2026-06-10T23:26:42 - INFO: Slick 0.0.8a12

🟢 Starting Calibration Pipeline

=== Processing MDC059_W045_2026_05_29 ===
2026-06-10T23:26:42 - INFO: Camera ID: 12131329
2026-06-10T23:26:42 - INFO: Wand ID: 
2026-06-10T23:26:42 - INFO: Recorded At: 2026-05-29T15:57:59Z

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                        Step 1: Digitization                       
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[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 510 stationary frames, 544 remain


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→ 500 Phase 2: corners:  95%|█████████▍| 515/544 [00:03<00:00, 159.96it/s]

                                                                          [PASS2] Frame 0000294: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000295: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000296: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000297: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000410: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000411: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000412: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000413: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000759: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000850: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000853: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000860: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000863: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001074: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001077: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001120: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001121: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000296: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001120: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 11 frames fixed, 7 untrusted, 544 total


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→ 500 Phase 3: finalize:  99%|█████████▉| 539/544 [00:10<00:00, 53.56it/s]

                                                                          
[TIMING] 500 — 544 frames
  Phase 1  radial detect   (parallel)  :   7.93s  (14.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.81s  (7.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.57s
  Phase 3  finalize        (sequential):  10.29s  (18.9 ms/frame)
    decode + undistort                 :   5.45s  (10.0 ms/frame)
    corner apply + refinement          :   2.15s  (4.0 ms/frame)
    segment                            :   2.20s  (4.0 ms/frame)
    smooth                             :   0.30s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.59s


Processing Rigs:  50%|█████     | 1/2 [00:27<00:27, 27.51s/it][INFO] Filtered 688 stationary frames, 692 remain


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→ 1000 (692 images) Phase 1:  55%|█████▌    | 383/692 [00:05<00:03, 78.90it/s]

→ 1000 (692 images) Phase 1:  57%|█████▋    | 395/692 [00:06<00:04, 62.86it/s]

→ 1000 (692 images) Phase 1:  60%|█████▉    | 415/692 [00:06<00:03, 80.24it/s]

→ 1000 (692 images) Phase 1:  62%|██████▏   | 427/692 [00:06<00:04, 62.64it/s]

→ 1000 (692 images) Phase 1:  65%|██████▍   | 447/692 [00:06<00:03, 80.04it/s]

→ 1000 (692 images) Phase 1:  66%|██████▌   | 458/692 [00:07<00:03, 63.10it/s]

→ 1000 (692 images) Phase 1:  69%|██████▉   | 479/692 [00:07<00:02, 81.52it/s]

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→ 1000 (692 images) Phase 1:  74%|███████▍  | 511/692 [00:07<00:02, 82.80it/s]

→ 1000 (692 images) Phase 1:  76%|███████▌  | 523/692 [00:07<00:02, 64.67it/s]

→ 1000 (692 images) Phase 1:  78%|███████▊  | 543/692 [00:08<00:01, 82.73it/s]

→ 1000 (692 images) Phase 1:  80%|████████  | 555/692 [00:08<00:02, 64.96it/s]

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→ 1000 (692 images) Phase 1:  85%|████████▍ | 587/692 [00:08<00:01, 65.41it/s]

→ 1000 (692 images) Phase 1:  88%|████████▊ | 607/692 [00:08<00:01, 83.97it/s]

→ 1000 (692 images) Phase 1:  89%|████████▉ | 619/692 [00:09<00:01, 64.99it/s]

→ 1000 (692 images) Phase 1:  92%|█████████▏| 639/692 [00:09<00:00, 83.76it/s]

→ 1000 (692 images) Phase 1:  94%|█████████▍| 651/692 [00:09<00:00, 65.28it/s]

→ 1000 (692 images) Phase 1:  97%|█████████▋| 670/692 [00:09<00:00, 84.22it/s]

→ 1000 (692 images) Phase 1:  99%|█████████▊| 683/692 [00:09<00:00, 74.24it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/692 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/692 [00:00<04:05,  2.81it/s]

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→ 1000 Phase 2: corners:  53%|█████▎    | 366/692 [00:02<00:01, 169.91it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 385/692 [00:03<00:01, 174.32it/s]

→ 1000 Phase 2: corners:  58%|█████▊    | 404/692 [00:03<00:01, 177.58it/s]

→ 1000 Phase 2: corners:  61%|██████    | 422/692 [00:03<00:01, 175.51it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 442/692 [00:03<00:01, 182.36it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 462/692 [00:03<00:01, 183.78it/s]

→ 1000 Phase 2: corners:  70%|██████▉   | 481/692 [00:03<00:01, 183.24it/s]

→ 1000 Phase 2: corners:  72%|███████▏  | 501/692 [00:03<00:01, 183.23it/s]

→ 1000 Phase 2: corners:  75%|███████▌  | 521/692 [00:03<00:00, 187.20it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 540/692 [00:03<00:00, 186.32it/s]

→ 1000 Phase 2: corners:  81%|████████  | 559/692 [00:03<00:00, 182.88it/s]

→ 1000 Phase 2: corners:  84%|████████▎ | 578/692 [00:04<00:00, 184.32it/s]

→ 1000 Phase 2: corners:  86%|████████▋ | 597/692 [00:04<00:00, 178.00it/s]

→ 1000 Phase 2: corners:  89%|████████▉ | 616/692 [00:04<00:00, 180.81it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 635/692 [00:04<00:00, 182.22it/s]

→ 1000 Phase 2: corners:  95%|█████████▍| 654/692 [00:04<00:00, 179.15it/s]

                                                                           [PASS2] Frame 0000184: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000358: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000359: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000362: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000363: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000364: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000365: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000381: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000382: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000383: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000384: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000387: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000388: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000389: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000390: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000508: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000509: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000510: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000511: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000512: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000513: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000514: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000515: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000516: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000517: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000518: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000519: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000520: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000521: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001277: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001278: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001279: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001280: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001391: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001392: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001393: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001394: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001407: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001408: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001409: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001411: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001449: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001450: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001451: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001452: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000359: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000364: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000383: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000389: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000510: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000513: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000517: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000520: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001279: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001393: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001409: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001451: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 24 frames fixed, 25 untrusted, 692 total


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→ 1000 Phase 3: finalize:  79%|███████▉  | 549/692 [00:10<00:02, 53.82it/s]

→ 1000 Phase 3: finalize:  80%|████████  | 555/692 [00:10<00:02, 53.96it/s]

→ 1000 Phase 3: finalize:  81%|████████  | 561/692 [00:10<00:02, 54.09it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 567/692 [00:10<00:02, 54.26it/s]

→ 1000 Phase 3: finalize:  83%|████████▎ | 573/692 [00:10<00:02, 54.33it/s]

→ 1000 Phase 3: finalize:  84%|████████▎ | 579/692 [00:11<00:02, 54.39it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 585/692 [00:11<00:01, 54.44it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 591/692 [00:11<00:01, 54.48it/s]

→ 1000 Phase 3: finalize:  86%|████████▋ | 597/692 [00:11<00:01, 54.64it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 603/692 [00:11<00:01, 54.74it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 609/692 [00:11<00:01, 54.76it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 615/692 [00:11<00:01, 54.80it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 621/692 [00:11<00:01, 54.75it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 627/692 [00:11<00:01, 54.81it/s]

→ 1000 Phase 3: finalize:  91%|█████████▏| 633/692 [00:12<00:01, 54.83it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 639/692 [00:12<00:00, 54.72it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 645/692 [00:12<00:00, 54.59it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 651/692 [00:12<00:00, 54.60it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 657/692 [00:12<00:00, 54.42it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 663/692 [00:12<00:00, 54.25it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 669/692 [00:12<00:00, 54.13it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 675/692 [00:12<00:00, 53.94it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 681/692 [00:12<00:00, 53.71it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 687/692 [00:13<00:00, 53.46it/s]

                                                                           
[TIMING] 1000 — 692 frames
  Phase 1  radial detect   (parallel)  :  10.16s  (14.7 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.70s  (6.8 ms/frame)
  Phase 2b corner fix      (sequential):   1.27s
  Phase 3  finalize        (sequential):  13.18s  (19.0 ms/frame)
    decode + undistort                 :   6.80s  (9.8 ms/frame)
    corner apply + refinement          :   2.95s  (4.3 ms/frame)
    segment                            :   2.81s  (4.1 ms/frame)
    smooth                             :   0.38s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  29.31s


Processing Rigs: 100%|██████████| 2/2 [01:01<00:00, 31.29s/it]
Processing Rigs: 100%|██████████| 2/2 [01:01<00:00, 30.72s/it]
[💾] Saved RigFrameDataset to /working/MDC059_W045_2026_05_29/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1236 frames from 2 rigs to /working/MDC059_W045_2026_05_29/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC059_W045_2026_05_29/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 544
[📋] 1000: 100 frames selected from 692
[💾] Saved RigFrameDataset to /working/MDC059_W045_2026_05_29/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC059_W045_2026_05_29/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 [00:03<02:04,  3.19s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:00,  3.16s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:56,  3.16s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:53,  3.14s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:50,  3.14s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:46,  3.14s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:43,  3.14s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:40,  3.13s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:37,  3.13s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:34,  3.14s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:34<01:33,  3.23s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:38<01:31,  3.28s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:41<01:27,  3.24s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:44<01:24,  3.23s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:47<01:20,  3.20s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:50<01:16,  3.17s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:53<01:12,  3.15s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:57<01:09,  3.15s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:00<01:06,  3.16s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:03<01:03,  3.15s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:06<00:59,  3.14s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:09<00:56,  3.13s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:12<00:52,  3.12s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:15<00:49,  3.11s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:18<00:46,  3.10s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:22<00:43,  3.10s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:25<00:40,  3.10s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:28<00:37,  3.11s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:31<00:34,  3.11s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:34<00:32,  3.23s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:38<00:29,  3.28s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:41<00:26,  3.26s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:44<00:22,  3.25s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:47<00:19,  3.20s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:50<00:15,  3.16s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:53<00:12,  3.15s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:57<00:09,  3.13s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:00<00:06,  3.13s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:03<00:03,  3.12s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:05<00:00,  2.92s/it]
                                                                                       
[✅] Casing detection complete: cx=982.0, cy=738.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   1%|          | 1/100 [00:00<00:28,  3.44it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   1%|          | 1/100 [00:00<00:28,  3.44it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.44it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.44it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:27,  3.44it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   5%|▌         | 5/100 [00:00<00:27,  3.44it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   6%|▌         | 6/100 [00:00<00:05, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   6%|▌         | 6/100 [00:00<00:05, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   7%|▋         | 7/100 [00:00<00:05, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   8%|▊         | 8/100 [00:00<00:05, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   9%|▉         | 9/100 [00:00<00:05, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  10%|█         | 10/100 [00:00<00:05, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  11%|█         | 11/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  12%|█▏        | 12/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 321.212} (0.000):  13%|█▎        | 13/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.036 | Best: {'fx': 519.192} (0.036):  14%|█▍        | 14/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 519.192} (0.036):  15%|█▌        | 15/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.072 | Best: {'fx': 526.263} (0.072):  16%|█▌        | 16/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.080 | Best: {'fx': 533.333} (0.080):  17%|█▋        | 17/100 [00:00<00:04, 17.88it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.080 | Best: {'fx': 533.333} (0.080):  18%|█▊        | 18/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.044 | Best: {'fx': 533.333} (0.080):  18%|█▊        | 18/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.031 | Best: {'fx': 533.333} (0.080):  19%|█▉        | 19/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  20%|██        | 20/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.025 | Best: {'fx': 533.333} (0.080):  21%|██        | 21/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  22%|██▏       | 22/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.021 | Best: {'fx': 533.333} (0.080):  23%|██▎       | 23/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  24%|██▍       | 24/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.017 | Best: {'fx': 533.333} (0.080):  25%|██▌       | 25/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.019 | Best: {'fx': 533.333} (0.080):  26%|██▌       | 26/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  27%|██▋       | 27/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  28%|██▊       | 28/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  29%|██▉       | 29/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  30%|███       | 30/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.016 | Best: {'fx': 533.333} (0.080):  31%|███       | 31/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.014 | Best: {'fx': 533.333} (0.080):  32%|███▏      | 32/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.015 | Best: {'fx': 533.333} (0.080):  33%|███▎      | 33/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  34%|███▍      | 34/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  35%|███▌      | 35/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.080):  36%|███▌      | 36/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  37%|███▋      | 37/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.080):  38%|███▊      | 38/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.080):  39%|███▉      | 39/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  40%|████      | 40/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.002 | Best: {'fx': 533.333} (0.080):  41%|████      | 41/100 [00:00<00:01, 48.29it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.002 | Best: {'fx': 533.333} (0.080):  42%|████▏     | 42/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.080):  42%|████▏     | 42/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.080):  43%|████▎     | 43/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.080):  44%|████▍     | 44/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.080):  45%|████▌     | 45/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.080):  46%|████▌     | 46/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.080):  47%|████▋     | 47/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.005 | Best: {'fx': 533.333} (0.080):  48%|████▊     | 48/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  49%|████▉     | 49/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  50%|█████     | 50/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.023 | Best: {'fx': 533.333} (0.080):  51%|█████     | 51/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.080):  52%|█████▏    | 52/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.080):  53%|█████▎    | 53/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  54%|█████▍    | 54/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  55%|█████▌    | 55/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  56%|█████▌    | 56/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  57%|█████▋    | 57/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  58%|█████▊    | 58/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  59%|█████▉    | 59/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  60%|██████    | 60/100 [00:00<00:00, 101.62it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  61%|██████    | 61/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  61%|██████    | 61/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  62%|██████▏   | 62/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  63%|██████▎   | 63/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  64%|██████▍   | 64/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.080):  65%|██████▌   | 65/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  66%|██████▌   | 66/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  67%|██████▋   | 67/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  68%|██████▊   | 68/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  69%|██████▉   | 69/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  70%|███████   | 70/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  71%|███████   | 71/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  72%|███████▏  | 72/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  73%|███████▎  | 73/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  74%|███████▍  | 74/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  75%|███████▌  | 75/100 [00:00<00:00, 123.56it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  76%|███████▌  | 76/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  76%|███████▌  | 76/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  77%|███████▋  | 77/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  78%|███████▊  | 78/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  79%|███████▉  | 79/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  80%|████████  | 80/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  81%|████████  | 81/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  82%|████████▏ | 82/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  83%|████████▎ | 83/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  84%|████████▍ | 84/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  85%|████████▌ | 85/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  86%|████████▌ | 86/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  87%|████████▋ | 87/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  88%|████████▊ | 88/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  89%|████████▉ | 89/100 [00:00<00:00, 127.19it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  90%|█████████ | 90/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  90%|█████████ | 90/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  91%|█████████ | 91/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  92%|█████████▏| 92/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  93%|█████████▎| 93/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  94%|█████████▍| 94/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  95%|█████████▌| 95/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  96%|█████████▌| 96/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  97%|█████████▋| 97/100 [00:00<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  98%|█████████▊| 98/100 [00:01<00:00, 130.65it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.080):  99%|█████████▉| 99/100 [00:01<00:00, 130.65it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.023 | Best: {'fx': 512.415} (0.023):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.023 | Best: {'fx': 512.415} (0.023):   2%|▏         | 1/50 [00:00<00:06,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.022 | Best: {'fx': 512.415} (0.023):   2%|▏         | 1/50 [00:00<00:06,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.018 | Best: {'fx': 512.415} (0.023):   4%|▍         | 2/50 [00:00<00:06,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.020 | Best: {'fx': 512.415} (0.023):   6%|▌         | 3/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.023 | Best: {'fx': 512.415} (0.023):   8%|▊         | 4/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.025 | Best: {'fx': 513.435} (0.025):  10%|█         | 5/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.027 | Best: {'fx': 514.456} (0.027):  12%|█▏        | 6/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.032 | Best: {'fx': 517.517} (0.032):  14%|█▍        | 7/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.028 | Best: {'fx': 517.517} (0.032):  16%|█▌        | 8/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.030 | Best: {'fx': 517.517} (0.032):  18%|█▊        | 9/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.035 | Best: {'fx': 518.537} (0.035):  20%|██        | 10/50 [00:00<00:05,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.037 | Best: {'fx': 519.558} (0.037):  22%|██▏       | 11/50 [00:00<00:04,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.039 | Best: {'fx': 520.578} (0.039):  24%|██▍       | 12/50 [00:00<00:04,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.044 | Best: {'fx': 521.599} (0.044):  26%|██▌       | 13/50 [00:00<00:04,  7.83it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.044 | Best: {'fx': 521.599} (0.044):  28%|██▊       | 14/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.048 | Best: {'fx': 522.619} (0.048):  28%|██▊       | 14/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.054 | Best: {'fx': 523.639} (0.054):  30%|███       | 15/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.059 | Best: {'fx': 524.66} (0.059):  32%|███▏      | 16/50 [00:00<00:00, 70.97it/s] 
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.091 | Best: {'fx': 528.741} (0.091):  34%|███▍      | 17/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.067 | Best: {'fx': 528.741} (0.091):  36%|███▌      | 18/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.087 | Best: {'fx': 528.741} (0.091):  38%|███▊      | 19/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.078 | Best: {'fx': 528.741} (0.091):  40%|████      | 20/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.103 | Best: {'fx': 529.762} (0.103):  42%|████▏     | 21/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.101 | Best: {'fx': 529.762} (0.103):  44%|████▍     | 22/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.096 | Best: {'fx': 529.762} (0.103):  46%|████▌     | 23/50 [00:00<00:00, 70.97it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.096 | Best: {'fx': 529.762} (0.103):  48%|████▊     | 24/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.068 | Best: {'fx': 529.762} (0.103):  48%|████▊     | 24/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.053 | Best: {'fx': 529.762} (0.103):  50%|█████     | 25/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.085 | Best: {'fx': 529.762} (0.103):  52%|█████▏    | 26/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.063 | Best: {'fx': 529.762} (0.103):  54%|█████▍    | 27/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.075 | Best: {'fx': 529.762} (0.103):  56%|█████▌    | 28/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.058 | Best: {'fx': 529.762} (0.103):  58%|█████▊    | 29/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.031 | Best: {'fx': 529.762} (0.103):  60%|██████    | 30/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.049 | Best: {'fx': 529.762} (0.103):  62%|██████▏   | 31/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.045 | Best: {'fx': 529.762} (0.103):  64%|██████▍   | 32/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.042 | Best: {'fx': 529.762} (0.103):  66%|██████▌   | 33/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.031 | Best: {'fx': 529.762} (0.103):  68%|██████▊   | 34/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.040 | Best: {'fx': 529.762} (0.103):  70%|███████   | 35/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.038 | Best: {'fx': 529.762} (0.103):  72%|███████▏  | 36/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.036 | Best: {'fx': 529.762} (0.103):  74%|███████▍  | 37/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.035 | Best: {'fx': 529.762} (0.103):  76%|███████▌  | 38/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.028 | Best: {'fx': 529.762} (0.103):  78%|███████▊  | 39/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.033 | Best: {'fx': 529.762} (0.103):  80%|████████  | 40/50 [00:00<00:00, 74.16it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.033 | Best: {'fx': 529.762} (0.103):  82%|████████▏ | 41/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.030 | Best: {'fx': 529.762} (0.103):  82%|████████▏ | 41/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.026 | Best: {'fx': 529.762} (0.103):  84%|████████▍ | 42/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.024 | Best: {'fx': 529.762} (0.103):  86%|████████▌ | 43/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.025 | Best: {'fx': 529.762} (0.103):  88%|████████▊ | 44/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.023 | Best: {'fx': 529.762} (0.103):  90%|█████████ | 45/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.027 | Best: {'fx': 529.762} (0.103):  92%|█████████▏| 46/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.025 | Best: {'fx': 529.762} (0.103):  94%|█████████▍| 47/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.026 | Best: {'fx': 529.762} (0.103):  96%|█████████▌| 48/50 [00:00<00:00, 105.64it/s]
Brute | Start: {'fx': 533.333} (0.080) | Current: 0.023 | Best: {'fx': 529.762} (0.103):  98%|█████████▊| 49/50 [00:00<00:00, 105.64it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.7619047619048
    px: 982.0
    py: 738.0
    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: 3.0611: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 3.0611, picklable_objective(x0) = 3.0639

