2026-05-12T02:53:41 - INFO: Slick 0.0.8a10

🟢 Starting Calibration Pipeline

=== Processing MDC015_W038_2025-05-14 ===
2026-05-12T02:53:42 - INFO: Camera ID: 14302215
2026-05-12T02:53:42 - INFO: Wand ID: 
2026-05-12T02:53:42 - INFO: Recorded At: 2025-05-14T16:05:32Z

-------------------------------------------------------------------
                        Step 1: Digitization                       
-------------------------------------------------------------------

[🔄] 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 1187 stationary frames, 228 remain


→ 1000 (228 images) Phase 1:   0%|          | 0/228 [00:00<?, ?it/s]

→ 1000 (228 images) Phase 1:   0%|          | 1/228 [00:00<01:52,  2.01it/s]

→ 1000 (228 images) Phase 1:   5%|▍         | 11/228 [00:00<00:09, 22.08it/s]

→ 1000 (228 images) Phase 1:  14%|█▍        | 32/228 [00:00<00:03, 57.62it/s]

→ 1000 (228 images) Phase 1:  18%|█▊        | 41/228 [00:01<00:04, 45.58it/s]

→ 1000 (228 images) Phase 1:  21%|██▏       | 49/228 [00:01<00:03, 51.89it/s]

→ 1000 (228 images) Phase 1:  29%|██▊       | 65/228 [00:01<00:03, 54.04it/s]

→ 1000 (228 images) Phase 1:  32%|███▏      | 74/228 [00:01<00:02, 57.31it/s]

→ 1000 (228 images) Phase 1:  43%|████▎     | 97/228 [00:01<00:02, 65.29it/s]

→ 1000 (228 images) Phase 1:  46%|████▋     | 106/228 [00:02<00:01, 66.21it/s]

→ 1000 (228 images) Phase 1:  56%|█████▌    | 128/228 [00:02<00:01, 93.24it/s]

→ 1000 (228 images) Phase 1:  61%|██████▏   | 140/228 [00:02<00:01, 64.90it/s]

→ 1000 (228 images) Phase 1:  71%|███████   | 161/228 [00:02<00:00, 68.59it/s]

→ 1000 (228 images) Phase 1:  75%|███████▍  | 170/228 [00:02<00:00, 67.25it/s]

→ 1000 (228 images) Phase 1:  84%|████████▍ | 192/228 [00:03<00:00, 92.39it/s]

→ 1000 (228 images) Phase 1:  89%|████████▉ | 204/228 [00:03<00:00, 67.99it/s]

→ 1000 (228 images) Phase 1:  99%|█████████▊| 225/228 [00:03<00:00, 83.83it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/228 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/228 [00:00<00:40,  5.58it/s]

→ 1000 Phase 2: corners:   6%|▌         | 13/228 [00:00<00:03, 54.50it/s]

→ 1000 Phase 2: corners:  12%|█▏        | 27/228 [00:00<00:02, 83.22it/s]

→ 1000 Phase 2: corners:  18%|█▊        | 41/228 [00:00<00:01, 102.55it/s]

→ 1000 Phase 2: corners:  25%|██▍       | 56/228 [00:00<00:01, 114.79it/s]

→ 1000 Phase 2: corners:  30%|███       | 69/228 [00:00<00:01, 114.61it/s]

→ 1000 Phase 2: corners:  36%|███▌      | 81/228 [00:00<00:01, 114.05it/s]

→ 1000 Phase 2: corners:  41%|████      | 94/228 [00:00<00:01, 118.73it/s]

→ 1000 Phase 2: corners:  47%|████▋     | 107/228 [00:01<00:01, 113.00it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 121/228 [00:01<00:00, 119.63it/s]

→ 1000 Phase 2: corners:  59%|█████▉    | 134/228 [00:01<00:00, 120.43it/s]

→ 1000 Phase 2: corners:  66%|██████▌   | 150/228 [00:01<00:00, 131.33it/s]

→ 1000 Phase 2: corners:  73%|███████▎  | 166/228 [00:01<00:00, 137.71it/s]

→ 1000 Phase 2: corners:  79%|███████▉  | 180/228 [00:01<00:00, 137.50it/s]

→ 1000 Phase 2: corners:  86%|████████▋ | 197/228 [00:01<00:00, 143.50it/s]

→ 1000 Phase 2: corners:  98%|█████████▊| 223/228 [00:01<00:00, 176.69it/s]

                                                                           [PASS2] Frame 0000341: fixed corners {2} (was_fixed=False, 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=True)
[PASS2] Frame 0001124: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001125: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001126: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001127: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001128: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001129: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001286: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001287: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001289: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001126: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001128: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 8 frames fixed, 7 untrusted, 228 total


→ 1000 Phase 3: finalize:   0%|          | 0/228 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   3%|▎         | 6/228 [00:00<00:04, 52.64it/s]

→ 1000 Phase 3: finalize:   6%|▌         | 13/228 [00:00<00:03, 57.92it/s]

→ 1000 Phase 3: finalize:   9%|▉         | 20/228 [00:00<00:03, 59.95it/s]

→ 1000 Phase 3: finalize:  12%|█▏        | 27/228 [00:00<00:03, 60.76it/s]

→ 1000 Phase 3: finalize:  15%|█▍        | 34/228 [00:00<00:03, 61.92it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 41/228 [00:00<00:03, 62.06it/s]

→ 1000 Phase 3: finalize:  21%|██        | 48/228 [00:00<00:02, 62.28it/s]

→ 1000 Phase 3: finalize:  24%|██▍       | 55/228 [00:00<00:02, 59.84it/s]

→ 1000 Phase 3: finalize:  27%|██▋       | 62/228 [00:01<00:02, 57.03it/s]

→ 1000 Phase 3: finalize:  30%|██▉       | 68/228 [00:01<00:02, 55.56it/s]

→ 1000 Phase 3: finalize:  32%|███▏      | 74/228 [00:01<00:02, 54.12it/s]

→ 1000 Phase 3: finalize:  35%|███▌      | 80/228 [00:01<00:02, 52.94it/s]

→ 1000 Phase 3: finalize:  38%|███▊      | 86/228 [00:01<00:02, 52.05it/s]

→ 1000 Phase 3: finalize:  40%|████      | 92/228 [00:01<00:02, 51.42it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 98/228 [00:01<00:02, 50.99it/s]

→ 1000 Phase 3: finalize:  46%|████▌     | 104/228 [00:01<00:02, 50.24it/s]

→ 1000 Phase 3: finalize:  48%|████▊     | 110/228 [00:02<00:02, 49.70it/s]

→ 1000 Phase 3: finalize:  50%|█████     | 115/228 [00:02<00:02, 49.15it/s]

→ 1000 Phase 3: finalize:  53%|█████▎    | 120/228 [00:02<00:02, 48.93it/s]

→ 1000 Phase 3: finalize:  55%|█████▍    | 125/228 [00:02<00:02, 48.76it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 130/228 [00:02<00:02, 48.72it/s]

→ 1000 Phase 3: finalize:  59%|█████▉    | 135/228 [00:02<00:01, 48.82it/s]

→ 1000 Phase 3: finalize:  61%|██████▏   | 140/228 [00:02<00:01, 48.84it/s]

→ 1000 Phase 3: finalize:  64%|██████▎   | 145/228 [00:02<00:01, 48.88it/s]

→ 1000 Phase 3: finalize:  66%|██████▌   | 150/228 [00:02<00:01, 49.12it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 156/228 [00:02<00:01, 49.90it/s]

→ 1000 Phase 3: finalize:  71%|███████   | 162/228 [00:03<00:01, 50.89it/s]

→ 1000 Phase 3: finalize:  74%|███████▎  | 168/228 [00:03<00:01, 51.85it/s]

→ 1000 Phase 3: finalize:  76%|███████▋  | 174/228 [00:03<00:01, 52.69it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 180/228 [00:03<00:00, 53.11it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 186/228 [00:03<00:00, 53.55it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 192/228 [00:03<00:00, 53.92it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 198/228 [00:03<00:00, 54.16it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 204/228 [00:03<00:00, 54.42it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 210/228 [00:03<00:00, 54.47it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 216/228 [00:04<00:00, 54.47it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 222/228 [00:04<00:00, 54.57it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 228/228 [00:04<00:00, 54.09it/s]

                                                                           
[TIMING] 1000 — 228 frames
  Phase 1  radial detect   (parallel)  :   3.62s  (15.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.84s  (8.1 ms/frame)
  Phase 2b corner fix      (sequential):   0.42s
  Phase 3  finalize        (sequential):   4.27s  (18.7 ms/frame)
    decode + undistort                 :   2.17s  (9.5 ms/frame)
    corner apply + refinement          :   1.00s  (4.4 ms/frame)
    segment                            :   0.90s  (3.9 ms/frame)
    smooth                             :   0.13s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.15s


Processing Rigs:  50%|█████     | 1/2 [00:14<00:14, 14.60s/it][INFO] Filtered 1127 stationary frames, 256 remain


→ 500 (256 images) Phase 1:   0%|          | 0/256 [00:00<?, ?it/s]

→ 500 (256 images) Phase 1:   0%|          | 1/256 [00:00<02:39,  1.60it/s]

→ 500 (256 images) Phase 1:  12%|█▎        | 32/256 [00:00<00:03, 58.96it/s]

→ 500 (256 images) Phase 1:  20%|█▉        | 51/256 [00:01<00:03, 53.69it/s]

→ 500 (256 images) Phase 1:  25%|██▌       | 65/256 [00:01<00:04, 46.58it/s]

→ 500 (256 images) Phase 1:  37%|███▋      | 94/256 [00:01<00:02, 79.23it/s]

→ 500 (256 images) Phase 1:  43%|████▎     | 110/256 [00:01<00:02, 62.69it/s]

→ 500 (256 images) Phase 1:  50%|█████     | 129/256 [00:02<00:02, 58.14it/s]

→ 500 (256 images) Phase 1:  60%|██████    | 154/256 [00:02<00:01, 81.67it/s]

→ 500 (256 images) Phase 1:  66%|██████▌   | 169/256 [00:02<00:01, 64.79it/s]

→ 500 (256 images) Phase 1:  75%|███████▍  | 191/256 [00:02<00:00, 83.51it/s]

→ 500 (256 images) Phase 1:  80%|████████  | 205/256 [00:03<00:00, 66.10it/s]

→ 500 (256 images) Phase 1:  88%|████████▊ | 225/256 [00:03<00:00, 63.18it/s]

→ 500 (256 images) Phase 1:  96%|█████████▋| 247/256 [00:03<00:00, 83.17it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/256 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/256 [00:00<01:07,  3.78it/s]

→ 500 Phase 2: corners:   8%|▊         | 21/256 [00:00<00:03, 65.32it/s]

→ 500 Phase 2: corners:  14%|█▍        | 36/256 [00:00<00:02, 89.21it/s]

→ 500 Phase 2: corners:  20%|█▉        | 51/256 [00:00<00:01, 106.38it/s]

→ 500 Phase 2: corners:  25%|██▌       | 64/256 [00:00<00:01, 107.25it/s]

→ 500 Phase 2: corners:  30%|██▉       | 76/256 [00:00<00:01, 110.82it/s]

