2026-09-13T20:40:47 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC021_W042_2025-07-14 ===
2026-09-13T20:40:47 - INFO: Camera ID: 15091853
2026-09-13T20:40:47 - INFO: Wand ID: 
2026-09-13T20:40:47 - INFO: Recorded At: 2025-07-14T13:04:06Z

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

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 1254 stationary frames, 231 remain


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→ 1000 (231 images) Phase 1:  97%|█████████▋| 225/231 [00:03<00:00, 81.35it/s]

                                                                              

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→ 1000 Phase 2: corners:  73%|███████▎  | 168/231 [00:01<00:00, 133.49it/s]

→ 1000 Phase 2: corners:  80%|███████▉  | 184/231 [00:01<00:00, 138.01it/s]

→ 1000 Phase 2: corners:  86%|████████▌ | 199/231 [00:01<00:00, 141.36it/s]

→ 1000 Phase 2: corners: 100%|██████████| 231/231 [00:01<00:00, 193.11it/s]

                                                                           [PASS2] Frame 0000353: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000500: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000501: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000502: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000503: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000509: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000510: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000511: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000512: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001274: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001275: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001276: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001277: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000501: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000502: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000511: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001276: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 9 frames fixed, 8 untrusted, 231 total


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→ 1000 Phase 3: finalize:  99%|█████████▊| 228/231 [00:04<00:00, 53.03it/s]

                                                                           
[TIMING] 1000 — 231 frames
  Phase 1  radial detect   (parallel)  :   3.74s  (16.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.82s  (7.9 ms/frame)
  Phase 2b corner fix      (sequential):   0.44s
  Phase 3  finalize        (sequential):   4.50s  (19.5 ms/frame)
    decode + undistort                 :   2.33s  (10.1 ms/frame)
    corner apply + refinement          :   1.04s  (4.5 ms/frame)
    segment                            :   0.92s  (4.0 ms/frame)
    smooth                             :   0.13s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.50s


Processing Rigs:  50%|█████     | 1/2 [00:17<00:17, 17.12s/it][INFO] Filtered 1068 stationary frames, 209 remain


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→ 500 Phase 2: corners:  56%|█████▌    | 116/209 [00:01<00:00, 122.09it/s]

→ 500 Phase 2: corners:  63%|██████▎   | 132/209 [00:01<00:00, 129.79it/s]

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→ 500 Phase 2: corners:  88%|████████▊ | 183/209 [00:01<00:00, 153.05it/s]

→ 500 Phase 2: corners:  96%|█████████▌| 201/209 [00:01<00:00, 160.51it/s]

                                                                          [PASS2] Frame 0000452: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000454: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000455: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001136: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001137: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001138: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001139: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000454: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001138: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 4 frames fixed, 4 untrusted, 209 total


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→ 500 Phase 3: finalize:  96%|█████████▌| 200/209 [00:03<00:00, 53.68it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 206/209 [00:03<00:00, 53.60it/s]

                                                                          
[TIMING] 500 — 209 frames
  Phase 1  radial detect   (parallel)  :   3.44s  (16.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.82s  (8.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.26s
  Phase 3  finalize        (sequential):   3.79s  (18.1 ms/frame)
    decode + undistort                 :   1.98s  (9.5 ms/frame)
    corner apply + refinement          :   0.77s  (3.7 ms/frame)
    segment                            :   0.83s  (4.0 ms/frame)
    smooth                             :   0.14s  (0.7 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   9.30s


Processing Rigs: 100%|██████████| 2/2 [00:30<00:00, 14.89s/it]
Processing Rigs: 100%|██████████| 2/2 [00:30<00:00, 15.23s/it]
[💾] Saved RigFrameDataset to /working/MDC021_W042_2025-07-14/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 440 frames from 2 rigs to /working/MDC021_W042_2025-07-14/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC021_W042_2025-07-14/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 231
[📋] 500: 100 frames selected from 209
[💾] Saved RigFrameDataset to /working/MDC021_W042_2025-07-14/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC021_W042_2025-07-14/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:04<03:10,  4.89s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:05<00:49,  1.35s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:34,  1.04it/s]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:24,  1.46it/s]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:05<00:17,  2.00it/s]
Computing max image from bright frames:  20%|██        | 8/40 [00:05<00:09,  3.46it/s]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:05<00:04,  5.92it/s]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:06<00:04,  6.42it/s]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:06<00:03,  7.41it/s]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:06<00:02,  8.72it/s]
Computing max image from bright frames:  48%|████▊     | 19/40 [00:06<00:02,  9.70it/s]
Computing max image from bright frames:  52%|█████▎    | 21/40 [00:06<00:02,  9.21it/s]
Computing max image from bright frames:  57%|█████▊    | 23/40 [00:06<00:01, 10.19it/s]
Computing max image from bright frames:  68%|██████▊   | 27/40 [00:07<00:00, 14.45it/s]
Computing max image from bright frames:  75%|███████▌  | 30/40 [00:07<00:00, 17.42it/s]
Computing max image from bright frames:  82%|████████▎ | 33/40 [00:08<00:01,  5.81it/s]
Computing max image from bright frames:  88%|████████▊ | 35/40 [00:08<00:00,  5.35it/s]
Computing max image from bright frames:  92%|█████████▎| 37/40 [00:09<00:00,  5.86it/s]
Computing max image from bright frames:  98%|█████████▊| 39/40 [00:09<00:00,  6.79it/s]
                                                                                       
[✅] Casing detection complete: cx=1030.0, cy=757.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   1%|          | 1/100 [00:00<00:29,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   1%|          | 1/100 [00:00<00:29,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   2%|▏         | 2/100 [00:00<00:29,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   3%|▎         | 3/100 [00:00<00:29,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   4%|▍         | 4/100 [00:00<00:28,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   5%|▌         | 5/100 [00:00<00:28,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   6%|▌         | 6/100 [00:00<00:28,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   7%|▋         | 7/100 [00:00<00:28,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   8%|▊         | 8/100 [00:00<00:27,  3.32it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 314.141} (0.000):   9%|▉         | 9/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.036 | Best: {'fx': 519.192} (0.036):   9%|▉         | 9/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 519.192} (0.036):  10%|█         | 10/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.045 | Best: {'fx': 540.404} (0.045):  11%|█         | 11/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.065 | Best: {'fx': 526.263} (0.065):  12%|█▏        | 12/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 526.263} (0.065):  13%|█▎        | 13/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 526.263} (0.065):  14%|█▍        | 14/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 526.263} (0.065):  15%|█▌        | 15/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.079 | Best: {'fx': 533.333} (0.079):  16%|█▌        | 16/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  17%|█▋        | 17/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.032 | Best: {'fx': 533.333} (0.079):  18%|█▊        | 18/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  19%|█▉        | 19/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.025 | Best: {'fx': 533.333} (0.079):  20%|██        | 20/100 [00:00<00:03, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  21%|██        | 21/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  22%|██▏       | 22/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.021 | Best: {'fx': 533.333} (0.079):  23%|██▎       | 23/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.019 | Best: {'fx': 533.333} (0.079):  24%|██▍       | 24/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.017 | Best: {'fx': 533.333} (0.079):  25%|██▌       | 25/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  26%|██▌       | 26/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  27%|██▋       | 27/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  28%|██▊       | 28/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  29%|██▉       | 29/100 [00:00<00:02, 26.63it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  30%|███       | 30/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.014 | Best: {'fx': 533.333} (0.079):  30%|███       | 30/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.016 | Best: {'fx': 533.333} (0.079):  31%|███       | 31/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  32%|███▏      | 32/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  33%|███▎      | 33/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  34%|███▍      | 34/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.015 | Best: {'fx': 533.333} (0.079):  35%|███▌      | 35/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  36%|███▌      | 36/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.014 | Best: {'fx': 533.333} (0.079):  37%|███▋      | 37/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  38%|███▊      | 38/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.079):  39%|███▉      | 39/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  40%|████      | 40/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  41%|████      | 41/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.079):  42%|████▏     | 42/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.022 | Best: {'fx': 533.333} (0.079):  43%|████▎     | 43/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.001 | Best: {'fx': 533.333} (0.079):  44%|████▍     | 44/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.004 | Best: {'fx': 533.333} (0.079):  45%|████▌     | 45/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  46%|████▌     | 46/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.079):  47%|████▋     | 47/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  48%|████▊     | 48/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.007 | Best: {'fx': 533.333} (0.079):  49%|████▉     | 49/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  50%|█████     | 50/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  51%|█████     | 51/100 [00:00<00:00, 81.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  52%|█████▏    | 52/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  52%|█████▏    | 52/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.079):  53%|█████▎    | 53/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  54%|█████▍    | 54/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  55%|█████▌    | 55/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  56%|█████▌    | 56/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  57%|█████▋    | 57/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  58%|█████▊    | 58/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  59%|█████▉    | 59/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  60%|██████    | 60/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  61%|██████    | 61/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  62%|██████▏   | 62/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  63%|██████▎   | 63/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  64%|██████▍   | 64/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.079):  65%|██████▌   | 65/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  66%|██████▌   | 66/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  67%|██████▋   | 67/100 [00:00<00:00, 121.25it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  68%|██████▊   | 68/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  68%|██████▊   | 68/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  69%|██████▉   | 69/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  70%|███████   | 70/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  71%|███████   | 71/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  72%|███████▏  | 72/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  73%|███████▎  | 73/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  74%|███████▍  | 74/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  75%|███████▌  | 75/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  76%|███████▌  | 76/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  77%|███████▋  | 77/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  78%|███████▊  | 78/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  79%|███████▉  | 79/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  80%|████████  | 80/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  81%|████████  | 81/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  82%|████████▏ | 82/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  83%|████████▎ | 83/100 [00:00<00:00, 129.36it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  84%|████████▍ | 84/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  84%|████████▍ | 84/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  85%|████████▌ | 85/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  86%|████████▌ | 86/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  87%|████████▋ | 87/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  88%|████████▊ | 88/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  89%|████████▉ | 89/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  90%|█████████ | 90/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  91%|█████████ | 91/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  92%|█████████▏| 92/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  93%|█████████▎| 93/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  94%|█████████▍| 94/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  95%|█████████▌| 95/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  96%|█████████▌| 96/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  97%|█████████▋| 97/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  98%|█████████▊| 98/100 [00:00<00:00, 134.41it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  99%|█████████▉| 99/100 [00:00<00:00, 121.10it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.079):  99%|█████████▉| 99/100 [00:01<00:00, 121.10it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.013 | Best: {'fx': 509.354} (0.013):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.013 | Best: {'fx': 509.354} (0.013):   2%|▏         | 1/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.016 | Best: {'fx': 510.374} (0.016):   2%|▏         | 1/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.011 | Best: {'fx': 510.374} (0.016):   4%|▍         | 2/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.021 | Best: {'fx': 511.395} (0.021):   6%|▌         | 3/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.027 | Best: {'fx': 514.456} (0.027):   8%|▊         | 4/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 516.497} (0.030):  10%|█         | 5/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.023 | Best: {'fx': 516.497} (0.030):  12%|█▏        | 6/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.026 | Best: {'fx': 516.497} (0.030):  14%|█▍        | 7/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 516.497} (0.030):  16%|█▌        | 8/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 517.517} (0.032):  18%|█▊        | 9/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.035 | Best: {'fx': 518.537} (0.035):  20%|██        | 10/50 [00:00<00:04,  9.90it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.035 | Best: {'fx': 518.537} (0.035):  22%|██▏       | 11/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.037 | Best: {'fx': 519.558} (0.037):  22%|██▏       | 11/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.041 | Best: {'fx': 520.578} (0.041):  24%|██▍       | 12/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.043 | Best: {'fx': 521.599} (0.043):  26%|██▌       | 13/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.046 | Best: {'fx': 522.619} (0.046):  28%|██▊       | 14/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.058 | Best: {'fx': 524.66} (0.058):  30%|███       | 15/50 [00:00<00:00, 58.12it/s] 
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.062 | Best: {'fx': 525.68} (0.062):  32%|███▏      | 16/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.051 | Best: {'fx': 525.68} (0.062):  34%|███▍      | 17/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.080 | Best: {'fx': 528.741} (0.080):  36%|███▌      | 18/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.067 | Best: {'fx': 528.741} (0.080):  38%|███▊      | 19/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.075 | Best: {'fx': 528.741} (0.080):  40%|████      | 20/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.075 | Best: {'fx': 528.741} (0.080):  42%|████▏     | 21/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.082 | Best: {'fx': 530.782} (0.082):  44%|████▍     | 22/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.088 | Best: {'fx': 529.762} (0.088):  46%|████▌     | 23/50 [00:00<00:00, 58.12it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.088 | Best: {'fx': 529.762} (0.088):  48%|████▊     | 24/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.090 | Best: {'fx': 531.803} (0.090):  48%|████▊     | 24/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.064 | Best: {'fx': 531.803} (0.090):  50%|█████     | 25/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.083 | Best: {'fx': 531.803} (0.090):  52%|█████▏    | 26/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.060 | Best: {'fx': 531.803} (0.090):  54%|█████▍    | 27/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.057 | Best: {'fx': 531.803} (0.090):  56%|█████▌    | 28/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.049 | Best: {'fx': 531.803} (0.090):  58%|█████▊    | 29/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.043 | Best: {'fx': 531.803} (0.090):  60%|██████    | 30/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.052 | Best: {'fx': 531.803} (0.090):  62%|██████▏   | 31/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.041 | Best: {'fx': 531.803} (0.090):  64%|██████▍   | 32/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.045 | Best: {'fx': 531.803} (0.090):  66%|██████▌   | 33/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.039 | Best: {'fx': 531.803} (0.090):  68%|██████▊   | 34/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.037 | Best: {'fx': 531.803} (0.090):  70%|███████   | 35/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 531.803} (0.090):  72%|███████▏  | 36/50 [00:00<00:00, 85.41it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.030 | Best: {'fx': 531.803} (0.090):  74%|███████▍  | 37/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.035 | Best: {'fx': 531.803} (0.090):  74%|███████▍  | 37/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.032 | Best: {'fx': 531.803} (0.090):  76%|███████▌  | 38/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.033 | Best: {'fx': 531.803} (0.090):  78%|███████▊  | 39/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.031 | Best: {'fx': 531.803} (0.090):  80%|████████  | 40/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 531.803} (0.090):  82%|████████▏ | 41/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.028 | Best: {'fx': 531.803} (0.090):  84%|████████▍ | 42/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.026 | Best: {'fx': 531.803} (0.090):  86%|████████▌ | 43/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.027 | Best: {'fx': 531.803} (0.090):  88%|████████▊ | 44/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 531.803} (0.090):  90%|█████████ | 45/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.025 | Best: {'fx': 531.803} (0.090):  92%|█████████▏| 46/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.025 | Best: {'fx': 531.803} (0.090):  94%|█████████▍| 47/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.024 | Best: {'fx': 531.803} (0.090):  96%|█████████▌| 48/50 [00:00<00:00, 102.02it/s]
Brute | Start: {'fx': 533.333} (0.079) | Current: 0.023 | Best: {'fx': 531.803} (0.090):  98%|█████████▊| 49/50 [00:00<00:00, 102.02it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 1030.0
    py: 757.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization

Initial score: 14.0357: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 14.0357, picklable_objective(x0) = 14.0887

