2026-05-20T02:22:50 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC044_W038_2026-03-10 ===
2026-05-20T02:22:50 - INFO: Camera ID: 6393994
2026-05-20T02:22:50 - INFO: Wand ID: 
2026-05-20T02:22:50 - INFO: Recorded At: 2026-03-10T14:27:08Z

-------------------------------------------------------------------
                        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 659 stationary frames, 160 remain


→ 1000 (160 images) Phase 1:   0%|          | 0/160 [00:00<?, ?it/s]

→ 1000 (160 images) Phase 1:   1%|          | 1/160 [00:00<01:20,  1.97it/s]

→ 1000 (160 images) Phase 1:  14%|█▍        | 23/160 [00:00<00:03, 44.53it/s]

→ 1000 (160 images) Phase 1:  21%|██        | 33/160 [00:00<00:03, 37.51it/s]

→ 1000 (160 images) Phase 1:  32%|███▏      | 51/160 [00:01<00:01, 61.85it/s]

→ 1000 (160 images) Phase 1:  40%|████      | 64/160 [00:01<00:01, 75.05it/s]

→ 1000 (160 images) Phase 1:  48%|████▊     | 76/160 [00:01<00:01, 57.60it/s]

→ 1000 (160 images) Phase 1:  56%|█████▌    | 89/160 [00:01<00:01, 69.32it/s]

→ 1000 (160 images) Phase 1:  62%|██████▏   | 99/160 [00:01<00:01, 56.02it/s]

→ 1000 (160 images) Phase 1:  74%|███████▍  | 118/160 [00:01<00:00, 79.04it/s]

→ 1000 (160 images) Phase 1:  81%|████████▏ | 130/160 [00:02<00:00, 59.94it/s]

→ 1000 (160 images) Phase 1:  94%|█████████▍| 151/160 [00:02<00:00, 84.17it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/160 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   1%|          | 1/160 [00:00<00:20,  7.89it/s]

→ 1000 Phase 2: corners:  11%|█▏        | 18/160 [00:00<00:01, 89.40it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 31/160 [00:00<00:01, 104.44it/s]

→ 1000 Phase 2: corners:  26%|██▋       | 42/160 [00:00<00:01, 106.07it/s]

→ 1000 Phase 2: corners:  39%|███▉      | 62/160 [00:00<00:00, 135.08it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 76/160 [00:00<00:00, 127.21it/s]

→ 1000 Phase 2: corners:  56%|█████▌    | 89/160 [00:00<00:00, 127.34it/s]

→ 1000 Phase 2: corners:  64%|██████▍   | 102/160 [00:00<00:00, 125.72it/s]

→ 1000 Phase 2: corners:  72%|███████▎  | 116/160 [00:00<00:00, 129.84it/s]

→ 1000 Phase 2: corners:  81%|████████▏ | 130/160 [00:01<00:00, 116.09it/s]

→ 1000 Phase 2: corners:  95%|█████████▌| 152/160 [00:01<00:00, 143.53it/s]

                                                                           [PASS2] Frame 0000331: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000334: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000337: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000339: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000342: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000345: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000346: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000445: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000447: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000448: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000450: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000453: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000466: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000469: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000471: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000585: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000586: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000604: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000933: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000934: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000936: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000937: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000939: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000940: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000942: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000943: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000945: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000946: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001090: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001092: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001093: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001095: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001096: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001099: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001102: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001104: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001105: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001107: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001108: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001110: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001111: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001113: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001114: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001117: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001119: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001237: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001240: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001242: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001243: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001245: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000330: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000450: fixed corners {0, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000451: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000586: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000604: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000945: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001093: fixed corners {1, 2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001095: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001096: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001104: fixed corners {0, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001105: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001245: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000330: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000447: fixed corners {1, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000448: fixed corners {1, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000586: fixed corners {1} (was_fixed=False, trusted=False)
[PASS2] Frame 0000604: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000940: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001095: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001096: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001098: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001104: fixed corners {0, 2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001105: fixed corners {0, 2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001245: fixed corners {1, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Summary: 31 frames fixed, 81 untrusted, 160 total


→ 1000 Phase 3: finalize:   0%|          | 0/160 [00:00<?, ?it/s]

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→ 1000 Phase 3: finalize:  37%|███▋      | 59/160 [00:01<00:01, 54.71it/s]

→ 1000 Phase 3: finalize:  41%|████      | 65/160 [00:01<00:01, 52.95it/s]

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→ 1000 Phase 3: finalize:  48%|████▊     | 77/160 [00:01<00:01, 50.94it/s]

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→ 1000 Phase 3: finalize:  56%|█████▌    | 89/160 [00:01<00:01, 49.96it/s]

→ 1000 Phase 3: finalize:  59%|█████▉    | 95/160 [00:01<00:01, 49.70it/s]

→ 1000 Phase 3: finalize:  62%|██████▎   | 100/160 [00:01<00:01, 49.65it/s]

→ 1000 Phase 3: finalize:  66%|██████▋   | 106/160 [00:01<00:01, 50.50it/s]

→ 1000 Phase 3: finalize:  70%|███████   | 112/160 [00:02<00:00, 51.47it/s]

→ 1000 Phase 3: finalize:  74%|███████▍  | 118/160 [00:02<00:00, 52.33it/s]

→ 1000 Phase 3: finalize:  78%|███████▊  | 124/160 [00:02<00:00, 53.32it/s]

→ 1000 Phase 3: finalize:  81%|████████▏ | 130/160 [00:02<00:00, 54.15it/s]

→ 1000 Phase 3: finalize:  85%|████████▌ | 136/160 [00:02<00:00, 54.57it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 142/160 [00:02<00:00, 54.89it/s]

→ 1000 Phase 3: finalize:  92%|█████████▎| 148/160 [00:02<00:00, 54.66it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 154/160 [00:02<00:00, 54.08it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 160/160 [00:02<00:00, 53.84it/s]

                                                                           
[TIMING] 1000 — 160 frames
  Phase 1  radial detect   (parallel)  :   2.54s  (15.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.28s  (8.0 ms/frame)
  Phase 2b corner fix      (sequential):   1.41s
  Phase 3  finalize        (sequential):   2.97s  (18.6 ms/frame)
    decode + undistort                 :   1.60s  (10.0 ms/frame)
    corner apply + refinement          :   0.61s  (3.8 ms/frame)
    segment                            :   0.61s  (3.8 ms/frame)
    smooth                             :   0.09s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.20s


Processing Rigs:  50%|█████     | 1/2 [00:11<00:11, 11.21s/it][INFO] Filtered 1200 stationary frames, 201 remain


→ 500 (201 images) Phase 1:   0%|          | 0/201 [00:00<?, ?it/s]

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→ 500 (201 images) Phase 1:  48%|████▊     | 97/201 [00:01<00:01, 55.67it/s]

→ 500 (201 images) Phase 1:  56%|█████▌    | 113/201 [00:02<00:01, 68.87it/s]

→ 500 (201 images) Phase 1:  64%|██████▍   | 129/201 [00:02<00:01, 60.88it/s]

→ 500 (201 images) Phase 1:  72%|███████▏  | 145/201 [00:02<00:00, 73.94it/s]

→ 500 (201 images) Phase 1:  80%|████████  | 161/201 [00:02<00:00, 63.89it/s]

→ 500 (201 images) Phase 1:  88%|████████▊ | 177/201 [00:02<00:00, 77.75it/s]

→ 500 (201 images) Phase 1:  96%|█████████▌| 193/201 [00:03<00:00, 77.93it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/201 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/201 [00:00<01:00,  3.31it/s]

→ 500 Phase 2: corners:   9%|▉         | 18/201 [00:00<00:03, 56.44it/s]

→ 500 Phase 2: corners:  14%|█▍        | 28/201 [00:00<00:02, 67.95it/s]

→ 500 Phase 2: corners:  21%|██        | 42/201 [00:00<00:01, 87.01it/s]

→ 500 Phase 2: corners:  28%|██▊       | 56/201 [00:00<00:01, 97.06it/s]

→ 500 Phase 2: corners:  34%|███▍      | 68/201 [00:00<00:01, 102.88it/s]

→ 500 Phase 2: corners:  40%|████      | 81/201 [00:00<00:01, 109.93it/s]

→ 500 Phase 2: corners:  48%|████▊     | 97/201 [00:01<00:00, 114.84it/s]

→ 500 Phase 2: corners:  56%|█████▌    | 112/201 [00:01<00:00, 124.21it/s]

→ 500 Phase 2: corners:  64%|██████▎   | 128/201 [00:01<00:00, 133.50it/s]

→ 500 Phase 2: corners:  71%|███████   | 142/201 [00:01<00:00, 134.96it/s]

→ 500 Phase 2: corners:  78%|███████▊  | 157/201 [00:01<00:00, 136.78it/s]

→ 500 Phase 2: corners:  87%|████████▋ | 174/201 [00:01<00:00, 146.32it/s]

                                                                          [PASS2] Summary: 0 frames fixed, 0 untrusted, 201 total


→ 500 Phase 3: finalize:   0%|          | 0/201 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   3%|▎         | 6/201 [00:00<00:03, 58.64it/s]

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→ 500 Phase 3: finalize:   9%|▉         | 18/201 [00:00<00:03, 59.65it/s]

→ 500 Phase 3: finalize:  12%|█▏        | 24/201 [00:00<00:02, 59.33it/s]

→ 500 Phase 3: finalize:  15%|█▍        | 30/201 [00:00<00:02, 59.13it/s]

→ 500 Phase 3: finalize:  18%|█▊        | 36/201 [00:00<00:02, 58.81it/s]

→ 500 Phase 3: finalize:  21%|██        | 42/201 [00:00<00:02, 58.47it/s]

→ 500 Phase 3: finalize:  24%|██▍       | 48/201 [00:00<00:02, 58.85it/s]

→ 500 Phase 3: finalize:  27%|██▋       | 54/201 [00:00<00:02, 59.02it/s]

→ 500 Phase 3: finalize:  30%|██▉       | 60/201 [00:01<00:02, 57.00it/s]

→ 500 Phase 3: finalize:  33%|███▎      | 66/201 [00:01<00:02, 55.74it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 72/201 [00:01<00:02, 54.74it/s]

→ 500 Phase 3: finalize:  39%|███▉      | 78/201 [00:01<00:02, 54.35it/s]

→ 500 Phase 3: finalize:  42%|████▏     | 84/201 [00:01<00:02, 54.16it/s]

→ 500 Phase 3: finalize:  45%|████▍     | 90/201 [00:01<00:02, 54.24it/s]

→ 500 Phase 3: finalize:  48%|████▊     | 96/201 [00:01<00:01, 54.34it/s]

→ 500 Phase 3: finalize:  51%|█████     | 102/201 [00:01<00:01, 54.43it/s]

→ 500 Phase 3: finalize:  54%|█████▎    | 108/201 [00:01<00:01, 54.38it/s]

→ 500 Phase 3: finalize:  57%|█████▋    | 114/201 [00:02<00:01, 54.74it/s]

→ 500 Phase 3: finalize:  60%|█████▉    | 120/201 [00:02<00:01, 54.69it/s]

→ 500 Phase 3: finalize:  63%|██████▎   | 126/201 [00:02<00:01, 54.24it/s]

→ 500 Phase 3: finalize:  66%|██████▌   | 132/201 [00:02<00:01, 54.10it/s]

→ 500 Phase 3: finalize:  69%|██████▊   | 138/201 [00:02<00:01, 53.99it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 144/201 [00:02<00:01, 54.06it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 150/201 [00:02<00:00, 53.99it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 156/201 [00:02<00:00, 53.90it/s]

→ 500 Phase 3: finalize:  81%|████████  | 162/201 [00:02<00:00, 53.89it/s]

→ 500 Phase 3: finalize:  84%|████████▎ | 168/201 [00:03<00:00, 53.95it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 174/201 [00:03<00:00, 53.97it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 180/201 [00:03<00:00, 53.94it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 186/201 [00:03<00:00, 53.98it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 192/201 [00:03<00:00, 53.68it/s]

→ 500 Phase 3: finalize:  99%|█████████▊| 198/201 [00:03<00:00, 53.51it/s]

                                                                          
[TIMING] 500 — 201 frames
  Phase 1  radial detect   (parallel)  :   3.28s  (16.3 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.73s  (8.6 ms/frame)
  Phase 2b corner fix      (sequential):   0.04s
  Phase 3  finalize        (sequential):   3.65s  (18.1 ms/frame)
    decode + undistort                 :   1.86s  (9.3 ms/frame)
    corner apply + refinement          :   0.79s  (3.9 ms/frame)
    segment                            :   0.81s  (4.1 ms/frame)
    smooth                             :   0.11s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.69s


Processing Rigs: 100%|██████████| 2/2 [00:24<00:00, 12.15s/it]
Processing Rigs: 100%|██████████| 2/2 [00:24<00:00, 12.01s/it]
[💾] Saved RigFrameDataset to /working/MDC044_W038_2026-03-10/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 361 frames from 2 rigs to /working/MDC044_W038_2026-03-10/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC044_W038_2026-03-10/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 160
[📋] 500: 100 frames selected from 201
[💾] Saved RigFrameDataset to /working/MDC044_W038_2026-03-10/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC044_W038_2026-03-10/calibration/debug/1_digitized/rig_frames.json

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

-> Calculating the casing center ...
[INFO] Using 20 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/20 [00:00<?, ?it/s]
Computing max image from bright frames:   5%|▌         | 1/20 [00:03<01:05,  3.43s/it]
Computing max image from bright frames:  20%|██        | 4/20 [00:03<00:10,  1.47it/s]
Computing max image from bright frames:  35%|███▌      | 7/20 [00:03<00:04,  2.92it/s]
Computing max image from bright frames:  45%|████▌     | 9/20 [00:03<00:02,  3.74it/s]
Computing max image from bright frames:  55%|█████▌    | 11/20 [00:04<00:01,  4.53it/s]
Computing max image from bright frames:  65%|██████▌   | 13/20 [00:04<00:01,  5.21it/s]
Computing max image from bright frames:  75%|███████▌  | 15/20 [00:04<00:00,  5.42it/s]
Computing max image from bright frames:  80%|████████  | 16/20 [00:04<00:00,  5.28it/s]
Computing max image from bright frames:  85%|████████▌ | 17/20 [00:05<00:00,  4.74it/s]
Computing max image from bright frames: 100%|██████████| 20/20 [00:05<00:00,  7.56it/s]
                                                                                       
