2026-07-17T03:49:04 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC013_W027_2026-07-16 ===
2026-07-17T03:49:04 - INFO: Camera ID: 14302213
2026-07-17T03:49:04 - INFO: Wand ID: 
2026-07-17T03:49:04 - INFO: Recorded At: 2026-07-16T12:48:13Z

-------------------------------------------------------------------
                        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 665 stationary frames, 615 remain


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                                                                           [PASS2] Frame 0000371: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000372: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000373: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000435: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000436: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000470: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000471: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000472: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000473: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000484: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000485: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000486: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000488: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000494: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000495: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000496: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000497: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000564: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000565: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000566: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000567: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000569: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000570: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000571: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000572: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000584: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000585: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000586: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000587: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000599: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001021: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001062: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001063: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001064: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001082: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001083: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001084: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001085: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001089: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001090: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001091: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001092: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001094: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001095: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001096: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001097: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001098: fixed corners {2} (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=False)
[PASS2] Frame 0001121: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001122: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001211: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001212: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001213: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001215: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001227: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001228: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001229: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001230: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000435: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000472: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000496: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000566: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000571: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000586: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001063: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001084: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001091: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001096: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001097: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001119: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001121: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001213: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001214: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001229: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000433: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 36 frames fixed, 32 untrusted, 615 total


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→ 1000 Phase 3: finalize:  74%|███████▍  | 454/615 [00:09<00:03, 52.07it/s]

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→ 1000 Phase 3: finalize:  85%|████████▍ | 520/615 [00:10<00:01, 53.78it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 526/615 [00:10<00:01, 53.78it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 532/615 [00:10<00:01, 53.89it/s]

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→ 1000 Phase 3: finalize:  90%|█████████ | 556/615 [00:10<00:01, 54.22it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 562/615 [00:11<00:00, 54.08it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 568/615 [00:11<00:00, 53.91it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 574/615 [00:11<00:00, 53.83it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 580/615 [00:11<00:00, 53.67it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 586/615 [00:11<00:00, 53.65it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 592/615 [00:11<00:00, 53.53it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 598/615 [00:11<00:00, 53.37it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 604/615 [00:11<00:00, 53.19it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 610/615 [00:11<00:00, 53.03it/s]

                                                                           
[TIMING] 1000 — 615 frames
  Phase 1  radial detect   (parallel)  :   8.86s  (14.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.28s  (7.0 ms/frame)
  Phase 2b corner fix      (sequential):   1.62s
  Phase 3  finalize        (sequential):  12.00s  (19.5 ms/frame)
    decode + undistort                 :   6.12s  (10.0 ms/frame)
    corner apply + refinement          :   2.79s  (4.5 ms/frame)
    segment                            :   2.55s  (4.1 ms/frame)
    smooth                             :   0.34s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  26.76s


Processing Rigs:  50%|█████     | 1/2 [00:32<00:32, 32.41s/it][INFO] Filtered 620 stationary frames, 592 remain


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→ 500 (592 images) Phase 1:  59%|█████▉    | 352/592 [00:05<00:02, 81.50it/s]

→ 500 (592 images) Phase 1:  62%|██████▏   | 365/592 [00:05<00:03, 63.79it/s]

→ 500 (592 images) Phase 1:  65%|██████▌   | 385/592 [00:06<00:03, 61.82it/s]

→ 500 (592 images) Phase 1:  67%|██████▋   | 399/592 [00:06<00:02, 72.39it/s]

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→ 500 (592 images) Phase 1:  72%|███████▏  | 429/592 [00:06<00:02, 66.64it/s]

→ 500 (592 images) Phase 1:  75%|███████▌  | 446/592 [00:06<00:01, 82.64it/s]

→ 500 (592 images) Phase 1:  77%|███████▋  | 458/592 [00:07<00:02, 63.61it/s]

→ 500 (592 images) Phase 1:  80%|████████  | 475/592 [00:07<00:01, 79.49it/s]

→ 500 (592 images) Phase 1:  82%|████████▏ | 487/592 [00:07<00:01, 62.59it/s]

→ 500 (592 images) Phase 1:  85%|████████▌ | 506/592 [00:07<00:01, 82.39it/s]

→ 500 (592 images) Phase 1:  88%|████████▊ | 518/592 [00:07<00:01, 63.88it/s]

→ 500 (592 images) Phase 1:  91%|█████████ | 537/592 [00:08<00:00, 83.45it/s]

→ 500 (592 images) Phase 1:  93%|█████████▎| 550/592 [00:08<00:00, 65.73it/s]

→ 500 (592 images) Phase 1:  95%|█████████▍| 561/592 [00:08<00:00, 72.67it/s]

→ 500 (592 images) Phase 1:  97%|█████████▋| 577/592 [00:08<00:00, 75.94it/s]

→ 500 (592 images) Phase 1: 100%|█████████▉| 591/592 [00:08<00:00, 86.90it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/592 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:  49%|████▉     | 292/592 [00:02<00:02, 148.42it/s]

→ 500 Phase 2: corners:  52%|█████▏    | 308/592 [00:02<00:01, 150.68it/s]

→ 500 Phase 2: corners:  55%|█████▍    | 325/592 [00:02<00:01, 153.98it/s]

→ 500 Phase 2: corners:  58%|█████▊    | 341/592 [00:03<00:01, 153.57it/s]

→ 500 Phase 2: corners:  60%|██████    | 357/592 [00:03<00:01, 151.23it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 373/592 [00:03<00:01, 151.19it/s]

→ 500 Phase 2: corners:  66%|██████▌   | 389/592 [00:03<00:01, 145.67it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 405/592 [00:03<00:01, 148.40it/s]

→ 500 Phase 2: corners:  71%|███████   | 421/592 [00:03<00:01, 151.10it/s]

→ 500 Phase 2: corners:  74%|███████▍  | 437/592 [00:03<00:01, 146.91it/s]

→ 500 Phase 2: corners:  76%|███████▋  | 452/592 [00:03<00:01, 134.62it/s]

→ 500 Phase 2: corners:  79%|███████▉  | 470/592 [00:03<00:00, 145.36it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 485/592 [00:04<00:00, 143.41it/s]

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→ 500 Phase 2: corners:  87%|████████▋ | 516/592 [00:04<00:00, 138.58it/s]

→ 500 Phase 2: corners:  90%|████████▉ | 532/592 [00:04<00:00, 143.75it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 548/592 [00:04<00:00, 147.22it/s]

→ 500 Phase 2: corners:  98%|█████████▊| 580/592 [00:04<00:00, 196.21it/s]

                                                                          [PASS2] Frame 0000345: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000346: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000347: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000348: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001175: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001176: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001177: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001178: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000347: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001177: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 4 untrusted, 592 total


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→ 500 Phase 3: finalize:  54%|█████▍    | 320/592 [00:05<00:05, 52.93it/s]

→ 500 Phase 3: finalize:  55%|█████▌    | 326/592 [00:06<00:05, 52.97it/s]

→ 500 Phase 3: finalize:  56%|█████▌    | 332/592 [00:06<00:04, 52.96it/s]

→ 500 Phase 3: finalize:  57%|█████▋    | 338/592 [00:06<00:04, 52.91it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 344/592 [00:06<00:04, 52.71it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 350/592 [00:06<00:04, 52.67it/s]

→ 500 Phase 3: finalize:  60%|██████    | 356/592 [00:06<00:04, 52.58it/s]

→ 500 Phase 3: finalize:  61%|██████    | 362/592 [00:06<00:04, 52.60it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 368/592 [00:06<00:04, 52.62it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 374/592 [00:06<00:04, 52.75it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 380/592 [00:07<00:04, 52.80it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 386/592 [00:07<00:03, 52.89it/s]

→ 500 Phase 3: finalize:  66%|██████▌   | 392/592 [00:07<00:03, 52.59it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 398/592 [00:07<00:03, 52.71it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 404/592 [00:07<00:03, 52.88it/s]

→ 500 Phase 3: finalize:  69%|██████▉   | 410/592 [00:07<00:03, 53.03it/s]

→ 500 Phase 3: finalize:  70%|███████   | 416/592 [00:07<00:03, 53.25it/s]

→ 500 Phase 3: finalize:  71%|███████▏  | 422/592 [00:07<00:03, 53.38it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 428/592 [00:07<00:03, 53.46it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 434/592 [00:08<00:02, 53.57it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 440/592 [00:08<00:02, 53.48it/s]

→ 500 Phase 3: finalize:  75%|███████▌  | 446/592 [00:08<00:02, 53.08it/s]

→ 500 Phase 3: finalize:  76%|███████▋  | 452/592 [00:08<00:02, 52.79it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 458/592 [00:08<00:02, 52.86it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 464/592 [00:08<00:02, 52.70it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 470/592 [00:08<00:02, 52.97it/s]

→ 500 Phase 3: finalize:  80%|████████  | 476/592 [00:08<00:02, 53.16it/s]

→ 500 Phase 3: finalize:  81%|████████▏ | 482/592 [00:08<00:02, 53.29it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 488/592 [00:09<00:01, 53.45it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 494/592 [00:09<00:01, 53.62it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 500/592 [00:09<00:01, 53.71it/s]

→ 500 Phase 3: finalize:  85%|████████▌ | 506/592 [00:09<00:01, 53.87it/s]

→ 500 Phase 3: finalize:  86%|████████▋ | 512/592 [00:09<00:01, 53.97it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 518/592 [00:09<00:01, 54.06it/s]

→ 500 Phase 3: finalize:  89%|████████▊ | 524/592 [00:09<00:01, 54.11it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 530/592 [00:09<00:01, 54.16it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 536/592 [00:09<00:01, 54.15it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 542/592 [00:10<00:00, 54.08it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 548/592 [00:10<00:00, 54.17it/s]

→ 500 Phase 3: finalize:  94%|█████████▎| 554/592 [00:10<00:00, 54.12it/s]

→ 500 Phase 3: finalize:  95%|█████████▍| 560/592 [00:10<00:00, 54.03it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 566/592 [00:10<00:00, 53.93it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 572/592 [00:10<00:00, 53.94it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 578/592 [00:10<00:00, 53.88it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 584/592 [00:10<00:00, 53.82it/s]

→ 500 Phase 3: finalize: 100%|█████████▉| 590/592 [00:10<00:00, 53.73it/s]

                                                                          
[TIMING] 500 — 592 frames
  Phase 1  radial detect   (parallel)  :   8.85s  (15.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.72s  (8.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.49s
  Phase 3  finalize        (sequential):  10.99s  (18.6 ms/frame)
    decode + undistort                 :   5.82s  (9.8 ms/frame)
    corner apply + refinement          :   2.22s  (3.7 ms/frame)
    segment                            :   2.43s  (4.1 ms/frame)
    smooth                             :   0.33s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  25.06s


Processing Rigs: 100%|██████████| 2/2 [01:01<00:00, 30.39s/it]
Processing Rigs: 100%|██████████| 2/2 [01:01<00:00, 30.69s/it]
[💾] Saved RigFrameDataset to /working/MDC013_W027_2026-07-16/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1207 frames from 2 rigs to /working/MDC013_W027_2026-07-16/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC013_W027_2026-07-16/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 615
[📋] 500: 100 frames selected from 592
[💾] Saved RigFrameDataset to /working/MDC013_W027_2026-07-16/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC013_W027_2026-07-16/calibration/debug/1_digitized/rig_frames.json

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

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

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:04<03:00,  4.64s/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 [00:05<00:43,  1.18s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:27,  1.31it/s]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:05<00:14,  2.31it/s]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:05<00:12,  2.64it/s]
Computing max image from bright frames:  20%|██        | 8/40 [00:05<00:09,  3.30it/s]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:05<00:04,  6.34it/s]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:03,  7.68it/s]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:06<00:04,  5.65it/s]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:06<00:03,  7.10it/s]
Computing max image from bright frames:  48%|████▊     | 19/40 [00:06<00:02,  7.17it/s]
Computing max image from bright frames:  52%|█████▎    | 21/40 [00:07<00:02,  8.00it/s]
Computing max image from bright frames:  60%|██████    | 24/40 [00:07<00:01, 10.64it/s]
Computing max image from bright frames:  65%|██████▌   | 26/40 [00:07<00:01, 11.95it/s]
Computing max image from bright frames:  70%|███████   | 28/40 [00:07<00:01, 11.10it/s]
Computing max image from bright frames:  78%|███████▊  | 31/40 [00:07<00:00, 13.76it/s]
Computing max image from bright frames:  82%|████████▎ | 33/40 [00:08<00:01,  5.11it/s]
Computing max image from bright frames:  88%|████████▊ | 35/40 [00:09<00:00,  5.84it/s]
Computing max image from bright frames:  92%|█████████▎| 37/40 [00:09<00:00,  7.01it/s]
Computing max image from bright frames:  98%|█████████▊| 39/40 [00:09<00:00,  8.14it/s]
                                                                                       
[✅] Casing detection complete: cx=984.0, cy=746.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   1%|          | 1/100 [00:00<00:33,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   1%|          | 1/100 [00:00<00:33,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:33,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   3%|▎         | 3/100 [00:00<00:32,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   4%|▍         | 4/100 [00:00<00:32,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   5%|▌         | 5/100 [00:00<00:32,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   6%|▌         | 6/100 [00:00<00:31,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   7%|▋         | 7/100 [00:00<00:31,  2.96it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   8%|▊         | 8/100 [00:00<00:04, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   8%|▊         | 8/100 [00:00<00:04, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   9%|▉         | 9/100 [00:00<00:04, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.033 | Best: {'fx': 519.192} (0.033):  10%|█         | 10/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 519.192} (0.033):  11%|█         | 11/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.053 | Best: {'fx': 526.263} (0.053):  12%|█▏        | 12/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 526.263} (0.053):  13%|█▎        | 13/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 526.263} (0.053):  14%|█▍        | 14/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.081 | Best: {'fx': 533.333} (0.081):  15%|█▌        | 15/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  16%|█▌        | 16/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  17%|█▋        | 17/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  18%|█▊        | 18/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.048 | Best: {'fx': 533.333} (0.081):  19%|█▉        | 19/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  20%|██        | 20/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.032 | Best: {'fx': 533.333} (0.081):  21%|██        | 21/100 [00:00<00:03, 22.58it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.032 | Best: {'fx': 533.333} (0.081):  22%|██▏       | 22/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.081):  22%|██▏       | 22/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  23%|██▎       | 23/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.081):  24%|██▍       | 24/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  25%|██▌       | 25/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.022 | Best: {'fx': 533.333} (0.081):  26%|██▌       | 26/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  27%|██▋       | 27/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  28%|██▊       | 28/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  29%|██▉       | 29/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  30%|███       | 30/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  31%|███       | 31/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  32%|███▏      | 32/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  33%|███▎      | 33/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.081):  34%|███▍      | 34/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  35%|███▌      | 35/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  36%|███▌      | 36/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  37%|███▋      | 37/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.081):  38%|███▊      | 38/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  39%|███▉      | 39/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  40%|████      | 40/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  41%|████      | 41/100 [00:00<00:01, 57.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  42%|████▏     | 42/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.004 | Best: {'fx': 533.333} (0.081):  42%|████▏     | 42/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.081):  43%|████▎     | 43/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  44%|████▍     | 44/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.081):  45%|████▌     | 45/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.081):  46%|████▌     | 46/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  47%|████▋     | 47/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.081):  48%|████▊     | 48/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  49%|████▉     | 49/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.022 | Best: {'fx': 533.333} (0.081):  50%|█████     | 50/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.081):  51%|█████     | 51/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.007 | Best: {'fx': 533.333} (0.081):  52%|█████▏    | 52/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  53%|█████▎    | 53/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  54%|█████▍    | 54/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  55%|█████▌    | 55/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  56%|█████▌    | 56/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  57%|█████▋    | 57/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  58%|█████▊    | 58/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  59%|█████▉    | 59/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  60%|██████    | 60/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  61%|██████    | 61/100 [00:00<00:00, 97.94it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  62%|██████▏   | 62/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  62%|██████▏   | 62/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.081):  63%|██████▎   | 63/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  64%|██████▍   | 64/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  65%|██████▌   | 65/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  66%|██████▌   | 66/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  67%|██████▋   | 67/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  68%|██████▊   | 68/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  69%|██████▉   | 69/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  70%|███████   | 70/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  71%|███████   | 71/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  72%|███████▏  | 72/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  73%|███████▎  | 73/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  74%|███████▍  | 74/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  75%|███████▌  | 75/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  76%|███████▌  | 76/100 [00:00<00:00, 124.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  77%|███████▋  | 77/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  77%|███████▋  | 77/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  78%|███████▊  | 78/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  79%|███████▉  | 79/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  80%|████████  | 80/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  81%|████████  | 81/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  82%|████████▏ | 82/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  83%|████████▎ | 83/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  84%|████████▍ | 84/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  85%|████████▌ | 85/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  86%|████████▌ | 86/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  87%|████████▋ | 87/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  88%|████████▊ | 88/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  89%|████████▉ | 89/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  90%|█████████ | 90/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  91%|█████████ | 91/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  92%|█████████▏| 92/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  93%|█████████▎| 93/100 [00:00<00:00, 124.02it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  94%|█████████▍| 94/100 [00:00<00:00, 130.52it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  94%|█████████▍| 94/100 [00:01<00:00, 130.52it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  95%|█████████▌| 95/100 [00:01<00:00, 130.52it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  96%|█████████▌| 96/100 [00:01<00:00, 130.52it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  97%|█████████▋| 97/100 [00:01<00:00, 130.52it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  98%|█████████▊| 98/100 [00:01<00:00, 130.52it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.081):  99%|█████████▉| 99/100 [00:01<00:00, 130.52it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.017 | Best: {'fx': 511.395} (0.017):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.017 | Best: {'fx': 511.395} (0.017):   2%|▏         | 1/50 [00:00<00:05,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.011 | Best: {'fx': 511.395} (0.017):   2%|▏         | 1/50 [00:00<00:05,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.012 | Best: {'fx': 511.395} (0.017):   4%|▍         | 2/50 [00:00<00:05,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.015 | Best: {'fx': 511.395} (0.017):   6%|▌         | 3/50 [00:00<00:05,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.022 | Best: {'fx': 512.415} (0.022):   8%|▊         | 4/50 [00:00<00:05,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 514.456} (0.024):  10%|█         | 5/50 [00:00<00:05,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.023 | Best: {'fx': 514.456} (0.024):  12%|█▏        | 6/50 [00:00<00:04,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 517.517} (0.029):  14%|█▍        | 7/50 [00:00<00:04,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 517.517} (0.029):  16%|█▌        | 8/50 [00:00<00:04,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.032 | Best: {'fx': 519.558} (0.032):  18%|█▊        | 9/50 [00:00<00:04,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.027 | Best: {'fx': 519.558} (0.032):  20%|██        | 10/50 [00:00<00:04,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.030 | Best: {'fx': 519.558} (0.032):  22%|██▏       | 11/50 [00:00<00:04,  8.86it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.030 | Best: {'fx': 519.558} (0.032):  24%|██▍       | 12/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.033 | Best: {'fx': 520.578} (0.033):  24%|██▍       | 12/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.037 | Best: {'fx': 521.599} (0.037):  26%|██▌       | 13/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.040 | Best: {'fx': 522.619} (0.040):  28%|██▊       | 14/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.047 | Best: {'fx': 524.66} (0.047):  30%|███       | 15/50 [00:00<00:00, 64.10it/s] 
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.043 | Best: {'fx': 524.66} (0.047):  32%|███▏      | 16/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.071 | Best: {'fx': 529.762} (0.071):  34%|███▍      | 17/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.051 | Best: {'fx': 529.762} (0.071):  36%|███▌      | 18/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.055 | Best: {'fx': 529.762} (0.071):  38%|███▊      | 19/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.069 | Best: {'fx': 529.762} (0.071):  40%|████      | 20/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.064 | Best: {'fx': 529.762} (0.071):  42%|████▏     | 21/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.079 | Best: {'fx': 530.782} (0.079):  44%|████▍     | 22/50 [00:00<00:00, 64.10it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.079 | Best: {'fx': 530.782} (0.079):  46%|████▌     | 23/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.081 | Best: {'fx': 531.803} (0.081):  46%|████▌     | 23/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.079 | Best: {'fx': 531.803} (0.081):  48%|████▊     | 24/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.077 | Best: {'fx': 531.803} (0.081):  50%|█████     | 25/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.075 | Best: {'fx': 531.803} (0.081):  52%|█████▏    | 26/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.067 | Best: {'fx': 531.803} (0.081):  54%|█████▍    | 27/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.060 | Best: {'fx': 531.803} (0.081):  56%|█████▌    | 28/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.054 | Best: {'fx': 531.803} (0.081):  58%|█████▊    | 29/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.043 | Best: {'fx': 531.803} (0.081):  60%|██████    | 30/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.051 | Best: {'fx': 531.803} (0.081):  62%|██████▏   | 31/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.050 | Best: {'fx': 531.803} (0.081):  64%|██████▍   | 32/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.045 | Best: {'fx': 531.803} (0.081):  66%|██████▌   | 33/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 531.803} (0.081):  68%|██████▊   | 34/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 531.803} (0.081):  70%|███████   | 35/50 [00:00<00:00, 78.03it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 531.803} (0.081):  72%|███████▏  | 36/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.036 | Best: {'fx': 531.803} (0.081):  72%|███████▏  | 36/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.032 | Best: {'fx': 531.803} (0.081):  74%|███████▍  | 37/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.032 | Best: {'fx': 531.803} (0.081):  76%|███████▌  | 38/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 531.803} (0.081):  78%|███████▊  | 39/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 531.803} (0.081):  80%|████████  | 40/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 531.803} (0.081):  82%|████████▏ | 41/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.027 | Best: {'fx': 531.803} (0.081):  84%|████████▍ | 42/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 531.803} (0.081):  86%|████████▌ | 43/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.028 | Best: {'fx': 531.803} (0.081):  88%|████████▊ | 44/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 531.803} (0.081):  90%|█████████ | 45/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 531.803} (0.081):  92%|█████████▏| 46/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.025 | Best: {'fx': 531.803} (0.081):  94%|█████████▍| 47/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.023 | Best: {'fx': 531.803} (0.081):  96%|█████████▌| 48/50 [00:00<00:00, 93.36it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 531.803} (0.081):  98%|█████████▊| 49/50 [00:00<00:00, 93.36it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 984.0
    py: 746.0
    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: 15.5491: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 15.5491, picklable_objective(x0) = 15.5355