Best score: 14.0887: : 0iter [00:01, ?iter/s] 
Best score: 14.0887: : 1iter [00:01,  1.84s/iter]
Best score: 14.0887: : 1iter [00:01,  1.84s/iter]
Best score: 14.0887: : 2iter [00:02,  1.84s/iter]
Best score: 14.0887: : 3iter [00:02,  1.86iter/s]
Best score: 14.0887: : 3iter [00:02,  1.86iter/s]
Best score: 14.0887: : 4iter [00:02,  1.86iter/s]
Best score: 14.0887: : 5iter [00:02,  1.86iter/s]
Best score: 14.0887: : 6iter [00:02,  1.86iter/s]
Best score: 14.0887: : 7iter [00:02,  5.27iter/s]
Best score: 14.0887: : 7iter [00:02,  5.27iter/s]
Best score: 14.0887: : 8iter [00:02,  5.27iter/s]
Best score: 14.0887: : 9iter [00:02,  5.27iter/s]
Best score: 14.0887: : 10iter [00:02,  5.27iter/s]
Best score: 14.0887: : 11iter [00:02,  9.20iter/s]
Best score: 14.0887: : 11iter [00:02,  9.20iter/s]
Best score: 14.0887: : 12iter [00:02,  9.20iter/s]
Best score: 14.0887: : 13iter [00:02,  9.20iter/s]
Best score: 14.0887: : 14iter [00:02,  9.20iter/s]
Best score: 14.0887: : 15iter [00:02, 13.47iter/s]
Best score: 14.0887: : 15iter [00:02, 13.47iter/s]
Best score: 14.0887: : 16iter [00:02, 13.47iter/s]
Best score: 14.0887: : 17iter [00:02, 13.47iter/s]
Best score: 14.0887: : 18iter [00:02, 13.47iter/s]
Best score: 14.0887: : 19iter [00:02, 17.84iter/s]
Best score: 14.0887: : 19iter [00:02, 17.84iter/s]
Best score: 14.0887: : 20iter [00:02, 17.84iter/s]
Best score: 14.0887: : 21iter [00:02, 17.84iter/s]
Best score: 14.0887: : 22iter [00:02, 17.84iter/s]
Best score: 14.0887: : 23iter [00:02, 22.00iter/s]
Best score: 14.0887: : 23iter [00:02, 22.00iter/s]
Best score: 14.0887: : 24iter [00:02, 22.00iter/s]
Best score: 14.0887: : 25iter [00:02, 22.00iter/s]
Best score: 14.0887: : 26iter [00:02, 22.00iter/s]
Best score: 14.0887: : 27iter [00:02, 25.83iter/s]
Best score: 14.0887: : 27iter [00:02, 25.83iter/s]
Best score: 14.0887: : 28iter [00:02, 25.83iter/s]
Best score: 14.0887: : 29iter [00:02, 25.83iter/s]
Best score: 14.0887: : 30iter [00:02, 25.83iter/s]
Best score: 14.0887: : 31iter [00:02, 29.01iter/s]
Best score: 14.0887: : 31iter [00:02, 29.01iter/s]
Best score: 14.0887: : 32iter [00:02, 29.01iter/s]
Best score: 14.0887: : 33iter [00:02, 29.01iter/s]
Best score: 14.0887: : 34iter [00:02, 29.01iter/s]
Best score: 14.0887: : 35iter [00:02, 31.74iter/s]
Best score: 14.0887: : 35iter [00:02, 31.74iter/s]
Best score: 14.0887: : 36iter [00:02, 31.74iter/s]
Best score: 14.0887: : 37iter [00:02, 31.74iter/s]
Best score: 14.0887: : 38iter [00:02, 31.74iter/s]
Best score: 14.0887: : 39iter [00:02, 33.82iter/s]
Best score: 14.0887: : 39iter [00:02, 33.82iter/s]
Best score: 14.0887: : 40iter [00:02, 33.82iter/s]
Best score: 14.0887: : 41iter [00:02, 33.82iter/s]
Best score: 14.0887: : 42iter [00:03, 33.82iter/s]
Best score: 14.0887: : 43iter [00:03, 35.25iter/s]
Best score: 14.0887: : 43iter [00:03, 35.25iter/s]
Best score: 14.0887: : 44iter [00:03, 35.25iter/s]
Best score: 14.0887: : 45iter [00:03, 35.25iter/s]
Best score: 14.0887: : 46iter [00:03, 35.25iter/s]
Best score: 14.0887: : 47iter [00:03, 36.53iter/s]
Best score: 14.0887: : 47iter [00:03, 36.53iter/s]
Best score: 14.0887: : 48iter [00:03, 36.53iter/s]
[ITER 50] Best: 14.0887
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1030.0000
  py: 757.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 14.0887: : 49iter [00:03, 36.53iter/s]
Best score: 14.0887: : 50iter [00:03, 36.53iter/s]
Best score: 14.0887: : 51iter [00:03, 37.28iter/s]
Best score: 14.0887: : 51iter [00:03, 37.28iter/s]
Best score: 14.0887: : 52iter [00:03, 37.28iter/s]
Best score: 14.0887: : 53iter [00:03, 37.28iter/s]
Best score: 14.0887: : 54iter [00:03, 37.28iter/s]
Best score: 14.0887: : 55iter [00:03, 38.03iter/s]
Best score: 14.0887: : 55iter [00:03, 38.03iter/s]
Best score: 14.0887: : 56iter [00:03, 38.03iter/s]
Best score: 14.0887: : 57iter [00:03, 38.03iter/s]
Best score: 14.0887: : 58iter [00:03, 38.03iter/s]
Best score: 14.0887: : 59iter [00:03, 38.03iter/s]
Best score: 14.0887: : 60iter [00:03, 38.56iter/s]
Best score: 14.0887: : 60iter [00:03, 38.56iter/s]
Best score: 14.0887: : 61iter [00:03, 38.56iter/s]
Best score: 14.0887: : 62iter [00:03, 38.56iter/s]
Best score: 14.0887: : 63iter [00:03, 38.56iter/s]
Best score: 14.0887: : 64iter [00:03, 38.93iter/s]
Best score: 14.0887: : 64iter [00:03, 38.93iter/s]
Best score: 14.0887: : 65iter [00:03, 38.93iter/s]
Best score: 14.0887: : 66iter [00:03, 38.93iter/s]
Best score: 14.0887: : 67iter [00:03, 38.93iter/s]
Best score: 14.0887: : 68iter [00:03, 39.23iter/s]
Best score: 14.0887: : 68iter [00:03, 39.23iter/s]
Best score: 14.0887: : 69iter [00:03, 39.23iter/s]
Best score: 14.0887: : 70iter [00:03, 39.23iter/s]
Best score: 14.0887: : 71iter [00:03, 39.23iter/s]
Best score: 14.0887: : 72iter [00:03, 39.10iter/s]
Best score: 14.0887: : 72iter [00:03, 39.10iter/s]
Best score: 14.0887: : 73iter [00:03, 39.10iter/s]
Best score: 14.0887: : 74iter [00:03, 39.10iter/s]
Best score: 14.0887: : 75iter [00:03, 39.10iter/s]
Best score: 14.0887: : 76iter [00:03, 39.31iter/s]
Best score: 14.0887: : 76iter [00:03, 39.31iter/s]
Best score: 14.0887: : 77iter [00:03, 39.31iter/s]
Best score: 14.0887: : 78iter [00:03, 39.31iter/s]
Best score: 14.0887: : 79iter [00:03, 39.31iter/s]
Best score: 14.0887: : 80iter [00:03, 39.20iter/s]
Best score: 14.0887: : 80iter [00:03, 39.20iter/s]
Best score: 14.0887: : 81iter [00:04, 39.20iter/s]
Best score: 14.0887: : 82iter [00:04, 39.20iter/s]
Best score: 14.0887: : 83iter [00:04, 39.20iter/s]
Best score: 14.0887: : 84iter [00:04, 39.20iter/s]
Best score: 14.0887: : 85iter [00:04, 39.53iter/s]
Best score: 14.0887: : 85iter [00:04, 39.53iter/s]
Best score: 14.0887: : 86iter [00:04, 39.53iter/s]
Best score: 14.0887: : 87iter [00:04, 39.53iter/s]
Best score: 14.0887: : 88iter [00:04, 39.53iter/s]
Best score: 14.0887: : 89iter [00:04, 39.53iter/s]
Best score: 14.0887: : 90iter [00:04, 39.47iter/s]
Best score: 14.0887: : 90iter [00:04, 39.47iter/s]
Best score: 14.0887: : 91iter [00:04, 39.47iter/s]
Best score: 14.0887: : 92iter [00:04, 39.47iter/s]
Best score: 14.0887: : 93iter [00:04, 39.47iter/s]
Best score: 14.0887: : 94iter [00:04, 39.47iter/s]
Best score: 14.0887: : 95iter [00:04, 39.79iter/s]
Best score: 14.0887: : 95iter [00:04, 39.79iter/s]
Best score: 14.0887: : 96iter [00:04, 39.79iter/s]
Best score: 14.0887: : 97iter [00:04, 39.79iter/s]
Best score: 14.0887: : 98iter [00:04, 39.79iter/s]
[ITER 100] Best: 14.0887
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1030.0000
  py: 757.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 14.0887: : 99iter [00:04, 39.79iter/s]
Best score: 14.0887: : 100iter [00:04, 39.84iter/s]
Best score: 14.0887: : 100iter [00:04, 39.84iter/s]
Best score: 14.0887: : 101iter [00:04, 39.84iter/s]
Best score: 14.0887: : 102iter [00:04, 39.84iter/s]
Best score: 14.0887: : 103iter [00:04, 39.84iter/s]
Best score: 14.0887: : 104iter [00:04, 37.36iter/s]
Best score: 14.0887: : 104iter [00:04, 37.36iter/s]
Best score: 14.0887: : 105iter [00:04, 37.36iter/s]
Best score: 14.0887: : 106iter [00:04, 37.36iter/s]
Best score: 14.0887: : 107iter [00:04, 37.36iter/s]
Best score: 14.0887: : 108iter [00:04, 37.94iter/s]
Best score: 14.0887: : 108iter [00:04, 37.94iter/s]
Best score: 14.0887: : 109iter [00:04, 37.94iter/s]
Best score: 14.0887: : 110iter [00:04, 37.94iter/s]
Best score: 14.0887: : 111iter [00:04, 37.94iter/s]
Best score: 14.0887: : 112iter [00:04, 38.25iter/s]
Best score: 14.0887: : 112iter [00:04, 38.25iter/s]
Best score: 14.0887: : 113iter [00:04, 38.25iter/s]
Best score: 14.0887: : 114iter [00:04, 38.25iter/s]
Best score: 14.0887: : 115iter [00:04, 38.25iter/s]
Best score: 14.0887: : 116iter [00:04, 38.72iter/s]
Best score: 14.0887: : 116iter [00:04, 38.72iter/s]
Best score: 14.0887: : 117iter [00:04, 38.72iter/s]
Best score: 14.0887: : 118iter [00:04, 38.72iter/s]
Best score: 14.0887: : 119iter [00:04, 38.72iter/s]
Best score: 14.0887: : 120iter [00:04, 38.71iter/s]
Best score: 14.0887: : 120iter [00:05, 38.71iter/s]
Best score: 14.0887: : 121iter [00:05, 38.71iter/s]
Best score: 14.0887: : 122iter [00:05, 38.71iter/s]
Best score: 14.0887: : 123iter [00:05, 38.71iter/s]
Best score: 14.0887: : 124iter [00:05, 39.03iter/s]
Best score: 14.0887: : 124iter [00:05, 39.03iter/s]
Best score: 14.0887: : 125iter [00:05, 39.03iter/s]
Best score: 14.0887: : 126iter [00:05, 39.03iter/s]
Best score: 14.0887: : 127iter [00:05, 39.03iter/s]
Best score: 14.0887: : 128iter [00:05, 39.03iter/s]
Best score: 14.0887: : 129iter [00:05, 39.36iter/s]
Best score: 14.0887: : 129iter [00:05, 39.36iter/s]
Best score: 14.0887: : 130iter [00:05, 39.36iter/s]
Best score: 14.0887: : 131iter [00:05, 39.36iter/s]
Best score: 14.0887: : 132iter [00:05, 39.36iter/s]
Best score: 14.0887: : 133iter [00:05, 39.24iter/s]
Best score: 14.0887: : 133iter [00:05, 39.24iter/s]
Best score: 14.0887: : 134iter [00:05, 39.24iter/s]
Best score: 14.0887: : 135iter [00:05, 39.24iter/s]
Best score: 14.0887: : 136iter [00:05, 39.24iter/s]
Best score: 14.0887: : 137iter [00:05, 39.24iter/s]
Best score: 14.0887: : 138iter [00:05, 39.57iter/s]
Best score: 14.0887: : 138iter [00:05, 39.57iter/s]
Best score: 14.0887: : 139iter [00:05, 39.57iter/s]
Best score: 14.0887: : 140iter [00:05, 39.57iter/s]
Best score: 14.0887: : 141iter [00:05, 39.57iter/s]
Best score: 14.0887: : 142iter [00:05, 39.51iter/s]
Best score: 14.0887: : 142iter [00:05, 39.51iter/s]
Best score: 14.0887: : 143iter [00:05, 39.51iter/s]
Best score: 14.0887: : 144iter [00:05, 39.51iter/s]
Best score: 14.0887: : 145iter [00:05, 39.51iter/s]
Best score: 14.0887: : 146iter [00:05, 39.65iter/s]
Best score: 14.0887: : 146iter [00:05, 39.65iter/s]
Best score: 14.0887: : 147iter [00:05, 39.65iter/s]
Best score: 14.0887: : 148iter [00:05, 39.65iter/s]
[ITER 150] Best: 14.0887
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1030.0000
  py: 757.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 14.0887: : 149iter [00:05, 39.65iter/s]
Best score: 14.0887: : 150iter [00:05, 39.60iter/s]
Best score: 14.0887: : 150iter [00:05, 39.60iter/s]
Best score: 14.0887: : 151iter [00:05, 39.60iter/s]
Best score: 14.0887: : 152iter [00:05, 39.60iter/s]
Best score: 14.0887: : 153iter [00:05, 39.60iter/s]
Best score: 14.0887: : 154iter [00:05, 32.50iter/s]
Best score: 14.0887: : 154iter [00:05, 32.50iter/s]
Best score: 14.0887: : 155iter [00:05, 32.50iter/s]
Best score: 14.0887: : 156iter [00:05, 32.50iter/s]
Best score: 14.0887: : 157iter [00:06, 32.50iter/s]
Best score: 14.0887: : 158iter [00:06, 32.50iter/s]
Best score: 14.0887: : 159iter [00:06, 34.81iter/s]
Best score: 14.0887: : 159iter [00:06, 34.81iter/s]
Best score: 14.0887: : 160iter [00:06, 34.81iter/s]
Best score: 14.0887: : 161iter [00:06, 34.81iter/s]
Best score: 14.0887: : 162iter [00:06, 34.81iter/s]
Best score: 14.0887: : 163iter [00:06, 35.98iter/s]
Best score: 14.0887: : 163iter [00:06, 35.98iter/s]
Best score: 14.0887: : 164iter [00:06, 35.98iter/s]
Best score: 14.0887: : 165iter [00:06, 35.98iter/s]
Best score: 14.0887: : 166iter [00:06, 35.98iter/s]
Best score: 14.0887: : 167iter [00:06, 35.98iter/s]
Best score: 14.0887: : 168iter [00:06, 37.32iter/s]
Best score: 14.0887: : 168iter [00:06, 37.32iter/s]
Best score: 14.0887: : 169iter [00:06, 37.32iter/s]
Best score: 14.0887: : 170iter [00:06, 37.32iter/s]
Best score: 14.0887: : 171iter [00:06, 37.32iter/s]
Best score: 14.0887: : 172iter [00:06, 37.70iter/s]
Best score: 14.0887: : 172iter [00:06, 37.70iter/s]
Best score: 14.0887: : 173iter [00:06, 37.70iter/s]
Best score: 14.0887: : 174iter [00:06, 37.70iter/s]
Best score: 14.0887: : 175iter [00:06, 37.70iter/s]
Best score: 14.0887: : 176iter [00:06, 37.70iter/s]
Best score: 14.0887: : 177iter [00:06, 38.57iter/s]
Best score: 14.0887: : 177iter [00:06, 38.57iter/s]
Best score: 14.0887: : 178iter [00:06, 38.57iter/s]
Best score: 14.0887: : 179iter [00:06, 38.57iter/s]
Best score: 14.0887: : 180iter [00:06, 38.57iter/s]
Best score: 14.0887: : 181iter [00:06, 38.69iter/s]
Best score: 14.0887: : 181iter [00:06, 38.69iter/s]
Best score: 14.0887: : 182iter [00:06, 38.69iter/s]
Best score: 14.0887: : 183iter [00:06, 38.69iter/s]
Best score: 14.0887: : 184iter [00:06, 38.69iter/s]
Best score: 14.0887: : 185iter [00:06, 38.67iter/s]
Best score: 14.0887: : 185iter [00:06, 38.67iter/s]
Best score: 14.0887: : 186iter [00:06, 38.67iter/s]
Best score: 14.0887: : 187iter [00:06, 38.67iter/s]
Best score: 14.0887: : 188iter [00:06, 38.67iter/s]
Best score: 14.0887: : 189iter [00:06, 38.86iter/s]
Best score: 14.0887: : 189iter [00:06, 38.86iter/s]
Best score: 14.0887: : 190iter [00:06, 38.86iter/s]
Best score: 14.0887: : 191iter [00:06, 38.86iter/s]
Best score: 14.0887: : 192iter [00:06, 38.86iter/s]
Best score: 14.0887: : 193iter [00:06, 39.16iter/s]
Best score: 14.0887: : 193iter [00:06, 39.16iter/s]
Best score: 14.0887: : 194iter [00:06, 39.16iter/s]
Best score: 14.0887: : 195iter [00:06, 39.16iter/s]
Best score: 14.0887: : 196iter [00:07, 39.16iter/s]
Best score: 14.0887: : 197iter [00:07, 39.34iter/s]
Best score: 14.0887: : 197iter [00:07, 39.34iter/s]
Best score: 14.0887: : 198iter [00:07, 39.34iter/s]
[ITER 200] Best: 14.0887
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 1030.0000
  py: 757.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 14.0887: : 199iter [00:07, 39.34iter/s]
Best score: 14.0887: : 200iter [00:07, 39.34iter/s]
Best score: 14.0887: : 201iter [00:07, 32.30iter/s]
Best score: 14.0887: : 201iter [00:07, 32.30iter/s]
Best score: 14.0887: : 202iter [00:07, 32.30iter/s]
Best score: 14.0887: : 203iter [00:07, 32.30iter/s]
Best score: 14.0887: : 204iter [00:07, 32.30iter/s]
Best score: 14.0887: : 205iter [00:07, 34.16iter/s]
Best score: 14.0887: : 205iter [00:07, 34.16iter/s]
Best score: 14.0887: : 206iter [00:07, 34.16iter/s]
Best score: 14.0887: : 207iter [00:07, 34.16iter/s]
Best score: 14.0887: : 208iter [00:07, 34.16iter/s]
Best score: 14.0887: : 209iter [00:07, 34.16iter/s]
Best score: 14.0887: : 210iter [00:07, 35.93iter/s]
Best score: 14.0887: : 210iter [00:07, 35.93iter/s]
Best score: 14.0887: : 211iter [00:07, 35.93iter/s]
Best score: 14.0887: : 212iter [00:07, 35.93iter/s]
Best score: 14.0887: : 213iter [00:07, 35.93iter/s]
Best score: 14.0887: : 214iter [00:07, 35.93iter/s]
Best score: 14.0887: : 215iter [00:07, 37.26iter/s]
Best score: 14.0887: : 215iter [00:07, 37.26iter/s]
Best score: 14.0887: : 216iter [00:07, 37.26iter/s]
Best score: 14.0887: : 217iter [00:07, 37.26iter/s]
Best score: 14.0887: : 218iter [00:07, 37.26iter/s]
Best score: 9.2457: : 219iter [00:07, 37.26iter/s] 
Best score: 9.2457: : 220iter [00:07, 38.09iter/s]
Best score: 9.2457: : 220iter [00:07, 38.09iter/s]
Best score: 9.2457: : 221iter [00:07, 38.09iter/s]
Best score: 9.2457: : 222iter [00:07, 38.09iter/s]
Best score: 9.2457: : 223iter [00:07, 38.09iter/s]
Best score: 9.2457: : 224iter [00:07, 38.09iter/s]
Best score: 9.2457: : 225iter [00:07, 38.73iter/s]
Best score: 9.2457: : 225iter [00:07, 38.73iter/s]
Best score: 9.2457: : 226iter [00:07, 38.73iter/s]
Best score: 9.2457: : 227iter [00:07, 38.73iter/s]
Best score: 9.2457: : 228iter [00:07, 38.73iter/s]
Best score: 9.2457: : 229iter [00:07, 38.73iter/s]
Best score: 9.2457: : 230iter [00:07, 39.12iter/s]
Best score: 9.2457: : 230iter [00:07, 39.12iter/s]
Best score: 9.2457: : 231iter [00:07, 39.12iter/s]
Best score: 9.2457: : 232iter [00:07, 39.12iter/s]
Best score: 9.2457: : 233iter [00:08, 39.12iter/s]
Best score: 9.2457: : 234iter [00:08, 39.12iter/s]
Best score: 9.2457: : 235iter [00:08, 39.49iter/s]
Best score: 9.2457: : 235iter [00:08, 39.49iter/s]
Best score: 9.2457: : 236iter [00:08, 39.49iter/s]
Best score: 9.2457: : 237iter [00:08, 39.49iter/s]
Best score: 9.2457: : 238iter [00:08, 39.49iter/s]
Best score: 9.2457: : 239iter [00:08, 39.49iter/s]
Best score: 9.2457: : 240iter [00:08, 39.57iter/s]
Best score: 9.2457: : 240iter [00:08, 39.57iter/s]
Best score: 9.2457: : 241iter [00:08, 39.57iter/s]
Best score: 9.2457: : 242iter [00:08, 39.57iter/s]
Best score: 9.2457: : 243iter [00:08, 39.57iter/s]
Best score: 9.2457: : 244iter [00:08, 39.57iter/s]
Best score: 9.2457: : 245iter [00:08, 39.96iter/s]
Best score: 9.2457: : 245iter [00:08, 39.96iter/s]
Best score: 9.2457: : 246iter [00:08, 39.96iter/s]
Best score: 9.2457: : 247iter [00:08, 39.96iter/s]
Best score: 9.2457: : 248iter [00:08, 39.96iter/s]
[ITER 250] Best: 9.2457
[PARAMS] Current best parameters:
  fx: 506.7672
  fy: 506.3243
  px: 1035.3240
  py: 759.8246
  k1: 0.05184249
  k2: -0.02912317
  k3: -0.00213301
  k4: 0.00413641
  p1: -0.00245151
  p2: -0.00985313
  p3: 0.00894860
  p4: -0.00840183
  s1: 0.0098
  s2: 0.0075
  s3: -0.0034
  s4: 0.0066
  tilt_x: 0.0099
  tilt_y: -0.0430
  scale: 3.1157
  rotation: 0.0800

