2026-07-15T23:35:34 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC015_W068_2026-07-15 ===
2026-07-15T23:35:34 - INFO: Camera ID: 14302215
2026-07-15T23:35:34 - INFO: Wand ID: 
2026-07-15T23:35:34 - INFO: Recorded At: 2026-07-15T12:44:15Z

-------------------------------------------------------------------
                        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 647 stationary frames, 572 remain


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→ 1000 Phase 2: corners:  94%|█████████▍| 538/572 [00:03<00:00, 154.23it/s]

                                                                           [PASS2] Frame 0000328: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000329: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000330: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000331: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000332: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000334: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000335: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000336: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000342: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000344: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000345: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000587: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000589: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000590: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000606: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000607: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000608: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000609: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000611: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000613: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000614: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000618: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000619: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000620: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000621: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000975: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000976: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000977: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000978: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000981: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000982: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000983: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000984: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000985: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000986: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000987: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000988: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000996: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000997: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000998: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000999: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001000: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001001: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001002: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001003: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001004: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001081: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001082: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001083: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001084: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001085: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001086: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001087: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001088: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001091: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001092: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001093: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001094: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001095: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001096: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001097: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001099: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001106: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001107: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001108: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001109: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001207: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001208: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001209: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001210: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001226: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001227: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001228: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001229: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001246: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001247: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001248: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001250: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001251: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001252: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001253: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001254: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001256: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001257: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001258: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001259: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001260: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000330: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000332: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000335: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000344: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000589: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000608: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000613: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000620: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000983: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000998: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001001: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001003: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001083: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001086: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001087: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001093: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001097: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001108: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001209: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001228: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001249: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001253: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001258: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001259: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 51 frames fixed, 48 untrusted, 572 total


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→ 1000 Phase 3: finalize:  38%|███▊      | 216/572 [00:04<00:07, 48.44it/s]

→ 1000 Phase 3: finalize:  39%|███▊      | 221/572 [00:04<00:07, 48.32it/s]

→ 1000 Phase 3: finalize:  40%|███▉      | 226/572 [00:04<00:07, 48.26it/s]

→ 1000 Phase 3: finalize:  40%|████      | 231/572 [00:04<00:07, 48.20it/s]

→ 1000 Phase 3: finalize:  41%|████▏     | 236/572 [00:04<00:06, 48.19it/s]

→ 1000 Phase 3: finalize:  42%|████▏     | 241/572 [00:04<00:06, 48.14it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 246/572 [00:04<00:06, 48.06it/s]

→ 1000 Phase 3: finalize:  44%|████▍     | 251/572 [00:04<00:06, 48.01it/s]

→ 1000 Phase 3: finalize:  45%|████▍     | 256/572 [00:05<00:06, 47.95it/s]

→ 1000 Phase 3: finalize:  46%|████▌     | 261/572 [00:05<00:06, 47.91it/s]

→ 1000 Phase 3: finalize:  47%|████▋     | 266/572 [00:05<00:06, 47.93it/s]

→ 1000 Phase 3: finalize:  47%|████▋     | 271/572 [00:05<00:06, 47.92it/s]

→ 1000 Phase 3: finalize:  48%|████▊     | 276/572 [00:05<00:06, 47.99it/s]

→ 1000 Phase 3: finalize:  49%|████▉     | 281/572 [00:05<00:06, 48.02it/s]

→ 1000 Phase 3: finalize:  50%|█████     | 286/572 [00:05<00:05, 48.01it/s]

→ 1000 Phase 3: finalize:  51%|█████     | 291/572 [00:05<00:05, 47.99it/s]

→ 1000 Phase 3: finalize:  52%|█████▏    | 296/572 [00:05<00:05, 47.69it/s]

→ 1000 Phase 3: finalize:  53%|█████▎    | 301/572 [00:05<00:05, 47.67it/s]

→ 1000 Phase 3: finalize:  53%|█████▎    | 306/572 [00:06<00:05, 47.70it/s]

→ 1000 Phase 3: finalize:  54%|█████▍    | 311/572 [00:06<00:05, 47.66it/s]

→ 1000 Phase 3: finalize:  55%|█████▌    | 316/572 [00:06<00:05, 47.64it/s]

→ 1000 Phase 3: finalize:  56%|█████▌    | 321/572 [00:06<00:05, 47.64it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 326/572 [00:06<00:05, 47.62it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 331/572 [00:06<00:05, 47.53it/s]

→ 1000 Phase 3: finalize:  59%|█████▊    | 336/572 [00:06<00:04, 47.56it/s]

→ 1000 Phase 3: finalize:  60%|█████▉    | 341/572 [00:06<00:04, 47.62it/s]

→ 1000 Phase 3: finalize:  60%|██████    | 346/572 [00:06<00:04, 47.59it/s]

→ 1000 Phase 3: finalize:  61%|██████▏   | 351/572 [00:07<00:04, 47.42it/s]

→ 1000 Phase 3: finalize:  62%|██████▏   | 356/572 [00:07<00:04, 47.65it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 361/572 [00:07<00:04, 47.84it/s]

→ 1000 Phase 3: finalize:  64%|██████▍   | 366/572 [00:07<00:04, 48.08it/s]

→ 1000 Phase 3: finalize:  65%|██████▍   | 371/572 [00:07<00:04, 48.23it/s]

→ 1000 Phase 3: finalize:  66%|██████▌   | 376/572 [00:07<00:04, 48.40it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 381/572 [00:07<00:03, 48.59it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 386/572 [00:07<00:03, 48.76it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 391/572 [00:07<00:03, 48.97it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 396/572 [00:07<00:03, 49.24it/s]

→ 1000 Phase 3: finalize:  70%|███████   | 402/572 [00:08<00:03, 49.56it/s]

→ 1000 Phase 3: finalize:  71%|███████▏  | 408/572 [00:08<00:03, 49.89it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 414/572 [00:08<00:03, 50.22it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 420/572 [00:08<00:03, 50.50it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 426/572 [00:08<00:02, 50.76it/s]

→ 1000 Phase 3: finalize:  76%|███████▌  | 432/572 [00:08<00:02, 51.04it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 438/572 [00:08<00:02, 51.03it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 444/572 [00:08<00:02, 51.13it/s]

→ 1000 Phase 3: finalize:  79%|███████▊  | 450/572 [00:09<00:02, 51.39it/s]

→ 1000 Phase 3: finalize:  80%|███████▉  | 456/572 [00:09<00:02, 51.45it/s]

→ 1000 Phase 3: finalize:  81%|████████  | 462/572 [00:09<00:02, 51.52it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 468/572 [00:09<00:02, 51.66it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 474/572 [00:09<00:01, 51.79it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 480/572 [00:09<00:01, 51.87it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 486/572 [00:09<00:01, 51.99it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 492/572 [00:09<00:01, 51.98it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 498/572 [00:09<00:01, 51.99it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 504/572 [00:10<00:01, 52.04it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 510/572 [00:10<00:01, 52.16it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 516/572 [00:10<00:01, 52.16it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 522/572 [00:10<00:00, 52.24it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 528/572 [00:10<00:00, 52.16it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 534/572 [00:10<00:00, 52.02it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 540/572 [00:10<00:00, 52.50it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 546/572 [00:10<00:00, 52.74it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 552/572 [00:10<00:00, 52.74it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 558/572 [00:11<00:00, 52.66it/s]

→ 1000 Phase 3: finalize:  99%|█████████▊| 564/572 [00:11<00:00, 52.67it/s]

→ 1000 Phase 3: finalize: 100%|█████████▉| 570/572 [00:11<00:00, 52.54it/s]

                                                                           
[TIMING] 1000 — 572 frames
  Phase 1  radial detect   (parallel)  :   8.22s  (14.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.07s  (7.1 ms/frame)
  Phase 2b corner fix      (sequential):   2.10s
  Phase 3  finalize        (sequential):  11.34s  (19.8 ms/frame)
    decode + undistort                 :   5.84s  (10.2 ms/frame)
    corner apply + refinement          :   2.68s  (4.7 ms/frame)
    segment                            :   2.31s  (4.0 ms/frame)
    smooth                             :   0.32s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  25.73s


Processing Rigs:  50%|█████     | 1/2 [00:29<00:29, 30.00s/it][INFO] Filtered 588 stationary frames, 523 remain


→ 500 (523 images) Phase 1:   0%|          | 0/523 [00:00<?, ?it/s]

→ 500 (523 images) Phase 1:   0%|          | 1/523 [00:00<05:52,  1.48it/s]

→ 500 (523 images) Phase 1:   1%|          | 6/523 [00:00<00:51,  9.96it/s]

→ 500 (523 images) Phase 1:   6%|▌         | 32/523 [00:00<00:08, 59.83it/s]

→ 500 (523 images) Phase 1:   9%|▉         | 46/523 [00:01<00:09, 48.28it/s]

→ 500 (523 images) Phase 1:  12%|█▏        | 65/523 [00:01<00:08, 53.73it/s]

→ 500 (523 images) Phase 1:  14%|█▍        | 74/523 [00:01<00:07, 58.03it/s]

→ 500 (523 images) Phase 1:  19%|█▊        | 97/523 [00:01<00:06, 63.01it/s]

→ 500 (523 images) Phase 1:  20%|██        | 105/523 [00:02<00:06, 64.05it/s]

→ 500 (523 images) Phase 1:  25%|██▍       | 129/523 [00:02<00:05, 67.77it/s]

→ 500 (523 images) Phase 1:  26%|██▌       | 137/523 [00:02<00:05, 67.46it/s]

→ 500 (523 images) Phase 1:  31%|███       | 161/523 [00:02<00:05, 70.33it/s]

→ 500 (523 images) Phase 1:  32%|███▏      | 169/523 [00:03<00:05, 68.61it/s]

→ 500 (523 images) Phase 1:  37%|███▋      | 193/523 [00:03<00:04, 71.41it/s]

→ 500 (523 images) Phase 1:  38%|███▊      | 201/523 [00:03<00:04, 71.11it/s]

→ 500 (523 images) Phase 1:  43%|████▎     | 223/523 [00:03<00:03, 95.03it/s]

→ 500 (523 images) Phase 1:  45%|████▍     | 234/523 [00:03<00:04, 67.96it/s]

→ 500 (523 images) Phase 1:  49%|████▉     | 255/523 [00:03<00:02, 90.85it/s]

→ 500 (523 images) Phase 1:  51%|█████     | 268/523 [00:04<00:03, 65.99it/s]

→ 500 (523 images) Phase 1:  55%|█████▌    | 289/523 [00:04<00:03, 61.08it/s]

→ 500 (523 images) Phase 1:  58%|█████▊    | 301/523 [00:04<00:03, 68.77it/s]

→ 500 (523 images) Phase 1:  61%|██████    | 318/523 [00:04<00:02, 84.14it/s]

→ 500 (523 images) Phase 1:  63%|██████▎   | 330/523 [00:05<00:03, 60.73it/s]

→ 500 (523 images) Phase 1:  67%|██████▋   | 353/523 [00:05<00:02, 65.17it/s]

→ 500 (523 images) Phase 1:  69%|██████▉   | 362/523 [00:05<00:02, 66.22it/s]

→ 500 (523 images) Phase 1:  74%|███████▎  | 385/523 [00:06<00:02, 68.68it/s]

→ 500 (523 images) Phase 1:  75%|███████▌  | 393/523 [00:06<00:01, 67.61it/s]

→ 500 (523 images) Phase 1:  80%|███████▉  | 417/523 [00:06<00:01, 71.53it/s]

→ 500 (523 images) Phase 1:  81%|████████▏ | 425/523 [00:06<00:01, 70.17it/s]

→ 500 (523 images) Phase 1:  86%|████████▌ | 449/523 [00:06<00:01, 72.83it/s]

→ 500 (523 images) Phase 1:  87%|████████▋ | 457/523 [00:07<00:00, 71.63it/s]

→ 500 (523 images) Phase 1:  92%|█████████▏| 480/523 [00:07<00:00, 97.90it/s]

→ 500 (523 images) Phase 1:  94%|█████████▍| 492/523 [00:07<00:00, 67.80it/s]

→ 500 (523 images) Phase 1:  98%|█████████▊| 513/523 [00:07<00:00, 82.24it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/523 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/523 [00:00<03:13,  2.70it/s]

→ 500 Phase 2: corners:   3%|▎         | 15/523 [00:00<00:12, 40.61it/s]

→ 500 Phase 2: corners:   5%|▌         | 27/523 [00:00<00:07, 62.96it/s]

→ 500 Phase 2: corners:   8%|▊         | 42/523 [00:00<00:05, 86.24it/s]

→ 500 Phase 2: corners:  10%|█         | 54/523 [00:00<00:05, 88.20it/s]

→ 500 Phase 2: corners:  13%|█▎        | 70/523 [00:00<00:04, 104.34it/s]

→ 500 Phase 2: corners:  16%|█▋        | 86/523 [00:01<00:03, 119.13it/s]

→ 500 Phase 2: corners:  19%|█▉        | 100/523 [00:01<00:03, 123.71it/s]

→ 500 Phase 2: corners:  22%|██▏       | 114/523 [00:01<00:03, 123.18it/s]

→ 500 Phase 2: corners:  25%|██▍       | 129/523 [00:01<00:03, 128.80it/s]

→ 500 Phase 2: corners:  27%|██▋       | 143/523 [00:01<00:03, 120.90it/s]

→ 500 Phase 2: corners:  30%|███       | 158/523 [00:01<00:02, 127.74it/s]

→ 500 Phase 2: corners:  33%|███▎      | 172/523 [00:01<00:02, 128.87it/s]

→ 500 Phase 2: corners:  36%|███▌      | 186/523 [00:01<00:02, 128.98it/s]

→ 500 Phase 2: corners:  38%|███▊      | 200/523 [00:01<00:02, 127.42it/s]

→ 500 Phase 2: corners:  41%|████      | 214/523 [00:02<00:02, 130.69it/s]

→ 500 Phase 2: corners:  44%|████▍     | 232/523 [00:02<00:02, 143.99it/s]

→ 500 Phase 2: corners:  47%|████▋     | 248/523 [00:02<00:01, 147.77it/s]

→ 500 Phase 2: corners:  51%|█████     | 265/523 [00:02<00:01, 153.90it/s]

→ 500 Phase 2: corners:  54%|█████▎    | 281/523 [00:02<00:01, 150.34it/s]

→ 500 Phase 2: corners:  57%|█████▋    | 297/523 [00:02<00:01, 117.59it/s]

→ 500 Phase 2: corners:  60%|█████▉    | 313/523 [00:02<00:01, 126.85it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 328/523 [00:02<00:01, 131.32it/s]

→ 500 Phase 2: corners:  66%|██████▌   | 345/523 [00:02<00:01, 139.66it/s]

→ 500 Phase 2: corners:  69%|██████▉   | 360/523 [00:03<00:01, 142.34it/s]

→ 500 Phase 2: corners:  72%|███████▏  | 375/523 [00:03<00:01, 143.82it/s]

→ 500 Phase 2: corners:  75%|███████▍  | 390/523 [00:03<00:00, 144.51it/s]

→ 500 Phase 2: corners:  78%|███████▊  | 406/523 [00:03<00:00, 146.07it/s]

→ 500 Phase 2: corners:  80%|████████  | 421/523 [00:03<00:00, 139.10it/s]

→ 500 Phase 2: corners:  84%|████████▎ | 438/523 [00:03<00:00, 146.92it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 454/523 [00:03<00:00, 150.49it/s]

→ 500 Phase 2: corners:  90%|█████████ | 472/523 [00:03<00:00, 157.59it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 488/523 [00:03<00:00, 156.93it/s]

                                                                          [PASS2] Summary: 0 frames fixed, 0 untrusted, 523 total


→ 500 Phase 3: finalize:   0%|          | 0/523 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   1%|          | 6/523 [00:00<00:08, 57.50it/s]

→ 500 Phase 3: finalize:   2%|▏         | 12/523 [00:00<00:08, 58.54it/s]

→ 500 Phase 3: finalize:   3%|▎         | 18/523 [00:00<00:08, 59.04it/s]

→ 500 Phase 3: finalize:   5%|▍         | 25/523 [00:00<00:08, 59.55it/s]

→ 500 Phase 3: finalize:   6%|▌         | 32/523 [00:00<00:08, 59.93it/s]

→ 500 Phase 3: finalize:   7%|▋         | 39/523 [00:00<00:08, 60.25it/s]

→ 500 Phase 3: finalize:   9%|▉         | 46/523 [00:00<00:07, 60.60it/s]

→ 500 Phase 3: finalize:  10%|█         | 53/523 [00:00<00:07, 60.61it/s]

→ 500 Phase 3: finalize:  11%|█▏        | 60/523 [00:00<00:07, 60.73it/s]

→ 500 Phase 3: finalize:  13%|█▎        | 67/523 [00:01<00:07, 58.13it/s]

→ 500 Phase 3: finalize:  14%|█▍        | 73/523 [00:01<00:07, 56.84it/s]

→ 500 Phase 3: finalize:  15%|█▌        | 79/523 [00:01<00:07, 55.89it/s]

→ 500 Phase 3: finalize:  16%|█▋        | 85/523 [00:01<00:07, 55.06it/s]

→ 500 Phase 3: finalize:  17%|█▋        | 91/523 [00:01<00:07, 54.68it/s]

→ 500 Phase 3: finalize:  19%|█▊        | 97/523 [00:01<00:07, 54.38it/s]

→ 500 Phase 3: finalize:  20%|█▉        | 103/523 [00:01<00:07, 54.06it/s]

→ 500 Phase 3: finalize:  21%|██        | 109/523 [00:01<00:07, 53.94it/s]

→ 500 Phase 3: finalize:  22%|██▏       | 115/523 [00:02<00:07, 53.85it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 121/523 [00:02<00:07, 53.74it/s]

→ 500 Phase 3: finalize:  24%|██▍       | 127/523 [00:02<00:07, 53.55it/s]

→ 500 Phase 3: finalize:  25%|██▌       | 133/523 [00:02<00:07, 53.58it/s]

→ 500 Phase 3: finalize:  27%|██▋       | 139/523 [00:02<00:07, 53.45it/s]

→ 500 Phase 3: finalize:  28%|██▊       | 145/523 [00:02<00:07, 53.45it/s]

→ 500 Phase 3: finalize:  29%|██▉       | 151/523 [00:02<00:06, 53.33it/s]

→ 500 Phase 3: finalize:  30%|███       | 157/523 [00:02<00:06, 53.25it/s]

→ 500 Phase 3: finalize:  31%|███       | 163/523 [00:02<00:06, 53.00it/s]

→ 500 Phase 3: finalize:  32%|███▏      | 169/523 [00:03<00:06, 52.82it/s]

→ 500 Phase 3: finalize:  33%|███▎      | 175/523 [00:03<00:06, 52.75it/s]

→ 500 Phase 3: finalize:  35%|███▍      | 181/523 [00:03<00:06, 52.92it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 187/523 [00:03<00:06, 53.15it/s]

→ 500 Phase 3: finalize:  37%|███▋      | 193/523 [00:03<00:06, 52.74it/s]

→ 500 Phase 3: finalize:  38%|███▊      | 199/523 [00:03<00:06, 52.25it/s]

→ 500 Phase 3: finalize:  39%|███▉      | 205/523 [00:03<00:06, 52.18it/s]

→ 500 Phase 3: finalize:  40%|████      | 211/523 [00:03<00:06, 52.00it/s]

→ 500 Phase 3: finalize:  41%|████▏     | 217/523 [00:03<00:05, 51.95it/s]

→ 500 Phase 3: finalize:  43%|████▎     | 223/523 [00:04<00:05, 51.87it/s]

→ 500 Phase 3: finalize:  44%|████▍     | 229/523 [00:04<00:05, 51.89it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 235/523 [00:04<00:05, 51.94it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 241/523 [00:04<00:05, 52.03it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 247/523 [00:04<00:05, 52.02it/s]

→ 500 Phase 3: finalize:  48%|████▊     | 253/523 [00:04<00:05, 51.86it/s]

→ 500 Phase 3: finalize:  50%|████▉     | 259/523 [00:04<00:05, 51.71it/s]

→ 500 Phase 3: finalize:  51%|█████     | 265/523 [00:04<00:04, 51.67it/s]

→ 500 Phase 3: finalize:  52%|█████▏    | 271/523 [00:05<00:04, 51.67it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 277/523 [00:05<00:04, 51.58it/s]