[✅] Casing detection complete: cx=1019.0, cy=751.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.011 | Best: {'fx': 300.0} (0.011):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.011 | Best: {'fx': 300.0} (0.011):   1%|          | 1/100 [00:00<00:11,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):   1%|          | 1/100 [00:00<00:11,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.008 | Best: {'fx': 300.0} (0.011):   2%|▏         | 2/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):   3%|▎         | 3/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):   4%|▍         | 4/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):   5%|▌         | 5/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):   6%|▌         | 6/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):   7%|▋         | 7/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.006 | Best: {'fx': 300.0} (0.011):   8%|▊         | 8/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):   9%|▉         | 9/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  10%|█         | 10/100 [00:00<00:10,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  11%|█         | 11/100 [00:00<00:09,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  12%|█▏        | 12/100 [00:00<00:09,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  13%|█▎        | 13/100 [00:00<00:09,  8.92it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  14%|█▍        | 14/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  14%|█▍        | 14/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  15%|█▌        | 15/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.007 | Best: {'fx': 300.0} (0.011):  16%|█▌        | 16/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.008 | Best: {'fx': 300.0} (0.011):  17%|█▋        | 17/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  18%|█▊        | 18/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  19%|█▉        | 19/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  20%|██        | 20/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.008 | Best: {'fx': 300.0} (0.011):  21%|██        | 21/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.008 | Best: {'fx': 300.0} (0.011):  22%|██▏       | 22/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  23%|██▎       | 23/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.008 | Best: {'fx': 300.0} (0.011):  24%|██▍       | 24/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  25%|██▌       | 25/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.008 | Best: {'fx': 300.0} (0.011):  26%|██▌       | 26/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  27%|██▋       | 27/100 [00:00<00:01, 69.26it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.009 | Best: {'fx': 300.0} (0.011):  28%|██▊       | 28/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.037 | Best: {'fx': 519.192} (0.037):  28%|██▊       | 28/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.011 | Best: {'fx': 519.192} (0.037):  29%|██▉       | 29/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.052 | Best: {'fx': 540.404} (0.052):  30%|███       | 30/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.014 | Best: {'fx': 540.404} (0.052):  31%|███       | 31/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.081 | Best: {'fx': 533.333} (0.081):  32%|███▏      | 32/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.026 | Best: {'fx': 533.333} (0.081):  33%|███▎      | 33/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.055 | Best: {'fx': 533.333} (0.081):  34%|███▍      | 34/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.038 | Best: {'fx': 533.333} (0.081):  35%|███▌      | 35/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.025 | Best: {'fx': 533.333} (0.081):  36%|███▌      | 36/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.032 | Best: {'fx': 533.333} (0.081):  37%|███▋      | 37/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.028 | Best: {'fx': 533.333} (0.081):  38%|███▊      | 38/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  39%|███▉      | 39/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  40%|████      | 40/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.023 | Best: {'fx': 533.333} (0.081):  41%|████      | 41/100 [00:00<00:00, 89.77it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.023 | Best: {'fx': 533.333} (0.081):  42%|████▏     | 42/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.022 | Best: {'fx': 533.333} (0.081):  42%|████▏     | 42/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.021 | Best: {'fx': 533.333} (0.081):  43%|████▎     | 43/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.020 | Best: {'fx': 533.333} (0.081):  44%|████▍     | 44/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  45%|████▌     | 45/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.020 | Best: {'fx': 533.333} (0.081):  46%|████▌     | 46/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  47%|████▋     | 47/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  48%|████▊     | 48/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  49%|████▉     | 49/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  50%|█████     | 50/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  51%|█████     | 51/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  52%|█████▏    | 52/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  53%|█████▎    | 53/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  54%|█████▍    | 54/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  55%|█████▌    | 55/100 [00:00<00:00, 107.03it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  56%|█████▌    | 56/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  56%|█████▌    | 56/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  57%|█████▋    | 57/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  58%|█████▊    | 58/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  59%|█████▉    | 59/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  60%|██████    | 60/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  61%|██████    | 61/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  62%|██████▏   | 62/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  63%|██████▎   | 63/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  64%|██████▍   | 64/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  65%|██████▌   | 65/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  66%|██████▌   | 66/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  67%|██████▋   | 67/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  68%|██████▊   | 68/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  69%|██████▉   | 69/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  70%|███████   | 70/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  71%|███████   | 71/100 [00:00<00:00, 117.94it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  72%|███████▏  | 72/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  72%|███████▏  | 72/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  73%|███████▎  | 73/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  74%|███████▍  | 74/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  75%|███████▌  | 75/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  76%|███████▌  | 76/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  77%|███████▋  | 77/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  78%|███████▊  | 78/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.016 | Best: {'fx': 533.333} (0.081):  79%|███████▉  | 79/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  80%|████████  | 80/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  81%|████████  | 81/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  82%|████████▏ | 82/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  83%|████████▎ | 83/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  84%|████████▍ | 84/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  85%|████████▌ | 85/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  86%|████████▌ | 86/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  87%|████████▋ | 87/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  88%|████████▊ | 88/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  89%|████████▉ | 89/100 [00:00<00:00, 126.20it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  90%|█████████ | 90/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.017 | Best: {'fx': 533.333} (0.081):  90%|█████████ | 90/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  91%|█████████ | 91/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  92%|█████████▏| 92/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  93%|█████████▎| 93/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  94%|█████████▍| 94/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  95%|█████████▌| 95/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.020 | Best: {'fx': 533.333} (0.081):  96%|█████████▌| 96/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.018 | Best: {'fx': 533.333} (0.081):  97%|█████████▋| 97/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  98%|█████████▊| 98/100 [00:00<00:00, 137.79it/s]
Brute | Start: {'fx': 500.0} (0.012) | Current: 0.019 | Best: {'fx': 533.333} (0.081):  99%|█████████▉| 99/100 [00:00<00:00, 137.79it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.020 | Best: {'fx': 509.354} (0.020):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.020 | Best: {'fx': 509.354} (0.020):   2%|▏         | 1/50 [00:00<00:05,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.017 | Best: {'fx': 509.354} (0.020):   2%|▏         | 1/50 [00:00<00:05,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.022 | Best: {'fx': 510.374} (0.022):   4%|▍         | 2/50 [00:00<00:05,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.024 | Best: {'fx': 511.395} (0.024):   6%|▌         | 3/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.030 | Best: {'fx': 513.435} (0.030):   8%|▊         | 4/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.032 | Best: {'fx': 515.476} (0.032):  10%|█         | 5/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.026 | Best: {'fx': 515.476} (0.032):  12%|█▏        | 6/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 515.476} (0.032):  14%|█▍        | 7/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.033 | Best: {'fx': 516.497} (0.033):  16%|█▌        | 8/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 517.517} (0.035):  18%|█▊        | 9/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.036 | Best: {'fx': 518.537} (0.036):  20%|██        | 10/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.042 | Best: {'fx': 521.599} (0.042):  22%|██▏       | 11/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.038 | Best: {'fx': 521.599} (0.042):  24%|██▍       | 12/50 [00:00<00:04,  9.40it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.038 | Best: {'fx': 521.599} (0.042):  26%|██▌       | 13/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.040 | Best: {'fx': 521.599} (0.042):  26%|██▌       | 13/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.044 | Best: {'fx': 522.619} (0.044):  28%|██▊       | 14/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.046 | Best: {'fx': 523.639} (0.046):  30%|███       | 15/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.050 | Best: {'fx': 524.66} (0.050):  32%|███▏      | 16/50 [00:00<00:00, 72.61it/s] 
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.052 | Best: {'fx': 525.68} (0.052):  34%|███▍      | 17/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.065 | Best: {'fx': 529.762} (0.065):  36%|███▌      | 18/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.056 | Best: {'fx': 529.762} (0.065):  38%|███▊      | 19/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.060 | Best: {'fx': 529.762} (0.065):  40%|████      | 20/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.066 | Best: {'fx': 528.741} (0.066):  42%|████▏     | 21/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.076 | Best: {'fx': 533.844} (0.076):  44%|████▍     | 22/50 [00:00<00:00, 72.61it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.076 | Best: {'fx': 533.844} (0.076):  46%|████▌     | 23/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.073 | Best: {'fx': 533.844} (0.076):  46%|████▌     | 23/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.081 | Best: {'fx': 532.823} (0.081):  48%|████▊     | 24/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.080 | Best: {'fx': 532.823} (0.081):  50%|█████     | 25/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.078 | Best: {'fx': 532.823} (0.081):  52%|█████▏    | 26/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.069 | Best: {'fx': 532.823} (0.081):  54%|█████▍    | 27/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.062 | Best: {'fx': 532.823} (0.081):  56%|█████▌    | 28/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.048 | Best: {'fx': 532.823} (0.081):  58%|█████▊    | 29/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.049 | Best: {'fx': 532.823} (0.081):  60%|██████    | 30/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.044 | Best: {'fx': 532.823} (0.081):  62%|██████▏   | 31/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.060 | Best: {'fx': 532.823} (0.081):  64%|██████▍   | 32/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.052 | Best: {'fx': 532.823} (0.081):  66%|██████▌   | 33/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.055 | Best: {'fx': 532.823} (0.081):  68%|██████▊   | 34/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.045 | Best: {'fx': 532.823} (0.081):  70%|███████   | 35/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.041 | Best: {'fx': 532.823} (0.081):  72%|███████▏  | 36/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 532.823} (0.081):  74%|███████▍  | 37/50 [00:00<00:00, 82.14it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.039 | Best: {'fx': 532.823} (0.081):  76%|███████▌  | 38/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.038 | Best: {'fx': 532.823} (0.081):  76%|███████▌  | 38/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.034 | Best: {'fx': 532.823} (0.081):  78%|███████▊  | 39/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.037 | Best: {'fx': 532.823} (0.081):  80%|████████  | 40/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.036 | Best: {'fx': 532.823} (0.081):  82%|████████▏ | 41/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.034 | Best: {'fx': 532.823} (0.081):  84%|████████▍ | 42/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.035 | Best: {'fx': 532.823} (0.081):  86%|████████▌ | 43/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 532.823} (0.081):  88%|████████▊ | 44/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.032 | Best: {'fx': 532.823} (0.081):  90%|█████████ | 45/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.029 | Best: {'fx': 532.823} (0.081):  92%|█████████▏| 46/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.031 | Best: {'fx': 532.823} (0.081):  94%|█████████▍| 47/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.032 | Best: {'fx': 532.823} (0.081):  96%|█████████▌| 48/50 [00:00<00:00, 103.76it/s]
Brute | Start: {'fx': 533.333} (0.081) | Current: 0.030 | Best: {'fx': 532.823} (0.081):  98%|█████████▊| 49/50 [00:00<00:00, 103.76it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 532.8231292517007
    px: 1019.0
    py: 751.0
    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 2 incomplete frames): 18 frames will be used for optimization
  Rigs: 1000(1000mm):18/20
  Total: 18/20 frames used for optimization

Initial score: 24.3620: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 24.3620, picklable_objective(x0) = 19.8123