Best score: 15.5355: : 0iter [00:01, ?iter/s] 
Best score: 15.5355: : 1iter [00:01,  1.84s/iter]
Best score: 15.5355: : 1iter [00:01,  1.84s/iter]
Best score: 15.5355: : 2iter [00:02,  1.84s/iter]
Best score: 15.5355: : 3iter [00:02,  1.84iter/s]
Best score: 15.5355: : 3iter [00:02,  1.84iter/s]
Best score: 15.5355: : 4iter [00:02,  1.84iter/s]
Best score: 15.5355: : 5iter [00:02,  1.84iter/s]
Best score: 15.5355: : 6iter [00:02,  1.84iter/s]
Best score: 15.5355: : 7iter [00:02,  5.21iter/s]
Best score: 15.5355: : 7iter [00:02,  5.21iter/s]
Best score: 15.5355: : 8iter [00:02,  5.21iter/s]
Best score: 15.5355: : 9iter [00:02,  5.21iter/s]
Best score: 15.5355: : 10iter [00:02,  5.21iter/s]
Best score: 15.5355: : 11iter [00:02,  9.09iter/s]
Best score: 15.5355: : 11iter [00:02,  9.09iter/s]
Best score: 15.5355: : 12iter [00:02,  9.09iter/s]
Best score: 15.5355: : 13iter [00:02,  9.09iter/s]
Best score: 15.5355: : 14iter [00:02,  9.09iter/s]
Best score: 15.5355: : 15iter [00:02, 13.34iter/s]
Best score: 15.5355: : 15iter [00:02, 13.34iter/s]
Best score: 15.5355: : 16iter [00:02, 13.34iter/s]
Best score: 15.5355: : 17iter [00:02, 13.34iter/s]
Best score: 15.5355: : 18iter [00:02, 13.34iter/s]
Best score: 15.5355: : 19iter [00:02, 17.70iter/s]
Best score: 15.5355: : 19iter [00:02, 17.70iter/s]
Best score: 15.5355: : 20iter [00:02, 17.70iter/s]
Best score: 15.5355: : 21iter [00:02, 17.70iter/s]
Best score: 15.5355: : 22iter [00:02, 17.70iter/s]
Best score: 15.5355: : 23iter [00:02, 21.84iter/s]
Best score: 15.5355: : 23iter [00:02, 21.84iter/s]
Best score: 15.5355: : 24iter [00:02, 21.84iter/s]
Best score: 15.5355: : 25iter [00:02, 21.84iter/s]
Best score: 15.5355: : 26iter [00:02, 21.84iter/s]
Best score: 15.5355: : 27iter [00:02, 21.84iter/s]
Best score: 15.5355: : 28iter [00:02, 26.44iter/s]
Best score: 15.5355: : 28iter [00:02, 26.44iter/s]
Best score: 15.5355: : 29iter [00:02, 26.44iter/s]
Best score: 15.5355: : 30iter [00:02, 26.44iter/s]
Best score: 15.5355: : 31iter [00:02, 26.44iter/s]
Best score: 15.5355: : 32iter [00:02, 29.33iter/s]
Best score: 15.5355: : 32iter [00:02, 29.33iter/s]
Best score: 15.5355: : 33iter [00:02, 29.33iter/s]
Best score: 15.5355: : 34iter [00:02, 29.33iter/s]
Best score: 15.5355: : 35iter [00:02, 29.33iter/s]
Best score: 15.5355: : 36iter [00:02, 31.88iter/s]
Best score: 15.5355: : 36iter [00:02, 31.88iter/s]
Best score: 15.5355: : 37iter [00:02, 31.88iter/s]
Best score: 15.5355: : 38iter [00:02, 31.88iter/s]
Best score: 15.5355: : 39iter [00:02, 31.88iter/s]
Best score: 15.5355: : 40iter [00:02, 33.78iter/s]
Best score: 15.5355: : 40iter [00:02, 33.78iter/s]
Best score: 15.5355: : 41iter [00:03, 33.78iter/s]
Best score: 15.5355: : 42iter [00:03, 33.78iter/s]
Best score: 15.5355: : 43iter [00:03, 33.78iter/s]
Best score: 15.5355: : 44iter [00:03, 35.42iter/s]
Best score: 15.5355: : 44iter [00:03, 35.42iter/s]
Best score: 15.5355: : 45iter [00:03, 35.42iter/s]
Best score: 15.5355: : 46iter [00:03, 35.42iter/s]
Best score: 15.5355: : 47iter [00:03, 35.42iter/s]
Best score: 15.5355: : 48iter [00:03, 36.63iter/s]
Best score: 15.5355: : 48iter [00:03, 36.63iter/s]
[ITER 50] Best: 15.5355
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 984.0000
  py: 746.0000
  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: 15.5355: : 49iter [00:03, 36.63iter/s]
Best score: 15.5355: : 50iter [00:03, 36.63iter/s]
Best score: 15.5355: : 51iter [00:03, 36.63iter/s]
Best score: 15.5355: : 52iter [00:03, 37.40iter/s]
Best score: 15.5355: : 52iter [00:03, 37.40iter/s]
Best score: 15.5355: : 53iter [00:03, 37.40iter/s]
Best score: 15.5355: : 54iter [00:03, 37.40iter/s]
Best score: 15.5355: : 55iter [00:03, 37.40iter/s]
Best score: 15.5355: : 56iter [00:03, 37.40iter/s]
Best score: 15.5355: : 57iter [00:03, 38.26iter/s]
Best score: 15.5355: : 57iter [00:03, 38.26iter/s]
Best score: 15.5355: : 58iter [00:03, 38.26iter/s]
Best score: 15.5355: : 59iter [00:03, 38.26iter/s]
Best score: 15.5355: : 60iter [00:03, 38.26iter/s]
Best score: 15.5355: : 61iter [00:03, 38.41iter/s]
Best score: 15.5355: : 61iter [00:03, 38.41iter/s]
Best score: 15.5355: : 62iter [00:03, 38.41iter/s]
Best score: 15.5355: : 63iter [00:03, 38.41iter/s]
Best score: 15.5355: : 64iter [00:03, 38.41iter/s]
Best score: 15.5355: : 65iter [00:03, 38.41iter/s]
Best score: 15.5355: : 66iter [00:03, 38.94iter/s]
Best score: 15.5355: : 66iter [00:03, 38.94iter/s]
Best score: 15.5355: : 67iter [00:03, 38.94iter/s]
Best score: 15.5355: : 68iter [00:03, 38.94iter/s]
Best score: 15.5355: : 69iter [00:03, 38.94iter/s]
Best score: 15.5355: : 70iter [00:03, 39.01iter/s]
Best score: 15.5355: : 70iter [00:03, 39.01iter/s]
Best score: 15.5355: : 71iter [00:03, 39.01iter/s]
Best score: 15.5355: : 72iter [00:03, 39.01iter/s]
Best score: 15.5355: : 73iter [00:03, 39.01iter/s]
Best score: 15.5355: : 74iter [00:03, 39.14iter/s]
Best score: 15.5355: : 74iter [00:03, 39.14iter/s]
Best score: 15.5355: : 75iter [00:03, 39.14iter/s]
Best score: 15.5355: : 76iter [00:03, 39.14iter/s]
Best score: 15.5355: : 77iter [00:03, 39.14iter/s]
Best score: 15.5355: : 78iter [00:03, 39.36iter/s]
Best score: 15.5355: : 78iter [00:03, 39.36iter/s]
Best score: 15.5355: : 79iter [00:03, 39.36iter/s]
Best score: 15.5355: : 80iter [00:03, 39.36iter/s]
Best score: 15.5355: : 81iter [00:04, 39.36iter/s]
Best score: 15.5355: : 82iter [00:04, 39.42iter/s]
Best score: 15.5355: : 82iter [00:04, 39.42iter/s]
Best score: 15.5355: : 83iter [00:04, 39.42iter/s]
Best score: 15.5355: : 84iter [00:04, 39.42iter/s]
Best score: 15.5355: : 85iter [00:04, 39.42iter/s]
Best score: 15.5355: : 86iter [00:04, 39.42iter/s]
Best score: 15.5355: : 87iter [00:04, 39.68iter/s]
Best score: 15.5355: : 87iter [00:04, 39.68iter/s]
Best score: 15.5355: : 88iter [00:04, 39.68iter/s]
Best score: 15.5355: : 89iter [00:04, 39.68iter/s]
Best score: 15.5355: : 90iter [00:04, 39.68iter/s]
Best score: 15.5355: : 91iter [00:04, 39.54iter/s]
Best score: 15.5355: : 91iter [00:04, 39.54iter/s]
Best score: 15.5355: : 92iter [00:04, 39.54iter/s]
Best score: 15.5355: : 93iter [00:04, 39.54iter/s]
Best score: 15.5355: : 94iter [00:04, 39.54iter/s]
Best score: 15.5355: : 95iter [00:04, 39.64iter/s]
Best score: 15.5355: : 95iter [00:04, 39.64iter/s]
Best score: 15.5355: : 96iter [00:04, 39.64iter/s]
Best score: 15.5355: : 97iter [00:04, 39.64iter/s]
Best score: 15.5355: : 98iter [00:04, 39.64iter/s]
[ITER 100] Best: 15.5355
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 984.0000
  py: 746.0000
  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: 15.5355: : 99iter [00:04, 39.64iter/s]
Best score: 15.5355: : 100iter [00:04, 39.76iter/s]
Best score: 15.5355: : 100iter [00:04, 39.76iter/s]
Best score: 15.5355: : 101iter [00:04, 39.76iter/s]
Best score: 15.5355: : 102iter [00:04, 39.76iter/s]
Best score: 15.5355: : 103iter [00:04, 39.76iter/s]
Best score: 15.5355: : 104iter [00:04, 37.11iter/s]
Best score: 15.5355: : 104iter [00:04, 37.11iter/s]
Best score: 15.5355: : 105iter [00:04, 37.11iter/s]
Best score: 15.5355: : 106iter [00:04, 37.11iter/s]
Best score: 15.5355: : 107iter [00:04, 37.11iter/s]
Best score: 15.5355: : 108iter [00:04, 37.85iter/s]
Best score: 15.5355: : 108iter [00:04, 37.85iter/s]
Best score: 15.5355: : 109iter [00:04, 37.85iter/s]
Best score: 15.5355: : 110iter [00:04, 37.85iter/s]
Best score: 15.5355: : 111iter [00:04, 37.85iter/s]
Best score: 15.5355: : 112iter [00:04, 38.16iter/s]
Best score: 15.5355: : 112iter [00:04, 38.16iter/s]
Best score: 15.5355: : 113iter [00:04, 38.16iter/s]
Best score: 15.5355: : 114iter [00:04, 38.16iter/s]
Best score: 15.5355: : 115iter [00:04, 38.16iter/s]
Best score: 15.5355: : 116iter [00:04, 38.67iter/s]
Best score: 15.5355: : 116iter [00:04, 38.67iter/s]
Best score: 15.5355: : 117iter [00:04, 38.67iter/s]
Best score: 15.5355: : 118iter [00:04, 38.67iter/s]
Best score: 15.5355: : 119iter [00:05, 38.67iter/s]
Best score: 15.5355: : 120iter [00:05, 38.78iter/s]
Best score: 15.5355: : 120iter [00:05, 38.78iter/s]
Best score: 15.5355: : 121iter [00:05, 38.78iter/s]
Best score: 15.5355: : 122iter [00:05, 38.78iter/s]
Best score: 15.5355: : 123iter [00:05, 38.78iter/s]
Best score: 15.5355: : 124iter [00:05, 39.01iter/s]
Best score: 15.5355: : 124iter [00:05, 39.01iter/s]
Best score: 15.5355: : 125iter [00:05, 39.01iter/s]
Best score: 15.5355: : 126iter [00:05, 39.01iter/s]
Best score: 15.5355: : 127iter [00:05, 39.01iter/s]
Best score: 15.5355: : 128iter [00:05, 39.29iter/s]
Best score: 15.5355: : 128iter [00:05, 39.29iter/s]
Best score: 15.5355: : 129iter [00:05, 39.29iter/s]
Best score: 15.5355: : 130iter [00:05, 39.29iter/s]
Best score: 15.5355: : 131iter [00:05, 39.29iter/s]
Best score: 15.5355: : 132iter [00:05, 39.13iter/s]
Best score: 15.5355: : 132iter [00:05, 39.13iter/s]
Best score: 15.5355: : 133iter [00:05, 39.13iter/s]
Best score: 15.5355: : 134iter [00:05, 39.13iter/s]
Best score: 15.5355: : 135iter [00:05, 39.13iter/s]
Best score: 15.5355: : 136iter [00:05, 39.29iter/s]
Best score: 15.5355: : 136iter [00:05, 39.29iter/s]
Best score: 15.5355: : 137iter [00:05, 39.29iter/s]
Best score: 15.5355: : 138iter [00:05, 39.29iter/s]
Best score: 15.5355: : 139iter [00:05, 39.29iter/s]
Best score: 15.5355: : 140iter [00:05, 39.13iter/s]
Best score: 15.5355: : 140iter [00:05, 39.13iter/s]
Best score: 15.5355: : 141iter [00:05, 39.13iter/s]
Best score: 15.5355: : 142iter [00:05, 39.13iter/s]
Best score: 15.5355: : 143iter [00:05, 39.13iter/s]
Best score: 15.5355: : 144iter [00:05, 39.13iter/s]
Best score: 15.5355: : 145iter [00:05, 39.46iter/s]
Best score: 15.5355: : 145iter [00:05, 39.46iter/s]
Best score: 15.5355: : 146iter [00:05, 39.46iter/s]
Best score: 15.5355: : 147iter [00:05, 39.46iter/s]
Best score: 15.5355: : 148iter [00:05, 39.46iter/s]
Best score: 15.5355: : 149iter [00:05, 39.60iter/s]
[ITER 150] Best: 15.5355
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 984.0000
  py: 746.0000
  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: 15.5355: : 149iter [00:05, 39.60iter/s]
Best score: 15.5355: : 150iter [00:05, 39.60iter/s]
Best score: 15.5355: : 151iter [00:05, 39.60iter/s]
Best score: 15.5355: : 152iter [00:05, 39.60iter/s]
Best score: 15.5355: : 153iter [00:05, 32.17iter/s]
Best score: 15.5355: : 153iter [00:05, 32.17iter/s]
Best score: 15.5355: : 154iter [00:05, 32.17iter/s]
Best score: 15.5355: : 155iter [00:05, 32.17iter/s]
Best score: 15.5355: : 156iter [00:06, 32.17iter/s]
Best score: 15.5355: : 157iter [00:06, 32.17iter/s]
Best score: 15.5355: : 158iter [00:06, 34.56iter/s]
Best score: 15.5355: : 158iter [00:06, 34.56iter/s]
Best score: 15.5355: : 159iter [00:06, 34.56iter/s]
Best score: 15.5355: : 160iter [00:06, 34.56iter/s]
Best score: 15.5355: : 161iter [00:06, 34.56iter/s]
Best score: 15.5355: : 162iter [00:06, 35.60iter/s]
Best score: 15.5355: : 162iter [00:06, 35.60iter/s]
Best score: 15.5355: : 163iter [00:06, 35.60iter/s]
Best score: 15.5355: : 164iter [00:06, 35.60iter/s]
Best score: 15.5355: : 165iter [00:06, 35.60iter/s]
Best score: 15.5355: : 166iter [00:06, 36.59iter/s]
Best score: 15.5355: : 166iter [00:06, 36.59iter/s]
Best score: 15.5355: : 167iter [00:06, 36.59iter/s]
Best score: 15.5355: : 168iter [00:06, 36.59iter/s]
Best score: 15.5355: : 169iter [00:06, 36.59iter/s]
Best score: 15.5355: : 170iter [00:06, 37.33iter/s]
Best score: 15.5355: : 170iter [00:06, 37.33iter/s]
Best score: 15.5355: : 171iter [00:06, 37.33iter/s]
Best score: 15.5355: : 172iter [00:06, 37.33iter/s]
Best score: 15.5355: : 173iter [00:06, 37.33iter/s]
Best score: 15.5355: : 174iter [00:06, 37.33iter/s]
Best score: 15.5355: : 175iter [00:06, 38.26iter/s]
Best score: 15.5355: : 175iter [00:06, 38.26iter/s]
Best score: 15.5355: : 176iter [00:06, 38.26iter/s]
Best score: 15.5355: : 177iter [00:06, 38.26iter/s]
Best score: 15.5355: : 178iter [00:06, 38.26iter/s]
Best score: 15.5355: : 179iter [00:06, 38.26iter/s]
Best score: 15.5355: : 180iter [00:06, 38.65iter/s]
Best score: 15.5355: : 180iter [00:06, 38.65iter/s]
Best score: 15.5355: : 181iter [00:06, 38.65iter/s]
Best score: 15.5355: : 182iter [00:06, 38.65iter/s]
Best score: 15.5355: : 183iter [00:06, 38.65iter/s]
Best score: 15.5355: : 184iter [00:06, 39.00iter/s]
Best score: 15.5355: : 184iter [00:06, 39.00iter/s]
Best score: 15.5355: : 185iter [00:06, 39.00iter/s]
Best score: 15.5355: : 186iter [00:06, 39.00iter/s]
Best score: 15.5355: : 187iter [00:06, 39.00iter/s]
Best score: 15.5355: : 188iter [00:06, 39.00iter/s]
Best score: 15.5355: : 189iter [00:06, 39.39iter/s]
Best score: 15.5355: : 189iter [00:06, 39.39iter/s]
Best score: 15.5355: : 190iter [00:06, 39.39iter/s]
Best score: 15.5355: : 191iter [00:06, 39.39iter/s]
Best score: 15.5355: : 192iter [00:06, 39.39iter/s]
Best score: 15.5355: : 193iter [00:06, 39.25iter/s]
Best score: 15.5355: : 193iter [00:06, 39.25iter/s]
Best score: 15.5355: : 194iter [00:06, 39.25iter/s]
Best score: 15.5355: : 195iter [00:07, 39.25iter/s]
Best score: 15.5355: : 196iter [00:07, 39.25iter/s]
Best score: 15.5355: : 197iter [00:07, 39.25iter/s]
Best score: 15.5355: : 198iter [00:07, 39.55iter/s]
Best score: 15.5355: : 198iter [00:07, 39.55iter/s]
[ITER 200] Best: 15.5355
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 984.0000
  py: 746.0000
  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: 15.5355: : 199iter [00:07, 39.55iter/s]
Best score: 15.5355: : 200iter [00:07, 39.55iter/s]
Best score: 15.5355: : 201iter [00:07, 39.55iter/s]
Best score: 15.5355: : 202iter [00:07, 32.86iter/s]
Best score: 15.5355: : 202iter [00:07, 32.86iter/s]
Best score: 15.5355: : 203iter [00:07, 32.86iter/s]
Best score: 15.5355: : 204iter [00:07, 32.86iter/s]
Best score: 15.5355: : 205iter [00:07, 32.86iter/s]
Best score: 15.5355: : 206iter [00:07, 32.86iter/s]
Best score: 15.5355: : 207iter [00:07, 34.98iter/s]
Best score: 15.5355: : 207iter [00:07, 34.98iter/s]
Best score: 15.5355: : 208iter [00:07, 34.98iter/s]
Best score: 15.5355: : 209iter [00:07, 34.98iter/s]
Best score: 15.5355: : 210iter [00:07, 34.98iter/s]
Best score: 15.5355: : 211iter [00:07, 36.13iter/s]
Best score: 15.5355: : 211iter [00:07, 36.13iter/s]
Best score: 15.5355: : 212iter [00:07, 36.13iter/s]
Best score: 15.5355: : 213iter [00:07, 36.13iter/s]
Best score: 15.5355: : 214iter [00:07, 36.13iter/s]
Best score: 15.5355: : 215iter [00:07, 36.13iter/s]
Best score: 15.5355: : 216iter [00:07, 37.36iter/s]
Best score: 15.5355: : 216iter [00:07, 37.36iter/s]
Best score: 15.5355: : 217iter [00:07, 37.36iter/s]
Best score: 15.5355: : 218iter [00:07, 37.36iter/s]
Best score: 15.0837: : 219iter [00:07, 37.36iter/s]
Best score: 15.0837: : 220iter [00:07, 37.71iter/s]
Best score: 15.0837: : 220iter [00:07, 37.71iter/s]
Best score: 15.0837: : 221iter [00:07, 37.71iter/s]
Best score: 15.0837: : 222iter [00:07, 37.71iter/s]
Best score: 15.0837: : 223iter [00:07, 37.71iter/s]
Best score: 15.0837: : 224iter [00:07, 37.71iter/s]
Best score: 15.0837: : 225iter [00:07, 38.56iter/s]
Best score: 15.0837: : 225iter [00:07, 38.56iter/s]
Best score: 15.0837: : 226iter [00:07, 38.56iter/s]
Best score: 15.0837: : 227iter [00:07, 38.56iter/s]
Best score: 15.0837: : 228iter [00:07, 38.56iter/s]
Best score: 15.0837: : 229iter [00:07, 38.56iter/s]
Best score: 15.0837: : 230iter [00:07, 38.85iter/s]
Best score: 15.0837: : 230iter [00:07, 38.85iter/s]
Best score: 15.0837: : 231iter [00:07, 38.85iter/s]
Best score: 15.0837: : 232iter [00:08, 38.85iter/s]
Best score: 15.0837: : 233iter [00:08, 38.85iter/s]
Best score: 15.0837: : 234iter [00:08, 38.85iter/s]
Best score: 15.0837: : 235iter [00:08, 39.37iter/s]
Best score: 15.0837: : 235iter [00:08, 39.37iter/s]
Best score: 15.0837: : 236iter [00:08, 39.37iter/s]
Best score: 15.0837: : 237iter [00:08, 39.37iter/s]
Best score: 15.0837: : 238iter [00:08, 39.37iter/s]
Best score: 15.0837: : 239iter [00:08, 39.37iter/s]
Best score: 15.0837: : 240iter [00:08, 39.55iter/s]
Best score: 15.0837: : 240iter [00:08, 39.55iter/s]
Best score: 15.0837: : 241iter [00:08, 39.55iter/s]
Best score: 15.0837: : 242iter [00:08, 39.55iter/s]
Best score: 15.0837: : 243iter [00:08, 39.55iter/s]
Best score: 15.0837: : 244iter [00:08, 39.55iter/s]
Best score: 15.0837: : 245iter [00:08, 39.84iter/s]
Best score: 15.0837: : 245iter [00:08, 39.84iter/s]
Best score: 15.0837: : 246iter [00:08, 39.84iter/s]
Best score: 15.0837: : 247iter [00:08, 39.84iter/s]
Best score: 15.0837: : 248iter [00:08, 39.84iter/s]
[ITER 250] Best: 5.5946
[PARAMS] Current best parameters:
  fx: 506.3139
  fy: 504.4921
  px: 982.8938
  py: 746.6563
  k1: 0.03356323
  k2: -0.00683197
  k3: 0.00224531
  k4: -0.00079051
  p1: -0.00461147
  p2: -0.00539388
  p3: -0.00396453
  p4: 0.00814214
  s1: -0.0064
  s2: -0.0013
  s3: 0.0050
  s4: -0.0043
  tilt_x: -0.0067
  tilt_y: -0.0068
  scale: 2.9750
  rotation: -0.1930

