  py: 766.8747
  k1: -0.14107223
  k2: -0.01304460
  k3: 0.03519755
  k4: -0.00985408
  p1: 0.01357708
  p2: 0.04137131
  p3: -0.04176867
  p4: 0.01452962
  s1: -0.0121
  s2: -0.0142
  s3: 0.0241
  s4: -0.0112
  tilt_x: -0.0625
  tilt_y: 0.0485
  scale: 2.9212
  rotation: -0.3813

Best score: 1.5055: : 399iter [00:12, 39.41iter/s]
Best score: 1.5055: : 400iter [00:12, 39.41iter/s]
Best score: 1.5055: : 401iter [00:12, 39.41iter/s]
Best score: 1.5055: : 402iter [00:12, 32.66iter/s]
Best score: 1.5055: : 402iter [00:12, 32.66iter/s]
Best score: 1.5055: : 403iter [00:12, 32.66iter/s]
Best score: 1.5055: : 404iter [00:12, 32.66iter/s]
Best score: 1.5055: : 405iter [00:12, 32.66iter/s]
Best score: 1.5055: : 406iter [00:12, 32.66iter/s]
Best score: 1.5055: : 407iter [00:12, 34.91iter/s]
Best score: 1.5055: : 407iter [00:12, 34.91iter/s]
Best score: 1.5055: : 408iter [00:12, 34.91iter/s]
Best score: 1.5055: : 409iter [00:12, 34.91iter/s]
Best score: 1.5055: : 410iter [00:12, 34.91iter/s]
Best score: 1.5055: : 411iter [00:12, 36.11iter/s]
Best score: 1.5055: : 411iter [00:12, 36.11iter/s]
Best score: 1.5055: : 412iter [00:12, 36.11iter/s]
Best score: 1.5055: : 413iter [00:12, 36.11iter/s]
Best score: 1.5055: : 414iter [00:12, 36.11iter/s]
Best score: 1.5055: : 415iter [00:12, 36.11iter/s]
Best score: 1.5055: : 416iter [00:12, 37.57iter/s]
Best score: 1.5055: : 416iter [00:12, 37.57iter/s]
Best score: 1.5055: : 417iter [00:12, 37.57iter/s]
Best score: 1.5055: : 418iter [00:12, 37.57iter/s]
Best score: 1.5055: : 419iter [00:13, 37.57iter/s]
Best score: 1.5055: : 420iter [00:13, 38.20iter/s]
Best score: 1.5055: : 420iter [00:13, 38.20iter/s]
Best score: 1.5055: : 421iter [00:13, 38.20iter/s]
Best score: 1.5055: : 422iter [00:13, 38.20iter/s]
Best score: 1.5055: : 423iter [00:13, 38.20iter/s]
Best score: 1.5055: : 424iter [00:13, 38.20iter/s]
Best score: 1.5055: : 425iter [00:13, 39.16iter/s]
Best score: 1.5055: : 425iter [00:13, 39.16iter/s]
Best score: 1.5055: : 426iter [00:13, 39.16iter/s]
Best score: 1.5055: : 427iter [00:13, 39.16iter/s]
Best score: 1.5055: : 428iter [00:13, 39.16iter/s]
Best score: 1.5055: : 429iter [00:13, 39.16iter/s]
Best score: 1.5055: : 430iter [00:13, 39.44iter/s]
Best score: 1.5055: : 430iter [00:13, 39.44iter/s]
Best score: 1.5055: : 431iter [00:13, 39.44iter/s]
Best score: 1.5055: : 432iter [00:13, 39.44iter/s]
Best score: 1.5055: : 433iter [00:13, 39.44iter/s]
Best score: 1.5055: : 434iter [00:13, 39.44iter/s]
Best score: 1.5055: : 435iter [00:13, 39.98iter/s]
Best score: 1.5055: : 435iter [00:13, 39.98iter/s]
Best score: 1.5055: : 436iter [00:13, 39.98iter/s]
Best score: 1.5055: : 437iter [00:13, 39.98iter/s]
Best score: 1.5055: : 438iter [00:13, 39.98iter/s]
Best score: 1.5055: : 439iter [00:13, 39.98iter/s]
Best score: 1.5055: : 440iter [00:13, 40.13iter/s]
Best score: 1.5055: : 440iter [00:13, 40.13iter/s]
Best score: 1.5055: : 441iter [00:13, 40.13iter/s]
Best score: 1.5055: : 442iter [00:13, 40.13iter/s]
Best score: 1.5055: : 443iter [00:13, 40.13iter/s]
Best score: 1.5055: : 444iter [00:13, 40.13iter/s]
Best score: 1.5055: : 445iter [00:13, 40.43iter/s]
Best score: 1.5055: : 445iter [00:13, 40.43iter/s]
Best score: 1.5055: : 446iter [00:13, 40.43iter/s]
Best score: 1.5055: : 447iter [00:13, 40.43iter/s]
Best score: 1.5055: : 448iter [00:13, 40.43iter/s]
[ITER 450] Best: 1.2506
[PARAMS] Current best parameters:
  fx: 673.7996
  fy: 673.8647
  px: 982.0645
  py: 766.2359
  k1: -0.11498583
  k2: -0.05051320
  k3: 0.05872423
  k4: -0.01499869
  p1: 0.00777908
  p2: 0.02839550
  p3: -0.04076487
  p4: 0.00710158
  s1: 0.0029
  s2: -0.0124
  s3: 0.0458
  s4: -0.0151
  tilt_x: -0.0576
  tilt_y: 0.0592
  scale: 2.9292
  rotation: -0.3774

Best score: 1.2506: : 449iter [00:13, 40.43iter/s]
Best score: 1.2506: : 450iter [00:13, 40.27iter/s]
Best score: 1.2506: : 450iter [00:13, 40.27iter/s]
Best score: 1.2506: : 451iter [00:13, 40.27iter/s]
Best score: 1.2506: : 452iter [00:13, 40.27iter/s]
Best score: 1.2506: : 453iter [00:13, 40.27iter/s]
Best score: 1.2506: : 454iter [00:13, 40.27iter/s]
Best score: 1.2506: : 455iter [00:13, 33.78iter/s]
Best score: 1.2506: : 455iter [00:13, 33.78iter/s]
Best score: 1.2506: : 456iter [00:13, 33.78iter/s]
Best score: 1.2506: : 457iter [00:14, 33.78iter/s]
Best score: 1.2506: : 458iter [00:14, 33.78iter/s]
Best score: 0.9308: : 459iter [00:14, 33.78iter/s]
Best score: 0.9308: : 460iter [00:14, 35.52iter/s]
Best score: 0.9308: : 460iter [00:14, 35.52iter/s]
Best score: 0.9308: : 461iter [00:14, 35.52iter/s]
Best score: 0.9308: : 462iter [00:14, 35.52iter/s]
Best score: 0.9308: : 463iter [00:14, 35.52iter/s]
Best score: 0.9308: : 464iter [00:14, 35.52iter/s]
Best score: 0.9308: : 465iter [00:14, 37.05iter/s]
Best score: 0.9308: : 465iter [00:14, 37.05iter/s]
Best score: 0.9308: : 466iter [00:14, 37.05iter/s]
Best score: 0.9308: : 467iter [00:14, 37.05iter/s]
Best score: 0.9308: : 468iter [00:14, 37.05iter/s]
Best score: 0.9308: : 469iter [00:14, 37.05iter/s]
Best score: 0.9308: : 470iter [00:14, 38.08iter/s]
Best score: 0.9308: : 470iter [00:14, 38.08iter/s]
Best score: 0.9308: : 471iter [00:14, 38.08iter/s]
Best score: 0.9308: : 472iter [00:14, 38.08iter/s]
Best score: 0.9308: : 473iter [00:14, 38.08iter/s]
Best score: 0.9308: : 474iter [00:14, 38.08iter/s]
Best score: 0.9308: : 475iter [00:14, 38.90iter/s]
Best score: 0.9308: : 475iter [00:14, 38.90iter/s]
Best score: 0.9308: : 476iter [00:14, 38.90iter/s]
Best score: 0.9308: : 477iter [00:14, 38.90iter/s]
Best score: 0.9308: : 478iter [00:14, 38.90iter/s]
Best score: 0.6729: : 479iter [00:14, 38.90iter/s]
Best score: 0.6729: : 480iter [00:14, 39.25iter/s]
Best score: 0.6729: : 480iter [00:14, 39.25iter/s]
Best score: 0.6729: : 481iter [00:14, 39.25iter/s]
Best score: 0.6729: : 482iter [00:14, 39.25iter/s]
Best score: 0.6729: : 483iter [00:14, 39.25iter/s]
Best score: 0.6729: : 484iter [00:14, 39.25iter/s]
Best score: 0.6729: : 485iter [00:14, 39.94iter/s]
Best score: 0.6729: : 485iter [00:14, 39.94iter/s]
Best score: 0.6729: : 486iter [00:14, 39.94iter/s]
Best score: 0.6729: : 487iter [00:14, 39.94iter/s]
Best score: 0.6729: : 488iter [00:14, 39.94iter/s]
Best score: 0.6729: : 489iter [00:14, 39.94iter/s]
Best score: 0.6729: : 490iter [00:14, 40.05iter/s]
Best score: 0.6729: : 490iter [00:14, 40.05iter/s]
Best score: 0.6729: : 491iter [00:14, 40.05iter/s]
Best score: 0.6729: : 492iter [00:14, 40.05iter/s]
Best score: 0.6729: : 493iter [00:14, 40.05iter/s]
Best score: 0.6729: : 494iter [00:14, 40.05iter/s]
Best score: 0.6729: : 495iter [00:14, 40.31iter/s]
Best score: 0.6729: : 495iter [00:14, 40.31iter/s]
Best score: 0.6729: : 496iter [00:14, 40.31iter/s]
Best score: 0.6729: : 497iter [00:15, 40.31iter/s]
Best score: 0.6729: : 498iter [00:15, 40.31iter/s]
[ITER 500] Best: 0.6729
[PARAMS] Current best parameters:
  fx: 676.7935
  fy: 677.4782
  px: 983.8776
  py: 767.6564
  k1: -0.11729694
  k2: -0.04251846
  k3: 0.05207680
  k4: -0.01365711
  p1: 0.00651633
  p2: 0.02546324
  p3: -0.03968346
  p4: 0.01553554
  s1: 0.0033
  s2: -0.0178
  s3: 0.0420
  s4: -0.0189
  tilt_x: -0.0467
  tilt_y: 0.0404
  scale: 2.8858
  rotation: -0.3678