→ 500 Phase 2: corners:  35%|███▍      | 89/256 [00:00<00:01, 114.69it/s]

→ 500 Phase 2: corners:  40%|███▉      | 102/256 [00:01<00:01, 111.11it/s]

→ 500 Phase 2: corners:  46%|████▌     | 118/256 [00:01<00:01, 121.30it/s]

→ 500 Phase 2: corners:  52%|█████▏    | 133/256 [00:01<00:00, 128.47it/s]

→ 500 Phase 2: corners:  57%|█████▋    | 147/256 [00:01<00:00, 119.67it/s]

→ 500 Phase 2: corners:  62%|██████▎   | 160/256 [00:01<00:00, 115.45it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 175/256 [00:01<00:00, 121.50it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 188/256 [00:01<00:00, 114.23it/s]

→ 500 Phase 2: corners:  79%|███████▉  | 203/256 [00:01<00:00, 122.10it/s]

→ 500 Phase 2: corners:  84%|████████▍ | 216/256 [00:01<00:00, 116.51it/s]

→ 500 Phase 2: corners:  91%|█████████ | 233/256 [00:02<00:00, 127.58it/s]

                                                                          [PASS2] Summary: 0 frames fixed, 0 untrusted, 256 total


→ 500 Phase 3: finalize:   0%|          | 0/256 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   3%|▎         | 7/256 [00:00<00:04, 61.26it/s]

→ 500 Phase 3: finalize:   5%|▌         | 14/256 [00:00<00:03, 64.27it/s]

→ 500 Phase 3: finalize:   8%|▊         | 21/256 [00:00<00:03, 64.79it/s]

→ 500 Phase 3: finalize:  11%|█         | 28/256 [00:00<00:03, 65.48it/s]

→ 500 Phase 3: finalize:  14%|█▎        | 35/256 [00:00<00:03, 66.21it/s]

→ 500 Phase 3: finalize:  16%|█▋        | 42/256 [00:00<00:03, 66.62it/s]

→ 500 Phase 3: finalize:  19%|█▉        | 49/256 [00:00<00:03, 66.83it/s]

→ 500 Phase 3: finalize:  22%|██▏       | 56/256 [00:00<00:03, 65.04it/s]

→ 500 Phase 3: finalize:  25%|██▍       | 63/256 [00:00<00:03, 60.90it/s]

→ 500 Phase 3: finalize:  27%|██▋       | 70/256 [00:01<00:03, 58.32it/s]

→ 500 Phase 3: finalize:  30%|██▉       | 76/256 [00:01<00:03, 56.91it/s]

→ 500 Phase 3: finalize:  32%|███▏      | 82/256 [00:01<00:03, 55.91it/s]

→ 500 Phase 3: finalize:  34%|███▍      | 88/256 [00:01<00:03, 54.94it/s]

→ 500 Phase 3: finalize:  37%|███▋      | 94/256 [00:01<00:02, 54.07it/s]

→ 500 Phase 3: finalize:  39%|███▉      | 100/256 [00:01<00:02, 53.54it/s]

→ 500 Phase 3: finalize:  41%|████▏     | 106/256 [00:01<00:02, 53.36it/s]

→ 500 Phase 3: finalize:  44%|████▍     | 112/256 [00:01<00:02, 53.18it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 118/256 [00:02<00:02, 53.04it/s]

→ 500 Phase 3: finalize:  48%|████▊     | 124/256 [00:02<00:02, 52.90it/s]

→ 500 Phase 3: finalize:  51%|█████     | 130/256 [00:02<00:02, 52.81it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 136/256 [00:02<00:02, 52.73it/s]

→ 500 Phase 3: finalize:  55%|█████▌    | 142/256 [00:02<00:02, 52.70it/s]

→ 500 Phase 3: finalize:  58%|█████▊    | 148/256 [00:02<00:02, 52.74it/s]

→ 500 Phase 3: finalize:  60%|██████    | 154/256 [00:02<00:01, 52.71it/s]

→ 500 Phase 3: finalize:  62%|██████▎   | 160/256 [00:02<00:01, 52.74it/s]

→ 500 Phase 3: finalize:  65%|██████▍   | 166/256 [00:02<00:01, 52.66it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 172/256 [00:03<00:01, 52.68it/s]

→ 500 Phase 3: finalize:  70%|██████▉   | 178/256 [00:03<00:01, 52.87it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 184/256 [00:03<00:01, 53.27it/s]

→ 500 Phase 3: finalize:  74%|███████▍  | 190/256 [00:03<00:01, 53.52it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 196/256 [00:03<00:01, 53.63it/s]

→ 500 Phase 3: finalize:  79%|███████▉  | 202/256 [00:03<00:01, 53.78it/s]

→ 500 Phase 3: finalize:  81%|████████▏ | 208/256 [00:03<00:00, 53.65it/s]

→ 500 Phase 3: finalize:  84%|████████▎ | 214/256 [00:03<00:00, 53.78it/s]

→ 500 Phase 3: finalize:  86%|████████▌ | 220/256 [00:03<00:00, 53.79it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 226/256 [00:04<00:00, 53.90it/s]

→ 500 Phase 3: finalize:  91%|█████████ | 232/256 [00:04<00:00, 53.95it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 238/256 [00:04<00:00, 54.01it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 244/256 [00:04<00:00, 53.71it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 250/256 [00:04<00:00, 53.61it/s]

→ 500 Phase 3: finalize: 100%|██████████| 256/256 [00:04<00:00, 53.65it/s]

                                                                          
[TIMING] 500 — 256 frames
  Phase 1  radial detect   (parallel)  :   3.87s  (15.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.22s  (8.7 ms/frame)
  Phase 2b corner fix      (sequential):   0.05s
  Phase 3  finalize        (sequential):   4.62s  (18.0 ms/frame)
    decode + undistort                 :   2.41s  (9.4 ms/frame)
    corner apply + refinement          :   0.97s  (3.8 ms/frame)
    segment                            :   1.01s  (4.0 ms/frame)
    smooth                             :   0.14s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.76s


Processing Rigs: 100%|██████████| 2/2 [00:29<00:00, 14.90s/it]
Processing Rigs: 100%|██████████| 2/2 [00:29<00:00, 14.86s/it]
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 484 frames from 2 rigs to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 228
[📋] 500: 100 frames selected from 256
[💾] Saved RigFrameDataset to /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC015_W038_2025-05-14/calibration/debug/1_digitized/rig_frames.json

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

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

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:03<02:01,  3.12s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<01:58,  3.12s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:54,  3.09s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:51,  3.10s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:48,  3.09s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:45,  3.09s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:21<01:43,  3.14s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:41,  3.16s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:28<01:38,  3.18s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:35,  3.18s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:34<01:32,  3.20s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:37<01:29,  3.20s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:40<01:25,  3.16s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:44<01:21,  3.15s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:47<01:18,  3.13s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:50<01:14,  3.12s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:53<01:11,  3.11s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:56<01:08,  3.10s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [00:59<01:04,  3.09s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:00<00:52,  2.61s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:04<00:52,  2.78s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:07<00:52,  2.89s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:10<00:50,  2.98s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:13<00:48,  3.04s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:16<00:46,  3.07s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:19<00:43,  3.09s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:23<00:40,  3.10s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:26<00:37,  3.11s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:29<00:34,  3.13s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:32<00:31,  3.16s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:35<00:28,  3.18s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:39<00:25,  3.21s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:42<00:22,  3.21s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:45<00:19,  3.20s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:48<00:16,  3.21s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:51<00:12,  3.19s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:55<00:09,  3.18s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [01:58<00:06,  3.18s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:01<00:03,  3.19s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:03<00:00,  2.90s/it]
                                                                                       