Best score: 5.5946: : 249iter [00:08, 39.84iter/s] 
Best score: 5.5946: : 250iter [00:08, 39.84iter/s]
Best score: 5.5946: : 250iter [00:08, 39.84iter/s]
Best score: 5.5946: : 251iter [00:08, 39.84iter/s]
Best score: 5.5946: : 252iter [00:08, 39.84iter/s]
Best score: 5.5946: : 253iter [00:08, 39.84iter/s]
Best score: 5.5946: : 254iter [00:08, 33.36iter/s]
Best score: 5.5946: : 254iter [00:08, 33.36iter/s]
Best score: 5.5946: : 255iter [00:08, 33.36iter/s]
Best score: 5.5946: : 256iter [00:08, 33.36iter/s]
Best score: 5.5946: : 257iter [00:08, 33.36iter/s]
Best score: 5.5946: : 258iter [00:08, 33.36iter/s]
Best score: 5.5946: : 259iter [00:08, 35.34iter/s]
Best score: 5.5946: : 259iter [00:08, 35.34iter/s]
Best score: 5.5946: : 260iter [00:08, 35.34iter/s]
Best score: 5.5946: : 261iter [00:08, 35.34iter/s]
Best score: 5.5946: : 262iter [00:08, 35.34iter/s]
Best score: 5.5946: : 263iter [00:08, 35.34iter/s]
Best score: 5.5946: : 264iter [00:08, 36.71iter/s]
Best score: 5.5946: : 264iter [00:08, 36.71iter/s]
Best score: 5.5946: : 265iter [00:08, 36.71iter/s]
Best score: 5.5946: : 266iter [00:08, 36.71iter/s]
Best score: 5.5946: : 267iter [00:08, 36.71iter/s]
Best score: 5.5946: : 268iter [00:08, 36.71iter/s]
Best score: 5.5946: : 269iter [00:08, 37.69iter/s]
Best score: 5.5946: : 269iter [00:09, 37.69iter/s]
Best score: 5.5946: : 270iter [00:09, 37.69iter/s]
Best score: 5.5946: : 271iter [00:09, 37.69iter/s]
Best score: 5.5946: : 272iter [00:09, 37.69iter/s]
Best score: 5.5946: : 273iter [00:09, 38.16iter/s]
Best score: 5.5946: : 273iter [00:09, 38.16iter/s]
Best score: 5.5946: : 274iter [00:09, 38.16iter/s]
Best score: 5.5946: : 275iter [00:09, 38.16iter/s]
Best score: 5.5946: : 276iter [00:09, 38.16iter/s]
Best score: 5.5946: : 277iter [00:09, 38.16iter/s]
Best score: 5.5946: : 278iter [00:09, 38.84iter/s]
Best score: 5.5946: : 278iter [00:09, 38.84iter/s]
Best score: 4.4714: : 279iter [00:09, 38.84iter/s]
Best score: 4.4714: : 280iter [00:09, 38.84iter/s]
Best score: 4.4714: : 281iter [00:09, 38.84iter/s]
Best score: 4.4714: : 282iter [00:09, 38.99iter/s]
Best score: 4.4714: : 282iter [00:09, 38.99iter/s]
Best score: 4.4714: : 283iter [00:09, 38.99iter/s]
Best score: 4.4714: : 284iter [00:09, 38.99iter/s]
Best score: 4.4714: : 285iter [00:09, 38.99iter/s]
Best score: 4.4714: : 286iter [00:09, 38.99iter/s]
Best score: 4.4714: : 287iter [00:09, 39.47iter/s]
Best score: 4.4714: : 287iter [00:09, 39.47iter/s]
Best score: 4.4714: : 288iter [00:09, 39.47iter/s]
Best score: 3.0851: : 289iter [00:09, 39.47iter/s]
Best score: 3.0851: : 290iter [00:09, 39.47iter/s]
Best score: 3.0851: : 291iter [00:09, 39.44iter/s]
Best score: 3.0851: : 291iter [00:09, 39.44iter/s]
Best score: 3.0851: : 292iter [00:09, 39.44iter/s]
Best score: 3.0851: : 293iter [00:09, 39.44iter/s]
Best score: 3.0851: : 294iter [00:09, 39.44iter/s]
Best score: 3.0851: : 295iter [00:09, 39.44iter/s]
Best score: 3.0851: : 296iter [00:09, 39.73iter/s]
Best score: 3.0851: : 296iter [00:09, 39.73iter/s]
Best score: 3.0851: : 297iter [00:09, 39.73iter/s]
Best score: 3.0851: : 298iter [00:09, 39.73iter/s]
[ITER 300] Best: 2.6530
[PARAMS] Current best parameters:
  fx: 518.7476
  fy: 517.6166
  px: 981.3656
  py: 747.9507
  k1: 0.00727312
  k2: 0.00039644
  k3: 0.00405261
  k4: -0.00197490
  p1: 0.00544604
  p2: 0.00171930
  p3: -0.00330020
  p4: 0.00953364
  s1: -0.0138
  s2: -0.0040
  s3: -0.0069
  s4: -0.0074
  tilt_x: -0.0152
  tilt_y: 0.0108
  scale: 3.0336
  rotation: -0.1940

Best score: 2.6530: : 299iter [00:09, 39.73iter/s]
Best score: 2.6530: : 300iter [00:09, 39.68iter/s]
Best score: 2.6530: : 300iter [00:09, 39.68iter/s]
Best score: 2.6530: : 301iter [00:09, 39.68iter/s]
Best score: 2.6530: : 302iter [00:09, 39.68iter/s]
Best score: 2.6530: : 303iter [00:09, 39.68iter/s]
Best score: 2.6530: : 304iter [00:09, 32.46iter/s]
Best score: 2.6530: : 304iter [00:09, 32.46iter/s]
Best score: 2.6530: : 305iter [00:09, 32.46iter/s]
Best score: 2.6530: : 306iter [00:10, 32.46iter/s]
Best score: 2.6530: : 307iter [00:10, 32.46iter/s]
Best score: 2.6530: : 308iter [00:10, 34.23iter/s]
Best score: 2.6530: : 308iter [00:10, 34.23iter/s]
Best score: 2.6093: : 309iter [00:10, 34.23iter/s]
Best score: 2.6093: : 310iter [00:10, 34.23iter/s]
Best score: 2.6093: : 311iter [00:10, 34.23iter/s]
Best score: 2.6093: : 312iter [00:10, 35.30iter/s]
Best score: 2.6093: : 312iter [00:10, 35.30iter/s]
Best score: 2.6093: : 313iter [00:10, 35.30iter/s]
Best score: 2.6093: : 314iter [00:10, 35.30iter/s]
Best score: 2.6093: : 315iter [00:10, 35.30iter/s]
Best score: 2.6093: : 316iter [00:10, 36.47iter/s]
Best score: 2.6093: : 316iter [00:10, 36.47iter/s]
Best score: 2.6093: : 317iter [00:10, 36.47iter/s]
Best score: 2.6093: : 318iter [00:10, 36.47iter/s]
Best score: 1.3877: : 319iter [00:10, 36.47iter/s]
Best score: 1.3877: : 320iter [00:10, 37.11iter/s]
Best score: 1.3877: : 320iter [00:10, 37.11iter/s]
Best score: 1.3877: : 321iter [00:10, 37.11iter/s]
Best score: 1.3877: : 322iter [00:10, 37.11iter/s]
Best score: 1.3877: : 323iter [00:10, 37.11iter/s]
Best score: 1.3877: : 324iter [00:10, 37.76iter/s]
Best score: 1.3877: : 324iter [00:10, 37.76iter/s]
Best score: 1.3877: : 325iter [00:10, 37.76iter/s]
Best score: 1.3877: : 326iter [00:10, 37.76iter/s]
Best score: 1.3877: : 327iter [00:10, 37.76iter/s]
Best score: 1.3877: : 328iter [00:10, 37.76iter/s]
Best score: 1.3877: : 329iter [00:10, 38.68iter/s]
Best score: 1.3877: : 329iter [00:10, 38.68iter/s]
Best score: 1.3877: : 330iter [00:10, 38.68iter/s]
Best score: 1.3877: : 331iter [00:10, 38.68iter/s]
Best score: 1.3877: : 332iter [00:10, 38.68iter/s]
Best score: 1.3877: : 333iter [00:10, 38.91iter/s]
Best score: 1.3877: : 333iter [00:10, 38.91iter/s]
Best score: 1.3877: : 334iter [00:10, 38.91iter/s]
Best score: 1.3877: : 335iter [00:10, 38.91iter/s]
Best score: 1.3877: : 336iter [00:10, 38.91iter/s]
Best score: 1.3877: : 337iter [00:10, 38.91iter/s]
Best score: 1.3877: : 338iter [00:10, 39.31iter/s]
Best score: 1.3877: : 338iter [00:10, 39.31iter/s]
Best score: 1.3877: : 339iter [00:10, 39.31iter/s]
Best score: 1.3877: : 340iter [00:10, 39.31iter/s]
Best score: 1.3877: : 341iter [00:10, 39.31iter/s]
Best score: 1.3877: : 342iter [00:10, 39.39iter/s]
Best score: 1.3877: : 342iter [00:10, 39.39iter/s]
Best score: 1.3877: : 343iter [00:10, 39.39iter/s]
Best score: 1.3877: : 344iter [00:10, 39.39iter/s]
Best score: 1.3877: : 345iter [00:10, 39.39iter/s]
Best score: 1.3877: : 346iter [00:11, 39.39iter/s]
Best score: 1.3877: : 347iter [00:11, 39.66iter/s]
Best score: 1.3877: : 347iter [00:11, 39.66iter/s]
Best score: 1.3877: : 348iter [00:11, 39.66iter/s]
[ITER 350] Best: 1.2567
[PARAMS] Current best parameters:
  fx: 495.8658
  fy: 495.2598
  px: 981.9028
  py: 751.1371
  k1: 0.04058732
  k2: -0.02459225
  k3: 0.02567614
  k4: -0.00842141
  p1: 0.00195015
  p2: -0.00065041
  p3: -0.00580951
  p4: 0.01100453
  s1: -0.0013
  s2: -0.0072
  s3: -0.0022
  s4: -0.0059
  tilt_x: 0.0035
  tilt_y: 0.0052
  scale: 3.0282
  rotation: -0.2224

