2026-05-17T22:40:36 - INFO: Slick 0.0.8a11

🟢 Starting Calibration Pipeline

=== Processing MDC001_W099(6)_2026-03-02 ===
2026-05-17T22:40:36 - INFO: Camera ID: 12151194
2026-05-17T22:40:36 - INFO: Wand ID: 
2026-05-17T22:40:36 - INFO: Recorded At: 2026-03-02T16:41:45Z

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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 4 las0.zip files to process
Processing Rigs:   0%|          | 0/4 [00:00<?, ?it/s][INFO] Filtered 562 stationary frames, 161 remain

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→ 2000L Phase 2: corners:  98%|█████████▊| 157/161 [00:01<00:00, 145.51it/s][A
                                                                            [A[PASS2] Frame 0000898: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000899: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000901: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000902: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000911: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000913: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000914: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000916: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001094: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001096: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001097: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001099: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000901: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000914: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001097: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 6 frames fixed, 10 untrusted, 161 total

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                                                                            [A
[TIMING] 2000L — 161 frames
  Phase 1  radial detect   (parallel)  :   2.67s  (16.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.38s  (8.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.31s
  Phase 3  finalize        (sequential):   3.02s  (18.8 ms/frame)
    decode + undistort                 :   1.62s  (10.0 ms/frame)
    corner apply + refinement          :   0.63s  (3.9 ms/frame)
    segment                            :   0.63s  (3.9 ms/frame)
    smooth                             :   0.09s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   7.39s

Processing Rigs:  25%|██▌       | 1/4 [00:10<00:30, 10.02s/it][INFO] Filtered 1091 stationary frames, 288 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 0 untrusted, 288 total

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                                                                           [A
[TIMING] 1500 — 288 frames
  Phase 1  radial detect   (parallel)  :   4.39s  (15.3 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.31s  (8.0 ms/frame)
  Phase 2b corner fix      (sequential):   0.06s
  Phase 3  finalize        (sequential):   5.27s  (18.3 ms/frame)
    decode + undistort                 :   2.78s  (9.7 ms/frame)
    corner apply + refinement          :   1.10s  (3.8 ms/frame)
    segment                            :   1.14s  (4.0 ms/frame)
    smooth                             :   0.16s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  12.03s

Processing Rigs:  50%|█████     | 2/4 [00:26<00:27, 13.68s/it][INFO] Filtered 910 stationary frames, 195 remain

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                                                                           [A[PASS2] Frame 0000225: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000226: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000225: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 1 untrusted, 195 total

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                                                                           [A
[TIMING] 1000 — 195 frames
  Phase 1  radial detect   (parallel)  :   3.33s  (17.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.87s  (9.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.16s
  Phase 3  finalize        (sequential):   3.41s  (17.5 ms/frame)
    decode + undistort                 :   1.84s  (9.4 ms/frame)
    corner apply + refinement          :   0.65s  (3.4 ms/frame)
    segment                            :   0.75s  (3.8 ms/frame)
    smooth                             :   0.10s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.77s

Processing Rigs:  75%|███████▌  | 3/4 [00:38<00:12, 12.95s/it][INFO] Filtered 648 stationary frames, 170 remain

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                                                                            [A[PASS2] Frame 0000449: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000450: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000453: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000459: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000461: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000464: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000645: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000647: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000648: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000650: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000839: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000840: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000842: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000843: fixed corners {1, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000845: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000846: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000447: fixed corners {1, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000452: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000462: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000648: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000842: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000843: fixed corners {0, 1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000845: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000846: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000446: fixed corners {0} (was_fixed=False, trusted=False)
[PASS2] Frame 0000842: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000843: fixed corners {0, 1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000845: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000846: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Summary: 8 frames fixed, 17 untrusted, 170 total

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                                                                            [A
[TIMING] 2000R — 170 frames
  Phase 1  radial detect   (parallel)  :   3.10s  (18.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.75s  (10.3 ms/frame)
  Phase 2b corner fix      (sequential):   0.52s
  Phase 3  finalize        (sequential):   3.15s  (18.5 ms/frame)
    decode + undistort                 :   1.60s  (9.4 ms/frame)
    corner apply + refinement          :   0.74s  (4.3 ms/frame)
    segment                            :   0.67s  (3.9 ms/frame)
    smooth                             :   0.09s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.53s

Processing Rigs: 100%|██████████| 4/4 [00:49<00:00, 12.36s/it]Processing Rigs: 100%|██████████| 4/4 [00:49<00:00, 12.45s/it]
[💾] Saved RigFrameDataset to /working/MDC001_W099(6)_2026-03-02/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 814 frames from 4 rigs to /working/MDC001_W099(6)_2026-03-02/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC001_W099(6)_2026-03-02/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 2000L: 100 frames selected from 161
[📋] 1500: 100 frames selected from 288
[📋] 1000: 100 frames selected from 195
[📋] 2000R: 100 frames selected from 170
[💾] Saved RigFrameDataset to /working/MDC001_W099(6)_2026-03-02/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC001_W099(6)_2026-03-02/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
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from bright frames:  42%|████▎     | 34/80 [01:47<02:26,  3.19s/it]Computing max image from bright frames:  44%|████▍     | 35/80 [01:50<02:21,  3.15s/it]Computing max image from bright frames:  45%|████▌     | 36/80 [01:54<02:17,  3.13s/it]Computing max image from bright frames:  46%|████▋     | 37/80 [01:57<02:13,  3.11s/it]Computing max image from bright frames:  48%|████▊     | 38/80 [02:00<02:09,  3.10s/it]Computing max image from bright frames:  49%|████▉     | 39/80 [02:03<02:06,  3.09s/it]Computing max image from bright frames:  50%|█████     | 40/80 [02:04<01:43,  2.59s/it]Computing max image from bright frames:  51%|█████▏    | 41/80 [02:07<01:46,  2.73s/it]Computing max image from bright frames:  52%|█████▎    | 42/80 [02:10<01:47,  2.83s/it]Computing max image from bright frames:  54%|█████▍    | 43/80 [02:13<01:47,  2.90s/it]Computing max image from bright frames:  55%|█████▌    | 44/80 [02:16<01:46,  2.95s/it]Computing max image from bright frames:  56%|█████▋    | 45/80 [02:19<01:44,  2.98s/it]Computing max image from bright frames:  57%|█████▊    | 46/80 [02:22<01:41,  3.00s/it]Computing max image from bright frames:  59%|█████▉    | 47/80 [02:26<01:39,  3.02s/it]Computing max image from bright frames:  60%|██████    | 48/80 [02:29<01:36,  3.02s/it]Computing max image from bright frames:  61%|██████▏   | 49/80 [02:32<01:34,  3.05s/it]Computing max image from bright frames:  62%|██████▎   | 50/80 [02:35<01:32,  3.09s/it]Computing max image from bright frames:  64%|██████▍   | 51/80 [02:38<01:30,  3.13s/it]Computing max image from bright frames:  65%|██████▌   | 52/80 [02:41<01:27,  3.14s/it]Computing max image from bright frames:  66%|██████▋   | 53/80 [02:44<01:25,  3.16s/it]Computing max image from bright frames:  68%|██████▊   | 54/80 [02:48<01:23,  3.21s/it]Computing max image from bright frames:  69%|██████▉   | 55/80 [02:51<01:19,  3.17s/it]Computing max image from bright frames:  70%|███████   | 56/80 [02:54<01:15,  3.14s/it]Computing max image from bright frames:  71%|███████▏  | 57/80 [02:57<01:11,  3.11s/it]Computing max image from bright frames:  72%|███████▎  | 58/80 [03:00<01:08,  3.10s/it]Computing max image from bright frames:  74%|███████▍  | 59/80 [03:03<01:05,  3.10s/it]Computing max image from bright frames:  75%|███████▌  | 60/80 [03:06<00:58,  2.90s/it]Computing max image from bright frames:  76%|███████▋  | 61/80 [03:09<00:55,  2.94s/it]Computing max image from bright frames:  78%|███████▊  | 62/80 [03:12<00:53,  2.96s/it]Computing max image from bright frames:  79%|███████▉  | 63/80 [03:15<00:50,  2.97s/it]Computing max image from bright frames:  80%|████████  | 64/80 [03:18<00:48,  3.04s/it]Computing max image from bright frames:  81%|████████▏ | 65/80 [03:21<00:47,  3.20s/it]Computing max image from bright frames:  82%|████████▎ | 66/80 [03:24<00:44,  3.15s/it]Computing max image from bright frames:  84%|████████▍ | 67/80 [03:28<00:40,  3.14s/it]Computing max image from bright frames:  85%|████████▌ | 68/80 [03:31<00:37,  3.16s/it]Computing max image from bright frames:  86%|████████▋ | 69/80 [03:34<00:34,  3.17s/it]Computing max image from bright frames:  88%|████████▊ | 70/80 [03:37<00:31,  3.16s/it]Computing max image from bright frames:  89%|████████▉ | 71/80 [03:40<00:28,  3.14s/it]Computing max image from bright frames:  90%|█████████ | 72/80 [03:43<00:25,  3.13s/it]Computing max image from bright frames:  91%|█████████▏| 73/80 [03:46<00:22,  3.15s/it]Computing max image from bright frames:  92%|█████████▎| 74/80 [03:50<00:18,  3.16s/it]Computing max image from bright frames:  94%|█████████▍| 75/80 [03:53<00:15,  3.19s/it]Computing max image from bright frames:  95%|█████████▌| 76/80 [03:56<00:12,  3.21s/it]Computing max image from bright frames:  96%|█████████▋| 77/80 [03:59<00:09,  3.23s/it]Computing max image from bright frames:  98%|█████████▊| 78/80 [04:03<00:06,  3.24s/it]Computing max image from bright frames:  99%|█████████▉| 79/80 [04:06<00:03,  3.23s/it]Computing max image from bright frames: 100%|██████████| 80/80 [04:09<00:00,  3.24s/it]                                                                                       [✅] Casing detection complete: cx=1014.0, cy=771.0