Best score: 9.2457: : 249iter [00:08, 39.96iter/s]
Best score: 9.2457: : 250iter [00:08, 39.89iter/s]
Best score: 9.2457: : 250iter [00:08, 39.89iter/s]
Best score: 9.2457: : 251iter [00:08, 39.89iter/s]
Best score: 9.2457: : 252iter [00:08, 39.89iter/s]
Best score: 9.2457: : 253iter [00:08, 39.89iter/s]
Best score: 9.2457: : 254iter [00:08, 33.46iter/s]
Best score: 9.2457: : 254iter [00:08, 33.46iter/s]
Best score: 9.2457: : 255iter [00:08, 33.46iter/s]
Best score: 9.2457: : 256iter [00:08, 33.46iter/s]
Best score: 9.2457: : 257iter [00:08, 33.46iter/s]
Best score: 9.2457: : 258iter [00:08, 33.46iter/s]
Best score: 9.2457: : 259iter [00:08, 35.27iter/s]
Best score: 7.8100: : 259iter [00:08, 35.27iter/s]
Best score: 7.8100: : 260iter [00:08, 35.27iter/s]
Best score: 7.8100: : 261iter [00:08, 35.27iter/s]
Best score: 7.8100: : 262iter [00:08, 35.27iter/s]
Best score: 7.8100: : 263iter [00:08, 36.37iter/s]
Best score: 7.8100: : 263iter [00:08, 36.37iter/s]
Best score: 7.8100: : 264iter [00:08, 36.37iter/s]
Best score: 7.8100: : 265iter [00:08, 36.37iter/s]
Best score: 7.8100: : 266iter [00:08, 36.37iter/s]
Best score: 7.8100: : 267iter [00:08, 36.37iter/s]
Best score: 7.8100: : 268iter [00:08, 37.76iter/s]
Best score: 7.8100: : 268iter [00:08, 37.76iter/s]
Best score: 5.3227: : 269iter [00:08, 37.76iter/s]
Best score: 5.3227: : 270iter [00:09, 37.76iter/s]
Best score: 5.3227: : 271iter [00:09, 37.76iter/s]
Best score: 5.3227: : 272iter [00:09, 37.76iter/s]
Best score: 5.3227: : 273iter [00:09, 38.55iter/s]
Best score: 5.3227: : 273iter [00:09, 38.55iter/s]
Best score: 5.3227: : 274iter [00:09, 38.55iter/s]
Best score: 5.3227: : 275iter [00:09, 38.55iter/s]
Best score: 5.3227: : 276iter [00:09, 38.55iter/s]
Best score: 5.3227: : 277iter [00:09, 38.55iter/s]
Best score: 5.3227: : 278iter [00:09, 39.23iter/s]
Best score: 5.3227: : 278iter [00:09, 39.23iter/s]
Best score: 5.3016: : 279iter [00:09, 39.23iter/s]
Best score: 5.3016: : 280iter [00:09, 39.23iter/s]
Best score: 5.3016: : 281iter [00:09, 39.23iter/s]
Best score: 5.3016: : 282iter [00:09, 39.40iter/s]
Best score: 5.3016: : 282iter [00:09, 39.40iter/s]
Best score: 5.3016: : 283iter [00:09, 39.40iter/s]
Best score: 5.3016: : 284iter [00:09, 39.40iter/s]
Best score: 5.3016: : 285iter [00:09, 39.40iter/s]
Best score: 5.3016: : 286iter [00:09, 39.40iter/s]
Best score: 5.3016: : 287iter [00:09, 39.75iter/s]
Best score: 5.3016: : 287iter [00:09, 39.75iter/s]
Best score: 5.3016: : 288iter [00:09, 39.75iter/s]
Best score: 3.3492: : 289iter [00:09, 39.75iter/s]
Best score: 3.3492: : 290iter [00:09, 39.75iter/s]
Best score: 3.3492: : 291iter [00:09, 39.66iter/s]
Best score: 3.3492: : 291iter [00:09, 39.66iter/s]
Best score: 3.3492: : 292iter [00:09, 39.66iter/s]
Best score: 3.3492: : 293iter [00:09, 39.66iter/s]
Best score: 3.3492: : 294iter [00:09, 39.66iter/s]
Best score: 3.3492: : 295iter [00:09, 39.66iter/s]
Best score: 3.3492: : 296iter [00:09, 39.93iter/s]
Best score: 3.3492: : 296iter [00:09, 39.93iter/s]
Best score: 3.3492: : 297iter [00:09, 39.93iter/s]
Best score: 3.3492: : 298iter [00:09, 39.93iter/s]
[ITER 300] Best: 1.9479
[PARAMS] Current best parameters:
  fx: 540.0088
  fy: 539.6984
  px: 1033.7835
  py: 757.4397
  k1: 0.00325203
  k2: 0.00780661
  k3: -0.01175158
  k4: 0.00225055
  p1: -0.00497585
  p2: 0.01332158
  p3: -0.00411687
  p4: 0.00686749
  s1: -0.0097
  s2: -0.0026
  s3: 0.0087
  s4: -0.0100
  tilt_x: 0.0036
  tilt_y: 0.0103
  scale: 2.9332
  rotation: 0.4659

Best score: 1.9479: : 299iter [00:09, 39.93iter/s]
Best score: 1.9479: : 300iter [00:09, 39.86iter/s]
Best score: 1.9479: : 300iter [00:09, 39.86iter/s]
Best score: 1.9479: : 301iter [00:09, 39.86iter/s]
Best score: 1.9479: : 302iter [00:09, 39.86iter/s]
Best score: 1.9479: : 303iter [00:09, 39.86iter/s]
Best score: 1.9479: : 304iter [00:09, 33.11iter/s]
Best score: 1.9479: : 304iter [00:09, 33.11iter/s]
Best score: 1.9479: : 305iter [00:09, 33.11iter/s]
Best score: 1.9479: : 306iter [00:09, 33.11iter/s]
Best score: 1.9479: : 307iter [00:09, 33.11iter/s]
Best score: 1.9479: : 308iter [00:10, 33.11iter/s]
Best score: 1.9479: : 309iter [00:10, 35.26iter/s]
Best score: 1.9479: : 309iter [00:10, 35.26iter/s]
Best score: 1.9479: : 310iter [00:10, 35.26iter/s]
Best score: 1.9479: : 311iter [00:10, 35.26iter/s]
Best score: 1.9479: : 312iter [00:10, 35.26iter/s]
Best score: 1.9479: : 313iter [00:10, 36.27iter/s]
Best score: 1.9479: : 313iter [00:10, 36.27iter/s]
Best score: 1.9479: : 314iter [00:10, 36.27iter/s]
Best score: 1.9479: : 315iter [00:10, 36.27iter/s]
Best score: 1.9479: : 316iter [00:10, 36.27iter/s]
Best score: 1.9479: : 317iter [00:10, 36.27iter/s]
Best score: 1.9479: : 318iter [00:10, 37.52iter/s]
Best score: 1.9479: : 318iter [00:10, 37.52iter/s]
Best score: 1.9012: : 319iter [00:10, 37.52iter/s]
Best score: 1.9012: : 320iter [00:10, 37.52iter/s]
Best score: 1.9012: : 321iter [00:10, 37.52iter/s]
Best score: 1.9012: : 322iter [00:10, 38.11iter/s]
Best score: 1.9012: : 322iter [00:10, 38.11iter/s]
Best score: 1.9012: : 323iter [00:10, 38.11iter/s]
Best score: 1.9012: : 324iter [00:10, 38.11iter/s]
Best score: 1.9012: : 325iter [00:10, 38.11iter/s]
Best score: 1.9012: : 326iter [00:10, 38.11iter/s]
Best score: 1.9012: : 327iter [00:10, 39.03iter/s]
Best score: 1.9012: : 327iter [00:10, 39.03iter/s]
Best score: 1.9012: : 328iter [00:10, 39.03iter/s]
Best score: 1.9012: : 329iter [00:10, 39.03iter/s]
Best score: 1.9012: : 330iter [00:10, 39.03iter/s]
Best score: 1.9012: : 331iter [00:10, 39.13iter/s]
Best score: 1.9012: : 331iter [00:10, 39.13iter/s]
Best score: 1.9012: : 332iter [00:10, 39.13iter/s]
Best score: 1.9012: : 333iter [00:10, 39.13iter/s]
Best score: 1.9012: : 334iter [00:10, 39.13iter/s]
Best score: 1.9012: : 335iter [00:10, 39.13iter/s]
Best score: 1.9012: : 336iter [00:10, 39.62iter/s]
Best score: 1.9012: : 336iter [00:10, 39.62iter/s]
Best score: 1.9012: : 337iter [00:10, 39.62iter/s]
Best score: 1.9012: : 338iter [00:10, 39.62iter/s]
Best score: 1.9012: : 339iter [00:10, 39.62iter/s]
Best score: 1.9012: : 340iter [00:10, 39.59iter/s]
Best score: 1.9012: : 340iter [00:10, 39.59iter/s]
Best score: 1.9012: : 341iter [00:10, 39.59iter/s]
Best score: 1.9012: : 342iter [00:10, 39.59iter/s]
Best score: 1.9012: : 343iter [00:10, 39.59iter/s]
Best score: 1.9012: : 344iter [00:10, 39.62iter/s]
Best score: 1.9012: : 344iter [00:10, 39.62iter/s]
Best score: 1.9012: : 345iter [00:10, 39.62iter/s]
Best score: 1.9012: : 346iter [00:10, 39.62iter/s]
Best score: 1.9012: : 347iter [00:10, 39.62iter/s]
Best score: 1.9012: : 348iter [00:11, 39.62iter/s]
Best score: 1.9012: : 349iter [00:11, 40.07iter/s]
[ITER 350] Best: 1.3454
[PARAMS] Current best parameters:
  fx: 529.2724
  fy: 528.9100
  px: 1035.0300
  py: 757.8142
  k1: 0.01592503
  k2: 0.00314797
  k3: -0.01341794
  k4: 0.00388824
  p1: -0.00348922
  p2: 0.01140918
  p3: -0.00148159
  p4: 0.00210564
  s1: -0.0082
  s2: 0.0015
  s3: 0.0028
  s4: -0.0031
  tilt_x: 0.0055
  tilt_y: -0.0035
  scale: 2.9767
  rotation: 0.5653

Best score: 1.3454: : 349iter [00:11, 40.07iter/s]
Best score: 1.3454: : 350iter [00:11, 40.07iter/s]
Best score: 1.3454: : 351iter [00:11, 40.07iter/s]
Best score: 1.3454: : 352iter [00:11, 40.07iter/s]
Best score: 1.3454: : 353iter [00:11, 40.07iter/s]
Best score: 1.3454: : 354iter [00:11, 33.48iter/s]
Best score: 1.3454: : 354iter [00:11, 33.48iter/s]
Best score: 1.3454: : 355iter [00:11, 33.48iter/s]
Best score: 1.3454: : 356iter [00:11, 33.48iter/s]
Best score: 1.3454: : 357iter [00:11, 33.48iter/s]
Best score: 1.3454: : 358iter [00:11, 33.48iter/s]
Best score: 1.3454: : 359iter [00:11, 29.73iter/s]
Best score: 1.0164: : 359iter [00:11, 29.73iter/s]
Best score: 1.0164: : 360iter [00:11, 29.73iter/s]
Best score: 1.0164: : 361iter [00:11, 29.73iter/s]
Best score: 1.0164: : 362iter [00:11, 29.73iter/s]
Best score: 1.0164: : 363iter [00:11, 31.82iter/s]
Best score: 1.0164: : 363iter [00:11, 31.82iter/s]
Best score: 1.0164: : 364iter [00:11, 31.82iter/s]
Best score: 1.0164: : 365iter [00:11, 31.82iter/s]
Best score: 1.0164: : 366iter [00:11, 31.82iter/s]
Best score: 1.0164: : 367iter [00:11, 31.82iter/s]
Best score: 1.0164: : 368iter [00:11, 34.21iter/s]
Best score: 1.0164: : 368iter [00:11, 34.21iter/s]
Best score: 1.0164: : 369iter [00:11, 34.21iter/s]
Best score: 1.0164: : 370iter [00:11, 34.21iter/s]
Best score: 1.0164: : 371iter [00:11, 34.21iter/s]
Best score: 1.0164: : 372iter [00:11, 35.57iter/s]
Best score: 1.0164: : 372iter [00:11, 35.57iter/s]
Best score: 1.0164: : 373iter [00:11, 35.57iter/s]
Best score: 1.0164: : 374iter [00:11, 35.57iter/s]
Best score: 1.0164: : 375iter [00:11, 35.57iter/s]
Best score: 1.0164: : 376iter [00:11, 35.57iter/s]
Best score: 1.0164: : 377iter [00:11, 37.10iter/s]
Best score: 1.0164: : 377iter [00:11, 37.10iter/s]
Best score: 1.0164: : 378iter [00:11, 37.10iter/s]
Best score: 1.0164: : 379iter [00:11, 37.10iter/s]
Best score: 1.0164: : 380iter [00:11, 37.10iter/s]
Best score: 1.0164: : 381iter [00:11, 37.76iter/s]
Best score: 1.0164: : 381iter [00:11, 37.76iter/s]
Best score: 1.0164: : 382iter [00:12, 37.76iter/s]
Best score: 1.0164: : 383iter [00:12, 37.76iter/s]
Best score: 1.0164: : 384iter [00:12, 37.76iter/s]
Best score: 1.0164: : 385iter [00:12, 37.76iter/s]
Best score: 1.0164: : 386iter [00:12, 38.65iter/s]
Best score: 1.0164: : 386iter [00:12, 38.65iter/s]
Best score: 1.0164: : 387iter [00:12, 38.65iter/s]
Best score: 1.0164: : 388iter [00:12, 38.65iter/s]
Best score: 0.8867: : 389iter [00:12, 38.65iter/s]
Best score: 0.8867: : 390iter [00:12, 38.89iter/s]
Best score: 0.8867: : 390iter [00:12, 38.89iter/s]
Best score: 0.8867: : 391iter [00:12, 38.89iter/s]
Best score: 0.8867: : 392iter [00:12, 38.89iter/s]
Best score: 0.8867: : 393iter [00:12, 38.89iter/s]
Best score: 0.8867: : 394iter [00:12, 38.89iter/s]
Best score: 0.8867: : 395iter [00:12, 39.47iter/s]
Best score: 0.8867: : 395iter [00:12, 39.47iter/s]
Best score: 0.8867: : 396iter [00:12, 39.47iter/s]
Best score: 0.8867: : 397iter [00:12, 39.47iter/s]
Best score: 0.8867: : 398iter [00:12, 39.47iter/s]
[ITER 400] Best: 0.8667
[PARAMS] Current best parameters:
  fx: 537.2386
  fy: 537.0984
  px: 1030.5771
  py: 756.2305
  k1: 0.00078527
  k2: -0.00804083
  k3: 0.00785637
  k4: -0.00320936
  p1: -0.00367031
  p2: 0.01523395
  p3: -0.00444139
  p4: 0.00504696
  s1: -0.0156
  s2: -0.0006
  s3: 0.0109
  s4: -0.0087
  tilt_x: -0.0051
  tilt_y: 0.0263
  scale: 2.9887
  rotation: 0.6316