Best score: 1.2567: : 349iter [00:11, 39.66iter/s]
Best score: 1.2567: : 350iter [00:11, 39.66iter/s]
Best score: 1.2567: : 351iter [00:11, 32.41iter/s]
Best score: 1.2567: : 351iter [00:11, 32.41iter/s]
Best score: 1.2567: : 352iter [00:11, 32.41iter/s]
Best score: 1.2567: : 353iter [00:11, 32.41iter/s]
Best score: 1.2567: : 354iter [00:11, 32.41iter/s]
Best score: 1.2567: : 355iter [00:11, 32.41iter/s]
Best score: 1.2567: : 356iter [00:11, 34.79iter/s]
Best score: 1.2567: : 356iter [00:11, 34.79iter/s]
Best score: 1.2567: : 357iter [00:11, 34.79iter/s]
Best score: 1.2567: : 358iter [00:11, 34.79iter/s]
Best score: 1.2567: : 359iter [00:11, 34.79iter/s]
Best score: 1.2567: : 360iter [00:11, 35.78iter/s]
Best score: 1.2567: : 360iter [00:11, 35.78iter/s]
Best score: 1.2567: : 361iter [00:11, 35.78iter/s]
Best score: 1.2567: : 362iter [00:11, 35.78iter/s]
Best score: 1.2567: : 363iter [00:11, 35.78iter/s]
Best score: 1.2567: : 364iter [00:11, 29.13iter/s]
Best score: 1.2567: : 364iter [00:11, 29.13iter/s]
Best score: 1.2567: : 365iter [00:11, 29.13iter/s]
Best score: 1.2567: : 366iter [00:11, 29.13iter/s]
Best score: 1.2567: : 367iter [00:11, 29.13iter/s]
Best score: 1.2567: : 368iter [00:11, 29.13iter/s]
Best score: 1.2567: : 369iter [00:11, 32.19iter/s]
Best score: 1.2567: : 369iter [00:11, 32.19iter/s]
Best score: 1.2567: : 370iter [00:11, 32.19iter/s]
Best score: 1.2567: : 371iter [00:11, 32.19iter/s]
Best score: 1.2567: : 372iter [00:11, 32.19iter/s]
Best score: 1.2567: : 373iter [00:11, 33.94iter/s]
Best score: 1.2567: : 373iter [00:11, 33.94iter/s]
Best score: 1.2567: : 374iter [00:11, 33.94iter/s]
Best score: 1.2567: : 375iter [00:11, 33.94iter/s]
Best score: 1.2567: : 376iter [00:11, 33.94iter/s]
Best score: 1.2567: : 377iter [00:11, 33.94iter/s]
Best score: 1.2567: : 378iter [00:11, 35.81iter/s]
Best score: 1.2567: : 378iter [00:12, 35.81iter/s]
Best score: 0.9687: : 379iter [00:12, 35.81iter/s]
Best score: 0.9687: : 380iter [00:12, 35.81iter/s]
Best score: 0.9687: : 381iter [00:12, 35.81iter/s]
Best score: 0.9687: : 382iter [00:12, 36.78iter/s]
Best score: 0.9687: : 382iter [00:12, 36.78iter/s]
Best score: 0.9687: : 383iter [00:12, 36.78iter/s]
Best score: 0.9687: : 384iter [00:12, 36.78iter/s]
Best score: 0.9687: : 385iter [00:12, 36.78iter/s]
Best score: 0.9687: : 386iter [00:12, 36.78iter/s]
Best score: 0.9687: : 387iter [00:12, 37.94iter/s]
Best score: 0.9687: : 387iter [00:12, 37.94iter/s]
Best score: 0.9687: : 388iter [00:12, 37.94iter/s]
Best score: 0.9687: : 389iter [00:12, 37.94iter/s]
Best score: 0.9687: : 390iter [00:12, 37.94iter/s]
Best score: 0.9687: : 391iter [00:12, 37.94iter/s]
Best score: 0.9687: : 392iter [00:12, 38.65iter/s]
Best score: 0.9687: : 392iter [00:12, 38.65iter/s]
Best score: 0.9687: : 393iter [00:12, 38.65iter/s]
Best score: 0.9687: : 394iter [00:12, 38.65iter/s]
Best score: 0.9687: : 395iter [00:12, 38.65iter/s]
Best score: 0.9687: : 396iter [00:12, 38.65iter/s]
Best score: 0.9687: : 397iter [00:12, 39.19iter/s]
Best score: 0.9687: : 397iter [00:12, 39.19iter/s]
Best score: 0.9687: : 398iter [00:12, 39.19iter/s]
[ITER 400] Best: 0.9474
[PARAMS] Current best parameters:
  fx: 506.0738
  fy: 505.8619
  px: 984.6402
  py: 745.1874
  k1: 0.03355105
  k2: -0.02530804
  k3: 0.02140549
  k4: -0.00631634
  p1: -0.00634491
  p2: -0.00362016
  p3: -0.00281259
  p4: 0.01034643
  s1: -0.0024
  s2: -0.0056
  s3: 0.0080
  s4: -0.0099
  tilt_x: -0.0093
  tilt_y: -0.0165
  scale: 3.0276
  rotation: -0.2015

Best score: 0.9474: : 399iter [00:12, 39.19iter/s]
Best score: 0.9474: : 400iter [00:12, 39.19iter/s]
Best score: 0.9474: : 401iter [00:12, 32.44iter/s]
Best score: 0.9474: : 401iter [00:12, 32.44iter/s]
Best score: 0.9474: : 402iter [00:12, 32.44iter/s]
Best score: 0.9474: : 403iter [00:12, 32.44iter/s]
Best score: 0.9474: : 404iter [00:12, 32.44iter/s]
Best score: 0.9474: : 405iter [00:12, 32.44iter/s]
Best score: 0.9474: : 406iter [00:12, 34.56iter/s]
Best score: 0.9474: : 406iter [00:12, 34.56iter/s]
Best score: 0.9474: : 407iter [00:12, 34.56iter/s]
Best score: 0.9474: : 408iter [00:12, 34.56iter/s]
Best score: 0.9474: : 409iter [00:12, 34.56iter/s]
Best score: 0.9474: : 410iter [00:12, 35.79iter/s]
Best score: 0.9474: : 410iter [00:12, 35.79iter/s]
Best score: 0.9474: : 411iter [00:12, 35.79iter/s]
Best score: 0.9474: : 412iter [00:12, 35.79iter/s]
Best score: 0.9474: : 413iter [00:12, 35.79iter/s]
Best score: 0.9474: : 414iter [00:12, 35.79iter/s]
Best score: 0.9474: : 415iter [00:12, 37.17iter/s]
Best score: 0.9474: : 415iter [00:13, 37.17iter/s]
Best score: 0.9474: : 416iter [00:13, 37.17iter/s]
Best score: 0.9474: : 417iter [00:13, 37.17iter/s]
Best score: 0.9474: : 418iter [00:13, 37.17iter/s]
Best score: 0.9474: : 419iter [00:13, 37.17iter/s]
Best score: 0.9474: : 420iter [00:13, 37.85iter/s]
Best score: 0.9474: : 420iter [00:13, 37.85iter/s]
Best score: 0.9474: : 421iter [00:13, 37.85iter/s]
Best score: 0.9474: : 422iter [00:13, 37.85iter/s]
Best score: 0.9474: : 423iter [00:13, 37.85iter/s]
Best score: 0.9474: : 424iter [00:13, 37.85iter/s]
Best score: 0.9474: : 425iter [00:13, 38.61iter/s]
Best score: 0.9474: : 425iter [00:13, 38.61iter/s]
Best score: 0.9474: : 426iter [00:13, 38.61iter/s]
Best score: 0.9474: : 427iter [00:13, 38.61iter/s]
Best score: 0.9474: : 428iter [00:13, 38.61iter/s]
Best score: 0.9474: : 429iter [00:13, 38.61iter/s]
Best score: 0.9474: : 430iter [00:13, 38.93iter/s]
Best score: 0.9474: : 430iter [00:13, 38.93iter/s]
Best score: 0.9474: : 431iter [00:13, 38.93iter/s]
Best score: 0.9474: : 432iter [00:13, 38.93iter/s]
Best score: 0.9474: : 433iter [00:13, 38.93iter/s]
Best score: 0.9474: : 434iter [00:13, 38.93iter/s]
Best score: 0.9474: : 435iter [00:13, 39.41iter/s]
Best score: 0.9474: : 435iter [00:13, 39.41iter/s]
Best score: 0.9474: : 436iter [00:13, 39.41iter/s]
Best score: 0.9474: : 437iter [00:13, 39.41iter/s]
Best score: 0.9474: : 438iter [00:13, 39.41iter/s]
Best score: 0.8779: : 439iter [00:13, 39.41iter/s]
Best score: 0.8779: : 440iter [00:13, 39.43iter/s]
Best score: 0.8779: : 440iter [00:13, 39.43iter/s]
Best score: 0.8779: : 441iter [00:13, 39.43iter/s]
Best score: 0.8779: : 442iter [00:13, 39.43iter/s]
Best score: 0.8779: : 443iter [00:13, 39.43iter/s]
Best score: 0.8779: : 444iter [00:13, 39.43iter/s]
Best score: 0.8779: : 445iter [00:13, 39.86iter/s]
Best score: 0.8779: : 445iter [00:13, 39.86iter/s]
Best score: 0.8779: : 446iter [00:13, 39.86iter/s]
Best score: 0.8779: : 447iter [00:13, 39.86iter/s]
Best score: 0.8779: : 448iter [00:13, 39.86iter/s]
[ITER 450] Best: 0.7794
[PARAMS] Current best parameters:
  fx: 506.5783
  fy: 506.2050
  px: 986.1695
  py: 745.6144
  k1: 0.04967874
  k2: -0.03831557
  k3: 0.02167686
  k4: -0.00522022
  p1: -0.00485034
  p2: -0.00564019
  p3: -0.00224096
  p4: 0.01032939
  s1: 0.0016
  s2: -0.0067
  s3: 0.0110
  s4: -0.0123
  tilt_x: -0.0088
  tilt_y: -0.0278
  scale: 3.0129
  rotation: -0.0571

Best score: 0.7794: : 449iter [00:13, 39.86iter/s]
Best score: 0.7794: : 450iter [00:13, 39.87iter/s]
Best score: 0.7794: : 450iter [00:13, 39.87iter/s]
Best score: 0.7794: : 451iter [00:13, 39.87iter/s]
Best score: 0.7794: : 452iter [00:14, 39.87iter/s]
Best score: 0.7794: : 453iter [00:14, 39.87iter/s]
Best score: 0.7794: : 454iter [00:14, 33.34iter/s]
Best score: 0.7794: : 454iter [00:14, 33.34iter/s]
Best score: 0.7794: : 455iter [00:14, 33.34iter/s]
Best score: 0.7794: : 456iter [00:14, 33.34iter/s]
Best score: 0.7794: : 457iter [00:14, 33.34iter/s]
Best score: 0.7794: : 458iter [00:14, 33.34iter/s]
Best score: 0.7794: : 459iter [00:14, 35.41iter/s]
Best score: 0.6340: : 459iter [00:14, 35.41iter/s]
Best score: 0.6340: : 460iter [00:14, 35.41iter/s]
Best score: 0.6340: : 461iter [00:14, 35.41iter/s]
Best score: 0.6340: : 462iter [00:14, 35.41iter/s]
Best score: 0.6340: : 463iter [00:14, 36.38iter/s]
Best score: 0.6340: : 463iter [00:14, 36.38iter/s]
Best score: 0.6340: : 464iter [00:14, 36.38iter/s]
Best score: 0.6340: : 465iter [00:14, 36.38iter/s]
Best score: 0.6340: : 466iter [00:14, 36.38iter/s]
Best score: 0.6340: : 467iter [00:14, 36.38iter/s]
Best score: 0.6340: : 468iter [00:14, 37.59iter/s]
Best score: 0.6340: : 468iter [00:14, 37.59iter/s]
Best score: 0.6295: : 469iter [00:14, 37.59iter/s]
Best score: 0.6295: : 470iter [00:14, 37.59iter/s]
Best score: 0.6295: : 471iter [00:14, 37.59iter/s]
Best score: 0.6295: : 472iter [00:14, 38.14iter/s]
Best score: 0.6295: : 472iter [00:14, 38.14iter/s]
Best score: 0.6295: : 473iter [00:14, 38.14iter/s]
Best score: 0.6295: : 474iter [00:14, 38.14iter/s]
Best score: 0.6295: : 475iter [00:14, 38.14iter/s]
Best score: 0.6295: : 476iter [00:14, 38.14iter/s]
Best score: 0.6295: : 477iter [00:14, 38.93iter/s]
Best score: 0.6295: : 477iter [00:14, 38.93iter/s]
Best score: 0.6295: : 478iter [00:14, 38.93iter/s]
Best score: 0.5689: : 479iter [00:14, 38.93iter/s]
Best score: 0.5689: : 480iter [00:14, 38.93iter/s]
Best score: 0.5689: : 481iter [00:14, 38.94iter/s]
Best score: 0.5689: : 481iter [00:14, 38.94iter/s]
Best score: 0.5689: : 482iter [00:14, 38.94iter/s]
Best score: 0.5689: : 483iter [00:14, 38.94iter/s]
Best score: 0.5689: : 484iter [00:14, 38.94iter/s]
Best score: 0.5689: : 485iter [00:14, 38.94iter/s]
Best score: 0.5689: : 486iter [00:14, 39.59iter/s]
Best score: 0.5689: : 486iter [00:14, 39.59iter/s]
Best score: 0.5689: : 487iter [00:14, 39.59iter/s]
Best score: 0.5689: : 488iter [00:14, 39.59iter/s]
Best score: 0.5638: : 489iter [00:14, 39.59iter/s]
Best score: 0.5638: : 490iter [00:14, 39.56iter/s]
Best score: 0.5638: : 490iter [00:14, 39.56iter/s]
Best score: 0.5638: : 491iter [00:14, 39.56iter/s]
Best score: 0.5638: : 492iter [00:15, 39.56iter/s]
Best score: 0.5638: : 493iter [00:15, 39.56iter/s]
Best score: 0.5638: : 494iter [00:15, 39.56iter/s]
Best score: 0.5638: : 495iter [00:15, 39.87iter/s]
Best score: 0.5638: : 495iter [00:15, 39.87iter/s]
Best score: 0.5638: : 496iter [00:15, 39.87iter/s]
Best score: 0.5638: : 497iter [00:15, 39.87iter/s]
Best score: 0.5638: : 498iter [00:15, 39.87iter/s]
[ITER 500] Best: 0.5072
[PARAMS] Current best parameters:
  fx: 502.7288
  fy: 501.7073
  px: 981.1877
  py: 751.1522
  k1: 0.06794503
  k2: -0.04943418
  k3: 0.02577134
  k4: -0.00621959
  p1: 0.01698803
  p2: -0.00295758
  p3: 0.00797485
  p4: -0.00303425
  s1: -0.0063
  s2: 0.0025
  s3: -0.0223
  s4: 0.0024
  tilt_x: -0.0239
  tilt_y: 0.0028
  scale: 2.9555
  rotation: -0.1566

Best score: 0.5072: : 499iter [00:15, 39.87iter/s]
Best score: 0.5072: : 500iter [00:15, 39.79iter/s]
Best score: 0.5072: : 500iter [00:15, 39.79iter/s]
Best score: 0.5072: : 501iter [00:15, 39.79iter/s]
Best score: 0.5072: : 502iter [00:15, 39.79iter/s]
Best score: 0.5072: : 503iter [00:15, 39.79iter/s]
Best score: 0.5072: : 504iter [00:15, 33.09iter/s]
Best score: 0.5072: : 504iter [00:15, 33.09iter/s]
Best score: 0.5072: : 505iter [00:15, 33.09iter/s]
Best score: 0.5072: : 506iter [00:15, 33.09iter/s]
Best score: 0.5072: : 507iter [00:15, 33.09iter/s]
Best score: 0.5072: : 508iter [00:15, 33.09iter/s]
Best score: 0.5072: : 509iter [00:15, 35.08iter/s]
Best score: 0.5072: : 509iter [00:15, 35.08iter/s]
Best score: 0.5072: : 510iter [00:15, 35.08iter/s]
Best score: 0.5072: : 511iter [00:15, 35.08iter/s]
Best score: 0.5072: : 512iter [00:15, 35.08iter/s]
Best score: 0.5072: : 513iter [00:15, 36.15iter/s]
Best score: 0.5072: : 513iter [00:15, 36.15iter/s]
Best score: 0.5072: : 514iter [00:15, 36.15iter/s]
Best score: 0.5072: : 515iter [00:15, 36.15iter/s]
Best score: 0.5072: : 516iter [00:15, 36.15iter/s]
Best score: 0.5072: : 517iter [00:15, 36.15iter/s]
Best score: 0.5072: : 518iter [00:15, 37.44iter/s]
Best score: 0.5072: : 518iter [00:15, 37.44iter/s]
Best score: 0.3938: : 519iter [00:15, 37.44iter/s]
Best score: 0.3938: : 520iter [00:15, 37.44iter/s]
Best score: 0.3938: : 521iter [00:15, 37.44iter/s]
Best score: 0.3938: : 522iter [00:15, 38.07iter/s]
Best score: 0.3938: : 522iter [00:15, 38.07iter/s]
Best score: 0.3938: : 523iter [00:15, 38.07iter/s]
Best score: 0.3938: : 524iter [00:15, 38.07iter/s]
Best score: 0.3938: : 525iter [00:15, 38.07iter/s]
Best score: 0.3938: : 526iter [00:15, 38.07iter/s]
Best score: 0.3938: : 527iter [00:15, 38.74iter/s]
Best score: 0.3938: : 527iter [00:15, 38.74iter/s]
Best score: 0.3938: : 528iter [00:15, 38.74iter/s]
Best score: 0.3564: : 529iter [00:16, 38.74iter/s]
Best score: 0.3564: : 530iter [00:16, 38.74iter/s]
Best score: 0.3564: : 531iter [00:16, 38.95iter/s]
Best score: 0.3564: : 531iter [00:16, 38.95iter/s]
Best score: 0.3564: : 532iter [00:16, 38.95iter/s]
Best score: 0.3564: : 533iter [00:16, 38.95iter/s]
Best score: 0.3564: : 534iter [00:16, 38.95iter/s]
Best score: 0.3564: : 535iter [00:16, 38.95iter/s]
Best score: 0.3564: : 536iter [00:16, 39.49iter/s]
Best score: 0.3564: : 536iter [00:16, 39.49iter/s]
Best score: 0.3564: : 537iter [00:16, 39.49iter/s]
Best score: 0.3564: : 538iter [00:16, 39.49iter/s]
Best score: 0.3564: : 539iter [00:16, 39.49iter/s]
Best score: 0.3564: : 540iter [00:16, 39.49iter/s]
Best score: 0.3564: : 540iter [00:16, 39.49iter/s]
Best score: 0.3564: : 541iter [00:16, 39.49iter/s]
Best score: 0.3564: : 542iter [00:16, 39.49iter/s]
Best score: 0.3564: : 543iter [00:16, 39.49iter/s]
Best score: 0.3564: : 544iter [00:16, 39.49iter/s]
Best score: 0.3564: : 545iter [00:16, 39.88iter/s]
Best score: 0.3564: : 545iter [00:16, 39.88iter/s]
Best score: 0.3564: : 546iter [00:16, 39.88iter/s]
Best score: 0.3564: : 547iter [00:16, 39.88iter/s]
Best score: 0.3564: : 548iter [00:16, 39.88iter/s]
[ITER 550] Best: 0.3353
[PARAMS] Current best parameters:
  fx: 499.5240
  fy: 499.1868
  px: 985.5216
  py: 749.4204
  k1: 0.09558737
  k2: -0.07118983
  k3: 0.02827015
  k4: -0.00491806
  p1: 0.00708434
  p2: -0.00715483
  p3: 0.00297972
  p4: 0.00015866
  s1: 0.0031
  s2: 0.0009
  s3: -0.0062
  s4: -0.0009
  tilt_x: -0.0136
  tilt_y: -0.0260
  scale: 2.9250
  rotation: -0.1243