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

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:24,  4.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:24,  4.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:23,  4.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:23,  4.08it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:07, 13.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   4%|▍         | 4/100 [00:00<00:07, 13.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:07, 13.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 342.424} (0.001):   6%|▌         | 6/100 [00:00<00:06, 13.47it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 342.424} (0.001):   7%|▋         | 7/100 [00:00<00:05, 16.31it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:05, 16.31it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.002 | Best: {'fx': 370.707} (0.002):   8%|▊         | 8/100 [00:00<00:05, 16.31it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 370.707} (0.002):   9%|▉         | 9/100 [00:00<00:05, 16.31it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:04, 20.28it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.001 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:04, 20.28it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 20.28it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 20.28it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 20.28it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  14%|█▍        | 14/100 [00:00<00:03, 25.48it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  14%|█▍        | 14/100 [00:00<00:03, 25.48it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  15%|█▌        | 15/100 [00:00<00:03, 25.48it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 25.48it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.004 | Best: {'fx': 413.131} (0.005):  17%|█▋        | 17/100 [00:00<00:03, 22.80it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  17%|█▋        | 17/100 [00:00<00:03, 22.80it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:00<00:03, 22.80it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  19%|█▉        | 19/100 [00:00<00:03, 22.80it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.007 | Best: {'fx': 441.414} (0.009):  20%|██        | 20/100 [00:00<00:03, 22.80it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:00<00:03, 22.80it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  22%|██▏       | 22/100 [00:00<00:02, 27.69it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  22%|██▏       | 22/100 [00:01<00:02, 27.69it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.016 | Best: {'fx': 462.626} (0.016):  23%|██▎       | 23/100 [00:01<00:02, 27.69it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 27.69it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  25%|██▌       | 25/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 483.838} (0.025):  25%|██▌       | 25/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 483.838} (0.025):  26%|██▌       | 26/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.050 | Best: {'fx': 505.051} (0.050):  27%|██▋       | 27/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.041 | Best: {'fx': 505.051} (0.050):  28%|██▊       | 28/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.061 | Best: {'fx': 512.121} (0.061):  29%|██▉       | 29/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.032 | Best: {'fx': 512.121} (0.061):  30%|███       | 30/100 [00:01<00:03, 22.37it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.032 | Best: {'fx': 512.121} (0.061):  31%|███       | 31/100 [00:01<00:02, 30.21it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.079 | Best: {'fx': 519.192} (0.079):  31%|███       | 31/100 [00:01<00:02, 30.21it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.072 | Best: {'fx': 519.192} (0.079):  32%|███▏      | 32/100 [00:01<00:02, 30.21it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.105 | Best: {'fx': 526.263} (0.105):  33%|███▎      | 33/100 [00:01<00:02, 30.21it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.085 | Best: {'fx': 526.263} (0.105):  34%|███▍      | 34/100 [00:01<00:02, 30.21it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.085 | Best: {'fx': 526.263} (0.105):  35%|███▌      | 35/100 [00:01<00:03, 18.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.104 | Best: {'fx': 526.263} (0.105):  35%|███▌      | 35/100 [00:01<00:03, 18.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.045 | Best: {'fx': 526.263} (0.105):  36%|███▌      | 36/100 [00:01<00:03, 18.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.060 | Best: {'fx': 526.263} (0.105):  37%|███▋      | 37/100 [00:01<00:03, 18.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.054 | Best: {'fx': 526.263} (0.105):  38%|███▊      | 38/100 [00:01<00:03, 18.91it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.054 | Best: {'fx': 526.263} (0.105):  39%|███▉      | 39/100 [00:01<00:02, 22.32it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.038 | Best: {'fx': 526.263} (0.105):  39%|███▉      | 39/100 [00:01<00:02, 22.32it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.036 | Best: {'fx': 526.263} (0.105):  40%|████      | 40/100 [00:01<00:02, 22.32it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.033 | Best: {'fx': 526.263} (0.105):  41%|████      | 41/100 [00:01<00:02, 22.32it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.049 | Best: {'fx': 526.263} (0.105):  42%|████▏     | 42/100 [00:01<00:02, 22.32it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.049 | Best: {'fx': 526.263} (0.105):  43%|████▎     | 43/100 [00:01<00:02, 24.79it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.042 | Best: {'fx': 526.263} (0.105):  43%|████▎     | 43/100 [00:01<00:02, 24.79it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.026 | Best: {'fx': 526.263} (0.105):  44%|████▍     | 44/100 [00:01<00:02, 24.79it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.034 | Best: {'fx': 526.263} (0.105):  45%|████▌     | 45/100 [00:02<00:02, 24.79it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 526.263} (0.105):  46%|████▌     | 46/100 [00:02<00:02, 24.79it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 526.263} (0.105):  47%|████▋     | 47/100 [00:02<00:01, 27.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.027 | Best: {'fx': 526.263} (0.105):  47%|████▋     | 47/100 [00:02<00:01, 27.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.032 | Best: {'fx': 526.263} (0.105):  48%|████▊     | 48/100 [00:02<00:01, 27.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.030 | Best: {'fx': 526.263} (0.105):  49%|████▉     | 49/100 [00:02<00:01, 27.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.031 | Best: {'fx': 526.263} (0.105):  50%|█████     | 50/100 [00:02<00:01, 27.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.031 | Best: {'fx': 526.263} (0.105):  51%|█████     | 51/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.030 | Best: {'fx': 526.263} (0.105):  51%|█████     | 51/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.026 | Best: {'fx': 526.263} (0.105):  52%|█████▏    | 52/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.028 | Best: {'fx': 526.263} (0.105):  53%|█████▎    | 53/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 526.263} (0.105):  54%|█████▍    | 54/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 526.263} (0.105):  55%|█████▌    | 55/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.027 | Best: {'fx': 526.263} (0.105):  56%|█████▌    | 56/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 526.263} (0.105):  57%|█████▋    | 57/100 [00:02<00:01, 27.20it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 526.263} (0.105):  58%|█████▊    | 58/100 [00:02<00:01, 35.17it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.105):  58%|█████▊    | 58/100 [00:02<00:01, 35.17it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.105):  59%|█████▉    | 59/100 [00:02<00:01, 35.17it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.105):  60%|██████    | 60/100 [00:02<00:01, 35.17it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 526.263} (0.105):  61%|██████    | 61/100 [00:02<00:01, 35.17it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 526.263} (0.105):  62%|██████▏   | 62/100 [00:02<00:01, 35.17it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 526.263} (0.105):  63%|██████▎   | 63/100 [00:02<00:01, 36.65it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.023 | Best: {'fx': 526.263} (0.105):  63%|██████▎   | 63/100 [00:02<00:01, 36.65it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.025 | Best: {'fx': 526.263} (0.105):  64%|██████▍   | 64/100 [00:02<00:00, 36.65it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.105):  65%|██████▌   | 65/100 [00:02<00:00, 36.65it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  66%|██████▌   | 66/100 [00:02<00:00, 36.65it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  67%|██████▋   | 67/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.024 | Best: {'fx': 526.263} (0.105):  67%|██████▋   | 67/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  68%|██████▊   | 68/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  69%|██████▉   | 69/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  70%|███████   | 70/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.105):  71%|███████   | 71/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  72%|███████▏  | 72/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  73%|███████▎  | 73/100 [00:02<00:00, 37.43it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.021 | Best: {'fx': 526.263} (0.105):  74%|███████▍  | 74/100 [00:02<00:00, 41.62it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.022 | Best: {'fx': 526.263} (0.105):  74%|███████▍  | 74/100 [00:02<00:00, 41.62it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  75%|███████▌  | 75/100 [00:02<00:00, 41.62it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  76%|███████▌  | 76/100 [00:02<00:00, 41.62it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  77%|███████▋  | 77/100 [00:02<00:00, 41.62it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  78%|███████▊  | 78/100 [00:02<00:00, 41.62it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  79%|███████▉  | 79/100 [00:02<00:00, 41.49it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  79%|███████▉  | 79/100 [00:02<00:00, 41.49it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  80%|████████  | 80/100 [00:02<00:00, 41.49it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  81%|████████  | 81/100 [00:02<00:00, 41.49it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  82%|████████▏ | 82/100 [00:02<00:00, 41.49it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  83%|████████▎ | 83/100 [00:02<00:00, 41.49it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  84%|████████▍ | 84/100 [00:02<00:00, 41.16it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  84%|████████▍ | 84/100 [00:02<00:00, 41.16it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  85%|████████▌ | 85/100 [00:03<00:00, 41.16it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  86%|████████▌ | 86/100 [00:03<00:00, 41.16it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  87%|████████▋ | 87/100 [00:03<00:00, 41.16it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  88%|████████▊ | 88/100 [00:03<00:00, 41.16it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  89%|████████▉ | 89/100 [00:03<00:00, 37.99it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.020 | Best: {'fx': 526.263} (0.105):  89%|████████▉ | 89/100 [00:03<00:00, 37.99it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  90%|█████████ | 90/100 [00:03<00:00, 37.99it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  91%|█████████ | 91/100 [00:03<00:00, 37.99it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  92%|█████████▏| 92/100 [00:03<00:00, 37.99it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  93%|█████████▎| 93/100 [00:03<00:00, 34.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  93%|█████████▎| 93/100 [00:03<00:00, 34.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  94%|█████████▍| 94/100 [00:03<00:00, 34.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  95%|█████████▌| 95/100 [00:03<00:00, 34.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  96%|█████████▌| 96/100 [00:03<00:00, 34.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  97%|█████████▋| 97/100 [00:03<00:00, 34.94it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  98%|█████████▊| 98/100 [00:03<00:00, 36.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  98%|█████████▊| 98/100 [00:03<00:00, 36.01it/s]Brute | Start: {'fx': 500.0} (0.042) | Current: 0.019 | Best: {'fx': 526.263} (0.105):  99%|█████████▉| 99/100 [00:03<00:00, 36.01it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.043 | Best: {'fx': 501.263} (0.043):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.043 | Best: {'fx': 501.263} (0.043):   2%|▏         | 1/50 [00:00<00:26,  1.82it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.045 | Best: {'fx': 502.283} (0.045):   2%|▏         | 1/50 [00:00<00:26,  1.82it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.047 | Best: {'fx': 503.303} (0.047):   4%|▍         | 2/50 [00:00<00:26,  1.82it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.049 | Best: {'fx': 504.324} (0.049):   6%|▌         | 3/50 [00:00<00:25,  1.82it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.051 | Best: {'fx': 506.365} (0.051):   8%|▊         | 4/50 [00:00<00:25,  1.82it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.051 | Best: {'fx': 506.365} (0.051):  10%|█         | 5/50 [00:00<00:04,  9.68it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.050 | Best: {'fx': 506.365} (0.051):  10%|█         | 5/50 [00:00<00:04,  9.68it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.052 | Best: {'fx': 507.385} (0.052):  12%|█▏        | 6/50 [00:00<00:04,  9.68it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.054 | Best: {'fx': 508.405} (0.054):  14%|█▍        | 7/50 [00:00<00:04,  9.68it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.054 | Best: {'fx': 508.405} (0.054):  16%|█▌        | 8/50 [00:00<00:03, 13.43it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.058 | Best: {'fx': 510.446} (0.058):  16%|█▌        | 8/50 [00:00<00:03, 13.43it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.055 | Best: {'fx': 510.446} (0.058):  18%|█▊        | 9/50 [00:00<00:03, 13.43it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.060 | Best: {'fx': 511.467} (0.060):  20%|██        | 10/50 [00:00<00:02, 13.43it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.060 | Best: {'fx': 511.467} (0.060):  22%|██▏       | 11/50 [00:00<00:02, 15.72it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.066 | Best: {'fx': 514.528} (0.066):  22%|██▏       | 11/50 [00:00<00:02, 15.72it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.067 | Best: {'fx': 515.548} (0.067):  24%|██▍       | 12/50 [00:00<00:02, 15.72it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.061 | Best: {'fx': 515.548} (0.067):  26%|██▌       | 13/50 [00:01<00:02, 15.72it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.061 | Best: {'fx': 515.548} (0.067):  28%|██▊       | 14/50 [00:01<00:01, 18.84it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.063 | Best: {'fx': 515.548} (0.067):  28%|██▊       | 14/50 [00:01<00:01, 18.84it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.069 | Best: {'fx': 516.569} (0.069):  30%|███       | 15/50 [00:01<00:01, 18.84it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.076 | Best: {'fx': 518.61} (0.076):  32%|███▏      | 16/50 [00:01<00:01, 18.84it/s] Brute | Start: {'fx': 526.263} (0.105) | Current: 0.082 | Best: {'fx': 520.65} (0.082):  34%|███▍      | 17/50 [00:01<00:01, 18.84it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.082 | Best: {'fx': 520.65} (0.082):  36%|███▌      | 18/50 [00:01<00:01, 20.92it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.081 | Best: {'fx': 520.65} (0.082):  36%|███▌      | 18/50 [00:01<00:01, 20.92it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.072 | Best: {'fx': 520.65} (0.082):  38%|███▊      | 19/50 [00:01<00:01, 20.92it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.089 | Best: {'fx': 521.671} (0.089):  40%|████      | 20/50 [00:01<00:01, 20.92it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.096 | Best: {'fx': 523.712} (0.096):  42%|████▏     | 21/50 [00:01<00:01, 20.92it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.092 | Best: {'fx': 523.712} (0.096):  44%|████▍     | 22/50 [00:01<00:01, 20.92it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.092 | Best: {'fx': 523.712} (0.096):  46%|████▌     | 23/50 [00:01<00:01, 24.44it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.100 | Best: {'fx': 524.732} (0.100):  46%|████▌     | 23/50 [00:01<00:01, 24.44it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.108 | Best: {'fx': 526.773} (0.108):  48%|████▊     | 24/50 [00:01<00:01, 24.44it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.105 | Best: {'fx': 526.773} (0.108):  50%|█████     | 25/50 [00:01<00:01, 24.44it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.105 | Best: {'fx': 526.773} (0.108):  52%|█████▏    | 26/50 [00:01<00:01, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.108 | Best: {'fx': 527.793} (0.108):  52%|█████▏    | 26/50 [00:01<00:01, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.097 | Best: {'fx': 527.793} (0.108):  54%|█████▍    | 27/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.102 | Best: {'fx': 527.793} (0.108):  56%|█████▌    | 28/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.105 | Best: {'fx': 527.793} (0.108):  58%|█████▊    | 29/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.099 | Best: {'fx': 527.793} (0.108):  60%|██████    | 30/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.089 | Best: {'fx': 527.793} (0.108):  62%|██████▏   | 31/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.104 | Best: {'fx': 527.793} (0.108):  64%|██████▍   | 32/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.096 | Best: {'fx': 527.793} (0.108):  66%|██████▌   | 33/50 [00:01<00:00, 23.35it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.096 | Best: {'fx': 527.793} (0.108):  68%|██████▊   | 34/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.093 | Best: {'fx': 527.793} (0.108):  68%|██████▊   | 34/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.087 | Best: {'fx': 527.793} (0.108):  70%|███████   | 35/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.107 | Best: {'fx': 527.793} (0.108):  72%|███████▏  | 36/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.107 | Best: {'fx': 527.793} (0.108):  74%|███████▍  | 37/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.107 | Best: {'fx': 527.793} (0.108):  76%|███████▌  | 38/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.082 | Best: {'fx': 527.793} (0.108):  78%|███████▊  | 39/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.079 | Best: {'fx': 527.793} (0.108):  80%|████████  | 40/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.084 | Best: {'fx': 527.793} (0.108):  82%|████████▏ | 41/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.072 | Best: {'fx': 527.793} (0.108):  84%|████████▍ | 42/50 [00:01<00:00, 35.32it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.072 | Best: {'fx': 527.793} (0.108):  86%|████████▌ | 43/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.074 | Best: {'fx': 527.793} (0.108):  86%|████████▌ | 43/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.077 | Best: {'fx': 527.793} (0.108):  88%|████████▊ | 44/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.076 | Best: {'fx': 527.793} (0.108):  90%|█████████ | 45/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.068 | Best: {'fx': 527.793} (0.108):  92%|█████████▏| 46/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.071 | Best: {'fx': 527.793} (0.108):  94%|█████████▍| 47/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.069 | Best: {'fx': 527.793} (0.108):  96%|█████████▌| 48/50 [00:01<00:00, 42.88it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.069 | Best: {'fx': 527.793} (0.108):  98%|█████████▊| 49/50 [00:01<00:00, 46.26it/s]Brute | Start: {'fx': 526.263} (0.105) | Current: 0.066 | Best: {'fx': 527.793} (0.108):  98%|█████████▊| 49/50 [00:01<00:00, 46.26it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 527.7932385075243