→ 500 Phase 3: finalize:  54%|█████▍    | 283/523 [00:05<00:04, 51.61it/s]

→ 500 Phase 3: finalize:  55%|█████▌    | 289/523 [00:05<00:04, 51.67it/s]

→ 500 Phase 3: finalize:  56%|█████▋    | 295/523 [00:05<00:04, 51.97it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 301/523 [00:05<00:04, 52.18it/s]

→ 500 Phase 3: finalize:  59%|█████▊    | 307/523 [00:05<00:04, 52.33it/s]

→ 500 Phase 3: finalize:  60%|█████▉    | 313/523 [00:05<00:04, 52.35it/s]

→ 500 Phase 3: finalize:  61%|██████    | 319/523 [00:05<00:03, 52.26it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 325/523 [00:06<00:03, 52.31it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 331/523 [00:06<00:03, 52.29it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 337/523 [00:06<00:03, 52.46it/s]

→ 500 Phase 3: finalize:  66%|██████▌   | 343/523 [00:06<00:03, 52.60it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 349/523 [00:06<00:03, 52.79it/s]

→ 500 Phase 3: finalize:  68%|██████▊   | 355/523 [00:06<00:03, 52.86it/s]

→ 500 Phase 3: finalize:  69%|██████▉   | 361/523 [00:06<00:03, 53.03it/s]

→ 500 Phase 3: finalize:  70%|███████   | 367/523 [00:06<00:02, 53.13it/s]

→ 500 Phase 3: finalize:  71%|███████▏  | 373/523 [00:06<00:02, 53.31it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 379/523 [00:07<00:02, 53.35it/s]

→ 500 Phase 3: finalize:  74%|███████▎  | 385/523 [00:07<00:02, 53.56it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 391/523 [00:07<00:02, 52.84it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 397/523 [00:07<00:02, 52.59it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 403/523 [00:07<00:02, 52.73it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 409/523 [00:07<00:02, 52.98it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 415/523 [00:07<00:02, 53.22it/s]

→ 500 Phase 3: finalize:  80%|████████  | 421/523 [00:07<00:01, 53.33it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 427/523 [00:07<00:01, 53.57it/s]

→ 500 Phase 3: finalize:  83%|████████▎ | 433/523 [00:08<00:01, 53.66it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 439/523 [00:08<00:01, 53.76it/s]

→ 500 Phase 3: finalize:  85%|████████▌ | 445/523 [00:08<00:01, 53.88it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 451/523 [00:08<00:01, 53.10it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 457/523 [00:08<00:01, 53.05it/s]

→ 500 Phase 3: finalize:  89%|████████▊ | 463/523 [00:08<00:01, 53.32it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 469/523 [00:08<00:01, 53.52it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 475/523 [00:08<00:00, 53.62it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 481/523 [00:08<00:00, 53.67it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 487/523 [00:09<00:00, 53.80it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 493/523 [00:09<00:00, 53.75it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 499/523 [00:09<00:00, 53.80it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 505/523 [00:09<00:00, 53.77it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 511/523 [00:09<00:00, 53.62it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 517/523 [00:09<00:00, 53.60it/s]

→ 500 Phase 3: finalize: 100%|██████████| 523/523 [00:09<00:00, 53.54it/s]

                                                                          
[TIMING] 500 — 523 frames
  Phase 1  radial detect   (parallel)  :   7.90s  (15.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.03s  (7.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.11s
  Phase 3  finalize        (sequential):   9.75s  (18.6 ms/frame)
    decode + undistort                 :   5.18s  (9.9 ms/frame)
    corner apply + refinement          :   2.02s  (3.9 ms/frame)
    segment                            :   2.09s  (4.0 ms/frame)
    smooth                             :   0.29s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  21.78s


Processing Rigs: 100%|██████████| 2/2 [00:55<00:00, 27.22s/it]
Processing Rigs: 100%|██████████| 2/2 [00:55<00:00, 27.64s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W068_2026-07-15/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1095 frames from 2 rigs to /working/MDC015_W068_2026-07-15/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W068_2026-07-15/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 572
[📋] 500: 100 frames selected from 523
[💾] Saved RigFrameDataset to /working/MDC015_W068_2026-07-15/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W068_2026-07-15/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<03:05,  4.76s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:04<00:48,  1.32s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:32,  1.09it/s]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:23,  1.52it/s]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:05<00:17,  1.98it/s]
Computing max image from bright frames:  20%|██        | 8/40 [00:05<00:09,  3.29it/s]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:05<00:04,  5.81it/s]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:05,  4.68it/s]
Computing max image from bright frames:  40%|████      | 16/40 [00:06<00:03,  7.04it/s]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:06<00:03,  6.27it/s]
Computing max image from bright frames:  50%|█████     | 20/40 [00:07<00:02,  7.41it/s]
Computing max image from bright frames:  55%|█████▌    | 22/40 [00:07<00:02,  8.01it/s]
Computing max image from bright frames:  60%|██████    | 24/40 [00:07<00:01,  8.31it/s]
Computing max image from bright frames:  68%|██████▊   | 27/40 [00:07<00:01, 11.10it/s]
Computing max image from bright frames:  72%|███████▎  | 29/40 [00:07<00:01, 10.67it/s]
Computing max image from bright frames:  78%|███████▊  | 31/40 [00:08<00:00,  9.66it/s]
Computing max image from bright frames:  82%|████████▎ | 33/40 [00:08<00:01,  5.75it/s]
Computing max image from bright frames:  88%|████████▊ | 35/40 [00:09<00:00,  5.73it/s]
Computing max image from bright frames:  92%|█████████▎| 37/40 [00:09<00:00,  6.88it/s]
Computing max image from bright frames:  95%|█████████▌| 38/40 [00:09<00:00,  7.04it/s]
Computing max image from bright frames:  98%|█████████▊| 39/40 [00:09<00:00,  7.40it/s]
Computing max image from bright frames: 100%|██████████| 40/40 [00:09<00:00,  7.02it/s]
                                                                                       
[✅] Casing detection complete: cx=998.0, cy=743.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:10,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:10,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:10,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:10,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:10,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:09,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:09,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:09,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:09,  9.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 55.24it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  24%|██▍       | 24/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 476.768} (0.000):  25%|██▌       | 25/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  26%|██▌       | 26/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 490.909} (0.001):  27%|██▋       | 27/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.061 | Best: {'fx': 526.263} (0.061):  28%|██▊       | 28/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.004 | Best: {'fx': 526.263} (0.061):  29%|██▉       | 29/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.007 | Best: {'fx': 526.263} (0.061):  30%|███       | 30/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 526.263} (0.061):  31%|███       | 31/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.032 | Best: {'fx': 526.263} (0.061):  32%|███▏      | 32/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.085 | Best: {'fx': 533.333} (0.085):  33%|███▎      | 33/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  34%|███▍      | 34/100 [00:00<00:00, 79.42it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.085):  35%|███▌      | 35/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.033 | Best: {'fx': 533.333} (0.085):  35%|███▌      | 35/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.049 | Best: {'fx': 533.333} (0.085):  36%|███▌      | 36/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.085):  37%|███▋      | 37/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.085):  38%|███▊      | 38/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.085):  39%|███▉      | 39/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.085):  40%|████      | 40/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.085):  41%|████      | 41/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.085):  42%|████▏     | 42/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.085):  43%|████▎     | 43/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.085):  44%|████▍     | 44/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.085):  45%|████▌     | 45/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.085):  46%|████▌     | 46/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.085):  47%|████▋     | 47/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.085):  48%|████▊     | 48/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.085):  49%|████▉     | 49/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.085):  50%|█████     | 50/100 [00:00<00:00, 97.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.085):  51%|█████     | 51/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.085):  51%|█████     | 51/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  52%|█████▏    | 52/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  53%|█████▎    | 53/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  54%|█████▍    | 54/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  55%|█████▌    | 55/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  56%|█████▌    | 56/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  57%|█████▋    | 57/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  58%|█████▊    | 58/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  59%|█████▉    | 59/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  60%|██████    | 60/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.085):  61%|██████    | 61/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  62%|██████▏   | 62/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  63%|██████▎   | 63/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  64%|██████▍   | 64/100 [00:00<00:00, 114.37it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  65%|██████▌   | 65/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  65%|██████▌   | 65/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  66%|██████▌   | 66/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  67%|██████▋   | 67/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  68%|██████▊   | 68/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  69%|██████▉   | 69/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  70%|███████   | 70/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  71%|███████   | 71/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  72%|███████▏  | 72/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  73%|███████▎  | 73/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  74%|███████▍  | 74/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  75%|███████▌  | 75/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  76%|███████▌  | 76/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  77%|███████▋  | 77/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  78%|███████▊  | 78/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  79%|███████▉  | 79/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  80%|████████  | 80/100 [00:00<00:00, 118.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  81%|████████  | 81/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  81%|████████  | 81/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  82%|████████▏ | 82/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  83%|████████▎ | 83/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  84%|████████▍ | 84/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  85%|████████▌ | 85/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  86%|████████▌ | 86/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  87%|████████▋ | 87/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  88%|████████▊ | 88/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  89%|████████▉ | 89/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  90%|█████████ | 90/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.008 | Best: {'fx': 533.333} (0.085):  91%|█████████ | 91/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  92%|█████████▏| 92/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  93%|█████████▎| 93/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  94%|█████████▍| 94/100 [00:00<00:00, 130.68it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  95%|█████████▌| 95/100 [00:00<00:00, 130.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  95%|█████████▌| 95/100 [00:00<00:00, 130.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  96%|█████████▌| 96/100 [00:00<00:00, 130.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  97%|█████████▋| 97/100 [00:00<00:00, 130.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  98%|█████████▊| 98/100 [00:00<00:00, 130.30it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.085):  99%|█████████▉| 99/100 [00:00<00:00, 130.30it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.015 | Best: {'fx': 509.354} (0.015):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.015 | Best: {'fx': 509.354} (0.015):   2%|▏         | 1/50 [00:00<00:06,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.011 | Best: {'fx': 509.354} (0.015):   2%|▏         | 1/50 [00:00<00:06,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.020 | Best: {'fx': 511.395} (0.020):   4%|▍         | 2/50 [00:00<00:06,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.017 | Best: {'fx': 511.395} (0.020):   6%|▌         | 3/50 [00:00<00:06,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.021 | Best: {'fx': 512.415} (0.021):   8%|▊         | 4/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.024 | Best: {'fx': 514.456} (0.024):  10%|█         | 5/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.023 | Best: {'fx': 514.456} (0.024):  12%|█▏        | 6/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.025 | Best: {'fx': 515.476} (0.025):  14%|█▍        | 7/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.027 | Best: {'fx': 516.497} (0.027):  16%|█▌        | 8/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.029 | Best: {'fx': 517.517} (0.029):  18%|█▊        | 9/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.046 | Best: {'fx': 523.639} (0.046):  20%|██        | 10/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.031 | Best: {'fx': 523.639} (0.046):  22%|██▏       | 11/50 [00:00<00:05,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.033 | Best: {'fx': 523.639} (0.046):  24%|██▍       | 12/50 [00:00<00:04,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.036 | Best: {'fx': 523.639} (0.046):  26%|██▌       | 13/50 [00:00<00:04,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.051 | Best: {'fx': 524.66} (0.051):  28%|██▊       | 14/50 [00:00<00:04,  7.75it/s] 
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.039 | Best: {'fx': 524.66} (0.051):  30%|███       | 15/50 [00:00<00:04,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.041 | Best: {'fx': 524.66} (0.051):  32%|███▏      | 16/50 [00:00<00:04,  7.75it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.041 | Best: {'fx': 524.66} (0.051):  34%|███▍      | 17/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.074 | Best: {'fx': 527.721} (0.074):  34%|███▍      | 17/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.056 | Best: {'fx': 527.721} (0.074):  36%|███▌      | 18/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.066 | Best: {'fx': 527.721} (0.074):  38%|███▊      | 19/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.088 | Best: {'fx': 530.782} (0.088):  40%|████      | 20/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.080 | Best: {'fx': 530.782} (0.088):  42%|████▏     | 21/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.084 | Best: {'fx': 530.782} (0.088):  44%|████▍     | 22/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.090 | Best: {'fx': 531.803} (0.090):  46%|████▌     | 23/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.091 | Best: {'fx': 532.823} (0.091):  48%|████▊     | 24/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.078 | Best: {'fx': 532.823} (0.091):  50%|█████     | 25/50 [00:00<00:00, 83.13it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.078 | Best: {'fx': 532.823} (0.091):  52%|█████▏    | 26/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.066 | Best: {'fx': 532.823} (0.091):  52%|█████▏    | 26/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.075 | Best: {'fx': 532.823} (0.091):  54%|█████▍    | 27/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.062 | Best: {'fx': 532.823} (0.091):  56%|█████▌    | 28/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.056 | Best: {'fx': 532.823} (0.091):  58%|█████▊    | 29/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.048 | Best: {'fx': 532.823} (0.091):  60%|██████    | 30/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.044 | Best: {'fx': 532.823} (0.091):  62%|██████▏   | 31/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.052 | Best: {'fx': 532.823} (0.091):  64%|██████▍   | 32/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.040 | Best: {'fx': 532.823} (0.091):  66%|██████▌   | 33/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.046 | Best: {'fx': 532.823} (0.091):  68%|██████▊   | 34/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.038 | Best: {'fx': 532.823} (0.091):  70%|███████   | 35/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.031 | Best: {'fx': 532.823} (0.091):  72%|███████▏  | 36/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.035 | Best: {'fx': 532.823} (0.091):  74%|███████▍  | 37/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.033 | Best: {'fx': 532.823} (0.091):  76%|███████▌  | 38/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.032 | Best: {'fx': 532.823} (0.091):  78%|███████▊  | 39/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.036 | Best: {'fx': 532.823} (0.091):  80%|████████  | 40/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.029 | Best: {'fx': 532.823} (0.091):  82%|████████▏ | 41/50 [00:00<00:00, 84.03it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.029 | Best: {'fx': 532.823} (0.091):  84%|████████▍ | 42/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.028 | Best: {'fx': 532.823} (0.091):  84%|████████▍ | 42/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.027 | Best: {'fx': 532.823} (0.091):  86%|████████▌ | 43/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.025 | Best: {'fx': 532.823} (0.091):  88%|████████▊ | 44/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.026 | Best: {'fx': 532.823} (0.091):  90%|█████████ | 45/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.024 | Best: {'fx': 532.823} (0.091):  92%|█████████▏| 46/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.026 | Best: {'fx': 532.823} (0.091):  94%|█████████▍| 47/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.024 | Best: {'fx': 532.823} (0.091):  96%|█████████▌| 48/50 [00:00<00:00, 106.11it/s]
Brute | Start: {'fx': 533.333} (0.085) | Current: 0.023 | Best: {'fx': 532.823} (0.091):  98%|█████████▊| 49/50 [00:00<00:00, 106.11it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 532.8231292517007
    px: 998.0
    py: 743.0
    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: 15.7837: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 15.7837, picklable_objective(x0) = 15.7834