Best score: 0.3353: : 549iter [00:16, 39.88iter/s]
Best score: 0.3353: : 550iter [00:16, 39.87iter/s]
Best score: 0.3353: : 550iter [00:16, 39.87iter/s]
Best score: 0.3353: : 551iter [00:16, 39.87iter/s]
Best score: 0.3353: : 552iter [00:16, 39.87iter/s]
Best score: 0.3353: : 553iter [00:16, 39.87iter/s]
Best score: 0.3353: : 554iter [00:16, 33.03iter/s]
Best score: 0.3353: : 554iter [00:16, 33.03iter/s]
Best score: 0.3353: : 555iter [00:16, 33.03iter/s]
Best score: 0.3353: : 556iter [00:16, 33.03iter/s]
Best score: 0.3353: : 557iter [00:16, 33.03iter/s]
Best score: 0.3353: : 558iter [00:16, 33.03iter/s]
Best score: 0.3353: : 559iter [00:16, 35.12iter/s]
Best score: 0.3313: : 559iter [00:16, 35.12iter/s]
Best score: 0.3313: : 560iter [00:16, 35.12iter/s]
Best score: 0.3313: : 561iter [00:16, 35.12iter/s]
Best score: 0.3313: : 562iter [00:16, 35.12iter/s]
Best score: 0.3313: : 563iter [00:16, 36.18iter/s]
Best score: 0.3313: : 563iter [00:16, 36.18iter/s]
Best score: 0.3313: : 564iter [00:16, 36.18iter/s]
Best score: 0.3313: : 565iter [00:16, 36.18iter/s]
Best score: 0.3313: : 566iter [00:17, 36.18iter/s]
Best score: 0.3313: : 567iter [00:17, 36.18iter/s]
Best score: 0.3313: : 568iter [00:17, 37.47iter/s]
Best score: 0.3313: : 568iter [00:17, 37.47iter/s]
Best score: 0.3313: : 569iter [00:17, 37.47iter/s]
Best score: 0.3313: : 570iter [00:17, 37.47iter/s]
Best score: 0.3313: : 571iter [00:17, 37.47iter/s]
Best score: 0.3313: : 572iter [00:17, 38.04iter/s]
Best score: 0.3313: : 572iter [00:17, 38.04iter/s]
Best score: 0.3313: : 573iter [00:17, 38.04iter/s]
Best score: 0.3313: : 574iter [00:17, 38.04iter/s]
Best score: 0.3313: : 575iter [00:17, 38.04iter/s]
Best score: 0.3313: : 576iter [00:17, 38.04iter/s]
Best score: 0.3313: : 577iter [00:17, 38.76iter/s]
Best score: 0.3313: : 577iter [00:17, 38.76iter/s]
Best score: 0.3313: : 578iter [00:17, 38.76iter/s]
Best score: 0.3313: : 579iter [00:17, 38.76iter/s]
Best score: 0.3313: : 580iter [00:17, 38.76iter/s]
Best score: 0.3313: : 581iter [00:17, 38.95iter/s]
Best score: 0.3313: : 581iter [00:17, 38.95iter/s]
Best score: 0.3313: : 582iter [00:17, 38.95iter/s]
Best score: 0.3313: : 583iter [00:17, 38.95iter/s]
Best score: 0.3313: : 584iter [00:17, 38.95iter/s]
Best score: 0.3313: : 585iter [00:17, 38.95iter/s]
Best score: 0.3313: : 586iter [00:17, 39.60iter/s]
Best score: 0.3313: : 586iter [00:17, 39.60iter/s]
Best score: 0.3313: : 587iter [00:17, 39.60iter/s]
Best score: 0.3313: : 588iter [00:17, 39.60iter/s]
Best score: 0.3086: : 589iter [00:17, 39.60iter/s]
Best score: 0.3086: : 590iter [00:17, 39.58iter/s]
Best score: 0.3086: : 590iter [00:17, 39.58iter/s]
Best score: 0.3086: : 591iter [00:17, 39.58iter/s]
Best score: 0.3086: : 592iter [00:17, 39.58iter/s]
Best score: 0.3086: : 593iter [00:17, 39.58iter/s]
Best score: 0.3086: : 594iter [00:17, 39.58iter/s]
Best score: 0.3086: : 595iter [00:17, 39.98iter/s]
Best score: 0.3086: : 595iter [00:17, 39.98iter/s]
Best score: 0.3086: : 596iter [00:17, 39.98iter/s]
Best score: 0.3086: : 597iter [00:17, 39.98iter/s]
Best score: 0.3086: : 598iter [00:17, 39.98iter/s]
[ITER 600] Best: 0.3086
[PARAMS] Current best parameters:
  fx: 501.6442
  fy: 501.2657
  px: 984.4277
  py: 749.7899
  k1: 0.09167407
  k2: -0.06844645
  k3: 0.02755818
  k4: -0.00500704
  p1: 0.00980131
  p2: -0.00588082
  p3: 0.00547216
  p4: -0.00139977
  s1: 0.0012
  s2: 0.0013
  s3: -0.0134
  s4: 0.0013
  tilt_x: -0.0173
  tilt_y: -0.0176
  scale: 2.9173
  rotation: -0.1290

Best score: 0.3086: : 599iter [00:17, 39.98iter/s]
Best score: 0.3086: : 600iter [00:17, 40.03iter/s]
Best score: 0.3086: : 600iter [00:17, 40.03iter/s]
Best score: 0.3086: : 601iter [00:17, 40.03iter/s]
Best score: 0.3086: : 602iter [00:17, 40.03iter/s]
Best score: 0.3086: : 603iter [00:18, 40.03iter/s]
Best score: 0.3086: : 604iter [00:18, 40.03iter/s]
Best score: 0.3086: : 605iter [00:18, 33.54iter/s]
Best score: 0.3086: : 605iter [00:18, 33.54iter/s]
Best score: 0.3086: : 606iter [00:18, 33.54iter/s]
Best score: 0.3086: : 607iter [00:18, 33.54iter/s]
Best score: 0.3086: : 608iter [00:18, 33.54iter/s]
Best score: 0.3002: : 609iter [00:18, 33.54iter/s]
Best score: 0.3002: : 610iter [00:18, 35.24iter/s]
Best score: 0.3002: : 610iter [00:18, 35.24iter/s]
Best score: 0.3002: : 611iter [00:18, 35.24iter/s]
Best score: 0.3002: : 612iter [00:18, 35.24iter/s]
Best score: 0.3002: : 613iter [00:18, 35.24iter/s]
Best score: 0.3002: : 614iter [00:18, 35.24iter/s]
Best score: 0.3002: : 615iter [00:18, 36.71iter/s]
Best score: 0.3002: : 615iter [00:18, 36.71iter/s]
Best score: 0.3002: : 616iter [00:18, 36.71iter/s]
Best score: 0.3002: : 617iter [00:18, 36.71iter/s]
Best score: 0.3002: : 618iter [00:18, 36.71iter/s]
Best score: 0.2970: : 619iter [00:18, 36.71iter/s]
Best score: 0.2970: : 620iter [00:18, 37.52iter/s]
Best score: 0.2970: : 620iter [00:18, 37.52iter/s]
Best score: 0.2970: : 621iter [00:18, 37.52iter/s]
Best score: 0.2970: : 622iter [00:18, 37.52iter/s]
Best score: 0.2970: : 623iter [00:18, 37.52iter/s]
Best score: 0.2970: : 624iter [00:18, 37.52iter/s]
Best score: 0.2970: : 625iter [00:18, 38.37iter/s]
Best score: 0.2970: : 625iter [00:18, 38.37iter/s]
Best score: 0.2970: : 626iter [00:18, 38.37iter/s]
Best score: 0.2970: : 627iter [00:18, 38.37iter/s]
Best score: 0.2970: : 628iter [00:18, 38.37iter/s]
Best score: 0.2970: : 629iter [00:18, 38.37iter/s]
Best score: 0.2970: : 630iter [00:18, 38.70iter/s]
Best score: 0.2970: : 630iter [00:18, 38.70iter/s]
Best score: 0.2970: : 631iter [00:18, 38.70iter/s]
Best score: 0.2970: : 632iter [00:18, 38.70iter/s]
Best score: 0.2970: : 633iter [00:18, 38.70iter/s]
Best score: 0.2970: : 634iter [00:18, 38.70iter/s]
Best score: 0.2970: : 635iter [00:18, 39.21iter/s]
Best score: 0.2970: : 635iter [00:18, 39.21iter/s]
Best score: 0.2970: : 636iter [00:18, 39.21iter/s]
Best score: 0.2970: : 637iter [00:18, 39.21iter/s]
Best score: 0.2970: : 638iter [00:18, 39.21iter/s]
Best score: 0.2856: : 639iter [00:18, 39.21iter/s]
Best score: 0.2856: : 640iter [00:18, 39.41iter/s]
Best score: 0.2856: : 640iter [00:18, 39.41iter/s]
Best score: 0.2856: : 641iter [00:18, 39.41iter/s]
Best score: 0.2856: : 642iter [00:18, 39.41iter/s]
Best score: 0.2856: : 643iter [00:19, 39.41iter/s]
Best score: 0.2856: : 644iter [00:19, 39.41iter/s]
Best score: 0.2856: : 645iter [00:19, 39.68iter/s]
Best score: 0.2856: : 645iter [00:19, 39.68iter/s]
Best score: 0.2856: : 646iter [00:19, 39.68iter/s]
Best score: 0.2856: : 647iter [00:19, 39.68iter/s]
Best score: 0.2856: : 648iter [00:19, 39.68iter/s]
[ITER 650] Best: 0.2856
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2856: : 649iter [00:19, 39.68iter/s]
Best score: 0.2856: : 650iter [00:19, 39.73iter/s]
[Periodic outlier @ iter 550] Removing 1000 frame 13 (score=0.72, threshold=0.71)
  Remaining frames: 19

Best score: 0.2856: : 650iter [00:19, 39.73iter/s]
Best score: 0.2856: : 651iter [00:19, 39.73iter/s]
Best score: 0.2856: : 652iter [00:19, 39.73iter/s]
Best score: 0.2856: : 653iter [00:19, 39.73iter/s]
Best score: 0.2856: : 654iter [00:19, 31.04iter/s]
Best score: 0.2856: : 654iter [00:19, 31.04iter/s]
Best score: 0.2856: : 655iter [00:19, 31.04iter/s]
Best score: 0.2856: : 656iter [00:19, 31.04iter/s]
Best score: 0.2856: : 657iter [00:19, 31.04iter/s]
Best score: 0.2856: : 658iter [00:19, 31.04iter/s]
Best score: 0.2856: : 659iter [00:19, 33.65iter/s]
Best score: 0.2644: : 659iter [00:19, 33.65iter/s]
Best score: 0.2644: : 660iter [00:19, 33.65iter/s]
Best score: 0.2644: : 661iter [00:19, 33.65iter/s]
Best score: 0.2644: : 662iter [00:19, 33.65iter/s]
Best score: 0.2644: : 663iter [00:19, 33.65iter/s]
Best score: 0.2644: : 664iter [00:19, 35.60iter/s]
Best score: 0.2644: : 664iter [00:19, 35.60iter/s]
Best score: 0.2644: : 665iter [00:19, 35.60iter/s]
Best score: 0.2644: : 666iter [00:19, 35.60iter/s]
Best score: 0.2644: : 667iter [00:19, 35.60iter/s]
Best score: 0.2644: : 668iter [00:19, 35.60iter/s]
Best score: 0.2644: : 669iter [00:19, 37.37iter/s]
Best score: 0.2644: : 669iter [00:19, 37.37iter/s]
Best score: 0.2644: : 670iter [00:19, 37.37iter/s]
Best score: 0.2644: : 671iter [00:19, 37.37iter/s]
Best score: 0.2644: : 672iter [00:19, 37.37iter/s]
Best score: 0.2644: : 673iter [00:19, 37.37iter/s]
Best score: 0.2644: : 674iter [00:19, 38.37iter/s]
Best score: 0.2644: : 674iter [00:19, 38.37iter/s]
Best score: 0.2644: : 675iter [00:19, 38.37iter/s]
Best score: 0.2644: : 676iter [00:19, 38.37iter/s]
Best score: 0.2644: : 677iter [00:19, 38.37iter/s]
Best score: 0.2644: : 678iter [00:19, 38.37iter/s]
Best score: 0.2644: : 679iter [00:19, 39.36iter/s]
Best score: 0.2644: : 679iter [00:19, 39.36iter/s]
Best score: 0.2644: : 680iter [00:20, 39.36iter/s]
Best score: 0.2644: : 681iter [00:20, 39.36iter/s]
Best score: 0.2644: : 682iter [00:20, 39.36iter/s]
Best score: 0.2644: : 683iter [00:20, 39.36iter/s]
Best score: 0.2644: : 684iter [00:20, 31.94iter/s]
Best score: 0.2644: : 684iter [00:20, 31.94iter/s]
Best score: 0.2644: : 685iter [00:20, 31.94iter/s]
Best score: 0.2644: : 686iter [00:20, 31.94iter/s]
Best score: 0.2644: : 687iter [00:20, 31.94iter/s]
Best score: 0.2644: : 688iter [00:20, 31.94iter/s]
Best score: 0.2644: : 689iter [00:20, 34.31iter/s]
Best score: 0.2644: : 689iter [00:20, 34.31iter/s]
Best score: 0.2644: : 690iter [00:20, 34.31iter/s]
Best score: 0.2644: : 691iter [00:20, 34.31iter/s]
Best score: 0.2644: : 692iter [00:20, 34.31iter/s]
Best score: 0.2644: : 693iter [00:20, 34.31iter/s]
Best score: 0.2644: : 694iter [00:20, 36.06iter/s]
Best score: 0.2644: : 694iter [00:20, 36.06iter/s]
Best score: 0.2644: : 695iter [00:20, 36.06iter/s]
Best score: 0.2644: : 696iter [00:20, 36.06iter/s]
Best score: 0.2644: : 697iter [00:20, 36.06iter/s]
Best score: 0.2644: : 698iter [00:20, 36.06iter/s]
Best score: 0.2644: : 699iter [00:20, 37.65iter/s]
[ITER 700] Best: 0.2644
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2644: : 699iter [00:20, 37.65iter/s]
[Periodic outlier @ iter 600] Removing 1000 frame 12 (score=0.71, threshold=0.63)
  Remaining frames: 18