Best score: 3.0639: : 0iter [00:01, ?iter/s] 
Best score: 3.0639: : 1iter [00:01,  1.85s/iter]
Best score: 3.0639: : 1iter [00:01,  1.85s/iter]
Best score: 3.0639: : 2iter [00:02,  1.85s/iter]
Best score: 3.0639: : 3iter [00:02,  1.83iter/s]
Best score: 3.0639: : 3iter [00:02,  1.83iter/s]
Best score: 3.0639: : 4iter [00:02,  1.83iter/s]
Best score: 3.0639: : 5iter [00:02,  1.83iter/s]
Best score: 3.0639: : 6iter [00:02,  1.83iter/s]
Best score: 3.0639: : 7iter [00:02,  5.19iter/s]
Best score: 3.0639: : 7iter [00:02,  5.19iter/s]
Best score: 3.0639: : 8iter [00:02,  5.19iter/s]
Best score: 3.0639: : 9iter [00:02,  5.19iter/s]
Best score: 3.0639: : 10iter [00:02,  5.19iter/s]
Best score: 3.0639: : 11iter [00:02,  9.05iter/s]
Best score: 3.0639: : 11iter [00:02,  9.05iter/s]
Best score: 3.0639: : 12iter [00:02,  9.05iter/s]
Best score: 3.0639: : 13iter [00:02,  9.05iter/s]
Best score: 3.0639: : 14iter [00:02,  9.05iter/s]
Best score: 3.0639: : 15iter [00:02, 13.29iter/s]
Best score: 3.0639: : 15iter [00:02, 13.29iter/s]
Best score: 3.0639: : 16iter [00:02, 13.29iter/s]
Best score: 3.0639: : 17iter [00:02, 13.29iter/s]
Best score: 3.0639: : 18iter [00:02, 13.29iter/s]
Best score: 3.0639: : 19iter [00:02, 17.61iter/s]
Best score: 3.0639: : 19iter [00:02, 17.61iter/s]
Best score: 3.0639: : 20iter [00:02, 17.61iter/s]
Best score: 3.0639: : 21iter [00:02, 17.61iter/s]
Best score: 3.0639: : 22iter [00:02, 17.61iter/s]
Best score: 3.0639: : 23iter [00:02, 21.64iter/s]
Best score: 3.0639: : 23iter [00:02, 21.64iter/s]
Best score: 3.0639: : 24iter [00:02, 21.64iter/s]
Best score: 3.0639: : 25iter [00:02, 21.64iter/s]
Best score: 3.0639: : 26iter [00:02, 21.64iter/s]
Best score: 3.0639: : 27iter [00:02, 25.40iter/s]
Best score: 3.0639: : 27iter [00:02, 25.40iter/s]
Best score: 3.0639: : 28iter [00:02, 25.40iter/s]
Best score: 3.0639: : 29iter [00:02, 25.40iter/s]
Best score: 3.0639: : 30iter [00:02, 25.40iter/s]
Best score: 3.0639: : 31iter [00:02, 28.57iter/s]
Best score: 3.0639: : 31iter [00:02, 28.57iter/s]
Best score: 3.0639: : 32iter [00:02, 28.57iter/s]
Best score: 3.0639: : 33iter [00:02, 28.57iter/s]
Best score: 3.0639: : 34iter [00:02, 28.57iter/s]
Best score: 3.0639: : 35iter [00:02, 31.32iter/s]
Best score: 3.0639: : 35iter [00:02, 31.32iter/s]
Best score: 3.0639: : 36iter [00:02, 31.32iter/s]
Best score: 3.0639: : 37iter [00:02, 31.32iter/s]
Best score: 3.0639: : 38iter [00:02, 31.32iter/s]
Best score: 3.0639: : 39iter [00:02, 33.57iter/s]
Best score: 3.0639: : 39iter [00:02, 33.57iter/s]
Best score: 3.0639: : 40iter [00:03, 33.57iter/s]
Best score: 3.0639: : 41iter [00:03, 33.57iter/s]
Best score: 3.0639: : 42iter [00:03, 33.57iter/s]
Best score: 3.0639: : 43iter [00:03, 34.97iter/s]
Best score: 3.0639: : 43iter [00:03, 34.97iter/s]
Best score: 3.0639: : 44iter [00:03, 34.97iter/s]
Best score: 3.0639: : 45iter [00:03, 34.97iter/s]
Best score: 3.0639: : 46iter [00:03, 34.97iter/s]
Best score: 3.0639: : 47iter [00:03, 36.25iter/s]
Best score: 3.0639: : 47iter [00:03, 36.25iter/s]
Best score: 3.0639: : 48iter [00:03, 36.25iter/s]
[ITER 50] Best: 3.0639
[PARAMS] Current best parameters:
  fx: 529.7619
  fy: 529.7619
  px: 982.0000
  py: 738.0000
  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: 3.0639: : 49iter [00:03, 36.25iter/s]
Best score: 3.0639: : 50iter [00:03, 36.25iter/s]
Best score: 3.0639: : 51iter [00:03, 36.88iter/s]
Best score: 3.0639: : 51iter [00:03, 36.88iter/s]
Best score: 3.0639: : 52iter [00:03, 36.88iter/s]
Best score: 3.0639: : 53iter [00:03, 36.88iter/s]
Best score: 3.0639: : 54iter [00:03, 36.88iter/s]
Best score: 3.0639: : 55iter [00:03, 37.60iter/s]
Best score: 3.0639: : 55iter [00:03, 37.60iter/s]
Best score: 3.0639: : 56iter [00:03, 37.60iter/s]
Best score: 3.0639: : 57iter [00:03, 37.60iter/s]
Best score: 3.0639: : 58iter [00:03, 37.60iter/s]
Best score: 3.0639: : 59iter [00:03, 38.23iter/s]
Best score: 3.0639: : 59iter [00:03, 38.23iter/s]
Best score: 3.0639: : 60iter [00:03, 38.23iter/s]
Best score: 3.0639: : 61iter [00:03, 38.23iter/s]
Best score: 3.0639: : 62iter [00:03, 38.23iter/s]
Best score: 3.0639: : 63iter [00:03, 38.51iter/s]
Best score: 3.0639: : 63iter [00:03, 38.51iter/s]
Best score: 3.0639: : 64iter [00:03, 38.51iter/s]
Best score: 3.0639: : 65iter [00:03, 38.51iter/s]
Best score: 3.0639: : 66iter [00:03, 38.51iter/s]
Best score: 3.0639: : 67iter [00:03, 38.90iter/s]
Best score: 3.0639: : 67iter [00:03, 38.90iter/s]
Best score: 3.0639: : 68iter [00:03, 38.90iter/s]
Best score: 3.0639: : 69iter [00:03, 38.90iter/s]
Best score: 3.0639: : 70iter [00:03, 38.90iter/s]
Best score: 3.0639: : 71iter [00:03, 38.77iter/s]
Best score: 3.0639: : 71iter [00:03, 38.77iter/s]
Best score: 3.0639: : 72iter [00:03, 38.77iter/s]
Best score: 3.0639: : 73iter [00:03, 38.77iter/s]
Best score: 3.0639: : 74iter [00:03, 38.77iter/s]
Best score: 3.0639: : 75iter [00:03, 38.96iter/s]
Best score: 3.0639: : 75iter [00:03, 38.96iter/s]
Best score: 3.0639: : 76iter [00:03, 38.96iter/s]
Best score: 3.0639: : 77iter [00:03, 38.96iter/s]
Best score: 3.0639: : 78iter [00:03, 38.96iter/s]
Best score: 3.0639: : 79iter [00:03, 39.26iter/s]
Best score: 3.0639: : 79iter [00:03, 39.26iter/s]
Best score: 3.0639: : 80iter [00:04, 39.26iter/s]
Best score: 3.0639: : 81iter [00:04, 39.26iter/s]
Best score: 3.0639: : 82iter [00:04, 39.26iter/s]
Best score: 3.0639: : 83iter [00:04, 39.18iter/s]
Best score: 3.0639: : 83iter [00:04, 39.18iter/s]
Best score: 3.0639: : 84iter [00:04, 39.18iter/s]
Best score: 3.0639: : 85iter [00:04, 39.18iter/s]
Best score: 3.0639: : 86iter [00:04, 39.18iter/s]
Best score: 3.0639: : 87iter [00:04, 39.29iter/s]
Best score: 3.0639: : 87iter [00:04, 39.29iter/s]
Best score: 3.0639: : 88iter [00:04, 39.29iter/s]
Best score: 3.0639: : 89iter [00:04, 39.29iter/s]
Best score: 3.0639: : 90iter [00:04, 39.29iter/s]
Best score: 3.0639: : 91iter [00:04, 39.15iter/s]
Best score: 3.0639: : 91iter [00:04, 39.15iter/s]
Best score: 3.0639: : 92iter [00:04, 39.15iter/s]
Best score: 3.0639: : 93iter [00:04, 39.15iter/s]
Best score: 3.0639: : 94iter [00:04, 39.15iter/s]
Best score: 3.0639: : 95iter [00:04, 39.15iter/s]
Best score: 3.0639: : 96iter [00:04, 39.58iter/s]
Best score: 3.0639: : 96iter [00:04, 39.58iter/s]
Best score: 3.0639: : 97iter [00:04, 39.58iter/s]
Best score: 3.0639: : 98iter [00:04, 39.58iter/s]
[ITER 100] Best: 3.0639
[PARAMS] Current best parameters:
  fx: 529.7619
  fy: 529.7619
  px: 982.0000
  py: 738.0000
  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: 3.0639: : 99iter [00:04, 39.58iter/s]
Best score: 3.0639: : 100iter [00:04, 39.52iter/s]
Best score: 3.0639: : 100iter [00:04, 39.52iter/s]
Best score: 3.0639: : 101iter [00:04, 39.52iter/s]
Best score: 3.0639: : 102iter [00:04, 39.52iter/s]
Best score: 3.0639: : 103iter [00:04, 39.52iter/s]
Best score: 3.0639: : 104iter [00:04, 36.73iter/s]
Best score: 3.0639: : 104iter [00:04, 36.73iter/s]
Best score: 3.0639: : 105iter [00:04, 36.73iter/s]
Best score: 3.0639: : 106iter [00:04, 36.73iter/s]
Best score: 3.0639: : 107iter [00:04, 36.73iter/s]
Best score: 3.0639: : 108iter [00:04, 37.44iter/s]
Best score: 3.0639: : 108iter [00:04, 37.44iter/s]
Best score: 3.0639: : 109iter [00:04, 37.44iter/s]
Best score: 3.0639: : 110iter [00:04, 37.44iter/s]
Best score: 3.0639: : 111iter [00:04, 37.44iter/s]
Best score: 3.0639: : 112iter [00:04, 37.70iter/s]
Best score: 3.0639: : 112iter [00:04, 37.70iter/s]
Best score: 3.0639: : 113iter [00:04, 37.70iter/s]
Best score: 3.0639: : 114iter [00:04, 37.70iter/s]
Best score: 3.0639: : 115iter [00:04, 37.70iter/s]
Best score: 3.0639: : 116iter [00:04, 38.18iter/s]
Best score: 3.0639: : 116iter [00:04, 38.18iter/s]
Best score: 3.0639: : 117iter [00:04, 38.18iter/s]
Best score: 3.0639: : 118iter [00:05, 38.18iter/s]
Best score: 3.0639: : 119iter [00:05, 38.18iter/s]
Best score: 3.0639: : 120iter [00:05, 38.33iter/s]
Best score: 3.0639: : 120iter [00:05, 38.33iter/s]
Best score: 3.0639: : 121iter [00:05, 38.33iter/s]
Best score: 3.0639: : 122iter [00:05, 38.33iter/s]
Best score: 3.0639: : 123iter [00:05, 38.33iter/s]
Best score: 3.0639: : 124iter [00:05, 38.64iter/s]
Best score: 3.0639: : 124iter [00:05, 38.64iter/s]
Best score: 3.0639: : 125iter [00:05, 38.64iter/s]
Best score: 3.0639: : 126iter [00:05, 38.64iter/s]
Best score: 3.0639: : 127iter [00:05, 38.64iter/s]
Best score: 3.0639: : 128iter [00:05, 38.87iter/s]
Best score: 3.0639: : 128iter [00:05, 38.87iter/s]
Best score: 3.0639: : 129iter [00:05, 38.87iter/s]
Best score: 3.0639: : 130iter [00:05, 38.87iter/s]
Best score: 3.0639: : 131iter [00:05, 38.87iter/s]
Best score: 3.0639: : 132iter [00:05, 38.89iter/s]
Best score: 3.0639: : 132iter [00:05, 38.89iter/s]
Best score: 3.0639: : 133iter [00:05, 38.89iter/s]
Best score: 3.0639: : 134iter [00:05, 38.89iter/s]
Best score: 3.0639: : 135iter [00:05, 38.89iter/s]
Best score: 3.0639: : 136iter [00:05, 39.06iter/s]
Best score: 3.0639: : 136iter [00:05, 39.06iter/s]
Best score: 3.0639: : 137iter [00:05, 39.06iter/s]
Best score: 3.0639: : 138iter [00:05, 39.06iter/s]
Best score: 3.0639: : 139iter [00:05, 39.06iter/s]
Best score: 3.0639: : 140iter [00:05, 38.87iter/s]
Best score: 3.0639: : 140iter [00:05, 38.87iter/s]
Best score: 3.0639: : 141iter [00:05, 38.87iter/s]
Best score: 3.0639: : 142iter [00:05, 38.87iter/s]
Best score: 3.0639: : 143iter [00:05, 38.87iter/s]
Best score: 3.0639: : 144iter [00:05, 38.87iter/s]
Best score: 3.0639: : 144iter [00:05, 38.87iter/s]
Best score: 3.0639: : 145iter [00:05, 38.87iter/s]
Best score: 3.0639: : 146iter [00:05, 38.87iter/s]
Best score: 3.0639: : 147iter [00:05, 38.87iter/s]
Best score: 3.0639: : 148iter [00:05, 38.57iter/s]
Best score: 3.0639: : 148iter [00:05, 38.57iter/s]
[ITER 150] Best: 3.0639
[PARAMS] Current best parameters:
  fx: 529.7619
  fy: 529.7619
  px: 982.0000
  py: 738.0000
  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: 3.0639: : 149iter [00:05, 38.57iter/s]
Best score: 3.0639: : 150iter [00:05, 38.57iter/s]
Best score: 3.0639: : 151iter [00:05, 38.57iter/s]
Best score: 3.0639: : 152iter [00:05, 31.36iter/s]
Best score: 3.0639: : 152iter [00:05, 31.36iter/s]
Best score: 3.0639: : 153iter [00:05, 31.36iter/s]
Best score: 3.0639: : 154iter [00:06, 31.36iter/s]
Best score: 3.0639: : 155iter [00:06, 31.36iter/s]
Best score: 3.0639: : 156iter [00:06, 33.20iter/s]
Best score: 3.0639: : 156iter [00:06, 33.20iter/s]
Best score: 3.0639: : 157iter [00:06, 33.20iter/s]
Best score: 3.0639: : 158iter [00:06, 33.20iter/s]
Best score: 3.0639: : 159iter [00:06, 33.20iter/s]
Best score: 3.0639: : 160iter [00:06, 34.63iter/s]
Best score: 3.0639: : 160iter [00:06, 34.63iter/s]
Best score: 3.0639: : 161iter [00:06, 34.63iter/s]
Best score: 3.0639: : 162iter [00:06, 34.63iter/s]
Best score: 3.0639: : 163iter [00:06, 34.63iter/s]
Best score: 3.0639: : 164iter [00:06, 35.98iter/s]
Best score: 3.0639: : 164iter [00:06, 35.98iter/s]
Best score: 3.0639: : 165iter [00:06, 35.98iter/s]
Best score: 3.0639: : 166iter [00:06, 35.98iter/s]
Best score: 3.0639: : 167iter [00:06, 35.98iter/s]
Best score: 3.0639: : 168iter [00:06, 37.09iter/s]
Best score: 3.0639: : 168iter [00:06, 37.09iter/s]
Best score: 3.0639: : 169iter [00:06, 37.09iter/s]
Best score: 3.0639: : 170iter [00:06, 37.09iter/s]
Best score: 3.0639: : 171iter [00:06, 37.09iter/s]
Best score: 3.0639: : 172iter [00:06, 37.52iter/s]
Best score: 3.0639: : 172iter [00:06, 37.52iter/s]
Best score: 3.0639: : 173iter [00:06, 37.52iter/s]
Best score: 3.0639: : 174iter [00:06, 37.52iter/s]
Best score: 3.0639: : 175iter [00:06, 37.52iter/s]
Best score: 3.0639: : 176iter [00:06, 37.90iter/s]
Best score: 3.0639: : 176iter [00:06, 37.90iter/s]
Best score: 3.0639: : 177iter [00:06, 37.90iter/s]
Best score: 3.0639: : 178iter [00:06, 37.90iter/s]
Best score: 3.0639: : 179iter [00:06, 37.90iter/s]
Best score: 3.0639: : 180iter [00:06, 38.15iter/s]
Best score: 3.0639: : 180iter [00:06, 38.15iter/s]
Best score: 3.0639: : 181iter [00:06, 38.15iter/s]
Best score: 3.0639: : 182iter [00:06, 38.15iter/s]
Best score: 3.0639: : 183iter [00:06, 38.15iter/s]
Best score: 3.0639: : 184iter [00:06, 38.15iter/s]
Best score: 3.0639: : 185iter [00:06, 38.85iter/s]
Best score: 3.0639: : 185iter [00:06, 38.85iter/s]
Best score: 3.0639: : 186iter [00:06, 38.85iter/s]
Best score: 3.0639: : 187iter [00:06, 38.85iter/s]
Best score: 3.0639: : 188iter [00:06, 38.85iter/s]
Best score: 3.0639: : 189iter [00:06, 38.85iter/s]
Best score: 3.0639: : 190iter [00:06, 39.08iter/s]
Best score: 3.0639: : 190iter [00:06, 39.08iter/s]
Best score: 3.0639: : 191iter [00:06, 39.08iter/s]
Best score: 3.0639: : 192iter [00:06, 39.08iter/s]
Best score: 3.0639: : 193iter [00:07, 39.08iter/s]
Best score: 3.0639: : 194iter [00:07, 39.08iter/s]
Best score: 3.0639: : 195iter [00:07, 39.44iter/s]
Best score: 3.0639: : 195iter [00:07, 39.44iter/s]
Best score: 3.0639: : 196iter [00:07, 39.44iter/s]
Best score: 3.0639: : 197iter [00:07, 39.44iter/s]
Best score: 3.0639: : 198iter [00:07, 39.44iter/s]
[ITER 200] Best: 3.0639
[PARAMS] Current best parameters:
  fx: 529.7619
  fy: 529.7619
  px: 982.0000
  py: 738.0000
  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: 3.0639: : 199iter [00:07, 39.44iter/s]
Best score: 3.0639: : 200iter [00:07, 39.43iter/s]
Best score: 3.0639: : 200iter [00:07, 39.43iter/s]
Best score: 3.0639: : 201iter [00:07, 39.43iter/s]
Best score: 3.0639: : 202iter [00:07, 39.43iter/s]
Best score: 3.0639: : 203iter [00:07, 39.43iter/s]
Best score: 3.0639: : 204iter [00:07, 32.96iter/s]
Best score: 3.0639: : 204iter [00:07, 32.96iter/s]
Best score: 3.0639: : 205iter [00:07, 32.96iter/s]
Best score: 3.0639: : 206iter [00:07, 32.96iter/s]
Best score: 3.0639: : 207iter [00:07, 32.96iter/s]
Best score: 3.0639: : 208iter [00:07, 32.96iter/s]
Best score: 3.0639: : 209iter [00:07, 35.02iter/s]
Best score: 3.0639: : 209iter [00:07, 35.02iter/s]
Best score: 3.0639: : 210iter [00:07, 35.02iter/s]
Best score: 3.0639: : 211iter [00:07, 35.02iter/s]
Best score: 3.0639: : 212iter [00:07, 35.02iter/s]
Best score: 3.0639: : 213iter [00:07, 36.18iter/s]
Best score: 3.0639: : 213iter [00:07, 36.18iter/s]
Best score: 3.0639: : 214iter [00:07, 36.18iter/s]
Best score: 3.0639: : 215iter [00:07, 36.18iter/s]
Best score: 3.0639: : 216iter [00:07, 36.18iter/s]
Best score: 3.0639: : 217iter [00:07, 36.18iter/s]
Best score: 3.0639: : 218iter [00:07, 37.49iter/s]
Best score: 3.0639: : 218iter [00:07, 37.49iter/s]
Best score: 3.0639: : 219iter [00:07, 37.49iter/s]
Best score: 3.0639: : 220iter [00:07, 37.49iter/s]
Best score: 3.0639: : 221iter [00:07, 37.49iter/s]
Best score: 3.0639: : 222iter [00:07, 38.03iter/s]
Best score: 3.0639: : 222iter [00:07, 38.03iter/s]
Best score: 3.0639: : 223iter [00:07, 38.03iter/s]
Best score: 3.0639: : 224iter [00:07, 38.03iter/s]
Best score: 3.0639: : 225iter [00:07, 38.03iter/s]
Best score: 3.0639: : 226iter [00:07, 38.03iter/s]
Best score: 3.0639: : 227iter [00:07, 38.81iter/s]
Best score: 3.0639: : 227iter [00:07, 38.81iter/s]
Best score: 3.0639: : 228iter [00:07, 38.81iter/s]
Best score: 3.0639: : 229iter [00:07, 38.81iter/s]
Best score: 3.0639: : 230iter [00:08, 38.81iter/s]
Best score: 3.0639: : 231iter [00:08, 39.10iter/s]
Best score: 3.0639: : 231iter [00:08, 39.10iter/s]
Best score: 3.0639: : 232iter [00:08, 39.10iter/s]
Best score: 3.0639: : 233iter [00:08, 39.10iter/s]
Best score: 3.0639: : 234iter [00:08, 39.10iter/s]
Best score: 3.0639: : 235iter [00:08, 39.10iter/s]
Best score: 3.0639: : 236iter [00:08, 39.51iter/s]
Best score: 3.0639: : 236iter [00:08, 39.51iter/s]
Best score: 3.0639: : 237iter [00:08, 39.51iter/s]
Best score: 3.0639: : 238iter [00:08, 39.51iter/s]
Best score: 3.0639: : 239iter [00:08, 39.51iter/s]
Best score: 3.0639: : 240iter [00:08, 39.57iter/s]
Best score: 3.0639: : 240iter [00:08, 39.57iter/s]
Best score: 3.0639: : 241iter [00:08, 39.57iter/s]
Best score: 3.0639: : 242iter [00:08, 39.57iter/s]
Best score: 3.0639: : 243iter [00:08, 39.57iter/s]
Best score: 3.0639: : 244iter [00:08, 39.57iter/s]
Best score: 3.0639: : 245iter [00:08, 39.84iter/s]
Best score: 3.0639: : 245iter [00:08, 39.84iter/s]
Best score: 3.0639: : 246iter [00:08, 39.84iter/s]
Best score: 3.0639: : 247iter [00:08, 39.84iter/s]
Best score: 3.0639: : 248iter [00:08, 39.84iter/s]
[ITER 250] Best: 2.5421
[PARAMS] Current best parameters:
  fx: 527.4077
  fy: 526.9035
  px: 986.3601
  py: 740.8776
  k1: 0.02076918
  k2: -0.00859704
  k3: -0.00848600
  k4: 0.00414810
  p1: -0.00010037
  p2: 0.00147486
  p3: -0.00592780
  p4: 0.00778910
  s1: -0.0114
  s2: 0.0013
  s3: 0.0114
  s4: -0.0109
  tilt_x: 0.0099
  tilt_y: -0.0167
  scale: 2.9886
  rotation: 0.1456