Best score: 0.8667: : 399iter [00:12, 39.47iter/s]
Best score: 0.8667: : 400iter [00:12, 39.61iter/s]
Best score: 0.8667: : 400iter [00:12, 39.61iter/s]
Best score: 0.8667: : 401iter [00:12, 39.61iter/s]
Best score: 0.8667: : 402iter [00:12, 39.61iter/s]
Best score: 0.8667: : 403iter [00:12, 39.61iter/s]
Best score: 0.8667: : 404iter [00:12, 33.19iter/s]
Best score: 0.8667: : 404iter [00:12, 33.19iter/s]
Best score: 0.8667: : 405iter [00:12, 33.19iter/s]
Best score: 0.8667: : 406iter [00:12, 33.19iter/s]
Best score: 0.8667: : 407iter [00:12, 33.19iter/s]
Best score: 0.8667: : 408iter [00:12, 33.19iter/s]
Best score: 0.8667: : 409iter [00:12, 35.23iter/s]
Best score: 0.8015: : 409iter [00:12, 35.23iter/s]
Best score: 0.8015: : 410iter [00:12, 35.23iter/s]
Best score: 0.8015: : 411iter [00:12, 35.23iter/s]
Best score: 0.8015: : 412iter [00:12, 35.23iter/s]
Best score: 0.8015: : 413iter [00:12, 36.24iter/s]
Best score: 0.8015: : 413iter [00:12, 36.24iter/s]
Best score: 0.8015: : 414iter [00:12, 36.24iter/s]
Best score: 0.8015: : 415iter [00:12, 36.24iter/s]
Best score: 0.8015: : 416iter [00:12, 36.24iter/s]
Best score: 0.8015: : 417iter [00:12, 36.24iter/s]
Best score: 0.8015: : 418iter [00:12, 37.58iter/s]
Best score: 0.8015: : 418iter [00:12, 37.58iter/s]
Best score: 0.6228: : 419iter [00:13, 37.58iter/s]
Best score: 0.6228: : 420iter [00:13, 37.58iter/s]
Best score: 0.6228: : 421iter [00:13, 37.58iter/s]
Best score: 0.6228: : 422iter [00:13, 38.17iter/s]
Best score: 0.6228: : 422iter [00:13, 38.17iter/s]
Best score: 0.6228: : 423iter [00:13, 38.17iter/s]
Best score: 0.6228: : 424iter [00:13, 38.17iter/s]
Best score: 0.6228: : 425iter [00:13, 38.17iter/s]
Best score: 0.6228: : 426iter [00:13, 38.17iter/s]
Best score: 0.6228: : 427iter [00:13, 38.99iter/s]
Best score: 0.6228: : 427iter [00:13, 38.99iter/s]
Best score: 0.6228: : 428iter [00:13, 38.99iter/s]
Best score: 0.5513: : 429iter [00:13, 38.99iter/s]
Best score: 0.5513: : 430iter [00:13, 38.99iter/s]
Best score: 0.5513: : 431iter [00:13, 38.99iter/s]
Best score: 0.5513: : 432iter [00:13, 39.38iter/s]
Best score: 0.5513: : 432iter [00:13, 39.38iter/s]
Best score: 0.5513: : 433iter [00:13, 39.38iter/s]
Best score: 0.5513: : 434iter [00:13, 39.38iter/s]
Best score: 0.5513: : 435iter [00:13, 39.38iter/s]
Best score: 0.5513: : 436iter [00:13, 39.38iter/s]
Best score: 0.5513: : 437iter [00:13, 39.86iter/s]
Best score: 0.5513: : 437iter [00:13, 39.86iter/s]
Best score: 0.5513: : 438iter [00:13, 39.86iter/s]
Best score: 0.5513: : 439iter [00:13, 39.86iter/s]
Best score: 0.5513: : 440iter [00:13, 39.86iter/s]
Best score: 0.5513: : 441iter [00:13, 39.86iter/s]
Best score: 0.5513: : 442iter [00:13, 39.94iter/s]
Best score: 0.5513: : 442iter [00:13, 39.94iter/s]
Best score: 0.5513: : 443iter [00:13, 39.94iter/s]
Best score: 0.5513: : 444iter [00:13, 39.94iter/s]
Best score: 0.5513: : 445iter [00:13, 39.94iter/s]
Best score: 0.5513: : 446iter [00:13, 39.94iter/s]
Best score: 0.5513: : 447iter [00:13, 40.21iter/s]
Best score: 0.5513: : 447iter [00:13, 40.21iter/s]
Best score: 0.5513: : 448iter [00:13, 40.21iter/s]
[ITER 450] Best: 0.5510
[PARAMS] Current best parameters:
  fx: 541.0696
  fy: 540.6800
  px: 1031.6555
  py: 759.8711
  k1: -0.02178294
  k2: 0.00766477
  k3: 0.00355830
  k4: -0.00263492
  p1: 0.00213798
  p2: 0.01659015
  p3: -0.01552898
  p4: 0.00186647
  s1: -0.0044
  s2: 0.0013
  s3: 0.0144
  s4: -0.0017
  tilt_x: 0.0013
  tilt_y: 0.0220
  scale: 3.0476
  rotation: 0.6911

Best score: 0.5510: : 449iter [00:13, 40.21iter/s]
Best score: 0.5510: : 450iter [00:13, 40.21iter/s]
Best score: 0.5510: : 451iter [00:13, 40.21iter/s]
Best score: 0.5510: : 452iter [00:13, 33.74iter/s]
Best score: 0.5510: : 452iter [00:13, 33.74iter/s]
Best score: 0.5510: : 453iter [00:13, 33.74iter/s]
Best score: 0.5510: : 454iter [00:13, 33.74iter/s]
Best score: 0.5510: : 455iter [00:13, 33.74iter/s]
Best score: 0.5510: : 456iter [00:14, 33.74iter/s]
Best score: 0.5510: : 457iter [00:14, 35.54iter/s]
Best score: 0.5510: : 457iter [00:14, 35.54iter/s]
Best score: 0.5510: : 458iter [00:14, 35.54iter/s]
Best score: 0.5018: : 459iter [00:14, 35.54iter/s]
Best score: 0.5018: : 460iter [00:14, 35.54iter/s]
Best score: 0.5018: : 461iter [00:14, 36.36iter/s]
Best score: 0.5018: : 461iter [00:14, 36.36iter/s]
Best score: 0.5018: : 462iter [00:14, 36.36iter/s]
Best score: 0.5018: : 463iter [00:14, 36.36iter/s]
Best score: 0.5018: : 464iter [00:14, 36.36iter/s]
Best score: 0.5018: : 465iter [00:14, 36.36iter/s]
Best score: 0.5018: : 466iter [00:14, 37.55iter/s]
Best score: 0.5018: : 466iter [00:14, 37.55iter/s]
Best score: 0.5018: : 467iter [00:14, 37.55iter/s]
Best score: 0.5018: : 468iter [00:14, 37.55iter/s]
Best score: 0.4806: : 469iter [00:14, 37.55iter/s]
Best score: 0.4806: : 470iter [00:14, 38.09iter/s]
Best score: 0.4806: : 470iter [00:14, 38.09iter/s]
Best score: 0.4806: : 471iter [00:14, 38.09iter/s]
Best score: 0.4806: : 472iter [00:14, 38.09iter/s]
Best score: 0.4806: : 473iter [00:14, 38.09iter/s]
Best score: 0.4806: : 474iter [00:14, 38.09iter/s]
Best score: 0.4806: : 475iter [00:14, 38.99iter/s]
Best score: 0.4806: : 475iter [00:14, 38.99iter/s]
Best score: 0.4806: : 476iter [00:14, 38.99iter/s]
Best score: 0.4806: : 477iter [00:14, 38.99iter/s]
Best score: 0.4806: : 478iter [00:14, 38.99iter/s]
Best score: 0.4658: : 479iter [00:14, 38.99iter/s]
Best score: 0.4658: : 480iter [00:14, 39.36iter/s]
Best score: 0.4658: : 480iter [00:14, 39.36iter/s]
Best score: 0.4658: : 481iter [00:14, 39.36iter/s]
Best score: 0.4658: : 482iter [00:14, 39.36iter/s]
Best score: 0.4658: : 483iter [00:14, 39.36iter/s]
Best score: 0.4658: : 484iter [00:14, 39.36iter/s]
Best score: 0.4658: : 485iter [00:14, 39.66iter/s]
Best score: 0.4658: : 485iter [00:14, 39.66iter/s]
Best score: 0.4658: : 486iter [00:14, 39.66iter/s]
Best score: 0.4658: : 487iter [00:14, 39.66iter/s]
Best score: 0.4658: : 488iter [00:14, 39.66iter/s]
Best score: 0.4658: : 489iter [00:14, 39.66iter/s]
Best score: 0.4658: : 490iter [00:14, 39.73iter/s]
Best score: 0.4658: : 490iter [00:14, 39.73iter/s]
Best score: 0.4658: : 491iter [00:14, 39.73iter/s]
Best score: 0.4658: : 492iter [00:14, 39.73iter/s]
Best score: 0.4658: : 493iter [00:14, 39.73iter/s]
Best score: 0.4658: : 494iter [00:14, 39.73iter/s]
Best score: 0.4658: : 495iter [00:14, 40.07iter/s]
Best score: 0.4658: : 495iter [00:14, 40.07iter/s]
Best score: 0.4658: : 496iter [00:15, 40.07iter/s]
Best score: 0.4658: : 497iter [00:15, 40.07iter/s]
Best score: 0.4658: : 498iter [00:15, 40.07iter/s]
[ITER 500] Best: 0.4025
[PARAMS] Current best parameters:
  fx: 539.5240
  fy: 539.0947
  px: 1031.8067
  py: 759.3601
  k1: -0.00710193
  k2: 0.00313019
  k3: -0.00042057
  k4: -0.00098817
  p1: 0.00316153
  p2: 0.01362695
  p3: -0.01215565
  p4: 0.00055281
  s1: -0.0039
  s2: 0.0014
  s3: 0.0046
  s4: 0.0022
  tilt_x: -0.0039
  tilt_y: 0.0159
  scale: 2.9940
  rotation: 0.6765

Best score: 0.4025: : 499iter [00:15, 40.07iter/s]
Best score: 0.4025: : 500iter [00:15, 39.92iter/s]
Best score: 0.4025: : 500iter [00:15, 39.92iter/s]
Best score: 0.4025: : 501iter [00:15, 39.92iter/s]
Best score: 0.4025: : 502iter [00:15, 39.92iter/s]
Best score: 0.4025: : 503iter [00:15, 39.92iter/s]
Best score: 0.4025: : 504iter [00:15, 39.92iter/s]
Best score: 0.4025: : 505iter [00:15, 33.69iter/s]
Best score: 0.4025: : 505iter [00:15, 33.69iter/s]
Best score: 0.4025: : 506iter [00:15, 33.69iter/s]
Best score: 0.4025: : 507iter [00:15, 33.69iter/s]
Best score: 0.4025: : 508iter [00:15, 33.69iter/s]
Best score: 0.3945: : 509iter [00:15, 33.69iter/s]
Best score: 0.3945: : 510iter [00:15, 35.31iter/s]
Best score: 0.3945: : 510iter [00:15, 35.31iter/s]
Best score: 0.3945: : 511iter [00:15, 35.31iter/s]
Best score: 0.3945: : 512iter [00:15, 35.31iter/s]
Best score: 0.3945: : 513iter [00:15, 35.31iter/s]
Best score: 0.3945: : 514iter [00:15, 35.31iter/s]
Best score: 0.3945: : 515iter [00:15, 36.83iter/s]
Best score: 0.3945: : 515iter [00:15, 36.83iter/s]
Best score: 0.3945: : 516iter [00:15, 36.83iter/s]
Best score: 0.3945: : 517iter [00:15, 36.83iter/s]
Best score: 0.3945: : 518iter [00:15, 36.83iter/s]
Best score: 0.3917: : 519iter [00:15, 36.83iter/s]
Best score: 0.3917: : 520iter [00:15, 37.70iter/s]
Best score: 0.3917: : 520iter [00:15, 37.70iter/s]
Best score: 0.3917: : 521iter [00:15, 37.70iter/s]
Best score: 0.3917: : 522iter [00:15, 37.70iter/s]
Best score: 0.3917: : 523iter [00:15, 37.70iter/s]
Best score: 0.3917: : 524iter [00:15, 37.70iter/s]
Best score: 0.3917: : 525iter [00:15, 38.61iter/s]
Best score: 0.3917: : 525iter [00:15, 38.61iter/s]
Best score: 0.3917: : 526iter [00:15, 38.61iter/s]
Best score: 0.3917: : 527iter [00:15, 38.61iter/s]
Best score: 0.3917: : 528iter [00:15, 38.61iter/s]
Best score: 0.3818: : 529iter [00:15, 38.61iter/s]
Best score: 0.3818: : 530iter [00:15, 39.12iter/s]
Best score: 0.3818: : 530iter [00:15, 39.12iter/s]
Best score: 0.3818: : 531iter [00:15, 39.12iter/s]
Best score: 0.3818: : 532iter [00:15, 39.12iter/s]
Best score: 0.3818: : 533iter [00:16, 39.12iter/s]
Best score: 0.3818: : 534iter [00:16, 39.12iter/s]
Best score: 0.3818: : 535iter [00:16, 39.52iter/s]
Best score: 0.3818: : 535iter [00:16, 39.52iter/s]
Best score: 0.3818: : 536iter [00:16, 39.52iter/s]
Best score: 0.3818: : 537iter [00:16, 39.52iter/s]
Best score: 0.3818: : 538iter [00:16, 39.52iter/s]
Best score: 0.3818: : 539iter [00:16, 39.52iter/s]
Best score: 0.3818: : 540iter [00:16, 39.57iter/s]
Best score: 0.3818: : 540iter [00:16, 39.57iter/s]
Best score: 0.3818: : 541iter [00:16, 39.57iter/s]
Best score: 0.3818: : 542iter [00:16, 39.57iter/s]
Best score: 0.3818: : 543iter [00:16, 39.57iter/s]
Best score: 0.3818: : 544iter [00:16, 39.57iter/s]
Best score: 0.3818: : 545iter [00:16, 40.03iter/s]
Best score: 0.3818: : 545iter [00:16, 40.03iter/s]
Best score: 0.3818: : 546iter [00:16, 40.03iter/s]
Best score: 0.3818: : 547iter [00:16, 40.03iter/s]
Best score: 0.3818: : 548iter [00:16, 40.03iter/s]
[ITER 550] Best: 0.3788
[PARAMS] Current best parameters:
  fx: 531.7160
  fy: 531.3777
  px: 1030.6063
  py: 757.6018
  k1: 0.00282525
  k2: -0.00358069
  k3: 0.00261608
  k4: -0.00130201
  p1: -0.00319045
  p2: 0.01336913
  p3: -0.01030852
  p4: 0.00519357
  s1: -0.0081
  s2: -0.0022
  s3: 0.0106
  s4: -0.0040
  tilt_x: -0.0007
  tilt_y: 0.0220
  scale: 3.0185
  rotation: 0.6508