[✅] Casing detection complete: cx=998.0, cy=744.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:25,  3.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:25,  3.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   2%|▏         | 2/100 [00:00<00:25,  3.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   3%|▎         | 3/100 [00:00<00:25,  3.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   4%|▍         | 4/100 [00:00<00:24,  3.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   5%|▌         | 5/100 [00:00<00:24,  3.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   6%|▌         | 6/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   6%|▌         | 6/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   7%|▋         | 7/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   8%|▊         | 8/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   9%|▉         | 9/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  10%|█         | 10/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  11%|█         | 11/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  12%|█▏        | 12/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  13%|█▎        | 13/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  14%|█▍        | 14/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  15%|█▌        | 15/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  16%|█▌        | 16/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  17%|█▋        | 17/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  18%|█▊        | 18/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.034 | Best: {'fx': 519.192} (0.034):  19%|█▉        | 19/100 [00:00<00:04, 19.23it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.034 | Best: {'fx': 519.192} (0.034):  20%|██        | 20/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.062 | Best: {'fx': 526.263} (0.062):  20%|██        | 20/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.096 | Best: {'fx': 533.333} (0.096):  21%|██        | 21/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.047 | Best: {'fx': 533.333} (0.096):  22%|██▏       | 22/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  23%|██▎       | 23/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.033 | Best: {'fx': 533.333} (0.096):  24%|██▍       | 24/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  25%|██▌       | 25/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.026 | Best: {'fx': 533.333} (0.096):  26%|██▌       | 26/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.096):  27%|██▋       | 27/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.096):  28%|██▊       | 28/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.019 | Best: {'fx': 533.333} (0.096):  29%|██▉       | 29/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  30%|███       | 30/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  31%|███       | 31/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  32%|███▏      | 32/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.096):  33%|███▎      | 33/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.096):  34%|███▍      | 34/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  35%|███▌      | 35/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.096):  36%|███▌      | 36/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.096):  37%|███▋      | 37/100 [00:00<00:01, 53.64it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.096):  38%|███▊      | 38/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  38%|███▊      | 38/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  39%|███▉      | 39/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.003 | Best: {'fx': 533.333} (0.096):  40%|████      | 40/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.096):  41%|████      | 41/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.096):  42%|████▏     | 42/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.096):  43%|████▎     | 43/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.006 | Best: {'fx': 533.333} (0.096):  44%|████▍     | 44/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.096):  45%|████▌     | 45/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.096):  46%|████▌     | 46/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.096):  47%|████▋     | 47/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.096):  48%|████▊     | 48/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.096):  49%|████▉     | 49/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.096):  50%|█████     | 50/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  51%|█████     | 51/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.096):  52%|█████▏    | 52/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.096):  53%|█████▎    | 53/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  54%|█████▍    | 54/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  55%|█████▌    | 55/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  56%|█████▌    | 56/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  57%|█████▋    | 57/100 [00:00<00:00, 90.13it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  58%|█████▊    | 58/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  58%|█████▊    | 58/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  59%|█████▉    | 59/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  60%|██████    | 60/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  61%|██████    | 61/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  62%|██████▏   | 62/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  63%|██████▎   | 63/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  64%|██████▍   | 64/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.096):  65%|██████▌   | 65/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  66%|██████▌   | 66/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  67%|██████▋   | 67/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  68%|██████▊   | 68/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  69%|██████▉   | 69/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  70%|███████   | 70/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  71%|███████   | 71/100 [00:00<00:00, 121.62it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  72%|███████▏  | 72/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  72%|███████▏  | 72/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  73%|███████▎  | 73/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  74%|███████▍  | 74/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  75%|███████▌  | 75/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  76%|███████▌  | 76/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  77%|███████▋  | 77/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  78%|███████▊  | 78/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  79%|███████▉  | 79/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  80%|████████  | 80/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  81%|████████  | 81/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  82%|████████▏ | 82/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  83%|████████▎ | 83/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  84%|████████▍ | 84/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  85%|████████▌ | 85/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  86%|████████▌ | 86/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  87%|████████▋ | 87/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  88%|████████▊ | 88/100 [00:00<00:00, 118.86it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  89%|████████▉ | 89/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  89%|████████▉ | 89/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  90%|█████████ | 90/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  91%|█████████ | 91/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  92%|█████████▏| 92/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  93%|█████████▎| 93/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  94%|█████████▍| 94/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  95%|█████████▌| 95/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  96%|█████████▌| 96/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  97%|█████████▋| 97/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  98%|█████████▊| 98/100 [00:00<00:00, 130.26it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.096):  99%|█████████▉| 99/100 [00:01<00:00, 130.26it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.018 | Best: {'fx': 510.374} (0.018):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.018 | Best: {'fx': 510.374} (0.018):   2%|▏         | 1/50 [00:00<00:05,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.016 | Best: {'fx': 510.374} (0.018):   2%|▏         | 1/50 [00:00<00:05,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.015 | Best: {'fx': 510.374} (0.018):   4%|▍         | 2/50 [00:00<00:05,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.022 | Best: {'fx': 511.395} (0.022):   6%|▌         | 3/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.025 | Best: {'fx': 513.435} (0.025):   8%|▊         | 4/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.023 | Best: {'fx': 513.435} (0.025):  10%|█         | 5/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.029 | Best: {'fx': 516.497} (0.029):  12%|█▏        | 6/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.026 | Best: {'fx': 516.497} (0.029):  14%|█▍        | 7/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.027 | Best: {'fx': 516.497} (0.029):  16%|█▌        | 8/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.035 | Best: {'fx': 519.558} (0.035):  18%|█▊        | 9/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.031 | Best: {'fx': 519.558} (0.035):  20%|██        | 10/50 [00:00<00:04,  9.53it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.031 | Best: {'fx': 519.558} (0.035):  22%|██▏       | 11/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.044 | Best: {'fx': 522.619} (0.044):  22%|██▏       | 11/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.032 | Best: {'fx': 522.619} (0.044):  24%|██▍       | 12/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.047 | Best: {'fx': 523.639} (0.047):  26%|██▌       | 13/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.038 | Best: {'fx': 523.639} (0.047):  28%|██▊       | 14/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.040 | Best: {'fx': 523.639} (0.047):  30%|███       | 15/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.060 | Best: {'fx': 525.68} (0.060):  32%|███▏      | 16/50 [00:00<00:00, 59.73it/s] 
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.053 | Best: {'fx': 525.68} (0.060):  34%|███▍      | 17/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.085 | Best: {'fx': 528.741} (0.085):  36%|███▌      | 18/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.067 | Best: {'fx': 528.741} (0.085):  38%|███▊      | 19/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.078 | Best: {'fx': 528.741} (0.085):  40%|████      | 20/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.092 | Best: {'fx': 529.762} (0.092):  42%|████▏     | 21/50 [00:00<00:00, 59.73it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.092 | Best: {'fx': 529.762} (0.092):  44%|████▍     | 22/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.096 | Best: {'fx': 530.782} (0.096):  44%|████▍     | 22/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.102 | Best: {'fx': 531.803} (0.102):  46%|████▌     | 23/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.089 | Best: {'fx': 531.803} (0.102):  48%|████▊     | 24/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.094 | Best: {'fx': 531.803} (0.102):  50%|█████     | 25/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.073 | Best: {'fx': 531.803} (0.102):  52%|█████▏    | 26/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.070 | Best: {'fx': 531.803} (0.102):  54%|█████▍    | 27/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.062 | Best: {'fx': 531.803} (0.102):  56%|█████▌    | 28/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.038 | Best: {'fx': 531.803} (0.102):  58%|█████▊    | 29/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.052 | Best: {'fx': 531.803} (0.102):  60%|██████    | 30/50 [00:00<00:00, 80.82it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.052 | Best: {'fx': 531.803} (0.102):  62%|██████▏   | 31/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.056 | Best: {'fx': 531.803} (0.102):  62%|██████▏   | 31/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.051 | Best: {'fx': 531.803} (0.102):  64%|██████▍   | 32/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.037 | Best: {'fx': 531.803} (0.102):  66%|██████▌   | 33/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.045 | Best: {'fx': 531.803} (0.102):  68%|██████▊   | 34/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.033 | Best: {'fx': 531.803} (0.102):  70%|███████   | 35/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.041 | Best: {'fx': 531.803} (0.102):  72%|███████▏  | 36/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.042 | Best: {'fx': 531.803} (0.102):  74%|███████▍  | 37/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.034 | Best: {'fx': 531.803} (0.102):  76%|███████▌  | 38/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.032 | Best: {'fx': 531.803} (0.102):  78%|███████▊  | 39/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.031 | Best: {'fx': 531.803} (0.102):  80%|████████  | 40/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.029 | Best: {'fx': 531.803} (0.102):  82%|████████▏ | 41/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.030 | Best: {'fx': 531.803} (0.102):  84%|████████▍ | 42/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.024 | Best: {'fx': 531.803} (0.102):  86%|████████▌ | 43/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.027 | Best: {'fx': 531.803} (0.102):  88%|████████▊ | 44/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.027 | Best: {'fx': 531.803} (0.102):  90%|█████████ | 45/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.026 | Best: {'fx': 531.803} (0.102):  92%|█████████▏| 46/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.023 | Best: {'fx': 531.803} (0.102):  94%|█████████▍| 47/50 [00:00<00:00, 81.52it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.023 | Best: {'fx': 531.803} (0.102):  96%|█████████▌| 48/50 [00:00<00:00, 111.88it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.025 | Best: {'fx': 531.803} (0.102):  96%|█████████▌| 48/50 [00:00<00:00, 111.88it/s]
Brute | Start: {'fx': 533.333} (0.096) | Current: 0.024 | Best: {'fx': 531.803} (0.102):  98%|█████████▊| 49/50 [00:00<00:00, 111.88it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 998.0
    py: 744.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


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

Initial score: 7.0662: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 7.0662, picklable_objective(x0) = 7.0762