Best score: 2.5421: : 249iter [00:08, 39.84iter/s]
Best score: 2.5421: : 250iter [00:08, 39.76iter/s]
Best score: 2.5421: : 250iter [00:08, 39.76iter/s]
Best score: 2.5421: : 251iter [00:08, 39.76iter/s]
Best score: 2.5421: : 252iter [00:08, 39.76iter/s]
Best score: 2.5421: : 253iter [00:08, 39.76iter/s]
Best score: 2.5421: : 254iter [00:08, 32.91iter/s]
Best score: 2.5421: : 254iter [00:08, 32.91iter/s]
Best score: 2.5421: : 255iter [00:08, 32.91iter/s]
Best score: 2.5421: : 256iter [00:08, 32.91iter/s]
Best score: 2.5421: : 257iter [00:08, 32.91iter/s]
Best score: 2.5421: : 258iter [00:08, 32.91iter/s]
Best score: 2.5421: : 259iter [00:08, 35.02iter/s]
Best score: 2.3620: : 259iter [00:08, 35.02iter/s]
Best score: 2.3620: : 260iter [00:08, 35.02iter/s]
Best score: 2.3620: : 261iter [00:08, 35.02iter/s]
Best score: 2.3620: : 262iter [00:08, 35.02iter/s]
Best score: 2.3620: : 263iter [00:08, 36.06iter/s]
Best score: 2.3620: : 263iter [00:08, 36.06iter/s]
Best score: 2.3620: : 264iter [00:08, 36.06iter/s]
Best score: 2.3620: : 265iter [00:08, 36.06iter/s]
Best score: 2.3620: : 266iter [00:08, 36.06iter/s]
Best score: 2.3620: : 267iter [00:09, 36.06iter/s]
Best score: 2.3620: : 268iter [00:09, 37.40iter/s]
Best score: 2.3620: : 268iter [00:09, 37.40iter/s]
Best score: 2.3620: : 269iter [00:09, 37.40iter/s]
Best score: 2.3620: : 270iter [00:09, 37.40iter/s]
Best score: 2.3620: : 271iter [00:09, 37.40iter/s]
Best score: 2.3620: : 272iter [00:09, 37.96iter/s]
Best score: 2.3620: : 272iter [00:09, 37.96iter/s]
Best score: 2.3620: : 273iter [00:09, 37.96iter/s]
Best score: 2.3620: : 274iter [00:09, 37.96iter/s]
Best score: 2.3620: : 275iter [00:09, 37.96iter/s]
Best score: 2.3620: : 276iter [00:09, 37.96iter/s]
Best score: 2.3620: : 277iter [00:09, 38.63iter/s]
Best score: 2.3620: : 277iter [00:09, 38.63iter/s]
Best score: 2.3620: : 278iter [00:09, 38.63iter/s]
Best score: 1.6073: : 279iter [00:09, 38.63iter/s]
Best score: 1.6073: : 280iter [00:09, 38.63iter/s]
Best score: 1.6073: : 281iter [00:09, 38.87iter/s]
Best score: 1.6073: : 281iter [00:09, 38.87iter/s]
Best score: 1.6073: : 282iter [00:09, 38.87iter/s]
Best score: 1.6073: : 283iter [00:09, 38.87iter/s]
Best score: 1.6073: : 284iter [00:09, 38.87iter/s]
Best score: 1.6073: : 285iter [00:09, 38.87iter/s]
Best score: 1.6073: : 286iter [00:09, 39.27iter/s]
Best score: 1.6073: : 286iter [00:09, 39.27iter/s]
Best score: 1.6073: : 287iter [00:09, 39.27iter/s]
Best score: 1.6073: : 288iter [00:09, 39.27iter/s]
Best score: 1.6073: : 289iter [00:09, 39.27iter/s]
Best score: 1.6073: : 290iter [00:09, 39.31iter/s]
Best score: 1.6073: : 290iter [00:09, 39.31iter/s]
Best score: 1.6073: : 291iter [00:09, 39.31iter/s]
Best score: 1.6073: : 292iter [00:09, 39.31iter/s]
Best score: 1.6073: : 293iter [00:09, 39.31iter/s]
Best score: 1.6073: : 294iter [00:09, 39.31iter/s]
Best score: 1.6073: : 295iter [00:09, 39.63iter/s]
Best score: 1.6073: : 295iter [00:09, 39.63iter/s]
Best score: 1.6073: : 296iter [00:09, 39.63iter/s]
Best score: 1.6073: : 297iter [00:09, 39.63iter/s]
Best score: 1.6073: : 298iter [00:09, 39.63iter/s]
[ITER 300] Best: 1.6073
[PARAMS] Current best parameters:
  fx: 539.5863
  fy: 539.2569
  px: 981.4516
  py: 742.6059
  k1: 0.00047175
  k2: 0.00148536
  k3: -0.01159488
  k4: 0.00423775
  p1: 0.00977571
  p2: 0.00162167
  p3: -0.00597367
  p4: 0.00321607
  s1: -0.0044
  s2: -0.0000
  s3: 0.0011
  s4: -0.0037
  tilt_x: -0.0001
  tilt_y: 0.0080
  scale: 3.0038
  rotation: 0.0727

Best score: 1.6073: : 299iter [00:09, 39.63iter/s]
Best score: 1.6073: : 300iter [00:09, 39.48iter/s]
Best score: 1.6073: : 300iter [00:09, 39.48iter/s]
Best score: 1.6073: : 301iter [00:09, 39.48iter/s]
Best score: 1.6073: : 302iter [00:09, 39.48iter/s]
Best score: 1.6073: : 303iter [00:09, 39.48iter/s]
Best score: 1.6073: : 304iter [00:09, 32.96iter/s]
Best score: 1.6073: : 304iter [00:10, 32.96iter/s]
Best score: 1.6073: : 305iter [00:10, 32.96iter/s]
Best score: 1.6073: : 306iter [00:10, 32.96iter/s]
Best score: 1.6073: : 307iter [00:10, 32.96iter/s]
Best score: 1.6073: : 308iter [00:10, 32.96iter/s]
Best score: 1.6073: : 309iter [00:10, 35.09iter/s]
Best score: 1.6073: : 309iter [00:10, 35.09iter/s]
Best score: 1.6073: : 310iter [00:10, 35.09iter/s]
Best score: 1.6073: : 311iter [00:10, 35.09iter/s]
Best score: 1.6073: : 312iter [00:10, 35.09iter/s]
Best score: 1.6073: : 313iter [00:10, 36.13iter/s]
Best score: 1.6073: : 313iter [00:10, 36.13iter/s]
Best score: 1.6073: : 314iter [00:10, 36.13iter/s]
Best score: 1.6073: : 315iter [00:10, 36.13iter/s]
Best score: 1.6073: : 316iter [00:10, 36.13iter/s]
Best score: 1.6073: : 317iter [00:10, 36.13iter/s]
Best score: 1.6073: : 318iter [00:10, 37.55iter/s]
Best score: 1.6073: : 318iter [00:10, 37.55iter/s]
Best score: 1.6073: : 319iter [00:10, 37.55iter/s]
Best score: 1.6073: : 320iter [00:10, 37.55iter/s]
Best score: 1.6073: : 321iter [00:10, 37.55iter/s]
Best score: 1.6073: : 322iter [00:10, 37.99iter/s]
Best score: 1.6073: : 322iter [00:10, 37.99iter/s]
Best score: 1.6073: : 323iter [00:10, 37.99iter/s]
Best score: 1.6073: : 324iter [00:10, 37.99iter/s]
Best score: 1.6073: : 325iter [00:10, 37.99iter/s]
Best score: 1.6073: : 326iter [00:10, 38.50iter/s]
Best score: 1.6073: : 326iter [00:10, 38.50iter/s]
Best score: 1.6073: : 327iter [00:10, 38.50iter/s]
Best score: 1.6073: : 328iter [00:10, 38.50iter/s]
Best score: 1.5287: : 329iter [00:10, 38.50iter/s]
Best score: 1.5287: : 330iter [00:10, 38.73iter/s]
Best score: 1.5287: : 330iter [00:10, 38.73iter/s]
Best score: 1.5287: : 331iter [00:10, 38.73iter/s]
Best score: 1.5287: : 332iter [00:10, 38.73iter/s]
Best score: 1.5287: : 333iter [00:10, 38.73iter/s]
Best score: 1.5287: : 334iter [00:10, 38.73iter/s]
Best score: 1.5287: : 335iter [00:10, 39.30iter/s]
Best score: 1.5287: : 335iter [00:10, 39.30iter/s]
Best score: 1.5287: : 336iter [00:10, 39.30iter/s]
Best score: 1.5287: : 337iter [00:10, 39.30iter/s]
Best score: 1.5287: : 338iter [00:10, 39.30iter/s]
Best score: 1.2320: : 339iter [00:10, 39.30iter/s]
Best score: 1.2320: : 340iter [00:10, 39.46iter/s]
Best score: 1.2320: : 340iter [00:10, 39.46iter/s]
Best score: 1.2320: : 341iter [00:10, 39.46iter/s]
Best score: 1.2320: : 342iter [00:10, 39.46iter/s]
Best score: 1.2320: : 343iter [00:10, 39.46iter/s]
Best score: 1.2320: : 344iter [00:11, 39.46iter/s]
Best score: 1.2320: : 345iter [00:11, 39.75iter/s]
Best score: 1.2320: : 345iter [00:11, 39.75iter/s]
Best score: 1.2320: : 346iter [00:11, 39.75iter/s]
Best score: 1.2320: : 347iter [00:11, 39.75iter/s]
Best score: 1.2320: : 348iter [00:11, 39.75iter/s]
[ITER 350] Best: 1.2320
[PARAMS] Current best parameters:
  fx: 551.1380
  fy: 551.2427
  px: 976.3113
  py: 740.9959
  k1: -0.01168010
  k2: 0.00293783
  k3: -0.00970065
  k4: 0.00327240
  p1: 0.00769263
  p2: -0.00101770
  p3: 0.00099346
  p4: -0.00123953
  s1: 0.0019
  s2: -0.0027
  s3: 0.0004
  s4: -0.0028
  tilt_x: -0.0042
  tilt_y: 0.0294
  scale: 2.9770
  rotation: -0.0188

Best score: 1.2320: : 349iter [00:11, 39.75iter/s]
Best score: 1.2320: : 350iter [00:11, 39.66iter/s]
Best score: 1.2320: : 350iter [00:11, 39.66iter/s]
Best score: 1.2320: : 351iter [00:11, 39.66iter/s]
Best score: 1.2320: : 352iter [00:11, 39.66iter/s]
Best score: 1.2320: : 353iter [00:11, 39.66iter/s]
Best score: 1.2320: : 354iter [00:11, 33.17iter/s]
Best score: 1.2320: : 354iter [00:11, 33.17iter/s]
Best score: 1.2320: : 355iter [00:11, 33.17iter/s]
Best score: 1.2320: : 356iter [00:11, 33.17iter/s]
Best score: 1.2320: : 357iter [00:11, 33.17iter/s]
Best score: 1.2320: : 358iter [00:11, 33.17iter/s]
Best score: 1.2320: : 359iter [00:11, 35.15iter/s]
Best score: 0.8815: : 359iter [00:11, 35.15iter/s]
Best score: 0.8815: : 360iter [00:11, 35.15iter/s]
Best score: 0.8815: : 361iter [00:11, 35.15iter/s]
Best score: 0.8815: : 362iter [00:11, 35.15iter/s]
Best score: 0.8815: : 363iter [00:11, 29.33iter/s]
Best score: 0.8815: : 363iter [00:11, 29.33iter/s]
Best score: 0.8815: : 364iter [00:11, 29.33iter/s]
Best score: 0.8815: : 365iter [00:11, 29.33iter/s]
Best score: 0.8815: : 366iter [00:11, 29.33iter/s]
Best score: 0.8815: : 367iter [00:11, 29.33iter/s]
Best score: 0.8815: : 368iter [00:11, 32.11iter/s]
Best score: 0.8815: : 368iter [00:11, 32.11iter/s]
Best score: 0.7385: : 369iter [00:11, 32.11iter/s]
Best score: 0.7385: : 370iter [00:11, 32.11iter/s]
Best score: 0.7385: : 371iter [00:11, 32.11iter/s]
Best score: 0.7385: : 372iter [00:11, 33.76iter/s]
Best score: 0.7385: : 372iter [00:11, 33.76iter/s]
Best score: 0.7385: : 373iter [00:11, 33.76iter/s]
Best score: 0.7385: : 374iter [00:11, 33.76iter/s]
Best score: 0.7385: : 375iter [00:11, 33.76iter/s]
Best score: 0.7385: : 376iter [00:11, 33.76iter/s]
Best score: 0.7385: : 377iter [00:11, 35.60iter/s]
Best score: 0.7385: : 377iter [00:12, 35.60iter/s]
Best score: 0.7385: : 378iter [00:12, 35.60iter/s]
Best score: 0.7385: : 379iter [00:12, 35.60iter/s]
Best score: 0.7385: : 380iter [00:12, 35.60iter/s]
Best score: 0.7385: : 381iter [00:12, 36.54iter/s]
Best score: 0.7385: : 381iter [00:12, 36.54iter/s]
Best score: 0.7385: : 382iter [00:12, 36.54iter/s]
Best score: 0.7385: : 383iter [00:12, 36.54iter/s]
Best score: 0.7385: : 384iter [00:12, 36.54iter/s]
Best score: 0.7385: : 385iter [00:12, 36.54iter/s]
Best score: 0.7385: : 386iter [00:12, 37.77iter/s]
Best score: 0.7385: : 386iter [00:12, 37.77iter/s]
Best score: 0.7385: : 387iter [00:12, 37.77iter/s]
Best score: 0.7385: : 388iter [00:12, 37.77iter/s]
Best score: 0.7049: : 389iter [00:12, 37.77iter/s]
Best score: 0.7049: : 390iter [00:12, 38.25iter/s]
Best score: 0.7049: : 390iter [00:12, 38.25iter/s]
Best score: 0.7049: : 391iter [00:12, 38.25iter/s]
Best score: 0.7049: : 392iter [00:12, 38.25iter/s]
Best score: 0.7049: : 393iter [00:12, 38.25iter/s]
Best score: 0.7049: : 394iter [00:12, 38.25iter/s]
Best score: 0.7049: : 395iter [00:12, 38.91iter/s]
Best score: 0.7049: : 395iter [00:12, 38.91iter/s]
Best score: 0.7049: : 396iter [00:12, 38.91iter/s]
Best score: 0.7049: : 397iter [00:12, 38.91iter/s]
Best score: 0.7049: : 398iter [00:12, 38.91iter/s]
Best score: 0.7049: : 399iter [00:12, 38.96iter/s]
[ITER 400] Best: 0.5353
[PARAMS] Current best parameters:
  fx: 554.9754
  fy: 554.2047
  px: 975.7524
  py: 741.4891
  k1: -0.03507472
  k2: 0.03123066
  k3: -0.02283764
  k4: 0.00535576
  p1: 0.00467596
  p2: -0.00188256
  p3: -0.00918722
  p4: 0.00597036
  s1: 0.0049
  s2: -0.0069
  s3: 0.0085
  s4: -0.0072
  tilt_x: 0.0039
  tilt_y: 0.0292
  scale: 2.9850
  rotation: -0.0690

