2026-05-06T22:20:56 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC027_W031_2026-05-06 ===
2026-05-06T22:20:56 - INFO: Camera ID: 15091869
2026-05-06T22:20:56 - INFO: Wand ID: 
2026-05-06T22:20:56 - INFO: Recorded At: 2026-05-06T04:10:38Z

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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 414 stationary frames, 380 remain


→ 500 (380 images) Phase 1:   0%|          | 0/380 [00:00<?, ?it/s]

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→ 500 (380 images) Phase 1:  62%|██████▏   | 234/380 [00:03<00:02, 60.55it/s]

→ 500 (380 images) Phase 1:  64%|██████▍   | 244/380 [00:04<00:01, 68.15it/s]

→ 500 (380 images) Phase 1:  68%|██████▊   | 257/380 [00:04<00:01, 80.88it/s]

→ 500 (380 images) Phase 1:  70%|███████   | 267/380 [00:04<00:01, 60.99it/s]

→ 500 (380 images) Phase 1:  74%|███████▎  | 280/380 [00:04<00:01, 73.30it/s]

→ 500 (380 images) Phase 1:  76%|███████▋  | 290/380 [00:04<00:01, 76.46it/s]

→ 500 (380 images) Phase 1:  79%|███████▊  | 299/380 [00:04<00:01, 61.23it/s]

→ 500 (380 images) Phase 1:  82%|████████▏ | 311/380 [00:04<00:00, 72.92it/s]

→ 500 (380 images) Phase 1:  85%|████████▌ | 323/380 [00:05<00:00, 74.25it/s]

→ 500 (380 images) Phase 1:  87%|████████▋ | 332/380 [00:05<00:00, 63.22it/s]

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→ 500 (380 images) Phase 1:  93%|█████████▎| 355/380 [00:05<00:00, 76.49it/s]

→ 500 (380 images) Phase 1:  96%|█████████▌| 364/380 [00:05<00:00, 68.38it/s]

→ 500 (380 images) Phase 1: 100%|█████████▉| 379/380 [00:05<00:00, 85.80it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/380 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:   7%|▋         | 27/380 [00:00<00:04, 83.55it/s]

→ 500 Phase 2: corners:  11%|█         | 41/380 [00:00<00:03, 102.35it/s]

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→ 500 Phase 2: corners:  23%|██▎       | 86/380 [00:00<00:02, 139.47it/s]

→ 500 Phase 2: corners:  28%|██▊       | 108/380 [00:00<00:01, 161.70it/s]

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→ 500 Phase 2: corners:  49%|████▉     | 187/380 [00:01<00:01, 161.35it/s]

→ 500 Phase 2: corners:  54%|█████▎    | 204/380 [00:01<00:01, 159.99it/s]

→ 500 Phase 2: corners:  58%|█████▊    | 221/380 [00:01<00:01, 158.46it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 238/380 [00:01<00:00, 151.77it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 254/380 [00:01<00:00, 153.00it/s]

→ 500 Phase 2: corners:  71%|███████   | 270/380 [00:01<00:00, 154.87it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 286/380 [00:02<00:00, 153.42it/s]

→ 500 Phase 2: corners:  80%|███████▉  | 303/380 [00:02<00:00, 154.77it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 319/380 [00:02<00:00, 155.50it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 335/380 [00:02<00:00, 153.96it/s]

→ 500 Phase 2: corners:  92%|█████████▏| 351/380 [00:02<00:00, 148.67it/s]

→ 500 Phase 2: corners:  97%|█████████▋| 369/380 [00:02<00:00, 156.40it/s]

                                                                          [PASS2] Frame 0000416: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000417: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000418: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000420: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000421: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000425: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000428: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000565: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000417: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000418: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000420: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000418: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000419: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000420: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Summary: 7 frames fixed, 62 untrusted, 380 total


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→ 500 Phase 3: finalize:  81%|████████  | 308/380 [00:06<00:01, 45.74it/s]

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→ 500 Phase 3: finalize:  90%|█████████ | 343/380 [00:07<00:00, 46.08it/s]

→ 500 Phase 3: finalize:  92%|█████████▏| 348/380 [00:07<00:00, 46.08it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 353/380 [00:07<00:00, 46.12it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 358/380 [00:07<00:00, 46.20it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 363/380 [00:07<00:00, 46.29it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 368/380 [00:07<00:00, 46.34it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 373/380 [00:07<00:00, 46.46it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 378/380 [00:07<00:00, 46.42it/s]

                                                                          
[TIMING] 500 — 380 frames
  Phase 1  radial detect   (parallel)  :   5.93s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.69s  (7.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.43s
  Phase 3  finalize        (sequential):   7.86s  (20.7 ms/frame)
    decode + undistort                 :   4.85s  (12.8 ms/frame)
    corner apply + refinement          :   1.27s  (3.3 ms/frame)
    segment                            :   1.34s  (3.5 ms/frame)
    smooth                             :   0.28s  (0.7 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  16.90s


Processing Rigs:  50%|█████     | 1/2 [00:21<00:21, 21.62s/it][INFO] Filtered 357 stationary frames, 453 remain


→ 1000 (453 images) Phase 1:   0%|          | 0/453 [00:00<?, ?it/s]

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→ 1000 (453 images) Phase 1:  64%|██████▍   | 289/453 [00:04<00:02, 63.79it/s]

→ 1000 (453 images) Phase 1:  66%|██████▌   | 298/453 [00:04<00:02, 67.15it/s]

→ 1000 (453 images) Phase 1:  69%|██████▉   | 313/453 [00:04<00:01, 81.99it/s]

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→ 1000 (453 images) Phase 1:  75%|███████▍  | 338/453 [00:05<00:01, 75.67it/s]

→ 1000 (453 images) Phase 1:  78%|███████▊  | 352/453 [00:05<00:01, 83.70it/s]

→ 1000 (453 images) Phase 1:  80%|████████  | 363/453 [00:05<00:01, 64.39it/s]

→ 1000 (453 images) Phase 1:  84%|████████▍ | 381/453 [00:05<00:00, 83.52it/s]

→ 1000 (453 images) Phase 1:  87%|████████▋ | 392/453 [00:06<00:00, 61.78it/s]

→ 1000 (453 images) Phase 1:  91%|█████████ | 411/453 [00:06<00:00, 82.47it/s]

→ 1000 (453 images) Phase 1:  93%|█████████▎| 423/453 [00:06<00:00, 62.12it/s]

→ 1000 (453 images) Phase 1:  98%|█████████▊| 445/453 [00:06<00:00, 87.10it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/453 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/453 [00:00<02:38,  2.86it/s]

→ 1000 Phase 2: corners:   3%|▎         | 13/453 [00:00<00:12, 36.52it/s]

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→ 1000 Phase 2: corners:   9%|▉         | 40/453 [00:00<00:05, 81.01it/s]

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→ 1000 Phase 2: corners:  63%|██████▎   | 287/453 [00:02<00:01, 158.30it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 305/453 [00:02<00:00, 163.50it/s]

→ 1000 Phase 2: corners:  71%|███████   | 322/453 [00:02<00:00, 163.91it/s]

→ 1000 Phase 2: corners:  75%|███████▍  | 339/453 [00:02<00:00, 154.13it/s]

→ 1000 Phase 2: corners:  78%|███████▊  | 355/453 [00:02<00:00, 154.33it/s]

→ 1000 Phase 2: corners:  82%|████████▏ | 371/453 [00:02<00:00, 153.67it/s]

→ 1000 Phase 2: corners:  85%|████████▌ | 387/453 [00:02<00:00, 148.01it/s]

→ 1000 Phase 2: corners:  89%|████████▊ | 402/453 [00:03<00:00, 146.12it/s]

→ 1000 Phase 2: corners:  92%|█████████▏| 418/453 [00:03<00:00, 148.27it/s]

→ 1000 Phase 2: corners:  96%|█████████▌| 434/453 [00:03<00:00, 145.99it/s]

                                                                           [PASS2] Frame 0000319: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000320: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000321: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000322: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000461: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000462: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000465: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000466: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000469: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000470: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000630: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000631: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000633: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000648: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000785: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000786: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000787: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000788: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000321: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000465: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000633: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000787: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 10 frames fixed, 13 untrusted, 453 total


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→ 1000 Phase 3: finalize:  95%|█████████▍| 430/453 [00:09<00:00, 44.79it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 435/453 [00:09<00:00, 45.04it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 440/453 [00:09<00:00, 45.27it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 445/453 [00:09<00:00, 45.45it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 450/453 [00:10<00:00, 45.59it/s]

                                                                           
[TIMING] 1000 — 453 frames
  Phase 1  radial detect   (parallel)  :   6.97s  (15.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   3.39s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   0.65s
  Phase 3  finalize        (sequential):  10.11s  (22.3 ms/frame)
    decode + undistort                 :   5.68s  (12.5 ms/frame)
    corner apply + refinement          :   2.19s  (4.8 ms/frame)
    segment                            :   1.82s  (4.0 ms/frame)
    smooth                             :   0.27s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  21.11s


Processing Rigs: 100%|██████████| 2/2 [00:47<00:00, 24.12s/it]
Processing Rigs: 100%|██████████| 2/2 [00:47<00:00, 23.75s/it]
[💾] Saved RigFrameDataset to /working/MDC027_W031_2026-05-06/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 833 frames from 2 rigs to /working/MDC027_W031_2026-05-06/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC027_W031_2026-05-06/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 380
[📋] 1000: 100 frames selected from 453
[💾] Saved RigFrameDataset to /working/MDC027_W031_2026-05-06/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC027_W031_2026-05-06/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<02:06,  3.25s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:03,  3.25s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:59,  3.24s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:56,  3.23s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:52,  3.22s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:19<01:49,  3.23s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:46,  3.22s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:43,  3.22s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:29<01:40,  3.24s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:32<01:36,  3.23s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:35<01:33,  3.24s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:38<01:30,  3.23s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:42<01:27,  3.23s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:45<01:24,  3.25s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:48<01:21,  3.25s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:51<01:17,  3.25s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:55<01:14,  3.24s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:58<01:11,  3.25s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:01<01:08,  3.25s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:04<01:05,  3.25s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:07<01:01,  3.22s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:11<00:57,  3.21s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:14<00:54,  3.20s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:17<00:50,  3.18s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:20<00:47,  3.19s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:23<00:44,  3.19s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:27<00:41,  3.18s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:30<00:38,  3.18s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:33<00:34,  3.17s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:36<00:31,  3.18s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:39<00:28,  3.19s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:42<00:25,  3.18s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:46<00:22,  3.17s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:49<00:19,  3.17s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:52<00:15,  3.16s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:55<00:12,  3.16s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:58<00:09,  3.16s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:01<00:06,  3.16s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:04<00:03,  3.15s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:08<00:00,  3.15s/it]
                                                                                       
[✅] Casing detection complete: cx=1021.0, cy=752.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:11,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   2%|▏         | 2/100 [00:00<00:11,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   3%|▎         | 3/100 [00:00<00:11,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   4%|▍         | 4/100 [00:00<00:11,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   5%|▌         | 5/100 [00:00<00:11,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   6%|▌         | 6/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   7%|▋         | 7/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   8%|▊         | 8/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   9%|▉         | 9/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  10%|█         | 10/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  11%|█         | 11/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  12%|█▏        | 12/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  13%|█▎        | 13/100 [00:00<00:10,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  14%|█▍        | 14/100 [00:00<00:09,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  15%|█▌        | 15/100 [00:00<00:09,  8.63it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  16%|█▌        | 16/100 [00:00<00:01, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  17%|█▋        | 17/100 [00:00<00:01, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  18%|█▊        | 18/100 [00:00<00:01, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  19%|█▉        | 19/100 [00:00<00:01, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  20%|██        | 20/100 [00:00<00:01, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  21%|██        | 21/100 [00:00<00:00, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  22%|██▏       | 22/100 [00:00<00:00, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 300.0} (0.000):  23%|██▎       | 23/100 [00:00<00:00, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  24%|██▍       | 24/100 [00:00<00:00, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 483.838} (0.001):  25%|██▌       | 25/100 [00:00<00:00, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  26%|██▌       | 26/100 [00:00<00:00, 79.46it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 483.838} (0.001):  27%|██▋       | 27/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 505.051} (0.010):  27%|██▋       | 27/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.002 | Best: {'fx': 505.051} (0.010):  28%|██▊       | 28/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.004 | Best: {'fx': 505.051} (0.010):  29%|██▉       | 29/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.024 | Best: {'fx': 554.545} (0.024):  30%|███       | 30/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.073 | Best: {'fx': 533.333} (0.073):  31%|███       | 31/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.022 | Best: {'fx': 533.333} (0.073):  32%|███▏      | 32/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.036 | Best: {'fx': 533.333} (0.073):  33%|███▎      | 33/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.079 | Best: {'fx': 526.263} (0.079):  34%|███▍      | 34/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.041 | Best: {'fx': 526.263} (0.079):  35%|███▌      | 35/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.029 | Best: {'fx': 526.263} (0.079):  36%|███▌      | 36/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 526.263} (0.079):  37%|███▋      | 37/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.020 | Best: {'fx': 526.263} (0.079):  38%|███▊      | 38/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.018 | Best: {'fx': 526.263} (0.079):  39%|███▉      | 39/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 526.263} (0.079):  40%|████      | 40/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 526.263} (0.079):  41%|████      | 41/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 526.263} (0.079):  42%|████▏     | 42/100 [00:00<00:00, 91.71it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 526.263} (0.079):  43%|████▎     | 43/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 526.263} (0.079):  43%|████▎     | 43/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 526.263} (0.079):  44%|████▍     | 44/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 526.263} (0.079):  45%|████▌     | 45/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 526.263} (0.079):  46%|████▌     | 46/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 526.263} (0.079):  47%|████▋     | 47/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 526.263} (0.079):  48%|████▊     | 48/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 526.263} (0.079):  49%|████▉     | 49/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 526.263} (0.079):  50%|█████     | 50/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  51%|█████     | 51/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  52%|█████▏    | 52/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  53%|█████▎    | 53/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  54%|█████▍    | 54/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  55%|█████▌    | 55/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  56%|█████▌    | 56/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  57%|█████▋    | 57/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 526.263} (0.079):  58%|█████▊    | 58/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  59%|█████▉    | 59/100 [00:00<00:00, 115.89it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  60%|██████    | 60/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  60%|██████    | 60/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  61%|██████    | 61/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  62%|██████▏   | 62/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  63%|██████▎   | 63/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  64%|██████▍   | 64/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  65%|██████▌   | 65/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  66%|██████▌   | 66/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  67%|██████▋   | 67/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  68%|██████▊   | 68/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  69%|██████▉   | 69/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  70%|███████   | 70/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  71%|███████   | 71/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  72%|███████▏  | 72/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  73%|███████▎  | 73/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  74%|███████▍  | 74/100 [00:00<00:00, 127.20it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  75%|███████▌  | 75/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  75%|███████▌  | 75/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  76%|███████▌  | 76/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  77%|███████▋  | 77/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  78%|███████▊  | 78/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  79%|███████▉  | 79/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  80%|████████  | 80/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  81%|████████  | 81/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  82%|████████▏ | 82/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  83%|████████▎ | 83/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  84%|████████▍ | 84/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  85%|████████▌ | 85/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  86%|████████▌ | 86/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  87%|████████▋ | 87/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  88%|████████▊ | 88/100 [00:00<00:00, 131.11it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  89%|████████▉ | 89/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  89%|████████▉ | 89/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  90%|█████████ | 90/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  91%|█████████ | 91/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  92%|█████████▏| 92/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  93%|█████████▎| 93/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  94%|█████████▍| 94/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  95%|█████████▌| 95/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  96%|█████████▌| 96/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  97%|█████████▋| 97/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  98%|█████████▊| 98/100 [00:00<00:00, 132.21it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 526.263} (0.079):  99%|█████████▉| 99/100 [00:00<00:00, 132.21it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.006 | Best: {'fx': 501.263} (0.006):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.006 | Best: {'fx': 501.263} (0.006):   2%|▏         | 1/50 [00:00<00:06,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.007 | Best: {'fx': 502.283} (0.007):   2%|▏         | 1/50 [00:00<00:06,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.008 | Best: {'fx': 503.303} (0.008):   4%|▍         | 2/50 [00:00<00:06,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.009 | Best: {'fx': 504.324} (0.009):   6%|▌         | 3/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.016 | Best: {'fx': 508.405} (0.016):   8%|▊         | 4/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.011 | Best: {'fx': 508.405} (0.016):  10%|█         | 5/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.013 | Best: {'fx': 508.405} (0.016):  12%|█▏        | 6/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.016 | Best: {'fx': 508.405} (0.016):  14%|█▍        | 7/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.017 | Best: {'fx': 509.426} (0.017):  16%|█▌        | 8/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.021 | Best: {'fx': 510.446} (0.021):  18%|█▊        | 9/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.022 | Best: {'fx': 511.467} (0.022):  20%|██        | 10/50 [00:00<00:05,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.024 | Best: {'fx': 512.487} (0.024):  22%|██▏       | 11/50 [00:00<00:04,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.025 | Best: {'fx': 513.508} (0.025):  24%|██▍       | 12/50 [00:00<00:04,  7.98it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.025 | Best: {'fx': 513.508} (0.025):  26%|██▌       | 13/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.027 | Best: {'fx': 514.528} (0.027):  26%|██▌       | 13/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.029 | Best: {'fx': 515.548} (0.029):  28%|██▊       | 14/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.030 | Best: {'fx': 516.569} (0.030):  30%|███       | 15/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.033 | Best: {'fx': 517.589} (0.033):  32%|███▏      | 16/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.035 | Best: {'fx': 518.61} (0.035):  34%|███▍      | 17/50 [00:00<00:00, 66.49it/s] 
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.038 | Best: {'fx': 519.63} (0.038):  36%|███▌      | 18/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.051 | Best: {'fx': 522.691} (0.051):  38%|███▊      | 19/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.041 | Best: {'fx': 522.691} (0.051):  40%|████      | 20/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.045 | Best: {'fx': 522.691} (0.051):  42%|████▏     | 21/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.055 | Best: {'fx': 523.712} (0.055):  44%|████▍     | 22/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.065 | Best: {'fx': 524.732} (0.065):  46%|████▌     | 23/50 [00:00<00:00, 66.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.065 | Best: {'fx': 524.732} (0.065):  48%|████▊     | 24/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.086 | Best: {'fx': 526.773} (0.086):  48%|████▊     | 24/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.074 | Best: {'fx': 526.773} (0.086):  50%|█████     | 25/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.097 | Best: {'fx': 528.814} (0.097):  52%|█████▏    | 26/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.068 | Best: {'fx': 528.814} (0.097):  54%|█████▍    | 27/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.093 | Best: {'fx': 528.814} (0.097):  56%|█████▌    | 28/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.062 | Best: {'fx': 528.814} (0.097):  58%|█████▊    | 29/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.087 | Best: {'fx': 528.814} (0.097):  60%|██████    | 30/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.102 | Best: {'fx': 529.834} (0.102):  62%|██████▏   | 31/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.078 | Best: {'fx': 529.834} (0.102):  64%|██████▍   | 32/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.095 | Best: {'fx': 529.834} (0.102):  66%|██████▌   | 33/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.045 | Best: {'fx': 529.834} (0.102):  68%|██████▊   | 34/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.051 | Best: {'fx': 529.834} (0.102):  70%|███████   | 35/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.056 | Best: {'fx': 529.834} (0.102):  72%|███████▏  | 36/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.035 | Best: {'fx': 529.834} (0.102):  74%|███████▍  | 37/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.042 | Best: {'fx': 529.834} (0.102):  76%|███████▌  | 38/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.034 | Best: {'fx': 529.834} (0.102):  78%|███████▊  | 39/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.049 | Best: {'fx': 529.834} (0.102):  80%|████████  | 40/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.041 | Best: {'fx': 529.834} (0.102):  82%|████████▏ | 41/50 [00:00<00:00, 80.49it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.041 | Best: {'fx': 529.834} (0.102):  84%|████████▍ | 42/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.037 | Best: {'fx': 529.834} (0.102):  84%|████████▍ | 42/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.032 | Best: {'fx': 529.834} (0.102):  86%|████████▌ | 43/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.029 | Best: {'fx': 529.834} (0.102):  88%|████████▊ | 44/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.031 | Best: {'fx': 529.834} (0.102):  90%|█████████ | 45/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.028 | Best: {'fx': 529.834} (0.102):  92%|█████████▏| 46/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.027 | Best: {'fx': 529.834} (0.102):  94%|█████████▍| 47/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.030 | Best: {'fx': 529.834} (0.102):  96%|█████████▌| 48/50 [00:00<00:00, 111.68it/s]
Brute | Start: {'fx': 526.263} (0.079) | Current: 0.026 | Best: {'fx': 529.834} (0.102):  98%|█████████▊| 49/50 [00:00<00:00, 111.68it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.8340548340549
    px: 1021.0
    py: 752.0
    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: 4.0967: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 4.0967, picklable_objective(x0) = 4.0966