Best score: 19.8123: : 0iter [00:01, ?iter/s] 
Best score: 19.8123: : 1iter [00:01,  1.93s/iter]
Best score: 19.8123: : 1iter [00:01,  1.93s/iter]
Best score: 19.8123: : 2iter [00:01,  1.93s/iter]
Best score: 19.8123: : 3iter [00:02,  1.93s/iter]
Best score: 19.8123: : 4iter [00:02,  1.93s/iter]
Best score: 19.8123: : 5iter [00:02,  1.93s/iter]
Best score: 19.8123: : 6iter [00:02,  3.88iter/s]
Best score: 19.8123: : 6iter [00:02,  3.88iter/s]
Best score: 19.8123: : 7iter [00:02,  3.88iter/s]
Best score: 19.8123: : 8iter [00:02,  3.88iter/s]
Best score: 19.8123: : 9iter [00:02,  3.88iter/s]
Best score: 19.8123: : 10iter [00:02,  3.88iter/s]
Best score: 19.8123: : 11iter [00:02,  7.85iter/s]
Best score: 19.8123: : 11iter [00:02,  7.85iter/s]
Best score: 19.8123: : 12iter [00:02,  7.85iter/s]
Best score: 19.8123: : 13iter [00:02,  7.85iter/s]
Best score: 19.8123: : 14iter [00:02,  7.85iter/s]
Best score: 19.8123: : 15iter [00:02,  7.85iter/s]
Best score: 19.8123: : 16iter [00:02, 12.29iter/s]
Best score: 19.8123: : 16iter [00:02, 12.29iter/s]
Best score: 19.8123: : 17iter [00:02, 12.29iter/s]
Best score: 19.8123: : 18iter [00:02, 12.29iter/s]
Best score: 19.8123: : 19iter [00:02, 12.29iter/s]
Best score: 19.8123: : 20iter [00:02, 12.29iter/s]
Best score: 19.8123: : 21iter [00:02, 16.93iter/s]
Best score: 19.8123: : 21iter [00:02, 16.93iter/s]
Best score: 19.8123: : 22iter [00:02, 16.93iter/s]
Best score: 19.8123: : 23iter [00:02, 16.93iter/s]
Best score: 19.8123: : 24iter [00:02, 16.93iter/s]
Best score: 19.8123: : 25iter [00:02, 16.93iter/s]
Best score: 19.8123: : 26iter [00:02, 21.60iter/s]
Best score: 19.8123: : 26iter [00:02, 21.60iter/s]
Best score: 19.8123: : 27iter [00:02, 21.60iter/s]
Best score: 19.8123: : 28iter [00:02, 21.60iter/s]
Best score: 19.8123: : 29iter [00:02, 21.60iter/s]
Best score: 19.8123: : 30iter [00:02, 21.60iter/s]
Best score: 19.8123: : 31iter [00:02, 25.86iter/s]
Best score: 19.8123: : 31iter [00:02, 25.86iter/s]
Best score: 19.8123: : 32iter [00:02, 25.86iter/s]
Best score: 19.8123: : 33iter [00:02, 25.86iter/s]
Best score: 19.8123: : 34iter [00:02, 25.86iter/s]
Best score: 19.8123: : 35iter [00:02, 25.86iter/s]
Best score: 19.8123: : 36iter [00:02, 29.77iter/s]
Best score: 19.8123: : 36iter [00:02, 29.77iter/s]
Best score: 19.8123: : 37iter [00:02, 29.77iter/s]
Best score: 19.8123: : 38iter [00:02, 29.77iter/s]
Best score: 19.8123: : 39iter [00:02, 29.77iter/s]
Best score: 19.8123: : 40iter [00:02, 29.77iter/s]
Best score: 19.8123: : 41iter [00:02, 32.85iter/s]
Best score: 19.8123: : 41iter [00:02, 32.85iter/s]
Best score: 19.8123: : 42iter [00:02, 32.85iter/s]
Best score: 19.8123: : 43iter [00:02, 32.85iter/s]
Best score: 19.8123: : 44iter [00:02, 32.85iter/s]
Best score: 19.8123: : 45iter [00:02, 32.85iter/s]
Best score: 19.8123: : 46iter [00:02, 35.54iter/s]
Best score: 19.8123: : 46iter [00:03, 35.54iter/s]
Best score: 19.8123: : 47iter [00:03, 35.54iter/s]
Best score: 19.8123: : 48iter [00:03, 35.54iter/s]
[ITER 50] Best: 19.8123
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 1019.0000
  py: 751.0000
  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: 19.8123: : 49iter [00:03, 35.54iter/s]
Best score: 19.8123: : 50iter [00:03, 35.54iter/s]
Best score: 19.8123: : 51iter [00:03, 37.52iter/s]
Best score: 19.8123: : 51iter [00:03, 37.52iter/s]
Best score: 19.8123: : 52iter [00:03, 37.52iter/s]
Best score: 19.8123: : 53iter [00:03, 37.52iter/s]
Best score: 19.8123: : 54iter [00:03, 37.52iter/s]
Best score: 19.8123: : 55iter [00:03, 37.52iter/s]
Best score: 19.8123: : 56iter [00:03, 39.10iter/s]
Best score: 19.8123: : 56iter [00:03, 39.10iter/s]
Best score: 19.8123: : 57iter [00:03, 39.10iter/s]
Best score: 19.8123: : 58iter [00:03, 39.10iter/s]
Best score: 19.8123: : 59iter [00:03, 39.10iter/s]
Best score: 19.8123: : 60iter [00:03, 39.10iter/s]
Best score: 19.8123: : 61iter [00:03, 40.15iter/s]
Best score: 19.8123: : 61iter [00:03, 40.15iter/s]
Best score: 19.8123: : 62iter [00:03, 40.15iter/s]
Best score: 19.8123: : 63iter [00:03, 40.15iter/s]
Best score: 19.8123: : 64iter [00:03, 40.15iter/s]
Best score: 19.8123: : 65iter [00:03, 40.15iter/s]
Best score: 19.8123: : 66iter [00:03, 41.26iter/s]
Best score: 19.8123: : 66iter [00:03, 41.26iter/s]
Best score: 19.8123: : 67iter [00:03, 41.26iter/s]
Best score: 19.8123: : 68iter [00:03, 41.26iter/s]
Best score: 19.8123: : 69iter [00:03, 41.26iter/s]
Best score: 19.8123: : 70iter [00:03, 41.26iter/s]
Best score: 19.8123: : 71iter [00:03, 41.68iter/s]
Best score: 19.8123: : 71iter [00:03, 41.68iter/s]
Best score: 19.8123: : 72iter [00:03, 41.68iter/s]
Best score: 19.8123: : 73iter [00:03, 41.68iter/s]
Best score: 19.8123: : 74iter [00:03, 41.68iter/s]
Best score: 19.8123: : 75iter [00:03, 41.68iter/s]
Best score: 19.8123: : 76iter [00:03, 42.16iter/s]
Best score: 19.8123: : 76iter [00:03, 42.16iter/s]
Best score: 19.8123: : 77iter [00:03, 42.16iter/s]
Best score: 19.8123: : 78iter [00:03, 42.16iter/s]
Best score: 19.8123: : 79iter [00:03, 42.16iter/s]
Best score: 19.8123: : 80iter [00:03, 42.16iter/s]
Best score: 19.8123: : 81iter [00:03, 42.24iter/s]
Best score: 19.8123: : 81iter [00:03, 42.24iter/s]
Best score: 19.8123: : 82iter [00:03, 42.24iter/s]
Best score: 19.8123: : 83iter [00:03, 42.24iter/s]
Best score: 19.8123: : 84iter [00:03, 42.24iter/s]
Best score: 19.8123: : 85iter [00:03, 42.24iter/s]
Best score: 19.8123: : 86iter [00:03, 42.69iter/s]
Best score: 19.8123: : 86iter [00:03, 42.69iter/s]
Best score: 19.8123: : 87iter [00:03, 42.69iter/s]
Best score: 19.8123: : 88iter [00:03, 42.69iter/s]
Best score: 19.8123: : 89iter [00:04, 42.69iter/s]
Best score: 19.8123: : 90iter [00:04, 42.69iter/s]
Best score: 19.8123: : 91iter [00:04, 42.76iter/s]
Best score: 19.8123: : 91iter [00:04, 42.76iter/s]
Best score: 19.8123: : 92iter [00:04, 42.76iter/s]
Best score: 19.8123: : 93iter [00:04, 42.76iter/s]
Best score: 19.8123: : 94iter [00:04, 42.76iter/s]
Best score: 19.8123: : 95iter [00:04, 42.76iter/s]
Best score: 19.8123: : 96iter [00:04, 42.99iter/s]
Best score: 19.8123: : 96iter [00:04, 42.99iter/s]
Best score: 19.8123: : 97iter [00:04, 42.99iter/s]
Best score: 19.8123: : 98iter [00:04, 42.99iter/s]
[ITER 100] Best: 19.8123
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 1019.0000
  py: 751.0000
  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: 19.8123: : 99iter [00:04, 42.99iter/s]
Best score: 19.8123: : 100iter [00:04, 42.99iter/s]
Best score: 19.8123: : 101iter [00:04, 40.56iter/s]
Best score: 19.8123: : 101iter [00:04, 40.56iter/s]
Best score: 19.8123: : 102iter [00:04, 40.56iter/s]
Best score: 19.8123: : 103iter [00:04, 40.56iter/s]
Best score: 19.8123: : 104iter [00:04, 40.56iter/s]
Best score: 19.8123: : 105iter [00:04, 40.56iter/s]
Best score: 19.8123: : 106iter [00:04, 41.17iter/s]
Best score: 19.8123: : 106iter [00:04, 41.17iter/s]
Best score: 19.8123: : 107iter [00:04, 41.17iter/s]
Best score: 19.8123: : 108iter [00:04, 41.17iter/s]
Best score: 19.8123: : 109iter [00:04, 41.17iter/s]
Best score: 19.8123: : 110iter [00:04, 41.17iter/s]
Best score: 19.8123: : 111iter [00:04, 41.73iter/s]
Best score: 19.8123: : 111iter [00:04, 41.73iter/s]
Best score: 19.8123: : 112iter [00:04, 41.73iter/s]
Best score: 19.8123: : 113iter [00:04, 41.73iter/s]
Best score: 19.8123: : 114iter [00:04, 41.73iter/s]
Best score: 19.8123: : 115iter [00:04, 41.73iter/s]
Best score: 19.8123: : 116iter [00:04, 42.14iter/s]
Best score: 19.8123: : 116iter [00:04, 42.14iter/s]
Best score: 19.8123: : 117iter [00:04, 42.14iter/s]
Best score: 19.8123: : 118iter [00:04, 42.14iter/s]
Best score: 19.8123: : 119iter [00:04, 42.14iter/s]
Best score: 19.8123: : 120iter [00:04, 42.14iter/s]
Best score: 19.8123: : 121iter [00:04, 42.20iter/s]
Best score: 19.8123: : 121iter [00:04, 42.20iter/s]
Best score: 19.8123: : 122iter [00:04, 42.20iter/s]
Best score: 19.8123: : 123iter [00:04, 42.20iter/s]
Best score: 19.8123: : 124iter [00:04, 42.20iter/s]
Best score: 19.8123: : 125iter [00:04, 42.20iter/s]
Best score: 19.8123: : 126iter [00:04, 42.48iter/s]
Best score: 19.8123: : 126iter [00:04, 42.48iter/s]
Best score: 19.8123: : 127iter [00:04, 42.48iter/s]
Best score: 19.8123: : 128iter [00:04, 42.48iter/s]
Best score: 19.8123: : 129iter [00:04, 42.48iter/s]
Best score: 19.8123: : 130iter [00:04, 42.48iter/s]
Best score: 19.8123: : 131iter [00:04, 42.56iter/s]
Best score: 19.8123: : 131iter [00:05, 42.56iter/s]
Best score: 19.8123: : 132iter [00:05, 42.56iter/s]
Best score: 19.8123: : 133iter [00:05, 42.56iter/s]
Best score: 19.8123: : 134iter [00:05, 42.56iter/s]
Best score: 19.8123: : 135iter [00:05, 42.56iter/s]
Best score: 19.8123: : 136iter [00:05, 42.70iter/s]
Best score: 19.8123: : 136iter [00:05, 42.70iter/s]
Best score: 19.8123: : 137iter [00:05, 42.70iter/s]
Best score: 19.8123: : 138iter [00:05, 42.70iter/s]
Best score: 19.8123: : 139iter [00:05, 42.70iter/s]
Best score: 19.8123: : 140iter [00:05, 42.70iter/s]
Best score: 19.8123: : 141iter [00:05, 42.66iter/s]
Best score: 19.8123: : 141iter [00:05, 42.66iter/s]
Best score: 19.8123: : 142iter [00:05, 42.66iter/s]
Best score: 19.8123: : 143iter [00:05, 42.66iter/s]
Best score: 19.8123: : 144iter [00:05, 42.66iter/s]
Best score: 19.8123: : 145iter [00:05, 42.66iter/s]
Best score: 19.8123: : 146iter [00:05, 42.90iter/s]
Best score: 19.8123: : 146iter [00:05, 42.90iter/s]
Best score: 19.8123: : 147iter [00:05, 42.90iter/s]
Best score: 19.8123: : 148iter [00:05, 42.90iter/s]
[ITER 150] Best: 19.8123
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 1019.0000
  py: 751.0000
  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: 19.8123: : 149iter [00:05, 42.90iter/s]
Best score: 19.8123: : 150iter [00:05, 42.90iter/s]
Best score: 19.8123: : 151iter [00:05, 36.44iter/s]
Best score: 19.8123: : 151iter [00:05, 36.44iter/s]
Best score: 19.8123: : 152iter [00:05, 36.44iter/s]
Best score: 19.8123: : 153iter [00:05, 36.44iter/s]
Best score: 19.8123: : 154iter [00:05, 36.44iter/s]
Best score: 19.8123: : 155iter [00:05, 36.44iter/s]
Best score: 19.8123: : 156iter [00:05, 38.25iter/s]
Best score: 19.8123: : 156iter [00:05, 38.25iter/s]
Best score: 19.8123: : 157iter [00:05, 38.25iter/s]
Best score: 19.8123: : 158iter [00:05, 38.25iter/s]
Best score: 19.8123: : 159iter [00:05, 38.25iter/s]
Best score: 19.8123: : 160iter [00:05, 38.25iter/s]
Best score: 19.8123: : 161iter [00:05, 39.48iter/s]
Best score: 19.8123: : 161iter [00:05, 39.48iter/s]
Best score: 19.8123: : 162iter [00:05, 39.48iter/s]
Best score: 19.8123: : 163iter [00:05, 39.48iter/s]
Best score: 19.8123: : 164iter [00:05, 39.48iter/s]
Best score: 19.8123: : 165iter [00:05, 39.48iter/s]
Best score: 19.8123: : 166iter [00:05, 40.56iter/s]
Best score: 19.8123: : 166iter [00:05, 40.56iter/s]
Best score: 19.8123: : 167iter [00:05, 40.56iter/s]
Best score: 19.8123: : 168iter [00:05, 40.56iter/s]
Best score: 19.8123: : 169iter [00:05, 40.56iter/s]
Best score: 19.8123: : 170iter [00:05, 40.56iter/s]
Best score: 19.8123: : 171iter [00:05, 41.18iter/s]
Best score: 19.8123: : 171iter [00:06, 41.18iter/s]
Best score: 19.8123: : 172iter [00:06, 41.18iter/s]
Best score: 19.8123: : 173iter [00:06, 41.18iter/s]
Best score: 19.8123: : 174iter [00:06, 41.18iter/s]
Best score: 19.8123: : 175iter [00:06, 41.18iter/s]
Best score: 19.8123: : 176iter [00:06, 41.77iter/s]
Best score: 19.8123: : 176iter [00:06, 41.77iter/s]
Best score: 19.8123: : 177iter [00:06, 41.77iter/s]
Best score: 19.8123: : 178iter [00:06, 41.77iter/s]
Best score: 19.8123: : 179iter [00:06, 41.77iter/s]
Best score: 19.8123: : 180iter [00:06, 41.77iter/s]
Best score: 19.8123: : 181iter [00:06, 42.06iter/s]
Best score: 19.8123: : 181iter [00:06, 42.06iter/s]
Best score: 19.8123: : 182iter [00:06, 42.06iter/s]
Best score: 19.8123: : 183iter [00:06, 42.06iter/s]
Best score: 19.8123: : 184iter [00:06, 42.06iter/s]
Best score: 19.8123: : 185iter [00:06, 42.06iter/s]
Best score: 19.8123: : 186iter [00:06, 42.43iter/s]
Best score: 19.8123: : 186iter [00:06, 42.43iter/s]
Best score: 19.8123: : 187iter [00:06, 42.43iter/s]
Best score: 19.8123: : 188iter [00:06, 42.43iter/s]
Best score: 19.8123: : 189iter [00:06, 42.43iter/s]
Best score: 19.8123: : 190iter [00:06, 42.43iter/s]
Best score: 19.8123: : 191iter [00:06, 42.52iter/s]
Best score: 19.8123: : 191iter [00:06, 42.52iter/s]
Best score: 19.8123: : 192iter [00:06, 42.52iter/s]
Best score: 19.8123: : 193iter [00:06, 42.52iter/s]
Best score: 19.8123: : 194iter [00:06, 42.52iter/s]
Best score: 19.8123: : 195iter [00:06, 42.52iter/s]
Best score: 19.8123: : 196iter [00:06, 42.87iter/s]
Best score: 19.8123: : 196iter [00:06, 42.87iter/s]
Best score: 19.8123: : 197iter [00:06, 42.87iter/s]
Best score: 19.8123: : 198iter [00:06, 42.87iter/s]
[ITER 200] Best: 19.8123
[PARAMS] Current best parameters:
  fx: 532.8231
  fy: 532.8231
  px: 1019.0000
  py: 751.0000
  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: 19.8123: : 199iter [00:06, 42.87iter/s]
Best score: 19.8123: : 200iter [00:06, 42.87iter/s]
Best score: 19.8123: : 201iter [00:06, 36.44iter/s]
Best score: 19.8123: : 201iter [00:06, 36.44iter/s]
Best score: 19.8123: : 202iter [00:06, 36.44iter/s]
Best score: 19.8123: : 203iter [00:06, 36.44iter/s]
Best score: 19.8123: : 204iter [00:06, 36.44iter/s]
Best score: 19.8123: : 205iter [00:06, 36.44iter/s]
Best score: 19.8123: : 206iter [00:06, 38.43iter/s]
Best score: 19.8123: : 206iter [00:06, 38.43iter/s]
Best score: 19.8123: : 207iter [00:06, 38.43iter/s]
Best score: 19.8123: : 208iter [00:06, 38.43iter/s]
Best score: 19.8123: : 209iter [00:06, 38.43iter/s]
Best score: 19.8123: : 210iter [00:06, 38.43iter/s]
Best score: 19.8123: : 211iter [00:06, 39.76iter/s]
Best score: 19.8123: : 211iter [00:06, 39.76iter/s]
Best score: 19.8123: : 212iter [00:07, 39.76iter/s]
Best score: 19.8123: : 213iter [00:07, 39.76iter/s]
Best score: 19.8123: : 214iter [00:07, 39.76iter/s]
Best score: 19.8123: : 215iter [00:07, 39.76iter/s]
Best score: 19.8123: : 216iter [00:07, 40.86iter/s]
Best score: 19.8123: : 216iter [00:07, 40.86iter/s]
Best score: 19.8123: : 217iter [00:07, 40.86iter/s]
Best score: 19.8123: : 218iter [00:07, 40.86iter/s]
Best score: 19.8123: : 219iter [00:07, 40.86iter/s]
Best score: 19.8123: : 220iter [00:07, 40.86iter/s]
Best score: 19.8123: : 221iter [00:07, 34.00iter/s]
Best score: 19.8123: : 221iter [00:07, 34.00iter/s]
Best score: 19.8123: : 222iter [00:07, 34.00iter/s]
Best score: 19.8123: : 223iter [00:07, 34.00iter/s]
Best score: 19.8123: : 224iter [00:07, 34.00iter/s]
Best score: 19.8123: : 225iter [00:07, 34.00iter/s]
Best score: 19.8123: : 226iter [00:07, 36.48iter/s]
Best score: 19.8123: : 226iter [00:07, 36.48iter/s]
Best score: 19.8123: : 227iter [00:07, 36.48iter/s]
Best score: 19.8123: : 228iter [00:07, 36.48iter/s]
Best score: 19.8123: : 229iter [00:07, 36.48iter/s]
Best score: 19.8123: : 230iter [00:07, 36.48iter/s]
Best score: 19.8123: : 231iter [00:07, 38.15iter/s]
Best score: 19.8123: : 231iter [00:07, 38.15iter/s]
Best score: 19.8123: : 232iter [00:07, 38.15iter/s]
Best score: 19.8123: : 233iter [00:07, 38.15iter/s]
Best score: 19.8123: : 234iter [00:07, 38.15iter/s]
Best score: 19.8123: : 235iter [00:07, 38.15iter/s]
Best score: 19.8123: : 236iter [00:07, 39.70iter/s]
Best score: 19.8123: : 236iter [00:07, 39.70iter/s]
Best score: 19.8123: : 237iter [00:07, 39.70iter/s]
Best score: 19.8123: : 238iter [00:07, 39.70iter/s]
Best score: 18.0140: : 239iter [00:07, 39.70iter/s]
Best score: 18.0140: : 240iter [00:07, 39.70iter/s]
Best score: 18.0140: : 241iter [00:07, 40.68iter/s]
Best score: 18.0140: : 241iter [00:07, 40.68iter/s]
Best score: 18.0140: : 242iter [00:07, 40.68iter/s]
Best score: 18.0140: : 243iter [00:07, 40.68iter/s]
Best score: 18.0140: : 244iter [00:07, 40.68iter/s]
Best score: 18.0140: : 245iter [00:07, 40.68iter/s]
Best score: 18.0140: : 246iter [00:07, 41.72iter/s]
Best score: 18.0140: : 246iter [00:07, 41.72iter/s]
Best score: 18.0140: : 247iter [00:07, 41.72iter/s]
Best score: 18.0140: : 248iter [00:07, 41.72iter/s]
[ITER 250] Best: 18.0140
[PARAMS] Current best parameters:
  fx: 535.8927
  fy: 534.6534
  px: 1026.6899
  py: 745.6132
  k1: 0.02962612
  k2: -0.04142684
  k3: 0.01464087
  k4: -0.00098830
  p1: 0.01201282
  p2: -0.01035628
  p3: 0.00196263
  p4: 0.00439824
  s1: -0.0165
  s2: 0.0152
  s3: -0.0415
  s4: 0.0068
  tilt_x: -0.0554
  tilt_y: -0.0409
  scale: 3.0017
  rotation: 0.2735

Best score: 18.0140: : 249iter [00:07, 41.72iter/s]
Best score: 18.0140: : 250iter [00:08, 41.72iter/s]
Best score: 18.0140: : 251iter [00:08, 35.80iter/s]
Best score: 18.0140: : 251iter [00:08, 35.80iter/s]
Best score: 18.0140: : 252iter [00:08, 35.80iter/s]
Best score: 18.0140: : 253iter [00:08, 35.80iter/s]
Best score: 18.0140: : 254iter [00:08, 35.80iter/s]
Best score: 18.0140: : 255iter [00:08, 35.80iter/s]
Best score: 18.0140: : 256iter [00:08, 37.92iter/s]
Best score: 18.0140: : 256iter [00:08, 37.92iter/s]
Best score: 18.0140: : 257iter [00:08, 37.92iter/s]
Best score: 18.0140: : 258iter [00:08, 37.92iter/s]
Best score: 9.6179: : 259iter [00:08, 37.92iter/s] 
Best score: 9.6179: : 260iter [00:08, 37.92iter/s]
Best score: 9.6179: : 261iter [00:08, 39.29iter/s]
Best score: 9.6179: : 261iter [00:08, 39.29iter/s]
Best score: 9.6179: : 262iter [00:08, 39.29iter/s]
Best score: 9.6179: : 263iter [00:08, 39.29iter/s]
Best score: 9.6179: : 264iter [00:08, 39.29iter/s]
Best score: 9.6179: : 265iter [00:08, 39.29iter/s]
Best score: 9.6179: : 266iter [00:08, 40.50iter/s]
Best score: 9.6179: : 266iter [00:08, 40.50iter/s]
Best score: 9.6179: : 267iter [00:08, 40.50iter/s]
Best score: 9.6179: : 268iter [00:08, 40.50iter/s]
Best score: 8.0033: : 269iter [00:08, 40.50iter/s]
Best score: 8.0033: : 270iter [00:08, 40.50iter/s]
Best score: 8.0033: : 271iter [00:08, 41.24iter/s]
Best score: 8.0033: : 271iter [00:08, 41.24iter/s]
Best score: 8.0033: : 272iter [00:08, 41.24iter/s]
Best score: 8.0033: : 273iter [00:08, 41.24iter/s]
Best score: 8.0033: : 274iter [00:08, 41.24iter/s]
Best score: 8.0033: : 275iter [00:08, 41.24iter/s]
Best score: 8.0033: : 276iter [00:08, 41.77iter/s]
Best score: 8.0033: : 276iter [00:08, 41.77iter/s]
Best score: 8.0033: : 277iter [00:08, 41.77iter/s]
Best score: 8.0033: : 278iter [00:08, 41.77iter/s]
Best score: 8.0033: : 279iter [00:08, 41.77iter/s]
Best score: 8.0033: : 280iter [00:08, 41.77iter/s]
Best score: 8.0033: : 281iter [00:08, 42.05iter/s]
Best score: 8.0033: : 281iter [00:08, 42.05iter/s]
Best score: 8.0033: : 282iter [00:08, 42.05iter/s]
Best score: 8.0033: : 283iter [00:08, 42.05iter/s]
Best score: 8.0033: : 284iter [00:08, 42.05iter/s]
Best score: 8.0033: : 285iter [00:08, 42.05iter/s]
Best score: 8.0033: : 286iter [00:08, 42.62iter/s]
Best score: 8.0033: : 286iter [00:08, 42.62iter/s]
Best score: 8.0033: : 287iter [00:08, 42.62iter/s]
Best score: 8.0033: : 288iter [00:08, 42.62iter/s]
Best score: 8.0033: : 289iter [00:08, 42.62iter/s]
Best score: 8.0033: : 290iter [00:08, 42.62iter/s]
Best score: 8.0033: : 291iter [00:08, 42.75iter/s]
Best score: 8.0033: : 291iter [00:09, 42.75iter/s]
Best score: 8.0033: : 292iter [00:09, 42.75iter/s]
Best score: 8.0033: : 293iter [00:09, 42.75iter/s]
Best score: 8.0033: : 294iter [00:09, 42.75iter/s]
Best score: 8.0033: : 295iter [00:09, 42.75iter/s]
Best score: 8.0033: : 296iter [00:09, 43.08iter/s]
Best score: 8.0033: : 296iter [00:09, 43.08iter/s]
Best score: 8.0033: : 297iter [00:09, 43.08iter/s]
Best score: 8.0033: : 298iter [00:09, 43.08iter/s]
[ITER 300] Best: 4.6100
[PARAMS] Current best parameters:
  fx: 536.4326
  fy: 535.1142
  px: 1023.0154
  py: 750.3990
  k1: 0.01974912
  k2: -0.01641510
  k3: -0.00040870
  k4: 0.00142845
  p1: 0.00509937
  p2: -0.01206949
  p3: 0.01904094
  p4: -0.00531137
  s1: -0.0156
  s2: 0.0137
  s3: -0.0215
  s4: 0.0045
  tilt_x: -0.0315
  tilt_y: -0.0290
  scale: 2.9654
  rotation: -0.0318

