2026-05-20T02:35:07 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC044_W038_2026-03-10 ===
2026-05-20T02:35:07 - INFO: Camera ID: 6393994
2026-05-20T02:35:07 - INFO: Wand ID: 
2026-05-20T02:35:07 - 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]

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→ 1000 (160 images) Phase 1:  21%|██        | 33/160 [00:01<00:03, 36.32it/s]

→ 1000 (160 images) Phase 1:  36%|███▌      | 57/160 [00:01<00:01, 67.10it/s]

→ 1000 (160 images) Phase 1:  43%|████▎     | 69/160 [00:01<00:01, 52.93it/s]

→ 1000 (160 images) Phase 1:  57%|█████▋    | 91/160 [00:01<00:00, 77.87it/s]

→ 1000 (160 images) Phase 1:  65%|██████▌   | 104/160 [00:01<00:00, 61.28it/s]

→ 1000 (160 images) Phase 1:  77%|███████▋  | 123/160 [00:02<00:00, 80.86it/s]

→ 1000 (160 images) Phase 1:  85%|████████▌ | 136/160 [00:02<00:00, 63.84it/s]

→ 1000 (160 images) Phase 1: 100%|██████████| 160/160 [00:02<00:00, 90.51it/s]

                                                                              

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

→ 1000 Phase 2: corners:   1%|          | 1/160 [00:00<00:21,  7.45it/s]

→ 1000 Phase 2: corners:  11%|█▏        | 18/160 [00:00<00:01, 89.20it/s]

→ 1000 Phase 2: corners:  21%|██▏       | 34/160 [00:00<00:01, 115.21it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 47/160 [00:00<00:00, 118.96it/s]

→ 1000 Phase 2: corners:  38%|███▊      | 60/160 [00:00<00:00, 112.16it/s]

→ 1000 Phase 2: corners:  48%|████▊     | 76/160 [00:00<00:00, 121.74it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 91/160 [00:00<00:00, 126.90it/s]

→ 1000 Phase 2: corners:  67%|██████▋   | 107/160 [00:00<00:00, 131.18it/s]

→ 1000 Phase 2: corners:  76%|███████▌  | 121/160 [00:01<00:00, 129.49it/s]

→ 1000 Phase 2: corners:  84%|████████▍ | 135/160 [00:01<00:00, 126.23it/s]

→ 1000 Phase 2: corners:  96%|█████████▋| 154/160 [00:01<00:00, 143.57it/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:  61%|██████    | 97/160 [00:01<00:01, 49.12it/s]

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→ 1000 Phase 3: finalize:  71%|███████▏  | 114/160 [00:02<00:00, 51.53it/s]

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→ 1000 Phase 3: finalize:  79%|███████▉  | 126/160 [00:02<00:00, 52.51it/s]

→ 1000 Phase 3: finalize:  82%|████████▎ | 132/160 [00:02<00:00, 53.94it/s]

→ 1000 Phase 3: finalize:  86%|████████▋ | 138/160 [00:02<00:00, 54.48it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 144/160 [00:02<00:00, 54.86it/s]

→ 1000 Phase 3: finalize:  94%|█████████▍| 150/160 [00:02<00:00, 55.02it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 156/160 [00:02<00:00, 54.77it/s]

                                                                           
[TIMING] 1000 — 160 frames
  Phase 1  radial detect   (parallel)  :   2.55s  (15.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.32s  (8.2 ms/frame)
  Phase 2b corner fix      (sequential):   1.39s
  Phase 3  finalize        (sequential):   2.94s  (18.4 ms/frame)
    decode + undistort                 :   1.56s  (9.7 ms/frame)
    corner apply + refinement          :   0.63s  (4.0 ms/frame)
    segment                            :   0.60s  (3.8 ms/frame)
    smooth                             :   0.10s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.20s


Processing Rigs:  50%|█████     | 1/2 [00:11<00:11, 11.16s/it][INFO] Filtered 1200 stationary frames, 201 remain


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→ 500 (201 images) Phase 1:  48%|████▊     | 97/201 [00:01<00:01, 60.44it/s]

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→ 500 (201 images) Phase 1:  64%|██████▍   | 129/201 [00:02<00:01, 67.08it/s]

→ 500 (201 images) Phase 1:  68%|██████▊   | 137/201 [00:02<00:00, 67.19it/s]

→ 500 (201 images) Phase 1:  80%|████████  | 161/201 [00:02<00:00, 72.34it/s]

→ 500 (201 images) Phase 1:  84%|████████▍ | 169/201 [00:02<00:00, 68.82it/s]

→ 500 (201 images) Phase 1:  96%|█████████▌| 193/201 [00:03<00:00, 86.74it/s]

                                                                             

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

→ 500 Phase 2: corners:   0%|          | 1/201 [00:00<00:59,  3.35it/s]

→ 500 Phase 2: corners:   8%|▊         | 16/201 [00:00<00:03, 48.52it/s]

→ 500 Phase 2: corners:  13%|█▎        | 27/201 [00:00<00:02, 65.58it/s]

→ 500 Phase 2: corners:  19%|█▉        | 39/201 [00:00<00:01, 81.39it/s]

→ 500 Phase 2: corners:  26%|██▌       | 52/201 [00:00<00:01, 95.26it/s]

→ 500 Phase 2: corners:  34%|███▍      | 68/201 [00:00<00:01, 112.33it/s]

→ 500 Phase 2: corners:  40%|████      | 81/201 [00:00<00:01, 114.37it/s]

→ 500 Phase 2: corners:  47%|████▋     | 95/201 [00:01<00:00, 119.77it/s]

→ 500 Phase 2: corners:  54%|█████▎    | 108/201 [00:01<00:00, 116.02it/s]

→ 500 Phase 2: corners:  60%|██████    | 121/201 [00:01<00:00, 114.55it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 135/201 [00:01<00:00, 118.53it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 151/201 [00:01<00:00, 125.03it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 169/201 [00:01<00:00, 138.71it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 186/201 [00:01<00:00, 145.25it/s]

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


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

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→ 500 Phase 3: finalize:  19%|█▉        | 39/201 [00:00<00:02, 59.53it/s]

→ 500 Phase 3: finalize:  22%|██▏       | 45/201 [00:00<00:02, 58.60it/s]

→ 500 Phase 3: finalize:  25%|██▌       | 51/201 [00:00<00:02, 57.86it/s]

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→ 500 Phase 3: finalize:  34%|███▍      | 69/201 [00:01<00:02, 55.03it/s]

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→ 500 Phase 3: finalize:  40%|████      | 81/201 [00:01<00:02, 54.30it/s]

→ 500 Phase 3: finalize:  43%|████▎     | 87/201 [00:01<00:02, 54.14it/s]

→ 500 Phase 3: finalize:  46%|████▋     | 93/201 [00:01<00:01, 54.42it/s]

→ 500 Phase 3: finalize:  49%|████▉     | 99/201 [00:01<00:01, 54.56it/s]

→ 500 Phase 3: finalize:  52%|█████▏    | 105/201 [00:01<00:01, 53.78it/s]

→ 500 Phase 3: finalize:  55%|█████▌    | 111/201 [00:01<00:01, 54.39it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 117/201 [00:02<00:01, 54.96it/s]

→ 500 Phase 3: finalize:  61%|██████    | 123/201 [00:02<00:01, 54.43it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 129/201 [00:02<00:01, 54.34it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 135/201 [00:02<00:01, 54.36it/s]

→ 500 Phase 3: finalize:  70%|███████   | 141/201 [00:02<00:01, 54.48it/s]

→ 500 Phase 3: finalize:  73%|███████▎  | 147/201 [00:02<00:00, 54.47it/s]

→ 500 Phase 3: finalize:  76%|███████▌  | 153/201 [00:02<00:00, 54.44it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 159/201 [00:02<00:00, 54.40it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 165/201 [00:02<00:00, 54.33it/s]

→ 500 Phase 3: finalize:  85%|████████▌ | 171/201 [00:03<00:00, 54.36it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 177/201 [00:03<00:00, 54.49it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 183/201 [00:03<00:00, 54.51it/s]

→ 500 Phase 3: finalize:  94%|█████████▍| 189/201 [00:03<00:00, 54.13it/s]

→ 500 Phase 3: finalize:  97%|█████████▋| 195/201 [00:03<00:00, 54.06it/s]

→ 500 Phase 3: finalize: 100%|██████████| 201/201 [00:03<00:00, 53.93it/s]

                                                                          
[TIMING] 500 — 201 frames
  Phase 1  radial detect   (parallel)  :   3.22s  (16.0 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.83s  (9.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.04s
  Phase 3  finalize        (sequential):   3.63s  (18.1 ms/frame)
    decode + undistort                 :   1.87s  (9.3 ms/frame)
    corner apply + refinement          :   0.77s  (3.8 ms/frame)
    segment                            :   0.81s  (4.0 ms/frame)
    smooth                             :   0.12s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.72s


Processing Rigs: 100%|██████████| 2/2 [00:23<00:00, 12.13s/it]
Processing Rigs: 100%|██████████| 2/2 [00:23<00:00, 11.99s/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<01:04,  3.39s/it]
Computing max image from bright frames:  10%|█         | 2/20 [00:03<00:26,  1.48s/it]
Computing max image from bright frames:  15%|█▌        | 3/20 [00:03<00:14,  1.17it/s]
Computing max image from bright frames:  25%|██▌       | 5/20 [00:03<00:06,  2.46it/s]
Computing max image from bright frames:  35%|███▌      | 7/20 [00:04<00:03,  3.59it/s]
Computing max image from bright frames:  45%|████▌     | 9/20 [00:04<00:02,  4.61it/s]
Computing max image from bright frames:  60%|██████    | 12/20 [00:04<00:01,  7.27it/s]
Computing max image from bright frames:  70%|███████   | 14/20 [00:04<00:00,  8.57it/s]
Computing max image from bright frames:  80%|████████  | 16/20 [00:04<00:00,  7.73it/s]
Computing max image from bright frames:  90%|█████████ | 18/20 [00:05<00:00,  5.29it/s]
Computing max image from bright frames: 100%|██████████| 20/20 [00:05<00:00,  6.23it/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} (0.015) | Current: 0.009 | Best: {'fx': 300.0} (0.009):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 300.0} (0.009):   1%|          | 1/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   1%|          | 1/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   2%|▏         | 2/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   3%|▎         | 3/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   4%|▍         | 4/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   5%|▌         | 5/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   6%|▌         | 6/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   7%|▋         | 7/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   8%|▊         | 8/100 [00:00<00:11,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.007 | Best: {'fx': 300.0} (0.009):   9%|▉         | 9/100 [00:00<00:10,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.008 | Best: {'fx': 300.0} (0.009):  10%|█         | 10/100 [00:00<00:10,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 300.0} (0.009):  11%|█         | 11/100 [00:00<00:10,  8.31it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 300.0} (0.009):  12%|█▏        | 12/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 300.0} (0.009):  12%|█▏        | 12/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 300.0} (0.009):  13%|█▎        | 13/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.010 | Best: {'fx': 420.202} (0.010):  14%|█▍        | 14/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 420.202} (0.010):  15%|█▌        | 15/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 420.202} (0.010):  16%|█▌        | 16/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 420.202} (0.010):  17%|█▋        | 17/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 420.202} (0.010):  18%|█▊        | 18/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 420.202} (0.010):  19%|█▉        | 19/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.009 | Best: {'fx': 420.202} (0.010):  20%|██        | 20/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.010 | Best: {'fx': 448.485} (0.010):  21%|██        | 21/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.010 | Best: {'fx': 455.556} (0.010):  22%|██▏       | 22/100 [00:00<00:01, 62.69it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.010 | Best: {'fx': 455.556} (0.010):  23%|██▎       | 23/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.013 | Best: {'fx': 490.909} (0.013):  23%|██▎       | 23/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.010 | Best: {'fx': 490.909} (0.013):  24%|██▍       | 24/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.011 | Best: {'fx': 490.909} (0.013):  25%|██▌       | 25/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.011 | Best: {'fx': 490.909} (0.013):  26%|██▌       | 26/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.012 | Best: {'fx': 490.909} (0.013):  27%|██▋       | 27/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.014 | Best: {'fx': 497.98} (0.014):  28%|██▊       | 28/100 [00:00<00:00, 79.91it/s] 
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 505.051} (0.017):  29%|██▉       | 29/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.030 | Best: {'fx': 512.121} (0.030):  30%|███       | 30/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.089 | Best: {'fx': 533.333} (0.089):  31%|███       | 31/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.042 | Best: {'fx': 533.333} (0.089):  32%|███▏      | 32/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.058 | Best: {'fx': 533.333} (0.089):  33%|███▎      | 33/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.043 | Best: {'fx': 533.333} (0.089):  34%|███▍      | 34/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.058 | Best: {'fx': 533.333} (0.089):  35%|███▌      | 35/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.036 | Best: {'fx': 533.333} (0.089):  36%|███▌      | 36/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.032 | Best: {'fx': 533.333} (0.089):  37%|███▋      | 37/100 [00:00<00:00, 79.91it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.032 | Best: {'fx': 533.333} (0.089):  38%|███▊      | 38/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.028 | Best: {'fx': 533.333} (0.089):  38%|███▊      | 38/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.030 | Best: {'fx': 533.333} (0.089):  39%|███▉      | 39/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.024 | Best: {'fx': 533.333} (0.089):  40%|████      | 40/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.026 | Best: {'fx': 533.333} (0.089):  41%|████      | 41/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.025 | Best: {'fx': 533.333} (0.089):  42%|████▏     | 42/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.022 | Best: {'fx': 533.333} (0.089):  43%|████▎     | 43/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.023 | Best: {'fx': 533.333} (0.089):  44%|████▍     | 44/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.021 | Best: {'fx': 533.333} (0.089):  45%|████▌     | 45/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.023 | Best: {'fx': 533.333} (0.089):  46%|████▌     | 46/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.021 | Best: {'fx': 533.333} (0.089):  47%|████▋     | 47/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.022 | Best: {'fx': 533.333} (0.089):  48%|████▊     | 48/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.020 | Best: {'fx': 533.333} (0.089):  49%|████▉     | 49/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.021 | Best: {'fx': 533.333} (0.089):  50%|█████     | 50/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  51%|█████     | 51/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.020 | Best: {'fx': 533.333} (0.089):  52%|█████▏    | 52/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.020 | Best: {'fx': 533.333} (0.089):  53%|█████▎    | 53/100 [00:00<00:00, 105.52it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.020 | Best: {'fx': 533.333} (0.089):  54%|█████▍    | 54/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.020 | Best: {'fx': 533.333} (0.089):  54%|█████▍    | 54/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  55%|█████▌    | 55/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  56%|█████▌    | 56/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  57%|█████▋    | 57/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  58%|█████▊    | 58/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.019 | Best: {'fx': 533.333} (0.089):  59%|█████▉    | 59/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  60%|██████    | 60/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  61%|██████    | 61/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  62%|██████▏   | 62/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  63%|██████▎   | 63/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  64%|██████▍   | 64/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  65%|██████▌   | 65/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  66%|██████▌   | 66/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  67%|██████▋   | 67/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.018 | Best: {'fx': 533.333} (0.089):  68%|██████▊   | 68/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  69%|██████▉   | 69/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  70%|███████   | 70/100 [00:00<00:00, 123.95it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  71%|███████   | 71/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  71%|███████   | 71/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  72%|███████▏  | 72/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  73%|███████▎  | 73/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  74%|███████▍  | 74/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  75%|███████▌  | 75/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  76%|███████▌  | 76/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  77%|███████▋  | 77/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  78%|███████▊  | 78/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  79%|███████▉  | 79/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.017 | Best: {'fx': 533.333} (0.089):  80%|████████  | 80/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  81%|████████  | 81/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  82%|████████▏ | 82/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  83%|████████▎ | 83/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  84%|████████▍ | 84/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  85%|████████▌ | 85/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  86%|████████▌ | 86/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  87%|████████▋ | 87/100 [00:00<00:00, 135.47it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  88%|████████▊ | 88/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  88%|████████▊ | 88/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  89%|████████▉ | 89/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  90%|█████████ | 90/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  91%|█████████ | 91/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  92%|█████████▏| 92/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  93%|█████████▎| 93/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  94%|█████████▍| 94/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  95%|█████████▌| 95/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  96%|█████████▌| 96/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  97%|█████████▋| 97/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  98%|█████████▊| 98/100 [00:00<00:00, 145.79it/s]
Brute | Start: {'fx': 500.0} (0.015) | Current: 0.016 | Best: {'fx': 533.333} (0.089):  99%|█████████▉| 99/100 [00:00<00:00, 145.79it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.021 | Best: {'fx': 508.333} (0.021):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.021 | Best: {'fx': 508.333} (0.021):   2%|▏         | 1/50 [00:00<00:05,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.023 | Best: {'fx': 509.354} (0.023):   2%|▏         | 1/50 [00:00<00:05,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.025 | Best: {'fx': 510.374} (0.025):   4%|▍         | 2/50 [00:00<00:05,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.028 | Best: {'fx': 511.395} (0.028):   6%|▌         | 3/50 [00:00<00:05,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.030 | Best: {'fx': 512.415} (0.030):   8%|▊         | 4/50 [00:00<00:05,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.033 | Best: {'fx': 513.435} (0.033):  10%|█         | 5/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.038 | Best: {'fx': 516.497} (0.038):  12%|█▏        | 6/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.036 | Best: {'fx': 516.497} (0.038):  14%|█▍        | 7/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.037 | Best: {'fx': 516.497} (0.038):  16%|█▌        | 8/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.040 | Best: {'fx': 517.517} (0.040):  18%|█▊        | 9/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.041 | Best: {'fx': 518.537} (0.041):  20%|██        | 10/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.042 | Best: {'fx': 519.558} (0.042):  22%|██▏       | 11/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.050 | Best: {'fx': 523.639} (0.050):  24%|██▍       | 12/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.043 | Best: {'fx': 523.639} (0.050):  26%|██▌       | 13/50 [00:00<00:04,  9.18it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.043 | Best: {'fx': 523.639} (0.050):  28%|██▊       | 14/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.046 | Best: {'fx': 523.639} (0.050):  28%|██▊       | 14/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.049 | Best: {'fx': 523.639} (0.050):  30%|███       | 15/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.053 | Best: {'fx': 524.66} (0.053):  32%|███▏      | 16/50 [00:00<00:00, 77.42it/s] 
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.064 | Best: {'fx': 527.721} (0.064):  34%|███▍      | 17/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.060 | Best: {'fx': 527.721} (0.064):  36%|███▌      | 18/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.055 | Best: {'fx': 527.721} (0.064):  38%|███▊      | 19/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.069 | Best: {'fx': 529.762} (0.069):  40%|████      | 20/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.068 | Best: {'fx': 529.762} (0.069):  42%|████▏     | 21/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.078 | Best: {'fx': 530.782} (0.078):  44%|████▍     | 22/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.082 | Best: {'fx': 531.803} (0.082):  46%|████▌     | 23/50 [00:00<00:00, 77.42it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.082 | Best: {'fx': 531.803} (0.082):  48%|████▊     | 24/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.089 | Best: {'fx': 532.823} (0.089):  48%|████▊     | 24/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.081 | Best: {'fx': 532.823} (0.089):  50%|█████     | 25/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.078 | Best: {'fx': 532.823} (0.089):  52%|█████▏    | 26/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.080 | Best: {'fx': 532.823} (0.089):  54%|█████▍    | 27/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.064 | Best: {'fx': 532.823} (0.089):  56%|█████▌    | 28/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.070 | Best: {'fx': 532.823} (0.089):  58%|█████▊    | 29/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.061 | Best: {'fx': 532.823} (0.089):  60%|██████    | 30/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.053 | Best: {'fx': 532.823} (0.089):  62%|██████▏   | 31/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.063 | Best: {'fx': 532.823} (0.089):  64%|██████▍   | 32/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.056 | Best: {'fx': 532.823} (0.089):  66%|██████▌   | 33/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.052 | Best: {'fx': 532.823} (0.089):  68%|██████▊   | 34/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.049 | Best: {'fx': 532.823} (0.089):  70%|███████   | 35/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.044 | Best: {'fx': 532.823} (0.089):  72%|███████▏  | 36/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.047 | Best: {'fx': 532.823} (0.089):  74%|███████▍  | 37/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.045 | Best: {'fx': 532.823} (0.089):  76%|███████▌  | 38/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.043 | Best: {'fx': 532.823} (0.089):  78%|███████▊  | 39/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.039 | Best: {'fx': 532.823} (0.089):  80%|████████  | 40/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.040 | Best: {'fx': 532.823} (0.089):  82%|████████▏ | 41/50 [00:00<00:00, 85.11it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.040 | Best: {'fx': 532.823} (0.089):  84%|████████▍ | 42/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.037 | Best: {'fx': 532.823} (0.089):  84%|████████▍ | 42/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.041 | Best: {'fx': 532.823} (0.089):  86%|████████▌ | 43/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.039 | Best: {'fx': 532.823} (0.089):  88%|████████▊ | 44/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.035 | Best: {'fx': 532.823} (0.089):  90%|█████████ | 45/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.037 | Best: {'fx': 532.823} (0.089):  92%|█████████▏| 46/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.035 | Best: {'fx': 532.823} (0.089):  94%|█████████▍| 47/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.035 | Best: {'fx': 532.823} (0.089):  96%|█████████▌| 48/50 [00:00<00:00, 118.77it/s]
Brute | Start: {'fx': 533.333} (0.089) | Current: 0.034 | Best: {'fx': 532.823} (0.089):  98%|█████████▊| 49/50 [00:00<00:00, 118.77it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 533.3333333333334
    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: 23.6674: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 23.6674, picklable_objective(x0) = 19.1420