Best score: 4.0966: : 0iter [00:01, ?iter/s] 
Best score: 4.0966: : 1iter [00:01,  1.94s/iter]
Best score: 4.0966: : 1iter [00:01,  1.94s/iter]
Best score: 4.0966: : 2iter [00:01,  1.94s/iter]
Best score: 4.0966: : 3iter [00:02,  1.94s/iter]
Best score: 4.0966: : 4iter [00:02,  1.94s/iter]
Best score: 4.0966: : 5iter [00:02,  3.21iter/s]
Best score: 4.0966: : 5iter [00:02,  3.21iter/s]
Best score: 4.0966: : 6iter [00:02,  3.21iter/s]
Best score: 4.0966: : 7iter [00:02,  3.21iter/s]
Best score: 4.0966: : 8iter [00:02,  3.21iter/s]
Best score: 4.0966: : 9iter [00:02,  6.47iter/s]
Best score: 4.0966: : 9iter [00:02,  6.47iter/s]
Best score: 4.0966: : 10iter [00:02,  6.47iter/s]
Best score: 4.0966: : 11iter [00:02,  6.47iter/s]
Best score: 4.0966: : 12iter [00:02,  6.47iter/s]
Best score: 4.0966: : 13iter [00:02, 10.20iter/s]
Best score: 4.0966: : 13iter [00:02, 10.20iter/s]
Best score: 4.0966: : 14iter [00:02, 10.20iter/s]
Best score: 4.0966: : 15iter [00:02, 10.20iter/s]
Best score: 4.0966: : 16iter [00:02, 10.20iter/s]
Best score: 4.0966: : 17iter [00:02, 14.27iter/s]
Best score: 4.0966: : 17iter [00:02, 14.27iter/s]
Best score: 4.0966: : 18iter [00:02, 14.27iter/s]
Best score: 4.0966: : 19iter [00:02, 14.27iter/s]
Best score: 4.0966: : 20iter [00:02, 14.27iter/s]
Best score: 4.0966: : 21iter [00:02, 18.36iter/s]
Best score: 4.0966: : 21iter [00:02, 18.36iter/s]
Best score: 4.0966: : 22iter [00:02, 18.36iter/s]
Best score: 4.0966: : 23iter [00:02, 18.36iter/s]
Best score: 4.0966: : 24iter [00:02, 18.36iter/s]
Best score: 4.0966: : 25iter [00:02, 22.36iter/s]
Best score: 4.0966: : 25iter [00:02, 22.36iter/s]
Best score: 4.0966: : 26iter [00:02, 22.36iter/s]
Best score: 4.0966: : 27iter [00:02, 22.36iter/s]
Best score: 4.0966: : 28iter [00:02, 22.36iter/s]
Best score: 4.0966: : 29iter [00:02, 25.99iter/s]
Best score: 4.0966: : 29iter [00:02, 25.99iter/s]
Best score: 4.0966: : 30iter [00:02, 25.99iter/s]
Best score: 4.0966: : 31iter [00:02, 25.99iter/s]
Best score: 4.0966: : 32iter [00:02, 25.99iter/s]
Best score: 4.0966: : 33iter [00:02, 29.00iter/s]
Best score: 4.0966: : 33iter [00:02, 29.00iter/s]
Best score: 4.0966: : 34iter [00:02, 29.00iter/s]
Best score: 4.0966: : 35iter [00:02, 29.00iter/s]
Best score: 4.0966: : 36iter [00:02, 29.00iter/s]
Best score: 4.0966: : 37iter [00:02, 31.57iter/s]
Best score: 4.0966: : 37iter [00:02, 31.57iter/s]
Best score: 4.0966: : 38iter [00:02, 31.57iter/s]
Best score: 4.0966: : 39iter [00:02, 31.57iter/s]
Best score: 4.0966: : 40iter [00:02, 31.57iter/s]
Best score: 4.0966: : 41iter [00:02, 33.47iter/s]
Best score: 4.0966: : 41iter [00:02, 33.47iter/s]
Best score: 4.0966: : 42iter [00:03, 33.47iter/s]
Best score: 4.0966: : 43iter [00:03, 33.47iter/s]
Best score: 4.0966: : 44iter [00:03, 33.47iter/s]
Best score: 4.0966: : 45iter [00:03, 35.11iter/s]
Best score: 4.0966: : 45iter [00:03, 35.11iter/s]
Best score: 4.0966: : 46iter [00:03, 35.11iter/s]
Best score: 4.0966: : 47iter [00:03, 35.11iter/s]
Best score: 4.0966: : 48iter [00:03, 35.11iter/s]
Best score: 4.0966: : 49iter [00:03, 36.15iter/s]
[ITER 50] Best: 4.0966
[PARAMS] Current best parameters:
  fx: 529.8341
  fy: 529.8341
  px: 1021.0000
  py: 752.0000
  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: 4.0966: : 49iter [00:03, 36.15iter/s]
Best score: 4.0966: : 50iter [00:03, 36.15iter/s]
Best score: 4.0966: : 51iter [00:03, 36.15iter/s]
Best score: 4.0966: : 52iter [00:03, 36.15iter/s]
Best score: 4.0966: : 53iter [00:03, 36.75iter/s]
Best score: 4.0966: : 53iter [00:03, 36.75iter/s]
Best score: 4.0966: : 54iter [00:03, 36.75iter/s]
Best score: 4.0966: : 55iter [00:03, 36.75iter/s]
Best score: 4.0966: : 56iter [00:03, 36.75iter/s]
Best score: 4.0966: : 57iter [00:03, 37.51iter/s]
Best score: 4.0966: : 57iter [00:03, 37.51iter/s]
Best score: 4.0966: : 58iter [00:03, 37.51iter/s]
Best score: 4.0966: : 59iter [00:03, 37.51iter/s]
Best score: 4.0966: : 60iter [00:03, 37.51iter/s]
Best score: 4.0966: : 61iter [00:03, 37.87iter/s]
Best score: 4.0966: : 61iter [00:03, 37.87iter/s]
Best score: 4.0966: : 62iter [00:03, 37.87iter/s]
Best score: 4.0966: : 63iter [00:03, 37.87iter/s]
Best score: 4.0966: : 64iter [00:03, 37.87iter/s]
Best score: 4.0966: : 65iter [00:03, 38.23iter/s]
Best score: 4.0966: : 65iter [00:03, 38.23iter/s]
Best score: 4.0966: : 66iter [00:03, 38.23iter/s]
Best score: 4.0966: : 67iter [00:03, 38.23iter/s]
Best score: 4.0966: : 68iter [00:03, 38.23iter/s]
Best score: 4.0966: : 69iter [00:03, 38.48iter/s]
Best score: 4.0966: : 69iter [00:03, 38.48iter/s]
Best score: 4.0966: : 70iter [00:03, 38.48iter/s]
Best score: 4.0966: : 71iter [00:03, 38.48iter/s]
Best score: 4.0966: : 72iter [00:03, 38.48iter/s]
Best score: 4.0966: : 73iter [00:03, 38.44iter/s]
Best score: 4.0966: : 73iter [00:03, 38.44iter/s]
Best score: 4.0966: : 74iter [00:03, 38.44iter/s]
Best score: 4.0966: : 75iter [00:03, 38.44iter/s]
Best score: 4.0966: : 76iter [00:03, 38.44iter/s]
Best score: 4.0966: : 77iter [00:03, 38.65iter/s]
Best score: 4.0966: : 77iter [00:03, 38.65iter/s]
Best score: 4.0966: : 78iter [00:03, 38.65iter/s]
Best score: 4.0966: : 79iter [00:03, 38.65iter/s]
Best score: 4.0966: : 80iter [00:04, 38.65iter/s]
Best score: 4.0966: : 81iter [00:04, 38.61iter/s]
Best score: 4.0966: : 81iter [00:04, 38.61iter/s]
Best score: 4.0966: : 82iter [00:04, 38.61iter/s]
Best score: 4.0966: : 83iter [00:04, 38.61iter/s]
Best score: 4.0966: : 84iter [00:04, 38.61iter/s]
Best score: 4.0966: : 85iter [00:04, 38.80iter/s]
Best score: 4.0966: : 85iter [00:04, 38.80iter/s]
Best score: 4.0966: : 86iter [00:04, 38.80iter/s]
Best score: 4.0966: : 87iter [00:04, 38.80iter/s]
Best score: 4.0966: : 88iter [00:04, 38.80iter/s]
Best score: 4.0966: : 89iter [00:04, 39.08iter/s]
Best score: 4.0966: : 89iter [00:04, 39.08iter/s]
Best score: 4.0966: : 90iter [00:04, 39.08iter/s]
Best score: 4.0966: : 91iter [00:04, 39.08iter/s]
Best score: 4.0966: : 92iter [00:04, 39.08iter/s]
Best score: 4.0966: : 93iter [00:04, 38.82iter/s]
Best score: 4.0966: : 93iter [00:04, 38.82iter/s]
Best score: 4.0966: : 94iter [00:04, 38.82iter/s]
Best score: 4.0966: : 95iter [00:04, 38.82iter/s]
Best score: 4.0966: : 96iter [00:04, 38.82iter/s]
Best score: 4.0966: : 97iter [00:04, 38.94iter/s]
Best score: 4.0966: : 97iter [00:04, 38.94iter/s]
Best score: 4.0966: : 98iter [00:04, 38.94iter/s]
[ITER 100] Best: 4.0966
[PARAMS] Current best parameters:
  fx: 529.8341
  fy: 529.8341
  px: 1021.0000
  py: 752.0000
  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: 4.0966: : 99iter [00:04, 38.94iter/s]
Best score: 4.0966: : 100iter [00:04, 38.94iter/s]
Best score: 4.0966: : 101iter [00:04, 36.11iter/s]
Best score: 4.0966: : 101iter [00:04, 36.11iter/s]
Best score: 4.0966: : 102iter [00:04, 36.11iter/s]
Best score: 4.0966: : 103iter [00:04, 36.11iter/s]
Best score: 4.0966: : 104iter [00:04, 36.11iter/s]
Best score: 4.0966: : 105iter [00:04, 37.11iter/s]
Best score: 4.0966: : 105iter [00:04, 37.11iter/s]
Best score: 4.0966: : 106iter [00:04, 37.11iter/s]
Best score: 4.0966: : 107iter [00:04, 37.11iter/s]
Best score: 4.0966: : 108iter [00:04, 37.11iter/s]
Best score: 4.0966: : 109iter [00:04, 37.79iter/s]
Best score: 4.0966: : 109iter [00:04, 37.79iter/s]
Best score: 4.0966: : 110iter [00:04, 37.79iter/s]
Best score: 4.0966: : 111iter [00:04, 37.79iter/s]
Best score: 4.0966: : 112iter [00:04, 37.79iter/s]
Best score: 4.0966: : 113iter [00:04, 38.06iter/s]
Best score: 4.0966: : 113iter [00:04, 38.06iter/s]
Best score: 4.0966: : 114iter [00:04, 38.06iter/s]
Best score: 4.0966: : 115iter [00:04, 38.06iter/s]
Best score: 4.0966: : 116iter [00:04, 38.06iter/s]
Best score: 4.0966: : 117iter [00:04, 38.55iter/s]
Best score: 4.0966: : 117iter [00:04, 38.55iter/s]
Best score: 4.0966: : 118iter [00:04, 38.55iter/s]
Best score: 4.0966: : 119iter [00:05, 38.55iter/s]
Best score: 4.0966: : 120iter [00:05, 38.55iter/s]
Best score: 4.0966: : 121iter [00:05, 38.59iter/s]
Best score: 4.0966: : 121iter [00:05, 38.59iter/s]
Best score: 4.0966: : 122iter [00:05, 38.59iter/s]
Best score: 4.0966: : 123iter [00:05, 38.59iter/s]
Best score: 4.0966: : 124iter [00:05, 38.59iter/s]
Best score: 4.0966: : 125iter [00:05, 38.81iter/s]
Best score: 4.0966: : 125iter [00:05, 38.81iter/s]
Best score: 4.0966: : 126iter [00:05, 38.81iter/s]
Best score: 4.0966: : 127iter [00:05, 38.81iter/s]
Best score: 4.0966: : 128iter [00:05, 38.81iter/s]
Best score: 4.0966: : 129iter [00:05, 38.89iter/s]
Best score: 4.0966: : 129iter [00:05, 38.89iter/s]
Best score: 4.0966: : 130iter [00:05, 38.89iter/s]
Best score: 4.0966: : 131iter [00:05, 38.89iter/s]
Best score: 4.0966: : 132iter [00:05, 38.89iter/s]
Best score: 4.0966: : 133iter [00:05, 38.78iter/s]
Best score: 4.0966: : 133iter [00:05, 38.78iter/s]
Best score: 4.0966: : 134iter [00:05, 38.78iter/s]
Best score: 4.0966: : 135iter [00:05, 38.78iter/s]
Best score: 4.0966: : 136iter [00:05, 38.78iter/s]
Best score: 4.0966: : 137iter [00:05, 38.97iter/s]
Best score: 4.0966: : 137iter [00:05, 38.97iter/s]
Best score: 4.0966: : 138iter [00:05, 38.97iter/s]
Best score: 4.0966: : 139iter [00:05, 38.97iter/s]
Best score: 4.0966: : 140iter [00:05, 38.97iter/s]
Best score: 4.0966: : 141iter [00:05, 38.94iter/s]
Best score: 4.0966: : 141iter [00:05, 38.94iter/s]
Best score: 4.0966: : 142iter [00:05, 38.94iter/s]
Best score: 4.0966: : 143iter [00:05, 38.94iter/s]
Best score: 4.0966: : 144iter [00:05, 38.94iter/s]
Best score: 4.0966: : 145iter [00:05, 39.10iter/s]
Best score: 4.0966: : 145iter [00:05, 39.10iter/s]
Best score: 4.0966: : 146iter [00:05, 39.10iter/s]
Best score: 4.0966: : 147iter [00:05, 39.10iter/s]
Best score: 4.0966: : 148iter [00:05, 39.10iter/s]
Best score: 4.0966: : 149iter [00:05, 39.10iter/s]
[ITER 150] Best: 4.0966
[PARAMS] Current best parameters:
  fx: 529.8341
  fy: 529.8341
  px: 1021.0000
  py: 752.0000
  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: 4.0966: : 149iter [00:05, 39.10iter/s]
Best score: 4.0966: : 150iter [00:05, 39.10iter/s]
Best score: 4.0966: : 151iter [00:05, 39.10iter/s]
Best score: 4.0966: : 152iter [00:05, 39.10iter/s]
Best score: 4.0966: : 153iter [00:05, 31.89iter/s]
Best score: 4.0966: : 153iter [00:05, 31.89iter/s]
Best score: 4.0966: : 154iter [00:05, 31.89iter/s]
Best score: 4.0966: : 155iter [00:06, 31.89iter/s]
Best score: 4.0966: : 156iter [00:06, 31.89iter/s]
Best score: 4.0966: : 157iter [00:06, 33.93iter/s]
Best score: 4.0966: : 157iter [00:06, 33.93iter/s]
Best score: 4.0966: : 158iter [00:06, 33.93iter/s]
Best score: 4.0966: : 159iter [00:06, 33.93iter/s]
Best score: 4.0966: : 160iter [00:06, 33.93iter/s]
Best score: 4.0966: : 161iter [00:06, 35.11iter/s]
Best score: 4.0966: : 161iter [00:06, 35.11iter/s]
Best score: 4.0966: : 162iter [00:06, 35.11iter/s]
Best score: 4.0966: : 163iter [00:06, 35.11iter/s]
Best score: 4.0966: : 164iter [00:06, 35.11iter/s]
Best score: 4.0966: : 165iter [00:06, 36.21iter/s]
Best score: 4.0966: : 165iter [00:06, 36.21iter/s]
Best score: 4.0966: : 166iter [00:06, 36.21iter/s]
Best score: 4.0966: : 167iter [00:06, 36.21iter/s]
Best score: 4.0966: : 168iter [00:06, 36.21iter/s]
Best score: 4.0966: : 169iter [00:06, 37.19iter/s]
Best score: 4.0966: : 169iter [00:06, 37.19iter/s]
Best score: 4.0966: : 170iter [00:06, 37.19iter/s]
Best score: 4.0966: : 171iter [00:06, 37.19iter/s]
Best score: 4.0966: : 172iter [00:06, 37.19iter/s]
Best score: 4.0966: : 173iter [00:06, 37.60iter/s]
Best score: 4.0966: : 173iter [00:06, 37.60iter/s]
Best score: 4.0966: : 174iter [00:06, 37.60iter/s]
Best score: 4.0966: : 175iter [00:06, 37.60iter/s]
Best score: 4.0966: : 176iter [00:06, 37.60iter/s]
Best score: 4.0966: : 177iter [00:06, 38.23iter/s]
Best score: 4.0966: : 177iter [00:06, 38.23iter/s]
Best score: 4.0966: : 178iter [00:06, 38.23iter/s]
Best score: 4.0966: : 179iter [00:06, 38.23iter/s]
Best score: 4.0966: : 180iter [00:06, 38.23iter/s]
Best score: 4.0966: : 181iter [00:06, 38.43iter/s]
Best score: 4.0966: : 181iter [00:06, 38.43iter/s]
Best score: 4.0966: : 182iter [00:06, 38.43iter/s]
Best score: 4.0966: : 183iter [00:06, 38.43iter/s]
Best score: 4.0966: : 184iter [00:06, 38.43iter/s]
Best score: 4.0966: : 185iter [00:06, 38.74iter/s]
Best score: 4.0966: : 185iter [00:06, 38.74iter/s]
Best score: 4.0966: : 186iter [00:06, 38.74iter/s]
Best score: 4.0966: : 187iter [00:06, 38.74iter/s]
Best score: 4.0966: : 188iter [00:06, 38.74iter/s]
Best score: 4.0966: : 189iter [00:06, 38.87iter/s]
Best score: 4.0966: : 189iter [00:06, 38.87iter/s]
Best score: 4.0966: : 190iter [00:06, 38.87iter/s]
Best score: 4.0966: : 191iter [00:06, 38.87iter/s]
Best score: 4.0966: : 192iter [00:06, 38.87iter/s]
Best score: 4.0966: : 193iter [00:06, 39.01iter/s]
Best score: 4.0966: : 193iter [00:06, 39.01iter/s]
Best score: 4.0966: : 194iter [00:07, 39.01iter/s]
Best score: 4.0966: : 195iter [00:07, 39.01iter/s]
Best score: 4.0966: : 196iter [00:07, 39.01iter/s]
Best score: 4.0966: : 197iter [00:07, 39.25iter/s]
Best score: 4.0966: : 197iter [00:07, 39.25iter/s]
Best score: 4.0966: : 198iter [00:07, 39.25iter/s]
[ITER 200] Best: 4.0966
[PARAMS] Current best parameters:
  fx: 529.8341
  fy: 529.8341
  px: 1021.0000
  py: 752.0000
  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: 4.0966: : 199iter [00:07, 39.25iter/s]
Best score: 4.0966: : 200iter [00:07, 39.25iter/s]
Best score: 4.0966: : 201iter [00:07, 31.89iter/s]
Best score: 4.0966: : 201iter [00:07, 31.89iter/s]
Best score: 4.0966: : 202iter [00:07, 31.89iter/s]
Best score: 4.0966: : 203iter [00:07, 31.89iter/s]
Best score: 4.0966: : 204iter [00:07, 31.89iter/s]
Best score: 4.0966: : 205iter [00:07, 31.89iter/s]
Best score: 4.0966: : 206iter [00:07, 34.34iter/s]
Best score: 4.0966: : 206iter [00:07, 34.34iter/s]
Best score: 4.0966: : 207iter [00:07, 34.34iter/s]
Best score: 4.0966: : 208iter [00:07, 34.34iter/s]
Best score: 4.0966: : 209iter [00:07, 34.34iter/s]
Best score: 4.0966: : 210iter [00:07, 35.71iter/s]
Best score: 4.0966: : 210iter [00:07, 35.71iter/s]
Best score: 4.0966: : 211iter [00:07, 35.71iter/s]
Best score: 4.0966: : 212iter [00:07, 35.71iter/s]
Best score: 4.0966: : 213iter [00:07, 35.71iter/s]
Best score: 4.0966: : 214iter [00:07, 35.71iter/s]
Best score: 4.0966: : 215iter [00:07, 37.10iter/s]
Best score: 4.0966: : 215iter [00:07, 37.10iter/s]
Best score: 4.0966: : 216iter [00:07, 37.10iter/s]
Best score: 4.0966: : 217iter [00:07, 37.10iter/s]
Best score: 4.0966: : 218iter [00:07, 37.10iter/s]
Best score: 4.0966: : 219iter [00:07, 37.10iter/s]
Best score: 4.0966: : 220iter [00:07, 37.87iter/s]
Best score: 4.0966: : 220iter [00:07, 37.87iter/s]
Best score: 4.0966: : 221iter [00:07, 37.87iter/s]
Best score: 4.0966: : 222iter [00:07, 37.87iter/s]
Best score: 4.0966: : 223iter [00:07, 37.87iter/s]
Best score: 4.0966: : 224iter [00:07, 37.87iter/s]
Best score: 4.0966: : 225iter [00:07, 38.65iter/s]
Best score: 4.0966: : 225iter [00:07, 38.65iter/s]
Best score: 4.0966: : 226iter [00:07, 38.65iter/s]
Best score: 4.0966: : 227iter [00:07, 38.65iter/s]
Best score: 4.0966: : 228iter [00:07, 38.65iter/s]
Best score: 4.0966: : 229iter [00:07, 38.90iter/s]
Best score: 4.0966: : 229iter [00:07, 38.90iter/s]
Best score: 4.0966: : 230iter [00:07, 38.90iter/s]
Best score: 4.0966: : 231iter [00:08, 38.90iter/s]
Best score: 4.0966: : 232iter [00:08, 38.90iter/s]
Best score: 4.0966: : 233iter [00:08, 39.02iter/s]
Best score: 4.0966: : 233iter [00:08, 39.02iter/s]
Best score: 4.0966: : 234iter [00:08, 39.02iter/s]
Best score: 4.0966: : 235iter [00:08, 39.02iter/s]
Best score: 4.0966: : 236iter [00:08, 39.02iter/s]
Best score: 4.0966: : 237iter [00:08, 39.02iter/s]
Best score: 4.0966: : 238iter [00:08, 39.44iter/s]
Best score: 4.0966: : 238iter [00:08, 39.44iter/s]
Best score: 4.0966: : 239iter [00:08, 39.44iter/s]
Best score: 4.0966: : 240iter [00:08, 39.44iter/s]
Best score: 4.0966: : 241iter [00:08, 39.44iter/s]
Best score: 4.0966: : 242iter [00:08, 39.45iter/s]
Best score: 4.0966: : 242iter [00:08, 39.45iter/s]
Best score: 4.0966: : 243iter [00:08, 39.45iter/s]
Best score: 4.0966: : 244iter [00:08, 39.45iter/s]
Best score: 4.0966: : 245iter [00:08, 39.45iter/s]
Best score: 4.0966: : 246iter [00:08, 39.45iter/s]
Best score: 4.0966: : 247iter [00:08, 39.69iter/s]
Best score: 4.0966: : 247iter [00:08, 39.69iter/s]
Best score: 4.0966: : 248iter [00:08, 39.69iter/s]
[ITER 250] Best: 3.4965
[PARAMS] Current best parameters:
  fx: 537.8300
  fy: 536.8755
  px: 1020.9171
  py: 757.9544
  k1: -0.01800576
  k2: 0.01339367
  k3: -0.00911919
  k4: 0.00238323
  p1: 0.00804275
  p2: -0.00017422
  p3: -0.00494691
  p4: 0.00132948
  s1: -0.0054
  s2: 0.0007
  s3: -0.0067
  s4: 0.0031
  tilt_x: 0.0101
  tilt_y: -0.0005
  scale: 3.0579
  rotation: -0.4548