Best score: 4.6100: : 299iter [00:09, 43.08iter/s]
Best score: 4.6100: : 300iter [00:09, 43.08iter/s]
Best score: 4.6100: : 301iter [00:09, 36.54iter/s]
Best score: 4.6100: : 301iter [00:09, 36.54iter/s]
Best score: 4.6100: : 302iter [00:09, 36.54iter/s]
Best score: 4.6100: : 303iter [00:09, 36.54iter/s]
Best score: 4.6100: : 304iter [00:09, 36.54iter/s]
Best score: 4.6100: : 305iter [00:09, 36.54iter/s]
Best score: 4.6100: : 306iter [00:09, 38.39iter/s]
Best score: 4.6100: : 306iter [00:09, 38.39iter/s]
Best score: 4.6100: : 307iter [00:09, 38.39iter/s]
Best score: 4.6100: : 308iter [00:09, 38.39iter/s]
Best score: 2.7569: : 309iter [00:09, 38.39iter/s]
Best score: 2.7569: : 310iter [00:09, 38.39iter/s]
Best score: 2.7569: : 311iter [00:09, 39.65iter/s]
Best score: 2.7569: : 311iter [00:09, 39.65iter/s]
Best score: 2.7569: : 312iter [00:09, 39.65iter/s]
Best score: 2.7569: : 313iter [00:09, 39.65iter/s]
Best score: 2.7569: : 314iter [00:09, 39.65iter/s]
Best score: 2.7569: : 315iter [00:09, 39.65iter/s]
Best score: 2.7569: : 316iter [00:09, 40.78iter/s]
Best score: 2.7569: : 316iter [00:09, 40.78iter/s]
Best score: 2.7569: : 317iter [00:09, 40.78iter/s]
Best score: 2.7569: : 318iter [00:09, 40.78iter/s]
Best score: 2.7569: : 319iter [00:09, 40.78iter/s]
Best score: 2.7569: : 320iter [00:09, 40.78iter/s]
Best score: 2.7569: : 321iter [00:09, 41.43iter/s]
Best score: 2.7569: : 321iter [00:09, 41.43iter/s]
Best score: 2.7569: : 322iter [00:09, 41.43iter/s]
Best score: 2.7569: : 323iter [00:09, 41.43iter/s]
Best score: 2.7569: : 324iter [00:09, 41.43iter/s]
Best score: 2.7569: : 325iter [00:09, 41.43iter/s]
Best score: 2.7569: : 326iter [00:09, 42.07iter/s]
Best score: 2.7569: : 326iter [00:09, 42.07iter/s]
Best score: 2.7569: : 327iter [00:09, 42.07iter/s]
Best score: 2.7569: : 328iter [00:09, 42.07iter/s]
Best score: 2.0059: : 329iter [00:09, 42.07iter/s]
Best score: 2.0059: : 330iter [00:09, 42.07iter/s]
Best score: 2.0059: : 331iter [00:09, 42.35iter/s]
Best score: 2.0059: : 331iter [00:09, 42.35iter/s]
Best score: 2.0059: : 332iter [00:10, 42.35iter/s]
Best score: 2.0059: : 333iter [00:10, 42.35iter/s]
Best score: 2.0059: : 334iter [00:10, 42.35iter/s]
Best score: 2.0059: : 335iter [00:10, 42.35iter/s]
Best score: 2.0059: : 336iter [00:10, 42.87iter/s]
Best score: 2.0059: : 336iter [00:10, 42.87iter/s]
Best score: 2.0059: : 337iter [00:10, 42.87iter/s]
Best score: 2.0059: : 338iter [00:10, 42.87iter/s]
Best score: 2.0059: : 339iter [00:10, 42.87iter/s]
Best score: 2.0059: : 340iter [00:10, 42.87iter/s]
Best score: 2.0059: : 341iter [00:10, 42.89iter/s]
Best score: 2.0059: : 341iter [00:10, 42.89iter/s]
Best score: 2.0059: : 342iter [00:10, 42.89iter/s]
Best score: 2.0059: : 343iter [00:10, 42.89iter/s]
Best score: 2.0059: : 344iter [00:10, 42.89iter/s]
Best score: 2.0059: : 345iter [00:10, 42.89iter/s]
Best score: 2.0059: : 346iter [00:10, 43.26iter/s]
Best score: 2.0059: : 346iter [00:10, 43.26iter/s]
Best score: 2.0059: : 347iter [00:10, 43.26iter/s]
Best score: 2.0059: : 348iter [00:10, 43.26iter/s]
[ITER 350] Best: 2.0059
[PARAMS] Current best parameters:
  fx: 545.6897
  fy: 544.3954
  px: 1021.8357
  py: 752.6703
  k1: 0.02285969
  k2: -0.04866367
  k3: 0.02402851
  k4: -0.00434212
  p1: 0.00598399
  p2: -0.01062457
  p3: 0.01590120
  p4: -0.00959981
  s1: -0.0084
  s2: 0.0127
  s3: -0.0114
  s4: 0.0022
  tilt_x: -0.0233
  tilt_y: -0.0254
  scale: 2.9798
  rotation: -0.1517

Best score: 2.0059: : 349iter [00:10, 43.26iter/s]
Best score: 2.0059: : 350iter [00:10, 43.26iter/s]
Best score: 2.0059: : 351iter [00:10, 36.78iter/s]
Best score: 2.0059: : 351iter [00:10, 36.78iter/s]
Best score: 2.0059: : 352iter [00:10, 36.78iter/s]
Best score: 2.0059: : 353iter [00:10, 36.78iter/s]
Best score: 2.0059: : 354iter [00:10, 36.78iter/s]
Best score: 2.0059: : 355iter [00:10, 36.78iter/s]
Best score: 2.0059: : 356iter [00:10, 38.74iter/s]
Best score: 2.0059: : 356iter [00:10, 38.74iter/s]
Best score: 2.0059: : 357iter [00:10, 38.74iter/s]
Best score: 2.0059: : 358iter [00:10, 38.74iter/s]
Best score: 1.9625: : 359iter [00:10, 38.74iter/s]
Best score: 1.9625: : 360iter [00:10, 38.74iter/s]
Best score: 1.9625: : 361iter [00:10, 39.94iter/s]
Best score: 1.9625: : 361iter [00:10, 39.94iter/s]
Best score: 1.9625: : 362iter [00:10, 39.94iter/s]
Best score: 1.9625: : 363iter [00:10, 39.94iter/s]
Best score: 1.9625: : 364iter [00:10, 39.94iter/s]
Best score: 1.9625: : 365iter [00:10, 39.94iter/s]
Best score: 1.9625: : 366iter [00:10, 41.16iter/s]
Best score: 1.9625: : 366iter [00:10, 41.16iter/s]
Best score: 1.9625: : 367iter [00:10, 41.16iter/s]
Best score: 1.9625: : 368iter [00:10, 41.16iter/s]
Best score: 1.8114: : 369iter [00:10, 41.16iter/s]
Best score: 1.8114: : 370iter [00:10, 41.16iter/s]
Best score: 1.8114: : 371iter [00:10, 41.82iter/s]
Best score: 1.8114: : 371iter [00:10, 41.82iter/s]
Best score: 1.8114: : 372iter [00:10, 41.82iter/s]
Best score: 1.8114: : 373iter [00:11, 41.82iter/s]
Best score: 1.8114: : 374iter [00:11, 41.82iter/s]
Best score: 1.8114: : 375iter [00:11, 41.82iter/s]
Best score: 1.8114: : 376iter [00:11, 42.48iter/s]
Best score: 1.8114: : 376iter [00:11, 42.48iter/s]
Best score: 1.8114: : 377iter [00:11, 42.48iter/s]
Best score: 1.8114: : 378iter [00:11, 42.48iter/s]
Best score: 1.5496: : 379iter [00:11, 42.48iter/s]
Best score: 1.5496: : 380iter [00:11, 42.48iter/s]
Best score: 1.5496: : 381iter [00:11, 42.64iter/s]
Best score: 1.5496: : 381iter [00:11, 42.64iter/s]
Best score: 1.5496: : 382iter [00:11, 42.64iter/s]
Best score: 1.5496: : 383iter [00:11, 42.64iter/s]
Best score: 1.5496: : 384iter [00:11, 42.64iter/s]
Best score: 1.5496: : 385iter [00:11, 42.64iter/s]
Best score: 1.5496: : 386iter [00:11, 42.97iter/s]
Best score: 1.5496: : 386iter [00:11, 42.97iter/s]
Best score: 1.5496: : 387iter [00:11, 42.97iter/s]
Best score: 1.5496: : 388iter [00:11, 42.97iter/s]
Best score: 1.5496: : 389iter [00:11, 42.97iter/s]
Best score: 1.5496: : 390iter [00:11, 42.97iter/s]
Best score: 1.5496: : 391iter [00:11, 43.12iter/s]
Best score: 1.5496: : 391iter [00:11, 43.12iter/s]
Best score: 1.5496: : 392iter [00:11, 43.12iter/s]
Best score: 1.5496: : 393iter [00:11, 43.12iter/s]
Best score: 1.5496: : 394iter [00:11, 43.12iter/s]
Best score: 1.5496: : 395iter [00:11, 43.12iter/s]
Best score: 1.5496: : 396iter [00:11, 43.29iter/s]
Best score: 1.5496: : 396iter [00:11, 43.29iter/s]
Best score: 1.5496: : 397iter [00:11, 43.29iter/s]
Best score: 1.5496: : 398iter [00:11, 43.29iter/s]
[ITER 400] Best: 1.4870
[PARAMS] Current best parameters:
  fx: 536.0164
  fy: 535.1305
  px: 1022.3115
  py: 750.0491
  k1: 0.04368608
  k2: -0.04173079
  k3: 0.00820766
  k4: 0.00045135
  p1: 0.01229645
  p2: -0.00370965
  p3: 0.00347697
  p4: -0.00099587
  s1: 0.0037
  s2: -0.0003
  s3: -0.0171
  s4: 0.0025
  tilt_x: -0.0499
  tilt_y: -0.0185
  scale: 2.9315
  rotation: 0.0110

Best score: 1.4870: : 399iter [00:11, 43.29iter/s]
Best score: 1.4870: : 400iter [00:11, 43.29iter/s]
Best score: 1.4870: : 401iter [00:11, 36.79iter/s]
Best score: 1.4870: : 401iter [00:11, 36.79iter/s]
Best score: 1.4870: : 402iter [00:11, 36.79iter/s]
Best score: 1.4870: : 403iter [00:11, 36.79iter/s]
Best score: 1.4870: : 404iter [00:11, 36.79iter/s]
Best score: 1.4870: : 405iter [00:11, 36.79iter/s]
Best score: 1.4870: : 406iter [00:11, 38.76iter/s]
Best score: 1.4870: : 406iter [00:11, 38.76iter/s]
Best score: 1.4870: : 407iter [00:11, 38.76iter/s]
Best score: 1.4870: : 408iter [00:11, 38.76iter/s]
Best score: 1.0138: : 409iter [00:11, 38.76iter/s]
Best score: 1.0138: : 410iter [00:11, 38.76iter/s]
Best score: 1.0138: : 411iter [00:11, 40.02iter/s]
Best score: 1.0138: : 411iter [00:11, 40.02iter/s]
Best score: 1.0138: : 412iter [00:11, 40.02iter/s]
Best score: 1.0138: : 413iter [00:12, 40.02iter/s]
Best score: 1.0138: : 414iter [00:12, 40.02iter/s]
Best score: 1.0138: : 415iter [00:12, 40.02iter/s]
Best score: 1.0138: : 416iter [00:12, 41.03iter/s]
Best score: 1.0138: : 416iter [00:12, 41.03iter/s]
Best score: 1.0138: : 417iter [00:12, 41.03iter/s]
Best score: 1.0138: : 418iter [00:12, 41.03iter/s]
Best score: 1.0138: : 419iter [00:12, 41.03iter/s]
Best score: 1.0138: : 420iter [00:12, 41.03iter/s]
Best score: 1.0138: : 421iter [00:12, 41.68iter/s]
Best score: 1.0138: : 421iter [00:12, 41.68iter/s]
Best score: 1.0138: : 422iter [00:12, 41.68iter/s]
Best score: 1.0138: : 423iter [00:12, 41.68iter/s]
Best score: 1.0138: : 424iter [00:12, 41.68iter/s]
Best score: 1.0138: : 425iter [00:12, 41.68iter/s]
Best score: 1.0138: : 426iter [00:12, 42.33iter/s]
Best score: 1.0138: : 426iter [00:12, 42.33iter/s]
Best score: 1.0138: : 427iter [00:12, 42.33iter/s]
Best score: 1.0138: : 428iter [00:12, 42.33iter/s]
Best score: 1.0131: : 429iter [00:12, 42.33iter/s]
Best score: 1.0131: : 430iter [00:12, 42.33iter/s]
Best score: 1.0131: : 431iter [00:12, 42.58iter/s]
Best score: 1.0131: : 431iter [00:12, 42.58iter/s]
Best score: 1.0131: : 432iter [00:12, 42.58iter/s]
Best score: 1.0131: : 433iter [00:12, 42.58iter/s]
Best score: 1.0131: : 434iter [00:12, 42.58iter/s]
Best score: 1.0131: : 435iter [00:12, 42.58iter/s]
Best score: 1.0131: : 436iter [00:12, 43.11iter/s]
Best score: 1.0131: : 436iter [00:12, 43.11iter/s]
Best score: 1.0131: : 437iter [00:12, 43.11iter/s]
Best score: 1.0131: : 438iter [00:12, 43.11iter/s]
Best score: 1.0131: : 439iter [00:12, 43.11iter/s]
Best score: 1.0131: : 440iter [00:12, 43.11iter/s]
Best score: 1.0131: : 441iter [00:12, 43.21iter/s]
Best score: 1.0131: : 441iter [00:12, 43.21iter/s]
Best score: 1.0131: : 442iter [00:12, 43.21iter/s]
Best score: 1.0131: : 443iter [00:12, 43.21iter/s]
Best score: 1.0131: : 444iter [00:12, 43.21iter/s]
Best score: 1.0131: : 445iter [00:12, 43.21iter/s]
Best score: 1.0131: : 446iter [00:12, 43.40iter/s]
Best score: 1.0131: : 446iter [00:12, 43.40iter/s]
Best score: 1.0131: : 447iter [00:12, 43.40iter/s]
Best score: 1.0131: : 448iter [00:12, 43.40iter/s]
[ITER 450] Best: 1.0131
[PARAMS] Current best parameters:
  fx: 539.5405
  fy: 538.6615
  px: 1021.5870
  py: 749.0585
  k1: 0.04530570
  k2: -0.06147106
  k3: 0.02685595
  k4: -0.00468802
  p1: 0.02063058
  p2: -0.00175945
  p3: -0.00307578
  p4: 0.00043072
  s1: 0.0049
  s2: 0.0002
  s3: -0.0197
  s4: 0.0003
  tilt_x: -0.0712
  tilt_y: -0.0084
  scale: 2.9373
  rotation: -0.0518