    px: 1014.0
    py: 771.0
    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 3 incomplete frames): 77 frames will be used for optimization
  Rigs: 2000L(2000mm):19/20, 1500(1500mm):20/20, 1000(1000mm):19/20, 2000R(2000mm):19/20
  Total: 77/80 frames used for optimization
Initial score: 5.7657: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 5.7657, picklable_objective(x0) = 5.7256
Best score: 5.7256: : 0iter [00:02, ?iter/s] Best score: 5.7256: : 1iter [00:02,  2.00s/iter]Best score: 5.7256: : 1iter [00:02,  2.00s/iter]Best score: 5.7256: : 2iter [00:02,  2.00s/iter]Best score: 5.7256: : 3iter [00:02,  1.72iter/s]Best score: 5.7256: : 3iter [00:02,  1.72iter/s]Best score: 5.7256: : 4iter [00:02,  1.72iter/s]Best score: 5.7256: : 5iter [00:02,  3.07iter/s]Best score: 5.7256: : 5iter [00:02,  3.07iter/s]Best score: 5.7256: : 6iter [00:02,  3.07iter/s]Best score: 5.7256: : 7iter [00:02,  4.48iter/s]Best score: 5.7256: : 7iter [00:02,  4.48iter/s]Best score: 5.7256: : 8iter [00:02,  4.48iter/s]Best score: 5.7256: : 9iter [00:02,  5.87iter/s]Best score: 5.7256: : 9iter [00:02,  5.87iter/s]Best score: 5.7256: : 10iter [00:02,  5.87iter/s]Best score: 5.7256: : 11iter [00:02,  7.10iter/s]Best score: 5.7256: : 11iter [00:02,  7.10iter/s]Best score: 5.7256: : 12iter [00:03,  7.10iter/s]Best score: 5.7256: : 13iter [00:03,  8.22iter/s]Best score: 5.7256: : 13iter [00:03,  8.22iter/s]Best score: 5.7256: : 14iter [00:03,  8.22iter/s]Best score: 5.7256: : 15iter [00:03,  9.15iter/s]Best score: 5.7256: : 15iter [00:03,  9.15iter/s]Best score: 5.7256: : 16iter [00:03,  9.15iter/s]Best score: 5.7256: : 17iter [00:03,  9.90iter/s]Best score: 5.7256: : 17iter [00:03,  9.90iter/s]Best score: 5.7256: : 18iter [00:03,  9.90iter/s]Best score: 5.7256: : 19iter [00:03, 10.49iter/s]Best score: 5.7256: : 19iter [00:03, 10.49iter/s]Best score: 5.7256: : 20iter [00:03, 10.49iter/s]Best score: 5.7256: : 21iter [00:03, 10.76iter/s]Best score: 5.7256: : 21iter [00:03, 10.76iter/s]Best score: 5.7256: : 22iter [00:03, 10.76iter/s]Best score: 5.7256: : 23iter [00:03, 11.09iter/s]Best score: 5.7256: : 23iter [00:03, 11.09iter/s]Best score: 5.7256: : 24iter [00:04, 11.09iter/s]Best score: 5.7256: : 25iter [00:04, 11.33iter/s]Best score: 5.7256: : 25iter [00:04, 11.33iter/s]Best score: 5.7256: : 26iter [00:04, 11.33iter/s]Best score: 5.7256: : 27iter [00:04, 11.50iter/s]Best score: 5.7256: : 27iter [00:04, 11.50iter/s]Best score: 5.7256: : 28iter [00:04, 11.50iter/s]Best score: 5.7256: : 29iter [00:04, 11.63iter/s]Best score: 5.7256: : 29iter [00:04, 11.63iter/s]Best score: 5.7256: : 30iter [00:04, 11.63iter/s]Best score: 5.7256: : 31iter [00:04, 11.60iter/s]Best score: 5.7256: : 31iter [00:04, 11.60iter/s]Best score: 5.7256: : 32iter [00:04, 11.60iter/s]Best score: 5.7256: : 33iter [00:04, 11.70iter/s]Best score: 5.7256: : 33iter [00:04, 11.70iter/s]Best score: 5.7256: : 34iter [00:04, 11.70iter/s]Best score: 5.7256: : 35iter [00:04, 11.84iter/s]Best score: 5.7256: : 35iter [00:04, 11.84iter/s]Best score: 5.7256: : 36iter [00:05, 11.84iter/s]Best score: 5.7256: : 37iter [00:05, 11.93iter/s]Best score: 5.7256: : 37iter [00:05, 11.93iter/s]Best score: 5.7256: : 38iter [00:05, 11.93iter/s]Best score: 5.7256: : 39iter [00:05, 12.01iter/s]Best score: 5.7256: : 39iter [00:05, 12.01iter/s]Best score: 5.7256: : 40iter [00:05, 12.01iter/s]Best score: 5.7256: : 41iter [00:05, 11.85iter/s]Best score: 5.7256: : 41iter [00:05, 11.85iter/s]Best score: 5.7256: : 42iter [00:05, 11.85iter/s]Best score: 5.7256: : 43iter [00:05, 11.94iter/s]Best score: 5.7256: : 43iter [00:05, 11.94iter/s]Best score: 5.7256: : 44iter [00:05, 11.94iter/s]Best score: 5.7256: : 45iter [00:05, 12.05iter/s]Best score: 5.7256: : 45iter [00:05, 12.05iter/s]Best score: 5.7256: : 46iter [00:05, 12.05iter/s]Best score: 5.7256: : 47iter [00:05, 12.09iter/s]Best score: 5.7256: : 47iter [00:05, 12.09iter/s]Best score: 5.7256: : 48iter [00:06, 12.09iter/s]Best score: 5.7256: : 49iter [00:06, 12.13iter/s]
[ITER 50] Best: 5.7256
[PARAMS] Current best parameters:
  fx: 527.7932
  fy: 527.7932
  px: 1014.0000
  py: 771.0000
  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: 5.7256: : 49iter [00:06, 12.13iter/s]Best score: 5.7256: : 50iter [00:06, 12.13iter/s]Best score: 5.7256: : 51iter [00:06, 12.03iter/s]Best score: 5.7256: : 51iter [00:06, 12.03iter/s]Best score: 5.7256: : 52iter [00:06, 12.03iter/s]Best score: 5.7256: : 53iter [00:06, 12.06iter/s]Best score: 5.7256: : 53iter [00:06, 12.06iter/s]Best score: 5.7256: : 54iter [00:06, 12.06iter/s]Best score: 5.7256: : 55iter [00:06, 12.12iter/s]Best score: 5.7256: : 55iter [00:06, 12.12iter/s]Best score: 5.7256: : 56iter [00:06, 12.12iter/s]Best score: 5.7256: : 57iter [00:06, 12.13iter/s]Best score: 5.7256: : 57iter [00:06, 12.13iter/s]Best score: 5.7256: : 58iter [00:06, 12.13iter/s]Best score: 5.7256: : 59iter [00:06, 12.15iter/s]Best score: 5.7256: : 59iter [00:06, 12.15iter/s]Best score: 5.7256: : 60iter [00:07, 12.15iter/s]Best score: 5.7256: : 61iter [00:07, 12.04iter/s]Best score: 5.7256: : 61iter [00:07, 12.04iter/s]Best score: 5.7256: : 62iter [00:07, 12.04iter/s]Best score: 5.7256: : 63iter [00:07, 12.06iter/s]Best score: 5.7256: : 63iter [00:07, 12.06iter/s]Best score: 5.7256: : 64iter [00:07, 12.06iter/s]Best score: 5.7256: : 65iter [00:07, 12.12iter/s]Best score: 5.7256: : 65iter [00:07, 12.12iter/s]Best score: 5.7256: : 66iter [00:07, 12.12iter/s]Best score: 5.7256: : 67iter [00:07, 12.21iter/s]Best score: 5.7256: : 67iter [00:07, 12.21iter/s]Best score: 5.7256: : 68iter [00:07, 12.21iter/s]Best score: 5.7256: : 69iter [00:07, 12.19iter/s]Best score: 5.7256: : 69iter [00:07, 12.19iter/s]Best score: 5.7256: : 70iter [00:07, 12.19iter/s]Best score: 5.7256: : 71iter [00:07, 12.00iter/s]Best score: 5.7256: : 71iter [00:07, 12.00iter/s]Best score: 5.7256: : 72iter [00:08, 12.00iter/s]Best score: 5.7256: : 73iter [00:08, 12.10iter/s]Best score: 5.7256: : 73iter [00:08, 12.10iter/s]Best score: 5.7256: : 74iter [00:08, 12.10iter/s]Best score: 5.7256: : 75iter [00:08, 12.14iter/s]Best score: 5.7256: : 75iter [00:08, 12.14iter/s]Best score: 5.7256: : 76iter [00:08, 12.14iter/s]Best score: 5.7256: : 77iter [00:08, 12.19iter/s]Best score: 5.7256: : 77iter [00:08, 12.19iter/s]Best score: 5.7256: : 78iter [00:08, 12.19iter/s]Best score: 5.7256: : 79iter [00:08, 12.24iter/s]Best score: 4.8988: : 79iter [00:08, 12.24iter/s]Best score: 4.8988: : 80iter [00:08, 12.24iter/s]Best score: 4.8988: : 81iter [00:08, 12.04iter/s]Best score: 4.8988: : 81iter [00:08, 12.04iter/s]Best score: 4.8988: : 82iter [00:08, 12.04iter/s]Best score: 4.8988: : 83iter [00:08, 12.13iter/s]Best score: 4.8988: : 83iter [00:08, 12.13iter/s]Best score: 4.8988: : 84iter [00:08, 12.13iter/s]Best score: 4.8988: : 85iter [00:08, 12.14iter/s]Best score: 4.8988: : 85iter [00:09, 12.14iter/s]Best score: 4.8988: : 86iter [00:09, 12.14iter/s]Best score: 4.8988: : 87iter [00:09, 12.11iter/s]Best score: 4.8988: : 87iter [00:09, 12.11iter/s]Best score: 4.8988: : 88iter [00:09, 12.11iter/s]Best score: 4.8988: : 89iter [00:09, 12.16iter/s]Best score: 4.8988: : 89iter [00:09, 12.16iter/s]Best score: 4.8988: : 90iter [00:09, 12.16iter/s]Best score: 4.8988: : 91iter [00:09, 12.03iter/s]Best score: 4.8988: : 91iter [00:09, 12.03iter/s]Best score: 4.8988: : 92iter [00:09, 12.03iter/s]Best score: 4.8988: : 93iter [00:09, 12.13iter/s]Best score: 4.8988: : 93iter [00:09, 12.13iter/s]Best score: 4.8988: : 94iter [00:09, 12.13iter/s]Best score: 4.8988: : 95iter [00:09, 12.17iter/s]Best score: 4.8988: : 95iter [00:09, 12.17iter/s]Best score: 4.8988: : 96iter [00:09, 12.17iter/s]Best score: 4.8988: : 97iter [00:09, 12.17iter/s]Best score: 4.8988: : 97iter [00:10, 12.17iter/s]Best score: 4.8988: : 98iter [00:10, 12.17iter/s]Best score: 4.8988: : 99iter [00:10, 12.17iter/s]
[ITER 100] Best: 4.8988
[PARAMS] Current best parameters:
  fx: 540.3712
  fy: 539.7800
  px: 1014.7085
  py: 767.9842
  k1: -0.01652043
  k2: 0.03315089
  k3: -0.03021074
  k4: 0.00788479
  p1: -0.00780668
  p2: -0.00243570
  p3: -0.00797625
  p4: -0.00642938
  s1: -0.0053
  s2: 0.0191
  s3: -0.0048
  s4: -0.0013
  tilt_x: 0.0069
  tilt_y: -0.0071
  scale: 2.9447
  rotation: -0.0679
Best score: 4.8988: : 99iter [00:10, 12.17iter/s]Best score: 4.8988: : 100iter [00:10, 12.17iter/s]Best score: 4.8988: : 101iter [00:10, 10.36iter/s]Best score: 4.8988: : 101iter [00:10, 10.36iter/s]Best score: 4.8988: : 102iter [00:10, 10.36iter/s]Best score: 4.8988: : 103iter [00:10, 10.74iter/s]Best score: 4.8988: : 103iter [00:10, 10.74iter/s]Best score: 4.8988: : 104iter [00:10, 10.74iter/s]Best score: 4.8988: : 105iter [00:10, 11.06iter/s]Best score: 4.8988: : 105iter [00:10, 11.06iter/s]Best score: 4.8988: : 106iter [00:10, 11.06iter/s]Best score: 4.8988: : 107iter [00:10, 11.31iter/s]Best score: 4.8988: : 107iter [00:10, 11.31iter/s]Best score: 4.8988: : 108iter [00:11, 11.31iter/s]Best score: 4.8988: : 109iter [00:11, 11.49iter/s]Best score: 4.8988: : 109iter [00:11, 11.49iter/s]Best score: 4.8988: : 110iter [00:11, 11.49iter/s]Best score: 4.8988: : 111iter [00:11, 11.42iter/s]Best score: 4.8988: : 111iter [00:11, 11.42iter/s]Best score: 4.8988: : 112iter [00:11, 11.42iter/s]Best score: 4.8988: : 113iter [00:11, 11.62iter/s]Best score: 4.8988: : 113iter [00:11, 11.62iter/s]Best score: 4.8988: : 114iter [00:11, 11.62iter/s]Best score: 4.8988: : 115iter [00:11, 11.70iter/s]Best score: 4.8988: : 115iter [00:11, 11.70iter/s]Best score: 4.8988: : 116iter [00:11, 11.70iter/s]Best score: 4.8988: : 117iter [00:11, 11.79iter/s]Best score: 4.8988: : 117iter [00:11, 11.79iter/s]Best score: 4.8988: : 118iter [00:11, 11.79iter/s]Best score: 4.8988: : 119iter [00:11, 11.86iter/s]Best score: 4.8988: : 119iter [00:12, 11.86iter/s]Best score: 4.8988: : 120iter [00:12, 11.86iter/s]Best score: 4.8988: : 121iter [00:12, 11.67iter/s]Best score: 4.8988: : 121iter [00:12, 11.67iter/s]Best score: 4.8988: : 122iter [00:12, 11.67iter/s]Best score: 4.8988: : 123iter [00:12, 11.78iter/s]Best score: 4.8988: : 123iter [00:12, 11.78iter/s]Best score: 4.8988: : 124iter [00:12, 11.78iter/s]Best score: 4.8988: : 125iter [00:12, 11.85iter/s]Best score: 4.8988: : 125iter [00:12, 11.85iter/s]Best score: 4.8988: : 126iter [00:12, 11.85iter/s]Best score: 4.8988: : 127iter [00:12, 11.85iter/s]Best score: 4.8988: : 127iter [00:12, 11.85iter/s]Best score: 4.8988: : 128iter [00:12, 11.85iter/s]Best score: 4.8988: : 129iter [00:12, 11.85iter/s]Best score: 4.8988: : 129iter [00:12, 11.85iter/s]Best score: 4.8988: : 130iter [00:12, 11.85iter/s]Best score: 4.8988: : 131iter [00:12, 11.73iter/s]Best score: 4.8988: : 131iter [00:13, 11.73iter/s]Best score: 4.8988: : 132iter [00:13, 11.73iter/s]Best score: 4.8988: : 133iter [00:13, 11.82iter/s]Best score: 4.8988: : 133iter [00:13, 11.82iter/s]Best score: 4.8988: : 134iter [00:13, 11.82iter/s]Best score: 4.8988: : 135iter [00:13, 11.83iter/s]Best score: 4.8988: : 135iter [00:13, 11.83iter/s]Best score: 4.8988: : 136iter [00:13, 11.83iter/s]Best score: 4.8988: : 137iter [00:13, 11.86iter/s]Best score: 4.8988: : 137iter [00:13, 11.86iter/s]Best score: 4.8988: : 138iter [00:13, 11.86iter/s]Best score: 4.8988: : 139iter [00:13, 11.94iter/s]Best score: 4.8988: : 139iter [00:13, 11.94iter/s]Best score: 4.8988: : 140iter [00:13, 11.94iter/s]Best score: 4.8988: : 141iter [00:13, 11.76iter/s]Best score: 4.8988: : 141iter [00:13, 11.76iter/s]Best score: 4.8988: : 142iter [00:13, 11.76iter/s]Best score: 4.8988: : 143iter [00:13, 11.85iter/s]Best score: 4.8988: : 143iter [00:14, 11.85iter/s]Best score: 4.8988: : 144iter [00:14, 11.85iter/s]Best score: 4.8988: : 145iter [00:14, 11.87iter/s]Best score: 4.8988: : 145iter [00:14, 11.87iter/s]Best score: 4.8988: : 146iter [00:14, 11.87iter/s]Best score: 4.8988: : 147iter [00:14, 11.94iter/s]Best score: 4.8988: : 147iter [00:14, 11.94iter/s]Best score: 4.8988: : 148iter [00:14, 11.94iter/s]Best score: 4.8988: : 149iter [00:14, 12.00iter/s]
[ITER 150] Best: 4.8988
[PARAMS] Current best parameters:
  fx: 540.3712
  fy: 539.7800
  px: 1014.7085
  py: 767.9842
  k1: -0.01652043
  k2: 0.03315089
  k3: -0.03021074
  k4: 0.00788479
  p1: -0.00780668
  p2: -0.00243570
  p3: -0.00797625
  p4: -0.00642938
  s1: -0.0053
  s2: 0.0191
  s3: -0.0048
  s4: -0.0013
  tilt_x: 0.0069
  tilt_y: -0.0071
  scale: 2.9447
  rotation: -0.0679
Best score: 4.8988: : 149iter [00:14, 12.00iter/s]Best score: 4.8988: : 150iter [00:14, 12.00iter/s]Best score: 4.8988: : 151iter [00:14,  7.81iter/s]Best score: 4.8988: : 151iter [00:14,  7.81iter/s]Best score: 4.8988: : 152iter [00:15,  7.81iter/s]Best score: 4.8988: : 153iter [00:15,  8.73iter/s]Best score: 4.8988: : 153iter [00:15,  8.73iter/s]Best score: 4.8988: : 154iter [00:15,  8.73iter/s]Best score: 4.8988: : 155iter [00:15,  9.54iter/s]Best score: 4.8988: : 155iter [00:15,  9.54iter/s]Best score: 4.8988: : 156iter [00:15,  9.54iter/s]Best score: 4.8988: : 157iter [00:15, 10.20iter/s]Best score: 4.8988: : 157iter [00:15, 10.20iter/s]Best score: 4.8988: : 158iter [00:15, 10.20iter/s]Best score: 4.8988: : 159iter [00:15, 10.75iter/s]Best score: 4.8988: : 159iter [00:15, 10.75iter/s]Best score: 4.8988: : 160iter [00:15, 10.75iter/s]Best score: 4.8988: : 161iter [00:15, 10.95iter/s]Best score: 4.8988: : 161iter [00:15, 10.95iter/s]Best score: 4.8988: : 162iter [00:15, 10.95iter/s]Best score: 4.8988: : 163iter [00:15, 11.24iter/s]Best score: 4.8988: : 163iter [00:15, 11.24iter/s]Best score: 4.8988: : 164iter [00:16, 11.24iter/s]Best score: 4.8988: : 165iter [00:16, 11.46iter/s]Best score: 4.8988: : 165iter [00:16, 11.46iter/s]Best score: 4.8988: : 166iter [00:16, 11.46iter/s]Best score: 4.8988: : 167iter [00:16, 11.66iter/s]Best score: 4.8988: : 167iter [00:16, 11.66iter/s]Best score: 4.8988: : 168iter [00:16, 11.66iter/s]Best score: 4.8988: : 169iter [00:16, 11.86iter/s]Best score: 4.8988: : 169iter [00:16, 11.86iter/s]Best score: 4.8988: : 170iter [00:16, 11.86iter/s]Best score: 4.8988: : 171iter [00:16, 11.81iter/s]Best score: 4.8988: : 171iter [00:16, 11.81iter/s]Best score: 4.8988: : 172iter [00:16, 11.81iter/s]Best score: 4.8988: : 173iter [00:16, 11.97iter/s]Best score: 4.8988: : 173iter [00:16, 11.97iter/s]Best score: 4.8988: : 174iter [00:16, 11.97iter/s]Best score: 4.8988: : 175iter [00:16, 12.06iter/s]Best score: 4.8988: : 175iter [00:16, 12.06iter/s]Best score: 4.8988: : 176iter [00:17, 12.06iter/s]Best score: 4.8988: : 177iter [00:17, 12.17iter/s]Best score: 4.8988: : 177iter [00:17, 12.17iter/s]Best score: 4.8988: : 178iter [00:17, 12.17iter/s]Best score: 4.8988: : 179iter [00:17,  9.53iter/s]Best score: 4.8988: : 179iter [00:17,  9.53iter/s]Best score: 4.8988: : 180iter [00:17,  9.53iter/s]Best score: 4.8988: : 181iter [00:17, 10.08iter/s]Best score: 4.8988: : 181iter [00:17, 10.08iter/s]Best score: 4.8988: : 182iter [00:17, 10.08iter/s]Best score: 4.8988: : 183iter [00:17, 10.66iter/s]Best score: 4.8988: : 183iter [00:17, 10.66iter/s]Best score: 4.8988: : 184iter [00:17, 10.66iter/s]Best score: 4.8988: : 185iter [00:17, 11.15iter/s]Best score: 4.8988: : 185iter [00:17, 11.15iter/s]Best score: 4.8988: : 186iter [00:18, 11.15iter/s]Best score: 4.8988: : 187iter [00:18, 11.44iter/s]Best score: 4.8988: : 187iter [00:18, 11.44iter/s]Best score: 4.8988: : 188iter [00:18, 11.44iter/s]Best score: 4.8988: : 189iter [00:18, 11.63iter/s]Best score: 4.8988: : 189iter [00:18, 11.63iter/s]Best score: 4.8988: : 190iter [00:18, 11.63iter/s]Best score: 4.8988: : 191iter [00:18, 11.60iter/s]Best score: 4.8988: : 191iter [00:18, 11.60iter/s]Best score: 4.8988: : 192iter [00:18, 11.60iter/s]Best score: 4.8988: : 193iter [00:18, 11.84iter/s]Best score: 4.8988: : 193iter [00:18, 11.84iter/s]Best score: 4.8988: : 194iter [00:18, 11.84iter/s]Best score: 4.8988: : 195iter [00:18, 11.98iter/s]Best score: 4.8988: : 195iter [00:18, 11.98iter/s]Best score: 4.8988: : 196iter [00:18, 11.98iter/s]Best score: 4.8988: : 197iter [00:18, 12.11iter/s]Best score: 4.8988: : 197iter [00:18, 12.11iter/s]Best score: 4.8988: : 198iter [00:19, 12.11iter/s]Best score: 4.8988: : 199iter [00:19, 12.22iter/s]
[ITER 200] Best: 3.9791
[PARAMS] Current best parameters:
  fx: 535.2069
  fy: 536.3320
  px: 1013.7047
  py: 768.4730
  k1: 0.01104765
  k2: 0.02296078
  k3: -0.04760525
  k4: 0.01726088
  p1: -0.01354588
  p2: -0.00820949
  p3: -0.00438738
  p4: 0.00159181
  s1: -0.0064
  s2: 0.0118
  s3: -0.0026
  s4: 0.0119
  tilt_x: 0.0010
  tilt_y: -0.0023
  scale: 2.9255
  rotation: 0.2131