Best score: 3.4965: : 249iter [00:08, 39.69iter/s]
Best score: 3.4965: : 250iter [00:08, 39.69iter/s]
Best score: 3.4965: : 251iter [00:08, 32.54iter/s]
Best score: 3.4965: : 251iter [00:08, 32.54iter/s]
Best score: 3.4965: : 252iter [00:08, 32.54iter/s]
Best score: 3.4965: : 253iter [00:08, 32.54iter/s]
Best score: 3.4965: : 254iter [00:08, 32.54iter/s]
Best score: 3.4965: : 255iter [00:08, 34.29iter/s]
Best score: 3.4965: : 255iter [00:08, 34.29iter/s]
Best score: 3.4965: : 256iter [00:08, 34.29iter/s]
Best score: 3.4965: : 257iter [00:08, 34.29iter/s]
Best score: 3.4965: : 258iter [00:08, 34.29iter/s]
Best score: 3.4965: : 259iter [00:08, 34.29iter/s]
Best score: 3.4965: : 260iter [00:08, 35.92iter/s]
Best score: 3.4965: : 260iter [00:08, 35.92iter/s]
Best score: 3.4965: : 261iter [00:08, 35.92iter/s]
Best score: 3.4965: : 262iter [00:08, 35.92iter/s]
Best score: 3.4965: : 263iter [00:08, 35.92iter/s]
Best score: 3.4965: : 264iter [00:08, 36.90iter/s]
Best score: 3.4965: : 264iter [00:08, 36.90iter/s]
Best score: 3.4965: : 265iter [00:08, 36.90iter/s]
Best score: 3.4965: : 266iter [00:08, 36.90iter/s]
Best score: 3.4965: : 267iter [00:09, 36.90iter/s]
Best score: 3.4965: : 268iter [00:09, 36.90iter/s]
Best score: 3.4965: : 269iter [00:09, 37.86iter/s]
Best score: 2.9556: : 269iter [00:09, 37.86iter/s]
Best score: 2.9556: : 270iter [00:09, 37.86iter/s]
Best score: 2.9556: : 271iter [00:09, 37.86iter/s]
Best score: 2.9556: : 272iter [00:09, 37.86iter/s]
Best score: 2.9556: : 273iter [00:09, 38.00iter/s]
Best score: 2.9556: : 273iter [00:09, 38.00iter/s]
Best score: 2.9556: : 274iter [00:09, 38.00iter/s]
Best score: 2.9556: : 275iter [00:09, 38.00iter/s]
Best score: 2.9556: : 276iter [00:09, 38.00iter/s]
Best score: 2.9556: : 277iter [00:09, 38.50iter/s]
Best score: 2.9556: : 277iter [00:09, 38.50iter/s]
Best score: 2.9556: : 278iter [00:09, 38.50iter/s]
Best score: 1.7519: : 279iter [00:09, 38.50iter/s]
Best score: 1.7519: : 280iter [00:09, 38.50iter/s]
Best score: 1.7519: : 281iter [00:09, 38.60iter/s]
Best score: 1.7519: : 281iter [00:09, 38.60iter/s]
Best score: 1.7519: : 282iter [00:09, 38.60iter/s]
Best score: 1.7519: : 283iter [00:09, 38.60iter/s]
Best score: 1.7519: : 284iter [00:09, 38.60iter/s]
Best score: 1.7519: : 285iter [00:09, 38.89iter/s]
Best score: 1.7519: : 285iter [00:09, 38.89iter/s]
Best score: 1.7519: : 286iter [00:09, 38.89iter/s]
Best score: 1.7519: : 287iter [00:09, 38.89iter/s]
Best score: 1.7519: : 288iter [00:09, 38.89iter/s]
Best score: 1.7519: : 289iter [00:09, 39.12iter/s]
Best score: 1.4849: : 289iter [00:09, 39.12iter/s]
Best score: 1.4849: : 290iter [00:09, 39.12iter/s]
Best score: 1.4849: : 291iter [00:09, 39.12iter/s]
Best score: 1.4849: : 292iter [00:09, 39.12iter/s]
Best score: 1.4849: : 293iter [00:09, 39.14iter/s]
Best score: 1.4849: : 293iter [00:09, 39.14iter/s]
Best score: 1.4849: : 294iter [00:09, 39.14iter/s]
Best score: 1.4849: : 295iter [00:09, 39.14iter/s]
Best score: 1.4849: : 296iter [00:09, 39.14iter/s]
Best score: 1.4849: : 297iter [00:09, 30.71iter/s]
Best score: 1.4849: : 297iter [00:09, 30.71iter/s]
Best score: 1.4849: : 298iter [00:09, 30.71iter/s]
[ITER 300] Best: 0.7939
[PARAMS] Current best parameters:
  fx: 524.2504
  fy: 523.9929
  px: 1022.5494
  py: 747.2055
  k1: 0.05405803
  k2: -0.04961378
  k3: 0.01866696
  k4: -0.00325673
  p1: -0.00451702
  p2: -0.00643306
  p3: 0.00797267
  p4: -0.00225843
  s1: 0.0035
  s2: -0.0013
  s3: -0.0032
  s4: 0.0008
  tilt_x: -0.0162
  tilt_y: -0.0200
  scale: 2.9144
  rotation: -0.0836

Best score: 0.7939: : 299iter [00:09, 30.71iter/s]
Best score: 0.7939: : 300iter [00:10, 30.71iter/s]
Best score: 0.7939: : 301iter [00:10, 27.64iter/s]
Best score: 0.7939: : 301iter [00:10, 27.64iter/s]
Best score: 0.7939: : 302iter [00:10, 27.64iter/s]
Best score: 0.7939: : 303iter [00:10, 27.64iter/s]
Best score: 0.7939: : 304iter [00:10, 27.64iter/s]
Best score: 0.7939: : 305iter [00:10, 27.64iter/s]
Best score: 0.7939: : 306iter [00:10, 31.04iter/s]
Best score: 0.7939: : 306iter [00:10, 31.04iter/s]
Best score: 0.7939: : 307iter [00:10, 31.04iter/s]
Best score: 0.7939: : 308iter [00:10, 31.04iter/s]
Best score: 0.7939: : 309iter [00:10, 31.04iter/s]
Best score: 0.7939: : 310iter [00:10, 33.04iter/s]
Best score: 0.7939: : 310iter [00:10, 33.04iter/s]
Best score: 0.7939: : 311iter [00:10, 33.04iter/s]
Best score: 0.7939: : 312iter [00:10, 33.04iter/s]
Best score: 0.7939: : 313iter [00:10, 33.04iter/s]
Best score: 0.7939: : 314iter [00:10, 33.04iter/s]
Best score: 0.7939: : 315iter [00:10, 35.19iter/s]
Best score: 0.7939: : 315iter [00:10, 35.19iter/s]
Best score: 0.7939: : 316iter [00:10, 35.19iter/s]
Best score: 0.7939: : 317iter [00:10, 35.19iter/s]
Best score: 0.7939: : 318iter [00:10, 35.19iter/s]
Best score: 0.7051: : 319iter [00:10, 35.19iter/s]
Best score: 0.7051: : 320iter [00:10, 36.44iter/s]
Best score: 0.7051: : 320iter [00:10, 36.44iter/s]
Best score: 0.7051: : 321iter [00:10, 36.44iter/s]
Best score: 0.7051: : 322iter [00:10, 36.44iter/s]
Best score: 0.7051: : 323iter [00:10, 36.44iter/s]
Best score: 0.7051: : 324iter [00:10, 36.44iter/s]
Best score: 0.7051: : 325iter [00:10, 37.61iter/s]
Best score: 0.7051: : 325iter [00:10, 37.61iter/s]
Best score: 0.7051: : 326iter [00:10, 37.61iter/s]
Best score: 0.7051: : 327iter [00:10, 37.61iter/s]
Best score: 0.7051: : 328iter [00:10, 37.61iter/s]
Best score: 0.7051: : 329iter [00:10, 38.17iter/s]
Best score: 0.7051: : 329iter [00:10, 38.17iter/s]
Best score: 0.7051: : 330iter [00:10, 38.17iter/s]
Best score: 0.7051: : 331iter [00:10, 38.17iter/s]
Best score: 0.7051: : 332iter [00:10, 38.17iter/s]
Best score: 0.7051: : 333iter [00:10, 38.40iter/s]
Best score: 0.7051: : 333iter [00:10, 38.40iter/s]
Best score: 0.7051: : 334iter [00:10, 38.40iter/s]
Best score: 0.7051: : 335iter [00:10, 38.40iter/s]
Best score: 0.7051: : 336iter [00:10, 38.40iter/s]
Best score: 0.7051: : 337iter [00:10, 38.40iter/s]
Best score: 0.7051: : 338iter [00:10, 39.01iter/s]
Best score: 0.7051: : 338iter [00:10, 39.01iter/s]
Best score: 0.7051: : 339iter [00:10, 39.01iter/s]
Best score: 0.7051: : 340iter [00:11, 39.01iter/s]
Best score: 0.7051: : 341iter [00:11, 39.01iter/s]
Best score: 0.7051: : 342iter [00:11, 38.97iter/s]
Best score: 0.7051: : 342iter [00:11, 38.97iter/s]
Best score: 0.7051: : 343iter [00:11, 38.97iter/s]
Best score: 0.7051: : 344iter [00:11, 38.97iter/s]
Best score: 0.7051: : 345iter [00:11, 38.97iter/s]
Best score: 0.7051: : 346iter [00:11, 39.25iter/s]
Best score: 0.7051: : 346iter [00:11, 39.25iter/s]
Best score: 0.7051: : 347iter [00:11, 39.25iter/s]
Best score: 0.7051: : 348iter [00:11, 39.25iter/s]
[ITER 350] Best: 0.6999
[PARAMS] Current best parameters:
  fx: 527.3397
  fy: 527.0528
  px: 1023.2559
  py: 747.3637
  k1: 0.03915175
  k2: -0.04252919
  k3: 0.01639374
  k4: -0.00234623
  p1: -0.00721483
  p2: 0.00120269
  p3: 0.00640282
  p4: -0.00503406
  s1: 0.0009
  s2: 0.0005
  s3: -0.0005
  s4: 0.0040
  tilt_x: -0.0088
  tilt_y: -0.0071
  scale: 2.9695
  rotation: -0.1938