Best score: 7.0762: : 0iter [00:01, ?iter/s] 
Best score: 7.0762: : 1iter [00:01,  1.83s/iter]
Best score: 7.0762: : 1iter [00:01,  1.83s/iter]
Best score: 7.0762: : 2iter [00:02,  1.83s/iter]
Best score: 7.0762: : 3iter [00:02,  1.86iter/s]
Best score: 7.0762: : 3iter [00:02,  1.86iter/s]
Best score: 7.0762: : 4iter [00:02,  1.86iter/s]
Best score: 7.0762: : 5iter [00:02,  1.86iter/s]
Best score: 7.0762: : 6iter [00:02,  1.86iter/s]
Best score: 7.0762: : 7iter [00:02,  5.27iter/s]
Best score: 7.0762: : 7iter [00:02,  5.27iter/s]
Best score: 7.0762: : 8iter [00:02,  5.27iter/s]
Best score: 7.0762: : 9iter [00:02,  5.27iter/s]
Best score: 7.0762: : 10iter [00:02,  5.27iter/s]
Best score: 7.0762: : 11iter [00:02,  9.18iter/s]
Best score: 7.0762: : 11iter [00:02,  9.18iter/s]
Best score: 7.0762: : 12iter [00:02,  9.18iter/s]
Best score: 7.0762: : 13iter [00:02,  9.18iter/s]
Best score: 7.0762: : 14iter [00:02,  9.18iter/s]
Best score: 7.0762: : 15iter [00:02, 13.45iter/s]
Best score: 7.0762: : 15iter [00:02, 13.45iter/s]
Best score: 7.0762: : 16iter [00:02, 13.45iter/s]
Best score: 7.0762: : 17iter [00:02, 13.45iter/s]
Best score: 7.0762: : 18iter [00:02, 13.45iter/s]
Best score: 7.0762: : 19iter [00:02, 17.75iter/s]
Best score: 7.0762: : 19iter [00:02, 17.75iter/s]
Best score: 7.0762: : 20iter [00:02, 17.75iter/s]
Best score: 7.0762: : 21iter [00:02, 17.75iter/s]
Best score: 7.0762: : 22iter [00:02, 17.75iter/s]
Best score: 7.0762: : 23iter [00:02, 21.91iter/s]
Best score: 7.0762: : 23iter [00:02, 21.91iter/s]
Best score: 7.0762: : 24iter [00:02, 21.91iter/s]
Best score: 7.0762: : 25iter [00:02, 21.91iter/s]
Best score: 7.0762: : 26iter [00:02, 21.91iter/s]
Best score: 7.0762: : 27iter [00:02, 25.75iter/s]
Best score: 7.0762: : 27iter [00:02, 25.75iter/s]
Best score: 7.0762: : 28iter [00:02, 25.75iter/s]
Best score: 7.0762: : 29iter [00:02, 25.75iter/s]
Best score: 7.0762: : 30iter [00:02, 25.75iter/s]
Best score: 7.0762: : 31iter [00:02, 28.99iter/s]
Best score: 7.0762: : 31iter [00:02, 28.99iter/s]
Best score: 7.0762: : 32iter [00:02, 28.99iter/s]
Best score: 7.0762: : 33iter [00:02, 28.99iter/s]
Best score: 7.0762: : 34iter [00:02, 28.99iter/s]
Best score: 7.0762: : 35iter [00:02, 31.73iter/s]
Best score: 7.0762: : 35iter [00:02, 31.73iter/s]
Best score: 7.0762: : 36iter [00:02, 31.73iter/s]
Best score: 7.0762: : 37iter [00:02, 31.73iter/s]
Best score: 7.0762: : 38iter [00:02, 31.73iter/s]
Best score: 7.0762: : 39iter [00:02, 33.89iter/s]
Best score: 7.0762: : 39iter [00:02, 33.89iter/s]
Best score: 7.0762: : 40iter [00:02, 33.89iter/s]
Best score: 7.0762: : 41iter [00:02, 33.89iter/s]
Best score: 7.0762: : 42iter [00:03, 33.89iter/s]
Best score: 7.0762: : 43iter [00:03, 35.23iter/s]
Best score: 7.0762: : 43iter [00:03, 35.23iter/s]
Best score: 7.0762: : 44iter [00:03, 35.23iter/s]
Best score: 7.0762: : 45iter [00:03, 35.23iter/s]
Best score: 7.0762: : 46iter [00:03, 35.23iter/s]
Best score: 7.0762: : 47iter [00:03, 35.23iter/s]
Best score: 7.0762: : 48iter [00:03, 36.83iter/s]
Best score: 7.0762: : 48iter [00:03, 36.83iter/s]
[ITER 50] Best: 7.0762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 744.0000
  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: 7.0762: : 49iter [00:03, 36.83iter/s]
Best score: 7.0762: : 50iter [00:03, 36.83iter/s]
Best score: 7.0762: : 51iter [00:03, 36.83iter/s]
Best score: 7.0762: : 52iter [00:03, 37.44iter/s]
Best score: 7.0762: : 52iter [00:03, 37.44iter/s]
Best score: 7.0762: : 53iter [00:03, 37.44iter/s]
Best score: 7.0762: : 54iter [00:03, 37.44iter/s]
Best score: 7.0762: : 55iter [00:03, 37.44iter/s]
Best score: 7.0762: : 56iter [00:03, 38.12iter/s]
Best score: 7.0762: : 56iter [00:03, 38.12iter/s]
Best score: 7.0762: : 57iter [00:03, 38.12iter/s]
Best score: 7.0762: : 58iter [00:03, 38.12iter/s]
Best score: 7.0762: : 59iter [00:03, 38.12iter/s]
Best score: 7.0762: : 60iter [00:03, 38.37iter/s]
Best score: 7.0762: : 60iter [00:03, 38.37iter/s]
Best score: 7.0762: : 61iter [00:03, 38.37iter/s]
Best score: 7.0762: : 62iter [00:03, 38.37iter/s]
Best score: 7.0762: : 63iter [00:03, 38.37iter/s]
Best score: 7.0762: : 64iter [00:03, 38.37iter/s]
Best score: 7.0762: : 65iter [00:03, 38.97iter/s]
Best score: 7.0762: : 65iter [00:03, 38.97iter/s]
Best score: 7.0762: : 66iter [00:03, 38.97iter/s]
Best score: 7.0762: : 67iter [00:03, 38.97iter/s]
Best score: 7.0762: : 68iter [00:03, 38.97iter/s]
Best score: 7.0762: : 69iter [00:03, 38.97iter/s]
Best score: 7.0762: : 70iter [00:03, 39.19iter/s]
Best score: 7.0762: : 70iter [00:03, 39.19iter/s]
Best score: 7.0762: : 71iter [00:03, 39.19iter/s]
Best score: 7.0762: : 72iter [00:03, 39.19iter/s]
Best score: 7.0762: : 73iter [00:03, 39.19iter/s]
Best score: 7.0762: : 74iter [00:03, 39.19iter/s]
Best score: 7.0762: : 75iter [00:03, 39.48iter/s]
Best score: 7.0762: : 75iter [00:03, 39.48iter/s]
Best score: 7.0762: : 76iter [00:03, 39.48iter/s]
Best score: 7.0762: : 77iter [00:03, 39.48iter/s]
Best score: 7.0762: : 78iter [00:03, 39.48iter/s]
Best score: 7.0762: : 79iter [00:03, 39.48iter/s]
Best score: 7.0762: : 80iter [00:03, 39.51iter/s]
Best score: 7.0762: : 80iter [00:03, 39.51iter/s]
Best score: 7.0762: : 81iter [00:03, 39.51iter/s]
Best score: 7.0762: : 82iter [00:04, 39.51iter/s]
Best score: 7.0762: : 83iter [00:04, 39.51iter/s]
Best score: 7.0762: : 84iter [00:04, 39.51iter/s]
Best score: 7.0762: : 85iter [00:04, 39.84iter/s]
Best score: 7.0762: : 85iter [00:04, 39.84iter/s]
Best score: 7.0762: : 86iter [00:04, 39.84iter/s]
Best score: 7.0762: : 87iter [00:04, 39.84iter/s]
Best score: 7.0762: : 88iter [00:04, 39.84iter/s]
Best score: 7.0762: : 89iter [00:04, 39.84iter/s]
Best score: 7.0762: : 90iter [00:04, 39.85iter/s]
Best score: 7.0762: : 90iter [00:04, 39.85iter/s]
Best score: 7.0762: : 91iter [00:04, 39.85iter/s]
Best score: 7.0762: : 92iter [00:04, 39.85iter/s]
Best score: 7.0762: : 93iter [00:04, 39.85iter/s]
Best score: 7.0762: : 94iter [00:04, 39.85iter/s]
Best score: 7.0762: : 95iter [00:04, 40.10iter/s]
Best score: 7.0762: : 95iter [00:04, 40.10iter/s]
Best score: 7.0762: : 96iter [00:04, 40.10iter/s]
Best score: 7.0762: : 97iter [00:04, 40.10iter/s]
Best score: 7.0762: : 98iter [00:04, 40.10iter/s]
[ITER 100] Best: 7.0762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 744.0000
  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: 7.0762: : 99iter [00:04, 40.10iter/s]
Best score: 7.0762: : 100iter [00:04, 40.12iter/s]
Best score: 7.0762: : 100iter [00:04, 40.12iter/s]
Best score: 7.0762: : 101iter [00:04, 40.12iter/s]
Best score: 7.0762: : 102iter [00:04, 40.12iter/s]
Best score: 7.0762: : 103iter [00:04, 40.12iter/s]
Best score: 7.0762: : 104iter [00:04, 40.12iter/s]
Best score: 7.0762: : 105iter [00:04, 37.81iter/s]
Best score: 7.0762: : 105iter [00:04, 37.81iter/s]
Best score: 7.0762: : 106iter [00:04, 37.81iter/s]
Best score: 7.0762: : 107iter [00:04, 37.81iter/s]
Best score: 7.0762: : 108iter [00:04, 37.81iter/s]
Best score: 7.0762: : 109iter [00:04, 38.29iter/s]
Best score: 7.0762: : 109iter [00:04, 38.29iter/s]
Best score: 7.0762: : 110iter [00:04, 38.29iter/s]
Best score: 7.0762: : 111iter [00:04, 38.29iter/s]
Best score: 7.0762: : 112iter [00:04, 38.29iter/s]
Best score: 7.0762: : 113iter [00:04, 38.45iter/s]
Best score: 7.0762: : 113iter [00:04, 38.45iter/s]
Best score: 7.0762: : 114iter [00:04, 38.45iter/s]
Best score: 7.0762: : 115iter [00:04, 38.45iter/s]
Best score: 7.0762: : 116iter [00:04, 38.45iter/s]
Best score: 7.0762: : 117iter [00:04, 38.80iter/s]
Best score: 7.0762: : 117iter [00:04, 38.80iter/s]
Best score: 7.0762: : 118iter [00:04, 38.80iter/s]
Best score: 7.0762: : 119iter [00:04, 38.80iter/s]
Best score: 7.0762: : 120iter [00:04, 38.80iter/s]
Best score: 7.0762: : 121iter [00:04, 38.92iter/s]
Best score: 7.0762: : 121iter [00:05, 38.92iter/s]
Best score: 7.0762: : 122iter [00:05, 38.92iter/s]
Best score: 7.0762: : 123iter [00:05, 38.92iter/s]
Best score: 7.0762: : 124iter [00:05, 38.92iter/s]
Best score: 7.0762: : 125iter [00:05, 39.13iter/s]
Best score: 7.0762: : 125iter [00:05, 39.13iter/s]
Best score: 7.0762: : 126iter [00:05, 39.13iter/s]
Best score: 7.0762: : 127iter [00:05, 39.13iter/s]
Best score: 7.0762: : 128iter [00:05, 39.13iter/s]
Best score: 7.0762: : 129iter [00:05, 39.32iter/s]
Best score: 7.0762: : 129iter [00:05, 39.32iter/s]
Best score: 7.0762: : 130iter [00:05, 39.32iter/s]
Best score: 7.0762: : 131iter [00:05, 39.32iter/s]
Best score: 7.0762: : 132iter [00:05, 39.32iter/s]
Best score: 7.0762: : 133iter [00:05, 39.38iter/s]
Best score: 7.0762: : 133iter [00:05, 39.38iter/s]
Best score: 7.0762: : 134iter [00:05, 39.38iter/s]
Best score: 7.0762: : 135iter [00:05, 39.38iter/s]
Best score: 7.0762: : 136iter [00:05, 39.38iter/s]
Best score: 7.0762: : 137iter [00:05, 39.56iter/s]
Best score: 7.0762: : 137iter [00:05, 39.56iter/s]
Best score: 7.0762: : 138iter [00:05, 39.56iter/s]
Best score: 7.0762: : 139iter [00:05, 39.56iter/s]
Best score: 7.0762: : 140iter [00:05, 39.56iter/s]
Best score: 7.0762: : 141iter [00:05, 39.43iter/s]
Best score: 7.0762: : 141iter [00:05, 39.43iter/s]
Best score: 7.0762: : 142iter [00:05, 39.43iter/s]
Best score: 7.0762: : 143iter [00:05, 39.43iter/s]
Best score: 7.0762: : 144iter [00:05, 39.43iter/s]
Best score: 7.0762: : 145iter [00:05, 39.43iter/s]
Best score: 7.0762: : 146iter [00:05, 39.65iter/s]
Best score: 7.0762: : 146iter [00:05, 39.65iter/s]
Best score: 7.0762: : 147iter [00:05, 39.65iter/s]
Best score: 7.0762: : 148iter [00:05, 39.65iter/s]
[ITER 150] Best: 7.0762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 744.0000
  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: 7.0762: : 149iter [00:05, 39.65iter/s]
Best score: 7.0762: : 150iter [00:05, 39.54iter/s]
Best score: 7.0762: : 150iter [00:05, 39.54iter/s]
Best score: 7.0762: : 151iter [00:05, 39.54iter/s]
Best score: 7.0762: : 152iter [00:05, 39.54iter/s]
Best score: 7.0762: : 153iter [00:05, 39.54iter/s]
Best score: 7.0762: : 154iter [00:05, 32.53iter/s]
Best score: 7.0762: : 154iter [00:05, 32.53iter/s]
Best score: 7.0762: : 155iter [00:05, 32.53iter/s]
Best score: 7.0762: : 156iter [00:05, 32.53iter/s]
Best score: 7.0762: : 157iter [00:06, 32.53iter/s]
Best score: 7.0762: : 158iter [00:06, 34.40iter/s]
Best score: 7.0762: : 158iter [00:06, 34.40iter/s]
Best score: 7.0762: : 159iter [00:06, 34.40iter/s]
Best score: 7.0762: : 160iter [00:06, 34.40iter/s]
Best score: 7.0762: : 161iter [00:06, 34.40iter/s]
Best score: 7.0762: : 162iter [00:06, 35.66iter/s]
Best score: 7.0762: : 162iter [00:06, 35.66iter/s]
Best score: 7.0762: : 163iter [00:06, 35.66iter/s]
Best score: 7.0762: : 164iter [00:06, 35.66iter/s]
Best score: 7.0762: : 165iter [00:06, 35.66iter/s]
Best score: 7.0762: : 166iter [00:06, 36.74iter/s]
Best score: 7.0762: : 166iter [00:06, 36.74iter/s]
Best score: 7.0762: : 167iter [00:06, 36.74iter/s]
Best score: 7.0762: : 168iter [00:06, 36.74iter/s]
Best score: 7.0762: : 169iter [00:06, 36.74iter/s]
Best score: 7.0762: : 170iter [00:06, 37.36iter/s]
Best score: 7.0762: : 170iter [00:06, 37.36iter/s]
Best score: 7.0762: : 171iter [00:06, 37.36iter/s]
Best score: 7.0762: : 172iter [00:06, 37.36iter/s]
Best score: 7.0762: : 173iter [00:06, 37.36iter/s]
Best score: 7.0762: : 174iter [00:06, 38.04iter/s]
Best score: 7.0762: : 174iter [00:06, 38.04iter/s]
Best score: 7.0762: : 175iter [00:06, 38.04iter/s]
Best score: 7.0762: : 176iter [00:06, 38.04iter/s]
Best score: 7.0762: : 177iter [00:06, 38.04iter/s]
Best score: 7.0762: : 178iter [00:06, 38.58iter/s]
Best score: 7.0762: : 178iter [00:06, 38.58iter/s]
Best score: 7.0762: : 179iter [00:06, 38.58iter/s]
Best score: 7.0762: : 180iter [00:06, 38.58iter/s]
Best score: 7.0762: : 181iter [00:06, 38.58iter/s]
Best score: 7.0762: : 182iter [00:06, 38.81iter/s]
Best score: 7.0762: : 182iter [00:06, 38.81iter/s]
Best score: 7.0762: : 183iter [00:06, 38.81iter/s]
Best score: 7.0762: : 184iter [00:06, 38.81iter/s]
Best score: 7.0762: : 185iter [00:06, 38.81iter/s]
Best score: 7.0762: : 186iter [00:06, 39.05iter/s]
Best score: 7.0762: : 186iter [00:06, 39.05iter/s]
Best score: 7.0762: : 187iter [00:06, 39.05iter/s]
Best score: 7.0762: : 188iter [00:06, 39.05iter/s]
Best score: 7.0762: : 189iter [00:06, 39.05iter/s]
Best score: 7.0762: : 190iter [00:06, 38.94iter/s]
Best score: 7.0762: : 190iter [00:06, 38.94iter/s]
Best score: 7.0762: : 191iter [00:06, 38.94iter/s]
Best score: 7.0762: : 192iter [00:06, 38.94iter/s]
Best score: 7.0762: : 193iter [00:06, 38.94iter/s]
Best score: 7.0762: : 194iter [00:06, 39.25iter/s]
Best score: 7.0762: : 194iter [00:06, 39.25iter/s]
Best score: 7.0762: : 195iter [00:06, 39.25iter/s]
Best score: 7.0762: : 196iter [00:06, 39.25iter/s]
Best score: 7.0762: : 197iter [00:07, 39.25iter/s]
Best score: 7.0762: : 198iter [00:07, 39.38iter/s]
Best score: 7.0762: : 198iter [00:07, 39.38iter/s]
[ITER 200] Best: 7.0762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 744.0000
  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: 7.0762: : 199iter [00:07, 39.38iter/s]
Best score: 7.0762: : 200iter [00:07, 39.38iter/s]
Best score: 7.0762: : 201iter [00:07, 39.38iter/s]
Best score: 7.0762: : 202iter [00:07, 32.13iter/s]
Best score: 7.0762: : 202iter [00:07, 32.13iter/s]
Best score: 7.0762: : 203iter [00:07, 32.13iter/s]
Best score: 7.0762: : 204iter [00:07, 32.13iter/s]
Best score: 7.0762: : 205iter [00:07, 32.13iter/s]
Best score: 7.0762: : 206iter [00:07, 32.13iter/s]
Best score: 7.0762: : 207iter [00:07, 34.53iter/s]
Best score: 7.0762: : 207iter [00:07, 34.53iter/s]
Best score: 7.0762: : 208iter [00:07, 34.53iter/s]
Best score: 7.0762: : 209iter [00:07, 34.53iter/s]
Best score: 7.0762: : 210iter [00:07, 34.53iter/s]
Best score: 7.0762: : 211iter [00:07, 35.89iter/s]
Best score: 7.0762: : 211iter [00:07, 35.89iter/s]
Best score: 7.0762: : 212iter [00:07, 35.89iter/s]
Best score: 7.0762: : 213iter [00:07, 35.89iter/s]
Best score: 7.0762: : 214iter [00:07, 35.89iter/s]
Best score: 7.0762: : 215iter [00:07, 35.89iter/s]
Best score: 7.0762: : 216iter [00:07, 37.22iter/s]
Best score: 7.0762: : 216iter [00:07, 37.22iter/s]
Best score: 7.0762: : 217iter [00:07, 37.22iter/s]
Best score: 7.0762: : 218iter [00:07, 37.22iter/s]
Best score: 7.0762: : 219iter [00:07, 37.22iter/s]
Best score: 7.0762: : 220iter [00:07, 37.92iter/s]
Best score: 7.0762: : 220iter [00:07, 37.92iter/s]
Best score: 7.0762: : 221iter [00:07, 37.92iter/s]
Best score: 7.0762: : 222iter [00:07, 37.92iter/s]
Best score: 7.0762: : 223iter [00:07, 37.92iter/s]
Best score: 7.0762: : 224iter [00:07, 37.92iter/s]
Best score: 7.0762: : 225iter [00:07, 38.87iter/s]
Best score: 7.0762: : 225iter [00:07, 38.87iter/s]
Best score: 7.0762: : 226iter [00:07, 38.87iter/s]
Best score: 7.0762: : 227iter [00:07, 38.87iter/s]
Best score: 7.0762: : 228iter [00:07, 38.87iter/s]
Best score: 7.0762: : 229iter [00:07, 38.87iter/s]
Best score: 7.0762: : 230iter [00:07, 39.02iter/s]
Best score: 7.0762: : 230iter [00:07, 39.02iter/s]
Best score: 7.0762: : 231iter [00:07, 39.02iter/s]
Best score: 7.0762: : 232iter [00:07, 39.02iter/s]
Best score: 7.0762: : 233iter [00:07, 39.02iter/s]
Best score: 7.0762: : 234iter [00:08, 39.02iter/s]
Best score: 7.0762: : 235iter [00:08, 39.57iter/s]
Best score: 7.0762: : 235iter [00:08, 39.57iter/s]
Best score: 7.0762: : 236iter [00:08, 39.57iter/s]
Best score: 7.0762: : 237iter [00:08, 39.57iter/s]
Best score: 7.0762: : 238iter [00:08, 39.57iter/s]
Best score: 7.0762: : 239iter [00:08, 39.57iter/s]
Best score: 7.0762: : 240iter [00:08, 39.78iter/s]
Best score: 7.0762: : 240iter [00:08, 39.78iter/s]
Best score: 7.0762: : 241iter [00:08, 39.78iter/s]
Best score: 7.0762: : 242iter [00:08, 39.78iter/s]
Best score: 7.0762: : 243iter [00:08, 39.78iter/s]
Best score: 7.0762: : 244iter [00:08, 39.67iter/s]
Best score: 7.0762: : 244iter [00:08, 39.67iter/s]
Best score: 7.0762: : 245iter [00:08, 39.67iter/s]
Best score: 7.0762: : 246iter [00:08, 39.67iter/s]
Best score: 7.0762: : 247iter [00:08, 39.67iter/s]
Best score: 7.0762: : 248iter [00:08, 39.67iter/s]
Best score: 7.0762: : 249iter [00:08, 40.06iter/s]
[ITER 250] Best: 7.0762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 744.0000
  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: 7.0762: : 249iter [00:08, 40.06iter/s]
Best score: 7.0762: : 250iter [00:08, 40.06iter/s]
Best score: 7.0762: : 251iter [00:08, 40.06iter/s]
Best score: 7.0762: : 252iter [00:08, 40.06iter/s]
Best score: 7.0762: : 253iter [00:08, 40.06iter/s]
Best score: 7.0762: : 254iter [00:08, 33.46iter/s]
Best score: 7.0762: : 254iter [00:08, 33.46iter/s]
Best score: 7.0762: : 255iter [00:08, 33.46iter/s]
Best score: 7.0762: : 256iter [00:08, 33.46iter/s]
Best score: 7.0762: : 257iter [00:08, 33.46iter/s]
Best score: 7.0762: : 258iter [00:08, 33.46iter/s]
Best score: 7.0762: : 259iter [00:08, 35.45iter/s]
Best score: 7.0762: : 259iter [00:08, 35.45iter/s]
Best score: 7.0762: : 260iter [00:08, 35.45iter/s]
Best score: 7.0762: : 261iter [00:08, 35.45iter/s]
Best score: 7.0762: : 262iter [00:08, 35.45iter/s]
Best score: 7.0762: : 263iter [00:08, 35.45iter/s]
Best score: 7.0762: : 264iter [00:08, 36.84iter/s]
Best score: 7.0762: : 264iter [00:08, 36.84iter/s]
Best score: 7.0762: : 265iter [00:08, 36.84iter/s]
Best score: 7.0762: : 266iter [00:08, 36.84iter/s]
Best score: 7.0762: : 267iter [00:08, 36.84iter/s]
Best score: 7.0762: : 268iter [00:08, 36.84iter/s]
Best score: 7.0762: : 269iter [00:08, 38.02iter/s]
Best score: 4.7287: : 269iter [00:08, 38.02iter/s]
Best score: 4.7287: : 270iter [00:08, 38.02iter/s]
Best score: 4.7287: : 271iter [00:09, 38.02iter/s]
Best score: 4.7287: : 272iter [00:09, 38.02iter/s]
Best score: 4.7287: : 273iter [00:09, 38.49iter/s]
Best score: 4.7287: : 273iter [00:09, 38.49iter/s]
Best score: 4.7287: : 274iter [00:09, 38.49iter/s]
Best score: 4.7287: : 275iter [00:09, 38.49iter/s]
Best score: 4.7287: : 276iter [00:09, 38.49iter/s]
Best score: 4.7287: : 277iter [00:09, 38.49iter/s]
Best score: 4.7287: : 278iter [00:09, 39.18iter/s]
Best score: 4.7287: : 278iter [00:09, 39.18iter/s]
Best score: 4.4965: : 279iter [00:09, 39.18iter/s]
Best score: 4.4965: : 280iter [00:09, 39.18iter/s]
Best score: 4.4965: : 281iter [00:09, 39.18iter/s]
Best score: 4.4965: : 282iter [00:09, 39.34iter/s]
Best score: 4.4965: : 282iter [00:09, 39.34iter/s]
Best score: 4.4965: : 283iter [00:09, 39.34iter/s]
Best score: 4.4965: : 284iter [00:09, 39.34iter/s]
Best score: 4.4965: : 285iter [00:09, 39.34iter/s]
Best score: 4.4965: : 286iter [00:09, 39.34iter/s]
Best score: 4.4965: : 287iter [00:09, 39.73iter/s]
Best score: 4.4965: : 287iter [00:09, 39.73iter/s]
Best score: 4.4965: : 288iter [00:09, 39.73iter/s]
Best score: 4.4965: : 289iter [00:09, 39.73iter/s]
Best score: 4.4965: : 290iter [00:09, 39.73iter/s]
Best score: 4.4965: : 291iter [00:09, 39.78iter/s]
Best score: 4.4965: : 291iter [00:09, 39.78iter/s]
Best score: 4.4965: : 292iter [00:09, 39.78iter/s]
Best score: 4.4965: : 293iter [00:09, 39.78iter/s]
Best score: 4.4965: : 294iter [00:09, 39.78iter/s]
Best score: 4.4965: : 295iter [00:09, 39.78iter/s]
Best score: 4.4965: : 296iter [00:09, 40.09iter/s]
Best score: 4.4965: : 296iter [00:09, 40.09iter/s]
Best score: 4.4965: : 297iter [00:09, 40.09iter/s]
Best score: 4.4965: : 298iter [00:09, 40.09iter/s]
[ITER 300] Best: 3.6183
[PARAMS] Current best parameters:
  fx: 548.0362
  fy: 548.4548
  px: 996.8252
  py: 744.2325
  k1: -0.02322508
  k2: -0.01678718
  k3: 0.02313694
  k4: -0.00659024
  p1: 0.00388340
  p2: 0.00003095
  p3: -0.00583466
  p4: -0.00104779
  s1: 0.0027
  s2: -0.0001
  s3: 0.0074
  s4: -0.0038
  tilt_x: -0.0069
  tilt_y: 0.0108
  scale: 3.0674
  rotation: -0.4387