Best score: 0.2644: : 700iter [00:20, 37.65iter/s]
Best score: 0.2644: : 701iter [00:20, 37.65iter/s]
Best score: 0.2644: : 702iter [00:20, 37.65iter/s]
Best score: 0.2644: : 703iter [00:20, 30.04iter/s]
Best score: 0.2644: : 703iter [00:20, 30.04iter/s]
Best score: 0.2644: : 704iter [00:20, 30.04iter/s]
Best score: 0.2644: : 705iter [00:20, 30.04iter/s]
Best score: 0.2644: : 706iter [00:20, 30.04iter/s]
Best score: 0.2644: : 707iter [00:20, 30.04iter/s]
Best score: 0.2644: : 708iter [00:20, 33.27iter/s]
Best score: 0.2644: : 708iter [00:20, 33.27iter/s]
Best score: 0.2420: : 709iter [00:20, 33.27iter/s]
Best score: 0.2420: : 710iter [00:20, 33.27iter/s]
Best score: 0.2420: : 711iter [00:20, 33.27iter/s]
Best score: 0.2420: : 712iter [00:21, 33.27iter/s]
Best score: 0.2420: : 713iter [00:21, 35.60iter/s]
Best score: 0.2420: : 713iter [00:21, 35.60iter/s]
Best score: 0.2420: : 714iter [00:21, 35.60iter/s]
Best score: 0.2420: : 715iter [00:21, 35.60iter/s]
Best score: 0.2420: : 716iter [00:21, 35.60iter/s]
Best score: 0.2420: : 717iter [00:21, 35.60iter/s]
Best score: 0.2420: : 718iter [00:21, 37.79iter/s]
Best score: 0.2420: : 718iter [00:21, 37.79iter/s]
Best score: 0.2420: : 719iter [00:21, 37.79iter/s]
Best score: 0.2420: : 720iter [00:21, 37.79iter/s]
Best score: 0.2420: : 721iter [00:21, 37.79iter/s]
Best score: 0.2420: : 722iter [00:21, 37.79iter/s]
Best score: 0.2420: : 723iter [00:21, 39.08iter/s]
Best score: 0.2420: : 723iter [00:21, 39.08iter/s]
Best score: 0.2420: : 724iter [00:21, 39.08iter/s]
Best score: 0.2420: : 725iter [00:21, 39.08iter/s]
Best score: 0.2420: : 726iter [00:21, 39.08iter/s]
Best score: 0.2420: : 727iter [00:21, 39.08iter/s]
Best score: 0.2420: : 728iter [00:21, 40.29iter/s]
Best score: 0.2420: : 728iter [00:21, 40.29iter/s]
Best score: 0.2420: : 729iter [00:21, 40.29iter/s]
Best score: 0.2420: : 730iter [00:21, 40.29iter/s]
Best score: 0.2420: : 731iter [00:21, 40.29iter/s]
Best score: 0.2420: : 732iter [00:21, 40.29iter/s]
Best score: 0.2420: : 733iter [00:21, 40.73iter/s]
Best score: 0.2420: : 733iter [00:21, 40.73iter/s]
Best score: 0.2420: : 734iter [00:21, 40.73iter/s]
Best score: 0.2420: : 735iter [00:21, 40.73iter/s]
Best score: 0.2420: : 736iter [00:21, 40.73iter/s]
Best score: 0.2420: : 737iter [00:21, 40.73iter/s]
Best score: 0.2420: : 738iter [00:21, 41.48iter/s]
Best score: 0.2420: : 738iter [00:21, 41.48iter/s]
Best score: 0.2420: : 739iter [00:21, 41.48iter/s]
Best score: 0.2420: : 740iter [00:21, 41.48iter/s]
Best score: 0.2420: : 741iter [00:21, 41.48iter/s]
Best score: 0.2420: : 742iter [00:21, 41.48iter/s]
Best score: 0.2420: : 743iter [00:21, 41.82iter/s]
Best score: 0.2420: : 743iter [00:21, 41.82iter/s]
Best score: 0.2420: : 744iter [00:21, 41.82iter/s]
Best score: 0.2420: : 745iter [00:21, 41.82iter/s]
Best score: 0.2420: : 746iter [00:21, 41.82iter/s]
Best score: 0.2420: : 747iter [00:21, 41.82iter/s]
Best score: 0.2420: : 748iter [00:21, 42.42iter/s]
Best score: 0.2420: : 748iter [00:21, 42.42iter/s]
[ITER 750] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 749iter [00:21, 42.42iter/s]
Best score: 0.2420: : 750iter [00:21, 42.42iter/s]
Best score: 0.2420: : 751iter [00:21, 42.42iter/s]
Best score: 0.2420: : 752iter [00:22, 42.42iter/s]
Best score: 0.2420: : 753iter [00:22, 35.51iter/s]
Best score: 0.2420: : 753iter [00:22, 35.51iter/s]
Best score: 0.2420: : 754iter [00:22, 35.51iter/s]
Best score: 0.2420: : 755iter [00:22, 35.51iter/s]
Best score: 0.2420: : 756iter [00:22, 35.51iter/s]
Best score: 0.2420: : 757iter [00:22, 35.51iter/s]
Best score: 0.2420: : 758iter [00:22, 37.62iter/s]
Best score: 0.2420: : 758iter [00:22, 37.62iter/s]
Best score: 0.2420: : 759iter [00:22, 37.62iter/s]
Best score: 0.2420: : 760iter [00:22, 37.62iter/s]
Best score: 0.2420: : 761iter [00:22, 37.62iter/s]
Best score: 0.2420: : 762iter [00:22, 37.62iter/s]
Best score: 0.2420: : 763iter [00:22, 39.12iter/s]
Best score: 0.2420: : 763iter [00:22, 39.12iter/s]
Best score: 0.2420: : 764iter [00:22, 39.12iter/s]
Best score: 0.2420: : 765iter [00:22, 39.12iter/s]
Best score: 0.2420: : 766iter [00:22, 39.12iter/s]
Best score: 0.2420: : 767iter [00:22, 39.12iter/s]
Best score: 0.2420: : 768iter [00:22, 40.41iter/s]
Best score: 0.2420: : 768iter [00:22, 40.41iter/s]
Best score: 0.2420: : 769iter [00:22, 40.41iter/s]
Best score: 0.2420: : 770iter [00:22, 40.41iter/s]
Best score: 0.2420: : 771iter [00:22, 40.41iter/s]
Best score: 0.2420: : 772iter [00:22, 40.41iter/s]
Best score: 0.2420: : 773iter [00:22, 40.99iter/s]
Best score: 0.2420: : 773iter [00:22, 40.99iter/s]
Best score: 0.2420: : 774iter [00:22, 40.99iter/s]
Best score: 0.2420: : 775iter [00:22, 40.99iter/s]
Best score: 0.2420: : 776iter [00:22, 40.99iter/s]
Best score: 0.2420: : 777iter [00:22, 40.99iter/s]
Best score: 0.2420: : 778iter [00:22, 41.89iter/s]
Best score: 0.2420: : 778iter [00:22, 41.89iter/s]
Best score: 0.2420: : 779iter [00:22, 41.89iter/s]
Best score: 0.2420: : 780iter [00:22, 41.89iter/s]
Best score: 0.2420: : 781iter [00:22, 41.89iter/s]
Best score: 0.2420: : 782iter [00:22, 41.89iter/s]
Best score: 0.2420: : 783iter [00:22, 42.13iter/s]
Best score: 0.2420: : 783iter [00:22, 42.13iter/s]
Best score: 0.2420: : 784iter [00:22, 42.13iter/s]
Best score: 0.2420: : 785iter [00:22, 42.13iter/s]
Best score: 0.2420: : 786iter [00:22, 42.13iter/s]
Best score: 0.2420: : 787iter [00:22, 42.13iter/s]
Best score: 0.2420: : 788iter [00:22, 42.63iter/s]
Best score: 0.2420: : 788iter [00:22, 42.63iter/s]
Best score: 0.2420: : 789iter [00:22, 42.63iter/s]
Best score: 0.2420: : 790iter [00:22, 42.63iter/s]
Best score: 0.2420: : 791iter [00:22, 42.63iter/s]
Best score: 0.2420: : 792iter [00:22, 42.63iter/s]
Best score: 0.2420: : 793iter [00:22, 42.51iter/s]
Best score: 0.2420: : 793iter [00:22, 42.51iter/s]
Best score: 0.2420: : 794iter [00:22, 42.51iter/s]
Best score: 0.2420: : 795iter [00:23, 42.51iter/s]
Best score: 0.2420: : 796iter [00:23, 42.51iter/s]
Best score: 0.2420: : 797iter [00:23, 42.51iter/s]
Best score: 0.2420: : 798iter [00:23, 42.88iter/s]
Best score: 0.2420: : 798iter [00:23, 42.88iter/s]
[ITER 800] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 799iter [00:23, 42.88iter/s]
Best score: 0.2420: : 800iter [00:23, 42.88iter/s]
Best score: 0.2420: : 801iter [00:23, 42.88iter/s]
Best score: 0.2420: : 802iter [00:23, 42.88iter/s]
Best score: 0.2420: : 803iter [00:23, 35.73iter/s]
Best score: 0.2420: : 803iter [00:23, 35.73iter/s]
Best score: 0.2420: : 804iter [00:23, 35.73iter/s]
Best score: 0.2420: : 805iter [00:23, 35.73iter/s]
Best score: 0.2420: : 806iter [00:23, 35.73iter/s]
Best score: 0.2420: : 807iter [00:23, 35.73iter/s]
Best score: 0.2420: : 808iter [00:23, 37.80iter/s]
Best score: 0.2420: : 808iter [00:23, 37.80iter/s]
Best score: 0.2420: : 809iter [00:23, 37.80iter/s]
Best score: 0.2420: : 810iter [00:23, 37.80iter/s]
Best score: 0.2420: : 811iter [00:23, 37.80iter/s]
Best score: 0.2420: : 812iter [00:23, 37.80iter/s]
Best score: 0.2420: : 813iter [00:23, 39.16iter/s]
Best score: 0.2420: : 813iter [00:23, 39.16iter/s]
Best score: 0.2420: : 814iter [00:23, 39.16iter/s]
Best score: 0.2420: : 815iter [00:23, 39.16iter/s]
Best score: 0.2420: : 816iter [00:23, 39.16iter/s]
Best score: 0.2420: : 817iter [00:23, 39.16iter/s]
Best score: 0.2420: : 818iter [00:23, 40.38iter/s]
Best score: 0.2420: : 818iter [00:23, 40.38iter/s]
Best score: 0.2420: : 819iter [00:23, 40.38iter/s]
Best score: 0.2420: : 820iter [00:23, 40.38iter/s]
Best score: 0.2420: : 821iter [00:23, 40.38iter/s]
Best score: 0.2420: : 822iter [00:23, 40.38iter/s]
Best score: 0.2420: : 823iter [00:23, 40.90iter/s]
Best score: 0.2420: : 823iter [00:23, 40.90iter/s]
Best score: 0.2420: : 824iter [00:23, 40.90iter/s]
Best score: 0.2420: : 825iter [00:23, 40.90iter/s]
Best score: 0.2420: : 826iter [00:23, 40.90iter/s]
Best score: 0.2420: : 827iter [00:23, 40.90iter/s]
Best score: 0.2420: : 828iter [00:23, 41.63iter/s]
Best score: 0.2420: : 828iter [00:23, 41.63iter/s]
Best score: 0.2420: : 829iter [00:23, 41.63iter/s]
Best score: 0.2420: : 830iter [00:23, 41.63iter/s]
Best score: 0.2420: : 831iter [00:23, 41.63iter/s]
Best score: 0.2420: : 832iter [00:23, 41.63iter/s]
Best score: 0.2420: : 833iter [00:23, 41.95iter/s]
Best score: 0.2420: : 833iter [00:23, 41.95iter/s]
Best score: 0.2420: : 834iter [00:23, 41.95iter/s]
Best score: 0.2420: : 835iter [00:24, 41.95iter/s]
Best score: 0.2420: : 836iter [00:24, 41.95iter/s]
Best score: 0.2420: : 837iter [00:24, 41.95iter/s]
Best score: 0.2420: : 838iter [00:24, 42.46iter/s]
Best score: 0.2420: : 838iter [00:24, 42.46iter/s]
Best score: 0.2420: : 839iter [00:24, 42.46iter/s]
Best score: 0.2420: : 840iter [00:24, 42.46iter/s]
Best score: 0.2420: : 841iter [00:24, 42.46iter/s]
Best score: 0.2420: : 842iter [00:24, 42.46iter/s]
Best score: 0.2420: : 843iter [00:24, 42.60iter/s]
Best score: 0.2420: : 843iter [00:24, 42.60iter/s]
Best score: 0.2420: : 844iter [00:24, 42.60iter/s]
Best score: 0.2420: : 845iter [00:24, 42.60iter/s]
Best score: 0.2420: : 846iter [00:24, 42.60iter/s]
Best score: 0.2420: : 847iter [00:24, 42.60iter/s]
Best score: 0.2420: : 848iter [00:24, 42.94iter/s]
Best score: 0.2420: : 848iter [00:24, 42.94iter/s]
[ITER 850] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 849iter [00:24, 42.94iter/s]
Best score: 0.2420: : 850iter [00:24, 42.94iter/s]
Best score: 0.2420: : 851iter [00:24, 42.94iter/s]
Best score: 0.2420: : 852iter [00:24, 42.94iter/s]
Best score: 0.2420: : 853iter [00:24, 35.87iter/s]
Best score: 0.2420: : 853iter [00:24, 35.87iter/s]
Best score: 0.2420: : 854iter [00:24, 35.87iter/s]
Best score: 0.2420: : 855iter [00:24, 35.87iter/s]
Best score: 0.2420: : 856iter [00:24, 35.87iter/s]
Best score: 0.2420: : 857iter [00:24, 35.87iter/s]
Best score: 0.2420: : 858iter [00:24, 37.99iter/s]
Best score: 0.2420: : 858iter [00:24, 37.99iter/s]
Best score: 0.2420: : 859iter [00:24, 37.99iter/s]
Best score: 0.2420: : 860iter [00:24, 37.99iter/s]
Best score: 0.2420: : 861iter [00:24, 37.99iter/s]
Best score: 0.2420: : 862iter [00:24, 37.99iter/s]
Best score: 0.2420: : 863iter [00:24, 39.44iter/s]
Best score: 0.2420: : 863iter [00:24, 39.44iter/s]
Best score: 0.2420: : 864iter [00:24, 39.44iter/s]
Best score: 0.2420: : 865iter [00:24, 39.44iter/s]
Best score: 0.2420: : 866iter [00:24, 39.44iter/s]
Best score: 0.2420: : 867iter [00:24, 39.44iter/s]
Best score: 0.2420: : 868iter [00:24, 40.62iter/s]
Best score: 0.2420: : 868iter [00:24, 40.62iter/s]
Best score: 0.2420: : 869iter [00:24, 40.62iter/s]
Best score: 0.2420: : 870iter [00:24, 40.62iter/s]
Best score: 0.2420: : 871iter [00:24, 40.62iter/s]
Best score: 0.2420: : 872iter [00:24, 40.62iter/s]
Best score: 0.2420: : 873iter [00:24, 41.30iter/s]
Best score: 0.2420: : 873iter [00:24, 41.30iter/s]
Best score: 0.2420: : 874iter [00:24, 41.30iter/s]
Best score: 0.2420: : 875iter [00:25, 41.30iter/s]
Best score: 0.2420: : 876iter [00:25, 41.30iter/s]
Best score: 0.2420: : 877iter [00:25, 41.30iter/s]
Best score: 0.2420: : 878iter [00:25, 42.16iter/s]
Best score: 0.2420: : 878iter [00:25, 42.16iter/s]
Best score: 0.2420: : 879iter [00:25, 42.16iter/s]
Best score: 0.2420: : 880iter [00:25, 42.16iter/s]
Best score: 0.2420: : 881iter [00:25, 42.16iter/s]
Best score: 0.2420: : 882iter [00:25, 42.16iter/s]
Best score: 0.2420: : 883iter [00:25, 42.56iter/s]
Best score: 0.2420: : 883iter [00:25, 42.56iter/s]
Best score: 0.2420: : 884iter [00:25, 42.56iter/s]
Best score: 0.2420: : 885iter [00:25, 42.56iter/s]
Best score: 0.2420: : 886iter [00:25, 42.56iter/s]
Best score: 0.2420: : 887iter [00:25, 42.56iter/s]
Best score: 0.2420: : 888iter [00:25, 43.30iter/s]
Best score: 0.2420: : 888iter [00:25, 43.30iter/s]
Best score: 0.2420: : 889iter [00:25, 43.30iter/s]
Best score: 0.2420: : 890iter [00:25, 43.30iter/s]
Best score: 0.2420: : 891iter [00:25, 43.30iter/s]
Best score: 0.2420: : 892iter [00:25, 43.30iter/s]
Best score: 0.2420: : 893iter [00:25, 43.28iter/s]
Best score: 0.2420: : 893iter [00:25, 43.28iter/s]
Best score: 0.2420: : 894iter [00:25, 43.28iter/s]
Best score: 0.2420: : 895iter [00:25, 43.28iter/s]
Best score: 0.2420: : 896iter [00:25, 43.28iter/s]
Best score: 0.2420: : 897iter [00:25, 43.28iter/s]
Best score: 0.2420: : 898iter [00:25, 43.59iter/s]
Best score: 0.2420: : 898iter [00:25, 43.59iter/s]
[ITER 900] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 899iter [00:25, 43.59iter/s]
Best score: 0.2420: : 900iter [00:25, 43.59iter/s]
Best score: 0.2420: : 901iter [00:25, 43.59iter/s]
Best score: 0.2420: : 902iter [00:25, 43.59iter/s]
Best score: 0.2420: : 903iter [00:25, 36.43iter/s]