Best score: 1.0131: : 449iter [00:12, 43.40iter/s]
Best score: 1.0131: : 450iter [00:12, 43.40iter/s]
Best score: 1.0131: : 451iter [00:12, 36.81iter/s]
Best score: 1.0131: : 451iter [00:12, 36.81iter/s]
Best score: 1.0131: : 452iter [00:12, 36.81iter/s]
Best score: 1.0131: : 453iter [00:12, 36.81iter/s]
Best score: 1.0131: : 454iter [00:13, 36.81iter/s]
Best score: 1.0131: : 455iter [00:13, 36.81iter/s]
Best score: 1.0131: : 456iter [00:13, 38.53iter/s]
Best score: 1.0131: : 456iter [00:13, 38.53iter/s]
Best score: 1.0131: : 457iter [00:13, 38.53iter/s]
Best score: 1.0131: : 458iter [00:13, 38.53iter/s]
Best score: 1.0131: : 459iter [00:13, 38.53iter/s]
Best score: 1.0131: : 460iter [00:13, 38.53iter/s]
Best score: 1.0131: : 461iter [00:13, 39.88iter/s]
Best score: 1.0131: : 461iter [00:13, 39.88iter/s]
Best score: 1.0131: : 462iter [00:13, 39.88iter/s]
Best score: 1.0131: : 463iter [00:13, 39.88iter/s]
Best score: 1.0131: : 464iter [00:13, 39.88iter/s]
Best score: 1.0131: : 465iter [00:13, 39.88iter/s]
Best score: 1.0131: : 466iter [00:13, 41.05iter/s]
Best score: 1.0131: : 466iter [00:13, 41.05iter/s]
Best score: 1.0131: : 467iter [00:13, 41.05iter/s]
Best score: 1.0131: : 468iter [00:13, 41.05iter/s]
Best score: 0.9543: : 469iter [00:13, 41.05iter/s]
Best score: 0.9543: : 470iter [00:13, 41.05iter/s]
Best score: 0.9543: : 471iter [00:13, 41.72iter/s]
Best score: 0.9543: : 471iter [00:13, 41.72iter/s]
Best score: 0.9543: : 472iter [00:13, 41.72iter/s]
Best score: 0.9543: : 473iter [00:13, 41.72iter/s]
Best score: 0.9543: : 474iter [00:13, 41.72iter/s]
Best score: 0.9543: : 475iter [00:13, 41.72iter/s]
Best score: 0.9543: : 476iter [00:13, 42.44iter/s]
Best score: 0.9543: : 476iter [00:13, 42.44iter/s]
Best score: 0.9543: : 477iter [00:13, 42.44iter/s]
Best score: 0.9543: : 478iter [00:13, 42.44iter/s]
Best score: 0.9514: : 479iter [00:13, 42.44iter/s]
Best score: 0.9514: : 480iter [00:13, 42.44iter/s]
Best score: 0.9514: : 481iter [00:13, 42.64iter/s]
Best score: 0.9514: : 481iter [00:13, 42.64iter/s]
Best score: 0.9514: : 482iter [00:13, 42.64iter/s]
Best score: 0.9514: : 483iter [00:13, 42.64iter/s]
Best score: 0.9514: : 484iter [00:13, 42.64iter/s]
Best score: 0.9514: : 485iter [00:13, 42.64iter/s]
Best score: 0.9514: : 486iter [00:13, 43.04iter/s]
Best score: 0.9514: : 486iter [00:13, 43.04iter/s]
Best score: 0.9514: : 487iter [00:13, 43.04iter/s]
Best score: 0.9514: : 488iter [00:13, 43.04iter/s]
Best score: 0.9514: : 489iter [00:13, 43.04iter/s]
Best score: 0.9514: : 490iter [00:13, 43.04iter/s]
Best score: 0.9514: : 491iter [00:13, 43.14iter/s]
Best score: 0.9514: : 491iter [00:13, 43.14iter/s]
Best score: 0.9514: : 492iter [00:13, 43.14iter/s]
Best score: 0.9514: : 493iter [00:13, 43.14iter/s]
Best score: 0.9514: : 494iter [00:13, 43.14iter/s]
Best score: 0.9514: : 495iter [00:13, 43.14iter/s]
Best score: 0.9514: : 496iter [00:13, 43.53iter/s]
Best score: 0.9514: : 496iter [00:13, 43.53iter/s]
Best score: 0.9514: : 497iter [00:14, 43.53iter/s]
Best score: 0.9514: : 498iter [00:14, 43.53iter/s]
[ITER 500] Best: 0.9261
[PARAMS] Current best parameters:
  fx: 539.9318
  fy: 538.9662
  px: 1021.4407
  py: 749.8638
  k1: 0.05090789
  k2: -0.06765368
  k3: 0.02762989
  k4: -0.00420088
  p1: 0.02287745
  p2: -0.00033799
  p3: -0.00104670
  p4: -0.00177341
  s1: 0.0040
  s2: 0.0015
  s3: -0.0207
  s4: 0.0012
  tilt_x: -0.0723
  tilt_y: -0.0041
  scale: 2.9331
  rotation: -0.0722

Best score: 0.9261: : 499iter [00:14, 43.53iter/s]
Best score: 0.9261: : 500iter [00:14, 43.53iter/s]
Best score: 0.9261: : 501iter [00:14, 36.78iter/s]
Best score: 0.9261: : 501iter [00:14, 36.78iter/s]
Best score: 0.9261: : 502iter [00:14, 36.78iter/s]
Best score: 0.9261: : 503iter [00:14, 36.78iter/s]
Best score: 0.9261: : 504iter [00:14, 36.78iter/s]
Best score: 0.9261: : 505iter [00:14, 36.78iter/s]
Best score: 0.9261: : 506iter [00:14, 38.78iter/s]
Best score: 0.9261: : 506iter [00:14, 38.78iter/s]
Best score: 0.9261: : 507iter [00:14, 38.78iter/s]
Best score: 0.9261: : 508iter [00:14, 38.78iter/s]
Best score: 0.8894: : 509iter [00:14, 38.78iter/s]
Best score: 0.8894: : 510iter [00:14, 38.78iter/s]
Best score: 0.8894: : 511iter [00:14, 40.00iter/s]
Best score: 0.8894: : 511iter [00:14, 40.00iter/s]
Best score: 0.8894: : 512iter [00:14, 40.00iter/s]
Best score: 0.8894: : 513iter [00:14, 40.00iter/s]
Best score: 0.8894: : 514iter [00:14, 40.00iter/s]
Best score: 0.8894: : 515iter [00:14, 40.00iter/s]
Best score: 0.8894: : 516iter [00:14, 41.15iter/s]
Best score: 0.8894: : 516iter [00:14, 41.15iter/s]
Best score: 0.8894: : 517iter [00:14, 41.15iter/s]
Best score: 0.8894: : 518iter [00:14, 41.15iter/s]
Best score: 0.8894: : 519iter [00:14, 41.15iter/s]
Best score: 0.8894: : 520iter [00:14, 41.15iter/s]
Best score: 0.8894: : 521iter [00:14, 41.77iter/s]
Best score: 0.8894: : 521iter [00:14, 41.77iter/s]
Best score: 0.8894: : 522iter [00:14, 41.77iter/s]
Best score: 0.8894: : 523iter [00:14, 41.77iter/s]
Best score: 0.8894: : 524iter [00:14, 41.77iter/s]
Best score: 0.8894: : 525iter [00:14, 41.77iter/s]
Best score: 0.8894: : 526iter [00:14, 42.39iter/s]
Best score: 0.8894: : 526iter [00:14, 42.39iter/s]
Best score: 0.8894: : 527iter [00:14, 42.39iter/s]
Best score: 0.8894: : 528iter [00:14, 42.39iter/s]
Best score: 0.8894: : 529iter [00:14, 42.39iter/s]
Best score: 0.8894: : 530iter [00:14, 42.39iter/s]
Best score: 0.8894: : 531iter [00:14, 42.66iter/s]
Best score: 0.8894: : 531iter [00:14, 42.66iter/s]
Best score: 0.8894: : 532iter [00:14, 42.66iter/s]
Best score: 0.8894: : 533iter [00:14, 42.66iter/s]
Best score: 0.8894: : 534iter [00:14, 42.66iter/s]
Best score: 0.8894: : 535iter [00:15, 42.66iter/s]
Best score: 0.8894: : 536iter [00:15, 35.78iter/s]
Best score: 0.8894: : 536iter [00:15, 35.78iter/s]
Best score: 0.8894: : 537iter [00:15, 35.78iter/s]
Best score: 0.8894: : 538iter [00:15, 35.78iter/s]
Best score: 0.8607: : 539iter [00:15, 35.78iter/s]
Best score: 0.8607: : 540iter [00:15, 35.78iter/s]
Best score: 0.8607: : 541iter [00:15, 37.71iter/s]
Best score: 0.8607: : 541iter [00:15, 37.71iter/s]
Best score: 0.8607: : 542iter [00:15, 37.71iter/s]
Best score: 0.8607: : 543iter [00:15, 37.71iter/s]
Best score: 0.8607: : 544iter [00:15, 37.71iter/s]
Best score: 0.8607: : 545iter [00:15, 37.71iter/s]
Best score: 0.8607: : 546iter [00:15, 39.37iter/s]
Best score: 0.8607: : 546iter [00:15, 39.37iter/s]
Best score: 0.8607: : 547iter [00:15, 39.37iter/s]
Best score: 0.8607: : 548iter [00:15, 39.37iter/s]
[ITER 550] Best: 0.8598
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.8598: : 549iter [00:15, 39.37iter/s]
[Periodic outlier @ iter 450] Removing 1000 frame 6 (score=3.78, threshold=3.30)
  Remaining frames: 17

Best score: 0.8598: : 550iter [00:15, 39.37iter/s]
Best score: 0.8598: : 551iter [00:15, 32.52iter/s]
Best score: 0.8598: : 551iter [00:15, 32.52iter/s]
Best score: 0.8598: : 552iter [00:15, 32.52iter/s]
Best score: 0.8598: : 553iter [00:15, 32.52iter/s]
Best score: 0.8598: : 554iter [00:15, 32.52iter/s]
Best score: 0.8598: : 555iter [00:15, 32.52iter/s]
Best score: 0.8598: : 556iter [00:15, 35.56iter/s]
Best score: 0.8598: : 556iter [00:15, 35.56iter/s]
Best score: 0.8598: : 557iter [00:15, 35.56iter/s]
Best score: 0.8598: : 558iter [00:15, 35.56iter/s]
Best score: 0.6887: : 559iter [00:15, 35.56iter/s]
Best score: 0.6887: : 560iter [00:15, 35.56iter/s]
Best score: 0.6887: : 561iter [00:15, 37.89iter/s]
Best score: 0.6887: : 561iter [00:15, 37.89iter/s]
Best score: 0.6887: : 562iter [00:15, 37.89iter/s]
Best score: 0.6887: : 563iter [00:15, 37.89iter/s]
Best score: 0.6887: : 564iter [00:15, 37.89iter/s]
Best score: 0.6887: : 565iter [00:15, 37.89iter/s]
Best score: 0.6887: : 566iter [00:15, 39.95iter/s]
Best score: 0.6887: : 566iter [00:15, 39.95iter/s]
Best score: 0.6887: : 567iter [00:15, 39.95iter/s]
Best score: 0.6887: : 568iter [00:15, 39.95iter/s]
Best score: 0.6887: : 569iter [00:15, 39.95iter/s]
Best score: 0.6887: : 570iter [00:15, 39.95iter/s]
Best score: 0.6887: : 571iter [00:15, 40.54iter/s]
Best score: 0.6887: : 571iter [00:15, 40.54iter/s]
Best score: 0.6887: : 572iter [00:15, 40.54iter/s]
Best score: 0.6887: : 573iter [00:15, 40.54iter/s]
Best score: 0.6887: : 574iter [00:16, 40.54iter/s]
Best score: 0.6887: : 575iter [00:16, 40.54iter/s]
Best score: 0.6887: : 576iter [00:16, 41.60iter/s]
Best score: 0.6887: : 576iter [00:16, 41.60iter/s]
Best score: 0.6887: : 577iter [00:16, 41.60iter/s]
Best score: 0.6887: : 578iter [00:16, 41.60iter/s]
Best score: 0.6887: : 579iter [00:16, 41.60iter/s]
Best score: 0.6887: : 580iter [00:16, 41.60iter/s]
Best score: 0.6887: : 581iter [00:16, 42.56iter/s]
Best score: 0.6887: : 581iter [00:16, 42.56iter/s]
Best score: 0.6887: : 582iter [00:16, 42.56iter/s]
Best score: 0.6887: : 583iter [00:16, 42.56iter/s]
Best score: 0.6887: : 584iter [00:16, 42.56iter/s]
Best score: 0.6887: : 585iter [00:16, 42.56iter/s]
Best score: 0.6887: : 586iter [00:16, 43.49iter/s]
Best score: 0.6887: : 586iter [00:16, 43.49iter/s]
Best score: 0.6887: : 587iter [00:16, 43.49iter/s]
Best score: 0.6887: : 588iter [00:16, 43.49iter/s]
Best score: 0.6887: : 589iter [00:16, 43.49iter/s]
Best score: 0.6887: : 590iter [00:16, 43.49iter/s]
Best score: 0.6887: : 591iter [00:16, 43.93iter/s]
Best score: 0.6887: : 591iter [00:16, 43.93iter/s]
Best score: 0.6887: : 592iter [00:16, 43.93iter/s]
Best score: 0.6887: : 593iter [00:16, 43.93iter/s]
Best score: 0.6887: : 594iter [00:16, 43.93iter/s]
Best score: 0.6887: : 595iter [00:16, 43.93iter/s]
Best score: 0.6887: : 596iter [00:16, 44.41iter/s]
Best score: 0.6887: : 596iter [00:16, 44.41iter/s]
Best score: 0.6887: : 597iter [00:16, 44.41iter/s]
Best score: 0.6887: : 598iter [00:16, 44.41iter/s]
[ITER 600] Best: 0.6887
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.6887: : 599iter [00:16, 44.41iter/s]
[Periodic outlier @ iter 500] Removing 1000 frame 17 (score=2.25, threshold=1.89)
  Remaining frames: 16

Best score: 0.6887: : 600iter [00:16, 44.41iter/s]
Best score: 0.6887: : 601iter [00:16, 35.33iter/s]
Best score: 0.6887: : 601iter [00:16, 35.33iter/s]
Best score: 0.6887: : 602iter [00:16, 35.33iter/s]
Best score: 0.6887: : 603iter [00:16, 35.33iter/s]
Best score: 0.6887: : 604iter [00:16, 35.33iter/s]
Best score: 0.6887: : 605iter [00:16, 35.33iter/s]
Best score: 0.6887: : 606iter [00:16, 38.35iter/s]
Best score: 0.6887: : 606iter [00:16, 38.35iter/s]
Best score: 0.6887: : 607iter [00:16, 38.35iter/s]
Best score: 0.6887: : 608iter [00:16, 38.35iter/s]
Best score: 0.5897: : 609iter [00:16, 38.35iter/s]
Best score: 0.5897: : 610iter [00:16, 38.35iter/s]
Best score: 0.5897: : 611iter [00:16, 40.60iter/s]
Best score: 0.5897: : 611iter [00:16, 40.60iter/s]
Best score: 0.5897: : 612iter [00:16, 40.60iter/s]
Best score: 0.5897: : 613iter [00:16, 40.60iter/s]
Best score: 0.5897: : 614iter [00:16, 40.60iter/s]
Best score: 0.5897: : 615iter [00:16, 40.60iter/s]
Best score: 0.5897: : 616iter [00:16, 42.53iter/s]
Best score: 0.5897: : 616iter [00:17, 42.53iter/s]
Best score: 0.5897: : 617iter [00:17, 42.53iter/s]
Best score: 0.5897: : 618iter [00:17, 42.53iter/s]
Best score: 0.5897: : 619iter [00:17, 42.53iter/s]
Best score: 0.5897: : 620iter [00:17, 42.53iter/s]
Best score: 0.5897: : 621iter [00:17, 43.70iter/s]
Best score: 0.5897: : 621iter [00:17, 43.70iter/s]
Best score: 0.5897: : 622iter [00:17, 43.70iter/s]
Best score: 0.5897: : 623iter [00:17, 43.70iter/s]
Best score: 0.5897: : 624iter [00:17, 43.70iter/s]
Best score: 0.5897: : 625iter [00:17, 43.70iter/s]
Best score: 0.5897: : 626iter [00:17, 44.79iter/s]
Best score: 0.5897: : 626iter [00:17, 44.79iter/s]
Best score: 0.5897: : 627iter [00:17, 44.79iter/s]
Best score: 0.5897: : 628iter [00:17, 44.79iter/s]
Best score: 0.5897: : 629iter [00:17, 44.79iter/s]
Best score: 0.5897: : 630iter [00:17, 44.79iter/s]
Best score: 0.5897: : 631iter [00:17, 45.40iter/s]
Best score: 0.5897: : 631iter [00:17, 45.40iter/s]
Best score: 0.5897: : 632iter [00:17, 45.40iter/s]
Best score: 0.5897: : 633iter [00:17, 45.40iter/s]
Best score: 0.5897: : 634iter [00:17, 45.40iter/s]
Best score: 0.5897: : 635iter [00:17, 45.40iter/s]
Best score: 0.5897: : 636iter [00:17, 46.16iter/s]
Best score: 0.5897: : 636iter [00:17, 46.16iter/s]
Best score: 0.5897: : 637iter [00:17, 46.16iter/s]
Best score: 0.5897: : 638iter [00:17, 46.16iter/s]
Best score: 0.5897: : 639iter [00:17, 46.16iter/s]
Best score: 0.5897: : 640iter [00:17, 46.16iter/s]
Best score: 0.5897: : 641iter [00:17, 46.50iter/s]
Best score: 0.5897: : 641iter [00:17, 46.50iter/s]
Best score: 0.5897: : 642iter [00:17, 46.50iter/s]
Best score: 0.5897: : 643iter [00:17, 46.50iter/s]
Best score: 0.5897: : 644iter [00:17, 46.50iter/s]
Best score: 0.5897: : 645iter [00:17, 46.50iter/s]
Best score: 0.5897: : 646iter [00:17, 46.76iter/s]
Best score: 0.5897: : 646iter [00:17, 46.76iter/s]
Best score: 0.5897: : 647iter [00:17, 46.76iter/s]
Best score: 0.5897: : 648iter [00:17, 46.76iter/s]
[ITER 650] Best: 0.5897
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.5897: : 649iter [00:17, 46.76iter/s]
[Periodic outlier @ iter 550] Removing 1000 frame 0 (score=2.00, threshold=1.85)
  Remaining frames: 15