Best score: 3.6183: : 299iter [00:09, 40.09iter/s]
Best score: 3.6183: : 300iter [00:09, 40.09iter/s]
Best score: 3.6183: : 301iter [00:09, 33.24iter/s]
Best score: 3.6183: : 301iter [00:09, 33.24iter/s]
Best score: 3.6183: : 302iter [00:09, 33.24iter/s]
Best score: 3.6183: : 303iter [00:09, 33.24iter/s]
Best score: 3.6183: : 304iter [00:09, 33.24iter/s]
Best score: 3.6183: : 305iter [00:09, 34.79iter/s]
Best score: 3.6183: : 305iter [00:09, 34.79iter/s]
Best score: 3.6183: : 306iter [00:09, 34.79iter/s]
Best score: 3.6183: : 307iter [00:09, 34.79iter/s]
Best score: 3.6183: : 308iter [00:10, 34.79iter/s]
Best score: 3.5163: : 309iter [00:10, 34.79iter/s]
Best score: 3.5163: : 310iter [00:10, 36.27iter/s]
Best score: 3.5163: : 310iter [00:10, 36.27iter/s]
Best score: 3.5163: : 311iter [00:10, 36.27iter/s]
Best score: 3.5163: : 312iter [00:10, 36.27iter/s]
Best score: 3.5163: : 313iter [00:10, 36.27iter/s]
Best score: 3.5163: : 314iter [00:10, 36.27iter/s]
Best score: 3.5163: : 315iter [00:10, 37.52iter/s]
Best score: 3.5163: : 315iter [00:10, 37.52iter/s]
Best score: 3.5163: : 316iter [00:10, 37.52iter/s]
Best score: 3.5163: : 317iter [00:10, 37.52iter/s]
Best score: 3.5163: : 318iter [00:10, 37.52iter/s]
Best score: 3.1181: : 319iter [00:10, 37.52iter/s]
Best score: 3.1181: : 320iter [00:10, 38.15iter/s]
Best score: 3.1181: : 320iter [00:10, 38.15iter/s]
Best score: 3.1181: : 321iter [00:10, 38.15iter/s]
Best score: 3.1181: : 322iter [00:10, 38.15iter/s]
Best score: 3.1181: : 323iter [00:10, 38.15iter/s]
Best score: 3.1181: : 324iter [00:10, 38.15iter/s]
Best score: 3.1181: : 325iter [00:10, 38.87iter/s]
Best score: 3.1181: : 325iter [00:10, 38.87iter/s]
Best score: 3.1181: : 326iter [00:10, 38.87iter/s]
Best score: 3.1181: : 327iter [00:10, 38.87iter/s]
Best score: 3.1181: : 328iter [00:10, 38.87iter/s]
Best score: 3.1181: : 329iter [00:10, 38.87iter/s]
Best score: 3.1181: : 330iter [00:10, 39.20iter/s]
Best score: 3.1181: : 330iter [00:10, 39.20iter/s]
Best score: 3.1181: : 331iter [00:10, 39.20iter/s]
Best score: 3.1181: : 332iter [00:10, 39.20iter/s]
Best score: 3.1181: : 333iter [00:10, 39.20iter/s]
Best score: 3.1181: : 334iter [00:10, 39.20iter/s]
Best score: 3.1181: : 335iter [00:10, 39.58iter/s]
Best score: 3.1181: : 335iter [00:10, 39.58iter/s]
Best score: 3.1181: : 336iter [00:10, 39.58iter/s]
Best score: 3.1181: : 337iter [00:10, 39.58iter/s]
Best score: 3.1181: : 338iter [00:10, 39.58iter/s]
Best score: 1.9396: : 339iter [00:10, 39.58iter/s]
Best score: 1.9396: : 340iter [00:10, 39.74iter/s]
Best score: 1.9396: : 340iter [00:10, 39.74iter/s]
Best score: 1.9396: : 341iter [00:10, 39.74iter/s]
Best score: 1.9396: : 342iter [00:10, 39.74iter/s]
Best score: 1.9396: : 343iter [00:10, 39.74iter/s]
Best score: 1.9396: : 344iter [00:10, 39.74iter/s]
Best score: 1.9396: : 345iter [00:10, 40.00iter/s]
Best score: 1.9396: : 345iter [00:10, 40.00iter/s]
Best score: 1.9396: : 346iter [00:10, 40.00iter/s]
Best score: 1.9396: : 347iter [00:10, 40.00iter/s]
Best score: 1.9396: : 348iter [00:11, 40.00iter/s]
[ITER 350] Best: 1.8686
[PARAMS] Current best parameters:
  fx: 556.7967
  fy: 556.4098
  px: 996.0304
  py: 742.9610
  k1: -0.01444745
  k2: -0.05159963
  k3: 0.05046694
  k4: -0.01387295
  p1: 0.01053281
  p2: -0.00495737
  p3: -0.00915668
  p4: 0.00191946
  s1: 0.0023
  s2: 0.0021
  s3: 0.0098
  s4: -0.0084
  tilt_x: -0.0299
  tilt_y: 0.0035
  scale: 3.0561
  rotation: -0.5129