Best score: 0.5353: : 399iter [00:12, 38.96iter/s]
Best score: 0.5353: : 400iter [00:12, 38.96iter/s]
Best score: 0.5353: : 401iter [00:12, 38.96iter/s]
Best score: 0.5353: : 402iter [00:12, 38.96iter/s]
Best score: 0.5353: : 403iter [00:12, 32.21iter/s]
Best score: 0.5353: : 403iter [00:12, 32.21iter/s]
Best score: 0.5353: : 404iter [00:12, 32.21iter/s]
Best score: 0.5353: : 405iter [00:12, 32.21iter/s]
Best score: 0.5353: : 406iter [00:12, 32.21iter/s]
Best score: 0.5353: : 407iter [00:12, 32.21iter/s]
Best score: 0.5353: : 408iter [00:12, 34.49iter/s]
Best score: 0.5353: : 408iter [00:12, 34.49iter/s]
Best score: 0.4537: : 409iter [00:12, 34.49iter/s]
Best score: 0.4537: : 410iter [00:12, 34.49iter/s]
Best score: 0.4537: : 411iter [00:12, 34.49iter/s]
Best score: 0.4537: : 412iter [00:12, 35.70iter/s]
Best score: 0.4537: : 412iter [00:12, 35.70iter/s]
Best score: 0.4537: : 413iter [00:13, 35.70iter/s]
Best score: 0.4537: : 414iter [00:13, 35.70iter/s]
Best score: 0.4537: : 415iter [00:13, 35.70iter/s]
Best score: 0.4537: : 416iter [00:13, 35.70iter/s]
Best score: 0.4537: : 417iter [00:13, 37.20iter/s]
Best score: 0.4537: : 417iter [00:13, 37.20iter/s]
Best score: 0.4537: : 418iter [00:13, 37.20iter/s]
Best score: 0.3748: : 419iter [00:13, 37.20iter/s]
Best score: 0.3748: : 420iter [00:13, 37.20iter/s]
Best score: 0.3748: : 421iter [00:13, 37.70iter/s]
Best score: 0.3748: : 421iter [00:13, 37.70iter/s]
Best score: 0.3748: : 422iter [00:13, 37.70iter/s]
Best score: 0.3748: : 423iter [00:13, 37.70iter/s]
Best score: 0.3748: : 424iter [00:13, 37.70iter/s]
Best score: 0.3748: : 425iter [00:13, 37.70iter/s]
Best score: 0.3748: : 426iter [00:13, 38.53iter/s]
Best score: 0.3748: : 426iter [00:13, 38.53iter/s]
Best score: 0.3748: : 427iter [00:13, 38.53iter/s]
Best score: 0.3748: : 428iter [00:13, 38.53iter/s]
Best score: 0.3695: : 429iter [00:13, 38.53iter/s]
Best score: 0.3695: : 430iter [00:13, 38.75iter/s]
Best score: 0.3695: : 430iter [00:13, 38.75iter/s]
Best score: 0.3695: : 431iter [00:13, 38.75iter/s]
Best score: 0.3695: : 432iter [00:13, 38.75iter/s]
Best score: 0.3695: : 433iter [00:13, 38.75iter/s]
Best score: 0.3695: : 434iter [00:13, 38.75iter/s]
Best score: 0.3695: : 435iter [00:13, 39.19iter/s]
Best score: 0.3695: : 435iter [00:13, 39.19iter/s]
Best score: 0.3695: : 436iter [00:13, 39.19iter/s]
Best score: 0.3695: : 437iter [00:13, 39.19iter/s]
Best score: 0.3695: : 438iter [00:13, 39.19iter/s]
Best score: 0.3131: : 439iter [00:13, 39.19iter/s]
Best score: 0.3131: : 440iter [00:13, 39.34iter/s]
Best score: 0.3131: : 440iter [00:13, 39.34iter/s]
Best score: 0.3131: : 441iter [00:13, 39.34iter/s]
Best score: 0.3131: : 442iter [00:13, 39.34iter/s]
Best score: 0.3131: : 443iter [00:13, 39.34iter/s]
Best score: 0.3131: : 444iter [00:13, 39.34iter/s]
Best score: 0.3131: : 445iter [00:13, 39.76iter/s]
Best score: 0.3131: : 445iter [00:13, 39.76iter/s]
Best score: 0.3131: : 446iter [00:13, 39.76iter/s]
Best score: 0.3131: : 447iter [00:13, 39.76iter/s]
Best score: 0.3131: : 448iter [00:13, 39.76iter/s]
[ITER 450] Best: 0.3131
[PARAMS] Current best parameters:
  fx: 552.2524
  fy: 551.8057
  px: 979.3588
  py: 741.3013
  k1: -0.03966324
  k2: 0.03610407
  k3: -0.02132165
  k4: 0.00408492
  p1: -0.00669713
  p2: -0.00394147
  p3: 0.00829041
  p4: 0.00814448
  s1: -0.0114
  s2: -0.0061
  s3: -0.0008
  s4: -0.0077
  tilt_x: 0.0256
  tilt_y: 0.0064
  scale: 2.9939
  rotation: 0.0307

Best score: 0.3131: : 449iter [00:13, 39.76iter/s]
Best score: 0.3131: : 450iter [00:13, 39.69iter/s]
Best score: 0.3131: : 450iter [00:14, 39.69iter/s]
Best score: 0.3131: : 451iter [00:14, 39.69iter/s]
Best score: 0.3131: : 452iter [00:14, 39.69iter/s]
Best score: 0.3131: : 453iter [00:14, 39.69iter/s]
Best score: 0.3131: : 454iter [00:14, 33.15iter/s]
Best score: 0.3131: : 454iter [00:14, 33.15iter/s]
Best score: 0.3131: : 455iter [00:14, 33.15iter/s]
Best score: 0.3131: : 456iter [00:14, 33.15iter/s]
Best score: 0.3131: : 457iter [00:14, 33.15iter/s]
Best score: 0.3131: : 458iter [00:14, 34.69iter/s]
Best score: 0.3131: : 458iter [00:14, 34.69iter/s]
Best score: 0.3131: : 459iter [00:14, 34.69iter/s]
Best score: 0.3131: : 460iter [00:14, 34.69iter/s]
Best score: 0.3131: : 461iter [00:14, 34.69iter/s]
Best score: 0.3131: : 462iter [00:14, 35.86iter/s]
Best score: 0.3131: : 462iter [00:14, 35.86iter/s]
Best score: 0.3131: : 463iter [00:14, 35.86iter/s]
Best score: 0.3131: : 464iter [00:14, 35.86iter/s]
Best score: 0.3131: : 465iter [00:14, 35.86iter/s]
Best score: 0.3131: : 466iter [00:14, 35.86iter/s]
Best score: 0.3131: : 467iter [00:14, 37.26iter/s]
Best score: 0.3131: : 467iter [00:14, 37.26iter/s]
Best score: 0.3131: : 468iter [00:14, 37.26iter/s]
Best score: 0.3131: : 469iter [00:14, 37.26iter/s]
Best score: 0.3131: : 470iter [00:14, 37.26iter/s]
Best score: 0.3131: : 471iter [00:14, 37.68iter/s]
Best score: 0.3131: : 471iter [00:14, 37.68iter/s]
Best score: 0.3131: : 472iter [00:14, 37.68iter/s]
Best score: 0.3131: : 473iter [00:14, 37.68iter/s]
Best score: 0.3131: : 474iter [00:14, 37.68iter/s]
Best score: 0.3131: : 475iter [00:14, 37.68iter/s]
Best score: 0.3131: : 476iter [00:14, 38.44iter/s]
Best score: 0.3131: : 476iter [00:14, 38.44iter/s]
Best score: 0.3131: : 477iter [00:14, 38.44iter/s]
Best score: 0.3131: : 478iter [00:14, 38.44iter/s]
Best score: 0.3131: : 479iter [00:14, 38.44iter/s]
Best score: 0.3131: : 480iter [00:14, 38.63iter/s]
Best score: 0.3131: : 480iter [00:14, 38.63iter/s]
Best score: 0.3131: : 481iter [00:14, 38.63iter/s]
Best score: 0.3131: : 482iter [00:14, 38.63iter/s]
Best score: 0.3131: : 483iter [00:14, 38.63iter/s]
Best score: 0.3131: : 484iter [00:14, 38.63iter/s]
Best score: 0.3131: : 485iter [00:14, 39.19iter/s]
Best score: 0.3131: : 485iter [00:14, 39.19iter/s]
Best score: 0.3131: : 486iter [00:14, 39.19iter/s]
Best score: 0.3131: : 487iter [00:14, 39.19iter/s]
Best score: 0.3131: : 488iter [00:14, 39.19iter/s]
Best score: 0.3131: : 489iter [00:14, 39.19iter/s]
Best score: 0.3131: : 490iter [00:14, 39.33iter/s]
Best score: 0.3131: : 490iter [00:15, 39.33iter/s]
Best score: 0.3131: : 491iter [00:15, 39.33iter/s]
Best score: 0.3131: : 492iter [00:15, 39.33iter/s]
Best score: 0.3131: : 493iter [00:15, 39.33iter/s]
Best score: 0.3131: : 494iter [00:15, 39.33iter/s]
Best score: 0.3131: : 495iter [00:15, 39.61iter/s]
Best score: 0.3131: : 495iter [00:15, 39.61iter/s]
Best score: 0.3131: : 496iter [00:15, 39.61iter/s]
Best score: 0.3131: : 497iter [00:15, 39.61iter/s]
Best score: 0.3131: : 498iter [00:15, 39.61iter/s]
[ITER 500] Best: 0.3074
[PARAMS] Current best parameters:
  fx: 552.6251
  fy: 552.1043
  px: 979.1456
  py: 740.7147
  k1: -0.04573165
  k2: 0.04023604
  k3: -0.02108753
  k4: 0.00360476
  p1: -0.00770373
  p2: -0.00500070
  p3: 0.00905039
  p4: 0.00742340
  s1: -0.0108
  s2: -0.0059
  s3: -0.0035
  s4: -0.0061
  tilt_x: 0.0253
  tilt_y: 0.0059
  scale: 3.0063
  rotation: 0.0108

Best score: 0.3074: : 499iter [00:15, 39.61iter/s]
Best score: 0.3074: : 500iter [00:15, 39.57iter/s]
Best score: 0.3074: : 500iter [00:15, 39.57iter/s]
Best score: 0.3074: : 501iter [00:15, 39.57iter/s]
Best score: 0.3074: : 502iter [00:15, 39.57iter/s]
Best score: 0.3074: : 503iter [00:15, 39.57iter/s]
Best score: 0.3074: : 504iter [00:15, 32.99iter/s]
Best score: 0.3074: : 504iter [00:15, 32.99iter/s]
Best score: 0.3074: : 505iter [00:15, 32.99iter/s]
Best score: 0.3074: : 506iter [00:15, 32.99iter/s]
Best score: 0.3074: : 507iter [00:15, 32.99iter/s]
Best score: 0.3074: : 508iter [00:15, 34.48iter/s]
Best score: 0.3074: : 508iter [00:15, 34.48iter/s]
Best score: 0.2988: : 509iter [00:15, 34.48iter/s]
Best score: 0.2988: : 510iter [00:15, 34.48iter/s]
Best score: 0.2988: : 511iter [00:15, 34.48iter/s]
Best score: 0.2988: : 512iter [00:15, 35.67iter/s]
Best score: 0.2988: : 512iter [00:15, 35.67iter/s]
Best score: 0.2988: : 513iter [00:15, 35.67iter/s]
Best score: 0.2988: : 514iter [00:15, 35.67iter/s]
Best score: 0.2988: : 515iter [00:15, 35.67iter/s]
Best score: 0.2988: : 516iter [00:15, 35.67iter/s]
Best score: 0.2988: : 517iter [00:15, 37.18iter/s]
Best score: 0.2988: : 517iter [00:15, 37.18iter/s]
Best score: 0.2988: : 518iter [00:15, 37.18iter/s]
Best score: 0.2761: : 519iter [00:15, 37.18iter/s]
Best score: 0.2761: : 520iter [00:15, 37.18iter/s]
Best score: 0.2761: : 521iter [00:15, 37.72iter/s]
Best score: 0.2761: : 521iter [00:15, 37.72iter/s]
Best score: 0.2761: : 522iter [00:15, 37.72iter/s]
Best score: 0.2761: : 523iter [00:15, 37.72iter/s]
Best score: 0.2761: : 524iter [00:15, 37.72iter/s]
Best score: 0.2761: : 525iter [00:15, 37.72iter/s]
Best score: 0.2761: : 526iter [00:15, 38.53iter/s]
Best score: 0.2761: : 526iter [00:15, 38.53iter/s]
Best score: 0.2761: : 527iter [00:16, 38.53iter/s]
Best score: 0.2761: : 528iter [00:16, 38.53iter/s]
Best score: 0.2740: : 529iter [00:16, 38.53iter/s]
Best score: 0.2740: : 530iter [00:16, 38.83iter/s]
Best score: 0.2740: : 530iter [00:16, 38.83iter/s]
Best score: 0.2740: : 531iter [00:16, 38.83iter/s]
Best score: 0.2740: : 532iter [00:16, 38.83iter/s]
Best score: 0.2740: : 533iter [00:16, 38.83iter/s]
Best score: 0.2740: : 534iter [00:16, 38.83iter/s]
Best score: 0.2740: : 535iter [00:16, 39.30iter/s]
Best score: 0.2740: : 535iter [00:16, 39.30iter/s]
Best score: 0.2740: : 536iter [00:16, 39.30iter/s]
Best score: 0.2740: : 537iter [00:16, 39.30iter/s]
Best score: 0.2740: : 538iter [00:16, 39.30iter/s]
Best score: 0.2740: : 539iter [00:16, 39.30iter/s]
Best score: 0.2740: : 540iter [00:16, 39.28iter/s]
Best score: 0.2740: : 540iter [00:16, 39.28iter/s]
Best score: 0.2740: : 541iter [00:16, 39.28iter/s]
Best score: 0.2740: : 542iter [00:16, 39.28iter/s]
Best score: 0.2740: : 543iter [00:16, 39.28iter/s]
Best score: 0.2740: : 544iter [00:16, 39.28iter/s]
Best score: 0.2740: : 545iter [00:16, 39.58iter/s]
Best score: 0.2740: : 545iter [00:16, 39.58iter/s]
Best score: 0.2740: : 546iter [00:16, 39.58iter/s]
Best score: 0.2740: : 547iter [00:16, 39.58iter/s]
Best score: 0.2740: : 548iter [00:16, 39.58iter/s]
Best score: 0.2740: : 549iter [00:16, 39.67iter/s]
[ITER 550] Best: 0.2679
[PARAMS] Current best parameters:
  fx: 551.5135
  fy: 550.9593
  px: 978.5714
  py: 740.0311
  k1: -0.06251666
  k2: 0.06657040
  k3: -0.03545591
  k4: 0.00642319
  p1: -0.00922727
  p2: -0.01142216
  p3: 0.00591583
  p4: 0.00767663
  s1: -0.0009
  s2: -0.0069
  s3: 0.0025
  s4: -0.0074
  tilt_x: 0.0242
  tilt_y: -0.0051
  scale: 3.0269
  rotation: 0.0207

