2026-09-11T00:45:04 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC050_W019(6)_2026-09-09 ===
2026-09-11T00:45:04 - INFO: Camera ID: 6396146
2026-09-11T00:45:04 - INFO: Wand ID: 
2026-09-11T00:45:04 - INFO: Recorded At: 2026-09-09T13:20:49Z

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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 506 stationary frames, 537 remain

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                                                                            [A[PASS2] Frame 0000757: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000810: fixed corners {0} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 1 untrusted, 537 total

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                                                                            [A
[TIMING] 2000R — 537 frames
  Phase 1  radial detect   (parallel)  :   7.83s  (14.6 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.06s  (7.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.24s
  Phase 3  finalize        (sequential):  10.20s  (19.0 ms/frame)
    decode + undistort                 :   5.44s  (10.1 ms/frame)
    corner apply + refinement          :   2.15s  (4.0 ms/frame)
    segment                            :   2.13s  (4.0 ms/frame)
    smooth                             :   0.30s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.33s

Processing Rigs:  25%|██▌       | 1/4 [00:25<01:17, 25.97s/it][INFO] Filtered 777 stationary frames, 584 remain

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                                                                            [A[PASS2] Frame 0000893: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000909: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000893: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000893: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Summary: 1 frames fixed, 3 untrusted, 584 total

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                                                                            [A
[TIMING] 2000L — 584 frames
  Phase 1  radial detect   (parallel)  :   8.62s  (14.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.21s  (7.2 ms/frame)
  Phase 2b corner fix      (sequential):   0.39s
  Phase 3  finalize        (sequential):   9.53s  (16.3 ms/frame)
    decode + undistort                 :   4.25s  (7.3 ms/frame)
    corner apply + refinement          :   2.45s  (4.2 ms/frame)
    segment                            :   2.31s  (4.0 ms/frame)
    smooth                             :   0.33s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  22.75s

Processing Rigs:  50%|█████     | 2/4 [00:53<00:53, 26.64s/it][INFO] Filtered 499 stationary frames, 567 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 2 untrusted, 567 total

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                                                                           [A
[TIMING] 1000 — 567 frames
  Phase 1  radial detect   (parallel)  :   8.39s  (14.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.00s  (7.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.12s
  Phase 3  finalize        (sequential):   8.70s  (15.3 ms/frame)
    decode + undistort                 :   4.16s  (7.3 ms/frame)
    corner apply + refinement          :   1.88s  (3.3 ms/frame)
    segment                            :   2.17s  (3.8 ms/frame)
    smooth                             :   0.31s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  21.20s

Processing Rigs:  75%|███████▌  | 3/4 [01:17<00:25, 25.78s/it][INFO] Filtered 547 stationary frames, 744 remain

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                                                                           [A[PASS2] Summary: 0 frames fixed, 1 untrusted, 744 total

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                                                                           [A
[TIMING] 1500 — 744 frames
  Phase 1  radial detect   (parallel)  :  11.26s  (15.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.39s  (7.2 ms/frame)
  Phase 2b corner fix      (sequential):   0.16s
  Phase 3  finalize        (sequential):  11.80s  (15.9 ms/frame)
    decode + undistort                 :   5.46s  (7.3 ms/frame)
    corner apply + refinement          :   2.75s  (3.7 ms/frame)
    segment                            :   2.94s  (3.9 ms/frame)
    smooth                             :   0.41s  (0.5 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  28.61s

Processing Rigs: 100%|██████████| 4/4 [01:50<00:00, 28.55s/it]Processing Rigs: 100%|██████████| 4/4 [01:50<00:00, 27.66s/it]
[💾] Saved RigFrameDataset to /working/MDC050_W019(6)_2026-09-09/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 2432 frames from 4 rigs to /working/MDC050_W019(6)_2026-09-09/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC050_W019(6)_2026-09-09/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 2000R: 100 frames selected from 537
[📋] 2000L: 100 frames selected from 584
[📋] 1000: 100 frames selected from 567
[📋] 1500: 100 frames selected from 744
[💾] Saved RigFrameDataset to /working/MDC050_W019(6)_2026-09-09/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC050_W019(6)_2026-09-09/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 [00:05<07:29,  5.69s/it]Computing max image from bright frames:   4%|▍         | 3/80 [00:05<01:57,  1.53s/it]Computing max image from bright frames:   5%|▌         | 4/80 [00:05<01:18,  1.04s/it]Computing max image from bright frames:   6%|▋         | 5/80 [00:06<00:54,  1.37it/s]Computing max image from bright frames:   9%|▉         | 7/80 [00:06<00:29,  2.44it/s]Computing max image from bright frames:  11%|█▏        | 9/80 [00:06<00:20,  3.53it/s]Computing max image from bright frames:  14%|█▍        | 11/80 [00:06<00:14,  4.69it/s]Computing max image from bright frames:  16%|█▋        | 13/80 [00:06<00:11,  5.75it/s]Computing max image from bright frames:  19%|█▉        | 15/80 [00:06<00:09,  6.78it/s]Computing max image from bright frames:  21%|██▏       | 17/80 [00:07<00:08,  7.68it/s]Computing max image from bright frames:  24%|██▍       | 19/80 [00:07<00:07,  8.60it/s]Computing max image from bright frames:  26%|██▋       | 21/80 [00:07<00:06,  8.63it/s]Computing max image from bright frames:  29%|██▉       | 23/80 [00:07<00:06,  9.49it/s]Computing max image from bright frames:  31%|███▏      | 25/80 [00:07<00:05,  9.54it/s]Computing max image from bright frames:  34%|███▍      | 27/80 [00:08<00:05, 10.08it/s]Computing max image from bright frames:  36%|███▋      | 29/80 [00:08<00:04, 10.37it/s]Computing max image from bright frames:  39%|███▉      | 31/80 [00:08<00:05,  9.61it/s]Computing max image from bright frames:  41%|████▏     | 33/80 [00:12<00:29,  1.61it/s]Computing max image from bright frames:  42%|████▎     | 34/80 [00:12<00:25,  1.82it/s]Computing max image from bright frames:  45%|████▌     | 36/80 [00:12<00:17,  2.55it/s]Computing max image from bright frames:  48%|████▊     | 38/80 [00:12<00:12,  3.44it/s]Computing max image from bright frames:  50%|█████     | 40/80 [00:12<00:08,  4.60it/s]Computing max image from bright frames:  52%|█████▎    | 42/80 [00:12<00:06,  5.95it/s]Computing max image from bright frames:  55%|█████▌    | 44/80 [00:13<00:04,  7.30it/s]Computing max image from bright frames:  57%|█████▊    | 46/80 [00:13<00:03,  8.77it/s]Computing max image from bright frames:  60%|██████    | 48/80 [00:13<00:03,  9.50it/s]Computing max image from bright frames:  62%|██████▎   | 50/80 [00:13<00:03,  9.56it/s]Computing max image from bright frames:  65%|██████▌   | 52/80 [00:13<00:02, 10.47it/s]Computing max image from bright frames:  68%|██████▊   | 54/80 [00:13<00:02,  9.99it/s]Computing max image from bright frames:  70%|███████   | 56/80 [00:14<00:02, 10.88it/s]Computing max image from bright frames:  72%|███████▎  | 58/80 [00:14<00:02, 10.30it/s]Computing max image from bright frames:  75%|███████▌  | 60/80 [00:14<00:01, 11.26it/s]Computing max image from bright frames:  79%|███████▉  | 63/80 [00:14<00:01, 13.76it/s]Computing max image from bright frames:  81%|████████▏ | 65/80 [00:16<00:05,  2.99it/s]Computing max image from bright frames:  85%|████████▌ | 68/80 [00:16<00:02,  4.39it/s]Computing max image from bright frames:  90%|█████████ | 72/80 [00:16<00:01,  6.50it/s]Computing max image from bright frames:  95%|█████████▌| 76/80 [00:17<00:00,  9.28it/s]Computing max image from bright frames:  99%|█████████▉| 79/80 [00:17<00:00,  9.52it/s]                                                                                       [✅] Casing detection complete: cx=997.0, cy=736.0