Best score: 19.1420: : 0iter [00:01, ?iter/s] 
Best score: 19.1420: : 1iter [00:01,  1.94s/iter]
Best score: 19.1420: : 1iter [00:01,  1.94s/iter]
Best score: 19.1420: : 2iter [00:01,  1.94s/iter]
Best score: 19.1420: : 3iter [00:02,  1.94s/iter]
Best score: 19.1420: : 4iter [00:02,  1.94s/iter]
Best score: 19.1420: : 5iter [00:02,  1.94s/iter]
Best score: 19.1420: : 6iter [00:02,  3.87iter/s]
Best score: 19.1420: : 6iter [00:02,  3.87iter/s]
Best score: 19.1420: : 7iter [00:02,  3.87iter/s]
Best score: 19.1420: : 8iter [00:02,  3.87iter/s]
Best score: 19.1420: : 9iter [00:02,  3.87iter/s]
Best score: 19.1420: : 10iter [00:02,  3.87iter/s]
Best score: 19.1420: : 11iter [00:02,  7.84iter/s]
Best score: 19.1420: : 11iter [00:02,  7.84iter/s]
Best score: 19.1420: : 12iter [00:02,  7.84iter/s]
Best score: 19.1420: : 13iter [00:02,  7.84iter/s]
Best score: 19.1420: : 14iter [00:02,  7.84iter/s]
Best score: 19.1420: : 15iter [00:02,  7.84iter/s]
Best score: 19.1420: : 16iter [00:02, 12.32iter/s]
Best score: 19.1420: : 16iter [00:02, 12.32iter/s]
Best score: 19.1420: : 17iter [00:02, 12.32iter/s]
Best score: 19.1420: : 18iter [00:02, 12.32iter/s]
Best score: 19.1420: : 19iter [00:02, 12.32iter/s]
Best score: 19.1420: : 20iter [00:02, 12.32iter/s]
Best score: 19.1420: : 21iter [00:02, 16.99iter/s]
Best score: 19.1420: : 21iter [00:02, 16.99iter/s]
Best score: 19.1420: : 22iter [00:02, 16.99iter/s]
Best score: 19.1420: : 23iter [00:02, 16.99iter/s]
Best score: 19.1420: : 24iter [00:02, 16.99iter/s]
Best score: 19.1420: : 25iter [00:02, 16.99iter/s]
Best score: 19.1420: : 26iter [00:02, 21.66iter/s]
Best score: 19.1420: : 26iter [00:02, 21.66iter/s]
Best score: 19.1420: : 27iter [00:02, 21.66iter/s]
Best score: 19.1420: : 28iter [00:02, 21.66iter/s]
Best score: 19.1420: : 29iter [00:02, 21.66iter/s]
Best score: 19.1420: : 30iter [00:02, 21.66iter/s]
Best score: 19.1420: : 31iter [00:02, 25.97iter/s]
Best score: 19.1420: : 31iter [00:02, 25.97iter/s]
Best score: 19.1420: : 32iter [00:02, 25.97iter/s]
Best score: 19.1420: : 33iter [00:02, 25.97iter/s]
Best score: 19.1420: : 34iter [00:02, 25.97iter/s]
Best score: 19.1420: : 35iter [00:02, 25.97iter/s]
Best score: 19.1420: : 36iter [00:02, 29.80iter/s]
Best score: 19.1420: : 36iter [00:02, 29.80iter/s]
Best score: 19.1420: : 37iter [00:02, 29.80iter/s]
Best score: 19.1420: : 38iter [00:02, 29.80iter/s]
Best score: 19.1420: : 39iter [00:02, 29.80iter/s]
Best score: 19.1420: : 40iter [00:02, 29.80iter/s]
Best score: 19.1420: : 41iter [00:02, 33.01iter/s]
Best score: 19.1420: : 41iter [00:02, 33.01iter/s]
Best score: 19.1420: : 42iter [00:02, 33.01iter/s]
Best score: 19.1420: : 43iter [00:02, 33.01iter/s]
Best score: 19.1420: : 44iter [00:02, 33.01iter/s]
Best score: 19.1420: : 45iter [00:02, 33.01iter/s]
Best score: 19.1420: : 46iter [00:02, 35.76iter/s]
Best score: 19.1420: : 46iter [00:03, 35.76iter/s]
Best score: 19.1420: : 47iter [00:03, 35.76iter/s]
Best score: 19.1420: : 48iter [00:03, 35.76iter/s]
[ITER 50] Best: 19.1420
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  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: 19.1420: : 49iter [00:03, 35.76iter/s]
Best score: 19.1420: : 50iter [00:03, 35.76iter/s]
Best score: 19.1420: : 51iter [00:03, 37.75iter/s]
Best score: 19.1420: : 51iter [00:03, 37.75iter/s]
Best score: 19.1420: : 52iter [00:03, 37.75iter/s]
Best score: 19.1420: : 53iter [00:03, 37.75iter/s]
Best score: 19.1420: : 54iter [00:03, 37.75iter/s]
Best score: 19.1420: : 55iter [00:03, 37.75iter/s]
Best score: 19.1420: : 56iter [00:03, 39.41iter/s]
Best score: 19.1420: : 56iter [00:03, 39.41iter/s]
Best score: 19.1420: : 57iter [00:03, 39.41iter/s]
Best score: 19.1420: : 58iter [00:03, 39.41iter/s]
Best score: 19.1420: : 59iter [00:03, 39.41iter/s]
Best score: 19.1420: : 60iter [00:03, 39.41iter/s]
Best score: 19.1420: : 61iter [00:03, 40.42iter/s]
Best score: 19.1420: : 61iter [00:03, 40.42iter/s]
Best score: 19.1420: : 62iter [00:03, 40.42iter/s]
Best score: 19.1420: : 63iter [00:03, 40.42iter/s]
Best score: 19.1420: : 64iter [00:03, 40.42iter/s]
Best score: 19.1420: : 65iter [00:03, 40.42iter/s]
Best score: 19.1420: : 66iter [00:03, 41.41iter/s]
Best score: 19.1420: : 66iter [00:03, 41.41iter/s]
Best score: 19.1420: : 67iter [00:03, 41.41iter/s]
Best score: 19.1420: : 68iter [00:03, 41.41iter/s]
Best score: 19.1420: : 69iter [00:03, 41.41iter/s]
Best score: 19.1420: : 70iter [00:03, 41.41iter/s]
Best score: 19.1420: : 71iter [00:03, 41.83iter/s]
Best score: 19.1420: : 71iter [00:03, 41.83iter/s]
Best score: 19.1420: : 72iter [00:03, 41.83iter/s]
Best score: 19.1420: : 73iter [00:03, 41.83iter/s]
Best score: 19.1420: : 74iter [00:03, 41.83iter/s]
Best score: 19.1420: : 75iter [00:03, 41.83iter/s]
Best score: 19.1420: : 76iter [00:03, 42.48iter/s]
Best score: 19.1420: : 76iter [00:03, 42.48iter/s]
Best score: 19.1420: : 77iter [00:03, 42.48iter/s]
Best score: 19.1420: : 78iter [00:03, 42.48iter/s]
Best score: 19.1420: : 79iter [00:03, 42.48iter/s]
Best score: 19.1420: : 80iter [00:03, 42.48iter/s]
Best score: 19.1420: : 81iter [00:03, 42.80iter/s]
Best score: 19.1420: : 81iter [00:03, 42.80iter/s]
Best score: 19.1420: : 82iter [00:03, 42.80iter/s]
Best score: 19.1420: : 83iter [00:03, 42.80iter/s]
Best score: 19.1420: : 84iter [00:03, 42.80iter/s]
Best score: 19.1420: : 85iter [00:03, 42.80iter/s]
Best score: 19.1420: : 86iter [00:03, 43.12iter/s]
Best score: 19.1420: : 86iter [00:03, 43.12iter/s]
Best score: 19.1420: : 87iter [00:03, 43.12iter/s]
Best score: 19.1420: : 88iter [00:03, 43.12iter/s]
Best score: 19.1420: : 89iter [00:03, 43.12iter/s]
Best score: 19.1420: : 90iter [00:04, 43.12iter/s]
Best score: 19.1420: : 91iter [00:04, 43.29iter/s]
Best score: 19.1420: : 91iter [00:04, 43.29iter/s]
Best score: 19.1420: : 92iter [00:04, 43.29iter/s]
Best score: 19.1420: : 93iter [00:04, 43.29iter/s]
Best score: 19.1420: : 94iter [00:04, 43.29iter/s]
Best score: 19.1420: : 95iter [00:04, 43.29iter/s]
Best score: 19.1420: : 96iter [00:04, 43.56iter/s]
Best score: 19.1420: : 96iter [00:04, 43.56iter/s]
Best score: 19.1420: : 97iter [00:04, 43.56iter/s]
Best score: 19.1420: : 98iter [00:04, 43.56iter/s]
[ITER 100] Best: 19.1420
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  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: 19.1420: : 99iter [00:04, 43.56iter/s]
Best score: 19.1420: : 100iter [00:04, 43.56iter/s]
Best score: 19.1420: : 101iter [00:04, 40.95iter/s]
Best score: 19.1420: : 101iter [00:04, 40.95iter/s]
Best score: 19.1420: : 102iter [00:04, 40.95iter/s]
Best score: 19.1420: : 103iter [00:04, 40.95iter/s]
Best score: 19.1420: : 104iter [00:04, 40.95iter/s]
Best score: 19.1420: : 105iter [00:04, 40.95iter/s]
Best score: 19.1420: : 106iter [00:04, 41.66iter/s]
Best score: 19.1420: : 106iter [00:04, 41.66iter/s]
Best score: 19.1420: : 107iter [00:04, 41.66iter/s]
Best score: 19.1420: : 108iter [00:04, 41.66iter/s]
Best score: 19.1420: : 109iter [00:04, 41.66iter/s]
Best score: 19.1420: : 110iter [00:04, 41.66iter/s]
Best score: 19.1420: : 111iter [00:04, 41.94iter/s]
Best score: 19.1420: : 111iter [00:04, 41.94iter/s]
Best score: 19.1420: : 112iter [00:04, 41.94iter/s]
Best score: 19.1420: : 113iter [00:04, 41.94iter/s]
Best score: 19.1420: : 114iter [00:04, 41.94iter/s]
Best score: 19.1420: : 115iter [00:04, 41.94iter/s]
Best score: 19.1420: : 116iter [00:04, 42.43iter/s]
Best score: 19.1420: : 116iter [00:04, 42.43iter/s]
Best score: 19.1420: : 117iter [00:04, 42.43iter/s]
Best score: 19.1420: : 118iter [00:04, 42.43iter/s]
Best score: 19.1420: : 119iter [00:04, 42.43iter/s]
Best score: 19.1420: : 120iter [00:04, 42.43iter/s]
Best score: 19.1420: : 121iter [00:04, 42.49iter/s]
Best score: 19.1420: : 121iter [00:04, 42.49iter/s]
Best score: 19.1420: : 122iter [00:04, 42.49iter/s]
Best score: 19.1420: : 123iter [00:04, 42.49iter/s]
Best score: 19.1420: : 124iter [00:04, 42.49iter/s]
Best score: 19.1420: : 125iter [00:04, 42.49iter/s]
Best score: 19.1420: : 126iter [00:04, 42.76iter/s]
Best score: 19.1420: : 126iter [00:04, 42.76iter/s]
Best score: 19.1420: : 127iter [00:04, 42.76iter/s]
Best score: 19.1420: : 128iter [00:04, 42.76iter/s]
Best score: 19.1420: : 129iter [00:04, 42.76iter/s]
Best score: 19.1420: : 130iter [00:04, 42.76iter/s]
Best score: 19.1420: : 131iter [00:04, 42.72iter/s]
Best score: 19.1420: : 131iter [00:04, 42.72iter/s]
Best score: 19.1420: : 132iter [00:05, 42.72iter/s]
Best score: 19.1420: : 133iter [00:05, 42.72iter/s]
Best score: 19.1420: : 134iter [00:05, 42.72iter/s]
Best score: 19.1420: : 135iter [00:05, 42.72iter/s]
Best score: 19.1420: : 136iter [00:05, 43.05iter/s]
Best score: 19.1420: : 136iter [00:05, 43.05iter/s]
Best score: 19.1420: : 137iter [00:05, 43.05iter/s]
Best score: 19.1420: : 138iter [00:05, 43.05iter/s]
Best score: 19.1420: : 139iter [00:05, 43.05iter/s]
Best score: 19.1420: : 140iter [00:05, 43.05iter/s]
Best score: 19.1420: : 141iter [00:05, 42.93iter/s]
Best score: 19.1420: : 141iter [00:05, 42.93iter/s]
Best score: 19.1420: : 142iter [00:05, 42.93iter/s]
Best score: 19.1420: : 143iter [00:05, 42.93iter/s]
Best score: 19.1420: : 144iter [00:05, 42.93iter/s]
Best score: 19.1420: : 145iter [00:05, 42.93iter/s]
Best score: 19.1420: : 146iter [00:05, 43.06iter/s]
Best score: 19.1420: : 146iter [00:05, 43.06iter/s]
Best score: 19.1420: : 147iter [00:05, 43.06iter/s]
Best score: 19.1420: : 148iter [00:05, 43.06iter/s]
[ITER 150] Best: 19.1420
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  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: 19.1420: : 149iter [00:05, 43.06iter/s]
Best score: 19.1420: : 150iter [00:05, 43.06iter/s]
Best score: 19.1420: : 151iter [00:05, 36.43iter/s]
Best score: 19.1420: : 151iter [00:05, 36.43iter/s]
Best score: 19.1420: : 152iter [00:05, 36.43iter/s]
Best score: 19.1420: : 153iter [00:05, 36.43iter/s]
Best score: 19.1420: : 154iter [00:05, 36.43iter/s]
Best score: 19.1420: : 155iter [00:05, 36.43iter/s]
Best score: 19.1420: : 156iter [00:05, 38.39iter/s]
Best score: 19.1420: : 156iter [00:05, 38.39iter/s]
Best score: 19.1420: : 157iter [00:05, 38.39iter/s]
Best score: 19.1420: : 158iter [00:05, 38.39iter/s]
Best score: 19.1420: : 159iter [00:05, 38.39iter/s]
Best score: 19.1420: : 160iter [00:05, 38.39iter/s]
Best score: 19.1420: : 161iter [00:05, 39.65iter/s]
Best score: 19.1420: : 161iter [00:05, 39.65iter/s]
Best score: 19.1420: : 162iter [00:05, 39.65iter/s]
Best score: 19.1420: : 163iter [00:05, 39.65iter/s]
Best score: 19.1420: : 164iter [00:05, 39.65iter/s]
Best score: 19.1420: : 165iter [00:05, 39.65iter/s]
Best score: 19.1420: : 166iter [00:05, 40.78iter/s]
Best score: 19.1420: : 166iter [00:05, 40.78iter/s]
Best score: 19.1420: : 167iter [00:05, 40.78iter/s]
Best score: 19.1420: : 168iter [00:05, 40.78iter/s]
Best score: 19.1420: : 169iter [00:05, 40.78iter/s]
Best score: 19.1420: : 170iter [00:05, 40.78iter/s]
Best score: 19.1420: : 171iter [00:05, 41.37iter/s]
Best score: 19.1420: : 171iter [00:05, 41.37iter/s]
Best score: 19.1420: : 172iter [00:06, 41.37iter/s]
Best score: 19.1420: : 173iter [00:06, 41.37iter/s]
Best score: 19.1420: : 174iter [00:06, 41.37iter/s]
Best score: 19.1420: : 175iter [00:06, 41.37iter/s]
Best score: 19.1420: : 176iter [00:06, 42.04iter/s]
Best score: 19.1420: : 176iter [00:06, 42.04iter/s]
Best score: 19.1420: : 177iter [00:06, 42.04iter/s]
Best score: 19.1420: : 178iter [00:06, 42.04iter/s]
Best score: 19.1420: : 179iter [00:06, 42.04iter/s]
Best score: 19.1420: : 180iter [00:06, 42.04iter/s]
Best score: 19.1420: : 181iter [00:06, 42.33iter/s]
Best score: 19.1420: : 181iter [00:06, 42.33iter/s]
Best score: 19.1420: : 182iter [00:06, 42.33iter/s]
Best score: 19.1420: : 183iter [00:06, 42.33iter/s]
Best score: 19.1420: : 184iter [00:06, 42.33iter/s]
Best score: 19.1420: : 185iter [00:06, 42.33iter/s]
Best score: 19.1420: : 186iter [00:06, 42.68iter/s]
Best score: 19.1420: : 186iter [00:06, 42.68iter/s]
Best score: 19.1420: : 187iter [00:06, 42.68iter/s]
Best score: 19.1420: : 188iter [00:06, 42.68iter/s]
Best score: 19.1420: : 189iter [00:06, 42.68iter/s]
Best score: 19.1420: : 190iter [00:06, 42.68iter/s]
Best score: 19.1420: : 191iter [00:06, 42.69iter/s]
Best score: 19.1420: : 191iter [00:06, 42.69iter/s]
Best score: 19.1420: : 192iter [00:06, 42.69iter/s]
Best score: 19.1420: : 193iter [00:06, 42.69iter/s]
Best score: 19.1420: : 194iter [00:06, 42.69iter/s]
Best score: 19.1420: : 195iter [00:06, 42.69iter/s]
Best score: 19.1420: : 196iter [00:06, 42.87iter/s]
Best score: 19.1420: : 196iter [00:06, 42.87iter/s]
Best score: 19.1420: : 197iter [00:06, 42.87iter/s]
Best score: 19.1420: : 198iter [00:06, 42.87iter/s]
[ITER 200] Best: 19.1420
[PARAMS] Current best parameters:
  fx: 533.3333
  fy: 533.3333
  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: 19.1420: : 199iter [00:06, 42.87iter/s]
Best score: 19.1420: : 200iter [00:06, 42.87iter/s]
Best score: 19.1420: : 201iter [00:06, 36.43iter/s]
Best score: 19.1420: : 201iter [00:06, 36.43iter/s]
Best score: 19.1420: : 202iter [00:06, 36.43iter/s]
Best score: 19.1420: : 203iter [00:06, 36.43iter/s]
Best score: 19.1420: : 204iter [00:06, 36.43iter/s]
Best score: 19.1420: : 205iter [00:06, 36.43iter/s]
Best score: 19.1420: : 206iter [00:06, 38.39iter/s]
Best score: 19.1420: : 206iter [00:06, 38.39iter/s]
Best score: 19.1420: : 207iter [00:06, 38.39iter/s]
Best score: 19.1420: : 208iter [00:06, 38.39iter/s]
Best score: 19.1420: : 209iter [00:06, 38.39iter/s]
Best score: 19.1420: : 210iter [00:06, 38.39iter/s]
Best score: 19.1420: : 211iter [00:06, 39.80iter/s]
Best score: 19.1420: : 211iter [00:06, 39.80iter/s]
Best score: 19.1420: : 212iter [00:06, 39.80iter/s]
Best score: 19.1420: : 213iter [00:07, 39.80iter/s]
Best score: 19.1420: : 214iter [00:07, 39.80iter/s]
Best score: 19.1420: : 215iter [00:07, 39.80iter/s]
Best score: 19.1420: : 216iter [00:07, 41.12iter/s]
Best score: 19.1420: : 216iter [00:07, 41.12iter/s]
Best score: 19.1420: : 217iter [00:07, 41.12iter/s]
Best score: 19.1420: : 218iter [00:07, 41.12iter/s]
Best score: 19.1420: : 219iter [00:07, 41.12iter/s]
Best score: 19.1420: : 220iter [00:07, 41.12iter/s]
Best score: 19.1420: : 221iter [00:07, 41.79iter/s]
Best score: 19.1420: : 221iter [00:07, 41.79iter/s]
Best score: 19.1420: : 222iter [00:07, 41.79iter/s]
Best score: 19.1420: : 223iter [00:07, 41.79iter/s]
Best score: 19.1420: : 224iter [00:07, 41.79iter/s]
Best score: 19.1420: : 225iter [00:07, 41.79iter/s]
Best score: 19.1420: : 226iter [00:07, 42.49iter/s]
Best score: 19.1420: : 226iter [00:07, 42.49iter/s]
Best score: 19.1420: : 227iter [00:07, 42.49iter/s]
Best score: 19.1420: : 228iter [00:07, 42.49iter/s]
Best score: 19.1420: : 229iter [00:07, 42.49iter/s]
Best score: 19.1420: : 230iter [00:07, 42.49iter/s]
Best score: 19.1420: : 231iter [00:07, 42.83iter/s]
Best score: 19.1420: : 231iter [00:07, 42.83iter/s]
Best score: 19.1420: : 232iter [00:07, 42.83iter/s]
Best score: 19.1420: : 233iter [00:07, 42.83iter/s]
Best score: 19.1420: : 234iter [00:07, 42.83iter/s]
Best score: 19.1420: : 235iter [00:07, 42.83iter/s]
Best score: 19.1420: : 236iter [00:07, 43.34iter/s]
Best score: 19.1420: : 236iter [00:07, 43.34iter/s]
Best score: 19.1420: : 237iter [00:07, 43.34iter/s]
Best score: 19.1420: : 238iter [00:07, 43.34iter/s]
Best score: 15.1612: : 239iter [00:07, 43.34iter/s]
Best score: 15.1612: : 240iter [00:07, 43.34iter/s]
Best score: 15.1612: : 241iter [00:07, 43.28iter/s]
Best score: 15.1612: : 241iter [00:07, 43.28iter/s]
Best score: 15.1612: : 242iter [00:07, 43.28iter/s]
Best score: 15.1612: : 243iter [00:07, 43.28iter/s]
Best score: 15.1612: : 244iter [00:07, 43.28iter/s]
Best score: 15.1612: : 245iter [00:07, 43.28iter/s]
Best score: 15.1612: : 246iter [00:07, 43.67iter/s]
Best score: 15.1612: : 246iter [00:07, 43.67iter/s]
Best score: 15.1612: : 247iter [00:07, 43.67iter/s]
Best score: 15.1612: : 248iter [00:07, 43.67iter/s]
[ITER 250] Best: 8.1746
[PARAMS] Current best parameters:
  fx: 521.9892
  fy: 520.8863
  px: 1021.0214
  py: 746.5061
  k1: 0.05473381
  k2: -0.03851300
  k3: 0.00565787
  k4: 0.00120968
  p1: -0.01006246
  p2: 0.00606799
  p3: -0.01072458
  p4: 0.00998217
  s1: -0.0184
  s2: 0.0035
  s3: 0.0004
  s4: -0.0028
  tilt_x: -0.0206
  tilt_y: 0.0047
  scale: 2.9549
  rotation: 0.0383