Best score: 0.2679: : 549iter [00:16, 39.67iter/s]
Best score: 0.2679: : 550iter [00:16, 39.67iter/s]
Best score: 0.2679: : 551iter [00:16, 39.67iter/s]
Best score: 0.2679: : 552iter [00:16, 39.67iter/s]
Best score: 0.2679: : 553iter [00:16, 32.67iter/s]
Best score: 0.2679: : 553iter [00:16, 32.67iter/s]
Best score: 0.2679: : 554iter [00:16, 32.67iter/s]
Best score: 0.2679: : 555iter [00:16, 32.67iter/s]
Best score: 0.2679: : 556iter [00:16, 32.67iter/s]
Best score: 0.2679: : 557iter [00:16, 34.40iter/s]
Best score: 0.2679: : 557iter [00:16, 34.40iter/s]
Best score: 0.2679: : 558iter [00:16, 34.40iter/s]
Best score: 0.2621: : 559iter [00:16, 34.40iter/s]
Best score: 0.2621: : 560iter [00:16, 34.40iter/s]
Best score: 0.2621: : 561iter [00:16, 35.70iter/s]
Best score: 0.2621: : 561iter [00:16, 35.70iter/s]
Best score: 0.2621: : 562iter [00:16, 35.70iter/s]
Best score: 0.2621: : 563iter [00:17, 35.70iter/s]
Best score: 0.2621: : 564iter [00:17, 35.70iter/s]
Best score: 0.2621: : 565iter [00:17, 35.70iter/s]
Best score: 0.2621: : 566iter [00:17, 37.20iter/s]
Best score: 0.2621: : 566iter [00:17, 37.20iter/s]
Best score: 0.2621: : 567iter [00:17, 37.20iter/s]
Best score: 0.2621: : 568iter [00:17, 37.20iter/s]
Best score: 0.2577: : 569iter [00:17, 37.20iter/s]
Best score: 0.2577: : 570iter [00:17, 37.83iter/s]
Best score: 0.2577: : 570iter [00:17, 37.83iter/s]
Best score: 0.2577: : 571iter [00:17, 37.83iter/s]
Best score: 0.2577: : 572iter [00:17, 37.83iter/s]
Best score: 0.2577: : 573iter [00:17, 37.83iter/s]
Best score: 0.2577: : 574iter [00:17, 37.83iter/s]
Best score: 0.2577: : 575iter [00:17, 38.69iter/s]
Best score: 0.2577: : 575iter [00:17, 38.69iter/s]
Best score: 0.2577: : 576iter [00:17, 38.69iter/s]
Best score: 0.2577: : 577iter [00:17, 38.69iter/s]
Best score: 0.2577: : 578iter [00:17, 38.69iter/s]
Best score: 0.2577: : 579iter [00:17, 38.69iter/s]
Best score: 0.2577: : 580iter [00:17, 38.92iter/s]
Best score: 0.2577: : 580iter [00:17, 38.92iter/s]
Best score: 0.2577: : 581iter [00:17, 38.92iter/s]
Best score: 0.2577: : 582iter [00:17, 38.92iter/s]
Best score: 0.2577: : 583iter [00:17, 38.92iter/s]
Best score: 0.2577: : 584iter [00:17, 38.92iter/s]
Best score: 0.2577: : 585iter [00:17, 39.36iter/s]
Best score: 0.2577: : 585iter [00:17, 39.36iter/s]
Best score: 0.2577: : 586iter [00:17, 39.36iter/s]
Best score: 0.2577: : 587iter [00:17, 39.36iter/s]
Best score: 0.2577: : 588iter [00:17, 39.36iter/s]
Best score: 0.2577: : 589iter [00:17, 39.36iter/s]
Best score: 0.2577: : 590iter [00:17, 39.46iter/s]
Best score: 0.2577: : 590iter [00:17, 39.46iter/s]
Best score: 0.2577: : 591iter [00:17, 39.46iter/s]
Best score: 0.2577: : 592iter [00:17, 39.46iter/s]
Best score: 0.2577: : 593iter [00:17, 39.46iter/s]
Best score: 0.2577: : 594iter [00:17, 39.46iter/s]
Best score: 0.2577: : 595iter [00:17, 39.67iter/s]
Best score: 0.2577: : 595iter [00:17, 39.67iter/s]
Best score: 0.2577: : 596iter [00:17, 39.67iter/s]
Best score: 0.2577: : 597iter [00:17, 39.67iter/s]
Best score: 0.2577: : 598iter [00:17, 39.67iter/s]
[ITER 600] Best: 0.2533
[PARAMS] Current best parameters:
  fx: 548.5571
  fy: 547.8769
  px: 979.3112
  py: 737.5517
  k1: -0.05262350
  k2: 0.05692594
  k3: -0.03100593
  k4: 0.00566294
  p1: -0.01159697
  p2: -0.00963241
  p3: 0.00409316
  p4: 0.00932785
  s1: -0.0002
  s2: -0.0084
  s3: 0.0055
  s4: -0.0096
  tilt_x: 0.0164
  tilt_y: -0.0054
  scale: 3.0226
  rotation: 0.0187

Best score: 0.2533: : 599iter [00:17, 39.67iter/s]
Best score: 0.2533: : 600iter [00:17, 39.68iter/s]
Best score: 0.2533: : 600iter [00:18, 39.68iter/s]
Best score: 0.2533: : 601iter [00:18, 39.68iter/s]
Best score: 0.2533: : 602iter [00:18, 39.68iter/s]
Best score: 0.2533: : 603iter [00:18, 39.68iter/s]
Best score: 0.2533: : 604iter [00:18, 33.17iter/s]
Best score: 0.2533: : 604iter [00:18, 33.17iter/s]
Best score: 0.2533: : 605iter [00:18, 33.17iter/s]
Best score: 0.2533: : 606iter [00:18, 33.17iter/s]
Best score: 0.2533: : 607iter [00:18, 33.17iter/s]
Best score: 0.2533: : 608iter [00:18, 33.17iter/s]
Best score: 0.2533: : 609iter [00:18, 35.07iter/s]
Best score: 0.2374: : 609iter [00:18, 35.07iter/s]
Best score: 0.2374: : 610iter [00:18, 35.07iter/s]
Best score: 0.2374: : 611iter [00:18, 35.07iter/s]
Best score: 0.2374: : 612iter [00:18, 35.07iter/s]
Best score: 0.2374: : 613iter [00:18, 36.04iter/s]
Best score: 0.2374: : 613iter [00:18, 36.04iter/s]
Best score: 0.2374: : 614iter [00:18, 36.04iter/s]
Best score: 0.2374: : 615iter [00:18, 36.04iter/s]
Best score: 0.2374: : 616iter [00:18, 36.04iter/s]
Best score: 0.2374: : 617iter [00:18, 37.04iter/s]
Best score: 0.2374: : 617iter [00:18, 37.04iter/s]
Best score: 0.2374: : 618iter [00:18, 37.04iter/s]
Best score: 0.2374: : 619iter [00:18, 37.04iter/s]
Best score: 0.2374: : 620iter [00:18, 37.04iter/s]
Best score: 0.2374: : 621iter [00:18, 37.56iter/s]
Best score: 0.2374: : 621iter [00:18, 37.56iter/s]
Best score: 0.2374: : 622iter [00:18, 37.56iter/s]
Best score: 0.2374: : 623iter [00:18, 37.56iter/s]
Best score: 0.2374: : 624iter [00:18, 37.56iter/s]
Best score: 0.2374: : 625iter [00:18, 37.56iter/s]
Best score: 0.2374: : 626iter [00:18, 38.59iter/s]
Best score: 0.2374: : 626iter [00:18, 38.59iter/s]
Best score: 0.2374: : 627iter [00:18, 38.59iter/s]
Best score: 0.2374: : 628iter [00:18, 38.59iter/s]
Best score: 0.2374: : 629iter [00:18, 38.59iter/s]
Best score: 0.2374: : 630iter [00:18, 38.82iter/s]
Best score: 0.2374: : 630iter [00:18, 38.82iter/s]
Best score: 0.2374: : 631iter [00:18, 38.82iter/s]
Best score: 0.2374: : 632iter [00:18, 38.82iter/s]
Best score: 0.2374: : 633iter [00:18, 38.82iter/s]
Best score: 0.2374: : 634iter [00:18, 38.82iter/s]
Best score: 0.2374: : 635iter [00:18, 39.27iter/s]
Best score: 0.2374: : 635iter [00:18, 39.27iter/s]
Best score: 0.2374: : 636iter [00:18, 39.27iter/s]
Best score: 0.2374: : 637iter [00:18, 39.27iter/s]
Best score: 0.2374: : 638iter [00:18, 39.27iter/s]
Best score: 0.2374: : 639iter [00:18, 39.46iter/s]
Best score: 0.2361: : 639iter [00:18, 39.46iter/s]
Best score: 0.2361: : 640iter [00:19, 39.46iter/s]
Best score: 0.2361: : 641iter [00:19, 39.46iter/s]
Best score: 0.2361: : 642iter [00:19, 39.46iter/s]
Best score: 0.2361: : 643iter [00:19, 39.28iter/s]
Best score: 0.2361: : 643iter [00:19, 39.28iter/s]
Best score: 0.2361: : 644iter [00:19, 39.28iter/s]
Best score: 0.2361: : 645iter [00:19, 39.28iter/s]
Best score: 0.2361: : 646iter [00:19, 39.28iter/s]
Best score: 0.2361: : 647iter [00:19, 39.28iter/s]
Best score: 0.2361: : 648iter [00:19, 39.67iter/s]
Best score: 0.2361: : 648iter [00:19, 39.67iter/s]
[ITER 650] Best: 0.2351
[PARAMS] Current best parameters:
  fx: 546.3511
  fy: 545.6937
  px: 979.6677
  py: 737.0510
  k1: -0.05493617
  k2: 0.06326851
  k3: -0.03418721
  k4: 0.00617231
  p1: -0.01444150
  p2: -0.01145126
  p3: 0.00286981
  p4: 0.01100192
  s1: 0.0041
  s2: -0.0106
  s3: 0.0087
  s4: -0.0110
  tilt_x: 0.0192
  tilt_y: -0.0105
  scale: 3.0263
  rotation: 0.0025

Best score: 0.2351: : 649iter [00:19, 39.67iter/s]
Best score: 0.2351: : 650iter [00:19, 39.67iter/s]
Best score: 0.2351: : 651iter [00:19, 39.67iter/s]
Best score: 0.2351: : 652iter [00:19, 32.66iter/s]
Best score: 0.2351: : 652iter [00:19, 32.66iter/s]
Best score: 0.2351: : 653iter [00:19, 32.66iter/s]
Best score: 0.2351: : 654iter [00:19, 32.66iter/s]
Best score: 0.2351: : 655iter [00:19, 32.66iter/s]
Best score: 0.2351: : 656iter [00:19, 32.66iter/s]
Best score: 0.2351: : 657iter [00:19, 34.94iter/s]
Best score: 0.2351: : 657iter [00:19, 34.94iter/s]
Best score: 0.2351: : 658iter [00:19, 34.94iter/s]
Best score: 0.2351: : 659iter [00:19, 34.94iter/s]
Best score: 0.2351: : 660iter [00:19, 34.94iter/s]
Best score: 0.2351: : 661iter [00:19, 36.14iter/s]
Best score: 0.2351: : 661iter [00:19, 36.14iter/s]
Best score: 0.2351: : 662iter [00:19, 36.14iter/s]
Best score: 0.2351: : 663iter [00:19, 36.14iter/s]
Best score: 0.2351: : 664iter [00:19, 36.14iter/s]
Best score: 0.2351: : 665iter [00:19, 36.14iter/s]
Best score: 0.2351: : 666iter [00:19, 37.53iter/s]
Best score: 0.2351: : 666iter [00:19, 37.53iter/s]
Best score: 0.2351: : 667iter [00:19, 37.53iter/s]
Best score: 0.2351: : 668iter [00:19, 37.53iter/s]
Best score: 0.2308: : 669iter [00:19, 37.53iter/s]
Best score: 0.2308: : 670iter [00:19, 37.99iter/s]
Best score: 0.2308: : 670iter [00:19, 37.99iter/s]
Best score: 0.2308: : 671iter [00:19, 37.99iter/s]
Best score: 0.2308: : 672iter [00:19, 37.99iter/s]
Best score: 0.2308: : 673iter [00:19, 37.99iter/s]
Best score: 0.2308: : 674iter [00:19, 37.99iter/s]
Best score: 0.2308: : 675iter [00:19, 38.78iter/s]
Best score: 0.2308: : 675iter [00:19, 38.78iter/s]
Best score: 0.2308: : 676iter [00:19, 38.78iter/s]
Best score: 0.2308: : 677iter [00:20, 38.78iter/s]
Best score: 0.2308: : 678iter [00:20, 38.78iter/s]
Best score: 0.2308: : 679iter [00:20, 38.78iter/s]
Best score: 0.2308: : 680iter [00:20, 39.07iter/s]
Best score: 0.2308: : 680iter [00:20, 39.07iter/s]
Best score: 0.2308: : 681iter [00:20, 39.07iter/s]
Best score: 0.2308: : 682iter [00:20, 39.07iter/s]
Best score: 0.2308: : 683iter [00:20, 39.07iter/s]
Best score: 0.2308: : 684iter [00:20, 31.42iter/s]
Best score: 0.2308: : 684iter [00:20, 31.42iter/s]
Best score: 0.2308: : 685iter [00:20, 31.42iter/s]
Best score: 0.2308: : 686iter [00:20, 31.42iter/s]
Best score: 0.2308: : 687iter [00:20, 31.42iter/s]
Best score: 0.2308: : 688iter [00:20, 33.39iter/s]
Best score: 0.2308: : 688iter [00:20, 33.39iter/s]
Best score: 0.2308: : 689iter [00:20, 33.39iter/s]
Best score: 0.2308: : 690iter [00:20, 33.39iter/s]
Best score: 0.2308: : 691iter [00:20, 33.39iter/s]
Best score: 0.2308: : 692iter [00:20, 34.84iter/s]
Best score: 0.2308: : 692iter [00:20, 34.84iter/s]
Best score: 0.2308: : 693iter [00:20, 34.84iter/s]
Best score: 0.2308: : 694iter [00:20, 34.84iter/s]
Best score: 0.2308: : 695iter [00:20, 34.84iter/s]
Best score: 0.2308: : 696iter [00:20, 34.84iter/s]
Best score: 0.2308: : 697iter [00:20, 36.49iter/s]
Best score: 0.2308: : 697iter [00:20, 36.49iter/s]
Best score: 0.2308: : 698iter [00:20, 36.49iter/s]
[ITER 700] Best: 0.2302
[PARAMS] Current best parameters:
  fx: 543.6081
  fy: 543.0032
  px: 980.6486
  py: 736.1764
  k1: -0.04739758
  k2: 0.05465476
  k3: -0.02896365
  k4: 0.00503366
  p1: -0.01411100
  p2: -0.01039500
  p3: 0.00209444
  p4: 0.01321396
  s1: 0.0047
  s2: -0.0128
  s3: 0.0083
  s4: -0.0132
  tilt_x: 0.0143
  tilt_y: -0.0136
  scale: 3.0247
  rotation: -0.0071

Best score: 0.2302: : 699iter [00:20, 36.49iter/s]
Best score: 0.2302: : 700iter [00:20, 36.49iter/s]
Best score: 0.2302: : 701iter [00:20, 30.77iter/s]
Best score: 0.2302: : 701iter [00:20, 30.77iter/s]
Best score: 0.2302: : 702iter [00:20, 30.77iter/s]
Best score: 0.2302: : 703iter [00:20, 30.77iter/s]
Best score: 0.2302: : 704iter [00:20, 30.77iter/s]
Best score: 0.2302: : 705iter [00:20, 30.77iter/s]
Best score: 0.2302: : 706iter [00:20, 33.39iter/s]
Best score: 0.2302: : 706iter [00:20, 33.39iter/s]
Best score: 0.2302: : 707iter [00:20, 33.39iter/s]
Best score: 0.2302: : 708iter [00:20, 33.39iter/s]
Best score: 0.2298: : 709iter [00:20, 33.39iter/s]
Best score: 0.2298: : 710iter [00:20, 34.87iter/s]
Best score: 0.2298: : 710iter [00:21, 34.87iter/s]
Best score: 0.2298: : 711iter [00:21, 34.87iter/s]
Best score: 0.2298: : 712iter [00:21, 34.87iter/s]
Best score: 0.2298: : 713iter [00:21, 34.87iter/s]
Best score: 0.2298: : 714iter [00:21, 34.87iter/s]
Best score: 0.2298: : 715iter [00:21, 36.53iter/s]
Best score: 0.2298: : 715iter [00:21, 36.53iter/s]
Best score: 0.2298: : 716iter [00:21, 36.53iter/s]
Best score: 0.2298: : 717iter [00:21, 36.53iter/s]
Best score: 0.2298: : 718iter [00:21, 36.53iter/s]
Best score: 0.2288: : 719iter [00:21, 36.53iter/s]
Best score: 0.2288: : 720iter [00:21, 37.46iter/s]
Best score: 0.2288: : 720iter [00:21, 37.46iter/s]
Best score: 0.2288: : 721iter [00:21, 37.46iter/s]
Best score: 0.2288: : 722iter [00:21, 37.46iter/s]
Best score: 0.2288: : 723iter [00:21, 37.46iter/s]
Best score: 0.2288: : 724iter [00:21, 37.46iter/s]
Best score: 0.2288: : 725iter [00:21, 38.33iter/s]
Best score: 0.2288: : 725iter [00:21, 38.33iter/s]
Best score: 0.2288: : 726iter [00:21, 38.33iter/s]
Best score: 0.2288: : 727iter [00:21, 38.33iter/s]
Best score: 0.2288: : 728iter [00:21, 38.33iter/s]
Best score: 0.2263: : 729iter [00:21, 38.33iter/s]
Best score: 0.2263: : 730iter [00:21, 38.60iter/s]
Best score: 0.2263: : 730iter [00:21, 38.60iter/s]
Best score: 0.2263: : 731iter [00:21, 38.60iter/s]
Best score: 0.2263: : 732iter [00:21, 38.60iter/s]
Best score: 0.2263: : 733iter [00:21, 38.60iter/s]
Best score: 0.2263: : 734iter [00:21, 38.87iter/s]
Best score: 0.2263: : 734iter [00:21, 38.87iter/s]
Best score: 0.2263: : 735iter [00:21, 38.87iter/s]
Best score: 0.2263: : 736iter [00:21, 38.87iter/s]
Best score: 0.2263: : 737iter [00:21, 38.87iter/s]
Best score: 0.2263: : 738iter [00:21, 38.87iter/s]
Best score: 0.2263: : 739iter [00:21, 39.41iter/s]
Best score: 0.2210: : 739iter [00:21, 39.41iter/s]
Best score: 0.2210: : 740iter [00:21, 39.41iter/s]
Best score: 0.2210: : 741iter [00:21, 39.41iter/s]
Best score: 0.2210: : 742iter [00:21, 39.41iter/s]
Best score: 0.2210: : 743iter [00:21, 39.36iter/s]
Best score: 0.2210: : 743iter [00:21, 39.36iter/s]
Best score: 0.2210: : 744iter [00:21, 39.36iter/s]
Best score: 0.2210: : 745iter [00:21, 39.36iter/s]
Best score: 0.2210: : 746iter [00:21, 39.36iter/s]
Best score: 0.2210: : 747iter [00:21, 39.36iter/s]
Best score: 0.2210: : 748iter [00:21, 39.73iter/s]
Best score: 0.2210: : 748iter [00:21, 39.73iter/s]
[ITER 750] Best: 0.2210
[PARAMS] Current best parameters:
  fx: 540.2071
  fy: 539.5798
  px: 981.3858
  py: 738.0409
  k1: -0.03529131
  k2: 0.04358018
  k3: -0.02385189
  k4: 0.00408333
  p1: -0.01078574
  p2: -0.00728867
  p3: -0.00185284
  p4: 0.01128289
  s1: 0.0067
  s2: -0.0111
  s3: 0.0106
  s4: -0.0113
  tilt_x: 0.0171
  tilt_y: -0.0110
  scale: 3.0126
  rotation: -0.0159

