
🟢 Starting Calibration Pipeline

=== Processing MDC042_W007(6)_2025-05-28 ===

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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 943 stationary frames, 234 remain

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                                                                            [A[PASS2] Frame 0000346: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000347: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000348: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000349: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000536: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000348: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000536: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000536: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Summary: 3 frames fixed, 3 untrusted, 234 total

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                                                                            [A
[TIMING] 2000R — 234 frames
  Phase 1  radial detect   (parallel)  :   3.66s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.89s  (8.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.25s
  Phase 3  finalize        (sequential):   4.45s  (19.0 ms/frame)
    decode + undistort                 :   2.34s  (10.0 ms/frame)
    corner apply + refinement          :   1.00s  (4.3 ms/frame)
    segment                            :   0.90s  (3.9 ms/frame)
    smooth                             :   0.13s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.25s

Processing Rigs:  25%|██▌       | 1/4 [00:13<00:41, 13.83s/it][INFO] Filtered 1048 stationary frames, 241 remain

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                                                                            [A[PASS2] Frame 0000769: fixed corners {1} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 0 untrusted, 241 total

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                                                                            [A
[TIMING] 2000L — 241 frames
  Phase 1  radial detect   (parallel)  :   3.76s  (15.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.13s  (8.8 ms/frame)
  Phase 2b corner fix      (sequential):   0.11s
  Phase 3  finalize        (sequential):   4.45s  (18.5 ms/frame)
    decode + undistort                 :   2.27s  (9.4 ms/frame)
    corner apply + refinement          :   1.04s  (4.3 ms/frame)
    segment                            :   0.93s  (3.9 ms/frame)
    smooth                             :   0.13s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.46s

Processing Rigs:  50%|█████     | 2/4 [00:28<00:28, 14.16s/it][INFO] Filtered 1293 stationary frames, 302 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 302 total

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                                                                           [A
[TIMING] 1500 — 302 frames
  Phase 1  radial detect   (parallel)  :   4.61s  (15.3 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.46s  (8.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.06s
  Phase 3  finalize        (sequential):   5.42s  (18.0 ms/frame)
    decode + undistort                 :   2.89s  (9.6 ms/frame)
    corner apply + refinement          :   1.13s  (3.7 ms/frame)
    segment                            :   1.15s  (3.8 ms/frame)
    smooth                             :   0.16s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  12.56s

Processing Rigs:  75%|███████▌  | 3/4 [00:45<00:15, 15.64s/it][INFO] Filtered 1078 stationary frames, 228 remain

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                                                                           [A[PASS2] Frame 0000490: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 1 frames fixed, 1 untrusted, 228 total

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                                                                           [A
[TIMING] 1000 — 228 frames
  Phase 1  radial detect   (parallel)  :   3.89s  (17.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.08s  (9.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.10s
  Phase 3  finalize        (sequential):   3.92s  (17.2 ms/frame)
    decode + undistort                 :   2.15s  (9.4 ms/frame)
    corner apply + refinement          :   0.73s  (3.2 ms/frame)
    segment                            :   0.85s  (3.7 ms/frame)
    smooth                             :   0.12s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.99s

Processing Rigs: 100%|██████████| 4/4 [00:59<00:00, 15.00s/it]Processing Rigs: 100%|██████████| 4/4 [00:59<00:00, 14.91s/it]
[💾] Saved RigFrameDataset to /working/MDC042_W007(6)_2025-05-28/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1005 frames from 4 rigs to /working/MDC042_W007(6)_2025-05-28/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC042_W007(6)_2025-05-28/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 2000R: 100 frames selected from 234
[📋] 2000L: 100 frames selected from 241
[📋] 1500: 100 frames selected from 302
[📋] 1000: 100 frames selected from 228
[💾] Saved RigFrameDataset to /working/MDC042_W007(6)_2025-05-28/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC042_W007(6)_2025-05-28/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 80 total sampled bright frames
Computing max image from bright frames:   0%|          | 0/80 [00:00<?, ?it/s]Computing max image from bright frames:   1%|▏         | 1/80 [00:03<04:07,  3.13s/it]Computing max image from bright frames:   2%|▎         | 2/80 [00:06<04:02,  3.11s/it]Computing max image from bright frames:   4%|▍         | 3/80 [00:09<03:58,  3.09s/it]Computing max image from bright frames:   5%|▌         | 4/80 [00:12<03:54,  3.09s/it]Computing max image from bright frames:   6%|▋         | 5/80 [00:15<03:51,  3.09s/it]Computing max image from bright frames:   8%|▊         | 6/80 [00:18<03:49,  3.09s/it]Computing max image from bright frames:   9%|▉         | 7/80 [00:21<03:46,  3.10s/it]Computing max image from bright frames:  10%|█         | 8/80 [00:24<03:45,  3.13s/it]Computing max image from bright frames:  11%|█▏        | 9/80 [00:28<03:44,  3.16s/it]Computing max image from bright frames:  12%|█▎        | 10/80 [00:31<03:42,  3.17s/it]Computing max image from bright frames:  14%|█▍        | 11/80 [00:34<03:39,  3.18s/it]Computing max image from bright frames:  15%|█▌        | 12/80 [00:37<03:37,  3.19s/it]Computing max image from bright frames:  16%|█▋        | 13/80 [00:40<03:33,  3.19s/it]Computing max image from bright frames:  18%|█▊        | 14/80 [00:44<03:29,  3.17s/it]Computing max image from bright frames:  19%|█▉        | 15/80 [00:47<03:24,  3.15s/it]Computing max image from bright frames:  20%|██        | 16/80 [00:50<03:20,  3.13s/it]Computing max image from bright frames:  21%|██▏       | 17/80 [00:53<03:16,  3.11s/it]Computing max image from bright frames:  22%|██▎       | 18/80 [00:56<03:13,  3.12s/it]Computing max image from bright frames:  24%|██▍       | 19/80 [00:59<03:10,  3.12s/it]Computing max image from bright frames:  25%|██▌       | 20/80 [01:01<02:54,  2.92s/it]Computing max image from bright frames:  26%|██▋       | 21/80 [01:05<02:57,  3.01s/it]Computing max image from bright frames:  28%|██▊       | 22/80 [01:09<03:22,  3.50s/it]Computing max image from bright frames:  29%|██▉       | 23/80 [01:13<03:13,  3.39s/it]Computing max image from bright frames:  30%|███       | 24/80 [01:16<03:06,  3.32s/it]Computing max image from bright frames:  31%|███▏      | 25/80 [01:19<02:59,  3.27s/it]Computing max image from bright frames:  32%|███▎      | 26/80 [01:22<02:54,  3.23s/it]Computing max image from bright frames:  34%|███▍      | 27/80 [01:25<02:50,  3.21s/it]Computing max image from bright frames:  35%|███▌      | 28/80 [01:28<02:46,  3.20s/it]Computing max image from bright frames:  36%|███▋      | 29/80 [01:31<02:42,  3.19s/it]Computing max image from bright frames:  38%|███▊      | 30/80 [01:35<02:39,  3.19s/it]Computing max image from bright frames:  39%|███▉      | 31/80 [01:38<02:36,  3.20s/it]Computing max image from bright frames:  40%|████      | 32/80 [01:41<02:33,  3.21s/it]Computing max image from bright frames:  41%|████▏     | 33/80 [01:44<02:30,  3.20s/it]Computing max image from bright frames:  42%|████▎     | 34/80 [01:47<02:26,  3.20s/it]Computing max image from bright frames:  44%|████▍     | 35/80 [01:51<02:22,  3.18s/it]Computing max image from bright frames:  45%|████▌     | 36/80 [01:54<02:18,  3.15s/it]Computing max image from bright frames:  46%|████▋     | 37/80 [01:57<02:15,  3.14s/it]Computing max image from bright frames:  48%|████▊     | 38/80 [02:00<02:11,  3.13s/it]Computing max image from bright frames:  49%|████▉     | 39/80 [02:03<02:08,  3.12s/it]Computing max image from bright frames:  50%|█████     | 40/80 [02:05<01:45,  2.65s/it]Computing max image from bright frames:  51%|█████▏    | 41/80 [02:08<01:48,  2.77s/it]Computing max image from bright frames:  52%|█████▎    | 42/80 [02:11<01:49,  2.87s/it]Computing max image from bright frames:  54%|█████▍    | 43/80 [02:14<01:48,  2.93s/it]Computing max image from bright frames:  55%|█████▌    | 44/80 [02:17<01:47,  2.98s/it]Computing max image from bright frames:  56%|█████▋    | 45/80 [02:20<01:45,  3.01s/it]Computing max image from bright frames:  57%|█████▊    | 46/80 [02:23<01:43,  3.03s/it]Computing max image from bright frames:  59%|█████▉    | 47/80 [02:26<01:40,  3.05s/it]Computing max image from bright frames:  60%|██████    | 48/80 [02:29<01:37,  3.06s/it]Computing max image from bright frames:  61%|██████▏   | 49/80 [02:32<01:35,  3.08s/it]Computing max image from bright frames:  62%|██████▎   | 50/80 [02:35<01:32,  3.10s/it]Computing max image from bright frames:  64%|██████▍   | 51/80 [02:39<01:30,  3.11s/it]Computing max image from bright frames:  65%|██████▌   | 52/80 [02:42<01:27,  3.12s/it]Computing max image from bright frames:  66%|██████▋   | 53/80 [02:45<01:24,  3.13s/it]Computing max image from bright frames:  68%|██████▊   | 54/80 [02:48<01:20,  3.10s/it]Computing max image from bright frames:  69%|██████▉   | 55/80 [02:51<01:17,  3.10s/it]Computing max image from bright frames:  70%|███████   | 56/80 [02:54<01:14,  3.09s/it]Computing max image from bright frames:  71%|███████▏  | 57/80 [02:57<01:11,  3.09s/it]Computing max image from bright frames:  72%|███████▎  | 58/80 [03:00<01:08,  3.09s/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 [03:05<00:53,  2.67s/it]Computing max image from bright frames:  76%|███████▋  | 61/80 [03:08<00:53,  2.80s/it]Computing max image from bright frames:  78%|███████▊  | 62/80 [03:11<00:51,  2.88s/it]Computing max image from bright frames:  79%|███████▉  | 63/80 [03:14<00:50,  2.95s/it]Computing max image from bright frames:  80%|████████  | 64/80 [03:17<00:47,  3.00s/it]Computing max image from bright frames:  81%|████████▏ | 65/80 [03:21<00:45,  3.03s/it]Computing max image from bright frames:  82%|████████▎ | 66/80 [03:24<00:42,  3.06s/it]Computing max image from bright frames:  84%|████████▍ | 67/80 [03:27<00:39,  3.07s/it]Computing max image from bright frames:  85%|████████▌ | 68/80 [03:30<00:37,  3.11s/it]Computing max image from bright frames:  86%|████████▋ | 69/80 [03:33<00:34,  3.13s/it]Computing max image from bright frames:  88%|████████▊ | 70/80 [03:36<00:31,  3.16s/it]Computing max image from bright frames:  89%|████████▉ | 71/80 [03:40<00:28,  3.17s/it]Computing max image from bright frames:  90%|█████████ | 72/80 [03:43<00:25,  3.19s/it]Computing max image from bright frames:  91%|█████████▏| 73/80 [03:46<00:22,  3.20s/it]Computing max image from bright frames:  92%|█████████▎| 74/80 [03:49<00:19,  3.20s/it]Computing max image from bright frames:  94%|█████████▍| 75/80 [03:52<00:15,  3.17s/it]Computing max image from bright frames:  95%|█████████▌| 76/80 [03:55<00:12,  3.15s/it]Computing max image from bright frames:  96%|█████████▋| 77/80 [03:59<00:09,  3.14s/it]Computing max image from bright frames:  98%|█████████▊| 78/80 [04:02<00:06,  3.13s/it]Computing max image from bright frames:  99%|█████████▉| 79/80 [04:05<00:03,  3.12s/it]Computing max image from bright frames: 100%|██████████| 80/80 [04:06<00:00,  2.63s/it]                                                                                       [✅] Casing detection complete: cx=1001.0, cy=766.0