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

  0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:27,  3.61it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 300.0} (0.000):   1%|          | 1/100 [00:00<00:27,  3.61it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:27,  3.61it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   3%|▎         | 3/100 [00:00<00:26,  3.61it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 321.212} (0.000):   4%|▍         | 4/100 [00:00<00:07, 12.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:07, 12.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 335.354} (0.000):   5%|▌         | 5/100 [00:00<00:07, 12.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   6%|▌         | 6/100 [00:00<00:07, 12.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.001 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:06, 15.16it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.000 | Best: {'fx': 349.495} (0.001):   7%|▋         | 7/100 [00:00<00:06, 15.16it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.001 | Best: {'fx': 356.566} (0.001):   8%|▊         | 8/100 [00:00<00:06, 15.16it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.003 | Best: {'fx': 377.778} (0.003):   9%|▉         | 9/100 [00:00<00:06, 15.16it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.003 | Best: {'fx': 377.778} (0.003):  10%|█         | 10/100 [00:00<00:05, 17.94it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.002 | Best: {'fx': 377.778} (0.003):  10%|█         | 10/100 [00:00<00:05, 17.94it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.001 | Best: {'fx': 377.778} (0.003):  11%|█         | 11/100 [00:00<00:04, 17.94it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  12%|█▏        | 12/100 [00:00<00:04, 17.94it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.004 | Best: {'fx': 391.919} (0.004):  13%|█▎        | 13/100 [00:00<00:04, 17.94it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  14%|█▍        | 14/100 [00:00<00:04, 17.94it/s] Brute | Start: {'fx': 500.0} (0.044) | Current: 0.004 | Best: {'fx': 398.99} (0.004):  15%|█▌        | 15/100 [00:00<00:03, 26.08it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  15%|█▌        | 15/100 [00:00<00:03, 26.08it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.005 | Best: {'fx': 413.131} (0.005):  16%|█▌        | 16/100 [00:00<00:03, 26.08it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  17%|█▋        | 17/100 [00:00<00:03, 26.08it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.006 | Best: {'fx': 420.202} (0.006):  18%|█▊        | 18/100 [00:00<00:03, 24.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.006 | Best: {'fx': 427.273} (0.006):  18%|█▊        | 18/100 [00:00<00:03, 24.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.007 | Best: {'fx': 434.343} (0.007):  19%|█▉        | 19/100 [00:00<00:03, 24.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.009 | Best: {'fx': 441.414} (0.009):  20%|██        | 20/100 [00:00<00:03, 24.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  21%|██        | 21/100 [00:01<00:03, 24.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.011 | Best: {'fx': 448.485} (0.011):  22%|██▏       | 22/100 [00:01<00:03, 25.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.013 | Best: {'fx': 455.556} (0.013):  22%|██▏       | 22/100 [00:01<00:03, 25.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.016 | Best: {'fx': 462.626} (0.016):  23%|██▎       | 23/100 [00:01<00:03, 25.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  24%|██▍       | 24/100 [00:01<00:02, 25.63it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.017 | Best: {'fx': 469.697} (0.017):  25%|██▌       | 25/100 [00:01<00:02, 25.95it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 476.768} (0.021):  25%|██▌       | 25/100 [00:01<00:02, 25.95it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.026 | Best: {'fx': 483.838} (0.026):  26%|██▌       | 26/100 [00:01<00:02, 25.95it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.033 | Best: {'fx': 490.909} (0.033):  27%|██▋       | 27/100 [00:01<00:02, 25.95it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.033 | Best: {'fx': 490.909} (0.033):  28%|██▊       | 28/100 [00:01<00:02, 26.28it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.042 | Best: {'fx': 497.98} (0.042):  28%|██▊       | 28/100 [00:01<00:02, 26.28it/s] Brute | Start: {'fx': 500.0} (0.044) | Current: 0.050 | Best: {'fx': 505.051} (0.050):  29%|██▉       | 29/100 [00:01<00:02, 26.28it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.063 | Best: {'fx': 512.121} (0.063):  30%|███       | 30/100 [00:01<00:02, 26.28it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.063 | Best: {'fx': 512.121} (0.063):  31%|███       | 31/100 [00:01<00:02, 23.65it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.106 | Best: {'fx': 526.263} (0.106):  31%|███       | 31/100 [00:01<00:02, 23.65it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.068 | Best: {'fx': 526.263} (0.106):  32%|███▏      | 32/100 [00:01<00:02, 23.65it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.080 | Best: {'fx': 526.263} (0.106):  33%|███▎      | 33/100 [00:01<00:02, 23.65it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.080 | Best: {'fx': 526.263} (0.106):  34%|███▍      | 34/100 [00:01<00:03, 16.98it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.058 | Best: {'fx': 526.263} (0.106):  34%|███▍      | 34/100 [00:01<00:03, 16.98it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.082 | Best: {'fx': 526.263} (0.106):  35%|███▌      | 35/100 [00:01<00:03, 16.98it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.050 | Best: {'fx': 526.263} (0.106):  36%|███▌      | 36/100 [00:01<00:03, 16.98it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.101 | Best: {'fx': 526.263} (0.106):  37%|███▋      | 37/100 [00:01<00:03, 16.98it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.046 | Best: {'fx': 526.263} (0.106):  38%|███▊      | 38/100 [00:01<00:03, 16.98it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.046 | Best: {'fx': 526.263} (0.106):  39%|███▉      | 39/100 [00:01<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.039 | Best: {'fx': 526.263} (0.106):  39%|███▉      | 39/100 [00:01<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.037 | Best: {'fx': 526.263} (0.106):  40%|████      | 40/100 [00:01<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.043 | Best: {'fx': 526.263} (0.106):  41%|████      | 41/100 [00:01<00:02, 22.34it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.043 | Best: {'fx': 526.263} (0.106):  42%|████▏     | 42/100 [00:01<00:02, 23.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.035 | Best: {'fx': 526.263} (0.106):  42%|████▏     | 42/100 [00:01<00:02, 23.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.033 | Best: {'fx': 526.263} (0.106):  43%|████▎     | 43/100 [00:02<00:02, 23.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.032 | Best: {'fx': 526.263} (0.106):  44%|████▍     | 44/100 [00:02<00:02, 23.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.032 | Best: {'fx': 526.263} (0.106):  45%|████▌     | 45/100 [00:02<00:02, 23.41it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.030 | Best: {'fx': 526.263} (0.106):  45%|████▌     | 45/100 [00:02<00:02, 23.41it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.030 | Best: {'fx': 526.263} (0.106):  46%|████▌     | 46/100 [00:02<00:02, 23.41it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.027 | Best: {'fx': 526.263} (0.106):  47%|████▋     | 47/100 [00:02<00:02, 23.41it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.027 | Best: {'fx': 526.263} (0.106):  48%|████▊     | 48/100 [00:02<00:02, 19.82it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.026 | Best: {'fx': 526.263} (0.106):  48%|████▊     | 48/100 [00:02<00:02, 19.82it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.026 | Best: {'fx': 526.263} (0.106):  49%|████▉     | 49/100 [00:02<00:02, 19.82it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.028 | Best: {'fx': 526.263} (0.106):  50%|█████     | 50/100 [00:02<00:02, 19.82it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.028 | Best: {'fx': 526.263} (0.106):  51%|█████     | 51/100 [00:02<00:02, 21.66it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.022 | Best: {'fx': 526.263} (0.106):  51%|█████     | 51/100 [00:02<00:02, 21.66it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.029 | Best: {'fx': 526.263} (0.106):  52%|█████▏    | 52/100 [00:02<00:02, 21.66it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.024 | Best: {'fx': 526.263} (0.106):  53%|█████▎    | 53/100 [00:02<00:02, 21.66it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.028 | Best: {'fx': 526.263} (0.106):  54%|█████▍    | 54/100 [00:02<00:02, 21.66it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.025 | Best: {'fx': 526.263} (0.106):  55%|█████▌    | 55/100 [00:02<00:02, 21.66it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.025 | Best: {'fx': 526.263} (0.106):  56%|█████▌    | 56/100 [00:02<00:01, 27.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.025 | Best: {'fx': 526.263} (0.106):  56%|█████▌    | 56/100 [00:02<00:01, 27.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  57%|█████▋    | 57/100 [00:02<00:01, 27.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.024 | Best: {'fx': 526.263} (0.106):  58%|█████▊    | 58/100 [00:02<00:01, 27.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.022 | Best: {'fx': 526.263} (0.106):  59%|█████▉    | 59/100 [00:02<00:01, 27.62it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.022 | Best: {'fx': 526.263} (0.106):  60%|██████    | 60/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.024 | Best: {'fx': 526.263} (0.106):  60%|██████    | 60/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  61%|██████    | 61/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.023 | Best: {'fx': 526.263} (0.106):  62%|██████▏   | 62/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.022 | Best: {'fx': 526.263} (0.106):  63%|██████▎   | 63/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  64%|██████▍   | 64/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.023 | Best: {'fx': 526.263} (0.106):  65%|██████▌   | 65/100 [00:02<00:01, 30.50it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.023 | Best: {'fx': 526.263} (0.106):  66%|██████▌   | 66/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  66%|██████▌   | 66/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  67%|██████▋   | 67/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.023 | Best: {'fx': 526.263} (0.106):  68%|██████▊   | 68/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  69%|██████▉   | 69/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.022 | Best: {'fx': 526.263} (0.106):  70%|███████   | 70/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  71%|███████   | 71/100 [00:02<00:00, 37.80it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.021 | Best: {'fx': 526.263} (0.106):  72%|███████▏  | 72/100 [00:02<00:00, 42.73it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  72%|███████▏  | 72/100 [00:02<00:00, 42.73it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  73%|███████▎  | 73/100 [00:02<00:00, 42.73it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  74%|███████▍  | 74/100 [00:02<00:00, 42.73it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  75%|███████▌  | 75/100 [00:02<00:00, 42.73it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  76%|███████▌  | 76/100 [00:02<00:00, 42.73it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  77%|███████▋  | 77/100 [00:02<00:00, 40.97it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  77%|███████▋  | 77/100 [00:02<00:00, 40.97it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  78%|███████▊  | 78/100 [00:03<00:00, 40.97it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  79%|███████▉  | 79/100 [00:03<00:00, 40.97it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  80%|████████  | 80/100 [00:03<00:00, 40.97it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  81%|████████  | 81/100 [00:03<00:00, 40.97it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.020 | Best: {'fx': 526.263} (0.106):  82%|████████▏ | 82/100 [00:03<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  82%|████████▏ | 82/100 [00:03<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  83%|████████▎ | 83/100 [00:03<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  84%|████████▍ | 84/100 [00:03<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  85%|████████▌ | 85/100 [00:03<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  86%|████████▌ | 86/100 [00:03<00:00, 40.57it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  87%|████████▋ | 87/100 [00:03<00:00, 38.18it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  87%|████████▋ | 87/100 [00:03<00:00, 38.18it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  88%|████████▊ | 88/100 [00:03<00:00, 38.18it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  89%|████████▉ | 89/100 [00:03<00:00, 38.18it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  90%|█████████ | 90/100 [00:03<00:00, 38.18it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  91%|█████████ | 91/100 [00:03<00:00, 33.46it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  91%|█████████ | 91/100 [00:03<00:00, 33.46it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  92%|█████████▏| 92/100 [00:03<00:00, 33.46it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  93%|█████████▎| 93/100 [00:03<00:00, 33.46it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  94%|█████████▍| 94/100 [00:03<00:00, 33.46it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  95%|█████████▌| 95/100 [00:03<00:00, 32.05it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.019 | Best: {'fx': 526.263} (0.106):  95%|█████████▌| 95/100 [00:03<00:00, 32.05it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  96%|█████████▌| 96/100 [00:03<00:00, 32.05it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  97%|█████████▋| 97/100 [00:03<00:00, 32.05it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  98%|█████████▊| 98/100 [00:03<00:00, 32.05it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  99%|█████████▉| 99/100 [00:03<00:00, 29.44it/s]Brute | Start: {'fx': 500.0} (0.044) | Current: 0.018 | Best: {'fx': 526.263} (0.106):  99%|█████████▉| 99/100 [00:03<00:00, 29.44it/s]                                                                                                                                         0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.045 | Best: {'fx': 501.263} (0.045):   0%|          | 0/50 [00:00<?, ?it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.045 | Best: {'fx': 501.263} (0.045):   2%|▏         | 1/50 [00:00<00:28,  1.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.047 | Best: {'fx': 502.283} (0.047):   2%|▏         | 1/50 [00:00<00:28,  1.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.048 | Best: {'fx': 503.303} (0.048):   4%|▍         | 2/50 [00:00<00:27,  1.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.048 | Best: {'fx': 503.303} (0.048):   6%|▌         | 3/50 [00:00<00:08,  5.34it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.053 | Best: {'fx': 506.365} (0.053):   6%|▌         | 3/50 [00:00<00:08,  5.34it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.051 | Best: {'fx': 506.365} (0.053):   8%|▊         | 4/50 [00:00<00:08,  5.34it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.049 | Best: {'fx': 506.365} (0.053):  10%|█         | 5/50 [00:00<00:08,  5.34it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.049 | Best: {'fx': 506.365} (0.053):  12%|█▏        | 6/50 [00:00<00:04,  9.44it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.056 | Best: {'fx': 508.405} (0.056):  12%|█▏        | 6/50 [00:00<00:04,  9.44it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.054 | Best: {'fx': 508.405} (0.056):  14%|█▍        | 7/50 [00:00<00:04,  9.44it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.061 | Best: {'fx': 511.467} (0.061):  16%|█▌        | 8/50 [00:00<00:04,  9.44it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.060 | Best: {'fx': 511.467} (0.061):  18%|█▊        | 9/50 [00:00<00:04,  9.44it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.058 | Best: {'fx': 511.467} (0.061):  20%|██        | 10/50 [00:01<00:04,  9.44it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.058 | Best: {'fx': 511.467} (0.061):  22%|██▏       | 11/50 [00:01<00:02, 15.93it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.067 | Best: {'fx': 513.508} (0.067):  22%|██▏       | 11/50 [00:01<00:02, 15.93it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.064 | Best: {'fx': 513.508} (0.067):  24%|██▍       | 12/50 [00:01<00:02, 15.93it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.078 | Best: {'fx': 518.61} (0.078):  26%|██▌       | 13/50 [00:01<00:02, 15.93it/s] Brute | Start: {'fx': 526.263} (0.106) | Current: 0.078 | Best: {'fx': 518.61} (0.078):  28%|██▊       | 14/50 [00:01<00:02, 14.32it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.073 | Best: {'fx': 518.61} (0.078):  28%|██▊       | 14/50 [00:01<00:02, 14.32it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.074 | Best: {'fx': 518.61} (0.078):  30%|███       | 15/50 [00:01<00:02, 14.32it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.082 | Best: {'fx': 519.63} (0.082):  32%|███▏      | 16/50 [00:01<00:02, 14.32it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.085 | Best: {'fx': 520.65} (0.085):  34%|███▍      | 17/50 [00:01<00:02, 14.32it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.068 | Best: {'fx': 520.65} (0.085):  36%|███▌      | 18/50 [00:01<00:02, 14.32it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.068 | Best: {'fx': 520.65} (0.085):  38%|███▊      | 19/50 [00:01<00:01, 20.20it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.071 | Best: {'fx': 520.65} (0.085):  38%|███▊      | 19/50 [00:01<00:01, 20.20it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.089 | Best: {'fx': 521.671} (0.089):  40%|████      | 20/50 [00:01<00:01, 20.20it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.092 | Best: {'fx': 522.691} (0.092):  42%|████▏     | 21/50 [00:01<00:01, 20.20it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.092 | Best: {'fx': 522.691} (0.092):  44%|████▍     | 22/50 [00:01<00:01, 20.30it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.093 | Best: {'fx': 536.977} (0.093):  44%|████▍     | 22/50 [00:01<00:01, 20.30it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.099 | Best: {'fx': 533.916} (0.099):  46%|████▌     | 23/50 [00:01<00:01, 20.30it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.095 | Best: {'fx': 533.916} (0.099):  48%|████▊     | 24/50 [00:01<00:01, 20.30it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.095 | Best: {'fx': 533.916} (0.099):  50%|█████     | 25/50 [00:01<00:01, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.106 | Best: {'fx': 527.793} (0.106):  50%|█████     | 25/50 [00:01<00:01, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.097 | Best: {'fx': 527.793} (0.106):  52%|█████▏    | 26/50 [00:01<00:01, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.102 | Best: {'fx': 527.793} (0.106):  54%|█████▍    | 27/50 [00:01<00:01, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.090 | Best: {'fx': 527.793} (0.106):  56%|█████▌    | 28/50 [00:01<00:01, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.100 | Best: {'fx': 527.793} (0.106):  58%|█████▊    | 29/50 [00:01<00:00, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.108 | Best: {'fx': 529.834} (0.108):  60%|██████    | 30/50 [00:01<00:00, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.105 | Best: {'fx': 529.834} (0.108):  62%|██████▏   | 31/50 [00:01<00:00, 21.72it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.105 | Best: {'fx': 529.834} (0.108):  64%|██████▍   | 32/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.100 | Best: {'fx': 529.834} (0.108):  64%|██████▍   | 32/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.087 | Best: {'fx': 529.834} (0.108):  66%|██████▌   | 33/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.106 | Best: {'fx': 529.834} (0.108):  68%|██████▊   | 34/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.105 | Best: {'fx': 529.834} (0.108):  70%|███████   | 35/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.104 | Best: {'fx': 529.834} (0.108):  72%|███████▏  | 36/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.083 | Best: {'fx': 529.834} (0.108):  74%|███████▍  | 37/50 [00:01<00:00, 31.