Best score: 0.6729: : 499iter [00:15, 40.31iter/s]
Best score: 0.6729: : 500iter [00:15, 40.18iter/s]
Best score: 0.6729: : 500iter [00:15, 40.18iter/s]
Best score: 0.6729: : 501iter [00:15, 40.18iter/s]
Best score: 0.6729: : 502iter [00:15, 40.18iter/s]
Best score: 0.6729: : 503iter [00:15, 40.18iter/s]
Best score: 0.6729: : 504iter [00:15, 40.18iter/s]
Best score: 0.6729: : 505iter [00:15, 33.99iter/s]
Best score: 0.6729: : 505iter [00:15, 33.99iter/s]
Best score: 0.6729: : 506iter [00:15, 33.99iter/s]
Best score: 0.6729: : 507iter [00:15, 33.99iter/s]
Best score: 0.6729: : 508iter [00:15, 33.99iter/s]
Best score: 0.6729: : 509iter [00:15, 33.99iter/s]
Best score: 0.6729: : 510iter [00:15, 35.64iter/s]
Best score: 0.6729: : 510iter [00:15, 35.64iter/s]
Best score: 0.6729: : 511iter [00:15, 35.64iter/s]
Best score: 0.6729: : 512iter [00:15, 35.64iter/s]
Best score: 0.6729: : 513iter [00:15, 35.64iter/s]
Best score: 0.6729: : 514iter [00:15, 35.64iter/s]
Best score: 0.6729: : 515iter [00:15, 37.17iter/s]
Best score: 0.6729: : 515iter [00:15, 37.17iter/s]
Best score: 0.6729: : 516iter [00:15, 37.17iter/s]
Best score: 0.6729: : 517iter [00:15, 37.17iter/s]
Best score: 0.6729: : 518iter [00:15, 37.17iter/s]
Best score: 0.6729: : 519iter [00:15, 37.17iter/s]
Best score: 0.6729: : 520iter [00:15, 38.00iter/s]
Best score: 0.6729: : 520iter [00:15, 38.00iter/s]
Best score: 0.6729: : 521iter [00:15, 38.00iter/s]
Best score: 0.6729: : 522iter [00:15, 38.00iter/s]
Best score: 0.6729: : 523iter [00:15, 38.00iter/s]
Best score: 0.6729: : 524iter [00:15, 38.46iter/s]
Best score: 0.6729: : 524iter [00:15, 38.46iter/s]
Best score: 0.6729: : 525iter [00:15, 38.46iter/s]
Best score: 0.6729: : 526iter [00:15, 38.46iter/s]
Best score: 0.6729: : 527iter [00:15, 38.46iter/s]
Best score: 0.6729: : 528iter [00:15, 38.46iter/s]
Best score: 0.6729: : 529iter [00:15, 39.18iter/s]
Best score: 0.6729: : 529iter [00:15, 39.18iter/s]
Best score: 0.6729: : 530iter [00:15, 39.18iter/s]
Best score: 0.6729: : 531iter [00:15, 39.18iter/s]
Best score: 0.6729: : 532iter [00:15, 39.18iter/s]
Best score: 0.6729: : 533iter [00:15, 39.32iter/s]
Best score: 0.6729: : 533iter [00:15, 39.32iter/s]
Best score: 0.6729: : 534iter [00:15, 39.32iter/s]
Best score: 0.6729: : 535iter [00:16, 39.32iter/s]
Best score: 0.6729: : 536iter [00:16, 39.32iter/s]
Best score: 0.6729: : 537iter [00:16, 39.32iter/s]
Best score: 0.6729: : 538iter [00:16, 39.82iter/s]
Best score: 0.6729: : 538iter [00:16, 39.82iter/s]
Best score: 0.6688: : 539iter [00:16, 39.82iter/s]
Best score: 0.6688: : 540iter [00:16, 39.82iter/s]
Best score: 0.6688: : 541iter [00:16, 39.82iter/s]
Best score: 0.6688: : 542iter [00:16, 39.82iter/s]
Best score: 0.6688: : 543iter [00:16, 39.91iter/s]
Best score: 0.6688: : 543iter [00:16, 39.91iter/s]
Best score: 0.6688: : 544iter [00:16, 39.91iter/s]
Best score: 0.6688: : 545iter [00:16, 39.91iter/s]
Best score: 0.6688: : 546iter [00:16, 39.91iter/s]
Best score: 0.6688: : 547iter [00:16, 39.91iter/s]
Best score: 0.6688: : 548iter [00:16, 40.36iter/s]
Best score: 0.6688: : 548iter [00:16, 40.36iter/s]
[ITER 550] Best: 0.6451
[PARAMS] Current best parameters:
  fx: 679.2123
  fy: 680.0588
  px: 984.4317
  py: 767.1098
  k1: -0.12762395
  k2: -0.03243877
  k3: 0.04864678
  k4: -0.01341766
  p1: 0.00800495
  p2: 0.03034185
  p3: -0.03826171
  p4: 0.01280106
  s1: -0.0057
  s2: -0.0129
  s3: 0.0389
  s4: -0.0168
  tilt_x: -0.0538
  tilt_y: 0.0453
  scale: 2.9002
  rotation: -0.3663

Best score: 0.6451: : 549iter [00:16, 40.36iter/s]
Best score: 0.6451: : 550iter [00:16, 40.36iter/s]
Best score: 0.6451: : 551iter [00:16, 40.36iter/s]
Best score: 0.6451: : 552iter [00:16, 40.36iter/s]
Best score: 0.6451: : 553iter [00:16, 33.87iter/s]
Best score: 0.6451: : 553iter [00:16, 33.87iter/s]
Best score: 0.6451: : 554iter [00:16, 33.87iter/s]
Best score: 0.6451: : 555iter [00:16, 33.87iter/s]
Best score: 0.6451: : 556iter [00:16, 33.87iter/s]
Best score: 0.6451: : 557iter [00:16, 33.87iter/s]
Best score: 0.6451: : 558iter [00:16, 35.70iter/s]
Best score: 0.6451: : 558iter [00:16, 35.70iter/s]
Best score: 0.6451: : 559iter [00:16, 35.70iter/s]
Best score: 0.6451: : 560iter [00:16, 35.70iter/s]
Best score: 0.6451: : 561iter [00:16, 35.70iter/s]
Best score: 0.6451: : 562iter [00:16, 35.70iter/s]
Best score: 0.6451: : 563iter [00:16, 37.04iter/s]
Best score: 0.6451: : 563iter [00:16, 37.04iter/s]
Best score: 0.6451: : 564iter [00:16, 37.04iter/s]
Best score: 0.6451: : 565iter [00:16, 37.04iter/s]
Best score: 0.6451: : 566iter [00:16, 37.04iter/s]
Best score: 0.6451: : 567iter [00:16, 37.04iter/s]
Best score: 0.6451: : 568iter [00:16, 38.15iter/s]
Best score: 0.6451: : 568iter [00:16, 38.15iter/s]
Best score: 0.6451: : 569iter [00:16, 38.15iter/s]
Best score: 0.6451: : 570iter [00:16, 38.15iter/s]
Best score: 0.6451: : 571iter [00:16, 38.15iter/s]
Best score: 0.6451: : 572iter [00:17, 38.15iter/s]
Best score: 0.6451: : 573iter [00:17, 38.80iter/s]
Best score: 0.6451: : 573iter [00:17, 38.80iter/s]
Best score: 0.6451: : 574iter [00:17, 38.80iter/s]
Best score: 0.6451: : 575iter [00:17, 38.80iter/s]
Best score: 0.6451: : 576iter [00:17, 38.80iter/s]
Best score: 0.6451: : 577iter [00:17, 38.80iter/s]
Best score: 0.6451: : 578iter [00:17, 39.55iter/s]
Best score: 0.6451: : 578iter [00:17, 39.55iter/s]
Best score: 0.5758: : 579iter [00:17, 39.55iter/s]
Best score: 0.5758: : 580iter [00:17, 39.55iter/s]
Best score: 0.5758: : 581iter [00:17, 39.55iter/s]
Best score: 0.5758: : 582iter [00:17, 39.55iter/s]
Best score: 0.5758: : 583iter [00:17, 39.79iter/s]
Best score: 0.5758: : 583iter [00:17, 39.79iter/s]
Best score: 0.5758: : 584iter [00:17, 39.79iter/s]
Best score: 0.5758: : 585iter [00:17, 39.79iter/s]
Best score: 0.5758: : 586iter [00:17, 39.79iter/s]
Best score: 0.5758: : 587iter [00:17, 39.79iter/s]
Best score: 0.5758: : 588iter [00:17, 40.14iter/s]
Best score: 0.5758: : 588iter [00:17, 40.14iter/s]
Best score: 0.5221: : 589iter [00:17, 40.14iter/s]
Best score: 0.5221: : 590iter [00:17, 40.14iter/s]
Best score: 0.5221: : 591iter [00:17, 40.14iter/s]
Best score: 0.5221: : 592iter [00:17, 40.14iter/s]
Best score: 0.5221: : 593iter [00:17, 40.30iter/s]
Best score: 0.5221: : 593iter [00:17, 40.30iter/s]
Best score: 0.5221: : 594iter [00:17, 40.30iter/s]
Best score: 0.5221: : 595iter [00:17, 40.30iter/s]
Best score: 0.5221: : 596iter [00:17, 40.30iter/s]
Best score: 0.5221: : 597iter [00:17, 40.30iter/s]
Best score: 0.5221: : 598iter [00:17, 40.53iter/s]
Best score: 0.5221: : 598iter [00:17, 40.53iter/s]
[ITER 600] Best: 0.5221
[PARAMS] Current best parameters:
  fx: 679.9558
  fy: 680.9596
  px: 985.6291
  py: 768.4410
  k1: -0.13366161
  k2: -0.02666842
  k3: 0.04680814
  k4: -0.01327884
  p1: 0.01385207
  p2: 0.03180249
  p3: -0.03490093
  p4: 0.00682533
  s1: -0.0104
  s2: -0.0071
  s3: 0.0338
  s4: -0.0129
  tilt_x: -0.0566
  tilt_y: 0.0409
  scale: 2.9129
  rotation: -0.3815

Best score: 0.5221: : 599iter [00:17, 40.53iter/s]
Best score: 0.5221: : 600iter [00:17, 40.53iter/s]
Best score: 0.5221: : 601iter [00:17, 40.53iter/s]
Best score: 0.5221: : 602iter [00:17, 40.53iter/s]
Best score: 0.5221: : 603iter [00:17, 33.98iter/s]
Best score: 0.5221: : 603iter [00:17, 33.98iter/s]
Best score: 0.5221: : 604iter [00:17, 33.98iter/s]
Best score: 0.5221: : 605iter [00:17, 33.98iter/s]
Best score: 0.5221: : 606iter [00:17, 33.98iter/s]
Best score: 0.5221: : 607iter [00:17, 33.98iter/s]
Best score: 0.5221: : 608iter [00:17, 35.92iter/s]
Best score: 0.5221: : 608iter [00:17, 35.92iter/s]
Best score: 0.4838: : 609iter [00:17, 35.92iter/s]
Best score: 0.4838: : 610iter [00:18, 35.92iter/s]
Best score: 0.4838: : 611iter [00:18, 35.92iter/s]
Best score: 0.4838: : 612iter [00:18, 36.78iter/s]
Best score: 0.4838: : 612iter [00:18, 36.78iter/s]
Best score: 0.4838: : 613iter [00:18, 36.78iter/s]
Best score: 0.4838: : 614iter [00:18, 36.78iter/s]
Best score: 0.4838: : 615iter [00:18, 36.78iter/s]
Best score: 0.4838: : 616iter [00:18, 36.78iter/s]
Best score: 0.4838: : 617iter [00:18, 38.00iter/s]
Best score: 0.4838: : 617iter [00:18, 38.00iter/s]
Best score: 0.4838: : 618iter [00:18, 38.00iter/s]
Best score: 0.4617: : 619iter [00:18, 38.00iter/s]
Best score: 0.4617: : 620iter [00:18, 38.00iter/s]
Best score: 0.4617: : 621iter [00:18, 38.50iter/s]
Best score: 0.4617: : 621iter [00:18, 38.50iter/s]
Best score: 0.4617: : 622iter [00:18, 38.50iter/s]
Best score: 0.4617: : 623iter [00:18, 38.50iter/s]
Best score: 0.4617: : 624iter [00:18, 38.50iter/s]
Best score: 0.4617: : 625iter [00:18, 38.50iter/s]
Best score: 0.4617: : 626iter [00:18, 39.01iter/s]
Best score: 0.4617: : 626iter [00:18, 39.01iter/s]
Best score: 0.4617: : 627iter [00:18, 39.01iter/s]
Best score: 0.4617: : 628iter [00:18, 39.01iter/s]
Best score: 0.4446: : 629iter [00:18, 39.01iter/s]
Best score: 0.4446: : 630iter [00:18, 39.24iter/s]
Best score: 0.4446: : 630iter [00:18, 39.24iter/s]
Best score: 0.4446: : 631iter [00:18, 39.24iter/s]
Best score: 0.4446: : 632iter [00:18, 39.24iter/s]
Best score: 0.4446: : 633iter [00:18, 39.24iter/s]
Best score: 0.4446: : 634iter [00:18, 39.24iter/s]
Best score: 0.4446: : 635iter [00:18, 39.78iter/s]
Best score: 0.4446: : 635iter [00:18, 39.78iter/s]
Best score: 0.4446: : 636iter [00:18, 39.78iter/s]
Best score: 0.4446: : 637iter [00:18, 39.78iter/s]
Best score: 0.4446: : 638iter [00:18, 39.78iter/s]
Best score: 0.4296: : 639iter [00:18, 39.78iter/s]
Best score: 0.4296: : 640iter [00:18, 40.01iter/s]
Best score: 0.4296: : 640iter [00:18, 40.01iter/s]
Best score: 0.4296: : 641iter [00:18, 40.01iter/s]
Best score: 0.4296: : 642iter [00:18, 40.01iter/s]
Best score: 0.4296: : 643iter [00:18, 40.01iter/s]
Best score: 0.4296: : 644iter [00:18, 40.01iter/s]
Best score: 0.4296: : 645iter [00:18, 40.31iter/s]
Best score: 0.4296: : 645iter [00:18, 40.31iter/s]
Best score: 0.4296: : 646iter [00:18, 40.31iter/s]
Best score: 0.4296: : 647iter [00:18, 40.31iter/s]
Best score: 0.4296: : 648iter [00:18, 40.31iter/s]
[ITER 650] Best: 0.4269
[PARAMS] Current best parameters:
  fx: 684.8491
  fy: 685.7879
  px: 987.1012
  py: 767.9179
  k1: -0.15759859
  k2: 0.00645488
  k3: 0.02783366
  k4: -0.00931050
  p1: 0.00975851
  p2: 0.01920403
  p3: -0.02074435
  p4: 0.00756116
  s1: -0.0098
  s2: -0.0065
  s3: 0.0224
  s4: -0.0122
  tilt_x: -0.0522
  tilt_y: 0.0066
  scale: 2.9234
  rotation: -0.3158