Best score: 0.3788: : 549iter [00:16, 40.03iter/s]
Best score: 0.3788: : 550iter [00:16, 40.07iter/s]
Best score: 0.3788: : 550iter [00:16, 40.07iter/s]
Best score: 0.3788: : 551iter [00:16, 40.07iter/s]
Best score: 0.3788: : 552iter [00:16, 40.07iter/s]
Best score: 0.3788: : 553iter [00:16, 40.07iter/s]
Best score: 0.3788: : 554iter [00:16, 40.07iter/s]
Best score: 0.3788: : 555iter [00:16, 34.03iter/s]
Best score: 0.3788: : 555iter [00:16, 34.03iter/s]
Best score: 0.3788: : 556iter [00:16, 34.03iter/s]
Best score: 0.3788: : 557iter [00:16, 34.03iter/s]
Best score: 0.3788: : 558iter [00:16, 34.03iter/s]
Best score: 0.3502: : 559iter [00:16, 34.03iter/s]
Best score: 0.3502: : 560iter [00:16, 35.53iter/s]
Best score: 0.3502: : 560iter [00:16, 35.53iter/s]
Best score: 0.3502: : 561iter [00:16, 35.53iter/s]
Best score: 0.3502: : 562iter [00:16, 35.53iter/s]
Best score: 0.3502: : 563iter [00:16, 35.53iter/s]
Best score: 0.3502: : 564iter [00:16, 35.53iter/s]
Best score: 0.3502: : 565iter [00:16, 36.91iter/s]
Best score: 0.3502: : 565iter [00:16, 36.91iter/s]
Best score: 0.3502: : 566iter [00:16, 36.91iter/s]
Best score: 0.3502: : 567iter [00:16, 36.91iter/s]
Best score: 0.3502: : 568iter [00:16, 36.91iter/s]
Best score: 0.3502: : 569iter [00:16, 36.91iter/s]
Best score: 0.3502: : 570iter [00:16, 37.79iter/s]
Best score: 0.3502: : 570iter [00:16, 37.79iter/s]
Best score: 0.3502: : 571iter [00:17, 37.79iter/s]
Best score: 0.3502: : 572iter [00:17, 37.79iter/s]
Best score: 0.3502: : 573iter [00:17, 37.79iter/s]
Best score: 0.3502: : 574iter [00:17, 37.79iter/s]
Best score: 0.3502: : 575iter [00:17, 38.72iter/s]
Best score: 0.3502: : 575iter [00:17, 38.72iter/s]
Best score: 0.3502: : 576iter [00:17, 38.72iter/s]
Best score: 0.3502: : 577iter [00:17, 38.72iter/s]
Best score: 0.3502: : 578iter [00:17, 38.72iter/s]
Best score: 0.3502: : 579iter [00:17, 38.72iter/s]
Best score: 0.3502: : 580iter [00:17, 39.04iter/s]
Best score: 0.3502: : 580iter [00:17, 39.04iter/s]
Best score: 0.3502: : 581iter [00:17, 39.04iter/s]
Best score: 0.3502: : 582iter [00:17, 39.04iter/s]
Best score: 0.3502: : 583iter [00:17, 39.04iter/s]
Best score: 0.3502: : 584iter [00:17, 39.04iter/s]
Best score: 0.3502: : 585iter [00:17, 39.32iter/s]
Best score: 0.3502: : 585iter [00:17, 39.32iter/s]
Best score: 0.3502: : 586iter [00:17, 39.32iter/s]
Best score: 0.3502: : 587iter [00:17, 39.32iter/s]
Best score: 0.3502: : 588iter [00:17, 39.32iter/s]
Best score: 0.3349: : 589iter [00:17, 39.32iter/s]
Best score: 0.3349: : 590iter [00:17, 39.48iter/s]
Best score: 0.3349: : 590iter [00:17, 39.48iter/s]
Best score: 0.3349: : 591iter [00:17, 39.48iter/s]
Best score: 0.3349: : 592iter [00:17, 39.48iter/s]
Best score: 0.3349: : 593iter [00:17, 39.48iter/s]
Best score: 0.3349: : 594iter [00:17, 39.48iter/s]
Best score: 0.3349: : 595iter [00:17, 39.80iter/s]
Best score: 0.3349: : 595iter [00:17, 39.80iter/s]
Best score: 0.3349: : 596iter [00:17, 39.80iter/s]
Best score: 0.3349: : 597iter [00:17, 39.80iter/s]
Best score: 0.3349: : 598iter [00:17, 39.80iter/s]
[ITER 600] Best: 0.2667
[PARAMS] Current best parameters:
  fx: 527.4214
  fy: 527.1278
  px: 1028.0819
  py: 755.7459
  k1: 0.02969703
  k2: -0.03535038
  k3: 0.01949101
  k4: -0.00467921
  p1: -0.00215208
  p2: 0.00993212
  p3: 0.00668245
  p4: 0.00055871
  s1: -0.0210
  s2: 0.0010
  s3: -0.0092
  s4: 0.0005
  tilt_x: -0.0111
  tilt_y: 0.0274
  scale: 2.9900
  rotation: 0.6441

Best score: 0.2667: : 599iter [00:17, 39.80iter/s]
Best score: 0.2667: : 600iter [00:17, 39.84iter/s]
Best score: 0.2667: : 600iter [00:17, 39.84iter/s]
Best score: 0.2667: : 601iter [00:17, 39.84iter/s]
Best score: 0.2667: : 602iter [00:17, 39.84iter/s]
Best score: 0.2667: : 603iter [00:17, 39.84iter/s]
Best score: 0.2667: : 604iter [00:17, 33.54iter/s]
Best score: 0.2667: : 604iter [00:17, 33.54iter/s]
Best score: 0.2667: : 605iter [00:17, 33.54iter/s]
Best score: 0.2667: : 606iter [00:17, 33.54iter/s]
Best score: 0.2667: : 607iter [00:17, 33.54iter/s]
Best score: 0.2667: : 608iter [00:18, 33.54iter/s]
Best score: 0.2667: : 609iter [00:18, 35.47iter/s]
Best score: 0.2667: : 609iter [00:18, 35.47iter/s]
Best score: 0.2667: : 610iter [00:18, 35.47iter/s]
Best score: 0.2667: : 611iter [00:18, 35.47iter/s]
Best score: 0.2667: : 612iter [00:18, 35.47iter/s]
Best score: 0.2667: : 613iter [00:18, 36.47iter/s]
Best score: 0.2667: : 613iter [00:18, 36.47iter/s]
Best score: 0.2667: : 614iter [00:18, 36.47iter/s]
Best score: 0.2667: : 615iter [00:18, 36.47iter/s]
Best score: 0.2667: : 616iter [00:18, 36.47iter/s]
Best score: 0.2667: : 617iter [00:18, 36.47iter/s]
Best score: 0.2667: : 618iter [00:18, 37.76iter/s]
Best score: 0.2667: : 618iter [00:18, 37.76iter/s]
Best score: 0.2667: : 619iter [00:18, 37.76iter/s]
Best score: 0.2667: : 620iter [00:18, 37.76iter/s]
Best score: 0.2667: : 621iter [00:18, 37.76iter/s]
Best score: 0.2667: : 622iter [00:18, 38.13iter/s]
Best score: 0.2667: : 622iter [00:18, 38.13iter/s]
Best score: 0.2667: : 623iter [00:18, 38.13iter/s]
Best score: 0.2667: : 624iter [00:18, 38.13iter/s]
Best score: 0.2667: : 625iter [00:18, 38.13iter/s]
Best score: 0.2667: : 626iter [00:18, 38.13iter/s]
Best score: 0.2667: : 627iter [00:18, 38.92iter/s]
Best score: 0.2667: : 627iter [00:18, 38.92iter/s]
Best score: 0.2667: : 628iter [00:18, 38.92iter/s]
Best score: 0.2667: : 629iter [00:18, 38.92iter/s]
Best score: 0.2667: : 630iter [00:18, 38.92iter/s]
Best score: 0.2667: : 631iter [00:18, 39.05iter/s]
Best score: 0.2667: : 631iter [00:18, 39.05iter/s]
Best score: 0.2667: : 632iter [00:18, 39.05iter/s]
Best score: 0.2667: : 633iter [00:18, 39.05iter/s]
Best score: 0.2667: : 634iter [00:18, 39.05iter/s]
Best score: 0.2667: : 635iter [00:18, 39.05iter/s]
Best score: 0.2667: : 636iter [00:18, 39.57iter/s]
Best score: 0.2667: : 636iter [00:18, 39.57iter/s]
Best score: 0.2667: : 637iter [00:18, 39.57iter/s]
Best score: 0.2667: : 638iter [00:18, 39.57iter/s]
Best score: 0.2667: : 639iter [00:18, 39.57iter/s]
Best score: 0.2667: : 640iter [00:18, 39.59iter/s]
Best score: 0.2667: : 640iter [00:18, 39.59iter/s]
Best score: 0.2667: : 641iter [00:18, 39.59iter/s]
Best score: 0.2667: : 642iter [00:18, 39.59iter/s]
Best score: 0.2667: : 643iter [00:18, 39.59iter/s]
Best score: 0.2667: : 644iter [00:18, 39.59iter/s]
Best score: 0.2667: : 645iter [00:18, 40.07iter/s]
Best score: 0.2667: : 645iter [00:18, 40.07iter/s]
Best score: 0.2667: : 646iter [00:18, 40.07iter/s]
Best score: 0.2667: : 647iter [00:18, 40.07iter/s]
Best score: 0.2667: : 648iter [00:19, 40.07iter/s]
[ITER 650] Best: 0.2584
[PARAMS] Current best parameters:
  fx: 529.5983
  fy: 529.2384
  px: 1026.5180
  py: 756.2058
  k1: 0.02768620
  k2: -0.02940156
  k3: 0.01310214
  k4: -0.00287154
  p1: -0.00185728
  p2: 0.01009710
  p3: 0.00838751
  p4: -0.00018440
  s1: -0.0233
  s2: 0.0017
  s3: -0.0124
  s4: 0.0020
  tilt_x: -0.0093
  tilt_y: 0.0365
  scale: 2.9791
  rotation: 0.6343

Best score: 0.2584: : 649iter [00:19, 40.07iter/s]
Best score: 0.2584: : 650iter [00:19, 39.69iter/s]
Best score: 0.2584: : 650iter [00:19, 39.69iter/s]
Best score: 0.2584: : 651iter [00:19, 39.69iter/s]
Best score: 0.2584: : 652iter [00:19, 39.69iter/s]
Best score: 0.2584: : 653iter [00:19, 39.69iter/s]
Best score: 0.2584: : 654iter [00:19, 33.14iter/s]
Best score: 0.2584: : 654iter [00:19, 33.14iter/s]
Best score: 0.2584: : 655iter [00:19, 33.14iter/s]
Best score: 0.2584: : 656iter [00:19, 33.14iter/s]
Best score: 0.2584: : 657iter [00:19, 33.14iter/s]
Best score: 0.2584: : 658iter [00:19, 33.14iter/s]
Best score: 0.2584: : 659iter [00:19, 35.23iter/s]
Best score: 0.2584: : 659iter [00:19, 35.23iter/s]
Best score: 0.2584: : 660iter [00:19, 35.23iter/s]
Best score: 0.2584: : 661iter [00:19, 35.23iter/s]
Best score: 0.2584: : 662iter [00:19, 35.23iter/s]
Best score: 0.2584: : 663iter [00:19, 36.29iter/s]
Best score: 0.2584: : 663iter [00:19, 36.29iter/s]
Best score: 0.2584: : 664iter [00:19, 36.29iter/s]
Best score: 0.2584: : 665iter [00:19, 36.29iter/s]
Best score: 0.2584: : 666iter [00:19, 36.29iter/s]
Best score: 0.2584: : 667iter [00:19, 36.29iter/s]
Best score: 0.2584: : 668iter [00:19, 37.61iter/s]
Best score: 0.2584: : 668iter [00:19, 37.61iter/s]
Best score: 0.2584: : 669iter [00:19, 37.61iter/s]
Best score: 0.2584: : 670iter [00:19, 37.61iter/s]
Best score: 0.2584: : 671iter [00:19, 37.61iter/s]
Best score: 0.2584: : 672iter [00:19, 38.04iter/s]
Best score: 0.2584: : 672iter [00:19, 38.04iter/s]
Best score: 0.2584: : 673iter [00:19, 38.04iter/s]
Best score: 0.2584: : 674iter [00:19, 38.04iter/s]
Best score: 0.2584: : 675iter [00:19, 38.04iter/s]
Best score: 0.2584: : 676iter [00:19, 38.04iter/s]
Best score: 0.2584: : 677iter [00:19, 38.79iter/s]
Best score: 0.2584: : 677iter [00:19, 38.79iter/s]
Best score: 0.2584: : 678iter [00:19, 38.79iter/s]
Best score: 0.2492: : 679iter [00:19, 38.79iter/s]
Best score: 0.2492: : 680iter [00:19, 38.79iter/s]
Best score: 0.2492: : 681iter [00:19, 31.95iter/s]
Best score: 0.2492: : 681iter [00:19, 31.95iter/s]
Best score: 0.2492: : 682iter [00:20, 31.95iter/s]
Best score: 0.2492: : 683iter [00:20, 31.95iter/s]
Best score: 0.2492: : 684iter [00:20, 31.95iter/s]
Best score: 0.2492: : 685iter [00:20, 33.76iter/s]
Best score: 0.2492: : 685iter [00:20, 33.76iter/s]
Best score: 0.2492: : 686iter [00:20, 33.76iter/s]
Best score: 0.2492: : 687iter [00:20, 33.76iter/s]
Best score: 0.2492: : 688iter [00:20, 33.76iter/s]
Best score: 0.2450: : 689iter [00:20, 33.76iter/s]
Best score: 0.2450: : 690iter [00:20, 35.50iter/s]
Best score: 0.2450: : 690iter [00:20, 35.50iter/s]
Best score: 0.2450: : 691iter [00:20, 35.50iter/s]
Best score: 0.2450: : 692iter [00:20, 35.50iter/s]
Best score: 0.2450: : 693iter [00:20, 35.50iter/s]
Best score: 0.2450: : 694iter [00:20, 35.50iter/s]
Best score: 0.2450: : 695iter [00:20, 36.97iter/s]
Best score: 0.2450: : 695iter [00:20, 36.97iter/s]
Best score: 0.2450: : 696iter [00:20, 36.97iter/s]
Best score: 0.2450: : 697iter [00:20, 36.97iter/s]
Best score: 0.2450: : 698iter [00:20, 36.97iter/s]
[ITER 700] Best: 0.2450
[PARAMS] Current best parameters:
  fx: 527.2861
  fy: 527.0199
  px: 1026.3993
  py: 755.3784
  k1: 0.03549054
  k2: -0.03793265
  k3: 0.01773506
  k4: -0.00381515
  p1: -0.00165444
  p2: 0.01017063
  p3: 0.01336100
  p4: -0.00060598
  s1: -0.0282
  s2: 0.0018
  s3: -0.0187
  s4: 0.0026
  tilt_x: -0.0139
  tilt_y: 0.0370
  scale: 2.9771
  rotation: 0.6248

Best score: 0.2450: : 699iter [00:20, 36.97iter/s]
Best score: 0.2450: : 700iter [00:20, 37.69iter/s]
Best score: 0.2450: : 700iter [00:20, 37.69iter/s]
Best score: 0.2450: : 701iter [00:20, 37.69iter/s]
Best score: 0.2450: : 702iter [00:20, 37.69iter/s]
Best score: 0.2450: : 703iter [00:20, 37.69iter/s]
Best score: 0.2450: : 704iter [00:20, 31.94iter/s]
Best score: 0.2450: : 704iter [00:20, 31.94iter/s]
Best score: 0.2450: : 705iter [00:20, 31.94iter/s]
Best score: 0.2450: : 706iter [00:20, 31.94iter/s]
Best score: 0.2450: : 707iter [00:20, 31.94iter/s]
Best score: 0.2450: : 708iter [00:20, 31.94iter/s]
Best score: 0.2450: : 709iter [00:20, 34.27iter/s]
Best score: 0.2417: : 709iter [00:20, 34.27iter/s]
Best score: 0.2417: : 710iter [00:20, 34.27iter/s]
Best score: 0.2417: : 711iter [00:20, 34.27iter/s]
Best score: 0.2417: : 712iter [00:20, 34.27iter/s]
Best score: 0.2417: : 713iter [00:20, 35.48iter/s]
Best score: 0.2417: : 713iter [00:20, 35.48iter/s]
Best score: 0.2417: : 714iter [00:20, 35.48iter/s]
Best score: 0.2417: : 715iter [00:20, 35.48iter/s]
Best score: 0.2417: : 716iter [00:20, 35.48iter/s]
Best score: 0.2417: : 717iter [00:20, 36.53iter/s]
Best score: 0.2417: : 717iter [00:20, 36.53iter/s]
Best score: 0.2417: : 718iter [00:21, 36.53iter/s]
Best score: 0.2391: : 719iter [00:21, 36.53iter/s]
Best score: 0.2391: : 720iter [00:21, 36.53iter/s]
Best score: 0.2391: : 721iter [00:21, 37.22iter/s]
Best score: 0.2391: : 721iter [00:21, 37.22iter/s]
Best score: 0.2391: : 722iter [00:21, 37.22iter/s]
Best score: 0.2391: : 723iter [00:21, 37.22iter/s]
Best score: 0.2391: : 724iter [00:21, 37.22iter/s]
Best score: 0.2391: : 725iter [00:21, 37.22iter/s]
Best score: 0.2391: : 726iter [00:21, 38.14iter/s]
Best score: 0.2391: : 726iter [00:21, 38.14iter/s]
Best score: 0.2391: : 727iter [00:21, 38.14iter/s]
Best score: 0.2391: : 728iter [00:21, 38.14iter/s]
Best score: 0.2331: : 729iter [00:21, 38.14iter/s]
Best score: 0.2331: : 730iter [00:21, 38.48iter/s]
Best score: 0.2331: : 730iter [00:21, 38.48iter/s]
Best score: 0.2331: : 731iter [00:21, 38.48iter/s]
Best score: 0.2331: : 732iter [00:21, 38.48iter/s]
Best score: 0.2331: : 733iter [00:21, 38.48iter/s]
Best score: 0.2331: : 734iter [00:21, 38.72iter/s]
Best score: 0.2331: : 734iter [00:21, 38.72iter/s]
Best score: 0.2331: : 735iter [00:21, 38.72iter/s]
Best score: 0.2331: : 736iter [00:21, 38.72iter/s]
Best score: 0.2331: : 737iter [00:21, 38.72iter/s]
Best score: 0.2331: : 738iter [00:21, 38.72iter/s]
Best score: 0.2331: : 739iter [00:21, 39.30iter/s]
Best score: 0.2329: : 739iter [00:21, 39.30iter/s]
Best score: 0.2329: : 740iter [00:21, 39.30iter/s]
Best score: 0.2329: : 741iter [00:21, 39.30iter/s]
Best score: 0.2329: : 742iter [00:21, 39.30iter/s]
Best score: 0.2329: : 743iter [00:21, 39.33iter/s]
Best score: 0.2329: : 743iter [00:21, 39.33iter/s]
Best score: 0.2329: : 744iter [00:21, 39.33iter/s]
Best score: 0.2329: : 745iter [00:21, 39.33iter/s]
Best score: 0.2329: : 746iter [00:21, 39.33iter/s]
Best score: 0.2329: : 747iter [00:21, 39.33iter/s]
Best score: 0.2329: : 748iter [00:21, 39.60iter/s]
Best score: 0.2329: : 748iter [00:21, 39.60iter/s]
[ITER 750] Best: 0.2278
[PARAMS] Current best parameters:
  fx: 525.8989
  fy: 525.7037
  px: 1027.6388
  py: 753.6726
  k1: 0.03620526
  k2: -0.04216654
  k3: 0.02265685
  k4: -0.00518026
  p1: -0.00273647
  p2: 0.00965004
  p3: 0.01850420
  p4: -0.00265458
  s1: -0.0310
  s2: 0.0033
  s3: -0.0239
  s4: 0.0044
  tilt_x: -0.0201
  tilt_y: 0.0300
  scale: 2.9834
  rotation: 0.6007