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.083 | Best: {'fx': 529.834} (0.108):  76%|███████▌  | 38/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.107 | Best: {'fx': 529.834} (0.108):  76%|███████▌  | 38/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.079 | Best: {'fx': 529.834} (0.108):  78%|███████▊  | 39/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.075 | Best: {'fx': 529.834} (0.108):  80%|████████  | 40/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.077 | Best: {'fx': 529.834} (0.108):  82%|████████▏ | 41/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.070 | Best: {'fx': 529.834} (0.108):  84%|████████▍ | 42/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.068 | Best: {'fx': 529.834} (0.108):  86%|████████▌ | 43/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.072 | Best: {'fx': 529.834} (0.108):  88%|████████▊ | 44/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.067 | Best: {'fx': 529.834} (0.108):  90%|█████████ | 45/50 [00:01<00:00, 38.40it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.067 | Best: {'fx': 529.834} (0.108):  92%|█████████▏| 46/50 [00:01<00:00, 48.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.071 | Best: {'fx': 529.834} (0.108):  92%|█████████▏| 46/50 [00:01<00:00, 48.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.065 | Best: {'fx': 529.834} (0.108):  94%|█████████▍| 47/50 [00:01<00:00, 48.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.063 | Best: {'fx': 529.834} (0.108):  96%|█████████▌| 48/50 [00:02<00:00, 48.74it/s]Brute | Start: {'fx': 526.263} (0.106) | Current: 0.061 | Best: {'fx': 529.834} (0.108):  98%|█████████▊| 49/50 [00:02<00:00, 48.74it/s]                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 529.8340548340549
    px: 997.0
    py: 736.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 80 frames will be used for optimization
  Rigs: 2000R(2000mm):20/20, 2000L(2000mm):20/20, 1000(1000mm):20/20, 1500(1500mm):20/20
  Total: 80/80 frames used for optimization
Initial score: 10.5931: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 10.5931, picklable_objective(x0) = 10.6021
Best score: 10.6021: : 0iter [00:02, ?iter/s] Best score: 10.6021: : 1iter [00:02,  2.02s/iter]Best score: 10.6021: : 1iter [00:02,  2.02s/iter]Best score: 10.6021: : 2iter [00:02,  2.02s/iter]Best score: 10.6021: : 3iter [00:02,  1.70iter/s]Best score: 10.6021: : 3iter [00:02,  1.70iter/s]Best score: 10.6021: : 4iter [00:02,  1.70iter/s]Best score: 10.6021: : 5iter [00:02,  3.03iter/s]Best score: 10.6021: : 5iter [00:02,  3.03iter/s]Best score: 10.6021: : 6iter [00:02,  3.03iter/s]Best score: 10.6021: : 7iter [00:02,  4.40iter/s]Best score: 10.6021: : 7iter [00:02,  4.40iter/s]Best score: 10.6021: : 8iter [00:02,  4.40iter/s]Best score: 10.6021: : 9iter [00:02,  5.74iter/s]Best score: 10.6021: : 9iter [00:02,  5.74iter/s]Best score: 10.6021: : 10iter [00:02,  5.74iter/s]Best score: 10.6021: : 11iter [00:02,  6.92iter/s]Best score: 10.6021: : 11iter [00:02,  6.92iter/s]Best score: 10.6021: : 12iter [00:03,  6.92iter/s]Best score: 10.6021: : 13iter [00:03,  8.00iter/s]Best score: 10.6021: : 13iter [00:03,  8.00iter/s]Best score: 10.6021: : 14iter [00:03,  8.00iter/s]Best score: 10.6021: : 15iter [00:03,  8.89iter/s]Best score: 10.6021: : 15iter [00:03,  8.89iter/s]Best score: 10.6021: : 16iter [00:03,  8.89iter/s]Best score: 10.6021: : 17iter [00:03,  9.60iter/s]Best score: 10.6021: : 17iter [00:03,  9.60iter/s]Best score: 10.6021: : 18iter [00:03,  9.60iter/s]Best score: 10.6021: : 19iter [00:03, 10.14iter/s]Best score: 10.6021: : 19iter [00:03, 10.14iter/s]Best score: 10.6021: : 20iter [00:03, 10.14iter/s]Best score: 10.6021: : 21iter [00:03, 10.38iter/s]Best score: 10.6021: : 21iter [00:03, 10.38iter/s]Best score: 10.6021: : 22iter [00:03, 10.38iter/s]Best score: 10.6021: : 23iter [00:03, 10.69iter/s]Best score: 10.6021: : 23iter [00:04, 10.69iter/s]Best score: 10.6021: : 24iter [00:04, 10.69iter/s]Best score: 10.6021: : 25iter [00:04, 10.99iter/s]Best score: 10.6021: : 25iter [00:04, 10.99iter/s]Best score: 10.6021: : 26iter [00:04, 10.99iter/s]Best score: 10.6021: : 27iter [00:04, 11.15iter/s]Best score: 10.6021: : 27iter [00:04, 11.15iter/s]Best score: 10.6021: : 28iter [00:04, 11.15iter/s]Best score: 10.6021: : 29iter [00:04, 11.29iter/s]Best score: 10.6021: : 29iter [00:04, 11.29iter/s]Best score: 10.6021: : 30iter [00:04, 11.29iter/s]Best score: 10.6021: : 31iter [00:04, 11.18iter/s]Best score: 10.6021: : 31iter [00:04, 11.18iter/s]Best score: 10.6021: : 32iter [00:04, 11.18iter/s]Best score: 10.6021: : 33iter [00:04, 11.29iter/s]Best score: 10.6021: : 33iter [00:04, 11.29iter/s]Best score: 10.6021: : 34iter [00:04, 11.29iter/s]Best score: 10.6021: : 35iter [00:04, 11.38iter/s]Best score: 10.6021: : 35iter [00:05, 11.38iter/s]Best score: 10.6021: : 36iter [00:05, 11.38iter/s]Best score: 10.6021: : 37iter [00:05, 11.49iter/s]Best score: 10.6021: : 37iter [00:05, 11.49iter/s]Best score: 10.6021: : 38iter [00:05, 11.49iter/s]Best score: 10.6021: : 39iter [00:05, 11.54iter/s]Best score: 10.6021: : 39iter [00:05, 11.54iter/s]Best score: 10.6021: : 40iter [00:05, 11.54iter/s]Best score: 10.6021: : 41iter [00:05, 11.45iter/s]Best score: 10.6021: : 41iter [00:05, 11.45iter/s]Best score: 10.6021: : 42iter [00:05, 11.45iter/s]Best score: 10.6021: : 43iter [00:05, 11.54iter/s]Best score: 10.6021: : 43iter [00:05, 11.54iter/s]Best score: 10.6021: : 44iter [00:05, 11.54iter/s]Best score: 10.6021: : 45iter [00:05, 11.61iter/s]Best score: 10.6021: : 45iter [00:05, 11.61iter/s]Best score: 10.6021: : 46iter [00:06, 11.61iter/s]Best score: 10.6021: : 47iter [00:06, 11.70iter/s]Best score: 10.6021: : 47iter [00:06, 11.70iter/s]Best score: 10.6021: : 48iter [00:06, 11.70iter/s]Best score: 10.6021: : 49iter [00:06, 11.70iter/s]
[ITER 50] Best: 10.6021
[PARAMS] Current best parameters:
  fx: 529.8341
  fy: 529.8341
  px: 997.0000
  py: 736.0000
  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: 10.6021: : 49iter [00:06, 11.70iter/s]Best score: 10.6021: : 50iter [00:06, 11.70iter/s]Best score: 10.6021: : 51iter [00:06, 11.50iter/s]Best score: 10.6021: : 51iter [00:06, 11.50iter/s]Best score: 10.6021: : 52iter [00:06, 11.50iter/s]Best score: 10.6021: : 53iter [00:06, 11.55iter/s]Best score: 10.6021: : 53iter [00:06, 11.55iter/s]Best score: 10.6021: : 54iter [00:06, 11.55iter/s]Best score: 10.6021: : 55iter [00:06, 11.57iter/s]Best score: 10.6021: : 55iter [00:06, 11.57iter/s]Best score: 10.6021: : 56iter [00:06, 11.57iter/s]Best score: 10.6021: : 57iter [00:06, 11.67iter/s]Best score: 10.6021: : 57iter [00:06, 11.67iter/s]Best score: 10.6021: : 58iter [00:07, 11.67iter/s]Best score: 10.6021: : 59iter [00:07, 11.72iter/s]Best score: 10.6021: : 59iter [00:07, 11.72iter/s]Best score: 10.6021: : 60iter [00:07, 11.72iter/s]Best score: 10.6021: : 61iter [00:07, 11.62iter/s]Best score: 10.6021: : 61iter [00:07, 11.62iter/s]Best score: 10.6021: : 62iter [00:07, 11.62iter/s]Best score: 10.6021: : 63iter [00:07, 11.71iter/s]Best score: 10.6021: : 63iter [00:07, 11.71iter/s]Best score: 10.6021: : 64iter [00:07, 11.71iter/s]Best score: 10.6021: : 65iter [00:07, 11.81iter/s]Best score: 10.6021: : 65iter [00:07, 11.81iter/s]Best score: 10.6021: : 66iter [00:07, 11.81iter/s]Best score: 10.6021: : 67iter [00:07, 11.81iter/s]Best score: 10.6021: : 67iter [00:07, 11.81iter/s]Best score: 10.6021: : 68iter [00:07, 11.81iter/s]Best score: 10.6021: : 69iter [00:07, 11.85iter/s]Best score: 6.5226: : 69iter [00:07, 11.85iter/s] Best score: 6.5226: : 70iter [00:08, 11.85iter/s]Best score: 6.5226: : 71iter [00:08, 11.63iter/s]Best score: 6.5226: : 71iter [00:08, 11.63iter/s]Best score: 6.5226: : 72iter [00:08, 11.63iter/s]Best score: 6.5226: : 73iter [00:08, 11.65iter/s]Best score: 6.5226: : 73iter [00:08, 11.65iter/s]Best score: 6.5226: : 74iter [00:08, 11.65iter/s]Best score: 6.5226: : 75iter [00:08, 11.73iter/s]Best score: 6.5226: : 75iter [00:08, 11.73iter/s]Best score: 6.5226: : 76iter [00:08, 11.73iter/s]Best score: 6.5226: : 77iter [00:08, 11.74iter/s]Best score: 6.5226: : 77iter [00:08, 11.74iter/s]Best score: 6.5226: : 78iter [00:08, 11.74iter/s]Best score: 6.5226: : 79iter [00:08, 11.83iter/s]Best score: 1.8896: : 79iter [00:08, 11.83iter/s]Best score: 1.8896: : 80iter [00:08, 11.83iter/s]Best score: 1.8896: : 81iter [00:08, 11.66iter/s]Best score: 1.8896: : 81iter [00:09, 11.66iter/s]Best score: 1.8896: : 82iter [00:09, 11.66iter/s]Best score: 1.8896: : 83iter [00:09, 11.69iter/s]Best score: 1.8896: : 83iter [00:09, 11.69iter/s]Best score: 1.8896: : 84iter [00:09, 11.69iter/s]Best score: 1.8896: : 85iter [00:09, 11.77iter/s]Best score: 1.8896: : 85iter [00:09, 11.77iter/s]Best score: 1.8896: : 86iter [00:09, 11.77iter/s]Best score: 1.8896: : 87iter [00:09, 11.81iter/s]Best score: 1.8896: : 87iter [00:09, 11.81iter/s]Best score: 1.8896: : 88iter [00:09, 11.81iter/s]Best score: 1.8896: : 89iter [00:09, 11.87iter/s]Best score: 1.8896: : 89iter [00:09, 11.87iter/s]Best score: 1.8896: : 90iter [00:09, 11.87iter/s]Best score: 1.8896: : 91iter [00:09, 11.72iter/s]Best score: 1.8896: : 91iter [00:09, 11.72iter/s]Best score: 1.8896: : 92iter [00:09, 11.72iter/s]Best score: 1.8896: : 93iter [00:09, 11.79iter/s]Best score: 1.8896: : 93iter [00:10, 11.79iter/s]Best score: 1.8896: : 94iter [00:10, 11.79iter/s]Best score: 1.8896: : 95iter [00:10, 11.87iter/s]Best score: 1.8896: : 95iter [00:10, 11.87iter/s]Best score: 1.8896: : 96iter [00:10, 11.87iter/s]Best score: 1.8896: : 97iter [00:10, 11.88iter/s]Best score: 1.8896: : 97iter [00:10, 11.88iter/s]Best score: 1.8896: : 98iter [00:10, 11.88iter/s]Best score: 1.8896: : 99iter [00:10, 11.87iter/s]
[ITER 100] Best: 1.8896
[PARAMS] Current best parameters:
  fx: 550.9770
  fy: 549.3755
  px: 998.2432
  py: 737.1558
  k1: -0.02206161
  k2: 0.01155716
  k3: -0.01200062
  k4: 0.00369468
  p1: -0.00554687
  p2: -0.00313214
  p3: -0.00903167
  p4: 0.00272658
  s1: -0.0044
  s2: 0.0046
  s3: 0.0001
  s4: 0.0028
  tilt_x: -0.0097
  tilt_y: -0.0266
  scale: 2.9806
  rotation: -0.3450
Best score: 1.8896: : 99iter [00:10, 11.87iter/s]Best score: 1.8896: : 100iter [00:10, 11.87iter/s]Best score: 1.8896: : 101iter [00:10, 10.11iter/s]Best score: 1.8896: : 101iter [00:10, 10.11iter/s]Best score: 1.8896: : 102iter [00:10, 10.11iter/s]Best score: 1.8896: : 103iter [00:10, 10.48iter/s]Best score: 1.8896: : 103iter [00:10, 10.48iter/s]Best score: 1.8896: : 104iter [00:11, 10.48iter/s]Best score: 1.8896: : 105iter [00:11, 10.78iter/s]Best score: 1.8896: : 105iter [00:11, 10.78iter/s]Best score: 1.8896: : 106iter [00:11, 10.78iter/s]Best score: 1.8896: : 107iter [00:11, 10.93iter/s]Best score: 1.8896: : 107iter [00:11, 10.93iter/s]Best score: 1.8896: : 108iter [00:11, 10.93iter/s]Best score: 1.8896: : 109iter [00:11, 11.15iter/s]Best score: 1.8896: : 109iter [00:11, 11.15iter/s]Best score: 1.8896: : 110iter [00:11, 11.15iter/s]Best score: 1.8896: : 111iter [00:11, 11.12iter/s]Best score: 1.8896: : 111iter [00:11, 11.12iter/s]Best score: 1.8896: : 112iter [00:11, 11.12iter/s]Best score: 1.8896: : 113iter [00:11, 11.23iter/s]Best score: 1.8896: : 113iter [00:11, 11.23iter/s]Best score: 1.8896: : 114iter [00:11, 11.23iter/s]Best score: 1.8896: : 115iter [00:11, 11.35iter/s]Best score: 1.8896: : 115iter [00:12, 11.35iter/s]Best score: 1.8896: : 116iter [00:12, 11.35iter/s]Best score: 1.8896: : 117iter [00:12, 11.44iter/s]Best score: 1.8896: : 117iter [00:12, 11.44iter/s]Best score: 1.8896: : 118iter [00:12, 11.44iter/s]Best score: 1.8896: : 119iter [00:12, 11.49iter/s]Best score: 1.8896: : 119iter [00:12, 11.49iter/s]Best score: 1.8896: : 120iter [00:12, 11.49iter/s]Best score: 1.8896: : 121iter [00:12, 11.34iter/s]Best score: 1.8896: : 121iter [00:12, 11.34iter/s]Best score: 1.8896: : 122iter [00:12, 11.34iter/s]Best score: 1.8896: : 123iter [00:12, 11.43iter/s]Best score: 1.8896: : 123iter [00:12, 11.43iter/s]Best score: 1.8896: : 124iter [00:12, 11.43iter/s]Best score: 1.8896: : 125iter [00:12, 11.54iter/s]Best score: 1.8896: : 125iter [00:12, 11.54iter/s]Best score: 1.8896: : 126iter [00:12, 11.54iter/s]Best score: 1.8896: : 127iter [00:12, 11.58iter/s]Best score: 1.8896: : 127iter [00:13, 11.58iter/s]Best score: 1.8896: : 128iter [00:13, 11.58iter/s]Best score: 1.8896: : 129iter [00:13, 11.56iter/s]Best score: 1.8896: : 129iter [00:13, 11.56iter/s]Best score: 1.8896: : 130iter [00:13, 11.56iter/s]Best score: 1.8896: : 131iter [00:13, 11.39iter/s]Best score: 1.8896: : 131iter [00:13, 11.39iter/s]Best score: 1.8896: : 132iter [00:13, 11.39iter/s]Best score: 1.8896: : 133iter [00:13, 11.49iter/s]Best score: 1.8896: : 133iter [00:13, 11.49iter/s]Best score: 1.8896: : 134iter [00:13, 11.49iter/s]Best score: 1.8896: : 135iter [00:13, 11.52iter/s]Best score: 1.8896: : 135iter [00:13, 11.52iter/s]Best score: 1.8896: : 136iter [00:13, 11.52iter/s]Best score: 1.8896: : 137iter [00:13, 11.57iter/s]Best score: 1.8896: : 137iter [00:13, 11.57iter/s]Best score: 1.8896: : 138iter [00:14, 11.57iter/s]Best score: 1.8896: : 139iter [00:14, 11.60iter/s]Best score: 1.8896: : 139iter [00:14, 11.60iter/s]Best score: 1.8896: : 140iter [00:14, 11.60iter/s]Best score: 1.8896: : 141iter [00:14, 11.47iter/s]Best score: 1.8896: : 141iter [00:14, 11.47iter/s]Best score: 1.8896: : 142iter [00:14, 11.47iter/s]Best score: 1.8896: : 143iter [00:14, 11.54iter/s]Best score: 1.8896: : 143iter [00:14, 11.54iter/s]Best score: 1.8896: : 144iter [00:14, 11.54iter/s]Best score: 1.8896: : 145iter [00:14, 11.63iter/s]Best score: 1.8896: : 145iter [00:14, 11.63iter/s]Best score: 1.8896: : 146iter [00:14, 11.63iter/s]Best score: 1.8896: : 147iter [00:14, 11.63iter/s]Best score: 1.8896: : 147iter [00:14, 11.63iter/s]Best score: 1.8896: : 148iter [00:14, 11.63iter/s]Best score: 1.8896: : 149iter [00:14, 11.65iter/s]
[ITER 150] Best: 1.8896
[PARAMS] Current best parameters:
  fx: 550.9770
  fy: 549.3755
  px: 998.2432
  py: 737.1558
  k1: -0.02206161
  k2: 0.01155716
  k3: -0.01200062
  k4: 0.00369468
  p1: -0.00554687
  p2: -0.00313214
  p3: -0.00903167
  p4: 0.00272658
  s1: -0.0044
  s2: 0.0046
  s3: 0.0001
  s4: 0.0028
  tilt_x: -0.0097
  tilt_y: -0.0266
  scale: 2.9806
  rotation: -0.3450
Best score: 1.8896: : 149iter [00:14, 11.65iter/s]Best score: 1.8896: : 150iter [00:15, 11.65iter/s]Best score: 1.8896: : 151iter [00:15,  7.48iter/s]Best score: 1.8896: : 151iter [00:15,  7.48iter/s]Best score: 1.8896: : 152iter [00:15,  7.48iter/s]Best score: 1.8896: : 153iter [00:15,  8.40iter/s]Best score: 1.8896: : 153iter [00:15,  8.40iter/s]Best score: 1.8896: : 154iter [00:15,  8.40iter/s]Best score: 1.8896: : 155iter [00:15,  9.18iter/s]Best score: 1.8896: : 155iter [00:15,  9.18iter/s]Best score: 1.8896: : 156iter [00:15,  9.18iter/s]Best score: 1.8896: : 157iter [00:15,  9.80iter/s]Best score: 1.8896: : 157iter [00:15,  9.80iter/s]Best score: 1.8896: : 158iter [00:16,  9.80iter/s]Best score: 1.8896: : 159iter [00:16, 10.33iter/s]Best score: 1.8896: : 159iter [00:16, 10.33iter/s]Best score: 1.8896: : 160iter [00:16, 10.33iter/s]Best score: 1.8896: : 161iter [00:16, 10.54iter/s]Best score: 1.8896: : 161iter [00:16, 10.54iter/s]Best score: 1.8896: : 162iter [00:16, 10.54iter/s]Best score: 1.8896: : 163iter [00:16, 10.92iter/s]Best score: 1.8896: : 163iter [00:16, 10.92iter/s]Best score: 1.8896: : 164iter [00:16, 10.92iter/s]Best score: 1.8896: : 165iter [00:16, 11.22iter/s]Best score: 1.8896: : 165iter [00:16, 11.22iter/s]Best score: 1.8896: : 166iter [00:16, 11.22iter/s]Best score: 1.8896: : 167iter [00:16, 11.44iter/s]Best score: 1.8896: : 167iter [00:16, 11.44iter/s]Best score: 1.8896: : 168iter [00:16, 11.44iter/s]Best score: 1.8896: : 169iter [00:16, 11.57iter/s]Best score: 1.8896: : 169iter [00:16, 11.57iter/s]Best score: 1.8896: : 170iter [00:17, 11.57iter/s]Best score: 1.8896: : 171iter [00:17, 11.46iter/s]Best score: 1.8896: : 171iter [00:17, 11.46iter/s]Best score: 1.8896: : 172iter [00:17, 11.46iter/s]Best score: 1.8896: : 173iter [00:17, 11.58iter/s]Best score: 1.8896: : 173iter [00:17, 11.58iter/s]Best score: 1.8896: : 174iter [00:17, 11.58iter/s]Best score: 1.8896: : 175iter [00:17, 11.69iter/s]Best score: 1.8896: : 175iter [00:17, 11.69iter/s]Best score: 1.8896: : 176iter [00:17, 11.69iter/s]Best score: 1.8896: : 177iter [00:17, 11.78iter/s]Best score: 1.8896: : 177iter [00:17, 11.78iter/s]Best score: 1.8896: : 178iter [00:17, 11.78iter/s]Best score: 1.8896: : 179iter [00:17, 11.81iter/s]Best score: 1.8896: : 179iter [00:17, 11.81iter/s]Best score: 1.8896: : 180iter [00:17, 11.81iter/s]Best score: 1.8896: : 181iter [00:17, 11.68iter/s]Best score: 1.8896: : 181iter [00:18, 11.68iter/s]Best score: 1.8896: : 182iter [00:18, 11.68iter/s]Best score: 1.8896: : 183iter [00:18, 11.72iter/s]Best score: 1.8896: : 183iter [00:18, 11.72iter/s]Best score: 1.8896: : 184iter [00:18, 11.72iter/s]Best score: 1.8896: : 185iter [00:18, 11.83iter/s]Best score: 1.8896: : 185iter [00:18, 11.83iter/s]Best score: 1.8896: : 186iter [00:18, 11.83iter/s]Best score: 1.8896: : 187iter [00:18, 11.90iter/s]Best score: 1.8896: : 187iter [00:18, 11.90iter/s]Best score: 1.8896: : 188iter [00:18, 11.90iter/s]Best score: 1.8896: : 189iter [00:18, 11.97iter/s]Best score: 1.8896: : 189iter [00:18, 11.97iter/s]Best score: 1.8896: : 190iter [00:18, 11.97iter/s]Best score: 1.8896: : 191iter [00:18, 11.78iter/s]Best score: 1.8896: : 191iter [00:18, 11.78iter/s]Best score: 1.8896: : 192iter [00:18, 11.78iter/s]Best score: 1.8896: : 193iter [00:18, 11.83iter/s]Best score: 1.8896: : 193iter [00:19, 11.83iter/s]Best score: 1.8896: : 194iter [00:19, 11.83iter/s]Best score: 1.8896: : 195iter [00:19, 11.85iter/s]Best score: 1.8896: : 195iter [00:19, 11.85iter/s]Best score: 1.8896: : 196iter [00:19, 11.85iter/s]Best score: 1.8896: : 197iter [00:19, 11.89iter/s]Best score: 1.8896: : 197iter [00:19, 11.89iter/s]Best score: 1.8896: : 198iter [00:19, 11.89iter/s]Best score: 1.8896: : 199iter [00:19, 11.92iter/s]