Best score: 8.1746: : 249iter [00:07, 43.67iter/s] 
Best score: 8.1746: : 250iter [00:07, 43.67iter/s]
Best score: 8.1746: : 251iter [00:07, 36.83iter/s]
Best score: 8.1746: : 251iter [00:07, 36.83iter/s]
Best score: 8.1746: : 252iter [00:07, 36.83iter/s]
Best score: 8.1746: : 253iter [00:07, 36.83iter/s]
Best score: 8.1746: : 254iter [00:08, 36.83iter/s]
Best score: 8.1746: : 255iter [00:08, 36.83iter/s]
Best score: 8.1746: : 256iter [00:08, 38.92iter/s]
Best score: 8.1746: : 256iter [00:08, 38.92iter/s]
Best score: 8.1746: : 257iter [00:08, 38.92iter/s]
Best score: 8.1746: : 258iter [00:08, 38.92iter/s]
Best score: 8.1746: : 259iter [00:08, 38.92iter/s]
Best score: 8.1746: : 260iter [00:08, 38.92iter/s]
Best score: 8.1746: : 261iter [00:08, 40.16iter/s]
Best score: 8.1746: : 261iter [00:08, 40.16iter/s]
Best score: 8.1746: : 262iter [00:08, 40.16iter/s]
Best score: 8.1746: : 263iter [00:08, 40.16iter/s]
Best score: 8.1746: : 264iter [00:08, 40.16iter/s]
Best score: 8.1746: : 265iter [00:08, 40.16iter/s]
Best score: 8.1746: : 266iter [00:08, 41.36iter/s]
Best score: 8.1746: : 266iter [00:08, 41.36iter/s]
Best score: 8.1746: : 267iter [00:08, 41.36iter/s]
Best score: 8.1746: : 268iter [00:08, 41.36iter/s]
Best score: 4.8224: : 269iter [00:08, 41.36iter/s]
Best score: 4.8224: : 270iter [00:08, 41.36iter/s]
Best score: 4.8224: : 271iter [00:08, 42.01iter/s]
Best score: 4.8224: : 271iter [00:08, 42.01iter/s]
Best score: 4.8224: : 272iter [00:08, 42.01iter/s]
Best score: 4.8224: : 273iter [00:08, 42.01iter/s]
Best score: 4.8224: : 274iter [00:08, 42.01iter/s]
Best score: 4.8224: : 275iter [00:08, 42.01iter/s]
Best score: 4.8224: : 276iter [00:08, 42.64iter/s]
Best score: 4.8224: : 276iter [00:08, 42.64iter/s]
Best score: 4.8224: : 277iter [00:08, 42.64iter/s]
Best score: 4.8224: : 278iter [00:08, 42.64iter/s]
Best score: 3.7228: : 279iter [00:08, 42.64iter/s]
Best score: 3.7228: : 280iter [00:08, 42.64iter/s]
Best score: 3.7228: : 281iter [00:08, 42.89iter/s]
Best score: 3.7228: : 281iter [00:08, 42.89iter/s]
Best score: 3.7228: : 282iter [00:08, 42.89iter/s]
Best score: 3.7228: : 283iter [00:08, 42.89iter/s]
Best score: 3.7228: : 284iter [00:08, 42.89iter/s]
Best score: 3.7228: : 285iter [00:08, 42.89iter/s]
Best score: 3.7228: : 286iter [00:08, 43.31iter/s]
Best score: 3.7228: : 286iter [00:08, 43.31iter/s]
Best score: 3.7228: : 287iter [00:08, 43.31iter/s]
Best score: 3.7228: : 288iter [00:08, 43.31iter/s]
Best score: 2.6202: : 289iter [00:08, 43.31iter/s]
Best score: 2.6202: : 290iter [00:08, 43.31iter/s]
Best score: 2.6202: : 291iter [00:08, 43.27iter/s]
Best score: 2.6202: : 291iter [00:08, 43.27iter/s]
Best score: 2.6202: : 292iter [00:08, 43.27iter/s]
Best score: 2.6202: : 293iter [00:08, 43.27iter/s]
Best score: 2.6202: : 294iter [00:08, 43.27iter/s]
Best score: 2.6202: : 295iter [00:08, 43.27iter/s]
Best score: 2.6202: : 296iter [00:08, 43.57iter/s]
Best score: 2.6202: : 296iter [00:08, 43.57iter/s]
Best score: 2.6202: : 297iter [00:08, 43.57iter/s]
Best score: 2.6202: : 298iter [00:09, 43.57iter/s]
[ITER 300] Best: 2.3217
[PARAMS] Current best parameters:
  fx: 528.9720
  fy: 528.1115
  px: 1019.8708
  py: 743.5059
  k1: 0.03880635
  k2: -0.03149786
  k3: 0.01049006
  k4: -0.00201418
  p1: -0.00991094
  p2: -0.00010931
  p3: -0.00912752
  p4: 0.01165587
  s1: -0.0004
  s2: -0.0059
  s3: -0.0019
  s4: -0.0069
  tilt_x: -0.0348
  tilt_y: 0.0018
  scale: 2.9286
  rotation: -0.0698

Best score: 2.3217: : 299iter [00:09, 43.57iter/s]
Best score: 2.3217: : 300iter [00:09, 43.57iter/s]
Best score: 2.3217: : 301iter [00:09, 36.72iter/s]
Best score: 2.3217: : 301iter [00:09, 36.72iter/s]
Best score: 2.3217: : 302iter [00:09, 36.72iter/s]
Best score: 2.3217: : 303iter [00:09, 36.72iter/s]
Best score: 2.3217: : 304iter [00:09, 36.72iter/s]
Best score: 2.3217: : 305iter [00:09, 36.72iter/s]
Best score: 2.3217: : 306iter [00:09, 38.68iter/s]
Best score: 2.3217: : 306iter [00:09, 38.68iter/s]
Best score: 2.3217: : 307iter [00:09, 38.68iter/s]
Best score: 2.3217: : 308iter [00:09, 38.68iter/s]
Best score: 2.3217: : 309iter [00:09, 38.68iter/s]
Best score: 2.3217: : 310iter [00:09, 38.68iter/s]
Best score: 2.3217: : 311iter [00:09, 39.84iter/s]
Best score: 2.3217: : 311iter [00:09, 39.84iter/s]
Best score: 2.3217: : 312iter [00:09, 39.84iter/s]
Best score: 2.3217: : 313iter [00:09, 39.84iter/s]
Best score: 2.3217: : 314iter [00:09, 39.84iter/s]
Best score: 2.3217: : 315iter [00:09, 39.84iter/s]
Best score: 2.3217: : 316iter [00:09, 41.05iter/s]
Best score: 2.3217: : 316iter [00:09, 41.05iter/s]
Best score: 2.3217: : 317iter [00:09, 41.05iter/s]
Best score: 2.3217: : 318iter [00:09, 41.05iter/s]
Best score: 2.2810: : 319iter [00:09, 41.05iter/s]
Best score: 2.2810: : 320iter [00:09, 41.05iter/s]
Best score: 2.2810: : 321iter [00:09, 41.49iter/s]
Best score: 2.2810: : 321iter [00:09, 41.49iter/s]
Best score: 2.2810: : 322iter [00:09, 41.49iter/s]
Best score: 2.2810: : 323iter [00:09, 41.49iter/s]
Best score: 2.2810: : 324iter [00:09, 41.49iter/s]
Best score: 2.2810: : 325iter [00:09, 41.49iter/s]
Best score: 2.2810: : 326iter [00:09, 42.31iter/s]
Best score: 2.2810: : 326iter [00:09, 42.31iter/s]
Best score: 2.2810: : 327iter [00:09, 42.31iter/s]
Best score: 2.2810: : 328iter [00:09, 42.31iter/s]
Best score: 1.9574: : 329iter [00:09, 42.31iter/s]
Best score: 1.9574: : 330iter [00:09, 42.31iter/s]
Best score: 1.9574: : 331iter [00:09, 42.51iter/s]
Best score: 1.9574: : 331iter [00:09, 42.51iter/s]
Best score: 1.9574: : 332iter [00:09, 42.51iter/s]
Best score: 1.9574: : 333iter [00:09, 42.51iter/s]
Best score: 1.9574: : 334iter [00:09, 42.51iter/s]
Best score: 1.9574: : 335iter [00:09, 42.51iter/s]
Best score: 1.9574: : 336iter [00:09, 42.81iter/s]
Best score: 1.9574: : 336iter [00:09, 42.81iter/s]
Best score: 1.9574: : 337iter [00:09, 42.81iter/s]
Best score: 1.9574: : 338iter [00:10, 42.81iter/s]
Best score: 1.9574: : 339iter [00:10, 42.81iter/s]
Best score: 1.9574: : 340iter [00:10, 42.81iter/s]
Best score: 1.9574: : 341iter [00:10, 43.02iter/s]
Best score: 1.9574: : 341iter [00:10, 43.02iter/s]
Best score: 1.9574: : 342iter [00:10, 43.02iter/s]
Best score: 1.9574: : 343iter [00:10, 43.02iter/s]
Best score: 1.9574: : 344iter [00:10, 43.02iter/s]
Best score: 1.9574: : 345iter [00:10, 43.02iter/s]
Best score: 1.9574: : 346iter [00:10, 43.35iter/s]
Best score: 1.9574: : 346iter [00:10, 43.35iter/s]
Best score: 1.9574: : 347iter [00:10, 43.35iter/s]
Best score: 1.9574: : 348iter [00:10, 43.35iter/s]
[ITER 350] Best: 1.2392
[PARAMS] Current best parameters:
  fx: 532.9133
  fy: 532.4886
  px: 1019.3154
  py: 746.9635
  k1: 0.03825856
  k2: -0.02975886
  k3: 0.00310297
  k4: 0.00089408
  p1: -0.00565985
  p2: -0.00096615
  p3: -0.00371985
  p4: 0.00946827
  s1: -0.0039
  s2: -0.0044
  s3: 0.0017
  s4: -0.0070
  tilt_x: -0.0307
  tilt_y: 0.0058
  scale: 2.9414
  rotation: -0.0299