Best score: 0.2278: : 749iter [00:21, 39.60iter/s]
Best score: 0.2278: : 750iter [00:21, 39.60iter/s]
Best score: 0.2278: : 751iter [00:21, 39.60iter/s]
Best score: 0.2278: : 752iter [00:21, 32.70iter/s]
Best score: 0.2278: : 752iter [00:21, 32.70iter/s]
Best score: 0.2278: : 753iter [00:21, 32.70iter/s]
Best score: 0.2278: : 754iter [00:21, 32.70iter/s]
Best score: 0.2278: : 755iter [00:22, 32.70iter/s]
Best score: 0.2278: : 756iter [00:22, 32.70iter/s]
Best score: 0.2278: : 757iter [00:22, 34.86iter/s]
Best score: 0.2278: : 757iter [00:22, 34.86iter/s]
Best score: 0.2278: : 758iter [00:22, 34.86iter/s]
Best score: 0.2278: : 759iter [00:22, 34.86iter/s]
Best score: 0.2278: : 760iter [00:22, 34.86iter/s]
Best score: 0.2278: : 761iter [00:22, 36.06iter/s]
Best score: 0.2278: : 761iter [00:22, 36.06iter/s]
Best score: 0.2278: : 762iter [00:22, 36.06iter/s]
Best score: 0.2278: : 763iter [00:22, 36.06iter/s]
Best score: 0.2278: : 764iter [00:22, 36.06iter/s]
Best score: 0.2278: : 765iter [00:22, 36.06iter/s]
Best score: 0.2278: : 766iter [00:22, 37.45iter/s]
Best score: 0.2278: : 766iter [00:22, 37.45iter/s]
Best score: 0.2278: : 767iter [00:22, 37.45iter/s]
Best score: 0.2278: : 768iter [00:22, 37.45iter/s]
Best score: 0.2271: : 769iter [00:22, 37.45iter/s]
Best score: 0.2271: : 770iter [00:22, 37.98iter/s]
Best score: 0.2271: : 770iter [00:22, 37.98iter/s]
Best score: 0.2271: : 771iter [00:22, 37.98iter/s]
Best score: 0.2271: : 772iter [00:22, 37.98iter/s]
Best score: 0.2271: : 773iter [00:22, 37.98iter/s]
Best score: 0.2271: : 774iter [00:22, 37.98iter/s]
Best score: 0.2271: : 775iter [00:22, 38.89iter/s]
Best score: 0.2271: : 775iter [00:22, 38.89iter/s]
Best score: 0.2271: : 776iter [00:22, 38.89iter/s]
Best score: 0.2271: : 777iter [00:22, 38.89iter/s]
Best score: 0.2271: : 778iter [00:22, 38.89iter/s]
Best score: 0.2235: : 779iter [00:22, 38.89iter/s]
Best score: 0.2235: : 780iter [00:22, 39.18iter/s]
Best score: 0.2235: : 780iter [00:22, 39.18iter/s]
Best score: 0.2235: : 781iter [00:22, 39.18iter/s]
Best score: 0.2235: : 782iter [00:22, 39.18iter/s]
Best score: 0.2235: : 783iter [00:22, 39.18iter/s]
Best score: 0.2235: : 784iter [00:22, 39.18iter/s]
Best score: 0.2235: : 785iter [00:22, 39.56iter/s]
Best score: 0.2235: : 785iter [00:22, 39.56iter/s]
Best score: 0.2235: : 786iter [00:22, 39.56iter/s]
Best score: 0.2235: : 787iter [00:22, 39.56iter/s]
Best score: 0.2235: : 788iter [00:22, 39.56iter/s]
Best score: 0.2211: : 789iter [00:22, 39.56iter/s]
Best score: 0.2211: : 790iter [00:22, 39.59iter/s]
Best score: 0.2211: : 790iter [00:22, 39.59iter/s]
Best score: 0.2211: : 791iter [00:22, 39.59iter/s]
Best score: 0.2211: : 792iter [00:22, 39.59iter/s]
Best score: 0.2211: : 793iter [00:22, 39.59iter/s]
Best score: 0.2211: : 794iter [00:22, 39.69iter/s]
Best score: 0.2211: : 794iter [00:22, 39.69iter/s]
Best score: 0.2211: : 795iter [00:23, 39.69iter/s]
Best score: 0.2211: : 796iter [00:23, 39.69iter/s]
Best score: 0.2211: : 797iter [00:23, 39.69iter/s]
Best score: 0.2211: : 798iter [00:23, 39.69iter/s]
Best score: 0.2211: : 799iter [00:23, 40.03iter/s]
[ITER 800] Best: 0.2202
[PARAMS] Current best parameters:
  fx: 524.5583
  fy: 524.3719
  px: 1026.3885
  py: 753.0415
  k1: 0.04412083
  k2: -0.04731601
  k3: 0.02288484
  k4: -0.00480509
  p1: -0.00294626
  p2: 0.00858969
  p3: 0.02287119
  p4: -0.00374226
  s1: -0.0340
  s2: 0.0039
  s3: -0.0282
  s4: 0.0056
  tilt_x: -0.0231
  tilt_y: 0.0345
  scale: 2.9721
  rotation: 0.6000

Best score: 0.2202: : 799iter [00:23, 40.03iter/s]
Best score: 0.2202: : 800iter [00:23, 40.03iter/s]
Best score: 0.2202: : 801iter [00:23, 40.03iter/s]
Best score: 0.2202: : 802iter [00:23, 40.03iter/s]
Best score: 0.2202: : 803iter [00:23, 40.03iter/s]
Best score: 0.2202: : 804iter [00:23, 33.57iter/s]
Best score: 0.2202: : 804iter [00:23, 33.57iter/s]
Best score: 0.2202: : 805iter [00:23, 33.57iter/s]
Best score: 0.2202: : 806iter [00:23, 33.57iter/s]
Best score: 0.2202: : 807iter [00:23, 33.57iter/s]
Best score: 0.2202: : 808iter [00:23, 33.57iter/s]
Best score: 0.2202: : 809iter [00:23, 35.35iter/s]
Best score: 0.2183: : 809iter [00:23, 35.35iter/s]
Best score: 0.2183: : 810iter [00:23, 35.35iter/s]
Best score: 0.2183: : 811iter [00:23, 35.35iter/s]
Best score: 0.2183: : 812iter [00:23, 35.35iter/s]
Best score: 0.2183: : 813iter [00:23, 36.42iter/s]
Best score: 0.2183: : 813iter [00:23, 36.42iter/s]
Best score: 0.2183: : 814iter [00:23, 36.42iter/s]
Best score: 0.2183: : 815iter [00:23, 36.42iter/s]
Best score: 0.2183: : 816iter [00:23, 36.42iter/s]
Best score: 0.2183: : 817iter [00:23, 36.42iter/s]
Best score: 0.2183: : 818iter [00:23, 37.64iter/s]
Best score: 0.2183: : 818iter [00:23, 37.64iter/s]
Best score: 0.2159: : 819iter [00:23, 37.64iter/s]
Best score: 0.2159: : 820iter [00:23, 37.64iter/s]
Best score: 0.2159: : 821iter [00:23, 37.64iter/s]
Best score: 0.2159: : 822iter [00:23, 38.18iter/s]
Best score: 0.2159: : 822iter [00:23, 38.18iter/s]
Best score: 0.2159: : 823iter [00:23, 38.18iter/s]
Best score: 0.2159: : 824iter [00:23, 38.18iter/s]
Best score: 0.2159: : 825iter [00:23, 38.18iter/s]
Best score: 0.2159: : 826iter [00:23, 38.18iter/s]
Best score: 0.2159: : 827iter [00:23, 38.99iter/s]
Best score: 0.2159: : 827iter [00:23, 38.99iter/s]
Best score: 0.2159: : 828iter [00:23, 38.99iter/s]
Best score: 0.2153: : 829iter [00:23, 38.99iter/s]
Best score: 0.2153: : 830iter [00:23, 38.99iter/s]
Best score: 0.2153: : 831iter [00:23, 39.02iter/s]
Best score: 0.2153: : 831iter [00:23, 39.02iter/s]
Best score: 0.2153: : 832iter [00:24, 39.02iter/s]
Best score: 0.2153: : 833iter [00:24, 39.02iter/s]
Best score: 0.2153: : 834iter [00:24, 39.02iter/s]
Best score: 0.2153: : 835iter [00:24, 39.02iter/s]
Best score: 0.2153: : 836iter [00:24, 39.46iter/s]
Best score: 0.2153: : 836iter [00:24, 39.46iter/s]
Best score: 0.2153: : 837iter [00:24, 39.46iter/s]
Best score: 0.2153: : 838iter [00:24, 39.46iter/s]
Best score: 0.2113: : 839iter [00:24, 39.46iter/s]
Best score: 0.2113: : 840iter [00:24, 39.47iter/s]
Best score: 0.2113: : 840iter [00:24, 39.47iter/s]
Best score: 0.2113: : 841iter [00:24, 39.47iter/s]
Best score: 0.2113: : 842iter [00:24, 39.47iter/s]
Best score: 0.2113: : 843iter [00:24, 39.47iter/s]
Best score: 0.2113: : 844iter [00:24, 39.47iter/s]
Best score: 0.2113: : 845iter [00:24, 39.83iter/s]
Best score: 0.2113: : 845iter [00:24, 39.83iter/s]
Best score: 0.2113: : 846iter [00:24, 39.83iter/s]
Best score: 0.2113: : 847iter [00:24, 39.83iter/s]
Best score: 0.2113: : 848iter [00:24, 39.83iter/s]
Best score: 0.2113: : 849iter [00:24, 39.11iter/s]
[ITER 850] Best: 0.2113
[PARAMS] Current best parameters:
  fx: 519.6768
  fy: 519.5102
  px: 1027.0670
  py: 750.9431
  k1: 0.05416497
  k2: -0.05285986
  k3: 0.02466499
  k4: -0.00501757
  p1: -0.00541411
  p2: 0.00593306
  p3: 0.02529396
  p4: -0.00382191
  s1: -0.0338
  s2: 0.0042
  s3: -0.0291
  s4: 0.0054
  tilt_x: -0.0287
  tilt_y: 0.0255
  scale: 2.9667
  rotation: 0.5933

Best score: 0.2113: : 849iter [00:24, 39.11iter/s]
Best score: 0.2113: : 850iter [00:24, 39.11iter/s]
Best score: 0.2113: : 851iter [00:24, 39.11iter/s]
Best score: 0.2113: : 852iter [00:24, 39.11iter/s]
Best score: 0.2113: : 853iter [00:24, 32.41iter/s]
Best score: 0.2113: : 853iter [00:24, 32.41iter/s]
Best score: 0.2113: : 854iter [00:24, 32.41iter/s]
Best score: 0.2113: : 855iter [00:24, 32.41iter/s]
Best score: 0.2113: : 856iter [00:24, 32.41iter/s]
Best score: 0.2113: : 857iter [00:24, 32.41iter/s]
Best score: 0.2113: : 858iter [00:24, 34.67iter/s]
Best score: 0.2113: : 858iter [00:24, 34.67iter/s]
Best score: 0.2093: : 859iter [00:24, 34.67iter/s]
Best score: 0.2093: : 860iter [00:24, 34.67iter/s]
Best score: 0.2093: : 861iter [00:24, 34.67iter/s]
Best score: 0.2093: : 862iter [00:24, 35.92iter/s]
Best score: 0.2093: : 862iter [00:24, 35.92iter/s]
Best score: 0.2093: : 863iter [00:24, 35.92iter/s]
Best score: 0.2093: : 864iter [00:24, 35.92iter/s]
Best score: 0.2093: : 865iter [00:24, 35.92iter/s]
Best score: 0.2093: : 866iter [00:24, 35.92iter/s]
Best score: 0.2093: : 867iter [00:24, 37.30iter/s]
Best score: 0.2093: : 867iter [00:24, 37.30iter/s]
Best score: 0.2093: : 868iter [00:24, 37.30iter/s]
Best score: 0.2093: : 869iter [00:25, 37.30iter/s]
Best score: 0.2093: : 870iter [00:25, 37.30iter/s]
Best score: 0.2093: : 871iter [00:25, 37.91iter/s]
Best score: 0.2093: : 871iter [00:25, 37.91iter/s]
Best score: 0.2093: : 872iter [00:25, 37.91iter/s]
Best score: 0.2093: : 873iter [00:25, 37.91iter/s]
Best score: 0.2093: : 874iter [00:25, 37.91iter/s]
Best score: 0.2093: : 875iter [00:25, 37.91iter/s]
Best score: 0.2093: : 876iter [00:25, 38.80iter/s]
Best score: 0.2093: : 876iter [00:25, 38.80iter/s]
Best score: 0.2093: : 877iter [00:25, 38.80iter/s]
Best score: 0.2093: : 878iter [00:25, 38.80iter/s]
Best score: 0.2093: : 879iter [00:25, 38.80iter/s]
Best score: 0.2093: : 880iter [00:25, 39.08iter/s]
Best score: 0.2093: : 880iter [00:25, 39.08iter/s]
Best score: 0.2093: : 881iter [00:25, 39.08iter/s]
Best score: 0.2093: : 882iter [00:25, 39.08iter/s]
Best score: 0.2093: : 883iter [00:25, 39.08iter/s]
Best score: 0.2093: : 884iter [00:25, 39.08iter/s]
Best score: 0.2093: : 885iter [00:25, 39.54iter/s]
Best score: 0.2093: : 885iter [00:25, 39.54iter/s]
Best score: 0.2093: : 886iter [00:25, 39.54iter/s]
Best score: 0.2093: : 887iter [00:25, 39.54iter/s]
Best score: 0.2093: : 888iter [00:25, 39.54iter/s]
Best score: 0.2085: : 889iter [00:25, 39.54iter/s]
Best score: 0.2085: : 890iter [00:25, 39.56iter/s]
Best score: 0.2085: : 890iter [00:25, 39.56iter/s]
Best score: 0.2085: : 891iter [00:25, 39.56iter/s]
Best score: 0.2085: : 892iter [00:25, 39.56iter/s]
Best score: 0.2085: : 893iter [00:25, 39.56iter/s]
Best score: 0.2085: : 894iter [00:25, 39.56iter/s]
Best score: 0.2085: : 895iter [00:25, 39.93iter/s]
Best score: 0.2085: : 895iter [00:25, 39.93iter/s]
Best score: 0.2085: : 896iter [00:25, 39.93iter/s]
Best score: 0.2085: : 897iter [00:25, 39.93iter/s]
Best score: 0.2085: : 898iter [00:25, 39.93iter/s]
[ITER 900] Best: 0.2062
[PARAMS] Current best parameters:
  fx: 516.9764
  fy: 516.7616
  px: 1027.4033
  py: 750.0268
  k1: 0.05822929
  k2: -0.05268788
  k3: 0.02337460
  k4: -0.00459506
  p1: -0.00696811
  p2: 0.00295477
  p3: 0.02553514
  p4: -0.00304982
  s1: -0.0315
  s2: 0.0037
  s3: -0.0297
  s4: 0.0051
  tilt_x: -0.0295
  tilt_y: 0.0176
  scale: 2.9633
  rotation: 0.6039