Best score: 15.7834: : 0iter [00:01, ?iter/s] 
Best score: 15.7834: : 1iter [00:01,  1.95s/iter]
Best score: 15.7834: : 1iter [00:01,  1.95s/iter]
Best score: 15.7834: : 2iter [00:01,  1.95s/iter]
Best score: 15.7834: : 3iter [00:02,  1.95s/iter]
Best score: 15.7834: : 4iter [00:02,  1.95s/iter]
Best score: 15.7834: : 5iter [00:02,  3.21iter/s]
Best score: 15.7834: : 5iter [00:02,  3.21iter/s]
Best score: 15.7834: : 6iter [00:02,  3.21iter/s]
Best score: 15.7834: : 7iter [00:02,  3.21iter/s]
Best score: 15.7834: : 8iter [00:02,  3.21iter/s]
Best score: 15.7834: : 9iter [00:02,  6.47iter/s]
Best score: 15.7834: : 9iter [00:02,  6.47iter/s]
Best score: 15.7834: : 10iter [00:02,  6.47iter/s]
Best score: 15.7834: : 11iter [00:02,  6.47iter/s]
Best score: 15.7834: : 12iter [00:02,  6.47iter/s]
Best score: 15.7834: : 13iter [00:02, 10.21iter/s]
Best score: 15.7834: : 13iter [00:02, 10.21iter/s]
Best score: 15.7834: : 14iter [00:02, 10.21iter/s]
Best score: 15.7834: : 15iter [00:02, 10.21iter/s]
Best score: 15.7834: : 16iter [00:02, 10.21iter/s]
Best score: 15.7834: : 17iter [00:02, 10.21iter/s]
Best score: 15.7834: : 18iter [00:02, 15.18iter/s]
Best score: 15.7834: : 18iter [00:02, 15.18iter/s]
Best score: 15.7834: : 19iter [00:02, 15.18iter/s]
Best score: 15.7834: : 20iter [00:02, 15.18iter/s]
Best score: 15.7834: : 21iter [00:02, 15.18iter/s]
Best score: 15.7834: : 22iter [00:02, 19.11iter/s]
Best score: 15.7834: : 22iter [00:02, 19.11iter/s]
Best score: 15.7834: : 23iter [00:02, 19.11iter/s]
Best score: 15.7834: : 24iter [00:02, 19.11iter/s]
Best score: 15.7834: : 25iter [00:02, 19.11iter/s]
Best score: 15.7834: : 26iter [00:02, 19.11iter/s]
Best score: 15.7834: : 27iter [00:02, 23.75iter/s]
Best score: 15.7834: : 27iter [00:02, 23.75iter/s]
Best score: 15.7834: : 28iter [00:02, 23.75iter/s]
Best score: 15.7834: : 29iter [00:02, 23.75iter/s]
Best score: 15.7834: : 30iter [00:02, 23.75iter/s]
Best score: 15.7834: : 31iter [00:02, 26.92iter/s]
Best score: 15.7834: : 31iter [00:02, 26.92iter/s]
Best score: 15.7834: : 32iter [00:02, 26.92iter/s]
Best score: 15.7834: : 33iter [00:02, 26.92iter/s]
Best score: 15.7834: : 34iter [00:02, 26.92iter/s]
Best score: 15.7834: : 35iter [00:02, 29.76iter/s]
Best score: 15.7834: : 35iter [00:02, 29.76iter/s]
Best score: 15.7834: : 36iter [00:02, 29.76iter/s]
Best score: 15.7834: : 37iter [00:02, 29.76iter/s]
Best score: 15.7834: : 38iter [00:02, 29.76iter/s]
Best score: 15.7834: : 39iter [00:02, 32.16iter/s]
Best score: 15.7834: : 39iter [00:02, 32.16iter/s]
Best score: 15.7834: : 40iter [00:02, 32.16iter/s]
Best score: 15.7834: : 41iter [00:02, 32.16iter/s]
Best score: 15.7834: : 42iter [00:03, 32.16iter/s]
Best score: 15.7834: : 43iter [00:03, 33.88iter/s]
Best score: 15.7834: : 43iter [00:03, 33.88iter/s]
Best score: 15.7834: : 44iter [00:03, 33.88iter/s]
Best score: 15.7834: : 45iter [00:03, 33.88iter/s]
Best score: 15.7834: : 46iter [00:03, 33.88iter/s]
Best score: 15.7834: : 47iter [00:03, 35.45iter/s]
Best score: 15.7834: : 47iter [00:03, 35.45iter/s]
Best score: 15.7834: : 48iter [00:03, 35.45iter/s]
[ITER 50] Best: 15.7834
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 998.0000
  py: 743.0000
  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: 15.7834: : 49iter [00:03, 35.45iter/s]
Best score: 15.7834: : 50iter [00:03, 35.45iter/s]
Best score: 15.7834: : 51iter [00:03, 36.49iter/s]
Best score: 15.7834: : 51iter [00:03, 36.49iter/s]
Best score: 15.7834: : 52iter [00:03, 36.49iter/s]
Best score: 15.7834: : 53iter [00:03, 36.49iter/s]
Best score: 15.7834: : 54iter [00:03, 36.49iter/s]
Best score: 15.7834: : 55iter [00:03, 37.37iter/s]
Best score: 15.7834: : 55iter [00:03, 37.37iter/s]
Best score: 15.7834: : 56iter [00:03, 37.37iter/s]
Best score: 15.7834: : 57iter [00:03, 37.37iter/s]
Best score: 15.7834: : 58iter [00:03, 37.37iter/s]
Best score: 15.7834: : 59iter [00:03, 38.08iter/s]
Best score: 15.7834: : 59iter [00:03, 38.08iter/s]
Best score: 15.7834: : 60iter [00:03, 38.08iter/s]
Best score: 15.7834: : 61iter [00:03, 38.08iter/s]
Best score: 15.7834: : 62iter [00:03, 38.08iter/s]
Best score: 15.7834: : 63iter [00:03, 38.34iter/s]
Best score: 15.7834: : 63iter [00:03, 38.34iter/s]
Best score: 15.7834: : 64iter [00:03, 38.34iter/s]
Best score: 15.7834: : 65iter [00:03, 38.34iter/s]
Best score: 15.7834: : 66iter [00:03, 38.34iter/s]
Best score: 15.7834: : 67iter [00:03, 38.76iter/s]
Best score: 15.7834: : 67iter [00:03, 38.76iter/s]
Best score: 15.7834: : 68iter [00:03, 38.76iter/s]
Best score: 15.7834: : 69iter [00:03, 38.76iter/s]
Best score: 15.7834: : 70iter [00:03, 38.76iter/s]
Best score: 15.7834: : 71iter [00:03, 38.85iter/s]
Best score: 15.7834: : 71iter [00:03, 38.85iter/s]
Best score: 15.7834: : 72iter [00:03, 38.85iter/s]
Best score: 15.7834: : 73iter [00:03, 38.85iter/s]
Best score: 15.7834: : 74iter [00:03, 38.85iter/s]
Best score: 15.7834: : 75iter [00:03, 39.04iter/s]
Best score: 15.7834: : 75iter [00:03, 39.04iter/s]
Best score: 15.7834: : 76iter [00:03, 39.04iter/s]
Best score: 15.7834: : 77iter [00:03, 39.04iter/s]
Best score: 15.7834: : 78iter [00:03, 39.04iter/s]
Best score: 15.7834: : 79iter [00:03, 39.31iter/s]
Best score: 15.7834: : 79iter [00:03, 39.31iter/s]
Best score: 15.7834: : 80iter [00:03, 39.31iter/s]
Best score: 15.7834: : 81iter [00:04, 39.31iter/s]
Best score: 15.7834: : 82iter [00:04, 39.31iter/s]
Best score: 15.7834: : 83iter [00:04, 39.35iter/s]
Best score: 15.7834: : 83iter [00:04, 39.35iter/s]
Best score: 15.7834: : 84iter [00:04, 39.35iter/s]
Best score: 15.7834: : 85iter [00:04, 39.35iter/s]
Best score: 15.7834: : 86iter [00:04, 39.35iter/s]
Best score: 15.7834: : 87iter [00:04, 39.49iter/s]
Best score: 15.7834: : 87iter [00:04, 39.49iter/s]
Best score: 15.7834: : 88iter [00:04, 39.49iter/s]
Best score: 15.7834: : 89iter [00:04, 39.49iter/s]
Best score: 15.7834: : 90iter [00:04, 39.49iter/s]
Best score: 15.7834: : 91iter [00:04, 39.35iter/s]
Best score: 15.7834: : 91iter [00:04, 39.35iter/s]
Best score: 15.7834: : 92iter [00:04, 39.35iter/s]
Best score: 15.7834: : 93iter [00:04, 39.35iter/s]
Best score: 15.7834: : 94iter [00:04, 39.35iter/s]
Best score: 15.7834: : 95iter [00:04, 39.16iter/s]
Best score: 15.7834: : 95iter [00:04, 39.16iter/s]
Best score: 15.7834: : 96iter [00:04, 39.16iter/s]
Best score: 15.7834: : 97iter [00:04, 39.16iter/s]
Best score: 15.7834: : 98iter [00:04, 39.16iter/s]
Best score: 15.7834: : 99iter [00:04, 39.34iter/s]
[ITER 100] Best: 15.7834
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 998.0000
  py: 743.0000
  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: 15.7834: : 99iter [00:04, 39.34iter/s]
Best score: 15.7834: : 100iter [00:04, 39.34iter/s]
Best score: 15.7834: : 101iter [00:04, 39.34iter/s]
Best score: 15.7834: : 102iter [00:04, 39.34iter/s]
Best score: 15.7834: : 103iter [00:04, 36.72iter/s]
Best score: 15.7834: : 103iter [00:04, 36.72iter/s]
Best score: 15.7834: : 104iter [00:04, 36.72iter/s]
Best score: 15.7834: : 105iter [00:04, 36.72iter/s]
Best score: 15.7834: : 106iter [00:04, 36.72iter/s]
Best score: 15.7834: : 107iter [00:04, 37.61iter/s]
Best score: 15.7834: : 107iter [00:04, 37.61iter/s]
Best score: 15.7834: : 108iter [00:04, 37.61iter/s]
Best score: 15.7834: : 109iter [00:04, 37.61iter/s]
Best score: 15.7834: : 110iter [00:04, 37.61iter/s]
Best score: 15.7834: : 111iter [00:04, 37.99iter/s]
Best score: 15.7834: : 111iter [00:04, 37.99iter/s]
Best score: 15.7834: : 112iter [00:04, 37.99iter/s]
Best score: 15.7834: : 113iter [00:04, 37.99iter/s]
Best score: 15.7834: : 114iter [00:04, 37.99iter/s]
Best score: 15.7834: : 115iter [00:04, 38.47iter/s]
Best score: 15.7834: : 115iter [00:04, 38.47iter/s]
Best score: 15.7834: : 116iter [00:04, 38.47iter/s]
Best score: 15.7834: : 117iter [00:04, 38.47iter/s]
Best score: 15.7834: : 118iter [00:04, 38.47iter/s]
Best score: 15.7834: : 119iter [00:04, 38.77iter/s]
Best score: 15.7834: : 119iter [00:04, 38.77iter/s]
Best score: 15.7834: : 120iter [00:05, 38.77iter/s]
Best score: 15.7834: : 121iter [00:05, 38.77iter/s]
Best score: 15.7834: : 122iter [00:05, 38.77iter/s]
Best score: 15.7834: : 123iter [00:05, 38.00iter/s]
Best score: 15.7834: : 123iter [00:05, 38.00iter/s]
Best score: 15.7834: : 124iter [00:05, 38.00iter/s]
Best score: 15.7834: : 125iter [00:05, 38.00iter/s]
Best score: 15.7834: : 126iter [00:05, 38.00iter/s]
Best score: 15.7834: : 127iter [00:05, 38.35iter/s]
Best score: 15.7834: : 127iter [00:05, 38.35iter/s]
Best score: 15.7834: : 128iter [00:05, 38.35iter/s]
Best score: 15.7834: : 129iter [00:05, 38.35iter/s]
Best score: 15.7834: : 130iter [00:05, 38.35iter/s]
Best score: 15.7834: : 131iter [00:05, 38.60iter/s]
Best score: 15.7834: : 131iter [00:05, 38.60iter/s]
Best score: 15.7834: : 132iter [00:05, 38.60iter/s]
Best score: 15.7834: : 133iter [00:05, 38.60iter/s]
Best score: 15.7834: : 134iter [00:05, 38.60iter/s]
Best score: 15.7834: : 135iter [00:05, 38.97iter/s]
Best score: 15.7834: : 135iter [00:05, 38.97iter/s]
Best score: 15.7834: : 136iter [00:05, 38.97iter/s]
Best score: 15.7834: : 137iter [00:05, 38.97iter/s]
Best score: 15.7834: : 138iter [00:05, 38.97iter/s]
Best score: 15.7834: : 139iter [00:05, 39.16iter/s]
Best score: 15.7834: : 139iter [00:05, 39.16iter/s]
Best score: 15.7834: : 140iter [00:05, 39.16iter/s]
Best score: 15.7834: : 141iter [00:05, 39.16iter/s]
Best score: 15.7834: : 142iter [00:05, 39.16iter/s]
Best score: 15.7834: : 143iter [00:05, 39.07iter/s]
Best score: 15.7834: : 143iter [00:05, 39.07iter/s]
Best score: 15.7834: : 144iter [00:05, 39.07iter/s]
Best score: 15.7834: : 145iter [00:05, 39.07iter/s]
Best score: 15.7834: : 146iter [00:05, 39.07iter/s]
Best score: 15.7834: : 147iter [00:05, 39.32iter/s]
Best score: 15.7834: : 147iter [00:05, 39.32iter/s]
Best score: 15.7834: : 148iter [00:05, 39.32iter/s]
[ITER 150] Best: 15.7834
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 998.0000
  py: 743.0000
  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: 15.7834: : 149iter [00:05, 39.32iter/s]
Best score: 15.7834: : 150iter [00:05, 39.32iter/s]
Best score: 15.7834: : 151iter [00:05, 31.91iter/s]
Best score: 15.7834: : 151iter [00:05, 31.91iter/s]
Best score: 15.7834: : 152iter [00:05, 31.91iter/s]
Best score: 15.7834: : 153iter [00:05, 31.91iter/s]
Best score: 15.7834: : 154iter [00:05, 31.91iter/s]
Best score: 15.7834: : 155iter [00:05, 33.87iter/s]
Best score: 15.7834: : 155iter [00:05, 33.87iter/s]
Best score: 15.7834: : 156iter [00:06, 33.87iter/s]
Best score: 15.7834: : 157iter [00:06, 33.87iter/s]
Best score: 15.7834: : 158iter [00:06, 33.87iter/s]
Best score: 15.7834: : 159iter [00:06, 33.87iter/s]
Best score: 15.7834: : 160iter [00:06, 35.64iter/s]
Best score: 15.7834: : 160iter [00:06, 35.64iter/s]
Best score: 15.7834: : 161iter [00:06, 35.64iter/s]
Best score: 15.7834: : 162iter [00:06, 35.64iter/s]
Best score: 15.7834: : 163iter [00:06, 35.64iter/s]
Best score: 15.7834: : 164iter [00:06, 36.77iter/s]
Best score: 15.7834: : 164iter [00:06, 36.77iter/s]
Best score: 15.7834: : 165iter [00:06, 36.77iter/s]
Best score: 15.7834: : 166iter [00:06, 36.77iter/s]
Best score: 15.7834: : 167iter [00:06, 36.77iter/s]
Best score: 15.7834: : 168iter [00:06, 37.63iter/s]
Best score: 15.7834: : 168iter [00:06, 37.63iter/s]
Best score: 15.7834: : 169iter [00:06, 37.63iter/s]
Best score: 15.7834: : 170iter [00:06, 37.63iter/s]
Best score: 15.7834: : 171iter [00:06, 37.63iter/s]
Best score: 15.7834: : 172iter [00:06, 38.07iter/s]
Best score: 15.7834: : 172iter [00:06, 38.07iter/s]
Best score: 15.7834: : 173iter [00:06, 38.07iter/s]
Best score: 15.7834: : 174iter [00:06, 38.07iter/s]
Best score: 15.7834: : 175iter [00:06, 38.07iter/s]
Best score: 15.7834: : 176iter [00:06, 38.07iter/s]
Best score: 15.7834: : 177iter [00:06, 38.75iter/s]
Best score: 15.7834: : 177iter [00:06, 38.75iter/s]
Best score: 15.7834: : 178iter [00:06, 38.75iter/s]
Best score: 15.7834: : 179iter [00:06, 38.75iter/s]
Best score: 15.7834: : 180iter [00:06, 38.75iter/s]
Best score: 15.7834: : 181iter [00:06, 38.87iter/s]
Best score: 15.7834: : 181iter [00:06, 38.87iter/s]
Best score: 15.7834: : 182iter [00:06, 38.87iter/s]
Best score: 15.7834: : 183iter [00:06, 38.87iter/s]
Best score: 15.7834: : 184iter [00:06, 38.87iter/s]
Best score: 15.7834: : 185iter [00:06, 38.87iter/s]
Best score: 15.7834: : 186iter [00:06, 39.32iter/s]
Best score: 15.7834: : 186iter [00:06, 39.32iter/s]
Best score: 15.7834: : 187iter [00:06, 39.32iter/s]
Best score: 15.7834: : 188iter [00:06, 39.32iter/s]
Best score: 15.7834: : 189iter [00:06, 39.32iter/s]
Best score: 15.7834: : 190iter [00:06, 39.32iter/s]
Best score: 15.7834: : 191iter [00:06, 39.57iter/s]
Best score: 15.7834: : 191iter [00:06, 39.57iter/s]
Best score: 15.7834: : 192iter [00:06, 39.57iter/s]
Best score: 15.7834: : 193iter [00:06, 39.57iter/s]
Best score: 15.7834: : 194iter [00:06, 39.57iter/s]
Best score: 15.7834: : 195iter [00:06, 39.57iter/s]
Best score: 15.7834: : 196iter [00:06, 39.81iter/s]
Best score: 15.7834: : 196iter [00:07, 39.81iter/s]
Best score: 15.7834: : 197iter [00:07, 39.81iter/s]
Best score: 15.7834: : 198iter [00:07, 39.81iter/s]
[ITER 200] Best: 15.7834
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 998.0000
  py: 743.0000
  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: 15.7834: : 199iter [00:07, 39.81iter/s]
Best score: 15.7834: : 200iter [00:07, 39.79iter/s]
Best score: 15.7834: : 200iter [00:07, 39.79iter/s]
Best score: 15.7834: : 201iter [00:07, 39.79iter/s]
Best score: 15.7834: : 202iter [00:07, 39.79iter/s]
Best score: 15.7834: : 203iter [00:07, 39.79iter/s]
Best score: 15.7834: : 204iter [00:07, 33.00iter/s]
Best score: 15.7834: : 204iter [00:07, 33.00iter/s]
Best score: 15.7834: : 205iter [00:07, 33.00iter/s]
Best score: 15.7834: : 206iter [00:07, 33.00iter/s]
Best score: 15.7834: : 207iter [00:07, 33.00iter/s]
Best score: 15.7834: : 208iter [00:07, 33.00iter/s]
Best score: 15.7834: : 209iter [00:07, 35.23iter/s]
Best score: 15.7834: : 209iter [00:07, 35.23iter/s]
Best score: 15.7834: : 210iter [00:07, 35.23iter/s]
Best score: 15.7834: : 211iter [00:07, 35.23iter/s]
Best score: 15.7834: : 212iter [00:07, 35.23iter/s]
Best score: 15.7834: : 213iter [00:07, 36.34iter/s]
Best score: 15.7834: : 213iter [00:07, 36.34iter/s]
Best score: 15.7834: : 214iter [00:07, 36.34iter/s]
Best score: 15.7834: : 215iter [00:07, 36.34iter/s]
Best score: 15.7834: : 216iter [00:07, 36.34iter/s]
Best score: 15.7834: : 217iter [00:07, 36.34iter/s]
Best score: 15.7834: : 218iter [00:07, 37.69iter/s]
Best score: 15.7834: : 218iter [00:07, 37.69iter/s]
Best score: 14.7656: : 219iter [00:07, 37.69iter/s]
Best score: 14.7656: : 220iter [00:07, 37.69iter/s]
Best score: 14.7656: : 221iter [00:07, 37.69iter/s]
Best score: 14.7656: : 222iter [00:07, 37.69iter/s]
Best score: 14.7656: : 223iter [00:07, 38.45iter/s]
Best score: 14.7656: : 223iter [00:07, 38.45iter/s]
Best score: 14.7656: : 224iter [00:07, 38.45iter/s]
Best score: 14.7656: : 225iter [00:07, 38.45iter/s]
Best score: 14.7656: : 226iter [00:07, 38.45iter/s]
Best score: 14.7656: : 227iter [00:07, 38.45iter/s]
Best score: 14.7656: : 228iter [00:07, 39.22iter/s]
Best score: 14.7656: : 228iter [00:07, 39.22iter/s]
Best score: 14.7656: : 229iter [00:07, 39.22iter/s]
Best score: 14.7656: : 230iter [00:07, 39.22iter/s]
Best score: 14.7656: : 231iter [00:07, 39.22iter/s]
Best score: 14.7656: : 232iter [00:07, 39.27iter/s]
Best score: 14.7656: : 232iter [00:07, 39.27iter/s]
Best score: 14.7656: : 233iter [00:08, 39.27iter/s]
Best score: 14.7656: : 234iter [00:08, 39.27iter/s]
Best score: 14.7656: : 235iter [00:08, 39.27iter/s]
Best score: 14.7656: : 236iter [00:08, 39.27iter/s]
Best score: 14.7656: : 237iter [00:08, 39.68iter/s]
Best score: 14.7656: : 237iter [00:08, 39.68iter/s]
Best score: 14.7656: : 238iter [00:08, 39.68iter/s]
Best score: 10.0148: : 239iter [00:08, 39.68iter/s]
Best score: 10.0148: : 240iter [00:08, 39.68iter/s]
Best score: 10.0148: : 241iter [00:08, 39.68iter/s]
Best score: 10.0148: : 242iter [00:08, 39.91iter/s]
Best score: 10.0148: : 242iter [00:08, 39.91iter/s]
Best score: 10.0148: : 243iter [00:08, 39.91iter/s]
Best score: 10.0148: : 244iter [00:08, 39.91iter/s]
Best score: 10.0148: : 245iter [00:08, 39.91iter/s]
Best score: 10.0148: : 246iter [00:08, 39.91iter/s]
Best score: 10.0148: : 247iter [00:08, 40.04iter/s]
Best score: 10.0148: : 247iter [00:08, 40.04iter/s]
Best score: 10.0148: : 248iter [00:08, 40.04iter/s]
[ITER 250] Best: 9.2274
[PARAMS] Current best parameters:
  fx: 549.3591
  fy: 548.4840
  px: 996.0602
  py: 740.3252
  k1: -0.02725556
  k2: -0.02255788
  k3: 0.03461431
  k4: -0.01048333
  p1: -0.00496479
  p2: -0.00173601
  p3: -0.00412879
  p4: 0.00461621
  s1: -0.0062
  s2: 0.0022
  s3: 0.0045
  s4: 0.0026
  tilt_x: 0.0018
  tilt_y: 0.0051
  scale: 3.0756
  rotation: -0.3625

Best score: 9.2274: : 249iter [00:08, 40.04iter/s] 
Best score: 9.2274: : 250iter [00:08, 40.04iter/s]
Best score: 9.2274: : 251iter [00:08, 40.04iter/s]
Best score: 9.2274: : 252iter [00:08, 33.76iter/s]
Best score: 9.2274: : 252iter [00:08, 33.76iter/s]
Best score: 9.2274: : 253iter [00:08, 33.76iter/s]
Best score: 9.2274: : 254iter [00:08, 33.76iter/s]
Best score: 9.2274: : 255iter [00:08, 33.76iter/s]
Best score: 9.2274: : 256iter [00:08, 33.76iter/s]
Best score: 9.2274: : 257iter [00:08, 35.58iter/s]
Best score: 9.2274: : 257iter [00:08, 35.58iter/s]
Best score: 9.2274: : 258iter [00:08, 35.58iter/s]
Best score: 6.2969: : 259iter [00:08, 35.58iter/s]
Best score: 6.2969: : 260iter [00:08, 35.58iter/s]
Best score: 6.2969: : 261iter [00:08, 36.50iter/s]
Best score: 6.2969: : 261iter [00:08, 36.50iter/s]
Best score: 6.2969: : 262iter [00:08, 36.50iter/s]
Best score: 6.2969: : 263iter [00:08, 36.50iter/s]
Best score: 6.2969: : 264iter [00:08, 36.50iter/s]
Best score: 6.2969: : 265iter [00:08, 36.50iter/s]
Best score: 6.2969: : 266iter [00:08, 37.74iter/s]
Best score: 6.2969: : 266iter [00:08, 37.74iter/s]
Best score: 6.2969: : 267iter [00:08, 37.74iter/s]
Best score: 6.2969: : 268iter [00:08, 37.74iter/s]
Best score: 6.2969: : 269iter [00:08, 37.74iter/s]
Best score: 6.2969: : 270iter [00:08, 38.22iter/s]
Best score: 6.2969: : 270iter [00:09, 38.22iter/s]
Best score: 6.2969: : 271iter [00:09, 38.22iter/s]
Best score: 6.2969: : 272iter [00:09, 38.22iter/s]
Best score: 6.2969: : 273iter [00:09, 38.22iter/s]
Best score: 6.2969: : 274iter [00:09, 38.22iter/s]
Best score: 6.2969: : 275iter [00:09, 38.80iter/s]
Best score: 6.2969: : 275iter [00:09, 38.80iter/s]
Best score: 6.2969: : 276iter [00:09, 38.80iter/s]
Best score: 6.2969: : 277iter [00:09, 38.80iter/s]
Best score: 6.2969: : 278iter [00:09, 38.80iter/s]
Best score: 5.7690: : 279iter [00:09, 38.80iter/s]
Best score: 5.7690: : 280iter [00:09, 39.20iter/s]
Best score: 5.7690: : 280iter [00:09, 39.20iter/s]
Best score: 5.7690: : 281iter [00:09, 39.20iter/s]
Best score: 5.7690: : 282iter [00:09, 39.20iter/s]
Best score: 5.7690: : 283iter [00:09, 39.20iter/s]
Best score: 5.7690: : 284iter [00:09, 39.20iter/s]
Best score: 5.7690: : 285iter [00:09, 39.54iter/s]
Best score: 5.7690: : 285iter [00:09, 39.54iter/s]
Best score: 5.7690: : 286iter [00:09, 39.54iter/s]
Best score: 5.7690: : 287iter [00:09, 39.54iter/s]
Best score: 5.7690: : 288iter [00:09, 39.54iter/s]
Best score: 5.1362: : 289iter [00:09, 39.54iter/s]
Best score: 5.1362: : 290iter [00:09, 39.47iter/s]
Best score: 5.1362: : 290iter [00:09, 39.47iter/s]
Best score: 5.1362: : 291iter [00:09, 39.47iter/s]
Best score: 5.1362: : 292iter [00:09, 39.47iter/s]
Best score: 5.1362: : 293iter [00:09, 39.47iter/s]
Best score: 5.1362: : 294iter [00:09, 39.47iter/s]
Best score: 5.1362: : 295iter [00:09, 39.72iter/s]
Best score: 5.1362: : 295iter [00:09, 39.72iter/s]
Best score: 5.1362: : 296iter [00:09, 39.72iter/s]
Best score: 5.1362: : 297iter [00:09, 39.72iter/s]
Best score: 5.1362: : 298iter [00:09, 39.72iter/s]
[ITER 300] Best: 5.1298
[PARAMS] Current best parameters:
  fx: 536.1201
  fy: 533.5967
  px: 1000.8772
  py: 752.7606
  k1: 0.03968197
  k2: -0.05927490
  k3: 0.02572309
  k4: -0.00349626
  p1: 0.02460438
  p2: -0.00346463
  p3: 0.01069083
  p4: -0.00251831
  s1: -0.0052
  s2: 0.0052
  s3: -0.0186
  s4: -0.0024
  tilt_x: 0.0016
  tilt_y: -0.0170
  scale: 3.0157
  rotation: -0.7611