Best score: 1.2392: : 349iter [00:10, 43.35iter/s]
Best score: 1.2392: : 350iter [00:10, 43.35iter/s]
Best score: 1.2392: : 351iter [00:10, 36.66iter/s]
Best score: 1.2392: : 351iter [00:10, 36.66iter/s]
Best score: 1.2392: : 352iter [00:10, 36.66iter/s]
Best score: 1.2392: : 353iter [00:10, 36.66iter/s]
Best score: 1.2392: : 354iter [00:10, 36.66iter/s]
Best score: 1.2392: : 355iter [00:10, 36.66iter/s]
Best score: 1.2392: : 356iter [00:10, 38.57iter/s]
Best score: 1.2392: : 356iter [00:10, 38.57iter/s]
Best score: 1.2392: : 357iter [00:10, 38.57iter/s]
Best score: 1.2392: : 358iter [00:10, 38.57iter/s]
Best score: 1.2392: : 359iter [00:10, 38.57iter/s]
Best score: 1.2392: : 360iter [00:10, 38.57iter/s]
Best score: 1.2392: : 361iter [00:10, 39.93iter/s]
Best score: 1.2392: : 361iter [00:10, 39.93iter/s]
Best score: 1.2392: : 362iter [00:10, 39.93iter/s]
Best score: 1.2392: : 363iter [00:10, 39.93iter/s]
Best score: 1.2392: : 364iter [00:10, 39.93iter/s]
Best score: 1.2392: : 365iter [00:10, 39.93iter/s]
Best score: 1.2392: : 366iter [00:10, 41.17iter/s]
Best score: 1.2392: : 366iter [00:10, 41.17iter/s]
Best score: 1.2392: : 367iter [00:10, 41.17iter/s]
Best score: 1.2392: : 368iter [00:10, 41.17iter/s]
Best score: 1.2133: : 369iter [00:10, 41.17iter/s]
Best score: 1.2133: : 370iter [00:10, 41.17iter/s]
Best score: 1.2133: : 371iter [00:10, 41.63iter/s]
Best score: 1.2133: : 371iter [00:10, 41.63iter/s]
Best score: 1.2133: : 372iter [00:10, 41.63iter/s]
Best score: 1.2133: : 373iter [00:10, 41.63iter/s]
Best score: 1.2133: : 374iter [00:10, 41.63iter/s]
Best score: 1.2133: : 375iter [00:10, 41.63iter/s]
Best score: 1.2133: : 376iter [00:10, 42.43iter/s]
Best score: 1.2133: : 376iter [00:10, 42.43iter/s]
Best score: 1.2133: : 377iter [00:10, 42.43iter/s]
Best score: 1.2133: : 378iter [00:10, 42.43iter/s]
Best score: 1.0680: : 379iter [00:11, 42.43iter/s]
Best score: 1.0680: : 380iter [00:11, 42.43iter/s]
Best score: 1.0680: : 381iter [00:11, 42.61iter/s]
Best score: 1.0680: : 381iter [00:11, 42.61iter/s]
Best score: 1.0680: : 382iter [00:11, 42.61iter/s]
Best score: 1.0680: : 383iter [00:11, 42.61iter/s]
Best score: 1.0680: : 384iter [00:11, 42.61iter/s]
Best score: 1.0680: : 385iter [00:11, 42.61iter/s]
Best score: 1.0680: : 386iter [00:11, 43.10iter/s]
Best score: 1.0680: : 386iter [00:11, 43.10iter/s]
Best score: 1.0680: : 387iter [00:11, 43.10iter/s]
Best score: 1.0680: : 388iter [00:11, 43.10iter/s]
Best score: 1.0276: : 389iter [00:11, 43.10iter/s]
Best score: 1.0276: : 390iter [00:11, 43.10iter/s]
Best score: 1.0276: : 391iter [00:11, 35.27iter/s]
Best score: 1.0276: : 391iter [00:11, 35.27iter/s]
Best score: 1.0276: : 392iter [00:11, 35.27iter/s]
Best score: 1.0276: : 393iter [00:11, 35.27iter/s]
Best score: 1.0276: : 394iter [00:11, 35.27iter/s]
Best score: 1.0276: : 395iter [00:11, 35.27iter/s]
Best score: 1.0276: : 396iter [00:11, 37.52iter/s]
Best score: 1.0276: : 396iter [00:11, 37.52iter/s]
Best score: 1.0276: : 397iter [00:11, 37.52iter/s]
Best score: 1.0276: : 398iter [00:11, 37.52iter/s]
[ITER 400] Best: 0.8832
[PARAMS] Current best parameters:
  fx: 531.5303
  fy: 531.0270
  px: 1015.0722
  py: 753.0296
  k1: 0.01747255
  k2: -0.01772302
  k3: 0.00597514
  k4: -0.00102127
  p1: -0.00103266
  p2: -0.00742375
  p3: -0.00084856
  p4: -0.00237575
  s1: 0.0056
  s2: 0.0014
  s3: 0.0104
  s4: -0.0012
  tilt_x: -0.0075
  tilt_y: 0.0118
  scale: 3.0089
  rotation: 0.0037

Best score: 0.8832: : 399iter [00:11, 37.52iter/s]
Best score: 0.8832: : 400iter [00:11, 37.52iter/s]
Best score: 0.8832: : 401iter [00:11, 33.74iter/s]
Best score: 0.8832: : 401iter [00:11, 33.74iter/s]
Best score: 0.8832: : 402iter [00:11, 33.74iter/s]
Best score: 0.8832: : 403iter [00:11, 33.74iter/s]
Best score: 0.8832: : 404iter [00:11, 33.74iter/s]
Best score: 0.8832: : 405iter [00:11, 33.74iter/s]
Best score: 0.8832: : 406iter [00:11, 36.35iter/s]
Best score: 0.8832: : 406iter [00:11, 36.35iter/s]
Best score: 0.8832: : 407iter [00:11, 36.35iter/s]
Best score: 0.8832: : 408iter [00:11, 36.35iter/s]
Best score: 0.8036: : 409iter [00:11, 36.35iter/s]
Best score: 0.8036: : 410iter [00:11, 36.35iter/s]
Best score: 0.8036: : 411iter [00:11, 38.26iter/s]
Best score: 0.8036: : 411iter [00:11, 38.26iter/s]
Best score: 0.8036: : 412iter [00:11, 38.26iter/s]
Best score: 0.8036: : 413iter [00:11, 38.26iter/s]
Best score: 0.8036: : 414iter [00:11, 38.26iter/s]
Best score: 0.8036: : 415iter [00:11, 38.26iter/s]
Best score: 0.8036: : 416iter [00:11, 40.01iter/s]
Best score: 0.8036: : 416iter [00:12, 40.01iter/s]
Best score: 0.8036: : 417iter [00:12, 40.01iter/s]
Best score: 0.8036: : 418iter [00:12, 40.01iter/s]
Best score: 0.6735: : 419iter [00:12, 40.01iter/s]
Best score: 0.6735: : 420iter [00:12, 40.01iter/s]
Best score: 0.6735: : 421iter [00:12, 40.98iter/s]
Best score: 0.6735: : 421iter [00:12, 40.98iter/s]
Best score: 0.6735: : 422iter [00:12, 40.98iter/s]
Best score: 0.6735: : 423iter [00:12, 40.98iter/s]
Best score: 0.6735: : 424iter [00:12, 40.98iter/s]
Best score: 0.6735: : 425iter [00:12, 40.98iter/s]
Best score: 0.6735: : 426iter [00:12, 42.01iter/s]
Best score: 0.6735: : 426iter [00:12, 42.01iter/s]
Best score: 0.6735: : 427iter [00:12, 42.01iter/s]
Best score: 0.6735: : 428iter [00:12, 42.01iter/s]
Best score: 0.6735: : 429iter [00:12, 42.01iter/s]
Best score: 0.6735: : 430iter [00:12, 42.01iter/s]
Best score: 0.6735: : 431iter [00:12, 42.53iter/s]
Best score: 0.6735: : 431iter [00:12, 42.53iter/s]
Best score: 0.6735: : 432iter [00:12, 42.53iter/s]
Best score: 0.6735: : 433iter [00:12, 42.53iter/s]
Best score: 0.6735: : 434iter [00:12, 42.53iter/s]
Best score: 0.6735: : 435iter [00:12, 42.53iter/s]
Best score: 0.6735: : 436iter [00:12, 43.01iter/s]
Best score: 0.6735: : 436iter [00:12, 43.01iter/s]
Best score: 0.6735: : 437iter [00:12, 43.01iter/s]
Best score: 0.6735: : 438iter [00:12, 43.01iter/s]
Best score: 0.6586: : 439iter [00:12, 43.01iter/s]
Best score: 0.6586: : 440iter [00:12, 43.01iter/s]
Best score: 0.6586: : 441iter [00:12, 43.22iter/s]
Best score: 0.6586: : 441iter [00:12, 43.22iter/s]
Best score: 0.6586: : 442iter [00:12, 43.22iter/s]
Best score: 0.6586: : 443iter [00:12, 43.22iter/s]
Best score: 0.6586: : 444iter [00:12, 43.22iter/s]
Best score: 0.6586: : 445iter [00:12, 43.22iter/s]
Best score: 0.6586: : 446iter [00:12, 43.78iter/s]
Best score: 0.6586: : 446iter [00:12, 43.78iter/s]
Best score: 0.6586: : 447iter [00:12, 43.78iter/s]
Best score: 0.6586: : 448iter [00:12, 43.78iter/s]
[ITER 450] Best: 0.6586
[PARAMS] Current best parameters:
  fx: 531.8760
  fy: 531.1526
  px: 1017.0055
  py: 749.2933
  k1: 0.02243583
  k2: -0.02589970
  k3: 0.01151239
  k4: -0.00233639
  p1: 0.00013180
  p2: -0.00539348
  p3: -0.01301100
  p4: 0.00499306
  s1: 0.0138
  s2: -0.0032
  s3: 0.0163
  s4: -0.0075
  tilt_x: -0.0295
  tilt_y: 0.0084
  scale: 2.9940
  rotation: -0.0346

Best score: 0.6586: : 449iter [00:12, 43.78iter/s]
[Periodic outlier @ iter 350] Removing 1000 frame 6 (score=3.77, threshold=2.09)
  Remaining frames: 17

Best score: 0.6586: : 450iter [00:12, 43.78iter/s]
Best score: 0.6586: : 451iter [00:12, 34.49iter/s]
Best score: 0.6586: : 451iter [00:12, 34.49iter/s]
Best score: 0.6586: : 452iter [00:12, 34.49iter/s]
Best score: 0.6586: : 453iter [00:12, 34.49iter/s]
Best score: 0.6586: : 454iter [00:12, 34.49iter/s]
Best score: 0.6586: : 455iter [00:13, 34.49iter/s]
Best score: 0.6586: : 456iter [00:13, 37.23iter/s]
Best score: 0.6586: : 456iter [00:13, 37.23iter/s]
Best score: 0.6586: : 457iter [00:13, 37.23iter/s]
Best score: 0.6586: : 458iter [00:13, 37.23iter/s]
Best score: 0.4756: : 459iter [00:13, 37.23iter/s]
Best score: 0.4756: : 460iter [00:13, 37.23iter/s]
Best score: 0.4756: : 461iter [00:13, 39.23iter/s]
Best score: 0.4756: : 461iter [00:13, 39.23iter/s]
Best score: 0.4756: : 462iter [00:13, 39.23iter/s]
Best score: 0.4756: : 463iter [00:13, 39.23iter/s]
Best score: 0.4756: : 464iter [00:13, 39.23iter/s]
Best score: 0.4756: : 465iter [00:13, 39.23iter/s]
Best score: 0.4756: : 466iter [00:13, 40.99iter/s]
Best score: 0.4756: : 466iter [00:13, 40.99iter/s]
Best score: 0.4756: : 467iter [00:13, 40.99iter/s]
Best score: 0.4756: : 468iter [00:13, 40.99iter/s]
Best score: 0.4756: : 469iter [00:13, 40.99iter/s]
Best score: 0.4756: : 470iter [00:13, 40.99iter/s]
Best score: 0.4756: : 471iter [00:13, 42.04iter/s]
Best score: 0.4756: : 471iter [00:13, 42.04iter/s]
Best score: 0.4756: : 472iter [00:13, 42.04iter/s]
Best score: 0.4756: : 473iter [00:13, 42.04iter/s]
Best score: 0.4756: : 474iter [00:13, 42.04iter/s]
Best score: 0.4756: : 475iter [00:13, 42.04iter/s]
Best score: 0.4756: : 476iter [00:13, 43.17iter/s]
Best score: 0.4756: : 476iter [00:13, 43.17iter/s]
Best score: 0.4756: : 477iter [00:13, 43.17iter/s]
Best score: 0.4756: : 478iter [00:13, 43.17iter/s]
Best score: 0.4756: : 479iter [00:13, 43.17iter/s]
Best score: 0.4756: : 480iter [00:13, 43.17iter/s]
Best score: 0.4756: : 481iter [00:13, 43.77iter/s]
Best score: 0.4756: : 481iter [00:13, 43.77iter/s]
Best score: 0.4756: : 482iter [00:13, 43.77iter/s]
Best score: 0.4756: : 483iter [00:13, 43.77iter/s]
Best score: 0.4756: : 484iter [00:13, 43.77iter/s]
Best score: 0.4756: : 485iter [00:13, 43.77iter/s]
Best score: 0.4756: : 486iter [00:13, 44.48iter/s]
Best score: 0.4756: : 486iter [00:13, 44.48iter/s]
Best score: 0.4756: : 487iter [00:13, 44.48iter/s]
Best score: 0.4756: : 488iter [00:13, 44.48iter/s]
Best score: 0.4756: : 489iter [00:13, 44.48iter/s]
Best score: 0.4756: : 490iter [00:13, 44.48iter/s]
Best score: 0.4756: : 491iter [00:13, 44.57iter/s]
Best score: 0.4756: : 491iter [00:13, 44.57iter/s]
Best score: 0.4756: : 492iter [00:13, 44.57iter/s]
Best score: 0.4756: : 493iter [00:13, 44.57iter/s]
Best score: 0.4756: : 494iter [00:13, 44.57iter/s]
Best score: 0.4756: : 495iter [00:13, 44.57iter/s]
Best score: 0.4756: : 496iter [00:13, 44.81iter/s]
Best score: 0.4756: : 496iter [00:13, 44.81iter/s]
Best score: 0.4756: : 497iter [00:13, 44.81iter/s]
Best score: 0.4756: : 498iter [00:13, 44.81iter/s]
[ITER 500] Best: 0.4756
[PARAMS] Current best parameters:
  fx: 531.8760
  fy: 531.1526
  px: 1017.0055
  py: 749.2933
  k1: 0.02243583
  k2: -0.02589970
  k3: 0.01151239
  k4: -0.00233639
  p1: 0.00013180
  p2: -0.00539348
  p3: -0.01301100
  p4: 0.00499306
  s1: 0.0138
  s2: -0.0032
  s3: 0.0163
  s4: -0.0075
  tilt_x: -0.0295
  tilt_y: 0.0084
  scale: 2.9940
  rotation: -0.0346

Best score: 0.4756: : 499iter [00:13, 44.81iter/s]
[Periodic outlier @ iter 400] Removing 1000 frame 17 (score=2.04, threshold=1.86)
  Remaining frames: 16