Best score: 0.6999: : 349iter [00:11, 39.25iter/s]
Best score: 0.6999: : 350iter [00:11, 39.13iter/s]
Best score: 0.6999: : 350iter [00:11, 39.13iter/s]
Best score: 0.6999: : 351iter [00:11, 39.13iter/s]
Best score: 0.6999: : 352iter [00:11, 39.13iter/s]
Best score: 0.6999: : 353iter [00:11, 39.13iter/s]
Best score: 0.6999: : 354iter [00:11, 32.18iter/s]
Best score: 0.6999: : 354iter [00:11, 32.18iter/s]
Best score: 0.6999: : 355iter [00:11, 32.18iter/s]
Best score: 0.6999: : 356iter [00:11, 32.18iter/s]
Best score: 0.6999: : 357iter [00:11, 32.18iter/s]
Best score: 0.6999: : 358iter [00:11, 32.18iter/s]
Best score: 0.6999: : 359iter [00:11, 34.51iter/s]
Best score: 0.6811: : 359iter [00:11, 34.51iter/s]
Best score: 0.6811: : 360iter [00:11, 34.51iter/s]
Best score: 0.6811: : 361iter [00:11, 34.51iter/s]
Best score: 0.6811: : 362iter [00:11, 34.51iter/s]
Best score: 0.6811: : 363iter [00:11, 35.70iter/s]
Best score: 0.6811: : 363iter [00:11, 35.70iter/s]
Best score: 0.6811: : 364iter [00:11, 35.70iter/s]
Best score: 0.6811: : 365iter [00:11, 35.70iter/s]
Best score: 0.6811: : 366iter [00:11, 35.70iter/s]
Best score: 0.6811: : 367iter [00:11, 36.80iter/s]
Best score: 0.6811: : 367iter [00:11, 36.80iter/s]
Best score: 0.6811: : 368iter [00:11, 36.80iter/s]
Best score: 0.6811: : 369iter [00:11, 36.80iter/s]
Best score: 0.6811: : 370iter [00:11, 36.80iter/s]
Best score: 0.6811: : 371iter [00:11, 37.41iter/s]
Best score: 0.6811: : 371iter [00:11, 37.41iter/s]
Best score: 0.6811: : 372iter [00:11, 37.41iter/s]
Best score: 0.6811: : 373iter [00:11, 37.41iter/s]
Best score: 0.6811: : 374iter [00:11, 37.41iter/s]
Best score: 0.6811: : 375iter [00:11, 38.05iter/s]
Best score: 0.6811: : 375iter [00:11, 38.05iter/s]
Best score: 0.6811: : 376iter [00:12, 38.05iter/s]
Best score: 0.6811: : 377iter [00:12, 38.05iter/s]
Best score: 0.6811: : 378iter [00:12, 38.05iter/s]
Best score: 0.6811: : 379iter [00:12, 38.52iter/s]
Best score: 0.4352: : 379iter [00:12, 38.52iter/s]
Best score: 0.4352: : 380iter [00:12, 38.52iter/s]
Best score: 0.4352: : 381iter [00:12, 38.52iter/s]
Best score: 0.4352: : 382iter [00:12, 38.52iter/s]
Best score: 0.4352: : 383iter [00:12, 38.64iter/s]
Best score: 0.4352: : 383iter [00:12, 38.64iter/s]
Best score: 0.4352: : 384iter [00:12, 38.64iter/s]
Best score: 0.4352: : 385iter [00:12, 38.64iter/s]
Best score: 0.4352: : 386iter [00:12, 38.64iter/s]
Best score: 0.4352: : 387iter [00:12, 39.02iter/s]
Best score: 0.4352: : 387iter [00:12, 39.02iter/s]
Best score: 0.4352: : 388iter [00:12, 39.02iter/s]
Best score: 0.4064: : 389iter [00:12, 39.02iter/s]
Best score: 0.4064: : 390iter [00:12, 39.02iter/s]
Best score: 0.4064: : 391iter [00:12, 39.03iter/s]
Best score: 0.4064: : 391iter [00:12, 39.03iter/s]
Best score: 0.4064: : 392iter [00:12, 39.03iter/s]
Best score: 0.4064: : 393iter [00:12, 39.03iter/s]
Best score: 0.4064: : 394iter [00:12, 39.03iter/s]
Best score: 0.4064: : 395iter [00:12, 39.03iter/s]
Best score: 0.4064: : 396iter [00:12, 39.47iter/s]
Best score: 0.4064: : 396iter [00:12, 39.47iter/s]
Best score: 0.4064: : 397iter [00:12, 39.47iter/s]
Best score: 0.4064: : 398iter [00:12, 39.47iter/s]
[ITER 400] Best: 0.3536
[PARAMS] Current best parameters:
  fx: 531.8634
  fy: 531.5494
  px: 1021.4496
  py: 746.9777
  k1: 0.01990214
  k2: -0.02826058
  k3: 0.01599838
  k4: -0.00397155
  p1: -0.00424858
  p2: -0.00783974
  p3: 0.00485825
  p4: -0.00644762
  s1: 0.0033
  s2: 0.0055
  s3: -0.0019
  s4: 0.0048
  tilt_x: -0.0162
  tilt_y: -0.0142
  scale: 2.9708
  rotation: -0.1640

Best score: 0.3536: : 399iter [00:12, 39.47iter/s]
Best score: 0.3536: : 400iter [00:12, 39.43iter/s]
Best score: 0.3536: : 400iter [00:12, 39.43iter/s]
Best score: 0.3536: : 401iter [00:12, 39.43iter/s]
Best score: 0.3536: : 402iter [00:12, 39.43iter/s]
Best score: 0.3536: : 403iter [00:12, 39.43iter/s]
Best score: 0.3536: : 404iter [00:12, 32.37iter/s]
Best score: 0.3536: : 404iter [00:12, 32.37iter/s]
Best score: 0.3536: : 405iter [00:12, 32.37iter/s]
Best score: 0.3536: : 406iter [00:12, 32.37iter/s]
Best score: 0.3536: : 407iter [00:12, 32.37iter/s]
Best score: 0.3536: : 408iter [00:12, 32.37iter/s]
Best score: 0.3536: : 409iter [00:12, 34.75iter/s]
Best score: 0.3084: : 409iter [00:12, 34.75iter/s]
Best score: 0.3084: : 410iter [00:12, 34.75iter/s]
Best score: 0.3084: : 411iter [00:12, 34.75iter/s]
Best score: 0.3084: : 412iter [00:12, 34.75iter/s]
Best score: 0.3084: : 413iter [00:12, 35.93iter/s]
Best score: 0.3084: : 413iter [00:13, 35.93iter/s]
Best score: 0.3084: : 414iter [00:13, 35.93iter/s]
Best score: 0.3084: : 415iter [00:13, 35.93iter/s]
Best score: 0.3084: : 416iter [00:13, 35.93iter/s]
Best score: 0.3084: : 417iter [00:13, 36.93iter/s]
Best score: 0.3084: : 417iter [00:13, 36.93iter/s]
Best score: 0.3084: : 418iter [00:13, 36.93iter/s]
Best score: 0.3084: : 419iter [00:13, 36.93iter/s]
Best score: 0.3084: : 420iter [00:13, 36.93iter/s]
Best score: 0.3084: : 421iter [00:13, 37.52iter/s]
Best score: 0.3084: : 421iter [00:13, 37.52iter/s]
Best score: 0.3084: : 422iter [00:13, 37.52iter/s]
Best score: 0.3084: : 423iter [00:13, 37.52iter/s]
Best score: 0.3084: : 424iter [00:13, 37.52iter/s]
Best score: 0.3084: : 425iter [00:13, 37.52iter/s]
Best score: 0.3084: : 426iter [00:13, 38.38iter/s]
Best score: 0.3084: : 426iter [00:13, 38.38iter/s]
Best score: 0.3084: : 427iter [00:13, 38.38iter/s]
Best score: 0.3084: : 428iter [00:13, 38.38iter/s]
Best score: 0.3084: : 429iter [00:13, 38.38iter/s]
Best score: 0.3084: : 430iter [00:13, 38.50iter/s]
Best score: 0.3084: : 430iter [00:13, 38.50iter/s]
Best score: 0.3084: : 431iter [00:13, 38.50iter/s]
Best score: 0.3084: : 432iter [00:13, 38.50iter/s]
Best score: 0.3084: : 433iter [00:13, 38.50iter/s]
Best score: 0.3084: : 434iter [00:13, 38.50iter/s]
Best score: 0.3084: : 435iter [00:13, 39.05iter/s]
Best score: 0.3084: : 435iter [00:13, 39.05iter/s]
Best score: 0.3084: : 436iter [00:13, 39.05iter/s]
Best score: 0.3084: : 437iter [00:13, 39.05iter/s]
Best score: 0.3084: : 438iter [00:13, 39.05iter/s]
Best score: 0.3084: : 439iter [00:13, 39.05iter/s]
Best score: 0.3084: : 440iter [00:13, 39.19iter/s]
Best score: 0.3084: : 440iter [00:13, 39.19iter/s]
Best score: 0.3084: : 441iter [00:13, 39.19iter/s]
Best score: 0.3084: : 442iter [00:13, 39.19iter/s]
Best score: 0.3084: : 443iter [00:13, 39.19iter/s]
Best score: 0.3084: : 444iter [00:13, 39.19iter/s]
Best score: 0.3084: : 445iter [00:13, 39.47iter/s]
Best score: 0.3084: : 445iter [00:13, 39.47iter/s]
Best score: 0.3084: : 446iter [00:13, 39.47iter/s]
Best score: 0.3084: : 447iter [00:13, 39.47iter/s]
Best score: 0.3084: : 448iter [00:13, 39.47iter/s]
[ITER 450] Best: 0.3084
[PARAMS] Current best parameters:
  fx: 533.4634
  fy: 533.2283
  px: 1022.7855
  py: 748.3095
  k1: 0.02775949
  k2: -0.03560021
  k3: 0.01702274
  k4: -0.00375700
  p1: -0.00749125
  p2: -0.00556214
  p3: 0.00866168
  p4: -0.00636593
  s1: 0.0007
  s2: 0.0042
  s3: -0.0015
  s4: 0.0047
  tilt_x: -0.0044
  tilt_y: -0.0174
  scale: 2.9473
  rotation: -0.1571

Best score: 0.3084: : 449iter [00:13, 39.47iter/s]
Best score: 0.3084: : 450iter [00:13, 39.46iter/s]
Best score: 0.3084: : 450iter [00:14, 39.46iter/s]
Best score: 0.3084: : 451iter [00:14, 39.46iter/s]
Best score: 0.3084: : 452iter [00:14, 39.46iter/s]
Best score: 0.3084: : 453iter [00:14, 39.46iter/s]
Best score: 0.3084: : 454iter [00:14, 33.06iter/s]
Best score: 0.3084: : 454iter [00:14, 33.06iter/s]
Best score: 0.3084: : 455iter [00:14, 33.06iter/s]
Best score: 0.3084: : 456iter [00:14, 33.06iter/s]
Best score: 0.3084: : 457iter [00:14, 33.06iter/s]
Best score: 0.3084: : 458iter [00:14, 34.66iter/s]
Best score: 0.3084: : 458iter [00:14, 34.66iter/s]
Best score: 0.2994: : 459iter [00:14, 34.66iter/s]
Best score: 0.2994: : 460iter [00:14, 34.66iter/s]
Best score: 0.2994: : 461iter [00:14, 34.66iter/s]
Best score: 0.2994: : 462iter [00:14, 35.72iter/s]
Best score: 0.2994: : 462iter [00:14, 35.72iter/s]
Best score: 0.2994: : 463iter [00:14, 35.72iter/s]
Best score: 0.2994: : 464iter [00:14, 35.72iter/s]
Best score: 0.2994: : 465iter [00:14, 35.72iter/s]
Best score: 0.2994: : 466iter [00:14, 35.72iter/s]
Best score: 0.2994: : 467iter [00:14, 37.09iter/s]
Best score: 0.2994: : 467iter [00:14, 37.09iter/s]
Best score: 0.2994: : 468iter [00:14, 37.09iter/s]
Best score: 0.2978: : 469iter [00:14, 37.09iter/s]
Best score: 0.2978: : 470iter [00:14, 37.09iter/s]
Best score: 0.2978: : 471iter [00:14, 37.59iter/s]
Best score: 0.2978: : 471iter [00:14, 37.59iter/s]
Best score: 0.2978: : 472iter [00:14, 37.59iter/s]
Best score: 0.2978: : 473iter [00:14, 37.59iter/s]
Best score: 0.2978: : 474iter [00:14, 37.59iter/s]
Best score: 0.2978: : 475iter [00:14, 37.59iter/s]
Best score: 0.2978: : 476iter [00:14, 38.37iter/s]
Best score: 0.2978: : 476iter [00:14, 38.37iter/s]
Best score: 0.2978: : 477iter [00:14, 38.37iter/s]
Best score: 0.2978: : 478iter [00:14, 38.37iter/s]
Best score: 0.2969: : 479iter [00:14, 38.37iter/s]
Best score: 0.2969: : 480iter [00:14, 38.67iter/s]
Best score: 0.2969: : 480iter [00:14, 38.67iter/s]
Best score: 0.2969: : 481iter [00:14, 38.67iter/s]
Best score: 0.2969: : 482iter [00:14, 38.67iter/s]
Best score: 0.2969: : 483iter [00:14, 38.67iter/s]
Best score: 0.2969: : 484iter [00:14, 38.67iter/s]
Best score: 0.2969: : 485iter [00:14, 39.18iter/s]
Best score: 0.2969: : 485iter [00:14, 39.18iter/s]
Best score: 0.2969: : 486iter [00:14, 39.18iter/s]
Best score: 0.2969: : 487iter [00:14, 39.18iter/s]
Best score: 0.2969: : 488iter [00:14, 39.18iter/s]
Best score: 0.2969: : 489iter [00:14, 39.18iter/s]
Best score: 0.2969: : 489iter [00:15, 39.18iter/s]
Best score: 0.2969: : 490iter [00:15, 39.18iter/s]
Best score: 0.2969: : 491iter [00:15, 39.18iter/s]
Best score: 0.2969: : 492iter [00:15, 39.18iter/s]
Best score: 0.2969: : 493iter [00:15, 39.20iter/s]
Best score: 0.2969: : 493iter [00:15, 39.20iter/s]
Best score: 0.2969: : 494iter [00:15, 39.20iter/s]
Best score: 0.2969: : 495iter [00:15, 39.20iter/s]
Best score: 0.2969: : 496iter [00:15, 39.20iter/s]
Best score: 0.2969: : 497iter [00:15, 39.36iter/s]
Best score: 0.2969: : 497iter [00:15, 39.36iter/s]
Best score: 0.2969: : 498iter [00:15, 39.36iter/s]
[ITER 500] Best: 0.2659
[PARAMS] Current best parameters:
  fx: 533.8976
  fy: 533.5660
  px: 1022.3254
  py: 747.5167
  k1: 0.01288175
  k2: -0.01754794
  k3: 0.00898001
  k4: -0.00246312
  p1: -0.00553316
  p2: -0.00585402
  p3: 0.00592747
  p4: -0.00622857
  s1: 0.0014
  s2: 0.0050
  s3: -0.0016
  s4: 0.0043
  tilt_x: -0.0112
  tilt_y: -0.0157
  scale: 2.9672
  rotation: -0.1721

Best score: 0.2659: : 499iter [00:15, 39.36iter/s]
Best score: 0.2659: : 500iter [00:15, 39.36iter/s]
Best score: 0.2659: : 501iter [00:15, 32.06iter/s]
Best score: 0.2659: : 501iter [00:15, 32.06iter/s]
Best score: 0.2659: : 502iter [00:15, 32.06iter/s]
Best score: 0.2659: : 503iter [00:15, 32.06iter/s]
Best score: 0.2659: : 504iter [00:15, 32.06iter/s]
Best score: 0.2659: : 505iter [00:15, 32.06iter/s]
Best score: 0.2659: : 506iter [00:15, 34.48iter/s]
Best score: 0.2659: : 506iter [00:15, 34.48iter/s]
Best score: 0.2659: : 507iter [00:15, 34.48iter/s]
Best score: 0.2659: : 508iter [00:15, 34.48iter/s]
Best score: 0.2481: : 509iter [00:15, 34.48iter/s]
Best score: 0.2481: : 510iter [00:15, 35.68iter/s]
Best score: 0.2481: : 510iter [00:15, 35.68iter/s]
Best score: 0.2481: : 511iter [00:15, 35.68iter/s]
Best score: 0.2481: : 512iter [00:15, 35.68iter/s]
Best score: 0.2481: : 513iter [00:15, 35.68iter/s]
Best score: 0.2481: : 514iter [00:15, 35.68iter/s]
Best score: 0.2481: : 515iter [00:15, 37.12iter/s]
Best score: 0.2481: : 515iter [00:15, 37.12iter/s]
Best score: 0.2481: : 516iter [00:15, 37.12iter/s]
Best score: 0.2481: : 517iter [00:15, 37.12iter/s]
Best score: 0.2481: : 518iter [00:15, 37.12iter/s]
Best score: 0.2424: : 519iter [00:15, 37.12iter/s]
Best score: 0.2424: : 520iter [00:15, 37.92iter/s]
Best score: 0.2424: : 520iter [00:15, 37.92iter/s]
Best score: 0.2424: : 521iter [00:15, 37.92iter/s]
Best score: 0.2424: : 522iter [00:15, 37.92iter/s]
Best score: 0.2424: : 523iter [00:15, 37.92iter/s]
Best score: 0.2424: : 524iter [00:15, 37.92iter/s]
Best score: 0.2424: : 525iter [00:15, 38.69iter/s]
Best score: 0.2424: : 525iter [00:15, 38.69iter/s]
Best score: 0.2424: : 526iter [00:16, 38.69iter/s]
Best score: 0.2424: : 527iter [00:16, 38.69iter/s]
Best score: 0.2424: : 528iter [00:16, 38.69iter/s]
Best score: 0.2424: : 529iter [00:16, 38.69iter/s]
Best score: 0.2424: : 530iter [00:16, 38.85iter/s]
Best score: 0.2424: : 530iter [00:16, 38.85iter/s]
Best score: 0.2424: : 531iter [00:16, 38.85iter/s]
Best score: 0.2424: : 532iter [00:16, 38.85iter/s]
Best score: 0.2424: : 533iter [00:16, 38.85iter/s]
Best score: 0.2424: : 534iter [00:16, 39.06iter/s]
Best score: 0.2424: : 534iter [00:16, 39.06iter/s]
Best score: 0.2424: : 535iter [00:16, 39.06iter/s]
Best score: 0.2424: : 536iter [00:16, 39.06iter/s]
Best score: 0.2424: : 537iter [00:16, 39.06iter/s]
Best score: 0.2424: : 538iter [00:16, 39.26iter/s]
Best score: 0.2424: : 538iter [00:16, 39.26iter/s]
Best score: 0.2399: : 539iter [00:16, 39.26iter/s]
Best score: 0.2399: : 540iter [00:16, 39.26iter/s]
Best score: 0.2399: : 541iter [00:16, 39.26iter/s]
Best score: 0.2399: : 542iter [00:16, 39.11iter/s]
Best score: 0.2399: : 542iter [00:16, 39.11iter/s]
Best score: 0.2399: : 543iter [00:16, 39.11iter/s]
Best score: 0.2399: : 544iter [00:16, 39.11iter/s]
Best score: 0.2399: : 545iter [00:16, 39.11iter/s]
Best score: 0.2399: : 546iter [00:16, 39.11iter/s]
Best score: 0.2399: : 547iter [00:16, 39.43iter/s]
Best score: 0.2399: : 547iter [00:16, 39.43iter/s]
Best score: 0.2399: : 548iter [00:16, 39.43iter/s]
[ITER 550] Best: 0.2306
[PARAMS] Current best parameters:
  fx: 533.6626
  fy: 533.2382
  px: 1020.9378
  py: 746.9293
  k1: 0.00120830
  k2: -0.00265756
  k3: 0.00192023
  k4: -0.00120248
  p1: -0.00833218
  p2: -0.00451029
  p3: 0.00390203
  p4: -0.00526045
  s1: -0.0009
  s2: 0.0051
  s3: 0.0009
  s4: 0.0044
  tilt_x: -0.0084
  tilt_y: -0.0058
  scale: 2.9874
  rotation: -0.1272