Best score: 0.2210: : 749iter [00:21, 39.73iter/s]
Best score: 0.2210: : 750iter [00:22, 39.73iter/s]
Best score: 0.2210: : 751iter [00:22, 39.73iter/s]
Best score: 0.2210: : 752iter [00:22, 32.91iter/s]
Best score: 0.2210: : 752iter [00:22, 32.91iter/s]
Best score: 0.2210: : 753iter [00:22, 32.91iter/s]
Best score: 0.2210: : 754iter [00:22, 32.91iter/s]
Best score: 0.2210: : 755iter [00:22, 32.91iter/s]
Best score: 0.2210: : 756iter [00:22, 32.91iter/s]
Best score: 0.2210: : 757iter [00:22, 35.01iter/s]
Best score: 0.2210: : 757iter [00:22, 35.01iter/s]
Best score: 0.2210: : 758iter [00:22, 35.01iter/s]
Best score: 0.2210: : 759iter [00:22, 35.01iter/s]
Best score: 0.2210: : 760iter [00:22, 35.01iter/s]
Best score: 0.2210: : 761iter [00:22, 36.05iter/s]
Best score: 0.2210: : 761iter [00:22, 36.05iter/s]
Best score: 0.2210: : 762iter [00:22, 36.05iter/s]
Best score: 0.2210: : 763iter [00:22, 36.05iter/s]
Best score: 0.2210: : 764iter [00:22, 36.05iter/s]
Best score: 0.2210: : 765iter [00:22, 36.05iter/s]
Best score: 0.2210: : 766iter [00:22, 37.25iter/s]
Best score: 0.2210: : 766iter [00:22, 37.25iter/s]
Best score: 0.2210: : 767iter [00:22, 37.25iter/s]
Best score: 0.2210: : 768iter [00:22, 37.25iter/s]
Best score: 0.2210: : 769iter [00:22, 37.25iter/s]
Best score: 0.2210: : 770iter [00:22, 37.76iter/s]
Best score: 0.2210: : 770iter [00:22, 37.76iter/s]
Best score: 0.2210: : 771iter [00:22, 37.76iter/s]
Best score: 0.2210: : 772iter [00:22, 37.76iter/s]
Best score: 0.2210: : 773iter [00:22, 37.76iter/s]
Best score: 0.2210: : 774iter [00:22, 38.32iter/s]
Best score: 0.2210: : 774iter [00:22, 38.32iter/s]
Best score: 0.2210: : 775iter [00:22, 38.32iter/s]
Best score: 0.2210: : 776iter [00:22, 38.32iter/s]
Best score: 0.2210: : 777iter [00:22, 38.32iter/s]
Best score: 0.2210: : 778iter [00:22, 38.32iter/s]
Best score: 0.2210: : 779iter [00:22, 38.92iter/s]
Best score: 0.2207: : 779iter [00:22, 38.92iter/s]
Best score: 0.2207: : 780iter [00:22, 38.92iter/s]
Best score: 0.2207: : 781iter [00:22, 38.92iter/s]
Best score: 0.2207: : 782iter [00:22, 38.92iter/s]
Best score: 0.2207: : 783iter [00:22, 38.99iter/s]
Best score: 0.2207: : 783iter [00:22, 38.99iter/s]
Best score: 0.2207: : 784iter [00:22, 38.99iter/s]
Best score: 0.2207: : 785iter [00:22, 38.99iter/s]
Best score: 0.2207: : 786iter [00:23, 38.99iter/s]
Best score: 0.2207: : 787iter [00:23, 38.99iter/s]
Best score: 0.2207: : 788iter [00:23, 39.33iter/s]
Best score: 0.2207: : 788iter [00:23, 39.33iter/s]
Best score: 0.2201: : 789iter [00:23, 39.33iter/s]
Best score: 0.2201: : 790iter [00:23, 39.33iter/s]
Best score: 0.2201: : 791iter [00:23, 39.33iter/s]
Best score: 0.2201: : 792iter [00:23, 39.32iter/s]
Best score: 0.2201: : 792iter [00:23, 39.32iter/s]
Best score: 0.2201: : 793iter [00:23, 39.32iter/s]
Best score: 0.2201: : 794iter [00:23, 39.32iter/s]
Best score: 0.2201: : 795iter [00:23, 39.32iter/s]
Best score: 0.2201: : 796iter [00:23, 39.32iter/s]
Best score: 0.2201: : 797iter [00:23, 39.64iter/s]
Best score: 0.2201: : 797iter [00:23, 39.64iter/s]
Best score: 0.2201: : 798iter [00:23, 39.64iter/s]
[ITER 800] Best: 0.2169
[PARAMS] Current best parameters:
  fx: 539.6570
  fy: 539.0249
  px: 982.2566
  py: 737.3835
  k1: -0.03444968
  k2: 0.04064294
  k3: -0.02090777
  k4: 0.00326929
  p1: -0.01134345
  p2: -0.00678654
  p3: -0.00235611
  p4: 0.01170822
  s1: 0.0079
  s2: -0.0118
  s3: 0.0110
  s4: -0.0117
  tilt_x: 0.0149
  tilt_y: -0.0145
  scale: 3.0162
  rotation: -0.0165

Best score: 0.2169: : 799iter [00:23, 39.64iter/s]
Best score: 0.2169: : 800iter [00:23, 39.64iter/s]
Best score: 0.2169: : 801iter [00:23, 32.72iter/s]
Best score: 0.2169: : 801iter [00:23, 32.72iter/s]
Best score: 0.2169: : 802iter [00:23, 32.72iter/s]
Best score: 0.2169: : 803iter [00:23, 32.72iter/s]
Best score: 0.2169: : 804iter [00:23, 32.72iter/s]
Best score: 0.2169: : 805iter [00:23, 32.72iter/s]
Best score: 0.2169: : 806iter [00:23, 34.85iter/s]
Best score: 0.2169: : 806iter [00:23, 34.85iter/s]
Best score: 0.2169: : 807iter [00:23, 34.85iter/s]
Best score: 0.2169: : 808iter [00:23, 34.85iter/s]
Best score: 0.2160: : 809iter [00:23, 34.85iter/s]
Best score: 0.2160: : 810iter [00:23, 35.93iter/s]
Best score: 0.2160: : 810iter [00:23, 35.93iter/s]
Best score: 0.2160: : 811iter [00:23, 35.93iter/s]
Best score: 0.2160: : 812iter [00:23, 35.93iter/s]
Best score: 0.2160: : 813iter [00:23, 35.93iter/s]
Best score: 0.2160: : 814iter [00:23, 36.98iter/s]
Best score: 0.2160: : 814iter [00:23, 36.98iter/s]
Best score: 0.2160: : 815iter [00:23, 36.98iter/s]
Best score: 0.2160: : 816iter [00:23, 36.98iter/s]
Best score: 0.2160: : 817iter [00:23, 36.98iter/s]
Best score: 0.2160: : 818iter [00:23, 36.98iter/s]
Best score: 0.2160: : 819iter [00:23, 38.13iter/s]
Best score: 0.2157: : 819iter [00:23, 38.13iter/s]
Best score: 0.2157: : 820iter [00:23, 38.13iter/s]
Best score: 0.2157: : 821iter [00:23, 38.13iter/s]
Best score: 0.2157: : 822iter [00:23, 38.13iter/s]
Best score: 0.2157: : 823iter [00:23, 38.46iter/s]
Best score: 0.2157: : 823iter [00:24, 38.46iter/s]
Best score: 0.2157: : 824iter [00:24, 38.46iter/s]
Best score: 0.2157: : 825iter [00:24, 38.46iter/s]
Best score: 0.2157: : 826iter [00:24, 38.46iter/s]
Best score: 0.2157: : 827iter [00:24, 38.46iter/s]
Best score: 0.2157: : 828iter [00:24, 39.09iter/s]
Best score: 0.2157: : 828iter [00:24, 39.09iter/s]
Best score: 0.2146: : 829iter [00:24, 39.09iter/s]
Best score: 0.2146: : 830iter [00:24, 39.09iter/s]
Best score: 0.2146: : 831iter [00:24, 39.09iter/s]
Best score: 0.2146: : 832iter [00:24, 38.93iter/s]
Best score: 0.2146: : 832iter [00:24, 38.93iter/s]
Best score: 0.2146: : 833iter [00:24, 38.93iter/s]
Best score: 0.2146: : 834iter [00:24, 38.93iter/s]
Best score: 0.2146: : 835iter [00:24, 38.93iter/s]
Best score: 0.2146: : 836iter [00:24, 38.93iter/s]
Best score: 0.2146: : 837iter [00:24, 39.38iter/s]
Best score: 0.2146: : 837iter [00:24, 39.38iter/s]
Best score: 0.2146: : 838iter [00:24, 39.38iter/s]
Best score: 0.2144: : 839iter [00:24, 39.38iter/s]
Best score: 0.2144: : 840iter [00:24, 39.38iter/s]
Best score: 0.2144: : 841iter [00:24, 39.39iter/s]
Best score: 0.2144: : 841iter [00:24, 39.39iter/s]
Best score: 0.2144: : 842iter [00:24, 39.39iter/s]
Best score: 0.2144: : 843iter [00:24, 39.39iter/s]
Best score: 0.2144: : 844iter [00:24, 39.39iter/s]
Best score: 0.2144: : 845iter [00:24, 39.39iter/s]
Best score: 0.2144: : 846iter [00:24, 39.76iter/s]
Best score: 0.2144: : 846iter [00:24, 39.76iter/s]
Best score: 0.2144: : 847iter [00:24, 39.76iter/s]
Best score: 0.2144: : 848iter [00:24, 39.76iter/s]
[ITER 850] Best: 0.2143
[PARAMS] Current best parameters:
  fx: 538.1916
  fy: 537.5445
  px: 984.3018
  py: 737.6104
  k1: -0.02745561
  k2: 0.03325969
  k3: -0.01744966
  k4: 0.00267530
  p1: -0.01026063
  p2: -0.00484969
  p3: -0.00540058
  p4: 0.01165192
  s1: 0.0111
  s2: -0.0120
  s3: 0.0137
  s4: -0.0119
  tilt_x: 0.0139
  tilt_y: -0.0210
  scale: 3.0101
  rotation: -0.0284

Best score: 0.2143: : 849iter [00:24, 39.76iter/s]
Best score: 0.2143: : 850iter [00:24, 39.62iter/s]
Best score: 0.2143: : 850iter [00:24, 39.62iter/s]
Best score: 0.2143: : 851iter [00:24, 39.62iter/s]
Best score: 0.2143: : 852iter [00:24, 39.62iter/s]
Best score: 0.2143: : 853iter [00:24, 39.62iter/s]
Best score: 0.2143: : 854iter [00:24, 32.71iter/s]
Best score: 0.2143: : 854iter [00:24, 32.71iter/s]
Best score: 0.2143: : 855iter [00:24, 32.71iter/s]
Best score: 0.2143: : 856iter [00:24, 32.71iter/s]
Best score: 0.2143: : 857iter [00:24, 32.71iter/s]
Best score: 0.2143: : 858iter [00:24, 32.71iter/s]
Best score: 0.2143: : 859iter [00:24, 34.86iter/s]
Best score: 0.2143: : 859iter [00:24, 34.86iter/s]
Best score: 0.2143: : 860iter [00:25, 34.86iter/s]
Best score: 0.2143: : 861iter [00:25, 34.86iter/s]
Best score: 0.2143: : 862iter [00:25, 34.86iter/s]
Best score: 0.2143: : 863iter [00:25, 35.98iter/s]
Best score: 0.2143: : 863iter [00:25, 35.98iter/s]
Best score: 0.2143: : 864iter [00:25, 35.98iter/s]
Best score: 0.2143: : 865iter [00:25, 35.98iter/s]
Best score: 0.2143: : 866iter [00:25, 35.98iter/s]
Best score: 0.2143: : 867iter [00:25, 35.98iter/s]
Best score: 0.2143: : 868iter [00:25, 37.39iter/s]
Best score: 0.2143: : 868iter [00:25, 37.39iter/s]
Best score: 0.2132: : 869iter [00:25, 37.39iter/s]
Best score: 0.2132: : 870iter [00:25, 37.39iter/s]
Best score: 0.2132: : 871iter [00:25, 37.39iter/s]
Best score: 0.2132: : 872iter [00:25, 37.87iter/s]
Best score: 0.2132: : 872iter [00:25, 37.87iter/s]
Best score: 0.2132: : 873iter [00:25, 37.87iter/s]
Best score: 0.2132: : 874iter [00:25, 37.87iter/s]
Best score: 0.2132: : 875iter [00:25, 37.87iter/s]
Best score: 0.2132: : 876iter [00:25, 38.38iter/s]
Best score: 0.2132: : 876iter [00:25, 38.38iter/s]
Best score: 0.2132: : 877iter [00:25, 38.38iter/s]
Best score: 0.2132: : 878iter [00:25, 38.38iter/s]
Best score: 0.2132: : 879iter [00:25, 38.38iter/s]
Best score: 0.2132: : 880iter [00:25, 38.57iter/s]
Best score: 0.2132: : 880iter [00:25, 38.57iter/s]
Best score: 0.2132: : 881iter [00:25, 38.57iter/s]
Best score: 0.2132: : 882iter [00:25, 38.57iter/s]
Best score: 0.2132: : 883iter [00:25, 38.57iter/s]
Best score: 0.2132: : 884iter [00:25, 38.97iter/s]
Best score: 0.2132: : 884iter [00:25, 38.97iter/s]
Best score: 0.2132: : 885iter [00:25, 38.97iter/s]
Best score: 0.2132: : 886iter [00:25, 38.97iter/s]
Best score: 0.2132: : 887iter [00:25, 38.97iter/s]
Best score: 0.2132: : 888iter [00:25, 38.97iter/s]
Best score: 0.2132: : 889iter [00:25, 39.44iter/s]
Best score: 0.2130: : 889iter [00:25, 39.44iter/s]
Best score: 0.2130: : 890iter [00:25, 39.44iter/s]
Best score: 0.2130: : 891iter [00:25, 39.44iter/s]
Best score: 0.2130: : 892iter [00:25, 39.44iter/s]
Best score: 0.2130: : 893iter [00:25, 39.44iter/s]
Best score: 0.2130: : 893iter [00:25, 39.44iter/s]
Best score: 0.2130: : 894iter [00:25, 39.44iter/s]
Best score: 0.2130: : 895iter [00:25, 39.44iter/s]
Best score: 0.2130: : 896iter [00:25, 39.44iter/s]
Best score: 0.2130: : 897iter [00:25, 39.44iter/s]
Best score: 0.2130: : 898iter [00:25, 39.69iter/s]
Best score: 0.2130: : 898iter [00:25, 39.69iter/s]
[ITER 900] Best: 0.2130
[PARAMS] Current best parameters:
  fx: 540.2296
  fy: 539.6005
  px: 983.7357
  py: 738.0319
  k1: -0.03637481
  k2: 0.04228462
  k3: -0.02147920
  k4: 0.00332077
  p1: -0.01169509
  p2: -0.00689845
  p3: -0.00056087
  p4: 0.01101785
  s1: 0.0074
  s2: -0.0111
  s3: 0.0109
  s4: -0.0115
  tilt_x: 0.0189
  tilt_y: -0.0223
  scale: 3.0182
  rotation: -0.0277

Best score: 0.2130: : 899iter [00:25, 39.69iter/s]
Best score: 0.2130: : 900iter [00:26, 39.69iter/s]
Best score: 0.2130: : 901iter [00:26, 39.69iter/s]
Best score: 0.2130: : 902iter [00:26, 32.67iter/s]
Best score: 0.2130: : 902iter [00:26, 32.67iter/s]
Best score: 0.2130: : 903iter [00:26, 32.67iter/s]
Best score: 0.2130: : 904iter [00:26, 32.67iter/s]
Best score: 0.2130: : 905iter [00:26, 32.67iter/s]
Best score: 0.2130: : 906iter [00:26, 34.42iter/s]
Best score: 0.2130: : 906iter [00:26, 34.42iter/s]
Best score: 0.2130: : 907iter [00:26, 34.42iter/s]
Best score: 0.2130: : 908iter [00:26, 34.42iter/s]
Best score: 0.2130: : 909iter [00:26, 34.42iter/s]
Best score: 0.2130: : 910iter [00:26, 35.64iter/s]
Best score: 0.2130: : 910iter [00:26, 35.64iter/s]
Best score: 0.2130: : 911iter [00:26, 35.64iter/s]
Best score: 0.2130: : 912iter [00:26, 35.64iter/s]
Best score: 0.2130: : 913iter [00:26, 35.64iter/s]
Best score: 0.2130: : 914iter [00:26, 36.77iter/s]
Best score: 0.2130: : 914iter [00:26, 36.77iter/s]
Best score: 0.2130: : 915iter [00:26, 36.77iter/s]
Best score: 0.2130: : 916iter [00:26, 36.77iter/s]
Best score: 0.2130: : 917iter [00:26, 36.77iter/s]
Best score: 0.2130: : 918iter [00:26, 37.64iter/s]
Best score: 0.2130: : 918iter [00:26, 37.64iter/s]
Best score: 0.2120: : 919iter [00:26, 37.64iter/s]
Best score: 0.2120: : 920iter [00:26, 37.64iter/s]
Best score: 0.2120: : 921iter [00:26, 37.64iter/s]
Best score: 0.2120: : 922iter [00:26, 38.07iter/s]
Best score: 0.2120: : 922iter [00:26, 38.07iter/s]
Best score: 0.2120: : 923iter [00:26, 38.07iter/s]
Best score: 0.2120: : 924iter [00:26, 38.07iter/s]
Best score: 0.2120: : 925iter [00:26, 38.07iter/s]
Best score: 0.2120: : 926iter [00:26, 38.07iter/s]
Best score: 0.2120: : 927iter [00:26, 38.83iter/s]
Best score: 0.2120: : 927iter [00:26, 38.83iter/s]
Best score: 0.2120: : 928iter [00:26, 38.83iter/s]
Best score: 0.2119: : 929iter [00:26, 38.83iter/s]
Best score: 0.2119: : 930iter [00:26, 38.83iter/s]
Best score: 0.2119: : 931iter [00:26, 38.95iter/s]
Best score: 0.2119: : 931iter [00:26, 38.95iter/s]
Best score: 0.2119: : 932iter [00:26, 38.95iter/s]
Best score: 0.2119: : 933iter [00:26, 38.95iter/s]
Best score: 0.2119: : 934iter [00:26, 38.95iter/s]
Best score: 0.2119: : 935iter [00:26, 38.95iter/s]
Best score: 0.2119: : 936iter [00:26, 39.44iter/s]
Best score: 0.2119: : 936iter [00:27, 39.44iter/s]
Best score: 0.2119: : 937iter [00:27, 39.44iter/s]
Best score: 0.2119: : 938iter [00:27, 39.44iter/s]
Best score: 0.2118: : 939iter [00:27, 39.44iter/s]
Best score: 0.2118: : 940iter [00:27, 39.48iter/s]
Best score: 0.2118: : 940iter [00:27, 39.48iter/s]
Best score: 0.2118: : 941iter [00:27, 39.48iter/s]
Best score: 0.2118: : 942iter [00:27, 39.48iter/s]
Best score: 0.2118: : 943iter [00:27, 39.48iter/s]
Best score: 0.2118: : 944iter [00:27, 39.48iter/s]
Best score: 0.2118: : 945iter [00:27, 39.85iter/s]
Best score: 0.2118: : 945iter [00:27, 39.85iter/s]
Best score: 0.2118: : 946iter [00:27, 39.85iter/s]
Best score: 0.2118: : 947iter [00:27, 39.85iter/s]
Best score: 0.2118: : 948iter [00:27, 39.85iter/s]
[ITER 950] Best: 0.2117
[PARAMS] Current best parameters:
  fx: 539.6523
  fy: 538.9994
  px: 984.5557
  py: 738.0828
  k1: -0.03443695
  k2: 0.04100752
  k3: -0.02114361
  k4: 0.00329507
  p1: -0.01194972
  p2: -0.00656256
  p3: -0.00039568
  p4: 0.01122831
  s1: 0.0076
  s2: -0.0113
  s3: 0.0108
  s4: -0.0116
  tilt_x: 0.0197
  tilt_y: -0.0258
  scale: 3.0159
  rotation: -0.0331