[ITER 200] Best: 1.8896
[PARAMS] Current best parameters:
  fx: 550.9770
  fy: 549.3755
  px: 998.2432
  py: 737.1558
  k1: -0.02206161
  k2: 0.01155716
  k3: -0.01200062
  k4: 0.00369468
  p1: -0.00554687
  p2: -0.00313214
  p3: -0.00903167
  p4: 0.00272658
  s1: -0.0044
  s2: 0.0046
  s3: 0.0001
  s4: 0.0028
  tilt_x: -0.0097
  tilt_y: -0.0266
  scale: 2.9806
  rotation: -0.3450
Best score: 1.8896: : 199iter [00:19, 11.92iter/s]Best score: 1.8896: : 200iter [00:19, 11.92iter/s]Best score: 1.8896: : 201iter [00:19,  7.62iter/s]Best score: 1.8896: : 201iter [00:19,  7.62iter/s]Best score: 1.8896: : 202iter [00:20,  7.62iter/s]Best score: 1.8896: : 203iter [00:20,  8.54iter/s]Best score: 1.8896: : 203iter [00:20,  8.54iter/s]Best score: 1.8896: : 204iter [00:20,  8.54iter/s]Best score: 1.8896: : 205iter [00:20,  9.35iter/s]Best score: 1.8896: : 205iter [00:20,  9.35iter/s]Best score: 1.8896: : 206iter [00:20,  9.35iter/s]Best score: 1.8896: : 207iter [00:20, 10.02iter/s]Best score: 1.8896: : 207iter [00:20, 10.02iter/s]Best score: 1.8896: : 208iter [00:20, 10.02iter/s]Best score: 1.8896: : 209iter [00:20, 10.51iter/s]Best score: 1.8896: : 209iter [00:20, 10.51iter/s]Best score: 1.8896: : 210iter [00:20, 10.51iter/s]Best score: 1.8896: : 211iter [00:20, 10.74iter/s]Best score: 1.8896: : 211iter [00:20, 10.74iter/s]Best score: 1.8896: : 212iter [00:20, 10.74iter/s]Best score: 1.8896: : 213iter [00:20, 11.08iter/s]Best score: 1.8896: : 213iter [00:21, 11.08iter/s]Best score: 1.8896: : 214iter [00:21, 11.08iter/s]Best score: 1.8896: : 215iter [00:21, 11.34iter/s]Best score: 1.8896: : 215iter [00:21, 11.34iter/s]Best score: 1.8896: : 216iter [00:21, 11.34iter/s]Best score: 1.8896: : 217iter [00:21, 11.53iter/s]Best score: 1.8896: : 217iter [00:21, 11.53iter/s]Best score: 1.8896: : 218iter [00:21, 11.53iter/s]Best score: 1.8896: : 219iter [00:21, 11.64iter/s]Best score: 1.7603: : 219iter [00:21, 11.64iter/s]Best score: 1.7603: : 220iter [00:21, 11.64iter/s]Best score: 1.7603: : 221iter [00:21, 11.55iter/s]Best score: 1.7603: : 221iter [00:21, 11.55iter/s]Best score: 1.7603: : 222iter [00:21, 11.55iter/s]Best score: 1.7603: : 223iter [00:21, 11.65iter/s]Best score: 1.7603: : 223iter [00:21, 11.65iter/s]Best score: 1.7603: : 224iter [00:21, 11.65iter/s]Best score: 1.7603: : 225iter [00:21, 11.75iter/s]Best score: 1.7603: : 225iter [00:22, 11.75iter/s]Best score: 1.7603: : 226iter [00:22, 11.75iter/s]Best score: 1.7603: : 227iter [00:22, 11.77iter/s]Best score: 1.7603: : 227iter [00:22, 11.77iter/s]Best score: 1.7603: : 228iter [00:22, 11.77iter/s]Best score: 1.7603: : 229iter [00:22, 11.84iter/s]Best score: 1.7232: : 229iter [00:22, 11.84iter/s]Best score: 1.7232: : 230iter [00:22, 11.84iter/s]Best score: 1.7232: : 231iter [00:22, 11.71iter/s]Best score: 1.7232: : 231iter [00:22, 11.71iter/s]Best score: 1.7232: : 232iter [00:22, 11.71iter/s]Best score: 1.7232: : 233iter [00:22, 11.79iter/s]Best score: 1.7232: : 233iter [00:22, 11.79iter/s]Best score: 1.7232: : 234iter [00:22, 11.79iter/s]Best score: 1.7232: : 235iter [00:22, 11.86iter/s]Best score: 1.7232: : 235iter [00:22, 11.86iter/s]Best score: 1.7232: : 236iter [00:22, 11.86iter/s]Best score: 1.7232: : 237iter [00:22, 11.90iter/s]Best score: 1.7232: : 237iter [00:23, 11.90iter/s]Best score: 1.7232: : 238iter [00:23, 11.90iter/s]Best score: 1.7232: : 239iter [00:23, 11.94iter/s]Best score: 1.5306: : 239iter [00:23, 11.94iter/s]Best score: 1.5306: : 240iter [00:23, 11.94iter/s]Best score: 1.5306: : 241iter [00:23, 11.74iter/s]Best score: 1.5306: : 241iter [00:23, 11.74iter/s]Best score: 1.5306: : 242iter [00:23, 11.74iter/s]Best score: 1.5306: : 243iter [00:23, 11.83iter/s]Best score: 1.5306: : 243iter [00:23, 11.83iter/s]Best score: 1.5306: : 244iter [00:23, 11.83iter/s]Best score: 1.5306: : 245iter [00:23, 11.84iter/s]Best score: 1.5306: : 245iter [00:23, 11.84iter/s]Best score: 1.5306: : 246iter [00:23, 11.84iter/s]Best score: 1.5306: : 247iter [00:23, 11.89iter/s]Best score: 1.5306: : 247iter [00:23, 11.89iter/s]Best score: 1.5306: : 248iter [00:23, 11.89iter/s]Best score: 1.5306: : 249iter [00:23, 11.93iter/s]
[ITER 250] Best: 1.5306
[PARAMS] Current best parameters:
  fx: 544.4870
  fy: 544.4620
  px: 997.8579
  py: 741.3571
  k1: -0.01006194
  k2: 0.00807607
  k3: -0.01389575
  k4: 0.00458575
  p1: 0.00948739
  p2: -0.01058890
  p3: -0.01095528
  p4: 0.00776426
  s1: 0.0057
  s2: 0.0010
  s3: -0.0007
  s4: -0.0044
  tilt_x: -0.0155
  tilt_y: -0.0369
  scale: 2.9681
  rotation: -0.1521
Best score: 1.5306: : 249iter [00:24, 11.93iter/s]Best score: 1.5306: : 250iter [00:24, 11.93iter/s]Best score: 1.5306: : 251iter [00:24,  7.57iter/s]Best score: 1.5306: : 251iter [00:24,  7.57iter/s]Best score: 1.5306: : 252iter [00:24,  7.57iter/s]Best score: 1.5306: : 253iter [00:24,  8.48iter/s]Best score: 1.5306: : 253iter [00:24,  8.48iter/s]Best score: 1.5306: : 254iter [00:24,  8.48iter/s]Best score: 1.5306: : 255iter [00:24,  9.27iter/s]Best score: 1.5306: : 255iter [00:24,  9.27iter/s]Best score: 1.5306: : 256iter [00:24,  9.27iter/s]Best score: 1.5306: : 257iter [00:24,  9.93iter/s]Best score: 1.5306: : 257iter [00:25,  9.93iter/s]Best score: 1.5306: : 258iter [00:25,  9.93iter/s]Best score: 1.5306: : 259iter [00:25, 10.48iter/s]Best score: 1.5306: : 259iter [00:25, 10.48iter/s]Best score: 1.5306: : 260iter [00:25, 10.48iter/s]Best score: 1.5306: : 261iter [00:25, 10.74iter/s]Best score: 1.5306: : 261iter [00:25, 10.74iter/s]Best score: 1.5306: : 262iter [00:25, 10.74iter/s]Best score: 1.5306: : 263iter [00:25, 11.05iter/s]Best score: 1.5306: : 263iter [00:25, 11.05iter/s]Best score: 1.5306: : 264iter [00:25, 11.05iter/s]Best score: 1.5306: : 265iter [00:25,  8.55iter/s]Best score: 1.5306: : 265iter [00:25,  8.55iter/s]Best score: 1.5306: : 266iter [00:25,  8.55iter/s]Best score: 1.5306: : 267iter [00:25,  9.34iter/s]Best score: 1.5306: : 267iter [00:26,  9.34iter/s]Best score: 1.5306: : 268iter [00:26,  9.34iter/s]Best score: 1.5306: : 269iter [00:26,  9.99iter/s]Best score: 0.8272: : 269iter [00:26,  9.99iter/s]Best score: 0.8272: : 270iter [00:26,  9.99iter/s]Best score: 0.8272: : 271iter [00:26, 10.31iter/s]Best score: 0.8272: : 271iter [00:26, 10.31iter/s]Best score: 0.8272: : 272iter [00:26, 10.31iter/s]Best score: 0.8272: : 273iter [00:26, 10.72iter/s]Best score: 0.8272: : 273iter [00:26, 10.72iter/s]Best score: 0.8272: : 274iter [00:26, 10.72iter/s]Best score: 0.8272: : 275iter [00:26, 11.05iter/s]Best score: 0.8272: : 275iter [00:26, 11.05iter/s]Best score: 0.8272: : 276iter [00:26, 11.05iter/s]Best score: 0.8272: : 277iter [00:26, 11.30iter/s]Best score: 0.8272: : 277iter [00:26, 11.30iter/s]Best score: 0.8272: : 278iter [00:27, 11.30iter/s]Best score: 0.8272: : 279iter [00:27, 11.46iter/s]Best score: 0.8272: : 279iter [00:27, 11.46iter/s]Best score: 0.8272: : 280iter [00:27, 11.46iter/s]Best score: 0.8272: : 281iter [00:27, 11.36iter/s]Best score: 0.8272: : 281iter [00:27, 11.36iter/s]Best score: 0.8272: : 282iter [00:27, 11.36iter/s]Best score: 0.8272: : 283iter [00:27, 11.50iter/s]Best score: 0.8272: : 283iter [00:27, 11.50iter/s]Best score: 0.8272: : 284iter [00:27, 11.50iter/s]Best score: 0.8272: : 285iter [00:27, 11.59iter/s]Best score: 0.8272: : 285iter [00:27, 11.59iter/s]Best score: 0.8272: : 286iter [00:27, 11.59iter/s]Best score: 0.8272: : 287iter [00:27, 11.66iter/s]Best score: 0.8272: : 287iter [00:27, 11.66iter/s]Best score: 0.8272: : 288iter [00:27, 11.66iter/s]Best score: 0.8272: : 289iter [00:27, 11.75iter/s]Best score: 0.8272: : 289iter [00:27, 11.75iter/s]Best score: 0.8272: : 290iter [00:28, 11.75iter/s]Best score: 0.8272: : 291iter [00:28, 11.62iter/s]Best score: 0.8272: : 291iter [00:28, 11.62iter/s]Best score: 0.8272: : 292iter [00:28, 11.62iter/s]Best score: 0.8272: : 293iter [00:28, 11.71iter/s]Best score: 0.8272: : 293iter [00:28, 11.71iter/s]Best score: 0.8272: : 294iter [00:28, 11.71iter/s]Best score: 0.8272: : 295iter [00:28, 11.77iter/s]Best score: 0.8272: : 295iter [00:28, 11.77iter/s]Best score: 0.8272: : 296iter [00:28, 11.77iter/s]Best score: 0.8272: : 297iter [00:28, 11.80iter/s]Best score: 0.8272: : 297iter [00:28, 11.80iter/s]Best score: 0.8272: : 298iter [00:28, 11.80iter/s]Best score: 0.8272: : 299iter [00:28, 11.83iter/s]
[ITER 300] Best: 0.6513
[PARAMS] Current best parameters:
  fx: 551.7932
  fy: 550.8392
  px: 1003.8693
  py: 746.4323
  k1: -0.00041616
  k2: -0.02842804
  k3: 0.01787782
  k4: -0.00459426
  p1: 0.00968763
  p2: -0.00598853
  p3: -0.00131663
  p4: 0.00020479
  s1: 0.0020
  s2: 0.0075
  s3: 0.0010
  s4: -0.0044
  tilt_x: 0.0078
  tilt_y: -0.0513
  scale: 2.9544
  rotation: -0.4386
Best score: 0.6513: : 299iter [00:28, 11.83iter/s]Best score: 0.6513: : 300iter [00:29, 11.83iter/s]Best score: 0.6513: : 301iter [00:29,  7.57iter/s]Best score: 0.6513: : 301iter [00:29,  7.57iter/s]Best score: 0.6513: : 302iter [00:29,  7.57iter/s]Best score: 0.6513: : 303iter [00:29,  8.50iter/s]Best score: 0.6513: : 303iter [00:29,  8.50iter/s]Best score: 0.6513: : 304iter [00:29,  8.50iter/s]Best score: 0.6513: : 305iter [00:29,  9.31iter/s]Best score: 0.6513: : 305iter [00:29,  9.31iter/s]Best score: 0.6513: : 306iter [00:29,  9.31iter/s]Best score: 0.6513: : 307iter [00:29,  9.99iter/s]Best score: 0.6513: : 307iter [00:29,  9.99iter/s]Best score: 0.6513: : 308iter [00:29,  9.99iter/s]Best score: 0.6513: : 309iter [00:29, 10.47iter/s]Best score: 0.6513: : 309iter [00:29, 10.47iter/s]Best score: 0.6513: : 310iter [00:30, 10.47iter/s]Best score: 0.6513: : 311iter [00:30, 10.67iter/s]Best score: 0.6513: : 311iter [00:30, 10.67iter/s]Best score: 0.6513: : 312iter [00:30, 10.67iter/s]Best score: 0.6513: : 313iter [00:30, 11.01iter/s]Best score: 0.6513: : 313iter [00:30, 11.01iter/s]Best score: 0.6513: : 314iter [00:30, 11.01iter/s]Best score: 0.6513: : 315iter [00:30, 11.30iter/s]Best score: 0.6513: : 315iter [00:30, 11.30iter/s]Best score: 0.6513: : 316iter [00:30, 11.30iter/s]Best score: 0.6513: : 317iter [00:30, 11.52iter/s]Best score: 0.6513: : 317iter [00:30, 11.52iter/s]Best score: 0.6513: : 318iter [00:30, 11.52iter/s]Best score: 0.6513: : 319iter [00:30, 11.66iter/s]Best score: 0.4269: : 319iter [00:30, 11.66iter/s]Best score: 0.4269: : 320iter [00:30, 11.66iter/s]Best score: 0.4269: : 321iter [00:30, 11.58iter/s]Best score: 0.4269: : 321iter [00:30, 11.58iter/s]Best score: 0.4269: : 322iter [00:31, 11.58iter/s]Best score: 0.4269: : 323iter [00:31, 11.71iter/s]Best score: 0.4269: : 323iter [00:31, 11.71iter/s]Best score: 0.4269: : 324iter [00:31, 11.71iter/s]Best score: 0.4269: : 325iter [00:31, 11.71iter/s]Best score: 0.4269: : 325iter [00:31, 11.71iter/s]Best score: 0.4269: : 326iter [00:31, 11.71iter/s]Best score: 0.4269: : 327iter [00:31, 11.79iter/s]Best score: 0.4269: : 327iter [00:31, 11.79iter/s]Best score: 0.4269: : 328iter [00:31, 11.79iter/s]Best score: 0.4269: : 329iter [00:31, 11.85iter/s]Best score: 0.4269: : 329iter [00:31, 11.85iter/s]Best score: 0.4269: : 330iter [00:31, 11.85iter/s]Best score: 0.4269: : 331iter [00:31, 11.70iter/s]Best score: 0.4269: : 331iter [00:31, 11.70iter/s]Best score: 0.4269: : 332iter [00:31, 11.70iter/s]Best score: 0.4269: : 333iter [00:31, 11.78iter/s]Best score: 0.4269: : 333iter [00:31, 11.78iter/s]Best score: 0.4269: : 334iter [00:32, 11.78iter/s]Best score: 0.4269: : 335iter [00:32, 11.84iter/s]Best score: 0.4269: : 335iter [00:32, 11.84iter/s]Best score: 0.4269: : 336iter [00:32, 11.84iter/s]Best score: 0.4269: : 337iter [00:32, 11.89iter/s]Best score: 0.4269: : 337iter [00:32, 11.89iter/s]Best score: 0.4269: : 338iter [00:32, 11.89iter/s]Best score: 0.4269: : 339iter [00:32, 11.92iter/s]Best score: 0.4269: : 339iter [00:32, 11.92iter/s]Best score: 0.4269: : 340iter [00:32, 11.92iter/s]Best score: 0.4269: : 341iter [00:32, 11.73iter/s]Best score: 0.4269: : 341iter [00:32, 11.73iter/s]Best score: 0.4269: : 342iter [00:32, 11.73iter/s]Best score: 0.4269: : 343iter [00:32, 11.81iter/s]Best score: 0.4269: : 343iter [00:32, 11.81iter/s]Best score: 0.4269: : 344iter [00:32, 11.81iter/s]Best score: 0.4269: : 345iter [00:32, 11.83iter/s]Best score: 0.4269: : 345iter [00:32, 11.83iter/s]Best score: 0.4269: : 346iter [00:33, 11.83iter/s]Best score: 0.4269: : 347iter [00:33, 11.89iter/s]Best score: 0.4269: : 347iter [00:33, 11.89iter/s]Best score: 0.4269: : 348iter [00:33, 11.89iter/s]Best score: 0.4269: : 349iter [00:33, 11.93iter/s]
[ITER 350] Best: 0.4269
[PARAMS] Current best parameters:
  fx: 553.3050
  fy: 553.0261
  px: 999.1370
  py: 746.2386
  k1: 0.00795573
  k2: -0.04418349
  k3: 0.02338648
  k4: -0.00441298
  p1: 0.01162787
  p2: -0.00604267
  p3: -0.00808677
  p4: 0.00580478
  s1: 0.0080
  s2: 0.0016
  s3: 0.0028
  s4: -0.0054
  tilt_x: 0.0039
  tilt_y: -0.0252
  scale: 2.9535
  rotation: -0.5398
Best score: 0.4269: : 349iter [00:33, 11.93iter/s]Best score: 0.4269: : 350iter [00:33, 11.93iter/s]Best score: 0.4269: : 351iter [00:33,  7.58iter/s]Best score: 0.4269: : 351iter [00:33,  7.58iter/s]Best score: 0.4269: : 352iter [00:33,  7.58iter/s]Best score: 0.4269: : 353iter [00:33,  8.51iter/s]Best score: 0.4269: : 353iter [00:33,  8.51iter/s]Best score: 0.4269: : 354iter [00:34,  8.51iter/s]Best score: 0.4269: : 355iter [00:34,  9.29iter/s]Best score: 0.4269: : 355iter [00:34,  9.29iter/s]Best score: 0.4269: : 356iter [00:34,  9.29iter/s]Best score: 0.4269: : 357iter [00:34,  9.94iter/s]Best score: 0.4269: : 357iter [00:34,  9.94iter/s]Best score: 0.4269: : 358iter [00:34,  9.94iter/s]Best score: 0.4269: : 359iter [00:34, 10.49iter/s]Best score: 0.4269: : 359iter [00:34, 10.49iter/s]Best score: 0.4269: : 360iter [00:34, 10.49iter/s]Best score: 0.4269: : 361iter [00:34, 10.73iter/s]Best score: 0.4269: : 361iter [00:34, 10.73iter/s]Best score: 0.4269: : 362iter [00:34, 10.73iter/s]Best score: 0.4269: : 363iter [00:34, 11.05iter/s]Best score: 0.4269: : 363iter [00:34, 11.05iter/s]Best score: 0.4269: : 364iter [00:34, 11.05iter/s]Best score: 0.4269: : 365iter [00:34, 11.26iter/s]Best score: 0.4269: : 365iter [00:35, 11.26iter/s]Best score: 0.4269: : 366iter [00:35, 11.26iter/s]Best score: 0.4269: : 367iter [00:35, 11.43iter/s]Best score: 0.4269: : 367iter [00:35, 11.43iter/s]Best score: 0.4269: : 368iter [00:35, 11.43iter/s]Best score: 0.4269: : 369iter [00:35, 11.57iter/s]Best score: 0.3701: : 369iter [00:35, 11.57iter/s]Best score: 0.3701: : 370iter [00:35, 11.57iter/s]Best score: 0.3701: : 371iter [00:35, 11.50iter/s]Best score: 0.3701: : 371iter [00:35, 11.50iter/s]Best score: 0.3701: : 372iter [00:35, 11.50iter/s]Best score: 0.3701: : 373iter [00:35, 11.67iter/s]Best score: 0.3701: : 373iter [00:35, 11.67iter/s]Best score: 0.3701: : 374iter [00:35, 11.67iter/s]Best score: 0.3701: : 375iter [00:35, 11.78iter/s]Best score: 0.3701: : 375iter [00:35, 11.78iter/s]Best score: 0.3701: : 376iter [00:35, 11.78iter/s]Best score: 0.3701: : 377iter [00:35, 11.83iter/s]Best score: 0.3701: : 377iter [00:36, 11.83iter/s]Best score: 0.3701: : 378iter [00:36, 11.83iter/s]Best score: 0.3701: : 379iter [00:36, 11.85iter/s]Best score: 0.3701: : 379iter [00:36, 11.85iter/s]Best score: 0.3701: : 380iter [00:36, 11.85iter/s]Best score: 0.3701: : 381iter [00:36, 11.63iter/s]Best score: 0.3701: : 381iter [00:36, 11.63iter/s]Best score: 0.3701: : 382iter [00:36, 11.63iter/s]Best score: 0.3701: : 383iter [00:36, 11.74iter/s]Best score: 0.3701: : 383iter [00:36, 11.74iter/s]Best score: 0.3701: : 384iter [00:36, 11.74iter/s]Best score: 0.3701: : 385iter [00:36, 11.84iter/s]Best score: 0.3701: : 385iter [00:36, 11.84iter/s]Best score: 0.3701: : 386iter [00:36, 11.84iter/s]Best score: 0.3701: : 387iter [00:36, 11.89iter/s]Best score: 0.3701: : 387iter [00:36, 11.89iter/s]Best score: 0.3701: : 388iter [00:36, 11.89iter/s]Best score: 0.3701: : 389iter [00:36, 11.90iter/s]Best score: 0.3701: : 389iter [00:37, 11.90iter/s]Best score: 0.3701: : 390iter [00:37, 11.90iter/s]Best score: 0.3701: : 391iter [00:37, 11.72iter/s]Best score: 0.3701: : 391iter [00:37, 11.72iter/s]Best score: 0.3701: : 392iter [00:37, 11.72iter/s]Best score: 0.3701: : 393iter [00:37, 11.77iter/s]Best score: 0.3701: : 393iter [00:37, 11.77iter/s]Best score: 0.3701: : 394iter [00:37, 11.77iter/s]Best score: 0.3701: : 395iter [00:37, 11.84iter/s]Best score: 0.3701: : 395iter [00:37, 11.84iter/s]Best score: 0.3701: : 396iter [00:37, 11.84iter/s]Best score: 0.3701: : 397iter [00:37, 11.86iter/s]Best score: 0.3701: : 397iter [00:37, 11.86iter/s]Best score: 0.3701: : 398iter [00:37, 11.86iter/s]Best score: 0.3701: : 399iter [00:37, 11.86iter/s]
[ITER 400] Best: 0.2888
[PARAMS] Current best parameters:
  fx: 546.7224
  fy: 546.3602
  px: 1003.5140
  py: 742.1430
  k1: -0.00809714
  k2: -0.00943879
  k3: 0.00478591
  k4: -0.00118291
  p1: 0.00385070
  p2: 0.00326552
  p3: 0.00763090
  p4: 0.00246898
  s1: -0.0095
  s2: 0.0001
  s3: -0.0087
  s4: -0.0022
  tilt_x: -0.0022
  tilt_y: -0.0285
  scale: 2.9743
  rotation: -0.4857