Best score: 0.2306: : 549iter [00:16, 39.43iter/s]
Best score: 0.2306: : 550iter [00:16, 39.43iter/s]
Best score: 0.2306: : 551iter [00:16, 32.68iter/s]
Best score: 0.2306: : 551iter [00:16, 32.68iter/s]
Best score: 0.2306: : 552iter [00:16, 32.68iter/s]
Best score: 0.2306: : 553iter [00:16, 32.68iter/s]
Best score: 0.2306: : 554iter [00:16, 32.68iter/s]
Best score: 0.2306: : 555iter [00:16, 32.68iter/s]
Best score: 0.2306: : 556iter [00:16, 34.85iter/s]
Best score: 0.2306: : 556iter [00:16, 34.85iter/s]
Best score: 0.2306: : 557iter [00:16, 34.85iter/s]
Best score: 0.2306: : 558iter [00:16, 34.85iter/s]
Best score: 0.2248: : 559iter [00:16, 34.85iter/s]
Best score: 0.2248: : 560iter [00:16, 35.84iter/s]
Best score: 0.2248: : 560iter [00:16, 35.84iter/s]
Best score: 0.2248: : 561iter [00:16, 35.84iter/s]
Best score: 0.2248: : 562iter [00:17, 35.84iter/s]
Best score: 0.2248: : 563iter [00:17, 35.84iter/s]
Best score: 0.2248: : 564iter [00:17, 35.84iter/s]
Best score: 0.2248: : 565iter [00:17, 37.18iter/s]
Best score: 0.2248: : 565iter [00:17, 37.18iter/s]
Best score: 0.2248: : 566iter [00:17, 37.18iter/s]
Best score: 0.2248: : 567iter [00:17, 37.18iter/s]
Best score: 0.2248: : 568iter [00:17, 37.18iter/s]
Best score: 0.2248: : 569iter [00:17, 37.88iter/s]
Best score: 0.2239: : 569iter [00:17, 37.88iter/s]
Best score: 0.2239: : 570iter [00:17, 37.88iter/s]
Best score: 0.2239: : 571iter [00:17, 37.88iter/s]
Best score: 0.2239: : 572iter [00:17, 37.88iter/s]
Best score: 0.2239: : 573iter [00:17, 38.13iter/s]
Best score: 0.2239: : 573iter [00:17, 38.13iter/s]
Best score: 0.2239: : 574iter [00:17, 38.13iter/s]
Best score: 0.2239: : 575iter [00:17, 38.13iter/s]
Best score: 0.2239: : 576iter [00:17, 38.13iter/s]
Best score: 0.2239: : 577iter [00:17, 38.60iter/s]
Best score: 0.2239: : 577iter [00:17, 38.60iter/s]
Best score: 0.2239: : 578iter [00:17, 38.60iter/s]
Best score: 0.2234: : 579iter [00:17, 38.60iter/s]
Best score: 0.2234: : 580iter [00:17, 38.60iter/s]
Best score: 0.2234: : 581iter [00:17, 38.80iter/s]
Best score: 0.2234: : 581iter [00:17, 38.80iter/s]
Best score: 0.2234: : 582iter [00:17, 38.80iter/s]
Best score: 0.2234: : 583iter [00:17, 38.80iter/s]
Best score: 0.2234: : 584iter [00:17, 38.80iter/s]
Best score: 0.2234: : 585iter [00:17, 38.80iter/s]
Best score: 0.2234: : 586iter [00:17, 39.25iter/s]
Best score: 0.2234: : 586iter [00:17, 39.25iter/s]
Best score: 0.2234: : 587iter [00:17, 39.25iter/s]
Best score: 0.2234: : 588iter [00:17, 39.25iter/s]
Best score: 0.2234: : 589iter [00:17, 39.25iter/s]
Best score: 0.2234: : 590iter [00:17, 39.19iter/s]
Best score: 0.2234: : 590iter [00:17, 39.19iter/s]
Best score: 0.2234: : 591iter [00:17, 39.19iter/s]
Best score: 0.2234: : 592iter [00:17, 39.19iter/s]
Best score: 0.2234: : 593iter [00:17, 39.19iter/s]
Best score: 0.2234: : 594iter [00:17, 39.34iter/s]
Best score: 0.2234: : 594iter [00:17, 39.34iter/s]
Best score: 0.2234: : 595iter [00:17, 39.34iter/s]
Best score: 0.2234: : 596iter [00:17, 39.34iter/s]
Best score: 0.2234: : 597iter [00:17, 39.34iter/s]
Best score: 0.2234: : 598iter [00:17, 39.42iter/s]
Best score: 0.2234: : 598iter [00:17, 39.42iter/s]
[ITER 600] Best: 0.2209
[PARAMS] Current best parameters:
  fx: 534.7243
  fy: 534.3895
  px: 1020.4388
  py: 747.3657
  k1: 0.00395388
  k2: -0.00718148
  k3: 0.00349942
  k4: -0.00129133
  p1: -0.00815515
  p2: -0.00489285
  p3: 0.00387849
  p4: -0.00545088
  s1: -0.0025
  s2: 0.0063
  s3: -0.0001
  s4: 0.0050
  tilt_x: -0.0066
  tilt_y: -0.0040
  scale: 2.9833
  rotation: -0.1490

Best score: 0.2209: : 599iter [00:18, 39.42iter/s]
Best score: 0.2209: : 600iter [00:18, 39.42iter/s]
Best score: 0.2209: : 601iter [00:18, 39.42iter/s]
Best score: 0.2209: : 602iter [00:18, 26.31iter/s]
Best score: 0.2209: : 602iter [00:18, 26.31iter/s]
Best score: 0.2209: : 603iter [00:18, 26.31iter/s]
Best score: 0.2209: : 604iter [00:18, 26.31iter/s]
Best score: 0.2209: : 605iter [00:18, 26.31iter/s]
Best score: 0.2209: : 606iter [00:18, 26.31iter/s]
Best score: 0.2209: : 607iter [00:18, 29.82iter/s]
Best score: 0.2209: : 607iter [00:18, 29.82iter/s]
Best score: 0.2209: : 608iter [00:18, 29.82iter/s]
Best score: 0.2159: : 609iter [00:18, 29.82iter/s]
Best score: 0.2159: : 610iter [00:18, 29.82iter/s]
Best score: 0.2159: : 611iter [00:18, 31.85iter/s]
Best score: 0.2159: : 611iter [00:18, 31.85iter/s]
Best score: 0.2159: : 612iter [00:18, 31.85iter/s]
Best score: 0.2159: : 613iter [00:18, 31.85iter/s]
Best score: 0.2159: : 614iter [00:18, 31.85iter/s]
Best score: 0.2159: : 615iter [00:18, 33.77iter/s]
Best score: 0.2159: : 615iter [00:18, 33.77iter/s]
Best score: 0.2159: : 616iter [00:18, 33.77iter/s]
Best score: 0.2159: : 617iter [00:18, 33.77iter/s]
Best score: 0.2159: : 618iter [00:18, 33.77iter/s]
Best score: 0.2159: : 619iter [00:18, 33.77iter/s]
Best score: 0.2159: : 620iter [00:18, 35.53iter/s]
Best score: 0.2159: : 620iter [00:18, 35.53iter/s]
Best score: 0.2159: : 621iter [00:18, 35.53iter/s]
Best score: 0.2159: : 622iter [00:18, 35.53iter/s]
Best score: 0.2159: : 623iter [00:18, 35.53iter/s]
Best score: 0.2159: : 624iter [00:18, 35.53iter/s]
Best score: 0.2159: : 625iter [00:18, 36.91iter/s]
Best score: 0.2159: : 625iter [00:18, 36.91iter/s]
Best score: 0.2159: : 626iter [00:18, 36.91iter/s]
Best score: 0.2159: : 627iter [00:18, 36.91iter/s]
Best score: 0.2159: : 628iter [00:18, 36.91iter/s]
Best score: 0.2132: : 629iter [00:18, 36.91iter/s]
Best score: 0.2132: : 630iter [00:18, 37.57iter/s]
Best score: 0.2132: : 630iter [00:18, 37.57iter/s]
Best score: 0.2132: : 631iter [00:18, 37.57iter/s]
Best score: 0.2132: : 632iter [00:18, 37.57iter/s]
Best score: 0.2132: : 633iter [00:18, 37.57iter/s]
Best score: 0.2132: : 634iter [00:18, 37.57iter/s]
Best score: 0.2132: : 635iter [00:18, 38.40iter/s]
Best score: 0.2132: : 635iter [00:19, 38.40iter/s]
Best score: 0.2132: : 636iter [00:19, 38.40iter/s]
Best score: 0.2132: : 637iter [00:19, 38.40iter/s]
Best score: 0.2132: : 638iter [00:19, 38.40iter/s]
Best score: 0.2132: : 639iter [00:19, 38.40iter/s]
Best score: 0.2132: : 640iter [00:19, 38.68iter/s]
Best score: 0.2132: : 640iter [00:19, 38.68iter/s]
Best score: 0.2132: : 641iter [00:19, 38.68iter/s]
Best score: 0.2132: : 642iter [00:19, 38.68iter/s]
Best score: 0.2132: : 643iter [00:19, 38.68iter/s]
Best score: 0.2132: : 644iter [00:19, 38.68iter/s]
Best score: 0.2132: : 645iter [00:19, 39.19iter/s]
Best score: 0.2132: : 645iter [00:19, 39.19iter/s]
Best score: 0.2132: : 646iter [00:19, 39.19iter/s]
Best score: 0.2132: : 647iter [00:19, 39.19iter/s]
Best score: 0.2132: : 648iter [00:19, 39.19iter/s]
[ITER 650] Best: 0.2132
[PARAMS] Current best parameters:
  fx: 533.3775
  fy: 533.0406
  px: 1020.4062
  py: 747.9284
  k1: 0.00754340
  k2: -0.01319890
  k3: 0.00805643
  k4: -0.00240336
  p1: -0.00776276
  p2: -0.00474957
  p3: 0.00371580
  p4: -0.00542152
  s1: -0.0019
  s2: 0.0057
  s3: 0.0006
  s4: 0.0049
  tilt_x: -0.0047
  tilt_y: -0.0038
  scale: 2.9864
  rotation: -0.1520

Best score: 0.2132: : 649iter [00:19, 39.19iter/s]
Best score: 0.2132: : 650iter [00:19, 39.27iter/s]
Best score: 0.2132: : 650iter [00:19, 39.27iter/s]
Best score: 0.2132: : 651iter [00:19, 39.27iter/s]
Best score: 0.2132: : 652iter [00:19, 39.27iter/s]
Best score: 0.2132: : 653iter [00:19, 39.27iter/s]
Best score: 0.2132: : 654iter [00:19, 32.92iter/s]
Best score: 0.2132: : 654iter [00:19, 32.92iter/s]
Best score: 0.2132: : 655iter [00:19, 32.92iter/s]
Best score: 0.2132: : 656iter [00:19, 32.92iter/s]
Best score: 0.2132: : 657iter [00:19, 32.92iter/s]
Best score: 0.2132: : 658iter [00:19, 32.92iter/s]
Best score: 0.2132: : 659iter [00:19, 34.99iter/s]
Best score: 0.2113: : 659iter [00:19, 34.99iter/s]
Best score: 0.2113: : 660iter [00:19, 34.99iter/s]
Best score: 0.2113: : 661iter [00:19, 34.99iter/s]
Best score: 0.2113: : 662iter [00:19, 34.99iter/s]
Best score: 0.2113: : 663iter [00:19, 35.98iter/s]
Best score: 0.2113: : 663iter [00:19, 35.98iter/s]
Best score: 0.2113: : 664iter [00:19, 35.98iter/s]
Best score: 0.2113: : 665iter [00:19, 35.98iter/s]
Best score: 0.2113: : 666iter [00:19, 35.98iter/s]
Best score: 0.2113: : 667iter [00:19, 36.93iter/s]
Best score: 0.2113: : 667iter [00:19, 36.93iter/s]
Best score: 0.2113: : 668iter [00:19, 36.93iter/s]
Best score: 0.2109: : 669iter [00:19, 36.93iter/s]
Best score: 0.2109: : 670iter [00:19, 36.93iter/s]
Best score: 0.2109: : 671iter [00:19, 37.47iter/s]
Best score: 0.2109: : 671iter [00:20, 37.47iter/s]
Best score: 0.2109: : 672iter [00:20, 37.47iter/s]
Best score: 0.2109: : 673iter [00:20, 37.47iter/s]
Best score: 0.2109: : 674iter [00:20, 37.47iter/s]
Best score: 0.2109: : 675iter [00:20, 37.47iter/s]
Best score: 0.2109: : 676iter [00:20, 38.32iter/s]
Best score: 0.2109: : 676iter [00:20, 38.32iter/s]
Best score: 0.2109: : 677iter [00:20, 38.32iter/s]
Best score: 0.2109: : 678iter [00:20, 38.32iter/s]
Best score: 0.2075: : 679iter [00:20, 38.32iter/s]
Best score: 0.2075: : 680iter [00:20, 38.56iter/s]
Best score: 0.2075: : 680iter [00:20, 38.56iter/s]
Best score: 0.2075: : 681iter [00:20, 38.56iter/s]
Best score: 0.2075: : 682iter [00:20, 38.56iter/s]
Best score: 0.2075: : 683iter [00:20, 38.56iter/s]
Best score: 0.2075: : 684iter [00:20, 38.56iter/s]
Best score: 0.2075: : 685iter [00:20, 39.06iter/s]
Best score: 0.2075: : 685iter [00:20, 39.06iter/s]
Best score: 0.2075: : 686iter [00:20, 39.06iter/s]
Best score: 0.2075: : 687iter [00:20, 39.06iter/s]
Best score: 0.2075: : 688iter [00:20, 39.06iter/s]
Best score: 0.2075: : 689iter [00:20, 39.06iter/s]
Best score: 0.2075: : 690iter [00:20, 39.18iter/s]
Best score: 0.2075: : 690iter [00:20, 39.18iter/s]
Best score: 0.2075: : 691iter [00:20, 39.18iter/s]
Best score: 0.2075: : 692iter [00:20, 39.18iter/s]
Best score: 0.2075: : 693iter [00:20, 39.18iter/s]
Best score: 0.2075: : 694iter [00:20, 39.18iter/s]
Best score: 0.2075: : 695iter [00:20, 39.59iter/s]
Best score: 0.2075: : 695iter [00:20, 39.59iter/s]
Best score: 0.2075: : 696iter [00:20, 39.59iter/s]
Best score: 0.2075: : 697iter [00:20, 39.59iter/s]
Best score: 0.2075: : 698iter [00:20, 39.59iter/s]
[ITER 700] Best: 0.2070
[PARAMS] Current best parameters:
  fx: 533.9322
  fy: 533.6645
  px: 1021.9516
  py: 746.3907
  k1: -0.03099136
  k2: 0.04217851
  k3: -0.02184332
  k4: 0.00343630
  p1: -0.00656988
  p2: -0.00478567
  p3: -0.00128960
  p4: -0.00271893
  s1: 0.0050
  s2: 0.0027
  s3: 0.0027
  s4: 0.0025
  tilt_x: -0.0149
  tilt_y: -0.0117
  scale: 3.0318
  rotation: -0.1424

Best score: 0.2070: : 699iter [00:20, 39.59iter/s]
Best score: 0.2070: : 700iter [00:20, 39.46iter/s]
Best score: 0.2070: : 700iter [00:20, 39.46iter/s]
Best score: 0.2070: : 701iter [00:20, 39.46iter/s]
Best score: 0.2070: : 702iter [00:20, 39.46iter/s]
Best score: 0.2070: : 703iter [00:20, 39.46iter/s]
Best score: 0.2070: : 704iter [00:20, 32.83iter/s]
Best score: 0.2070: : 704iter [00:20, 32.83iter/s]
Best score: 0.2070: : 705iter [00:20, 32.83iter/s]
Best score: 0.2070: : 706iter [00:20, 32.83iter/s]
Best score: 0.2070: : 707iter [00:20, 32.83iter/s]
Best score: 0.2070: : 708iter [00:21, 32.83iter/s]
Best score: 0.2070: : 709iter [00:21, 34.88iter/s]
Best score: 0.2069: : 709iter [00:21, 34.88iter/s]
Best score: 0.2069: : 710iter [00:21, 34.88iter/s]
Best score: 0.2069: : 711iter [00:21, 34.88iter/s]
Best score: 0.2069: : 712iter [00:21, 34.88iter/s]
Best score: 0.2069: : 713iter [00:21, 35.80iter/s]
Best score: 0.2069: : 713iter [00:21, 35.80iter/s]
Best score: 0.2069: : 714iter [00:21, 35.80iter/s]
Best score: 0.2069: : 715iter [00:21, 35.80iter/s]
Best score: 0.2069: : 716iter [00:21, 35.80iter/s]
Best score: 0.2069: : 717iter [00:21, 36.86iter/s]
Best score: 0.2069: : 717iter [00:21, 36.86iter/s]
Best score: 0.2069: : 718iter [00:21, 36.86iter/s]
Best score: 0.2069: : 719iter [00:21, 36.86iter/s]
Best score: 0.2069: : 720iter [00:21, 36.86iter/s]
Best score: 0.2069: : 721iter [00:21, 37.34iter/s]
Best score: 0.2069: : 721iter [00:21, 37.34iter/s]
Best score: 0.2069: : 722iter [00:21, 37.34iter/s]
Best score: 0.2069: : 723iter [00:21, 37.34iter/s]
Best score: 0.2069: : 724iter [00:21, 37.34iter/s]
Best score: 0.2069: : 725iter [00:21, 37.34iter/s]
Best score: 0.2069: : 726iter [00:21, 38.24iter/s]
Best score: 0.2069: : 726iter [00:21, 38.24iter/s]
Best score: 0.2069: : 727iter [00:21, 38.24iter/s]
Best score: 0.2069: : 728iter [00:21, 38.24iter/s]
Best score: 0.2050: : 729iter [00:21, 38.24iter/s]
Best score: 0.2050: : 730iter [00:21, 38.60iter/s]
Best score: 0.2050: : 730iter [00:21, 38.60iter/s]
Best score: 0.2050: : 731iter [00:21, 38.60iter/s]
Best score: 0.2050: : 732iter [00:21, 38.60iter/s]
Best score: 0.2050: : 733iter [00:21, 38.60iter/s]
Best score: 0.2050: : 734iter [00:21, 38.60iter/s]
Best score: 0.2050: : 735iter [00:21, 39.17iter/s]
Best score: 0.2050: : 735iter [00:21, 39.17iter/s]
Best score: 0.2050: : 736iter [00:21, 39.17iter/s]
Best score: 0.2050: : 737iter [00:21, 39.17iter/s]
Best score: 0.2050: : 738iter [00:21, 39.17iter/s]
Best score: 0.2050: : 739iter [00:21, 39.37iter/s]
Best score: 0.2033: : 739iter [00:21, 39.37iter/s]
Best score: 0.2033: : 740iter [00:21, 39.37iter/s]
Best score: 0.2033: : 741iter [00:21, 39.37iter/s]
Best score: 0.2033: : 742iter [00:21, 39.37iter/s]
Best score: 0.2033: : 743iter [00:21, 39.12iter/s]
Best score: 0.2033: : 743iter [00:21, 39.12iter/s]
Best score: 0.2033: : 744iter [00:21, 39.12iter/s]
Best score: 0.2033: : 745iter [00:21, 39.12iter/s]
Best score: 0.2033: : 746iter [00:21, 39.12iter/s]
Best score: 0.2033: : 747iter [00:22, 39.12iter/s]
Best score: 0.2033: : 748iter [00:22, 39.42iter/s]
Best score: 0.2033: : 748iter [00:22, 39.42iter/s]
[ITER 750] Best: 0.2032
[PARAMS] Current best parameters:
  fx: 533.0082
  fy: 532.7609
  px: 1021.5712
  py: 745.8050
  k1: -0.03070330
  k2: 0.04291415
  k3: -0.02205038
  k4: 0.00340667
  p1: -0.00682266
  p2: -0.00461700
  p3: -0.00244869
  p4: -0.00266651
  s1: 0.0057
  s2: 0.0026
  s3: 0.0035
  s4: 0.0024
  tilt_x: -0.0172
  tilt_y: -0.0096
  scale: 3.0319
  rotation: -0.1363