Best score: 0.4756: : 500iter [00:14, 44.81iter/s]
Best score: 0.4756: : 501iter [00:14, 35.76iter/s]
Best score: 0.4756: : 501iter [00:14, 35.76iter/s]
Best score: 0.4756: : 502iter [00:14, 35.76iter/s]
Best score: 0.4756: : 503iter [00:14, 35.76iter/s]
Best score: 0.4756: : 504iter [00:14, 35.76iter/s]
Best score: 0.4756: : 505iter [00:14, 35.76iter/s]
Best score: 0.4756: : 506iter [00:14, 38.74iter/s]
Best score: 0.4756: : 506iter [00:14, 38.74iter/s]
Best score: 0.4756: : 507iter [00:14, 38.74iter/s]
Best score: 0.4756: : 508iter [00:14, 38.74iter/s]
Best score: 0.3785: : 509iter [00:14, 38.74iter/s]
Best score: 0.3785: : 510iter [00:14, 38.74iter/s]
Best score: 0.3785: : 511iter [00:14, 40.93iter/s]
Best score: 0.3785: : 511iter [00:14, 40.93iter/s]
Best score: 0.3785: : 512iter [00:14, 40.93iter/s]
Best score: 0.3785: : 513iter [00:14, 40.93iter/s]
Best score: 0.3785: : 514iter [00:14, 40.93iter/s]
Best score: 0.3785: : 515iter [00:14, 40.93iter/s]
Best score: 0.3785: : 516iter [00:14, 42.84iter/s]
Best score: 0.3785: : 516iter [00:14, 42.84iter/s]
Best score: 0.3785: : 517iter [00:14, 42.84iter/s]
Best score: 0.3785: : 518iter [00:14, 42.84iter/s]
Best score: 0.3785: : 519iter [00:14, 42.84iter/s]
Best score: 0.3785: : 520iter [00:14, 42.84iter/s]
Best score: 0.3785: : 521iter [00:14, 44.17iter/s]
Best score: 0.3785: : 521iter [00:14, 44.17iter/s]
Best score: 0.3785: : 522iter [00:14, 44.17iter/s]
Best score: 0.3785: : 523iter [00:14, 44.17iter/s]
Best score: 0.3785: : 524iter [00:14, 44.17iter/s]
Best score: 0.3785: : 525iter [00:14, 44.17iter/s]
Best score: 0.3785: : 526iter [00:14, 45.39iter/s]
Best score: 0.3785: : 526iter [00:14, 45.39iter/s]
Best score: 0.3785: : 527iter [00:14, 45.39iter/s]
Best score: 0.3785: : 528iter [00:14, 45.39iter/s]
Best score: 0.3785: : 529iter [00:14, 45.39iter/s]
Best score: 0.3785: : 530iter [00:14, 45.39iter/s]
Best score: 0.3785: : 531iter [00:14, 45.78iter/s]
Best score: 0.3785: : 531iter [00:14, 45.78iter/s]
Best score: 0.3785: : 532iter [00:14, 45.78iter/s]
Best score: 0.3785: : 533iter [00:14, 45.78iter/s]
Best score: 0.3785: : 534iter [00:14, 45.78iter/s]
Best score: 0.3785: : 535iter [00:14, 45.78iter/s]
Best score: 0.3785: : 536iter [00:14, 46.49iter/s]
Best score: 0.3785: : 536iter [00:14, 46.49iter/s]
Best score: 0.3785: : 537iter [00:14, 46.49iter/s]
Best score: 0.3785: : 538iter [00:14, 46.49iter/s]
Best score: 0.3785: : 539iter [00:14, 46.49iter/s]
Best score: 0.3785: : 540iter [00:14, 46.49iter/s]
Best score: 0.3785: : 541iter [00:14, 46.77iter/s]
Best score: 0.3785: : 541iter [00:14, 46.77iter/s]
Best score: 0.3785: : 542iter [00:14, 46.77iter/s]
Best score: 0.3785: : 543iter [00:14, 46.77iter/s]
Best score: 0.3785: : 544iter [00:15, 46.77iter/s]
Best score: 0.3785: : 545iter [00:15, 46.77iter/s]
Best score: 0.3785: : 546iter [00:15, 47.13iter/s]
Best score: 0.3785: : 546iter [00:15, 47.13iter/s]
Best score: 0.3785: : 547iter [00:15, 47.13iter/s]
Best score: 0.3785: : 548iter [00:15, 47.13iter/s]
[ITER 550] Best: 0.3785
[PARAMS] Current best parameters:
  fx: 531.8760
  fy: 531.1526
  px: 1017.0055
  py: 749.2933
  k1: 0.02243583
  k2: -0.02589970
  k3: 0.01151239
  k4: -0.00233639
  p1: 0.00013180
  p2: -0.00539348
  p3: -0.01301100
  p4: 0.00499306
  s1: 0.0138
  s2: -0.0032
  s3: 0.0163
  s4: -0.0075
  tilt_x: -0.0295
  tilt_y: 0.0084
  scale: 2.9940
  rotation: -0.0346

Best score: 0.3785: : 549iter [00:15, 47.13iter/s]
Best score: 0.3785: : 550iter [00:15, 47.13iter/s]
Best score: 0.3785: : 551iter [00:15, 39.81iter/s]
Best score: 0.3785: : 551iter [00:15, 39.81iter/s]
Best score: 0.3785: : 552iter [00:15, 39.81iter/s]
Best score: 0.3785: : 553iter [00:15, 39.81iter/s]
Best score: 0.3785: : 554iter [00:15, 39.81iter/s]
Best score: 0.3785: : 555iter [00:15, 39.81iter/s]
Best score: 0.3785: : 556iter [00:15, 42.06iter/s]
Best score: 0.3785: : 556iter [00:15, 42.06iter/s]
Best score: 0.3785: : 557iter [00:15, 42.06iter/s]
Best score: 0.3785: : 558iter [00:15, 42.06iter/s]
Best score: 0.3785: : 559iter [00:15, 42.06iter/s]
Best score: 0.3785: : 560iter [00:15, 42.06iter/s]
Best score: 0.3785: : 561iter [00:15, 43.60iter/s]
Best score: 0.3785: : 561iter [00:15, 43.60iter/s]
Best score: 0.3785: : 562iter [00:15, 43.60iter/s]
Best score: 0.3785: : 563iter [00:15, 43.60iter/s]
Best score: 0.3785: : 564iter [00:15, 43.60iter/s]
Best score: 0.3785: : 565iter [00:15, 43.60iter/s]
Best score: 0.3785: : 566iter [00:15, 44.88iter/s]
Best score: 0.3785: : 566iter [00:15, 44.88iter/s]
Best score: 0.3785: : 567iter [00:15, 44.88iter/s]
Best score: 0.3785: : 568iter [00:15, 44.88iter/s]
Best score: 0.3785: : 569iter [00:15, 44.88iter/s]
Best score: 0.3785: : 570iter [00:15, 44.88iter/s]
Best score: 0.3785: : 571iter [00:15, 45.68iter/s]
Best score: 0.3785: : 571iter [00:15, 45.68iter/s]
Best score: 0.3785: : 572iter [00:15, 45.68iter/s]
Best score: 0.3785: : 573iter [00:15, 45.68iter/s]
Best score: 0.3785: : 574iter [00:15, 45.68iter/s]
Best score: 0.3785: : 575iter [00:15, 45.68iter/s]
Best score: 0.3785: : 576iter [00:15, 46.52iter/s]
Best score: 0.3785: : 576iter [00:15, 46.52iter/s]
Best score: 0.3785: : 577iter [00:15, 46.52iter/s]
Best score: 0.3785: : 578iter [00:15, 46.52iter/s]
Best score: 0.3785: : 579iter [00:15, 46.52iter/s]
Best score: 0.3785: : 580iter [00:15, 46.52iter/s]
Best score: 0.3785: : 581iter [00:15, 46.96iter/s]
Best score: 0.3785: : 581iter [00:15, 46.96iter/s]
Best score: 0.3785: : 582iter [00:15, 46.96iter/s]
Best score: 0.3785: : 583iter [00:15, 46.96iter/s]
Best score: 0.3785: : 584iter [00:15, 46.96iter/s]
Best score: 0.3785: : 585iter [00:15, 46.96iter/s]
Best score: 0.3785: : 586iter [00:15, 47.49iter/s]
Best score: 0.3785: : 586iter [00:15, 47.49iter/s]
Best score: 0.3785: : 587iter [00:15, 47.49iter/s]
Best score: 0.3785: : 588iter [00:15, 47.49iter/s]
Best score: 0.3785: : 589iter [00:16, 47.49iter/s]
Best score: 0.3785: : 590iter [00:16, 47.49iter/s]
Best score: 0.3785: : 591iter [00:16, 47.58iter/s]
Best score: 0.3785: : 591iter [00:16, 47.58iter/s]
Best score: 0.3785: : 592iter [00:16, 47.58iter/s]
Best score: 0.3785: : 593iter [00:16, 47.58iter/s]
Best score: 0.3785: : 594iter [00:16, 47.58iter/s]
Best score: 0.3785: : 595iter [00:16, 47.58iter/s]
Best score: 0.3785: : 596iter [00:16, 47.90iter/s]
Best score: 0.3785: : 596iter [00:16, 47.90iter/s]
Best score: 0.3785: : 597iter [00:16, 47.90iter/s]
Best score: 0.3785: : 598iter [00:16, 47.90iter/s]
[ITER 600] Best: 0.3785
[PARAMS] Current best parameters:
  fx: 531.8760
  fy: 531.1526
  px: 1017.0055
  py: 749.2933
  k1: 0.02243583
  k2: -0.02589970
  k3: 0.01151239
  k4: -0.00233639
  p1: 0.00013180
  p2: -0.00539348
  p3: -0.01301100
  p4: 0.00499306
  s1: 0.0138
  s2: -0.0032
  s3: 0.0163
  s4: -0.0075
  tilt_x: -0.0295
  tilt_y: 0.0084
  scale: 2.9940
  rotation: -0.0346

Best score: 0.3785: : 599iter [00:16, 47.90iter/s]
Best score: 0.3785: : 600iter [00:16, 47.90iter/s]
Best score: 0.3785: : 601iter [00:16, 40.36iter/s]
Best score: 0.3785: : 601iter [00:16, 40.36iter/s]
Best score: 0.3785: : 602iter [00:16, 40.36iter/s]
Best score: 0.3785: : 603iter [00:16, 40.36iter/s]
Best score: 0.3785: : 604iter [00:16, 40.36iter/s]
Best score: 0.3785: : 605iter [00:16, 40.36iter/s]
Best score: 0.3785: : 606iter [00:16, 42.56iter/s]
Best score: 0.3785: : 606iter [00:16, 42.56iter/s]
Best score: 0.3785: : 607iter [00:16, 42.56iter/s]
Best score: 0.3785: : 608iter [00:16, 42.56iter/s]
Best score: 0.3785: : 609iter [00:16, 42.56iter/s]
Best score: 0.3785: : 610iter [00:16, 42.56iter/s]
Best score: 0.3785: : 611iter [00:16, 43.98iter/s]
Best score: 0.3785: : 611iter [00:16, 43.98iter/s]
Best score: 0.3785: : 612iter [00:16, 43.98iter/s]
Best score: 0.3785: : 613iter [00:16, 43.98iter/s]
Best score: 0.3785: : 614iter [00:16, 43.98iter/s]
Best score: 0.3785: : 615iter [00:16, 43.98iter/s]
Best score: 0.3785: : 616iter [00:16, 45.41iter/s]
Best score: 0.3785: : 616iter [00:16, 45.41iter/s]
Best score: 0.3785: : 617iter [00:16, 45.41iter/s]
Best score: 0.3785: : 618iter [00:16, 45.41iter/s]
Best score: 0.3785: : 619iter [00:16, 45.41iter/s]
Best score: 0.3785: : 620iter [00:16, 45.41iter/s]
Best score: 0.3785: : 621iter [00:16, 46.21iter/s]
Best score: 0.3785: : 621iter [00:16, 46.21iter/s]
Best score: 0.3785: : 622iter [00:16, 46.21iter/s]
Best score: 0.3785: : 623iter [00:16, 46.21iter/s]
Best score: 0.3785: : 624iter [00:16, 46.21iter/s]
Best score: 0.3785: : 625iter [00:16, 46.21iter/s]
Best score: 0.3785: : 626iter [00:16, 46.84iter/s]
Best score: 0.3785: : 626iter [00:16, 46.84iter/s]
Best score: 0.3785: : 627iter [00:16, 46.84iter/s]
Best score: 0.3785: : 628iter [00:16, 46.84iter/s]
Best score: 0.3785: : 629iter [00:16, 46.84iter/s]
Best score: 0.3785: : 630iter [00:16, 46.84iter/s]
Best score: 0.3785: : 631iter [00:16, 47.14iter/s]
Best score: 0.3785: : 631iter [00:16, 47.14iter/s]
Best score: 0.3785: : 632iter [00:16, 47.14iter/s]
Best score: 0.3785: : 633iter [00:16, 47.14iter/s]
Best score: 0.3785: : 634iter [00:17, 47.14iter/s]
Best score: 0.3785: : 635iter [00:17, 47.14iter/s]
Best score: 0.3785: : 636iter [00:17, 47.74iter/s]
Best score: 0.3785: : 636iter [00:17, 47.74iter/s]
Best score: 0.3785: : 637iter [00:17, 47.74iter/s]
Best score: 0.3785: : 638iter [00:17, 47.74iter/s]
Best score: 0.3785: : 639iter [00:17, 47.74iter/s]
Best score: 0.3785: : 640iter [00:17, 47.74iter/s]
Best score: 0.3785: : 641iter [00:17, 47.70iter/s]
Best score: 0.3785: : 641iter [00:17, 47.70iter/s]
Best score: 0.3785: : 642iter [00:17, 47.70iter/s]
Best score: 0.3785: : 643iter [00:17, 47.70iter/s]
Best score: 0.3785: : 644iter [00:17, 47.70iter/s]
Best score: 0.3785: : 645iter [00:17, 47.70iter/s]
Best score: 0.3785: : 646iter [00:17, 48.15iter/s]
Best score: 0.3785: : 646iter [00:17, 48.15iter/s]
Best score: 0.3785: : 647iter [00:17, 48.15iter/s]
Best score: 0.3785: : 648iter [00:17, 48.15iter/s]
[ITER 650] Best: 0.3785
[PARAMS] Current best parameters:
  fx: 531.8760
  fy: 531.1526
  px: 1017.0055
  py: 749.2933
  k1: 0.02243583
  k2: -0.02589970
  k3: 0.01151239
  k4: -0.00233639
  p1: 0.00013180
  p2: -0.00539348
  p3: -0.01301100
  p4: 0.00499306
  s1: 0.0138
  s2: -0.0032
  s3: 0.0163
  s4: -0.0075
  tilt_x: -0.0295
  tilt_y: 0.0084
  scale: 2.9940
  rotation: -0.0346

Best score: 0.3785: : 649iter [00:17, 48.15iter/s]
Best score: 0.3785: : 650iter [00:17, 48.15iter/s]
Best score: 0.3785: : 651iter [00:17, 40.50iter/s]
Best score: 0.3785: : 651iter [00:17, 40.50iter/s]
Best score: 0.3785: : 652iter [00:17, 40.50iter/s]
Best score: 0.3785: : 653iter [00:17, 40.50iter/s]
Best score: 0.3785: : 654iter [00:17, 40.50iter/s]
Best score: 0.3785: : 655iter [00:17, 40.50iter/s]
Best score: 0.3785: : 656iter [00:17, 42.68iter/s]
Best score: 0.3785: : 656iter [00:17, 42.68iter/s]
Best score: 0.3785: : 657iter [00:17, 42.68iter/s]
Best score: 0.3785: : 658iter [00:17, 42.68iter/s]
Best score: 0.3785: : 659iter [00:17, 42.68iter/s]
Best score: 0.3785: : 660iter [00:17, 42.68iter/s]
Best score: 0.3785: : 661iter [00:17, 44.16iter/s]
Best score: 0.3785: : 661iter [00:17, 44.16iter/s]
Best score: 0.3785: : 662iter [00:17, 44.16iter/s]
Best score: 0.3785: : 663iter [00:17, 44.16iter/s]
Best score: 0.3785: : 664iter [00:17, 44.16iter/s]
Best score: 0.3785: : 665iter [00:17, 44.16iter/s]
Best score: 0.3785: : 666iter [00:17, 45.49iter/s]
Best score: 0.3785: : 666iter [00:17, 45.49iter/s]
Best score: 0.3785: : 667iter [00:17, 45.49iter/s]
Best score: 0.3785: : 668iter [00:17, 45.49iter/s]
Best score: 0.3785: : 669iter [00:17, 45.49iter/s]
Best score: 0.3785: : 670iter [00:17, 45.49iter/s]
Best score: 0.3785: : 671iter [00:17, 46.20iter/s]
Best score: 0.3785: : 671iter [00:17, 46.20iter/s]
Best score: 0.3785: : 672iter [00:17, 46.20iter/s]
Best score: 0.3785: : 673iter [00:17, 46.20iter/s]
Best score: 0.3785: : 674iter [00:17, 46.20iter/s]
Best score: 0.3785: : 675iter [00:17, 46.20iter/s]
Best score: 0.3785: : 676iter [00:17, 47.00iter/s]
Best score: 0.3785: : 676iter [00:17, 47.00iter/s]
Best score: 0.3785: : 677iter [00:17, 47.00iter/s]
Best score: 0.3785: : 678iter [00:17, 47.00iter/s]
Best score: 0.3785: : 679iter [00:18, 47.00iter/s]
Best score: 0.3785: : 680iter [00:18, 47.00iter/s]
Best score: 0.3785: : 681iter [00:18, 47.24iter/s]
Best score: 0.3785: : 681iter [00:18, 47.24iter/s]
Best score: 0.3785: : 682iter [00:18, 47.24iter/s]
Best score: 0.3785: : 683iter [00:18, 47.24iter/s]
Best score: 0.3785: : 684iter [00:18, 47.24iter/s]
Best score: 0.3785: : 685iter [00:18, 47.24iter/s]
Best score: 0.3785: : 686iter [00:18, 47.70iter/s]
Best score: 0.3785: : 686iter [00:18, 47.70iter/s]
Best score: 0.3785: : 687iter [00:18, 47.70iter/s]
Best score: 0.3785: : 688iter [00:18, 47.70iter/s]
Best score: 0.3785: : 689iter [00:18, 47.70iter/s]
Best score: 0.3785: : 690iter [00:18, 47.70iter/s]
Best score: 0.3785: : 691iter [00:18, 47.75iter/s]
Best score: 0.3785: : 691iter [00:18, 47.75iter/s]
Best score: 0.3785: : 692iter [00:18, 47.75iter/s]
Best score: 0.3785: : 693iter [00:18, 47.75iter/s]
Best score: 0.3785: : 694iter [00:18, 47.75iter/s]
Best score: 0.3785: : 695iter [00:18, 47.75iter/s]
Best score: 0.3785: : 696iter [00:18, 47.92iter/s]
Best score: 0.3785: : 696iter [00:18, 47.92iter/s]
Best score: 0.3785: : 697iter [00:18, 47.92iter/s]
Best score: 0.3785: : 698iter [00:18, 47.92iter/s]
[ITER 700] Best: 0.3785
[PARAMS] Current best parameters:
  fx: 531.8760
  fy: 531.1526
  px: 1017.0055
  py: 749.2933
  k1: 0.02243583
  k2: -0.02589970
  k3: 0.01151239
  k4: -0.00233639
  p1: 0.00013180
  p2: -0.00539348
  p3: -0.01301100
  p4: 0.00499306
  s1: 0.0138
  s2: -0.0032
  s3: 0.0163
  s4: -0.0075
  tilt_x: -0.0295
  tilt_y: 0.0084
  scale: 2.9940
  rotation: -0.0346