Best score: 0.2888: : 399iter [00:37, 11.86iter/s]Best score: 0.2888: : 400iter [00:38, 11.86iter/s]Best score: 0.2888: : 401iter [00:38,  7.56iter/s]Best score: 0.2888: : 401iter [00:38,  7.56iter/s]Best score: 0.2888: : 402iter [00:38,  7.56iter/s]Best score: 0.2888: : 403iter [00:38,  8.50iter/s]Best score: 0.2888: : 403iter [00:38,  8.50iter/s]Best score: 0.2888: : 404iter [00:38,  8.50iter/s]Best score: 0.2888: : 405iter [00:38,  9.32iter/s]Best score: 0.2888: : 405iter [00:38,  9.32iter/s]Best score: 0.2888: : 406iter [00:38,  9.32iter/s]Best score: 0.2888: : 407iter [00:38,  9.99iter/s]Best score: 0.2888: : 407iter [00:38,  9.99iter/s]Best score: 0.2888: : 408iter [00:38,  9.99iter/s]Best score: 0.2888: : 409iter [00:38, 10.50iter/s]Best score: 0.2831: : 409iter [00:39, 10.50iter/s]Best score: 0.2831: : 410iter [00:39, 10.50iter/s]Best score: 0.2831: : 411iter [00:39, 10.73iter/s]Best score: 0.2831: : 411iter [00:39, 10.73iter/s]Best score: 0.2831: : 412iter [00:39, 10.73iter/s]Best score: 0.2831: : 413iter [00:39, 11.01iter/s]Best score: 0.2831: : 413iter [00:39, 11.01iter/s]Best score: 0.2831: : 414iter [00:39, 11.01iter/s]Best score: 0.2831: : 415iter [00:39, 11.26iter/s]Best score: 0.2831: : 415iter [00:39, 11.26iter/s]Best score: 0.2831: : 416iter [00:39, 11.26iter/s]Best score: 0.2831: : 417iter [00:39, 11.47iter/s]Best score: 0.2831: : 417iter [00:39, 11.47iter/s]Best score: 0.2831: : 418iter [00:39, 11.47iter/s]Best score: 0.2831: : 419iter [00:39, 11.59iter/s]Best score: 0.2709: : 419iter [00:39, 11.59iter/s]Best score: 0.2709: : 420iter [00:39, 11.59iter/s]Best score: 0.2709: : 421iter [00:39, 11.53iter/s]Best score: 0.2709: : 421iter [00:40, 11.53iter/s]Best score: 0.2709: : 422iter [00:40, 11.53iter/s]Best score: 0.2709: : 423iter [00:40, 11.68iter/s]Best score: 0.2709: : 423iter [00:40, 11.68iter/s]Best score: 0.2709: : 424iter [00:40, 11.68iter/s]Best score: 0.2709: : 425iter [00:40, 11.78iter/s]Best score: 0.2709: : 425iter [00:40, 11.78iter/s]Best score: 0.2709: : 426iter [00:40, 11.78iter/s]Best score: 0.2709: : 427iter [00:40, 11.86iter/s]Best score: 0.2709: : 427iter [00:40, 11.86iter/s]Best score: 0.2709: : 428iter [00:40, 11.86iter/s]Best score: 0.2709: : 429iter [00:40, 11.91iter/s]Best score: 0.2553: : 429iter [00:40, 11.91iter/s]Best score: 0.2553: : 430iter [00:40, 11.91iter/s]Best score: 0.2553: : 431iter [00:40, 11.76iter/s]Best score: 0.2553: : 431iter [00:40, 11.76iter/s]Best score: 0.2553: : 432iter [00:40, 11.76iter/s]Best score: 0.2553: : 433iter [00:40, 11.83iter/s]Best score: 0.2553: : 433iter [00:41, 11.83iter/s]Best score: 0.2553: : 434iter [00:41, 11.83iter/s]Best score: 0.2553: : 435iter [00:41, 11.88iter/s]Best score: 0.2553: : 435iter [00:41, 11.88iter/s]Best score: 0.2553: : 436iter [00:41, 11.88iter/s]Best score: 0.2553: : 437iter [00:41, 11.92iter/s]Best score: 0.2553: : 437iter [00:41, 11.92iter/s]Best score: 0.2553: : 438iter [00:41, 11.92iter/s]Best score: 0.2553: : 439iter [00:41, 11.95iter/s]Best score: 0.2414: : 439iter [00:41, 11.95iter/s]Best score: 0.2414: : 440iter [00:41, 11.95iter/s]Best score: 0.2414: : 441iter [00:41, 11.78iter/s]Best score: 0.2414: : 441iter [00:41, 11.78iter/s]Best score: 0.2414: : 442iter [00:41, 11.78iter/s]Best score: 0.2414: : 443iter [00:41, 11.86iter/s]Best score: 0.2414: : 443iter [00:41, 11.86iter/s]Best score: 0.2414: : 444iter [00:41, 11.86iter/s]Best score: 0.2414: : 445iter [00:41, 11.86iter/s]Best score: 0.2414: : 445iter [00:42, 11.86iter/s]Best score: 0.2414: : 446iter [00:42, 11.86iter/s]Best score: 0.2414: : 447iter [00:42, 11.89iter/s]Best score: 0.2414: : 447iter [00:42, 11.89iter/s]Best score: 0.2414: : 448iter [00:42, 11.89iter/s]Best score: 0.2414: : 449iter [00:42, 11.89iter/s]
[ITER 450] Best: 0.2327
[PARAMS] Current best parameters:
  fx: 545.2088
  fy: 545.1412
  px: 1001.5834
  py: 741.0905
  k1: -0.01635995
  k2: -0.00089481
  k3: 0.00300928
  k4: -0.00128208
  p1: -0.00472885
  p2: -0.00657390
  p3: 0.01871408
  p4: 0.00501806
  s1: -0.0087
  s2: -0.0050
  s3: -0.0125
  s4: -0.0053
  tilt_x: 0.0109
  tilt_y: -0.0383
  scale: 2.9964
  rotation: -0.5085
Best score: 0.2327: : 449iter [00:42, 11.89iter/s]Best score: 0.2327: : 450iter [00:42, 11.89iter/s]Best score: 0.2327: : 451iter [00:42,  7.62iter/s]Best score: 0.2327: : 451iter [00:42,  7.62iter/s]Best score: 0.2327: : 452iter [00:42,  7.62iter/s]Best score: 0.2327: : 453iter [00:42,  8.57iter/s]Best score: 0.2327: : 453iter [00:43,  8.57iter/s]Best score: 0.2327: : 454iter [00:43,  8.57iter/s]Best score: 0.2327: : 455iter [00:43,  9.38iter/s]Best score: 0.2327: : 455iter [00:43,  9.38iter/s]Best score: 0.2327: : 456iter [00:43,  9.38iter/s]Best score: 0.2327: : 457iter [00:43, 10.05iter/s]Best score: 0.2327: : 457iter [00:43, 10.05iter/s]Best score: 0.2327: : 458iter [00:43, 10.05iter/s]Best score: 0.2327: : 459iter [00:43, 10.57iter/s]Best score: 0.2327: : 459iter [00:43, 10.57iter/s]Best score: 0.2327: : 460iter [00:43, 10.57iter/s]Best score: 0.2327: : 461iter [00:43, 10.81iter/s]Best score: 0.2327: : 461iter [00:43, 10.81iter/s]Best score: 0.2327: : 462iter [00:43, 10.81iter/s]Best score: 0.2327: : 463iter [00:43, 11.15iter/s]Best score: 0.2327: : 463iter [00:43, 11.15iter/s]Best score: 0.2327: : 464iter [00:43, 11.15iter/s]Best score: 0.2327: : 465iter [00:43, 11.41iter/s]Best score: 0.2327: : 465iter [00:44, 11.41iter/s]Best score: 0.2327: : 466iter [00:44, 11.41iter/s]Best score: 0.2327: : 467iter [00:44, 11.58iter/s]Best score: 0.2327: : 467iter [00:44, 11.58iter/s]Best score: 0.2327: : 468iter [00:44, 11.58iter/s]Best score: 0.2327: : 469iter [00:44, 11.68iter/s]Best score: 0.2227: : 469iter [00:44, 11.68iter/s]Best score: 0.2227: : 470iter [00:44, 11.68iter/s]Best score: 0.2227: : 471iter [00:44, 11.57iter/s]Best score: 0.2227: : 471iter [00:44, 11.57iter/s]Best score: 0.2227: : 472iter [00:44, 11.57iter/s]Best score: 0.2227: : 473iter [00:44, 11.71iter/s]Best score: 0.2227: : 473iter [00:44, 11.71iter/s]Best score: 0.2227: : 474iter [00:44, 11.71iter/s]Best score: 0.2227: : 475iter [00:44, 11.81iter/s]Best score: 0.2227: : 475iter [00:44, 11.81iter/s]Best score: 0.2227: : 476iter [00:44, 11.81iter/s]Best score: 0.2227: : 477iter [00:44, 11.83iter/s]Best score: 0.2227: : 477iter [00:45, 11.83iter/s]Best score: 0.2227: : 478iter [00:45, 11.83iter/s]Best score: 0.2227: : 479iter [00:45, 11.89iter/s]Best score: 0.2202: : 479iter [00:45, 11.89iter/s]Best score: 0.2202: : 480iter [00:45, 11.89iter/s]Best score: 0.2202: : 481iter [00:45, 11.74iter/s]Best score: 0.2202: : 481iter [00:45, 11.74iter/s]Best score: 0.2202: : 482iter [00:45, 11.74iter/s]Best score: 0.2202: : 483iter [00:45, 11.80iter/s]Best score: 0.2202: : 483iter [00:45, 11.80iter/s]Best score: 0.2202: : 484iter [00:45, 11.80iter/s]Best score: 0.2202: : 485iter [00:45, 11.86iter/s]Best score: 0.2202: : 485iter [00:45, 11.86iter/s]Best score: 0.2202: : 486iter [00:45, 11.86iter/s]Best score: 0.2202: : 487iter [00:45, 11.87iter/s]Best score: 0.2202: : 487iter [00:45, 11.87iter/s]Best score: 0.2202: : 488iter [00:46, 11.87iter/s]Best score: 0.2202: : 489iter [00:46, 11.89iter/s]Best score: 0.2202: : 489iter [00:46, 11.89iter/s]Best score: 0.2202: : 490iter [00:46, 11.89iter/s]Best score: 0.2202: : 491iter [00:46, 11.67iter/s]Best score: 0.2202: : 491iter [00:46, 11.67iter/s]Best score: 0.2202: : 492iter [00:46, 11.67iter/s]Best score: 0.2202: : 493iter [00:46, 11.73iter/s]Best score: 0.2202: : 493iter [00:46, 11.73iter/s]Best score: 0.2202: : 494iter [00:46, 11.73iter/s]Best score: 0.2202: : 495iter [00:46, 11.81iter/s]Best score: 0.2202: : 495iter [00:46, 11.81iter/s]Best score: 0.2202: : 496iter [00:46, 11.81iter/s]Best score: 0.2202: : 497iter [00:46, 11.88iter/s]Best score: 0.2202: : 497iter [00:46, 11.88iter/s]Best score: 0.2202: : 498iter [00:46, 11.88iter/s]Best score: 0.2202: : 499iter [00:46, 11.89iter/s]
[ITER 500] Best: 0.2166
[PARAMS] Current best parameters:
  fx: 543.7459
  fy: 543.7727
  px: 999.5904
  py: 739.8565
  k1: -0.03040230
  k2: 0.02759999
  k3: -0.01619613
  k4: 0.00316436
  p1: -0.00799008
  p2: -0.00684083
  p3: 0.02355839
  p4: 0.00458536
  s1: -0.0128
  s2: -0.0068
  s3: -0.0120
  s4: -0.0072
  tilt_x: 0.0103
  tilt_y: -0.0291
  scale: 3.0079
  rotation: -0.4847
Best score: 0.2166: : 499iter [00:46, 11.89iter/s]Best score: 0.2166: : 500iter [00:47, 11.89iter/s]Best score: 0.2166: : 501iter [00:47,  7.59iter/s]Best score: 0.2166: : 501iter [00:47,  7.59iter/s]Best score: 0.2166: : 502iter [00:47,  7.59iter/s]Best score: 0.2166: : 503iter [00:47,  8.55iter/s]Best score: 0.2166: : 503iter [00:47,  8.55iter/s]Best score: 0.2166: : 504iter [00:47,  8.55iter/s]Best score: 0.2166: : 505iter [00:47,  9.38iter/s]Best score: 0.2166: : 505iter [00:47,  9.38iter/s]Best score: 0.2166: : 506iter [00:47,  9.38iter/s]Best score: 0.2166: : 507iter [00:47, 10.04iter/s]Best score: 0.2166: : 507iter [00:47, 10.04iter/s]Best score: 0.2166: : 508iter [00:48, 10.04iter/s]Best score: 0.2166: : 509iter [00:48, 10.56iter/s]Best score: 0.2128: : 509iter [00:48, 10.56iter/s]Best score: 0.2128: : 510iter [00:48, 10.56iter/s]Best score: 0.2128: : 511iter [00:48, 10.78iter/s]Best score: 0.2128: : 511iter [00:48, 10.78iter/s]Best score: 0.2128: : 512iter [00:48, 10.78iter/s]Best score: 0.2128: : 513iter [00:48, 11.13iter/s]Best score: 0.2128: : 513iter [00:48, 11.13iter/s]Best score: 0.2128: : 514iter [00:48, 11.13iter/s]Best score: 0.2128: : 515iter [00:48, 11.34iter/s]Best score: 0.2128: : 515iter [00:48, 11.34iter/s]Best score: 0.2128: : 516iter [00:48, 11.34iter/s]Best score: 0.2128: : 517iter [00:48, 11.54iter/s]Best score: 0.2128: : 517iter [00:48, 11.54iter/s]Best score: 0.2128: : 518iter [00:48, 11.54iter/s]Best score: 0.2128: : 519iter [00:48, 11.66iter/s]Best score: 0.2097: : 519iter [00:48, 11.66iter/s]Best score: 0.2097: : 520iter [00:49, 11.66iter/s]Best score: 0.2097: : 521iter [00:49, 11.57iter/s]Best score: 0.2097: : 521iter [00:49, 11.57iter/s]Best score: 0.2097: : 522iter [00:49, 11.57iter/s]Best score: 0.2097: : 523iter [00:49, 11.67iter/s]Best score: 0.2097: : 523iter [00:49, 11.67iter/s]Best score: 0.2097: : 524iter [00:49, 11.67iter/s]Best score: 0.2097: : 525iter [00:49, 11.80iter/s]Best score: 0.2097: : 525iter [00:49, 11.80iter/s]Best score: 0.2097: : 526iter [00:49, 11.80iter/s]Best score: 0.2097: : 527iter [00:49, 11.86iter/s]Best score: 0.2097: : 527iter [00:49, 11.86iter/s]Best score: 0.2097: : 528iter [00:49, 11.86iter/s]Best score: 0.2097: : 529iter [00:49, 11.92iter/s]Best score: 0.2097: : 529iter [00:49, 11.92iter/s]Best score: 0.2097: : 530iter [00:49, 11.92iter/s]Best score: 0.2097: : 531iter [00:49, 11.70iter/s]Best score: 0.2097: : 531iter [00:49, 11.70iter/s]Best score: 0.2097: : 532iter [00:50, 11.70iter/s]Best score: 0.2097: : 533iter [00:50, 11.74iter/s]Best score: 0.2097: : 533iter [00:50, 11.74iter/s]Best score: 0.2097: : 534iter [00:50, 11.74iter/s]Best score: 0.2097: : 535iter [00:50, 11.81iter/s]Best score: 0.2097: : 535iter [00:50, 11.81iter/s]Best score: 0.2097: : 536iter [00:50, 11.81iter/s]Best score: 0.2097: : 537iter [00:50, 11.85iter/s]Best score: 0.2097: : 537iter [00:50, 11.85iter/s]Best score: 0.2097: : 538iter [00:50, 11.85iter/s]Best score: 0.2097: : 539iter [00:50, 11.92iter/s]Best score: 0.2063: : 539iter [00:50, 11.92iter/s]Best score: 0.2063: : 540iter [00:50, 11.92iter/s]Best score: 0.2063: : 541iter [00:50, 11.76iter/s]Best score: 0.2063: : 541iter [00:50, 11.76iter/s]Best score: 0.2063: : 542iter [00:50, 11.76iter/s]Best score: 0.2063: : 543iter [00:50, 11.85iter/s]Best score: 0.2063: : 543iter [00:50, 11.85iter/s]Best score: 0.2063: : 544iter [00:51, 11.85iter/s]Best score: 0.2063: : 545iter [00:51, 11.91iter/s]Best score: 0.2063: : 545iter [00:51, 11.91iter/s]Best score: 0.2063: : 546iter [00:51, 11.91iter/s]Best score: 0.2063: : 547iter [00:51, 11.95iter/s]Best score: 0.2063: : 547iter [00:51, 11.95iter/s]Best score: 0.2063: : 548iter [00:51, 11.95iter/s]Best score: 0.2063: : 549iter [00:51, 11.98iter/s]
[ITER 550] Best: 0.2026
[PARAMS] Current best parameters:
  fx: 545.7145
  fy: 545.9484
  px: 998.5368
  py: 738.8634
  k1: -0.03356201
  k2: 0.03234313
  k3: -0.02064488
  k4: 0.00442863
  p1: -0.00598750
  p2: 0.00118612
  p3: 0.01629203
  p4: 0.00191655
  s1: -0.0160
  s2: -0.0031
  s3: -0.0090
  s4: -0.0039
  tilt_x: 0.0019
  tilt_y: -0.0081
  scale: 3.0027
  rotation: -0.4987
Best score: 0.2026: : 549iter [00:51, 11.98iter/s]Best score: 0.2026: : 550iter [00:51, 11.98iter/s]Best score: 0.2026: : 551iter [00:51,  7.60iter/s]Best score: 0.2026: : 551iter [00:51,  7.60iter/s]Best score: 0.2026: : 552iter [00:52,  7.60iter/s]Best score: 0.2026: : 553iter [00:52,  8.54iter/s]Best score: 0.2026: : 553iter [00:52,  8.54iter/s]Best score: 0.2026: : 554iter [00:52,  8.54iter/s]Best score: 0.2026: : 555iter [00:52,  9.36iter/s]Best score: 0.2026: : 555iter [00:52,  9.36iter/s]Best score: 0.2026: : 556iter [00:52,  9.36iter/s]Best score: 0.2026: : 557iter [00:52,  9.99iter/s]Best score: 0.2026: : 557iter [00:52,  9.99iter/s]Best score: 0.2026: : 558iter [00:52,  9.99iter/s]Best score: 0.2026: : 559iter [00:52, 10.53iter/s]Best score: 0.2010: : 559iter [00:52, 10.53iter/s]Best score: 0.2010: : 560iter [00:52, 10.53iter/s]Best score: 0.2010: : 561iter [00:52, 10.78iter/s]Best score: 0.2010: : 561iter [00:52, 10.78iter/s]Best score: 0.2010: : 562iter [00:52, 10.78iter/s]Best score: 0.2010: : 563iter [00:52, 11.13iter/s]Best score: 0.2010: : 563iter [00:52, 11.13iter/s]Best score: 0.2010: : 564iter [00:53, 11.13iter/s]Best score: 0.2010: : 565iter [00:53, 11.39iter/s]Best score: 0.2010: : 565iter [00:53, 11.39iter/s]Best score: 0.2010: : 566iter [00:53, 11.39iter/s]Best score: 0.2010: : 567iter [00:53, 11.58iter/s]Best score: 0.2010: : 567iter [00:53, 11.58iter/s]Best score: 0.2010: : 568iter [00:53, 11.58iter/s]Best score: 0.2010: : 569iter [00:53, 11.72iter/s]Best score: 0.1989: : 569iter [00:53, 11.72iter/s]Best score: 0.1989: : 570iter [00:53, 11.72iter/s]Best score: 0.1989: : 571iter [00:53, 11.62iter/s]Best score: 0.1989: : 571iter [00:53, 11.62iter/s]Best score: 0.1989: : 572iter [00:53, 11.62iter/s]Best score: 0.1989: : 573iter [00:53, 11.75iter/s]Best score: 0.1989: : 573iter [00:53, 11.75iter/s]Best score: 0.1989: : 574iter [00:53, 11.75iter/s]Best score: 0.1989: : 575iter [00:53, 11.86iter/s]Best score: 0.1989: : 575iter [00:53, 11.86iter/s]Best score: 0.1989: : 576iter [00:54, 11.86iter/s]Best score: 0.1989: : 577iter [00:54, 11.91iter/s]Best score: 0.1989: : 577iter [00:54, 11.91iter/s]Best score: 0.1989: : 578iter [00:54, 11.91iter/s]Best score: 0.1989: : 579iter [00:54, 11.93iter/s]Best score: 0.1983: : 579iter [00:54, 11.93iter/s]Best score: 0.1983: : 580iter [00:54, 11.93iter/s]Best score: 0.1983: : 581iter [00:54, 11.76iter/s]Best score: 0.1983: : 581iter [00:54, 11.76iter/s]Best score: 0.1983: : 582iter [00:54, 11.76iter/s]Best score: 0.1983: : 583iter [00:54, 11.85iter/s]Best score: 0.1983: : 583iter [00:54, 11.85iter/s]Best score: 0.1983: : 584iter [00:54, 11.85iter/s]Best score: 0.1983: : 585iter [00:54, 11.91iter/s]Best score: 0.1983: : 585iter [00:54, 11.91iter/s]Best score: 0.1983: : 586iter [00:54, 11.91iter/s]Best score: 0.1983: : 587iter [00:54, 11.96iter/s]Best score: 0.1983: : 587iter [00:54, 11.96iter/s]Best score: 0.1983: : 588iter [00:55, 11.96iter/s]Best score: 0.1983: : 589iter [00:55, 11.98iter/s]Best score: 0.1975: : 589iter [00:55, 11.98iter/s]Best score: 0.1975: : 590iter [00:55, 11.98iter/s]Best score: 0.1975: : 591iter [00:55, 11.80iter/s]Best score: 0.1975: : 591iter [00:55, 11.80iter/s]Best score: 0.1975: : 592iter [00:55, 11.80iter/s]Best score: 0.1975: : 593iter [00:55, 11.84iter/s]Best score: 0.1975: : 593iter [00:55, 11.84iter/s]Best score: 0.1975: : 594iter [00:55, 11.84iter/s]Best score: 0.1975: : 595iter [00:55, 11.91iter/s]Best score: 0.1975: : 595iter [00:55, 11.91iter/s]Best score: 0.1975: : 596iter [00:55, 11.91iter/s]Best score: 0.1975: : 597iter [00:55, 11.96iter/s]Best score: 0.1975: : 597iter [00:55, 11.96iter/s]Best score: 0.1975: : 598iter [00:55, 11.96iter/s]Best score: 0.1975: : 599iter [00:55, 11.97iter/s]
[ITER 600] Best: 0.1968
[PARAMS] Current best parameters:
  fx: 545.6404
  fy: 545.8009
  px: 998.8878
  py: 738.8194
  k1: -0.03011679
  k2: 0.02397323
  k3: -0.01341185
  k4: 0.00238655
  p1: -0.00567257
  p2: 0.00114893
  p3: 0.01461370
  p4: 0.00375543
  s1: -0.0149
  s2: -0.0037
  s3: -0.0082
  s4: -0.0054
  tilt_x: 0.0011
  tilt_y: -0.0098
  scale: 3.0005
  rotation: -0.4896
Best score: 0.1968: : 599iter [00:55, 11.97iter/s]Best score: 0.1968: : 600iter [00:56, 11.97iter/s]Best score: 0.1968: : 601iter [00:56,  7.58iter/s]Best score: 0.1968: : 601iter [00:56,  7.58iter/s]Best score: 0.1968: : 602iter [00:56,  7.58iter/s]Best score: 0.1968: : 603iter [00:56,  8.51iter/s]Best score: 0.1968: : 603iter [00:56,  8.51iter/s]Best score: 0.1968: : 604iter [00:56,  8.51iter/s]Best score: 0.1968: : 605iter [00:56,  9.29iter/s]Best score: 0.1968: : 605iter [00:56,  9.29iter/s]Best score: 0.1968: : 606iter [00:56,  9.29iter/s]Best score: 0.1968: : 607iter [00:56,  9.97iter/s]Best score: 0.1968: : 607iter [00:56,  9.97iter/s]Best score: 0.1968: : 608iter [00:57,  9.97iter/s]Best score: 0.1968: : 609iter [00:57, 10.50iter/s]Best score: 0.1950: : 609iter [00:57, 10.50iter/s]Best score: 0.1950: : 610iter [00:57, 10.50iter/s]Best score: 0.1950: : 611iter [00:57, 10.71iter/s]Best score: 0.1950: : 611iter [00:57, 10.71iter/s]Best score: 0.1950: : 612iter [00:57, 10.71iter/s]Best score: 0.1950: : 613iter [00:57, 11.07iter/s]Best score: 0.1950: : 613iter [00:57, 11.07iter/s]Best score: 0.1950: : 614iter [00:57, 11.07iter/s]Best score: 0.1950: : 615iter [00:57, 11.34iter/s]Best score: 0.1950: : 615iter [00:57, 11.34iter/s]Best score: 0.1950: : 616iter [00:57, 11.34iter/s]Best score: 0.1950: : 617iter [00:57, 11.54iter/s]Best score: 0.1950: : 617iter [00:57, 11.54iter/s]Best score: 0.1950: : 618iter [00:57, 11.54iter/s]Best score: 0.1950: : 619iter [00:57, 11.68iter/s]Best score: 0.1950: : 619iter [00:57, 11.68iter/s]Best score: 0.1950: : 620iter [00:58, 11.68iter/s]Best score: 0.1950: : 621iter [00:58, 11.58iter/s]Best score: 0.1950: : 621iter [00:58, 11.58iter/s]Best score: 0.1950: : 622iter [00:58, 11.58iter/s]Best score: 0.1950: : 623iter [00:58, 11.62iter/s]Best score: 0.1950: : 623iter [00:58, 11.62iter/s]Best score: 0.1950: : 624iter [00:58, 11.62iter/s]Best score: 0.1950: : 625iter [00:58, 11.73iter/s]Best score: 0.1950: : 625iter [00:58, 11.73iter/s]Best score: 0.1950: : 626iter [00:58, 11.73iter/s]Best score: 0.1950: : 627iter [00:58, 11.83iter/s]Best score: 0.1950: : 627iter [00:58, 11.83iter/s]Best score: 0.1950: : 628iter [00:58, 11.83iter/s]Best score: 0.1950: : 629iter [00:58, 11.89iter/s]Best score: 0.1942: : 629iter [00:58, 11.89iter/s]Best score: 0.1942: : 630iter [00:58, 11.89iter/s]Best score: 0.1942: : 631iter [00:58, 11.72iter/s]Best score: 0.1942: : 631iter [00:58, 11.72iter/s]Best score: 0.1942: : 632iter [00:59, 11.72iter/s]Best score: 0.1942: : 633iter [00:59, 11.76iter/s]Best score: 0.1942: : 633iter [00:59, 11.76iter/s]Best score: 0.1942: : 634iter [00:59, 11.76iter/s]Best score: 0.1942: : 635iter [00:59, 11.85iter/s]Best score: 0.1942: : 635iter [00:59, 11.85iter/s]Best score: 0.1942: : 636iter [00:59, 11.85iter/s]Best score: 0.1942: : 637iter [00:59, 11.92iter/s]Best score: 0.1942: : 637iter [00:59, 11.92iter/s]Best score: 0.1942: : 638iter [00:59, 11.92iter/s]Best score: 0.1942: : 639iter [00:59, 11.97iter/s]Best score: 0.1935: : 639iter [00:59, 11.97iter/s]Best score: 0.1935: : 640iter [00:59, 11.97iter/s]Best score: 0.1935: : 641iter [00:59, 11.80iter/s]Best score: 0.1935: : 641iter [00:59, 11.80iter/s]Best score: 0.1935: : 642iter [00:59, 11.80iter/s]Best score: 0.1935: : 643iter [00:59, 11.83iter/s]Best score: 0.1935: : 643iter [01:00, 11.83iter/s]Best score: 0.1935: : 644iter [01:00, 11.83iter/s]Best score: 0.1935: : 645iter [01:00, 11.85iter/s]Best score: 0.1935: : 645iter [01:00, 11.85iter/s]Best score: 0.1935: : 646iter [01:00, 11.85iter/s]Best score: 0.1935: : 647iter [01:00, 11.89iter/s]Best score: 0.1935: : 647iter [01:00, 11.89iter/s]Best score: 0.1935: : 648iter [01:00, 11.89iter/s]Best score: 0.1935: : 649iter [01:00, 11.85iter/s]