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

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:27,  3.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:27,  3.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:27,  3.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:27,  3.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:26,  3.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:26,  3.58it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   6%|▌         | 6/100 [00:00<00:05, 17.72it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:05, 17.72it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.000 | Best: {'fx': 349.495} (0.000):   7%|▋         | 7/100 [00:00<00:05, 17.72it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:05, 17.72it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   9%|▉         | 9/100 [00:00<00:04, 19.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.001 | Best: {'fx': 363.636} (0.001):   9%|▉         | 9/100 [00:00<00:04, 19.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.002 | Best: {'fx': 370.707} (0.002):  10%|█         | 10/100 [00:00<00:04, 19.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 19.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 19.96it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 384.848} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 24.76it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:03, 24.76it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  14%|█▍        | 14/100 [00:00<00:03, 24.76it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  15%|█▌        | 15/100 [00:00<00:03, 24.76it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 24.04it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 24.04it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.008 | Best: {'fx': 434.343} (0.008):  17%|█▋        | 17/100 [00:00<00:03, 24.04it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.007 | Best: {'fx': 434.343} (0.008):  18%|█▊        | 18/100 [00:00<00:03, 24.04it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.007 | Best: {'fx': 434.343} (0.008):  19%|█▉        | 19/100 [00:00<00:03, 25.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.006 | Best: {'fx': 434.343} (0.008):  19%|█▉        | 19/100 [00:00<00:03, 25.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  20%|██        | 20/100 [00:00<00:03, 25.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:00<00:03, 25.21it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  22%|██▏       | 22/100 [00:00<00:02, 26.22it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  22%|██▏       | 22/100 [00:00<00:02, 26.22it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.015 | Best: {'fx': 462.626} (0.015):  23%|██▎       | 23/100 [00:01<00:02, 26.22it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 26.22it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  25%|██▌       | 25/100 [00:01<00:02, 26.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 483.838} (0.024):  25%|██▌       | 25/100 [00:01<00:02, 26.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 483.838} (0.024):  26%|██▌       | 26/100 [00:01<00:02, 26.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.040 | Best: {'fx': 497.98} (0.040):  27%|██▋       | 27/100 [00:01<00:02, 26.07it/s] Brute | Start: {'fx': 500.0} (0.043) | Current: 0.049 | Best: {'fx': 505.051} (0.049):  28%|██▊       | 28/100 [00:01<00:02, 26.07it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.049 | Best: {'fx': 505.051} (0.049):  29%|██▉       | 29/100 [00:01<00:02, 27.06it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.031 | Best: {'fx': 505.051} (0.049):  29%|██▉       | 29/100 [00:01<00:02, 27.06it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.060 | Best: {'fx': 512.121} (0.060):  30%|███       | 30/100 [00:01<00:02, 27.06it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.078 | Best: {'fx': 519.192} (0.078):  31%|███       | 31/100 [00:01<00:02, 27.06it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.078 | Best: {'fx': 519.192} (0.078):  32%|███▏      | 32/100 [00:01<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.104 | Best: {'fx': 533.333} (0.104):  32%|███▏      | 32/100 [00:01<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.092 | Best: {'fx': 533.333} (0.104):  33%|███▎      | 33/100 [00:01<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.071 | Best: {'fx': 533.333} (0.104):  34%|███▍      | 34/100 [00:01<00:03, 18.43it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.071 | Best: {'fx': 533.333} (0.104):  35%|███▌      | 35/100 [00:01<00:03, 20.32it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.086 | Best: {'fx': 533.333} (0.104):  35%|███▌      | 35/100 [00:01<00:03, 20.32it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.049 | Best: {'fx': 533.333} (0.104):  36%|███▌      | 36/100 [00:01<00:03, 20.32it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.054 | Best: {'fx': 533.333} (0.104):  37%|███▋      | 37/100 [00:01<00:03, 20.32it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.045 | Best: {'fx': 533.333} (0.104):  38%|███▊      | 38/100 [00:01<00:03, 20.32it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.060 | Best: {'fx': 533.333} (0.104):  39%|███▉      | 39/100 [00:01<00:03, 20.32it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.060 | Best: {'fx': 533.333} (0.104):  40%|████      | 40/100 [00:01<00:02, 26.36it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.040 | Best: {'fx': 533.333} (0.104):  40%|████      | 40/100 [00:01<00:02, 26.36it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.038 | Best: {'fx': 533.333} (0.104):  41%|████      | 41/100 [00:01<00:02, 26.36it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.034 | Best: {'fx': 533.333} (0.104):  42%|████▏     | 42/100 [00:01<00:02, 26.36it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.042 | Best: {'fx': 533.333} (0.104):  43%|████▎     | 43/100 [00:01<00:02, 26.36it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.042 | Best: {'fx': 533.333} (0.104):  44%|████▍     | 44/100 [00:01<00:02, 24.77it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.036 | Best: {'fx': 533.333} (0.104):  44%|████▍     | 44/100 [00:01<00:02, 24.77it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.030 | Best: {'fx': 533.333} (0.104):  45%|████▌     | 45/100 [00:01<00:02, 24.77it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.030 | Best: {'fx': 533.333} (0.104):  46%|████▌     | 46/100 [00:02<00:02, 24.77it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.029 | Best: {'fx': 533.333} (0.104):  47%|████▋     | 47/100 [00:02<00:02, 24.77it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.032 | Best: {'fx': 533.333} (0.104):  48%|████▊     | 48/100 [00:02<00:02, 24.77it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.032 | Best: {'fx': 533.333} (0.104):  49%|████▉     | 49/100 [00:02<00:01, 27.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 533.333} (0.104):  49%|████▉     | 49/100 [00:02<00:01, 27.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.031 | Best: {'fx': 533.333} (0.104):  50%|█████     | 50/100 [00:02<00:01, 27.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.027 | Best: {'fx': 533.333} (0.104):  51%|█████     | 51/100 [00:02<00:01, 27.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 533.333} (0.104):  52%|█████▏    | 52/100 [00:02<00:01, 27.12it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.028 | Best: {'fx': 533.333} (0.104):  53%|█████▎    | 53/100 [00:02<00:01, 29.84it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 533.333} (0.104):  53%|█████▎    | 53/100 [00:02<00:01, 29.84it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.026 | Best: {'fx': 533.333} (0.104):  54%|█████▍    | 54/100 [00:02<00:01, 29.84it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.026 | Best: {'fx': 533.333} (0.104):  55%|█████▌    | 55/100 [00:02<00:01, 29.84it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.104):  56%|█████▌    | 56/100 [00:02<00:01, 29.84it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.104):  57%|█████▋    | 57/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.104):  57%|█████▋    | 57/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.104):  58%|█████▊    | 58/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.104):  59%|█████▉    | 59/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.104):  60%|██████    | 60/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 533.333} (0.104):  61%|██████    | 61/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.104):  62%|██████▏   | 62/100 [00:02<00:01, 30.09it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.104):  63%|██████▎   | 63/100 [00:02<00:01, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.104):  63%|██████▎   | 63/100 [00:02<00:01, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.104):  64%|██████▍   | 64/100 [00:02<00:01, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.022 | Best: {'fx': 533.333} (0.104):  65%|██████▌   | 65/100 [00:02<00:00, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  66%|██████▌   | 66/100 [00:02<00:00, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  67%|██████▋   | 67/100 [00:02<00:00, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.025 | Best: {'fx': 533.333} (0.104):  68%|██████▊   | 68/100 [00:02<00:00, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.023 | Best: {'fx': 533.333} (0.104):  69%|██████▉   | 69/100 [00:02<00:00, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.104):  70%|███████   | 70/100 [00:02<00:00, 35.02it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.024 | Best: {'fx': 533.333} (0.104):  71%|███████   | 71/100 [00:02<00:00, 44.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  71%|███████   | 71/100 [00:02<00:00, 44.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  72%|███████▏  | 72/100 [00:02<00:00, 44.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  73%|███████▎  | 73/100 [00:02<00:00, 44.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  74%|███████▍  | 74/100 [00:02<00:00, 44.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  75%|███████▌  | 75/100 [00:02<00:00, 44.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  76%|███████▌  | 76/100 [00:02<00:00, 43.70it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  76%|███████▌  | 76/100 [00:02<00:00, 43.70it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  77%|███████▋  | 77/100 [00:02<00:00, 43.70it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.021 | Best: {'fx': 533.333} (0.104):  78%|███████▊  | 78/100 [00:02<00:00, 43.70it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  79%|███████▉  | 79/100 [00:02<00:00, 43.70it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  80%|████████  | 80/100 [00:02<00:00, 43.70it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  81%|████████  | 81/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  81%|████████  | 81/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  82%|████████▏ | 82/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  83%|████████▎ | 83/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  84%|████████▍ | 84/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  85%|████████▌ | 85/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  86%|████████▌ | 86/100 [00:02<00:00, 36.53it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  87%|████████▋ | 87/100 [00:02<00:00, 40.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  87%|████████▋ | 87/100 [00:02<00:00, 40.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  88%|████████▊ | 88/100 [00:03<00:00, 40.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  89%|████████▉ | 89/100 [00:03<00:00, 40.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  90%|█████████ | 90/100 [00:03<00:00, 40.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  91%|█████████ | 91/100 [00:03<00:00, 40.49it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  92%|█████████▏| 92/100 [00:03<00:00, 32.80it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.020 | Best: {'fx': 533.333} (0.104):  92%|█████████▏| 92/100 [00:03<00:00, 32.80it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  93%|█████████▎| 93/100 [00:03<00:00, 32.80it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  94%|█████████▍| 94/100 [00:03<00:00, 32.80it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  95%|█████████▌| 95/100 [00:03<00:00, 32.80it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  96%|█████████▌| 96/100 [00:03<00:00, 32.80it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  97%|█████████▋| 97/100 [00:03<00:00, 35.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  97%|█████████▋| 97/100 [00:03<00:00, 35.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  98%|█████████▊| 98/100 [00:03<00:00, 35.91it/s]Brute | Start: {'fx': 500.0} (0.043) | Current: 0.019 | Best: {'fx': 533.333} (0.104):  99%|█████████▉| 99/100 [00:03<00:00, 35.91it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.053 | Best: {'fx': 508.333} (0.053):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.053 | Best: {'fx': 508.333} (0.053):   2%|▏         | 1/50 [00:00<00:24,  1.97it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.055 | Best: {'fx': 509.354} (0.055):   2%|▏         | 1/50 [00:00<00:24,  1.97it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.057 | Best: {'fx': 510.374} (0.057):   4%|▍         | 2/50 [00:00<00:24,  1.97it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.058 | Best: {'fx': 511.395} (0.058):   6%|▌         | 3/50 [00:00<00:23,  1.97it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.058 | Best: {'fx': 511.395} (0.058):   8%|▊         | 4/50 [00:00<00:05,  7.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.061 | Best: {'fx': 512.415} (0.061):   8%|▊         | 4/50 [00:00<00:05,  7.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.064 | Best: {'fx': 513.435} (0.064):  10%|█         | 5/50 [00:00<00:05,  7.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.068 | Best: {'fx': 515.476} (0.068):  12%|█▏        | 6/50 [00:00<00:05,  7.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.068 | Best: {'fx': 515.476} (0.068):  14%|█▍        | 7/50 [00:00<00:03, 10.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.070 | Best: {'fx': 516.497} (0.070):  14%|█▍        | 7/50 [00:00<00:03, 10.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.076 | Best: {'fx': 518.537} (0.076):  16%|█▌        | 8/50 [00:00<00:03, 10.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.079 | Best: {'fx': 519.558} (0.079):  18%|█▊        | 9/50 [00:00<00:03, 10.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.065 | Best: {'fx': 519.558} (0.079):  20%|██        | 10/50 [00:00<00:03, 10.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.072 | Best: {'fx': 519.558} (0.079):  22%|██▏       | 11/50 [00:00<00:03, 10.83it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.072 | Best: {'fx': 519.558} (0.079):  24%|██▍       | 12/50 [00:00<00:02, 16.73it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.084 | Best: {'fx': 522.619} (0.084):  24%|██▍       | 12/50 [00:00<00:02, 16.73it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.080 | Best: {'fx': 522.619} (0.084):  26%|██▌       | 13/50 [00:00<00:02, 16.73it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.082 | Best: {'fx': 522.619} (0.084):  28%|██▊       | 14/50 [00:00<00:02, 16.73it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.084 | Best: {'fx': 523.639} (0.084):  30%|███       | 15/50 [00:01<00:02, 16.73it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.091 | Best: {'fx': 525.68} (0.091):  32%|███▏      | 16/50 [00:01<00:02, 16.73it/s] Brute | Start: {'fx': 533.333} (0.104) | Current: 0.091 | Best: {'fx': 525.68} (0.091):  34%|███▍      | 17/50 [00:01<00:01, 22.51it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.089 | Best: {'fx': 525.68} (0.091):  34%|███▍      | 17/50 [00:01<00:01, 22.51it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.094 | Best: {'fx': 526.701} (0.094):  36%|███▌      | 18/50 [00:01<00:01, 22.51it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.104 | Best: {'fx': 530.782} (0.104):  38%|███▊      | 19/50 [00:01<00:01, 22.51it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.104 | Best: {'fx': 530.782} (0.104):  40%|████      | 20/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.101 | Best: {'fx': 530.782} (0.104):  40%|████      | 20/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.103 | Best: {'fx': 530.782} (0.104):  42%|████▏     | 21/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.097 | Best: {'fx': 530.782} (0.104):  44%|████▍     | 22/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.104 | Best: {'fx': 532.823} (0.104):  46%|████▌     | 23/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.098 | Best: {'fx': 532.823} (0.104):  48%|████▊     | 24/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.087 | Best: {'fx': 532.823} (0.104):  50%|█████     | 25/50 [00:01<00:01, 19.34it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.087 | Best: {'fx': 532.823} (0.104):  52%|█████▏    | 26/50 [00:01<00:00, 27.03it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.102 | Best: {'fx': 532.823} (0.104):  52%|█████▏    | 26/50 [00:01<00:00, 27.03it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.084 | Best: {'fx': 532.823} (0.104):  54%|█████▍    | 27/50 [00:01<00:00, 27.03it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.100 | Best: {'fx': 532.823} (0.104):  56%|█████▌    | 28/50 [00:01<00:00, 27.03it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.103 | Best: {'fx': 532.823} (0.104):  58%|█████▊    | 29/50 [00:01<00:00, 27.03it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.103 | Best: {'fx': 532.823} (0.104):  60%|██████    | 30/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.092 | Best: {'fx': 532.823} (0.104):  60%|██████    | 30/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.072 | Best: {'fx': 532.823} (0.104):  62%|██████▏   | 31/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.090 | Best: {'fx': 532.823} (0.104):  64%|██████▍   | 32/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.074 | Best: {'fx': 532.823} (0.104):  66%|██████▌   | 33/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.081 | Best: {'fx': 532.823} (0.104):  68%|██████▊   | 34/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.096 | Best: {'fx': 532.823} (0.104):  70%|███████   | 35/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.078 | Best: {'fx': 532.823} (0.104):  72%|███████▏  | 36/50 [00:01<00:00, 26.14it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.078 | Best: {'fx': 532.823} (0.104):  74%|███████▍  | 37/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.068 | Best: {'fx': 532.823} (0.104):  74%|███████▍  | 37/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.070 | Best: {'fx': 532.823} (0.104):  76%|███████▌  | 38/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.076 | Best: {'fx': 532.823} (0.104):  78%|███████▊  | 39/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.071 | Best: {'fx': 532.823} (0.104):  80%|████████  | 40/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.066 | Best: {'fx': 532.823} (0.104):  82%|████████▏ | 41/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.063 | Best: {'fx': 532.823} (0.104):  84%|████████▍ | 42/50 [00:01<00:00, 35.54it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.063 | Best: {'fx': 532.823} (0.104):  86%|████████▌ | 43/50 [00:01<00:00, 40.95it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.065 | Best: {'fx': 532.823} (0.104):  86%|████████▌ | 43/50 [00:01<00:00, 40.95it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.062 | Best: {'fx': 532.823} (0.104):  88%|████████▊ | 44/50 [00:01<00:00, 40.95it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.060 | Best: {'fx': 532.823} (0.104):  90%|█████████ | 45/50 [00:01<00:00, 40.95it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.059 | Best: {'fx': 532.823} (0.104):  92%|█████████▏| 46/50 [00:01<00:00, 40.95it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.056 | Best: {'fx': 532.823} (0.104):  94%|█████████▍| 47/50 [00:01<00:00, 40.95it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.056 | Best: {'fx': 532.823} (0.104):  96%|█████████▌| 48/50 [00:01<00:00, 38.37it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.057 | Best: {'fx': 532.823} (0.104):  96%|█████████▌| 48/50 [00:01<00:00, 38.37it/s]Brute | Start: {'fx': 533.333} (0.104) | Current: 0.058 | Best: {'fx': 532.823} (0.104):  98%|█████████▊| 49/50 [00:01<00:00, 38.37it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.3333333333334
    px: 1001.0
    py: 766.0
    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 1 incomplete frames): 79 frames will be used for optimization
  Rigs: 2000R(2000mm):20/20, 2000L(2000mm):20/20, 1500(1500mm):19/20, 1000(1000mm):20/20
  Total: 79/80 frames used for optimization
Initial score: 14.6764: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 14.6764, picklable_objective(x0) = 14.3309