Best score: 1.8686: : 349iter [00:11, 40.00iter/s]
Best score: 1.8686: : 350iter [00:11, 40.03iter/s]
Best score: 1.8686: : 350iter [00:11, 40.03iter/s]
Best score: 1.8686: : 351iter [00:11, 40.03iter/s]
Best score: 1.8686: : 352iter [00:11, 40.03iter/s]
Best score: 1.8686: : 353iter [00:11, 40.03iter/s]
Best score: 1.8686: : 354iter [00:11, 40.03iter/s]
Best score: 1.8686: : 355iter [00:11, 33.83iter/s]
Best score: 1.8686: : 355iter [00:11, 33.83iter/s]
Best score: 1.8686: : 356iter [00:11, 33.83iter/s]
Best score: 1.8686: : 357iter [00:11, 33.83iter/s]
Best score: 1.8686: : 358iter [00:11, 33.83iter/s]
Best score: 1.8686: : 359iter [00:11, 33.83iter/s]
Best score: 1.8686: : 360iter [00:11, 35.53iter/s]
Best score: 1.8686: : 360iter [00:11, 35.53iter/s]
Best score: 1.8686: : 361iter [00:11, 35.53iter/s]
Best score: 1.8686: : 362iter [00:11, 35.53iter/s]
Best score: 1.8686: : 363iter [00:11, 35.53iter/s]
Best score: 1.8686: : 364iter [00:11, 30.63iter/s]
Best score: 1.8686: : 364iter [00:11, 30.63iter/s]
Best score: 1.8686: : 365iter [00:11, 30.63iter/s]
Best score: 1.8686: : 366iter [00:11, 30.63iter/s]
Best score: 1.8686: : 367iter [00:11, 30.63iter/s]
Best score: 1.8686: : 368iter [00:11, 30.63iter/s]
Best score: 1.8686: : 369iter [00:11, 33.31iter/s]
Best score: 1.7746: : 369iter [00:11, 33.31iter/s]
Best score: 1.7746: : 370iter [00:11, 33.31iter/s]
Best score: 1.7746: : 371iter [00:11, 33.31iter/s]
Best score: 1.7746: : 372iter [00:11, 33.31iter/s]
Best score: 1.7746: : 373iter [00:11, 34.19iter/s]
Best score: 1.7746: : 373iter [00:11, 34.19iter/s]
Best score: 1.7746: : 374iter [00:11, 34.19iter/s]
Best score: 1.7746: : 375iter [00:11, 34.19iter/s]
Best score: 1.7746: : 376iter [00:11, 34.19iter/s]
Best score: 1.7746: : 377iter [00:11, 34.19iter/s]
Best score: 1.7746: : 378iter [00:11, 35.99iter/s]
Best score: 1.7746: : 378iter [00:11, 35.99iter/s]
Best score: 1.6498: : 379iter [00:11, 35.99iter/s]
Best score: 1.6498: : 380iter [00:11, 35.99iter/s]
Best score: 1.6498: : 381iter [00:11, 35.99iter/s]
Best score: 1.6498: : 382iter [00:11, 36.82iter/s]
Best score: 1.6498: : 382iter [00:12, 36.82iter/s]
Best score: 1.6498: : 383iter [00:12, 36.82iter/s]
Best score: 1.6498: : 384iter [00:12, 36.82iter/s]
Best score: 1.6498: : 385iter [00:12, 36.82iter/s]
Best score: 1.6498: : 386iter [00:12, 36.82iter/s]
Best score: 1.6498: : 387iter [00:12, 37.96iter/s]
Best score: 1.6498: : 387iter [00:12, 37.96iter/s]
Best score: 1.6498: : 388iter [00:12, 37.96iter/s]
Best score: 1.3162: : 389iter [00:12, 37.96iter/s]
Best score: 1.3162: : 390iter [00:12, 37.96iter/s]
Best score: 1.3162: : 391iter [00:12, 38.38iter/s]
Best score: 1.3162: : 391iter [00:12, 38.38iter/s]
Best score: 1.3162: : 392iter [00:12, 38.38iter/s]
Best score: 1.3162: : 393iter [00:12, 38.38iter/s]
Best score: 1.3162: : 394iter [00:12, 38.38iter/s]
Best score: 1.3162: : 395iter [00:12, 38.38iter/s]
Best score: 1.3162: : 396iter [00:12, 39.21iter/s]
Best score: 1.3162: : 396iter [00:12, 39.21iter/s]
Best score: 1.3162: : 397iter [00:12, 39.21iter/s]
Best score: 1.3162: : 398iter [00:12, 39.21iter/s]
[ITER 400] Best: 1.1380
[PARAMS] Current best parameters:
  fx: 550.7770
  fy: 550.8943
  px: 1002.1013
  py: 742.8041
  k1: -0.01261806
  k2: -0.01673338
  k3: 0.01831838
  k4: -0.00590087
  p1: 0.00455852
  p2: 0.00256042
  p3: -0.00980521
  p4: -0.00116833
  s1: 0.0112
  s2: -0.0006
  s3: 0.0077
  s4: -0.0004
  tilt_x: -0.0190
  tilt_y: -0.0125
  scale: 2.9937
  rotation: -0.4818