Best score: 0.2062: : 899iter [00:25, 39.93iter/s]
Best score: 0.2062: : 900iter [00:25, 39.75iter/s]
Best score: 0.2062: : 900iter [00:25, 39.75iter/s]
Best score: 0.2062: : 901iter [00:25, 39.75iter/s]
Best score: 0.2062: : 902iter [00:25, 39.75iter/s]
Best score: 0.2062: : 903iter [00:25, 39.75iter/s]
Best score: 0.2062: : 904iter [00:25, 33.27iter/s]
Best score: 0.2062: : 904iter [00:25, 33.27iter/s]
Best score: 0.2062: : 905iter [00:25, 33.27iter/s]
Best score: 0.2062: : 906iter [00:26, 33.27iter/s]
Best score: 0.2062: : 907iter [00:26, 33.27iter/s]
Best score: 0.2062: : 908iter [00:26, 34.81iter/s]
Best score: 0.2062: : 908iter [00:26, 34.81iter/s]
Best score: 0.2051: : 909iter [00:26, 34.81iter/s]
Best score: 0.2051: : 910iter [00:26, 34.81iter/s]
Best score: 0.2051: : 911iter [00:26, 34.81iter/s]
Best score: 0.2051: : 912iter [00:26, 35.93iter/s]
Best score: 0.2051: : 912iter [00:26, 35.93iter/s]
Best score: 0.2051: : 913iter [00:26, 35.93iter/s]
Best score: 0.2051: : 914iter [00:26, 35.93iter/s]
Best score: 0.2051: : 915iter [00:26, 35.93iter/s]
Best score: 0.2051: : 916iter [00:26, 35.93iter/s]
Best score: 0.2051: : 917iter [00:26, 37.29iter/s]
Best score: 0.2051: : 917iter [00:26, 37.29iter/s]
Best score: 0.2051: : 918iter [00:26, 37.29iter/s]
Best score: 0.2051: : 919iter [00:26, 37.29iter/s]
Best score: 0.2051: : 920iter [00:26, 37.29iter/s]
Best score: 0.2051: : 921iter [00:26, 37.83iter/s]
Best score: 0.2051: : 921iter [00:26, 37.83iter/s]
Best score: 0.2051: : 922iter [00:26, 37.83iter/s]
Best score: 0.2051: : 923iter [00:26, 37.83iter/s]
Best score: 0.2051: : 924iter [00:26, 37.83iter/s]
Best score: 0.2051: : 925iter [00:26, 37.83iter/s]
Best score: 0.2051: : 926iter [00:26, 38.75iter/s]
Best score: 0.2051: : 926iter [00:26, 38.75iter/s]
Best score: 0.2051: : 927iter [00:26, 38.75iter/s]
Best score: 0.2051: : 928iter [00:26, 38.75iter/s]
Best score: 0.2051: : 929iter [00:26, 38.75iter/s]
Best score: 0.2051: : 930iter [00:26, 38.84iter/s]
Best score: 0.2051: : 930iter [00:26, 38.84iter/s]
Best score: 0.2051: : 931iter [00:26, 38.84iter/s]
Best score: 0.2051: : 932iter [00:26, 38.84iter/s]
Best score: 0.2051: : 933iter [00:26, 38.84iter/s]
Best score: 0.2051: : 934iter [00:26, 38.84iter/s]
Best score: 0.2051: : 935iter [00:26, 39.28iter/s]
Best score: 0.2051: : 935iter [00:26, 39.28iter/s]
Best score: 0.2051: : 936iter [00:26, 39.28iter/s]
Best score: 0.2051: : 937iter [00:26, 39.28iter/s]
Best score: 0.2051: : 938iter [00:26, 39.28iter/s]
Best score: 0.2051: : 939iter [00:26, 39.28iter/s]
Best score: 0.2051: : 940iter [00:26, 39.47iter/s]
Best score: 0.2051: : 940iter [00:26, 39.47iter/s]
Best score: 0.2051: : 941iter [00:26, 39.47iter/s]
Best score: 0.2051: : 942iter [00:26, 39.47iter/s]
Best score: 0.2051: : 943iter [00:26, 39.47iter/s]
Best score: 0.2051: : 944iter [00:26, 39.47iter/s]
Best score: 0.2051: : 945iter [00:26, 39.79iter/s]
Best score: 0.2051: : 945iter [00:26, 39.79iter/s]
Best score: 0.2051: : 946iter [00:27, 39.79iter/s]
Best score: 0.2051: : 947iter [00:27, 39.79iter/s]
Best score: 0.2051: : 948iter [00:27, 39.79iter/s]
[ITER 950] Best: 0.2051
[PARAMS] Current best parameters:
  fx: 517.5523
  fy: 517.3517
  px: 1026.9726
  py: 750.1190
  k1: 0.05700405
  k2: -0.05317422
  k3: 0.02436049
  k4: -0.00489300
  p1: -0.00693360
  p2: 0.00374904
  p3: 0.02673360
  p4: -0.00331502
  s1: -0.0336
  s2: 0.0039
  s3: -0.0305
  s4: 0.0052
  tilt_x: -0.0293
  tilt_y: 0.0216
  scale: 2.9660
  rotation: 0.5984

Best score: 0.2051: : 949iter [00:27, 39.79iter/s]
Best score: 0.2051: : 950iter [00:27, 39.67iter/s]
Best score: 0.2051: : 950iter [00:27, 39.67iter/s]
Best score: 0.2051: : 951iter [00:27, 39.67iter/s]
Best score: 0.2051: : 952iter [00:27, 39.67iter/s]
Best score: 0.2051: : 953iter [00:27, 39.67iter/s]
Best score: 0.2051: : 954iter [00:27, 33.27iter/s]
Best score: 0.2051: : 954iter [00:27, 33.27iter/s]
Best score: 0.2051: : 955iter [00:27, 33.27iter/s]
Best score: 0.2051: : 956iter [00:27, 33.27iter/s]
Best score: 0.2051: : 957iter [00:27, 33.27iter/s]
Best score: 0.2051: : 958iter [00:27, 33.27iter/s]
Best score: 0.2051: : 959iter [00:27, 35.16iter/s]
Best score: 0.2051: : 959iter [00:27, 35.16iter/s]
Best score: 0.2051: : 960iter [00:27, 35.16iter/s]
Best score: 0.2051: : 961iter [00:27, 35.16iter/s]
Best score: 0.2051: : 962iter [00:27, 35.16iter/s]
Best score: 0.2051: : 963iter [00:27, 36.07iter/s]
Best score: 0.2051: : 963iter [00:27, 36.07iter/s]
Best score: 0.2051: : 964iter [00:27, 36.07iter/s]
Best score: 0.2051: : 965iter [00:27, 36.07iter/s]
Best score: 0.2051: : 966iter [00:27, 36.07iter/s]
Best score: 0.2051: : 967iter [00:27, 36.07iter/s]
Best score: 0.2051: : 968iter [00:27, 37.31iter/s]
Best score: 0.2051: : 968iter [00:27, 37.31iter/s]
Best score: 0.2049: : 969iter [00:27, 37.31iter/s]
Best score: 0.2049: : 970iter [00:27, 37.31iter/s]
Best score: 0.2049: : 971iter [00:27, 37.31iter/s]
Best score: 0.2049: : 972iter [00:27, 37.82iter/s]
Best score: 0.2049: : 972iter [00:27, 37.82iter/s]
Best score: 0.2049: : 973iter [00:27, 37.82iter/s]
Best score: 0.2049: : 974iter [00:27, 37.82iter/s]
Best score: 0.2049: : 975iter [00:27, 37.82iter/s]
Best score: 0.2049: : 976iter [00:27, 37.82iter/s]
Best score: 0.2049: : 977iter [00:27, 38.63iter/s]
Best score: 0.2049: : 977iter [00:27, 38.63iter/s]
Best score: 0.2049: : 978iter [00:27, 38.63iter/s]
Best score: 0.2049: : 979iter [00:27, 38.63iter/s]
Best score: 0.2049: : 980iter [00:27, 38.63iter/s]
Best score: 0.2049: : 981iter [00:27, 38.78iter/s]
Best score: 0.2049: : 981iter [00:27, 38.78iter/s]
Best score: 0.2049: : 982iter [00:27, 38.78iter/s]
Best score: 0.2049: : 983iter [00:28, 38.78iter/s]
Best score: 0.2049: : 984iter [00:28, 38.78iter/s]
Best score: 0.2049: : 985iter [00:28, 38.78iter/s]
Best score: 0.2049: : 986iter [00:28, 39.23iter/s]
Best score: 0.2049: : 986iter [00:28, 39.23iter/s]
Best score: 0.2049: : 987iter [00:28, 39.23iter/s]
Best score: 0.2049: : 988iter [00:28, 39.23iter/s]
Best score: 0.2042: : 989iter [00:28, 39.23iter/s]
Best score: 0.2042: : 990iter [00:28, 39.38iter/s]
Best score: 0.2042: : 990iter [00:28, 39.38iter/s]
Best score: 0.2042: : 991iter [00:28, 39.38iter/s]
Best score: 0.2042: : 992iter [00:28, 39.38iter/s]
Best score: 0.2042: : 993iter [00:28, 39.38iter/s]
Best score: 0.2042: : 994iter [00:28, 39.38iter/s]
Best score: 0.2042: : 995iter [00:28, 39.83iter/s]
Best score: 0.2042: : 995iter [00:28, 39.83iter/s]
Best score: 0.2042: : 996iter [00:28, 39.83iter/s]
Best score: 0.2042: : 997iter [00:28, 39.83iter/s]
Best score: 0.2042: : 998iter [00:28, 39.83iter/s]
Best score: 0.2042: : 999iter [00:28, 32.80iter/s]
[ITER 1000] Best: 0.2042
[PARAMS] Current best parameters:
  fx: 516.3130
  fy: 516.1283
  px: 1027.6699
  py: 749.6018
  k1: 0.05846298
  k2: -0.05415826
  k3: 0.02511930
  k4: -0.00507644
  p1: -0.00733317
  p2: 0.00367569
  p3: 0.02843346
  p4: -0.00411451
  s1: -0.0341
  s2: 0.0045
  s3: -0.0318
  s4: 0.0058
  tilt_x: -0.0309
  tilt_y: 0.0180
  scale: 2.9679
  rotation: 0.5971

Best score: 0.2042: : 999iter [00:28, 32.80iter/s]
Best score: 0.2042: : 1000iter [00:28, 32.80iter/s]
Best score: 0.2042: : 1001iter [00:28, 32.80iter/s]
Best score: 0.2042: : 1002iter [00:28, 32.80iter/s]
Best score: 0.2042: : 1003iter [00:28, 29.03iter/s]
Best score: 0.2042: : 1003iter [00:28, 29.03iter/s]
Best score: 0.2042: : 1004iter [00:28, 29.03iter/s]
Best score: 0.2042: : 1005iter [00:28, 29.03iter/s]
Best score: 0.2042: : 1006iter [00:28, 29.03iter/s]
Best score: 0.2042: : 1007iter [00:28, 29.03iter/s]
Best score: 0.2042: : 1008iter [00:28, 32.06iter/s]
Best score: 0.2042: : 1008iter [00:28, 32.06iter/s]
Best score: 0.2038: : 1009iter [00:28, 32.06iter/s]
Best score: 0.2038: : 1010iter [00:28, 32.06iter/s]
Best score: 0.2038: : 1011iter [00:28, 32.06iter/s]
Best score: 0.2038: : 1012iter [00:28, 32.06iter/s]
Best score: 0.2038: : 1013iter [00:28, 34.32iter/s]
Best score: 0.2038: : 1013iter [00:28, 34.32iter/s]
Best score: 0.2038: : 1014iter [00:28, 34.32iter/s]
Best score: 0.2038: : 1015iter [00:28, 34.32iter/s]
Best score: 0.2038: : 1016iter [00:28, 34.32iter/s]
Best score: 0.2038: : 1017iter [00:29, 34.32iter/s]
Best score: 0.2038: : 1018iter [00:29, 36.04iter/s]
Best score: 0.2038: : 1018iter [00:29, 36.04iter/s]
Best score: 0.2037: : 1019iter [00:29, 36.04iter/s]
Best score: 0.2037: : 1020iter [00:29, 36.04iter/s]
Best score: 0.2037: : 1021iter [00:29, 36.04iter/s]
Best score: 0.2037: : 1022iter [00:29, 36.85iter/s]
Best score: 0.2037: : 1022iter [00:29, 36.85iter/s]
Best score: 0.2037: : 1023iter [00:29, 36.85iter/s]
Best score: 0.2037: : 1024iter [00:29, 36.85iter/s]
Best score: 0.2037: : 1025iter [00:29, 36.85iter/s]
Best score: 0.2037: : 1026iter [00:29, 36.85iter/s]
Best score: 0.2037: : 1027iter [00:29, 37.90iter/s]
Best score: 0.2037: : 1027iter [00:29, 37.90iter/s]
Best score: 0.2037: : 1028iter [00:29, 37.90iter/s]
Best score: 0.2037: : 1029iter [00:29, 37.90iter/s]
Best score: 0.2037: : 1030iter [00:29, 37.90iter/s]
Best score: 0.2037: : 1031iter [00:29, 38.26iter/s]
Best score: 0.2037: : 1031iter [00:29, 38.26iter/s]
Best score: 0.2037: : 1032iter [00:29, 38.26iter/s]
Best score: 0.2037: : 1033iter [00:29, 38.26iter/s]
Best score: 0.2037: : 1034iter [00:29, 38.26iter/s]
Best score: 0.2037: : 1035iter [00:29, 38.26iter/s]
Best score: 0.2037: : 1036iter [00:29, 38.89iter/s]
Best score: 0.2037: : 1036iter [00:29, 38.89iter/s]
Best score: 0.2037: : 1037iter [00:29, 38.89iter/s]
Best score: 0.2037: : 1038iter [00:29, 38.89iter/s]
Best score: 0.2030: : 1039iter [00:29, 38.89iter/s]
Best score: 0.2030: : 1040iter [00:29, 38.94iter/s]
Best score: 0.2030: : 1040iter [00:29, 38.94iter/s]
Best score: 0.2030: : 1041iter [00:29, 38.94iter/s]
Best score: 0.2030: : 1042iter [00:29, 38.94iter/s]
Best score: 0.2030: : 1043iter [00:29, 38.94iter/s]
Best score: 0.2030: : 1044iter [00:29, 38.94iter/s]
Best score: 0.2030: : 1045iter [00:29, 39.37iter/s]
Best score: 0.2030: : 1045iter [00:29, 39.37iter/s]
Best score: 0.2030: : 1046iter [00:29, 39.37iter/s]
Best score: 0.2030: : 1047iter [00:29, 39.37iter/s]
Best score: 0.2030: : 1048iter [00:29, 39.37iter/s]
[ITER 1050] Best: 0.2030
[PARAMS] Current best parameters:
  fx: 516.5814
  fy: 516.3975
  px: 1027.5644
  py: 749.4194
  k1: 0.05541364
  k2: -0.04981957
  k3: 0.02265621
  k4: -0.00457900
  p1: -0.00691262
  p2: 0.00400481
  p3: 0.02799806
  p4: -0.00390972
  s1: -0.0344
  s2: 0.0044
  s3: -0.0319
  s4: 0.0056
  tilt_x: -0.0327
  tilt_y: 0.0192
  scale: 2.9706
  rotation: 0.5945