Best score: 0.2420: : 903iter [00:25, 36.43iter/s]
Best score: 0.2420: : 904iter [00:25, 36.43iter/s]
Best score: 0.2420: : 905iter [00:25, 36.43iter/s]
Best score: 0.2420: : 906iter [00:25, 36.43iter/s]
Best score: 0.2420: : 907iter [00:25, 36.43iter/s]
Best score: 0.2420: : 908iter [00:25, 38.59iter/s]
Best score: 0.2420: : 908iter [00:25, 38.59iter/s]
Best score: 0.2420: : 909iter [00:25, 38.59iter/s]
Best score: 0.2420: : 910iter [00:25, 38.59iter/s]
Best score: 0.2420: : 911iter [00:25, 38.59iter/s]
Best score: 0.2420: : 912iter [00:25, 38.59iter/s]
Best score: 0.2420: : 913iter [00:25, 39.96iter/s]
Best score: 0.2420: : 913iter [00:25, 39.96iter/s]
Best score: 0.2420: : 914iter [00:25, 39.96iter/s]
Best score: 0.2420: : 915iter [00:25, 39.96iter/s]
Best score: 0.2420: : 916iter [00:26, 39.96iter/s]
Best score: 0.2420: : 917iter [00:26, 39.96iter/s]
Best score: 0.2420: : 918iter [00:26, 41.16iter/s]
Best score: 0.2420: : 918iter [00:26, 41.16iter/s]
Best score: 0.2420: : 919iter [00:26, 41.16iter/s]
Best score: 0.2420: : 920iter [00:26, 41.16iter/s]
Best score: 0.2420: : 921iter [00:26, 41.16iter/s]
Best score: 0.2420: : 922iter [00:26, 41.16iter/s]
Best score: 0.2420: : 923iter [00:26, 41.51iter/s]
Best score: 0.2420: : 923iter [00:26, 41.51iter/s]
Best score: 0.2420: : 924iter [00:26, 41.51iter/s]
Best score: 0.2420: : 925iter [00:26, 41.51iter/s]
Best score: 0.2420: : 926iter [00:26, 41.51iter/s]
Best score: 0.2420: : 927iter [00:26, 41.51iter/s]
Best score: 0.2420: : 928iter [00:26, 42.43iter/s]
Best score: 0.2420: : 928iter [00:26, 42.43iter/s]
Best score: 0.2420: : 929iter [00:26, 42.43iter/s]
Best score: 0.2420: : 930iter [00:26, 42.43iter/s]
Best score: 0.2420: : 931iter [00:26, 42.43iter/s]
Best score: 0.2420: : 932iter [00:26, 42.43iter/s]
Best score: 0.2420: : 933iter [00:26, 42.71iter/s]
Best score: 0.2420: : 933iter [00:26, 42.71iter/s]
Best score: 0.2420: : 934iter [00:26, 42.71iter/s]
Best score: 0.2420: : 935iter [00:26, 42.71iter/s]
Best score: 0.2420: : 936iter [00:26, 42.71iter/s]
Best score: 0.2420: : 937iter [00:26, 42.71iter/s]
Best score: 0.2420: : 938iter [00:26, 43.21iter/s]
Best score: 0.2420: : 938iter [00:26, 43.21iter/s]
Best score: 0.2420: : 939iter [00:26, 43.21iter/s]
Best score: 0.2420: : 940iter [00:26, 43.21iter/s]
Best score: 0.2420: : 941iter [00:26, 43.21iter/s]
Best score: 0.2420: : 942iter [00:26, 43.21iter/s]
Best score: 0.2420: : 943iter [00:26, 43.32iter/s]
Best score: 0.2420: : 943iter [00:26, 43.32iter/s]
Best score: 0.2420: : 944iter [00:26, 43.32iter/s]
Best score: 0.2420: : 945iter [00:26, 43.32iter/s]
Best score: 0.2420: : 946iter [00:26, 43.32iter/s]
Best score: 0.2420: : 947iter [00:26, 43.32iter/s]
Best score: 0.2420: : 948iter [00:26, 43.65iter/s]
Best score: 0.2420: : 948iter [00:26, 43.65iter/s]
[ITER 950] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 949iter [00:26, 43.65iter/s]
Best score: 0.2420: : 950iter [00:26, 43.65iter/s]
Best score: 0.2420: : 951iter [00:26, 43.65iter/s]
Best score: 0.2420: : 952iter [00:26, 43.65iter/s]
Best score: 0.2420: : 953iter [00:26, 36.48iter/s]
Best score: 0.2420: : 953iter [00:26, 36.48iter/s]
Best score: 0.2420: : 954iter [00:26, 36.48iter/s]
Best score: 0.2420: : 955iter [00:26, 36.48iter/s]
Best score: 0.2420: : 956iter [00:27, 36.48iter/s]
Best score: 0.2420: : 957iter [00:27, 36.48iter/s]
Best score: 0.2420: : 958iter [00:27, 38.41iter/s]
Best score: 0.2420: : 958iter [00:27, 38.41iter/s]
Best score: 0.2420: : 959iter [00:27, 38.41iter/s]
Best score: 0.2420: : 960iter [00:27, 38.41iter/s]
Best score: 0.2420: : 961iter [00:27, 38.41iter/s]
Best score: 0.2420: : 962iter [00:27, 38.41iter/s]
Best score: 0.2420: : 963iter [00:27, 39.80iter/s]
Best score: 0.2420: : 963iter [00:27, 39.80iter/s]
Best score: 0.2420: : 964iter [00:27, 39.80iter/s]
Best score: 0.2420: : 965iter [00:27, 39.80iter/s]
Best score: 0.2420: : 966iter [00:27, 39.80iter/s]
Best score: 0.2420: : 967iter [00:27, 39.80iter/s]
Best score: 0.2420: : 968iter [00:27, 41.14iter/s]
Best score: 0.2420: : 968iter [00:27, 41.14iter/s]
Best score: 0.2420: : 969iter [00:27, 41.14iter/s]
Best score: 0.2420: : 970iter [00:27, 41.14iter/s]
Best score: 0.2420: : 971iter [00:27, 41.14iter/s]
Best score: 0.2420: : 972iter [00:27, 41.14iter/s]
Best score: 0.2420: : 973iter [00:27, 41.93iter/s]
Best score: 0.2420: : 973iter [00:27, 41.93iter/s]
Best score: 0.2420: : 974iter [00:27, 41.93iter/s]
Best score: 0.2420: : 975iter [00:27, 41.93iter/s]
Best score: 0.2420: : 976iter [00:27, 41.93iter/s]
Best score: 0.2420: : 977iter [00:27, 41.93iter/s]
Best score: 0.2420: : 978iter [00:27, 42.63iter/s]
Best score: 0.2420: : 978iter [00:27, 42.63iter/s]
Best score: 0.2420: : 979iter [00:27, 42.63iter/s]
Best score: 0.2420: : 980iter [00:27, 42.63iter/s]
Best score: 0.2420: : 981iter [00:27, 42.63iter/s]
Best score: 0.2420: : 982iter [00:27, 42.63iter/s]
Best score: 0.2420: : 983iter [00:27, 42.89iter/s]
Best score: 0.2420: : 983iter [00:27, 42.89iter/s]
Best score: 0.2420: : 984iter [00:27, 42.89iter/s]
Best score: 0.2420: : 985iter [00:27, 42.89iter/s]
Best score: 0.2420: : 986iter [00:27, 42.89iter/s]
Best score: 0.2420: : 987iter [00:27, 42.89iter/s]
Best score: 0.2420: : 988iter [00:27, 43.36iter/s]
Best score: 0.2420: : 988iter [00:27, 43.36iter/s]
Best score: 0.2420: : 989iter [00:27, 43.36iter/s]
Best score: 0.2420: : 990iter [00:27, 43.36iter/s]
Best score: 0.2420: : 991iter [00:27, 43.36iter/s]
Best score: 0.2420: : 992iter [00:27, 43.36iter/s]
Best score: 0.2420: : 993iter [00:27, 43.40iter/s]
Best score: 0.2420: : 993iter [00:27, 43.40iter/s]
Best score: 0.2420: : 994iter [00:27, 43.40iter/s]
Best score: 0.2420: : 995iter [00:27, 43.40iter/s]
Best score: 0.2420: : 996iter [00:27, 43.40iter/s]
Best score: 0.2420: : 997iter [00:27, 43.40iter/s]
Best score: 0.2420: : 998iter [00:27, 43.56iter/s]
Best score: 0.2420: : 998iter [00:28, 43.56iter/s]
[ITER 1000] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 999iter [00:28, 43.56iter/s]
Best score: 0.2420: : 1000iter [00:28, 43.56iter/s]
Best score: 0.2420: : 1001iter [00:28, 43.56iter/s]
Best score: 0.2420: : 1002iter [00:28, 43.56iter/s]
Best score: 0.2420: : 1003iter [00:28, 29.70iter/s]
Best score: 0.2420: : 1003iter [00:28, 29.70iter/s]
Best score: 0.2420: : 1004iter [00:28, 29.70iter/s]
Best score: 0.2420: : 1005iter [00:28, 29.70iter/s]
Best score: 0.2420: : 1006iter [00:28, 29.70iter/s]
Best score: 0.2420: : 1007iter [00:28, 29.70iter/s]
Best score: 0.2420: : 1008iter [00:28, 32.96iter/s]
Best score: 0.2420: : 1008iter [00:28, 32.96iter/s]
Best score: 0.2420: : 1009iter [00:28, 32.96iter/s]
Best score: 0.2420: : 1010iter [00:28, 32.96iter/s]
Best score: 0.2420: : 1011iter [00:28, 32.96iter/s]
Best score: 0.2420: : 1012iter [00:28, 32.96iter/s]
Best score: 0.2420: : 1013iter [00:28, 35.51iter/s]
Best score: 0.2420: : 1013iter [00:28, 35.51iter/s]
Best score: 0.2420: : 1014iter [00:28, 35.51iter/s]
Best score: 0.2420: : 1015iter [00:28, 35.51iter/s]
Best score: 0.2420: : 1016iter [00:28, 35.51iter/s]
Best score: 0.2420: : 1017iter [00:28, 35.51iter/s]
Best score: 0.2420: : 1018iter [00:28, 37.79iter/s]
Best score: 0.2420: : 1018iter [00:28, 37.79iter/s]
Best score: 0.2420: : 1019iter [00:28, 37.79iter/s]
Best score: 0.2420: : 1020iter [00:28, 37.79iter/s]
Best score: 0.2420: : 1021iter [00:28, 37.79iter/s]
Best score: 0.2420: : 1022iter [00:28, 37.79iter/s]
Best score: 0.2420: : 1023iter [00:28, 39.44iter/s]
Best score: 0.2420: : 1023iter [00:28, 39.44iter/s]
Best score: 0.2420: : 1024iter [00:28, 39.44iter/s]
Best score: 0.2420: : 1025iter [00:28, 39.44iter/s]
Best score: 0.2420: : 1026iter [00:28, 39.44iter/s]
Best score: 0.2420: : 1027iter [00:28, 39.44iter/s]
Best score: 0.2420: : 1028iter [00:28, 40.84iter/s]
Best score: 0.2420: : 1028iter [00:28, 40.84iter/s]
Best score: 0.2420: : 1029iter [00:28, 40.84iter/s]
Best score: 0.2420: : 1030iter [00:28, 40.84iter/s]
Best score: 0.2420: : 1031iter [00:28, 40.84iter/s]
Best score: 0.2420: : 1032iter [00:28, 40.84iter/s]
Best score: 0.2420: : 1033iter [00:28, 41.17iter/s]
Best score: 0.2420: : 1033iter [00:28, 41.17iter/s]
Best score: 0.2420: : 1034iter [00:28, 41.17iter/s]
Best score: 0.2420: : 1035iter [00:28, 41.17iter/s]
Best score: 0.2420: : 1036iter [00:29, 41.17iter/s]
Best score: 0.2420: : 1037iter [00:29, 41.17iter/s]
Best score: 0.2420: : 1038iter [00:29, 42.03iter/s]
Best score: 0.2420: : 1038iter [00:29, 42.03iter/s]
Best score: 0.2420: : 1039iter [00:29, 42.03iter/s]
Best score: 0.2420: : 1040iter [00:29, 42.03iter/s]
Best score: 0.2420: : 1041iter [00:29, 42.03iter/s]
Best score: 0.2420: : 1042iter [00:29, 42.03iter/s]
Best score: 0.2420: : 1043iter [00:29, 42.50iter/s]
Best score: 0.2420: : 1043iter [00:29, 42.50iter/s]
Best score: 0.2420: : 1044iter [00:29, 42.50iter/s]
Best score: 0.2420: : 1045iter [00:29, 42.50iter/s]
Best score: 0.2420: : 1046iter [00:29, 42.50iter/s]
Best score: 0.2420: : 1047iter [00:29, 42.50iter/s]
Best score: 0.2420: : 1048iter [00:29, 42.98iter/s]
Best score: 0.2420: : 1048iter [00:29, 42.98iter/s]
[ITER 1050] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 1049iter [00:29, 42.98iter/s]
Best score: 0.2420: : 1050iter [00:29, 42.98iter/s]
Best score: 0.2420: : 1051iter [00:29, 42.98iter/s]
Best score: 0.2420: : 1052iter [00:29, 42.98iter/s]
Best score: 0.2420: : 1053iter [00:29, 36.00iter/s]
Best score: 0.2420: : 1053iter [00:29, 36.00iter/s]
Best score: 0.2420: : 1054iter [00:29, 36.00iter/s]
Best score: 0.2420: : 1055iter [00:29, 36.00iter/s]
Best score: 0.2420: : 1056iter [00:29, 36.00iter/s]
Best score: 0.2420: : 1057iter [00:29, 36.00iter/s]
Best score: 0.2420: : 1058iter [00:29, 38.24iter/s]
Best score: 0.2420: : 1058iter [00:29, 38.24iter/s]
Best score: 0.2420: : 1059iter [00:29, 38.24iter/s]
Best score: 0.2420: : 1060iter [00:29, 38.24iter/s]
Best score: 0.2420: : 1061iter [00:29, 38.24iter/s]
Best score: 0.2420: : 1062iter [00:29, 38.24iter/s]
Best score: 0.2420: : 1063iter [00:29, 39.77iter/s]
Best score: 0.2420: : 1063iter [00:29, 39.77iter/s]
Best score: 0.2420: : 1064iter [00:29, 39.77iter/s]
Best score: 0.2420: : 1065iter [00:29, 39.77iter/s]
Best score: 0.2420: : 1066iter [00:29, 39.77iter/s]
Best score: 0.2420: : 1067iter [00:29, 39.77iter/s]
Best score: 0.2420: : 1068iter [00:29, 41.20iter/s]
Best score: 0.2420: : 1068iter [00:29, 41.20iter/s]
Best score: 0.2420: : 1069iter [00:29, 41.20iter/s]
Best score: 0.2420: : 1070iter [00:29, 41.20iter/s]
Best score: 0.2420: : 1071iter [00:29, 41.20iter/s]
Best score: 0.2420: : 1072iter [00:29, 41.20iter/s]
Best score: 0.2420: : 1073iter [00:29, 41.76iter/s]
Best score: 0.2420: : 1073iter [00:29, 41.76iter/s]
Best score: 0.2420: : 1074iter [00:29, 41.76iter/s]
Best score: 0.2420: : 1075iter [00:29, 41.76iter/s]
Best score: 0.2420: : 1076iter [00:29, 41.76iter/s]
Best score: 0.2420: : 1077iter [00:30, 41.76iter/s]
Best score: 0.2420: : 1078iter [00:30, 42.30iter/s]
Best score: 0.2420: : 1078iter [00:30, 42.30iter/s]
Best score: 0.2420: : 1079iter [00:30, 42.30iter/s]
Best score: 0.2420: : 1080iter [00:30, 42.30iter/s]
Best score: 0.2420: : 1081iter [00:30, 42.30iter/s]
Best score: 0.2420: : 1082iter [00:30, 42.30iter/s]
Best score: 0.2420: : 1083iter [00:30, 42.61iter/s]
Best score: 0.2420: : 1083iter [00:30, 42.61iter/s]
Best score: 0.2420: : 1084iter [00:30, 42.61iter/s]
Best score: 0.2420: : 1085iter [00:30, 42.61iter/s]
Best score: 0.2420: : 1086iter [00:30, 42.61iter/s]
Best score: 0.2420: : 1087iter [00:30, 42.61iter/s]
Best score: 0.2420: : 1088iter [00:30, 43.17iter/s]
Best score: 0.2420: : 1088iter [00:30, 43.17iter/s]
Best score: 0.2420: : 1089iter [00:30, 43.17iter/s]
Best score: 0.2420: : 1090iter [00:30, 43.17iter/s]
Best score: 0.2420: : 1091iter [00:30, 43.17iter/s]
Best score: 0.2420: : 1092iter [00:30, 43.17iter/s]
Best score: 0.2420: : 1093iter [00:30, 43.37iter/s]
Best score: 0.2420: : 1093iter [00:30, 43.37iter/s]
Best score: 0.2420: : 1094iter [00:30, 43.37iter/s]
Best score: 0.2420: : 1095iter [00:30, 43.37iter/s]
Best score: 0.2420: : 1096iter [00:30, 43.37iter/s]
Best score: 0.2420: : 1097iter [00:30, 43.37iter/s]
Best score: 0.2420: : 1098iter [00:30, 43.76iter/s]
Best score: 0.2420: : 1098iter [00:30, 43.76iter/s]
[ITER 1100] Best: 0.2420
[PARAMS] Current best parameters:
  fx: 500.9521
  fy: 500.4854
  px: 984.7708
  py: 748.9903
  k1: 0.08438017
  k2: -0.06147167
  k3: 0.02656503
  k4: -0.00531769
  p1: 0.00754443
  p2: -0.00599900
  p3: 0.00452833
  p4: -0.00021508
  s1: 0.0014
  s2: 0.0006
  s3: -0.0096
  s4: -0.0003
  tilt_x: -0.0166
  tilt_y: -0.0197
  scale: 2.9318
  rotation: -0.1418