Best score: 0.3785: : 699iter [00:18, 47.92iter/s]
Best score: 0.3785: : 700iter [00:18, 47.92iter/s]
Best score: 0.3785: : 701iter [00:18, 33.35iter/s]
Best score: 0.3785: : 701iter [00:18, 33.35iter/s]
Best score: 0.3785: : 702iter [00:18, 33.35iter/s]
Best score: 0.3785: : 703iter [00:18, 33.35iter/s]
Best score: 0.3785: : 704iter [00:18, 33.35iter/s]
Best score: 0.3785: : 705iter [00:18, 33.35iter/s]
Best score: 0.3785: : 706iter [00:18, 36.85iter/s]
Best score: 0.3785: : 706iter [00:18, 36.85iter/s]
Best score: 0.3785: : 707iter [00:18, 36.85iter/s]
Best score: 0.3785: : 708iter [00:18, 36.85iter/s]
Best score: 0.3785: : 709iter [00:18, 36.85iter/s]
Best score: 0.3785: : 710iter [00:18, 36.85iter/s]
Best score: 0.3785: : 711iter [00:18, 39.63iter/s]
Best score: 0.3785: : 711iter [00:18, 39.63iter/s]
Best score: 0.3785: : 712iter [00:18, 39.63iter/s]
Best score: 0.3785: : 713iter [00:18, 39.63iter/s]
Best score: 0.3785: : 714iter [00:18, 39.63iter/s]
Best score: 0.3785: : 715iter [00:18, 39.63iter/s]
Best score: 0.3785: : 716iter [00:18, 42.05iter/s]
Best score: 0.3785: : 716iter [00:18, 42.05iter/s]
Best score: 0.3785: : 717iter [00:18, 42.05iter/s]
Best score: 0.3785: : 718iter [00:18, 42.05iter/s]
Best score: 0.3785: : 719iter [00:18, 42.05iter/s]
Best score: 0.3785: : 720iter [00:19, 42.05iter/s]
Best score: 0.3785: : 721iter [00:19, 43.57iter/s]
Best score: 0.3785: : 721iter [00:19, 43.57iter/s]
Best score: 0.3785: : 722iter [00:19, 43.57iter/s]
Best score: 0.3785: : 723iter [00:19, 43.57iter/s]
Best score: 0.3785: : 724iter [00:19, 43.57iter/s]
Best score: 0.3785: : 725iter [00:19, 43.57iter/s]
Best score: 0.3785: : 726iter [00:19, 45.09iter/s]
Best score: 0.3785: : 726iter [00:19, 45.09iter/s]
Best score: 0.3785: : 727iter [00:19, 45.09iter/s]
Best score: 0.3785: : 728iter [00:19, 45.09iter/s]
Best score: 0.3785: : 729iter [00:19, 45.09iter/s]
Best score: 0.3785: : 730iter [00:19, 45.09iter/s]
Best score: 0.3785: : 731iter [00:19, 45.94iter/s]
Best score: 0.3785: : 731iter [00:19, 45.94iter/s]
Best score: 0.3785: : 732iter [00:19, 45.94iter/s]
Best score: 0.3785: : 733iter [00:19, 45.94iter/s]
Best score: 0.3785: : 734iter [00:19, 45.94iter/s]
Best score: 0.3785: : 735iter [00:19, 45.94iter/s]
Best score: 0.3785: : 736iter [00:19, 46.74iter/s]
Best score: 0.3785: : 736iter [00:19, 46.74iter/s]
Best score: 0.3785: : 737iter [00:19, 46.74iter/s]
Best score: 0.3785: : 738iter [00:19, 46.74iter/s]
Best score: 0.3785: : 739iter [00:19, 46.74iter/s]
Best score: 0.3785: : 740iter [00:19, 46.74iter/s]
Best score: 0.3785: : 741iter [00:19, 46.89iter/s]
Best score: 0.3785: : 741iter [00:19, 46.89iter/s]
Best score: 0.3785: : 742iter [00:19, 46.89iter/s]
Best score: 0.3785: : 743iter [00:19, 46.89iter/s]
Best score: 0.3785: : 744iter [00:19, 46.89iter/s]
Best score: 0.3785: : 745iter [00:19, 46.89iter/s]
Best score: 0.3785: : 746iter [00:19, 47.40iter/s]
Best score: 0.3785: : 746iter [00:19, 47.40iter/s]
Best score: 0.3785: : 747iter [00:19, 47.40iter/s]
Best score: 0.3785: : 748iter [00:19, 47.40iter/s]
[ITER 750] Best: 0.3695
[PARAMS] Current best parameters:
  fx: 529.5688
  fy: 528.9562
  px: 1017.1088
  py: 750.0395
  k1: 0.01780066
  k2: -0.01667086
  k3: 0.00706589
  k4: -0.00161578
  p1: 0.00085036
  p2: -0.00372874
  p3: -0.01261775
  p4: 0.00322370
  s1: 0.0136
  s2: -0.0025
  s3: 0.0190
  s4: -0.0075
  tilt_x: -0.0264
  tilt_y: 0.0108
  scale: 3.0022
  rotation: -0.0274

Best score: 0.3695: : 749iter [00:19, 47.40iter/s]
Best score: 0.3695: : 750iter [00:19, 47.40iter/s]
Best score: 0.3695: : 751iter [00:19, 39.92iter/s]
Best score: 0.3695: : 751iter [00:19, 39.92iter/s]
Best score: 0.3695: : 752iter [00:19, 39.92iter/s]
Best score: 0.3695: : 753iter [00:19, 39.92iter/s]
Best score: 0.3695: : 754iter [00:19, 39.92iter/s]
Best score: 0.3695: : 755iter [00:19, 39.92iter/s]
Best score: 0.3695: : 756iter [00:19, 42.25iter/s]
Best score: 0.3695: : 756iter [00:19, 42.25iter/s]
Best score: 0.3695: : 757iter [00:19, 42.25iter/s]
Best score: 0.3695: : 758iter [00:19, 42.25iter/s]
Best score: 0.3695: : 759iter [00:19, 42.25iter/s]
Best score: 0.3695: : 760iter [00:19, 42.25iter/s]
Best score: 0.3695: : 761iter [00:19, 43.75iter/s]
Best score: 0.3695: : 761iter [00:19, 43.75iter/s]
Best score: 0.3695: : 762iter [00:19, 43.75iter/s]
Best score: 0.3695: : 763iter [00:19, 43.75iter/s]
Best score: 0.3695: : 764iter [00:19, 43.75iter/s]
Best score: 0.3695: : 765iter [00:20, 43.75iter/s]
Best score: 0.3695: : 766iter [00:20, 45.13iter/s]
Best score: 0.3695: : 766iter [00:20, 45.13iter/s]
Best score: 0.3695: : 767iter [00:20, 45.13iter/s]
Best score: 0.3695: : 768iter [00:20, 45.13iter/s]
Best score: 0.3695: : 769iter [00:20, 45.13iter/s]
Best score: 0.3695: : 770iter [00:20, 45.13iter/s]
Best score: 0.3695: : 771iter [00:20, 45.92iter/s]
Best score: 0.3695: : 771iter [00:20, 45.92iter/s]
Best score: 0.3695: : 772iter [00:20, 45.92iter/s]
Best score: 0.3695: : 773iter [00:20, 45.92iter/s]
Best score: 0.3695: : 774iter [00:20, 45.92iter/s]
Best score: 0.3695: : 775iter [00:20, 45.92iter/s]
Best score: 0.3695: : 776iter [00:20, 46.83iter/s]
Best score: 0.3695: : 776iter [00:20, 46.83iter/s]
Best score: 0.3695: : 777iter [00:20, 46.83iter/s]
Best score: 0.3695: : 778iter [00:20, 46.83iter/s]
Best score: 0.3695: : 779iter [00:20, 46.83iter/s]
Best score: 0.3695: : 780iter [00:20, 46.83iter/s]
Best score: 0.3695: : 781iter [00:20, 47.17iter/s]
Best score: 0.3695: : 781iter [00:20, 47.17iter/s]
Best score: 0.3695: : 782iter [00:20, 47.17iter/s]
Best score: 0.3695: : 783iter [00:20, 47.17iter/s]
Best score: 0.3695: : 784iter [00:20, 47.17iter/s]
Best score: 0.3695: : 785iter [00:20, 47.17iter/s]
Best score: 0.3695: : 786iter [00:20, 47.51iter/s]
Best score: 0.3695: : 786iter [00:20, 47.51iter/s]
Best score: 0.3695: : 787iter [00:20, 47.51iter/s]
Best score: 0.3695: : 788iter [00:20, 47.51iter/s]
Best score: 0.3613: : 789iter [00:20, 47.51iter/s]
Best score: 0.3613: : 790iter [00:20, 47.51iter/s]
Best score: 0.3613: : 791iter [00:20, 47.51iter/s]
Best score: 0.3613: : 791iter [00:20, 47.51iter/s]
Best score: 0.3613: : 792iter [00:20, 47.51iter/s]
Best score: 0.3613: : 793iter [00:20, 47.51iter/s]
Best score: 0.3613: : 794iter [00:20, 47.51iter/s]
Best score: 0.3613: : 795iter [00:20, 47.51iter/s]
Best score: 0.3613: : 796iter [00:20, 47.99iter/s]
Best score: 0.3613: : 796iter [00:20, 47.99iter/s]
Best score: 0.3613: : 797iter [00:20, 47.99iter/s]
Best score: 0.3613: : 798iter [00:20, 47.99iter/s]
[ITER 800] Best: 0.3567
[PARAMS] Current best parameters:
  fx: 532.6773
  fy: 532.1036
  px: 1016.5937
  py: 748.8227
  k1: 0.02910996
  k2: -0.02957910
  k3: 0.00948969
  k4: -0.00124924
  p1: 0.00182229
  p2: -0.00658688
  p3: -0.01321936
  p4: 0.00549144
  s1: 0.0166
  s2: -0.0044
  s3: 0.0158
  s4: -0.0084
  tilt_x: -0.0348
  tilt_y: 0.0082
  scale: 2.9748
  rotation: -0.0163

Best score: 0.3567: : 799iter [00:20, 47.99iter/s]
Best score: 0.3567: : 800iter [00:20, 47.99iter/s]
Best score: 0.3567: : 801iter [00:20, 40.36iter/s]
Best score: 0.3567: : 801iter [00:20, 40.36iter/s]
Best score: 0.3567: : 802iter [00:20, 40.36iter/s]
Best score: 0.3567: : 803iter [00:20, 40.36iter/s]
Best score: 0.3567: : 804iter [00:20, 40.36iter/s]
Best score: 0.3567: : 805iter [00:20, 40.36iter/s]
Best score: 0.3567: : 806iter [00:20, 42.55iter/s]
Best score: 0.3567: : 806iter [00:20, 42.55iter/s]
Best score: 0.3567: : 807iter [00:20, 42.55iter/s]
Best score: 0.3567: : 808iter [00:20, 42.55iter/s]
Best score: 0.3496: : 809iter [00:20, 42.55iter/s]
Best score: 0.3496: : 810iter [00:20, 42.55iter/s]
Best score: 0.3496: : 811iter [00:20, 44.01iter/s]
Best score: 0.3496: : 811iter [00:21, 44.01iter/s]
Best score: 0.3496: : 812iter [00:21, 44.01iter/s]
Best score: 0.3496: : 813iter [00:21, 44.01iter/s]
Best score: 0.3496: : 814iter [00:21, 44.01iter/s]
Best score: 0.3496: : 815iter [00:21, 44.01iter/s]
Best score: 0.3496: : 816iter [00:21, 45.32iter/s]
Best score: 0.3496: : 816iter [00:21, 45.32iter/s]
Best score: 0.3496: : 817iter [00:21, 45.32iter/s]
Best score: 0.3496: : 818iter [00:21, 45.32iter/s]
Best score: 0.3324: : 819iter [00:21, 45.32iter/s]
Best score: 0.3324: : 820iter [00:21, 45.32iter/s]
Best score: 0.3324: : 821iter [00:21, 46.10iter/s]
Best score: 0.3324: : 821iter [00:21, 46.10iter/s]
Best score: 0.3324: : 822iter [00:21, 46.10iter/s]
Best score: 0.3324: : 823iter [00:21, 46.10iter/s]
Best score: 0.3324: : 824iter [00:21, 46.10iter/s]
Best score: 0.3324: : 825iter [00:21, 46.10iter/s]
Best score: 0.3324: : 826iter [00:21, 46.97iter/s]
Best score: 0.3324: : 826iter [00:21, 46.97iter/s]
Best score: 0.3324: : 827iter [00:21, 46.97iter/s]
Best score: 0.3324: : 828iter [00:21, 46.97iter/s]
Best score: 0.3324: : 829iter [00:21, 46.97iter/s]
Best score: 0.3324: : 830iter [00:21, 46.97iter/s]
Best score: 0.3324: : 831iter [00:21, 47.05iter/s]
Best score: 0.3324: : 831iter [00:21, 47.05iter/s]
Best score: 0.3324: : 832iter [00:21, 47.05iter/s]
Best score: 0.3324: : 833iter [00:21, 47.05iter/s]
Best score: 0.3324: : 834iter [00:21, 47.05iter/s]
Best score: 0.3324: : 835iter [00:21, 47.05iter/s]
Best score: 0.3324: : 836iter [00:21, 47.78iter/s]
Best score: 0.3324: : 836iter [00:21, 47.78iter/s]
Best score: 0.3324: : 837iter [00:21, 47.78iter/s]
Best score: 0.3324: : 838iter [00:21, 47.78iter/s]
Best score: 0.3212: : 839iter [00:21, 47.78iter/s]
Best score: 0.3212: : 840iter [00:21, 47.78iter/s]
Best score: 0.3212: : 841iter [00:21, 47.90iter/s]
Best score: 0.3212: : 841iter [00:21, 47.90iter/s]
Best score: 0.3212: : 842iter [00:21, 47.90iter/s]
Best score: 0.3212: : 843iter [00:21, 47.90iter/s]
Best score: 0.3212: : 844iter [00:21, 47.90iter/s]
Best score: 0.3212: : 845iter [00:21, 47.90iter/s]
Best score: 0.3212: : 846iter [00:21, 48.35iter/s]
Best score: 0.3212: : 846iter [00:21, 48.35iter/s]
Best score: 0.3212: : 847iter [00:21, 48.35iter/s]
Best score: 0.3212: : 848iter [00:21, 48.35iter/s]
[ITER 850] Best: 0.3212
[PARAMS] Current best parameters:
  fx: 535.8562
  fy: 535.4214
  px: 1015.1842
  py: 750.0748
  k1: 0.02015145
  k2: -0.02526882
  k3: 0.00923980
  k4: -0.00147591
  p1: 0.00146894
  p2: -0.01093832
  p3: -0.01251486
  p4: 0.00380102
  s1: 0.0178
  s2: -0.0022
  s3: 0.0160
  s4: -0.0071
  tilt_x: -0.0278
  tilt_y: 0.0062
  scale: 2.9869
  rotation: -0.0353