Best score: 3.9791: : 199iter [00:19, 12.22iter/s]Best score: 3.9791: : 200iter [00:19, 12.22iter/s]Best score: 3.9791: : 201iter [00:19,  7.89iter/s]Best score: 3.9791: : 201iter [00:19,  7.89iter/s]Best score: 3.9791: : 202iter [00:19,  7.89iter/s]Best score: 3.9791: : 203iter [00:19,  8.86iter/s]Best score: 3.9791: : 203iter [00:19,  8.86iter/s]Best score: 3.9791: : 204iter [00:19,  8.86iter/s]Best score: 3.9791: : 205iter [00:19,  9.67iter/s]Best score: 3.9791: : 205iter [00:19,  9.67iter/s]Best score: 3.9791: : 206iter [00:19,  9.67iter/s]Best score: 3.9791: : 207iter [00:19, 10.32iter/s]Best score: 3.9791: : 207iter [00:20, 10.32iter/s]Best score: 3.9791: : 208iter [00:20, 10.32iter/s]Best score: 3.9791: : 209iter [00:20, 10.85iter/s]Best score: 2.1843: : 209iter [00:20, 10.85iter/s]Best score: 2.1843: : 210iter [00:20, 10.85iter/s]Best score: 2.1843: : 211iter [00:20, 11.07iter/s]Best score: 2.1843: : 211iter [00:20, 11.07iter/s]Best score: 2.1843: : 212iter [00:20, 11.07iter/s]Best score: 2.1843: : 213iter [00:20, 11.41iter/s]Best score: 2.1843: : 213iter [00:20, 11.41iter/s]Best score: 2.1843: : 214iter [00:20, 11.41iter/s]Best score: 2.1843: : 215iter [00:20, 11.69iter/s]Best score: 2.1843: : 215iter [00:20, 11.69iter/s]Best score: 2.1843: : 216iter [00:20, 11.69iter/s]Best score: 2.1843: : 217iter [00:20, 11.83iter/s]Best score: 2.1843: : 217iter [00:20, 11.83iter/s]Best score: 2.1843: : 218iter [00:20, 11.83iter/s]Best score: 2.1843: : 219iter [00:20, 11.97iter/s]Best score: 2.1843: : 219iter [00:21, 11.97iter/s]Best score: 2.1843: : 220iter [00:21, 11.97iter/s]Best score: 2.1843: : 221iter [00:21, 11.88iter/s]Best score: 2.1843: : 221iter [00:21, 11.88iter/s]Best score: 2.1843: : 222iter [00:21, 11.88iter/s]Best score: 2.1843: : 223iter [00:21, 11.99iter/s]Best score: 2.1843: : 223iter [00:21, 11.99iter/s]Best score: 2.1843: : 224iter [00:21, 11.99iter/s]Best score: 2.1843: : 225iter [00:21, 12.06iter/s]Best score: 2.1843: : 225iter [00:21, 12.06iter/s]Best score: 2.1843: : 226iter [00:21, 12.06iter/s]Best score: 2.1843: : 227iter [00:21, 12.10iter/s]Best score: 2.1843: : 227iter [00:21, 12.10iter/s]Best score: 2.1843: : 228iter [00:21, 12.10iter/s]Best score: 2.1843: : 229iter [00:21, 12.17iter/s]Best score: 1.5467: : 229iter [00:21, 12.17iter/s]Best score: 1.5467: : 230iter [00:21, 12.17iter/s]Best score: 1.5467: : 231iter [00:21, 11.98iter/s]Best score: 1.5467: : 231iter [00:22, 11.98iter/s]Best score: 1.5467: : 232iter [00:22, 11.98iter/s]Best score: 1.5467: : 233iter [00:22, 12.10iter/s]Best score: 1.5467: : 233iter [00:22, 12.10iter/s]Best score: 1.5467: : 234iter [00:22, 12.10iter/s]Best score: 1.5467: : 235iter [00:22, 12.18iter/s]Best score: 1.5467: : 235iter [00:22, 12.18iter/s]Best score: 1.5467: : 236iter [00:22, 12.18iter/s]Best score: 1.5467: : 237iter [00:22, 12.26iter/s]Best score: 1.5467: : 237iter [00:22, 12.26iter/s]Best score: 1.5467: : 238iter [00:22, 12.26iter/s]Best score: 1.5467: : 239iter [00:22, 12.29iter/s]Best score: 1.3577: : 239iter [00:22, 12.29iter/s]Best score: 1.3577: : 240iter [00:22, 12.29iter/s]Best score: 1.3577: : 241iter [00:22, 12.10iter/s]Best score: 1.3577: : 241iter [00:22, 12.10iter/s]Best score: 1.3577: : 242iter [00:22, 12.10iter/s]Best score: 1.3577: : 243iter [00:22, 12.18iter/s]Best score: 1.3577: : 243iter [00:23, 12.18iter/s]Best score: 1.3577: : 244iter [00:23, 12.18iter/s]Best score: 1.3577: : 245iter [00:23, 12.24iter/s]Best score: 1.3577: : 245iter [00:23, 12.24iter/s]Best score: 1.3577: : 246iter [00:23, 12.24iter/s]Best score: 1.3577: : 247iter [00:23, 12.29iter/s]Best score: 1.3577: : 247iter [00:23, 12.29iter/s]Best score: 1.3577: : 248iter [00:23, 12.29iter/s]Best score: 1.3577: : 249iter [00:23, 12.26iter/s]
[ITER 250] Best: 0.7626
[PARAMS] Current best parameters:
  fx: 534.6464
  fy: 535.0247
  px: 1022.7824
  py: 771.5305
  k1: 0.01551504
  k2: -0.00503402
  k3: -0.01159799
  k4: 0.00455068
  p1: 0.00400525
  p2: 0.01560610
  p3: -0.01106953
  p4: -0.00747276
  s1: 0.0087
  s2: 0.0064
  s3: 0.0070
  s4: 0.0088
  tilt_x: -0.0239
  tilt_y: -0.0082
  scale: 2.9324
  rotation: 0.2277
Best score: 0.7626: : 249iter [00:23, 12.26iter/s]Best score: 0.7626: : 250iter [00:23, 12.26iter/s]Best score: 0.7626: : 251iter [00:23,  7.88iter/s]Best score: 0.7626: : 251iter [00:23,  7.88iter/s]Best score: 0.7626: : 252iter [00:24,  7.88iter/s]Best score: 0.7626: : 253iter [00:24,  8.80iter/s]Best score: 0.7626: : 253iter [00:24,  8.80iter/s]Best score: 0.7626: : 254iter [00:24,  8.80iter/s]Best score: 0.7626: : 255iter [00:24,  9.60iter/s]Best score: 0.7626: : 255iter [00:24,  9.60iter/s]Best score: 0.7626: : 256iter [00:24,  9.60iter/s]Best score: 0.7626: : 257iter [00:24, 10.27iter/s]Best score: 0.7626: : 257iter [00:24, 10.27iter/s]Best score: 0.7626: : 258iter [00:24, 10.27iter/s]Best score: 0.7626: : 259iter [00:24, 10.75iter/s]Best score: 0.5797: : 259iter [00:24, 10.75iter/s]Best score: 0.5797: : 260iter [00:24, 10.75iter/s]Best score: 0.5797: : 261iter [00:24, 10.96iter/s]Best score: 0.5797: : 261iter [00:24, 10.96iter/s]Best score: 0.5797: : 262iter [00:24, 10.96iter/s]Best score: 0.5797: : 263iter [00:24, 11.32iter/s]Best score: 0.5797: : 263iter [00:24, 11.32iter/s]Best score: 0.5797: : 264iter [00:25, 11.32iter/s]Best score: 0.5797: : 265iter [00:25, 11.57iter/s]Best score: 0.5797: : 265iter [00:25, 11.57iter/s]Best score: 0.5797: : 266iter [00:25, 11.57iter/s]Best score: 0.5797: : 267iter [00:25, 11.75iter/s]Best score: 0.5797: : 267iter [00:25, 11.75iter/s]Best score: 0.5797: : 268iter [00:25, 11.75iter/s]Best score: 0.5797: : 269iter [00:25, 11.89iter/s]Best score: 0.5797: : 269iter [00:25, 11.89iter/s]Best score: 0.5797: : 270iter [00:25, 11.89iter/s]Best score: 0.5797: : 271iter [00:25, 11.79iter/s]Best score: 0.5797: : 271iter [00:25, 11.79iter/s]Best score: 0.5797: : 272iter [00:25, 11.79iter/s]Best score: 0.5797: : 273iter [00:25, 11.91iter/s]Best score: 0.5797: : 273iter [00:25, 11.91iter/s]Best score: 0.5797: : 274iter [00:25, 11.91iter/s]Best score: 0.5797: : 275iter [00:25, 12.01iter/s]Best score: 0.5797: : 275iter [00:25, 12.01iter/s]Best score: 0.5797: : 276iter [00:26, 12.01iter/s]Best score: 0.5797: : 277iter [00:26, 12.11iter/s]Best score: 0.5797: : 277iter [00:26, 12.11iter/s]Best score: 0.5797: : 278iter [00:26, 12.11iter/s]Best score: 0.5797: : 279iter [00:26, 12.18iter/s]Best score: 0.5416: : 279iter [00:26, 12.18iter/s]Best score: 0.5416: : 280iter [00:26, 12.18iter/s]Best score: 0.5416: : 281iter [00:26, 12.02iter/s]Best score: 0.5416: : 281iter [00:26, 12.02iter/s]Best score: 0.5416: : 282iter [00:26, 12.02iter/s]Best score: 0.5416: : 283iter [00:26, 12.12iter/s]Best score: 0.5416: : 283iter [00:26, 12.12iter/s]Best score: 0.5416: : 284iter [00:26, 12.12iter/s]Best score: 0.5416: : 285iter [00:26, 12.14iter/s]Best score: 0.5416: : 285iter [00:26, 12.14iter/s]Best score: 0.5416: : 286iter [00:26, 12.14iter/s]Best score: 0.5416: : 287iter [00:26, 12.21iter/s]Best score: 0.5416: : 287iter [00:26, 12.21iter/s]Best score: 0.5416: : 288iter [00:27, 12.21iter/s]Best score: 0.5416: : 289iter [00:27, 12.25iter/s]Best score: 0.5081: : 289iter [00:27, 12.25iter/s]Best score: 0.5081: : 290iter [00:27, 12.25iter/s]Best score: 0.5081: : 291iter [00:27, 12.01iter/s]Best score: 0.5081: : 291iter [00:27, 12.01iter/s]Best score: 0.5081: : 292iter [00:27, 12.01iter/s]Best score: 0.5081: : 293iter [00:27, 12.10iter/s]Best score: 0.5081: : 293iter [00:27, 12.10iter/s]Best score: 0.5081: : 294iter [00:27, 12.10iter/s]Best score: 0.5081: : 295iter [00:27, 12.17iter/s]Best score: 0.5081: : 295iter [00:27, 12.17iter/s]Best score: 0.5081: : 296iter [00:27, 12.17iter/s]Best score: 0.5081: : 297iter [00:27, 12.23iter/s]Best score: 0.5081: : 297iter [00:27, 12.23iter/s]Best score: 0.5081: : 298iter [00:27, 12.23iter/s]Best score: 0.5081: : 299iter [00:27, 12.28iter/s]
[ITER 300] Best: 0.4542
[PARAMS] Current best parameters:
  fx: 525.0500
  fy: 525.5897
  px: 1018.3849
  py: 780.8757
  k1: 0.02483269
  k2: -0.01271401
  k3: -0.00508103
  k4: 0.00303578
  p1: 0.01239642
  p2: 0.01255908
  p3: -0.01312370
  p4: -0.00043433
  s1: 0.0011
  s2: 0.0034
  s3: 0.0113
  s4: -0.0001
  tilt_x: 0.0028
  tilt_y: 0.0065
  scale: 2.9506
  rotation: 0.1821
Best score: 0.4542: : 299iter [00:27, 12.28iter/s]Best score: 0.4542: : 300iter [00:28, 12.28iter/s]Best score: 0.4542: : 301iter [00:28,  7.89iter/s]Best score: 0.4542: : 301iter [00:28,  7.89iter/s]Best score: 0.4542: : 302iter [00:28,  7.89iter/s]Best score: 0.4542: : 303iter [00:28,  8.83iter/s]Best score: 0.4542: : 303iter [00:28,  8.83iter/s]Best score: 0.4542: : 304iter [00:28,  8.83iter/s]Best score: 0.4542: : 305iter [00:28,  9.57iter/s]Best score: 0.4542: : 305iter [00:28,  9.57iter/s]Best score: 0.4542: : 306iter [00:28,  9.57iter/s]Best score: 0.4542: : 307iter [00:28, 10.25iter/s]Best score: 0.4542: : 307iter [00:28, 10.25iter/s]Best score: 0.4542: : 308iter [00:28, 10.25iter/s]Best score: 0.4542: : 309iter [00:28, 10.80iter/s]Best score: 0.4542: : 309iter [00:29, 10.80iter/s]Best score: 0.4542: : 310iter [00:29, 10.80iter/s]Best score: 0.4542: : 311iter [00:29, 11.04iter/s]Best score: 0.4542: : 311iter [00:29, 11.04iter/s]Best score: 0.4542: : 312iter [00:29, 11.04iter/s]Best score: 0.4542: : 313iter [00:29, 11.38iter/s]Best score: 0.4542: : 313iter [00:29, 11.38iter/s]Best score: 0.4542: : 314iter [00:29, 11.38iter/s]Best score: 0.4542: : 315iter [00:29, 11.67iter/s]Best score: 0.4542: : 315iter [00:29, 11.67iter/s]Best score: 0.4542: : 316iter [00:29, 11.67iter/s]Best score: 0.4542: : 317iter [00:29, 11.86iter/s]Best score: 0.4542: : 317iter [00:29, 11.86iter/s]Best score: 0.4542: : 318iter [00:29, 11.86iter/s]Best score: 0.4542: : 319iter [00:29, 12.00iter/s]Best score: 0.4311: : 319iter [00:29, 12.00iter/s]Best score: 0.4311: : 320iter [00:29, 12.00iter/s]Best score: 0.4311: : 321iter [00:29, 11.89iter/s]Best score: 0.4311: : 321iter [00:30, 11.89iter/s]Best score: 0.4311: : 322iter [00:30, 11.89iter/s]Best score: 0.4311: : 323iter [00:30, 12.01iter/s]Best score: 0.4311: : 323iter [00:30, 12.01iter/s]Best score: 0.4311: : 324iter [00:30, 12.01iter/s]Best score: 0.4311: : 325iter [00:30, 12.05iter/s]Best score: 0.4311: : 325iter [00:30, 12.05iter/s]Best score: 0.4311: : 326iter [00:30, 12.05iter/s]Best score: 0.4311: : 327iter [00:30, 12.15iter/s]Best score: 0.4311: : 327iter [00:30, 12.15iter/s]Best score: 0.4311: : 328iter [00:30, 12.15iter/s]Best score: 0.4311: : 329iter [00:30, 12.22iter/s]Best score: 0.4002: : 329iter [00:30, 12.22iter/s]Best score: 0.4002: : 330iter [00:30, 12.22iter/s]Best score: 0.4002: : 331iter [00:30, 12.00iter/s]Best score: 0.4002: : 331iter [00:30, 12.00iter/s]Best score: 0.4002: : 332iter [00:30, 12.00iter/s]Best score: 0.4002: : 333iter [00:30, 12.11iter/s]Best score: 0.4002: : 333iter [00:31, 12.11iter/s]Best score: 0.4002: : 334iter [00:31, 12.11iter/s]Best score: 0.4002: : 335iter [00:31, 12.20iter/s]Best score: 0.4002: : 335iter [00:31, 12.20iter/s]Best score: 0.4002: : 336iter [00:31, 12.20iter/s]Best score: 0.4002: : 337iter [00:31, 12.22iter/s]Best score: 0.4002: : 337iter [00:31, 12.22iter/s]Best score: 0.4002: : 338iter [00:31, 12.22iter/s]Best score: 0.4002: : 339iter [00:31, 12.28iter/s]Best score: 0.3747: : 339iter [00:31, 12.28iter/s]Best score: 0.3747: : 340iter [00:31, 12.28iter/s]Best score: 0.3747: : 341iter [00:31, 12.10iter/s]Best score: 0.3747: : 341iter [00:31, 12.10iter/s]Best score: 0.3747: : 342iter [00:31, 12.10iter/s]Best score: 0.3747: : 343iter [00:31, 12.12iter/s]Best score: 0.3747: : 343iter [00:31, 12.12iter/s]Best score: 0.3747: : 344iter [00:31, 12.12iter/s]Best score: 0.3747: : 345iter [00:31, 12.18iter/s]Best score: 0.3747: : 345iter [00:31, 12.18iter/s]Best score: 0.3747: : 346iter [00:32, 12.18iter/s]Best score: 0.3747: : 347iter [00:32, 12.24iter/s]Best score: 0.3747: : 347iter [00:32, 12.24iter/s]Best score: 0.3747: : 348iter [00:32, 12.24iter/s]Best score: 0.3747: : 349iter [00:32, 12.27iter/s]
[ITER 350] Best: 0.3435
[PARAMS] Current best parameters:
  fx: 524.8312
  fy: 525.7225
  px: 1016.2546
  py: 782.5119
  k1: 0.01276095
  k2: -0.00620685
  k3: -0.00064971
  k4: 0.00045104
  p1: 0.00606874
  p2: 0.00830380
  p3: -0.00341962
  p4: 0.00356256
  s1: -0.0046
  s2: -0.0044
  s3: 0.0051
  s4: -0.0018
  tilt_x: 0.0221
  tilt_y: 0.0059
  scale: 2.9731
  rotation: 0.1507
Best score: 0.3435: : 349iter [00:32, 12.27iter/s]Best score: 0.3435: : 350iter [00:32, 12.27iter/s]Best score: 0.3435: : 351iter [00:32,  7.90iter/s]Best score: 0.3435: : 351iter [00:32,  7.90iter/s]Best score: 0.3435: : 352iter [00:32,  7.90iter/s]Best score: 0.3435: : 353iter [00:32,  8.86iter/s]Best score: 0.3435: : 353iter [00:32,  8.86iter/s]Best score: 0.3435: : 354iter [00:33,  8.86iter/s]Best score: 0.3435: : 355iter [00:33,  9.69iter/s]Best score: 0.3435: : 355iter [00:33,  9.69iter/s]Best score: 0.3435: : 356iter [00:33,  9.69iter/s]Best score: 0.3435: : 357iter [00:33, 10.34iter/s]Best score: 0.3435: : 357iter [00:33, 10.34iter/s]Best score: 0.3435: : 358iter [00:33, 10.34iter/s]Best score: 0.3435: : 359iter [00:33, 10.86iter/s]Best score: 0.2992: : 359iter [00:33, 10.86iter/s]Best score: 0.2992: : 360iter [00:33, 10.86iter/s]Best score: 0.2992: : 361iter [00:33, 11.09iter/s]Best score: 0.2992: : 361iter [00:33, 11.09iter/s]Best score: 0.2992: : 362iter [00:33, 11.09iter/s]Best score: 0.2992: : 363iter [00:33, 11.45iter/s]Best score: 0.2992: : 363iter [00:33, 11.45iter/s]Best score: 0.2992: : 364iter [00:33, 11.45iter/s]Best score: 0.2992: : 365iter [00:33, 11.72iter/s]Best score: 0.2992: : 365iter [00:33, 11.72iter/s]Best score: 0.2992: : 366iter [00:34, 11.72iter/s]Best score: 0.2992: : 367iter [00:34, 11.90iter/s]Best score: 0.2992: : 367iter [00:34, 11.90iter/s]Best score: 0.2992: : 368iter [00:34, 11.90iter/s]Best score: 0.2992: : 369iter [00:34, 12.04iter/s]Best score: 0.2992: : 369iter [00:34, 12.04iter/s]Best score: 0.2992: : 370iter [00:34, 12.04iter/s]Best score: 0.2992: : 371iter [00:34, 11.95iter/s]Best score: 0.2992: : 371iter [00:34, 11.95iter/s]Best score: 0.2992: : 372iter [00:34, 11.95iter/s]Best score: 0.2992: : 373iter [00:34, 12.08iter/s]Best score: 0.2992: : 373iter [00:34, 12.08iter/s]Best score: 0.2992: : 374iter [00:34, 12.08iter/s]Best score: 0.2992: : 375iter [00:34, 12.19iter/s]Best score: 0.2992: : 375iter [00:34, 12.19iter/s]Best score: 0.2992: : 376iter [00:34, 12.19iter/s]Best score: 0.2992: : 377iter [00:34, 12.26iter/s]Best score: 0.2992: : 377iter [00:34, 12.26iter/s]Best score: 0.2992: : 378iter [00:34, 12.26iter/s]Best score: 0.2992: : 379iter [00:34, 12.32iter/s]Best score: 0.2894: : 379iter [00:35, 12.32iter/s]Best score: 0.2894: : 380iter [00:35, 12.32iter/s]Best score: 0.2894: : 381iter [00:35, 12.10iter/s]Best score: 0.2894: : 381iter [00:35, 12.10iter/s]Best score: 0.2894: : 382iter [00:35, 12.10iter/s]Best score: 0.2894: : 383iter [00:35, 12.18iter/s]Best score: 0.2894: : 383iter [00:35, 12.18iter/s]Best score: 0.2894: : 384iter [00:35, 12.18iter/s]Best score: 0.2894: : 385iter [00:35, 12.25iter/s]Best score: 0.2894: : 385iter [00:35, 12.25iter/s]Best score: 0.2894: : 386iter [00:35, 12.25iter/s]Best score: 0.2894: : 387iter [00:35, 12.29iter/s]Best score: 0.2894: : 387iter [00:35, 12.29iter/s]Best score: 0.2894: : 388iter [00:35, 12.29iter/s]Best score: 0.2894: : 389iter [00:35, 12.31iter/s]Best score: 0.2589: : 389iter [00:35, 12.31iter/s]Best score: 0.2589: : 390iter [00:35, 12.31iter/s]Best score: 0.2589: : 391iter [00:35, 12.13iter/s]Best score: 0.2589: : 391iter [00:36, 12.13iter/s]Best score: 0.2589: : 392iter [00:36, 12.13iter/s]Best score: 0.2589: : 393iter [00:36, 12.22iter/s]Best score: 0.2589: : 393iter [00:36, 12.22iter/s]Best score: 0.2589: : 394iter [00:36, 12.22iter/s]Best score: 0.2589: : 395iter [00:36, 12.27iter/s]Best score: 0.2589: : 395iter [00:36, 12.27iter/s]Best score: 0.2589: : 396iter [00:36, 12.27iter/s]Best score: 0.2589: : 397iter [00:36, 12.32iter/s]Best score: 0.2589: : 397iter [00:36, 12.32iter/s]Best score: 0.2589: : 398iter [00:36, 12.32iter/s]Best score: 0.2589: : 399iter [00:36, 12.30iter/s]
[ITER 400] Best: 0.2315
[PARAMS] Current best parameters:
  fx: 524.0523
  fy: 524.9427
  px: 1019.8026
  py: 774.8689
  k1: 0.01027672
  k2: -0.00073316
  k3: -0.00385164
  k4: 0.00110309
  p1: 0.00692357
  p2: 0.01455950
  p3: -0.00644397
  p4: -0.00986756
  s1: -0.0037
  s2: 0.0103
  s3: 0.0055
  s4: 0.0068
  tilt_x: -0.0152
  tilt_y: 0.0030
  scale: 2.9737
  rotation: 0.1942
Best score: 0.2315: : 399iter [00:36, 12.30iter/s]Best score: 0.2315: : 400iter [00:37, 12.30iter/s]Best score: 0.2315: : 401iter [00:37,  7.92iter/s]Best score: 0.2315: : 401iter [00:37,  7.92iter/s]Best score: 0.2315: : 402iter [00:37,  7.92iter/s]Best score: 0.2315: : 403iter [00:37,  8.86iter/s]Best score: 0.2315: : 403iter [00:37,  8.86iter/s]Best score: 0.2315: : 404iter [00:37,  8.86iter/s]Best score: 0.2315: : 405iter [00:37,  9.68iter/s]Best score: 0.2315: : 405iter [00:37,  9.68iter/s]Best score: 0.2315: : 406iter [00:37,  9.68iter/s]Best score: 0.2315: : 407iter [00:37, 10.35iter/s]Best score: 0.2315: : 407iter [00:37, 10.35iter/s]Best score: 0.2315: : 408iter [00:37, 10.35iter/s]Best score: 0.2315: : 409iter [00:37, 10.87iter/s]Best score: 0.2315: : 409iter [00:37, 10.87iter/s]Best score: 0.2315: : 410iter [00:37, 10.87iter/s]Best score: 0.2315: : 411iter [00:37, 11.12iter/s]Best score: 0.2315: : 411iter [00:37, 11.12iter/s]Best score: 0.2315: : 412iter [00:38, 11.12iter/s]Best score: 0.2315: : 413iter [00:38, 11.47iter/s]Best score: 0.2315: : 413iter [00:38, 11.47iter/s]Best score: 0.2315: : 414iter [00:38, 11.47iter/s]Best score: 0.2315: : 415iter [00:38, 11.68iter/s]Best score: 0.2315: : 415iter [00:38, 11.68iter/s]Best score: 0.2315: : 416iter [00:38, 11.68iter/s]Best score: 0.2315: : 417iter [00:38, 11.85iter/s]Best score: 0.2315: : 417iter [00:38, 11.85iter/s]Best score: 0.2315: : 418iter [00:38, 11.85iter/s]Best score: 0.2315: : 419iter [00:38, 12.00iter/s]Best score: 0.2315: : 419iter [00:38, 12.00iter/s]Best score: 0.2315: : 420iter [00:38, 12.00iter/s]Best score: 0.2315: : 421iter [00:38, 11.92iter/s]Best score: 0.2315: : 421iter [00:38, 11.92iter/s]Best score: 0.2315: : 422iter [00:38, 11.92iter/s]Best score: 0.2315: : 423iter [00:38, 12.06iter/s]Best score: 0.2315: : 423iter [00:38, 12.06iter/s]Best score: 0.2315: : 424iter [00:39, 12.06iter/s]Best score: 0.2315: : 425iter [00:39, 12.17iter/s]Best score: 0.2315: : 425iter [00:39, 12.17iter/s]Best score: 0.2315: : 426iter [00:39, 12.17iter/s]Best score: 0.2315: : 427iter [00:39, 12.24iter/s]Best score: 0.2315: : 427iter [00:39, 12.24iter/s]Best score: 0.2315: : 428iter [00:39, 12.24iter/s]Best score: 0.2315: : 429iter [00:39, 12.30iter/s]Best score: 0.2218: : 429iter [00:39, 12.30iter/s]Best score: 0.2218: : 430iter [00:39, 12.30iter/s]Best score: 0.2218: : 431iter [00:39, 12.12iter/s]Best score: 0.2218: : 431iter [00:39, 12.12iter/s]Best score: 0.2218: : 432iter [00:39, 12.12iter/s]Best score: 0.2218: : 433iter [00:39, 12.21iter/s]Best score: 0.2218: : 433iter [00:39, 12.21iter/s]Best score: 0.2218: : 434iter [00:39, 12.21iter/s]Best score: 0.2218: : 435iter [00:39, 12.25iter/s]Best score: 0.2218: : 435iter [00:39, 12.25iter/s]Best score: 0.2218: : 436iter [00:40, 12.25iter/s]Best score: 0.2218: : 437iter [00:40, 12.28iter/s]Best score: 0.2218: : 437iter [00:40, 12.28iter/s]Best score: 0.2218: : 438iter [00:40, 12.28iter/s]Best score: 0.2218: : 439iter [00:40, 12.32iter/s]Best score: 0.2212: : 439iter [00:40, 12.32iter/s]Best score: 0.2212: : 440iter [00:40, 12.32iter/s]Best score: 0.2212: : 441iter [00:40, 12.14iter/s]Best score: 0.2212: : 441iter [00:40, 12.14iter/s]Best score: 0.2212: : 442iter [00:40, 12.14iter/s]Best score: 0.2212: : 443iter [00:40, 12.21iter/s]Best score: 0.2212: : 443iter [00:40, 12.21iter/s]Best score: 0.2212: : 444iter [00:40, 12.21iter/s]Best score: 0.2212: : 445iter [00:40, 12.24iter/s]Best score: 0.2212: : 445iter [00:40, 12.24iter/s]Best score: 0.2212: : 446iter [00:40, 12.24iter/s]Best score: 0.2212: : 447iter [00:40, 12.28iter/s]Best score: 0.2212: : 447iter [00:40, 12.28iter/s]Best score: 0.2212: : 448iter [00:41, 12.28iter/s]Best score: 0.2212: : 449iter [00:41, 12.29iter/s]