Best score: 14.3309: : 0iter [00:01, ?iter/s] Best score: 14.3309: : 1iter [00:01,  1.99s/iter]Best score: 14.3309: : 1iter [00:02,  1.99s/iter]Best score: 14.3309: : 2iter [00:02,  1.99s/iter]Best score: 14.3309: : 3iter [00:02,  1.73iter/s]Best score: 14.3309: : 3iter [00:02,  1.73iter/s]Best score: 14.3309: : 4iter [00:02,  1.73iter/s]Best score: 14.3309: : 5iter [00:02,  3.08iter/s]Best score: 14.3309: : 5iter [00:02,  3.08iter/s]Best score: 14.3309: : 6iter [00:02,  3.08iter/s]Best score: 14.3309: : 7iter [00:02,  4.48iter/s]Best score: 14.3309: : 7iter [00:02,  4.48iter/s]Best score: 14.3309: : 8iter [00:02,  4.48iter/s]Best score: 14.3309: : 9iter [00:02,  5.84iter/s]Best score: 14.3309: : 9iter [00:02,  5.84iter/s]Best score: 14.3309: : 10iter [00:02,  5.84iter/s]Best score: 14.3309: : 11iter [00:02,  7.03iter/s]Best score: 14.3309: : 11iter [00:02,  7.03iter/s]Best score: 14.3309: : 12iter [00:03,  7.03iter/s]Best score: 14.3309: : 13iter [00:03,  8.12iter/s]Best score: 14.3309: : 13iter [00:03,  8.12iter/s]Best score: 14.3309: : 14iter [00:03,  8.12iter/s]Best score: 14.3309: : 15iter [00:03,  9.00iter/s]Best score: 14.3309: : 15iter [00:03,  9.00iter/s]Best score: 14.3309: : 16iter [00:03,  9.00iter/s]Best score: 14.3309: : 17iter [00:03,  9.70iter/s]Best score: 14.3309: : 17iter [00:03,  9.70iter/s]Best score: 14.3309: : 18iter [00:03,  9.70iter/s]Best score: 14.3309: : 19iter [00:03, 10.25iter/s]Best score: 14.3309: : 19iter [00:03, 10.25iter/s]Best score: 14.3309: : 20iter [00:03, 10.25iter/s]Best score: 14.3309: : 21iter [00:03, 10.53iter/s]Best score: 14.3309: : 21iter [00:03, 10.53iter/s]Best score: 14.3309: : 22iter [00:03, 10.53iter/s]Best score: 14.3309: : 23iter [00:03, 10.89iter/s]Best score: 14.3309: : 23iter [00:03, 10.89iter/s]Best score: 14.3309: : 24iter [00:04, 10.89iter/s]Best score: 14.3309: : 25iter [00:04, 11.12iter/s]Best score: 14.3309: : 25iter [00:04, 11.12iter/s]Best score: 14.3309: : 26iter [00:04, 11.12iter/s]Best score: 14.3309: : 27iter [00:04, 11.28iter/s]Best score: 14.3309: : 27iter [00:04, 11.28iter/s]Best score: 14.3309: : 28iter [00:04, 11.28iter/s]Best score: 14.3309: : 29iter [00:04, 11.40iter/s]Best score: 14.3309: : 29iter [00:04, 11.40iter/s]Best score: 14.3309: : 30iter [00:04, 11.40iter/s]Best score: 14.3309: : 31iter [00:04, 11.39iter/s]Best score: 14.3309: : 31iter [00:04, 11.39iter/s]Best score: 14.3309: : 32iter [00:04, 11.39iter/s]Best score: 14.3309: : 33iter [00:04, 11.49iter/s]Best score: 14.3309: : 33iter [00:04, 11.49iter/s]Best score: 14.3309: : 34iter [00:04, 11.49iter/s]Best score: 14.3309: : 35iter [00:04, 11.57iter/s]Best score: 14.3309: : 35iter [00:04, 11.57iter/s]Best score: 14.3309: : 36iter [00:05, 11.57iter/s]Best score: 14.3309: : 37iter [00:05, 11.64iter/s]Best score: 14.3309: : 37iter [00:05, 11.64iter/s]Best score: 14.3309: : 38iter [00:05, 11.64iter/s]Best score: 14.3309: : 39iter [00:05, 11.72iter/s]Best score: 14.3309: : 39iter [00:05, 11.72iter/s]Best score: 14.3309: : 40iter [00:05, 11.72iter/s]Best score: 14.3309: : 41iter [00:05, 11.61iter/s]Best score: 14.3309: : 41iter [00:05, 11.61iter/s]Best score: 14.3309: : 42iter [00:05, 11.61iter/s]Best score: 14.3309: : 43iter [00:05, 11.67iter/s]Best score: 14.3309: : 43iter [00:05, 11.67iter/s]Best score: 14.3309: : 44iter [00:05, 11.67iter/s]Best score: 14.3309: : 45iter [00:05, 11.71iter/s]Best score: 14.3309: : 45iter [00:05, 11.71iter/s]Best score: 14.3309: : 46iter [00:05, 11.71iter/s]Best score: 14.3309: : 47iter [00:05, 11.76iter/s]Best score: 14.3309: : 47iter [00:06, 11.76iter/s]Best score: 14.3309: : 48iter [00:06, 11.76iter/s]Best score: 14.3309: : 49iter [00:06, 11.82iter/s]
[ITER 50] Best: 14.3309
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  px: 1001.0000
  py: 766.0000
  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: 14.3309: : 49iter [00:06, 11.82iter/s]Best score: 14.3309: : 50iter [00:06, 11.82iter/s]Best score: 14.3309: : 51iter [00:06, 11.67iter/s]Best score: 14.3309: : 51iter [00:06, 11.67iter/s]Best score: 14.3309: : 52iter [00:06, 11.67iter/s]Best score: 14.3309: : 53iter [00:06, 11.77iter/s]Best score: 14.3309: : 53iter [00:06, 11.77iter/s]Best score: 14.3309: : 54iter [00:06, 11.77iter/s]Best score: 14.3309: : 55iter [00:06, 11.87iter/s]Best score: 14.3309: : 55iter [00:06, 11.87iter/s]Best score: 14.3309: : 56iter [00:06, 11.87iter/s]Best score: 14.3309: : 57iter [00:06, 11.90iter/s]Best score: 14.3309: : 57iter [00:06, 11.90iter/s]Best score: 14.3309: : 58iter [00:06, 11.90iter/s]Best score: 14.3309: : 59iter [00:06, 11.94iter/s]Best score: 14.3309: : 59iter [00:07, 11.94iter/s]Best score: 14.3309: : 60iter [00:07, 11.94iter/s]Best score: 14.3309: : 61iter [00:07, 11.83iter/s]Best score: 14.3309: : 61iter [00:07, 11.83iter/s]Best score: 14.3309: : 62iter [00:07, 11.83iter/s]Best score: 14.3309: : 63iter [00:07, 11.85iter/s]Best score: 14.3309: : 63iter [00:07, 11.85iter/s]Best score: 14.3309: : 64iter [00:07, 11.85iter/s]Best score: 14.3309: : 65iter [00:07, 11.87iter/s]Best score: 14.3309: : 65iter [00:07, 11.87iter/s]Best score: 14.3309: : 66iter [00:07, 11.87iter/s]Best score: 14.3309: : 67iter [00:07, 11.90iter/s]Best score: 14.3309: : 67iter [00:07, 11.90iter/s]Best score: 14.3309: : 68iter [00:07, 11.90iter/s]Best score: 14.3309: : 69iter [00:07, 11.98iter/s]Best score: 7.1581: : 69iter [00:07, 11.98iter/s] Best score: 7.1581: : 70iter [00:07, 11.98iter/s]Best score: 7.1581: : 71iter [00:07, 11.79iter/s]Best score: 7.1581: : 71iter [00:08, 11.79iter/s]Best score: 7.1581: : 72iter [00:08, 11.79iter/s]Best score: 7.1581: : 73iter [00:08, 11.87iter/s]Best score: 7.1581: : 73iter [00:08, 11.87iter/s]Best score: 7.1581: : 74iter [00:08, 11.87iter/s]Best score: 7.1581: : 75iter [00:08, 11.92iter/s]Best score: 7.1581: : 75iter [00:08, 11.92iter/s]Best score: 7.1581: : 76iter [00:08, 11.92iter/s]Best score: 7.1581: : 77iter [00:08, 11.91iter/s]Best score: 7.1581: : 77iter [00:08, 11.91iter/s]Best score: 7.1581: : 78iter [00:08, 11.91iter/s]Best score: 7.1581: : 79iter [00:08, 11.91iter/s]Best score: 7.1581: : 79iter [00:08, 11.91iter/s]Best score: 7.1581: : 80iter [00:08, 11.91iter/s]Best score: 7.1581: : 81iter [00:08, 11.69iter/s]Best score: 7.1581: : 81iter [00:08, 11.69iter/s]Best score: 7.1581: : 82iter [00:08, 11.69iter/s]Best score: 7.1581: : 83iter [00:08, 11.78iter/s]Best score: 7.1581: : 83iter [00:09, 11.78iter/s]Best score: 7.1581: : 84iter [00:09, 11.78iter/s]Best score: 7.1581: : 85iter [00:09, 11.85iter/s]Best score: 7.1581: : 85iter [00:09, 11.85iter/s]Best score: 7.1581: : 86iter [00:09, 11.85iter/s]Best score: 7.1581: : 87iter [00:09, 11.86iter/s]Best score: 7.1581: : 87iter [00:09, 11.86iter/s]Best score: 7.1581: : 88iter [00:09, 11.86iter/s]Best score: 7.1581: : 89iter [00:09, 11.94iter/s]Best score: 7.1581: : 89iter [00:09, 11.94iter/s]Best score: 7.1581: : 90iter [00:09, 11.94iter/s]Best score: 7.1581: : 91iter [00:09, 11.73iter/s]Best score: 7.1581: : 91iter [00:09, 11.73iter/s]Best score: 7.1581: : 92iter [00:09, 11.73iter/s]Best score: 7.1581: : 93iter [00:09, 11.78iter/s]Best score: 7.1581: : 93iter [00:09, 11.78iter/s]Best score: 7.1581: : 94iter [00:09, 11.78iter/s]Best score: 7.1581: : 95iter [00:09, 11.83iter/s]Best score: 7.1581: : 95iter [00:10, 11.83iter/s]Best score: 7.1581: : 96iter [00:10, 11.83iter/s]Best score: 7.1581: : 97iter [00:10, 11.86iter/s]Best score: 7.1581: : 97iter [00:10, 11.86iter/s]Best score: 7.1581: : 98iter [00:10, 11.86iter/s]Best score: 7.1581: : 99iter [00:10, 11.91iter/s]
[ITER 100] Best: 6.2312
[PARAMS] Current best parameters:
  fx: 526.2140
  fy: 525.9946
  px: 991.0553
  py: 763.4345
  k1: 0.02113751
  k2: 0.02126445
  k3: -0.03623304
  k4: 0.01099082
  p1: -0.01160815
  p2: -0.00354049
  p3: 0.00343181
  p4: -0.01187150
  s1: -0.0076
  s2: 0.0142
  s3: 0.0276
  s4: -0.0158
  tilt_x: -0.0071
  tilt_y: 0.0273
  scale: 2.9280
  rotation: 0.4993
Best score: 6.2312: : 99iter [00:10, 11.91iter/s]Best score: 6.2312: : 100iter [00:10, 11.91iter/s]Best score: 6.2312: : 101iter [00:10, 10.19iter/s]Best score: 6.2312: : 101iter [00:10, 10.19iter/s]Best score: 6.2312: : 102iter [00:10, 10.19iter/s]Best score: 6.2312: : 103iter [00:10, 10.59iter/s]Best score: 6.2312: : 103iter [00:10, 10.59iter/s]Best score: 6.2312: : 104iter [00:10, 10.59iter/s]Best score: 6.2312: : 105iter [00:10, 10.88iter/s]Best score: 6.2312: : 105iter [00:11, 10.88iter/s]Best score: 6.2312: : 106iter [00:11, 10.88iter/s]Best score: 6.2312: : 107iter [00:11, 11.15iter/s]Best score: 6.2312: : 107iter [00:11, 11.15iter/s]Best score: 6.2312: : 108iter [00:11, 11.15iter/s]Best score: 6.2312: : 109iter [00:11, 11.30iter/s]Best score: 6.2312: : 109iter [00:11, 11.30iter/s]Best score: 6.2312: : 110iter [00:11, 11.30iter/s]Best score: 6.2312: : 111iter [00:11, 11.22iter/s]Best score: 6.2312: : 111iter [00:11, 11.22iter/s]Best score: 6.2312: : 112iter [00:11, 11.22iter/s]Best score: 6.2312: : 113iter [00:11, 11.37iter/s]Best score: 6.2312: : 113iter [00:11, 11.37iter/s]Best score: 6.2312: : 114iter [00:11, 11.37iter/s]Best score: 6.2312: : 115iter [00:11, 11.46iter/s]Best score: 6.2312: : 115iter [00:11, 11.46iter/s]Best score: 6.2312: : 116iter [00:11, 11.46iter/s]Best score: 6.2312: : 117iter [00:11, 11.59iter/s]Best score: 6.2312: : 117iter [00:12, 11.59iter/s]Best score: 6.2312: : 118iter [00:12, 11.59iter/s]Best score: 6.2312: : 119iter [00:12, 11.65iter/s]Best score: 6.2312: : 119iter [00:12, 11.65iter/s]Best score: 6.2312: : 120iter [00:12, 11.65iter/s]Best score: 6.2312: : 121iter [00:12, 11.47iter/s]Best score: 6.2312: : 121iter [00:12, 11.47iter/s]Best score: 6.2312: : 122iter [00:12, 11.47iter/s]Best score: 6.2312: : 123iter [00:12, 11.49iter/s]Best score: 6.2312: : 123iter [00:12, 11.49iter/s]Best score: 6.2312: : 124iter [00:12, 11.49iter/s]Best score: 6.2312: : 125iter [00:12, 11.57iter/s]Best score: 6.2312: : 125iter [00:12, 11.57iter/s]Best score: 6.2312: : 126iter [00:12, 11.57iter/s]Best score: 6.2312: : 127iter [00:12, 11.65iter/s]Best score: 6.2312: : 127iter [00:12, 11.65iter/s]Best score: 6.2312: : 128iter [00:12, 11.65iter/s]Best score: 6.2312: : 129iter [00:12, 11.72iter/s]Best score: 6.2312: : 129iter [00:13, 11.72iter/s]Best score: 6.2312: : 130iter [00:13, 11.72iter/s]Best score: 6.2312: : 131iter [00:13, 11.56iter/s]Best score: 6.2312: : 131iter [00:13, 11.56iter/s]Best score: 6.2312: : 132iter [00:13, 11.56iter/s]Best score: 6.2312: : 133iter [00:13, 11.65iter/s]Best score: 6.2312: : 133iter [00:13, 11.65iter/s]Best score: 6.2312: : 134iter [00:13, 11.65iter/s]Best score: 6.2312: : 135iter [00:13, 11.71iter/s]Best score: 6.2312: : 135iter [00:13, 11.71iter/s]Best score: 6.2312: : 136iter [00:13, 11.71iter/s]Best score: 6.2312: : 137iter [00:13, 11.80iter/s]Best score: 6.2312: : 137iter [00:13, 11.80iter/s]Best score: 6.2312: : 138iter [00:13, 11.80iter/s]Best score: 6.2312: : 139iter [00:13, 11.87iter/s]Best score: 6.2312: : 139iter [00:13, 11.87iter/s]Best score: 6.2312: : 140iter [00:14, 11.87iter/s]Best score: 6.2312: : 141iter [00:14, 11.72iter/s]Best score: 6.2312: : 141iter [00:14, 11.72iter/s]Best score: 6.2312: : 142iter [00:14, 11.72iter/s]Best score: 6.2312: : 143iter [00:14, 11.78iter/s]Best score: 6.2312: : 143iter [00:14, 11.78iter/s]Best score: 6.2312: : 144iter [00:14, 11.78iter/s]Best score: 6.2312: : 145iter [00:14, 11.85iter/s]Best score: 6.2312: : 145iter [00:14, 11.85iter/s]Best score: 6.2312: : 146iter [00:14, 11.85iter/s]Best score: 6.2312: : 147iter [00:14, 11.79iter/s]Best score: 6.2312: : 147iter [00:14, 11.79iter/s]Best score: 6.2312: : 148iter [00:14, 11.79iter/s]Best score: 6.2312: : 149iter [00:14, 11.83iter/s]
[ITER 150] Best: 6.2312
[PARAMS] Current best parameters:
  fx: 526.2140
  fy: 525.9946
  px: 991.0553
  py: 763.4345
  k1: 0.02113751
  k2: 0.02126445
  k3: -0.03623304
  k4: 0.01099082
  p1: -0.01160815
  p2: -0.00354049
  p3: 0.00343181
  p4: -0.01187150
  s1: -0.0076
  s2: 0.0142
  s3: 0.0276
  s4: -0.0158
  tilt_x: -0.0071
  tilt_y: 0.0273
  scale: 2.9280
  rotation: 0.4993
Best score: 6.2312: : 149iter [00:14, 11.83iter/s]
[Periodic outlier @ iter 50] Removing 2000L frame 12 (score=50.36, threshold=47.51)
  Remaining frames: 78
Best score: 6.2312: : 150iter [00:15, 11.83iter/s]Best score: 6.2312: : 151iter [00:15,  6.68iter/s]Best score: 6.2312: : 151iter [00:15,  6.68iter/s]Best score: 6.2312: : 152iter [00:15,  6.68iter/s]Best score: 6.2312: : 153iter [00:15,  7.68iter/s]Best score: 6.2312: : 153iter [00:15,  7.68iter/s]Best score: 6.2312: : 154iter [00:15,  7.68iter/s]Best score: 6.2312: : 155iter [00:15,  8.60iter/s]Best score: 6.2312: : 155iter [00:15,  8.60iter/s]Best score: 6.2312: : 156iter [00:15,  8.60iter/s]Best score: 6.2312: : 157iter [00:15,  9.41iter/s]Best score: 6.2312: : 157iter [00:15,  9.41iter/s]Best score: 6.2312: : 158iter [00:15,  9.41iter/s]Best score: 6.2312: : 159iter [00:15, 10.05iter/s]Best score: 5.8859: : 159iter [00:16, 10.05iter/s]Best score: 5.8859: : 160iter [00:16, 10.05iter/s]Best score: 5.8859: : 161iter [00:16, 10.42iter/s]Best score: 5.8859: : 161iter [00:16, 10.42iter/s]Best score: 5.8859: : 162iter [00:16, 10.42iter/s]Best score: 5.8859: : 163iter [00:16, 10.89iter/s]Best score: 5.8859: : 163iter [00:16, 10.89iter/s]Best score: 5.8859: : 164iter [00:16, 10.89iter/s]Best score: 5.8859: : 165iter [00:16, 11.16iter/s]Best score: 5.8859: : 165iter [00:16, 11.16iter/s]Best score: 5.8859: : 166iter [00:16, 11.16iter/s]Best score: 5.8859: : 167iter [00:16, 11.37iter/s]Best score: 5.8859: : 167iter [00:16, 11.37iter/s]Best score: 5.8859: : 168iter [00:16, 11.37iter/s]Best score: 5.8859: : 169iter [00:16, 11.55iter/s]Best score: 5.8859: : 169iter [00:16, 11.55iter/s]Best score: 5.8859: : 170iter [00:16, 11.55iter/s]Best score: 5.8859: : 171iter [00:16, 11.47iter/s]Best score: 5.8859: : 171iter [00:17, 11.47iter/s]Best score: 5.8859: : 172iter [00:17, 11.47iter/s]Best score: 5.8859: : 173iter [00:17,  9.21iter/s]Best score: 5.8859: : 173iter [00:17,  9.21iter/s]Best score: 5.8859: : 174iter [00:17,  9.21iter/s]Best score: 5.8859: : 175iter [00:17,  9.90iter/s]Best score: 5.8859: : 175iter [00:17,  9.90iter/s]Best score: 5.8859: : 176iter [00:17,  9.90iter/s]Best score: 5.8859: : 177iter [00:17, 10.48iter/s]Best score: 5.8859: : 177iter [00:17, 10.48iter/s]Best score: 5.8859: : 178iter [00:17, 10.48iter/s]Best score: 5.8859: : 179iter [00:17, 10.87iter/s]Best score: 5.8859: : 179iter [00:17, 10.87iter/s]Best score: 5.8859: : 180iter [00:18, 10.87iter/s]Best score: 5.8859: : 181iter [00:18,  8.76iter/s]Best score: 5.8859: : 181iter [00:18,  8.76iter/s]Best score: 5.8859: : 182iter [00:18,  8.76iter/s]Best score: 5.8859: : 183iter [00:18,  9.53iter/s]Best score: 5.8859: : 183iter [00:18,  9.53iter/s]Best score: 5.8859: : 184iter [00:18,  9.53iter/s]Best score: 5.8859: : 185iter [00:18, 10.20iter/s]Best score: 5.8859: : 185iter [00:18, 10.20iter/s]Best score: 5.8859: : 186iter [00:18, 10.20iter/s]Best score: 5.8859: : 187iter [00:18, 10.70iter/s]Best score: 5.8859: : 187iter [00:18, 10.70iter/s]Best score: 5.8859: : 188iter [00:18, 10.70iter/s]Best score: 5.8859: : 189iter [00:18, 11.06iter/s]Best score: 5.8859: : 189iter [00:18, 11.06iter/s]Best score: 5.8859: : 190iter [00:18, 11.06iter/s]Best score: 5.8859: : 191iter [00:18, 11.18iter/s]Best score: 5.8859: : 191iter [00:19, 11.18iter/s]Best score: 5.8859: : 192iter [00:19, 11.18iter/s]Best score: 5.8859: : 193iter [00:19, 11.39iter/s]Best score: 5.8859: : 193iter [00:19, 11.39iter/s]Best score: 5.8859: : 194iter [00:19, 11.39iter/s]Best score: 5.8859: : 195iter [00:19, 11.59iter/s]Best score: 5.8859: : 195iter [00:19, 11.59iter/s]Best score: 5.8859: : 196iter [00:19, 11.59iter/s]Best score: 5.8859: : 197iter [00:19, 11.78iter/s]Best score: 5.8859: : 197iter [00:19, 11.78iter/s]Best score: 5.8859: : 198iter [00:19, 11.78iter/s]Best score: 5.8859: : 199iter [00:19, 11.84iter/s]
[ITER 200] Best: 5.8859
[PARAMS] Current best parameters:
  fx: 526.2140
  fy: 525.9946
  px: 991.0553
  py: 763.4345
  k1: 0.02113751
  k2: 0.02126445
  k3: -0.03623304
  k4: 0.01099082
  p1: -0.01160815
  p2: -0.00354049
  p3: 0.00343181
  p4: -0.01187150
  s1: -0.0076
  s2: 0.0142
  s3: 0.0276
  s4: -0.0158
  tilt_x: -0.0071
  tilt_y: 0.0273
  scale: 2.9280
  rotation: 0.4993
Best score: 5.8859: : 199iter [00:19, 11.84iter/s]
[Periodic outlier @ iter 100] Removing 2000L frame 11 (score=49.39, threshold=46.14)
  Remaining frames: 77
Best score: 5.8859: : 200iter [00:20, 11.84iter/s]Best score: 5.8859: : 201iter [00:20,  6.70iter/s]Best score: 5.8859: : 201iter [00:20,  6.70iter/s]Best score: 5.8859: : 202iter [00:20,  6.70iter/s]Best score: 5.8859: : 203iter [00:20,  7.74iter/s]Best score: 5.8859: : 203iter [00:20,  7.74iter/s]Best score: 5.8859: : 204iter [00:20,  7.74iter/s]Best score: 5.8859: : 205iter [00:20,  8.68iter/s]Best score: 5.8859: : 205iter [00:20,  8.68iter/s]Best score: 5.8859: : 206iter [00:20,  8.68iter/s]Best score: 5.8859: : 207iter [00:20,  9.48iter/s]Best score: 5.8859: : 207iter [00:20,  9.48iter/s]Best score: 5.8859: : 208iter [00:20,  9.48iter/s]Best score: 5.8859: : 209iter [00:20, 10.16iter/s]Best score: 5.5324: : 209iter [00:20, 10.16iter/s]Best score: 5.5324: : 210iter [00:21, 10.16iter/s]Best score: 5.5324: : 211iter [00:21, 10.53iter/s]Best score: 5.5324: : 211iter [00:21, 10.53iter/s]Best score: 5.5324: : 212iter [00:21, 10.53iter/s]Best score: 5.5324: : 213iter [00:21, 10.95iter/s]Best score: 5.5324: : 213iter [00:21, 10.95iter/s]Best score: 5.5324: : 214iter [00:21, 10.95iter/s]Best score: 5.5324: : 215iter [00:21, 11.26iter/s]Best score: 5.5324: : 215iter [00:21, 11.26iter/s]Best score: 5.5324: : 216iter [00:21, 11.26iter/s]Best score: 5.5324: : 217iter [00:21, 11.52iter/s]Best score: 5.5324: : 217iter [00:21, 11.52iter/s]Best score: 5.5324: : 218iter [00:21, 11.52iter/s]Best score: 5.5324: : 219iter [00:21, 11.68iter/s]Best score: 5.5324: : 219iter [00:21, 11.68iter/s]Best score: 5.5324: : 220iter [00:21, 11.68iter/s]Best score: 5.5324: : 221iter [00:21, 11.62iter/s]Best score: 5.5324: : 221iter [00:21, 11.62iter/s]Best score: 5.5324: : 222iter [00:22, 11.62iter/s]Best score: 5.5324: : 223iter [00:22, 11.77iter/s]Best score: 5.5324: : 223iter [00:22, 11.77iter/s]Best score: 5.5324: : 224iter [00:22, 11.77iter/s]Best score: 5.5324: : 225iter [00:22, 11.93iter/s]Best score: 5.5324: : 225iter [00:22, 11.93iter/s]Best score: 5.5324: : 226iter [00:22, 11.93iter/s]Best score: 5.5324: : 227iter [00:22, 12.02iter/s]Best score: 5.5324: : 227iter [00:22, 12.02iter/s]Best score: 5.5324: : 228iter [00:22, 12.02iter/s]Best score: 5.5324: : 229iter [00:22, 12.07iter/s]Best score: 5.5324: : 229iter [00:22, 12.07iter/s]Best score: 5.5324: : 230iter [00:22, 12.07iter/s]Best score: 5.5324: : 231iter [00:22, 11.94iter/s]Best score: 5.5324: : 231iter [00:22, 11.94iter/s]Best score: 5.5324: : 232iter [00:22, 11.94iter/s]Best score: 5.5324: : 233iter [00:22, 12.01iter/s]Best score: 5.5324: : 233iter [00:22, 12.01iter/s]Best score: 5.5324: : 234iter [00:23, 12.01iter/s]Best score: 5.5324: : 235iter [00:23, 12.04iter/s]Best score: 5.5324: : 235iter [00:23, 12.04iter/s]Best score: 5.5324: : 236iter [00:23, 12.04iter/s]Best score: 5.5324: : 237iter [00:23, 12.05iter/s]Best score: 5.5324: : 237iter [00:23, 12.05iter/s]Best score: 5.5324: : 238iter [00:23, 12.05iter/s]Best score: 5.5324: : 239iter [00:23, 12.11iter/s]Best score: 5.5324: : 239iter [00:23, 12.11iter/s]Best score: 5.5324: : 240iter [00:23, 12.11iter/s]Best score: 5.5324: : 241iter [00:23, 11.97iter/s]Best score: 5.5324: : 241iter [00:23, 11.97iter/s]Best score: 5.5324: : 242iter [00:23, 11.97iter/s]Best score: 5.5324: : 243iter [00:23, 12.04iter/s]Best score: 5.5324: : 243iter [00:23, 12.04iter/s]Best score: 5.5324: : 244iter [00:23, 12.04iter/s]Best score: 5.5324: : 245iter [00:23, 12.10iter/s]Best score: 5.5324: : 245iter [00:23, 12.10iter/s]Best score: 5.5324: : 246iter [00:24, 12.10iter/s]Best score: 5.5324: : 247iter [00:24, 12.17iter/s]Best score: 5.5324: : 247iter [00:24, 12.17iter/s]Best score: 5.5324: : 248iter [00:24, 12.17iter/s]Best score: 5.5324: : 249iter [00:24, 12.26iter/s]
[ITER 250] Best: 5.5324
[PARAMS] Current best parameters:
  fx: 526.2140
  fy: 525.9946
  px: 991.0553
  py: 763.4345
  k1: 0.02113751
  k2: 0.02126445
  k3: -0.03623304
  k4: 0.01099082
  p1: -0.01160815
  p2: -0.00354049
  p3: 0.00343181
  p4: -0.01187150
  s1: -0.0076
  s2: 0.0142
  s3: 0.0276
  s4: -0.0158
  tilt_x: -0.0071
  tilt_y: 0.0273
  scale: 2.9280
  rotation: 0.4993