Best score: 0.4269: : 649iter [00:18, 40.31iter/s]
Best score: 0.4269: : 650iter [00:18, 40.12iter/s]
Best score: 0.4269: : 650iter [00:19, 40.12iter/s]
Best score: 0.4269: : 651iter [00:19, 40.12iter/s]
Best score: 0.4269: : 652iter [00:19, 40.12iter/s]
Best score: 0.4269: : 653iter [00:19, 40.12iter/s]
Best score: 0.4269: : 654iter [00:19, 40.12iter/s]
Best score: 0.4269: : 655iter [00:19, 33.75iter/s]
Best score: 0.4269: : 655iter [00:19, 33.75iter/s]
Best score: 0.4269: : 656iter [00:19, 33.75iter/s]
Best score: 0.4269: : 657iter [00:19, 33.75iter/s]
Best score: 0.4269: : 658iter [00:19, 33.75iter/s]
Best score: 0.4032: : 659iter [00:19, 33.75iter/s]
Best score: 0.4032: : 660iter [00:19, 35.51iter/s]
Best score: 0.4032: : 660iter [00:19, 35.51iter/s]
Best score: 0.4032: : 661iter [00:19, 35.51iter/s]
Best score: 0.4032: : 662iter [00:19, 35.51iter/s]
Best score: 0.4032: : 663iter [00:19, 35.51iter/s]
Best score: 0.4032: : 664iter [00:19, 35.51iter/s]
Best score: 0.4032: : 665iter [00:19, 36.98iter/s]
Best score: 0.4032: : 665iter [00:19, 36.98iter/s]
Best score: 0.4032: : 666iter [00:19, 36.98iter/s]
Best score: 0.4032: : 667iter [00:19, 36.98iter/s]
Best score: 0.4032: : 668iter [00:19, 36.98iter/s]
Best score: 0.3989: : 669iter [00:19, 36.98iter/s]
Best score: 0.3989: : 670iter [00:19, 38.04iter/s]
Best score: 0.3989: : 670iter [00:19, 38.04iter/s]
Best score: 0.3989: : 671iter [00:19, 38.04iter/s]
Best score: 0.3989: : 672iter [00:19, 38.04iter/s]
Best score: 0.3989: : 673iter [00:19, 38.04iter/s]
Best score: 0.3989: : 674iter [00:19, 38.04iter/s]
Best score: 0.3989: : 675iter [00:19, 38.98iter/s]
Best score: 0.3989: : 675iter [00:19, 38.98iter/s]
Best score: 0.3989: : 676iter [00:19, 38.98iter/s]
Best score: 0.3989: : 677iter [00:19, 38.98iter/s]
Best score: 0.3989: : 678iter [00:19, 38.98iter/s]
Best score: 0.3804: : 679iter [00:19, 38.98iter/s]
Best score: 0.3804: : 680iter [00:19, 39.21iter/s]
Best score: 0.3804: : 680iter [00:19, 39.21iter/s]
Best score: 0.3804: : 681iter [00:19, 39.21iter/s]
Best score: 0.3804: : 682iter [00:19, 39.21iter/s]
Best score: 0.3804: : 683iter [00:20, 39.21iter/s]
Best score: 0.3804: : 684iter [00:20, 31.39iter/s]
Best score: 0.3804: : 684iter [00:20, 31.39iter/s]
Best score: 0.3804: : 685iter [00:20, 31.39iter/s]
Best score: 0.3804: : 686iter [00:20, 31.39iter/s]
Best score: 0.3804: : 687iter [00:20, 31.39iter/s]
Best score: 0.3804: : 688iter [00:20, 31.39iter/s]
Best score: 0.3804: : 689iter [00:20, 33.70iter/s]
Best score: 0.3706: : 689iter [00:20, 33.70iter/s]
Best score: 0.3706: : 690iter [00:20, 33.70iter/s]
Best score: 0.3706: : 691iter [00:20, 33.70iter/s]
Best score: 0.3706: : 692iter [00:20, 33.70iter/s]
Best score: 0.3706: : 693iter [00:20, 35.02iter/s]
Best score: 0.3706: : 693iter [00:20, 35.02iter/s]
Best score: 0.3706: : 694iter [00:20, 35.02iter/s]
Best score: 0.3706: : 695iter [00:20, 35.02iter/s]
Best score: 0.3706: : 696iter [00:20, 35.02iter/s]
Best score: 0.3706: : 697iter [00:20, 35.02iter/s]
Best score: 0.3706: : 698iter [00:20, 36.67iter/s]
Best score: 0.3706: : 698iter [00:20, 36.67iter/s]
[ITER 700] Best: 0.3576
[PARAMS] Current best parameters:
  fx: 691.7231
  fy: 692.2494
  px: 991.3369
  py: 767.8932
  k1: -0.17588378
  k2: 0.03077576
  k3: 0.01233438
  k4: -0.00569430
  p1: 0.00853567
  p2: 0.01953580
  p3: -0.01767550
  p4: 0.00315587
  s1: -0.0091
  s2: -0.0030
  s3: 0.0217
  s4: -0.0086
  tilt_x: -0.0504
  tilt_y: -0.0205
  scale: 2.9284
  rotation: -0.2985

Best score: 0.3576: : 699iter [00:20, 36.67iter/s]
Best score: 0.3576: : 700iter [00:20, 36.67iter/s]
Best score: 0.3576: : 701iter [00:20, 36.67iter/s]
Best score: 0.3576: : 702iter [00:20, 31.27iter/s]
Best score: 0.3576: : 702iter [00:20, 31.27iter/s]
Best score: 0.3576: : 703iter [00:20, 31.27iter/s]
Best score: 0.3576: : 704iter [00:20, 31.27iter/s]
Best score: 0.3576: : 705iter [00:20, 31.27iter/s]
Best score: 0.3576: : 706iter [00:20, 31.27iter/s]
Best score: 0.3576: : 707iter [00:20, 33.72iter/s]
Best score: 0.3576: : 707iter [00:20, 33.72iter/s]
Best score: 0.3576: : 708iter [00:20, 33.72iter/s]
Best score: 0.3542: : 709iter [00:20, 33.72iter/s]
Best score: 0.3542: : 710iter [00:20, 33.72iter/s]
Best score: 0.3542: : 711iter [00:20, 35.03iter/s]
Best score: 0.3542: : 711iter [00:20, 35.03iter/s]
Best score: 0.3542: : 712iter [00:20, 35.03iter/s]
Best score: 0.3542: : 713iter [00:20, 35.03iter/s]
Best score: 0.3542: : 714iter [00:20, 35.03iter/s]
Best score: 0.3542: : 715iter [00:20, 36.19iter/s]
Best score: 0.3542: : 715iter [00:20, 36.19iter/s]
Best score: 0.3542: : 716iter [00:20, 36.19iter/s]
Best score: 0.3542: : 717iter [00:20, 36.19iter/s]
Best score: 0.3542: : 718iter [00:20, 36.19iter/s]
Best score: 0.3474: : 719iter [00:20, 36.19iter/s]
Best score: 0.3474: : 720iter [00:20, 37.28iter/s]
Best score: 0.3474: : 720iter [00:21, 37.28iter/s]
Best score: 0.3474: : 721iter [00:21, 37.28iter/s]
Best score: 0.3474: : 722iter [00:21, 37.28iter/s]
Best score: 0.3474: : 723iter [00:21, 37.28iter/s]
Best score: 0.3474: : 724iter [00:21, 37.28iter/s]
Best score: 0.3474: : 725iter [00:21, 38.31iter/s]
Best score: 0.3474: : 725iter [00:21, 38.31iter/s]
Best score: 0.3474: : 726iter [00:21, 38.31iter/s]
Best score: 0.3474: : 727iter [00:21, 38.31iter/s]
Best score: 0.3474: : 728iter [00:21, 38.31iter/s]
Best score: 0.3382: : 729iter [00:21, 38.31iter/s]
Best score: 0.3382: : 730iter [00:21, 38.82iter/s]
Best score: 0.3382: : 730iter [00:21, 38.82iter/s]
Best score: 0.3382: : 731iter [00:21, 38.82iter/s]
Best score: 0.3382: : 732iter [00:21, 38.82iter/s]
Best score: 0.3382: : 733iter [00:21, 38.82iter/s]
Best score: 0.3382: : 734iter [00:21, 38.82iter/s]
Best score: 0.3382: : 735iter [00:21, 39.21iter/s]
Best score: 0.3382: : 735iter [00:21, 39.21iter/s]
Best score: 0.3382: : 736iter [00:21, 39.21iter/s]
Best score: 0.3382: : 737iter [00:21, 39.21iter/s]
Best score: 0.3382: : 738iter [00:21, 39.21iter/s]
Best score: 0.3375: : 739iter [00:21, 39.21iter/s]
Best score: 0.3375: : 740iter [00:21, 39.33iter/s]
Best score: 0.3375: : 740iter [00:21, 39.33iter/s]
Best score: 0.3375: : 741iter [00:21, 39.33iter/s]
Best score: 0.3375: : 742iter [00:21, 39.33iter/s]
Best score: 0.3375: : 743iter [00:21, 39.33iter/s]
Best score: 0.3375: : 744iter [00:21, 39.33iter/s]
Best score: 0.3375: : 745iter [00:21, 39.80iter/s]
Best score: 0.3375: : 745iter [00:21, 39.80iter/s]
Best score: 0.3375: : 746iter [00:21, 39.80iter/s]
Best score: 0.3375: : 747iter [00:21, 39.80iter/s]
Best score: 0.3375: : 748iter [00:21, 39.80iter/s]
[ITER 750] Best: 0.3375
[PARAMS] Current best parameters:
  fx: 691.9566
  fy: 692.4567
  px: 990.7254
  py: 766.3770
  k1: -0.17769327
  k2: 0.03490528
  k3: 0.00923873
  k4: -0.00494248
  p1: 0.00319310
  p2: 0.01532422
  p3: -0.01613288
  p4: 0.00703011
  s1: -0.0070
  s2: -0.0065
  s3: 0.0239
  s4: -0.0127
  tilt_x: -0.0497
  tilt_y: -0.0246
  scale: 2.9271
  rotation: -0.2889