Best score: 1.1380: : 399iter [00:12, 39.21iter/s]
Best score: 1.1380: : 400iter [00:12, 39.27iter/s]
Best score: 1.1380: : 400iter [00:12, 39.27iter/s]
Best score: 1.1380: : 401iter [00:12, 39.27iter/s]
Best score: 1.1380: : 402iter [00:12, 39.27iter/s]
Best score: 1.1380: : 403iter [00:12, 39.27iter/s]
Best score: 1.1380: : 404iter [00:12, 32.59iter/s]
Best score: 1.1380: : 404iter [00:12, 32.59iter/s]
Best score: 1.1380: : 405iter [00:12, 32.59iter/s]
Best score: 1.1380: : 406iter [00:12, 32.59iter/s]
Best score: 1.1380: : 407iter [00:12, 32.59iter/s]
Best score: 1.1380: : 408iter [00:12, 32.59iter/s]
Best score: 1.1380: : 409iter [00:12, 34.93iter/s]
Best score: 1.1380: : 409iter [00:12, 34.93iter/s]
Best score: 1.1380: : 410iter [00:12, 34.93iter/s]
Best score: 1.1380: : 411iter [00:12, 34.93iter/s]
Best score: 1.1380: : 412iter [00:12, 34.93iter/s]
Best score: 1.1380: : 413iter [00:12, 36.05iter/s]
Best score: 1.1380: : 413iter [00:12, 36.05iter/s]
Best score: 1.1380: : 414iter [00:12, 36.05iter/s]
Best score: 1.1380: : 415iter [00:12, 36.05iter/s]
Best score: 1.1380: : 416iter [00:12, 36.05iter/s]
Best score: 1.1380: : 417iter [00:12, 36.05iter/s]
Best score: 1.1380: : 418iter [00:12, 37.47iter/s]
Best score: 1.1380: : 418iter [00:12, 37.47iter/s]
Best score: 0.8134: : 419iter [00:13, 37.47iter/s]
Best score: 0.8134: : 420iter [00:13, 37.47iter/s]
Best score: 0.8134: : 421iter [00:13, 37.47iter/s]
Best score: 0.8134: : 422iter [00:13, 38.12iter/s]
Best score: 0.8134: : 422iter [00:13, 38.12iter/s]
Best score: 0.8134: : 423iter [00:13, 38.12iter/s]
Best score: 0.8134: : 424iter [00:13, 38.12iter/s]
Best score: 0.8134: : 425iter [00:13, 38.12iter/s]
Best score: 0.8134: : 426iter [00:13, 38.12iter/s]
Best score: 0.8134: : 427iter [00:13, 39.08iter/s]
Best score: 0.8134: : 427iter [00:13, 39.08iter/s]
Best score: 0.8134: : 428iter [00:13, 39.08iter/s]
Best score: 0.8134: : 429iter [00:13, 39.08iter/s]
Best score: 0.8134: : 430iter [00:13, 39.08iter/s]
Best score: 0.8134: : 431iter [00:13, 39.27iter/s]
Best score: 0.8134: : 431iter [00:13, 39.27iter/s]
Best score: 0.8134: : 432iter [00:13, 39.27iter/s]
Best score: 0.8134: : 433iter [00:13, 39.27iter/s]
Best score: 0.8134: : 434iter [00:13, 39.27iter/s]
Best score: 0.8134: : 435iter [00:13, 39.27iter/s]
Best score: 0.8134: : 436iter [00:13, 39.86iter/s]
Best score: 0.8134: : 436iter [00:13, 39.86iter/s]
Best score: 0.8134: : 437iter [00:13, 39.86iter/s]
Best score: 0.8134: : 438iter [00:13, 39.86iter/s]
Best score: 0.7966: : 439iter [00:13, 39.86iter/s]
Best score: 0.7966: : 440iter [00:13, 39.86iter/s]
Best score: 0.7966: : 441iter [00:13, 39.80iter/s]
Best score: 0.7966: : 441iter [00:13, 39.80iter/s]
Best score: 0.7966: : 442iter [00:13, 39.80iter/s]
Best score: 0.7966: : 443iter [00:13, 39.80iter/s]
Best score: 0.7966: : 444iter [00:13, 39.80iter/s]
Best score: 0.7966: : 445iter [00:13, 39.80iter/s]
Best score: 0.7966: : 446iter [00:13, 40.08iter/s]
Best score: 0.7966: : 446iter [00:13, 40.08iter/s]
Best score: 0.7966: : 447iter [00:13, 40.08iter/s]
Best score: 0.7966: : 448iter [00:13, 40.08iter/s]
[ITER 450] Best: 0.5664
[PARAMS] Current best parameters:
  fx: 549.7367
  fy: 549.2556
  px: 1004.2430
  py: 749.3671
  k1: -0.03153378
  k2: 0.02809105
  k3: -0.01543455
  k4: 0.00246618
  p1: 0.00843365
  p2: 0.00774961
  p3: -0.00435240
  p4: -0.00591229
  s1: 0.0071
  s2: 0.0031
  s3: 0.0014
  s4: 0.0047
  tilt_x: 0.0077
  tilt_y: -0.0135
  scale: 2.9984
  rotation: -0.5094

Best score: 0.5664: : 449iter [00:13, 40.08iter/s]
Best score: 0.5664: : 450iter [00:13, 40.08iter/s]
Best score: 0.5664: : 451iter [00:13, 33.30iter/s]
Best score: 0.5664: : 451iter [00:13, 33.30iter/s]
Best score: 0.5664: : 452iter [00:13, 33.30iter/s]
Best score: 0.5664: : 453iter [00:13, 33.30iter/s]
Best score: 0.5664: : 454iter [00:13, 33.30iter/s]
Best score: 0.5664: : 455iter [00:13, 33.30iter/s]
Best score: 0.5664: : 456iter [00:13, 35.25iter/s]
Best score: 0.5664: : 456iter [00:14, 35.25iter/s]
Best score: 0.5664: : 457iter [00:14, 35.25iter/s]
Best score: 0.5664: : 458iter [00:14, 35.25iter/s]
Best score: 0.5664: : 459iter [00:14, 35.25iter/s]
Best score: 0.5664: : 460iter [00:14, 35.91iter/s]
Best score: 0.5664: : 460iter [00:14, 35.91iter/s]
Best score: 0.5664: : 461iter [00:14, 35.91iter/s]
Best score: 0.5664: : 462iter [00:14, 35.91iter/s]
Best score: 0.5664: : 463iter [00:14, 35.91iter/s]
Best score: 0.5664: : 464iter [00:14, 35.91iter/s]
Best score: 0.5664: : 465iter [00:14, 37.22iter/s]
Best score: 0.5664: : 465iter [00:14, 37.22iter/s]
Best score: 0.5664: : 466iter [00:14, 37.22iter/s]
Best score: 0.5664: : 467iter [00:14, 37.22iter/s]
Best score: 0.5664: : 468iter [00:14, 37.22iter/s]
Best score: 0.5231: : 469iter [00:14, 37.22iter/s]
Best score: 0.5231: : 470iter [00:14, 37.99iter/s]
Best score: 0.5231: : 470iter [00:14, 37.99iter/s]
Best score: 0.5231: : 471iter [00:14, 37.99iter/s]
Best score: 0.5231: : 472iter [00:14, 37.99iter/s]
Best score: 0.5231: : 473iter [00:14, 37.99iter/s]
Best score: 0.5231: : 474iter [00:14, 37.99iter/s]
Best score: 0.5231: : 475iter [00:14, 38.73iter/s]
Best score: 0.5231: : 475iter [00:14, 38.73iter/s]
Best score: 0.5231: : 476iter [00:14, 38.73iter/s]
Best score: 0.5231: : 477iter [00:14, 38.73iter/s]
Best score: 0.5231: : 478iter [00:14, 38.73iter/s]
Best score: 0.5038: : 479iter [00:14, 38.73iter/s]
Best score: 0.5038: : 480iter [00:14, 39.09iter/s]
Best score: 0.5038: : 480iter [00:14, 39.09iter/s]
Best score: 0.5038: : 481iter [00:14, 39.09iter/s]
Best score: 0.5038: : 482iter [00:14, 39.09iter/s]
Best score: 0.5038: : 483iter [00:14, 39.09iter/s]
Best score: 0.5038: : 484iter [00:14, 39.09iter/s]
Best score: 0.5038: : 485iter [00:14, 39.53iter/s]
Best score: 0.5038: : 485iter [00:14, 39.53iter/s]
Best score: 0.5038: : 486iter [00:14, 39.53iter/s]
Best score: 0.5038: : 487iter [00:14, 39.53iter/s]
Best score: 0.5038: : 488iter [00:14, 39.53iter/s]
Best score: 0.4997: : 489iter [00:14, 39.53iter/s]
Best score: 0.4997: : 490iter [00:14, 39.58iter/s]
Best score: 0.4997: : 490iter [00:14, 39.58iter/s]
Best score: 0.4997: : 491iter [00:14, 39.58iter/s]
Best score: 0.4997: : 492iter [00:14, 39.58iter/s]
Best score: 0.4997: : 493iter [00:14, 39.58iter/s]
Best score: 0.4997: : 494iter [00:14, 39.58iter/s]
Best score: 0.4997: : 495iter [00:14, 39.92iter/s]
Best score: 0.4997: : 495iter [00:14, 39.92iter/s]
Best score: 0.4997: : 496iter [00:15, 39.92iter/s]
Best score: 0.4997: : 497iter [00:15, 39.92iter/s]
Best score: 0.4997: : 498iter [00:15, 39.92iter/s]
[ITER 500] Best: 0.4683
[PARAMS] Current best parameters:
  fx: 548.2343
  fy: 547.7646
  px: 1003.2896
  py: 746.3668
  k1: -0.03142277
  k2: 0.01827515
  k3: -0.00427346
  k4: -0.00065473
  p1: 0.00356540
  p2: 0.00738073
  p3: -0.00751697
  p4: -0.00299481
  s1: 0.0069
  s2: 0.0013
  s3: 0.0081
  s4: 0.0008
  tilt_x: 0.0021
  tilt_y: -0.0095
  scale: 3.0235
  rotation: -0.4295