Best score: 5.5324: : 249iter [00:24, 12.26iter/s]Best score: 5.5324: : 250iter [00:24, 12.26iter/s]Best score: 5.5324: : 251iter [00:24,  7.88iter/s]Best score: 5.5324: : 251iter [00:24,  7.88iter/s]Best score: 5.5324: : 252iter [00:24,  7.88iter/s]Best score: 5.5324: : 253iter [00:24,  8.85iter/s]Best score: 5.5324: : 253iter [00:24,  8.85iter/s]Best score: 5.5324: : 254iter [00:24,  8.85iter/s]Best score: 5.5324: : 255iter [00:24,  9.64iter/s]Best score: 5.5324: : 255iter [00:25,  9.64iter/s]Best score: 5.5324: : 256iter [00:25,  9.64iter/s]Best score: 5.5324: : 257iter [00:25, 10.32iter/s]Best score: 5.5324: : 257iter [00:25, 10.32iter/s]Best score: 5.5324: : 258iter [00:25, 10.32iter/s]Best score: 5.5324: : 259iter [00:25, 10.88iter/s]Best score: 5.5324: : 259iter [00:25, 10.88iter/s]Best score: 5.5324: : 260iter [00:25, 10.88iter/s]Best score: 5.5324: : 261iter [00:25, 11.13iter/s]Best score: 5.5324: : 261iter [00:25, 11.13iter/s]Best score: 5.5324: : 262iter [00:25, 11.13iter/s]Best score: 5.5324: : 263iter [00:25, 11.47iter/s]Best score: 5.5324: : 263iter [00:25, 11.47iter/s]Best score: 5.5324: : 264iter [00:25, 11.47iter/s]Best score: 5.5324: : 265iter [00:25, 11.71iter/s]Best score: 5.5324: : 265iter [00:25, 11.71iter/s]Best score: 5.5324: : 266iter [00:25, 11.71iter/s]Best score: 5.5324: : 267iter [00:25, 11.95iter/s]Best score: 5.5324: : 267iter [00:26, 11.95iter/s]Best score: 5.5324: : 268iter [00:26, 11.95iter/s]Best score: 5.5324: : 269iter [00:26, 12.12iter/s]Best score: 4.3302: : 269iter [00:26, 12.12iter/s]Best score: 4.3302: : 270iter [00:26, 12.12iter/s]Best score: 4.3302: : 271iter [00:26, 12.05iter/s]Best score: 4.3302: : 271iter [00:26, 12.05iter/s]Best score: 4.3302: : 272iter [00:26, 12.05iter/s]Best score: 4.3302: : 273iter [00:26, 12.17iter/s]Best score: 4.3302: : 273iter [00:26, 12.17iter/s]Best score: 4.3302: : 274iter [00:26, 12.17iter/s]Best score: 4.3302: : 275iter [00:26, 12.27iter/s]Best score: 4.3302: : 275iter [00:26, 12.27iter/s]Best score: 4.3302: : 276iter [00:26, 12.27iter/s]Best score: 4.3302: : 277iter [00:26, 12.31iter/s]Best score: 4.3302: : 277iter [00:26, 12.31iter/s]Best score: 4.3302: : 278iter [00:26, 12.31iter/s]Best score: 4.3302: : 279iter [00:26, 12.38iter/s]Best score: 3.9850: : 279iter [00:27, 12.38iter/s]Best score: 3.9850: : 280iter [00:27, 12.38iter/s]Best score: 3.9850: : 281iter [00:27, 12.18iter/s]Best score: 3.9850: : 281iter [00:27, 12.18iter/s]Best score: 3.9850: : 282iter [00:27, 12.18iter/s]Best score: 3.9850: : 283iter [00:27, 12.26iter/s]Best score: 3.9850: : 283iter [00:27, 12.26iter/s]Best score: 3.9850: : 284iter [00:27, 12.26iter/s]Best score: 3.9850: : 285iter [00:27, 12.28iter/s]Best score: 3.9850: : 285iter [00:27, 12.28iter/s]Best score: 3.9850: : 286iter [00:27, 12.28iter/s]Best score: 3.9850: : 287iter [00:27, 12.33iter/s]Best score: 3.9850: : 287iter [00:27, 12.33iter/s]Best score: 3.9850: : 288iter [00:27, 12.33iter/s]Best score: 3.9850: : 289iter [00:27, 12.39iter/s]Best score: 3.1981: : 289iter [00:27, 12.39iter/s]Best score: 3.1981: : 290iter [00:27, 12.39iter/s]Best score: 3.1981: : 291iter [00:27, 12.17iter/s]Best score: 3.1981: : 291iter [00:27, 12.17iter/s]Best score: 3.1981: : 292iter [00:28, 12.17iter/s]Best score: 3.1981: : 293iter [00:28, 12.23iter/s]Best score: 3.1981: : 293iter [00:28, 12.23iter/s]Best score: 3.1981: : 294iter [00:28, 12.23iter/s]Best score: 3.1981: : 295iter [00:28, 12.26iter/s]Best score: 3.1981: : 295iter [00:28, 12.26iter/s]Best score: 3.1981: : 296iter [00:28, 12.26iter/s]Best score: 3.1981: : 297iter [00:28, 12.36iter/s]Best score: 3.1981: : 297iter [00:28, 12.36iter/s]Best score: 3.1981: : 298iter [00:28, 12.36iter/s]Best score: 3.1981: : 299iter [00:28, 12.39iter/s]
[ITER 300] Best: 2.1060
[PARAMS] Current best parameters:
  fx: 526.3528
  fy: 523.1376
  px: 988.2995
  py: 758.5258
  k1: 0.01610163
  k2: 0.01281238
  k3: -0.02467858
  k4: 0.00779776
  p1: -0.02215368
  p2: -0.00321829
  p3: 0.00043192
  p4: -0.01078262
  s1: -0.0243
  s2: 0.0239
  s3: 0.0233
  s4: -0.0006
  tilt_x: -0.0132
  tilt_y: 0.0319
  scale: 2.9854
  rotation: 0.2421
Best score: 2.1060: : 299iter [00:28, 12.39iter/s]Best score: 2.1060: : 300iter [00:29, 12.39iter/s]Best score: 2.1060: : 301iter [00:29,  7.94iter/s]Best score: 2.1060: : 301iter [00:29,  7.94iter/s]Best score: 2.1060: : 302iter [00:29,  7.94iter/s]Best score: 2.1060: : 303iter [00:29,  8.91iter/s]Best score: 2.1060: : 303iter [00:29,  8.91iter/s]Best score: 2.1060: : 304iter [00:29,  8.91iter/s]Best score: 2.1060: : 305iter [00:29,  9.75iter/s]Best score: 2.1060: : 305iter [00:29,  9.75iter/s]Best score: 2.1060: : 306iter [00:29,  9.75iter/s]Best score: 2.1060: : 307iter [00:29, 10.44iter/s]Best score: 2.1060: : 307iter [00:29, 10.44iter/s]Best score: 2.1060: : 308iter [00:29, 10.44iter/s]Best score: 2.1060: : 309iter [00:29, 10.96iter/s]Best score: 2.1060: : 309iter [00:29, 10.96iter/s]Best score: 2.1060: : 310iter [00:29, 10.96iter/s]Best score: 2.1060: : 311iter [00:29, 11.21iter/s]Best score: 2.1060: : 311iter [00:29, 11.21iter/s]Best score: 2.1060: : 312iter [00:29, 11.21iter/s]Best score: 2.1060: : 313iter [00:29, 11.57iter/s]Best score: 2.1060: : 313iter [00:30, 11.57iter/s]Best score: 2.1060: : 314iter [00:30, 11.57iter/s]Best score: 2.1060: : 315iter [00:30, 11.82iter/s]Best score: 2.1060: : 315iter [00:30, 11.82iter/s]Best score: 2.1060: : 316iter [00:30, 11.82iter/s]Best score: 2.1060: : 317iter [00:30, 11.98iter/s]Best score: 2.1060: : 317iter [00:30, 11.98iter/s]Best score: 2.1060: : 318iter [00:30, 11.98iter/s]Best score: 2.1060: : 319iter [00:30, 12.15iter/s]Best score: 1.7627: : 319iter [00:30, 12.15iter/s]Best score: 1.7627: : 320iter [00:30, 12.15iter/s]Best score: 1.7627: : 321iter [00:30, 12.04iter/s]Best score: 1.7627: : 321iter [00:30, 12.04iter/s]Best score: 1.7627: : 322iter [00:30, 12.04iter/s]Best score: 1.7627: : 323iter [00:30, 12.19iter/s]Best score: 1.7627: : 323iter [00:30, 12.19iter/s]Best score: 1.7627: : 324iter [00:30, 12.19iter/s]Best score: 1.7627: : 325iter [00:30, 12.28iter/s]Best score: 1.7627: : 325iter [00:31, 12.28iter/s]Best score: 1.7627: : 326iter [00:31, 12.28iter/s]Best score: 1.7627: : 327iter [00:31, 12.32iter/s]Best score: 1.7627: : 327iter [00:31, 12.32iter/s]Best score: 1.7627: : 328iter [00:31, 12.32iter/s]Best score: 1.7627: : 329iter [00:31, 12.36iter/s]Best score: 1.7627: : 329iter [00:31, 12.36iter/s]Best score: 1.7627: : 330iter [00:31, 12.36iter/s]Best score: 1.7627: : 331iter [00:31, 12.19iter/s]Best score: 1.7627: : 331iter [00:31, 12.19iter/s]Best score: 1.7627: : 332iter [00:31, 12.19iter/s]Best score: 1.7627: : 333iter [00:31, 12.30iter/s]Best score: 1.7627: : 333iter [00:31, 12.30iter/s]Best score: 1.7627: : 334iter [00:31, 12.30iter/s]Best score: 1.7627: : 335iter [00:31, 12.37iter/s]Best score: 1.7627: : 335iter [00:31, 12.37iter/s]Best score: 1.7627: : 336iter [00:31, 12.37iter/s]Best score: 1.7627: : 337iter [00:31, 12.38iter/s]Best score: 1.7627: : 337iter [00:32, 12.38iter/s]Best score: 1.7627: : 338iter [00:32, 12.38iter/s]Best score: 1.7627: : 339iter [00:32, 12.43iter/s]Best score: 1.5595: : 339iter [00:32, 12.43iter/s]Best score: 1.5595: : 340iter [00:32, 12.43iter/s]Best score: 1.5595: : 341iter [00:32, 12.21iter/s]Best score: 1.5595: : 341iter [00:32, 12.21iter/s]Best score: 1.5595: : 342iter [00:32, 12.21iter/s]Best score: 1.5595: : 343iter [00:32, 12.28iter/s]Best score: 1.5595: : 343iter [00:32, 12.28iter/s]Best score: 1.5595: : 344iter [00:32, 12.28iter/s]Best score: 1.5595: : 345iter [00:32, 12.35iter/s]Best score: 1.5595: : 345iter [00:32, 12.35iter/s]Best score: 1.5595: : 346iter [00:32, 12.35iter/s]Best score: 1.5595: : 347iter [00:32, 12.32iter/s]Best score: 1.5595: : 347iter [00:32, 12.32iter/s]Best score: 1.5595: : 348iter [00:32, 12.32iter/s]Best score: 1.5595: : 349iter [00:32, 12.36iter/s]
[ITER 350] Best: 1.4060
[PARAMS] Current best parameters:
  fx: 509.3899
  fy: 508.2339
  px: 989.3398
  py: 764.9204
  k1: 0.01837563
  k2: 0.02093180
  k3: -0.01919640
  k4: 0.00400033
  p1: -0.02035174
  p2: 0.00320344
  p3: 0.00071731
  p4: -0.00348563
  s1: -0.0304
  s2: 0.0174
  s3: 0.0298
  s4: -0.0071
  tilt_x: 0.0129
  tilt_y: 0.0449
  scale: 3.0103
  rotation: -0.0060
Best score: 1.4060: : 349iter [00:33, 12.36iter/s]Best score: 1.4060: : 350iter [00:33, 12.36iter/s]Best score: 1.4060: : 351iter [00:33,  7.91iter/s]Best score: 1.4060: : 351iter [00:33,  7.91iter/s]Best score: 1.4060: : 352iter [00:33,  7.91iter/s]Best score: 1.4060: : 353iter [00:33,  8.89iter/s]Best score: 1.4060: : 353iter [00:33,  8.89iter/s]Best score: 1.4060: : 354iter [00:33,  8.89iter/s]Best score: 1.4060: : 355iter [00:33,  9.68iter/s]Best score: 1.4060: : 355iter [00:33,  9.68iter/s]Best score: 1.4060: : 356iter [00:33,  9.68iter/s]Best score: 1.4060: : 357iter [00:33, 10.37iter/s]Best score: 1.4060: : 357iter [00:33, 10.37iter/s]Best score: 1.4060: : 358iter [00:34, 10.37iter/s]Best score: 1.4060: : 359iter [00:34, 10.89iter/s]Best score: 1.3603: : 359iter [00:34, 10.89iter/s]Best score: 1.3603: : 360iter [00:34, 10.89iter/s]Best score: 1.3603: : 361iter [00:34, 11.11iter/s]Best score: 1.3603: : 361iter [00:34, 11.11iter/s]Best score: 1.3603: : 362iter [00:34, 11.11iter/s]Best score: 1.3603: : 363iter [00:34, 11.50iter/s]Best score: 1.3603: : 363iter [00:34, 11.50iter/s]Best score: 1.3603: : 364iter [00:34, 11.50iter/s]Best score: 1.3603: : 365iter [00:34, 11.80iter/s]Best score: 1.3603: : 365iter [00:34, 11.80iter/s]Best score: 1.3603: : 366iter [00:34, 11.80iter/s]Best score: 1.3603: : 367iter [00:34, 11.99iter/s]Best score: 1.3603: : 367iter [00:34, 11.99iter/s]Best score: 1.3603: : 368iter [00:34, 11.99iter/s]Best score: 1.3603: : 369iter [00:34, 12.12iter/s]Best score: 1.2626: : 369iter [00:34, 12.12iter/s]Best score: 1.2626: : 370iter [00:35, 12.12iter/s]Best score: 1.2626: : 371iter [00:35, 11.94iter/s]Best score: 1.2626: : 371iter [00:35, 11.94iter/s]Best score: 1.2626: : 372iter [00:35, 11.94iter/s]Best score: 1.2626: : 373iter [00:35, 12.08iter/s]Best score: 1.2626: : 373iter [00:35, 12.08iter/s]Best score: 1.2626: : 374iter [00:35, 12.08iter/s]Best score: 1.2626: : 375iter [00:35, 12.14iter/s]Best score: 1.2626: : 375iter [00:35, 12.14iter/s]Best score: 1.2626: : 376iter [00:35, 12.14iter/s]Best score: 1.2626: : 377iter [00:35, 12.19iter/s]Best score: 1.2626: : 377iter [00:35, 12.19iter/s]Best score: 1.2626: : 378iter [00:35, 12.19iter/s]Best score: 1.2626: : 379iter [00:35, 12.26iter/s]Best score: 1.1631: : 379iter [00:35, 12.26iter/s]Best score: 1.1631: : 380iter [00:35, 12.26iter/s]Best score: 1.1631: : 381iter [00:35, 12.08iter/s]Best score: 1.1631: : 381iter [00:35, 12.08iter/s]Best score: 1.1631: : 382iter [00:35, 12.08iter/s]Best score: 1.1631: : 383iter [00:35, 12.13iter/s]Best score: 1.1631: : 383iter [00:36, 12.13iter/s]Best score: 1.1631: : 384iter [00:36, 12.13iter/s]Best score: 1.1631: : 385iter [00:36, 12.15iter/s]Best score: 1.1631: : 385iter [00:36, 12.15iter/s]Best score: 1.1631: : 386iter [00:36, 12.15iter/s]Best score: 1.1631: : 387iter [00:36, 12.25iter/s]Best score: 1.1631: : 387iter [00:36, 12.25iter/s]Best score: 1.1631: : 388iter [00:36, 12.25iter/s]Best score: 1.1631: : 389iter [00:36, 12.32iter/s]Best score: 0.9643: : 389iter [00:36, 12.32iter/s]Best score: 0.9643: : 390iter [00:36, 12.32iter/s]Best score: 0.9643: : 391iter [00:36, 12.16iter/s]Best score: 0.9643: : 391iter [00:36, 12.16iter/s]Best score: 0.9643: : 392iter [00:36, 12.16iter/s]Best score: 0.9643: : 393iter [00:36, 12.21iter/s]Best score: 0.9643: : 393iter [00:36, 12.21iter/s]Best score: 0.9643: : 394iter [00:36, 12.21iter/s]Best score: 0.9643: : 395iter [00:36, 12.24iter/s]Best score: 0.9643: : 395iter [00:37, 12.24iter/s]Best score: 0.9643: : 396iter [00:37, 12.24iter/s]Best score: 0.9643: : 397iter [00:37, 12.32iter/s]Best score: 0.9643: : 397iter [00:37, 12.32iter/s]Best score: 0.9643: : 398iter [00:37, 12.32iter/s]Best score: 0.9643: : 399iter [00:37, 12.36iter/s]
[ITER 400] Best: 0.9643
[PARAMS] Current best parameters:
  fx: 547.8779
  fy: 546.8757
  px: 994.2985
  py: 773.6496
  k1: -0.05862480
  k2: 0.09025829
  k3: -0.07014402
  k4: 0.01861987
  p1: -0.00046564
  p2: 0.01784484
  p3: -0.01998735
  p4: -0.00282816
  s1: -0.0008
  s2: -0.0041
  s3: 0.0345
  s4: -0.0111
  tilt_x: 0.0204
  tilt_y: 0.0446
  scale: 3.0490
  rotation: 0.0412
Best score: 0.9643: : 399iter [00:37, 12.36iter/s]Best score: 0.9643: : 400iter [00:37, 12.36iter/s]Best score: 0.9643: : 401iter [00:37,  7.91iter/s]Best score: 0.9643: : 401iter [00:37,  7.91iter/s]Best score: 0.9643: : 402iter [00:37,  7.91iter/s]Best score: 0.9643: : 403iter [00:37,  8.87iter/s]Best score: 0.9643: : 403iter [00:37,  8.87iter/s]Best score: 0.9643: : 404iter [00:38,  8.87iter/s]Best score: 0.9643: : 405iter [00:38,  9.68iter/s]Best score: 0.9643: : 405iter [00:38,  9.68iter/s]Best score: 0.9643: : 406iter [00:38,  9.68iter/s]Best score: 0.9643: : 407iter [00:38, 10.35iter/s]Best score: 0.9643: : 407iter [00:38, 10.35iter/s]Best score: 0.9643: : 408iter [00:38, 10.35iter/s]Best score: 0.9643: : 409iter [00:38, 10.85iter/s]Best score: 0.9643: : 409iter [00:38, 10.85iter/s]Best score: 0.9643: : 410iter [00:38, 10.85iter/s]Best score: 0.9643: : 411iter [00:38, 11.10iter/s]Best score: 0.9643: : 411iter [00:38, 11.10iter/s]Best score: 0.9643: : 412iter [00:38, 11.10iter/s]Best score: 0.9643: : 413iter [00:38, 11.49iter/s]Best score: 0.9643: : 413iter [00:38, 11.49iter/s]Best score: 0.9643: : 414iter [00:38, 11.49iter/s]Best score: 0.9643: : 415iter [00:38, 11.76iter/s]Best score: 0.9643: : 415iter [00:38, 11.76iter/s]Best score: 0.9643: : 416iter [00:39, 11.76iter/s]Best score: 0.9643: : 417iter [00:39, 11.97iter/s]Best score: 0.9643: : 417iter [00:39, 11.97iter/s]Best score: 0.9643: : 418iter [00:39, 11.97iter/s]Best score: 0.9643: : 419iter [00:39, 12.09iter/s]Best score: 0.9643: : 419iter [00:39, 12.09iter/s]Best score: 0.9643: : 420iter [00:39, 12.09iter/s]Best score: 0.9643: : 421iter [00:39, 12.01iter/s]Best score: 0.9643: : 421iter [00:39, 12.01iter/s]Best score: 0.9643: : 422iter [00:39, 12.01iter/s]Best score: 0.9643: : 423iter [00:39, 12.15iter/s]Best score: 0.9643: : 423iter [00:39, 12.15iter/s]Best score: 0.9643: : 424iter [00:39, 12.15iter/s]Best score: 0.9643: : 425iter [00:39, 12.25iter/s]Best score: 0.9643: : 425iter [00:39, 12.25iter/s]Best score: 0.9643: : 426iter [00:39, 12.25iter/s]Best score: 0.9643: : 427iter [00:39, 12.30iter/s]Best score: 0.9643: : 427iter [00:39, 12.30iter/s]Best score: 0.9643: : 428iter [00:40, 12.30iter/s]Best score: 0.9643: : 429iter [00:40, 12.27iter/s]Best score: 0.8013: : 429iter [00:40, 12.27iter/s]Best score: 0.8013: : 430iter [00:40, 12.27iter/s]Best score: 0.8013: : 431iter [00:40, 12.06iter/s]Best score: 0.8013: : 431iter [00:40, 12.06iter/s]Best score: 0.8013: : 432iter [00:40, 12.06iter/s]Best score: 0.8013: : 433iter [00:40, 12.14iter/s]Best score: 0.8013: : 433iter [00:40, 12.14iter/s]Best score: 0.8013: : 434iter [00:40, 12.14iter/s]Best score: 0.8013: : 435iter [00:40, 12.16iter/s]Best score: 0.8013: : 435iter [00:40, 12.16iter/s]Best score: 0.8013: : 436iter [00:40, 12.16iter/s]Best score: 0.8013: : 437iter [00:40, 12.24iter/s]Best score: 0.8013: : 437iter [00:40, 12.24iter/s]Best score: 0.8013: : 438iter [00:40, 12.24iter/s]Best score: 0.8013: : 439iter [00:40, 12.21iter/s]Best score: 0.8013: : 439iter [00:40, 12.21iter/s]Best score: 0.8013: : 440iter [00:41, 12.21iter/s]Best score: 0.8013: : 441iter [00:41, 12.05iter/s]Best score: 0.8013: : 441iter [00:41, 12.05iter/s]Best score: 0.8013: : 442iter [00:41, 12.05iter/s]Best score: 0.8013: : 443iter [00:41, 12.14iter/s]Best score: 0.8013: : 443iter [00:41, 12.14iter/s]Best score: 0.8013: : 444iter [00:41, 12.14iter/s]Best score: 0.8013: : 445iter [00:41, 12.21iter/s]Best score: 0.8013: : 445iter [00:41, 12.21iter/s]Best score: 0.8013: : 446iter [00:41, 12.21iter/s]Best score: 0.8013: : 447iter [00:41, 12.30iter/s]Best score: 0.8013: : 447iter [00:41, 12.30iter/s]Best score: 0.8013: : 448iter [00:41, 12.30iter/s]Best score: 0.8013: : 449iter [00:41, 12.35iter/s]
[ITER 450] Best: 0.7577
[PARAMS] Current best parameters:
  fx: 549.6220
  fy: 549.9856
  px: 998.1895
  py: 773.4912
  k1: -0.11223078
  k2: 0.17381881
  k3: -0.11923465
  k4: 0.02863079
  p1: 0.01172614
  p2: 0.01670755
  p3: -0.03813972
  p4: -0.00712911
  s1: 0.0113
  s2: 0.0110
  s3: 0.0462
  s4: -0.0078
  tilt_x: -0.0074
  tilt_y: 0.0203
  scale: 3.0975
  rotation: -0.0836
Best score: 0.7577: : 449iter [00:41, 12.35iter/s]Best score: 0.7577: : 450iter [00:42, 12.35iter/s]Best score: 0.7577: : 451iter [00:42,  7.91iter/s]Best score: 0.7577: : 451iter [00:42,  7.91iter/s]Best score: 0.7577: : 452iter [00:42,  7.91iter/s]Best score: 0.7577: : 453iter [00:42,  8.88iter/s]Best score: 0.7577: : 453iter [00:42,  8.88iter/s]Best score: 0.7577: : 454iter [00:42,  8.88iter/s]Best score: 0.7577: : 455iter [00:42,  9.72iter/s]Best score: 0.7577: : 455iter [00:42,  9.72iter/s]Best score: 0.7577: : 456iter [00:42,  9.72iter/s]Best score: 0.7577: : 457iter [00:42, 10.39iter/s]Best score: 0.7577: : 457iter [00:42, 10.39iter/s]Best score: 0.7577: : 458iter [00:42, 10.39iter/s]Best score: 0.7577: : 459iter [00:42, 10.90iter/s]Best score: 0.7174: : 459iter [00:42, 10.90iter/s]Best score: 0.7174: : 460iter [00:42, 10.90iter/s]Best score: 0.7174: : 461iter [00:42, 11.14iter/s]Best score: 0.7174: : 461iter [00:43, 11.14iter/s]Best score: 0.7174: : 462iter [00:43, 11.14iter/s]Best score: 0.7174: : 463iter [00:43, 11.48iter/s]Best score: 0.7174: : 463iter [00:43, 11.48iter/s]Best score: 0.7174: : 464iter [00:43, 11.48iter/s]Best score: 0.7174: : 465iter [00:43, 11.75iter/s]Best score: 0.7174: : 465iter [00:43, 11.75iter/s]Best score: 0.7174: : 466iter [00:43, 11.75iter/s]Best score: 0.7174: : 467iter [00:43, 11.94iter/s]Best score: 0.7174: : 467iter [00:43, 11.94iter/s]Best score: 0.7174: : 468iter [00:43, 11.94iter/s]Best score: 0.7174: : 469iter [00:43, 12.06iter/s]Best score: 0.7174: : 469iter [00:43, 12.06iter/s]Best score: 0.7174: : 470iter [00:43, 12.06iter/s]Best score: 0.7174: : 471iter [00:43, 11.95iter/s]Best score: 0.7174: : 471iter [00:43, 11.95iter/s]Best score: 0.7174: : 472iter [00:43, 11.95iter/s]Best score: 0.7174: : 473iter [00:43, 12.05iter/s]Best score: 0.7174: : 473iter [00:44, 12.05iter/s]Best score: 0.7174: : 474iter [00:44, 12.05iter/s]Best score: 0.7174: : 475iter [00:44, 12.12iter/s]Best score: 0.7174: : 475iter [00:44, 12.12iter/s]Best score: 0.7174: : 476iter [00:44, 12.12iter/s]Best score: 0.7174: : 477iter [00:44, 12.21iter/s]Best score: 0.7174: : 477iter [00:44, 12.21iter/s]Best score: 0.7174: : 478iter [00:44, 12.21iter/s]Best score: 0.7174: : 479iter [00:44, 12.28iter/s]Best score: 0.7174: : 479iter [00:44, 12.28iter/s]Best score: 0.7174: : 480iter [00:44, 12.28iter/s]Best score: 0.7174: : 481iter [00:44, 12.10iter/s]Best score: 0.7174: : 481iter [00:44, 12.10iter/s]Best score: 0.7174: : 482iter [00:44, 12.10iter/s]Best score: 0.7174: : 483iter [00:44, 12.19iter/s]Best score: 0.7174: : 483iter [00:44, 12.19iter/s]Best score: 0.7174: : 484iter [00:44, 12.19iter/s]Best score: 0.7174: : 485iter [00:44, 12.24iter/s]Best score: 0.7174: : 485iter [00:44, 12.24iter/s]Best score: 0.7174: : 486iter [00:45, 12.24iter/s]Best score: 0.7174: : 487iter [00:45, 12.30iter/s]Best score: 0.7174: : 487iter [00:45, 12.30iter/s]Best score: 0.7174: : 488iter [00:45, 12.30iter/s]Best score: 0.7174: : 489iter [00:45, 12.30iter/s]Best score: 0.5678: : 489iter [00:45, 12.30iter/s]Best score: 0.5678: : 490iter [00:45, 12.30iter/s]Best score: 0.5678: : 491iter [00:45, 12.14iter/s]Best score: 0.5678: : 491iter [00:45, 12.14iter/s]Best score: 0.5678: : 492iter [00:45, 12.14iter/s]Best score: 0.5678: : 493iter [00:45, 12.25iter/s]Best score: 0.5678: : 493iter [00:45, 12.25iter/s]Best score: 0.5678: : 494iter [00:45, 12.25iter/s]Best score: 0.5678: : 495iter [00:45, 12.33iter/s]Best score: 0.5678: : 495iter [00:45, 12.33iter/s]Best score: 0.5678: : 496iter [00:45, 12.33iter/s]Best score: 0.5678: : 497iter [00:45, 12.37iter/s]Best score: 0.5678: : 497iter [00:45, 12.37iter/s]Best score: 0.5678: : 498iter [00:46, 12.37iter/s]Best score: 0.5678: : 499iter [00:46, 12.38iter/s]
[ITER 500] Best: 0.5545
[PARAMS] Current best parameters:
  fx: 542.1376
  fy: 541.1064
  px: 993.0145
  py: 770.2501
  k1: -0.06542144
  k2: 0.09727652
  k3: -0.06425135
  k4: 0.01474007
  p1: -0.00474769
  p2: 0.01286838
  p3: -0.01870417
  p4: -0.00789691
  s1: -0.0032
  s2: 0.0109
  s3: 0.0337
  s4: -0.0011
  tilt_x: 0.0082
  tilt_y: 0.0424
  scale: 3.0707
  rotation: -0.1063
Best score: 0.5545: : 499iter [00:46, 12.38iter/s]Best score: 0.5545: : 500iter [00:46, 12.38iter/s]Best score: 0.5545: : 501iter [00:46,  7.91iter/s]Best score: 0.5545: : 501iter [00:46,  7.91iter/s]Best score: 0.5545: : 502iter [00:46,  7.91iter/s]Best score: 0.5545: : 503iter [00:46,  8.88iter/s]Best score: 0.5545: : 503iter [00:46,  8.88iter/s]Best score: 0.5545: : 504iter [00:46,  8.88iter/s]Best score: 0.5545: : 505iter [00:46,  9.70iter/s]Best score: 0.5545: : 505iter [00:46,  9.70iter/s]Best score: 0.5545: : 506iter [00:46,  9.70iter/s]Best score: 0.5545: : 507iter [00:46, 10.32iter/s]Best score: 0.5545: : 507iter [00:47, 10.32iter/s]Best score: 0.5545: : 508iter [00:47, 10.32iter/s]Best score: 0.5545: : 509iter [00:47, 10.84iter/s]Best score: 0.4643: : 509iter [00:47, 10.84iter/s]Best score: 0.4643: : 510iter [00:47, 10.84iter/s]Best score: 0.4643: : 511iter [00:47, 11.05iter/s]Best score: 0.4643: : 511iter [00:47, 11.05iter/s]Best score: 0.4643: : 512iter [00:47, 11.05iter/s]Best score: 0.4643: : 513iter [00:47, 11.41iter/s]Best score: 0.4643: : 513iter [00:47, 11.41iter/s]Best score: 0.4643: : 514iter [00:47, 11.41iter/s]Best score: 0.4643: : 515iter [00:47, 11.68iter/s]Best score: 0.4643: : 515iter [00:47, 11.68iter/s]Best score: 0.4643: : 516iter [00:47, 11.68iter/s]Best score: 0.4643: : 517iter [00:47, 11.92iter/s]Best score: 0.4643: : 517iter [00:47, 11.92iter/s]Best score: 0.4643: : 518iter [00:47, 11.92iter/s]Best score: 0.4643: : 519iter [00:47, 12.08iter/s]Best score: 0.4643: : 519iter [00:48, 12.08iter/s]Best score: 0.4643: : 520iter [00:48, 12.08iter/s]Best score: 0.4643: : 521iter [00:48, 11.90iter/s]Best score: 0.4643: : 521iter [00:48, 11.90iter/s]Best score: 0.4643: : 522iter [00:48, 11.90iter/s]Best score: 0.4643: : 523iter [00:48, 12.02iter/s]Best score: 0.4643: : 523iter [00:48, 12.02iter/s]Best score: 0.4643: : 524iter [00:48, 12.02iter/s]Best score: 0.4643: : 525iter [00:48, 12.14iter/s]Best score: 0.4643: : 525iter [00:48, 12.14iter/s]Best score: 0.4643: : 526iter [00:48, 12.14iter/s]Best score: 0.4643: : 527iter [00:48, 12.23iter/s]Best score: 0.4643: : 527iter [00:48, 12.23iter/s]Best score: 0.4643: : 528iter [00:48, 12.23iter/s]Best score: 0.4643: : 529iter [00:48, 12.26iter/s]Best score: 0.4643: : 529iter [00:48, 12.26iter/s]Best score: 0.4643: : 530iter [00:48, 12.26iter/s]Best score: 0.4643: : 531iter [00:48, 12.05iter/s]Best score: 0.4643: : 531iter [00:49, 12.05iter/s]Best score: 0.4643: : 532iter [00:49, 12.05iter/s]Best score: 0.4643: : 533iter [00:49, 12.15iter/s]Best score: 0.4643: : 533iter [00:49, 12.15iter/s]Best score: 0.4643: : 534iter [00:49, 12.15iter/s]Best score: 0.4643: : 535iter [00:49, 12.24iter/s]Best score: 0.4643: : 535iter [00:49, 12.24iter/s]Best score: 0.4643: : 536iter [00:49, 12.24iter/s]Best score: 0.4643: : 537iter [00:49, 12.32iter/s]Best score: 0.4643: : 537iter [00:49, 12.32iter/s]Best score: 0.4643: : 538iter [00:49, 12.32iter/s]Best score: 0.4643: : 539iter [00:49, 12.35iter/s]Best score: 0.4643: : 539iter [00:49, 12.35iter/s]Best score: 0.4643: : 540iter [00:49, 12.35iter/s]Best score: 0.4643: : 541iter [00:49, 12.16iter/s]Best score: 0.4643: : 541iter [00:49, 12.16iter/s]Best score: 0.4643: : 542iter [00:49, 12.16iter/s]Best score: 0.4643: : 543iter [00:49, 12.20iter/s]Best score: 0.4643: : 543iter [00:50, 12.20iter/s]Best score: 0.4643: : 544iter [00:50, 12.20iter/s]Best score: 0.4643: : 545iter [00:50, 12.26iter/s]Best score: 0.4643: : 545iter [00:50, 12.26iter/s]Best score: 0.4643: : 546iter [00:50, 12.26iter/s]Best score: 0.4643: : 547iter [00:50, 12.29iter/s]Best score: 0.4643: : 547iter [00:50, 12.29iter/s]Best score: 0.4643: : 548iter [00:50, 12.29iter/s]Best score: 0.4643: : 549iter [00:50, 12.35iter/s]