Best score: 0.2032: : 749iter [00:22, 39.42iter/s]
Best score: 0.2032: : 750iter [00:22, 39.42iter/s]
Best score: 0.2032: : 751iter [00:22, 39.42iter/s]
Best score: 0.2032: : 752iter [00:22, 32.53iter/s]
Best score: 0.2032: : 752iter [00:22, 32.53iter/s]
Best score: 0.2032: : 753iter [00:22, 32.53iter/s]
Best score: 0.2032: : 754iter [00:22, 32.53iter/s]
Best score: 0.2032: : 755iter [00:22, 32.53iter/s]
Best score: 0.2032: : 756iter [00:22, 34.17iter/s]
Best score: 0.2032: : 756iter [00:22, 34.17iter/s]
Best score: 0.2032: : 757iter [00:22, 34.17iter/s]
Best score: 0.2032: : 758iter [00:22, 34.17iter/s]
Best score: 0.2032: : 759iter [00:22, 34.17iter/s]
Best score: 0.2032: : 760iter [00:22, 35.46iter/s]
Best score: 0.2032: : 760iter [00:22, 35.46iter/s]
Best score: 0.2032: : 761iter [00:22, 35.46iter/s]
Best score: 0.2032: : 762iter [00:22, 35.46iter/s]
Best score: 0.2032: : 763iter [00:22, 35.46iter/s]
Best score: 0.2032: : 764iter [00:22, 36.60iter/s]
Best score: 0.2032: : 764iter [00:22, 36.60iter/s]
Best score: 0.2032: : 765iter [00:22, 36.60iter/s]
Best score: 0.2032: : 766iter [00:22, 36.60iter/s]
Best score: 0.2032: : 767iter [00:22, 36.60iter/s]
Best score: 0.2032: : 768iter [00:22, 37.53iter/s]
Best score: 0.2032: : 768iter [00:22, 37.53iter/s]
Best score: 0.2032: : 769iter [00:22, 37.53iter/s]
Best score: 0.2032: : 770iter [00:22, 37.53iter/s]
Best score: 0.2032: : 771iter [00:22, 37.53iter/s]
Best score: 0.2032: : 772iter [00:22, 37.93iter/s]
Best score: 0.2032: : 772iter [00:22, 37.93iter/s]
Best score: 0.2032: : 773iter [00:22, 37.93iter/s]
Best score: 0.2032: : 774iter [00:22, 37.93iter/s]
Best score: 0.2032: : 775iter [00:22, 37.93iter/s]
Best score: 0.2032: : 776iter [00:22, 38.38iter/s]
Best score: 0.2032: : 776iter [00:22, 38.38iter/s]
Best score: 0.2032: : 777iter [00:22, 38.38iter/s]
Best score: 0.2032: : 778iter [00:22, 38.38iter/s]
Best score: 0.2031: : 779iter [00:22, 38.38iter/s]
Best score: 0.2031: : 780iter [00:22, 38.62iter/s]
Best score: 0.2031: : 780iter [00:22, 38.62iter/s]
Best score: 0.2031: : 781iter [00:22, 38.62iter/s]
Best score: 0.2031: : 782iter [00:22, 38.62iter/s]
Best score: 0.2031: : 783iter [00:22, 38.62iter/s]
Best score: 0.2031: : 784iter [00:22, 38.93iter/s]
Best score: 0.2031: : 784iter [00:23, 38.93iter/s]
Best score: 0.2031: : 785iter [00:23, 38.93iter/s]
Best score: 0.2031: : 786iter [00:23, 38.93iter/s]
Best score: 0.2031: : 787iter [00:23, 38.93iter/s]
Best score: 0.2031: : 788iter [00:23, 39.22iter/s]
Best score: 0.2031: : 788iter [00:23, 39.22iter/s]
Best score: 0.2029: : 789iter [00:23, 39.22iter/s]
Best score: 0.2029: : 790iter [00:23, 39.22iter/s]
Best score: 0.2029: : 791iter [00:23, 39.22iter/s]
Best score: 0.2029: : 792iter [00:23, 39.01iter/s]
Best score: 0.2029: : 792iter [00:23, 39.01iter/s]
Best score: 0.2029: : 793iter [00:23, 39.01iter/s]
Best score: 0.2029: : 794iter [00:23, 39.01iter/s]
Best score: 0.2029: : 795iter [00:23, 39.01iter/s]
Best score: 0.2029: : 796iter [00:23, 39.23iter/s]
Best score: 0.2029: : 796iter [00:23, 39.23iter/s]
Best score: 0.2029: : 797iter [00:23, 39.23iter/s]
Best score: 0.2029: : 798iter [00:23, 39.23iter/s]
[ITER 800] Best: 0.2023
[PARAMS] Current best parameters:
  fx: 532.3931
  fy: 532.0585
  px: 1021.3553
  py: 745.4330
  k1: -0.04022279
  k2: 0.05772477
  k3: -0.02986844
  k4: 0.00485464
  p1: -0.00935560
  p2: -0.00507848
  p3: -0.00202478
  p4: -0.00170498
  s1: 0.0055
  s2: 0.0017
  s3: 0.0052
  s4: 0.0015
  tilt_x: -0.0139
  tilt_y: -0.0092
  scale: 3.0424
  rotation: -0.1377

Best score: 0.2023: : 799iter [00:23, 39.23iter/s]
Best score: 0.2023: : 800iter [00:23, 39.12iter/s]
Best score: 0.2023: : 800iter [00:23, 39.12iter/s]
Best score: 0.2023: : 801iter [00:23, 39.12iter/s]
Best score: 0.2023: : 802iter [00:23, 39.12iter/s]
Best score: 0.2023: : 803iter [00:23, 39.12iter/s]
Best score: 0.2023: : 804iter [00:23, 31.95iter/s]
Best score: 0.2023: : 804iter [00:23, 31.95iter/s]
Best score: 0.2023: : 805iter [00:23, 31.95iter/s]
Best score: 0.2023: : 806iter [00:23, 31.95iter/s]
Best score: 0.2023: : 807iter [00:23, 31.95iter/s]
Best score: 0.2023: : 808iter [00:23, 34.00iter/s]
Best score: 0.2023: : 808iter [00:23, 34.00iter/s]
Best score: 0.2023: : 809iter [00:23, 34.00iter/s]
Best score: 0.2023: : 810iter [00:23, 34.00iter/s]
Best score: 0.2023: : 811iter [00:23, 34.00iter/s]
Best score: 0.2023: : 812iter [00:23, 35.28iter/s]
Best score: 0.2023: : 812iter [00:23, 35.28iter/s]
Best score: 0.2023: : 813iter [00:23, 35.28iter/s]
Best score: 0.2023: : 814iter [00:23, 35.28iter/s]
Best score: 0.2023: : 815iter [00:23, 35.28iter/s]
Best score: 0.2023: : 816iter [00:23, 36.34iter/s]
Best score: 0.2023: : 816iter [00:23, 36.34iter/s]
Best score: 0.2023: : 817iter [00:23, 36.34iter/s]
Best score: 0.2023: : 818iter [00:23, 36.34iter/s]
Best score: 0.2023: : 819iter [00:23, 36.34iter/s]
Best score: 0.2023: : 820iter [00:23, 37.04iter/s]
Best score: 0.2023: : 820iter [00:24, 37.04iter/s]
Best score: 0.2023: : 821iter [00:24, 37.04iter/s]
Best score: 0.2023: : 822iter [00:24, 37.04iter/s]
Best score: 0.2023: : 823iter [00:24, 37.04iter/s]
Best score: 0.2023: : 824iter [00:24, 37.79iter/s]
Best score: 0.2023: : 824iter [00:24, 37.79iter/s]
Best score: 0.2023: : 825iter [00:24, 37.79iter/s]
Best score: 0.2023: : 826iter [00:24, 37.79iter/s]
Best score: 0.2023: : 827iter [00:24, 37.79iter/s]
Best score: 0.2023: : 828iter [00:24, 37.79iter/s]
Best score: 0.2023: : 829iter [00:24, 38.55iter/s]
Best score: 0.2021: : 829iter [00:24, 38.55iter/s]
Best score: 0.2021: : 830iter [00:24, 38.55iter/s]
Best score: 0.2021: : 831iter [00:24, 38.55iter/s]
Best score: 0.2021: : 832iter [00:24, 38.55iter/s]
Best score: 0.2021: : 833iter [00:24, 38.75iter/s]
Best score: 0.2021: : 833iter [00:24, 38.75iter/s]
Best score: 0.2021: : 834iter [00:24, 38.75iter/s]
Best score: 0.2021: : 835iter [00:24, 38.75iter/s]
Best score: 0.2021: : 836iter [00:24, 38.75iter/s]
Best score: 0.2021: : 837iter [00:24, 39.07iter/s]
Best score: 0.2021: : 837iter [00:24, 39.07iter/s]
Best score: 0.2021: : 838iter [00:24, 39.07iter/s]
Best score: 0.2019: : 839iter [00:24, 39.07iter/s]
Best score: 0.2019: : 840iter [00:24, 39.07iter/s]
Best score: 0.2019: : 841iter [00:24, 39.09iter/s]
Best score: 0.2019: : 841iter [00:24, 39.09iter/s]
Best score: 0.2019: : 842iter [00:24, 39.09iter/s]
Best score: 0.2019: : 843iter [00:24, 39.09iter/s]
Best score: 0.2019: : 844iter [00:24, 39.09iter/s]
Best score: 0.2019: : 845iter [00:24, 39.09iter/s]
Best score: 0.2019: : 846iter [00:24, 39.39iter/s]
Best score: 0.2019: : 846iter [00:24, 39.39iter/s]
Best score: 0.2019: : 847iter [00:24, 39.39iter/s]
Best score: 0.2019: : 848iter [00:24, 39.39iter/s]
[ITER 850] Best: 0.2019
[PARAMS] Current best parameters:
  fx: 532.7707
  fy: 532.5061
  px: 1021.3893
  py: 745.4228
  k1: -0.04080732
  k2: 0.05817036
  k3: -0.03024976
  k4: 0.00496643
  p1: -0.00830742
  p2: -0.00534349
  p3: -0.00221468
  p4: -0.00211480
  s1: 0.0061
  s2: 0.0020
  s3: 0.0041
  s4: 0.0021
  tilt_x: -0.0162
  tilt_y: -0.0100
  scale: 3.0429
  rotation: -0.1396

Best score: 0.2019: : 849iter [00:24, 39.39iter/s]
Best score: 0.2019: : 850iter [00:24, 39.22iter/s]
Best score: 0.2019: : 850iter [00:24, 39.22iter/s]
Best score: 0.2019: : 851iter [00:24, 39.22iter/s]
Best score: 0.2019: : 852iter [00:24, 39.22iter/s]
Best score: 0.2019: : 853iter [00:24, 39.22iter/s]
Best score: 0.2019: : 854iter [00:24, 32.25iter/s]
Best score: 0.2019: : 854iter [00:24, 32.25iter/s]
Best score: 0.2019: : 855iter [00:24, 32.25iter/s]
Best score: 0.2019: : 856iter [00:25, 32.25iter/s]
Best score: 0.2019: : 857iter [00:25, 32.25iter/s]
Best score: 0.2019: : 858iter [00:25, 34.05iter/s]
Best score: 0.2019: : 858iter [00:25, 34.05iter/s]
Best score: 0.2019: : 859iter [00:25, 34.05iter/s]
Best score: 0.2019: : 860iter [00:25, 34.05iter/s]
Best score: 0.2019: : 861iter [00:25, 34.05iter/s]
Best score: 0.2019: : 862iter [00:25, 35.27iter/s]
Best score: 0.2019: : 862iter [00:25, 35.27iter/s]
Best score: 0.2019: : 863iter [00:25, 35.27iter/s]
Best score: 0.2019: : 864iter [00:25, 35.27iter/s]
Best score: 0.2019: : 865iter [00:25, 35.27iter/s]
Best score: 0.2019: : 866iter [00:25, 36.44iter/s]
Best score: 0.2019: : 866iter [00:25, 36.44iter/s]
Best score: 0.2019: : 867iter [00:25, 36.44iter/s]
Best score: 0.2019: : 868iter [00:25, 36.44iter/s]
Best score: 0.2019: : 869iter [00:25, 36.44iter/s]
Best score: 0.2019: : 870iter [00:25, 37.21iter/s]
Best score: 0.2019: : 870iter [00:25, 37.21iter/s]
Best score: 0.2019: : 871iter [00:25, 37.21iter/s]
Best score: 0.2019: : 872iter [00:25, 37.21iter/s]
Best score: 0.2019: : 873iter [00:25, 37.21iter/s]
Best score: 0.2019: : 874iter [00:25, 37.21iter/s]
Best score: 0.2019: : 875iter [00:25, 38.27iter/s]
Best score: 0.2019: : 875iter [00:25, 38.27iter/s]
Best score: 0.2019: : 876iter [00:25, 38.27iter/s]
Best score: 0.2019: : 877iter [00:25, 38.27iter/s]
Best score: 0.2019: : 878iter [00:25, 38.27iter/s]
Best score: 0.2019: : 879iter [00:25, 38.47iter/s]
Best score: 0.2017: : 879iter [00:25, 38.47iter/s]
Best score: 0.2017: : 880iter [00:25, 38.47iter/s]
Best score: 0.2017: : 881iter [00:25, 38.47iter/s]
Best score: 0.2017: : 882iter [00:25, 38.47iter/s]
Best score: 0.2017: : 883iter [00:25, 38.65iter/s]
Best score: 0.2017: : 883iter [00:25, 38.65iter/s]
Best score: 0.2017: : 884iter [00:25, 38.65iter/s]
Best score: 0.2017: : 885iter [00:25, 38.65iter/s]
Best score: 0.2017: : 886iter [00:25, 38.65iter/s]
Best score: 0.2017: : 887iter [00:25, 38.65iter/s]
Best score: 0.2017: : 888iter [00:25, 39.12iter/s]
Best score: 0.2017: : 888iter [00:25, 39.12iter/s]
Best score: 0.2017: : 889iter [00:25, 39.12iter/s]
Best score: 0.2017: : 890iter [00:25, 39.12iter/s]
Best score: 0.2017: : 891iter [00:25, 39.12iter/s]
Best score: 0.2017: : 892iter [00:25, 39.12iter/s]
Best score: 0.2017: : 892iter [00:25, 39.12iter/s]
Best score: 0.2017: : 893iter [00:25, 39.12iter/s]
Best score: 0.2017: : 894iter [00:25, 39.12iter/s]
Best score: 0.2017: : 895iter [00:25, 39.12iter/s]
Best score: 0.2017: : 896iter [00:26, 39.12iter/s]
Best score: 0.2017: : 897iter [00:26, 39.53iter/s]
Best score: 0.2017: : 897iter [00:26, 39.53iter/s]
Best score: 0.2017: : 898iter [00:26, 39.53iter/s]
[ITER 900] Best: 0.2017
[PARAMS] Current best parameters:
  fx: 532.6340
  fy: 532.3361
  px: 1021.1146
  py: 745.8344
  k1: -0.03590574
  k2: 0.05060086
  k3: -0.02589382
  k4: 0.00408243
  p1: -0.00870718
  p2: -0.00535666
  p3: -0.00126223
  p4: -0.00229691
  s1: 0.0050
  s2: 0.0022
  s3: 0.0039
  s4: 0.0022
  tilt_x: -0.0132
  tilt_y: -0.0087
  scale: 3.0387
  rotation: -0.1395