[ITER 450] Best: 0.2212
[PARAMS] Current best parameters:
  fx: 524.6265
  fy: 525.3864
  px: 1019.3312
  py: 774.1335
  k1: 0.01002556
  k2: -0.00167452
  k3: -0.00294119
  k4: 0.00085500
  p1: 0.00495276
  p2: 0.01265781
  p3: -0.00505559
  p4: -0.00938601
  s1: -0.0035
  s2: 0.0099
  s3: 0.0048
  s4: 0.0072
  tilt_x: -0.0147
  tilt_y: 0.0013
  scale: 2.9736
  rotation: 0.2007
Best score: 0.2212: : 449iter [00:41, 12.29iter/s]Best score: 0.2212: : 450iter [00:41, 12.29iter/s]Best score: 0.2212: : 451iter [00:41,  7.87iter/s]Best score: 0.2212: : 451iter [00:41,  7.87iter/s]Best score: 0.2212: : 452iter [00:41,  7.87iter/s]Best score: 0.2212: : 453iter [00:41,  8.84iter/s]Best score: 0.2212: : 453iter [00:41,  8.84iter/s]Best score: 0.2212: : 454iter [00:41,  8.84iter/s]Best score: 0.2212: : 455iter [00:41,  9.67iter/s]Best score: 0.2212: : 455iter [00:41,  9.67iter/s]Best score: 0.2212: : 456iter [00:41,  9.67iter/s]Best score: 0.2212: : 457iter [00:41, 10.36iter/s]Best score: 0.2212: : 457iter [00:42, 10.36iter/s]Best score: 0.2212: : 458iter [00:42, 10.36iter/s]Best score: 0.2212: : 459iter [00:42, 10.88iter/s]Best score: 0.2160: : 459iter [00:42, 10.88iter/s]Best score: 0.2160: : 460iter [00:42, 10.88iter/s]Best score: 0.2160: : 461iter [00:42, 11.11iter/s]Best score: 0.2160: : 461iter [00:42, 11.11iter/s]Best score: 0.2160: : 462iter [00:42, 11.11iter/s]Best score: 0.2160: : 463iter [00:42, 11.47iter/s]Best score: 0.2160: : 463iter [00:42, 11.47iter/s]Best score: 0.2160: : 464iter [00:42, 11.47iter/s]Best score: 0.2160: : 465iter [00:42, 11.73iter/s]Best score: 0.2160: : 465iter [00:42, 11.73iter/s]Best score: 0.2160: : 466iter [00:42, 11.73iter/s]Best score: 0.2160: : 467iter [00:42, 11.91iter/s]Best score: 0.2160: : 467iter [00:42, 11.91iter/s]Best score: 0.2160: : 468iter [00:42, 11.91iter/s]Best score: 0.2160: : 469iter [00:42, 12.06iter/s]Best score: 0.2072: : 469iter [00:43, 12.06iter/s]Best score: 0.2072: : 470iter [00:43, 12.06iter/s]Best score: 0.2072: : 471iter [00:43, 11.96iter/s]Best score: 0.2072: : 471iter [00:43, 11.96iter/s]Best score: 0.2072: : 472iter [00:43, 11.96iter/s]Best score: 0.2072: : 473iter [00:43, 12.09iter/s]Best score: 0.2072: : 473iter [00:43, 12.09iter/s]Best score: 0.2072: : 474iter [00:43, 12.09iter/s]Best score: 0.2072: : 475iter [00:43, 12.19iter/s]Best score: 0.2072: : 475iter [00:43, 12.19iter/s]Best score: 0.2072: : 476iter [00:43, 12.19iter/s]Best score: 0.2072: : 477iter [00:43, 12.25iter/s]Best score: 0.2072: : 477iter [00:43, 12.25iter/s]Best score: 0.2072: : 478iter [00:43, 12.25iter/s]Best score: 0.2072: : 479iter [00:43, 12.30iter/s]Best score: 0.2067: : 479iter [00:43, 12.30iter/s]Best score: 0.2067: : 480iter [00:43, 12.30iter/s]Best score: 0.2067: : 481iter [00:43, 12.12iter/s]Best score: 0.2067: : 481iter [00:43, 12.12iter/s]Best score: 0.2067: : 482iter [00:44, 12.12iter/s]Best score: 0.2067: : 483iter [00:44, 12.21iter/s]Best score: 0.2067: : 483iter [00:44, 12.21iter/s]Best score: 0.2067: : 484iter [00:44, 12.21iter/s]Best score: 0.2067: : 485iter [00:44, 12.28iter/s]Best score: 0.2067: : 485iter [00:44, 12.28iter/s]Best score: 0.2067: : 486iter [00:44, 12.28iter/s]Best score: 0.2067: : 487iter [00:44, 12.30iter/s]Best score: 0.2067: : 487iter [00:44, 12.30iter/s]Best score: 0.2067: : 488iter [00:44, 12.30iter/s]Best score: 0.2067: : 489iter [00:44, 12.29iter/s]Best score: 0.1959: : 489iter [00:44, 12.29iter/s]Best score: 0.1959: : 490iter [00:44, 12.29iter/s]Best score: 0.1959: : 491iter [00:44, 12.12iter/s]Best score: 0.1959: : 491iter [00:44, 12.12iter/s]Best score: 0.1959: : 492iter [00:44, 12.12iter/s]Best score: 0.1959: : 493iter [00:44, 12.19iter/s]Best score: 0.1959: : 493iter [00:44, 12.19iter/s]Best score: 0.1959: : 494iter [00:45, 12.19iter/s]Best score: 0.1959: : 495iter [00:45, 12.25iter/s]Best score: 0.1959: : 495iter [00:45, 12.25iter/s]Best score: 0.1959: : 496iter [00:45, 12.25iter/s]Best score: 0.1959: : 497iter [00:45, 12.29iter/s]Best score: 0.1959: : 497iter [00:45, 12.29iter/s]Best score: 0.1959: : 498iter [00:45, 12.29iter/s]Best score: 0.1959: : 499iter [00:45, 12.33iter/s]
[ITER 500] Best: 0.1955
[PARAMS] Current best parameters:
  fx: 523.4398
  fy: 524.1340
  px: 1011.9409
  py: 772.5820
  k1: 0.01535700
  k2: -0.01070013
  k3: 0.00453455
  k4: -0.00126619
  p1: 0.00099450
  p2: -0.01117080
  p3: -0.00139561
  p4: 0.00276287
  s1: 0.0079
  s2: -0.0011
  s3: 0.0023
  s4: -0.0037
  tilt_x: -0.0139
  tilt_y: -0.0123
  scale: 2.9702
  rotation: 0.1866
Best score: 0.1955: : 499iter [00:45, 12.33iter/s]Best score: 0.1955: : 500iter [00:45, 12.33iter/s]Best score: 0.1955: : 501iter [00:45,  7.94iter/s]Best score: 0.1955: : 501iter [00:45,  7.94iter/s]Best score: 0.1955: : 502iter [00:46,  7.94iter/s]Best score: 0.1955: : 503iter [00:46,  8.90iter/s]Best score: 0.1955: : 503iter [00:46,  8.90iter/s]Best score: 0.1955: : 504iter [00:46,  8.90iter/s]Best score: 0.1955: : 505iter [00:46,  9.73iter/s]Best score: 0.1955: : 505iter [00:46,  9.73iter/s]Best score: 0.1955: : 506iter [00:46,  9.73iter/s]Best score: 0.1955: : 507iter [00:46, 10.40iter/s]Best score: 0.1955: : 507iter [00:46, 10.40iter/s]Best score: 0.1955: : 508iter [00:46, 10.40iter/s]Best score: 0.1955: : 509iter [00:46, 10.94iter/s]Best score: 0.1920: : 509iter [00:46, 10.94iter/s]Best score: 0.1920: : 510iter [00:46, 10.94iter/s]Best score: 0.1920: : 511iter [00:46, 11.16iter/s]Best score: 0.1920: : 511iter [00:46, 11.16iter/s]Best score: 0.1920: : 512iter [00:46, 11.16iter/s]Best score: 0.1920: : 513iter [00:46, 11.51iter/s]Best score: 0.1920: : 513iter [00:46, 11.51iter/s]Best score: 0.1920: : 514iter [00:46, 11.51iter/s]Best score: 0.1920: : 515iter [00:46, 11.76iter/s]Best score: 0.1920: : 515iter [00:47, 11.76iter/s]Best score: 0.1920: : 516iter [00:47, 11.76iter/s]Best score: 0.1920: : 517iter [00:47, 11.95iter/s]Best score: 0.1920: : 517iter [00:47, 11.95iter/s]Best score: 0.1920: : 518iter [00:47, 11.95iter/s]Best score: 0.1920: : 519iter [00:47, 12.09iter/s]Best score: 0.1920: : 519iter [00:47, 12.09iter/s]Best score: 0.1920: : 520iter [00:47, 12.09iter/s]Best score: 0.1920: : 521iter [00:47, 11.95iter/s]Best score: 0.1920: : 521iter [00:47, 11.95iter/s]Best score: 0.1920: : 522iter [00:47, 11.95iter/s]Best score: 0.1920: : 523iter [00:47, 12.08iter/s]Best score: 0.1920: : 523iter [00:47, 12.08iter/s]Best score: 0.1920: : 524iter [00:47, 12.08iter/s]Best score: 0.1920: : 525iter [00:47, 12.18iter/s]Best score: 0.1920: : 525iter [00:47, 12.18iter/s]Best score: 0.1920: : 526iter [00:47, 12.18iter/s]Best score: 0.1920: : 527iter [00:47, 12.21iter/s]Best score: 0.1920: : 527iter [00:48, 12.21iter/s]Best score: 0.1920: : 528iter [00:48, 12.21iter/s]Best score: 0.1920: : 529iter [00:48, 12.25iter/s]Best score: 0.1912: : 529iter [00:48, 12.25iter/s]Best score: 0.1912: : 530iter [00:48, 12.25iter/s]Best score: 0.1912: : 531iter [00:48, 12.08iter/s]Best score: 0.1912: : 531iter [00:48, 12.08iter/s]Best score: 0.1912: : 532iter [00:48, 12.08iter/s]Best score: 0.1912: : 533iter [00:48, 12.17iter/s]Best score: 0.1912: : 533iter [00:48, 12.17iter/s]Best score: 0.1912: : 534iter [00:48, 12.17iter/s]Best score: 0.1912: : 535iter [00:48, 12.24iter/s]Best score: 0.1912: : 535iter [00:48, 12.24iter/s]Best score: 0.1912: : 536iter [00:48, 12.24iter/s]Best score: 0.1912: : 537iter [00:48, 12.29iter/s]Best score: 0.1912: : 537iter [00:48, 12.29iter/s]Best score: 0.1912: : 538iter [00:48, 12.29iter/s]Best score: 0.1912: : 539iter [00:48, 12.28iter/s]Best score: 0.1894: : 539iter [00:49, 12.28iter/s]Best score: 0.1894: : 540iter [00:49, 12.28iter/s]Best score: 0.1894: : 541iter [00:49, 12.11iter/s]Best score: 0.1894: : 541iter [00:49, 12.11iter/s]Best score: 0.1894: : 542iter [00:49, 12.11iter/s]Best score: 0.1894: : 543iter [00:49, 12.20iter/s]Best score: 0.1894: : 543iter [00:49, 12.20iter/s]Best score: 0.1894: : 544iter [00:49, 12.20iter/s]Best score: 0.1894: : 545iter [00:49, 12.24iter/s]Best score: 0.1894: : 545iter [00:49, 12.24iter/s]Best score: 0.1894: : 546iter [00:49, 12.24iter/s]Best score: 0.1894: : 547iter [00:49, 12.29iter/s]Best score: 0.1894: : 547iter [00:49, 12.29iter/s]Best score: 0.1894: : 548iter [00:49, 12.29iter/s]Best score: 0.1894: : 549iter [00:49, 12.31iter/s]
[ITER 550] Best: 0.1885
[PARAMS] Current best parameters:
  fx: 526.3880
  fy: 527.1646
  px: 1011.0645
  py: 771.6795
  k1: 0.00280006
  k2: 0.00088732
  k3: -0.00005179
  k4: -0.00069951
  p1: -0.00147887
  p2: -0.00980007
  p3: 0.00542092
  p4: -0.00010203
  s1: 0.0005
  s2: 0.0000
  s3: -0.0005
  s4: -0.0029
  tilt_x: -0.0135
  tilt_y: -0.0053
  scale: 2.9821
  rotation: 0.1930
Best score: 0.1885: : 549iter [00:49, 12.31iter/s]Best score: 0.1885: : 550iter [00:50, 12.31iter/s]Best score: 0.1885: : 551iter [00:50,  7.85iter/s]Best score: 0.1885: : 551iter [00:50,  7.85iter/s]Best score: 0.1885: : 552iter [00:50,  7.85iter/s]Best score: 0.1885: : 553iter [00:50,  8.81iter/s]Best score: 0.1885: : 553iter [00:50,  8.81iter/s]Best score: 0.1885: : 554iter [00:50,  8.81iter/s]Best score: 0.1885: : 555iter [00:50,  9.65iter/s]Best score: 0.1885: : 555iter [00:50,  9.65iter/s]Best score: 0.1885: : 556iter [00:50,  9.65iter/s]Best score: 0.1885: : 557iter [00:50, 10.35iter/s]Best score: 0.1885: : 557iter [00:50, 10.35iter/s]Best score: 0.1885: : 558iter [00:50, 10.35iter/s]Best score: 0.1885: : 559iter [00:50, 10.85iter/s]Best score: 0.1858: : 559iter [00:50, 10.85iter/s]Best score: 0.1858: : 560iter [00:51, 10.85iter/s]Best score: 0.1858: : 561iter [00:51, 11.10iter/s]Best score: 0.1858: : 561iter [00:51, 11.10iter/s]Best score: 0.1858: : 562iter [00:51, 11.10iter/s]Best score: 0.1858: : 563iter [00:51, 11.39iter/s]Best score: 0.1858: : 563iter [00:51, 11.39iter/s]Best score: 0.1858: : 564iter [00:51, 11.39iter/s]Best score: 0.1858: : 565iter [00:51, 11.68iter/s]Best score: 0.1858: : 565iter [00:51, 11.68iter/s]Best score: 0.1858: : 566iter [00:51, 11.68iter/s]Best score: 0.1858: : 567iter [00:51, 11.90iter/s]Best score: 0.1858: : 567iter [00:51, 11.90iter/s]Best score: 0.1858: : 568iter [00:51, 11.90iter/s]Best score: 0.1858: : 569iter [00:51, 12.05iter/s]Best score: 0.1858: : 569iter [00:51, 12.05iter/s]Best score: 0.1858: : 570iter [00:51, 12.05iter/s]Best score: 0.1858: : 571iter [00:51, 11.95iter/s]Best score: 0.1858: : 571iter [00:51, 11.95iter/s]Best score: 0.1858: : 572iter [00:52, 11.95iter/s]Best score: 0.1858: : 573iter [00:52, 12.09iter/s]Best score: 0.1858: : 573iter [00:52, 12.09iter/s]Best score: 0.1858: : 574iter [00:52, 12.09iter/s]Best score: 0.1858: : 575iter [00:52, 12.15iter/s]Best score: 0.1858: : 575iter [00:52, 12.15iter/s]Best score: 0.1858: : 576iter [00:52, 12.15iter/s]Best score: 0.1858: : 577iter [00:52, 12.17iter/s]Best score: 0.1858: : 577iter [00:52, 12.17iter/s]Best score: 0.1858: : 578iter [00:52, 12.17iter/s]Best score: 0.1858: : 579iter [00:52, 12.23iter/s]Best score: 0.1846: : 579iter [00:52, 12.23iter/s]Best score: 0.1846: : 580iter [00:52, 12.23iter/s]Best score: 0.1846: : 581iter [00:52, 12.07iter/s]Best score: 0.1846: : 581iter [00:52, 12.07iter/s]Best score: 0.1846: : 582iter [00:52, 12.07iter/s]Best score: 0.1846: : 583iter [00:52, 12.17iter/s]Best score: 0.1846: : 583iter [00:52, 12.17iter/s]Best score: 0.1846: : 584iter [00:53, 12.17iter/s]Best score: 0.1846: : 585iter [00:53, 12.24iter/s]Best score: 0.1846: : 585iter [00:53, 12.24iter/s]Best score: 0.1846: : 586iter [00:53, 12.24iter/s]Best score: 0.1846: : 587iter [00:53, 12.30iter/s]Best score: 0.1846: : 587iter [00:53, 12.30iter/s]Best score: 0.1846: : 588iter [00:53, 12.30iter/s]Best score: 0.1846: : 589iter [00:53, 12.33iter/s]Best score: 0.1846: : 589iter [00:53, 12.33iter/s]Best score: 0.1846: : 590iter [00:53, 12.33iter/s]Best score: 0.1846: : 591iter [00:53, 12.15iter/s]Best score: 0.1846: : 591iter [00:53, 12.15iter/s]Best score: 0.1846: : 592iter [00:53, 12.15iter/s]Best score: 0.1846: : 593iter [00:53, 12.22iter/s]Best score: 0.1846: : 593iter [00:53, 12.22iter/s]Best score: 0.1846: : 594iter [00:53, 12.22iter/s]Best score: 0.1846: : 595iter [00:53, 12.28iter/s]Best score: 0.1846: : 595iter [00:53, 12.28iter/s]Best score: 0.1846: : 596iter [00:53, 12.28iter/s]Best score: 0.1846: : 597iter [00:53, 12.30iter/s]Best score: 0.1846: : 597iter [00:54, 12.30iter/s]Best score: 0.1846: : 598iter [00:54, 12.30iter/s]Best score: 0.1846: : 599iter [00:54, 12.33iter/s]
[ITER 600] Best: 0.1846
[PARAMS] Current best parameters:
  fx: 525.7386
  fy: 526.5262
  px: 1011.1508
  py: 771.2052
  k1: 0.00589995
  k2: -0.00174672
  k3: 0.00076171
  k4: -0.00074960
  p1: -0.00085351
  p2: -0.01202325
  p3: 0.00573197
  p4: 0.00021430
  s1: 0.0016
  s2: 0.0004
  s3: -0.0028
  s4: -0.0025
  tilt_x: -0.0164
  tilt_y: -0.0103
  scale: 2.9790
  rotation: 0.2053
Best score: 0.1846: : 599iter [00:54, 12.33iter/s]Best score: 0.1846: : 600iter [00:54, 12.33iter/s]Best score: 0.1846: : 601iter [00:54,  7.92iter/s]Best score: 0.1846: : 601iter [00:54,  7.92iter/s]Best score: 0.1846: : 602iter [00:54,  7.92iter/s]Best score: 0.1846: : 603iter [00:54,  8.88iter/s]Best score: 0.1846: : 603iter [00:54,  8.88iter/s]Best score: 0.1846: : 604iter [00:54,  8.88iter/s]Best score: 0.1846: : 605iter [00:54,  9.68iter/s]Best score: 0.1846: : 605iter [00:55,  9.68iter/s]Best score: 0.1846: : 606iter [00:55,  9.68iter/s]Best score: 0.1846: : 607iter [00:55, 10.36iter/s]Best score: 0.1846: : 607iter [00:55, 10.36iter/s]Best score: 0.1846: : 608iter [00:55, 10.36iter/s]Best score: 0.1846: : 609iter [00:55, 10.85iter/s]Best score: 0.1846: : 609iter [00:55, 10.85iter/s]Best score: 0.1846: : 610iter [00:55, 10.85iter/s]Best score: 0.1846: : 611iter [00:55, 11.10iter/s]Best score: 0.1846: : 611iter [00:55, 11.10iter/s]Best score: 0.1846: : 612iter [00:55, 11.10iter/s]Best score: 0.1846: : 613iter [00:55, 11.46iter/s]Best score: 0.1846: : 613iter [00:55, 11.46iter/s]Best score: 0.1846: : 614iter [00:55, 11.46iter/s]Best score: 0.1846: : 615iter [00:55, 11.68iter/s]Best score: 0.1846: : 615iter [00:55, 11.68iter/s]Best score: 0.1846: : 616iter [00:55, 11.68iter/s]Best score: 0.1846: : 617iter [00:55, 11.89iter/s]Best score: 0.1846: : 617iter [00:55, 11.89iter/s]Best score: 0.1846: : 618iter [00:56, 11.89iter/s]Best score: 0.1846: : 619iter [00:56, 12.03iter/s]Best score: 0.1844: : 619iter [00:56, 12.03iter/s]Best score: 0.1844: : 620iter [00:56, 12.03iter/s]Best score: 0.1844: : 621iter [00:56, 11.94iter/s]Best score: 0.1844: : 621iter [00:56, 11.94iter/s]Best score: 0.1844: : 622iter [00:56, 11.94iter/s]Best score: 0.1844: : 623iter [00:56, 12.07iter/s]Best score: 0.1844: : 623iter [00:56, 12.07iter/s]Best score: 0.1844: : 624iter [00:56, 12.07iter/s]Best score: 0.1844: : 625iter [00:56, 12.16iter/s]Best score: 0.1844: : 625iter [00:56, 12.16iter/s]Best score: 0.1844: : 626iter [00:56, 12.16iter/s]Best score: 0.1844: : 627iter [00:56, 12.24iter/s]Best score: 0.1844: : 627iter [00:56, 12.24iter/s]Best score: 0.1844: : 628iter [00:56, 12.24iter/s]Best score: 0.1844: : 629iter [00:56, 12.23iter/s]Best score: 0.1838: : 629iter [00:56, 12.23iter/s]Best score: 0.1838: : 630iter [00:57, 12.23iter/s]Best score: 0.1838: : 631iter [00:57, 12.08iter/s]Best score: 0.1838: : 631iter [00:57, 12.08iter/s]Best score: 0.1838: : 632iter [00:57, 12.08iter/s]Best score: 0.1838: : 633iter [00:57, 12.14iter/s]Best score: 0.1838: : 633iter [00:57, 12.14iter/s]Best score: 0.1838: : 634iter [00:57, 12.14iter/s]Best score: 0.1838: : 635iter [00:57, 12.22iter/s]Best score: 0.1838: : 635iter [00:57, 12.22iter/s]Best score: 0.1838: : 636iter [00:57, 12.22iter/s]Best score: 0.1838: : 637iter [00:57, 12.27iter/s]Best score: 0.1838: : 637iter [00:57, 12.27iter/s]Best score: 0.1838: : 638iter [00:57, 12.27iter/s]Best score: 0.1838: : 639iter [00:57, 12.26iter/s]Best score: 0.1830: : 639iter [00:57, 12.26iter/s]Best score: 0.1830: : 640iter [00:57, 12.26iter/s]Best score: 0.1830: : 641iter [00:57, 12.10iter/s]Best score: 0.1830: : 641iter [00:57, 12.10iter/s]Best score: 0.1830: : 642iter [00:58, 12.10iter/s]Best score: 0.1830: : 643iter [00:58, 12.19iter/s]Best score: 0.1830: : 643iter [00:58, 12.19iter/s]Best score: 0.1830: : 644iter [00:58, 12.19iter/s]Best score: 0.1830: : 645iter [00:58, 12.25iter/s]Best score: 0.1830: : 645iter [00:58, 12.25iter/s]Best score: 0.1830: : 646iter [00:58, 12.25iter/s]Best score: 0.1830: : 647iter [00:58, 12.20iter/s]Best score: 0.1830: : 647iter [00:58, 12.20iter/s]Best score: 0.1830: : 648iter [00:58, 12.20iter/s]Best score: 0.1830: : 649iter [00:58, 12.23iter/s]
[ITER 650] Best: 0.1829
[PARAMS] Current best parameters:
  fx: 524.8695
  fy: 525.6524
  px: 1011.7917
  py: 771.0900
  k1: 0.00847735
  k2: -0.00362605
  k3: 0.00136958
  k4: -0.00079508
  p1: -0.00081745
  p2: -0.01188765
  p3: 0.00266304
  p4: 0.00197619
  s1: 0.0045
  s2: -0.0010
  s3: -0.0002
  s4: -0.0042
  tilt_x: -0.0170
  tilt_y: -0.0130
  scale: 2.9766
  rotation: 0.1968