[ITER 550] Best: 0.4544
[PARAMS] Current best parameters:
  fx: 546.1952
  fy: 546.1722
  px: 995.5555
  py: 770.2103
  k1: -0.06482991
  k2: 0.10661364
  k3: -0.08001390
  k4: 0.02013742
  p1: -0.00215422
  p2: 0.01403519
  p3: -0.02547939
  p4: -0.00012711
  s1: 0.0019
  s2: 0.0063
  s3: 0.0346
  s4: -0.0060
  tilt_x: 0.0049
  tilt_y: 0.0350
  scale: 3.0448
  rotation: -0.0438
Best score: 0.4544: : 549iter [00:50, 12.35iter/s]Best score: 0.4544: : 550iter [00:50, 12.35iter/s]Best score: 0.4544: : 551iter [00:50,  7.90iter/s]Best score: 0.4544: : 551iter [00:50,  7.90iter/s]Best score: 0.4544: : 552iter [00:51,  7.90iter/s]Best score: 0.4544: : 553iter [00:51,  8.89iter/s]Best score: 0.4544: : 553iter [00:51,  8.89iter/s]Best score: 0.4544: : 554iter [00:51,  8.89iter/s]Best score: 0.4544: : 555iter [00:51,  9.73iter/s]Best score: 0.4544: : 555iter [00:51,  9.73iter/s]Best score: 0.4544: : 556iter [00:51,  9.73iter/s]Best score: 0.4544: : 557iter [00:51, 10.43iter/s]Best score: 0.4544: : 557iter [00:51, 10.43iter/s]Best score: 0.4544: : 558iter [00:51, 10.43iter/s]Best score: 0.4544: : 559iter [00:51, 10.96iter/s]Best score: 0.4536: : 559iter [00:51, 10.96iter/s]Best score: 0.4536: : 560iter [00:51, 10.96iter/s]Best score: 0.4536: : 561iter [00:51, 11.22iter/s]Best score: 0.4536: : 561iter [00:51, 11.22iter/s]Best score: 0.4536: : 562iter [00:51, 11.22iter/s]Best score: 0.4536: : 563iter [00:51, 11.52iter/s]Best score: 0.4536: : 563iter [00:51, 11.52iter/s]Best score: 0.4536: : 564iter [00:52, 11.52iter/s]Best score: 0.4536: : 565iter [00:52, 11.80iter/s]Best score: 0.4536: : 565iter [00:52, 11.80iter/s]Best score: 0.4536: : 566iter [00:52, 11.80iter/s]Best score: 0.4536: : 567iter [00:52, 12.01iter/s]Best score: 0.4536: : 567iter [00:52, 12.01iter/s]Best score: 0.4536: : 568iter [00:52, 12.01iter/s]Best score: 0.4536: : 569iter [00:52, 12.14iter/s]Best score: 0.3836: : 569iter [00:52, 12.14iter/s]Best score: 0.3836: : 570iter [00:52, 12.14iter/s]Best score: 0.3836: : 571iter [00:52, 12.04iter/s]Best score: 0.3836: : 571iter [00:52, 12.04iter/s]Best score: 0.3836: : 572iter [00:52, 12.04iter/s]Best score: 0.3836: : 573iter [00:52, 12.19iter/s]Best score: 0.3836: : 573iter [00:52, 12.19iter/s]Best score: 0.3836: : 574iter [00:52, 12.19iter/s]Best score: 0.3836: : 575iter [00:52, 12.25iter/s]Best score: 0.3836: : 575iter [00:52, 12.25iter/s]Best score: 0.3836: : 576iter [00:52, 12.25iter/s]Best score: 0.3836: : 577iter [00:52, 12.33iter/s]Best score: 0.3836: : 577iter [00:53, 12.33iter/s]Best score: 0.3836: : 578iter [00:53, 12.33iter/s]Best score: 0.3836: : 579iter [00:53, 12.39iter/s]Best score: 0.3479: : 579iter [00:53, 12.39iter/s]Best score: 0.3479: : 580iter [00:53, 12.39iter/s]Best score: 0.3479: : 581iter [00:53, 12.21iter/s]Best score: 0.3479: : 581iter [00:53, 12.21iter/s]Best score: 0.3479: : 582iter [00:53, 12.21iter/s]Best score: 0.3479: : 583iter [00:53, 12.31iter/s]Best score: 0.3479: : 583iter [00:53, 12.31iter/s]Best score: 0.3479: : 584iter [00:53, 12.31iter/s]Best score: 0.3479: : 585iter [00:53, 12.38iter/s]Best score: 0.3479: : 585iter [00:53, 12.38iter/s]Best score: 0.3479: : 586iter [00:53, 12.38iter/s]Best score: 0.3479: : 587iter [00:53, 12.43iter/s]Best score: 0.3479: : 587iter [00:53, 12.43iter/s]Best score: 0.3479: : 588iter [00:53, 12.43iter/s]Best score: 0.3479: : 589iter [00:53, 12.42iter/s]Best score: 0.3479: : 589iter [00:54, 12.42iter/s]Best score: 0.3479: : 590iter [00:54, 12.42iter/s]Best score: 0.3479: : 591iter [00:54, 12.25iter/s]Best score: 0.3479: : 591iter [00:54, 12.25iter/s]Best score: 0.3479: : 592iter [00:54, 12.25iter/s]Best score: 0.3479: : 593iter [00:54, 12.34iter/s]Best score: 0.3479: : 593iter [00:54, 12.34iter/s]Best score: 0.3479: : 594iter [00:54, 12.34iter/s]Best score: 0.3479: : 595iter [00:54, 12.41iter/s]Best score: 0.3479: : 595iter [00:54, 12.41iter/s]Best score: 0.3479: : 596iter [00:54, 12.41iter/s]Best score: 0.3479: : 597iter [00:54, 12.45iter/s]Best score: 0.3479: : 597iter [00:54, 12.45iter/s]Best score: 0.3479: : 598iter [00:54, 12.45iter/s]Best score: 0.3479: : 599iter [00:54, 12.45iter/s]
[ITER 600] Best: 0.3349
[PARAMS] Current best parameters:
  fx: 540.8407
  fy: 540.8976
  px: 995.6629
  py: 769.4050
  k1: -0.04234445
  k2: 0.05846206
  k3: -0.03812403
  k4: 0.00830575
  p1: -0.00490530
  p2: 0.00863222
  p3: -0.01460859
  p4: -0.00487560
  s1: -0.0034
  s2: 0.0076
  s3: 0.0264
  s4: -0.0009
  tilt_x: 0.0049
  tilt_y: 0.0209
  scale: 3.0473
  rotation: -0.0072
Best score: 0.3349: : 599iter [00:54, 12.45iter/s]Best score: 0.3349: : 600iter [00:55, 12.45iter/s]Best score: 0.3349: : 601iter [00:55,  7.96iter/s]Best score: 0.3349: : 601iter [00:55,  7.96iter/s]Best score: 0.3349: : 602iter [00:55,  7.96iter/s]Best score: 0.3349: : 603iter [00:55,  8.94iter/s]Best score: 0.3349: : 603iter [00:55,  8.94iter/s]Best score: 0.3349: : 604iter [00:55,  8.94iter/s]Best score: 0.3349: : 605iter [00:55,  9.76iter/s]Best score: 0.3349: : 605iter [00:55,  9.76iter/s]Best score: 0.3349: : 606iter [00:55,  9.76iter/s]Best score: 0.3349: : 607iter [00:55, 10.43iter/s]Best score: 0.3349: : 607iter [00:55, 10.43iter/s]Best score: 0.3349: : 608iter [00:55, 10.43iter/s]Best score: 0.3349: : 609iter [00:55, 10.97iter/s]Best score: 0.3032: : 609iter [00:55, 10.97iter/s]Best score: 0.3032: : 610iter [00:56, 10.97iter/s]Best score: 0.3032: : 611iter [00:56, 11.18iter/s]Best score: 0.3032: : 611iter [00:56, 11.18iter/s]Best score: 0.3032: : 612iter [00:56, 11.18iter/s]Best score: 0.3032: : 613iter [00:56, 11.53iter/s]Best score: 0.3032: : 613iter [00:56, 11.53iter/s]Best score: 0.3032: : 614iter [00:56, 11.53iter/s]Best score: 0.3032: : 615iter [00:56, 11.83iter/s]Best score: 0.3032: : 615iter [00:56, 11.83iter/s]Best score: 0.3032: : 616iter [00:56, 11.83iter/s]Best score: 0.3032: : 617iter [00:56, 12.04iter/s]Best score: 0.3032: : 617iter [00:56, 12.04iter/s]Best score: 0.3032: : 618iter [00:56, 12.04iter/s]Best score: 0.3032: : 619iter [00:56, 12.17iter/s]Best score: 0.2904: : 619iter [00:56, 12.17iter/s]Best score: 0.2904: : 620iter [00:56, 12.17iter/s]Best score: 0.2904: : 621iter [00:56, 12.02iter/s]Best score: 0.2904: : 621iter [00:56, 12.02iter/s]Best score: 0.2904: : 622iter [00:57, 12.02iter/s]Best score: 0.2904: : 623iter [00:57,  9.82iter/s]Best score: 0.2904: : 623iter [00:57,  9.82iter/s]Best score: 0.2904: : 624iter [00:57,  9.82iter/s]Best score: 0.2904: : 625iter [00:57, 10.44iter/s]Best score: 0.2904: : 625iter [00:57, 10.44iter/s]Best score: 0.2904: : 626iter [00:57, 10.44iter/s]Best score: 0.2904: : 627iter [00:57, 10.98iter/s]Best score: 0.2904: : 627iter [00:57, 10.98iter/s]Best score: 0.2904: : 628iter [00:57, 10.98iter/s]Best score: 0.2904: : 629iter [00:57, 11.39iter/s]Best score: 0.2904: : 629iter [00:57, 11.39iter/s]Best score: 0.2904: : 630iter [00:57, 11.39iter/s]Best score: 0.2904: : 631iter [00:57, 11.47iter/s]Best score: 0.2904: : 631iter [00:57, 11.47iter/s]Best score: 0.2904: : 632iter [00:57, 11.47iter/s]Best score: 0.2904: : 633iter [00:57, 11.78iter/s]Best score: 0.2904: : 633iter [00:58, 11.78iter/s]Best score: 0.2904: : 634iter [00:58, 11.78iter/s]Best score: 0.2904: : 635iter [00:58, 11.95iter/s]Best score: 0.2904: : 635iter [00:58, 11.95iter/s]Best score: 0.2904: : 636iter [00:58, 11.95iter/s]Best score: 0.2904: : 637iter [00:58, 12.13iter/s]Best score: 0.2904: : 637iter [00:58, 12.13iter/s]Best score: 0.2904: : 638iter [00:58, 12.13iter/s]Best score: 0.2904: : 639iter [00:58, 12.22iter/s]Best score: 0.2782: : 639iter [00:58, 12.22iter/s]Best score: 0.2782: : 640iter [00:58, 12.22iter/s]Best score: 0.2782: : 641iter [00:58, 12.10iter/s]Best score: 0.2782: : 641iter [00:58, 12.10iter/s]Best score: 0.2782: : 642iter [00:58, 12.10iter/s]Best score: 0.2782: : 643iter [00:58, 12.24iter/s]Best score: 0.2782: : 643iter [00:58, 12.24iter/s]Best score: 0.2782: : 644iter [00:58, 12.24iter/s]Best score: 0.2782: : 645iter [00:58, 12.28iter/s]Best score: 0.2782: : 645iter [00:59, 12.28iter/s]Best score: 0.2782: : 646iter [00:59, 12.28iter/s]Best score: 0.2782: : 647iter [00:59, 12.36iter/s]Best score: 0.2782: : 647iter [00:59, 12.36iter/s]Best score: 0.2782: : 648iter [00:59, 12.36iter/s]Best score: 0.2782: : 649iter [00:59, 12.37iter/s]
[ITER 650] Best: 0.2782
[PARAMS] Current best parameters:
  fx: 540.0291
  fy: 540.0115
  px: 993.6680
  py: 769.2120
  k1: -0.03250753
  k2: 0.04486168
  k3: -0.03111717
  k4: 0.00713044
  p1: -0.00677254
  p2: 0.00184047
  p3: -0.00665556
  p4: -0.00278795
  s1: -0.0044
  s2: 0.0050
  s3: 0.0168
  s4: -0.0005
  tilt_x: 0.0087
  tilt_y: 0.0177
  scale: 3.0383
  rotation: -0.0768
Best score: 0.2782: : 649iter [00:59, 12.37iter/s]Best score: 0.2782: : 650iter [00:59, 12.37iter/s]Best score: 0.2782: : 651iter [00:59,  7.92iter/s]Best score: 0.2782: : 651iter [00:59,  7.92iter/s]Best score: 0.2782: : 652iter [00:59,  7.92iter/s]Best score: 0.2782: : 653iter [00:59,  8.87iter/s]Best score: 0.2782: : 653iter [00:59,  8.87iter/s]Best score: 0.2782: : 654iter [01:00,  8.87iter/s]Best score: 0.2782: : 655iter [01:00,  9.72iter/s]Best score: 0.2782: : 655iter [01:00,  9.72iter/s]Best score: 0.2782: : 656iter [01:00,  9.72iter/s]Best score: 0.2782: : 657iter [01:00, 10.41iter/s]Best score: 0.2782: : 657iter [01:00, 10.41iter/s]Best score: 0.2782: : 658iter [01:00, 10.41iter/s]Best score: 0.2782: : 659iter [01:00, 10.99iter/s]Best score: 0.2779: : 659iter [01:00, 10.99iter/s]Best score: 0.2779: : 660iter [01:00, 10.99iter/s]Best score: 0.2779: : 661iter [01:00, 11.23iter/s]Best score: 0.2779: : 661iter [01:00, 11.23iter/s]Best score: 0.2779: : 662iter [01:00, 11.23iter/s]Best score: 0.2779: : 663iter [01:00, 11.60iter/s]Best score: 0.2779: : 663iter [01:00, 11.60iter/s]Best score: 0.2779: : 664iter [01:00, 11.60iter/s]Best score: 0.2779: : 665iter [01:00, 11.89iter/s]Best score: 0.2779: : 665iter [01:00, 11.89iter/s]Best score: 0.2779: : 666iter [01:01, 11.89iter/s]Best score: 0.2779: : 667iter [01:01, 12.08iter/s]Best score: 0.2779: : 667iter [01:01, 12.08iter/s]Best score: 0.2779: : 668iter [01:01, 12.08iter/s]Best score: 0.2779: : 669iter [01:01, 12.21iter/s]Best score: 0.2673: : 669iter [01:01, 12.21iter/s]Best score: 0.2673: : 670iter [01:01, 12.21iter/s]Best score: 0.2673: : 671iter [01:01, 12.08iter/s]Best score: 0.2673: : 671iter [01:01, 12.08iter/s]Best score: 0.2673: : 672iter [01:01, 12.08iter/s]Best score: 0.2673: : 673iter [01:01, 12.20iter/s]Best score: 0.2673: : 673iter [01:01, 12.20iter/s]Best score: 0.2673: : 674iter [01:01, 12.20iter/s]Best score: 0.2673: : 675iter [01:01, 12.25iter/s]Best score: 0.2673: : 675iter [01:01, 12.25iter/s]Best score: 0.2673: : 676iter [01:01, 12.25iter/s]Best score: 0.2673: : 677iter [01:01, 12.33iter/s]Best score: 0.2673: : 677iter [01:01, 12.33iter/s]Best score: 0.2673: : 678iter [01:01, 12.33iter/s]Best score: 0.2673: : 679iter [01:01, 12.30iter/s]Best score: 0.2656: : 679iter [01:02, 12.30iter/s]Best score: 0.2656: : 680iter [01:02, 12.30iter/s]Best score: 0.2656: : 681iter [01:02, 12.17iter/s]Best score: 0.2656: : 681iter [01:02, 12.17iter/s]Best score: 0.2656: : 682iter [01:02, 12.17iter/s]Best score: 0.2656: : 683iter [01:02, 12.29iter/s]Best score: 0.2656: : 683iter [01:02, 12.29iter/s]Best score: 0.2656: : 684iter [01:02, 12.29iter/s]Best score: 0.2656: : 685iter [01:02, 12.37iter/s]Best score: 0.2656: : 685iter [01:02, 12.37iter/s]Best score: 0.2656: : 686iter [01:02, 12.37iter/s]Best score: 0.2656: : 687iter [01:02, 12.42iter/s]Best score: 0.2656: : 687iter [01:02, 12.42iter/s]Best score: 0.2656: : 688iter [01:02, 12.42iter/s]Best score: 0.2656: : 689iter [01:02, 12.43iter/s]Best score: 0.2656: : 689iter [01:02, 12.43iter/s]Best score: 0.2656: : 690iter [01:02, 12.43iter/s]Best score: 0.2656: : 691iter [01:02, 12.25iter/s]Best score: 0.2656: : 691iter [01:03, 12.25iter/s]Best score: 0.2656: : 692iter [01:03, 12.25iter/s]Best score: 0.2656: : 693iter [01:03, 12.34iter/s]Best score: 0.2656: : 693iter [01:03, 12.34iter/s]Best score: 0.2656: : 694iter [01:03, 12.34iter/s]Best score: 0.2656: : 695iter [01:03, 12.34iter/s]Best score: 0.2656: : 695iter [01:03, 12.34iter/s]Best score: 0.2656: : 696iter [01:03, 12.34iter/s]Best score: 0.2656: : 697iter [01:03, 12.40iter/s]Best score: 0.2656: : 697iter [01:03, 12.40iter/s]Best score: 0.2656: : 698iter [01:03, 12.40iter/s]Best score: 0.2656: : 699iter [01:03, 12.39iter/s]
[ITER 700] Best: 0.2656
[PARAMS] Current best parameters:
  fx: 538.1866
  fy: 538.1303
  px: 993.8932
  py: 769.7831
  k1: -0.00859596
  k2: 0.00582110
  k3: -0.00594572
  k4: 0.00139785
  p1: -0.00620519
  p2: 0.00499380
  p3: -0.00292253
  p4: -0.00241249
  s1: -0.0097
  s2: 0.0038
  s3: 0.0132
  s4: -0.0000
  tilt_x: 0.0095
  tilt_y: 0.0234
  scale: 3.0232
  rotation: -0.0654
Best score: 0.2656: : 699iter [01:03, 12.39iter/s]Best score: 0.2656: : 700iter [01:04, 12.39iter/s]Best score: 0.2656: : 701iter [01:04,  7.95iter/s]Best score: 0.2656: : 701iter [01:04,  7.95iter/s]Best score: 0.2656: : 702iter [01:04,  7.95iter/s]Best score: 0.2656: : 703iter [01:04,  8.90iter/s]Best score: 0.2656: : 703iter [01:04,  8.90iter/s]Best score: 0.2656: : 704iter [01:04,  8.90iter/s]Best score: 0.2656: : 705iter [01:04,  9.76iter/s]Best score: 0.2656: : 705iter [01:04,  9.76iter/s]Best score: 0.2656: : 706iter [01:04,  9.76iter/s]Best score: 0.2656: : 707iter [01:04, 10.46iter/s]Best score: 0.2656: : 707iter [01:04, 10.46iter/s]Best score: 0.2656: : 708iter [01:04, 10.46iter/s]Best score: 0.2656: : 709iter [01:04, 11.01iter/s]Best score: 0.2614: : 709iter [01:04, 11.01iter/s]Best score: 0.2614: : 710iter [01:04, 11.01iter/s]Best score: 0.2614: : 711iter [01:04, 11.26iter/s]Best score: 0.2614: : 711iter [01:04, 11.26iter/s]Best score: 0.2614: : 712iter [01:05, 11.26iter/s]Best score: 0.2614: : 713iter [01:05, 11.62iter/s]Best score: 0.2614: : 713iter [01:05, 11.62iter/s]Best score: 0.2614: : 714iter [01:05, 11.62iter/s]Best score: 0.2614: : 715iter [01:05, 11.89iter/s]Best score: 0.2614: : 715iter [01:05, 11.89iter/s]Best score: 0.2614: : 716iter [01:05, 11.89iter/s]Best score: 0.2614: : 717iter [01:05, 12.09iter/s]Best score: 0.2614: : 717iter [01:05, 12.09iter/s]Best score: 0.2614: : 718iter [01:05, 12.09iter/s]Best score: 0.2614: : 719iter [01:05, 12.16iter/s]Best score: 0.2601: : 719iter [01:05, 12.16iter/s]Best score: 0.2601: : 720iter [01:05, 12.16iter/s]Best score: 0.2601: : 721iter [01:05, 12.08iter/s]Best score: 0.2601: : 721iter [01:05, 12.08iter/s]Best score: 0.2601: : 722iter [01:05, 12.08iter/s]Best score: 0.2601: : 723iter [01:05, 12.22iter/s]Best score: 0.2601: : 723iter [01:05, 12.22iter/s]Best score: 0.2601: : 724iter [01:05, 12.22iter/s]Best score: 0.2601: : 725iter [01:05, 12.33iter/s]Best score: 0.2601: : 725iter [01:06, 12.33iter/s]Best score: 0.2601: : 726iter [01:06, 12.33iter/s]Best score: 0.2601: : 727iter [01:06, 12.36iter/s]Best score: 0.2601: : 727iter [01:06, 12.36iter/s]Best score: 0.2601: : 728iter [01:06, 12.36iter/s]Best score: 0.2601: : 729iter [01:06, 12.41iter/s]Best score: 0.2561: : 729iter [01:06, 12.41iter/s]Best score: 0.2561: : 730iter [01:06, 12.41iter/s]Best score: 0.2561: : 731iter [01:06, 12.25iter/s]Best score: 0.2561: : 731iter [01:06, 12.25iter/s]Best score: 0.2561: : 732iter [01:06, 12.25iter/s]Best score: 0.2561: : 733iter [01:06, 12.34iter/s]Best score: 0.2561: : 733iter [01:06, 12.34iter/s]Best score: 0.2561: : 734iter [01:06, 12.34iter/s]Best score: 0.2561: : 735iter [01:06, 12.42iter/s]Best score: 0.2561: : 735iter [01:06, 12.42iter/s]Best score: 0.2561: : 736iter [01:06, 12.42iter/s]Best score: 0.2561: : 737iter [01:06, 12.44iter/s]Best score: 0.2561: : 737iter [01:07, 12.44iter/s]Best score: 0.2561: : 738iter [01:07, 12.44iter/s]Best score: 0.2561: : 739iter [01:07, 12.49iter/s]Best score: 0.2554: : 739iter [01:07, 12.49iter/s]Best score: 0.2554: : 740iter [01:07, 12.49iter/s]Best score: 0.2554: : 741iter [01:07, 12.28iter/s]Best score: 0.2554: : 741iter [01:07, 12.28iter/s]Best score: 0.2554: : 742iter [01:07, 12.28iter/s]Best score: 0.2554: : 743iter [01:07, 12.37iter/s]Best score: 0.2554: : 743iter [01:07, 12.37iter/s]Best score: 0.2554: : 744iter [01:07, 12.37iter/s]Best score: 0.2554: : 745iter [01:07, 12.37iter/s]Best score: 0.2554: : 745iter [01:07, 12.37iter/s]Best score: 0.2554: : 746iter [01:07, 12.37iter/s]Best score: 0.2554: : 747iter [01:07, 12.34iter/s]Best score: 0.2554: : 747iter [01:07, 12.34iter/s]Best score: 0.2554: : 748iter [01:07, 12.34iter/s]Best score: 0.2554: : 749iter [01:07, 12.39iter/s]
[ITER 750] Best: 0.2544
[PARAMS] Current best parameters:
  fx: 540.2016
  fy: 540.2463
  px: 995.6956
  py: 767.1011
  k1: -0.00014019
  k2: -0.01969791
  k3: 0.01472919
  k4: -0.00403626
  p1: -0.00462171
  p2: -0.00385315
  p3: -0.00112125
  p4: -0.00504936
  s1: -0.0023
  s2: 0.0073
  s3: 0.0057
  s4: 0.0033
  tilt_x: -0.0065
  tilt_y: -0.0033
  scale: 3.0186
  rotation: -0.0713
Best score: 0.2544: : 749iter [01:08, 12.39iter/s]Best score: 0.2544: : 750iter [01:08, 12.39iter/s]Best score: 0.2544: : 751iter [01:08,  7.91iter/s]Best score: 0.2544: : 751iter [01:08,  7.91iter/s]Best score: 0.2544: : 752iter [01:08,  7.91iter/s]Best score: 0.2544: : 753iter [01:08,  8.85iter/s]Best score: 0.2544: : 753iter [01:08,  8.85iter/s]Best score: 0.2544: : 754iter [01:08,  8.85iter/s]Best score: 0.2544: : 755iter [01:08,  9.69iter/s]Best score: 0.2544: : 755iter [01:08,  9.69iter/s]Best score: 0.2544: : 756iter [01:08,  9.69iter/s]Best score: 0.2544: : 757iter [01:08, 10.41iter/s]Best score: 0.2544: : 757iter [01:08, 10.41iter/s]Best score: 0.2544: : 758iter [01:09, 10.41iter/s]Best score: 0.2544: : 759iter [01:09, 10.95iter/s]Best score: 0.2544: : 759iter [01:09, 10.95iter/s]Best score: 0.2544: : 760iter [01:09, 10.95iter/s]Best score: 0.2544: : 761iter [01:09, 11.22iter/s]Best score: 0.2544: : 761iter [01:09, 11.22iter/s]Best score: 0.2544: : 762iter [01:09, 11.22iter/s]Best score: 0.2544: : 763iter [01:09, 11.59iter/s]Best score: 0.2544: : 763iter [01:09, 11.59iter/s]Best score: 0.2544: : 764iter [01:09, 11.59iter/s]Best score: 0.2544: : 765iter [01:09, 11.87iter/s]Best score: 0.2544: : 765iter [01:09, 11.87iter/s]Best score: 0.2544: : 766iter [01:09, 11.87iter/s]Best score: 0.2544: : 767iter [01:09, 12.08iter/s]Best score: 0.2544: : 767iter [01:09, 12.08iter/s]Best score: 0.2544: : 768iter [01:09, 12.08iter/s]Best score: 0.2544: : 769iter [01:09, 12.19iter/s]Best score: 0.2528: : 769iter [01:09, 12.19iter/s]Best score: 0.2528: : 770iter [01:10, 12.19iter/s]Best score: 0.2528: : 771iter [01:10, 12.09iter/s]Best score: 0.2528: : 771iter [01:10, 12.09iter/s]Best score: 0.2528: : 772iter [01:10, 12.09iter/s]Best score: 0.2528: : 773iter [01:10, 12.24iter/s]Best score: 0.2528: : 773iter [01:10, 12.24iter/s]Best score: 0.2528: : 774iter [01:10, 12.24iter/s]Best score: 0.2528: : 775iter [01:10, 12.35iter/s]Best score: 0.2528: : 775iter [01:10, 12.35iter/s]Best score: 0.2528: : 776iter [01:10, 12.35iter/s]Best score: 0.2528: : 777iter [01:10, 12.41iter/s]Best score: 0.2528: : 777iter [01:10, 12.41iter/s]Best score: 0.2528: : 778iter [01:10, 12.41iter/s]Best score: 0.2528: : 779iter [01:10, 12.44iter/s]Best score: 0.2524: : 779iter [01:10, 12.44iter/s]Best score: 0.2524: : 780iter [01:10, 12.44iter/s]Best score: 0.2524: : 781iter [01:10, 12.28iter/s]Best score: 0.2524: : 781iter [01:10, 12.28iter/s]Best score: 0.2524: : 782iter [01:10, 12.28iter/s]Best score: 0.2524: : 783iter [01:10, 12.35iter/s]Best score: 0.2524: : 783iter [01:11, 12.35iter/s]Best score: 0.2524: : 784iter [01:11, 12.35iter/s]Best score: 0.2524: : 785iter [01:11, 12.42iter/s]Best score: 0.2524: : 785iter [01:11, 12.42iter/s]Best score: 0.2524: : 786iter [01:11, 12.42iter/s]Best score: 0.2524: : 787iter [01:11, 12.38iter/s]Best score: 0.2524: : 787iter [01:11, 12.38iter/s]Best score: 0.2524: : 788iter [01:11, 12.38iter/s]Best score: 0.2524: : 789iter [01:11, 12.42iter/s]Best score: 0.2524: : 789iter [01:11, 12.42iter/s]Best score: 0.2524: : 790iter [01:11, 12.42iter/s]Best score: 0.2524: : 791iter [01:11, 12.20iter/s]Best score: 0.2524: : 791iter [01:11, 12.20iter/s]Best score: 0.2524: : 792iter [01:11, 12.20iter/s]Best score: 0.2524: : 793iter [01:11, 12.33iter/s]Best score: 0.2524: : 793iter [01:11, 12.33iter/s]Best score: 0.2524: : 794iter [01:11, 12.33iter/s]Best score: 0.2524: : 795iter [01:11, 12.40iter/s]Best score: 0.2524: : 795iter [01:12, 12.40iter/s]Best score: 0.2524: : 796iter [01:12, 12.40iter/s]Best score: 0.2524: : 797iter [01:12, 12.47iter/s]Best score: 0.2524: : 797iter [01:12, 12.47iter/s]Best score: 0.2524: : 798iter [01:12, 12.47iter/s]Best score: 0.2524: : 799iter [01:12, 12.41iter/s]
[ITER 800] Best: 0.2510