Best score: 0.5897: : 650iter [00:17, 46.76iter/s]
Best score: 0.5897: : 651iter [00:17, 36.99iter/s]
Best score: 0.5897: : 651iter [00:17, 36.99iter/s]
Best score: 0.5897: : 652iter [00:17, 36.99iter/s]
Best score: 0.5897: : 653iter [00:17, 36.99iter/s]
Best score: 0.5897: : 654iter [00:17, 36.99iter/s]
Best score: 0.5897: : 655iter [00:17, 36.99iter/s]
Best score: 0.5897: : 656iter [00:17, 36.99iter/s]
Best score: 0.5897: : 657iter [00:17, 40.74iter/s]
Best score: 0.5897: : 657iter [00:17, 40.74iter/s]
Best score: 0.5897: : 658iter [00:17, 40.74iter/s]
Best score: 0.4965: : 659iter [00:18, 40.74iter/s]
Best score: 0.4965: : 660iter [00:18, 40.74iter/s]
Best score: 0.4965: : 661iter [00:18, 40.74iter/s]
Best score: 0.4965: : 662iter [00:18, 42.94iter/s]
Best score: 0.4965: : 662iter [00:18, 42.94iter/s]
Best score: 0.4965: : 663iter [00:18, 42.94iter/s]
Best score: 0.4965: : 664iter [00:18, 42.94iter/s]
Best score: 0.4965: : 665iter [00:18, 42.94iter/s]
Best score: 0.4965: : 666iter [00:18, 42.94iter/s]
Best score: 0.4965: : 667iter [00:18, 42.94iter/s]
Best score: 0.4965: : 668iter [00:18, 45.21iter/s]
Best score: 0.4965: : 668iter [00:18, 45.21iter/s]
Best score: 0.4965: : 669iter [00:18, 45.21iter/s]
Best score: 0.4965: : 670iter [00:18, 45.21iter/s]
Best score: 0.4965: : 671iter [00:18, 45.21iter/s]
Best score: 0.4965: : 672iter [00:18, 45.21iter/s]
Best score: 0.4965: : 673iter [00:18, 46.40iter/s]
Best score: 0.4965: : 673iter [00:18, 46.40iter/s]
Best score: 0.4965: : 674iter [00:18, 46.40iter/s]
Best score: 0.4965: : 675iter [00:18, 46.40iter/s]
Best score: 0.4965: : 676iter [00:18, 46.40iter/s]
Best score: 0.4965: : 677iter [00:18, 46.40iter/s]
Best score: 0.4965: : 678iter [00:18, 46.40iter/s]
Best score: 0.4965: : 679iter [00:18, 47.79iter/s]
Best score: 0.4965: : 679iter [00:18, 47.79iter/s]
Best score: 0.4965: : 680iter [00:18, 47.79iter/s]
Best score: 0.4965: : 681iter [00:18, 47.79iter/s]
Best score: 0.4965: : 682iter [00:18, 47.79iter/s]
Best score: 0.4965: : 683iter [00:18, 47.79iter/s]
Best score: 0.4965: : 684iter [00:18, 48.33iter/s]
Best score: 0.4965: : 684iter [00:18, 48.33iter/s]
Best score: 0.4965: : 685iter [00:18, 48.33iter/s]
Best score: 0.4965: : 686iter [00:18, 48.33iter/s]
Best score: 0.4965: : 687iter [00:18, 48.33iter/s]
Best score: 0.4965: : 688iter [00:18, 48.33iter/s]
Best score: 0.4965: : 689iter [00:18, 48.33iter/s]
Best score: 0.4965: : 690iter [00:18, 48.88iter/s]
Best score: 0.4965: : 690iter [00:18, 48.88iter/s]
Best score: 0.4965: : 691iter [00:18, 48.88iter/s]
Best score: 0.4965: : 692iter [00:18, 48.88iter/s]
Best score: 0.4965: : 693iter [00:18, 48.88iter/s]
Best score: 0.4965: : 694iter [00:18, 48.88iter/s]
Best score: 0.4965: : 695iter [00:18, 48.88iter/s]
Best score: 0.4965: : 696iter [00:18, 49.46iter/s]
Best score: 0.4965: : 696iter [00:18, 49.46iter/s]
Best score: 0.4965: : 697iter [00:18, 49.46iter/s]
Best score: 0.4965: : 698iter [00:18, 49.46iter/s]
[ITER 700] Best: 0.4965
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.4965: : 699iter [00:18, 49.46iter/s]
Best score: 0.4965: : 700iter [00:18, 49.46iter/s]
Best score: 0.4965: : 701iter [00:18, 42.00iter/s]
Best score: 0.4965: : 701iter [00:18, 42.00iter/s]
Best score: 0.4965: : 702iter [00:18, 42.00iter/s]
Best score: 0.4965: : 703iter [00:18, 42.00iter/s]
Best score: 0.4965: : 704iter [00:18, 42.00iter/s]
Best score: 0.4965: : 705iter [00:18, 42.00iter/s]
Best score: 0.4965: : 706iter [00:19, 42.00iter/s]
Best score: 0.4965: : 707iter [00:19, 44.44iter/s]
Best score: 0.4965: : 707iter [00:19, 44.44iter/s]
Best score: 0.4965: : 708iter [00:19, 44.44iter/s]
Best score: 0.4965: : 709iter [00:19, 44.44iter/s]
Best score: 0.4965: : 710iter [00:19, 44.44iter/s]
Best score: 0.4965: : 711iter [00:19, 44.44iter/s]
Best score: 0.4965: : 712iter [00:19, 44.44iter/s]
Best score: 0.4965: : 713iter [00:19, 46.15iter/s]
Best score: 0.4965: : 713iter [00:19, 46.15iter/s]
Best score: 0.4965: : 714iter [00:19, 46.15iter/s]
Best score: 0.4965: : 715iter [00:19, 46.15iter/s]
Best score: 0.4965: : 716iter [00:19, 46.15iter/s]
Best score: 0.4965: : 717iter [00:19, 46.15iter/s]
Best score: 0.4965: : 718iter [00:19, 46.15iter/s]
Best score: 0.4965: : 719iter [00:19, 47.47iter/s]
Best score: 0.4965: : 719iter [00:19, 47.47iter/s]
Best score: 0.4965: : 720iter [00:19, 47.47iter/s]
Best score: 0.4965: : 721iter [00:19, 47.47iter/s]
Best score: 0.4965: : 722iter [00:19, 47.47iter/s]
Best score: 0.4965: : 723iter [00:19, 47.47iter/s]
Best score: 0.4965: : 724iter [00:19, 48.11iter/s]
Best score: 0.4965: : 724iter [00:19, 48.11iter/s]
Best score: 0.4965: : 725iter [00:19, 48.11iter/s]
Best score: 0.4965: : 726iter [00:19, 48.11iter/s]
Best score: 0.4965: : 727iter [00:19, 48.11iter/s]
Best score: 0.4965: : 728iter [00:19, 48.11iter/s]
Best score: 0.4965: : 729iter [00:19, 48.11iter/s]
Best score: 0.4965: : 730iter [00:19, 48.66iter/s]
Best score: 0.4965: : 730iter [00:19, 48.66iter/s]
Best score: 0.4965: : 731iter [00:19, 48.66iter/s]
Best score: 0.4965: : 732iter [00:19, 48.66iter/s]
Best score: 0.4965: : 733iter [00:19, 48.66iter/s]
Best score: 0.4965: : 734iter [00:19, 48.66iter/s]
Best score: 0.4965: : 735iter [00:19, 48.66iter/s]
Best score: 0.4965: : 736iter [00:19, 49.23iter/s]
Best score: 0.4965: : 736iter [00:19, 49.23iter/s]
Best score: 0.4965: : 737iter [00:19, 49.23iter/s]
Best score: 0.4965: : 738iter [00:19, 49.23iter/s]
Best score: 0.4965: : 739iter [00:19, 49.23iter/s]
Best score: 0.4965: : 740iter [00:19, 49.23iter/s]
Best score: 0.4965: : 741iter [00:19, 49.16iter/s]
Best score: 0.4965: : 741iter [00:19, 49.16iter/s]
Best score: 0.4965: : 742iter [00:19, 49.16iter/s]
Best score: 0.4965: : 743iter [00:19, 49.16iter/s]
Best score: 0.4965: : 744iter [00:19, 49.16iter/s]
Best score: 0.4965: : 745iter [00:19, 49.16iter/s]
Best score: 0.4965: : 746iter [00:19, 49.16iter/s]
Best score: 0.4965: : 747iter [00:19, 49.68iter/s]
Best score: 0.4965: : 747iter [00:19, 49.68iter/s]
Best score: 0.4965: : 748iter [00:19, 49.68iter/s]
[ITER 750] Best: 0.4965
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.4965: : 749iter [00:19, 49.68iter/s]
Best score: 0.4965: : 750iter [00:19, 49.68iter/s]
Best score: 0.4965: : 751iter [00:19, 49.68iter/s]
Best score: 0.4965: : 752iter [00:19, 42.34iter/s]
Best score: 0.4965: : 752iter [00:19, 42.34iter/s]
Best score: 0.4965: : 753iter [00:20, 42.34iter/s]
Best score: 0.4965: : 754iter [00:20, 42.34iter/s]
Best score: 0.4965: : 755iter [00:20, 42.34iter/s]
Best score: 0.4965: : 756iter [00:20, 42.34iter/s]
Best score: 0.4965: : 757iter [00:20, 42.34iter/s]
Best score: 0.4965: : 758iter [00:20, 44.97iter/s]
Best score: 0.4965: : 758iter [00:20, 44.97iter/s]
Best score: 0.4965: : 759iter [00:20, 44.97iter/s]
Best score: 0.4965: : 760iter [00:20, 44.97iter/s]
Best score: 0.4965: : 761iter [00:20, 44.97iter/s]
Best score: 0.4965: : 762iter [00:20, 44.97iter/s]
Best score: 0.4965: : 763iter [00:20, 44.97iter/s]
Best score: 0.4965: : 764iter [00:20, 46.52iter/s]
Best score: 0.4965: : 764iter [00:20, 46.52iter/s]
Best score: 0.4965: : 765iter [00:20, 46.52iter/s]
Best score: 0.4965: : 766iter [00:20, 46.52iter/s]
Best score: 0.4965: : 767iter [00:20, 46.52iter/s]
Best score: 0.4965: : 768iter [00:20, 46.52iter/s]
Best score: 0.4965: : 769iter [00:20, 46.52iter/s]
Best score: 0.4965: : 770iter [00:20, 47.73iter/s]
Best score: 0.4965: : 770iter [00:20, 47.73iter/s]
Best score: 0.4965: : 771iter [00:20, 47.73iter/s]
Best score: 0.4965: : 772iter [00:20, 47.73iter/s]
Best score: 0.4965: : 773iter [00:20, 47.73iter/s]
Best score: 0.4965: : 774iter [00:20, 47.73iter/s]
Best score: 0.4965: : 775iter [00:20, 47.73iter/s]
Best score: 0.4965: : 776iter [00:20, 48.83iter/s]
Best score: 0.4965: : 776iter [00:20, 48.83iter/s]
Best score: 0.4965: : 777iter [00:20, 48.83iter/s]
Best score: 0.4965: : 778iter [00:20, 48.83iter/s]
Best score: 0.4965: : 779iter [00:20, 48.83iter/s]
Best score: 0.4965: : 780iter [00:20, 48.83iter/s]
Best score: 0.4965: : 781iter [00:20, 48.83iter/s]
Best score: 0.4965: : 782iter [00:20, 49.32iter/s]
Best score: 0.4965: : 782iter [00:20, 49.32iter/s]
Best score: 0.4965: : 783iter [00:20, 49.32iter/s]
Best score: 0.4965: : 784iter [00:20, 49.32iter/s]
Best score: 0.4965: : 785iter [00:20, 49.32iter/s]
Best score: 0.4965: : 786iter [00:20, 49.32iter/s]
Best score: 0.4965: : 787iter [00:20, 49.32iter/s]
Best score: 0.4965: : 788iter [00:20, 49.95iter/s]
Best score: 0.4965: : 788iter [00:20, 49.95iter/s]
Best score: 0.4965: : 789iter [00:20, 49.95iter/s]
Best score: 0.4965: : 790iter [00:20, 49.95iter/s]
Best score: 0.4965: : 791iter [00:20, 49.95iter/s]
Best score: 0.4965: : 792iter [00:20, 49.95iter/s]
Best score: 0.4965: : 793iter [00:20, 49.95iter/s]
Best score: 0.4965: : 794iter [00:20, 50.24iter/s]
Best score: 0.4965: : 794iter [00:20, 50.24iter/s]
Best score: 0.4965: : 795iter [00:20, 50.24iter/s]
Best score: 0.4965: : 796iter [00:20, 50.24iter/s]
Best score: 0.4965: : 797iter [00:20, 50.24iter/s]
Best score: 0.4965: : 798iter [00:20, 50.24iter/s]
[ITER 800] Best: 0.4965
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.4965: : 799iter [00:20, 50.24iter/s]
Best score: 0.4965: : 800iter [00:20, 50.36iter/s]
Best score: 0.4965: : 800iter [00:20, 50.36iter/s]
Best score: 0.4965: : 801iter [00:21, 50.36iter/s]
Best score: 0.4965: : 802iter [00:21, 50.36iter/s]
Best score: 0.4965: : 803iter [00:21, 50.36iter/s]
Best score: 0.4965: : 804iter [00:21, 50.36iter/s]
Best score: 0.4965: : 805iter [00:21, 50.36iter/s]
Best score: 0.4965: : 806iter [00:21, 43.60iter/s]
Best score: 0.4965: : 806iter [00:21, 43.60iter/s]
Best score: 0.4965: : 807iter [00:21, 43.60iter/s]
Best score: 0.4965: : 808iter [00:21, 43.60iter/s]
Best score: 0.4965: : 809iter [00:21, 43.60iter/s]
Best score: 0.4965: : 810iter [00:21, 43.60iter/s]
Best score: 0.4965: : 811iter [00:21, 43.60iter/s]
Best score: 0.4965: : 812iter [00:21, 45.50iter/s]
Best score: 0.4965: : 812iter [00:21, 45.50iter/s]
Best score: 0.4965: : 813iter [00:21, 45.50iter/s]
Best score: 0.4965: : 814iter [00:21, 45.50iter/s]
Best score: 0.4965: : 815iter [00:21, 45.50iter/s]
Best score: 0.4965: : 816iter [00:21, 45.50iter/s]
Best score: 0.4965: : 817iter [00:21, 45.50iter/s]
Best score: 0.4965: : 818iter [00:21, 47.16iter/s]
Best score: 0.4965: : 818iter [00:21, 47.16iter/s]
Best score: 0.4965: : 819iter [00:21, 47.16iter/s]
Best score: 0.4965: : 820iter [00:21, 47.16iter/s]
Best score: 0.4965: : 821iter [00:21, 47.16iter/s]
Best score: 0.4965: : 822iter [00:21, 47.16iter/s]
Best score: 0.4965: : 823iter [00:21, 47.16iter/s]
Best score: 0.4965: : 824iter [00:21, 48.07iter/s]
Best score: 0.4965: : 824iter [00:21, 48.07iter/s]
Best score: 0.4965: : 825iter [00:21, 48.07iter/s]
Best score: 0.4965: : 826iter [00:21, 48.07iter/s]
Best score: 0.4965: : 827iter [00:21, 48.07iter/s]
Best score: 0.4965: : 828iter [00:21, 48.07iter/s]
Best score: 0.4965: : 829iter [00:21, 48.07iter/s]
Best score: 0.4965: : 830iter [00:21, 48.72iter/s]
Best score: 0.4965: : 830iter [00:21, 48.72iter/s]
Best score: 0.4965: : 831iter [00:21, 48.72iter/s]
Best score: 0.4965: : 832iter [00:21, 48.72iter/s]
Best score: 0.4965: : 833iter [00:21, 48.72iter/s]
Best score: 0.4965: : 834iter [00:21, 48.72iter/s]
Best score: 0.4965: : 835iter [00:21, 48.72iter/s]
Best score: 0.4965: : 836iter [00:21, 41.44iter/s]
Best score: 0.4965: : 836iter [00:21, 41.44iter/s]
Best score: 0.4965: : 837iter [00:21, 41.44iter/s]
Best score: 0.4965: : 838iter [00:21, 41.44iter/s]
Best score: 0.4965: : 839iter [00:21, 41.44iter/s]
Best score: 0.4965: : 840iter [00:21, 41.44iter/s]
Best score: 0.4965: : 841iter [00:21, 41.44iter/s]
Best score: 0.4965: : 842iter [00:21, 43.80iter/s]
Best score: 0.4965: : 842iter [00:21, 43.80iter/s]
Best score: 0.4965: : 843iter [00:21, 43.80iter/s]
Best score: 0.4965: : 844iter [00:21, 43.80iter/s]
Best score: 0.4965: : 845iter [00:21, 43.80iter/s]
Best score: 0.4965: : 846iter [00:21, 43.80iter/s]
Best score: 0.4965: : 847iter [00:21, 43.80iter/s]
Best score: 0.4965: : 848iter [00:21, 45.83iter/s]
Best score: 0.4965: : 848iter [00:22, 45.83iter/s]
[ITER 850] Best: 0.4965
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.4965: : 849iter [00:22, 45.83iter/s]
Best score: 0.4965: : 850iter [00:22, 45.83iter/s]
Best score: 0.4965: : 851iter [00:22, 45.83iter/s]
Best score: 0.4965: : 852iter [00:22, 45.83iter/s]
Best score: 0.4965: : 853iter [00:22, 40.50iter/s]
Best score: 0.4965: : 853iter [00:22, 40.50iter/s]
Best score: 0.4965: : 854iter [00:22, 40.50iter/s]
Best score: 0.4965: : 855iter [00:22, 40.50iter/s]
Best score: 0.4965: : 856iter [00:22, 40.50iter/s]
Best score: 0.4965: : 857iter [00:22, 40.50iter/s]
Best score: 0.4965: : 858iter [00:22, 40.50iter/s]
Best score: 0.4965: : 859iter [00:22, 43.30iter/s]
Best score: 0.4965: : 859iter [00:22, 43.30iter/s]
Best score: 0.4965: : 860iter [00:22, 43.30iter/s]
Best score: 0.4965: : 861iter [00:22, 43.30iter/s]
Best score: 0.4965: : 862iter [00:22, 43.30iter/s]
Best score: 0.4965: : 863iter [00:22, 43.30iter/s]
Best score: 0.4965: : 864iter [00:22, 43.30iter/s]
Best score: 0.4965: : 865iter [00:22, 45.33iter/s]
Best score: 0.4965: : 865iter [00:22, 45.33iter/s]
Best score: 0.4965: : 866iter [00:22, 45.33iter/s]
Best score: 0.4965: : 867iter [00:22, 45.33iter/s]
Best score: 0.4965: : 868iter [00:22, 45.33iter/s]
Best score: 0.4965: : 869iter [00:22, 45.33iter/s]
Best score: 0.4965: : 870iter [00:22, 45.33iter/s]
Best score: 0.4965: : 871iter [00:22, 46.78iter/s]
Best score: 0.4965: : 871iter [00:22, 46.78iter/s]
Best score: 0.4965: : 872iter [00:22, 46.78iter/s]
Best score: 0.4965: : 873iter [00:22, 46.78iter/s]
Best score: 0.4965: : 874iter [00:22, 46.78iter/s]
Best score: 0.4965: : 875iter [00:22, 46.78iter/s]
Best score: 0.4965: : 876iter [00:22, 46.78iter/s]
Best score: 0.4965: : 877iter [00:22, 48.04iter/s]
Best score: 0.4965: : 877iter [00:22, 48.04iter/s]
Best score: 0.4965: : 878iter [00:22, 48.04iter/s]
Best score: 0.4965: : 879iter [00:22, 48.04iter/s]
Best score: 0.4965: : 880iter [00:22, 48.04iter/s]
Best score: 0.4965: : 881iter [00:22, 48.04iter/s]
Best score: 0.4965: : 882iter [00:22, 48.04iter/s]
Best score: 0.4965: : 883iter [00:22, 48.72iter/s]
Best score: 0.4965: : 883iter [00:22, 48.72iter/s]
Best score: 0.4965: : 884iter [00:22, 48.72iter/s]
Best score: 0.4965: : 885iter [00:22, 48.72iter/s]
Best score: 0.4965: : 886iter [00:22, 48.72iter/s]
Best score: 0.4965: : 887iter [00:22, 48.72iter/s]
Best score: 0.4965: : 888iter [00:22, 48.72iter/s]
Best score: 0.4965: : 889iter [00:22, 49.52iter/s]
Best score: 0.4965: : 889iter [00:22, 49.52iter/s]
Best score: 0.4965: : 890iter [00:22, 49.52iter/s]
Best score: 0.4965: : 891iter [00:22, 49.52iter/s]
Best score: 0.4965: : 892iter [00:22, 49.52iter/s]
Best score: 0.4965: : 893iter [00:22, 49.52iter/s]
Best score: 0.4965: : 894iter [00:22, 49.52iter/s]
Best score: 0.4965: : 895iter [00:22, 49.70iter/s]
Best score: 0.4965: : 895iter [00:23, 49.70iter/s]
Best score: 0.4965: : 896iter [00:23, 49.70iter/s]
Best score: 0.4965: : 897iter [00:23, 49.70iter/s]
Best score: 0.4965: : 898iter [00:23, 49.70iter/s]
[ITER 900] Best: 0.4965
[PARAMS] Current best parameters:
  fx: 541.0839
  fy: 540.1177
  px: 1020.4490
  py: 749.9542
  k1: 0.05708294
  k2: -0.08778343
  k3: 0.04337518
  k4: -0.00802767
  p1: 0.02539283
  p2: 0.00258106
  p3: -0.00604148
  p4: -0.00184146
  s1: 0.0049
  s2: 0.0021
  s3: -0.0168
  s4: -0.0002
  tilt_x: -0.0764
  tilt_y: 0.0073
  scale: 2.9411
  rotation: -0.1113