Best score: 0.2017: : 899iter [00:26, 39.53iter/s]
Best score: 0.2017: : 900iter [00:26, 39.53iter/s]
Best score: 0.2017: : 901iter [00:26, 26.85iter/s]
Best score: 0.2017: : 901iter [00:26, 26.85iter/s]
Best score: 0.2017: : 902iter [00:26, 26.85iter/s]
Best score: 0.2017: : 903iter [00:26, 26.85iter/s]
Best score: 0.2017: : 904iter [00:26, 26.85iter/s]
Best score: 0.2017: : 905iter [00:26, 29.55iter/s]
Best score: 0.2017: : 905iter [00:26, 29.55iter/s]
Best score: 0.2017: : 906iter [00:26, 29.55iter/s]
Best score: 0.2017: : 907iter [00:26, 29.55iter/s]
Best score: 0.2017: : 908iter [00:26, 29.55iter/s]
Best score: 0.2016: : 909iter [00:26, 29.55iter/s]
Best score: 0.2016: : 910iter [00:26, 32.32iter/s]
Best score: 0.2016: : 910iter [00:26, 32.32iter/s]
Best score: 0.2016: : 911iter [00:26, 32.32iter/s]
Best score: 0.2016: : 912iter [00:26, 32.32iter/s]
Best score: 0.2016: : 913iter [00:26, 32.32iter/s]
Best score: 0.2016: : 914iter [00:26, 32.32iter/s]
Best score: 0.2016: : 915iter [00:26, 34.51iter/s]
Best score: 0.2016: : 915iter [00:26, 34.51iter/s]
Best score: 0.2016: : 916iter [00:26, 34.51iter/s]
Best score: 0.2016: : 917iter [00:26, 34.51iter/s]
Best score: 0.2016: : 918iter [00:26, 34.51iter/s]
Best score: 0.2016: : 919iter [00:26, 35.82iter/s]
Best score: 0.2015: : 919iter [00:26, 35.82iter/s]
Best score: 0.2015: : 920iter [00:26, 35.82iter/s]
Best score: 0.2015: : 921iter [00:26, 35.82iter/s]
Best score: 0.2015: : 922iter [00:26, 35.82iter/s]
Best score: 0.2015: : 923iter [00:26, 36.67iter/s]
Best score: 0.2015: : 923iter [00:26, 36.67iter/s]
Best score: 0.2015: : 924iter [00:26, 36.67iter/s]
Best score: 0.2015: : 925iter [00:26, 36.67iter/s]
Best score: 0.2015: : 926iter [00:26, 36.67iter/s]
Best score: 0.2015: : 927iter [00:26, 37.43iter/s]
Best score: 0.2015: : 927iter [00:26, 37.43iter/s]
Best score: 0.2015: : 928iter [00:26, 37.43iter/s]
Best score: 0.2015: : 929iter [00:27, 37.43iter/s]
Best score: 0.2015: : 930iter [00:27, 37.43iter/s]
Best score: 0.2015: : 931iter [00:27, 37.69iter/s]
Best score: 0.2015: : 931iter [00:27, 37.69iter/s]
Best score: 0.2015: : 932iter [00:27, 37.69iter/s]
Best score: 0.2015: : 933iter [00:27, 37.69iter/s]
Best score: 0.2015: : 934iter [00:27, 37.69iter/s]
Best score: 0.2015: : 935iter [00:27, 38.28iter/s]
Best score: 0.2015: : 935iter [00:27, 38.28iter/s]
Best score: 0.2015: : 936iter [00:27, 38.28iter/s]
Best score: 0.2015: : 937iter [00:27, 38.28iter/s]
Best score: 0.2015: : 938iter [00:27, 38.28iter/s]
Best score: 0.2015: : 939iter [00:27, 38.28iter/s]
Best score: 0.2015: : 940iter [00:27, 38.66iter/s]
Best score: 0.2015: : 940iter [00:27, 38.66iter/s]
Best score: 0.2015: : 941iter [00:27, 38.66iter/s]
Best score: 0.2015: : 942iter [00:27, 38.66iter/s]
Best score: 0.2015: : 943iter [00:27, 38.66iter/s]
Best score: 0.2015: : 944iter [00:27, 38.66iter/s]
Best score: 0.2015: : 945iter [00:27, 39.23iter/s]
Best score: 0.2015: : 945iter [00:27, 39.23iter/s]
Best score: 0.2015: : 946iter [00:27, 39.23iter/s]
Best score: 0.2015: : 947iter [00:27, 39.23iter/s]
Best score: 0.2015: : 948iter [00:27, 39.23iter/s]
Best score: 0.2015: : 949iter [00:27, 39.36iter/s]
[ITER 950] Best: 0.2014
[PARAMS] Current best parameters:
  fx: 532.0093
  fy: 531.7090
  px: 1021.3525
  py: 745.5977
  k1: -0.03968006
  k2: 0.05679454
  k3: -0.02907008
  k4: 0.00465376
  p1: -0.00869436
  p2: -0.00543184
  p3: -0.00171938
  p4: -0.00184167
  s1: 0.0057
  s2: 0.0017
  s3: 0.0044
  s4: 0.0017
  tilt_x: -0.0144
  tilt_y: -0.0100
  scale: 3.0438
  rotation: -0.1387

Best score: 0.2014: : 949iter [00:27, 39.36iter/s]
Best score: 0.2014: : 950iter [00:27, 39.36iter/s]
Best score: 0.2014: : 951iter [00:27, 39.36iter/s]
Best score: 0.2014: : 952iter [00:27, 39.36iter/s]
Best score: 0.2014: : 953iter [00:27, 32.25iter/s]
Best score: 0.2014: : 953iter [00:27, 32.25iter/s]
Best score: 0.2014: : 954iter [00:27, 32.25iter/s]
Best score: 0.2014: : 955iter [00:27, 32.25iter/s]
Best score: 0.2014: : 956iter [00:27, 32.25iter/s]
Best score: 0.2014: : 957iter [00:27, 34.08iter/s]
Best score: 0.2014: : 957iter [00:27, 34.08iter/s]
Best score: 0.2014: : 958iter [00:27, 34.08iter/s]
Best score: 0.2014: : 959iter [00:27, 34.08iter/s]
Best score: 0.2014: : 960iter [00:27, 34.08iter/s]
Best score: 0.2014: : 961iter [00:27, 35.32iter/s]
Best score: 0.2014: : 961iter [00:27, 35.32iter/s]
Best score: 0.2014: : 962iter [00:27, 35.32iter/s]
Best score: 0.2014: : 963iter [00:27, 35.32iter/s]
Best score: 0.2014: : 964iter [00:27, 35.32iter/s]
Best score: 0.2014: : 965iter [00:27, 36.56iter/s]
Best score: 0.2014: : 965iter [00:28, 36.56iter/s]
Best score: 0.2014: : 966iter [00:28, 36.56iter/s]
Best score: 0.2014: : 967iter [00:28, 36.56iter/s]
Best score: 0.2014: : 968iter [00:28, 36.56iter/s]
Best score: 0.2014: : 969iter [00:28, 37.41iter/s]
Best score: 0.2013: : 969iter [00:28, 37.41iter/s]
Best score: 0.2013: : 970iter [00:28, 37.41iter/s]
Best score: 0.2013: : 971iter [00:28, 37.41iter/s]
Best score: 0.2013: : 972iter [00:28, 37.41iter/s]
Best score: 0.2013: : 973iter [00:28, 37.82iter/s]
Best score: 0.2013: : 973iter [00:28, 37.82iter/s]
Best score: 0.2013: : 974iter [00:28, 37.82iter/s]
Best score: 0.2013: : 975iter [00:28, 37.82iter/s]
Best score: 0.2013: : 976iter [00:28, 37.82iter/s]
Best score: 0.2013: : 977iter [00:28, 38.43iter/s]
Best score: 0.2013: : 977iter [00:28, 38.43iter/s]
Best score: 0.2013: : 978iter [00:28, 38.43iter/s]
Best score: 0.2013: : 979iter [00:28, 38.43iter/s]
Best score: 0.2013: : 980iter [00:28, 38.43iter/s]
Best score: 0.2013: : 981iter [00:28, 38.70iter/s]
Best score: 0.2013: : 981iter [00:28, 38.70iter/s]
Best score: 0.2013: : 982iter [00:28, 38.70iter/s]
Best score: 0.2013: : 983iter [00:28, 38.70iter/s]
Best score: 0.2013: : 984iter [00:28, 38.70iter/s]
Best score: 0.2013: : 985iter [00:28, 38.70iter/s]
Best score: 0.2013: : 986iter [00:28, 39.30iter/s]
Best score: 0.2013: : 986iter [00:28, 39.30iter/s]
Best score: 0.2013: : 987iter [00:28, 39.30iter/s]
Best score: 0.2013: : 988iter [00:28, 39.30iter/s]
Best score: 0.2013: : 989iter [00:28, 39.30iter/s]
Best score: 0.2013: : 990iter [00:28, 39.26iter/s]
Best score: 0.2013: : 990iter [00:28, 39.26iter/s]
Best score: 0.2013: : 991iter [00:28, 39.26iter/s]
Best score: 0.2013: : 992iter [00:28, 39.26iter/s]
Best score: 0.2013: : 993iter [00:28, 39.26iter/s]
Best score: 0.2013: : 994iter [00:28, 39.47iter/s]
Best score: 0.2013: : 994iter [00:28, 39.47iter/s]
Best score: 0.2013: : 995iter [00:28, 39.47iter/s]
Best score: 0.2013: : 996iter [00:28, 39.47iter/s]
Best score: 0.2013: : 997iter [00:28, 39.47iter/s]
Best score: 0.2013: : 998iter [00:28, 39.47iter/s]
Best score: 0.2013: : 999iter [00:28, 39.84iter/s]
[ITER 1000] Best: 0.2013
[PARAMS] Current best parameters:
  fx: 531.7950
  fy: 531.4915
  px: 1021.1608
  py: 745.5238
  k1: -0.03919530
  k2: 0.05692337
  k3: -0.02942723
  k4: 0.00476669
  p1: -0.00879793
  p2: -0.00558385
  p3: -0.00175980
  p4: -0.00178941
  s1: 0.0058
  s2: 0.0017
  s3: 0.0044
  s4: 0.0017
  tilt_x: -0.0146
  tilt_y: -0.0094
  scale: 3.0428
  rotation: -0.1399

Best score: 0.2013: : 999iter [00:28, 39.84iter/s]
Best score: 0.2013: : 1000iter [00:28, 39.84iter/s]
Best score: 0.2013: : 1001iter [00:28, 39.84iter/s]
Best score: 0.2013: : 1002iter [00:29, 39.84iter/s]
Best score: 0.2013: : 1003iter [00:29, 32.46iter/s]
Best score: 0.2013: : 1003iter [00:29, 32.46iter/s]
Best score: 0.2013: : 1004iter [00:29, 32.46iter/s]
Best score: 0.2013: : 1005iter [00:29, 32.46iter/s]
Best score: 0.2013: : 1006iter [00:29, 32.46iter/s]
Best score: 0.2013: : 1007iter [00:29, 34.27iter/s]
Best score: 0.2013: : 1007iter [00:29, 34.27iter/s]
Best score: 0.2013: : 1008iter [00:29, 34.27iter/s]
Best score: 0.2011: : 1009iter [00:29, 34.27iter/s]
Best score: 0.2011: : 1010iter [00:29, 34.27iter/s]
Best score: 0.2011: : 1011iter [00:29, 35.43iter/s]
Best score: 0.2011: : 1011iter [00:29, 35.43iter/s]
Best score: 0.2011: : 1012iter [00:29, 35.43iter/s]
Best score: 0.2011: : 1013iter [00:29, 35.43iter/s]
Best score: 0.2011: : 1014iter [00:29, 35.43iter/s]
Best score: 0.2011: : 1015iter [00:29, 36.65iter/s]
Best score: 0.2011: : 1015iter [00:29, 36.65iter/s]
Best score: 0.2011: : 1016iter [00:29, 36.65iter/s]
Best score: 0.2011: : 1017iter [00:29, 36.65iter/s]
Best score: 0.2011: : 1018iter [00:29, 36.65iter/s]
Best score: 0.2011: : 1019iter [00:29, 37.57iter/s]
Best score: 0.2011: : 1019iter [00:29, 37.57iter/s]
Best score: 0.2011: : 1020iter [00:29, 37.57iter/s]
Best score: 0.2011: : 1021iter [00:29, 37.57iter/s]
Best score: 0.2011: : 1022iter [00:29, 37.57iter/s]
Best score: 0.2011: : 1023iter [00:29, 38.10iter/s]
Best score: 0.2011: : 1023iter [00:29, 38.10iter/s]
Best score: 0.2011: : 1024iter [00:29, 38.10iter/s]
Best score: 0.2011: : 1025iter [00:29, 38.10iter/s]
Best score: 0.2011: : 1026iter [00:29, 38.10iter/s]
Best score: 0.2011: : 1027iter [00:29, 38.10iter/s]
Best score: 0.2011: : 1028iter [00:29, 38.78iter/s]
Best score: 0.2011: : 1028iter [00:29, 38.78iter/s]
Best score: 0.2011: : 1029iter [00:29, 38.78iter/s]
Best score: 0.2011: : 1030iter [00:29, 38.78iter/s]
Best score: 0.2011: : 1031iter [00:29, 38.78iter/s]
Best score: 0.2011: : 1032iter [00:29, 38.84iter/s]
Best score: 0.2011: : 1032iter [00:29, 38.84iter/s]
Best score: 0.2011: : 1033iter [00:29, 38.84iter/s]
Best score: 0.2011: : 1034iter [00:29, 38.84iter/s]
Best score: 0.2011: : 1035iter [00:29, 38.84iter/s]
Best score: 0.2011: : 1036iter [00:29, 39.14iter/s]
Best score: 0.2011: : 1036iter [00:29, 39.14iter/s]
Best score: 0.2011: : 1037iter [00:29, 39.14iter/s]
Best score: 0.2011: : 1038iter [00:29, 39.14iter/s]
Best score: 0.2009: : 1039iter [00:29, 39.14iter/s]
Best score: 0.2009: : 1040iter [00:29, 39.09iter/s]
Best score: 0.2009: : 1040iter [00:29, 39.09iter/s]
Best score: 0.2009: : 1041iter [00:30, 39.09iter/s]
Best score: 0.2009: : 1042iter [00:30, 39.09iter/s]
Best score: 0.2009: : 1043iter [00:30, 39.09iter/s]
Best score: 0.2009: : 1044iter [00:30, 39.35iter/s]
Best score: 0.2009: : 1044iter [00:30, 39.35iter/s]
Best score: 0.2009: : 1045iter [00:30, 39.35iter/s]
Best score: 0.2009: : 1046iter [00:30, 39.35iter/s]
Best score: 0.2009: : 1047iter [00:30, 39.35iter/s]
Best score: 0.2009: : 1048iter [00:30, 39.53iter/s]
Best score: 0.2009: : 1048iter [00:30, 39.53iter/s]
[ITER 1050] Best: 0.2009
[PARAMS] Current best parameters:
  fx: 531.7488
  fy: 531.4460
  px: 1021.1611
  py: 745.4901
  k1: -0.03928426
  k2: 0.05718539
  k3: -0.02960359
  k4: 0.00480570
  p1: -0.00875731
  p2: -0.00553147
  p3: -0.00196830
  p4: -0.00174892
  s1: 0.0058
  s2: 0.0017
  s3: 0.0045
  s4: 0.0016
  tilt_x: -0.0147
  tilt_y: -0.0093
  scale: 3.0428
  rotation: -0.1392

Best score: 0.2009: : 1049iter [00:30, 39.53iter/s]
Best score: 0.2009: : 1050iter [00:30, 39.53iter/s]
Best score: 0.2009: : 1051iter [00:30, 39.53iter/s]
Best score: 0.2009: : 1052iter [00:30, 32.03iter/s]
Best score: 0.2009: : 1052iter [00:30, 32.03iter/s]
Best score: 0.2009: : 1053iter [00:30, 32.03iter/s]
Best score: 0.2009: : 1054iter [00:30, 32.03iter/s]
Best score: 0.2009: : 1055iter [00:30, 32.03iter/s]
Best score: 0.2009: : 1056iter [00:30, 34.02iter/s]
Best score: 0.2009: : 1056iter [00:30, 34.02iter/s]
Best score: 0.2009: : 1057iter [00:30, 34.02iter/s]
Best score: 0.2009: : 1058iter [00:30, 34.02iter/s]
Best score: 0.2008: : 1059iter [00:30, 34.02iter/s]
Best score: 0.2008: : 1060iter [00:30, 35.36iter/s]
Best score: 0.2008: : 1060iter [00:30, 35.36iter/s]
Best score: 0.2008: : 1061iter [00:30, 35.36iter/s]
Best score: 0.2008: : 1062iter [00:30, 35.36iter/s]
Best score: 0.2008: : 1063iter [00:30, 35.36iter/s]
Best score: 0.2008: : 1064iter [00:30, 35.36iter/s]
Best score: 0.2008: : 1065iter [00:30, 36.98iter/s]
Best score: 0.2008: : 1065iter [00:30, 36.98iter/s]
Best score: 0.2008: : 1066iter [00:30, 36.98iter/s]
Best score: 0.2008: : 1067iter [00:30, 36.98iter/s]
Best score: 0.2008: : 1068iter [00:30, 36.98iter/s]
Best score: 0.2008: : 1069iter [00:30, 37.74iter/s]
Best score: 0.2006: : 1069iter [00:30, 37.74iter/s]
Best score: 0.2006: : 1070iter [00:30, 37.74iter/s]
Best score: 0.2006: : 1071iter [00:30, 37.74iter/s]
Best score: 0.2006: : 1072iter [00:30, 37.74iter/s]
Best score: 0.2006: : 1073iter [00:30, 38.05iter/s]
Best score: 0.2006: : 1073iter [00:30, 38.05iter/s]
Best score: 0.2006: : 1074iter [00:30, 38.05iter/s]
Best score: 0.2006: : 1075iter [00:30, 38.05iter/s]
Best score: 0.2006: : 1076iter [00:30, 38.05iter/s]
Best score: 0.2006: : 1077iter [00:30, 38.60iter/s]
Best score: 0.2006: : 1077iter [00:30, 38.60iter/s]
Best score: 0.2006: : 1078iter [00:31, 38.60iter/s]
Best score: 0.2006: : 1079iter [00:31, 38.60iter/s]
Best score: 0.2006: : 1080iter [00:31, 38.60iter/s]
Best score: 0.2006: : 1081iter [00:31, 38.62iter/s]
Best score: 0.2006: : 1081iter [00:31, 38.62iter/s]
Best score: 0.2006: : 1082iter [00:31, 38.62iter/s]
Best score: 0.2006: : 1083iter [00:31, 38.62iter/s]
Best score: 0.2006: : 1084iter [00:31, 38.62iter/s]
Best score: 0.2006: : 1085iter [00:31, 38.98iter/s]
Best score: 0.2006: : 1085iter [00:31, 38.98iter/s]
Best score: 0.2006: : 1086iter [00:31, 38.98iter/s]
Best score: 0.2006: : 1087iter [00:31, 38.98iter/s]
Best score: 0.2006: : 1088iter [00:31, 38.98iter/s]
Best score: 0.2006: : 1089iter [00:31, 38.98iter/s]
Best score: 0.2006: : 1090iter [00:31, 39.11iter/s]
Best score: 0.2006: : 1090iter [00:31, 39.11iter/s]
Best score: 0.2006: : 1091iter [00:31, 39.11iter/s]
Best score: 0.2006: : 1092iter [00:31, 39.11iter/s]
Best score: 0.2006: : 1093iter [00:31, 39.11iter/s]
Best score: 0.2006: : 1094iter [00:31, 39.11iter/s]
Best score: 0.2006: : 1095iter [00:31, 39.53iter/s]
Best score: 0.2006: : 1095iter [00:31, 39.53iter/s]
Best score: 0.2006: : 1096iter [00:31, 39.53iter/s]
Best score: 0.2006: : 1097iter [00:31, 39.53iter/s]
Best score: 0.2006: : 1098iter [00:31, 39.53iter/s]
[ITER 1100] Best: 0.2006
[PARAMS] Current best parameters:
  fx: 531.6419
  fy: 531.3664
  px: 1021.4133
  py: 745.3841
  k1: -0.04009114
  k2: 0.05826430
  k3: -0.03002565
  k4: 0.00485401
  p1: -0.00787366
  p2: -0.00522296
  p3: -0.00383187
  p4: -0.00149103
  s1: 0.0077
  s2: 0.0013
  s3: 0.0052
  s4: 0.0014
  tilt_x: -0.0170
  tilt_y: -0.0100
  scale: 3.0442
  rotation: -0.1370

Best score: 0.2006: : 1099iter [00:31, 39.53iter/s]
Best score: 0.2006: : 1100iter [00:31, 39.61iter/s]
[📈] Running L-BFGS-B polish after 1100 DE iterations...