Best score: 5.1298: : 299iter [00:09, 39.72iter/s]
Best score: 5.1298: : 300iter [00:09, 39.77iter/s]
Best score: 5.1298: : 300iter [00:09, 39.77iter/s]
Best score: 5.1298: : 301iter [00:09, 39.77iter/s]
Best score: 5.1298: : 302iter [00:09, 39.77iter/s]
Best score: 5.1298: : 303iter [00:09, 39.77iter/s]
Best score: 5.1298: : 304iter [00:09, 33.34iter/s]
Best score: 5.1298: : 304iter [00:09, 33.34iter/s]
Best score: 5.1298: : 305iter [00:09, 33.34iter/s]
Best score: 5.1298: : 306iter [00:09, 33.34iter/s]
Best score: 5.1298: : 307iter [00:10, 33.34iter/s]
Best score: 5.1298: : 308iter [00:10, 33.34iter/s]
Best score: 5.1298: : 309iter [00:10, 35.34iter/s]
Best score: 5.1072: : 309iter [00:10, 35.34iter/s]
Best score: 5.1072: : 310iter [00:10, 35.34iter/s]
Best score: 5.1072: : 311iter [00:10, 35.34iter/s]
Best score: 5.1072: : 312iter [00:10, 35.34iter/s]
Best score: 5.1072: : 313iter [00:10, 36.39iter/s]
Best score: 5.1072: : 313iter [00:10, 36.39iter/s]
Best score: 5.1072: : 314iter [00:10, 36.39iter/s]
Best score: 5.1072: : 315iter [00:10, 36.39iter/s]
Best score: 5.1072: : 316iter [00:10, 36.39iter/s]
Best score: 5.1072: : 317iter [00:10, 36.39iter/s]
Best score: 5.1072: : 318iter [00:10, 37.63iter/s]
Best score: 5.1072: : 318iter [00:10, 37.63iter/s]
Best score: 4.2749: : 319iter [00:10, 37.63iter/s]
Best score: 4.2749: : 320iter [00:10, 37.63iter/s]
Best score: 4.2749: : 321iter [00:10, 37.63iter/s]
Best score: 4.2749: : 322iter [00:10, 38.13iter/s]
Best score: 4.2749: : 322iter [00:10, 38.13iter/s]
Best score: 4.2749: : 323iter [00:10, 38.13iter/s]
Best score: 4.2749: : 324iter [00:10, 38.13iter/s]
Best score: 4.2749: : 325iter [00:10, 38.13iter/s]
Best score: 4.2749: : 326iter [00:10, 38.13iter/s]
Best score: 4.2749: : 327iter [00:10, 38.99iter/s]
Best score: 4.2749: : 327iter [00:10, 38.99iter/s]
Best score: 4.2749: : 328iter [00:10, 38.99iter/s]
Best score: 4.1041: : 329iter [00:10, 38.99iter/s]
Best score: 4.1041: : 330iter [00:10, 38.99iter/s]
Best score: 4.1041: : 331iter [00:10, 39.21iter/s]
Best score: 4.1041: : 331iter [00:10, 39.21iter/s]
Best score: 4.1041: : 332iter [00:10, 39.21iter/s]
Best score: 4.1041: : 333iter [00:10, 39.21iter/s]
Best score: 4.1041: : 334iter [00:10, 39.21iter/s]
Best score: 4.1041: : 335iter [00:10, 39.21iter/s]
Best score: 4.1041: : 336iter [00:10, 39.42iter/s]
Best score: 4.1041: : 336iter [00:10, 39.42iter/s]
Best score: 4.1041: : 337iter [00:10, 39.42iter/s]
Best score: 4.1041: : 338iter [00:10, 39.42iter/s]
Best score: 2.8819: : 339iter [00:10, 39.42iter/s]
Best score: 2.8819: : 340iter [00:10, 39.37iter/s]
Best score: 2.8819: : 340iter [00:10, 39.37iter/s]
Best score: 2.8819: : 341iter [00:10, 39.37iter/s]
Best score: 2.8819: : 342iter [00:10, 39.37iter/s]
Best score: 2.8819: : 343iter [00:10, 39.37iter/s]
Best score: 2.8819: : 344iter [00:10, 39.37iter/s]
Best score: 2.8819: : 345iter [00:10, 39.75iter/s]
Best score: 2.8819: : 345iter [00:10, 39.75iter/s]
Best score: 2.8819: : 346iter [00:10, 39.75iter/s]
Best score: 2.8819: : 347iter [00:11, 39.75iter/s]
Best score: 2.8819: : 348iter [00:11, 39.75iter/s]
[ITER 350] Best: 2.6373
[PARAMS] Current best parameters:
  fx: 562.2069
  fy: 561.5977
  px: 1000.5434
  py: 741.1601
  k1: 0.01209527
  k2: -0.05778138
  k3: 0.03091654
  k4: -0.00589073
  p1: -0.00693833
  p2: -0.00060021
  p3: -0.01066373
  p4: 0.00181191
  s1: 0.0026
  s2: 0.0053
  s3: 0.0028
  s4: 0.0047
  tilt_x: 0.0113
  tilt_y: -0.0185
  scale: 2.9734
  rotation: -1.1471

Best score: 2.6373: : 349iter [00:11, 39.75iter/s]
Best score: 2.6373: : 350iter [00:11, 39.73iter/s]
Best score: 2.6373: : 350iter [00:11, 39.73iter/s]
Best score: 2.6373: : 351iter [00:11, 39.73iter/s]
Best score: 2.6373: : 352iter [00:11, 39.73iter/s]
Best score: 2.6373: : 353iter [00:11, 39.73iter/s]
Best score: 2.6373: : 354iter [00:11, 33.10iter/s]
Best score: 2.6373: : 354iter [00:11, 33.10iter/s]
Best score: 2.6373: : 355iter [00:11, 33.10iter/s]
Best score: 2.6373: : 356iter [00:11, 33.10iter/s]
Best score: 2.6373: : 357iter [00:11, 33.10iter/s]
Best score: 2.6373: : 358iter [00:11, 33.10iter/s]
Best score: 2.6373: : 359iter [00:11, 35.18iter/s]
Best score: 1.8433: : 359iter [00:11, 35.18iter/s]
Best score: 1.8433: : 360iter [00:11, 35.18iter/s]
Best score: 1.8433: : 361iter [00:11, 35.18iter/s]
Best score: 1.8433: : 362iter [00:11, 35.18iter/s]
Best score: 1.8433: : 363iter [00:11, 36.18iter/s]
Best score: 1.8433: : 363iter [00:11, 36.18iter/s]
Best score: 1.8433: : 364iter [00:11, 36.18iter/s]
Best score: 1.8433: : 365iter [00:11, 36.18iter/s]
Best score: 1.8433: : 366iter [00:11, 36.18iter/s]
Best score: 1.8433: : 367iter [00:11, 36.18iter/s]
Best score: 1.8433: : 368iter [00:11, 37.48iter/s]
Best score: 1.8433: : 368iter [00:11, 37.48iter/s]
Best score: 1.8433: : 369iter [00:11, 37.48iter/s]
Best score: 1.8433: : 370iter [00:11, 37.48iter/s]
Best score: 1.8433: : 371iter [00:11, 37.48iter/s]
Best score: 1.8433: : 372iter [00:11, 37.98iter/s]
Best score: 1.8433: : 372iter [00:11, 37.98iter/s]
Best score: 1.8433: : 373iter [00:11, 37.98iter/s]
Best score: 1.8433: : 374iter [00:11, 37.98iter/s]
Best score: 1.8433: : 375iter [00:11, 37.98iter/s]
Best score: 1.8433: : 376iter [00:11, 37.98iter/s]
Best score: 1.8433: : 377iter [00:11, 38.82iter/s]
Best score: 1.8433: : 377iter [00:11, 38.82iter/s]
Best score: 1.8433: : 378iter [00:11, 38.82iter/s]
Best score: 1.8433: : 379iter [00:11, 38.82iter/s]
Best score: 1.8433: : 380iter [00:11, 38.82iter/s]
Best score: 1.8433: : 381iter [00:11, 38.94iter/s]
Best score: 1.8433: : 381iter [00:11, 38.94iter/s]
Best score: 1.8433: : 382iter [00:11, 38.94iter/s]
Best score: 1.8433: : 383iter [00:11, 38.94iter/s]
Best score: 1.8433: : 384iter [00:12, 38.94iter/s]
Best score: 1.8433: : 385iter [00:12, 38.94iter/s]
Best score: 1.8433: : 386iter [00:12, 39.54iter/s]
Best score: 1.8433: : 386iter [00:12, 39.54iter/s]
Best score: 1.8433: : 387iter [00:12, 39.54iter/s]
Best score: 1.8433: : 388iter [00:12, 39.54iter/s]
Best score: 1.0557: : 389iter [00:12, 39.54iter/s]
Best score: 1.0557: : 390iter [00:12, 39.59iter/s]
Best score: 1.0557: : 390iter [00:12, 39.59iter/s]
Best score: 1.0557: : 391iter [00:12, 39.59iter/s]
Best score: 1.0557: : 392iter [00:12, 39.59iter/s]
Best score: 1.0557: : 393iter [00:12, 39.59iter/s]
Best score: 1.0557: : 394iter [00:12, 39.59iter/s]
Best score: 1.0557: : 395iter [00:12, 40.05iter/s]
Best score: 1.0557: : 395iter [00:12, 40.05iter/s]
Best score: 1.0557: : 396iter [00:12, 40.05iter/s]
Best score: 1.0557: : 397iter [00:12, 40.05iter/s]
Best score: 1.0557: : 398iter [00:12, 40.05iter/s]
[ITER 400] Best: 1.0557
[PARAMS] Current best parameters:
  fx: 551.4358
  fy: 550.8157
  px: 999.2303
  py: 741.6596
  k1: 0.05558405
  k2: -0.08202303
  k3: 0.02759481
  k4: -0.00209206
  p1: 0.00289135
  p2: -0.00448750
  p3: 0.00857844
  p4: -0.00129579
  s1: -0.0040
  s2: 0.0026
  s3: -0.0194
  s4: 0.0027
  tilt_x: -0.0064
  tilt_y: -0.0167
  scale: 2.9122
  rotation: -0.8715

Best score: 1.0557: : 399iter [00:12, 40.05iter/s]
Best score: 1.0557: : 400iter [00:12, 31.83iter/s]
Best score: 1.0557: : 400iter [00:12, 31.83iter/s]
Best score: 1.0557: : 401iter [00:12, 31.83iter/s]
Best score: 1.0557: : 402iter [00:12, 31.83iter/s]
Best score: 1.0557: : 403iter [00:12, 31.83iter/s]
Best score: 1.0557: : 404iter [00:12, 28.58iter/s]
Best score: 1.0557: : 404iter [00:12, 28.58iter/s]
Best score: 1.0557: : 405iter [00:12, 28.58iter/s]
Best score: 1.0557: : 406iter [00:12, 28.58iter/s]
Best score: 1.0557: : 407iter [00:12, 28.58iter/s]
Best score: 1.0557: : 408iter [00:12, 28.58iter/s]
Best score: 1.0557: : 409iter [00:12, 31.51iter/s]
Best score: 0.9697: : 409iter [00:12, 31.51iter/s]
Best score: 0.9697: : 410iter [00:12, 31.51iter/s]
Best score: 0.9697: : 411iter [00:12, 31.51iter/s]
Best score: 0.9697: : 412iter [00:12, 31.51iter/s]
Best score: 0.9697: : 413iter [00:12, 33.32iter/s]
Best score: 0.9697: : 413iter [00:12, 33.32iter/s]
Best score: 0.9697: : 414iter [00:12, 33.32iter/s]
Best score: 0.9697: : 415iter [00:12, 33.32iter/s]
Best score: 0.9697: : 416iter [00:12, 33.32iter/s]
Best score: 0.9697: : 417iter [00:12, 34.77iter/s]
Best score: 0.9697: : 417iter [00:13, 34.77iter/s]
Best score: 0.9697: : 418iter [00:13, 34.77iter/s]
Best score: 0.9074: : 419iter [00:13, 34.77iter/s]
Best score: 0.9074: : 420iter [00:13, 34.77iter/s]
Best score: 0.9074: : 421iter [00:13, 34.77iter/s]
Best score: 0.9074: : 422iter [00:13, 36.37iter/s]
Best score: 0.9074: : 422iter [00:13, 36.37iter/s]
Best score: 0.9074: : 423iter [00:13, 36.37iter/s]
Best score: 0.9074: : 424iter [00:13, 36.37iter/s]
Best score: 0.9074: : 425iter [00:13, 36.37iter/s]
Best score: 0.9074: : 426iter [00:13, 36.37iter/s]
Best score: 0.9074: : 427iter [00:13, 37.74iter/s]
Best score: 0.9074: : 427iter [00:13, 37.74iter/s]
Best score: 0.9074: : 428iter [00:13, 37.74iter/s]
Best score: 0.7625: : 429iter [00:13, 37.74iter/s]
Best score: 0.7625: : 430iter [00:13, 37.74iter/s]
Best score: 0.7625: : 431iter [00:13, 38.19iter/s]
Best score: 0.7625: : 431iter [00:13, 38.19iter/s]
Best score: 0.7625: : 432iter [00:13, 38.19iter/s]
Best score: 0.7625: : 433iter [00:13, 38.19iter/s]
Best score: 0.7625: : 434iter [00:13, 38.19iter/s]
Best score: 0.7625: : 435iter [00:13, 38.19iter/s]
Best score: 0.7625: : 436iter [00:13, 39.04iter/s]
Best score: 0.7625: : 436iter [00:13, 39.04iter/s]
Best score: 0.7625: : 437iter [00:13, 39.04iter/s]
Best score: 0.7625: : 438iter [00:13, 39.04iter/s]
Best score: 0.7266: : 439iter [00:13, 39.04iter/s]
Best score: 0.7266: : 440iter [00:13, 39.16iter/s]
Best score: 0.7266: : 440iter [00:13, 39.16iter/s]
Best score: 0.7266: : 441iter [00:13, 39.16iter/s]
Best score: 0.7266: : 442iter [00:13, 39.16iter/s]
Best score: 0.7266: : 443iter [00:13, 39.16iter/s]
Best score: 0.7266: : 444iter [00:13, 39.16iter/s]
Best score: 0.7266: : 445iter [00:13, 39.61iter/s]
Best score: 0.7266: : 445iter [00:13, 39.61iter/s]
Best score: 0.7266: : 446iter [00:13, 39.61iter/s]
Best score: 0.7266: : 447iter [00:13, 39.61iter/s]
Best score: 0.7266: : 448iter [00:13, 39.61iter/s]
[ITER 450] Best: 0.7266
[PARAMS] Current best parameters:
  fx: 553.7748
  fy: 553.3462
  px: 994.3006
  py: 742.6756
  k1: 0.04585661
  k2: -0.07421452
  k3: 0.02729330
  k4: -0.00298675
  p1: 0.00430704
  p2: -0.00556261
  p3: 0.01068023
  p4: 0.00055962
  s1: -0.0124
  s2: 0.0001
  s3: -0.0189
  s4: 0.0005
  tilt_x: -0.0029
  tilt_y: 0.0051
  scale: 2.9094
  rotation: -0.7500

Best score: 0.7266: : 449iter [00:13, 39.61iter/s]
Best score: 0.7266: : 450iter [00:13, 39.77iter/s]
Best score: 0.7266: : 450iter [00:13, 39.77iter/s]
Best score: 0.7266: : 451iter [00:13, 39.77iter/s]
Best score: 0.7266: : 452iter [00:13, 39.77iter/s]
Best score: 0.7266: : 453iter [00:13, 39.77iter/s]
Best score: 0.7266: : 454iter [00:13, 32.86iter/s]
Best score: 0.7266: : 454iter [00:14, 32.86iter/s]
Best score: 0.7266: : 455iter [00:14, 32.86iter/s]
Best score: 0.7266: : 456iter [00:14, 32.86iter/s]
Best score: 0.7266: : 457iter [00:14, 32.86iter/s]
Best score: 0.7266: : 458iter [00:14, 32.86iter/s]
Best score: 0.7266: : 459iter [00:14, 35.02iter/s]
Best score: 0.7159: : 459iter [00:14, 35.02iter/s]
Best score: 0.7159: : 460iter [00:14, 35.02iter/s]
Best score: 0.7159: : 461iter [00:14, 35.02iter/s]
Best score: 0.7159: : 462iter [00:14, 35.02iter/s]
Best score: 0.7159: : 463iter [00:14, 36.13iter/s]
Best score: 0.7159: : 463iter [00:14, 36.13iter/s]
Best score: 0.7159: : 464iter [00:14, 36.13iter/s]
Best score: 0.7159: : 465iter [00:14, 36.13iter/s]
Best score: 0.7159: : 466iter [00:14, 36.13iter/s]
Best score: 0.7159: : 467iter [00:14, 36.13iter/s]
Best score: 0.7159: : 468iter [00:14, 37.52iter/s]
Best score: 0.7159: : 468iter [00:14, 37.52iter/s]
Best score: 0.7047: : 469iter [00:14, 37.52iter/s]
Best score: 0.7047: : 470iter [00:14, 37.52iter/s]
Best score: 0.7047: : 471iter [00:14, 37.52iter/s]
Best score: 0.7047: : 472iter [00:14, 38.04iter/s]
Best score: 0.7047: : 472iter [00:14, 38.04iter/s]
Best score: 0.7047: : 473iter [00:14, 38.04iter/s]
Best score: 0.7047: : 474iter [00:14, 38.04iter/s]
Best score: 0.7047: : 475iter [00:14, 38.04iter/s]
Best score: 0.7047: : 476iter [00:14, 38.04iter/s]
Best score: 0.7047: : 477iter [00:14, 38.86iter/s]
Best score: 0.7047: : 477iter [00:14, 38.86iter/s]
Best score: 0.7047: : 478iter [00:14, 38.86iter/s]
Best score: 0.4654: : 479iter [00:14, 38.86iter/s]
Best score: 0.4654: : 480iter [00:14, 38.86iter/s]
Best score: 0.4654: : 481iter [00:14, 38.98iter/s]
Best score: 0.4654: : 481iter [00:14, 38.98iter/s]
Best score: 0.4654: : 482iter [00:14, 38.98iter/s]
Best score: 0.4654: : 483iter [00:14, 38.98iter/s]
Best score: 0.4654: : 484iter [00:14, 38.98iter/s]
Best score: 0.4654: : 485iter [00:14, 38.98iter/s]
Best score: 0.4654: : 486iter [00:14, 39.43iter/s]
Best score: 0.4654: : 486iter [00:14, 39.43iter/s]
Best score: 0.4654: : 487iter [00:14, 39.43iter/s]
Best score: 0.4654: : 488iter [00:14, 39.43iter/s]
Best score: 0.4654: : 489iter [00:14, 39.43iter/s]
Best score: 0.4654: : 490iter [00:14, 39.44iter/s]
Best score: 0.4654: : 490iter [00:14, 39.44iter/s]
Best score: 0.4654: : 491iter [00:14, 39.44iter/s]
Best score: 0.4654: : 492iter [00:14, 39.44iter/s]
Best score: 0.4654: : 493iter [00:14, 39.44iter/s]
Best score: 0.4654: : 494iter [00:15, 39.44iter/s]
Best score: 0.4654: : 495iter [00:15, 39.73iter/s]
Best score: 0.4654: : 495iter [00:15, 39.73iter/s]
Best score: 0.4654: : 496iter [00:15, 39.73iter/s]
Best score: 0.4654: : 497iter [00:15, 39.73iter/s]
Best score: 0.4654: : 498iter [00:15, 39.73iter/s]
[ITER 500] Best: 0.4654
[PARAMS] Current best parameters:
  fx: 550.2549
  fy: 549.7698
  px: 994.2187
  py: 739.4133
  k1: 0.03113855
  k2: -0.05443979
  k3: 0.02084723
  k4: -0.00265237
  p1: -0.00065734
  p2: -0.00754162
  p3: 0.00447460
  p4: 0.00087215
  s1: -0.0074
  s2: 0.0028
  s3: -0.0087
  s4: -0.0012
  tilt_x: -0.0114
  tilt_y: 0.0011
  scale: 2.9387
  rotation: -0.7869