Best score: 0.2420: : 1099iter [00:30, 43.76iter/s]
Best score: 0.2420: : 1100iter [00:30, 35.96iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.241993 -> 0.223731

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 23,229 → 23,229 within 3500mm ring
  Ring polish improved score: 0.223731 -> 0.223638
  Success: True
  Tangential distortion: p1=0.007333, p2=-0.005796, p3=0.003870, p4=-0.001127
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2236
[✅] Parameters copied to: /working/MDC013_W027_2026-07-16/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 500.9521225454255
    fy: 500.48537595644217
    px: 984.7708460724223
    py: 748.9903044894064
    k1: 0.08413514826374648
    k2: -0.06158058180421987
    k3: 0.02686428420850918
    k4: -0.005397950460623636
    p1: 0.007333150348040579
    p2: -0.005795775842067407
    p3: 0.0038700837899165188
    p4: -0.0011274958214638336
    scale: 2.932104507462349
    rotation: -0.14178684511814876
    mmpp: 1.2735883285082106

[🔄] Converting 1207 frames to PTD, creating debug images for 200 calibration frames...

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   🔄 500:  76%|███████▌  | 450/592 [00:02<00:00, 171.52frame/s]

   🔄 500:  79%|███████▉  | 468/592 [00:02<00:00, 171.55frame/s]

   🔄 500:  82%|████████▏ | 486/592 [00:02<00:00, 170.79frame/s]

   🔄 500:  85%|████████▌ | 504/592 [00:02<00:00, 171.04frame/s]

   🔄 500:  88%|████████▊ | 522/592 [00:03<00:00, 171.53frame/s]

   🔄 500:  91%|█████████ | 540/592 [00:03<00:00, 171.56frame/s]

   🔄 500:  94%|█████████▍| 558/592 [00:03<00:00, 171.56frame/s]

   🔄 500:  97%|█████████▋| 576/592 [00:03<00:00, 171.49frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:12<00:00,  6.24s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:12<00:00,  6.27s/rig]
[✅] Saved PTDs: /working/MDC013_W027_2026-07-16/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, 170.79frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 170.36frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 169.06frame/s]

   🔄 1000:  71%|███████   | 71/100 [00:00<00:00, 163.05frame/s]

   🔄 1000:  89%|████████▉ | 89/100 [00:00<00:00, 166.13frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.06rig/s]

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

   🔄 500:  17%|█▋        | 17/100 [00:00<00:00, 168.88frame/s]

   🔄 500:  34%|███▍      | 34/100 [00:00<00:00, 168.22frame/s]

   🔄 500:  52%|█████▏    | 52/100 [00:00<00:00, 170.17frame/s]

   🔄 500:  70%|███████   | 70/100 [00:00<00:00, 170.41frame/s]

   🔄 500:  88%|████████▊ | 88/100 [00:00<00:00, 170.91frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.06rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.06rig/s]
[✅] Saved PTDs: /working/MDC013_W027_2026-07-16/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%|▎         | 82156/3080448 [00:00<00:03, 821550.99px/s]
📤 Writing LUT:   6%|▌         | 169901/3080448 [00:00<00:03, 854429.50px/s]
📤 Writing LUT:   8%|▊         | 257719/3080448 [00:00<00:03, 865273.86px/s]
📤 Writing LUT:  11%|█         | 345680/3080448 [00:00<00:03, 870931.46px/s]
📤 Writing LUT:  14%|█▍        | 433655/3080448 [00:00<00:03, 874108.02px/s]
📤 Writing LUT:  17%|█▋        | 524301/3080448 [00:00<00:02, 885105.80px/s]
📤 Writing LUT:  20%|█▉        | 615470/3080448 [00:00<00:02, 893794.25px/s]
📤 Writing LUT:  23%|██▎       | 707325/3080448 [00:00<00:02, 901668.69px/s]
📤 Writing LUT:  26%|██▌       | 797492/3080448 [00:00<00:02, 891480.13px/s]
📤 Writing LUT:  29%|██▉       | 888483/3080448 [00:01<00:02, 897122.81px/s]
📤 Writing LUT:  32%|███▏      | 978217/3080448 [00:01<00:02, 895233.02px/s]
📤 Writing LUT:  35%|███▍      | 1070721/3080448 [00:01<00:02, 904252.00px/s]
📤 Writing LUT:  38%|███▊      | 1161162/3080448 [00:01<00:02, 903312.60px/s]
📤 Writing LUT:  41%|████      | 1251504/3080448 [00:01<00:02, 898931.49px/s]
📤 Writing LUT:  44%|████▎     | 1341539/3080448 [00:01<00:01, 899354.94px/s]
📤 Writing LUT:  47%|████▋     | 1433022/3080448 [00:01<00:01, 903993.11px/s]
📤 Writing LUT:  50%|████▉     | 1525335/3080448 [00:01<00:01, 909730.26px/s]
📤 Writing LUT:  52%|█████▏    | 1616315/3080448 [00:01<00:01, 904954.88px/s]
📤 Writing LUT:  55%|█████▌    | 1707113/3080448 [00:01<00:01, 905857.16px/s]
📤 Writing LUT:  58%|█████▊    | 1797706/3080448 [00:02<00:01, 904779.51px/s]
📤 Writing LUT:  61%|██████▏   | 1888189/3080448 [00:02<00:01, 899945.97px/s]
📤 Writing LUT:  64%|██████▍   | 1978983/3080448 [00:02<00:01, 902328.19px/s]
📤 Writing LUT:  67%|██████▋   | 2069223/3080448 [00:02<00:01, 895389.47px/s]
📤 Writing LUT:  70%|███████   | 2160471/3080448 [00:02<00:01, 900474.16px/s]
📤 Writing LUT:  73%|███████▎  | 2251349/3080448 [00:02<00:00, 902948.61px/s]
📤 Writing LUT:  76%|███████▌  | 2342300/3080448 [00:02<00:00, 904902.24px/s]
📤 Writing LUT:  79%|███████▉  | 2433366/3080448 [00:02<00:00, 906623.63px/s]
📤 Writing LUT:  82%|████████▏ | 2524036/3080448 [00:02<00:00, 906133.17px/s]
📤 Writing LUT:  85%|████████▍ | 2616485/3080448 [00:02<00:00, 911628.68px/s]
📤 Writing LUT:  88%|████████▊ | 2707653/3080448 [00:03<00:00, 910896.00px/s]
📤 Writing LUT:  91%|█████████ | 2799258/3080448 [00:03<00:00, 912438.91px/s]
📤 Writing LUT:  94%|█████████▍| 2891590/3080448 [00:03<00:00, 915699.52px/s]
📤 Writing LUT:  97%|█████████▋| 2983384/3080448 [00:03<00:00, 916370.79px/s]
📤 Writing LUT: 100%|█████████▉| 3075371/3080448 [00:03<00:00, 917418.33px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 900981.58px/s]
[✅] LUT written to: /working/MDC013_W027_2026-07-16/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC013_W027_2026-07-16' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC013_W027_2026-07-16/calibration/MDC013_W027_2026-07-16_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%|          | 0/615 [00:00<?, ?frame/s]

   🔥 1000:   1%|▏         | 8/615 [00:00<00:07, 77.94frame/s]

   🔥 1000:   3%|▎         | 17/615 [00:00<00:07, 82.62frame/s]

   🔥 1000:   4%|▍         | 26/615 [00:00<00:06, 85.03frame/s]

   🔥 1000:   6%|▌         | 35/615 [00:00<00:06, 86.41frame/s]

   🔥 1000:   7%|▋         | 45/615 [00:00<00:06, 88.07frame/s]

   🔥 1000:   9%|▉         | 54/615 [00:00<00:06, 88.11frame/s]

   🔥 1000:  10%|█         | 63/615 [00:00<00:06, 87.28frame/s]

   🔥 1000:  12%|█▏        | 72/615 [00:00<00:06, 85.85frame/s]

   🔥 1000:  13%|█▎        | 81/615 [00:00<00:06, 85.15frame/s]

   🔥 1000:  15%|█▍        | 90/615 [00:01<00:06, 84.70frame/s]

   🔥 1000:  16%|█▌        | 99/615 [00:01<00:06, 84.40frame/s]

   🔥 1000:  18%|█▊        | 108/615 [00:01<00:06, 83.41frame/s]

   🔥 1000:  19%|█▉        | 117/615 [00:01<00:06, 82.26frame/s]

   🔥 1000:  20%|██        | 126/615 [00:01<00:05, 81.97frame/s]

   🔥 1000:  22%|██▏       | 135/615 [00:01<00:05, 81.91frame/s]

   🔥 1000:  23%|██▎       | 144/615 [00:01<00:05, 81.73frame/s]

   🔥 1000:  25%|██▍       | 153/615 [00:01<00:05, 81.76frame/s]

   🔥 1000:  26%|██▋       | 162/615 [00:01<00:05, 82.28frame/s]

   🔥 1000:  28%|██▊       | 171/615 [00:02<00:05, 82.04frame/s]

   🔥 1000:  29%|██▉       | 180/615 [00:02<00:05, 82.39frame/s]

   🔥 1000:  31%|███       | 189/615 [00:02<00:05, 83.06frame/s]

   🔥 1000:  32%|███▏      | 198/615 [00:02<00:04, 84.43frame/s]

   🔥 1000:  34%|███▎      | 207/615 [00:02<00:04, 84.92frame/s]

   🔥 1000:  35%|███▌      | 216/615 [00:02<00:04, 86.05frame/s]

   🔥 1000:  37%|███▋      | 225/615 [00:02<00:04, 86.88frame/s]

   🔥 1000:  38%|███▊      | 235/615 [00:02<00:04, 88.09frame/s]

   🔥 1000:  40%|███▉      | 245/615 [00:02<00:04, 89.25frame/s]

   🔥 1000:  41%|████▏     | 254/615 [00:02<00:04, 86.74frame/s]

   🔥 1000:  43%|████▎     | 263/615 [00:03<00:04, 85.46frame/s]

   🔥 1000:  44%|████▍     | 272/615 [00:03<00:03, 86.05frame/s]

   🔥 1000:  46%|████▌     | 281/615 [00:03<00:03, 86.90frame/s]

   🔥 1000:  47%|████▋     | 291/615 [00:03<00:03, 88.13frame/s]

   🔥 1000:  49%|████▉     | 301/615 [00:03<00:03, 89.18frame/s]

   🔥 1000:  51%|█████     | 311/615 [00:03<00:03, 89.66frame/s]

   🔥 1000:  52%|█████▏    | 321/615 [00:03<00:03, 90.08frame/s]

   🔥 1000:  54%|█████▍    | 331/615 [00:03<00:03, 90.12frame/s]

   🔥 1000:  55%|█████▌    | 341/615 [00:03<00:03, 88.24frame/s]

   🔥 1000:  57%|█████▋    | 350/615 [00:04<00:03, 86.86frame/s]

   🔥 1000:  58%|█████▊    | 359/615 [00:04<00:02, 85.44frame/s]

   🔥 1000:  60%|█████▉    | 368/615 [00:04<00:02, 84.23frame/s]

   🔥 1000:  61%|██████▏   | 377/615 [00:04<00:02, 82.39frame/s]

   🔥 1000:  63%|██████▎   | 386/615 [00:04<00:02, 81.90frame/s]

   🔥 1000:  64%|██████▍   | 395/615 [00:04<00:02, 81.02frame/s]

   🔥 1000:  66%|██████▌   | 404/615 [00:04<00:02, 81.16frame/s]

   🔥 1000:  67%|██████▋   | 413/615 [00:04<00:02, 81.54frame/s]

   🔥 1000:  69%|██████▊   | 422/615 [00:04<00:02, 81.49frame/s]

   🔥 1000:  70%|███████   | 431/615 [00:05<00:02, 82.43frame/s]

   🔥 1000:  72%|███████▏  | 440/615 [00:05<00:02, 83.33frame/s]

   🔥 1000:  73%|███████▎  | 449/615 [00:05<00:01, 84.38frame/s]

   🔥 1000:  74%|███████▍  | 458/615 [00:05<00:01, 85.90frame/s]

   🔥 1000:  76%|███████▌  | 467/615 [00:05<00:01, 86.57frame/s]

   🔥 1000:  78%|███████▊  | 477/615 [00:05<00:01, 87.70frame/s]

   🔥 1000:  79%|███████▉  | 486/615 [00:05<00:01, 88.09frame/s]

   🔥 1000:  81%|████████  | 496/615 [00:05<00:01, 89.33frame/s]

   🔥 1000:  82%|████████▏ | 505/615 [00:05<00:01, 89.42frame/s]

   🔥 1000:  84%|████████▎ | 515/615 [00:06<00:01, 89.62frame/s]

   🔥 1000:  85%|████████▌ | 524/615 [00:06<00:01, 88.85frame/s]

   🔥 1000:  87%|████████▋ | 533/615 [00:06<00:00, 88.61frame/s]

   🔥 1000:  88%|████████▊ | 542/615 [00:06<00:00, 87.59frame/s]

   🔥 1000:  90%|████████▉ | 552/615 [00:06<00:00, 88.78frame/s]

   🔥 1000:  91%|█████████▏| 562/615 [00:06<00:00, 89.99frame/s]

   🔥 1000:  93%|█████████▎| 572/615 [00:06<00:00, 90.97frame/s]

   🔥 1000:  95%|█████████▍| 582/615 [00:06<00:00, 91.71frame/s]

   🔥 1000:  96%|█████████▋| 592/615 [00:06<00:00, 92.27frame/s]

   🔥 1000:  98%|█████████▊| 602/615 [00:06<00:00, 91.71frame/s]

   🔥 1000: 100%|█████████▉| 612/615 [00:07<00:00, 91.10frame/s]

                                                                [✅] 1000: /working/MDC013_W027_2026-07-16/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC013_W027_2026-07-16/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:10<00:10, 10.23s/rig]

   🔥 500:   0%|          | 0/592 [00:00<?, ?frame/s]

   🔥 500:   1%|▏         | 8/592 [00:00<00:07, 75.64frame/s]

   🔥 500:   3%|▎         | 16/592 [00:00<00:07, 76.72frame/s]

   🔥 500:   4%|▍         | 24/592 [00:00<00:07, 77.82frame/s]

   🔥 500:   5%|▌         | 32/592 [00:00<00:07, 77.80frame/s]

   🔥 500:   7%|▋         | 40/592 [00:00<00:07, 76.89frame/s]

   🔥 500:   8%|▊         | 48/592 [00:00<00:07, 77.57frame/s]

   🔥 500:   9%|▉         | 56/592 [00:00<00:06, 77.65frame/s]

   🔥 500:  11%|█         | 64/592 [00:00<00:06, 77.90frame/s]

   🔥 500:  12%|█▏        | 72/592 [00:00<00:06, 77.72frame/s]

   🔥 500:  14%|█▎        | 80/592 [00:01<00:06, 78.27frame/s]

   🔥 500:  15%|█▍        | 88/592 [00:01<00:06, 78.05frame/s]

   🔥 500:  16%|█▌        | 96/592 [00:01<00:06, 78.28frame/s]

   🔥 500:  18%|█▊        | 104/592 [00:01<00:06, 77.99frame/s]

   🔥 500:  19%|█▉        | 113/592 [00:01<00:06, 78.95frame/s]

   🔥 500:  20%|██        | 121/592 [00:01<00:06, 78.44frame/s]

   🔥 500:  22%|██▏       | 130/592 [00:01<00:05, 79.00frame/s]

   🔥 500:  23%|██▎       | 138/592 [00:01<00:05, 78.12frame/s]

   🔥 500:  25%|██▍       | 146/592 [00:01<00:05, 77.57frame/s]

   🔥 500:  26%|██▌       | 154/592 [00:01<00:05, 77.03frame/s]

   🔥 500:  27%|██▋       | 162/592 [00:02<00:05, 76.60frame/s]

   🔥 500:  29%|██▉       | 171/592 [00:02<00:05, 77.94frame/s]

   🔥 500:  30%|███       | 179/592 [00:02<00:05, 78.41frame/s]

   🔥 500:  32%|███▏      | 187/592 [00:02<00:05, 78.16frame/s]

   🔥 500:  33%|███▎      | 196/592 [00:02<00:05, 79.01frame/s]

   🔥 500:  34%|███▍      | 204/592 [00:02<00:04, 78.59frame/s]

   🔥 500:  36%|███▌      | 212/592 [00:02<00:04, 78.25frame/s]

   🔥 500:  37%|███▋      | 221/592 [00:02<00:04, 79.45frame/s]

   🔥 500:  39%|███▉      | 230/592 [00:02<00:04, 80.88frame/s]

   🔥 500:  40%|████      | 239/592 [00:03<00:04, 81.44frame/s]

   🔥 500:  42%|████▏     | 248/592 [00:03<00:04, 82.24frame/s]

   🔥 500:  43%|████▎     | 257/592 [00:03<00:04, 82.43frame/s]

   🔥 500:  45%|████▍     | 266/592 [00:03<00:03, 82.10frame/s]

   🔥 500:  46%|████▋     | 275/592 [00:03<00:03, 82.19frame/s]

   🔥 500:  48%|████▊     | 284/592 [00:03<00:03, 82.09frame/s]

   🔥 500:  49%|████▉     | 293/592 [00:03<00:03, 82.05frame/s]

   🔥 500:  51%|█████     | 302/592 [00:03<00:03, 81.53frame/s]

   🔥 500:  53%|█████▎    | 311/592 [00:03<00:03, 81.14frame/s]

   🔥 500:  54%|█████▍    | 320/592 [00:04<00:03, 81.07frame/s]

   🔥 500:  56%|█████▌    | 329/592 [00:04<00:03, 79.87frame/s]

   🔥 500:  57%|█████▋    | 337/592 [00:04<00:03, 78.33frame/s]

   🔥 500:  58%|█████▊    | 345/592 [00:04<00:03, 77.28frame/s]

   🔥 500:  60%|█████▉    | 353/592 [00:04<00:03, 77.02frame/s]

   🔥 500:  61%|██████    | 361/592 [00:04<00:03, 76.18frame/s]

   🔥 500:  62%|██████▏   | 369/592 [00:04<00:02, 76.47frame/s]

   🔥 500:  64%|██████▎   | 377/592 [00:04<00:02, 76.55frame/s]

   🔥 500:  65%|██████▌   | 385/592 [00:04<00:02, 76.15frame/s]

   🔥 500:  66%|██████▋   | 393/592 [00:04<00:02, 76.34frame/s]

   🔥 500:  68%|██████▊   | 401/592 [00:05<00:02, 76.74frame/s]

   🔥 500:  69%|██████▉   | 409/592 [00:05<00:02, 76.87frame/s]

   🔥 500:  70%|███████   | 417/592 [00:05<00:02, 77.65frame/s]

   🔥 500:  72%|███████▏  | 425/592 [00:05<00:02, 78.17frame/s]

   🔥 500:  73%|███████▎  | 433/592 [00:05<00:02, 78.54frame/s]

   🔥 500:  74%|███████▍  | 441/592 [00:05<00:01, 78.67frame/s]

   🔥 500:  76%|███████▌  | 450/592 [00:05<00:01, 79.59frame/s]

   🔥 500:  78%|███████▊  | 459/592 [00:05<00:01, 80.25frame/s]

   🔥 500:  79%|███████▉  | 468/592 [00:05<00:01, 80.72frame/s]

   🔥 500:  81%|████████  | 477/592 [00:06<00:01, 81.05frame/s]

   🔥 500:  82%|████████▏ | 486/592 [00:06<00:01, 81.22frame/s]

   🔥 500:  84%|████████▎ | 495/592 [00:06<00:01, 80.81frame/s]

   🔥 500:  85%|████████▌ | 504/592 [00:06<00:01, 79.67frame/s]

   🔥 500:  86%|████████▋ | 512/592 [00:06<00:01, 78.98frame/s]

   🔥 500:  88%|████████▊ | 520/592 [00:06<00:00, 78.18frame/s]

   🔥 500:  89%|████████▉ | 528/592 [00:06<00:00, 77.75frame/s]

   🔥 500:  91%|█████████ | 536/592 [00:06<00:00, 77.61frame/s]

   🔥 500:  92%|█████████▏| 544/592 [00:06<00:00, 78.15frame/s]

   🔥 500:  93%|█████████▎| 552/592 [00:07<00:00, 78.45frame/s]

   🔥 500:  95%|█████████▍| 560/592 [00:07<00:00, 78.16frame/s]

   🔥 500:  96%|█████████▌| 568/592 [00:07<00:00, 77.64frame/s]

   🔥 500:  97%|█████████▋| 576/592 [00:07<00:00, 77.84frame/s]

   🔥 500:  99%|█████████▉| 585/592 [00:07<00:00, 78.54frame/s]

                                                               [✅] 500: /working/MDC013_W027_2026-07-16/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC013_W027_2026-07-16/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:18<00:00,  9.32s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:18<00:00,  9.45s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC013_W027_2026-07-16/calibration/debug/5_heatmap
[🏁] Finished processing MDC013_W027_2026-07-16


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