Best score: 0.2006: : 1100iter [00:31, 34.78iter/s]
  Polish improved score: 0.200582 -> 0.200463

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 26,193 → 26,193 within 3500mm ring
  Ring polish did not improve score: 0.200463
  Success: True
  Tangential distortion: p1=-0.007877, p2=-0.005229, p3=-0.003832, p4=-0.001489
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2005
[✅] Parameters copied to: /working/MDC027_W031_2026-05-06/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 531.6418511062772
    fy: 531.3663837802724
    px: 1021.4133401728344
    py: 745.3840735084727
    k1: -0.04008898607341916
    k2: 0.0582651002512887
    k3: -0.030025434064333896
    k4: 0.004853508670218954
    p1: -0.007876649778813055
    p2: -0.005229458254307048
    p3: -0.0038319931930773364
    p4: -0.0014886353300588998
    scale: 3.044232564977081
    rotation: -0.13703831133373945
    mmpp: 1.2796943230162015
    nmpp: 1279694.3230162014

[🔄] Converting 833 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 500:   0%|          | 0/380 [00:00<?, ?frame/s]

   🔄 500:   5%|▍         | 18/380 [00:00<00:02, 174.09frame/s]

   🔄 500:   9%|▉         | 36/380 [00:00<00:01, 175.57frame/s]

   🔄 500:  14%|█▍        | 55/380 [00:00<00:01, 178.89frame/s]

   🔄 500:  21%|██        | 78/380 [00:00<00:01, 196.35frame/s]

   🔄 500:  29%|██▊       | 109/380 [00:00<00:01, 233.52frame/s]

   🔄 500:  35%|███▌      | 133/380 [00:00<00:01, 212.82frame/s]

   🔄 500:  45%|████▍     | 170/380 [00:00<00:00, 256.34frame/s]

   🔄 500:  52%|█████▏    | 197/380 [00:00<00:00, 228.41frame/s]

   🔄 500:  58%|█████▊    | 221/380 [00:01<00:00, 212.09frame/s]

   🔄 500:  64%|██████▍   | 243/380 [00:01<00:00, 201.54frame/s]

   🔄 500:  69%|██████▉   | 264/380 [00:01<00:00, 194.51frame/s]

   🔄 500:  75%|███████▍  | 284/380 [00:01<00:00, 190.10frame/s]

   🔄 500:  80%|████████  | 304/380 [00:01<00:00, 187.01frame/s]

   🔄 500:  85%|████████▌ | 323/380 [00:01<00:00, 184.96frame/s]

   🔄 500:  90%|█████████ | 342/380 [00:01<00:00, 183.58frame/s]

   🔄 500:  95%|█████████▌| 361/380 [00:01<00:00, 182.02frame/s]

   🔄 500: 100%|██████████| 380/380 [00:01<00:00, 180.88frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:03<00:03,  3.51s/rig]

   🔄 1000:   0%|          | 0/453 [00:00<?, ?frame/s]

   🔄 1000:   4%|▍         | 18/453 [00:00<00:02, 176.04frame/s]

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   🔄 1000:  12%|█▏        | 54/453 [00:00<00:02, 175.96frame/s]

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   🔄 1000:  28%|██▊       | 126/453 [00:00<00:01, 176.11frame/s]

   🔄 1000:  32%|███▏      | 144/453 [00:00<00:01, 176.32frame/s]

   🔄 1000:  36%|███▌      | 162/453 [00:00<00:01, 177.26frame/s]

   🔄 1000:  40%|███▉      | 180/453 [00:01<00:01, 178.01frame/s]

   🔄 1000:  44%|████▎     | 198/453 [00:01<00:01, 177.94frame/s]

   🔄 1000:  48%|████▊     | 216/453 [00:01<00:01, 177.69frame/s]

   🔄 1000:  52%|█████▏    | 234/453 [00:01<00:01, 177.70frame/s]

   🔄 1000:  56%|█████▌    | 252/453 [00:01<00:01, 177.51frame/s]

   🔄 1000:  60%|█████▉    | 270/453 [00:01<00:01, 177.49frame/s]

   🔄 1000:  64%|██████▎   | 288/453 [00:01<00:00, 176.70frame/s]

   🔄 1000:  68%|██████▊   | 306/453 [00:01<00:00, 175.88frame/s]

   🔄 1000:  72%|███████▏  | 324/453 [00:01<00:00, 175.21frame/s]

   🔄 1000:  75%|███████▌  | 342/453 [00:01<00:00, 174.84frame/s]

   🔄 1000:  79%|███████▉  | 360/453 [00:02<00:00, 174.57frame/s]

   🔄 1000:  83%|████████▎ | 378/453 [00:02<00:00, 174.50frame/s]

   🔄 1000:  87%|████████▋ | 396/453 [00:02<00:00, 174.27frame/s]

   🔄 1000:  91%|█████████▏| 414/453 [00:02<00:00, 173.73frame/s]

   🔄 1000:  95%|█████████▌| 432/453 [00:02<00:00, 173.95frame/s]

   🔄 1000:  99%|█████████▉| 450/453 [00:02<00:00, 174.40frame/s]

                                                                 
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.13s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:08<00:00,  4.04s/rig]
[✅] Saved PTDs: /working/MDC027_W031_2026-05-06/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 500:  19%|█▉        | 19/100 [00:00<00:00, 184.79frame/s]

   🔄 500:  48%|████▊     | 48/100 [00:00<00:00, 241.86frame/s]

   🔄 500:  73%|███████▎  | 73/100 [00:00<00:00, 207.22frame/s]

   🔄 500:  95%|█████████▌| 95/100 [00:00<00:00, 196.02frame/s]

                                                               
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.12rig/s]

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 171.82frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 172.94frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 120.81frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 138.05frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 149.50frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.03rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.04rig/s]
[✅] Saved PTDs: /working/MDC027_W031_2026-05-06/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 82968/3080448 [00:00<00:03, 829672.88px/s]
📤 Writing LUT:   5%|▌         | 168535/3080448 [00:00<00:03, 844964.54px/s]
📤 Writing LUT:   8%|▊         | 258145/3080448 [00:00<00:03, 868311.55px/s]
📤 Writing LUT:  11%|█         | 346438/3080448 [00:00<00:03, 874078.13px/s]
📤 Writing LUT:  14%|█▍        | 434234/3080448 [00:00<00:03, 875476.75px/s]
📤 Writing LUT:  17%|█▋        | 523480/3080448 [00:00<00:02, 881249.18px/s]
📤 Writing LUT:  20%|█▉        | 611605/3080448 [00:00<00:02, 875514.04px/s]
📤 Writing LUT:  23%|██▎       | 699164/3080448 [00:00<00:02, 872447.80px/s]
📤 Writing LUT:  26%|██▌       | 786415/3080448 [00:00<00:02, 870724.77px/s]
📤 Writing LUT:  28%|██▊       | 873492/3080448 [00:01<00:02, 869767.04px/s]
📤 Writing LUT:  31%|███▏      | 962987/3080448 [00:01<00:02, 877437.70px/s]
📤 Writing LUT:  34%|███▍      | 1053107/3080448 [00:01<00:02, 884642.11px/s]
📤 Writing LUT:  37%|███▋      | 1144335/3080448 [00:01<00:02, 892994.46px/s]
📤 Writing LUT:  40%|████      | 1233641/3080448 [00:01<00:02, 887534.91px/s]
📤 Writing LUT:  43%|████▎     | 1322835/3080448 [00:01<00:01, 888853.38px/s]
📤 Writing LUT:  46%|████▌     | 1414568/3080448 [00:01<00:01, 897389.88px/s]
📤 Writing LUT:  49%|████▉     | 1505409/3080448 [00:01<00:01, 900694.57px/s]
📤 Writing LUT:  52%|█████▏    | 1595486/3080448 [00:01<00:01, 895564.58px/s]
📤 Writing LUT:  55%|█████▍    | 1685054/3080448 [00:01<00:01, 890095.55px/s]
📤 Writing LUT:  58%|█████▊    | 1774077/3080448 [00:02<00:01, 884950.91px/s]
📤 Writing LUT:  60%|██████    | 1862584/3080448 [00:02<00:01, 881001.73px/s]
📤 Writing LUT:  63%|██████▎   | 1950693/3080448 [00:02<00:01, 878816.83px/s]
📤 Writing LUT:  66%|██████▌   | 2038580/3080448 [00:02<00:01, 876795.16px/s]
📤 Writing LUT:  69%|██████▉   | 2126263/3080448 [00:02<00:01, 876539.48px/s]
📤 Writing LUT:  72%|███████▏  | 2216001/3080448 [00:02<00:00, 882756.17px/s]
📤 Writing LUT:  75%|███████▍  | 2305458/3080448 [00:02<00:00, 886284.97px/s]
📤 Writing LUT:  78%|███████▊  | 2394337/3080448 [00:02<00:00, 887034.06px/s]
📤 Writing LUT:  81%|████████  | 2483207/3080448 [00:02<00:00, 887531.25px/s]
📤 Writing LUT:  83%|████████▎ | 2571963/3080448 [00:02<00:00, 886795.82px/s]
📤 Writing LUT:  86%|████████▋ | 2662028/3080448 [00:03<00:00, 890944.74px/s]
📤 Writing LUT:  89%|████████▉ | 2751724/3080448 [00:03<00:00, 892747.02px/s]
📤 Writing LUT:  92%|█████████▏| 2843455/3080448 [00:03<00:00, 900109.39px/s]
📤 Writing LUT:  95%|█████████▌| 2935067/3080448 [00:03<00:00, 904908.38px/s]
📤 Writing LUT:  98%|█████████▊| 3025560/3080448 [00:03<00:00, 904823.25px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 886132.91px/s]
[✅] LUT written to: /working/MDC027_W031_2026-05-06/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC027_W031_2026-05-06' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC027_W031_2026-05-06/calibration/MDC027_W031_2026-05-06_unknown.lut

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

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

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

   🔥 500:   0%|          | 0/380 [00:00<?, ?frame/s]

   🔥 500:   2%|▏         | 8/380 [00:00<00:05, 72.76frame/s]

   🔥 500:   4%|▍         | 16/380 [00:00<00:04, 76.62frame/s]

   🔥 500:   6%|▋         | 24/380 [00:00<00:04, 78.05frame/s]

   🔥 500:   9%|▊         | 33/380 [00:00<00:04, 78.91frame/s]

   🔥 500:  11%|█         | 42/380 [00:00<00:04, 79.77frame/s]

   🔥 500:  13%|█▎        | 50/380 [00:00<00:04, 79.36frame/s]

   🔥 500:  16%|█▌        | 59/380 [00:00<00:04, 80.10frame/s]

   🔥 500:  18%|█▊        | 68/380 [00:00<00:03, 81.23frame/s]

   🔥 500:  21%|██        | 79/380 [00:00<00:03, 89.62frame/s]

   🔥 500:  25%|██▌       | 95/380 [00:01<00:02, 108.67frame/s]

   🔥 500:  28%|██▊       | 107/380 [00:01<00:02, 109.92frame/s]

   🔥 500:  31%|███▏      | 119/380 [00:01<00:02, 102.94frame/s]

   🔥 500:  34%|███▍      | 130/380 [00:01<00:02, 97.49frame/s] 

   🔥 500:  37%|███▋      | 141/380 [00:01<00:02, 100.06frame/s]

   🔥 500:  42%|████▏     | 161/380 [00:01<00:01, 127.43frame/s]

   🔥 500:  46%|████▌     | 175/380 [00:01<00:01, 109.81frame/s]

   🔥 500:  49%|████▉     | 187/380 [00:01<00:01, 101.83frame/s]

   🔥 500:  52%|█████▏    | 198/380 [00:02<00:01, 95.07frame/s] 

   🔥 500:  55%|█████▍    | 208/380 [00:02<00:01, 91.02frame/s]

   🔥 500:  57%|█████▋    | 218/380 [00:02<00:01, 87.84frame/s]

   🔥 500:  60%|█████▉    | 227/380 [00:02<00:01, 85.59frame/s]

   🔥 500:  62%|██████▏   | 236/380 [00:02<00:01, 84.03frame/s]

   🔥 500:  64%|██████▍   | 245/380 [00:02<00:01, 82.06frame/s]

   🔥 500:  67%|██████▋   | 254/380 [00:02<00:01, 80.94frame/s]

   🔥 500:  69%|██████▉   | 263/380 [00:02<00:01, 79.77frame/s]

   🔥 500:  71%|███████▏  | 271/380 [00:03<00:01, 78.83frame/s]

   🔥 500:  73%|███████▎  | 279/380 [00:03<00:01, 76.56frame/s]

   🔥 500:  76%|███████▌  | 287/380 [00:03<00:01, 76.96frame/s]

   🔥 500:  78%|███████▊  | 295/380 [00:03<00:01, 76.49frame/s]

   🔥 500:  80%|███████▉  | 303/380 [00:03<00:01, 76.34frame/s]

   🔥 500:  82%|████████▏ | 311/380 [00:03<00:00, 76.67frame/s]

   🔥 500:  84%|████████▍ | 320/380 [00:03<00:00, 77.87frame/s]

   🔥 500:  87%|████████▋ | 329/380 [00:03<00:00, 78.60frame/s]

   🔥 500:  89%|████████▉ | 338/380 [00:03<00:00, 79.03frame/s]

   🔥 500:  91%|█████████▏| 347/380 [00:03<00:00, 79.53frame/s]

   🔥 500:  94%|█████████▎| 356/380 [00:04<00:00, 79.78frame/s]

   🔥 500:  96%|█████████▌| 365/380 [00:04<00:00, 79.85frame/s]

   🔥 500:  98%|█████████▊| 374/380 [00:04<00:00, 80.26frame/s]

                                                               [✅] 500: /working/MDC027_W031_2026-05-06/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC027_W031_2026-05-06/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.44s/rig]

   🔥 1000:   0%|          | 0/453 [00:00<?, ?frame/s]

   🔥 1000:   2%|▏         | 9/453 [00:00<00:05, 82.70frame/s]

   🔥 1000:   4%|▍         | 18/453 [00:00<00:05, 83.97frame/s]

   🔥 1000:   6%|▌         | 27/453 [00:00<00:05, 84.91frame/s]

   🔥 1000:   8%|▊         | 36/453 [00:00<00:04, 84.31frame/s]

   🔥 1000:  10%|▉         | 45/453 [00:00<00:04, 84.44frame/s]

   🔥 1000:  12%|█▏        | 54/453 [00:00<00:04, 84.38frame/s]

   🔥 1000:  14%|█▍        | 63/453 [00:00<00:04, 85.08frame/s]

   🔥 1000:  16%|█▌        | 72/453 [00:00<00:04, 85.95frame/s]

   🔥 1000:  18%|█▊        | 81/453 [00:00<00:04, 86.59frame/s]

   🔥 1000:  20%|█▉        | 90/453 [00:01<00:04, 87.15frame/s]

   🔥 1000:  22%|██▏       | 100/453 [00:01<00:04, 88.06frame/s]

   🔥 1000:  24%|██▍       | 110/453 [00:01<00:03, 88.64frame/s]

   🔥 1000:  26%|██▋       | 119/453 [00:01<00:03, 88.28frame/s]

   🔥 1000:  28%|██▊       | 128/453 [00:01<00:03, 87.07frame/s]

   🔥 1000:  30%|███       | 137/453 [00:01<00:03, 85.32frame/s]

   🔥 1000:  32%|███▏      | 146/453 [00:01<00:03, 84.33frame/s]

   🔥 1000:  34%|███▍      | 155/453 [00:01<00:03, 83.42frame/s]

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   🔥 1000:  38%|███▊      | 173/453 [00:02<00:03, 83.79frame/s]

   🔥 1000:  40%|████      | 182/453 [00:02<00:03, 84.66frame/s]

   🔥 1000:  42%|████▏     | 191/453 [00:02<00:03, 85.11frame/s]

   🔥 1000:  44%|████▍     | 200/453 [00:02<00:02, 85.74frame/s]

   🔥 1000:  46%|████▌     | 209/453 [00:02<00:02, 86.81frame/s]

   🔥 1000:  48%|████▊     | 218/453 [00:02<00:02, 87.52frame/s]

   🔥 1000:  50%|█████     | 227/453 [00:02<00:02, 86.48frame/s]

   🔥 1000:  52%|█████▏    | 236/453 [00:02<00:02, 84.12frame/s]

   🔥 1000:  54%|█████▍    | 245/453 [00:02<00:02, 82.21frame/s]

   🔥 1000:  56%|█████▌    | 254/453 [00:02<00:02, 81.82frame/s]

   🔥 1000:  58%|█████▊    | 263/453 [00:03<00:02, 82.01frame/s]

   🔥 1000:  60%|██████    | 272/453 [00:03<00:02, 82.39frame/s]

   🔥 1000:  62%|██████▏   | 281/453 [00:03<00:02, 82.52frame/s]

   🔥 1000:  64%|██████▍   | 290/453 [00:03<00:01, 82.45frame/s]

   🔥 1000:  66%|██████▌   | 299/453 [00:03<00:01, 82.59frame/s]

   🔥 1000:  68%|██████▊   | 308/453 [00:03<00:01, 82.43frame/s]

   🔥 1000:  70%|██████▉   | 317/453 [00:03<00:01, 82.71frame/s]

   🔥 1000:  72%|███████▏  | 326/453 [00:03<00:01, 83.00frame/s]

   🔥 1000:  74%|███████▍  | 335/453 [00:03<00:01, 83.21frame/s]

   🔥 1000:  76%|███████▌  | 344/453 [00:04<00:01, 83.22frame/s]

   🔥 1000:  78%|███████▊  | 353/453 [00:04<00:01, 83.57frame/s]

   🔥 1000:  80%|███████▉  | 362/453 [00:04<00:01, 82.69frame/s]

   🔥 1000:  82%|████████▏ | 371/453 [00:04<00:00, 82.70frame/s]

   🔥 1000:  84%|████████▍ | 380/453 [00:04<00:00, 82.34frame/s]

   🔥 1000:  86%|████████▌ | 389/453 [00:04<00:00, 83.09frame/s]

   🔥 1000:  88%|████████▊ | 398/453 [00:04<00:00, 84.59frame/s]

   🔥 1000:  90%|████████▉ | 407/453 [00:04<00:00, 84.66frame/s]

   🔥 1000:  92%|█████████▏| 416/453 [00:04<00:00, 84.62frame/s]

   🔥 1000:  94%|█████████▍| 425/453 [00:05<00:00, 84.32frame/s]

   🔥 1000:  96%|█████████▌| 434/453 [00:05<00:00, 84.75frame/s]

   🔥 1000:  98%|█████████▊| 443/453 [00:05<00:00, 84.96frame/s]

   🔥 1000: 100%|█████████▉| 452/453 [00:05<00:00, 85.26frame/s]

                                                                [✅] 1000: /working/MDC027_W031_2026-05-06/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC027_W031_2026-05-06/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  7.16s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:13<00:00,  6.90s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC027_W031_2026-05-06/calibration/debug/5_heatmap
[🏁] Finished processing MDC027_W031_2026-05-06


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