Best score: 0.2117: : 949iter [00:27, 39.85iter/s]
Best score: 0.2117: : 950iter [00:27, 39.89iter/s]
Best score: 0.2117: : 950iter [00:27, 39.89iter/s]
Best score: 0.2117: : 951iter [00:27, 39.89iter/s]
Best score: 0.2117: : 952iter [00:27, 39.89iter/s]
Best score: 0.2117: : 953iter [00:27, 39.89iter/s]
Best score: 0.2117: : 954iter [00:27, 32.95iter/s]
Best score: 0.2117: : 954iter [00:27, 32.95iter/s]
Best score: 0.2117: : 955iter [00:27, 32.95iter/s]
Best score: 0.2117: : 956iter [00:27, 32.95iter/s]
Best score: 0.2117: : 957iter [00:27, 32.95iter/s]
Best score: 0.2117: : 958iter [00:27, 32.95iter/s]
Best score: 0.2117: : 959iter [00:27, 34.94iter/s]
Best score: 0.2117: : 959iter [00:27, 34.94iter/s]
Best score: 0.2117: : 960iter [00:27, 34.94iter/s]
Best score: 0.2117: : 961iter [00:27, 34.94iter/s]
Best score: 0.2117: : 962iter [00:27, 34.94iter/s]
Best score: 0.2117: : 963iter [00:27, 36.05iter/s]
Best score: 0.2117: : 963iter [00:27, 36.05iter/s]
Best score: 0.2117: : 964iter [00:27, 36.05iter/s]
Best score: 0.2117: : 965iter [00:27, 36.05iter/s]
Best score: 0.2117: : 966iter [00:27, 36.05iter/s]
Best score: 0.2117: : 967iter [00:27, 36.05iter/s]
Best score: 0.2117: : 968iter [00:27, 37.32iter/s]
Best score: 0.2117: : 968iter [00:27, 37.32iter/s]
Best score: 0.2117: : 969iter [00:27, 37.32iter/s]
Best score: 0.2117: : 970iter [00:27, 37.32iter/s]
Best score: 0.2117: : 971iter [00:27, 37.32iter/s]
Best score: 0.2117: : 972iter [00:27, 37.75iter/s]
Best score: 0.2117: : 972iter [00:27, 37.75iter/s]
Best score: 0.2117: : 973iter [00:28, 37.75iter/s]
Best score: 0.2117: : 974iter [00:28, 37.75iter/s]
Best score: 0.2117: : 975iter [00:28, 37.75iter/s]
Best score: 0.2117: : 976iter [00:28, 37.75iter/s]
Best score: 0.2117: : 977iter [00:28, 38.50iter/s]
Best score: 0.2117: : 977iter [00:28, 38.50iter/s]
Best score: 0.2117: : 978iter [00:28, 38.50iter/s]
Best score: 0.2116: : 979iter [00:28, 38.50iter/s]
Best score: 0.2116: : 980iter [00:28, 38.50iter/s]
Best score: 0.2116: : 981iter [00:28, 38.73iter/s]
Best score: 0.2116: : 981iter [00:28, 38.73iter/s]
Best score: 0.2116: : 982iter [00:28, 38.73iter/s]
Best score: 0.2116: : 983iter [00:28, 38.73iter/s]
Best score: 0.2116: : 984iter [00:28, 38.73iter/s]
Best score: 0.2116: : 985iter [00:28, 38.94iter/s]
Best score: 0.2116: : 985iter [00:28, 38.94iter/s]
Best score: 0.2116: : 986iter [00:28, 38.94iter/s]
Best score: 0.2116: : 987iter [00:28, 38.94iter/s]
Best score: 0.2116: : 988iter [00:28, 38.94iter/s]
Best score: 0.2116: : 989iter [00:28, 39.00iter/s]
Best score: 0.2116: : 989iter [00:28, 39.00iter/s]
Best score: 0.2116: : 990iter [00:28, 39.00iter/s]
Best score: 0.2116: : 991iter [00:28, 39.00iter/s]
Best score: 0.2116: : 992iter [00:28, 39.00iter/s]
Best score: 0.2116: : 993iter [00:28, 38.92iter/s]
Best score: 0.2116: : 993iter [00:28, 38.92iter/s]
Best score: 0.2116: : 994iter [00:28, 38.92iter/s]
Best score: 0.2116: : 995iter [00:28, 38.92iter/s]
Best score: 0.2116: : 996iter [00:28, 38.92iter/s]
Best score: 0.2116: : 997iter [00:28, 38.90iter/s]
Best score: 0.2116: : 997iter [00:28, 38.90iter/s]
Best score: 0.2116: : 998iter [00:28, 38.90iter/s]
[ITER 1000] Best: 0.2116
[PARAMS] Current best parameters:
  fx: 540.7353
  fy: 540.0827
  px: 983.9702
  py: 737.8172
  k1: -0.03813289
  k2: 0.04487987
  k3: -0.02324508
  k4: 0.00373141
  p1: -0.01264343
  p2: -0.00741411
  p3: 0.00040130
  p4: 0.01128884
  s1: 0.0068
  s2: -0.0112
  s3: 0.0106
  s4: -0.0116
  tilt_x: 0.0197
  tilt_y: -0.0246
  scale: 3.0185
  rotation: -0.0299

Best score: 0.2116: : 999iter [00:28, 38.90iter/s]
Best score: 0.2116: : 1000iter [00:28, 38.90iter/s]
Best score: 0.2116: : 1001iter [00:28, 31.87iter/s]
Best score: 0.2116: : 1001iter [00:28, 31.87iter/s]
Best score: 0.2116: : 1002iter [00:28, 31.87iter/s]
Best score: 0.2116: : 1003iter [00:28, 31.87iter/s]
Best score: 0.2116: : 1004iter [00:28, 31.87iter/s]
Best score: 0.2116: : 1005iter [00:28, 27.29iter/s]
Best score: 0.2116: : 1005iter [00:28, 27.29iter/s]
Best score: 0.2116: : 1006iter [00:29, 27.29iter/s]
Best score: 0.2116: : 1007iter [00:29, 27.29iter/s]
Best score: 0.2116: : 1008iter [00:29, 27.29iter/s]
Best score: 0.2116: : 1009iter [00:29, 27.29iter/s]
Best score: 0.2116: : 1010iter [00:29, 30.49iter/s]
Best score: 0.2116: : 1010iter [00:29, 30.49iter/s]
Best score: 0.2116: : 1011iter [00:29, 30.49iter/s]
Best score: 0.2116: : 1012iter [00:29, 30.49iter/s]
Best score: 0.2116: : 1013iter [00:29, 30.49iter/s]
Best score: 0.2116: : 1014iter [00:29, 32.69iter/s]
Best score: 0.2116: : 1014iter [00:29, 32.69iter/s]
Best score: 0.2116: : 1015iter [00:29, 32.69iter/s]
Best score: 0.2116: : 1016iter [00:29, 32.69iter/s]
Best score: 0.2116: : 1017iter [00:29, 32.69iter/s]
Best score: 0.2116: : 1018iter [00:29, 32.69iter/s]
Best score: 0.2116: : 1019iter [00:29, 34.88iter/s]
Best score: 0.2116: : 1019iter [00:29, 34.88iter/s]
Best score: 0.2116: : 1020iter [00:29, 34.88iter/s]
Best score: 0.2116: : 1021iter [00:29, 34.88iter/s]
Best score: 0.2116: : 1022iter [00:29, 34.88iter/s]
Best score: 0.2116: : 1023iter [00:29, 36.05iter/s]
Best score: 0.2116: : 1023iter [00:29, 36.05iter/s]
Best score: 0.2116: : 1024iter [00:29, 36.05iter/s]
Best score: 0.2116: : 1025iter [00:29, 36.05iter/s]
Best score: 0.2116: : 1026iter [00:29, 36.05iter/s]
Best score: 0.2116: : 1027iter [00:29, 36.05iter/s]
Best score: 0.2116: : 1028iter [00:29, 37.39iter/s]
Best score: 0.2116: : 1028iter [00:29, 37.39iter/s]
Best score: 0.2115: : 1029iter [00:29, 37.39iter/s]
Best score: 0.2115: : 1030iter [00:29, 37.39iter/s]
Best score: 0.2115: : 1031iter [00:29, 37.39iter/s]
Best score: 0.2115: : 1032iter [00:29, 37.80iter/s]
Best score: 0.2115: : 1032iter [00:29, 37.80iter/s]
Best score: 0.2115: : 1033iter [00:29, 37.80iter/s]
Best score: 0.2115: : 1034iter [00:29, 37.80iter/s]
Best score: 0.2115: : 1035iter [00:29, 37.80iter/s]
Best score: 0.2115: : 1036iter [00:29, 37.80iter/s]
Best score: 0.2115: : 1037iter [00:29, 38.53iter/s]
Best score: 0.2115: : 1037iter [00:29, 38.53iter/s]
Best score: 0.2115: : 1038iter [00:29, 38.53iter/s]
Best score: 0.2112: : 1039iter [00:29, 38.53iter/s]
Best score: 0.2112: : 1040iter [00:29, 38.53iter/s]
Best score: 0.2112: : 1041iter [00:29, 38.76iter/s]
Best score: 0.2112: : 1041iter [00:29, 38.76iter/s]
Best score: 0.2112: : 1042iter [00:29, 38.76iter/s]
Best score: 0.2112: : 1043iter [00:29, 38.76iter/s]
Best score: 0.2112: : 1044iter [00:29, 38.76iter/s]
Best score: 0.2112: : 1045iter [00:30, 38.76iter/s]
Best score: 0.2112: : 1046iter [00:30, 39.21iter/s]
Best score: 0.2112: : 1046iter [00:30, 39.21iter/s]
Best score: 0.2112: : 1047iter [00:30, 39.21iter/s]
Best score: 0.2112: : 1048iter [00:30, 39.21iter/s]
[ITER 1050] Best: 0.2112
[PARAMS] Current best parameters:
  fx: 541.4090
  fy: 540.7478
  px: 984.5851
  py: 738.2920
  k1: -0.03991366
  k2: 0.04662997
  k3: -0.02419798
  k4: 0.00392215
  p1: -0.01347010
  p2: -0.00754055
  p3: 0.00337653
  p4: 0.00991758
  s1: 0.0048
  s2: -0.0100
  s3: 0.0090
  s4: -0.0104
  tilt_x: 0.0237
  tilt_y: -0.0279
  scale: 3.0200
  rotation: -0.0363

Best score: 0.2112: : 1049iter [00:30, 39.21iter/s]
Best score: 0.2112: : 1050iter [00:30, 39.16iter/s]
Best score: 0.2112: : 1050iter [00:30, 39.16iter/s]
Best score: 0.2112: : 1051iter [00:30, 39.16iter/s]
Best score: 0.2112: : 1052iter [00:30, 39.16iter/s]
Best score: 0.2112: : 1053iter [00:30, 39.16iter/s]
Best score: 0.2112: : 1054iter [00:30, 32.47iter/s]
Best score: 0.2112: : 1054iter [00:30, 32.47iter/s]
Best score: 0.2112: : 1055iter [00:30, 32.47iter/s]
Best score: 0.2112: : 1056iter [00:30, 32.47iter/s]
Best score: 0.2112: : 1057iter [00:30, 32.47iter/s]
Best score: 0.2112: : 1058iter [00:30, 32.47iter/s]
Best score: 0.2112: : 1059iter [00:30, 34.82iter/s]
Best score: 0.2112: : 1059iter [00:30, 34.82iter/s]
Best score: 0.2112: : 1060iter [00:30, 34.82iter/s]
Best score: 0.2112: : 1061iter [00:30, 34.82iter/s]
Best score: 0.2112: : 1062iter [00:30, 34.82iter/s]
Best score: 0.2112: : 1063iter [00:30, 35.84iter/s]
Best score: 0.2112: : 1063iter [00:30, 35.84iter/s]
Best score: 0.2112: : 1064iter [00:30, 35.84iter/s]
Best score: 0.2112: : 1065iter [00:30, 35.84iter/s]
Best score: 0.2112: : 1066iter [00:30, 35.84iter/s]
Best score: 0.2112: : 1067iter [00:30, 35.84iter/s]
Best score: 0.2112: : 1068iter [00:30, 37.07iter/s]
Best score: 0.2112: : 1068iter [00:30, 37.07iter/s]
Best score: 0.2111: : 1069iter [00:30, 37.07iter/s]
Best score: 0.2111: : 1070iter [00:30, 37.07iter/s]
Best score: 0.2111: : 1071iter [00:30, 37.07iter/s]
Best score: 0.2111: : 1072iter [00:30, 37.66iter/s]
Best score: 0.2111: : 1072iter [00:30, 37.66iter/s]
Best score: 0.2111: : 1073iter [00:30, 37.66iter/s]
Best score: 0.2111: : 1074iter [00:30, 37.66iter/s]
Best score: 0.2111: : 1075iter [00:30, 37.66iter/s]
Best score: 0.2111: : 1076iter [00:30, 37.66iter/s]
Best score: 0.2111: : 1077iter [00:30, 38.51iter/s]
Best score: 0.2111: : 1077iter [00:30, 38.51iter/s]
Best score: 0.2111: : 1078iter [00:30, 38.51iter/s]
Best score: 0.2111: : 1079iter [00:30, 38.51iter/s]
Best score: 0.2111: : 1080iter [00:30, 38.51iter/s]
Best score: 0.2111: : 1081iter [00:30, 38.70iter/s]
Best score: 0.2111: : 1081iter [00:30, 38.70iter/s]
Best score: 0.2111: : 1082iter [00:31, 38.70iter/s]
Best score: 0.2111: : 1083iter [00:31, 38.70iter/s]
Best score: 0.2111: : 1084iter [00:31, 38.70iter/s]
Best score: 0.2111: : 1085iter [00:31, 39.02iter/s]
Best score: 0.2111: : 1085iter [00:31, 39.02iter/s]
Best score: 0.2111: : 1086iter [00:31, 39.02iter/s]
Best score: 0.2111: : 1087iter [00:31, 39.02iter/s]
Best score: 0.2111: : 1088iter [00:31, 39.02iter/s]
Best score: 0.2109: : 1089iter [00:31, 39.02iter/s]
Best score: 0.2109: : 1090iter [00:31, 39.10iter/s]
Best score: 0.2109: : 1090iter [00:31, 39.10iter/s]
Best score: 0.2109: : 1091iter [00:31, 39.10iter/s]
Best score: 0.2109: : 1092iter [00:31, 39.10iter/s]
Best score: 0.2109: : 1093iter [00:31, 39.10iter/s]
Best score: 0.2109: : 1094iter [00:31, 39.10iter/s]
Best score: 0.2109: : 1095iter [00:31, 39.48iter/s]
Best score: 0.2109: : 1095iter [00:31, 39.48iter/s]
Best score: 0.2109: : 1096iter [00:31, 39.48iter/s]
Best score: 0.2109: : 1097iter [00:31, 39.48iter/s]
Best score: 0.2109: : 1098iter [00:31, 39.48iter/s]
[ITER 1100] Best: 0.2109
[PARAMS] Current best parameters:
  fx: 541.4251
  fy: 540.7637
  px: 984.6219
  py: 738.3875
  k1: -0.03943652
  k2: 0.04616111
  k3: -0.02409779
  k4: 0.00393131
  p1: -0.01320498
  p2: -0.00738066
  p3: 0.00364816
  p4: 0.00985621
  s1: 0.0046
  s2: -0.0100
  s3: 0.0086
  s4: -0.0103
  tilt_x: 0.0237
  tilt_y: -0.0278
  scale: 3.0193
  rotation: -0.0371

Best score: 0.2109: : 1099iter [00:31, 39.48iter/s]
Best score: 0.2109: : 1100iter [00:31, 39.51iter/s]
Best score: 0.2109: : 1100iter [00:31, 34.90iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.210907 -> 0.210795

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,895 → 25,895 within 3500mm ring
  Ring polish did not improve score: 0.210795
  Success: False
  Tangential distortion: p1=-0.013207, p2=-0.007387, p3=0.003646, p4=0.009853
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2108
[✅] Parameters copied to: /working/MDC059_W045_2026_05_29/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 541.4251070340885
    fy: 540.7637471024046
    px: 984.6218517893656
    py: 738.3874827444014
    k1: -0.039438598911837756
    k2: 0.04615896776484198
    k3: -0.024098964867072765
    k4: 0.003933524297122353
    p1: -0.013206903554216066
    p2: -0.007386541587292055
    p3: 0.0036462451797060003
    p4: 0.009852789701646516
    scale: 3.0192614104633666
    rotation: -0.03706345047001392
    mmpp: 1.2848698575209194

[🔄] Converting 1236 frames to PTD, creating debug images for 200 calibration frames...