Best score: 0.3212: : 849iter [00:21, 48.35iter/s]
Best score: 0.3212: : 850iter [00:21, 48.35iter/s]
Best score: 0.3212: : 851iter [00:21, 40.46iter/s]
Best score: 0.3212: : 851iter [00:21, 40.46iter/s]
Best score: 0.3212: : 852iter [00:21, 40.46iter/s]
Best score: 0.3212: : 853iter [00:21, 40.46iter/s]
Best score: 0.3212: : 854iter [00:21, 40.46iter/s]
Best score: 0.3212: : 855iter [00:21, 40.46iter/s]
Best score: 0.3212: : 856iter [00:21, 42.70iter/s]
Best score: 0.3212: : 856iter [00:22, 42.70iter/s]
Best score: 0.3212: : 857iter [00:22, 42.70iter/s]
Best score: 0.3212: : 858iter [00:22, 42.70iter/s]
Best score: 0.3212: : 859iter [00:22, 42.70iter/s]
Best score: 0.3212: : 860iter [00:22, 42.70iter/s]
Best score: 0.3212: : 861iter [00:22, 44.15iter/s]
Best score: 0.3212: : 861iter [00:22, 44.15iter/s]
Best score: 0.3212: : 862iter [00:22, 44.15iter/s]
Best score: 0.3212: : 863iter [00:22, 44.15iter/s]
Best score: 0.3212: : 864iter [00:22, 44.15iter/s]
Best score: 0.3212: : 865iter [00:22, 44.15iter/s]
Best score: 0.3212: : 866iter [00:22, 45.51iter/s]
Best score: 0.3212: : 866iter [00:22, 45.51iter/s]
Best score: 0.3212: : 867iter [00:22, 45.51iter/s]
Best score: 0.3212: : 868iter [00:22, 45.51iter/s]
Best score: 0.3212: : 869iter [00:22, 45.51iter/s]
Best score: 0.3212: : 870iter [00:22, 45.51iter/s]
Best score: 0.3212: : 871iter [00:22, 46.14iter/s]
Best score: 0.3212: : 871iter [00:22, 46.14iter/s]
Best score: 0.3212: : 872iter [00:22, 46.14iter/s]
Best score: 0.3212: : 873iter [00:22, 46.14iter/s]
Best score: 0.3212: : 874iter [00:22, 46.14iter/s]
Best score: 0.3212: : 875iter [00:22, 46.14iter/s]
Best score: 0.3212: : 876iter [00:22, 46.93iter/s]
Best score: 0.3212: : 876iter [00:22, 46.93iter/s]
Best score: 0.3212: : 877iter [00:22, 46.93iter/s]
Best score: 0.3212: : 878iter [00:22, 46.93iter/s]
Best score: 0.3142: : 879iter [00:22, 46.93iter/s]
Best score: 0.3142: : 880iter [00:22, 46.93iter/s]
Best score: 0.3142: : 881iter [00:22, 47.15iter/s]
Best score: 0.3142: : 881iter [00:22, 47.15iter/s]
Best score: 0.3142: : 882iter [00:22, 47.15iter/s]
Best score: 0.3142: : 883iter [00:22, 47.15iter/s]
Best score: 0.3142: : 884iter [00:22, 47.15iter/s]
Best score: 0.3142: : 885iter [00:22, 47.15iter/s]
Best score: 0.3142: : 886iter [00:22, 47.35iter/s]
Best score: 0.3142: : 886iter [00:22, 47.35iter/s]
Best score: 0.3142: : 887iter [00:22, 47.35iter/s]
Best score: 0.3142: : 888iter [00:22, 47.35iter/s]
Best score: 0.3086: : 889iter [00:22, 47.35iter/s]
Best score: 0.3086: : 890iter [00:22, 47.35iter/s]
Best score: 0.3086: : 891iter [00:22, 47.59iter/s]
Best score: 0.3086: : 891iter [00:22, 47.59iter/s]
Best score: 0.3086: : 892iter [00:22, 47.59iter/s]
Best score: 0.3086: : 893iter [00:22, 47.59iter/s]
Best score: 0.3086: : 894iter [00:22, 47.59iter/s]
Best score: 0.3086: : 895iter [00:22, 47.59iter/s]
Best score: 0.3086: : 896iter [00:22, 47.85iter/s]
Best score: 0.3086: : 896iter [00:22, 47.85iter/s]
Best score: 0.3086: : 897iter [00:22, 47.85iter/s]
Best score: 0.3086: : 898iter [00:22, 47.85iter/s]
[ITER 900] Best: 0.3047
[PARAMS] Current best parameters:
  fx: 536.5728
  fy: 536.2950
  px: 1016.0349
  py: 751.0629
  k1: 0.02145195
  k2: -0.02791145
  k3: 0.01079747
  k4: -0.00184546
  p1: 0.00323189
  p2: -0.01124078
  p3: -0.01467322
  p4: 0.00376981
  s1: 0.0205
  s2: -0.0020
  s3: 0.0156
  s4: -0.0062
  tilt_x: -0.0260
  tilt_y: 0.0015
  scale: 2.9809
  rotation: -0.0496

Best score: 0.3047: : 899iter [00:22, 47.85iter/s]
Best score: 0.3047: : 900iter [00:22, 47.85iter/s]
Best score: 0.3047: : 901iter [00:22, 40.25iter/s]
Best score: 0.3047: : 901iter [00:23, 40.25iter/s]
Best score: 0.3047: : 902iter [00:23, 40.25iter/s]
Best score: 0.3047: : 903iter [00:23, 40.25iter/s]
Best score: 0.3047: : 904iter [00:23, 40.25iter/s]
Best score: 0.3047: : 905iter [00:23, 40.25iter/s]
Best score: 0.3047: : 906iter [00:23, 42.54iter/s]
Best score: 0.3047: : 906iter [00:23, 42.54iter/s]
Best score: 0.3047: : 907iter [00:23, 42.54iter/s]
Best score: 0.3047: : 908iter [00:23, 42.54iter/s]
Best score: 0.2996: : 909iter [00:23, 42.54iter/s]
Best score: 0.2996: : 910iter [00:23, 42.54iter/s]
Best score: 0.2996: : 911iter [00:23, 43.88iter/s]
Best score: 0.2996: : 911iter [00:23, 43.88iter/s]
Best score: 0.2996: : 912iter [00:23, 43.88iter/s]
Best score: 0.2996: : 913iter [00:23, 43.88iter/s]
Best score: 0.2996: : 914iter [00:23, 43.88iter/s]
Best score: 0.2996: : 915iter [00:23, 43.88iter/s]
Best score: 0.2996: : 916iter [00:23, 45.18iter/s]
Best score: 0.2996: : 916iter [00:23, 45.18iter/s]
Best score: 0.2996: : 917iter [00:23, 45.18iter/s]
Best score: 0.2996: : 918iter [00:23, 45.18iter/s]
Best score: 0.2955: : 919iter [00:23, 45.18iter/s]
Best score: 0.2955: : 920iter [00:23, 45.18iter/s]
Best score: 0.2955: : 921iter [00:23, 45.71iter/s]
Best score: 0.2955: : 921iter [00:23, 45.71iter/s]
Best score: 0.2955: : 922iter [00:23, 45.71iter/s]
Best score: 0.2955: : 923iter [00:23, 45.71iter/s]
Best score: 0.2955: : 924iter [00:23, 45.71iter/s]
Best score: 0.2955: : 925iter [00:23, 45.71iter/s]
Best score: 0.2955: : 926iter [00:23, 46.50iter/s]
Best score: 0.2955: : 926iter [00:23, 46.50iter/s]
Best score: 0.2955: : 927iter [00:23, 46.50iter/s]
Best score: 0.2955: : 928iter [00:23, 46.50iter/s]
Best score: 0.2951: : 929iter [00:23, 46.50iter/s]
Best score: 0.2951: : 930iter [00:23, 46.50iter/s]
Best score: 0.2951: : 931iter [00:23, 46.79iter/s]
Best score: 0.2951: : 931iter [00:23, 46.79iter/s]
Best score: 0.2951: : 932iter [00:23, 46.79iter/s]
Best score: 0.2951: : 933iter [00:23, 46.79iter/s]
Best score: 0.2951: : 934iter [00:23, 46.79iter/s]
Best score: 0.2951: : 935iter [00:23, 46.79iter/s]
Best score: 0.2951: : 936iter [00:23, 47.33iter/s]
Best score: 0.2951: : 936iter [00:23, 47.33iter/s]
Best score: 0.2951: : 937iter [00:23, 47.33iter/s]
Best score: 0.2951: : 938iter [00:23, 47.33iter/s]
Best score: 0.2936: : 939iter [00:23, 47.33iter/s]
Best score: 0.2936: : 940iter [00:23, 47.33iter/s]
Best score: 0.2936: : 941iter [00:23, 47.41iter/s]
Best score: 0.2936: : 941iter [00:23, 47.41iter/s]
Best score: 0.2936: : 942iter [00:23, 47.41iter/s]
Best score: 0.2936: : 943iter [00:23, 47.41iter/s]
Best score: 0.2936: : 944iter [00:23, 47.41iter/s]
Best score: 0.2936: : 945iter [00:23, 47.41iter/s]
Best score: 0.2936: : 946iter [00:23, 47.68iter/s]
Best score: 0.2936: : 946iter [00:23, 47.68iter/s]
Best score: 0.2936: : 947iter [00:23, 47.68iter/s]
Best score: 0.2936: : 948iter [00:23, 47.68iter/s]
[ITER 950] Best: 0.2936
[PARAMS] Current best parameters:
  fx: 537.7087
  fy: 537.5716
  px: 1016.0374
  py: 750.0201
  k1: 0.02203512
  k2: -0.03044747
  k3: 0.01172378
  k4: -0.00186312
  p1: 0.00211692
  p2: -0.01367504
  p3: -0.01293253
  p4: 0.00400622
  s1: 0.0219
  s2: -0.0026
  s3: 0.0143
  s4: -0.0064
  tilt_x: -0.0293
  tilt_y: -0.0031
  scale: 2.9815
  rotation: -0.0126

Best score: 0.2936: : 949iter [00:24, 47.68iter/s]
Best score: 0.2936: : 950iter [00:24, 47.68iter/s]
Best score: 0.2936: : 951iter [00:24, 40.13iter/s]
Best score: 0.2936: : 951iter [00:24, 40.13iter/s]
Best score: 0.2936: : 952iter [00:24, 40.13iter/s]
Best score: 0.2936: : 953iter [00:24, 40.13iter/s]
Best score: 0.2936: : 954iter [00:24, 40.13iter/s]
Best score: 0.2936: : 955iter [00:24, 40.13iter/s]
Best score: 0.2936: : 956iter [00:24, 42.32iter/s]
Best score: 0.2936: : 956iter [00:24, 42.32iter/s]
Best score: 0.2936: : 957iter [00:24, 42.32iter/s]
Best score: 0.2936: : 958iter [00:24, 42.32iter/s]
Best score: 0.2926: : 959iter [00:24, 42.32iter/s]
Best score: 0.2926: : 960iter [00:24, 42.32iter/s]
Best score: 0.2926: : 961iter [00:24, 43.71iter/s]
Best score: 0.2926: : 961iter [00:24, 43.71iter/s]
Best score: 0.2926: : 962iter [00:24, 43.71iter/s]
Best score: 0.2926: : 963iter [00:24, 43.71iter/s]
Best score: 0.2926: : 964iter [00:24, 43.71iter/s]
Best score: 0.2926: : 965iter [00:24, 43.71iter/s]
Best score: 0.2926: : 966iter [00:24, 45.06iter/s]
Best score: 0.2926: : 966iter [00:24, 45.06iter/s]
Best score: 0.2926: : 967iter [00:24, 45.06iter/s]
Best score: 0.2926: : 968iter [00:24, 45.06iter/s]
Best score: 0.2895: : 969iter [00:24, 45.06iter/s]
Best score: 0.2895: : 970iter [00:24, 45.06iter/s]
Best score: 0.2895: : 971iter [00:24, 45.82iter/s]
Best score: 0.2895: : 971iter [00:24, 45.82iter/s]
Best score: 0.2895: : 972iter [00:24, 45.82iter/s]
Best score: 0.2895: : 973iter [00:24, 45.82iter/s]
Best score: 0.2895: : 974iter [00:24, 45.82iter/s]
Best score: 0.2895: : 975iter [00:24, 45.82iter/s]
Best score: 0.2895: : 976iter [00:24, 46.63iter/s]
Best score: 0.2895: : 976iter [00:24, 46.63iter/s]
Best score: 0.2895: : 977iter [00:24, 46.63iter/s]
Best score: 0.2895: : 978iter [00:24, 46.63iter/s]
Best score: 0.2889: : 979iter [00:24, 46.63iter/s]
Best score: 0.2889: : 980iter [00:24, 46.63iter/s]
Best score: 0.2889: : 981iter [00:24, 47.09iter/s]
Best score: 0.2889: : 981iter [00:24, 47.09iter/s]
Best score: 0.2889: : 982iter [00:24, 47.09iter/s]
Best score: 0.2889: : 983iter [00:24, 47.09iter/s]
Best score: 0.2889: : 984iter [00:24, 47.09iter/s]
Best score: 0.2889: : 985iter [00:24, 47.09iter/s]
Best score: 0.2889: : 986iter [00:24, 47.51iter/s]
Best score: 0.2889: : 986iter [00:24, 47.51iter/s]
Best score: 0.2889: : 987iter [00:24, 47.51iter/s]
Best score: 0.2889: : 988iter [00:24, 47.51iter/s]
Best score: 0.2875: : 989iter [00:24, 47.51iter/s]
Best score: 0.2875: : 990iter [00:24, 47.51iter/s]
Best score: 0.2875: : 991iter [00:24, 47.57iter/s]
Best score: 0.2875: : 991iter [00:24, 47.57iter/s]
Best score: 0.2875: : 992iter [00:24, 47.57iter/s]
Best score: 0.2875: : 993iter [00:24, 47.57iter/s]
Best score: 0.2875: : 994iter [00:25, 47.57iter/s]
Best score: 0.2875: : 995iter [00:25, 47.57iter/s]
Best score: 0.2875: : 996iter [00:25, 47.94iter/s]
Best score: 0.2875: : 996iter [00:25, 47.94iter/s]
Best score: 0.2875: : 997iter [00:25, 47.94iter/s]
Best score: 0.2875: : 998iter [00:25, 47.94iter/s]
[ITER 1000] Best: 0.2871
[PARAMS] Current best parameters:
  fx: 536.8886
  fy: 536.7618
  px: 1016.8194
  py: 749.8162
  k1: 0.02246719
  k2: -0.02992297
  k3: 0.01154703
  k4: -0.00187767
  p1: 0.00138287
  p2: -0.01429038
  p3: -0.01325144
  p4: 0.00464465
  s1: 0.0239
  s2: -0.0034
  s3: 0.0153
  s4: -0.0072
  tilt_x: -0.0286
  tilt_y: -0.0083
  scale: 2.9810
  rotation: -0.0177

Best score: 0.2871: : 999iter [00:25, 47.94iter/s]
Best score: 0.2871: : 1000iter [00:25, 47.94iter/s]
Best score: 0.2871: : 1001iter [00:25, 40.41iter/s]
Best score: 0.2871: : 1001iter [00:25, 40.41iter/s]
Best score: 0.2871: : 1002iter [00:25, 40.41iter/s]
Best score: 0.2871: : 1003iter [00:25, 40.41iter/s]
Best score: 0.2871: : 1004iter [00:25, 40.41iter/s]
Best score: 0.2871: : 1005iter [00:25, 40.41iter/s]
Best score: 0.2871: : 1006iter [00:25, 35.08iter/s]
Best score: 0.2871: : 1006iter [00:25, 35.08iter/s]
Best score: 0.2871: : 1007iter [00:25, 35.08iter/s]
Best score: 0.2871: : 1008iter [00:25, 35.08iter/s]
Best score: 0.2869: : 1009iter [00:25, 35.08iter/s]
Best score: 0.2869: : 1010iter [00:25, 35.08iter/s]
Best score: 0.2869: : 1011iter [00:25, 38.08iter/s]
Best score: 0.2869: : 1011iter [00:25, 38.08iter/s]
Best score: 0.2869: : 1012iter [00:25, 38.08iter/s]
Best score: 0.2869: : 1013iter [00:25, 38.08iter/s]
Best score: 0.2869: : 1014iter [00:25, 38.08iter/s]
Best score: 0.2869: : 1015iter [00:25, 38.08iter/s]
Best score: 0.2869: : 1016iter [00:25, 40.74iter/s]
Best score: 0.2869: : 1016iter [00:25, 40.74iter/s]
Best score: 0.2869: : 1017iter [00:25, 40.74iter/s]
Best score: 0.2869: : 1018iter [00:25, 40.74iter/s]
Best score: 0.2864: : 1019iter [00:25, 40.74iter/s]
Best score: 0.2864: : 1020iter [00:25, 40.74iter/s]
Best score: 0.2864: : 1021iter [00:25, 42.65iter/s]
Best score: 0.2864: : 1021iter [00:25, 42.65iter/s]
Best score: 0.2864: : 1022iter [00:25, 42.65iter/s]
Best score: 0.2864: : 1023iter [00:25, 42.65iter/s]
Best score: 0.2864: : 1024iter [00:25, 42.65iter/s]
Best score: 0.2864: : 1025iter [00:25, 42.65iter/s]
Best score: 0.2864: : 1026iter [00:25, 44.16iter/s]
Best score: 0.2864: : 1026iter [00:25, 44.16iter/s]
Best score: 0.2864: : 1027iter [00:25, 44.16iter/s]
Best score: 0.2864: : 1028iter [00:25, 44.16iter/s]
Best score: 0.2864: : 1029iter [00:25, 44.16iter/s]
Best score: 0.2864: : 1030iter [00:25, 44.16iter/s]
Best score: 0.2864: : 1031iter [00:25, 45.25iter/s]
Best score: 0.2864: : 1031iter [00:25, 45.25iter/s]
Best score: 0.2864: : 1032iter [00:25, 45.25iter/s]
Best score: 0.2864: : 1033iter [00:25, 45.25iter/s]
Best score: 0.2864: : 1034iter [00:25, 45.25iter/s]
Best score: 0.2864: : 1035iter [00:26, 45.25iter/s]
Best score: 0.2864: : 1036iter [00:26, 46.20iter/s]
Best score: 0.2864: : 1036iter [00:26, 46.20iter/s]
Best score: 0.2864: : 1037iter [00:26, 46.20iter/s]
Best score: 0.2864: : 1038iter [00:26, 46.20iter/s]
Best score: 0.2858: : 1039iter [00:26, 46.20iter/s]
Best score: 0.2858: : 1040iter [00:26, 46.20iter/s]
Best score: 0.2858: : 1041iter [00:26, 46.67iter/s]
Best score: 0.2858: : 1041iter [00:26, 46.67iter/s]
Best score: 0.2858: : 1042iter [00:26, 46.67iter/s]
Best score: 0.2858: : 1043iter [00:26, 46.67iter/s]
Best score: 0.2858: : 1044iter [00:26, 46.67iter/s]
Best score: 0.2858: : 1045iter [00:26, 46.67iter/s]
Best score: 0.2858: : 1046iter [00:26, 47.13iter/s]
Best score: 0.2858: : 1046iter [00:26, 47.13iter/s]
Best score: 0.2858: : 1047iter [00:26, 47.13iter/s]
Best score: 0.2858: : 1048iter [00:26, 47.13iter/s]
[ITER 1050] Best: 0.2853
[PARAMS] Current best parameters:
  fx: 535.8092
  fy: 535.6631
  px: 1017.2810
  py: 749.8431
  k1: 0.02331372
  k2: -0.02856282
  k3: 0.01032176
  k4: -0.00159111
  p1: 0.00198098
  p2: -0.01325242
  p3: -0.01430955
  p4: 0.00450332
  s1: 0.0245
  s2: -0.0032
  s3: 0.0160
  s4: -0.0071
  tilt_x: -0.0296
  tilt_y: -0.0084
  scale: 2.9800
  rotation: -0.0176