Best score: 0.3375: : 749iter [00:21, 39.80iter/s]
Best score: 0.3375: : 750iter [00:21, 39.78iter/s]
Best score: 0.3375: : 750iter [00:21, 39.78iter/s]
Best score: 0.3375: : 751iter [00:21, 39.78iter/s]
Best score: 0.3375: : 752iter [00:21, 39.78iter/s]
Best score: 0.3375: : 753iter [00:21, 39.78iter/s]
Best score: 0.3375: : 754iter [00:21, 33.26iter/s]
Best score: 0.3375: : 754iter [00:21, 33.26iter/s]
Best score: 0.3375: : 755iter [00:21, 33.26iter/s]
Best score: 0.3375: : 756iter [00:21, 33.26iter/s]
Best score: 0.3375: : 757iter [00:22, 33.26iter/s]
Best score: 0.3375: : 758iter [00:22, 34.80iter/s]
Best score: 0.3375: : 758iter [00:22, 34.80iter/s]
Best score: 0.3326: : 759iter [00:22, 34.80iter/s]
Best score: 0.3326: : 760iter [00:22, 34.80iter/s]
Best score: 0.3326: : 761iter [00:22, 34.80iter/s]
Best score: 0.3326: : 762iter [00:22, 36.01iter/s]
Best score: 0.3326: : 762iter [00:22, 36.01iter/s]
Best score: 0.3326: : 763iter [00:22, 36.01iter/s]
Best score: 0.3326: : 764iter [00:22, 36.01iter/s]
Best score: 0.3326: : 765iter [00:22, 36.01iter/s]
Best score: 0.3326: : 766iter [00:22, 36.01iter/s]
Best score: 0.3326: : 767iter [00:22, 37.50iter/s]
Best score: 0.3326: : 767iter [00:22, 37.50iter/s]
Best score: 0.3326: : 768iter [00:22, 37.50iter/s]
Best score: 0.3284: : 769iter [00:22, 37.50iter/s]
Best score: 0.3284: : 770iter [00:22, 37.50iter/s]
Best score: 0.3284: : 771iter [00:22, 38.04iter/s]
Best score: 0.3284: : 771iter [00:22, 38.04iter/s]
Best score: 0.3284: : 772iter [00:22, 38.04iter/s]
Best score: 0.3284: : 773iter [00:22, 38.04iter/s]
Best score: 0.3284: : 774iter [00:22, 38.04iter/s]
Best score: 0.3284: : 775iter [00:22, 38.04iter/s]
Best score: 0.3284: : 776iter [00:22, 38.90iter/s]
Best score: 0.3284: : 776iter [00:22, 38.90iter/s]
Best score: 0.3284: : 777iter [00:22, 38.90iter/s]
Best score: 0.3284: : 778iter [00:22, 38.90iter/s]
Best score: 0.3202: : 779iter [00:22, 38.90iter/s]
Best score: 0.3202: : 780iter [00:22, 38.94iter/s]
Best score: 0.3202: : 780iter [00:22, 38.94iter/s]
Best score: 0.3202: : 781iter [00:22, 38.94iter/s]
Best score: 0.3202: : 782iter [00:22, 38.94iter/s]
Best score: 0.3202: : 783iter [00:22, 38.94iter/s]
Best score: 0.3202: : 784iter [00:22, 38.94iter/s]
Best score: 0.3202: : 785iter [00:22, 39.33iter/s]
Best score: 0.3202: : 785iter [00:22, 39.33iter/s]
Best score: 0.3202: : 786iter [00:22, 39.33iter/s]
Best score: 0.3202: : 787iter [00:22, 39.33iter/s]
Best score: 0.3202: : 788iter [00:22, 39.33iter/s]
Best score: 0.3195: : 789iter [00:22, 39.33iter/s]
Best score: 0.3195: : 790iter [00:22, 39.18iter/s]
Best score: 0.3195: : 790iter [00:22, 39.18iter/s]
Best score: 0.3195: : 791iter [00:22, 39.18iter/s]
Best score: 0.3195: : 792iter [00:22, 39.18iter/s]
Best score: 0.3195: : 793iter [00:22, 39.18iter/s]
Best score: 0.3195: : 794iter [00:22, 39.18iter/s]
Best score: 0.3195: : 795iter [00:22, 39.50iter/s]
Best score: 0.3195: : 795iter [00:22, 39.50iter/s]
Best score: 0.3195: : 796iter [00:22, 39.50iter/s]
Best score: 0.3195: : 797iter [00:23, 39.50iter/s]
Best score: 0.3195: : 798iter [00:23, 39.50iter/s]
[ITER 800] Best: 0.3191
[PARAMS] Current best parameters:
  fx: 693.0463
  fy: 693.6143
  px: 991.2100
  py: 764.6928
  k1: -0.18608888
  k2: 0.04697966
  k3: 0.00234709
  k4: -0.00353253
  p1: 0.00165065
  p2: 0.01525310
  p3: -0.01525209
  p4: 0.00423820
  s1: -0.0057
  s2: -0.0046
  s3: 0.0219
  s4: -0.0092
  tilt_x: -0.0573
  tilt_y: -0.0283
  scale: 2.9387
  rotation: -0.2833

Best score: 0.3191: : 799iter [00:23, 39.50iter/s]
Best score: 0.3191: : 800iter [00:23, 39.51iter/s]
Best score: 0.3191: : 800iter [00:23, 39.51iter/s]
Best score: 0.3191: : 801iter [00:23, 39.51iter/s]
Best score: 0.3191: : 802iter [00:23, 39.51iter/s]
Best score: 0.3191: : 803iter [00:23, 39.51iter/s]
Best score: 0.3191: : 804iter [00:23, 33.12iter/s]
Best score: 0.3191: : 804iter [00:23, 33.12iter/s]
Best score: 0.3191: : 805iter [00:23, 33.12iter/s]
Best score: 0.3191: : 806iter [00:23, 33.12iter/s]
Best score: 0.3191: : 807iter [00:23, 33.12iter/s]
Best score: 0.3191: : 808iter [00:23, 33.12iter/s]
Best score: 0.3191: : 809iter [00:23, 35.25iter/s]
Best score: 0.3111: : 809iter [00:23, 35.25iter/s]
Best score: 0.3111: : 810iter [00:23, 35.25iter/s]
Best score: 0.3111: : 811iter [00:23, 35.25iter/s]
Best score: 0.3111: : 812iter [00:23, 35.25iter/s]
Best score: 0.3111: : 813iter [00:23, 36.25iter/s]
Best score: 0.3111: : 813iter [00:23, 36.25iter/s]
Best score: 0.3111: : 814iter [00:23, 36.25iter/s]
Best score: 0.3111: : 815iter [00:23, 36.25iter/s]
Best score: 0.3111: : 816iter [00:23, 36.25iter/s]
Best score: 0.3111: : 817iter [00:23, 36.25iter/s]
Best score: 0.3111: : 818iter [00:23, 37.57iter/s]
Best score: 0.3111: : 818iter [00:23, 37.57iter/s]
Best score: 0.3091: : 819iter [00:23, 37.57iter/s]
Best score: 0.3091: : 820iter [00:23, 37.57iter/s]
Best score: 0.3091: : 821iter [00:23, 37.57iter/s]
Best score: 0.3091: : 822iter [00:23, 37.98iter/s]
Best score: 0.3091: : 822iter [00:23, 37.98iter/s]
Best score: 0.3091: : 823iter [00:23, 37.98iter/s]
Best score: 0.3091: : 824iter [00:23, 37.98iter/s]
Best score: 0.3091: : 825iter [00:23, 37.98iter/s]
Best score: 0.3091: : 826iter [00:23, 37.98iter/s]
Best score: 0.3091: : 827iter [00:23, 38.81iter/s]
Best score: 0.3091: : 827iter [00:23, 38.81iter/s]
Best score: 0.3091: : 828iter [00:23, 38.81iter/s]
Best score: 0.3082: : 829iter [00:23, 38.81iter/s]
Best score: 0.3082: : 830iter [00:23, 38.81iter/s]
Best score: 0.3082: : 831iter [00:23, 38.90iter/s]
Best score: 0.3082: : 831iter [00:23, 38.90iter/s]
Best score: 0.3082: : 832iter [00:23, 38.90iter/s]
Best score: 0.3082: : 833iter [00:23, 38.90iter/s]
Best score: 0.3082: : 834iter [00:24, 38.90iter/s]
Best score: 0.3082: : 835iter [00:24, 38.90iter/s]
Best score: 0.3082: : 836iter [00:24, 39.48iter/s]
Best score: 0.3082: : 836iter [00:24, 39.48iter/s]
Best score: 0.3082: : 837iter [00:24, 39.48iter/s]
Best score: 0.3082: : 838iter [00:24, 39.48iter/s]
Best score: 0.3033: : 839iter [00:24, 39.48iter/s]
Best score: 0.3033: : 840iter [00:24, 39.55iter/s]
Best score: 0.3033: : 840iter [00:24, 39.55iter/s]
Best score: 0.3033: : 841iter [00:24, 39.55iter/s]
Best score: 0.3033: : 842iter [00:24, 39.55iter/s]
Best score: 0.3033: : 843iter [00:24, 39.55iter/s]
Best score: 0.3033: : 844iter [00:24, 39.55iter/s]
Best score: 0.3033: : 845iter [00:24, 39.94iter/s]
Best score: 0.3033: : 845iter [00:24, 39.94iter/s]
Best score: 0.3033: : 846iter [00:24, 39.94iter/s]
Best score: 0.3033: : 847iter [00:24, 39.94iter/s]
Best score: 0.3033: : 848iter [00:24, 39.94iter/s]
[ITER 850] Best: 0.3033
[PARAMS] Current best parameters:
  fx: 693.3736
  fy: 693.8467
  px: 992.0568
  py: 763.1134
  k1: -0.19802196
  k2: 0.06517972
  k3: -0.00824957
  k4: -0.00126105
  p1: -0.00036441
  p2: 0.01377073
  p3: -0.01196723
  p4: -0.00039734
  s1: -0.0065
  s2: 0.0004
  s3: 0.0179
  s4: -0.0038
  tilt_x: -0.0633
  tilt_y: -0.0361
  scale: 2.9588
  rotation: -0.2606