[PARAMS] Current best parameters:
  fx: 539.0716
  fy: 539.1685
  px: 996.5818
  py: 766.8041
  k1: -0.00130565
  k2: -0.01298810
  k3: 0.00886223
  k4: -0.00244956
  p1: -0.00295239
  p2: -0.00141746
  p3: -0.00369076
  p4: -0.00223293
  s1: -0.0011
  s2: 0.0045
  s3: 0.0074
  s4: -0.0006
  tilt_x: -0.0109
  tilt_y: -0.0026
  scale: 3.0180
  rotation: -0.0700
Best score: 0.2510: : 799iter [01:12, 12.41iter/s]Best score: 0.2510: : 800iter [01:12, 12.41iter/s]Best score: 0.2510: : 801iter [01:12,  7.94iter/s]Best score: 0.2510: : 801iter [01:12,  7.94iter/s]Best score: 0.2510: : 802iter [01:12,  7.94iter/s]Best score: 0.2510: : 803iter [01:12,  8.93iter/s]Best score: 0.2510: : 803iter [01:12,  8.93iter/s]Best score: 0.2510: : 804iter [01:13,  8.93iter/s]Best score: 0.2510: : 805iter [01:13,  9.78iter/s]Best score: 0.2510: : 805iter [01:13,  9.78iter/s]Best score: 0.2510: : 806iter [01:13,  9.78iter/s]Best score: 0.2510: : 807iter [01:13, 10.49iter/s]Best score: 0.2510: : 807iter [01:13, 10.49iter/s]Best score: 0.2510: : 808iter [01:13, 10.49iter/s]Best score: 0.2510: : 809iter [01:13, 11.06iter/s]Best score: 0.2510: : 809iter [01:13, 11.06iter/s]Best score: 0.2510: : 810iter [01:13, 11.06iter/s]Best score: 0.2510: : 811iter [01:13, 11.30iter/s]Best score: 0.2510: : 811iter [01:13, 11.30iter/s]Best score: 0.2510: : 812iter [01:13, 11.30iter/s]Best score: 0.2510: : 813iter [01:13, 11.68iter/s]Best score: 0.2510: : 813iter [01:13, 11.68iter/s]Best score: 0.2510: : 814iter [01:13, 11.68iter/s]Best score: 0.2510: : 815iter [01:13, 11.94iter/s]Best score: 0.2510: : 815iter [01:13, 11.94iter/s]Best score: 0.2510: : 816iter [01:14, 11.94iter/s]Best score: 0.2510: : 817iter [01:14, 12.15iter/s]Best score: 0.2510: : 817iter [01:14, 12.15iter/s]Best score: 0.2510: : 818iter [01:14, 12.15iter/s]Best score: 0.2510: : 819iter [01:14, 12.29iter/s]Best score: 0.2510: : 819iter [01:14, 12.29iter/s]Best score: 0.2510: : 820iter [01:14, 12.29iter/s]Best score: 0.2510: : 821iter [01:14, 12.17iter/s]Best score: 0.2510: : 821iter [01:14, 12.17iter/s]Best score: 0.2510: : 822iter [01:14, 12.17iter/s]Best score: 0.2510: : 823iter [01:14, 12.31iter/s]Best score: 0.2510: : 823iter [01:14, 12.31iter/s]Best score: 0.2510: : 824iter [01:14, 12.31iter/s]Best score: 0.2510: : 825iter [01:14, 12.39iter/s]Best score: 0.2510: : 825iter [01:14, 12.39iter/s]Best score: 0.2510: : 826iter [01:14, 12.39iter/s]Best score: 0.2510: : 827iter [01:14, 12.46iter/s]Best score: 0.2510: : 827iter [01:14, 12.46iter/s]Best score: 0.2510: : 828iter [01:14, 12.46iter/s]Best score: 0.2510: : 829iter [01:14, 12.50iter/s]Best score: 0.2500: : 829iter [01:15, 12.50iter/s]Best score: 0.2500: : 830iter [01:15, 12.50iter/s]Best score: 0.2500: : 831iter [01:15, 12.27iter/s]Best score: 0.2500: : 831iter [01:15, 12.27iter/s]Best score: 0.2500: : 832iter [01:15, 12.27iter/s]Best score: 0.2500: : 833iter [01:15, 12.35iter/s]Best score: 0.2500: : 833iter [01:15, 12.35iter/s]Best score: 0.2500: : 834iter [01:15, 12.35iter/s]Best score: 0.2500: : 835iter [01:15, 12.41iter/s]Best score: 0.2500: : 835iter [01:15, 12.41iter/s]Best score: 0.2500: : 836iter [01:15, 12.41iter/s]Best score: 0.2500: : 837iter [01:15, 12.47iter/s]Best score: 0.2500: : 837iter [01:15, 12.47iter/s]Best score: 0.2500: : 838iter [01:15, 12.47iter/s]Best score: 0.2500: : 839iter [01:15, 12.51iter/s]Best score: 0.2494: : 839iter [01:15, 12.51iter/s]Best score: 0.2494: : 840iter [01:15, 12.51iter/s]Best score: 0.2494: : 841iter [01:15, 12.34iter/s]Best score: 0.2494: : 841iter [01:16, 12.34iter/s]Best score: 0.2494: : 842iter [01:16, 12.34iter/s]Best score: 0.2494: : 843iter [01:16, 12.42iter/s]Best score: 0.2494: : 843iter [01:16, 12.42iter/s]Best score: 0.2494: : 844iter [01:16, 12.42iter/s]Best score: 0.2494: : 845iter [01:16, 12.48iter/s]Best score: 0.2494: : 845iter [01:16, 12.48iter/s]Best score: 0.2494: : 846iter [01:16, 12.48iter/s]Best score: 0.2494: : 847iter [01:16, 12.51iter/s]Best score: 0.2494: : 847iter [01:16, 12.51iter/s]Best score: 0.2494: : 848iter [01:16, 12.51iter/s]Best score: 0.2494: : 849iter [01:16, 12.51iter/s]
[ITER 850] Best: 0.2467
[PARAMS] Current best parameters:
  fx: 534.5277
  fy: 534.6680
  px: 996.2548
  py: 766.6726
  k1: 0.00410199
  k2: -0.01289043
  k3: 0.00741483
  k4: -0.00190568
  p1: 0.00012540
  p2: -0.00430762
  p3: 0.00060115
  p4: 0.00248323
  s1: -0.0027
  s2: -0.0003
  s3: -0.0012
  s4: -0.0039
  tilt_x: -0.0179
  tilt_y: -0.0068
  scale: 3.0195
  rotation: -0.0695
Best score: 0.2467: : 849iter [01:16, 12.51iter/s]Best score: 0.2467: : 850iter [01:17, 12.51iter/s]Best score: 0.2467: : 851iter [01:17,  7.96iter/s]Best score: 0.2467: : 851iter [01:17,  7.96iter/s]Best score: 0.2467: : 852iter [01:17,  7.96iter/s]Best score: 0.2467: : 853iter [01:17,  8.90iter/s]Best score: 0.2467: : 853iter [01:17,  8.90iter/s]Best score: 0.2467: : 854iter [01:17,  8.90iter/s]Best score: 0.2467: : 855iter [01:17,  9.72iter/s]Best score: 0.2467: : 855iter [01:17,  9.72iter/s]Best score: 0.2467: : 856iter [01:17,  9.72iter/s]Best score: 0.2467: : 857iter [01:17, 10.40iter/s]Best score: 0.2467: : 857iter [01:17, 10.40iter/s]Best score: 0.2467: : 858iter [01:17, 10.40iter/s]Best score: 0.2467: : 859iter [01:17, 10.97iter/s]Best score: 0.2466: : 859iter [01:17, 10.97iter/s]Best score: 0.2466: : 860iter [01:17, 10.97iter/s]Best score: 0.2466: : 861iter [01:17, 11.25iter/s]Best score: 0.2466: : 861iter [01:17, 11.25iter/s]Best score: 0.2466: : 862iter [01:18, 11.25iter/s]Best score: 0.2466: : 863iter [01:18, 11.60iter/s]Best score: 0.2466: : 863iter [01:18, 11.60iter/s]Best score: 0.2466: : 864iter [01:18, 11.60iter/s]Best score: 0.2466: : 865iter [01:18, 11.83iter/s]Best score: 0.2466: : 865iter [01:18, 11.83iter/s]Best score: 0.2466: : 866iter [01:18, 11.83iter/s]Best score: 0.2466: : 867iter [01:18, 12.06iter/s]Best score: 0.2466: : 867iter [01:18, 12.06iter/s]Best score: 0.2466: : 868iter [01:18, 12.06iter/s]Best score: 0.2466: : 869iter [01:18, 12.23iter/s]Best score: 0.2463: : 869iter [01:18, 12.23iter/s]Best score: 0.2463: : 870iter [01:18, 12.23iter/s]Best score: 0.2463: : 871iter [01:18, 12.13iter/s]Best score: 0.2463: : 871iter [01:18, 12.13iter/s]Best score: 0.2463: : 872iter [01:18, 12.13iter/s]Best score: 0.2463: : 873iter [01:18, 12.27iter/s]Best score: 0.2463: : 873iter [01:18, 12.27iter/s]Best score: 0.2463: : 874iter [01:18, 12.27iter/s]Best score: 0.2463: : 875iter [01:18, 12.35iter/s]Best score: 0.2463: : 875iter [01:19, 12.35iter/s]Best score: 0.2463: : 876iter [01:19, 12.35iter/s]Best score: 0.2463: : 877iter [01:19, 12.43iter/s]Best score: 0.2463: : 877iter [01:19, 12.43iter/s]Best score: 0.2463: : 878iter [01:19, 12.43iter/s]Best score: 0.2463: : 879iter [01:19, 12.47iter/s]Best score: 0.2459: : 879iter [01:19, 12.47iter/s]Best score: 0.2459: : 880iter [01:19, 12.47iter/s]Best score: 0.2459: : 881iter [01:19, 12.30iter/s]Best score: 0.2459: : 881iter [01:19, 12.30iter/s]Best score: 0.2459: : 882iter [01:19, 12.30iter/s]Best score: 0.2459: : 883iter [01:19, 12.29iter/s]Best score: 0.2459: : 883iter [01:19, 12.29iter/s]Best score: 0.2459: : 884iter [01:19, 12.29iter/s]Best score: 0.2459: : 885iter [01:19, 12.29iter/s]Best score: 0.2459: : 885iter [01:19, 12.29iter/s]Best score: 0.2459: : 886iter [01:19, 12.29iter/s]Best score: 0.2459: : 887iter [01:19, 12.28iter/s]Best score: 0.2459: : 887iter [01:20, 12.28iter/s]Best score: 0.2459: : 888iter [01:20, 12.28iter/s]Best score: 0.2459: : 889iter [01:20, 12.35iter/s]Best score: 0.2433: : 889iter [01:20, 12.35iter/s]Best score: 0.2433: : 890iter [01:20, 12.35iter/s]Best score: 0.2433: : 891iter [01:20, 12.22iter/s]Best score: 0.2433: : 891iter [01:20, 12.22iter/s]Best score: 0.2433: : 892iter [01:20, 12.22iter/s]Best score: 0.2433: : 893iter [01:20, 12.34iter/s]Best score: 0.2433: : 893iter [01:20, 12.34iter/s]Best score: 0.2433: : 894iter [01:20, 12.34iter/s]Best score: 0.2433: : 895iter [01:20, 12.44iter/s]Best score: 0.2433: : 895iter [01:20, 12.44iter/s]Best score: 0.2433: : 896iter [01:20, 12.44iter/s]Best score: 0.2433: : 897iter [01:20, 12.49iter/s]Best score: 0.2433: : 897iter [01:20, 12.49iter/s]Best score: 0.2433: : 898iter [01:20, 12.49iter/s]Best score: 0.2433: : 899iter [01:20, 12.54iter/s]
[ITER 900] Best: 0.2433
[PARAMS] Current best parameters:
  fx: 535.7822
  fy: 535.9846
  px: 995.5228
  py: 765.4741
  k1: -0.00271323
  k2: -0.00480994
  k3: 0.00330583
  k4: -0.00118265
  p1: 0.00070820
  p2: -0.00845190
  p3: 0.00011911
  p4: 0.00085250
  s1: 0.0009
  s2: 0.0012
  s3: -0.0031
  s4: -0.0021
  tilt_x: -0.0247
  tilt_y: -0.0118
  scale: 3.0251
  rotation: -0.0640
Best score: 0.2433: : 899iter [01:20, 12.54iter/s]Best score: 0.2433: : 900iter [01:21, 12.54iter/s]Best score: 0.2433: : 901iter [01:21,  8.01iter/s]Best score: 0.2433: : 901iter [01:21,  8.01iter/s]Best score: 0.2433: : 902iter [01:21,  8.01iter/s]Best score: 0.2433: : 903iter [01:21,  9.00iter/s]Best score: 0.2433: : 903iter [01:21,  9.00iter/s]Best score: 0.2433: : 904iter [01:21,  9.00iter/s]Best score: 0.2433: : 905iter [01:21,  9.85iter/s]Best score: 0.2433: : 905iter [01:21,  9.85iter/s]Best score: 0.2433: : 906iter [01:21,  9.85iter/s]Best score: 0.2433: : 907iter [01:21, 10.54iter/s]Best score: 0.2433: : 907iter [01:21, 10.54iter/s]Best score: 0.2433: : 908iter [01:22, 10.54iter/s]Best score: 0.2433: : 909iter [01:22, 11.08iter/s]Best score: 0.2431: : 909iter [01:22, 11.08iter/s]Best score: 0.2431: : 910iter [01:22, 11.08iter/s]Best score: 0.2431: : 911iter [01:22, 11.28iter/s]Best score: 0.2431: : 911iter [01:22, 11.28iter/s]Best score: 0.2431: : 912iter [01:22, 11.28iter/s]Best score: 0.2431: : 913iter [01:22, 11.65iter/s]Best score: 0.2431: : 913iter [01:22, 11.65iter/s]Best score: 0.2431: : 914iter [01:22, 11.65iter/s]Best score: 0.2431: : 915iter [01:22, 11.91iter/s]Best score: 0.2431: : 915iter [01:22, 11.91iter/s]Best score: 0.2431: : 916iter [01:22, 11.91iter/s]Best score: 0.2431: : 917iter [01:22, 12.10iter/s]Best score: 0.2431: : 917iter [01:22, 12.10iter/s]Best score: 0.2431: : 918iter [01:22, 12.10iter/s]Best score: 0.2431: : 919iter [01:22, 12.25iter/s]Best score: 0.2431: : 919iter [01:22, 12.25iter/s]Best score: 0.2431: : 920iter [01:22, 12.25iter/s]Best score: 0.2431: : 921iter [01:22, 12.13iter/s]Best score: 0.2431: : 921iter [01:23, 12.13iter/s]Best score: 0.2431: : 922iter [01:23, 12.13iter/s]Best score: 0.2431: : 923iter [01:23, 12.26iter/s]Best score: 0.2431: : 923iter [01:23, 12.26iter/s]Best score: 0.2431: : 924iter [01:23, 12.26iter/s]Best score: 0.2431: : 925iter [01:23, 12.32iter/s]Best score: 0.2431: : 925iter [01:23, 12.32iter/s]Best score: 0.2431: : 926iter [01:23, 12.32iter/s]Best score: 0.2431: : 927iter [01:23, 12.39iter/s]Best score: 0.2431: : 927iter [01:23, 12.39iter/s]Best score: 0.2431: : 928iter [01:23, 12.39iter/s]Best score: 0.2431: : 929iter [01:23, 12.47iter/s]Best score: 0.2424: : 929iter [01:23, 12.47iter/s]Best score: 0.2424: : 930iter [01:23, 12.47iter/s]Best score: 0.2424: : 931iter [01:23, 12.28iter/s]Best score: 0.2424: : 931iter [01:23, 12.28iter/s]Best score: 0.2424: : 932iter [01:23, 12.28iter/s]Best score: 0.2424: : 933iter [01:23, 12.32iter/s]Best score: 0.2424: : 933iter [01:24, 12.32iter/s]Best score: 0.2424: : 934iter [01:24, 12.32iter/s]Best score: 0.2424: : 935iter [01:24, 12.39iter/s]Best score: 0.2424: : 935iter [01:24, 12.39iter/s]Best score: 0.2424: : 936iter [01:24, 12.39iter/s]Best score: 0.2424: : 937iter [01:24, 12.46iter/s]Best score: 0.2424: : 937iter [01:24, 12.46iter/s]Best score: 0.2424: : 938iter [01:24, 12.46iter/s]Best score: 0.2424: : 939iter [01:24, 12.52iter/s]Best score: 0.2416: : 939iter [01:24, 12.52iter/s]Best score: 0.2416: : 940iter [01:24, 12.52iter/s]Best score: 0.2416: : 941iter [01:24, 12.32iter/s]Best score: 0.2416: : 941iter [01:24, 12.32iter/s]Best score: 0.2416: : 942iter [01:24, 12.32iter/s]Best score: 0.2416: : 943iter [01:24, 12.37iter/s]Best score: 0.2416: : 943iter [01:24, 12.37iter/s]Best score: 0.2416: : 944iter [01:24, 12.37iter/s]Best score: 0.2416: : 945iter [01:24, 12.40iter/s]Best score: 0.2416: : 945iter [01:24, 12.40iter/s]Best score: 0.2416: : 946iter [01:25, 12.40iter/s]Best score: 0.2416: : 947iter [01:25, 12.45iter/s]Best score: 0.2416: : 947iter [01:25, 12.45iter/s]Best score: 0.2416: : 948iter [01:25, 12.45iter/s]Best score: 0.2416: : 949iter [01:25, 12.51iter/s]
[ITER 950] Best: 0.2401
[PARAMS] Current best parameters:
  fx: 534.4370
  fy: 534.6661
  px: 995.4361
  py: 764.9661
  k1: -0.00014094
  k2: -0.00495494
  k3: 0.00232938
  k4: -0.00082874
  p1: 0.00450902
  p2: -0.00763132
  p3: -0.00237531
  p4: 0.00079267
  s1: 0.0032
  s2: 0.0008
  s3: -0.0045
  s4: -0.0022
  tilt_x: -0.0349
  tilt_y: -0.0097
  scale: 3.0237
  rotation: -0.0584
Best score: 0.2401: : 949iter [01:25, 12.51iter/s]Best score: 0.2401: : 950iter [01:25, 12.51iter/s]Best score: 0.2401: : 951iter [01:25,  7.99iter/s]Best score: 0.2401: : 951iter [01:25,  7.99iter/s]Best score: 0.2401: : 952iter [01:25,  7.99iter/s]Best score: 0.2401: : 953iter [01:25,  8.95iter/s]Best score: 0.2401: : 953iter [01:25,  8.95iter/s]Best score: 0.2401: : 954iter [01:26,  8.95iter/s]Best score: 0.2401: : 955iter [01:26,  9.76iter/s]Best score: 0.2401: : 955iter [01:26,  9.76iter/s]Best score: 0.2401: : 956iter [01:26,  9.76iter/s]Best score: 0.2401: : 957iter [01:26, 10.45iter/s]Best score: 0.2401: : 957iter [01:26, 10.45iter/s]Best score: 0.2401: : 958iter [01:26, 10.45iter/s]Best score: 0.2401: : 959iter [01:26, 11.03iter/s]Best score: 0.2395: : 959iter [01:26, 11.03iter/s]Best score: 0.2395: : 960iter [01:26, 11.03iter/s]Best score: 0.2395: : 961iter [01:26, 11.25iter/s]Best score: 0.2395: : 961iter [01:26, 11.25iter/s]Best score: 0.2395: : 962iter [01:26, 11.25iter/s]Best score: 0.2395: : 963iter [01:26, 11.57iter/s]Best score: 0.2395: : 963iter [01:26, 11.57iter/s]Best score: 0.2395: : 964iter [01:26, 11.57iter/s]Best score: 0.2395: : 965iter [01:26, 11.87iter/s]Best score: 0.2395: : 965iter [01:26, 11.87iter/s]Best score: 0.2395: : 966iter [01:26, 11.87iter/s]Best score: 0.2395: : 967iter [01:26, 12.07iter/s]Best score: 0.2395: : 967iter [01:27, 12.07iter/s]Best score: 0.2395: : 968iter [01:27, 12.07iter/s]Best score: 0.2395: : 969iter [01:27, 12.19iter/s]Best score: 0.2395: : 969iter [01:27, 12.19iter/s]Best score: 0.2395: : 970iter [01:27, 12.19iter/s]Best score: 0.2395: : 971iter [01:27, 11.99iter/s]Best score: 0.2395: : 971iter [01:27, 11.99iter/s]Best score: 0.2395: : 972iter [01:27, 11.99iter/s]Best score: 0.2395: : 973iter [01:27, 12.15iter/s]Best score: 0.2395: : 973iter [01:27, 12.15iter/s]Best score: 0.2395: : 974iter [01:27, 12.15iter/s]Best score: 0.2395: : 975iter [01:27, 12.30iter/s]Best score: 0.2395: : 975iter [01:27, 12.30iter/s]Best score: 0.2395: : 976iter [01:27, 12.30iter/s]Best score: 0.2395: : 977iter [01:27, 12.39iter/s]Best score: 0.2395: : 977iter [01:27, 12.39iter/s]Best score: 0.2395: : 978iter [01:27, 12.39iter/s]Best score: 0.2395: : 979iter [01:27, 12.41iter/s]Best score: 0.2393: : 979iter [01:28, 12.41iter/s]Best score: 0.2393: : 980iter [01:28, 12.41iter/s]Best score: 0.2393: : 981iter [01:28, 12.21iter/s]Best score: 0.2393: : 981iter [01:28, 12.21iter/s]Best score: 0.2393: : 982iter [01:28, 12.21iter/s]Best score: 0.2393: : 983iter [01:28, 12.21iter/s]Best score: 0.2393: : 983iter [01:28, 12.21iter/s]Best score: 0.2393: : 984iter [01:28, 12.21iter/s]Best score: 0.2393: : 985iter [01:28, 12.27iter/s]Best score: 0.2393: : 985iter [01:28, 12.27iter/s]Best score: 0.2393: : 986iter [01:28, 12.27iter/s]Best score: 0.2393: : 987iter [01:28, 12.37iter/s]Best score: 0.2393: : 987iter [01:28, 12.37iter/s]Best score: 0.2393: : 988iter [01:28, 12.37iter/s]Best score: 0.2393: : 989iter [01:28, 12.44iter/s]Best score: 0.2389: : 989iter [01:28, 12.44iter/s]Best score: 0.2389: : 990iter [01:28, 12.44iter/s]Best score: 0.2389: : 991iter [01:28, 12.23iter/s]Best score: 0.2389: : 991iter [01:29, 12.23iter/s]Best score: 0.2389: : 992iter [01:29, 12.23iter/s]Best score: 0.2389: : 993iter [01:29, 12.26iter/s]Best score: 0.2389: : 993iter [01:29, 12.26iter/s]Best score: 0.2389: : 994iter [01:29, 12.26iter/s]Best score: 0.2389: : 995iter [01:29, 12.36iter/s]Best score: 0.2389: : 995iter [01:29, 12.36iter/s]Best score: 0.2389: : 996iter [01:29, 12.36iter/s]Best score: 0.2389: : 997iter [01:29, 12.43iter/s]Best score: 0.2389: : 997iter [01:29, 12.43iter/s]Best score: 0.2389: : 998iter [01:29, 12.43iter/s]Best score: 0.2389: : 999iter [01:29, 12.47iter/s]
[ITER 1000] Best: 0.2388
[PARAMS] Current best parameters:
  fx: 535.0068
  fy: 535.3449
  px: 995.6615
  py: 764.6719
  k1: -0.00453481
  k2: 0.00099830
  k3: -0.00110269
  k4: -0.00011745
  p1: 0.00532459
  p2: -0.00570513
  p3: -0.00757629
  p4: 0.00135736
  s1: 0.0064
  s2: 0.0007
  s3: -0.0006
  s4: -0.0029
  tilt_x: -0.0375
  tilt_y: -0.0068
  scale: 3.0276
  rotation: -0.0634
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[ITER 1050] Best: 0.2366
[PARAMS] Current best parameters:
  fx: 529.7423
  fy: 530.0471
  px: 996.3817
  py: 764.0396
  k1: 0.00895083
  k2: -0.01173755
  k3: 0.00610706
  k4: -0.00169165
  p1: 0.01024891
  p2: -0.00034964
  p3: -0.00857662
  p4: 0.00254200
  s1: 0.0032
  s2: -0.0007
  s3: -0.0053
  s4: -0.0038
  tilt_x: -0.0503
  tilt_y: 0.0006
  scale: 3.0236
  rotation: -0.0676
Best score: 0.2366: : 1049iter [01:33, 12.52iter/s]Best score: 0.2366: : 1050iter [01:34, 12.52iter/s]Best score: 0.2366: : 1051iter [01:34,  7.87iter/s]Best score: 0.2366: : 1051iter [01:34,  7.87iter/s]Best score: 0.2366: : 1052iter [01:34,  7.87iter/s]Best score: 0.2366: : 1053iter [01:34,  8.85iter/s]Best score: 0.2366: : 1053iter [01:34,  8.85iter/s]Best score: 0.2366: : 1054iter [01:34,  8.85iter/s]Best score: 0.2366: : 1055iter [01:34,  9.69iter/s]Best score: 0.2366: : 1055iter [01:34,  9.69iter/s]Best score: 0.2366: : 1056iter [01:34,  9.69iter/s]Best score: 0.2366: : 1057iter [01:34, 10.39iter/s]Best score: 0.2366: : 1057iter [01:34, 10.39iter/s]Best score: 0.2366: : 1058iter [01:35, 10.39iter/s]Best score: 0.2366: : 1059iter [01:35, 10.88iter/s]Best score: 0.2364: : 1059iter [01:35, 10.88iter/s]Best score: 0.2364: : 1060iter [01:35, 10.88iter/s]Best score: 0.2364: : 1061iter [01:35, 11.17iter/s]Best score: 0.2364: : 1061iter [01:35, 11.17iter/s]Best score: 0.2364: : 1062iter [01:35, 11.17iter/s]Best score: 0.2364: : 1063iter [01:35, 11.52iter/s]Best score: 0.2364: : 1063iter [01:35, 11.52iter/s]Best score: 0.2364: : 1064iter [01:35, 11.52iter/s]Best score: 0.2364: : 1065iter [01:35, 11.83iter/s]Best score: 0.2364: : 1065iter [01:35, 11.83iter/s]Best score: 0.2364: : 1066iter [01:35, 11.83iter/s]Best score: 0.2364: : 1067iter [01:35, 12.03iter/s]Best score: 0.2364: : 1067iter [01:35, 12.03iter/s]Best score: 0.2364: : 1068iter [01:35, 12.03iter/s]Best score: 0.2364: : 1069iter [01:35, 12.20iter/s]Best score: 0.2362: : 1069iter [01:35, 12.20iter/s]Best score: 0.2362: : 1070iter [01:35, 12.20iter/s]Best score: 0.2362: : 1071iter [01:35, 12.01iter/s]Best score: 0.2362: : 1071iter [01:36, 12.01iter/s]Best score: 0.2362: : 1072iter [01:36, 12.01iter/s]Best score: 0.2362: : 1073iter [01:36, 12.07iter/s]Best score: 0.2362: : 1073iter [01:36, 12.07iter/s]Best score: 0.2362: : 1074iter [01:36, 12.07iter/s]Best score: 0.2362: : 1075iter [01:36, 12.22iter/s]Best score: 0.2362: : 1075iter [01:36, 12.22iter/s]Best score: 0.2362: : 1076iter [01:36, 12.22iter/s]Best score: 0.2362: : 1077iter [01:36, 12.33iter/s]Best score: 0.2362: : 1077iter [01:36, 12.33iter/s]Best score: 0.2362: : 1078iter [01:36, 12.33iter/s]Best score: 0.2362: : 1079iter [01:36, 12.34iter/s]Best score: 0.2359: : 1079iter [01:36, 12.34iter/s]Best score: 0.2359: : 1080iter [01:36, 12.34iter/s]Best score: 0.2359: : 1081iter [01:36, 12.21iter/s]Best score: 0.2359: : 1081iter [01:36, 12.21iter/s]Best score: 0.2359: : 1082iter [01:36, 12.21iter/s]Best score: 0.2359: : 1083iter [01:36, 12.33iter/s]Best score: 0.2359: : 1083iter [01:37, 12.33iter/s]Best score: 0.2359: : 1084iter [01:37, 12.33iter/s]Best score: 0.2359: : 1085iter [01:37, 12.43iter/s]Best score: 0.2359: : 1085iter [01:37, 12.43iter/s]Best score: 0.2359: : 1086iter [01:37, 12.43iter/s]Best score: 0.2359: : 1087iter [01:37, 12.48iter/s]Best score: 0.2359: : 1087iter [01:37, 12.48iter/s]Best score: 0.2359: : 1088iter [01:37, 12.48iter/s]Best score: 0.2359: : 1089iter [01:37, 12.36iter/s]Best score: 0.2359: : 1089iter [01:37, 12.36iter/s]Best score: 0.2359: : 1090iter [01:37, 12.36iter/s]Best score: 0.2359: : 1091iter [01:37, 12.23iter/s]Best score: 0.2359: : 1091iter [01:37, 12.23iter/s]Best score: 0.2359: : 1092iter [01:37, 12.23iter/s]Best score: 0.2359: : 1093iter [01:37, 12.35iter/s]Best score: 0.2359: : 1093iter [01:37, 12.35iter/s]Best score: 0.2359: : 1094iter [01:37, 12.35iter/s]Best score: 0.2359: : 1095iter [01:37, 12.44iter/s]Best score: 0.2359: : 1095iter [01:37, 12.44iter/s]Best score: 0.2359: : 1096iter [01:38, 12.44iter/s]Best score: 0.2359: : 1097iter [01:38, 12.51iter/s]Best score: 0.2359: : 1097iter [01:38, 12.51iter/s]Best score: 0.2359: : 1098iter [01:38, 12.51iter/s]Best score: 0.2359: : 1099iter [01:38, 12.55iter/s]
[ITER 1100] Best: 0.2359
[PARAMS] Current best parameters:
  fx: 529.3660
  fy: 529.6959
  px: 996.2079
  py: 764.1402
  k1: 0.00921000
  k2: -0.01101091
  k3: 0.00528196
  k4: -0.00143914
  p1: 0.01052971
  p2: 0.00073303
  p3: -0.00997305
  p4: 0.00223484
  s1: 0.0031
  s2: -0.0004
  s3: -0.0043
  s4: -0.0032
  tilt_x: -0.0504
  tilt_y: 0.0033
  scale: 3.0237
  rotation: -0.0788
Best score: 0.2359: : 1099iter [01:38, 12.55iter/s]Best score: 0.2359: : 1100iter [01:38, 11.15iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.235877 -> 0.235606