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   🔄 1000:  65%|██████▌   | 453/692 [00:02<00:01, 174.14frame/s]

   🔄 1000:  68%|██████▊   | 471/692 [00:02<00:01, 174.94frame/s]

   🔄 1000:  71%|███████   | 489/692 [00:02<00:01, 175.66frame/s]

   🔄 1000:  73%|███████▎  | 507/692 [00:02<00:01, 176.13frame/s]

   🔄 1000:  76%|███████▌  | 525/692 [00:02<00:00, 176.63frame/s]

   🔄 1000:  78%|███████▊  | 543/692 [00:03<00:00, 176.89frame/s]

   🔄 1000:  81%|████████  | 561/692 [00:03<00:00, 177.10frame/s]

   🔄 1000:  84%|████████▎ | 579/692 [00:03<00:00, 177.29frame/s]

   🔄 1000:  86%|████████▋ | 597/692 [00:03<00:00, 177.53frame/s]

   🔄 1000:  89%|████████▉ | 615/692 [00:03<00:00, 177.67frame/s]

   🔄 1000:  91%|█████████▏| 633/692 [00:03<00:00, 177.48frame/s]

   🔄 1000:  94%|█████████▍| 651/692 [00:03<00:00, 177.43frame/s]

   🔄 1000:  97%|█████████▋| 669/692 [00:03<00:00, 177.16frame/s]

   🔄 1000:  99%|█████████▉| 687/692 [00:04<00:00, 120.13frame/s]

                                                                 
📁 Rigs: 100%|██████████| 2/2 [00:12<00:00,  6.44s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:12<00:00,  6.31s/rig]
[✅] Saved PTDs: /working/MDC059_W045_2026_05_29/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 174.59frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 175.73frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 176.31frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 176.36frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 176.16frame/s]

                                                               
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.08rig/s]

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 176.10frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 172.90frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 171.28frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 172.02frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 173.14frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.00s/rig][✅] Saved PTDs: /working/MDC059_W045_2026_05_29/calibration/debug/4_undistorted/temp_ptd

📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.01rig/s]
[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%|▎         | 77830/3080448 [00:00<00:03, 778291.46px/s]
📤 Writing LUT:   5%|▌         | 163574/3080448 [00:00<00:03, 824848.52px/s]
📤 Writing LUT:   8%|▊         | 251540/3080448 [00:00<00:03, 849873.19px/s]
📤 Writing LUT:  11%|█         | 337183/3080448 [00:00<00:03, 852457.24px/s]
📤 Writing LUT:  14%|█▎        | 422429/3080448 [00:00<00:03, 852058.55px/s]
📤 Writing LUT:  17%|█▋        | 509198/3080448 [00:00<00:02, 857369.09px/s]
📤 Writing LUT:  19%|█▉        | 597171/3080448 [00:00<00:02, 864675.42px/s]
📤 Writing LUT:  22%|██▏       | 686470/3080448 [00:00<00:02, 873687.04px/s]
📤 Writing LUT:  25%|██▌       | 774493/3080448 [00:00<00:02, 875731.22px/s]
📤 Writing LUT:  28%|██▊       | 862067/3080448 [00:01<00:02, 875451.97px/s]
📤 Writing LUT:  31%|███       | 950756/3080448 [00:01<00:02, 878949.98px/s]
📤 Writing LUT:  34%|███▍      | 1040514/3080448 [00:01<00:02, 884613.39px/s]
📤 Writing LUT:  37%|███▋      | 1128976/3080448 [00:01<00:02, 884599.04px/s]
📤 Writing LUT:  40%|███▉      | 1217688/3080448 [00:01<00:02, 885360.03px/s]
📤 Writing LUT:  42%|████▏     | 1306225/3080448 [00:01<00:02, 883825.64px/s]
📤 Writing LUT:  45%|████▌     | 1396139/3080448 [00:01<00:01, 888427.77px/s]
📤 Writing LUT:  48%|████▊     | 1484983/3080448 [00:01<00:01, 886898.15px/s]
📤 Writing LUT:  51%|█████     | 1573675/3080448 [00:01<00:01, 882492.44px/s]
📤 Writing LUT:  54%|█████▍    | 1661930/3080448 [00:01<00:01, 879759.37px/s]
📤 Writing LUT:  57%|█████▋    | 1749911/3080448 [00:02<00:01, 877395.64px/s]
📤 Writing LUT:  60%|█████▉    | 1839814/3080448 [00:02<00:01, 883850.56px/s]
📤 Writing LUT:  63%|██████▎   | 1928206/3080448 [00:02<00:01, 879519.79px/s]
📤 Writing LUT:  66%|██████▌   | 2018164/3080448 [00:02<00:01, 885499.46px/s]
📤 Writing LUT:  68%|██████▊   | 2107568/3080448 [00:02<00:01, 888050.45px/s]
📤 Writing LUT:  71%|███████▏  | 2196381/3080448 [00:02<00:00, 885744.92px/s]
📤 Writing LUT:  74%|███████▍  | 2284962/3080448 [00:02<00:00, 883532.47px/s]
📤 Writing LUT:  77%|███████▋  | 2373320/3080448 [00:02<00:00, 881010.06px/s]
📤 Writing LUT:  80%|███████▉  | 2461425/3080448 [00:02<00:00, 879429.64px/s]
📤 Writing LUT:  83%|████████▎ | 2550569/3080448 [00:02<00:00, 883014.16px/s]
📤 Writing LUT:  86%|████████▌ | 2640365/3080448 [00:03<00:00, 887480.29px/s]
📤 Writing LUT:  89%|████████▊ | 2732202/3080448 [00:03<00:00, 896723.93px/s]
📤 Writing LUT:  92%|█████████▏| 2823291/3080448 [00:03<00:00, 900964.93px/s]
📤 Writing LUT:  95%|█████████▍| 2915373/3080448 [00:03<00:00, 906912.10px/s]
📤 Writing LUT:  98%|█████████▊| 3008404/3080448 [00:03<00:00, 913924.61px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 883042.15px/s]
[✅] LUT written to: /working/MDC059_W045_2026_05_29/calibration/debug/4_undistorted/output/LUT.lut
[✅] LUT copied to: /working/MDC059_W045_2026_05_29/calibration/MDC059_W045_unknown_2026_05_29.lut

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

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

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

   🔥 500:   0%|          | 0/544 [00:00<?, ?frame/s]

   🔥 500:   1%|▏         | 8/544 [00:00<00:07, 72.98frame/s]

   🔥 500:   3%|▎         | 16/544 [00:00<00:06, 76.12frame/s]

   🔥 500:   4%|▍         | 24/544 [00:00<00:06, 77.11frame/s]

   🔥 500:   6%|▌         | 32/544 [00:00<00:06, 77.51frame/s]

   🔥 500:   7%|▋         | 40/544 [00:00<00:06, 76.86frame/s]

   🔥 500:   9%|▉         | 48/544 [00:00<00:06, 77.17frame/s]

   🔥 500:  10%|█         | 56/544 [00:00<00:06, 77.13frame/s]

   🔥 500:  12%|█▏        | 64/544 [00:00<00:06, 77.42frame/s]

   🔥 500:  13%|█▎        | 72/544 [00:00<00:06, 77.41frame/s]

   🔥 500:  15%|█▍        | 80/544 [00:01<00:06, 77.20frame/s]

   🔥 500:  16%|█▌        | 88/544 [00:01<00:05, 77.30frame/s]

   🔥 500:  18%|█▊        | 96/544 [00:01<00:05, 77.10frame/s]

   🔥 500:  19%|█▉        | 105/544 [00:01<00:05, 78.46frame/s]

   🔥 500:  21%|██        | 113/544 [00:01<00:05, 78.51frame/s]

   🔥 500:  22%|██▏       | 121/544 [00:01<00:05, 77.72frame/s]

   🔥 500:  24%|██▍       | 130/544 [00:01<00:05, 78.67frame/s]

   🔥 500:  25%|██▌       | 138/544 [00:01<00:05, 78.94frame/s]

   🔥 500:  27%|██▋       | 146/544 [00:01<00:05, 78.10frame/s]

   🔥 500:  28%|██▊       | 154/544 [00:01<00:05, 77.37frame/s]

   🔥 500:  30%|██▉       | 162/544 [00:02<00:04, 77.68frame/s]

   🔥 500:  31%|███▏      | 170/544 [00:02<00:04, 78.10frame/s]

   🔥 500:  33%|███▎      | 178/544 [00:02<00:04, 77.27frame/s]

   🔥 500:  34%|███▍      | 186/544 [00:02<00:04, 76.60frame/s]

   🔥 500:  36%|███▌      | 194/544 [00:02<00:04, 76.33frame/s]

   🔥 500:  37%|███▋      | 202/544 [00:02<00:04, 76.95frame/s]

   🔥 500:  39%|███▊      | 210/544 [00:02<00:04, 77.50frame/s]

   🔥 500:  40%|████      | 219/544 [00:02<00:04, 78.72frame/s]

   🔥 500:  42%|████▏     | 228/544 [00:02<00:03, 79.92frame/s]

   🔥 500:  43%|████▎     | 236/544 [00:03<00:03, 79.88frame/s]

   🔥 500:  45%|████▍     | 244/544 [00:03<00:03, 79.79frame/s]

   🔥 500:  47%|████▋     | 253/544 [00:03<00:03, 80.09frame/s]

   🔥 500:  48%|████▊     | 262/544 [00:03<00:03, 79.60frame/s]

   🔥 500:  50%|████▉     | 270/544 [00:03<00:03, 79.38frame/s]

   🔥 500:  51%|█████     | 278/544 [00:03<00:03, 79.17frame/s]

   🔥 500:  53%|█████▎    | 286/544 [00:03<00:03, 78.78frame/s]

   🔥 500:  54%|█████▍    | 294/544 [00:03<00:03, 78.57frame/s]

   🔥 500:  56%|█████▌    | 302/544 [00:03<00:03, 78.29frame/s]

   🔥 500:  57%|█████▋    | 310/544 [00:03<00:02, 78.70frame/s]

   🔥 500:  58%|█████▊    | 318/544 [00:04<00:02, 78.65frame/s]

   🔥 500:  60%|█████▉    | 326/544 [00:04<00:02, 78.94frame/s]

   🔥 500:  61%|██████▏   | 334/544 [00:04<00:02, 78.98frame/s]

   🔥 500:  63%|██████▎   | 342/544 [00:04<00:02, 79.18frame/s]

   🔥 500:  64%|██████▍   | 350/544 [00:04<00:02, 79.35frame/s]

   🔥 500:  66%|██████▌   | 358/544 [00:04<00:02, 79.13frame/s]

   🔥 500:  67%|██████▋   | 367/544 [00:04<00:02, 79.67frame/s]

   🔥 500:  69%|██████▉   | 376/544 [00:04<00:02, 79.87frame/s]

   🔥 500:  71%|███████   | 384/544 [00:04<00:02, 79.72frame/s]

   🔥 500:  72%|███████▏  | 392/544 [00:05<00:01, 79.74frame/s]

   🔥 500:  74%|███████▎  | 400/544 [00:05<00:01, 79.75frame/s]

   🔥 500:  75%|███████▌  | 408/544 [00:05<00:01, 79.47frame/s]

   🔥 500:  76%|███████▋  | 416/544 [00:05<00:01, 79.21frame/s]

   🔥 500:  78%|███████▊  | 424/544 [00:05<00:01, 78.56frame/s]

   🔥 500:  79%|███████▉  | 432/544 [00:05<00:01, 78.72frame/s]

   🔥 500:  81%|████████  | 440/544 [00:05<00:01, 78.32frame/s]

   🔥 500:  82%|████████▏ | 448/544 [00:05<00:01, 78.08frame/s]

   🔥 500:  84%|████████▍ | 456/544 [00:05<00:01, 78.00frame/s]

   🔥 500:  85%|████████▌ | 464/544 [00:05<00:01, 78.25frame/s]

   🔥 500:  87%|████████▋ | 472/544 [00:06<00:00, 78.43frame/s]

   🔥 500:  88%|████████▊ | 480/544 [00:06<00:00, 78.04frame/s]

   🔥 500:  90%|████████▉ | 488/544 [00:06<00:00, 77.80frame/s]

   🔥 500:  91%|█████████ | 496/544 [00:06<00:00, 78.35frame/s]

   🔥 500:  93%|█████████▎| 504/544 [00:06<00:00, 78.01frame/s]

   🔥 500:  94%|█████████▍| 512/544 [00:06<00:00, 77.83frame/s]

   🔥 500:  96%|█████████▌| 520/544 [00:06<00:00, 78.15frame/s]

   🔥 500:  97%|█████████▋| 528/544 [00:06<00:00, 78.04frame/s]

   🔥 500:  99%|█████████▊| 536/544 [00:06<00:00, 78.27frame/s]

   🔥 500: 100%|██████████| 544/544 [00:06<00:00, 78.32frame/s]

                                                               [✅] 500: /working/MDC059_W045_2026_05_29/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC059_W045_2026_05_29/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:08<00:08,  8.11s/rig]

   🔥 1000:   0%|          | 0/692 [00:00<?, ?frame/s]

   🔥 1000:   1%|▏         | 9/692 [00:00<00:07, 86.41frame/s]

   🔥 1000:   3%|▎         | 18/692 [00:00<00:07, 86.14frame/s]

   🔥 1000:   4%|▍         | 27/692 [00:00<00:07, 85.44frame/s]

   🔥 1000:   5%|▌         | 36/692 [00:00<00:07, 86.54frame/s]

   🔥 1000:   7%|▋         | 45/692 [00:00<00:07, 87.64frame/s]

   🔥 1000:   8%|▊         | 55/692 [00:00<00:07, 89.23frame/s]

   🔥 1000:   9%|▉         | 64/692 [00:00<00:07, 88.69frame/s]

   🔥 1000:  11%|█         | 73/692 [00:00<00:07, 87.59frame/s]

   🔥 1000:  12%|█▏        | 82/692 [00:00<00:06, 87.32frame/s]

   🔥 1000:  13%|█▎        | 91/692 [00:01<00:06, 86.64frame/s]

   🔥 1000:  14%|█▍        | 100/692 [00:01<00:06, 85.27frame/s]

   🔥 1000:  16%|█▌        | 109/692 [00:01<00:06, 84.84frame/s]

   🔥 1000:  17%|█▋        | 118/692 [00:01<00:06, 83.95frame/s]

   🔥 1000:  18%|█▊        | 127/692 [00:01<00:06, 82.97frame/s]

   🔥 1000:  20%|█▉        | 136/692 [00:01<00:06, 82.74frame/s]

   🔥 1000:  21%|██        | 145/692 [00:01<00:06, 82.47frame/s]

   🔥 1000:  22%|██▏       | 154/692 [00:01<00:06, 82.33frame/s]

   🔥 1000:  24%|██▎       | 163/692 [00:01<00:06, 82.22frame/s]

   🔥 1000:  25%|██▍       | 172/692 [00:02<00:06, 81.99frame/s]

   🔥 1000:  26%|██▌       | 181/692 [00:02<00:06, 80.75frame/s]

   🔥 1000:  27%|██▋       | 190/692 [00:02<00:06, 81.08frame/s]

   🔥 1000:  29%|██▉       | 199/692 [00:02<00:06, 81.39frame/s]

   🔥 1000:  30%|███       | 208/692 [00:02<00:05, 81.13frame/s]

   🔥 1000:  31%|███▏      | 217/692 [00:02<00:05, 80.97frame/s]

   🔥 1000:  33%|███▎      | 226/692 [00:02<00:05, 81.08frame/s]

   🔥 1000:  34%|███▍      | 235/692 [00:02<00:05, 81.14frame/s]

   🔥 1000:  35%|███▌      | 244/692 [00:02<00:05, 81.52frame/s]

   🔥 1000:  37%|███▋      | 253/692 [00:03<00:05, 82.04frame/s]

   🔥 1000:  38%|███▊      | 262/692 [00:03<00:05, 82.41frame/s]

   🔥 1000:  39%|███▉      | 271/692 [00:03<00:05, 82.03frame/s]

   🔥 1000:  40%|████      | 280/692 [00:03<00:05, 82.23frame/s]

   🔥 1000:  42%|████▏     | 289/692 [00:03<00:04, 82.51frame/s]

   🔥 1000:  43%|████▎     | 298/692 [00:03<00:04, 83.69frame/s]

   🔥 1000:  45%|████▍     | 308/692 [00:03<00:04, 85.82frame/s]

   🔥 1000:  46%|████▌     | 317/692 [00:03<00:04, 86.42frame/s]

   🔥 1000:  47%|████▋     | 326/692 [00:03<00:04, 87.34frame/s]

   🔥 1000:  49%|████▊     | 336/692 [00:03<00:04, 88.14frame/s]

   🔥 1000:  50%|████▉     | 345/692 [00:04<00:03, 87.90frame/s]

   🔥 1000:  51%|█████     | 354/692 [00:04<00:03, 87.48frame/s]

   🔥 1000:  52%|█████▏    | 363/692 [00:04<00:03, 86.67frame/s]

   🔥 1000:  54%|█████▍    | 372/692 [00:04<00:03, 86.15frame/s]

   🔥 1000:  55%|█████▌    | 381/692 [00:04<00:03, 85.98frame/s]

   🔥 1000:  56%|█████▋    | 390/692 [00:04<00:03, 85.97frame/s]

   🔥 1000:  58%|█████▊    | 399/692 [00:04<00:03, 85.48frame/s]

   🔥 1000:  59%|█████▉    | 408/692 [00:04<00:03, 85.57frame/s]

   🔥 1000:  60%|██████    | 417/692 [00:04<00:03, 85.51frame/s]

   🔥 1000:  62%|██████▏   | 426/692 [00:05<00:03, 85.27frame/s]

   🔥 1000:  63%|██████▎   | 435/692 [00:05<00:03, 85.01frame/s]

   🔥 1000:  64%|██████▍   | 444/692 [00:05<00:02, 84.99frame/s]

   🔥 1000:  65%|██████▌   | 453/692 [00:05<00:02, 85.08frame/s]

   🔥 1000:  67%|██████▋   | 462/692 [00:05<00:02, 85.27frame/s]

   🔥 1000:  68%|██████▊   | 471/692 [00:05<00:02, 84.98frame/s]

   🔥 1000:  69%|██████▉   | 480/692 [00:05<00:02, 85.24frame/s]

   🔥 1000:  71%|███████   | 489/692 [00:05<00:02, 85.39frame/s]

   🔥 1000:  72%|███████▏  | 498/692 [00:05<00:02, 84.94frame/s]

   🔥 1000:  73%|███████▎  | 507/692 [00:05<00:02, 86.27frame/s]

   🔥 1000:  75%|███████▍  | 517/692 [00:06<00:01, 87.63frame/s]

   🔥 1000:  76%|███████▌  | 527/692 [00:06<00:01, 88.80frame/s]

   🔥 1000:  78%|███████▊  | 537/692 [00:06<00:01, 89.96frame/s]

   🔥 1000:  79%|███████▉  | 547/692 [00:06<00:01, 90.43frame/s]

   🔥 1000:  80%|████████  | 557/692 [00:06<00:01, 91.18frame/s]

   🔥 1000:  82%|████████▏ | 567/692 [00:06<00:01, 92.13frame/s]

   🔥 1000:  83%|████████▎ | 577/692 [00:06<00:01, 92.13frame/s]

   🔥 1000:  85%|████████▍ | 587/692 [00:06<00:01, 90.78frame/s]

   🔥 1000:  86%|████████▋ | 597/692 [00:06<00:01, 90.42frame/s]

   🔥 1000:  88%|████████▊ | 607/692 [00:07<00:00, 90.00frame/s]

   🔥 1000:  89%|████████▉ | 617/692 [00:07<00:00, 89.83frame/s]

   🔥 1000:  91%|█████████ | 627/692 [00:07<00:00, 90.10frame/s]

   🔥 1000:  92%|█████████▏| 637/692 [00:07<00:00, 90.67frame/s]

   🔥 1000:  93%|█████████▎| 647/692 [00:07<00:00, 90.84frame/s]

   🔥 1000:  95%|█████████▍| 657/692 [00:07<00:00, 91.21frame/s]

   🔥 1000:  96%|█████████▋| 667/692 [00:07<00:00, 90.14frame/s]

   🔥 1000:  98%|█████████▊| 677/692 [00:07<00:00, 88.58frame/s]

   🔥 1000:  99%|█████████▉| 686/692 [00:07<00:00, 87.86frame/s]

                                                                [✅] 1000: /working/MDC059_W045_2026_05_29/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC059_W045_2026_05_29/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:19<00:00,  9.83s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:19<00:00,  9.57s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC059_W045_2026_05_29/calibration/debug/5_heatmap
[🏁] Finished processing MDC059_W045_2026_05_29


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