Best score: 0.1829: : 649iter [00:58, 12.23iter/s]Best score: 0.1829: : 650iter [00:58, 12.23iter/s]Best score: 0.1829: : 651iter [00:58,  7.88iter/s]Best score: 0.1829: : 651iter [00:59,  7.88iter/s]Best score: 0.1829: : 652iter [00:59,  7.88iter/s]Best score: 0.1829: : 653iter [00:59,  8.82iter/s]Best score: 0.1829: : 653iter [00:59,  8.82iter/s]Best score: 0.1829: : 654iter [00:59,  8.82iter/s]Best score: 0.1829: : 655iter [00:59,  8.08iter/s]Best score: 0.1829: : 655iter [00:59,  8.08iter/s]Best score: 0.1829: : 656iter [00:59,  8.08iter/s]Best score: 0.1829: : 657iter [00:59,  9.02iter/s]Best score: 0.1829: : 657iter [00:59,  9.02iter/s]Best score: 0.1829: : 658iter [00:59,  9.02iter/s]Best score: 0.1829: : 659iter [00:59,  9.84iter/s]Best score: 0.1828: : 659iter [00:59,  9.84iter/s]Best score: 0.1828: : 660iter [00:59,  9.84iter/s]Best score: 0.1828: : 661iter [00:59, 10.33iter/s]Best score: 0.1828: : 661iter [01:00, 10.33iter/s]Best score: 0.1828: : 662iter [01:00, 10.33iter/s]Best score: 0.1828: : 663iter [01:00, 10.85iter/s]Best score: 0.1828: : 663iter [01:00, 10.85iter/s]Best score: 0.1828: : 664iter [01:00, 10.85iter/s]Best score: 0.1828: : 665iter [01:00, 11.17iter/s]Best score: 0.1828: : 665iter [01:00, 11.17iter/s]Best score: 0.1828: : 666iter [01:00, 11.17iter/s]Best score: 0.1828: : 667iter [01:00, 11.51iter/s]Best score: 0.1828: : 667iter [01:00, 11.51iter/s]Best score: 0.1828: : 668iter [01:00, 11.51iter/s]Best score: 0.1828: : 669iter [01:00, 11.76iter/s]Best score: 0.1828: : 669iter [01:00, 11.76iter/s]Best score: 0.1828: : 670iter [01:00, 11.76iter/s]Best score: 0.1828: : 671iter [01:00, 11.74iter/s]Best score: 0.1828: : 671iter [01:00, 11.74iter/s]Best score: 0.1828: : 672iter [01:00, 11.74iter/s]Best score: 0.1828: : 673iter [01:00, 11.93iter/s]Best score: 0.1828: : 673iter [01:01, 11.93iter/s]Best score: 0.1828: : 674iter [01:01, 11.93iter/s]Best score: 0.1828: : 675iter [01:01, 12.07iter/s]Best score: 0.1828: : 675iter [01:01, 12.07iter/s]Best score: 0.1828: : 676iter [01:01, 12.07iter/s]Best score: 0.1828: : 677iter [01:01, 12.17iter/s]Best score: 0.1828: : 677iter [01:01, 12.17iter/s]Best score: 0.1828: : 678iter [01:01, 12.17iter/s]Best score: 0.1828: : 679iter [01:01, 12.21iter/s]Best score: 0.1828: : 679iter [01:01, 12.21iter/s]Best score: 0.1828: : 680iter [01:01, 12.21iter/s]Best score: 0.1828: : 681iter [01:01, 12.01iter/s]Best score: 0.1828: : 681iter [01:01, 12.01iter/s]Best score: 0.1828: : 682iter [01:01, 12.01iter/s]Best score: 0.1828: : 683iter [01:01, 12.09iter/s]Best score: 0.1828: : 683iter [01:01, 12.09iter/s]Best score: 0.1828: : 684iter [01:01, 12.09iter/s]Best score: 0.1828: : 685iter [01:01, 12.19iter/s]Best score: 0.1828: : 685iter [01:01, 12.19iter/s]Best score: 0.1828: : 686iter [01:02, 12.19iter/s]Best score: 0.1828: : 687iter [01:02, 12.24iter/s]Best score: 0.1828: : 687iter [01:02, 12.24iter/s]Best score: 0.1828: : 688iter [01:02, 12.24iter/s]Best score: 0.1828: : 689iter [01:02, 12.23iter/s]Best score: 0.1828: : 689iter [01:02, 12.23iter/s]Best score: 0.1828: : 690iter [01:02, 12.23iter/s]Best score: 0.1828: : 691iter [01:02, 12.06iter/s]Best score: 0.1828: : 691iter [01:02, 12.06iter/s]Best score: 0.1828: : 692iter [01:02, 12.06iter/s]Best score: 0.1828: : 693iter [01:02, 12.16iter/s]Best score: 0.1828: : 693iter [01:02, 12.16iter/s]Best score: 0.1828: : 694iter [01:02, 12.16iter/s]Best score: 0.1828: : 695iter [01:02, 12.23iter/s]Best score: 0.1828: : 695iter [01:02, 12.23iter/s]Best score: 0.1828: : 696iter [01:02, 12.23iter/s]Best score: 0.1828: : 697iter [01:02, 12.27iter/s]Best score: 0.1828: : 697iter [01:02, 12.27iter/s]Best score: 0.1828: : 698iter [01:03, 12.27iter/s]Best score: 0.1828: : 699iter [01:03, 12.33iter/s]
[ITER 700] Best: 0.1828
[PARAMS] Current best parameters:
  fx: 524.8759
  fy: 525.6705
  px: 1011.7926
  py: 771.0303
  k1: 0.00844676
  k2: -0.00365189
  k3: 0.00141320
  k4: -0.00080920
  p1: -0.00079972
  p2: -0.01204042
  p3: 0.00299240
  p4: 0.00189488
  s1: 0.0043
  s2: -0.0008
  s3: -0.0006
  s4: -0.0041
  tilt_x: -0.0174
  tilt_y: -0.0133
  scale: 2.9767
  rotation: 0.1966
Best score: 0.1828: : 699iter [01:03, 12.33iter/s]Best score: 0.1828: : 700iter [01:03, 12.33iter/s]Best score: 0.1828: : 701iter [01:03,  7.91iter/s]Best score: 0.1828: : 701iter [01:03,  7.91iter/s]Best score: 0.1828: : 702iter [01:03,  7.91iter/s]Best score: 0.1828: : 703iter [01:03,  8.88iter/s]Best score: 0.1828: : 703iter [01:03,  8.88iter/s]Best score: 0.1828: : 704iter [01:03,  8.88iter/s]Best score: 0.1828: : 705iter [01:03,  9.71iter/s]Best score: 0.1828: : 705iter [01:03,  9.71iter/s]Best score: 0.1828: : 706iter [01:04,  9.71iter/s]Best score: 0.1828: : 707iter [01:04, 10.39iter/s]Best score: 0.1828: : 707iter [01:04, 10.39iter/s]Best score: 0.1828: : 708iter [01:04, 10.39iter/s]Best score: 0.1828: : 709iter [01:04, 10.83iter/s]Best score: 0.1826: : 709iter [01:04, 10.83iter/s]Best score: 0.1826: : 710iter [01:04, 10.83iter/s]Best score: 0.1826: : 711iter [01:04, 11.04iter/s]Best score: 0.1826: : 711iter [01:04, 11.04iter/s]Best score: 0.1826: : 712iter [01:04, 11.04iter/s]Best score: 0.1826: : 713iter [01:04, 11.39iter/s]Best score: 0.1826: : 713iter [01:04, 11.39iter/s]Best score: 0.1826: : 714iter [01:04, 11.39iter/s]Best score: 0.1826: : 715iter [01:04, 11.67iter/s]Best score: 0.1826: : 715iter [01:04, 11.67iter/s]Best score: 0.1826: : 716iter [01:04, 11.67iter/s]Best score: 0.1826: : 717iter [01:04, 11.88iter/s]Best score: 0.1826: : 717iter [01:04, 11.88iter/s]Best score: 0.1826: : 718iter [01:04, 11.88iter/s]Best score: 0.1826: : 719iter [01:04, 12.04iter/s]Best score: 0.1822: : 719iter [01:05, 12.04iter/s]Best score: 0.1822: : 720iter [01:05, 12.04iter/s]Best score: 0.1822: : 721iter [01:05, 11.95iter/s]Best score: 0.1822: : 721iter [01:05, 11.95iter/s]Best score: 0.1822: : 722iter [01:05, 11.95iter/s]Best score: 0.1822: : 723iter [01:05, 12.06iter/s]Best score: 0.1822: : 723iter [01:05, 12.06iter/s]Best score: 0.1822: : 724iter [01:05, 12.06iter/s]Best score: 0.1822: : 725iter [01:05, 12.10iter/s]Best score: 0.1822: : 725iter [01:05, 12.10iter/s]Best score: 0.1822: : 726iter [01:05, 12.10iter/s]Best score: 0.1822: : 727iter [01:05, 12.19iter/s]Best score: 0.1822: : 727iter [01:05, 12.19iter/s]Best score: 0.1822: : 728iter [01:05, 12.19iter/s]Best score: 0.1822: : 729iter [01:05, 12.26iter/s]Best score: 0.1818: : 729iter [01:05, 12.26iter/s]Best score: 0.1818: : 730iter [01:05, 12.26iter/s]Best score: 0.1818: : 731iter [01:05, 12.07iter/s]Best score: 0.1818: : 731iter [01:06, 12.07iter/s]Best score: 0.1818: : 732iter [01:06, 12.07iter/s]Best score: 0.1818: : 733iter [01:06, 12.14iter/s]Best score: 0.1818: : 733iter [01:06, 12.14iter/s]Best score: 0.1818: : 734iter [01:06, 12.14iter/s]Best score: 0.1818: : 735iter [01:06, 12.20iter/s]Best score: 0.1818: : 735iter [01:06, 12.20iter/s]Best score: 0.1818: : 736iter [01:06, 12.20iter/s]Best score: 0.1818: : 737iter [01:06, 12.24iter/s]Best score: 0.1818: : 737iter [01:06, 12.24iter/s]Best score: 0.1818: : 738iter [01:06, 12.24iter/s]Best score: 0.1818: : 739iter [01:06, 12.26iter/s]Best score: 0.1818: : 739iter [01:06, 12.26iter/s]Best score: 0.1818: : 740iter [01:06, 12.26iter/s]Best score: 0.1818: : 741iter [01:06, 12.09iter/s]Best score: 0.1818: : 741iter [01:06, 12.09iter/s]Best score: 0.1818: : 742iter [01:06, 12.09iter/s]Best score: 0.1818: : 743iter [01:06, 12.17iter/s]Best score: 0.1818: : 743iter [01:07, 12.17iter/s]Best score: 0.1818: : 744iter [01:07, 12.17iter/s]Best score: 0.1818: : 745iter [01:07, 12.24iter/s]Best score: 0.1818: : 745iter [01:07, 12.24iter/s]Best score: 0.1818: : 746iter [01:07, 12.24iter/s]Best score: 0.1818: : 747iter [01:07, 12.27iter/s]Best score: 0.1818: : 747iter [01:07, 12.27iter/s]Best score: 0.1818: : 748iter [01:07, 12.27iter/s]Best score: 0.1818: : 749iter [01:07, 12.30iter/s]
[ITER 750] Best: 0.1818
[PARAMS] Current best parameters:
  fx: 524.8149
  fy: 525.5985
  px: 1011.7371
  py: 770.6787
  k1: 0.01124112
  k2: -0.00881510
  k3: 0.00490609
  k4: -0.00163122
  p1: -0.00029036
  p2: -0.01356233
  p3: 0.00725813
  p4: -0.00029508
  s1: 0.0013
  s2: 0.0016
  s3: -0.0056
  s4: -0.0022
  tilt_x: -0.0199
  tilt_y: -0.0160
  scale: 2.9755
  rotation: 0.2030
Best score: 0.1818: : 749iter [01:07, 12.30iter/s]Best score: 0.1818: : 750iter [01:07, 12.30iter/s]Best score: 0.1818: : 751iter [01:07,  7.93iter/s]Best score: 0.1818: : 751iter [01:07,  7.93iter/s]Best score: 0.1818: : 752iter [01:08,  7.93iter/s]Best score: 0.1818: : 753iter [01:08,  8.87iter/s]Best score: 0.1818: : 753iter [01:08,  8.87iter/s]Best score: 0.1818: : 754iter [01:08,  8.87iter/s]Best score: 0.1818: : 755iter [01:08,  9.68iter/s]Best score: 0.1818: : 755iter [01:08,  9.68iter/s]Best score: 0.1818: : 756iter [01:08,  9.68iter/s]Best score: 0.1818: : 757iter [01:08, 10.37iter/s]Best score: 0.1818: : 757iter [01:08, 10.37iter/s]Best score: 0.1818: : 758iter [01:08, 10.37iter/s]Best score: 0.1818: : 759iter [01:08, 10.92iter/s]Best score: 0.1811: : 759iter [01:08, 10.92iter/s]Best score: 0.1811: : 760iter [01:08, 10.92iter/s]Best score: 0.1811: : 761iter [01:08, 11.14iter/s]Best score: 0.1811: : 761iter [01:08, 11.14iter/s]Best score: 0.1811: : 762iter [01:08, 11.14iter/s]Best score: 0.1811: : 763iter [01:08, 11.49iter/s]Best score: 0.1811: : 763iter [01:08, 11.49iter/s]Best score: 0.1811: : 764iter [01:09, 11.49iter/s]Best score: 0.1811: : 765iter [01:09, 11.75iter/s]Best score: 0.1811: : 765iter [01:09, 11.75iter/s]Best score: 0.1811: : 766iter [01:09, 11.75iter/s]Best score: 0.1811: : 767iter [01:09, 11.94iter/s]Best score: 0.1811: : 767iter [01:09, 11.94iter/s]Best score: 0.1811: : 768iter [01:09, 11.94iter/s]Best score: 0.1811: : 769iter [01:09, 12.08iter/s]Best score: 0.1809: : 769iter [01:09, 12.08iter/s]Best score: 0.1809: : 770iter [01:09, 12.08iter/s]Best score: 0.1809: : 771iter [01:09, 11.98iter/s]Best score: 0.1809: : 771iter [01:09, 11.98iter/s]Best score: 0.1809: : 772iter [01:09, 11.98iter/s]Best score: 0.1809: : 773iter [01:09, 12.09iter/s]Best score: 0.1809: : 773iter [01:09, 12.09iter/s]Best score: 0.1809: : 774iter [01:09, 12.09iter/s]Best score: 0.1809: : 775iter [01:09, 12.07iter/s]Best score: 0.1809: : 775iter [01:09, 12.07iter/s]Best score: 0.1809: : 776iter [01:10, 12.07iter/s]Best score: 0.1809: : 777iter [01:10, 12.05iter/s]Best score: 0.1809: : 777iter [01:10, 12.05iter/s]Best score: 0.1809: : 778iter [01:10, 12.05iter/s]Best score: 0.1809: : 779iter [01:10, 12.08iter/s]Best score: 0.1809: : 779iter [01:10, 12.08iter/s]Best score: 0.1809: : 780iter [01:10, 12.08iter/s]Best score: 0.1809: : 781iter [01:10, 11.87iter/s]Best score: 0.1809: : 781iter [01:10, 11.87iter/s]Best score: 0.1809: : 782iter [01:10, 11.87iter/s]Best score: 0.1809: : 783iter [01:10, 12.02iter/s]Best score: 0.1809: : 783iter [01:10, 12.02iter/s]Best score: 0.1809: : 784iter [01:10, 12.02iter/s]Best score: 0.1809: : 785iter [01:10, 12.10iter/s]Best score: 0.1809: : 785iter [01:10, 12.10iter/s]Best score: 0.1809: : 786iter [01:10, 12.10iter/s]Best score: 0.1809: : 787iter [01:10, 12.20iter/s]Best score: 0.1809: : 787iter [01:10, 12.20iter/s]Best score: 0.1809: : 788iter [01:11, 12.20iter/s]Best score: 0.1809: : 789iter [01:11, 12.27iter/s]Best score: 0.1809: : 789iter [01:11, 12.27iter/s]Best score: 0.1809: : 790iter [01:11, 12.27iter/s]Best score: 0.1809: : 791iter [01:11, 12.09iter/s]Best score: 0.1809: : 791iter [01:11, 12.09iter/s]Best score: 0.1809: : 792iter [01:11, 12.09iter/s]Best score: 0.1809: : 793iter [01:11, 12.18iter/s]Best score: 0.1809: : 793iter [01:11, 12.18iter/s]Best score: 0.1809: : 794iter [01:11, 12.18iter/s]Best score: 0.1809: : 795iter [01:11, 12.24iter/s]Best score: 0.1809: : 795iter [01:11, 12.24iter/s]Best score: 0.1809: : 796iter [01:11, 12.24iter/s]Best score: 0.1809: : 797iter [01:11, 12.24iter/s]Best score: 0.1809: : 797iter [01:11, 12.24iter/s]Best score: 0.1809: : 798iter [01:11, 12.24iter/s]Best score: 0.1809: : 799iter [01:11, 12.28iter/s]
[ITER 800] Best: 0.1808
[PARAMS] Current best parameters:
  fx: 525.2946
  fy: 526.1059
  px: 1011.2786
  py: 770.7323
  k1: 0.00967723
  k2: -0.00762201
  k3: 0.00452498
  k4: -0.00161120
  p1: -0.00030231
  p2: -0.01514289
  p3: 0.01280136
  p4: -0.00259567
  s1: -0.0029
  s2: 0.0038
  s3: -0.0112
  s4: 0.0002
  tilt_x: -0.0198
  tilt_y: -0.0171
  scale: 2.9770
  rotation: 0.2033
Best score: 0.1808: : 799iter [01:11, 12.28iter/s]Best score: 0.1808: : 800iter [01:12, 12.28iter/s]Best score: 0.1808: : 801iter [01:12,  7.87iter/s]Best score: 0.1808: : 801iter [01:12,  7.87iter/s]Best score: 0.1808: : 802iter [01:12,  7.87iter/s]Best score: 0.1808: : 803iter [01:12,  8.84iter/s]Best score: 0.1808: : 803iter [01:12,  8.84iter/s]Best score: 0.1808: : 804iter [01:12,  8.84iter/s]Best score: 0.1808: : 805iter [01:12,  9.68iter/s]Best score: 0.1808: : 805iter [01:12,  9.68iter/s]Best score: 0.1808: : 806iter [01:12,  9.68iter/s]Best score: 0.1808: : 807iter [01:12, 10.35iter/s]Best score: 0.1808: : 807iter [01:12, 10.35iter/s]Best score: 0.1808: : 808iter [01:12, 10.35iter/s]Best score: 0.1808: : 809iter [01:12, 10.89iter/s]Best score: 0.1808: : 809iter [01:13, 10.89iter/s]Best score: 0.1808: : 810iter [01:13, 10.89iter/s]Best score: 0.1808: : 811iter [01:13, 11.14iter/s]Best score: 0.1808: : 811iter [01:13, 11.14iter/s]Best score: 0.1808: : 812iter [01:13, 11.14iter/s]Best score: 0.1808: : 813iter [01:13, 11.50iter/s]Best score: 0.1808: : 813iter [01:13, 11.50iter/s]Best score: 0.1808: : 814iter [01:13, 11.50iter/s]Best score: 0.1808: : 815iter [01:13, 11.75iter/s]Best score: 0.1808: : 815iter [01:13, 11.75iter/s]Best score: 0.1808: : 816iter [01:13, 11.75iter/s]Best score: 0.1808: : 817iter [01:13, 11.93iter/s]Best score: 0.1808: : 817iter [01:13, 11.93iter/s]Best score: 0.1808: : 818iter [01:13, 11.93iter/s]Best score: 0.1808: : 819iter [01:13, 12.05iter/s]Best score: 0.1807: : 819iter [01:13, 12.05iter/s]Best score: 0.1807: : 820iter [01:13, 12.05iter/s]Best score: 0.1807: : 821iter [01:13, 11.97iter/s]Best score: 0.1807: : 821iter [01:14, 11.97iter/s]Best score: 0.1807: : 822iter [01:14, 11.97iter/s]Best score: 0.1807: : 823iter [01:14, 12.10iter/s]Best score: 0.1807: : 823iter [01:14, 12.10iter/s]Best score: 0.1807: : 824iter [01:14, 12.10iter/s]Best score: 0.1807: : 825iter [01:14, 12.20iter/s]Best score: 0.1807: : 825iter [01:14, 12.20iter/s]Best score: 0.1807: : 826iter [01:14, 12.20iter/s]Best score: 0.1807: : 827iter [01:14, 12.26iter/s]Best score: 0.1807: : 827iter [01:14, 12.26iter/s]Best score: 0.1807: : 828iter [01:14, 12.26iter/s]Best score: 0.1807: : 829iter [01:14, 12.28iter/s]Best score: 0.1807: : 829iter [01:14, 12.28iter/s]Best score: 0.1807: : 830iter [01:14, 12.28iter/s]Best score: 0.1807: : 831iter [01:14, 12.09iter/s]Best score: 0.1807: : 831iter [01:14, 12.09iter/s]Best score: 0.1807: : 832iter [01:14, 12.09iter/s]Best score: 0.1807: : 833iter [01:14, 12.16iter/s]Best score: 0.1807: : 833iter [01:14, 12.16iter/s]Best score: 0.1807: : 834iter [01:15, 12.16iter/s]Best score: 0.1807: : 835iter [01:15, 12.17iter/s]Best score: 0.1807: : 835iter [01:15, 12.17iter/s]Best score: 0.1807: : 836iter [01:15, 12.17iter/s]Best score: 0.1807: : 837iter [01:15, 12.20iter/s]Best score: 0.1807: : 837iter [01:15, 12.20iter/s]Best score: 0.1807: : 838iter [01:15, 12.20iter/s]Best score: 0.1807: : 839iter [01:15, 12.24iter/s]Best score: 0.1807: : 839iter [01:15, 12.24iter/s]Best score: 0.1807: : 840iter [01:15, 12.24iter/s]Best score: 0.1807: : 841iter [01:15, 12.05iter/s]Best score: 0.1807: : 841iter [01:15, 12.05iter/s]Best score: 0.1807: : 842iter [01:15, 12.05iter/s]Best score: 0.1807: : 843iter [01:15, 12.15iter/s]Best score: 0.1807: : 843iter [01:15, 12.15iter/s]Best score: 0.1807: : 844iter [01:15, 12.15iter/s]Best score: 0.1807: : 845iter [01:15, 12.22iter/s]Best score: 0.1807: : 845iter [01:15, 12.22iter/s]Best score: 0.1807: : 846iter [01:16, 12.22iter/s]Best score: 0.1807: : 847iter [01:16, 12.27iter/s]Best score: 0.1807: : 847iter [01:16, 12.27iter/s]Best score: 0.1807: : 848iter [01:16, 12.27iter/s]Best score: 0.1807: : 849iter [01:16, 12.31iter/s]
[ITER 850] Best: 0.1805
[PARAMS] Current best parameters:
  fx: 526.2277
  fy: 527.0554
  px: 1011.1004
  py: 770.5123
  k1: 0.00649543
  k2: -0.00414330
  k3: 0.00249180
  k4: -0.00116581
  p1: -0.00026137
  p2: -0.01670651
  p3: 0.01507632
  p4: -0.00330857
  s1: -0.0039
  s2: 0.0045
  s3: -0.0136
  s4: 0.0009
  tilt_x: -0.0209
  tilt_y: -0.0193
  scale: 2.9791
  rotation: 0.2106
Best score: 0.1805: : 849iter [01:16, 12.31iter/s]Best score: 0.1805: : 850iter [01:16, 12.31iter/s]Best score: 0.1805: : 851iter [01:16,  7.93iter/s]Best score: 0.1805: : 851iter [01:16,  7.93iter/s]Best score: 0.1805: : 852iter [01:16,  7.93iter/s]Best score: 0.1805: : 853iter [01:16,  8.88iter/s]Best score: 0.1805: : 853iter [01:16,  8.88iter/s]Best score: 0.1805: : 854iter [01:17,  8.88iter/s]Best score: 0.1805: : 855iter [01:17,  9.71iter/s]Best score: 0.1805: : 855iter [01:17,  9.71iter/s]Best score: 0.1805: : 856iter [01:17,  9.71iter/s]Best score: 0.1805: : 857iter [01:17, 10.38iter/s]Best score: 0.1805: : 857iter [01:17, 10.38iter/s]Best score: 0.1805: : 858iter [01:17, 10.38iter/s]Best score: 0.1805: : 859iter [01:17, 10.91iter/s]Best score: 0.1805: : 859iter [01:17, 10.91iter/s]Best score: 0.1805: : 860iter [01:17, 10.91iter/s]Best score: 0.1805: : 861iter [01:17, 11.13iter/s]Best score: 0.1805: : 861iter [01:17, 11.13iter/s]Best score: 0.1805: : 862iter [01:17, 11.13iter/s]Best score: 0.1805: : 863iter [01:17, 11.37iter/s]Best score: 0.1805: : 863iter [01:17, 11.37iter/s]Best score: 0.1805: : 864iter [01:17, 11.37iter/s]Best score: 0.1805: : 865iter [01:17, 11.64iter/s]Best score: 0.1805: : 865iter [01:17, 11.64iter/s]Best score: 0.1805: : 866iter [01:17, 11.64iter/s]Best score: 0.1805: : 867iter [01:17, 11.84iter/s]Best score: 0.1805: : 867iter [01:18, 11.84iter/s]Best score: 0.1805: : 868iter [01:18, 11.84iter/s]Best score: 0.1805: : 869iter [01:18, 12.01iter/s]Best score: 0.1805: : 869iter [01:18, 12.01iter/s]Best score: 0.1805: : 870iter [01:18, 12.01iter/s]Best score: 0.1805: : 871iter [01:18, 11.87iter/s]Best score: 0.1805: : 871iter [01:18, 11.87iter/s]Best score: 0.1805: : 872iter [01:18, 11.87iter/s]Best score: 0.1805: : 873iter [01:18, 12.02iter/s]Best score: 0.1805: : 873iter [01:18, 12.02iter/s]Best score: 0.1805: : 874iter [01:18, 12.02iter/s]Best score: 0.1805: : 875iter [01:18, 12.13iter/s]Best score: 0.1805: : 875iter [01:18, 12.13iter/s]Best score: 0.1805: : 876iter [01:18, 12.13iter/s]Best score: 0.1805: : 877iter [01:18, 12.18iter/s]Best score: 0.1805: : 877iter [01:18, 12.18iter/s]Best score: 0.1805: : 878iter [01:18, 12.18iter/s]Best score: 0.1805: : 879iter [01:18, 12.24iter/s]Best score: 0.1804: : 879iter [01:19, 12.24iter/s]Best score: 0.1804: : 880iter [01:19, 12.24iter/s]Best score: 0.1804: : 881iter [01:19, 12.04iter/s]Best score: 0.1804: : 881iter [01:19, 12.04iter/s]Best score: 0.1804: : 882iter [01:19, 12.04iter/s]Best score: 0.1804: : 883iter [01:19, 12.14iter/s]Best score: 0.1804: : 883iter [01:19, 12.14iter/s]Best score: 0.1804: : 884iter [01:19, 12.14iter/s]Best score: 0.1804: : 885iter [01:19, 12.22iter/s]Best score: 0.1804: : 885iter [01:19, 12.22iter/s]Best score: 0.1804: : 886iter [01:19, 12.22iter/s]Best score: 0.1804: : 887iter [01:19, 12.17iter/s]Best score: 0.1804: : 887iter [01:19, 12.17iter/s]Best score: 0.1804: : 888iter [01:19, 12.17iter/s]Best score: 0.1804: : 889iter [01:19, 12.23iter/s]Best score: 0.1803: : 889iter [01:19, 12.23iter/s]Best score: 0.1803: : 890iter [01:19, 12.23iter/s]Best score: 0.1803: : 891iter [01:19, 12.06iter/s]Best score: 0.1803: : 891iter [01:20, 12.06iter/s]Best score: 0.1803: : 892iter [01:20, 12.06iter/s]Best score: 0.1803: : 893iter [01:20, 12.06iter/s]Best score: 0.1803: : 893iter [01:20, 12.06iter/s]Best score: 0.1803: : 894iter [01:20, 12.06iter/s]Best score: 0.1803: : 895iter [01:20, 12.07iter/s]Best score: 0.1803: : 895iter [01:20, 12.07iter/s]Best score: 0.1803: : 896iter [01:20, 12.07iter/s]Best score: 0.1803: : 897iter [01:20, 12.07iter/s]Best score: 0.1803: : 897iter [01:20, 12.07iter/s]Best score: 0.1803: : 898iter [01:20, 12.07iter/s]Best score: 0.1803: : 899iter [01:20, 12.08iter/s]