Best score: 0.4965: : 899iter [00:23, 49.70iter/s]
Best score: 0.4965: : 900iter [00:23, 49.70iter/s]
Best score: 0.4965: : 901iter [00:23, 42.98iter/s]
Best score: 0.4965: : 901iter [00:23, 42.98iter/s]
Best score: 0.4965: : 902iter [00:23, 42.98iter/s]
Best score: 0.4965: : 903iter [00:23, 42.98iter/s]
Best score: 0.4965: : 904iter [00:23, 42.98iter/s]
Best score: 0.4965: : 905iter [00:23, 42.98iter/s]
Best score: 0.4965: : 906iter [00:23, 42.98iter/s]
Best score: 0.4965: : 907iter [00:23, 45.23iter/s]
Best score: 0.4965: : 907iter [00:23, 45.23iter/s]
Best score: 0.4965: : 908iter [00:23, 45.23iter/s]
Best score: 0.4965: : 909iter [00:23, 45.23iter/s]
Best score: 0.4965: : 910iter [00:23, 45.23iter/s]
Best score: 0.4965: : 911iter [00:23, 45.23iter/s]
Best score: 0.4965: : 912iter [00:23, 45.23iter/s]
Best score: 0.4965: : 913iter [00:23, 46.66iter/s]
Best score: 0.4965: : 913iter [00:23, 46.66iter/s]
Best score: 0.4965: : 914iter [00:23, 46.66iter/s]
Best score: 0.4965: : 915iter [00:23, 46.66iter/s]
Best score: 0.4965: : 916iter [00:23, 46.66iter/s]
Best score: 0.4965: : 917iter [00:23, 46.66iter/s]
Best score: 0.4965: : 918iter [00:23, 46.66iter/s]
Best score: 0.4965: : 919iter [00:23, 47.97iter/s]
Best score: 0.4965: : 919iter [00:23, 47.97iter/s]
Best score: 0.4965: : 920iter [00:23, 47.97iter/s]
Best score: 0.4965: : 921iter [00:23, 47.97iter/s]
Best score: 0.4965: : 922iter [00:23, 47.97iter/s]
Best score: 0.4965: : 923iter [00:23, 47.97iter/s]
Best score: 0.4965: : 924iter [00:23, 48.47iter/s]
Best score: 0.4965: : 924iter [00:23, 48.47iter/s]
Best score: 0.4965: : 925iter [00:23, 48.47iter/s]
Best score: 0.4965: : 926iter [00:23, 48.47iter/s]
Best score: 0.4965: : 927iter [00:23, 48.47iter/s]
Best score: 0.4965: : 928iter [00:23, 48.47iter/s]
Best score: 0.4881: : 929iter [00:23, 48.47iter/s]
Best score: 0.4881: : 930iter [00:23, 48.96iter/s]
Best score: 0.4881: : 930iter [00:23, 48.96iter/s]
Best score: 0.4881: : 931iter [00:23, 48.96iter/s]
Best score: 0.4881: : 932iter [00:23, 48.96iter/s]
Best score: 0.4881: : 933iter [00:23, 48.96iter/s]
Best score: 0.4881: : 934iter [00:23, 48.96iter/s]
Best score: 0.4881: : 935iter [00:23, 48.96iter/s]
Best score: 0.4881: : 936iter [00:23, 49.71iter/s]
Best score: 0.4881: : 936iter [00:23, 49.71iter/s]
Best score: 0.4881: : 937iter [00:23, 49.71iter/s]
Best score: 0.4881: : 938iter [00:23, 49.71iter/s]
Best score: 0.4825: : 939iter [00:23, 49.71iter/s]
Best score: 0.4825: : 940iter [00:23, 49.71iter/s]
Best score: 0.4825: : 941iter [00:23, 49.71iter/s]
Best score: 0.4825: : 942iter [00:23, 49.93iter/s]
Best score: 0.4825: : 942iter [00:23, 49.93iter/s]
Best score: 0.4825: : 943iter [00:24, 49.93iter/s]
Best score: 0.4825: : 944iter [00:24, 49.93iter/s]
Best score: 0.4825: : 945iter [00:24, 49.93iter/s]
Best score: 0.4825: : 946iter [00:24, 49.93iter/s]
Best score: 0.4825: : 947iter [00:24, 49.93iter/s]
Best score: 0.4825: : 948iter [00:24, 50.46iter/s]
Best score: 0.4825: : 948iter [00:24, 50.46iter/s]
[ITER 950] Best: 0.4644
[PARAMS] Current best parameters:
  fx: 538.2364
  fy: 537.4616
  px: 1020.3971
  py: 749.8117
  k1: 0.05925329
  k2: -0.08860181
  k3: 0.04455383
  k4: -0.00841343
  p1: 0.02644311
  p2: 0.00497234
  p3: -0.00702968
  p4: -0.00452286
  s1: 0.0055
  s2: 0.0032
  s3: -0.0179
  s4: 0.0029
  tilt_x: -0.0784
  tilt_y: 0.0117
  scale: 2.9469
  rotation: -0.1210

Best score: 0.4644: : 949iter [00:24, 50.46iter/s]
[Periodic outlier @ iter 850] Removing 1000 frame 4 (score=1.83, threshold=1.51)
  Remaining frames: 14

Best score: 0.4644: : 950iter [00:24, 50.46iter/s]
Best score: 0.4644: : 951iter [00:24, 50.46iter/s]
Best score: 0.4644: : 952iter [00:24, 50.46iter/s]
Best score: 0.4644: : 953iter [00:24, 50.46iter/s]
Best score: 0.4644: : 954iter [00:24, 41.06iter/s]
Best score: 0.4644: : 954iter [00:24, 41.06iter/s]
Best score: 0.4644: : 955iter [00:24, 41.06iter/s]
Best score: 0.4644: : 956iter [00:24, 41.06iter/s]
Best score: 0.4644: : 957iter [00:24, 41.06iter/s]
Best score: 0.4644: : 958iter [00:24, 41.06iter/s]
Best score: 0.3682: : 959iter [00:24, 41.06iter/s]
Best score: 0.3682: : 960iter [00:24, 43.68iter/s]
Best score: 0.3682: : 960iter [00:24, 43.68iter/s]
Best score: 0.3682: : 961iter [00:24, 43.68iter/s]
Best score: 0.3682: : 962iter [00:24, 43.68iter/s]
Best score: 0.3682: : 963iter [00:24, 43.68iter/s]
Best score: 0.3682: : 964iter [00:24, 43.68iter/s]
Best score: 0.3682: : 965iter [00:24, 43.68iter/s]
Best score: 0.3682: : 966iter [00:24, 46.27iter/s]
Best score: 0.3682: : 966iter [00:24, 46.27iter/s]
Best score: 0.3682: : 967iter [00:24, 46.27iter/s]
Best score: 0.3682: : 968iter [00:24, 46.27iter/s]
Best score: 0.3682: : 969iter [00:24, 46.27iter/s]
Best score: 0.3682: : 970iter [00:24, 46.27iter/s]
Best score: 0.3682: : 971iter [00:24, 46.27iter/s]
Best score: 0.3682: : 972iter [00:24, 47.90iter/s]
Best score: 0.3682: : 972iter [00:24, 47.90iter/s]
Best score: 0.3682: : 973iter [00:24, 47.90iter/s]
Best score: 0.3682: : 974iter [00:24, 47.90iter/s]
Best score: 0.3682: : 975iter [00:24, 47.90iter/s]
Best score: 0.3682: : 976iter [00:24, 47.90iter/s]
Best score: 0.3682: : 977iter [00:24, 47.90iter/s]
Best score: 0.3682: : 978iter [00:24, 49.46iter/s]
Best score: 0.3682: : 978iter [00:24, 49.46iter/s]
Best score: 0.3682: : 979iter [00:24, 49.46iter/s]
Best score: 0.3682: : 980iter [00:24, 49.46iter/s]
Best score: 0.3682: : 981iter [00:24, 49.46iter/s]
Best score: 0.3682: : 982iter [00:24, 49.46iter/s]
Best score: 0.3682: : 983iter [00:24, 49.46iter/s]
Best score: 0.3682: : 984iter [00:24, 50.41iter/s]
Best score: 0.3682: : 984iter [00:24, 50.41iter/s]
Best score: 0.3682: : 985iter [00:24, 50.41iter/s]
Best score: 0.3682: : 986iter [00:24, 50.41iter/s]
Best score: 0.3682: : 987iter [00:24, 50.41iter/s]
Best score: 0.3682: : 988iter [00:24, 50.41iter/s]
Best score: 0.3682: : 989iter [00:24, 50.41iter/s]
Best score: 0.3682: : 990iter [00:24, 51.20iter/s]
Best score: 0.3682: : 990iter [00:25, 51.20iter/s]
Best score: 0.3682: : 991iter [00:25, 51.20iter/s]
Best score: 0.3682: : 992iter [00:25, 51.20iter/s]
Best score: 0.3682: : 993iter [00:25, 51.20iter/s]
Best score: 0.3682: : 994iter [00:25, 51.20iter/s]
Best score: 0.3682: : 995iter [00:25, 51.20iter/s]
Best score: 0.3682: : 996iter [00:25, 51.80iter/s]
Best score: 0.3682: : 996iter [00:25, 51.80iter/s]
Best score: 0.3682: : 997iter [00:25, 51.80iter/s]
Best score: 0.3682: : 998iter [00:25, 51.80iter/s]
[ITER 1000] Best: 0.3682
[PARAMS] Current best parameters:
  fx: 538.2364
  fy: 537.4616
  px: 1020.3971
  py: 749.8117
  k1: 0.05925329
  k2: -0.08860181
  k3: 0.04455383
  k4: -0.00841343
  p1: 0.02644311
  p2: 0.00497234
  p3: -0.00702968
  p4: -0.00452286
  s1: 0.0055
  s2: 0.0032
  s3: -0.0179
  s4: 0.0029
  tilt_x: -0.0784
  tilt_y: 0.0117
  scale: 2.9469
  rotation: -0.1210