[ITER 650] Best: 0.1917
[PARAMS] Current best parameters:
  fx: 543.2835
  fy: 543.4563
  px: 998.6810
  py: 739.2540
  k1: -0.02811405
  k2: 0.02661043
  k3: -0.01668512
  k4: 0.00342128
  p1: -0.00288997
  p2: 0.00395515
  p3: 0.01910240
  p4: 0.00009413
  s1: -0.0215
  s2: -0.0011
  s3: -0.0163
  s4: -0.0006
  tilt_x: -0.0021
  tilt_y: -0.0034
  scale: 2.9999
  rotation: -0.4750
Best score: 0.1917: : 649iter [01:00, 11.85iter/s]Best score: 0.1917: : 650iter [01:00, 11.85iter/s]Best score: 0.1917: : 651iter [01:00,  7.56iter/s]Best score: 0.1917: : 651iter [01:00,  7.56iter/s]Best score: 0.1917: : 652iter [01:01,  7.56iter/s]Best score: 0.1917: : 653iter [01:01,  8.51iter/s]Best score: 0.1917: : 653iter [01:01,  8.51iter/s]Best score: 0.1917: : 654iter [01:01,  8.51iter/s]Best score: 0.1917: : 655iter [01:01,  9.31iter/s]Best score: 0.1917: : 655iter [01:01,  9.31iter/s]Best score: 0.1917: : 656iter [01:01,  9.31iter/s]Best score: 0.1917: : 657iter [01:01,  9.99iter/s]Best score: 0.1917: : 657iter [01:01,  9.99iter/s]Best score: 0.1917: : 658iter [01:01,  9.99iter/s]Best score: 0.1917: : 659iter [01:01, 10.54iter/s]Best score: 0.1911: : 659iter [01:01, 10.54iter/s]Best score: 0.1911: : 660iter [01:01, 10.54iter/s]Best score: 0.1911: : 661iter [01:01, 10.79iter/s]Best score: 0.1911: : 661iter [01:01, 10.79iter/s]Best score: 0.1911: : 662iter [01:01, 10.79iter/s]Best score: 0.1911: : 663iter [01:01, 11.11iter/s]Best score: 0.1911: : 663iter [01:02, 11.11iter/s]Best score: 0.1911: : 664iter [01:02, 11.11iter/s]Best score: 0.1911: : 665iter [01:02, 11.38iter/s]Best score: 0.1911: : 665iter [01:02, 11.38iter/s]Best score: 0.1911: : 666iter [01:02, 11.38iter/s]Best score: 0.1911: : 667iter [01:02, 11.58iter/s]Best score: 0.1911: : 667iter [01:02, 11.58iter/s]Best score: 0.1911: : 668iter [01:02, 11.58iter/s]Best score: 0.1911: : 669iter [01:02, 11.71iter/s]Best score: 0.1900: : 669iter [01:02, 11.71iter/s]Best score: 0.1900: : 670iter [01:02, 11.71iter/s]Best score: 0.1900: : 671iter [01:02, 11.59iter/s]Best score: 0.1900: : 671iter [01:02, 11.59iter/s]Best score: 0.1900: : 672iter [01:02, 11.59iter/s]Best score: 0.1900: : 673iter [01:02, 11.73iter/s]Best score: 0.1900: : 673iter [01:02, 11.73iter/s]Best score: 0.1900: : 674iter [01:02, 11.73iter/s]Best score: 0.1900: : 675iter [01:02, 11.80iter/s]Best score: 0.1900: : 675iter [01:03, 11.80iter/s]Best score: 0.1900: : 676iter [01:03, 11.80iter/s]Best score: 0.1900: : 677iter [01:03, 11.87iter/s]Best score: 0.1900: : 677iter [01:03, 11.87iter/s]Best score: 0.1900: : 678iter [01:03, 11.87iter/s]Best score: 0.1900: : 679iter [01:03, 11.91iter/s]Best score: 0.1887: : 679iter [01:03, 11.91iter/s]Best score: 0.1887: : 680iter [01:03, 11.91iter/s]Best score: 0.1887: : 681iter [01:03, 11.73iter/s]Best score: 0.1887: : 681iter [01:03, 11.73iter/s]Best score: 0.1887: : 682iter [01:03, 11.73iter/s]Best score: 0.1887: : 683iter [01:03, 11.82iter/s]Best score: 0.1887: : 683iter [01:03, 11.82iter/s]Best score: 0.1887: : 684iter [01:03, 11.82iter/s]Best score: 0.1887: : 685iter [01:03, 11.87iter/s]Best score: 0.1887: : 685iter [01:03, 11.87iter/s]Best score: 0.1887: : 686iter [01:03, 11.87iter/s]Best score: 0.1887: : 687iter [01:03, 11.92iter/s]Best score: 0.1887: : 687iter [01:04, 11.92iter/s]Best score: 0.1887: : 688iter [01:04, 11.92iter/s]Best score: 0.1887: : 689iter [01:04, 11.97iter/s]Best score: 0.1882: : 689iter [01:04, 11.97iter/s]Best score: 0.1882: : 690iter [01:04, 11.97iter/s]Best score: 0.1882: : 691iter [01:04, 11.78iter/s]Best score: 0.1882: : 691iter [01:04, 11.78iter/s]Best score: 0.1882: : 692iter [01:04, 11.78iter/s]Best score: 0.1882: : 693iter [01:04, 11.85iter/s]Best score: 0.1882: : 693iter [01:04, 11.85iter/s]Best score: 0.1882: : 694iter [01:04, 11.85iter/s]Best score: 0.1882: : 695iter [01:04, 11.91iter/s]Best score: 0.1882: : 695iter [01:04, 11.91iter/s]Best score: 0.1882: : 696iter [01:04, 11.91iter/s]Best score: 0.1882: : 697iter [01:04, 11.96iter/s]Best score: 0.1882: : 697iter [01:04, 11.96iter/s]Best score: 0.1882: : 698iter [01:04, 11.96iter/s]Best score: 0.1882: : 699iter [01:04, 11.98iter/s]
[ITER 700] Best: 0.1880
[PARAMS] Current best parameters:
  fx: 540.5766
  fy: 540.8513
  px: 999.0004
  py: 739.4597
  k1: -0.01866315
  k2: 0.01304586
  k3: -0.00730718
  k4: 0.00115699
  p1: 0.00358703
  p2: 0.01113945
  p3: 0.01459048
  p4: -0.00475779
  s1: -0.0240
  s2: 0.0039
  s3: -0.0185
  s4: 0.0048
  tilt_x: -0.0140
  tilt_y: 0.0097
  scale: 2.9973
  rotation: -0.4768
Best score: 0.1880: : 699iter [01:05, 11.98iter/s]Best score: 0.1880: : 700iter [01:05, 11.98iter/s]Best score: 0.1880: : 701iter [01:05,  7.58iter/s]Best score: 0.1880: : 701iter [01:05,  7.58iter/s]Best score: 0.1880: : 702iter [01:05,  7.58iter/s]Best score: 0.1880: : 703iter [01:05,  8.54iter/s]Best score: 0.1880: : 703iter [01:05,  8.54iter/s]Best score: 0.1880: : 704iter [01:05,  8.54iter/s]Best score: 0.1880: : 705iter [01:05,  9.36iter/s]Best score: 0.1880: : 705iter [01:05,  9.36iter/s]Best score: 0.1880: : 706iter [01:05,  9.36iter/s]Best score: 0.1880: : 707iter [01:05, 10.04iter/s]Best score: 0.1880: : 707iter [01:06, 10.04iter/s]Best score: 0.1880: : 708iter [01:06, 10.04iter/s]Best score: 0.1880: : 709iter [01:06, 10.57iter/s]Best score: 0.1875: : 709iter [01:06, 10.57iter/s]Best score: 0.1875: : 710iter [01:06, 10.57iter/s]Best score: 0.1875: : 711iter [01:06, 10.80iter/s]Best score: 0.1875: : 711iter [01:06, 10.80iter/s]Best score: 0.1875: : 712iter [01:06, 10.80iter/s]Best score: 0.1875: : 713iter [01:06,  8.70iter/s]Best score: 0.1875: : 713iter [01:06,  8.70iter/s]Best score: 0.1875: : 714iter [01:06,  8.70iter/s]Best score: 0.1875: : 715iter [01:06,  9.49iter/s]Best score: 0.1875: : 715iter [01:06,  9.49iter/s]Best score: 0.1875: : 716iter [01:06,  9.49iter/s]Best score: 0.1875: : 717iter [01:06, 10.12iter/s]Best score: 0.1875: : 717iter [01:07, 10.12iter/s]Best score: 0.1875: : 718iter [01:07, 10.12iter/s]Best score: 0.1875: : 719iter [01:07, 10.64iter/s]Best score: 0.1875: : 719iter [01:07, 10.64iter/s]Best score: 0.1875: : 720iter [01:07, 10.64iter/s]Best score: 0.1875: : 721iter [01:07, 10.85iter/s]Best score: 0.1875: : 721iter [01:07, 10.85iter/s]Best score: 0.1875: : 722iter [01:07, 10.85iter/s]Best score: 0.1875: : 723iter [01:07, 11.19iter/s]Best score: 0.1875: : 723iter [01:07, 11.19iter/s]Best score: 0.1875: : 724iter [01:07, 11.19iter/s]Best score: 0.1875: : 725iter [01:07, 11.43iter/s]Best score: 0.1875: : 725iter [01:07, 11.43iter/s]Best score: 0.1875: : 726iter [01:07, 11.43iter/s]Best score: 0.1875: : 727iter [01:07, 11.62iter/s]Best score: 0.1875: : 727iter [01:07, 11.62iter/s]Best score: 0.1875: : 728iter [01:07, 11.62iter/s]Best score: 0.1875: : 729iter [01:07, 11.70iter/s]Best score: 0.1867: : 729iter [01:08, 11.70iter/s]Best score: 0.1867: : 730iter [01:08, 11.70iter/s]Best score: 0.1867: : 731iter [01:08, 11.56iter/s]Best score: 0.1867: : 731iter [01:08, 11.56iter/s]Best score: 0.1867: : 732iter [01:08, 11.56iter/s]Best score: 0.1867: : 733iter [01:08, 11.67iter/s]Best score: 0.1867: : 733iter [01:08, 11.67iter/s]Best score: 0.1867: : 734iter [01:08, 11.67iter/s]Best score: 0.1867: : 735iter [01:08, 11.79iter/s]Best score: 0.1867: : 735iter [01:08, 11.79iter/s]Best score: 0.1867: : 736iter [01:08, 11.79iter/s]Best score: 0.1867: : 737iter [01:08, 11.86iter/s]Best score: 0.1867: : 737iter [01:08, 11.86iter/s]Best score: 0.1867: : 738iter [01:08, 11.86iter/s]Best score: 0.1867: : 739iter [01:08, 11.93iter/s]Best score: 0.1861: : 739iter [01:08, 11.93iter/s]Best score: 0.1861: : 740iter [01:08, 11.93iter/s]Best score: 0.1861: : 741iter [01:08, 11.77iter/s]Best score: 0.1861: : 741iter [01:09, 11.77iter/s]Best score: 0.1861: : 742iter [01:09, 11.77iter/s]Best score: 0.1861: : 743iter [01:09, 11.86iter/s]Best score: 0.1861: : 743iter [01:09, 11.86iter/s]Best score: 0.1861: : 744iter [01:09, 11.86iter/s]Best score: 0.1861: : 745iter [01:09, 11.92iter/s]Best score: 0.1861: : 745iter [01:09, 11.92iter/s]Best score: 0.1861: : 746iter [01:09, 11.92iter/s]Best score: 0.1861: : 747iter [01:09, 11.95iter/s]Best score: 0.1861: : 747iter [01:09, 11.95iter/s]Best score: 0.1861: : 748iter [01:09, 11.95iter/s]Best score: 0.1861: : 749iter [01:09, 11.91iter/s]
[ITER 750] Best: 0.1857
[PARAMS] Current best parameters:
  fx: 539.0383
  fy: 539.1144
  px: 996.8694
  py: 740.1832
  k1: -0.02104582
  k2: 0.02022011
  k3: -0.01186771
  k4: 0.00211693
  p1: 0.00394023
  p2: 0.00650967
  p3: 0.01706697
  p4: -0.00590637
  s1: -0.0238
  s2: 0.0051
  s3: -0.0195
  s4: 0.0051
  tilt_x: -0.0115
  tilt_y: 0.0109
  scale: 3.0010
  rotation: -0.4733
Best score: 0.1857: : 749iter [01:09, 11.91iter/s]Best score: 0.1857: : 750iter [01:10, 11.91iter/s]Best score: 0.1857: : 751iter [01:10,  7.51iter/s]Best score: 0.1857: : 751iter [01:10,  7.51iter/s]Best score: 0.1857: : 752iter [01:10,  7.51iter/s]Best score: 0.1857: : 753iter [01:10,  8.42iter/s]Best score: 0.1857: : 753iter [01:10,  8.42iter/s]Best score: 0.1857: : 754iter [01:10,  8.42iter/s]Best score: 0.1857: : 755iter [01:10,  9.25iter/s]Best score: 0.1857: : 755iter [01:10,  9.25iter/s]Best score: 0.1857: : 756iter [01:10,  9.25iter/s]Best score: 0.1857: : 757iter [01:10,  9.95iter/s]Best score: 0.1857: : 757iter [01:10,  9.95iter/s]Best score: 0.1857: : 758iter [01:10,  9.95iter/s]Best score: 0.1857: : 759iter [01:10, 10.49iter/s]Best score: 0.1856: : 759iter [01:10, 10.49iter/s]Best score: 0.1856: : 760iter [01:10, 10.49iter/s]Best score: 0.1856: : 761iter [01:10, 10.76iter/s]Best score: 0.1856: : 761iter [01:11, 10.76iter/s]Best score: 0.1856: : 762iter [01:11, 10.76iter/s]Best score: 0.1856: : 763iter [01:11, 11.07iter/s]Best score: 0.1856: : 763iter [01:11, 11.07iter/s]Best score: 0.1856: : 764iter [01:11, 11.07iter/s]Best score: 0.1856: : 765iter [01:11, 11.28iter/s]Best score: 0.1856: : 765iter [01:11, 11.28iter/s]Best score: 0.1856: : 766iter [01:11, 11.28iter/s]Best score: 0.1856: : 767iter [01:11, 11.51iter/s]Best score: 0.1856: : 767iter [01:11, 11.51iter/s]Best score: 0.1856: : 768iter [01:11, 11.51iter/s]Best score: 0.1856: : 769iter [01:11, 11.68iter/s]Best score: 0.1855: : 769iter [01:11, 11.68iter/s]Best score: 0.1855: : 770iter [01:11, 11.68iter/s]Best score: 0.1855: : 771iter [01:11, 11.59iter/s]Best score: 0.1855: : 771iter [01:11, 11.59iter/s]Best score: 0.1855: : 772iter [01:11, 11.59iter/s]Best score: 0.1855: : 773iter [01:11, 11.71iter/s]Best score: 0.1855: : 773iter [01:12, 11.71iter/s]Best score: 0.1855: : 774iter [01:12, 11.71iter/s]Best score: 0.1855: : 775iter [01:12, 11.81iter/s]Best score: 0.1855: : 775iter [01:12, 11.81iter/s]Best score: 0.1855: : 776iter [01:12, 11.81iter/s]Best score: 0.1855: : 777iter [01:12, 11.83iter/s]Best score: 0.1855: : 777iter [01:12, 11.83iter/s]Best score: 0.1855: : 778iter [01:12, 11.83iter/s]Best score: 0.1855: : 779iter [01:12, 11.85iter/s]Best score: 0.1853: : 779iter [01:12, 11.85iter/s]Best score: 0.1853: : 780iter [01:12, 11.85iter/s]Best score: 0.1853: : 781iter [01:12, 11.69iter/s]Best score: 0.1853: : 781iter [01:12, 11.69iter/s]Best score: 0.1853: : 782iter [01:12, 11.69iter/s]Best score: 0.1853: : 783iter [01:12, 11.78iter/s]Best score: 0.1853: : 783iter [01:12, 11.78iter/s]Best score: 0.1853: : 784iter [01:12, 11.78iter/s]Best score: 0.1853: : 785iter [01:12, 11.86iter/s]Best score: 0.1853: : 785iter [01:13, 11.86iter/s]Best score: 0.1853: : 786iter [01:13, 11.86iter/s]Best score: 0.1853: : 787iter [01:13, 11.92iter/s]Best score: 0.1853: : 787iter [01:13, 11.92iter/s]Best score: 0.1853: : 788iter [01:13, 11.92iter/s]Best score: 0.1853: : 789iter [01:13, 11.96iter/s]Best score: 0.1852: : 789iter [01:13, 11.96iter/s]Best score: 0.1852: : 790iter [01:13, 11.96iter/s]Best score: 0.1852: : 791iter [01:13, 11.77iter/s]Best score: 0.1852: : 791iter [01:13, 11.77iter/s]Best score: 0.1852: : 792iter [01:13, 11.77iter/s]Best score: 0.1852: : 793iter [01:13, 11.78iter/s]Best score: 0.1852: : 793iter [01:13, 11.78iter/s]Best score: 0.1852: : 794iter [01:13, 11.78iter/s]Best score: 0.1852: : 795iter [01:13, 11.87iter/s]Best score: 0.1852: : 795iter [01:13, 11.87iter/s]Best score: 0.1852: : 796iter [01:14, 11.87iter/s]Best score: 0.1852: : 797iter [01:14, 11.93iter/s]Best score: 0.1852: : 797iter [01:14, 11.93iter/s]Best score: 0.1852: : 798iter [01:14, 11.93iter/s]Best score: 0.1852: : 799iter [01:14, 11.98iter/s]
[ITER 800] Best: 0.1852
[PARAMS] Current best parameters:
  fx: 537.4069
  fy: 537.5413
  px: 997.3637
  py: 739.9826
  k1: -0.02206920
  k2: 0.02540905
  k3: -0.01575916
  k4: 0.00309263
  p1: 0.00379386
  p2: 0.00775650
  p3: 0.01998604
  p4: -0.00773607
  s1: -0.0275
  s2: 0.0069
  s3: -0.0229
  s4: 0.0073
  tilt_x: -0.0122
  tilt_y: 0.0110
  scale: 3.0032
  rotation: -0.4720
Best score: 0.1852: : 799iter [01:14, 11.98iter/s]Best score: 0.1852: : 800iter [01:14, 11.98iter/s]Best score: 0.1852: : 801iter [01:14,  7.63iter/s]Best score: 0.1852: : 801iter [01:14,  7.63iter/s]Best score: 0.1852: : 802iter [01:14,  7.63iter/s]Best score: 0.1852: : 803iter [01:14,  8.58iter/s]Best score: 0.1852: : 803iter [01:14,  8.58iter/s]Best score: 0.1852: : 804iter [01:14,  8.58iter/s]Best score: 0.1852: : 805iter [01:14,  9.37iter/s]Best score: 0.1852: : 805iter [01:15,  9.37iter/s]Best score: 0.1852: : 806iter [01:15,  9.37iter/s]Best score: 0.1852: : 807iter [01:15, 10.03iter/s]Best score: 0.1852: : 807iter [01:15, 10.03iter/s]Best score: 0.1852: : 808iter [01:15, 10.03iter/s]Best score: 0.1852: : 809iter [01:15, 10.56iter/s]Best score: 0.1852: : 809iter [01:15, 10.56iter/s]Best score: 0.1852: : 810iter [01:15, 10.56iter/s]Best score: 0.1852: : 811iter [01:15, 10.81iter/s]Best score: 0.1852: : 811iter [01:15, 10.81iter/s]Best score: 0.1852: : 812iter [01:15, 10.81iter/s]Best score: 0.1852: : 813iter [01:15, 11.14iter/s]Best score: 0.1852: : 813iter [01:15, 11.14iter/s]Best score: 0.1852: : 814iter [01:15, 11.14iter/s]Best score: 0.1852: : 815iter [01:15, 11.31iter/s]Best score: 0.1852: : 815iter [01:15, 11.31iter/s]Best score: 0.1852: : 816iter [01:15, 11.31iter/s]Best score: 0.1852: : 817iter [01:15, 11.51iter/s]Best score: 0.1852: : 817iter [01:16, 11.51iter/s]Best score: 0.1852: : 818iter [01:16, 11.51iter/s]Best score: 0.1852: : 819iter [01:16, 11.63iter/s]Best score: 0.1850: : 819iter [01:16, 11.63iter/s]Best score: 0.1850: : 820iter [01:16, 11.63iter/s]Best score: 0.1850: : 821iter [01:16, 11.52iter/s]Best score: 0.1850: : 821iter [01:16, 11.52iter/s]Best score: 0.1850: : 822iter [01:16, 11.52iter/s]Best score: 0.1850: : 823iter [01:16, 11.68iter/s]Best score: 0.1850: : 823iter [01:16, 11.68iter/s]Best score: 0.1850: : 824iter [01:16, 11.68iter/s]Best score: 0.1850: : 825iter [01:16, 11.80iter/s]Best score: 0.1850: : 825iter [01:16, 11.80iter/s]Best score: 0.1850: : 826iter [01:16, 11.80iter/s]Best score: 0.1850: : 827iter [01:16, 11.88iter/s]Best score: 0.1850: : 827iter [01:16, 11.88iter/s]Best score: 0.1850: : 828iter [01:17, 11.88iter/s]Best score: 0.1850: : 829iter [01:17, 11.92iter/s]Best score: 0.1850: : 829iter [01:17, 11.92iter/s]Best score: 0.1850: : 830iter [01:17, 11.92iter/s]Best score: 0.1850: : 831iter [01:17, 11.76iter/s]Best score: 0.1850: : 831iter [01:17, 11.76iter/s]Best score: 0.1850: : 832iter [01:17, 11.76iter/s]Best score: 0.1850: : 833iter [01:17, 11.85iter/s]Best score: 0.1850: : 833iter [01:17, 11.85iter/s]Best score: 0.1850: : 834iter [01:17, 11.85iter/s]Best score: 0.1850: : 835iter [01:17, 11.91iter/s]Best score: 0.1850: : 835iter [01:17, 11.91iter/s]Best score: 0.1850: : 836iter [01:17, 11.91iter/s]Best score: 0.1850: : 837iter [01:17, 11.91iter/s]Best score: 0.1850: : 837iter [01:17, 11.91iter/s]Best score: 0.1850: : 838iter [01:17, 11.91iter/s]Best score: 0.1850: : 839iter [01:17, 11.85iter/s]Best score: 0.1850: : 839iter [01:17, 11.85iter/s]Best score: 0.1850: : 840iter [01:18, 11.85iter/s]Best score: 0.1850: : 841iter [01:18, 11.67iter/s]Best score: 0.1850: : 841iter [01:18, 11.67iter/s]Best score: 0.1850: : 842iter [01:18, 11.67iter/s]Best score: 0.1850: : 843iter [01:18, 11.78iter/s]Best score: 0.1850: : 843iter [01:18, 11.78iter/s]Best score: 0.1850: : 844iter [01:18, 11.78iter/s]Best score: 0.1850: : 845iter [01:18, 11.81iter/s]Best score: 0.1850: : 845iter [01:18, 11.81iter/s]Best score: 0.1850: : 846iter [01:18, 11.81iter/s]Best score: 0.1850: : 847iter [01:18, 11.87iter/s]Best score: 0.1850: : 847iter [01:18, 11.87iter/s]Best score: 0.1850: : 848iter [01:18, 11.87iter/s]Best score: 0.1850: : 849iter [01:18, 11.94iter/s]
[ITER 850] Best: 0.1848
[PARAMS] Current best parameters:
  fx: 536.9836
  fy: 537.1009
  px: 997.0516
  py: 740.3111
  k1: -0.02007331
  k2: 0.02285880
  k3: -0.01429474
  k4: 0.00279877
  p1: 0.00489980
  p2: 0.00796065
  p3: 0.02125894
  p4: -0.00925294
  s1: -0.0289
  s2: 0.0082
  s3: -0.0251
  s4: 0.0089
  tilt_x: -0.0127
  tilt_y: 0.0130
  scale: 3.0020
  rotation: -0.4747
Best score: 0.1848: : 849iter [01:18, 11.94iter/s]Best score: 0.1848: : 850iter [01:19, 11.94iter/s]Best score: 0.1848: : 851iter [01:19,  7.61iter/s]Best score: 0.1848: : 851iter [01:19,  7.61iter/s]Best score: 0.1848: : 852iter [01:19,  7.61iter/s]Best score: 0.1848: : 853iter [01:19,  8.54iter/s]Best score: 0.1848: : 853iter [01:19,  8.54iter/s]Best score: 0.1848: : 854iter [01:19,  8.54iter/s]Best score: 0.1848: : 855iter [01:19,  9.33iter/s]Best score: 0.1848: : 855iter [01:19,  9.33iter/s]Best score: 0.1848: : 856iter [01:19,  9.33iter/s]Best score: 0.1848: : 857iter [01:19, 10.01iter/s]Best score: 0.1848: : 857iter [01:19, 10.01iter/s]Best score: 0.1848: : 858iter [01:19, 10.01iter/s]Best score: 0.1848: : 859iter [01:19, 10.49iter/s]Best score: 0.1843: : 859iter [01:19, 10.49iter/s]Best score: 0.1843: : 860iter [01:20, 10.49iter/s]Best score: 0.1843: : 861iter [01:20, 10.74iter/s]Best score: 0.1843: : 861iter [01:20, 10.74iter/s]Best score: 0.1843: : 862iter [01:20, 10.74iter/s]Best score: 0.1843: : 863iter [01:20, 11.11iter/s]Best score: 0.1843: : 863iter [01:20, 11.11iter/s]Best score: 0.1843: : 864iter [01:20, 11.11iter/s]Best score: 0.1843: : 865iter [01:20, 11.38iter/s]Best score: 0.1843: : 865iter [01:20, 11.38iter/s]Best score: 0.1843: : 866iter [01:20, 11.38iter/s]Best score: 0.1843: : 867iter [01:20, 11.57iter/s]Best score: 0.1843: : 867iter [01:20, 11.57iter/s]Best score: 0.1843: : 868iter [01:20, 11.57iter/s]Best score: 0.1843: : 869iter [01:20, 11.65iter/s]Best score: 0.1840: : 869iter [01:20, 11.65iter/s]Best score: 0.1840: : 870iter [01:20, 11.65iter/s]Best score: 0.1840: : 871iter [01:20, 11.53iter/s]Best score: 0.1840: : 871iter [01:20, 11.53iter/s]Best score: 0.1840: : 872iter [01:21, 11.53iter/s]Best score: 0.1840: : 873iter [01:21, 11.68iter/s]Best score: 0.1840: : 873iter [01:21, 11.68iter/s]Best score: 0.1840: : 874iter [01:21, 11.68iter/s]Best score: 0.1840: : 875iter [01:21, 11.78iter/s]Best score: 0.1840: : 875iter [01:21, 11.78iter/s]Best score: 0.1840: : 876iter [01:21, 11.78iter/s]Best score: 0.1840: : 877iter [01:21, 11.86iter/s]Best score: 0.1840: : 877iter [01:21, 11.86iter/s]Best score: 0.1840: : 878iter [01:21, 11.86iter/s]Best score: 0.1840: : 879iter [01:21, 11.90iter/s]Best score: 0.1839: : 879iter [01:21, 11.90iter/s]Best score: 0.1839: : 880iter [01:21, 11.90iter/s]Best score: 0.1839: : 881iter [01:21, 11.73iter/s]Best score: 0.1839: : 881iter [01:21, 11.73iter/s]Best score: 0.1839: : 882iter [01:21, 11.73iter/s]Best score: 0.1839: : 883iter [01:21, 11.73iter/s]Best score: 0.1839: : 883iter [01:21, 11.73iter/s]Best score: 0.1839: : 884iter [01:22, 11.73iter/s]Best score: 0.1839: : 885iter [01:22, 11.83iter/s]Best score: 0.1839: : 885iter [01:22, 11.83iter/s]Best score: 0.1839: : 886iter [01:22, 11.83iter/s]Best score: 0.1839: : 887iter [01:22, 11.89iter/s]Best score: 0.1839: : 887iter [01:22, 11.89iter/s]Best score: 0.1839: : 888iter [01:22, 11.89iter/s]Best score: 0.1839: : 889iter [01:22, 11.95iter/s]Best score: 0.1839: : 889iter [01:22, 11.95iter/s]Best score: 0.1839: : 890iter [01:22, 11.95iter/s]Best score: 0.1839: : 891iter [01:22, 11.78iter/s]Best score: 0.1839: : 891iter [01:22, 11.78iter/s]Best score: 0.1839: : 892iter [01:22, 11.78iter/s]Best score: 0.1839: : 893iter [01:22, 11.87iter/s]Best score: 0.1839: : 893iter [01:22, 11.87iter/s]Best score: 0.1839: : 894iter [01:22, 11.87iter/s]Best score: 0.1839: : 895iter [01:22, 11.92iter/s]Best score: 0.1839: : 895iter [01:22, 11.92iter/s]Best score: 0.1839: : 896iter [01:23, 11.92iter/s]Best score: 0.1839: : 897iter [01:23, 11.97iter/s]Best score: 0.1839: : 897iter [01:23, 11.97iter/s]Best score: 0.1839: : 898iter [01:23, 11.97iter/s]Best score: 0.1839: : 899iter [01:23, 12.01iter/s]