Best score: 0.4654: : 499iter [00:15, 39.73iter/s]
Best score: 0.4654: : 500iter [00:15, 39.64iter/s]
Best score: 0.4654: : 500iter [00:15, 39.64iter/s]
Best score: 0.4654: : 501iter [00:15, 39.64iter/s]
Best score: 0.4654: : 502iter [00:15, 39.64iter/s]
Best score: 0.4654: : 503iter [00:15, 39.64iter/s]
Best score: 0.4654: : 504iter [00:15, 33.00iter/s]
Best score: 0.4654: : 504iter [00:15, 33.00iter/s]
Best score: 0.4654: : 505iter [00:15, 33.00iter/s]
Best score: 0.4654: : 506iter [00:15, 33.00iter/s]
Best score: 0.4654: : 507iter [00:15, 33.00iter/s]
Best score: 0.4654: : 508iter [00:15, 33.00iter/s]
Best score: 0.4654: : 509iter [00:15, 35.04iter/s]
Best score: 0.4510: : 509iter [00:15, 35.04iter/s]
Best score: 0.4510: : 510iter [00:15, 35.04iter/s]
Best score: 0.4510: : 511iter [00:15, 35.04iter/s]
Best score: 0.4510: : 512iter [00:15, 35.04iter/s]
Best score: 0.4510: : 513iter [00:15, 36.09iter/s]
Best score: 0.4510: : 513iter [00:15, 36.09iter/s]
Best score: 0.4510: : 514iter [00:15, 36.09iter/s]
Best score: 0.4510: : 515iter [00:15, 36.09iter/s]
Best score: 0.4510: : 516iter [00:15, 36.09iter/s]
Best score: 0.4510: : 517iter [00:15, 36.09iter/s]
Best score: 0.4510: : 518iter [00:15, 37.37iter/s]
Best score: 0.4510: : 518iter [00:15, 37.37iter/s]
Best score: 0.4446: : 519iter [00:15, 37.37iter/s]
Best score: 0.4446: : 520iter [00:15, 37.37iter/s]
Best score: 0.4446: : 521iter [00:15, 37.37iter/s]
Best score: 0.4446: : 522iter [00:15, 37.97iter/s]
Best score: 0.4446: : 522iter [00:15, 37.97iter/s]
Best score: 0.4446: : 523iter [00:15, 37.97iter/s]
Best score: 0.4446: : 524iter [00:15, 37.97iter/s]
Best score: 0.4446: : 525iter [00:15, 37.97iter/s]
Best score: 0.4446: : 526iter [00:15, 37.97iter/s]
Best score: 0.4446: : 527iter [00:15, 38.61iter/s]
Best score: 0.4446: : 527iter [00:15, 38.61iter/s]
Best score: 0.4446: : 528iter [00:15, 38.61iter/s]
Best score: 0.4255: : 529iter [00:15, 38.61iter/s]
Best score: 0.4255: : 530iter [00:15, 38.61iter/s]
Best score: 0.4255: : 531iter [00:15, 38.81iter/s]
Best score: 0.4255: : 531iter [00:16, 38.81iter/s]
Best score: 0.4255: : 532iter [00:16, 38.81iter/s]
Best score: 0.4255: : 533iter [00:16, 38.81iter/s]
Best score: 0.4255: : 534iter [00:16, 38.81iter/s]
Best score: 0.4255: : 535iter [00:16, 38.81iter/s]
Best score: 0.4255: : 536iter [00:16, 39.29iter/s]
Best score: 0.4255: : 536iter [00:16, 39.29iter/s]
Best score: 0.4255: : 537iter [00:16, 39.29iter/s]
Best score: 0.4255: : 538iter [00:16, 39.29iter/s]
Best score: 0.4082: : 539iter [00:16, 39.29iter/s]
Best score: 0.4082: : 540iter [00:16, 39.43iter/s]
Best score: 0.4082: : 540iter [00:16, 39.43iter/s]
Best score: 0.4082: : 541iter [00:16, 39.43iter/s]
Best score: 0.4082: : 542iter [00:16, 39.43iter/s]
Best score: 0.4082: : 543iter [00:16, 39.43iter/s]
Best score: 0.4082: : 544iter [00:16, 39.43iter/s]
Best score: 0.4082: : 545iter [00:16, 39.81iter/s]
Best score: 0.4082: : 545iter [00:16, 39.81iter/s]
Best score: 0.4082: : 546iter [00:16, 39.81iter/s]
Best score: 0.4082: : 547iter [00:16, 39.81iter/s]
Best score: 0.4082: : 548iter [00:16, 39.81iter/s]
[ITER 550] Best: 0.3849
[PARAMS] Current best parameters:
  fx: 554.0439
  fy: 553.5863
  px: 999.9492
  py: 740.6598
  k1: 0.03122624
  k2: -0.06788996
  k3: 0.03269188
  k4: -0.00571716
  p1: 0.00084779
  p2: -0.00257371
  p3: 0.00923559
  p4: -0.00072114
  s1: -0.0056
  s2: 0.0010
  s3: -0.0119
  s4: -0.0013
  tilt_x: -0.0067
  tilt_y: -0.0174
  scale: 2.9484
  rotation: -0.7881

Best score: 0.3849: : 549iter [00:16, 39.81iter/s]
Best score: 0.3849: : 550iter [00:16, 39.82iter/s]
Best score: 0.3849: : 550iter [00:16, 39.82iter/s]
Best score: 0.3849: : 551iter [00:16, 39.82iter/s]
Best score: 0.3849: : 552iter [00:16, 39.82iter/s]
Best score: 0.3849: : 553iter [00:16, 39.82iter/s]
Best score: 0.3849: : 554iter [00:16, 33.20iter/s]
Best score: 0.3849: : 554iter [00:16, 33.20iter/s]
Best score: 0.3849: : 555iter [00:16, 33.20iter/s]
Best score: 0.3849: : 556iter [00:16, 33.20iter/s]
Best score: 0.3849: : 557iter [00:16, 33.20iter/s]
Best score: 0.3849: : 558iter [00:16, 33.20iter/s]
Best score: 0.3849: : 559iter [00:16, 35.31iter/s]
Best score: 0.3849: : 559iter [00:16, 35.31iter/s]
Best score: 0.3849: : 560iter [00:16, 35.31iter/s]
Best score: 0.3849: : 561iter [00:16, 35.31iter/s]
Best score: 0.3849: : 562iter [00:16, 35.31iter/s]
Best score: 0.3849: : 563iter [00:16, 36.44iter/s]
Best score: 0.3849: : 563iter [00:16, 36.44iter/s]
Best score: 0.3849: : 564iter [00:16, 36.44iter/s]
Best score: 0.3849: : 565iter [00:16, 36.44iter/s]
Best score: 0.3849: : 566iter [00:16, 36.44iter/s]
Best score: 0.3849: : 567iter [00:16, 36.44iter/s]
Best score: 0.3849: : 568iter [00:16, 37.77iter/s]
Best score: 0.3849: : 568iter [00:17, 37.77iter/s]
Best score: 0.3617: : 569iter [00:17, 37.77iter/s]
Best score: 0.3617: : 570iter [00:17, 37.77iter/s]
Best score: 0.3617: : 571iter [00:17, 37.77iter/s]
Best score: 0.3617: : 572iter [00:17, 38.15iter/s]
Best score: 0.3617: : 572iter [00:17, 38.15iter/s]
Best score: 0.3617: : 573iter [00:17, 38.15iter/s]
Best score: 0.3617: : 574iter [00:17, 38.15iter/s]
Best score: 0.3617: : 575iter [00:17, 38.15iter/s]
Best score: 0.3617: : 576iter [00:17, 38.15iter/s]
Best score: 0.3617: : 577iter [00:17, 38.96iter/s]
Best score: 0.3617: : 577iter [00:17, 38.96iter/s]
Best score: 0.3617: : 578iter [00:17, 38.96iter/s]
Best score: 0.3523: : 579iter [00:17, 38.96iter/s]
Best score: 0.3523: : 580iter [00:17, 38.96iter/s]
Best score: 0.3523: : 581iter [00:17, 39.09iter/s]
Best score: 0.3523: : 581iter [00:17, 39.09iter/s]
Best score: 0.3523: : 582iter [00:17, 39.09iter/s]
Best score: 0.3523: : 583iter [00:17, 39.09iter/s]
Best score: 0.3523: : 584iter [00:17, 39.09iter/s]
Best score: 0.3523: : 585iter [00:17, 39.09iter/s]
Best score: 0.3523: : 586iter [00:17, 39.49iter/s]
Best score: 0.3523: : 586iter [00:17, 39.49iter/s]
Best score: 0.3523: : 587iter [00:17, 39.49iter/s]
Best score: 0.3523: : 588iter [00:17, 39.49iter/s]
Best score: 0.3318: : 589iter [00:17, 39.49iter/s]
Best score: 0.3318: : 590iter [00:17, 39.53iter/s]
Best score: 0.3318: : 590iter [00:17, 39.53iter/s]
Best score: 0.3318: : 591iter [00:17, 39.53iter/s]
Best score: 0.3318: : 592iter [00:17, 39.53iter/s]
Best score: 0.3318: : 593iter [00:17, 39.53iter/s]
Best score: 0.3318: : 594iter [00:17, 39.53iter/s]
Best score: 0.3318: : 595iter [00:17, 40.00iter/s]
Best score: 0.3318: : 595iter [00:17, 40.00iter/s]
Best score: 0.3318: : 596iter [00:17, 40.00iter/s]
Best score: 0.3318: : 597iter [00:17, 40.00iter/s]
Best score: 0.3318: : 598iter [00:17, 40.00iter/s]
[ITER 600] Best: 0.3272
[PARAMS] Current best parameters:
  fx: 548.5517
  fy: 548.1229
  px: 993.8599
  py: 739.0332
  k1: 0.00916881
  k2: -0.03127274
  k3: 0.01588244
  k4: -0.00329026
  p1: -0.00043587
  p2: -0.00868634
  p3: 0.00216245
  p4: -0.00101584
  s1: -0.0025
  s2: 0.0037
  s3: -0.0071
  s4: 0.0006
  tilt_x: -0.0137
  tilt_y: 0.0021
  scale: 2.9799
  rotation: -0.7947

Best score: 0.3272: : 599iter [00:17, 40.00iter/s]
Best score: 0.3272: : 600iter [00:17, 39.93iter/s]
Best score: 0.3272: : 600iter [00:17, 39.93iter/s]
Best score: 0.3272: : 601iter [00:17, 39.93iter/s]
Best score: 0.3272: : 602iter [00:17, 39.93iter/s]
Best score: 0.3272: : 603iter [00:17, 39.93iter/s]
Best score: 0.3272: : 604iter [00:17, 33.21iter/s]
Best score: 0.3272: : 604iter [00:17, 33.21iter/s]
Best score: 0.3272: : 605iter [00:18, 33.21iter/s]
Best score: 0.3272: : 606iter [00:18, 33.21iter/s]
Best score: 0.3272: : 607iter [00:18, 33.21iter/s]
Best score: 0.3272: : 608iter [00:18, 33.21iter/s]
Best score: 0.3272: : 609iter [00:18, 35.16iter/s]
Best score: 0.2959: : 609iter [00:18, 35.16iter/s]
Best score: 0.2959: : 610iter [00:18, 35.16iter/s]
Best score: 0.2959: : 611iter [00:18, 35.16iter/s]
Best score: 0.2959: : 612iter [00:18, 35.16iter/s]
Best score: 0.2959: : 613iter [00:18, 36.18iter/s]
Best score: 0.2959: : 613iter [00:18, 36.18iter/s]
Best score: 0.2959: : 614iter [00:18, 36.18iter/s]
Best score: 0.2959: : 615iter [00:18, 36.18iter/s]
Best score: 0.2959: : 616iter [00:18, 36.18iter/s]
Best score: 0.2959: : 617iter [00:18, 36.18iter/s]
Best score: 0.2959: : 618iter [00:18, 37.37iter/s]
Best score: 0.2959: : 618iter [00:18, 37.37iter/s]
Best score: 0.2959: : 619iter [00:18, 37.37iter/s]
Best score: 0.2959: : 620iter [00:18, 37.37iter/s]
Best score: 0.2959: : 621iter [00:18, 37.37iter/s]
Best score: 0.2959: : 622iter [00:18, 37.95iter/s]
Best score: 0.2959: : 622iter [00:18, 37.95iter/s]
Best score: 0.2959: : 623iter [00:18, 37.95iter/s]
Best score: 0.2959: : 624iter [00:18, 37.95iter/s]
Best score: 0.2959: : 625iter [00:18, 37.95iter/s]
Best score: 0.2959: : 626iter [00:18, 37.95iter/s]
Best score: 0.2959: : 627iter [00:18, 38.82iter/s]
Best score: 0.2959: : 627iter [00:18, 38.82iter/s]
Best score: 0.2959: : 628iter [00:18, 38.82iter/s]
Best score: 0.2958: : 629iter [00:18, 38.82iter/s]
Best score: 0.2958: : 630iter [00:18, 38.82iter/s]
Best score: 0.2958: : 631iter [00:18, 38.99iter/s]
Best score: 0.2958: : 631iter [00:18, 38.99iter/s]
Best score: 0.2958: : 632iter [00:18, 38.99iter/s]
Best score: 0.2958: : 633iter [00:18, 38.99iter/s]
Best score: 0.2958: : 634iter [00:18, 38.99iter/s]
Best score: 0.2958: : 635iter [00:18, 38.99iter/s]
Best score: 0.2958: : 636iter [00:18, 39.48iter/s]
Best score: 0.2958: : 636iter [00:18, 39.48iter/s]
Best score: 0.2958: : 637iter [00:18, 39.48iter/s]
Best score: 0.2958: : 638iter [00:18, 39.48iter/s]
Best score: 0.2958: : 639iter [00:18, 39.48iter/s]
Best score: 0.2958: : 640iter [00:18, 39.56iter/s]
Best score: 0.2958: : 640iter [00:18, 39.56iter/s]
Best score: 0.2958: : 641iter [00:18, 39.56iter/s]
Best score: 0.2958: : 642iter [00:18, 39.56iter/s]
Best score: 0.2958: : 643iter [00:18, 39.56iter/s]
Best score: 0.2958: : 644iter [00:18, 39.56iter/s]
Best score: 0.2958: : 645iter [00:18, 39.88iter/s]
Best score: 0.2958: : 645iter [00:19, 39.88iter/s]
Best score: 0.2958: : 646iter [00:19, 39.88iter/s]
Best score: 0.2958: : 647iter [00:19, 39.88iter/s]
Best score: 0.2958: : 648iter [00:19, 39.88iter/s]
[ITER 650] Best: 0.2942
[PARAMS] Current best parameters:
  fx: 547.8101
  fy: 547.4733
  px: 993.9021
  py: 739.9055
  k1: 0.00337222
  k2: -0.02356076
  k3: 0.01292055
  k4: -0.00297832
  p1: 0.00283308
  p2: -0.00866107
  p3: 0.00302027
  p4: -0.00292046
  s1: -0.0005
  s2: 0.0041
  s3: -0.0106
  s4: 0.0022
  tilt_x: -0.0150
  tilt_y: 0.0017
  scale: 2.9895
  rotation: -0.7899

Best score: 0.2942: : 649iter [00:19, 39.88iter/s]
Best score: 0.2942: : 650iter [00:19, 39.71iter/s]
Best score: 0.2942: : 650iter [00:19, 39.71iter/s]
Best score: 0.2942: : 651iter [00:19, 39.71iter/s]
Best score: 0.2942: : 652iter [00:19, 39.71iter/s]
Best score: 0.2942: : 653iter [00:19, 39.71iter/s]
Best score: 0.2942: : 654iter [00:19, 33.23iter/s]
Best score: 0.2942: : 654iter [00:19, 33.23iter/s]
Best score: 0.2942: : 655iter [00:19, 33.23iter/s]
Best score: 0.2942: : 656iter [00:19, 33.23iter/s]
Best score: 0.2942: : 657iter [00:19, 33.23iter/s]
Best score: 0.2942: : 658iter [00:19, 33.23iter/s]
Best score: 0.2942: : 659iter [00:19, 35.27iter/s]
Best score: 0.2942: : 659iter [00:19, 35.27iter/s]
Best score: 0.2942: : 660iter [00:19, 35.27iter/s]
Best score: 0.2942: : 661iter [00:19, 35.27iter/s]
Best score: 0.2942: : 662iter [00:19, 35.27iter/s]
Best score: 0.2942: : 663iter [00:19, 36.38iter/s]
Best score: 0.2942: : 663iter [00:19, 36.38iter/s]
Best score: 0.2942: : 664iter [00:19, 36.38iter/s]
Best score: 0.2942: : 665iter [00:19, 36.38iter/s]
Best score: 0.2942: : 666iter [00:19, 36.38iter/s]
Best score: 0.2942: : 667iter [00:19, 36.38iter/s]
Best score: 0.2942: : 668iter [00:19, 37.57iter/s]
Best score: 0.2942: : 668iter [00:19, 37.57iter/s]
Best score: 0.2942: : 669iter [00:19, 37.57iter/s]
Best score: 0.2942: : 670iter [00:19, 37.57iter/s]
Best score: 0.2942: : 671iter [00:19, 37.57iter/s]
Best score: 0.2942: : 672iter [00:19, 38.10iter/s]
Best score: 0.2942: : 672iter [00:19, 38.10iter/s]
Best score: 0.2942: : 673iter [00:19, 38.10iter/s]
Best score: 0.2942: : 674iter [00:19, 38.10iter/s]
Best score: 0.2942: : 675iter [00:19, 38.10iter/s]
Best score: 0.2942: : 676iter [00:19, 38.10iter/s]
Best score: 0.2942: : 677iter [00:19, 38.99iter/s]
Best score: 0.2942: : 677iter [00:19, 38.99iter/s]
Best score: 0.2942: : 678iter [00:19, 38.99iter/s]
Best score: 0.2932: : 679iter [00:19, 38.99iter/s]
Best score: 0.2932: : 680iter [00:19, 38.99iter/s]
Best score: 0.2932: : 681iter [00:19, 39.06iter/s]
Best score: 0.2932: : 681iter [00:19, 39.06iter/s]
Best score: 0.2932: : 682iter [00:20, 39.06iter/s]
Best score: 0.2932: : 683iter [00:20, 39.06iter/s]
Best score: 0.2932: : 684iter [00:20, 39.06iter/s]
Best score: 0.2932: : 685iter [00:20, 39.06iter/s]
Best score: 0.2932: : 686iter [00:20, 39.52iter/s]
Best score: 0.2932: : 686iter [00:20, 39.52iter/s]
Best score: 0.2932: : 687iter [00:20, 39.52iter/s]
Best score: 0.2932: : 688iter [00:20, 39.52iter/s]
Best score: 0.2875: : 689iter [00:20, 39.52iter/s]
Best score: 0.2875: : 690iter [00:20, 39.54iter/s]
Best score: 0.2875: : 690iter [00:20, 39.54iter/s]
Best score: 0.2875: : 691iter [00:20, 39.54iter/s]
Best score: 0.2875: : 692iter [00:20, 39.54iter/s]
Best score: 0.2875: : 693iter [00:20, 39.54iter/s]
Best score: 0.2875: : 694iter [00:20, 39.54iter/s]
Best score: 0.2875: : 695iter [00:20, 39.78iter/s]
Best score: 0.2875: : 695iter [00:20, 39.78iter/s]
Best score: 0.2875: : 696iter [00:20, 39.78iter/s]
Best score: 0.2875: : 697iter [00:20, 39.78iter/s]
Best score: 0.2875: : 698iter [00:20, 39.78iter/s]
[ITER 700] Best: 0.2874
[PARAMS] Current best parameters:
  fx: 551.0343
  fy: 550.6384
  px: 997.6512
  py: 739.6035
  k1: 0.00804016
  k2: -0.03220485
  k3: 0.01620947
  k4: -0.00331384
  p1: 0.00159286
  p2: -0.00722525
  p3: 0.00433199
  p4: -0.00382200
  s1: 0.0013
  s2: 0.0043
  s3: -0.0110
  s4: 0.0031
  tilt_x: -0.0142
  tilt_y: -0.0143
  scale: 2.9742
  rotation: -0.7886