Best score: 0.3033: : 849iter [00:24, 39.94iter/s]
Best score: 0.3033: : 850iter [00:24, 39.73iter/s]
Best score: 0.3033: : 850iter [00:24, 39.73iter/s]
Best score: 0.3033: : 851iter [00:24, 39.73iter/s]
Best score: 0.3033: : 852iter [00:24, 39.73iter/s]
Best score: 0.3033: : 853iter [00:24, 39.73iter/s]
Best score: 0.3033: : 854iter [00:24, 32.94iter/s]
Best score: 0.3033: : 854iter [00:24, 32.94iter/s]
Best score: 0.3033: : 855iter [00:24, 32.94iter/s]
Best score: 0.3033: : 856iter [00:24, 32.94iter/s]
Best score: 0.3033: : 857iter [00:24, 32.94iter/s]
Best score: 0.3033: : 858iter [00:24, 32.94iter/s]
Best score: 0.3033: : 859iter [00:24, 35.11iter/s]
Best score: 0.3033: : 859iter [00:24, 35.11iter/s]
Best score: 0.3033: : 860iter [00:24, 35.11iter/s]
Best score: 0.3033: : 861iter [00:24, 35.11iter/s]
Best score: 0.3033: : 862iter [00:24, 35.11iter/s]
Best score: 0.3033: : 863iter [00:24, 36.23iter/s]
Best score: 0.3033: : 863iter [00:24, 36.23iter/s]
Best score: 0.3033: : 864iter [00:24, 36.23iter/s]
Best score: 0.3033: : 865iter [00:24, 36.23iter/s]
Best score: 0.3033: : 866iter [00:24, 36.23iter/s]
Best score: 0.3033: : 867iter [00:24, 36.23iter/s]
Best score: 0.3033: : 868iter [00:24, 37.49iter/s]
Best score: 0.3033: : 868iter [00:24, 37.49iter/s]
Best score: 0.3033: : 869iter [00:24, 37.49iter/s]
Best score: 0.3033: : 870iter [00:24, 37.49iter/s]
Best score: 0.3033: : 871iter [00:25, 37.49iter/s]
Best score: 0.3033: : 872iter [00:25, 38.00iter/s]
Best score: 0.3033: : 872iter [00:25, 38.00iter/s]
Best score: 0.3033: : 873iter [00:25, 38.00iter/s]
Best score: 0.3033: : 874iter [00:25, 38.00iter/s]
Best score: 0.3033: : 875iter [00:25, 38.00iter/s]
Best score: 0.3033: : 876iter [00:25, 38.00iter/s]
Best score: 0.3033: : 877iter [00:25, 39.01iter/s]
Best score: 0.3033: : 877iter [00:25, 39.01iter/s]
Best score: 0.3033: : 878iter [00:25, 39.01iter/s]
Best score: 0.3032: : 879iter [00:25, 39.01iter/s]
Best score: 0.3032: : 880iter [00:25, 39.01iter/s]
Best score: 0.3032: : 881iter [00:25, 39.18iter/s]
Best score: 0.3032: : 881iter [00:25, 39.18iter/s]
Best score: 0.3032: : 882iter [00:25, 39.18iter/s]
Best score: 0.3032: : 883iter [00:25, 39.18iter/s]
Best score: 0.3032: : 884iter [00:25, 39.18iter/s]
Best score: 0.3032: : 885iter [00:25, 39.18iter/s]
Best score: 0.3032: : 886iter [00:25, 39.77iter/s]
Best score: 0.3032: : 886iter [00:25, 39.77iter/s]
Best score: 0.3032: : 887iter [00:25, 39.77iter/s]
Best score: 0.3032: : 888iter [00:25, 39.77iter/s]
Best score: 0.3022: : 889iter [00:25, 39.77iter/s]
Best score: 0.3022: : 890iter [00:25, 39.72iter/s]
Best score: 0.3022: : 890iter [00:25, 39.72iter/s]
Best score: 0.3022: : 891iter [00:25, 39.72iter/s]
Best score: 0.3022: : 892iter [00:25, 39.72iter/s]
Best score: 0.3022: : 893iter [00:25, 39.72iter/s]
Best score: 0.3022: : 894iter [00:25, 39.72iter/s]
Best score: 0.3022: : 895iter [00:25, 40.05iter/s]
Best score: 0.3022: : 895iter [00:25, 40.05iter/s]
Best score: 0.3022: : 896iter [00:25, 40.05iter/s]
Best score: 0.3022: : 897iter [00:25, 40.05iter/s]
Best score: 0.3022: : 898iter [00:25, 40.05iter/s]
[ITER 900] Best: 0.3008
[PARAMS] Current best parameters:
  fx: 693.4164
  fy: 693.8916
  px: 991.6462
  py: 762.2444
  k1: -0.19737820
  k2: 0.06572298
  k3: -0.00954285
  k4: -0.00079850
  p1: -0.00230395
  p2: 0.01244109
  p3: -0.01249928
  p4: 0.00054434
  s1: -0.0047
  s2: -0.0011
  s3: 0.0185
  s4: -0.0038
  tilt_x: -0.0651
  tilt_y: -0.0363
  scale: 2.9558
  rotation: -0.2668

Best score: 0.3008: : 899iter [00:25, 40.05iter/s]
Best score: 0.3008: : 900iter [00:25, 40.05iter/s]
Best score: 0.3008: : 900iter [00:25, 40.05iter/s]
Best score: 0.3008: : 901iter [00:25, 40.05iter/s]
Best score: 0.3008: : 902iter [00:25, 40.05iter/s]
Best score: 0.3008: : 903iter [00:25, 40.05iter/s]
Best score: 0.3008: : 904iter [00:25, 40.05iter/s]
Best score: 0.3008: : 905iter [00:25, 33.51iter/s]
Best score: 0.3008: : 905iter [00:25, 33.51iter/s]
Best score: 0.3008: : 906iter [00:25, 33.51iter/s]
Best score: 0.3008: : 907iter [00:25, 33.51iter/s]
Best score: 0.3008: : 908iter [00:26, 33.51iter/s]
Best score: 0.3008: : 909iter [00:26, 33.51iter/s]
Best score: 0.3008: : 910iter [00:26, 35.27iter/s]
Best score: 0.3008: : 910iter [00:26, 35.27iter/s]
Best score: 0.3008: : 911iter [00:26, 35.27iter/s]
Best score: 0.3008: : 912iter [00:26, 35.27iter/s]
Best score: 0.3008: : 913iter [00:26, 35.27iter/s]
Best score: 0.3008: : 914iter [00:26, 35.27iter/s]
Best score: 0.3008: : 915iter [00:26, 36.79iter/s]
Best score: 0.3008: : 915iter [00:26, 36.79iter/s]
Best score: 0.3008: : 916iter [00:26, 36.79iter/s]
Best score: 0.3008: : 917iter [00:26, 36.79iter/s]
Best score: 0.3008: : 918iter [00:26, 36.79iter/s]
Best score: 0.3008: : 919iter [00:26, 36.79iter/s]
Best score: 0.3008: : 920iter [00:26, 37.67iter/s]
Best score: 0.3008: : 920iter [00:26, 37.67iter/s]
Best score: 0.3008: : 921iter [00:26, 37.67iter/s]
Best score: 0.3008: : 922iter [00:26, 37.67iter/s]
Best score: 0.3008: : 923iter [00:26, 37.67iter/s]
Best score: 0.3008: : 924iter [00:26, 37.67iter/s]
Best score: 0.3008: : 925iter [00:26, 38.50iter/s]
Best score: 0.3008: : 925iter [00:26, 38.50iter/s]
Best score: 0.3008: : 926iter [00:26, 38.50iter/s]
Best score: 0.3008: : 927iter [00:26, 38.50iter/s]
Best score: 0.3008: : 928iter [00:26, 38.50iter/s]
Best score: 0.3005: : 929iter [00:26, 38.50iter/s]
Best score: 0.3005: : 930iter [00:26, 39.00iter/s]
Best score: 0.3005: : 930iter [00:26, 39.00iter/s]
Best score: 0.3005: : 931iter [00:26, 39.00iter/s]
Best score: 0.3005: : 932iter [00:26, 39.00iter/s]
Best score: 0.3005: : 933iter [00:26, 39.00iter/s]
Best score: 0.3005: : 934iter [00:26, 39.00iter/s]
Best score: 0.3005: : 935iter [00:26, 39.55iter/s]
Best score: 0.3005: : 935iter [00:26, 39.55iter/s]
Best score: 0.3005: : 936iter [00:26, 39.55iter/s]
Best score: 0.3005: : 937iter [00:26, 39.55iter/s]
Best score: 0.3005: : 938iter [00:26, 39.55iter/s]
Best score: 0.2993: : 939iter [00:26, 39.55iter/s]
Best score: 0.2993: : 940iter [00:26, 39.69iter/s]
Best score: 0.2993: : 940iter [00:26, 39.69iter/s]
Best score: 0.2993: : 941iter [00:26, 39.69iter/s]
Best score: 0.2993: : 942iter [00:26, 39.69iter/s]
Best score: 0.2993: : 943iter [00:26, 39.69iter/s]
Best score: 0.2993: : 944iter [00:26, 39.69iter/s]
Best score: 0.2993: : 945iter [00:26, 39.87iter/s]
Best score: 0.2993: : 945iter [00:26, 39.87iter/s]
Best score: 0.2993: : 946iter [00:26, 39.87iter/s]
Best score: 0.2993: : 947iter [00:26, 39.87iter/s]
Best score: 0.2993: : 948iter [00:27, 39.87iter/s]
[ITER 950] Best: 0.2993
[PARAMS] Current best parameters:
  fx: 693.5357
  fy: 693.9919
  px: 991.8830
  py: 762.1525
  k1: -0.20020811
  k2: 0.07061475
  k3: -0.01271264
  k4: -0.00006332
  p1: -0.00268797
  p2: 0.01213916
  p3: -0.01231195
  p4: 0.00070293
  s1: -0.0047
  s2: -0.0008
  s3: 0.0176
  s4: -0.0029
  tilt_x: -0.0649
  tilt_y: -0.0383
  scale: 2.9586
  rotation: -0.2583

Best score: 0.2993: : 949iter [00:27, 39.87iter/s]
Best score: 0.2993: : 950iter [00:27, 39.68iter/s]
Best score: 0.2993: : 950iter [00:27, 39.68iter/s]
Best score: 0.2993: : 951iter [00:27, 39.68iter/s]
Best score: 0.2993: : 952iter [00:27, 39.68iter/s]
Best score: 0.2993: : 953iter [00:27, 39.68iter/s]
Best score: 0.2993: : 954iter [00:27, 33.26iter/s]
Best score: 0.2993: : 954iter [00:27, 33.26iter/s]
Best score: 0.2993: : 955iter [00:27, 33.26iter/s]
Best score: 0.2993: : 956iter [00:27, 33.26iter/s]
Best score: 0.2993: : 957iter [00:27, 33.26iter/s]
Best score: 0.2993: : 958iter [00:27, 33.26iter/s]
Best score: 0.2993: : 959iter [00:27, 35.29iter/s]
Best score: 0.2993: : 959iter [00:27, 35.29iter/s]
Best score: 0.2993: : 960iter [00:27, 35.29iter/s]
Best score: 0.2993: : 961iter [00:27, 35.29iter/s]
Best score: 0.2993: : 962iter [00:27, 35.29iter/s]
Best score: 0.2993: : 963iter [00:27, 36.39iter/s]
Best score: 0.2993: : 963iter [00:27, 36.39iter/s]
Best score: 0.2993: : 964iter [00:27, 36.39iter/s]
Best score: 0.2993: : 965iter [00:27, 36.39iter/s]
Best score: 0.2993: : 966iter [00:27, 36.39iter/s]
Best score: 0.2993: : 967iter [00:27, 36.39iter/s]
Best score: 0.2993: : 968iter [00:27, 37.59iter/s]
Best score: 0.2993: : 968iter [00:27, 37.59iter/s]
Best score: 0.2993: : 969iter [00:27, 37.59iter/s]
Best score: 0.2993: : 970iter [00:27, 37.59iter/s]
Best score: 0.2993: : 971iter [00:27, 37.59iter/s]
Best score: 0.2993: : 972iter [00:27, 38.09iter/s]
Best score: 0.2993: : 972iter [00:27, 38.09iter/s]
Best score: 0.2993: : 973iter [00:27, 38.09iter/s]
Best score: 0.2993: : 974iter [00:27, 38.09iter/s]
Best score: 0.2993: : 975iter [00:27, 38.09iter/s]
Best score: 0.2993: : 976iter [00:27, 38.09iter/s]
Best score: 0.2993: : 977iter [00:27, 38.98iter/s]
Best score: 0.2993: : 977iter [00:27, 38.98iter/s]
Best score: 0.2993: : 978iter [00:27, 38.98iter/s]
Best score: 0.2991: : 979iter [00:27, 38.98iter/s]
Best score: 0.2991: : 980iter [00:27, 38.98iter/s]
Best score: 0.2991: : 981iter [00:27, 39.12iter/s]
Best score: 0.2991: : 981iter [00:27, 39.12iter/s]
Best score: 0.2991: : 982iter [00:27, 39.12iter/s]
Best score: 0.2991: : 983iter [00:27, 39.12iter/s]
Best score: 0.2991: : 984iter [00:27, 39.12iter/s]
Best score: 0.2991: : 985iter [00:28, 39.12iter/s]
Best score: 0.2991: : 986iter [00:28, 39.65iter/s]
Best score: 0.2991: : 986iter [00:28, 39.65iter/s]
Best score: 0.2991: : 987iter [00:28, 39.65iter/s]
Best score: 0.2991: : 988iter [00:28, 39.65iter/s]
Best score: 0.2991: : 989iter [00:28, 39.65iter/s]
Best score: 0.2991: : 990iter [00:28, 39.49iter/s]
Best score: 0.2991: : 990iter [00:28, 39.49iter/s]
Best score: 0.2991: : 991iter [00:28, 39.49iter/s]
Best score: 0.2991: : 992iter [00:28, 39.49iter/s]
Best score: 0.2991: : 993iter [00:28, 39.49iter/s]
Best score: 0.2991: : 994iter [00:28, 39.10iter/s]
Best score: 0.2991: : 994iter [00:28, 39.10iter/s]
Best score: 0.2991: : 995iter [00:28, 39.10iter/s]
Best score: 0.2991: : 996iter [00:28, 39.10iter/s]
Best score: 0.2991: : 997iter [00:28, 39.10iter/s]
Best score: 0.2991: : 998iter [00:28, 39.10iter/s]
Best score: 0.2991: : 998iter [00:28, 39.10iter/s]
[ITER 1000] Best: 0.2977
[PARAMS] Current best parameters:
  fx: 693.3205
  fy: 693.8611
  px: 992.7592
  py: 762.1696
  k1: -0.20388633
  k2: 0.07604878
  k3: -0.01548695
  k4: 0.00042323
  p1: -0.00201813
  p2: 0.01413490
  p3: -0.01276404
  p4: -0.00017546
  s1: -0.0058
  s2: 0.0006
  s3: 0.0174
  s4: -0.0020
  tilt_x: -0.0662
  tilt_y: -0.0399
  scale: 2.9653
  rotation: -0.2556