[ITER 900] Best: 0.1803
[PARAMS] Current best parameters:
  fx: 526.0984
  fy: 526.9182
  px: 1011.1318
  py: 770.5288
  k1: 0.00772997
  k2: -0.00593376
  k3: 0.00355300
  k4: -0.00139141
  p1: -0.00006136
  p2: -0.01674100
  p3: 0.01590114
  p4: -0.00374847
  s1: -0.0046
  s2: 0.0050
  s3: -0.0146
  s4: 0.0012
  tilt_x: -0.0212
  tilt_y: -0.0195
  scale: 2.9783
  rotation: 0.2069
Best score: 0.1803: : 899iter [01:20, 12.08iter/s]Best score: 0.1803: : 900iter [01:21, 12.08iter/s]Best score: 0.1803: : 901iter [01:21,  7.82iter/s]Best score: 0.1803: : 901iter [01:21,  7.82iter/s]Best score: 0.1803: : 902iter [01:21,  7.82iter/s]Best score: 0.1803: : 903iter [01:21,  8.76iter/s]Best score: 0.1803: : 903iter [01:21,  8.76iter/s]Best score: 0.1803: : 904iter [01:21,  8.76iter/s]Best score: 0.1803: : 905iter [01:21,  9.61iter/s]Best score: 0.1803: : 905iter [01:21,  9.61iter/s]Best score: 0.1803: : 906iter [01:21,  9.61iter/s]Best score: 0.1803: : 907iter [01:21, 10.31iter/s]Best score: 0.1803: : 907iter [01:21, 10.31iter/s]Best score: 0.1803: : 908iter [01:21, 10.31iter/s]Best score: 0.1803: : 909iter [01:21, 10.86iter/s]Best score: 0.1803: : 909iter [01:21, 10.86iter/s]Best score: 0.1803: : 910iter [01:21, 10.86iter/s]Best score: 0.1803: : 911iter [01:21, 11.07iter/s]Best score: 0.1803: : 911iter [01:21, 11.07iter/s]Best score: 0.1803: : 912iter [01:22, 11.07iter/s]Best score: 0.1803: : 913iter [01:22, 11.44iter/s]Best score: 0.1803: : 913iter [01:22, 11.44iter/s]Best score: 0.1803: : 914iter [01:22, 11.44iter/s]Best score: 0.1803: : 915iter [01:22, 11.72iter/s]Best score: 0.1803: : 915iter [01:22, 11.72iter/s]Best score: 0.1803: : 916iter [01:22, 11.72iter/s]Best score: 0.1803: : 917iter [01:22, 11.92iter/s]Best score: 0.1803: : 917iter [01:22, 11.92iter/s]Best score: 0.1803: : 918iter [01:22, 11.92iter/s]Best score: 0.1803: : 919iter [01:22, 12.02iter/s]Best score: 0.1803: : 919iter [01:22, 12.02iter/s]Best score: 0.1803: : 920iter [01:22, 12.02iter/s]Best score: 0.1803: : 921iter [01:22, 11.85iter/s]Best score: 0.1803: : 921iter [01:22, 11.85iter/s]Best score: 0.1803: : 922iter [01:22, 11.85iter/s]Best score: 0.1803: : 923iter [01:22, 11.91iter/s]Best score: 0.1803: : 923iter [01:22, 11.91iter/s]Best score: 0.1803: : 924iter [01:23, 11.91iter/s]Best score: 0.1803: : 925iter [01:23, 11.98iter/s]Best score: 0.1803: : 925iter [01:23, 11.98iter/s]Best score: 0.1803: : 926iter [01:23, 11.98iter/s]Best score: 0.1803: : 927iter [01:23, 12.02iter/s]Best score: 0.1803: : 927iter [01:23, 12.02iter/s]Best score: 0.1803: : 928iter [01:23, 12.02iter/s]Best score: 0.1803: : 929iter [01:23, 12.06iter/s]Best score: 0.1803: : 929iter [01:23, 12.06iter/s]Best score: 0.1803: : 930iter [01:23, 12.06iter/s]Best score: 0.1803: : 931iter [01:23, 11.87iter/s]Best score: 0.1803: : 931iter [01:23, 11.87iter/s]Best score: 0.1803: : 932iter [01:23, 11.87iter/s]Best score: 0.1803: : 933iter [01:23, 12.02iter/s]Best score: 0.1803: : 933iter [01:23, 12.02iter/s]Best score: 0.1803: : 934iter [01:23, 12.02iter/s]Best score: 0.1803: : 935iter [01:23, 12.08iter/s]Best score: 0.1803: : 935iter [01:23, 12.08iter/s]Best score: 0.1803: : 936iter [01:24, 12.08iter/s]Best score: 0.1803: : 937iter [01:24, 12.18iter/s]Best score: 0.1803: : 937iter [01:24, 12.18iter/s]Best score: 0.1803: : 938iter [01:24, 12.18iter/s]Best score: 0.1803: : 939iter [01:24, 12.23iter/s]Best score: 0.1803: : 939iter [01:24, 12.23iter/s]Best score: 0.1803: : 940iter [01:24, 12.23iter/s]Best score: 0.1803: : 941iter [01:24, 11.99iter/s]Best score: 0.1803: : 941iter [01:24, 11.99iter/s]Best score: 0.1803: : 942iter [01:24, 11.99iter/s]Best score: 0.1803: : 943iter [01:24, 12.09iter/s]Best score: 0.1803: : 943iter [01:24, 12.09iter/s]Best score: 0.1803: : 944iter [01:24, 12.09iter/s]Best score: 0.1803: : 945iter [01:24, 12.18iter/s]Best score: 0.1803: : 945iter [01:24, 12.18iter/s]Best score: 0.1803: : 946iter [01:24, 12.18iter/s]Best score: 0.1803: : 947iter [01:24, 12.23iter/s]Best score: 0.1803: : 947iter [01:24, 12.23iter/s]Best score: 0.1803: : 948iter [01:25, 12.23iter/s]Best score: 0.1803: : 949iter [01:25, 12.29iter/s]
[ITER 950] Best: 0.1802
[PARAMS] Current best parameters:
  fx: 525.8967
  fy: 526.7094
  px: 1011.0735
  py: 770.6407
  k1: 0.00887509
  k2: -0.00779944
  k3: 0.00484308
  k4: -0.00169970
  p1: -0.00018895
  p2: -0.01711115
  p3: 0.01676504
  p4: -0.00373571
  s1: -0.0053
  s2: 0.0051
  s3: -0.0153
  s4: 0.0013
  tilt_x: -0.0204
  tilt_y: -0.0200
  scale: 2.9779
  rotation: 0.2088
Best score: 0.1802: : 949iter [01:25, 12.29iter/s]Best score: 0.1802: : 950iter [01:25, 12.29iter/s]Best score: 0.1802: : 951iter [01:25,  7.92iter/s]Best score: 0.1802: : 951iter [01:25,  7.92iter/s]Best score: 0.1802: : 952iter [01:25,  7.92iter/s]Best score: 0.1802: : 953iter [01:25,  8.88iter/s]Best score: 0.1802: : 953iter [01:25,  8.88iter/s]Best score: 0.1802: : 954iter [01:25,  8.88iter/s]Best score: 0.1802: : 955iter [01:25,  9.71iter/s]Best score: 0.1802: : 955iter [01:25,  9.71iter/s]Best score: 0.1802: : 956iter [01:25,  9.71iter/s]Best score: 0.1802: : 957iter [01:25, 10.31iter/s]Best score: 0.1802: : 957iter [01:26, 10.31iter/s]Best score: 0.1802: : 958iter [01:26, 10.31iter/s]Best score: 0.1802: : 959iter [01:26, 10.78iter/s]Best score: 0.1802: : 959iter [01:26, 10.78iter/s]Best score: 0.1802: : 960iter [01:26, 10.78iter/s]Best score: 0.1802: : 961iter [01:26, 10.97iter/s]Best score: 0.1802: : 961iter [01:26, 10.97iter/s]Best score: 0.1802: : 962iter [01:26, 10.97iter/s]Best score: 0.1802: : 963iter [01:26, 11.35iter/s]Best score: 0.1802: : 963iter [01:26, 11.35iter/s]Best score: 0.1802: : 964iter [01:26, 11.35iter/s]Best score: 0.1802: : 965iter [01:26, 11.65iter/s]Best score: 0.1802: : 965iter [01:26, 11.65iter/s]Best score: 0.1802: : 966iter [01:26, 11.65iter/s]Best score: 0.1802: : 967iter [01:26, 11.87iter/s]Best score: 0.1802: : 967iter [01:26, 11.87iter/s]Best score: 0.1802: : 968iter [01:26, 11.87iter/s]Best score: 0.1802: : 969iter [01:26, 12.03iter/s]Best score: 0.1802: : 969iter [01:27, 12.03iter/s]Best score: 0.1802: : 970iter [01:27, 12.03iter/s]Best score: 0.1802: : 971iter [01:27, 11.93iter/s]Best score: 0.1802: : 971iter [01:27, 11.93iter/s]Best score: 0.1802: : 972iter [01:27, 11.93iter/s]Best score: 0.1802: : 973iter [01:27, 12.02iter/s]Best score: 0.1802: : 973iter [01:27, 12.02iter/s]Best score: 0.1802: : 974iter [01:27, 12.02iter/s]Best score: 0.1802: : 975iter [01:27, 12.13iter/s]Best score: 0.1802: : 975iter [01:27, 12.13iter/s]Best score: 0.1802: : 976iter [01:27, 12.13iter/s]Best score: 0.1802: : 977iter [01:27, 12.17iter/s]Best score: 0.1802: : 977iter [01:27, 12.17iter/s]Best score: 0.1802: : 978iter [01:27, 12.17iter/s]Best score: 0.1802: : 979iter [01:27, 12.18iter/s]Best score: 0.1802: : 979iter [01:27, 12.18iter/s]Best score: 0.1802: : 980iter [01:27, 12.18iter/s]Best score: 0.1802: : 981iter [01:27, 12.04iter/s]Best score: 0.1802: : 981iter [01:28, 12.04iter/s]Best score: 0.1802: : 982iter [01:28, 12.04iter/s]Best score: 0.1802: : 983iter [01:28, 12.13iter/s]Best score: 0.1802: : 983iter [01:28, 12.13iter/s]Best score: 0.1802: : 984iter [01:28, 12.13iter/s]Best score: 0.1802: : 985iter [01:28, 12.19iter/s]Best score: 0.1802: : 985iter [01:28, 12.19iter/s]Best score: 0.1802: : 986iter [01:28, 12.19iter/s]Best score: 0.1802: : 987iter [01:28, 12.26iter/s]Best score: 0.1802: : 987iter [01:28, 12.26iter/s]Best score: 0.1802: : 988iter [01:28, 12.26iter/s]Best score: 0.1802: : 989iter [01:28, 12.17iter/s]Best score: 0.1802: : 989iter [01:28, 12.17iter/s]Best score: 0.1802: : 990iter [01:28, 12.17iter/s]Best score: 0.1802: : 991iter [01:28, 12.02iter/s]Best score: 0.1802: : 991iter [01:28, 12.02iter/s]Best score: 0.1802: : 992iter [01:28, 12.02iter/s]Best score: 0.1802: : 993iter [01:28, 12.08iter/s]Best score: 0.1802: : 993iter [01:29, 12.08iter/s]Best score: 0.1802: : 994iter [01:29, 12.08iter/s]Best score: 0.1802: : 995iter [01:29, 12.17iter/s]Best score: 0.1802: : 995iter [01:29, 12.17iter/s]Best score: 0.1802: : 996iter [01:29, 12.17iter/s]Best score: 0.1802: : 997iter [01:29, 12.25iter/s]Best score: 0.1802: : 997iter [01:29, 12.25iter/s]Best score: 0.1802: : 998iter [01:29, 12.25iter/s]Best score: 0.1802: : 999iter [01:29, 12.24iter/s]
[ITER 1000] Best: 0.1802
[PARAMS] Current best parameters:
  fx: 525.7613
  fy: 526.5730
  px: 1010.9819
  py: 770.8272
  k1: 0.00975400
  k2: -0.00905290
  k3: 0.00562125
  k4: -0.00187227
  p1: -0.00031539
  p2: -0.01690714
  p3: 0.01719607
  p4: -0.00392978
  s1: -0.0060
  s2: 0.0052
  s3: -0.0153
  s4: 0.0014
  tilt_x: -0.0193
  tilt_y: -0.0192
  scale: 2.9772
  rotation: 0.2093
Best score: 0.1802: : 999iter [01:29, 12.24iter/s]Best score: 0.1802: : 1000iter [01:29, 12.24iter/s]Best score: 0.1802: : 1001iter [01:29,  7.85iter/s]Best score: 0.1802: : 1001iter [01:29,  7.85iter/s]Best score: 0.1802: : 1002iter [01:30,  7.85iter/s]Best score: 0.1802: : 1003iter [01:30,  8.82iter/s]Best score: 0.1802: : 1003iter [01:30,  8.82iter/s]Best score: 0.1802: : 1004iter [01:30,  8.82iter/s]Best score: 0.1802: : 1005iter [01:30,  9.64iter/s]Best score: 0.1802: : 1005iter [01:30,  9.64iter/s]Best score: 0.1802: : 1006iter [01:30,  9.64iter/s]Best score: 0.1802: : 1007iter [01:30, 10.33iter/s]Best score: 0.1802: : 1007iter [01:30, 10.33iter/s]Best score: 0.1802: : 1008iter [01:30, 10.33iter/s]Best score: 0.1802: : 1009iter [01:30, 10.87iter/s]Best score: 0.1802: : 1009iter [01:30, 10.87iter/s]Best score: 0.1802: : 1010iter [01:30, 10.87iter/s]Best score: 0.1802: : 1011iter [01:30, 11.11iter/s]Best score: 0.1802: : 1011iter [01:30, 11.11iter/s]Best score: 0.1802: : 1012iter [01:30, 11.11iter/s]Best score: 0.1802: : 1013iter [01:30, 11.42iter/s]Best score: 0.1802: : 1013iter [01:30, 11.42iter/s]Best score: 0.1802: : 1014iter [01:31, 11.42iter/s]Best score: 0.1802: : 1015iter [01:31, 11.62iter/s]Best score: 0.1802: : 1015iter [01:31, 11.62iter/s]Best score: 0.1802: : 1016iter [01:31, 11.62iter/s]Best score: 0.1802: : 1017iter [01:31, 11.84iter/s]Best score: 0.1802: : 1017iter [01:31, 11.84iter/s]Best score: 0.1802: : 1018iter [01:31, 11.84iter/s]Best score: 0.1802: : 1019iter [01:31, 12.00iter/s]Best score: 0.1801: : 1019iter [01:31, 12.00iter/s]Best score: 0.1801: : 1020iter [01:31, 12.00iter/s]Best score: 0.1801: : 1021iter [01:31, 11.91iter/s]Best score: 0.1801: : 1021iter [01:31, 11.91iter/s]Best score: 0.1801: : 1022iter [01:31, 11.91iter/s]Best score: 0.1801: : 1023iter [01:31, 11.93iter/s]Best score: 0.1801: : 1023iter [01:31, 11.93iter/s]Best score: 0.1801: : 1024iter [01:31, 11.93iter/s]Best score: 0.1801: : 1025iter [01:31, 12.03iter/s]Best score: 0.1801: : 1025iter [01:31, 12.03iter/s]Best score: 0.1801: : 1026iter [01:32, 12.03iter/s]Best score: 0.1801: : 1027iter [01:32, 12.04iter/s]Best score: 0.1801: : 1027iter [01:32, 12.04iter/s]Best score: 0.1801: : 1028iter [01:32, 12.04iter/s]Best score: 0.1801: : 1029iter [01:32, 12.11iter/s]Best score: 0.1801: : 1029iter [01:32, 12.11iter/s]Best score: 0.1801: : 1030iter [01:32, 12.11iter/s]Best score: 0.1801: : 1031iter [01:32, 11.99iter/s]Best score: 0.1801: : 1031iter [01:32, 11.99iter/s]Best score: 0.1801: : 1032iter [01:32, 11.99iter/s]Best score: 0.1801: : 1033iter [01:32, 12.05iter/s]Best score: 0.1801: : 1033iter [01:32, 12.05iter/s]Best score: 0.1801: : 1034iter [01:32, 12.05iter/s]Best score: 0.1801: : 1035iter [01:32, 12.07iter/s]Best score: 0.1801: : 1035iter [01:32, 12.07iter/s]Best score: 0.1801: : 1036iter [01:32, 12.07iter/s]Best score: 0.1801: : 1037iter [01:32, 12.08iter/s]Best score: 0.1801: : 1037iter [01:32, 12.08iter/s]Best score: 0.1801: : 1038iter [01:33, 12.08iter/s]Best score: 0.1801: : 1039iter [01:33, 12.12iter/s]Best score: 0.1801: : 1039iter [01:33, 12.12iter/s]Best score: 0.1801: : 1040iter [01:33, 12.12iter/s]Best score: 0.1801: : 1041iter [01:33, 11.98iter/s]Best score: 0.1801: : 1041iter [01:33, 11.98iter/s]Best score: 0.1801: : 1042iter [01:33, 11.98iter/s]Best score: 0.1801: : 1043iter [01:33, 12.11iter/s]Best score: 0.1801: : 1043iter [01:33, 12.11iter/s]Best score: 0.1801: : 1044iter [01:33, 12.11iter/s]Best score: 0.1801: : 1045iter [01:33, 12.19iter/s]Best score: 0.1801: : 1045iter [01:33, 12.19iter/s]Best score: 0.1801: : 1046iter [01:33, 12.19iter/s]Best score: 0.1801: : 1047iter [01:33, 12.15iter/s]Best score: 0.1801: : 1047iter [01:33, 12.15iter/s]Best score: 0.1801: : 1048iter [01:33, 12.15iter/s]Best score: 0.1801: : 1049iter [01:33, 12.15iter/s]
[ITER 1050] Best: 0.1801
[PARAMS] Current best parameters:
  fx: 525.6716
  fy: 526.4855
  px: 1010.7711
  py: 770.8740
  k1: 0.01022365
  k2: -0.00964643
  k3: 0.00594497
  k4: -0.00193529
  p1: -0.00049446
  p2: -0.01713844
  p3: 0.01760845
  p4: -0.00391236
  s1: -0.0064
  s2: 0.0052
  s3: -0.0156
  s4: 0.0014
  tilt_x: -0.0187
  tilt_y: -0.0186
  scale: 2.9768
  rotation: 0.2105
Best score: 0.1801: : 1049iter [01:33, 12.15iter/s]Best score: 0.1801: : 1050iter [01:34, 12.15iter/s]Best score: 0.1801: : 1051iter [01:34,  7.83iter/s]Best score: 0.1801: : 1051iter [01:34,  7.83iter/s]Best score: 0.1801: : 1052iter [01:34,  7.83iter/s]Best score: 0.1801: : 1053iter [01:34,  8.80iter/s]Best score: 0.1801: : 1053iter [01:34,  8.80iter/s]Best score: 0.1801: : 1054iter [01:34,  8.80iter/s]Best score: 0.1801: : 1055iter [01:34,  9.60iter/s]Best score: 0.1801: : 1055iter [01:34,  9.60iter/s]Best score: 0.1801: : 1056iter [01:34,  9.60iter/s]Best score: 0.1801: : 1057iter [01:34, 10.29iter/s]Best score: 0.1801: : 1057iter [01:34, 10.29iter/s]Best score: 0.1801: : 1058iter [01:34, 10.29iter/s]Best score: 0.1801: : 1059iter [01:34, 10.85iter/s]Best score: 0.1801: : 1059iter [01:35, 10.85iter/s]Best score: 0.1801: : 1060iter [01:35, 10.85iter/s]Best score: 0.1801: : 1061iter [01:35, 11.09iter/s]Best score: 0.1801: : 1061iter [01:35, 11.09iter/s]Best score: 0.1801: : 1062iter [01:35, 11.09iter/s]Best score: 0.1801: : 1063iter [01:35, 11.47iter/s]Best score: 0.1801: : 1063iter [01:35, 11.47iter/s]Best score: 0.1801: : 1064iter [01:35, 11.47iter/s]Best score: 0.1801: : 1065iter [01:35, 11.73iter/s]Best score: 0.1801: : 1065iter [01:35, 11.73iter/s]Best score: 0.1801: : 1066iter [01:35, 11.73iter/s]Best score: 0.1801: : 1067iter [01:35, 11.91iter/s]Best score: 0.1801: : 1067iter [01:35, 11.91iter/s]Best score: 0.1801: : 1068iter [01:35, 11.91iter/s]Best score: 0.1801: : 1069iter [01:35, 12.05iter/s]Best score: 0.1800: : 1069iter [01:35, 12.05iter/s]Best score: 0.1800: : 1070iter [01:35, 12.05iter/s]Best score: 0.1800: : 1071iter [01:35, 11.92iter/s]Best score: 0.1800: : 1071iter [01:36, 11.92iter/s]Best score: 0.1800: : 1072iter [01:36, 11.92iter/s]Best score: 0.1800: : 1073iter [01:36, 12.06iter/s]Best score: 0.1800: : 1073iter [01:36, 12.06iter/s]Best score: 0.1800: : 1074iter [01:36, 12.06iter/s]Best score: 0.1800: : 1075iter [01:36, 12.12iter/s]Best score: 0.1800: : 1075iter [01:36, 12.12iter/s]Best score: 0.1800: : 1076iter [01:36, 12.12iter/s]Best score: 0.1800: : 1077iter [01:36, 12.20iter/s]Best score: 0.1800: : 1077iter [01:36, 12.20iter/s]Best score: 0.1800: : 1078iter [01:36, 12.20iter/s]Best score: 0.1800: : 1079iter [01:36, 12.25iter/s]Best score: 0.1800: : 1079iter [01:36, 12.25iter/s]Best score: 0.1800: : 1080iter [01:36, 12.25iter/s]Best score: 0.1800: : 1081iter [01:36, 12.08iter/s]Best score: 0.1800: : 1081iter [01:36, 12.08iter/s]Best score: 0.1800: : 1082iter [01:36, 12.08iter/s]Best score: 0.1800: : 1083iter [01:36, 12.18iter/s]Best score: 0.1800: : 1083iter [01:37, 12.18iter/s]Best score: 0.1800: : 1084iter [01:37, 12.18iter/s]Best score: 0.1800: : 1085iter [01:37, 12.25iter/s]Best score: 0.1800: : 1085iter [01:37, 12.25iter/s]Best score: 0.1800: : 1086iter [01:37, 12.25iter/s]Best score: 0.1800: : 1087iter [01:37, 12.30iter/s]Best score: 0.1800: : 1087iter [01:37, 12.30iter/s]Best score: 0.1800: : 1088iter [01:37, 12.30iter/s]Best score: 0.1800: : 1089iter [01:37, 12.34iter/s]Best score: 0.1800: : 1089iter [01:37, 12.34iter/s]Best score: 0.1800: : 1090iter [01:37, 12.34iter/s]Best score: 0.1800: : 1091iter [01:37, 12.10iter/s]Best score: 0.1800: : 1091iter [01:37, 12.10iter/s]Best score: 0.1800: : 1092iter [01:37, 12.10iter/s]Best score: 0.1800: : 1093iter [01:37, 12.10iter/s]Best score: 0.1800: : 1093iter [01:37, 12.10iter/s]Best score: 0.1800: : 1094iter [01:37, 12.10iter/s]Best score: 0.1800: : 1095iter [01:37, 12.19iter/s]Best score: 0.1800: : 1095iter [01:38, 12.19iter/s]Best score: 0.1800: : 1096iter [01:38, 12.19iter/s]Best score: 0.1800: : 1097iter [01:38, 12.19iter/s]Best score: 0.1800: : 1097iter [01:38, 12.19iter/s]Best score: 0.1800: : 1098iter [01:38, 12.19iter/s]Best score: 0.1800: : 1099iter [01:38, 12.22iter/s]
[ITER 1100] Best: 0.1800
[PARAMS] Current best parameters:
  fx: 525.9840
  fy: 526.7862
  px: 1010.5841
  py: 770.8391
  k1: 0.00926690
  k2: -0.00812156
  k3: 0.00475549
  k4: -0.00162548
  p1: -0.00110453
  p2: -0.01776671
  p3: 0.01949881
  p4: -0.00423841
  s1: -0.0079
  s2: 0.0056
  s3: -0.0169
  s4: 0.0018
  tilt_x: -0.0177
  tilt_y: -0.0190
  scale: 2.9768
  rotation: 0.2079
Best score: 0.1800: : 1099iter [01:38, 12.22iter/s]Best score: 0.1800: : 1100iter [01:38, 11.15iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.179994 -> 0.179985