Best score: 0.2874: : 699iter [00:20, 39.78iter/s]
Best score: 0.2874: : 700iter [00:20, 39.88iter/s]
Best score: 0.2874: : 700iter [00:20, 39.88iter/s]
Best score: 0.2874: : 701iter [00:20, 39.88iter/s]
Best score: 0.2874: : 702iter [00:20, 39.88iter/s]
Best score: 0.2874: : 703iter [00:20, 39.88iter/s]
Best score: 0.2874: : 704iter [00:20, 33.23iter/s]
Best score: 0.2874: : 704iter [00:20, 33.23iter/s]
Best score: 0.2874: : 705iter [00:20, 33.23iter/s]
Best score: 0.2874: : 706iter [00:20, 33.23iter/s]
Best score: 0.2874: : 707iter [00:20, 33.23iter/s]
Best score: 0.2874: : 708iter [00:20, 33.23iter/s]
Best score: 0.2874: : 709iter [00:20, 35.32iter/s]
Best score: 0.2874: : 709iter [00:20, 35.32iter/s]
Best score: 0.2874: : 710iter [00:20, 35.32iter/s]
Best score: 0.2874: : 711iter [00:20, 35.32iter/s]
Best score: 0.2874: : 712iter [00:20, 35.32iter/s]
Best score: 0.2874: : 713iter [00:20, 36.43iter/s]
Best score: 0.2874: : 713iter [00:20, 36.43iter/s]
Best score: 0.2874: : 714iter [00:20, 36.43iter/s]
Best score: 0.2874: : 715iter [00:20, 36.43iter/s]
Best score: 0.2874: : 716iter [00:21, 36.43iter/s]
Best score: 0.2874: : 717iter [00:21, 30.22iter/s]
Best score: 0.2874: : 717iter [00:21, 30.22iter/s]
Best score: 0.2874: : 718iter [00:21, 30.22iter/s]
Best score: 0.2814: : 719iter [00:21, 30.22iter/s]
Best score: 0.2814: : 720iter [00:21, 30.22iter/s]
Best score: 0.2814: : 721iter [00:21, 32.45iter/s]
Best score: 0.2814: : 721iter [00:21, 32.45iter/s]
Best score: 0.2814: : 722iter [00:21, 32.45iter/s]
Best score: 0.2814: : 723iter [00:21, 32.45iter/s]
Best score: 0.2814: : 724iter [00:21, 32.45iter/s]
Best score: 0.2814: : 725iter [00:21, 32.45iter/s]
Best score: 0.2814: : 726iter [00:21, 34.78iter/s]
Best score: 0.2814: : 726iter [00:21, 34.78iter/s]
Best score: 0.2814: : 727iter [00:21, 34.78iter/s]
Best score: 0.2814: : 728iter [00:21, 34.78iter/s]
Best score: 0.2814: : 729iter [00:21, 34.78iter/s]
Best score: 0.2814: : 730iter [00:21, 35.94iter/s]
Best score: 0.2814: : 730iter [00:21, 35.94iter/s]
Best score: 0.2814: : 731iter [00:21, 35.94iter/s]
Best score: 0.2814: : 732iter [00:21, 35.94iter/s]
Best score: 0.2814: : 733iter [00:21, 35.94iter/s]
Best score: 0.2814: : 734iter [00:21, 35.94iter/s]
Best score: 0.2814: : 735iter [00:21, 37.47iter/s]
Best score: 0.2814: : 735iter [00:21, 37.47iter/s]
Best score: 0.2814: : 736iter [00:21, 37.47iter/s]
Best score: 0.2814: : 737iter [00:21, 37.47iter/s]
Best score: 0.2814: : 738iter [00:21, 37.47iter/s]
Best score: 0.2789: : 739iter [00:21, 37.47iter/s]
Best score: 0.2789: : 740iter [00:21, 38.10iter/s]
Best score: 0.2789: : 740iter [00:21, 38.10iter/s]
Best score: 0.2789: : 741iter [00:21, 38.10iter/s]
Best score: 0.2789: : 742iter [00:21, 38.10iter/s]
Best score: 0.2789: : 743iter [00:21, 38.10iter/s]
Best score: 0.2789: : 744iter [00:21, 38.10iter/s]
Best score: 0.2789: : 745iter [00:21, 38.76iter/s]
Best score: 0.2789: : 745iter [00:21, 38.76iter/s]
Best score: 0.2789: : 746iter [00:21, 38.76iter/s]
Best score: 0.2789: : 747iter [00:21, 38.76iter/s]
Best score: 0.2789: : 748iter [00:21, 38.76iter/s]
[ITER 750] Best: 0.2754
[PARAMS] Current best parameters:
  fx: 549.1411
  fy: 548.7720
  px: 995.5939
  py: 738.0324
  k1: 0.00165891
  k2: -0.01849603
  k3: 0.00803039
  k4: -0.00167997
  p1: 0.00330843
  p2: -0.00921955
  p3: 0.00351926
  p4: -0.00323946
  s1: 0.0027
  s2: 0.0035
  s3: -0.0138
  s4: 0.0028
  tilt_x: -0.0257
  tilt_y: -0.0081
  scale: 2.9818
  rotation: -0.7932

Best score: 0.2754: : 749iter [00:21, 38.76iter/s]
Best score: 0.2754: : 750iter [00:21, 39.03iter/s]
Best score: 0.2754: : 750iter [00:21, 39.03iter/s]
Best score: 0.2754: : 751iter [00:21, 39.03iter/s]
Best score: 0.2754: : 752iter [00:21, 39.03iter/s]
Best score: 0.2754: : 753iter [00:22, 39.03iter/s]
Best score: 0.2754: : 754iter [00:22, 32.81iter/s]
Best score: 0.2754: : 754iter [00:22, 32.81iter/s]
Best score: 0.2754: : 755iter [00:22, 32.81iter/s]
Best score: 0.2754: : 756iter [00:22, 32.81iter/s]
Best score: 0.2754: : 757iter [00:22, 32.81iter/s]
Best score: 0.2754: : 758iter [00:22, 32.81iter/s]
Best score: 0.2754: : 759iter [00:22, 34.93iter/s]
Best score: 0.2754: : 759iter [00:22, 34.93iter/s]
Best score: 0.2754: : 760iter [00:22, 34.93iter/s]
Best score: 0.2754: : 761iter [00:22, 34.93iter/s]
Best score: 0.2754: : 762iter [00:22, 34.93iter/s]
Best score: 0.2754: : 763iter [00:22, 36.04iter/s]
Best score: 0.2754: : 763iter [00:22, 36.04iter/s]
Best score: 0.2754: : 764iter [00:22, 36.04iter/s]
Best score: 0.2754: : 765iter [00:22, 36.04iter/s]
Best score: 0.2754: : 766iter [00:22, 36.04iter/s]
Best score: 0.2754: : 767iter [00:22, 36.04iter/s]
Best score: 0.2754: : 768iter [00:22, 37.42iter/s]
Best score: 0.2754: : 768iter [00:22, 37.42iter/s]
Best score: 0.2750: : 769iter [00:22, 37.42iter/s]
Best score: 0.2750: : 770iter [00:22, 37.42iter/s]
Best score: 0.2750: : 771iter [00:22, 37.42iter/s]
Best score: 0.2750: : 772iter [00:22, 37.96iter/s]
Best score: 0.2750: : 772iter [00:22, 37.96iter/s]
Best score: 0.2750: : 773iter [00:22, 37.96iter/s]
Best score: 0.2750: : 774iter [00:22, 37.96iter/s]
Best score: 0.2750: : 775iter [00:22, 37.96iter/s]
Best score: 0.2750: : 776iter [00:22, 37.96iter/s]
Best score: 0.2750: : 777iter [00:22, 38.93iter/s]
Best score: 0.2750: : 777iter [00:22, 38.93iter/s]
Best score: 0.2750: : 778iter [00:22, 38.93iter/s]
Best score: 0.2739: : 779iter [00:22, 38.93iter/s]
Best score: 0.2739: : 780iter [00:22, 38.93iter/s]
Best score: 0.2739: : 781iter [00:22, 39.08iter/s]
Best score: 0.2739: : 781iter [00:22, 39.08iter/s]
Best score: 0.2739: : 782iter [00:22, 39.08iter/s]
Best score: 0.2739: : 783iter [00:22, 39.08iter/s]
Best score: 0.2739: : 784iter [00:22, 39.08iter/s]
Best score: 0.2739: : 785iter [00:22, 39.08iter/s]
Best score: 0.2739: : 786iter [00:22, 39.46iter/s]
Best score: 0.2739: : 786iter [00:22, 39.46iter/s]
Best score: 0.2739: : 787iter [00:22, 39.46iter/s]
Best score: 0.2739: : 788iter [00:22, 39.46iter/s]
Best score: 0.2726: : 789iter [00:22, 39.46iter/s]
Best score: 0.2726: : 790iter [00:22, 39.40iter/s]
Best score: 0.2726: : 790iter [00:22, 39.40iter/s]
Best score: 0.2726: : 791iter [00:22, 39.40iter/s]
Best score: 0.2726: : 792iter [00:22, 39.40iter/s]
Best score: 0.2726: : 793iter [00:23, 39.40iter/s]
Best score: 0.2726: : 794iter [00:23, 39.40iter/s]
Best score: 0.2726: : 795iter [00:23, 39.71iter/s]
Best score: 0.2726: : 795iter [00:23, 39.71iter/s]
Best score: 0.2726: : 796iter [00:23, 39.71iter/s]
Best score: 0.2726: : 797iter [00:23, 39.71iter/s]
Best score: 0.2726: : 798iter [00:23, 39.71iter/s]
[ITER 800] Best: 0.2653
[PARAMS] Current best parameters:
  fx: 548.4349
  fy: 548.2314
  px: 996.9658
  py: 738.3223
  k1: -0.00202553
  k2: -0.01233082
  k3: 0.00485998
  k4: -0.00112391
  p1: 0.00327488
  p2: -0.01022850
  p3: -0.00067860
  p4: -0.00152458
  s1: 0.0082
  s2: 0.0022
  s3: -0.0100
  s4: 0.0016
  tilt_x: -0.0241
  tilt_y: -0.0174
  scale: 2.9869
  rotation: -0.7868

Best score: 0.2653: : 799iter [00:23, 39.71iter/s]
Best score: 0.2653: : 800iter [00:23, 39.75iter/s]
Best score: 0.2653: : 800iter [00:23, 39.75iter/s]
Best score: 0.2653: : 801iter [00:23, 39.75iter/s]
Best score: 0.2653: : 802iter [00:23, 39.75iter/s]
Best score: 0.2653: : 803iter [00:23, 39.75iter/s]
Best score: 0.2653: : 804iter [00:23, 33.08iter/s]
Best score: 0.2653: : 804iter [00:23, 33.08iter/s]
Best score: 0.2653: : 805iter [00:23, 33.08iter/s]
Best score: 0.2653: : 806iter [00:23, 33.08iter/s]
Best score: 0.2653: : 807iter [00:23, 33.08iter/s]
Best score: 0.2653: : 808iter [00:23, 33.08iter/s]
Best score: 0.2653: : 809iter [00:23, 35.15iter/s]
Best score: 0.2653: : 809iter [00:23, 35.15iter/s]
Best score: 0.2653: : 810iter [00:23, 35.15iter/s]
Best score: 0.2653: : 811iter [00:23, 35.15iter/s]
Best score: 0.2653: : 812iter [00:23, 35.15iter/s]
Best score: 0.2653: : 813iter [00:23, 36.13iter/s]
Best score: 0.2653: : 813iter [00:23, 36.13iter/s]
Best score: 0.2653: : 814iter [00:23, 36.13iter/s]
Best score: 0.2653: : 815iter [00:23, 36.13iter/s]
Best score: 0.2653: : 816iter [00:23, 36.13iter/s]
Best score: 0.2653: : 817iter [00:23, 36.13iter/s]
Best score: 0.2653: : 818iter [00:23, 37.47iter/s]
Best score: 0.2653: : 818iter [00:23, 37.47iter/s]
Best score: 0.2653: : 819iter [00:23, 37.47iter/s]
Best score: 0.2653: : 820iter [00:23, 37.47iter/s]
Best score: 0.2653: : 821iter [00:23, 37.47iter/s]
Best score: 0.2653: : 822iter [00:23, 37.98iter/s]
Best score: 0.2653: : 822iter [00:23, 37.98iter/s]
Best score: 0.2653: : 823iter [00:23, 37.98iter/s]
Best score: 0.2653: : 824iter [00:23, 37.98iter/s]
Best score: 0.2653: : 825iter [00:23, 37.98iter/s]
Best score: 0.2653: : 826iter [00:23, 37.98iter/s]
Best score: 0.2653: : 827iter [00:23, 38.68iter/s]
Best score: 0.2653: : 827iter [00:23, 38.68iter/s]
Best score: 0.2653: : 828iter [00:23, 38.68iter/s]
Best score: 0.2638: : 829iter [00:23, 38.68iter/s]
Best score: 0.2638: : 830iter [00:24, 38.68iter/s]
Best score: 0.2638: : 831iter [00:24, 38.81iter/s]
Best score: 0.2638: : 831iter [00:24, 38.81iter/s]
Best score: 0.2638: : 832iter [00:24, 38.81iter/s]
Best score: 0.2638: : 833iter [00:24, 38.81iter/s]
Best score: 0.2638: : 834iter [00:24, 38.81iter/s]
Best score: 0.2638: : 835iter [00:24, 38.81iter/s]
Best score: 0.2638: : 836iter [00:24, 39.42iter/s]
Best score: 0.2638: : 836iter [00:24, 39.42iter/s]
Best score: 0.2638: : 837iter [00:24, 39.42iter/s]
Best score: 0.2638: : 838iter [00:24, 39.42iter/s]
Best score: 0.2624: : 839iter [00:24, 39.42iter/s]
Best score: 0.2624: : 840iter [00:24, 39.32iter/s]
Best score: 0.2624: : 840iter [00:24, 39.32iter/s]
Best score: 0.2624: : 841iter [00:24, 39.32iter/s]
Best score: 0.2624: : 842iter [00:24, 39.32iter/s]
Best score: 0.2624: : 843iter [00:24, 39.32iter/s]
Best score: 0.2624: : 844iter [00:24, 39.32iter/s]
Best score: 0.2624: : 845iter [00:24, 39.66iter/s]
Best score: 0.2624: : 845iter [00:24, 39.66iter/s]
Best score: 0.2624: : 846iter [00:24, 39.66iter/s]
Best score: 0.2624: : 847iter [00:24, 39.66iter/s]
Best score: 0.2624: : 848iter [00:24, 39.66iter/s]
[ITER 850] Best: 0.2624
[PARAMS] Current best parameters:
  fx: 546.0203
  fy: 545.9183
  px: 996.2310
  py: 737.6328
  k1: -0.01039738
  k2: 0.00203592
  k3: -0.00222378
  k4: 0.00007673
  p1: 0.00321725
  p2: -0.01212385
  p3: -0.00175702
  p4: -0.00107337
  s1: 0.0104
  s2: 0.0017
  s3: -0.0086
  s4: 0.0006
  tilt_x: -0.0275
  tilt_y: -0.0175
  scale: 3.0023
  rotation: -0.7886

Best score: 0.2624: : 849iter [00:24, 39.66iter/s]
Best score: 0.2624: : 850iter [00:24, 39.51iter/s]
Best score: 0.2624: : 850iter [00:24, 39.51iter/s]
Best score: 0.2624: : 851iter [00:24, 39.51iter/s]
Best score: 0.2624: : 852iter [00:24, 39.51iter/s]
Best score: 0.2624: : 853iter [00:24, 39.51iter/s]
Best score: 0.2624: : 854iter [00:24, 32.99iter/s]
Best score: 0.2624: : 854iter [00:24, 32.99iter/s]
Best score: 0.2624: : 855iter [00:24, 32.99iter/s]
Best score: 0.2624: : 856iter [00:24, 32.99iter/s]
Best score: 0.2624: : 857iter [00:24, 32.99iter/s]
Best score: 0.2624: : 858iter [00:24, 32.99iter/s]
Best score: 0.2624: : 859iter [00:24, 35.11iter/s]
Best score: 0.2606: : 859iter [00:24, 35.11iter/s]
Best score: 0.2606: : 860iter [00:24, 35.11iter/s]
Best score: 0.2606: : 861iter [00:24, 35.11iter/s]
Best score: 0.2606: : 862iter [00:24, 35.11iter/s]
Best score: 0.2606: : 863iter [00:24, 36.20iter/s]
Best score: 0.2606: : 863iter [00:24, 36.20iter/s]
Best score: 0.2606: : 864iter [00:24, 36.20iter/s]
Best score: 0.2606: : 865iter [00:24, 36.20iter/s]
Best score: 0.2606: : 866iter [00:24, 36.20iter/s]
Best score: 0.2606: : 867iter [00:25, 36.20iter/s]
Best score: 0.2606: : 868iter [00:25, 37.60iter/s]
Best score: 0.2606: : 868iter [00:25, 37.60iter/s]
Best score: 0.2591: : 869iter [00:25, 37.60iter/s]
Best score: 0.2591: : 870iter [00:25, 37.60iter/s]
Best score: 0.2591: : 871iter [00:25, 37.60iter/s]
Best score: 0.2591: : 872iter [00:25, 38.03iter/s]
Best score: 0.2591: : 872iter [00:25, 38.03iter/s]
Best score: 0.2591: : 873iter [00:25, 38.03iter/s]
Best score: 0.2591: : 874iter [00:25, 38.03iter/s]
Best score: 0.2591: : 875iter [00:25, 38.03iter/s]
Best score: 0.2591: : 876iter [00:25, 38.03iter/s]
Best score: 0.2591: : 877iter [00:25, 38.70iter/s]
Best score: 0.2591: : 877iter [00:25, 38.70iter/s]
Best score: 0.2591: : 878iter [00:25, 38.70iter/s]
Best score: 0.2574: : 879iter [00:25, 38.70iter/s]
Best score: 0.2574: : 880iter [00:25, 38.70iter/s]
Best score: 0.2574: : 881iter [00:25, 38.82iter/s]
Best score: 0.2574: : 881iter [00:25, 38.82iter/s]
Best score: 0.2574: : 882iter [00:25, 38.82iter/s]
Best score: 0.2574: : 883iter [00:25, 38.82iter/s]
Best score: 0.2574: : 884iter [00:25, 38.82iter/s]
Best score: 0.2574: : 885iter [00:25, 38.82iter/s]
Best score: 0.2574: : 886iter [00:25, 39.35iter/s]
Best score: 0.2574: : 886iter [00:25, 39.35iter/s]
Best score: 0.2574: : 887iter [00:25, 39.35iter/s]
Best score: 0.2574: : 888iter [00:25, 39.35iter/s]
Best score: 0.2574: : 889iter [00:25, 39.35iter/s]
Best score: 0.2574: : 890iter [00:25, 39.48iter/s]
Best score: 0.2574: : 890iter [00:25, 39.48iter/s]
Best score: 0.2574: : 891iter [00:25, 39.48iter/s]
Best score: 0.2574: : 892iter [00:25, 39.48iter/s]
Best score: 0.2574: : 893iter [00:25, 39.48iter/s]
Best score: 0.2574: : 894iter [00:25, 39.48iter/s]
Best score: 0.2574: : 895iter [00:25, 40.01iter/s]
Best score: 0.2574: : 895iter [00:25, 40.01iter/s]
Best score: 0.2574: : 896iter [00:25, 40.01iter/s]
Best score: 0.2574: : 897iter [00:25, 40.01iter/s]
Best score: 0.2574: : 898iter [00:25, 40.01iter/s]
[ITER 900] Best: 0.2574
[PARAMS] Current best parameters:
  fx: 542.7636
  fy: 542.6358
  px: 996.7545
  py: 737.2976
  k1: -0.01774165
  k2: 0.01859249
  k3: -0.01200487
  k4: 0.00207929
  p1: 0.00185441
  p2: -0.01426120
  p3: -0.00238313
  p4: 0.00052209
  s1: 0.0143
  s2: -0.0001
  s3: -0.0070
  s4: -0.0009
  tilt_x: -0.0264
  tilt_y: -0.0244
  scale: 3.0153
  rotation: -0.7735