Best score: 0.2977: : 999iter [00:28, 39.10iter/s]
Best score: 0.2977: : 1000iter [00:28, 39.10iter/s]
Best score: 0.2977: : 1001iter [00:28, 39.10iter/s]
Best score: 0.2977: : 1002iter [00:28, 32.12iter/s]
Best score: 0.2977: : 1002iter [00:28, 32.12iter/s]
Best score: 0.2977: : 1003iter [00:28, 32.12iter/s]
Best score: 0.2977: : 1004iter [00:28, 32.12iter/s]
Best score: 0.2977: : 1005iter [00:28, 32.12iter/s]
Best score: 0.2977: : 1006iter [00:28, 27.31iter/s]
Best score: 0.2977: : 1006iter [00:28, 27.31iter/s]
Best score: 0.2977: : 1007iter [00:28, 27.31iter/s]
Best score: 0.2977: : 1008iter [00:28, 27.31iter/s]
Best score: 0.2977: : 1009iter [00:28, 27.31iter/s]
Best score: 0.2977: : 1010iter [00:28, 30.03iter/s]
Best score: 0.2977: : 1010iter [00:28, 30.03iter/s]
Best score: 0.2977: : 1011iter [00:28, 30.03iter/s]
Best score: 0.2977: : 1012iter [00:28, 30.03iter/s]
Best score: 0.2977: : 1013iter [00:28, 30.03iter/s]
Best score: 0.2977: : 1014iter [00:28, 30.03iter/s]
Best score: 0.2977: : 1015iter [00:28, 32.97iter/s]
Best score: 0.2977: : 1015iter [00:28, 32.97iter/s]
Best score: 0.2977: : 1016iter [00:28, 32.97iter/s]
Best score: 0.2977: : 1017iter [00:28, 32.97iter/s]
Best score: 0.2977: : 1018iter [00:29, 32.97iter/s]
Best score: 0.2977: : 1019iter [00:29, 32.97iter/s]
Best score: 0.2977: : 1020iter [00:29, 34.93iter/s]
Best score: 0.2977: : 1020iter [00:29, 34.93iter/s]
Best score: 0.2977: : 1021iter [00:29, 34.93iter/s]
Best score: 0.2977: : 1022iter [00:29, 34.93iter/s]
Best score: 0.2977: : 1023iter [00:29, 34.93iter/s]
Best score: 0.2977: : 1024iter [00:29, 34.93iter/s]
Best score: 0.2977: : 1025iter [00:29, 36.57iter/s]
Best score: 0.2977: : 1025iter [00:29, 36.57iter/s]
Best score: 0.2977: : 1026iter [00:29, 36.57iter/s]
Best score: 0.2977: : 1027iter [00:29, 36.57iter/s]
Best score: 0.2977: : 1028iter [00:29, 36.57iter/s]
Best score: 0.2977: : 1029iter [00:29, 36.57iter/s]
Best score: 0.2977: : 1030iter [00:29, 37.61iter/s]
Best score: 0.2977: : 1030iter [00:29, 37.61iter/s]
Best score: 0.2977: : 1031iter [00:29, 37.61iter/s]
Best score: 0.2977: : 1032iter [00:29, 37.61iter/s]
Best score: 0.2977: : 1033iter [00:29, 37.61iter/s]
Best score: 0.2977: : 1034iter [00:29, 37.61iter/s]
Best score: 0.2977: : 1035iter [00:29, 38.73iter/s]
Best score: 0.2977: : 1035iter [00:29, 38.73iter/s]
Best score: 0.2977: : 1036iter [00:29, 38.73iter/s]
Best score: 0.2977: : 1037iter [00:29, 38.73iter/s]
Best score: 0.2977: : 1038iter [00:29, 38.73iter/s]
Best score: 0.2975: : 1039iter [00:29, 38.73iter/s]
Best score: 0.2975: : 1040iter [00:29, 39.08iter/s]
Best score: 0.2975: : 1040iter [00:29, 39.08iter/s]
Best score: 0.2975: : 1041iter [00:29, 39.08iter/s]
Best score: 0.2975: : 1042iter [00:29, 39.08iter/s]
Best score: 0.2975: : 1043iter [00:29, 39.08iter/s]
Best score: 0.2975: : 1044iter [00:29, 39.08iter/s]
Best score: 0.2975: : 1045iter [00:29, 39.67iter/s]
Best score: 0.2975: : 1045iter [00:29, 39.67iter/s]
Best score: 0.2975: : 1046iter [00:29, 39.67iter/s]
Best score: 0.2975: : 1047iter [00:29, 39.67iter/s]
Best score: 0.2975: : 1048iter [00:29, 39.67iter/s]
[ITER 1050] Best: 0.2969
[PARAMS] Current best parameters:
  fx: 693.2047
  fy: 693.7989
  px: 992.4818
  py: 761.6821
  k1: -0.20496151
  k2: 0.07880992
  k3: -0.01770058
  k4: 0.00099838
  p1: -0.00264683
  p2: 0.01351579
  p3: -0.01044335
  p4: 0.00022888
  s1: -0.0075
  s2: -0.0001
  s3: 0.0153
  s4: -0.0025
  tilt_x: -0.0681
  tilt_y: -0.0394
  scale: 2.9662
  rotation: -0.2558

Best score: 0.2969: : 1049iter [00:29, 39.67iter/s]
Best score: 0.2969: : 1050iter [00:29, 39.84iter/s]
Best score: 0.2969: : 1050iter [00:29, 39.84iter/s]
Best score: 0.2969: : 1051iter [00:29, 39.84iter/s]
Best score: 0.2969: : 1052iter [00:29, 39.84iter/s]
Best score: 0.2969: : 1053iter [00:29, 39.84iter/s]
Best score: 0.2969: : 1054iter [00:29, 39.84iter/s]
Best score: 0.2969: : 1055iter [00:29, 33.58iter/s]
Best score: 0.2969: : 1055iter [00:30, 33.58iter/s]
Best score: 0.2969: : 1056iter [00:30, 33.58iter/s]
Best score: 0.2969: : 1057iter [00:30, 33.58iter/s]
Best score: 0.2969: : 1058iter [00:30, 33.58iter/s]
Best score: 0.2969: : 1059iter [00:30, 33.58iter/s]
Best score: 0.2969: : 1060iter [00:30, 35.27iter/s]
Best score: 0.2969: : 1060iter [00:30, 35.27iter/s]
Best score: 0.2969: : 1061iter [00:30, 35.27iter/s]
Best score: 0.2969: : 1062iter [00:30, 35.27iter/s]
Best score: 0.2969: : 1063iter [00:30, 35.27iter/s]
Best score: 0.2969: : 1064iter [00:30, 35.27iter/s]
Best score: 0.2969: : 1065iter [00:30, 36.73iter/s]
Best score: 0.2969: : 1065iter [00:30, 36.73iter/s]
Best score: 0.2969: : 1066iter [00:30, 36.73iter/s]
Best score: 0.2969: : 1067iter [00:30, 36.73iter/s]
Best score: 0.2969: : 1068iter [00:30, 36.73iter/s]
Best score: 0.2966: : 1069iter [00:30, 36.73iter/s]
Best score: 0.2966: : 1070iter [00:30, 37.66iter/s]
Best score: 0.2966: : 1070iter [00:30, 37.66iter/s]
Best score: 0.2966: : 1071iter [00:30, 37.66iter/s]
Best score: 0.2966: : 1072iter [00:30, 37.66iter/s]
Best score: 0.2966: : 1073iter [00:30, 37.66iter/s]
Best score: 0.2966: : 1074iter [00:30, 37.66iter/s]
Best score: 0.2966: : 1075iter [00:30, 38.52iter/s]
Best score: 0.2966: : 1075iter [00:30, 38.52iter/s]
Best score: 0.2966: : 1076iter [00:30, 38.52iter/s]
Best score: 0.2966: : 1077iter [00:30, 38.52iter/s]
Best score: 0.2966: : 1078iter [00:30, 38.52iter/s]
Best score: 0.2966: : 1079iter [00:30, 38.52iter/s]
Best score: 0.2966: : 1080iter [00:30, 39.03iter/s]
Best score: 0.2966: : 1080iter [00:30, 39.03iter/s]
Best score: 0.2966: : 1081iter [00:30, 39.03iter/s]
Best score: 0.2966: : 1082iter [00:30, 39.03iter/s]
Best score: 0.2966: : 1083iter [00:30, 39.03iter/s]
Best score: 0.2966: : 1084iter [00:30, 39.03iter/s]
Best score: 0.2966: : 1085iter [00:30, 39.62iter/s]
Best score: 0.2966: : 1085iter [00:30, 39.62iter/s]
Best score: 0.2966: : 1086iter [00:30, 39.62iter/s]
Best score: 0.2966: : 1087iter [00:30, 39.62iter/s]
Best score: 0.2966: : 1088iter [00:30, 39.62iter/s]
Best score: 0.2964: : 1089iter [00:30, 39.62iter/s]
Best score: 0.2964: : 1090iter [00:30, 39.71iter/s]
Best score: 0.2964: : 1090iter [00:30, 39.71iter/s]
Best score: 0.2964: : 1091iter [00:30, 39.71iter/s]
Best score: 0.2964: : 1092iter [00:30, 39.71iter/s]
Best score: 0.2964: : 1093iter [00:30, 39.71iter/s]
Best score: 0.2964: : 1094iter [00:30, 39.71iter/s]
Best score: 0.2964: : 1095iter [00:30, 39.94iter/s]
Best score: 0.2964: : 1095iter [00:30, 39.94iter/s]
Best score: 0.2964: : 1096iter [00:31, 39.94iter/s]
Best score: 0.2964: : 1097iter [00:31, 39.94iter/s]
Best score: 0.2964: : 1098iter [00:31, 39.94iter/s]
[ITER 1100] Best: 0.2964
[PARAMS] Current best parameters:
  fx: 693.2782
  fy: 693.8570
  px: 992.7493
  py: 761.7421
  k1: -0.20540721
  k2: 0.07935442
  k3: -0.01798461
  k4: 0.00105918
  p1: -0.00230199
  p2: 0.01428501
  p3: -0.01192994
  p4: -0.00048847
  s1: -0.0064
  s2: 0.0004
  s3: 0.0162
  s4: -0.0014
  tilt_x: -0.0684
  tilt_y: -0.0397
  scale: 2.9670
  rotation: -0.2526

Best score: 0.2964: : 1099iter [00:31, 39.94iter/s]
Best score: 0.2964: : 1100iter [00:31, 39.88iter/s]
Best score: 0.2964: : 1100iter [00:31, 35.28iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.296370 -> 0.295593