Best score: 0.2853: : 1049iter [00:26, 47.13iter/s]
Best score: 0.2853: : 1050iter [00:26, 47.13iter/s]
Best score: 0.2853: : 1051iter [00:26, 39.82iter/s]
Best score: 0.2853: : 1051iter [00:26, 39.82iter/s]
Best score: 0.2853: : 1052iter [00:26, 39.82iter/s]
Best score: 0.2853: : 1053iter [00:26, 39.82iter/s]
Best score: 0.2853: : 1054iter [00:26, 39.82iter/s]
Best score: 0.2853: : 1055iter [00:26, 39.82iter/s]
Best score: 0.2853: : 1056iter [00:26, 42.09iter/s]
Best score: 0.2853: : 1056iter [00:26, 42.09iter/s]
Best score: 0.2853: : 1057iter [00:26, 42.09iter/s]
Best score: 0.2853: : 1058iter [00:26, 42.09iter/s]
Best score: 0.2853: : 1059iter [00:26, 42.09iter/s]
Best score: 0.2853: : 1060iter [00:26, 42.09iter/s]
Best score: 0.2853: : 1061iter [00:26, 43.59iter/s]
Best score: 0.2853: : 1061iter [00:26, 43.59iter/s]
Best score: 0.2853: : 1062iter [00:26, 43.59iter/s]
Best score: 0.2853: : 1063iter [00:26, 43.59iter/s]
Best score: 0.2853: : 1064iter [00:26, 43.59iter/s]
Best score: 0.2853: : 1065iter [00:26, 43.59iter/s]
Best score: 0.2853: : 1066iter [00:26, 45.03iter/s]
Best score: 0.2853: : 1066iter [00:26, 45.03iter/s]
Best score: 0.2853: : 1067iter [00:26, 45.03iter/s]
Best score: 0.2853: : 1068iter [00:26, 45.03iter/s]
Best score: 0.2853: : 1069iter [00:26, 45.03iter/s]
Best score: 0.2853: : 1070iter [00:26, 45.03iter/s]
Best score: 0.2853: : 1071iter [00:26, 45.78iter/s]
Best score: 0.2853: : 1071iter [00:26, 45.78iter/s]
Best score: 0.2853: : 1072iter [00:26, 45.78iter/s]
Best score: 0.2853: : 1073iter [00:26, 45.78iter/s]
Best score: 0.2853: : 1074iter [00:26, 45.78iter/s]
Best score: 0.2853: : 1075iter [00:26, 45.78iter/s]
Best score: 0.2853: : 1076iter [00:26, 46.67iter/s]
Best score: 0.2853: : 1076iter [00:26, 46.67iter/s]
Best score: 0.2853: : 1077iter [00:26, 46.67iter/s]
Best score: 0.2853: : 1078iter [00:26, 46.67iter/s]
Best score: 0.2853: : 1079iter [00:26, 46.67iter/s]
Best score: 0.2853: : 1080iter [00:27, 46.67iter/s]
Best score: 0.2853: : 1081iter [00:27, 46.97iter/s]
Best score: 0.2853: : 1081iter [00:27, 46.97iter/s]
Best score: 0.2853: : 1082iter [00:27, 46.97iter/s]
Best score: 0.2853: : 1083iter [00:27, 46.97iter/s]
Best score: 0.2853: : 1084iter [00:27, 46.97iter/s]
Best score: 0.2853: : 1085iter [00:27, 46.97iter/s]
Best score: 0.2853: : 1086iter [00:27, 47.34iter/s]
Best score: 0.2853: : 1086iter [00:27, 47.34iter/s]
Best score: 0.2853: : 1087iter [00:27, 47.34iter/s]
Best score: 0.2853: : 1088iter [00:27, 47.34iter/s]
Best score: 0.2848: : 1089iter [00:27, 47.34iter/s]
Best score: 0.2848: : 1090iter [00:27, 47.34iter/s]
Best score: 0.2848: : 1091iter [00:27, 47.61iter/s]
Best score: 0.2848: : 1091iter [00:27, 47.61iter/s]
Best score: 0.2848: : 1092iter [00:27, 47.61iter/s]
Best score: 0.2848: : 1093iter [00:27, 47.61iter/s]
Best score: 0.2848: : 1094iter [00:27, 47.61iter/s]
Best score: 0.2848: : 1095iter [00:27, 47.61iter/s]
Best score: 0.2848: : 1096iter [00:27, 47.92iter/s]
Best score: 0.2848: : 1096iter [00:27, 47.92iter/s]
Best score: 0.2848: : 1097iter [00:27, 47.92iter/s]
Best score: 0.2848: : 1098iter [00:27, 47.92iter/s]
[ITER 1100] Best: 0.2848
[PARAMS] Current best parameters:
  fx: 533.9153
  fy: 533.7466
  px: 1016.9005
  py: 749.8599
  k1: 0.02284578
  k2: -0.02495916
  k3: 0.00823427
  k4: -0.00119148
  p1: 0.00283504
  p2: -0.01176951
  p3: -0.01662570
  p4: 0.00456250
  s1: 0.0250
  s2: -0.0033
  s3: 0.0172
  s4: -0.0070
  tilt_x: -0.0311
  tilt_y: -0.0036
  scale: 2.9834
  rotation: -0.0248

Best score: 0.2848: : 1099iter [00:27, 47.92iter/s]
Best score: 0.2848: : 1100iter [00:27, 40.04iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.284763 -> 0.284647

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 20,765 → 20,765 within 3500mm ring
  Ring polish improved score: 0.284647 -> 0.283481
  Success: True
  Tangential distortion: p1=0.002835, p2=-0.011769, p3=-0.016625, p4=0.004563
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2835
[✅] 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: 533.9152959829809
    fy: 533.7466060141285
    px: 1016.9005278213159
    py: 749.8599300113624
    k1: 0.022847183147936042
    k2: -0.02495757521469721
    k3: 0.00823477574402323
    k4: -0.0011918601360258693
    p1: 0.0028348560569540033
    p2: -0.0117691835048236
    p3: -0.016625214982082442
    p4: 0.004562962216500052
    scale: 2.9834195889839483
    rotation: -0.024753998504967276
    mmpp: 1.2830567825456038

[🔄] 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:  17%|█▋        | 27/160 [00:00<00:00, 267.69frame/s]

   🔄 1000:  34%|███▍      | 54/160 [00:00<00:00, 212.68frame/s]

   🔄 1000:  48%|████▊     | 76/160 [00:00<00:00, 195.45frame/s]

   🔄 1000:  60%|██████    | 96/160 [00:00<00:00, 187.39frame/s]

   🔄 1000:  72%|███████▏  | 115/160 [00:00<00:00, 183.66frame/s]

   🔄 1000:  84%|████████▍ | 134/160 [00:00<00:00, 181.66frame/s]

   🔄 1000:  96%|█████████▌| 153/160 [00:00<00:00, 180.19frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.47s/rig]

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

   🔄 500:   3%|▎         | 7/201 [00:00<00:03, 50.96frame/s]

   🔄 500:  12%|█▏        | 25/201 [00:00<00:01, 115.86frame/s]

   🔄 500:  21%|██▏       | 43/201 [00:00<00:01, 142.06frame/s]

   🔄 500:  30%|███       | 61/201 [00:00<00:00, 155.38frame/s]

   🔄 500:  39%|███▉      | 79/201 [00:00<00:00, 163.07frame/s]

   🔄 500:  49%|████▉     | 98/201 [00:00<00:00, 170.43frame/s]

   🔄 500:  59%|█████▊    | 118/201 [00:00<00:00, 177.03frame/s]

   🔄 500:  68%|██████▊   | 136/201 [00:00<00:00, 176.13frame/s]

   🔄 500:  77%|███████▋  | 154/201 [00:00<00:00, 175.71frame/s]

   🔄 500:  86%|████████▌ | 172/201 [00:01<00:00, 175.37frame/s]

   🔄 500:  95%|█████████▍| 190/201 [00:01<00:00, 174.86frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:03<00:00,  1.82s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:03<00:00,  1.77s/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, 239.54frame/s]

   🔄 1000:  49%|████▉     | 49/100 [00:00<00:00, 197.26frame/s]

   🔄 1000:  70%|███████   | 70/100 [00:00<00:00, 185.65frame/s]

   🔄 1000:  89%|████████▉ | 89/100 [00:00<00:00, 182.37frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.07rig/s]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 176.07frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 175.93frame/s]

   🔄 500:  55%|█████▌    | 55/100 [00:00<00:00, 181.90frame/s]

   🔄 500:  74%|███████▍  | 74/100 [00:00<00:00, 181.46frame/s]

   🔄 500:  93%|█████████▎| 93/100 [00:00<00:00, 179.02frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.01rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.02rig/s]
[✅] 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%|▎         | 80879/3080448 [00:00<00:03, 808786.91px/s]
📤 Writing LUT:   5%|▌         | 168451/3080448 [00:00<00:03, 848154.97px/s]
📤 Writing LUT:   8%|▊         | 257789/3080448 [00:00<00:03, 868802.19px/s]
📤 Writing LUT:  11%|█         | 346112/3080448 [00:00<00:03, 874495.96px/s]
📤 Writing LUT:  14%|█▍        | 435970/3080448 [00:00<00:02, 883180.35px/s]
📤 Writing LUT:  17%|█▋        | 525278/3080448 [00:00<00:02, 886544.36px/s]
📤 Writing LUT:  20%|█▉        | 614106/3080448 [00:00<00:02, 887109.29px/s]
📤 Writing LUT:  23%|██▎       | 704251/3080448 [00:00<00:02, 891672.91px/s]
📤 Writing LUT:  26%|██▌       | 793419/3080448 [00:00<00:02, 884928.72px/s]
📤 Writing LUT:  29%|██▊       | 881923/3080448 [00:01<00:02, 881575.68px/s]
📤 Writing LUT:  32%|███▏      | 970921/3080448 [00:01<00:02, 884131.25px/s]
📤 Writing LUT:  34%|███▍      | 1060632/3080448 [00:01<00:02, 888059.87px/s]
📤 Writing LUT:  37%|███▋      | 1149873/3080448 [00:01<00:02, 889372.61px/s]
📤 Writing LUT:  40%|████      | 1241061/3080448 [00:01<00:02, 896154.05px/s]
📤 Writing LUT:  43%|████▎     | 1331067/3080448 [00:01<00:01, 897326.69px/s]
📤 Writing LUT:  46%|████▌     | 1420804/3080448 [00:01<00:01, 894675.68px/s]
📤 Writing LUT:  49%|████▉     | 1510276/3080448 [00:01<00:01, 890821.60px/s]
📤 Writing LUT:  52%|█████▏    | 1599364/3080448 [00:01<00:01, 886872.25px/s]
📤 Writing LUT:  55%|█████▍    | 1688282/3080448 [00:01<00:01, 887559.77px/s]
📤 Writing LUT:  58%|█████▊    | 1777648/3080448 [00:02<00:01, 889379.26px/s]
📤 Writing LUT:  61%|██████    | 1867962/3080448 [00:02<00:01, 893493.42px/s]
📤 Writing LUT:  64%|██████▎   | 1959145/3080448 [00:02<00:01, 898982.97px/s]
📤 Writing LUT:  67%|██████▋   | 2050002/3080448 [00:02<00:01, 901853.59px/s]
📤 Writing LUT:  70%|██████▉   | 2142283/3080448 [00:02<00:01, 908132.34px/s]
📤 Writing LUT:  73%|███████▎  | 2234042/3080448 [00:02<00:00, 910966.74px/s]
📤 Writing LUT:  76%|███████▌  | 2326414/3080448 [00:02<00:00, 914788.61px/s]
📤 Writing LUT:  79%|███████▊  | 2419117/3080448 [00:02<00:00, 918457.48px/s]
📤 Writing LUT:  82%|████████▏ | 2511758/3080448 [00:02<00:00, 920841.64px/s]
📤 Writing LUT:  85%|████████▍ | 2603844/3080448 [00:02<00:00, 918427.74px/s]
📤 Writing LUT:  88%|████████▊ | 2696332/3080448 [00:03<00:00, 920355.96px/s]
📤 Writing LUT:  91%|█████████ | 2788370/3080448 [00:03<00:00, 915883.51px/s]
📤 Writing LUT:  94%|█████████▎| 2880539/3080448 [00:03<00:00, 917610.26px/s]
📤 Writing LUT:  96%|█████████▋| 2972403/3080448 [00:03<00:00, 917917.84px/s]
📤 Writing LUT: 100%|█████████▉| 3065692/3080448 [00:03<00:00, 922396.80px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 899107.73px/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, 129.48frame/s]

   🔥 1000:  16%|█▋        | 26/160 [00:00<00:01, 107.06frame/s]

   🔥 1000:  23%|██▎       | 37/160 [00:00<00:01, 99.91frame/s] 

   🔥 1000:  30%|███       | 48/160 [00:00<00:01, 92.00frame/s]

   🔥 1000:  36%|███▋      | 58/160 [00:00<00:01, 86.98frame/s]

   🔥 1000:  42%|████▏     | 67/160 [00:00<00:01, 84.82frame/s]

   🔥 1000:  48%|████▊     | 76/160 [00:00<00:01, 83.84frame/s]

   🔥 1000:  53%|█████▎    | 85/160 [00:00<00:00, 84.15frame/s]

   🔥 1000:  59%|█████▉    | 94/160 [00:01<00:00, 84.52frame/s]

   🔥 1000:  64%|██████▍   | 103/160 [00:01<00:00, 83.21frame/s]

   🔥 1000:  70%|███████   | 112/160 [00:01<00:00, 83.71frame/s]

   🔥 1000:  76%|███████▌  | 121/160 [00:01<00:00, 84.59frame/s]

   🔥 1000:  81%|████████▏ | 130/160 [00:01<00:00, 85.29frame/s]

   🔥 1000:  87%|████████▋ | 139/160 [00:01<00:00, 86.25frame/s]

   🔥 1000:  92%|█████████▎| 148/160 [00:01<00:00, 86.30frame/s]

   🔥 1000:  98%|█████████▊| 157/160 [00:01<00:00, 86.46frame/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,  4.78s/rig]

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

   🔥 500:   4%|▍         | 8/201 [00:00<00:02, 75.04frame/s]

   🔥 500:   8%|▊         | 16/201 [00:00<00:02, 76.94frame/s]

   🔥 500:  12%|█▏        | 24/201 [00:00<00:02, 76.73frame/s]

   🔥 500:  16%|█▌        | 32/201 [00:00<00:02, 76.96frame/s]

   🔥 500:  20%|██        | 41/201 [00:00<00:02, 78.17frame/s]

   🔥 500:  24%|██▍       | 49/201 [00:00<00:01, 78.64frame/s]

   🔥 500:  29%|██▉       | 58/201 [00:00<00:01, 78.84frame/s]

   🔥 500:  33%|███▎      | 66/201 [00:00<00:01, 78.67frame/s]

   🔥 500:  37%|███▋      | 74/201 [00:00<00:01, 78.18frame/s]

   🔥 500:  41%|████      | 82/201 [00:01<00:01, 78.46frame/s]

   🔥 500:  45%|████▌     | 91/201 [00:01<00:01, 79.40frame/s]

   🔥 500:  50%|████▉     | 100/201 [00:01<00:01, 79.54frame/s]

   🔥 500:  54%|█████▎    | 108/201 [00:01<00:01, 78.78frame/s]

   🔥 500:  59%|█████▊    | 118/201 [00:01<00:01, 81.70frame/s]

   🔥 500:  63%|██████▎   | 127/201 [00:01<00:00, 80.42frame/s]

   🔥 500:  68%|██████▊   | 136/201 [00:01<00:00, 78.66frame/s]

   🔥 500:  72%|███████▏  | 144/201 [00:01<00:00, 78.55frame/s]

   🔥 500:  76%|███████▌  | 152/201 [00:01<00:00, 78.42frame/s]

   🔥 500:  80%|███████▉  | 160/201 [00:02<00:00, 78.49frame/s]

   🔥 500:  84%|████████▎ | 168/201 [00:02<00:00, 78.66frame/s]

   🔥 500:  88%|████████▊ | 176/201 [00:02<00:00, 78.65frame/s]

   🔥 500:  92%|█████████▏| 184/201 [00:02<00:00, 78.59frame/s]

   🔥 500:  96%|█████████▌| 192/201 [00:02<00:00, 78.90frame/s]

   🔥 500: 100%|█████████▉| 200/201 [00:02<00:00, 78.32frame/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,  4.14s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:08<00:00,  4.24s/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


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