[ITER 900] Best: 0.1838
[PARAMS] Current best parameters:
  fx: 535.3069
  fy: 535.3977
  px: 996.6637
  py: 740.4859
  k1: -0.01647275
  k2: 0.01903063
  k3: -0.01183827
  k4: 0.00224449
  p1: 0.00775487
  p2: 0.01052956
  p3: 0.02049000
  p4: -0.01101532
  s1: -0.0315
  s2: 0.0103
  s3: -0.0268
  s4: 0.0105
  tilt_x: -0.0178
  tilt_y: 0.0200
  scale: 3.0029
  rotation: -0.4763
Best score: 0.1838: : 899iter [01:23, 12.01iter/s]Best score: 0.1838: : 900iter [01:23, 12.01iter/s]Best score: 0.1838: : 901iter [01:23,  7.64iter/s]Best score: 0.1838: : 901iter [01:23,  7.64iter/s]Best score: 0.1838: : 902iter [01:23,  7.64iter/s]Best score: 0.1838: : 903iter [01:23,  8.58iter/s]Best score: 0.1838: : 903iter [01:23,  8.58iter/s]Best score: 0.1838: : 904iter [01:24,  8.58iter/s]Best score: 0.1838: : 905iter [01:24,  9.39iter/s]Best score: 0.1838: : 905iter [01:24,  9.39iter/s]Best score: 0.1838: : 906iter [01:24,  9.39iter/s]Best score: 0.1838: : 907iter [01:24, 10.05iter/s]Best score: 0.1838: : 907iter [01:24, 10.05iter/s]Best score: 0.1838: : 908iter [01:24, 10.05iter/s]Best score: 0.1838: : 909iter [01:24, 10.57iter/s]Best score: 0.1837: : 909iter [01:24, 10.57iter/s]Best score: 0.1837: : 910iter [01:24, 10.57iter/s]Best score: 0.1837: : 911iter [01:24, 10.76iter/s]Best score: 0.1837: : 911iter [01:24, 10.76iter/s]Best score: 0.1837: : 912iter [01:24, 10.76iter/s]Best score: 0.1837: : 913iter [01:24, 11.08iter/s]Best score: 0.1837: : 913iter [01:24, 11.08iter/s]Best score: 0.1837: : 914iter [01:24, 11.08iter/s]Best score: 0.1837: : 915iter [01:24, 11.30iter/s]Best score: 0.1837: : 915iter [01:24, 11.30iter/s]Best score: 0.1837: : 916iter [01:25, 11.30iter/s]Best score: 0.1837: : 917iter [01:25, 11.53iter/s]Best score: 0.1837: : 917iter [01:25, 11.53iter/s]Best score: 0.1837: : 918iter [01:25, 11.53iter/s]Best score: 0.1837: : 919iter [01:25, 11.69iter/s]Best score: 0.1836: : 919iter [01:25, 11.69iter/s]Best score: 0.1836: : 920iter [01:25, 11.69iter/s]Best score: 0.1836: : 921iter [01:25, 11.58iter/s]Best score: 0.1836: : 921iter [01:25, 11.58iter/s]Best score: 0.1836: : 922iter [01:25, 11.58iter/s]Best score: 0.1836: : 923iter [01:25, 11.73iter/s]Best score: 0.1836: : 923iter [01:25, 11.73iter/s]Best score: 0.1836: : 924iter [01:25, 11.73iter/s]Best score: 0.1836: : 925iter [01:25, 11.81iter/s]Best score: 0.1836: : 925iter [01:25, 11.81iter/s]Best score: 0.1836: : 926iter [01:25, 11.81iter/s]Best score: 0.1836: : 927iter [01:25, 11.87iter/s]Best score: 0.1836: : 927iter [01:25, 11.87iter/s]Best score: 0.1836: : 928iter [01:26, 11.87iter/s]Best score: 0.1836: : 929iter [01:26, 11.91iter/s]Best score: 0.1835: : 929iter [01:26, 11.91iter/s]Best score: 0.1835: : 930iter [01:26, 11.91iter/s]Best score: 0.1835: : 931iter [01:26, 11.74iter/s]Best score: 0.1835: : 931iter [01:26, 11.74iter/s]Best score: 0.1835: : 932iter [01:26, 11.74iter/s]Best score: 0.1835: : 933iter [01:26, 11.83iter/s]Best score: 0.1835: : 933iter [01:26, 11.83iter/s]Best score: 0.1835: : 934iter [01:26, 11.83iter/s]Best score: 0.1835: : 935iter [01:26, 11.92iter/s]Best score: 0.1835: : 935iter [01:26, 11.92iter/s]Best score: 0.1835: : 936iter [01:26, 11.92iter/s]Best score: 0.1835: : 937iter [01:26, 11.96iter/s]Best score: 0.1835: : 937iter [01:26, 11.96iter/s]Best score: 0.1835: : 938iter [01:26, 11.96iter/s]Best score: 0.1835: : 939iter [01:26, 11.97iter/s]Best score: 0.1835: : 939iter [01:26, 11.97iter/s]Best score: 0.1835: : 940iter [01:27, 11.97iter/s]Best score: 0.1835: : 941iter [01:27, 11.78iter/s]Best score: 0.1835: : 941iter [01:27, 11.78iter/s]Best score: 0.1835: : 942iter [01:27, 11.78iter/s]Best score: 0.1835: : 943iter [01:27, 11.87iter/s]Best score: 0.1835: : 943iter [01:27, 11.87iter/s]Best score: 0.1835: : 944iter [01:27, 11.87iter/s]Best score: 0.1835: : 945iter [01:27, 11.91iter/s]Best score: 0.1835: : 945iter [01:27, 11.91iter/s]Best score: 0.1835: : 946iter [01:27, 11.91iter/s]Best score: 0.1835: : 947iter [01:27, 11.93iter/s]Best score: 0.1835: : 947iter [01:27, 11.93iter/s]Best score: 0.1835: : 948iter [01:27, 11.93iter/s]Best score: 0.1835: : 949iter [01:27, 11.95iter/s]
[ITER 950] Best: 0.1835
[PARAMS] Current best parameters:
  fx: 537.3698
  fy: 537.4831
  px: 996.5308
  py: 740.4975
  k1: -0.01702388
  k2: 0.01634085
  k3: -0.01000428
  k4: 0.00184667
  p1: 0.00708219
  p2: 0.01020225
  p3: 0.01921258
  p4: -0.00941112
  s1: -0.0301
  s2: 0.0088
  s3: -0.0249
  s4: 0.0089
  tilt_x: -0.0163
  tilt_y: 0.0200
  scale: 3.0010
  rotation: -0.4743
Best score: 0.1835: : 949iter [01:27, 11.95iter/s]Best score: 0.1835: : 950iter [01:28, 11.95iter/s]Best score: 0.1835: : 951iter [01:28,  7.62iter/s]Best score: 0.1835: : 951iter [01:28,  7.62iter/s]Best score: 0.1835: : 952iter [01:28,  7.62iter/s]Best score: 0.1835: : 953iter [01:28,  8.56iter/s]Best score: 0.1835: : 953iter [01:28,  8.56iter/s]Best score: 0.1835: : 954iter [01:28,  8.56iter/s]Best score: 0.1835: : 955iter [01:28,  9.38iter/s]Best score: 0.1835: : 955iter [01:28,  9.38iter/s]Best score: 0.1835: : 956iter [01:28,  9.38iter/s]Best score: 0.1835: : 957iter [01:28, 10.03iter/s]Best score: 0.1835: : 957iter [01:28, 10.03iter/s]Best score: 0.1835: : 958iter [01:28, 10.03iter/s]Best score: 0.1835: : 959iter [01:28, 10.52iter/s]Best score: 0.1833: : 959iter [01:28, 10.52iter/s]Best score: 0.1833: : 960iter [01:29, 10.52iter/s]Best score: 0.1833: : 961iter [01:29, 10.76iter/s]Best score: 0.1833: : 961iter [01:29, 10.76iter/s]Best score: 0.1833: : 962iter [01:29, 10.76iter/s]Best score: 0.1833: : 963iter [01:29, 11.12iter/s]Best score: 0.1833: : 963iter [01:29, 11.12iter/s]Best score: 0.1833: : 964iter [01:29, 11.12iter/s]Best score: 0.1833: : 965iter [01:29, 11.39iter/s]Best score: 0.1833: : 965iter [01:29, 11.39iter/s]Best score: 0.1833: : 966iter [01:29, 11.39iter/s]Best score: 0.1833: : 967iter [01:29, 11.56iter/s]Best score: 0.1833: : 967iter [01:29, 11.56iter/s]Best score: 0.1833: : 968iter [01:29, 11.56iter/s]Best score: 0.1833: : 969iter [01:29, 11.71iter/s]Best score: 0.1832: : 969iter [01:29, 11.71iter/s]Best score: 0.1832: : 970iter [01:29, 11.71iter/s]Best score: 0.1832: : 971iter [01:29, 11.61iter/s]Best score: 0.1832: : 971iter [01:29, 11.61iter/s]Best score: 0.1832: : 972iter [01:30, 11.61iter/s]Best score: 0.1832: : 973iter [01:30, 11.68iter/s]Best score: 0.1832: : 973iter [01:30, 11.68iter/s]Best score: 0.1832: : 974iter [01:30, 11.68iter/s]Best score: 0.1832: : 975iter [01:30, 11.77iter/s]Best score: 0.1832: : 975iter [01:30, 11.77iter/s]Best score: 0.1832: : 976iter [01:30, 11.77iter/s]Best score: 0.1832: : 977iter [01:30, 11.84iter/s]Best score: 0.1832: : 977iter [01:30, 11.84iter/s]Best score: 0.1832: : 978iter [01:30, 11.84iter/s]Best score: 0.1832: : 979iter [01:30, 11.91iter/s]Best score: 0.1831: : 979iter [01:30, 11.91iter/s]Best score: 0.1831: : 980iter [01:30, 11.91iter/s]Best score: 0.1831: : 981iter [01:30, 11.75iter/s]Best score: 0.1831: : 981iter [01:30, 11.75iter/s]Best score: 0.1831: : 982iter [01:30, 11.75iter/s]Best score: 0.1831: : 983iter [01:30, 11.84iter/s]Best score: 0.1831: : 983iter [01:30, 11.84iter/s]Best score: 0.1831: : 984iter [01:31, 11.84iter/s]Best score: 0.1831: : 985iter [01:31, 11.92iter/s]Best score: 0.1831: : 985iter [01:31, 11.92iter/s]Best score: 0.1831: : 986iter [01:31, 11.92iter/s]Best score: 0.1831: : 987iter [01:31, 11.90iter/s]Best score: 0.1831: : 987iter [01:31, 11.90iter/s]Best score: 0.1831: : 988iter [01:31, 11.90iter/s]Best score: 0.1831: : 989iter [01:31, 11.95iter/s]Best score: 0.1831: : 989iter [01:31, 11.95iter/s]Best score: 0.1831: : 990iter [01:31, 11.95iter/s]Best score: 0.1831: : 991iter [01:31, 11.73iter/s]Best score: 0.1831: : 991iter [01:31, 11.73iter/s]Best score: 0.1831: : 992iter [01:31, 11.73iter/s]Best score: 0.1831: : 993iter [01:31, 11.82iter/s]Best score: 0.1831: : 993iter [01:31, 11.82iter/s]Best score: 0.1831: : 994iter [01:31, 11.82iter/s]Best score: 0.1831: : 995iter [01:31, 11.84iter/s]Best score: 0.1831: : 995iter [01:32, 11.84iter/s]Best score: 0.1831: : 996iter [01:32, 11.84iter/s]Best score: 0.1831: : 997iter [01:32, 11.81iter/s]Best score: 0.1831: : 997iter [01:32, 11.81iter/s]Best score: 0.1831: : 998iter [01:32, 11.81iter/s]Best score: 0.1831: : 999iter [01:32, 11.76iter/s]
[ITER 1000] Best: 0.1831
[PARAMS] Current best parameters:
  fx: 538.3740
  fy: 538.5024
  px: 996.5199
  py: 740.2026
  k1: -0.01821219
  k2: 0.01606430
  k3: -0.00953170
  k4: 0.00172302
  p1: 0.00591319
  p2: 0.01050190
  p3: 0.02027476
  p4: -0.00944599
  s1: -0.0315
  s2: 0.0088
  s3: -0.0253
  s4: 0.0090
  tilt_x: -0.0153
  tilt_y: 0.0207
  scale: 3.0013
  rotation: -0.4757
Best score: 0.1831: : 999iter [01:32, 11.76iter/s]Best score: 0.1831: : 1000iter [01:32, 11.76iter/s]Best score: 0.1831: : 1001iter [01:32,  7.49iter/s]Best score: 0.1831: : 1001iter [01:32,  7.49iter/s]Best score: 0.1831: : 1002iter [01:32,  7.49iter/s]Best score: 0.1831: : 1003iter [01:32,  8.44iter/s]Best score: 0.1831: : 1003iter [01:33,  8.44iter/s]Best score: 0.1831: : 1004iter [01:33,  8.44iter/s]Best score: 0.1831: : 1005iter [01:33,  9.26iter/s]Best score: 0.1831: : 1005iter [01:33,  9.26iter/s]Best score: 0.1831: : 1006iter [01:33,  9.26iter/s]Best score: 0.1831: : 1007iter [01:33,  9.92iter/s]Best score: 0.1831: : 1007iter [01:33,  9.92iter/s]Best score: 0.1831: : 1008iter [01:33,  9.92iter/s]Best score: 0.1831: : 1009iter [01:33, 10.44iter/s]Best score: 0.1829: : 1009iter [01:33, 10.44iter/s]Best score: 0.1829: : 1010iter [01:33, 10.44iter/s]Best score: 0.1829: : 1011iter [01:33, 10.71iter/s]Best score: 0.1829: : 1011iter [01:33, 10.71iter/s]Best score: 0.1829: : 1012iter [01:33, 10.71iter/s]Best score: 0.1829: : 1013iter [01:33, 11.09iter/s]Best score: 0.1829: : 1013iter [01:33, 11.09iter/s]Best score: 0.1829: : 1014iter [01:33, 11.09iter/s]Best score: 0.1829: : 1015iter [01:33, 11.36iter/s]Best score: 0.1829: : 1015iter [01:34, 11.36iter/s]Best score: 0.1829: : 1016iter [01:34, 11.36iter/s]Best score: 0.1829: : 1017iter [01:34, 11.49iter/s]Best score: 0.1829: : 1017iter [01:34, 11.49iter/s]Best score: 0.1829: : 1018iter [01:34, 11.49iter/s]Best score: 0.1829: : 1019iter [01:34, 11.64iter/s]Best score: 0.1829: : 1019iter [01:34, 11.64iter/s]Best score: 0.1829: : 1020iter [01:34, 11.64iter/s]Best score: 0.1829: : 1021iter [01:34, 11.55iter/s]Best score: 0.1829: : 1021iter [01:34, 11.55iter/s]Best score: 0.1829: : 1022iter [01:34, 11.55iter/s]Best score: 0.1829: : 1023iter [01:34, 11.71iter/s]Best score: 0.1829: : 1023iter [01:34, 11.71iter/s]Best score: 0.1829: : 1024iter [01:34, 11.71iter/s]Best score: 0.1829: : 1025iter [01:34, 11.82iter/s]Best score: 0.1829: : 1025iter [01:34, 11.82iter/s]Best score: 0.1829: : 1026iter [01:34, 11.82iter/s]Best score: 0.1829: : 1027iter [01:34, 11.86iter/s]Best score: 0.1829: : 1027iter [01:35, 11.86iter/s]Best score: 0.1829: : 1028iter [01:35, 11.86iter/s]Best score: 0.1829: : 1029iter [01:35, 11.86iter/s]Best score: 0.1824: : 1029iter [01:35, 11.86iter/s]Best score: 0.1824: : 1030iter [01:35, 11.86iter/s]Best score: 0.1824: : 1031iter [01:35, 11.73iter/s]Best score: 0.1824: : 1031iter [01:35, 11.73iter/s]Best score: 0.1824: : 1032iter [01:35, 11.73iter/s]Best score: 0.1824: : 1033iter [01:35, 11.81iter/s]Best score: 0.1824: : 1033iter [01:35, 11.81iter/s]Best score: 0.1824: : 1034iter [01:35, 11.81iter/s]Best score: 0.1824: : 1035iter [01:35, 11.89iter/s]Best score: 0.1824: : 1035iter [01:35, 11.89iter/s]Best score: 0.1824: : 1036iter [01:35, 11.89iter/s]Best score: 0.1824: : 1037iter [01:35, 11.94iter/s]Best score: 0.1824: : 1037iter [01:35, 11.94iter/s]Best score: 0.1824: : 1038iter [01:35, 11.94iter/s]Best score: 0.1824: : 1039iter [01:35, 11.97iter/s]Best score: 0.1823: : 1039iter [01:36, 11.97iter/s]Best score: 0.1823: : 1040iter [01:36, 11.97iter/s]Best score: 0.1823: : 1041iter [01:36, 11.76iter/s]Best score: 0.1823: : 1041iter [01:36, 11.76iter/s]Best score: 0.1823: : 1042iter [01:36, 11.76iter/s]Best score: 0.1823: : 1043iter [01:36, 11.85iter/s]Best score: 0.1823: : 1043iter [01:36, 11.85iter/s]Best score: 0.1823: : 1044iter [01:36, 11.85iter/s]Best score: 0.1823: : 1045iter [01:36, 11.91iter/s]Best score: 0.1823: : 1045iter [01:36, 11.91iter/s]Best score: 0.1823: : 1046iter [01:36, 11.91iter/s]Best score: 0.1823: : 1047iter [01:36, 11.93iter/s]Best score: 0.1823: : 1047iter [01:36, 11.93iter/s]Best score: 0.1823: : 1048iter [01:36, 11.93iter/s]Best score: 0.1823: : 1049iter [01:36, 11.97iter/s]
[ITER 1050] Best: 0.1822
[PARAMS] Current best parameters:
  fx: 540.8586
  fy: 541.0418
  px: 996.3712
  py: 739.6448
  k1: -0.02134898
  k2: 0.01680794
  k3: -0.00973008
  k4: 0.00176934
  p1: 0.00264337
  p2: 0.00963910
  p3: 0.02431369
  p4: -0.00953070
  s1: -0.0350
  s2: 0.0089
  s3: -0.0262
  s4: 0.0086
  tilt_x: -0.0114
  tilt_y: 0.0196
  scale: 3.0012
  rotation: -0.4763
Best score: 0.1822: : 1049iter [01:36, 11.97iter/s]Best score: 0.1822: : 1050iter [01:37, 11.97iter/s]Best score: 0.1822: : 1051iter [01:37,  7.62iter/s]Best score: 0.1822: : 1051iter [01:37,  7.62iter/s]Best score: 0.1822: : 1052iter [01:37,  7.62iter/s]Best score: 0.1822: : 1053iter [01:37,  8.56iter/s]Best score: 0.1822: : 1053iter [01:37,  8.56iter/s]Best score: 0.1822: : 1054iter [01:37,  8.56iter/s]Best score: 0.1822: : 1055iter [01:37,  9.38iter/s]Best score: 0.1822: : 1055iter [01:37,  9.38iter/s]Best score: 0.1822: : 1056iter [01:37,  9.38iter/s]Best score: 0.1822: : 1057iter [01:37, 10.05iter/s]Best score: 0.1822: : 1057iter [01:37, 10.05iter/s]Best score: 0.1822: : 1058iter [01:37, 10.05iter/s]Best score: 0.1822: : 1059iter [01:37, 10.57iter/s]Best score: 0.1821: : 1059iter [01:38, 10.57iter/s]Best score: 0.1821: : 1060iter [01:38, 10.57iter/s]Best score: 0.1821: : 1061iter [01:38, 10.78iter/s]Best score: 0.1821: : 1061iter [01:38, 10.78iter/s]Best score: 0.1821: : 1062iter [01:38, 10.78iter/s]Best score: 0.1821: : 1063iter [01:38, 11.13iter/s]Best score: 0.1821: : 1063iter [01:38, 11.13iter/s]Best score: 0.1821: : 1064iter [01:38, 11.13iter/s]Best score: 0.1821: : 1065iter [01:38, 11.34iter/s]Best score: 0.1821: : 1065iter [01:38, 11.34iter/s]Best score: 0.1821: : 1066iter [01:38, 11.34iter/s]Best score: 0.1821: : 1067iter [01:38, 11.52iter/s]Best score: 0.1821: : 1067iter [01:38, 11.52iter/s]Best score: 0.1821: : 1068iter [01:38, 11.52iter/s]Best score: 0.1821: : 1069iter [01:38, 11.68iter/s]Best score: 0.1820: : 1069iter [01:38, 11.68iter/s]Best score: 0.1820: : 1070iter [01:38, 11.68iter/s]Best score: 0.1820: : 1071iter [01:38, 11.59iter/s]Best score: 0.1820: : 1071iter [01:39, 11.59iter/s]Best score: 0.1820: : 1072iter [01:39, 11.59iter/s]Best score: 0.1820: : 1073iter [01:39, 11.69iter/s]Best score: 0.1820: : 1073iter [01:39, 11.69iter/s]Best score: 0.1820: : 1074iter [01:39, 11.69iter/s]Best score: 0.1820: : 1075iter [01:39, 11.79iter/s]Best score: 0.1820: : 1075iter [01:39, 11.79iter/s]Best score: 0.1820: : 1076iter [01:39, 11.79iter/s]Best score: 0.1820: : 1077iter [01:39, 11.77iter/s]Best score: 0.1820: : 1077iter [01:39, 11.77iter/s]Best score: 0.1820: : 1078iter [01:39, 11.77iter/s]Best score: 0.1820: : 1079iter [01:39, 11.84iter/s]Best score: 0.1815: : 1079iter [01:39, 11.84iter/s]Best score: 0.1815: : 1080iter [01:39, 11.84iter/s]Best score: 0.1815: : 1081iter [01:39, 11.69iter/s]Best score: 0.1815: : 1081iter [01:39, 11.69iter/s]Best score: 0.1815: : 1082iter [01:39, 11.69iter/s]Best score: 0.1815: : 1083iter [01:39, 11.80iter/s]Best score: 0.1815: : 1083iter [01:40, 11.80iter/s]Best score: 0.1815: : 1084iter [01:40, 11.80iter/s]Best score: 0.1815: : 1085iter [01:40, 11.87iter/s]Best score: 0.1815: : 1085iter [01:40, 11.87iter/s]Best score: 0.1815: : 1086iter [01:40, 11.87iter/s]Best score: 0.1815: : 1087iter [01:40, 11.91iter/s]Best score: 0.1815: : 1087iter [01:40, 11.91iter/s]Best score: 0.1815: : 1088iter [01:40, 11.91iter/s]Best score: 0.1815: : 1089iter [01:40, 11.94iter/s]Best score: 0.1812: : 1089iter [01:40, 11.94iter/s]Best score: 0.1812: : 1090iter [01:40, 11.94iter/s]Best score: 0.1812: : 1091iter [01:40, 11.79iter/s]Best score: 0.1812: : 1091iter [01:40, 11.79iter/s]Best score: 0.1812: : 1092iter [01:40, 11.79iter/s]Best score: 0.1812: : 1093iter [01:40, 11.84iter/s]Best score: 0.1812: : 1093iter [01:40, 11.84iter/s]Best score: 0.1812: : 1094iter [01:40, 11.84iter/s]Best score: 0.1812: : 1095iter [01:40, 11.83iter/s]Best score: 0.1812: : 1095iter [01:41, 11.83iter/s]Best score: 0.1812: : 1096iter [01:41, 11.83iter/s]Best score: 0.1812: : 1097iter [01:41, 11.89iter/s]Best score: 0.1812: : 1097iter [01:41, 11.89iter/s]Best score: 0.1812: : 1098iter [01:41, 11.89iter/s]Best score: 0.1812: : 1099iter [01:41, 11.88iter/s]
[ITER 1100] Best: 0.1808
[PARAMS] Current best parameters:
  fx: 541.9700
  fy: 542.2270
  px: 994.9439
  py: 738.8196
  k1: -0.01748058
  k2: 0.00827671
  k3: -0.00440501
  k4: 0.00064105
  p1: -0.00114457
  p2: 0.00755388
  p3: 0.03116847
  p4: -0.01036447
  s1: -0.0415
  s2: 0.0101
  s3: -0.0303
  s4: 0.0097
  tilt_x: -0.0078
  tilt_y: 0.0223
  scale: 2.9964
  rotation: -0.4763
Best score: 0.1808: : 1099iter [01:41, 11.88iter/s]Best score: 0.1808: : 1100iter [01:41, 10.81iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish did not improve score: 0.180797