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,836 → 25,836 within 3500mm ring
  Ring polish improved score: 0.295593 -> 0.294818
  Success: True
  Tangential distortion: p1=-0.002320, p2=0.014300, p3=-0.011897, p4=-0.000466
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2948
[✅] Parameters copied to: /working/MDC101_W036_2026-10-01/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 693.2782320518417
    fy: 693.8569878660445
    px: 992.7493439496772
    py: 761.7420729218612
    k1: -0.2054140971765336
    k2: 0.07935689146723028
    k3: -0.01796700895256442
    k4: 0.0010484139036610548
    p1: -0.002320366239054939
    p2: 0.014300041433599598
    p3: -0.011896550786241605
    p4: -0.00046621775526042055
    scale: 2.9670729251690275
    rotation: -0.252562098075899
    mmpp: 1.2676979814787963

[🔄] Converting 1149 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/544 [00:00<?, ?frame/s]

   🔄 1000:   3%|▎         | 18/544 [00:00<00:03, 170.89frame/s]

   🔄 1000:   7%|▋         | 36/544 [00:00<00:02, 172.86frame/s]

   🔄 1000:  10%|▉         | 54/544 [00:00<00:02, 174.75frame/s]

   🔄 1000:  13%|█▎        | 72/544 [00:00<00:02, 175.89frame/s]

   🔄 1000:  17%|█▋        | 90/544 [00:00<00:02, 176.42frame/s]

   🔄 1000:  20%|█▉        | 108/544 [00:00<00:02, 174.85frame/s]

   🔄 1000:  23%|██▎       | 126/544 [00:00<00:02, 175.51frame/s]

   🔄 1000:  26%|██▋       | 144/544 [00:00<00:02, 175.46frame/s]

   🔄 1000:  30%|██▉       | 162/544 [00:00<00:02, 176.03frame/s]

   🔄 1000:  33%|███▎      | 180/544 [00:01<00:02, 176.10frame/s]

   🔄 1000:  36%|███▋      | 198/544 [00:01<00:01, 175.42frame/s]

   🔄 1000:  40%|███▉      | 216/544 [00:01<00:01, 175.00frame/s]

   🔄 1000:  43%|████▎     | 234/544 [00:01<00:01, 174.47frame/s]

   🔄 1000:  46%|████▋     | 252/544 [00:01<00:01, 174.22frame/s]

   🔄 1000:  50%|████▉     | 270/544 [00:01<00:01, 174.09frame/s]

   🔄 1000:  53%|█████▎    | 288/544 [00:01<00:01, 173.46frame/s]

   🔄 1000:  56%|█████▋    | 306/544 [00:01<00:01, 173.72frame/s]

   🔄 1000:  60%|█████▉    | 324/544 [00:01<00:01, 173.97frame/s]

   🔄 1000:  63%|██████▎   | 342/544 [00:01<00:01, 174.06frame/s]

   🔄 1000:  66%|██████▌   | 360/544 [00:02<00:01, 174.15frame/s]

   🔄 1000:  69%|██████▉   | 378/544 [00:02<00:00, 174.62frame/s]

   🔄 1000:  73%|███████▎  | 396/544 [00:02<00:00, 175.40frame/s]

   🔄 1000:  76%|███████▌  | 414/544 [00:02<00:00, 175.92frame/s]

   🔄 1000:  79%|███████▉  | 432/544 [00:02<00:00, 176.21frame/s]

   🔄 1000:  83%|████████▎ | 450/544 [00:02<00:00, 176.26frame/s]

   🔄 1000:  86%|████████▌ | 468/544 [00:02<00:00, 174.75frame/s]

   🔄 1000:  89%|████████▉ | 486/544 [00:02<00:00, 175.70frame/s]

   🔄 1000:  93%|█████████▎| 504/544 [00:02<00:00, 176.27frame/s]

   🔄 1000:  96%|█████████▌| 522/544 [00:02<00:00, 176.50frame/s]

   🔄 1000:  99%|█████████▉| 540/544 [00:03<00:00, 176.43frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:05<00:05,  5.64s/rig]

   🔄 500:   0%|          | 0/605 [00:00<?, ?frame/s]

   🔄 500:   3%|▎         | 18/605 [00:00<00:03, 177.50frame/s]

   🔄 500:   6%|▌         | 36/605 [00:00<00:03, 177.99frame/s]

   🔄 500:   9%|▉         | 54/605 [00:00<00:03, 178.59frame/s]

   🔄 500:  12%|█▏        | 73/605 [00:00<00:02, 179.78frame/s]

   🔄 500:  15%|█▌        | 92/605 [00:00<00:02, 180.87frame/s]

   🔄 500:  18%|█▊        | 111/605 [00:00<00:02, 181.39frame/s]

   🔄 500:  21%|██▏       | 130/605 [00:00<00:02, 181.37frame/s]

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   🔄 500:  55%|█████▍    | 330/605 [00:01<00:01, 176.17frame/s]

   🔄 500:  58%|█████▊    | 348/605 [00:01<00:01, 176.30frame/s]

   🔄 500:  60%|██████    | 366/605 [00:02<00:01, 176.32frame/s]

   🔄 500:  63%|██████▎   | 384/605 [00:02<00:01, 175.85frame/s]

   🔄 500:  66%|██████▋   | 402/605 [00:02<00:01, 175.68frame/s]

   🔄 500:  69%|██████▉   | 420/605 [00:02<00:01, 175.51frame/s]

   🔄 500:  72%|███████▏  | 438/605 [00:02<00:00, 175.54frame/s]

   🔄 500:  75%|███████▌  | 456/605 [00:02<00:00, 175.59frame/s]

   🔄 500:  78%|███████▊  | 474/605 [00:02<00:00, 175.91frame/s]

   🔄 500:  81%|████████▏ | 492/605 [00:02<00:00, 176.22frame/s]

   🔄 500:  84%|████████▍ | 510/605 [00:02<00:00, 176.58frame/s]

   🔄 500:  87%|████████▋ | 528/605 [00:02<00:00, 176.71frame/s]

   🔄 500:  90%|█████████ | 546/605 [00:03<00:00, 176.56frame/s]

   🔄 500:  93%|█████████▎| 564/605 [00:03<00:00, 176.31frame/s]

   🔄 500:  96%|█████████▌| 582/605 [00:03<00:00, 175.91frame/s]

   🔄 500:  99%|█████████▉| 600/605 [00:03<00:00, 175.59frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.85s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.81s/rig]
[✅] Saved PTDs: /working/MDC101_W036_2026-10-01/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 175.48frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 175.62frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 174.34frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 174.18frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 175.24frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.03s/rig]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 176.78frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 175.01frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 174.62frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 174.96frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 175.87frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.04s/rig]
[✅] Saved PTDs: /working/MDC101_W036_2026-10-01/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%|▎         | 93631/3080448 [00:00<00:03, 936301.96px/s]
📤 Writing LUT:   6%|▋         | 192703/3080448 [00:00<00:02, 968311.65px/s]
📤 Writing LUT:   9%|▉         | 289614/3080448 [00:00<00:02, 968665.01px/s]
📤 Writing LUT:  13%|█▎        | 386481/3080448 [00:00<00:02, 960058.30px/s]
📤 Writing LUT:  16%|█▌        | 482499/3080448 [00:00<00:02, 938589.58px/s]
📤 Writing LUT:  19%|█▊        | 576437/3080448 [00:00<00:02, 906173.01px/s]
📤 Writing LUT:  22%|██▏       | 667260/3080448 [00:00<00:02, 886365.38px/s]
📤 Writing LUT:  25%|██▍       | 756057/3080448 [00:00<00:02, 875323.80px/s]
📤 Writing LUT:  27%|██▋       | 843685/3080448 [00:00<00:02, 868704.66px/s]
📤 Writing LUT:  30%|███       | 930609/3080448 [00:01<00:02, 862488.65px/s]
📤 Writing LUT:  33%|███▎      | 1016886/3080448 [00:01<00:02, 855095.72px/s]
📤 Writing LUT:  36%|███▌      | 1102410/3080448 [00:01<00:02, 852182.92px/s]
📤 Writing LUT:  39%|███▊      | 1187633/3080448 [00:01<00:02, 850734.83px/s]
📤 Writing LUT:  41%|████▏     | 1272847/3080448 [00:01<00:02, 851151.88px/s]
📤 Writing LUT:  44%|████▍     | 1358598/3080448 [00:01<00:02, 853047.74px/s]
📤 Writing LUT:  47%|████▋     | 1444139/3080448 [00:01<00:01, 853751.91px/s]
📤 Writing LUT:  50%|████▉     | 1529517/3080448 [00:01<00:01, 853160.00px/s]
📤 Writing LUT:  52%|█████▏    | 1614835/3080448 [00:01<00:01, 852614.50px/s]
📤 Writing LUT:  55%|█████▌    | 1700225/3080448 [00:01<00:01, 852998.32px/s]
📤 Writing LUT:  58%|█████▊    | 1785526/3080448 [00:02<00:01, 852150.08px/s]
📤 Writing LUT:  61%|██████    | 1871141/3080448 [00:02<00:01, 853347.55px/s]
📤 Writing LUT:  64%|██████▎   | 1956477/3080448 [00:02<00:01, 852613.33px/s]
📤 Writing LUT:  66%|██████▋   | 2041739/3080448 [00:02<00:01, 851507.53px/s]
📤 Writing LUT:  69%|██████▉   | 2127011/3080448 [00:02<00:01, 851867.16px/s]
📤 Writing LUT:  72%|███████▏  | 2212199/3080448 [00:02<00:01, 850939.48px/s]
📤 Writing LUT:  75%|███████▍  | 2297294/3080448 [00:02<00:00, 850285.36px/s]
📤 Writing LUT:  77%|███████▋  | 2382323/3080448 [00:02<00:00, 846817.60px/s]
📤 Writing LUT:  80%|████████  | 2467503/3080448 [00:02<00:00, 848305.86px/s]
📤 Writing LUT:  83%|████████▎ | 2553712/3080448 [00:02<00:00, 852428.30px/s]
📤 Writing LUT:  86%|████████▌ | 2640229/3080448 [00:03<00:00, 856241.56px/s]
📤 Writing LUT:  89%|████████▊ | 2726936/3080448 [00:03<00:00, 859484.54px/s]
📤 Writing LUT:  91%|█████████▏| 2813639/3080448 [00:03<00:00, 861744.97px/s]
📤 Writing LUT:  94%|█████████▍| 2900740/3080448 [00:03<00:00, 864521.57px/s]
📤 Writing LUT:  97%|█████████▋| 2988044/3080448 [00:03<00:00, 867075.49px/s]
📤 Writing LUT: 100%|█████████▉| 3075528/3080448 [00:03<00:00, 869402.43px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 867668.15px/s]
[✅] LUT written to: /working/MDC101_W036_2026-10-01/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC101_W036_2026-10-01' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC101_W036_2026-10-01/calibration/MDC101_W036_2026-10-01_unknown.lut

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

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

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

   🔥 1000:   0%|          | 0/544 [00:00<?, ?frame/s]

   🔥 1000:   1%|▏         | 8/544 [00:00<00:06, 78.59frame/s]

   🔥 1000:   3%|▎         | 17/544 [00:00<00:06, 83.91frame/s]

   🔥 1000:   5%|▍         | 26/544 [00:00<00:06, 85.96frame/s]

   🔥 1000:   6%|▋         | 35/544 [00:00<00:05, 86.57frame/s]

   🔥 1000:   8%|▊         | 44/544 [00:00<00:05, 87.52frame/s]

   🔥 1000:  10%|▉         | 53/544 [00:00<00:05, 87.18frame/s]

   🔥 1000:  12%|█▏        | 63/544 [00:00<00:05, 88.46frame/s]

   🔥 1000:  13%|█▎        | 72/544 [00:00<00:05, 88.89frame/s]

   🔥 1000:  15%|█▍        | 81/544 [00:00<00:05, 89.00frame/s]

   🔥 1000:  17%|█▋        | 90/544 [00:01<00:05, 88.41frame/s]

   🔥 1000:  18%|█▊        | 99/544 [00:01<00:05, 86.65frame/s]

   🔥 1000:  20%|█▉        | 108/544 [00:01<00:05, 87.06frame/s]