Best score: 0.2574: : 899iter [00:25, 40.01iter/s]
Best score: 0.2574: : 900iter [00:25, 40.00iter/s]
Best score: 0.2574: : 900iter [00:25, 40.00iter/s]
Best score: 0.2574: : 901iter [00:25, 40.00iter/s]
Best score: 0.2574: : 902iter [00:25, 40.00iter/s]
Best score: 0.2574: : 903iter [00:25, 40.00iter/s]
Best score: 0.2574: : 904iter [00:26, 40.00iter/s]
Best score: 0.2574: : 905iter [00:26, 33.69iter/s]
Best score: 0.2574: : 905iter [00:26, 33.69iter/s]
Best score: 0.2574: : 906iter [00:26, 33.69iter/s]
Best score: 0.2574: : 907iter [00:26, 33.69iter/s]
Best score: 0.2574: : 908iter [00:26, 33.69iter/s]
Best score: 0.2574: : 909iter [00:26, 33.69iter/s]
Best score: 0.2574: : 910iter [00:26, 35.37iter/s]
Best score: 0.2574: : 910iter [00:26, 35.37iter/s]
Best score: 0.2574: : 911iter [00:26, 35.37iter/s]
Best score: 0.2574: : 912iter [00:26, 35.37iter/s]
Best score: 0.2574: : 913iter [00:26, 35.37iter/s]
Best score: 0.2574: : 914iter [00:26, 35.37iter/s]
Best score: 0.2574: : 915iter [00:26, 36.76iter/s]
Best score: 0.2574: : 915iter [00:26, 36.76iter/s]
Best score: 0.2574: : 916iter [00:26, 36.76iter/s]
Best score: 0.2574: : 917iter [00:26, 36.76iter/s]
Best score: 0.2574: : 918iter [00:26, 36.76iter/s]
Best score: 0.2569: : 919iter [00:26, 36.76iter/s]
Best score: 0.2569: : 920iter [00:26, 37.67iter/s]
Best score: 0.2569: : 920iter [00:26, 37.67iter/s]
Best score: 0.2569: : 921iter [00:26, 37.67iter/s]
Best score: 0.2569: : 922iter [00:26, 37.67iter/s]
Best score: 0.2569: : 923iter [00:26, 37.67iter/s]
Best score: 0.2569: : 924iter [00:26, 37.67iter/s]
Best score: 0.2569: : 925iter [00:26, 38.45iter/s]
Best score: 0.2569: : 925iter [00:26, 38.45iter/s]
Best score: 0.2569: : 926iter [00:26, 38.45iter/s]
Best score: 0.2569: : 927iter [00:26, 38.45iter/s]
Best score: 0.2569: : 928iter [00:26, 38.45iter/s]
Best score: 0.2569: : 929iter [00:26, 38.45iter/s]
Best score: 0.2569: : 930iter [00:26, 38.90iter/s]
Best score: 0.2569: : 930iter [00:26, 38.90iter/s]
Best score: 0.2569: : 931iter [00:26, 38.90iter/s]
Best score: 0.2569: : 932iter [00:26, 38.90iter/s]
Best score: 0.2569: : 933iter [00:26, 38.90iter/s]
Best score: 0.2569: : 934iter [00:26, 38.90iter/s]
Best score: 0.2569: : 935iter [00:26, 39.52iter/s]
Best score: 0.2569: : 935iter [00:26, 39.52iter/s]
Best score: 0.2569: : 936iter [00:26, 39.52iter/s]
Best score: 0.2569: : 937iter [00:26, 39.52iter/s]
Best score: 0.2569: : 938iter [00:26, 39.52iter/s]
Best score: 0.2569: : 939iter [00:26, 39.34iter/s]
Best score: 0.2567: : 939iter [00:26, 39.34iter/s]
Best score: 0.2567: : 940iter [00:26, 39.34iter/s]
Best score: 0.2567: : 941iter [00:26, 39.34iter/s]
Best score: 0.2567: : 942iter [00:26, 39.34iter/s]
Best score: 0.2567: : 943iter [00:26, 39.36iter/s]
Best score: 0.2567: : 943iter [00:26, 39.36iter/s]
Best score: 0.2567: : 944iter [00:27, 39.36iter/s]
Best score: 0.2567: : 945iter [00:27, 39.36iter/s]
Best score: 0.2567: : 946iter [00:27, 39.36iter/s]
Best score: 0.2567: : 947iter [00:27, 39.36iter/s]
Best score: 0.2567: : 948iter [00:27, 39.78iter/s]
Best score: 0.2567: : 948iter [00:27, 39.78iter/s]
[ITER 950] Best: 0.2565
[PARAMS] Current best parameters:
  fx: 545.5960
  fy: 545.4540
  px: 996.3566
  py: 737.6698
  k1: -0.01574237
  k2: 0.01104484
  k3: -0.00758576
  k4: 0.00122523
  p1: 0.00273577
  p2: -0.01329629
  p3: -0.00198148
  p4: -0.00033361
  s1: 0.0124
  s2: 0.0009
  s3: -0.0079
  s4: -0.0000
  tilt_x: -0.0264
  tilt_y: -0.0204
  scale: 3.0097
  rotation: -0.7767

Best score: 0.2565: : 949iter [00:27, 39.78iter/s]
Best score: 0.2565: : 950iter [00:27, 39.78iter/s]
Best score: 0.2565: : 951iter [00:27, 39.78iter/s]
Best score: 0.2565: : 952iter [00:27, 32.90iter/s]
Best score: 0.2565: : 952iter [00:27, 32.90iter/s]
Best score: 0.2565: : 953iter [00:27, 32.90iter/s]
Best score: 0.2565: : 954iter [00:27, 32.90iter/s]
Best score: 0.2565: : 955iter [00:27, 32.90iter/s]
Best score: 0.2565: : 956iter [00:27, 32.90iter/s]
Best score: 0.2565: : 957iter [00:27, 35.05iter/s]
Best score: 0.2565: : 957iter [00:27, 35.05iter/s]
Best score: 0.2565: : 958iter [00:27, 35.05iter/s]
Best score: 0.2561: : 959iter [00:27, 35.05iter/s]
Best score: 0.2561: : 960iter [00:27, 35.05iter/s]
Best score: 0.2561: : 961iter [00:27, 36.11iter/s]
Best score: 0.2561: : 961iter [00:27, 36.11iter/s]
Best score: 0.2561: : 962iter [00:27, 36.11iter/s]
Best score: 0.2561: : 963iter [00:27, 36.11iter/s]
Best score: 0.2561: : 964iter [00:27, 36.11iter/s]
Best score: 0.2561: : 965iter [00:27, 36.11iter/s]
Best score: 0.2561: : 966iter [00:27, 37.51iter/s]
Best score: 0.2561: : 966iter [00:27, 37.51iter/s]
Best score: 0.2561: : 967iter [00:27, 37.51iter/s]
Best score: 0.2561: : 968iter [00:27, 37.51iter/s]
Best score: 0.2559: : 969iter [00:27, 37.51iter/s]
Best score: 0.2559: : 970iter [00:27, 38.09iter/s]
Best score: 0.2559: : 970iter [00:27, 38.09iter/s]
Best score: 0.2559: : 971iter [00:27, 38.09iter/s]
Best score: 0.2559: : 972iter [00:27, 38.09iter/s]
Best score: 0.2559: : 973iter [00:27, 38.09iter/s]
Best score: 0.2559: : 974iter [00:27, 38.09iter/s]
Best score: 0.2559: : 975iter [00:27, 38.84iter/s]
Best score: 0.2559: : 975iter [00:27, 38.84iter/s]
Best score: 0.2559: : 976iter [00:27, 38.84iter/s]
Best score: 0.2559: : 977iter [00:27, 38.84iter/s]
Best score: 0.2559: : 978iter [00:27, 38.84iter/s]
Best score: 0.2559: : 979iter [00:27, 38.84iter/s]
Best score: 0.2559: : 980iter [00:27, 39.11iter/s]
Best score: 0.2559: : 980iter [00:27, 39.11iter/s]
Best score: 0.2559: : 981iter [00:28, 39.11iter/s]
Best score: 0.2559: : 982iter [00:28, 39.11iter/s]
Best score: 0.2559: : 983iter [00:28, 39.11iter/s]
Best score: 0.2559: : 984iter [00:28, 39.11iter/s]
Best score: 0.2559: : 985iter [00:28, 39.55iter/s]
Best score: 0.2559: : 985iter [00:28, 39.55iter/s]
Best score: 0.2559: : 986iter [00:28, 39.55iter/s]
Best score: 0.2559: : 987iter [00:28, 39.55iter/s]
Best score: 0.2559: : 988iter [00:28, 39.55iter/s]
Best score: 0.2554: : 989iter [00:28, 39.55iter/s]
Best score: 0.2554: : 990iter [00:28, 39.54iter/s]
Best score: 0.2554: : 990iter [00:28, 39.54iter/s]
Best score: 0.2554: : 991iter [00:28, 39.54iter/s]
Best score: 0.2554: : 992iter [00:28, 39.54iter/s]
Best score: 0.2554: : 993iter [00:28, 39.54iter/s]
Best score: 0.2554: : 994iter [00:28, 39.54iter/s]
Best score: 0.2554: : 995iter [00:28, 39.71iter/s]
Best score: 0.2554: : 995iter [00:28, 39.71iter/s]
Best score: 0.2554: : 996iter [00:28, 39.71iter/s]
Best score: 0.2554: : 997iter [00:28, 39.71iter/s]
Best score: 0.2554: : 998iter [00:28, 39.71iter/s]
[ITER 1000] Best: 0.2554
[PARAMS] Current best parameters:
  fx: 545.8233
  fy: 545.7243
  px: 996.5453
  py: 737.3269
  k1: -0.02040531
  k2: 0.01828851
  k3: -0.01197312
  k4: 0.00217369
  p1: 0.00264641
  p2: -0.01390870
  p3: -0.00395996
  p4: 0.00057762
  s1: 0.0153
  s2: -0.0001
  s3: -0.0067
  s4: -0.0006
  tilt_x: -0.0280
  tilt_y: -0.0226
  scale: 3.0143
  rotation: -0.7716

Best score: 0.2554: : 999iter [00:28, 39.71iter/s]
Best score: 0.2554: : 1000iter [00:28, 39.71iter/s]
Best score: 0.2554: : 1000iter [00:28, 39.71iter/s]
Best score: 0.2554: : 1001iter [00:28, 39.71iter/s]
Best score: 0.2554: : 1002iter [00:28, 39.71iter/s]
Best score: 0.2554: : 1003iter [00:28, 39.71iter/s]
Best score: 0.2554: : 1004iter [00:28, 33.15iter/s]
Best score: 0.2554: : 1004iter [00:28, 33.15iter/s]
Best score: 0.2554: : 1005iter [00:28, 33.15iter/s]
Best score: 0.2554: : 1006iter [00:28, 33.15iter/s]
Best score: 0.2554: : 1007iter [00:28, 33.15iter/s]
Best score: 0.2554: : 1008iter [00:28, 33.15iter/s]
Best score: 0.2554: : 1009iter [00:28, 35.13iter/s]
Best score: 0.2554: : 1009iter [00:28, 35.13iter/s]
Best score: 0.2554: : 1010iter [00:28, 35.13iter/s]
Best score: 0.2554: : 1011iter [00:28, 35.13iter/s]
Best score: 0.2554: : 1012iter [00:28, 35.13iter/s]
Best score: 0.2554: : 1013iter [00:28, 36.11iter/s]
Best score: 0.2554: : 1013iter [00:28, 36.11iter/s]
Best score: 0.2554: : 1014iter [00:28, 36.11iter/s]
Best score: 0.2554: : 1015iter [00:28, 36.11iter/s]
Best score: 0.2554: : 1016iter [00:28, 36.11iter/s]
Best score: 0.2554: : 1017iter [00:28, 36.11iter/s]
Best score: 0.2554: : 1018iter [00:28, 37.41iter/s]
Best score: 0.2554: : 1018iter [00:29, 37.41iter/s]
Best score: 0.2551: : 1019iter [00:29, 37.41iter/s]
Best score: 0.2551: : 1020iter [00:29, 37.41iter/s]
Best score: 0.2551: : 1021iter [00:29, 37.41iter/s]
Best score: 0.2551: : 1022iter [00:29, 37.94iter/s]
Best score: 0.2551: : 1022iter [00:29, 37.94iter/s]
Best score: 0.2551: : 1023iter [00:29, 37.94iter/s]
Best score: 0.2551: : 1024iter [00:29, 37.94iter/s]
Best score: 0.2551: : 1025iter [00:29, 37.94iter/s]
Best score: 0.2551: : 1026iter [00:29, 38.48iter/s]
Best score: 0.2551: : 1026iter [00:29, 38.48iter/s]
Best score: 0.2551: : 1027iter [00:29, 38.48iter/s]
Best score: 0.2551: : 1028iter [00:29, 38.48iter/s]
Best score: 0.2551: : 1029iter [00:29, 38.48iter/s]
Best score: 0.2551: : 1030iter [00:29, 38.77iter/s]
Best score: 0.2551: : 1030iter [00:29, 38.77iter/s]
Best score: 0.2551: : 1031iter [00:29, 38.77iter/s]
Best score: 0.2551: : 1032iter [00:29, 38.77iter/s]
Best score: 0.2551: : 1033iter [00:29, 38.77iter/s]
Best score: 0.2551: : 1034iter [00:29, 30.89iter/s]
Best score: 0.2551: : 1034iter [00:29, 30.89iter/s]
Best score: 0.2551: : 1035iter [00:29, 30.89iter/s]
Best score: 0.2551: : 1036iter [00:29, 30.89iter/s]
Best score: 0.2551: : 1037iter [00:29, 30.89iter/s]
Best score: 0.2551: : 1038iter [00:29, 30.89iter/s]
Best score: 0.2551: : 1039iter [00:29, 33.60iter/s]
Best score: 0.2551: : 1039iter [00:29, 33.60iter/s]
Best score: 0.2551: : 1040iter [00:29, 33.60iter/s]
Best score: 0.2551: : 1041iter [00:29, 33.60iter/s]
Best score: 0.2551: : 1042iter [00:29, 33.60iter/s]
Best score: 0.2551: : 1043iter [00:29, 35.11iter/s]
Best score: 0.2551: : 1043iter [00:29, 35.11iter/s]
Best score: 0.2551: : 1044iter [00:29, 35.11iter/s]
Best score: 0.2551: : 1045iter [00:29, 35.11iter/s]
Best score: 0.2551: : 1046iter [00:29, 35.11iter/s]
Best score: 0.2551: : 1047iter [00:29, 35.11iter/s]
Best score: 0.2551: : 1048iter [00:29, 36.87iter/s]
Best score: 0.2551: : 1048iter [00:29, 36.87iter/s]
[ITER 1050] Best: 0.2551
[PARAMS] Current best parameters:
  fx: 546.3315
  fy: 546.2313
  px: 996.6406
  py: 737.5278
  k1: -0.01844410
  k2: 0.01458940
  k3: -0.00985059
  k4: 0.00174046
  p1: 0.00282607
  p2: -0.01363933
  p3: -0.00401770
  p4: 0.00043830
  s1: 0.0150
  s2: 0.0001
  s3: -0.0065
  s4: -0.0006
  tilt_x: -0.0273
  tilt_y: -0.0225
  scale: 3.0110
  rotation: -0.7712

Best score: 0.2551: : 1049iter [00:29, 36.87iter/s]
Best score: 0.2551: : 1050iter [00:29, 36.87iter/s]
Best score: 0.2551: : 1051iter [00:30, 36.87iter/s]
Best score: 0.2551: : 1052iter [00:30, 31.31iter/s]
Best score: 0.2551: : 1052iter [00:30, 31.31iter/s]
Best score: 0.2551: : 1053iter [00:30, 31.31iter/s]
Best score: 0.2551: : 1054iter [00:30, 31.31iter/s]
Best score: 0.2551: : 1055iter [00:30, 31.31iter/s]
Best score: 0.2551: : 1056iter [00:30, 31.31iter/s]
Best score: 0.2551: : 1057iter [00:30, 33.81iter/s]
Best score: 0.2551: : 1057iter [00:30, 33.81iter/s]
Best score: 0.2551: : 1058iter [00:30, 33.81iter/s]
Best score: 0.2549: : 1059iter [00:30, 33.81iter/s]
Best score: 0.2549: : 1060iter [00:30, 33.81iter/s]
Best score: 0.2549: : 1061iter [00:30, 35.15iter/s]
Best score: 0.2549: : 1061iter [00:30, 35.15iter/s]
Best score: 0.2549: : 1062iter [00:30, 35.15iter/s]
Best score: 0.2549: : 1063iter [00:30, 35.15iter/s]
Best score: 0.2549: : 1064iter [00:30, 35.15iter/s]
Best score: 0.2549: : 1065iter [00:30, 35.15iter/s]
Best score: 0.2549: : 1066iter [00:30, 36.81iter/s]
Best score: 0.2549: : 1066iter [00:30, 36.81iter/s]
Best score: 0.2549: : 1067iter [00:30, 36.81iter/s]
Best score: 0.2549: : 1068iter [00:30, 36.81iter/s]
Best score: 0.2549: : 1069iter [00:30, 36.81iter/s]
Best score: 0.2549: : 1070iter [00:30, 36.81iter/s]
Best score: 0.2549: : 1071iter [00:30, 37.89iter/s]
Best score: 0.2549: : 1071iter [00:30, 37.89iter/s]
Best score: 0.2549: : 1072iter [00:30, 37.89iter/s]
Best score: 0.2549: : 1073iter [00:30, 37.89iter/s]
Best score: 0.2549: : 1074iter [00:30, 37.89iter/s]
Best score: 0.2549: : 1075iter [00:30, 37.89iter/s]
Best score: 0.2549: : 1076iter [00:30, 38.79iter/s]
Best score: 0.2549: : 1076iter [00:30, 38.79iter/s]
Best score: 0.2549: : 1077iter [00:30, 38.79iter/s]
Best score: 0.2549: : 1078iter [00:30, 38.79iter/s]
Best score: 0.2548: : 1079iter [00:30, 38.79iter/s]
Best score: 0.2548: : 1080iter [00:30, 39.04iter/s]
Best score: 0.2548: : 1080iter [00:30, 39.04iter/s]
Best score: 0.2548: : 1081iter [00:30, 39.04iter/s]
Best score: 0.2548: : 1082iter [00:30, 39.04iter/s]
Best score: 0.2548: : 1083iter [00:30, 39.04iter/s]
Best score: 0.2548: : 1084iter [00:30, 39.04iter/s]
Best score: 0.2548: : 1085iter [00:30, 39.57iter/s]
Best score: 0.2548: : 1085iter [00:30, 39.57iter/s]
Best score: 0.2548: : 1086iter [00:30, 39.57iter/s]
Best score: 0.2548: : 1087iter [00:30, 39.57iter/s]
Best score: 0.2548: : 1088iter [00:30, 39.57iter/s]
Best score: 0.2547: : 1089iter [00:30, 39.57iter/s]
Best score: 0.2547: : 1090iter [00:30, 39.54iter/s]
Best score: 0.2547: : 1090iter [00:30, 39.54iter/s]
Best score: 0.2547: : 1091iter [00:31, 39.54iter/s]
Best score: 0.2547: : 1092iter [00:31, 39.54iter/s]
Best score: 0.2547: : 1093iter [00:31, 39.54iter/s]
Best score: 0.2547: : 1094iter [00:31, 39.54iter/s]
Best score: 0.2547: : 1095iter [00:31, 39.82iter/s]
Best score: 0.2547: : 1095iter [00:31, 39.82iter/s]
Best score: 0.2547: : 1096iter [00:31, 39.82iter/s]
Best score: 0.2547: : 1097iter [00:31, 39.82iter/s]
Best score: 0.2547: : 1098iter [00:31, 39.82iter/s]
[ITER 1100] Best: 0.2546
[PARAMS] Current best parameters:
  fx: 546.2331
  fy: 546.1515
  px: 996.5265
  py: 737.2123
  k1: -0.02271622
  k2: 0.02084256
  k3: -0.01311268
  k4: 0.00234636
  p1: 0.00309060
  p2: -0.01376872
  p3: -0.00453706
  p4: 0.00074671
  s1: 0.0158
  s2: -0.0003
  s3: -0.0067
  s4: -0.0008
  tilt_x: -0.0295
  tilt_y: -0.0222
  scale: 3.0162
  rotation: -0.7695

Best score: 0.2546: : 1099iter [00:31, 39.82iter/s]
Best score: 0.2546: : 1100iter [00:31, 39.84iter/s]
Best score: 0.2546: : 1100iter [00:31, 35.16iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.254646 -> 0.254541

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,936 → 25,936 within 3500mm ring
  Ring polish did not improve score: 0.254541
  Success: True
  Tangential distortion: p1=0.003097, p2=-0.013775, p3=-0.004537, p4=0.000748
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2545
[✅] Parameters copied to: /working/MDC015_W068_2026-07-15/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 546.233053606183
    fy: 546.151524487757
    px: 996.5265100184477
    py: 737.2123291790443
    k1: -0.022712219294272185
    k2: 0.020845005958989013
    k3: -0.013112849597734202
    k4: 0.0023448158533142267
    p1: 0.003097157861613282
    p2: -0.013774942488248522
    p3: -0.004536962626568544
    p4: 0.0007482930839714365
    scale: 3.0161895364098763
    rotation: -0.7695091687564513
    mmpp: 1.2816823081608915

[🔄] Converting 1095 frames to PTD, creating debug images for 200 calibration frames...