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 96,643 → 88,991 within 3500mm ring
  Ring polish did not improve score: 0.179985
  Success: True
  Tangential distortion: p1=-0.001106, p2=-0.017772, p3=0.019512, p4=-0.004234
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1800
[✅] Parameters copied to: /working/MDC001_W099(6)_2026-03-02/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 525.9839626995696
    fy: 526.7862067720905
    px: 1010.5840680985298
    py: 770.8390894554286
    k1: 0.009271109550951085
    k2: -0.008123880710209103
    k3: 0.004754523848603765
    k4: -0.001624854142974115
    p1: -0.0011059141165973571
    p2: -0.01777249325306747
    p3: 0.019511973918728876
    p4: -0.004234343673610193
    scale: 2.9767637118764942
    rotation: 0.20786049364325776
    mmpp: 3.444461456293551

[🔄] Converting 814 frames to PTD, creating debug images for 400 calibration frames...
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[✅] Saved PTDs: /working/MDC001_W099(6)_2026-03-02/calibration/debug/4_undistorted/output/ptd
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[✅] Saved PTDs: /working/MDC001_W099(6)_2026-03-02/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.
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[✅] LUT written to: /working/MDC001_W099(6)_2026-03-02/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC001_W099(6)_2026-03-02' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC001_W099(6)_2026-03-02/calibration/MDC001_W099(6)_2026-03-02_unknown.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)
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                                                                 [A[✅] 2000L: /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:03<00:09,  3.28s/rig]
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                                                                [A[✅] 1500: /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/1500_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 2/4 [00:08<00:08,  4.19s/rig]
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                                                                [A[✅] 1000: /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:10<00:03,  3.59s/rig]
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                                                                 [A[✅] 2000R: /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:14<00:00,  3.54s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:14<00:00,  3.61s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC001_W099(6)_2026-03-02/calibration/debug/5_heatmap
[🏁] Finished processing MDC001_W099(6)_2026-03-02


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