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 96,637 → 88,221 within 3500mm ring
  Ring polish did not improve score: 0.235606
  Success: True
  Tangential distortion: p1=0.010517, p2=0.000729, p3=-0.009986, p4=0.002263
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2356
[✅] Parameters copied to: /working/MDC042_W007(6)_2025-05-28/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 529.3660290385272
    fy: 529.6958938375993
    px: 996.2078931960108
    py: 764.1401890474517
    k1: 0.009134378830901931
    k2: -0.010950813831403915
    k3: 0.00530490267204631
    k4: -0.0014540690629828016
    p1: 0.010517174757039567
    p2: 0.0007288952231947099
    p3: -0.009985588571646248
    p4: 0.0022626119890165556
    scale: 3.0234733201622546
    rotation: -0.0788018105685678
    mmpp: 3.5145381498780925
    nmpp: 3514538.1498780926

[🔄] Converting 1005 frames to PTD, creating debug images for 400 calibration frames...
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   🔄 1000:  20%|██        | 20/100 [00:00<00:00, 194.76frame/s][A
   🔄 1000:  40%|████      | 40/100 [00:00<00:00, 190.19frame/s][A
   🔄 1000:  60%|██████    | 60/100 [00:00<00:00, 189.36frame/s][A
   🔄 1000:  79%|███████▉  | 79/100 [00:00<00:00, 189.20frame/s][A
   🔄 1000:  98%|█████████▊| 98/100 [00:00<00:00, 188.40frame/s][A
                                                                [A📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.01rig/s]📁 Rigs: 100%|██████████| 4/4 [00:03<00:00,  1.00rig/s]
[✅] Saved PTDs: /working/MDC042_W007(6)_2025-05-28/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.
📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]📤 Writing LUT:   3%|▎         | 86150/3080448 [00:00<00:03, 861494.66px/s]📤 Writing LUT:   6%|▌         | 176610/3080448 [00:00<00:03, 886847.44px/s]📤 Writing LUT:   9%|▉         | 269636/3080448 [00:00<00:03, 906668.88px/s]📤 Writing LUT:  12%|█▏        | 365701/3080448 [00:00<00:02, 927979.43px/s]📤 Writing LUT:  15%|█▍        | 460395/3080448 [00:00<00:02, 934816.25px/s]📤 Writing LUT:  18%|█▊        | 555067/3080448 [00:00<00:02, 938862.31px/s]📤 Writing LUT:  21%|██        | 649288/3080448 [00:00<00:02, 939956.31px/s]📤 Writing LUT:  24%|██▍       | 743500/3080448 [00:00<00:02, 940643.27px/s]📤 Writing LUT:  27%|██▋       | 837565/3080448 [00:00<00:02, 936974.39px/s]📤 Writing LUT:  30%|███       | 931266/3080448 [00:01<00:02, 933303.91px/s]📤 Writing LUT:  33%|███▎      | 1024601/3080448 [00:01<00:02, 932448.62px/s]📤 Writing LUT:  36%|███▋      | 1118892/3080448 [00:01<00:02, 935611.65px/s]📤 Writing LUT:  39%|███▉      | 1212992/3080448 [00:01<00:01, 937237.14px/s]📤 Writing LUT:  42%|████▏     | 1306719/3080448 [00:01<00:01, 936160.59px/s]📤 Writing LUT:  45%|████▌     | 1400338/3080448 [00:01<00:01, 936020.68px/s]📤 Writing LUT:  48%|████▊     | 1493942/3080448 [00:01<00:01, 932528.32px/s]📤 Writing LUT:  52%|█████▏    | 1587608/3080448 [00:01<00:01, 933762.34px/s]📤 Writing LUT:  55%|█████▍    | 1680988/3080448 [00:01<00:01, 931121.22px/s]📤 Writing LUT:  58%|█████▊    | 1774104/3080448 [00:01<00:01, 930131.21px/s]📤 Writing LUT:  61%|██████    | 1869263/3080448 [00:02<00:01, 936550.17px/s]📤 Writing LUT:  64%|██████▎   | 1963503/3080448 [00:02<00:01, 938298.80px/s]📤 Writing LUT:  67%|██████▋   | 2057336/3080448 [00:02<00:01, 937073.41px/s]📤 Writing LUT:  70%|██████▉   | 2152684/3080448 [00:02<00:00, 941984.64px/s]📤 Writing LUT:  73%|███████▎  | 2246886/3080448 [00:02<00:00, 938447.24px/s]📤 Writing LUT:  76%|███████▌  | 2340736/3080448 [00:02<00:00, 936523.56px/s]📤 Writing LUT:  79%|███████▉  | 2434392/3080448 [00:02<00:00, 934520.01px/s]📤 Writing LUT:  82%|████████▏ | 2528237/3080448 [00:02<00:00, 935692.93px/s]📤 Writing LUT:  85%|████████▌ | 2623305/3080448 [00:02<00:00, 940173.23px/s]📤 Writing LUT:  88%|████████▊ | 2718284/3080448 [00:02<00:00, 943049.80px/s]📤 Writing LUT:  91%|█████████▏| 2812592/3080448 [00:03<00:00, 941817.40px/s]📤 Writing LUT:  94%|█████████▍| 2908016/3080448 [00:03<00:00, 945535.84px/s]📤 Writing LUT:  97%|█████████▋| 3002572/3080448 [00:03<00:00, 943384.37px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 935562.11px/s]
[✅] LUT written to: /working/MDC042_W007(6)_2025-05-28/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC042_W007(6)_2025-05-28' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC042_W007(6)_2025-05-28/calibration/MDC042_W007(6)_2025-05-28_unknown.lut