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📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.53s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:11<00:00,  5.60s/rig]
[✅] Saved PTDs: /working/MDC015_W068_2026-07-15/calibration/debug/4_undistorted/output/ptd

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📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.09rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.09rig/s]
[✅] Saved PTDs: /working/MDC015_W068_2026-07-15/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%|▎         | 85916/3080448 [00:00<00:03, 859158.77px/s]
📤 Writing LUT:   6%|▌         | 180115/3080448 [00:00<00:03, 907880.96px/s]
📤 Writing LUT:   9%|▉         | 270904/3080448 [00:00<00:03, 896352.15px/s]
📤 Writing LUT:  12%|█▏        | 362352/3080448 [00:00<00:03, 903451.32px/s]
📤 Writing LUT:  15%|█▍        | 452714/3080448 [00:00<00:02, 892602.38px/s]
📤 Writing LUT:  18%|█▊        | 542003/3080448 [00:00<00:02, 886747.77px/s]
📤 Writing LUT:  20%|██        | 630698/3080448 [00:00<00:02, 880946.99px/s]
📤 Writing LUT:  23%|██▎       | 718807/3080448 [00:00<00:02, 878005.31px/s]
📤 Writing LUT:  26%|██▋       | 808641/3080448 [00:00<00:02, 884289.76px/s]
📤 Writing LUT:  29%|██▉       | 898594/3080448 [00:01<00:02, 888956.70px/s]
📤 Writing LUT:  32%|███▏      | 987502/3080448 [00:01<00:02, 884435.15px/s]
📤 Writing LUT:  35%|███▍      | 1077288/3080448 [00:01<00:02, 888488.25px/s]
📤 Writing LUT:  38%|███▊      | 1166776/3080448 [00:01<00:02, 890411.70px/s]
📤 Writing LUT:  41%|████      | 1255826/3080448 [00:01<00:02, 887797.94px/s]
📤 Writing LUT:  44%|████▎     | 1345667/3080448 [00:01<00:01, 890979.83px/s]
📤 Writing LUT:  47%|████▋     | 1434801/3080448 [00:01<00:01, 891086.88px/s]
📤 Writing LUT:  49%|████▉     | 1523915/3080448 [00:01<00:01, 884931.89px/s]
📤 Writing LUT:  52%|█████▏    | 1612806/3080448 [00:01<00:01, 886116.69px/s]
📤 Writing LUT:  55%|█████▌    | 1701427/3080448 [00:01<00:01, 882507.42px/s]
📤 Writing LUT:  58%|█████▊    | 1791279/3080448 [00:02<00:01, 887284.42px/s]
📤 Writing LUT:  61%|██████    | 1880016/3080448 [00:02<00:01, 885225.33px/s]
📤 Writing LUT:  64%|██████▍   | 1970446/3080448 [00:02<00:01, 890922.60px/s]
📤 Writing LUT:  67%|██████▋   | 2059546/3080448 [00:02<00:01, 887054.90px/s]
📤 Writing LUT:  70%|██████▉   | 2148260/3080448 [00:02<00:01, 878213.47px/s]
📤 Writing LUT:  73%|███████▎  | 2238407/3080448 [00:02<00:00, 885112.98px/s]
📤 Writing LUT:  76%|███████▌  | 2326941/3080448 [00:02<00:00, 884920.11px/s]
📤 Writing LUT:  78%|███████▊  | 2415594/3080448 [00:02<00:00, 885398.19px/s]
📤 Writing LUT:  81%|████████▏ | 2504145/3080448 [00:02<00:00, 884806.45px/s]
📤 Writing LUT:  84%|████████▍ | 2594204/3080448 [00:02<00:00, 889526.16px/s]
📤 Writing LUT:  87%|████████▋ | 2685662/3080448 [00:03<00:00, 897023.99px/s]
📤 Writing LUT:  90%|█████████ | 2775906/3080448 [00:03<00:00, 898643.32px/s]
📤 Writing LUT:  93%|█████████▎| 2867606/3080448 [00:03<00:00, 904142.64px/s]
📤 Writing LUT:  96%|█████████▌| 2959503/3080448 [00:03<00:00, 908587.06px/s]
📤 Writing LUT:  99%|█████████▉| 3050365/3080448 [00:03<00:00, 906436.81px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 890809.48px/s]
[✅] LUT written to: /working/MDC015_W068_2026-07-15/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W068_2026-07-15' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W068_2026-07-15/calibration/MDC015_W068_2026-07-15_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%|          | 0/572 [00:00<?, ?frame/s]

   🔥 1000:   1%|▏         | 8/572 [00:00<00:07, 79.09frame/s]

   🔥 1000:   3%|▎         | 18/572 [00:00<00:06, 86.38frame/s]

   🔥 1000:   5%|▍         | 28/572 [00:00<00:06, 88.53frame/s]

   🔥 1000:   7%|▋         | 38/572 [00:00<00:05, 90.62frame/s]

   🔥 1000:   8%|▊         | 48/572 [00:00<00:05, 91.89frame/s]

   🔥 1000:  10%|█         | 58/572 [00:00<00:05, 90.73frame/s]

   🔥 1000:  12%|█▏        | 68/572 [00:00<00:05, 88.54frame/s]

   🔥 1000:  13%|█▎        | 77/572 [00:00<00:05, 87.03frame/s]

   🔥 1000:  15%|█▌        | 86/572 [00:00<00:05, 86.22frame/s]

   🔥 1000:  17%|█▋        | 95/572 [00:01<00:05, 84.97frame/s]

   🔥 1000:  18%|█▊        | 104/572 [00:01<00:05, 83.53frame/s]

   🔥 1000:  20%|█▉        | 113/572 [00:01<00:05, 84.52frame/s]

   🔥 1000:  22%|██▏       | 123/572 [00:01<00:05, 86.28frame/s]

   🔥 1000:  23%|██▎       | 132/572 [00:01<00:05, 85.87frame/s]

   🔥 1000:  25%|██▍       | 141/572 [00:01<00:05, 85.13frame/s]

   🔥 1000:  26%|██▌       | 150/572 [00:01<00:04, 84.46frame/s]

   🔥 1000:  28%|██▊       | 159/572 [00:01<00:04, 83.54frame/s]

   🔥 1000:  29%|██▉       | 168/572 [00:01<00:04, 83.23frame/s]

   🔥 1000:  31%|███       | 177/572 [00:02<00:04, 82.88frame/s]

   🔥 1000:  33%|███▎      | 186/572 [00:02<00:04, 83.27frame/s]

   🔥 1000:  34%|███▍      | 195/572 [00:02<00:04, 83.34frame/s]

   🔥 1000:  36%|███▌      | 204/572 [00:02<00:04, 84.36frame/s]

   🔥 1000:  37%|███▋      | 214/572 [00:02<00:04, 86.37frame/s]

   🔥 1000:  39%|███▉      | 223/572 [00:02<00:04, 86.64frame/s]

   🔥 1000:  41%|████      | 232/572 [00:02<00:04, 84.79frame/s]

   🔥 1000:  42%|████▏     | 241/572 [00:02<00:04, 82.55frame/s]

   🔥 1000:  44%|████▎     | 250/572 [00:02<00:03, 82.31frame/s]

   🔥 1000:  45%|████▌     | 259/572 [00:03<00:03, 82.32frame/s]

   🔥 1000:  47%|████▋     | 268/572 [00:03<00:03, 82.11frame/s]

   🔥 1000:  48%|████▊     | 277/572 [00:03<00:03, 82.27frame/s]

   🔥 1000:  50%|█████     | 286/572 [00:03<00:03, 82.78frame/s]

   🔥 1000:  52%|█████▏    | 295/572 [00:03<00:03, 83.63frame/s]

   🔥 1000:  53%|█████▎    | 304/572 [00:03<00:03, 85.16frame/s]

   🔥 1000:  55%|█████▍    | 313/572 [00:03<00:03, 85.43frame/s]

   🔥 1000:  56%|█████▋    | 322/572 [00:03<00:02, 85.29frame/s]

   🔥 1000:  58%|█████▊    | 331/572 [00:03<00:02, 84.72frame/s]

   🔥 1000:  59%|█████▉    | 340/572 [00:04<00:02, 84.05frame/s]

   🔥 1000:  61%|██████    | 349/572 [00:04<00:02, 82.96frame/s]

   🔥 1000:  63%|██████▎   | 358/572 [00:04<00:02, 82.71frame/s]

   🔥 1000:  64%|██████▍   | 367/572 [00:04<00:02, 82.62frame/s]

   🔥 1000:  66%|██████▌   | 376/572 [00:04<00:02, 82.80frame/s]

   🔥 1000:  67%|██████▋   | 385/572 [00:04<00:02, 82.88frame/s]

   🔥 1000:  69%|██████▉   | 394/572 [00:04<00:02, 82.95frame/s]

   🔥 1000:  70%|███████   | 403/572 [00:04<00:02, 83.03frame/s]

   🔥 1000:  72%|███████▏  | 412/572 [00:04<00:01, 82.72frame/s]

   🔥 1000:  74%|███████▎  | 421/572 [00:04<00:01, 82.91frame/s]

   🔥 1000:  75%|███████▌  | 430/572 [00:05<00:01, 82.98frame/s]

   🔥 1000:  77%|███████▋  | 439/572 [00:05<00:01, 83.46frame/s]

   🔥 1000:  78%|███████▊  | 448/572 [00:05<00:01, 83.30frame/s]

   🔥 1000:  80%|███████▉  | 457/572 [00:05<00:01, 83.06frame/s]

   🔥 1000:  81%|████████▏ | 466/572 [00:05<00:01, 83.79frame/s]

   🔥 1000:  83%|████████▎ | 475/572 [00:05<00:01, 84.45frame/s]

   🔥 1000:  85%|████████▍ | 484/572 [00:05<00:01, 85.91frame/s]

   🔥 1000:  86%|████████▋ | 494/572 [00:05<00:00, 87.86frame/s]

   🔥 1000:  88%|████████▊ | 503/572 [00:05<00:00, 87.63frame/s]

   🔥 1000:  90%|████████▉ | 512/572 [00:06<00:00, 87.68frame/s]

   🔥 1000:  91%|█████████▏| 522/572 [00:06<00:00, 88.92frame/s]

   🔥 1000:  93%|█████████▎| 532/572 [00:06<00:00, 89.57frame/s]

   🔥 1000:  95%|█████████▍| 542/572 [00:06<00:00, 89.97frame/s]

   🔥 1000:  97%|█████████▋| 552/572 [00:06<00:00, 90.83frame/s]

   🔥 1000:  98%|█████████▊| 562/572 [00:06<00:00, 91.53frame/s]

   🔥 1000: 100%|██████████| 572/572 [00:06<00:00, 92.24frame/s]

                                                                [✅] 1000: /working/MDC015_W068_2026-07-15/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC015_W068_2026-07-15/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:09<00:09,  9.68s/rig]

   🔥 500:   0%|          | 0/523 [00:00<?, ?frame/s]

   🔥 500:   2%|▏         | 8/523 [00:00<00:07, 72.06frame/s]

   🔥 500:   3%|▎         | 16/523 [00:00<00:06, 74.42frame/s]

   🔥 500:   5%|▍         | 24/523 [00:00<00:06, 75.67frame/s]

   🔥 500:   6%|▌         | 32/523 [00:00<00:06, 76.14frame/s]

   🔥 500:   8%|▊         | 40/523 [00:00<00:06, 76.32frame/s]

   🔥 500:   9%|▉         | 48/523 [00:00<00:06, 76.62frame/s]

   🔥 500:  11%|█         | 56/523 [00:00<00:06, 76.41frame/s]

   🔥 500:  12%|█▏        | 64/523 [00:00<00:05, 76.89frame/s]

   🔥 500:  14%|█▍        | 72/523 [00:00<00:05, 76.51frame/s]

   🔥 500:  15%|█▌        | 80/523 [00:01<00:05, 74.72frame/s]

   🔥 500:  17%|█▋        | 88/523 [00:01<00:05, 74.17frame/s]

   🔥 500:  18%|█▊        | 96/523 [00:01<00:05, 73.93frame/s]

   🔥 500:  20%|█▉        | 104/523 [00:01<00:05, 72.94frame/s]

   🔥 500:  21%|██▏       | 112/523 [00:01<00:05, 72.99frame/s]

   🔥 500:  23%|██▎       | 120/523 [00:01<00:05, 72.87frame/s]

   🔥 500:  24%|██▍       | 128/523 [00:01<00:05, 73.93frame/s]

   🔥 500:  26%|██▌       | 136/523 [00:01<00:05, 74.87frame/s]

   🔥 500:  28%|██▊       | 144/523 [00:01<00:05, 75.15frame/s]

   🔥 500:  29%|██▉       | 152/523 [00:02<00:04, 75.10frame/s]

   🔥 500:  31%|███       | 160/523 [00:02<00:04, 74.58frame/s]

   🔥 500:  32%|███▏      | 168/523 [00:02<00:04, 74.50frame/s]

   🔥 500:  34%|███▎      | 176/523 [00:02<00:04, 73.10frame/s]

   🔥 500:  35%|███▌      | 184/523 [00:02<00:04, 73.64frame/s]

   🔥 500:  37%|███▋      | 192/523 [00:02<00:04, 74.32frame/s]

   🔥 500:  38%|███▊      | 200/523 [00:02<00:04, 74.67frame/s]

   🔥 500:  40%|███▉      | 208/523 [00:02<00:04, 74.93frame/s]

   🔥 500:  41%|████▏     | 216/523 [00:02<00:04, 75.24frame/s]

   🔥 500:  43%|████▎     | 224/523 [00:02<00:03, 75.41frame/s]

   🔥 500:  44%|████▍     | 232/523 [00:03<00:03, 76.29frame/s]

   🔥 500:  46%|████▌     | 240/523 [00:03<00:03, 76.93frame/s]

   🔥 500:  47%|████▋     | 248/523 [00:03<00:03, 77.28frame/s]

   🔥 500:  49%|████▉     | 256/523 [00:03<00:03, 77.31frame/s]

   🔥 500:  50%|█████     | 264/523 [00:03<00:03, 77.54frame/s]

   🔥 500:  52%|█████▏    | 272/523 [00:03<00:03, 77.41frame/s]

   🔥 500:  54%|█████▎    | 280/523 [00:03<00:03, 77.06frame/s]

   🔥 500:  55%|█████▌    | 288/523 [00:03<00:03, 76.77frame/s]

   🔥 500:  57%|█████▋    | 296/523 [00:03<00:02, 76.23frame/s]

   🔥 500:  58%|█████▊    | 304/523 [00:04<00:02, 76.11frame/s]

   🔥 500:  60%|█████▉    | 312/523 [00:04<00:02, 76.21frame/s]

   🔥 500:  61%|██████    | 320/523 [00:04<00:02, 76.31frame/s]

   🔥 500:  63%|██████▎   | 328/523 [00:04<00:02, 76.32frame/s]

   🔥 500:  64%|██████▍   | 336/523 [00:04<00:02, 75.71frame/s]

   🔥 500:  66%|██████▌   | 344/523 [00:04<00:02, 75.77frame/s]

   🔥 500:  67%|██████▋   | 352/523 [00:04<00:02, 76.04frame/s]

   🔥 500:  69%|██████▉   | 360/523 [00:04<00:02, 76.19frame/s]

   🔥 500:  70%|███████   | 368/523 [00:04<00:02, 76.53frame/s]

   🔥 500:  72%|███████▏  | 376/523 [00:04<00:01, 76.86frame/s]

   🔥 500:  73%|███████▎  | 384/523 [00:05<00:01, 77.05frame/s]

   🔥 500:  75%|███████▍  | 392/523 [00:05<00:01, 76.90frame/s]

   🔥 500:  76%|███████▋  | 400/523 [00:05<00:01, 77.15frame/s]

   🔥 500:  78%|███████▊  | 408/523 [00:05<00:01, 76.94frame/s]

   🔥 500:  80%|███████▉  | 416/523 [00:05<00:01, 77.06frame/s]

   🔥 500:  81%|████████  | 424/523 [00:05<00:01, 77.08frame/s]

   🔥 500:  83%|████████▎ | 432/523 [00:05<00:01, 77.51frame/s]

   🔥 500:  84%|████████▍ | 440/523 [00:05<00:01, 77.26frame/s]

   🔥 500:  86%|████████▌ | 448/523 [00:05<00:00, 77.43frame/s]

   🔥 500:  87%|████████▋ | 456/523 [00:06<00:00, 77.45frame/s]

   🔥 500:  89%|████████▊ | 464/523 [00:06<00:00, 77.39frame/s]

   🔥 500:  90%|█████████ | 472/523 [00:06<00:00, 77.03frame/s]

   🔥 500:  92%|█████████▏| 480/523 [00:06<00:00, 76.53frame/s]

   🔥 500:  93%|█████████▎| 488/523 [00:06<00:00, 76.53frame/s]

   🔥 500:  95%|█████████▍| 496/523 [00:06<00:00, 75.91frame/s]

   🔥 500:  96%|█████████▋| 504/523 [00:06<00:00, 75.61frame/s]

   🔥 500:  98%|█████████▊| 512/523 [00:06<00:00, 75.96frame/s]

   🔥 500:  99%|█████████▉| 520/523 [00:06<00:00, 76.02frame/s]

                                                               [✅] 500: /working/MDC015_W068_2026-07-15/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC015_W068_2026-07-15/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:17<00:00,  8.72s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:17<00:00,  8.87s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC015_W068_2026-07-15/calibration/debug/5_heatmap
[🏁] Finished processing MDC015_W068_2026-07-15


================================================================================================================================
================================================================================================================================