Best score: 0.3682: : 999iter [00:25, 51.80iter/s]
Best score: 0.3682: : 1000iter [00:25, 51.80iter/s]
Best score: 0.3682: : 1001iter [00:25, 51.80iter/s]
Best score: 0.3682: : 1002iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1002iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1003iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1004iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1005iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1006iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1007iter [00:25, 44.56iter/s]
Best score: 0.3682: : 1008iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1008iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1009iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1010iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1011iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1012iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1013iter [00:25, 46.95iter/s]
Best score: 0.3682: : 1014iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1014iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1015iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1016iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1017iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1018iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1019iter [00:25, 48.59iter/s]
Best score: 0.3682: : 1020iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1020iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1021iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1022iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1023iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1024iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1025iter [00:25, 49.73iter/s]
Best score: 0.3682: : 1026iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1026iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1027iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1028iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1029iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1030iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1031iter [00:25, 50.82iter/s]
Best score: 0.3682: : 1032iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1032iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1033iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1034iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1035iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1036iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1037iter [00:25, 51.47iter/s]
Best score: 0.3682: : 1038iter [00:25, 52.14iter/s]
Best score: 0.3682: : 1038iter [00:25, 52.14iter/s]
Best score: 0.3682: : 1039iter [00:25, 52.14iter/s]
Best score: 0.3682: : 1040iter [00:26, 52.14iter/s]
Best score: 0.3682: : 1041iter [00:26, 52.14iter/s]
Best score: 0.3682: : 1042iter [00:26, 52.14iter/s]
Best score: 0.3682: : 1043iter [00:26, 52.14iter/s]
Best score: 0.3682: : 1044iter [00:26, 52.31iter/s]
Best score: 0.3682: : 1044iter [00:26, 52.31iter/s]
Best score: 0.3682: : 1045iter [00:26, 52.31iter/s]
Best score: 0.3682: : 1046iter [00:26, 52.31iter/s]
Best score: 0.3682: : 1047iter [00:26, 52.31iter/s]
Best score: 0.3682: : 1048iter [00:26, 52.31iter/s]
[ITER 1050] Best: 0.3682
[PARAMS] Current best parameters:
  fx: 538.2364
  fy: 537.4616
  px: 1020.3971
  py: 749.8117
  k1: 0.05925329
  k2: -0.08860181
  k3: 0.04455383
  k4: -0.00841343
  p1: 0.02644311
  p2: 0.00497234
  p3: -0.00702968
  p4: -0.00452286
  s1: 0.0055
  s2: 0.0032
  s3: -0.0179
  s4: 0.0029
  tilt_x: -0.0784
  tilt_y: 0.0117
  scale: 2.9469
  rotation: -0.1210

Best score: 0.3682: : 1049iter [00:26, 52.31iter/s]
Best score: 0.3682: : 1050iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1050iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1051iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1052iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1053iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1054iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1055iter [00:26, 52.44iter/s]
Best score: 0.3682: : 1056iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1056iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1057iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1058iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1059iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1060iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1061iter [00:26, 45.34iter/s]
Best score: 0.3682: : 1062iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1062iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1063iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1064iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1065iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1066iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1067iter [00:26, 47.26iter/s]
Best score: 0.3682: : 1068iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1068iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1069iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1070iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1071iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1072iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1073iter [00:26, 49.07iter/s]
Best score: 0.3682: : 1074iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1074iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1075iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1076iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1077iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1078iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1079iter [00:26, 49.87iter/s]
Best score: 0.3682: : 1080iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1080iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1081iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1082iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1083iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1084iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1085iter [00:26, 50.66iter/s]
Best score: 0.3682: : 1086iter [00:26, 51.54iter/s]
Best score: 0.3682: : 1086iter [00:26, 51.54iter/s]
Best score: 0.3682: : 1087iter [00:26, 51.54iter/s]
Best score: 0.3682: : 1088iter [00:26, 51.54iter/s]
Best score: 0.3682: : 1089iter [00:26, 51.54iter/s]
Best score: 0.3682: : 1090iter [00:27, 51.54iter/s]
Best score: 0.3682: : 1091iter [00:27, 51.54iter/s]
Best score: 0.3682: : 1092iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1092iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1093iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1094iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1095iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1096iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1097iter [00:27, 51.93iter/s]
Best score: 0.3682: : 1098iter [00:27, 52.39iter/s]
Best score: 0.3682: : 1098iter [00:27, 52.39iter/s]
[ITER 1100] Best: 0.3682
[PARAMS] Current best parameters:
  fx: 538.2364
  fy: 537.4616
  px: 1020.3971
  py: 749.8117
  k1: 0.05925329
  k2: -0.08860181
  k3: 0.04455383
  k4: -0.00841343
  p1: 0.02644311
  p2: 0.00497234
  p3: -0.00702968
  p4: -0.00452286
  s1: 0.0055
  s2: 0.0032
  s3: -0.0179
  s4: 0.0029
  tilt_x: -0.0784
  tilt_y: 0.0117
  scale: 2.9469
  rotation: -0.1210

Best score: 0.3682: : 1099iter [00:27, 52.39iter/s]
Best score: 0.3682: : 1100iter [00:27, 40.37iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.368185 -> 0.354927

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 18,231 → 18,231 within 3500mm ring
  Ring polish did not improve score: 0.354927
  Success: True
  Tangential distortion: p1=0.026270, p2=0.005020, p3=-0.007496, p4=-0.003863
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3549
[✅] Parameters copied to: /working/MDC044_W038_2026-03-10/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 538.2363823292302
    fy: 537.4615374690261
    px: 1020.3970392954915
    py: 749.8116675305778
    k1: 0.059406215195294536
    k2: -0.0884916808744331
    k3: 0.04448272434874793
    k4: -0.00842457689698706
    p1: 0.026270062893809967
    p2: 0.005019509552505901
    p3: -0.007496484832782965
    p4: -0.00386336482311683
    scale: 2.945107292289947
    rotation: -0.12099551675389436
    mmpp: 1.2793783021063203

[🔄] Converting 361 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/160 [00:00<?, ?frame/s]

   🔄 1000:  18%|█▊        | 28/160 [00:00<00:00, 266.71frame/s]

   🔄 1000:  34%|███▍      | 55/160 [00:00<00:00, 208.17frame/s]

   🔄 1000:  48%|████▊     | 77/160 [00:00<00:00, 192.31frame/s]

   🔄 1000:  61%|██████    | 97/160 [00:00<00:00, 184.69frame/s]

   🔄 1000:  72%|███████▎  | 116/160 [00:00<00:00, 181.40frame/s]

   🔄 1000:  84%|████████▍ | 135/160 [00:00<00:00, 179.60frame/s]

   🔄 1000:  96%|█████████▋| 154/160 [00:00<00:00, 178.11frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.58s/rig]

   🔄 500:   0%|          | 0/201 [00:00<?, ?frame/s]

   🔄 500:   9%|▉         | 18/201 [00:00<00:01, 176.77frame/s]

   🔄 500:  18%|█▊        | 36/201 [00:00<00:00, 176.36frame/s]

   🔄 500:  27%|██▋       | 54/201 [00:00<00:00, 176.22frame/s]

   🔄 500:  36%|███▌      | 72/201 [00:00<00:00, 176.40frame/s]

   🔄 500:  45%|████▌     | 91/201 [00:00<00:00, 179.07frame/s]

   🔄 500:  55%|█████▍    | 110/201 [00:00<00:00, 182.14frame/s]

   🔄 500:  64%|██████▍   | 129/201 [00:00<00:00, 183.02frame/s]

   🔄 500:  74%|███████▎  | 148/201 [00:00<00:00, 180.66frame/s]

   🔄 500:  83%|████████▎ | 167/201 [00:00<00:00, 178.99frame/s]

   🔄 500:  92%|█████████▏| 185/201 [00:01<00:00, 177.62frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:03<00:00,  1.81s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:03<00:00,  1.78s/rig]
[✅] Saved PTDs: /working/MDC044_W038_2026-03-10/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  25%|██▌       | 25/100 [00:00<00:00, 247.51frame/s]

   🔄 1000:  50%|█████     | 50/100 [00:00<00:00, 194.79frame/s]

   🔄 1000:  71%|███████   | 71/100 [00:00<00:00, 183.26frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 179.78frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.06s/rig]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 171.94frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 169.72frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 173.62frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 172.81frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 173.68frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.05s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.05s/rig]
[✅] Saved PTDs: /working/MDC044_W038_2026-03-10/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%|▎         | 90681/3080448 [00:00<00:03, 906808.70px/s]
📤 Writing LUT:   6%|▌         | 191030/3080448 [00:00<00:02, 963676.51px/s]
📤 Writing LUT:   9%|▉         | 288115/3080448 [00:00<00:02, 966949.33px/s]
📤 Writing LUT:  12%|█▏        | 384810/3080448 [00:00<00:02, 954238.99px/s]
📤 Writing LUT:  16%|█▌        | 480259/3080448 [00:00<00:02, 947663.28px/s]
📤 Writing LUT:  19%|█▊        | 575044/3080448 [00:00<00:02, 940077.29px/s]
📤 Writing LUT:  22%|██▏       | 669069/3080448 [00:00<00:02, 933271.17px/s]
📤 Writing LUT:  25%|██▍       | 762620/3080448 [00:00<00:02, 933971.15px/s]
📤 Writing LUT:  28%|██▊       | 856027/3080448 [00:00<00:02, 932637.73px/s]
📤 Writing LUT:  31%|███       | 949297/3080448 [00:01<00:02, 931003.56px/s]
📤 Writing LUT:  34%|███▍      | 1042520/3080448 [00:01<00:02, 931376.11px/s]
📤 Writing LUT:  37%|███▋      | 1136007/3080448 [00:01<00:02, 932433.18px/s]
📤 Writing LUT:  40%|███▉      | 1229253/3080448 [00:01<00:01, 931188.45px/s]
📤 Writing LUT:  43%|████▎     | 1322374/3080448 [00:01<00:01, 928328.15px/s]
📤 Writing LUT:  46%|████▌     | 1416651/3080448 [00:01<00:01, 932662.15px/s]
📤 Writing LUT:  49%|████▉     | 1510528/3080448 [00:01<00:01, 934493.98px/s]
📤 Writing LUT:  52%|█████▏    | 1603981/3080448 [00:01<00:01, 934018.61px/s]
📤 Writing LUT:  55%|█████▌    | 1697540/3080448 [00:01<00:01, 934488.85px/s]
📤 Writing LUT:  58%|█████▊    | 1790991/3080448 [00:01<00:01, 932622.64px/s]
📤 Writing LUT:  61%|██████    | 1884255/3080448 [00:02<00:01, 930572.52px/s]
📤 Writing LUT:  64%|██████▍   | 1977320/3080448 [00:02<00:01, 930592.65px/s]
📤 Writing LUT:  67%|██████▋   | 2071450/3080448 [00:02<00:01, 933797.26px/s]
📤 Writing LUT:  70%|███████   | 2165243/3080448 [00:02<00:00, 935034.43px/s]
📤 Writing LUT:  73%|███████▎  | 2258748/3080448 [00:02<00:00, 934540.49px/s]
📤 Writing LUT:  76%|███████▋  | 2352203/3080448 [00:02<00:00, 932076.72px/s]
📤 Writing LUT:  79%|███████▉  | 2446322/3080448 [00:02<00:00, 934802.00px/s]
📤 Writing LUT:  82%|████████▏ | 2539960/3080448 [00:02<00:00, 935274.15px/s]
📤 Writing LUT:  86%|████████▌ | 2633871/3080448 [00:02<00:00, 936422.40px/s]
📤 Writing LUT:  89%|████████▊ | 2729425/3080448 [00:02<00:00, 942151.57px/s]
📤 Writing LUT:  92%|█████████▏| 2823642/3080448 [00:03<00:00, 939784.66px/s]
📤 Writing LUT:  95%|█████████▍| 2917623/3080448 [00:03<00:00, 938950.18px/s]
📤 Writing LUT:  98%|█████████▊| 3013359/3080448 [00:03<00:00, 944460.77px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 937293.53px/s]
[✅] LUT written to: /working/MDC044_W038_2026-03-10/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC044_W038_2026-03-10' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC044_W038_2026-03-10/calibration/MDC044_W038_2026-03-10_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%|          | 0/160 [00:00<?, ?frame/s]

   🔥 1000:   8%|▊         | 13/160 [00:00<00:01, 124.02frame/s]

   🔥 1000:  16%|█▋        | 26/160 [00:00<00:01, 99.93frame/s] 

   🔥 1000:  23%|██▎       | 37/160 [00:00<00:01, 96.21frame/s]

   🔥 1000:  29%|██▉       | 47/160 [00:00<00:01, 89.84frame/s]

   🔥 1000:  36%|███▌      | 57/160 [00:00<00:01, 84.53frame/s]

   🔥 1000:  41%|████▏     | 66/160 [00:00<00:01, 82.05frame/s]

   🔥 1000:  47%|████▋     | 75/160 [00:00<00:01, 80.93frame/s]

   🔥 1000:  52%|█████▎    | 84/160 [00:00<00:00, 81.78frame/s]

   🔥 1000:  58%|█████▊    | 93/160 [00:01<00:00, 83.03frame/s]

   🔥 1000:  64%|██████▍   | 102/160 [00:01<00:00, 82.24frame/s]

   🔥 1000:  69%|██████▉   | 111/160 [00:01<00:00, 82.09frame/s]

   🔥 1000:  75%|███████▌  | 120/160 [00:01<00:00, 81.79frame/s]

   🔥 1000:  81%|████████  | 129/160 [00:01<00:00, 83.49frame/s]

   🔥 1000:  86%|████████▋ | 138/160 [00:01<00:00, 84.89frame/s]

   🔥 1000:  92%|█████████▏| 147/160 [00:01<00:00, 84.84frame/s]

   🔥 1000:  98%|█████████▊| 156/160 [00:01<00:00, 83.99frame/s]

                                                                [✅] 1000: /working/MDC044_W038_2026-03-10/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC044_W038_2026-03-10/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:04<00:04,  4.96s/rig]

   🔥 500:   0%|          | 0/201 [00:00<?, ?frame/s]

   🔥 500:   4%|▍         | 8/201 [00:00<00:02, 71.16frame/s]

   🔥 500:   8%|▊         | 16/201 [00:00<00:02, 73.92frame/s]

   🔥 500:  12%|█▏        | 24/201 [00:00<00:02, 73.49frame/s]

   🔥 500:  16%|█▌        | 32/201 [00:00<00:02, 74.42frame/s]

   🔥 500:  20%|█▉        | 40/201 [00:00<00:02, 74.89frame/s]

   🔥 500:  24%|██▍       | 48/201 [00:00<00:02, 75.03frame/s]

   🔥 500:  28%|██▊       | 56/201 [00:00<00:01, 74.62frame/s]

   🔥 500:  32%|███▏      | 64/201 [00:00<00:01, 75.23frame/s]

   🔥 500:  36%|███▌      | 72/201 [00:00<00:01, 75.90frame/s]

   🔥 500:  40%|███▉      | 80/201 [00:01<00:01, 76.98frame/s]

   🔥 500:  44%|████▍     | 88/201 [00:01<00:01, 77.73frame/s]

   🔥 500:  48%|████▊     | 97/201 [00:01<00:01, 78.88frame/s]

   🔥 500:  52%|█████▏    | 105/201 [00:01<00:01, 78.64frame/s]

   🔥 500:  57%|█████▋    | 114/201 [00:01<00:01, 79.59frame/s]

   🔥 500:  61%|██████    | 122/201 [00:01<00:00, 79.64frame/s]

   🔥 500:  65%|██████▍   | 130/201 [00:01<00:00, 77.27frame/s]

   🔥 500:  69%|██████▊   | 138/201 [00:01<00:00, 76.03frame/s]

   🔥 500:  73%|███████▎  | 146/201 [00:01<00:00, 75.52frame/s]

   🔥 500:  77%|███████▋  | 154/201 [00:02<00:00, 75.68frame/s]

   🔥 500:  81%|████████  | 162/201 [00:02<00:00, 75.68frame/s]

   🔥 500:  85%|████████▍ | 170/201 [00:02<00:00, 75.64frame/s]

   🔥 500:  89%|████████▊ | 178/201 [00:02<00:00, 75.05frame/s]

   🔥 500:  93%|█████████▎| 186/201 [00:02<00:00, 49.94frame/s]

   🔥 500:  97%|█████████▋| 194/201 [00:02<00:00, 55.33frame/s]

                                                               [✅] 500: /working/MDC044_W038_2026-03-10/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC044_W038_2026-03-10/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:08<00:00,  4.39s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:08<00:00,  4.48s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC044_W038_2026-03-10/calibration/debug/5_heatmap
[🏁] Finished processing MDC044_W038_2026-03-10


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