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 100,695 → 93,158 within 3500mm ring
  Ring polish did not improve score: 0.180797
  Success: False
  Tangential distortion: p1=-0.001145, p2=0.007554, p3=0.031168, p4=-0.010364
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.1808
[✅] Parameters copied to: /working/MDC050_W019(6)_2026-09-09/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 541.9699918804487
    fy: 542.2269542515446
    px: 994.943937934691
    py: 738.8195620316549
    k1: -0.017480578424043467
    k2: 0.008276707981924689
    k3: -0.004405009063867304
    k4: 0.0006410511209250958
    p1: -0.0011445738386370973
    p2: 0.007553878763867772
    p3: 0.031168473798840857
    p4: -0.010364469537376643
    scale: 2.9963886107536153
    rotation: -0.47625997504059936
    mmpp: 3.433108471013462

[🔄] Converting 2432 frames to PTD, creating debug images for 400 calibration frames...
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                                                                 [A📁 Rigs: 100%|██████████| 4/4 [00:24<00:00,  6.43s/rig]📁 Rigs: 100%|██████████| 4/4 [00:24<00:00,  6.19s/rig]
[✅] Saved PTDs: /working/MDC050_W019(6)_2026-09-09/calibration/debug/4_undistorted/output/ptd
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   🔄 2000R:  90%|█████████ | 90/100 [00:00<00:00, 177.28frame/s][A
                                                                 [A📁 Rigs:  25%|██▌       | 1/4 [00:00<00:02,  1.07rig/s]
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   🔄 2000L:  93%|█████████▎| 93/100 [00:00<00:00, 175.81frame/s][A
                                                                 [A📁 Rigs:  50%|█████     | 2/4 [00:02<00:02,  1.05s/rig]
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   🔄 1000:  19%|█▉        | 19/100 [00:00<00:00, 183.06frame/s][A
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                                                                [A📁 Rigs:  75%|███████▌  | 3/4 [00:02<00:00,  1.01rig/s]
   🔄 1500:   0%|          | 0/100 [00:00<?, ?frame/s][A
   🔄 1500:  18%|█▊        | 18/100 [00:00<00:00, 177.21frame/s][A
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   🔄 1500:  54%|█████▍    | 54/100 [00:00<00:00, 177.17frame/s][A
   🔄 1500:  72%|███████▏  | 72/100 [00:00<00:00, 177.18frame/s][A
   🔄 1500:  90%|█████████ | 90/100 [00:00<00:00, 176.72frame/s][A
                                                                [A📁 Rigs: 100%|██████████| 4/4 [00:04<00:00,  1.04s/rig]📁 Rigs: 100%|██████████| 4/4 [00:04<00:00,  1.03s/rig]
[✅] Saved PTDs: /working/MDC050_W019(6)_2026-09-09/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%|▎         | 77380/3080448 [00:00<00:03, 773791.51px/s]📤 Writing LUT:   5%|▌         | 160807/3080448 [00:00<00:03, 809366.25px/s]📤 Writing LUT:   8%|▊         | 244526/3080448 [00:00<00:03, 822069.69px/s]📤 Writing LUT:  11%|█         | 328362/3080448 [00:00<00:03, 828498.10px/s]📤 Writing LUT:  13%|█▎        | 412319/3080448 [00:00<00:03, 832490.26px/s]📤 Writing LUT:  16%|█▌        | 496651/3080448 [00:00<00:03, 836169.93px/s]📤 Writing LUT:  19%|█▉        | 580268/3080448 [00:00<00:02, 835505.96px/s]📤 Writing LUT:  22%|██▏       | 663819/3080448 [00:00<00:02, 834700.16px/s]📤 Writing LUT:  24%|██▍       | 747290/3080448 [00:00<00:02, 832780.70px/s]📤 Writing LUT:  27%|██▋       | 830931/3080448 [00:01<00:02, 833897.57px/s]📤 Writing LUT:  30%|██▉       | 914354/3080448 [00:01<00:02, 833995.74px/s]📤 Writing LUT:  32%|███▏      | 997755/3080448 [00:01<00:02, 832387.76px/s]📤 Writing LUT:  35%|███▌      | 1081662/3080448 [00:01<00:02, 834404.37px/s]📤 Writing LUT:  38%|███▊      | 1167176/3080448 [00:01<00:02, 840655.43px/s]📤 Writing LUT:  41%|████      | 1252397/3080448 [00:01<00:02, 844133.73px/s]📤 Writing LUT:  43%|████▎     | 1337475/3080448 [00:01<00:02, 846130.01px/s]📤 Writing LUT:  46%|████▌     | 1422090/3080448 [00:01<00:01, 842437.67px/s]📤 Writing LUT:  49%|████▉     | 1506339/3080448 [00:01<00:01, 840511.05px/s]📤 Writing LUT:  52%|█████▏    | 1590595/3080448 [00:01<00:01, 841122.41px/s]📤 Writing LUT:  54%|█████▍    | 1675295/3080448 [00:02<00:01, 842879.37px/s]📤 Writing LUT:  57%|█████▋    | 1760301/3080448 [00:02<00:01, 845027.74px/s]📤 Writing LUT:  60%|█████▉    | 1845050/3080448 [00:02<00:01, 845765.33px/s]📤 Writing LUT:  63%|██████▎   | 1930408/3080448 [00:02<00:01, 848107.13px/s]📤 Writing LUT:  65%|██████▌   | 2015221/3080448 [00:02<00:01, 847292.25px/s]📤 Writing LUT:  68%|██████▊   | 2099952/3080448 [00:02<00:01, 844294.12px/s]📤 Writing LUT:  71%|███████   | 2184385/3080448 [00:02<00:01, 844290.65px/s]📤 Writing LUT:  74%|███████▎  | 2269274/3080448 [00:02<00:00, 845666.98px/s]📤 Writing LUT:  76%|███████▋  | 2354502/3080448 [00:02<00:00, 847646.19px/s]📤 Writing LUT:  79%|███████▉  | 2440779/3080448 [00:02<00:00, 852175.51px/s]📤 Writing LUT:  82%|████████▏ | 2527596/3080448 [00:03<00:00, 856969.76px/s]📤 Writing LUT:  85%|████████▍ | 2613295/3080448 [00:03<00:00, 855858.88px/s]📤 Writing LUT:  88%|████████▊ | 2700287/3080448 [00:03<00:00, 860070.16px/s]📤 Writing LUT:  90%|█████████ | 2787336/3080448 [00:03<00:00, 863192.91px/s]📤 Writing LUT:  93%|█████████▎| 2874423/3080448 [00:03<00:00, 865491.73px/s]📤 Writing LUT:  96%|█████████▌| 2962320/3080448 [00:03<00:00, 869533.03px/s]📤 Writing LUT:  99%|█████████▉| 3049894/3080448 [00:03<00:00, 871394.16px/s]📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 846057.84px/s]
[✅] LUT written to: /working/MDC050_W019(6)_2026-09-09/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC050_W019(6)_2026-09-09' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC050_W019(6)_2026-09-09/calibration/MDC050_W019(6)_2026-09-09_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]
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   🔥 2000R: 100%|█████████▉| 535/537 [00:06<00:00, 76.72frame/s][A
                                                                 [A[✅] 2000R: /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/2000R_heatmap.png
[✅] 2000R (fitted): /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/2000R_heatmap_fitted.png
📁 Rigs (heatmap):  25%|██▌       | 1/4 [00:07<00:23,  7.74s/rig]
   🔥 2000L:   0%|          | 0/584 [00:00<?, ?frame/s][A
   🔥 2000L:   2%|▏         | 9/584 [00:00<00:07, 81.01frame/s][A
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                                                                 [A[✅] 2000L: /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/2000L_heatmap.png
[✅] 2000L (fitted): /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/2000L_heatmap_fitted.png
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                                                                [A[✅] 1000: /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/1000_heatmap_fitted.png
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                                                                [A[✅] 1500: /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/1500_heatmap.png
[✅] 1500 (fitted): /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap/1500_heatmap_fitted.png
📁 Rigs (heatmap): 100%|██████████| 4/4 [00:33<00:00,  8.60s/rig]📁 Rigs (heatmap): 100%|██████████| 4/4 [00:33<00:00,  8.27s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC050_W019(6)_2026-09-09/calibration/debug/5_heatmap
[🏁] Finished processing MDC050_W019(6)_2026-09-09


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