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

[ℹ️] Detected device type: SMD_P3D (rigs: [1000, 1500, 2000]mm)
📁 Rigs (heatmap):   0%|          | 0/4 [00:00<?, ?rig/s]
   🔥 2000R:   0%|          | 0/234 [00:00<?, ?frame/s][A
   🔥 2000R:   3%|▎         | 8/234 [00:00<00:03, 72.64frame/s][A
   🔥 2000R:   7%|▋         | 16/234 [00:00<00:02, 75.45frame/s][A
   🔥 2000R:  10%|█         | 24/234 [00:00<00:02, 76.66frame/s][A
   🔥 2000R:  14%|█▎        | 32/234 [00:00<00:02, 77.90frame/s][A
   🔥 2000R:  18%|█▊        | 41/234 [00:00<00:02, 79.46frame/s][A
   🔥 2000R:  21%|██▏       | 50/234 [00:00<00:02, 81.29frame/s][A
   🔥 2000R:  25%|██▌       | 59/234 [00:00<00:02, 83.61frame/s][A
   🔥 2000R:  29%|██▉       | 69/234 [00:00<00:01, 87.29frame/s][A
   🔥 2000R:  34%|███▍      | 79/234 [00:00<00:01, 89.32frame/s][A
   🔥 2000R:  38%|███▊      | 89/234 [00:01<00:01, 90.77frame/s][A
   🔥 2000R:  42%|████▏     | 99/234 [00:01<00:01, 92.15frame/s][A
   🔥 2000R:  47%|████▋     | 109/234 [00:01<00:01, 92.55frame/s][A
   🔥 2000R:  51%|█████     | 119/234 [00:01<00:01, 91.63frame/s][A
   🔥 2000R:  55%|█████▌    | 129/234 [00:01<00:01, 92.17frame/s][A
   🔥 2000R:  59%|█████▉    | 139/234 [00:01<00:01, 94.18frame/s][A
   🔥 2000R:  64%|██████▍   | 150/234 [00:01<00:00, 96.92frame/s][A
   🔥 2000R:  69%|██████▉   | 161/234 [00:01<00:00, 99.10frame/s][A
   🔥 2000R:  74%|███████▎  | 172/234 [00:01<00:00, 101.36frame/s][A
   🔥 2000R:  78%|███████▊  | 183/234 [00:02<00:00, 99.19frame/s] [A
   🔥 2000R:  82%|████████▏ | 193/234 [00:02<00:00, 96.39frame/s][A
   🔥 2000R:  87%|████████▋ | 203/234 [00:02<00:00, 95.16frame/s][A
   🔥 2000R:  91%|█████████ | 213/234 [00:02<00:00, 90.99frame/s][A
   🔥 2000R:  95%|█████████▌| 223/234 [00:02<00:00, 87.07frame/s][A
   🔥 2000R:  99%|█████████▉| 232/234 [00:02<00:00, 84.34frame/s][A
                                                                 [A[✅] 2000R: /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:04<00:12,  4.05s/rig]
   🔥 2000L:   0%|          | 0/241 [00:00<?, ?frame/s][A
   🔥 2000L:   3%|▎         | 8/241 [00:00<00:03, 77.35frame/s][A
   🔥 2000L:   7%|▋         | 17/241 [00:00<00:02, 79.89frame/s][A
   🔥 2000L:  11%|█         | 26/241 [00:00<00:02, 80.62frame/s][A
   🔥 2000L:  15%|█▍        | 35/241 [00:00<00:02, 82.20frame/s][A
   🔥 2000L:  19%|█▊        | 45/241 [00:00<00:02, 85.82frame/s][A
   🔥 2000L:  23%|██▎       | 55/241 [00:00<00:02, 88.18frame/s][A
   🔥 2000L:  27%|██▋       | 66/241 [00:00<00:01, 93.65frame/s][A
   🔥 2000L:  32%|███▏      | 77/241 [00:00<00:01, 97.38frame/s][A
   🔥 2000L:  37%|███▋      | 88/241 [00:00<00:01, 100.52frame/s][A
   🔥 2000L:  41%|████      | 99/241 [00:01<00:01, 98.32frame/s] [A
   🔥 2000L:  45%|████▌     | 109/241 [00:01<00:01, 97.28frame/s][A
   🔥 2000L:  49%|████▉     | 119/241 [00:01<00:01, 96.51frame/s][A
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   🔥 2000L:  58%|█████▊    | 140/241 [00:01<00:01, 100.01frame/s][A
   🔥 2000L:  63%|██████▎   | 151/241 [00:01<00:00, 102.56frame/s][A
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   🔥 2000L:  72%|███████▏  | 173/241 [00:01<00:00, 101.79frame/s][A
   🔥 2000L:  76%|███████▋  | 184/241 [00:01<00:00, 97.98frame/s] [A
   🔥 2000L:  80%|████████  | 194/241 [00:02<00:00, 93.99frame/s][A
   🔥 2000L:  85%|████████▍ | 204/241 [00:02<00:00, 90.18frame/s][A
   🔥 2000L:  89%|████████▉ | 214/241 [00:02<00:00, 86.69frame/s][A
   🔥 2000L:  93%|█████████▎| 223/241 [00:02<00:00, 85.04frame/s][A
   🔥 2000L:  96%|█████████▋| 232/241 [00:02<00:00, 83.33frame/s][A
   🔥 2000L: 100%|██████████| 241/241 [00:02<00:00, 82.16frame/s][A
                                                                 [A[✅] 2000L: /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
📁 Rigs (heatmap):  50%|█████     | 2/4 [00:08<00:08,  4.06s/rig]
   🔥 1500:   0%|          | 0/302 [00:00<?, ?frame/s][A
   🔥 1500:   3%|▎         | 10/302 [00:00<00:03, 91.12frame/s][A
   🔥 1500:   7%|▋         | 20/302 [00:00<00:03, 92.29frame/s][A
   🔥 1500:  10%|▉         | 30/302 [00:00<00:02, 93.25frame/s][A
   🔥 1500:  13%|█▎        | 40/302 [00:00<00:02, 92.58frame/s][A
   🔥 1500:  17%|█▋        | 50/302 [00:00<00:02, 88.91frame/s][A
   🔥 1500:  20%|█▉        | 59/302 [00:00<00:02, 86.03frame/s][A
   🔥 1500:  23%|██▎       | 68/302 [00:00<00:02, 84.07frame/s][A
   🔥 1500:  25%|██▌       | 77/302 [00:00<00:02, 82.18frame/s][A
   🔥 1500:  28%|██▊       | 86/302 [00:01<00:02, 81.67frame/s][A
   🔥 1500:  31%|███▏      | 95/302 [00:01<00:02, 80.73frame/s][A
   🔥 1500:  34%|███▍      | 104/302 [00:01<00:02, 80.97frame/s][A
   🔥 1500:  37%|███▋      | 113/302 [00:01<00:02, 80.66frame/s][A
   🔥 1500:  40%|████      | 122/302 [00:01<00:02, 80.59frame/s][A
   🔥 1500:  43%|████▎     | 131/302 [00:01<00:02, 81.17frame/s][A
   🔥 1500:  46%|████▋     | 140/302 [00:01<00:01, 81.81frame/s][A
   🔥 1500:  50%|████▉     | 150/302 [00:01<00:01, 85.68frame/s][A
   🔥 1500:  53%|█████▎    | 160/302 [00:01<00:01, 87.47frame/s][A
   🔥 1500:  56%|█████▌    | 169/302 [00:01<00:01, 87.35frame/s][A
   🔥 1500:  59%|█████▉    | 178/302 [00:02<00:01, 86.53frame/s][A
   🔥 1500:  62%|██████▏   | 187/302 [00:02<00:01, 85.37frame/s][A
   🔥 1500:  65%|██████▍   | 196/302 [00:02<00:01, 84.19frame/s][A
   🔥 1500:  68%|██████▊   | 205/302 [00:02<00:01, 83.69frame/s][A
   🔥 1500:  71%|███████   | 214/302 [00:02<00:01, 82.78frame/s][A
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   🔥 1500:  77%|███████▋  | 232/302 [00:02<00:00, 82.63frame/s][A
   🔥 1500:  80%|███████▉  | 241/302 [00:02<00:00, 83.73frame/s][A
   🔥 1500:  83%|████████▎ | 251/302 [00:02<00:00, 85.81frame/s][A
   🔥 1500:  86%|████████▋ | 261/302 [00:03<00:00, 88.00frame/s][A
   🔥 1500:  90%|████████▉ | 271/302 [00:03<00:00, 90.64frame/s][A
   🔥 1500:  93%|█████████▎| 281/302 [00:03<00:00, 92.47frame/s][A
   🔥 1500:  96%|█████████▋| 291/302 [00:03<00:00, 93.12frame/s][A
   🔥 1500: 100%|█████████▉| 301/302 [00:03<00:00, 94.18frame/s][A
                                                                [A[✅] 1500: /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/1500_heatmap_fitted.png
📁 Rigs (heatmap):  75%|███████▌  | 3/4 [00:12<00:04,  4.40s/rig]
   🔥 1000:   0%|          | 0/228 [00:00<?, ?frame/s][A
   🔥 1000:   4%|▍         | 9/228 [00:00<00:02, 87.77frame/s][A
   🔥 1000:   8%|▊         | 18/228 [00:00<00:02, 86.76frame/s][A
   🔥 1000:  12%|█▏        | 27/228 [00:00<00:02, 88.06frame/s][A
   🔥 1000:  16%|█▌        | 36/228 [00:00<00:02, 88.58frame/s][A
   🔥 1000:  20%|██        | 46/228 [00:00<00:01, 91.16frame/s][A
   🔥 1000:  25%|██▍       | 56/228 [00:00<00:01, 92.48frame/s][A
   🔥 1000:  29%|██▉       | 66/228 [00:00<00:01, 93.29frame/s][A
   🔥 1000:  33%|███▎      | 76/228 [00:00<00:01, 93.68frame/s][A
   🔥 1000:  38%|███▊      | 86/228 [00:00<00:01, 94.06frame/s][A
   🔥 1000:  42%|████▏     | 96/228 [00:01<00:01, 94.65frame/s][A
   🔥 1000:  46%|████▋     | 106/228 [00:01<00:01, 92.62frame/s][A
   🔥 1000:  51%|█████     | 116/228 [00:01<00:01, 90.20frame/s][A
   🔥 1000:  55%|█████▌    | 126/228 [00:01<00:01, 88.36frame/s][A
   🔥 1000:  59%|█████▉    | 135/228 [00:01<00:01, 87.53frame/s][A
   🔥 1000:  63%|██████▎   | 144/228 [00:01<00:00, 86.87frame/s][A
   🔥 1000:  67%|██████▋   | 153/228 [00:01<00:00, 86.06frame/s][A
   🔥 1000:  71%|███████   | 162/228 [00:01<00:00, 85.34frame/s][A
   🔥 1000:  75%|███████▌  | 171/228 [00:01<00:00, 84.98frame/s][A
   🔥 1000:  79%|███████▉  | 180/228 [00:02<00:00, 85.61frame/s][A
   🔥 1000:  83%|████████▎ | 189/228 [00:02<00:00, 86.30frame/s][A
   🔥 1000:  87%|████████▋ | 198/228 [00:02<00:00, 87.03frame/s][A
   🔥 1000:  91%|█████████ | 207/228 [00:02<00:00, 87.64frame/s][A
   🔥 1000:  95%|█████████▍| 216/228 [00:02<00:00, 87.63frame/s][A
   🔥 1000:  99%|█████████▊| 225/228 [00:02<00:00, 87.43frame/s][A
                                                                [A[✅] 1000: /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap/1000_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:16<00:00,  3.94s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:16<00:00,  4.04s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC042_W007(6)_2025-05-28/calibration/debug/5_heatmap
[🏁] Finished processing MDC042_W007(6)_2025-05-28


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