Best score: 0.2030: : 1049iter [00:29, 39.37iter/s]
Best score: 0.2030: : 1050iter [00:29, 39.39iter/s]
Best score: 0.2030: : 1050iter [00:29, 39.39iter/s]
Best score: 0.2030: : 1051iter [00:29, 39.39iter/s]
Best score: 0.2030: : 1052iter [00:29, 39.39iter/s]
Best score: 0.2030: : 1053iter [00:30, 39.39iter/s]
Best score: 0.2030: : 1054iter [00:30, 32.98iter/s]
Best score: 0.2030: : 1054iter [00:30, 32.98iter/s]
Best score: 0.2030: : 1055iter [00:30, 32.98iter/s]
Best score: 0.2030: : 1056iter [00:30, 32.98iter/s]
Best score: 0.2030: : 1057iter [00:30, 32.98iter/s]
Best score: 0.2030: : 1058iter [00:30, 32.98iter/s]
Best score: 0.2030: : 1059iter [00:30, 35.03iter/s]
Best score: 0.2028: : 1059iter [00:30, 35.03iter/s]
Best score: 0.2028: : 1060iter [00:30, 35.03iter/s]
Best score: 0.2028: : 1061iter [00:30, 35.03iter/s]
Best score: 0.2028: : 1062iter [00:30, 35.03iter/s]
Best score: 0.2028: : 1063iter [00:30, 36.03iter/s]
Best score: 0.2028: : 1063iter [00:30, 36.03iter/s]
Best score: 0.2028: : 1064iter [00:30, 36.03iter/s]
Best score: 0.2028: : 1065iter [00:30, 36.03iter/s]
Best score: 0.2028: : 1066iter [00:30, 36.03iter/s]
Best score: 0.2028: : 1067iter [00:30, 36.03iter/s]
Best score: 0.2028: : 1068iter [00:30, 37.35iter/s]
Best score: 0.2028: : 1068iter [00:30, 37.35iter/s]
Best score: 0.2028: : 1069iter [00:30, 37.35iter/s]
Best score: 0.2028: : 1070iter [00:30, 37.35iter/s]
Best score: 0.2028: : 1071iter [00:30, 37.35iter/s]
Best score: 0.2028: : 1072iter [00:30, 37.91iter/s]
Best score: 0.2028: : 1072iter [00:30, 37.91iter/s]
Best score: 0.2028: : 1073iter [00:30, 37.91iter/s]
Best score: 0.2028: : 1074iter [00:30, 37.91iter/s]
Best score: 0.2028: : 1075iter [00:30, 37.91iter/s]
Best score: 0.2028: : 1076iter [00:30, 37.91iter/s]
Best score: 0.2028: : 1077iter [00:30, 38.66iter/s]
Best score: 0.2028: : 1077iter [00:30, 38.66iter/s]
Best score: 0.2028: : 1078iter [00:30, 38.66iter/s]
Best score: 0.2028: : 1079iter [00:30, 38.66iter/s]
Best score: 0.2028: : 1080iter [00:30, 38.66iter/s]
Best score: 0.2028: : 1081iter [00:30, 38.87iter/s]
Best score: 0.2028: : 1081iter [00:30, 38.87iter/s]
Best score: 0.2028: : 1082iter [00:30, 38.87iter/s]
Best score: 0.2028: : 1083iter [00:30, 38.87iter/s]
Best score: 0.2028: : 1084iter [00:30, 38.87iter/s]
Best score: 0.2028: : 1085iter [00:30, 38.87iter/s]
Best score: 0.2028: : 1086iter [00:30, 39.31iter/s]
Best score: 0.2028: : 1086iter [00:30, 39.31iter/s]
Best score: 0.2028: : 1087iter [00:30, 39.31iter/s]
Best score: 0.2028: : 1088iter [00:30, 39.31iter/s]
Best score: 0.2027: : 1089iter [00:30, 39.31iter/s]
Best score: 0.2027: : 1090iter [00:30, 39.35iter/s]
Best score: 0.2027: : 1090iter [00:30, 39.35iter/s]
Best score: 0.2027: : 1091iter [00:30, 39.35iter/s]
Best score: 0.2027: : 1092iter [00:30, 39.35iter/s]
Best score: 0.2027: : 1093iter [00:31, 39.35iter/s]
Best score: 0.2027: : 1094iter [00:31, 39.35iter/s]
Best score: 0.2027: : 1095iter [00:31, 39.60iter/s]
Best score: 0.2027: : 1095iter [00:31, 39.60iter/s]
Best score: 0.2027: : 1096iter [00:31, 39.60iter/s]
Best score: 0.2027: : 1097iter [00:31, 39.60iter/s]
Best score: 0.2027: : 1098iter [00:31, 39.60iter/s]
[ITER 1100] Best: 0.2027
[PARAMS] Current best parameters:
  fx: 517.0511
  fy: 516.8781
  px: 1027.7709
  py: 749.4271
  k1: 0.05346458
  k2: -0.04809886
  k3: 0.02199458
  k4: -0.00449258
  p1: -0.00693766
  p2: 0.00450464
  p3: 0.02730013
  p4: -0.00370130
  s1: -0.0342
  s2: 0.0042
  s3: -0.0313
  s4: 0.0054
  tilt_x: -0.0326
  tilt_y: 0.0192
  scale: 2.9723
  rotation: 0.5927

Best score: 0.2027: : 1099iter [00:31, 39.60iter/s]
Best score: 0.2027: : 1100iter [00:31, 39.48iter/s]
Best score: 0.2027: : 1100iter [00:31, 35.22iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.202676 -> 0.202317

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,879 → 25,879 within 3500mm ring
  Ring polish did not improve score: 0.202317
  Success: True
  Tangential distortion: p1=-0.006941, p2=0.004513, p3=0.027292, p4=-0.003712
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2023
[✅] Parameters copied to: /working/MDC021_W042_2025-07-14/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 517.0510925229489
    fy: 516.8780964103041
    px: 1027.7708777592914
    py: 749.4270540899653
    k1: 0.05347059409134216
    k2: -0.048091287089743646
    k3: 0.02199358628289071
    k4: -0.004496631162624821
    p1: -0.00694058574926554
    p2: 0.004512773195600465
    p3: 0.02729235654155027
    p4: -0.0037123145561592636
    scale: 2.9722918561140914
    rotation: 0.592739196693543
    mmpp: 1.2850398661186737

[🔄] Converting 440 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/231 [00:00<?, ?frame/s]

   🔄 1000:   8%|▊         | 18/231 [00:00<00:01, 179.83frame/s]

   🔄 1000:  16%|█▌        | 36/231 [00:00<00:01, 179.31frame/s]

   🔄 1000:  23%|██▎       | 54/231 [00:00<00:00, 179.09frame/s]

   🔄 1000:  31%|███       | 72/231 [00:00<00:00, 178.29frame/s]

   🔄 1000:  39%|███▉      | 90/231 [00:00<00:00, 177.40frame/s]

   🔄 1000:  47%|████▋     | 108/231 [00:00<00:00, 176.30frame/s]

   🔄 1000:  55%|█████▍    | 126/231 [00:00<00:00, 175.68frame/s]

   🔄 1000:  62%|██████▏   | 144/231 [00:00<00:00, 175.28frame/s]

   🔄 1000:  70%|███████   | 162/231 [00:00<00:00, 175.71frame/s]

   🔄 1000:  78%|███████▊  | 180/231 [00:01<00:00, 176.47frame/s]

   🔄 1000:  86%|████████▌ | 198/231 [00:01<00:00, 177.13frame/s]

   🔄 1000:  94%|█████████▎| 216/231 [00:01<00:00, 176.95frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.35s/rig]

   🔄 500:   0%|          | 0/209 [00:00<?, ?frame/s]

   🔄 500:   9%|▊         | 18/209 [00:00<00:01, 179.33frame/s]

   🔄 500:  17%|█▋        | 36/209 [00:00<00:00, 179.47frame/s]

   🔄 500:  26%|██▌       | 54/209 [00:00<00:00, 179.43frame/s]

   🔄 500:  34%|███▍      | 72/209 [00:00<00:00, 179.44frame/s]

   🔄 500:  44%|████▎     | 91/209 [00:00<00:00, 180.12frame/s]

   🔄 500:  53%|█████▎    | 110/209 [00:00<00:00, 181.55frame/s]

   🔄 500:  62%|██████▏   | 129/209 [00:00<00:00, 182.40frame/s]

   🔄 500:  71%|███████   | 148/209 [00:00<00:00, 181.48frame/s]

   🔄 500:  80%|███████▉  | 167/209 [00:00<00:00, 181.17frame/s]

   🔄 500:  89%|████████▉ | 186/209 [00:01<00:00, 180.59frame/s]

   🔄 500:  98%|█████████▊| 205/209 [00:01<00:00, 179.26frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.16s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.19s/rig]
[✅] Saved PTDs: /working/MDC021_W042_2025-07-14/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  19%|█▉        | 19/100 [00:00<00:00, 184.45frame/s]

   🔄 1000:  38%|███▊      | 38/100 [00:00<00:00, 179.98frame/s]

   🔄 1000:  57%|█████▋    | 57/100 [00:00<00:00, 175.88frame/s]

   🔄 1000:  75%|███████▌  | 75/100 [00:00<00:00, 175.55frame/s]

   🔄 1000:  93%|█████████▎| 93/100 [00:00<00:00, 176.20frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.01rig/s]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 178.09frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 178.18frame/s]

   🔄 500:  55%|█████▌    | 55/100 [00:00<00:00, 180.34frame/s]

   🔄 500:  74%|███████▍  | 74/100 [00:00<00:00, 130.72frame/s]

   🔄 500:  92%|█████████▏| 92/100 [00:00<00:00, 144.37frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.01s/rig]
[✅] Saved PTDs: /working/MDC021_W042_2025-07-14/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%|▎         | 87423/3080448 [00:00<00:03, 874137.05px/s]
📤 Writing LUT:   6%|▌         | 179173/3080448 [00:00<00:03, 899637.75px/s]
📤 Writing LUT:   9%|▊         | 269137/3080448 [00:00<00:03, 892687.77px/s]
📤 Writing LUT:  12%|█▏        | 358413/3080448 [00:00<00:03, 868963.77px/s]
📤 Writing LUT:  14%|█▍        | 445397/3080448 [00:00<00:03, 851149.52px/s]
📤 Writing LUT:  17%|█▋        | 530598/3080448 [00:00<00:03, 842111.25px/s]
📤 Writing LUT:  20%|█▉        | 614861/3080448 [00:00<00:02, 834611.94px/s]
📤 Writing LUT:  23%|██▎       | 698352/3080448 [00:00<00:02, 827189.97px/s]
📤 Writing LUT:  25%|██▌       | 781088/3080448 [00:00<00:02, 824557.35px/s]
📤 Writing LUT:  28%|██▊       | 863552/3080448 [00:01<00:02, 821296.35px/s]
📤 Writing LUT:  31%|███       | 945684/3080448 [00:01<00:02, 819884.01px/s]
📤 Writing LUT:  33%|███▎      | 1027673/3080448 [00:01<00:02, 818792.26px/s]
📤 Writing LUT:  36%|███▌      | 1110375/3080448 [00:01<00:02, 821258.96px/s]
📤 Writing LUT:  39%|███▊      | 1193271/3080448 [00:01<00:02, 823567.26px/s]
📤 Writing LUT:  41%|████▏     | 1276882/3080448 [00:01<00:02, 827330.52px/s]
📤 Writing LUT:  44%|████▍     | 1360634/3080448 [00:01<00:02, 830386.27px/s]
📤 Writing LUT:  47%|████▋     | 1443868/3080448 [00:01<00:01, 830970.31px/s]
📤 Writing LUT:  50%|████▉     | 1527364/3080448 [00:01<00:01, 832165.75px/s]
📤 Writing LUT:  52%|█████▏    | 1611749/3080448 [00:01<00:01, 835669.04px/s]
📤 Writing LUT:  55%|█████▌    | 1696564/3080448 [00:02<00:01, 839410.68px/s]
📤 Writing LUT:  58%|█████▊    | 1781291/3080448 [00:02<00:01, 841767.56px/s]
📤 Writing LUT:  61%|██████    | 1866116/3080448 [00:02<00:01, 843710.15px/s]
📤 Writing LUT:  63%|██████▎   | 1950488/3080448 [00:02<00:01, 842481.64px/s]
📤 Writing LUT:  66%|██████▌   | 2034738/3080448 [00:02<00:01, 842366.40px/s]
📤 Writing LUT:  69%|██████▉   | 2119981/3080448 [00:02<00:01, 845371.19px/s]
📤 Writing LUT:  72%|███████▏  | 2204941/3080448 [00:02<00:01, 846636.31px/s]
📤 Writing LUT:  74%|███████▍  | 2289606/3080448 [00:02<00:00, 845783.19px/s]
📤 Writing LUT:  77%|███████▋  | 2374185/3080448 [00:02<00:00, 845479.18px/s]
📤 Writing LUT:  80%|███████▉  | 2458735/3080448 [00:02<00:00, 845483.24px/s]
📤 Writing LUT:  83%|████████▎ | 2544232/3080448 [00:03<00:00, 848325.19px/s]
📤 Writing LUT:  85%|████████▌ | 2632783/3080448 [00:03<00:00, 859475.04px/s]
📤 Writing LUT:  88%|████████▊ | 2720496/3080448 [00:03<00:00, 864767.56px/s]
📤 Writing LUT:  91%|█████████ | 2808497/3080448 [00:03<00:00, 869337.00px/s]
📤 Writing LUT:  94%|█████████▍| 2895432/3080448 [00:03<00:00, 869226.77px/s]
📤 Writing LUT:  97%|█████████▋| 2983114/3080448 [00:03<00:00, 871500.78px/s]
📤 Writing LUT: 100%|█████████▉| 3070500/3080448 [00:03<00:00, 872208.07px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 846335.33px/s]
[✅] LUT written to: /working/MDC021_W042_2025-07-14/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC021_W042_2025-07-14' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC021_W042_2025-07-14/calibration/MDC021_W042_2025-07-14_unknown.lut

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

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

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

   🔥 1000:   0%|          | 0/231 [00:00<?, ?frame/s]

   🔥 1000:   4%|▍         | 9/231 [00:00<00:02, 83.41frame/s]

   🔥 1000:   8%|▊         | 18/231 [00:00<00:02, 85.88frame/s]

   🔥 1000:  12%|█▏        | 27/231 [00:00<00:02, 74.08frame/s]

   🔥 1000:  16%|█▌        | 37/231 [00:00<00:02, 80.38frame/s]

   🔥 1000:  20%|██        | 47/231 [00:00<00:02, 84.14frame/s]

   🔥 1000:  24%|██▍       | 56/231 [00:00<00:02, 84.96frame/s]

   🔥 1000:  28%|██▊       | 65/231 [00:00<00:01, 86.25frame/s]

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   🔥 1000:  36%|███▋      | 84/231 [00:00<00:01, 88.18frame/s]

   🔥 1000:  40%|████      | 93/231 [00:01<00:01, 88.49frame/s]

   🔥 1000:  44%|████▍     | 102/231 [00:01<00:01, 88.20frame/s]

   🔥 1000:  48%|████▊     | 111/231 [00:01<00:01, 85.92frame/s]

   🔥 1000:  52%|█████▏    | 120/231 [00:01<00:01, 84.67frame/s]

   🔥 1000:  56%|█████▌    | 129/231 [00:01<00:01, 84.84frame/s]

   🔥 1000:  60%|█████▉    | 138/231 [00:01<00:01, 85.66frame/s]

   🔥 1000:  64%|██████▎   | 147/231 [00:01<00:00, 86.59frame/s]

   🔥 1000:  68%|██████▊   | 156/231 [00:01<00:00, 86.88frame/s]

   🔥 1000:  71%|███████▏  | 165/231 [00:01<00:00, 85.75frame/s]

   🔥 1000:  75%|███████▌  | 174/231 [00:02<00:00, 85.83frame/s]

   🔥 1000:  79%|███████▉  | 183/231 [00:02<00:00, 86.15frame/s]

   🔥 1000:  83%|████████▎ | 192/231 [00:02<00:00, 86.63frame/s]

   🔥 1000:  87%|████████▋ | 202/231 [00:02<00:00, 87.68frame/s]

   🔥 1000:  91%|█████████▏| 211/231 [00:02<00:00, 87.98frame/s]

   🔥 1000:  95%|█████████▌| 220/231 [00:02<00:00, 88.56frame/s]

   🔥 1000:  99%|█████████▉| 229/231 [00:02<00:00, 88.43frame/s]

                                                                [✅] 1000: /working/MDC021_W042_2025-07-14/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC021_W042_2025-07-14/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.66s/rig]

   🔥 500:   0%|          | 0/209 [00:00<?, ?frame/s]

   🔥 500:   4%|▍         | 8/209 [00:00<00:02, 73.27frame/s]

   🔥 500:   8%|▊         | 16/209 [00:00<00:02, 75.79frame/s]

   🔥 500:  11%|█▏        | 24/209 [00:00<00:02, 76.14frame/s]

   🔥 500:  15%|█▌        | 32/209 [00:00<00:02, 77.10frame/s]

   🔥 500:  19%|█▉        | 40/209 [00:00<00:02, 77.72frame/s]

   🔥 500:  23%|██▎       | 49/209 [00:00<00:02, 78.61frame/s]

   🔥 500:  27%|██▋       | 57/209 [00:00<00:01, 78.85frame/s]

   🔥 500:  31%|███       | 65/209 [00:00<00:01, 78.48frame/s]

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   🔥 500:  39%|███▉      | 81/209 [00:01<00:01, 79.09frame/s]

   🔥 500:  43%|████▎     | 89/209 [00:01<00:01, 79.00frame/s]

   🔥 500:  46%|████▋     | 97/209 [00:01<00:01, 79.13frame/s]

   🔥 500:  50%|█████     | 105/209 [00:01<00:01, 79.23frame/s]

   🔥 500:  54%|█████▍    | 113/209 [00:01<00:01, 77.92frame/s]

   🔥 500:  58%|█████▊    | 121/209 [00:01<00:01, 77.24frame/s]

   🔥 500:  62%|██████▏   | 129/209 [00:01<00:01, 75.97frame/s]

   🔥 500:  66%|██████▌   | 137/209 [00:01<00:00, 75.72frame/s]

   🔥 500:  69%|██████▉   | 145/209 [00:01<00:00, 75.21frame/s]

   🔥 500:  73%|███████▎  | 153/209 [00:01<00:00, 76.28frame/s]

   🔥 500:  77%|███████▋  | 161/209 [00:02<00:00, 76.55frame/s]

   🔥 500:  81%|████████  | 169/209 [00:02<00:00, 76.53frame/s]

   🔥 500:  85%|████████▍ | 177/209 [00:02<00:00, 76.36frame/s]

   🔥 500:  89%|████████▊ | 185/209 [00:02<00:00, 75.57frame/s]

   🔥 500:  92%|█████████▏| 193/209 [00:02<00:00, 76.54frame/s]

   🔥 500:  96%|█████████▌| 201/209 [00:02<00:00, 76.89frame/s]

   🔥 500: 100%|██████████| 209/209 [00:02<00:00, 76.66frame/s]

                                                               [✅] 500: /working/MDC021_W042_2025-07-14/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC021_W042_2025-07-14/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.71s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.85s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC021_W042_2025-07-14/calibration/debug/5_heatmap
[🏁] Finished processing MDC021_W042_2025-07-14


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