   🔥 1000:  22%|██▏       | 117/544 [00:01<00:04, 87.74frame/s]

   🔥 1000:  23%|██▎       | 126/544 [00:01<00:04, 86.35frame/s]

   🔥 1000:  25%|██▍       | 135/544 [00:01<00:04, 86.07frame/s]

   🔥 1000:  26%|██▋       | 144/544 [00:01<00:04, 86.18frame/s]

   🔥 1000:  28%|██▊       | 153/544 [00:01<00:04, 86.50frame/s]

   🔥 1000:  30%|██▉       | 162/544 [00:01<00:04, 86.18frame/s]

   🔥 1000:  31%|███▏      | 171/544 [00:01<00:04, 86.80frame/s]

   🔥 1000:  33%|███▎      | 180/544 [00:02<00:04, 87.62frame/s]

   🔥 1000:  35%|███▍      | 190/544 [00:02<00:03, 88.72frame/s]

   🔥 1000:  37%|███▋      | 199/544 [00:02<00:03, 88.74frame/s]

   🔥 1000:  38%|███▊      | 209/544 [00:02<00:03, 89.26frame/s]

   🔥 1000:  40%|████      | 218/544 [00:02<00:03, 89.45frame/s]

   🔥 1000:  42%|████▏     | 227/544 [00:02<00:03, 86.88frame/s]

   🔥 1000:  43%|████▎     | 236/544 [00:02<00:03, 85.21frame/s]

   🔥 1000:  45%|████▌     | 245/544 [00:02<00:03, 84.47frame/s]

   🔥 1000:  47%|████▋     | 254/544 [00:02<00:03, 84.21frame/s]

   🔥 1000:  48%|████▊     | 263/544 [00:03<00:03, 84.23frame/s]

   🔥 1000:  50%|█████     | 272/544 [00:03<00:03, 84.79frame/s]

   🔥 1000:  52%|█████▏    | 281/544 [00:03<00:03, 85.25frame/s]

   🔥 1000:  53%|█████▎    | 290/544 [00:03<00:02, 86.43frame/s]

   🔥 1000:  55%|█████▌    | 300/544 [00:03<00:02, 88.02frame/s]

   🔥 1000:  57%|█████▋    | 310/544 [00:03<00:02, 89.30frame/s]

   🔥 1000:  59%|█████▉    | 320/544 [00:03<00:02, 90.28frame/s]

   🔥 1000:  61%|██████    | 330/544 [00:03<00:02, 90.31frame/s]

   🔥 1000:  62%|██████▎   | 340/544 [00:03<00:02, 89.15frame/s]

   🔥 1000:  64%|██████▍   | 349/544 [00:04<00:02, 86.56frame/s]

   🔥 1000:  66%|██████▌   | 358/544 [00:04<00:02, 85.25frame/s]

   🔥 1000:  67%|██████▋   | 367/544 [00:04<00:02, 84.52frame/s]

   🔥 1000:  69%|██████▉   | 376/544 [00:04<00:02, 83.53frame/s]

   🔥 1000:  71%|███████   | 385/544 [00:04<00:01, 84.13frame/s]

   🔥 1000:  72%|███████▏  | 394/544 [00:04<00:01, 84.55frame/s]

   🔥 1000:  74%|███████▍  | 403/544 [00:04<00:01, 85.68frame/s]

   🔥 1000:  76%|███████▌  | 413/544 [00:04<00:01, 87.15frame/s]

   🔥 1000:  78%|███████▊  | 422/544 [00:04<00:01, 87.95frame/s]

   🔥 1000:  79%|███████▉  | 431/544 [00:04<00:01, 88.43frame/s]

   🔥 1000:  81%|████████  | 441/544 [00:05<00:01, 89.20frame/s]

   🔥 1000:  83%|████████▎ | 451/544 [00:05<00:01, 89.56frame/s]

   🔥 1000:  85%|████████▍ | 460/544 [00:05<00:00, 89.62frame/s]

   🔥 1000:  86%|████████▋ | 470/544 [00:05<00:00, 90.57frame/s]

   🔥 1000:  88%|████████▊ | 480/544 [00:05<00:00, 91.24frame/s]

   🔥 1000:  90%|█████████ | 490/544 [00:05<00:00, 90.99frame/s]

   🔥 1000:  92%|█████████▏| 500/544 [00:05<00:00, 90.03frame/s]

   🔥 1000:  94%|█████████▍| 510/544 [00:05<00:00, 89.28frame/s]

   🔥 1000:  95%|█████████▌| 519/544 [00:05<00:00, 89.03frame/s]

   🔥 1000:  97%|█████████▋| 528/544 [00:06<00:00, 88.69frame/s]

   🔥 1000:  99%|█████████▊| 537/544 [00:06<00:00, 87.63frame/s]

                                                                [✅] 1000: /working/MDC101_W036_2026-10-01/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC101_W036_2026-10-01/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:09<00:09,  9.18s/rig]

   🔥 500:   0%|          | 0/605 [00:00<?, ?frame/s]

   🔥 500:   1%|▏         | 8/605 [00:00<00:07, 76.62frame/s]

   🔥 500:   3%|▎         | 17/605 [00:00<00:07, 79.10frame/s]

   🔥 500:   4%|▍         | 26/605 [00:00<00:07, 79.88frame/s]

   🔥 500:   6%|▌         | 35/605 [00:00<00:07, 80.20frame/s]

   🔥 500:   7%|▋         | 44/605 [00:00<00:06, 80.39frame/s]

   🔥 500:   9%|▉         | 53/605 [00:00<00:06, 80.47frame/s]

   🔥 500:  10%|█         | 62/605 [00:00<00:06, 80.54frame/s]

   🔥 500:  12%|█▏        | 71/605 [00:00<00:06, 81.32frame/s]

   🔥 500:  13%|█▎        | 80/605 [00:00<00:06, 81.63frame/s]

   🔥 500:  15%|█▍        | 89/605 [00:01<00:06, 82.10frame/s]

   🔥 500:  16%|█▌        | 98/605 [00:01<00:06, 82.72frame/s]

   🔥 500:  18%|█▊        | 107/605 [00:01<00:05, 83.94frame/s]

   🔥 500:  19%|█▉        | 116/605 [00:01<00:05, 85.48frame/s]

   🔥 500:  21%|██        | 126/605 [00:01<00:05, 87.16frame/s]

   🔥 500:  22%|██▏       | 135/605 [00:01<00:05, 86.72frame/s]

   🔥 500:  24%|██▍       | 144/605 [00:01<00:05, 85.66frame/s]

   🔥 500:  25%|██▌       | 153/605 [00:01<00:05, 84.69frame/s]

   🔥 500:  27%|██▋       | 162/605 [00:01<00:05, 83.75frame/s]

   🔥 500:  28%|██▊       | 171/605 [00:02<00:05, 83.28frame/s]

   🔥 500:  30%|██▉       | 180/605 [00:02<00:05, 82.68frame/s]

   🔥 500:  31%|███       | 189/605 [00:02<00:05, 81.94frame/s]

   🔥 500:  33%|███▎      | 198/605 [00:02<00:05, 80.31frame/s]

   🔥 500:  34%|███▍      | 207/605 [00:02<00:04, 79.90frame/s]

   🔥 500:  36%|███▌      | 215/605 [00:02<00:04, 79.87frame/s]

   🔥 500:  37%|███▋      | 223/605 [00:02<00:04, 79.21frame/s]

   🔥 500:  38%|███▊      | 231/605 [00:02<00:04, 78.40frame/s]

   🔥 500:  40%|███▉      | 239/605 [00:02<00:04, 78.41frame/s]

   🔥 500:  41%|████      | 247/605 [00:03<00:04, 78.40frame/s]

   🔥 500:  42%|████▏     | 256/605 [00:03<00:04, 79.59frame/s]

   🔥 500:  44%|████▍     | 265/605 [00:03<00:04, 80.17frame/s]

   🔥 500:  45%|████▌     | 274/605 [00:03<00:04, 80.28frame/s]

   🔥 500:  47%|████▋     | 283/605 [00:03<00:03, 80.52frame/s]

   🔥 500:  48%|████▊     | 292/605 [00:03<00:03, 81.10frame/s]

   🔥 500:  50%|████▉     | 301/605 [00:03<00:03, 80.77frame/s]

   🔥 500:  51%|█████     | 310/605 [00:03<00:03, 80.57frame/s]

   🔥 500:  53%|█████▎    | 319/605 [00:03<00:03, 79.81frame/s]

   🔥 500:  54%|█████▍    | 327/605 [00:04<00:03, 78.91frame/s]

   🔥 500:  55%|█████▌    | 335/605 [00:04<00:03, 78.55frame/s]

   🔥 500:  57%|█████▋    | 343/605 [00:04<00:03, 78.18frame/s]

   🔥 500:  58%|█████▊    | 351/605 [00:04<00:03, 77.91frame/s]

   🔥 500:  59%|█████▉    | 359/605 [00:04<00:03, 77.97frame/s]

   🔥 500:  61%|██████    | 367/605 [00:04<00:03, 77.30frame/s]

   🔥 500:  62%|██████▏   | 375/605 [00:04<00:02, 77.60frame/s]

   🔥 500:  63%|██████▎   | 383/605 [00:04<00:02, 77.97frame/s]

   🔥 500:  65%|██████▍   | 391/605 [00:04<00:02, 77.77frame/s]

   🔥 500:  66%|██████▌   | 399/605 [00:04<00:02, 78.06frame/s]

   🔥 500:  67%|██████▋   | 407/605 [00:05<00:02, 78.46frame/s]

   🔥 500:  69%|██████▊   | 415/605 [00:05<00:02, 78.30frame/s]

   🔥 500:  70%|██████▉   | 423/605 [00:05<00:02, 78.59frame/s]

   🔥 500:  71%|███████   | 431/605 [00:05<00:02, 78.63frame/s]

   🔥 500:  73%|███████▎  | 440/605 [00:05<00:02, 79.49frame/s]

   🔥 500:  74%|███████▍  | 449/605 [00:05<00:01, 80.10frame/s]

   🔥 500:  76%|███████▌  | 458/605 [00:05<00:01, 80.82frame/s]

   🔥 500:  77%|███████▋  | 467/605 [00:05<00:01, 81.48frame/s]

   🔥 500:  79%|███████▊  | 476/605 [00:05<00:01, 81.87frame/s]

   🔥 500:  80%|████████  | 485/605 [00:06<00:01, 82.11frame/s]

   🔥 500:  82%|████████▏ | 494/605 [00:06<00:01, 82.23frame/s]

   🔥 500:  83%|████████▎ | 503/605 [00:06<00:01, 81.96frame/s]

   🔥 500:  85%|████████▍ | 512/605 [00:06<00:01, 81.62frame/s]

   🔥 500:  86%|████████▌ | 521/605 [00:06<00:01, 81.27frame/s]

   🔥 500:  88%|████████▊ | 530/605 [00:06<00:00, 81.47frame/s]

   🔥 500:  89%|████████▉ | 539/605 [00:06<00:00, 81.26frame/s]

   🔥 500:  91%|█████████ | 548/605 [00:06<00:00, 81.21frame/s]

   🔥 500:  92%|█████████▏| 557/605 [00:06<00:00, 80.67frame/s]

   🔥 500:  94%|█████████▎| 566/605 [00:07<00:00, 80.48frame/s]

   🔥 500:  95%|█████████▌| 575/605 [00:07<00:00, 80.73frame/s]

   🔥 500:  97%|█████████▋| 584/605 [00:07<00:00, 81.02frame/s]

   🔥 500:  98%|█████████▊| 593/605 [00:07<00:00, 80.67frame/s]

   🔥 500: 100%|█████████▉| 602/605 [00:07<00:00, 80.43frame/s]

                                                               [✅] 500: /working/MDC101_W036_2026-10-01/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC101_W036_2026-10-01/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:17<00:00,  8.88s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:17<00:00,  8.92s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC101_W036_2026-10-01/calibration/debug/5_heatmap
[🏁] Finished processing MDC101_W036_2026-10-01


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