Best score: 0.4683: : 499iter [00:15, 39.92iter/s]
Best score: 0.4683: : 500iter [00:15, 39.86iter/s]
Best score: 0.4683: : 500iter [00:15, 39.86iter/s]
Best score: 0.4683: : 501iter [00:15, 39.86iter/s]
Best score: 0.4683: : 502iter [00:15, 39.86iter/s]
Best score: 0.4683: : 503iter [00:15, 39.86iter/s]
Best score: 0.4683: : 504iter [00:15, 33.38iter/s]
Best score: 0.4683: : 504iter [00:15, 33.38iter/s]
Best score: 0.4683: : 505iter [00:15, 33.38iter/s]
Best score: 0.4683: : 506iter [00:15, 33.38iter/s]
Best score: 0.4683: : 507iter [00:15, 33.38iter/s]
Best score: 0.4683: : 508iter [00:15, 33.38iter/s]
Best score: 0.4683: : 509iter [00:15, 35.42iter/s]
Best score: 0.4648: : 509iter [00:15, 35.42iter/s]
Best score: 0.4648: : 510iter [00:15, 35.42iter/s]
Best score: 0.4648: : 511iter [00:15, 35.42iter/s]
Best score: 0.4648: : 512iter [00:15, 35.42iter/s]
Best score: 0.4648: : 513iter [00:15, 35.42iter/s]
Best score: 0.4648: : 514iter [00:15, 36.79iter/s]
Best score: 0.4648: : 514iter [00:15, 36.79iter/s]
Best score: 0.4648: : 515iter [00:15, 36.79iter/s]
Best score: 0.4648: : 516iter [00:15, 36.79iter/s]
Best score: 0.4648: : 517iter [00:15, 36.79iter/s]
Best score: 0.4648: : 518iter [00:15, 36.79iter/s]
Best score: 0.4648: : 519iter [00:15, 37.92iter/s]
Best score: 0.4561: : 519iter [00:15, 37.92iter/s]
Best score: 0.4561: : 520iter [00:15, 37.92iter/s]
Best score: 0.4561: : 521iter [00:15, 37.92iter/s]
Best score: 0.4561: : 522iter [00:15, 37.92iter/s]
Best score: 0.4561: : 523iter [00:15, 38.06iter/s]
Best score: 0.4561: : 523iter [00:15, 38.06iter/s]
Best score: 0.4561: : 524iter [00:15, 38.06iter/s]
Best score: 0.4561: : 525iter [00:15, 38.06iter/s]
Best score: 0.4561: : 526iter [00:15, 38.06iter/s]
Best score: 0.4561: : 527iter [00:15, 38.06iter/s]
Best score: 0.4561: : 528iter [00:15, 38.88iter/s]
Best score: 0.4561: : 528iter [00:15, 38.88iter/s]
Best score: 0.4408: : 529iter [00:15, 38.88iter/s]
Best score: 0.4408: : 530iter [00:15, 38.88iter/s]
Best score: 0.4408: : 531iter [00:15, 38.88iter/s]
Best score: 0.4408: : 532iter [00:15, 39.11iter/s]
Best score: 0.4408: : 532iter [00:15, 39.11iter/s]
Best score: 0.4408: : 533iter [00:16, 39.11iter/s]
Best score: 0.4408: : 534iter [00:16, 39.11iter/s]
Best score: 0.4408: : 535iter [00:16, 39.11iter/s]
Best score: 0.4408: : 536iter [00:16, 39.11iter/s]
Best score: 0.4408: : 537iter [00:16, 39.64iter/s]
Best score: 0.4408: : 537iter [00:16, 39.64iter/s]
Best score: 0.4408: : 538iter [00:16, 39.64iter/s]
Best score: 0.4408: : 539iter [00:16, 39.64iter/s]
Best score: 0.4408: : 540iter [00:16, 39.64iter/s]
Best score: 0.4408: : 541iter [00:16, 39.64iter/s]
Best score: 0.4408: : 542iter [00:16, 39.89iter/s]
Best score: 0.4408: : 542iter [00:16, 39.89iter/s]
Best score: 0.4408: : 543iter [00:16, 39.89iter/s]
Best score: 0.4408: : 544iter [00:16, 39.89iter/s]
Best score: 0.4408: : 545iter [00:16, 39.89iter/s]
Best score: 0.4408: : 546iter [00:16, 39.89iter/s]
Best score: 0.4408: : 547iter [00:16, 40.26iter/s]
Best score: 0.4408: : 547iter [00:16, 40.26iter/s]
Best score: 0.4408: : 548iter [00:16, 40.26iter/s]
[ITER 550] Best: 0.4343
[PARAMS] Current best parameters:
  fx: 550.2060
  fy: 549.7064
  px: 1001.3738
  py: 746.6190
  k1: -0.03419932
  k2: 0.01951592
  k3: -0.00524254
  k4: -0.00032070
  p1: 0.00272687
  p2: 0.00663189
  p3: -0.00790106
  p4: -0.00160925
  s1: 0.0040
  s2: 0.0012
  s3: 0.0083
  s4: 0.0005
  tilt_x: 0.0043
  tilt_y: -0.0014
  scale: 3.0279
  rotation: -0.4470

Best score: 0.4343: : 549iter [00:16, 40.26iter/s]
Best score: 0.4343: : 550iter [00:16, 40.26iter/s]
Best score: 0.4343: : 551iter [00:16, 40.26iter/s]
Best score: 0.4343: : 552iter [00:16, 33.68iter/s]
Best score: 0.4343: : 552iter [00:16, 33.68iter/s]
Best score: 0.4343: : 553iter [00:16, 33.68iter/s]
Best score: 0.4343: : 554iter [00:16, 33.68iter/s]
Best score: 0.4343: : 555iter [00:16, 33.68iter/s]
Best score: 0.4343: : 556iter [00:16, 33.68iter/s]
Best score: 0.4343: : 557iter [00:16, 35.54iter/s]
Best score: 0.4343: : 557iter [00:16, 35.54iter/s]
Best score: 0.4343: : 558iter [00:16, 35.54iter/s]
Best score: 0.4050: : 559iter [00:16, 35.54iter/s]
Best score: 0.4050: : 560iter [00:16, 35.54iter/s]
Best score: 0.4050: : 561iter [00:16, 36.44iter/s]
Best score: 0.4050: : 561iter [00:16, 36.44iter/s]
Best score: 0.4050: : 562iter [00:16, 36.44iter/s]
Best score: 0.4050: : 563iter [00:16, 36.44iter/s]
Best score: 0.4050: : 564iter [00:16, 36.44iter/s]
Best score: 0.4050: : 565iter [00:16, 36.44iter/s]
Best score: 0.4050: : 566iter [00:16, 37.69iter/s]
Best score: 0.4050: : 566iter [00:16, 37.69iter/s]
Best score: 0.4050: : 567iter [00:16, 37.69iter/s]
Best score: 0.4050: : 568iter [00:16, 37.69iter/s]
Best score: 0.3767: : 569iter [00:16, 37.69iter/s]
Best score: 0.3767: : 570iter [00:16, 38.18iter/s]
Best score: 0.3767: : 570iter [00:17, 38.18iter/s]
Best score: 0.3767: : 571iter [00:17, 38.18iter/s]
Best score: 0.3767: : 572iter [00:17, 38.18iter/s]
Best score: 0.3767: : 573iter [00:17, 38.18iter/s]
Best score: 0.3767: : 574iter [00:17, 38.18iter/s]
Best score: 0.3767: : 575iter [00:17, 39.02iter/s]
Best score: 0.3767: : 575iter [00:17, 39.02iter/s]
Best score: 0.3767: : 576iter [00:17, 39.02iter/s]
Best score: 0.3767: : 577iter [00:17, 39.02iter/s]
Best score: 0.3767: : 578iter [00:17, 39.02iter/s]
Best score: 0.3743: : 579iter [00:17, 39.02iter/s]
Best score: 0.3743: : 580iter [00:17, 39.41iter/s]
Best score: 0.3743: : 580iter [00:17, 39.41iter/s]
Best score: 0.3743: : 581iter [00:17, 39.41iter/s]
Best score: 0.3743: : 582iter [00:17, 39.41iter/s]
Best score: 0.3743: : 583iter [00:17, 39.41iter/s]
Best score: 0.3743: : 584iter [00:17, 39.41iter/s]
Best score: 0.3743: : 585iter [00:17, 39.93iter/s]
Best score: 0.3743: : 585iter [00:17, 39.93iter/s]
Best score: 0.3743: : 586iter [00:17, 39.93iter/s]
Best score: 0.3743: : 587iter [00:17, 39.93iter/s]
Best score: 0.3743: : 588iter [00:17, 39.93iter/s]
Best score: 0.3650: : 589iter [00:17, 39.93iter/s]
Best score: 0.3650: : 590iter [00:17, 39.84iter/s]
Best score: 0.3650: : 590iter [00:17, 39.84iter/s]
Best score: 0.3650: : 591iter [00:17, 39.84iter/s]
Best score: 0.3650: : 592iter [00:17, 39.84iter/s]
Best score: 0.3650: : 593iter [00:17, 39.84iter/s]
Best score: 0.3650: : 594iter [00:17, 39.84iter/s]
Best score: 0.3650: : 595iter [00:17, 40.18iter/s]
Best score: 0.3650: : 595iter [00:17, 40.18iter/s]
Best score: 0.3650: : 596iter [00:17, 40.18iter/s]
Best score: 0.3650: : 597iter [00:17, 40.18iter/s]
Best score: 0.3650: : 598iter [00:17, 40.18iter/s]
[ITER 600] Best: 0.3624
[PARAMS] Current best parameters:
  fx: 548.0797
  fy: 547.6943
  px: 996.2543
  py: 742.1880
  k1: -0.08221617
  k2: 0.09728887
  k3: -0.04947971
  k4: 0.00855742
  p1: 0.00015991
  p2: 0.00019982
  p3: -0.02247803
  p4: 0.01041873
  s1: 0.0207
  s2: -0.0109
  s3: 0.0223
  s4: -0.0119
  tilt_x: -0.0138
  tilt_y: 0.0118
  scale: 3.0801
  rotation: -0.4370

Best score: 0.3624: : 599iter [00:17, 40.18iter/s]
Best score: 0.3624: : 600iter [00:17, 40.17iter/s]
Best score: 0.3624: : 600iter [00:17, 40.17iter/s]
Best score: 0.3624: : 601iter [00:17, 40.17iter/s]
Best score: 0.3624: : 602iter [00:17, 40.17iter/s]
Best score: 0.3624: : 603iter [00:17, 40.17iter/s]
Best score: 0.3624: : 604iter [00:17, 40.17iter/s]
Best score: 0.3624: : 605iter [00:17, 33.78iter/s]
Best score: 0.3624: : 605iter [00:17, 33.78iter/s]
Best score: 0.3624: : 606iter [00:17, 33.78iter/s]
Best score: 0.3624: : 607iter [00:18, 33.78iter/s]
Best score: 0.3624: : 608iter [00:18, 33.78iter/s]
Best score: 0.3624: : 609iter [00:18, 33.78iter/s]
Best score: 0.3624: : 610iter [00:18, 35.45iter/s]
Best score: 0.3624: : 610iter [00:18, 35.45iter/s]
Best score: 0.3624: : 611iter [00:18, 35.45iter/s]
Best score: 0.3624: : 612iter [00:18, 35.45iter/s]
Best score: 0.3624: : 613iter [00:18, 35.45iter/s]
Best score: 0.3624: : 614iter [00:18, 35.45iter/s]
Best score: 0.3624: : 615iter [00:18, 36.87iter/s]
Best score: 0.3624: : 615iter [00:18, 36.87iter/s]
Best score: 0.3624: : 616iter [00:18, 36.87iter/s]
Best score: 0.3624: : 617iter [00:18, 36.87iter/s]
Best score: 0.3624: : 618iter [00:18, 36.87iter/s]
Best score: 0.3559: : 619iter [00:18, 36.87iter/s]
Best score: 0.3559: : 620iter [00:18, 37.75iter/s]
Best score: 0.3559: : 620iter [00:18, 37.75iter/s]
Best score: 0.3559: : 621iter [00:18, 37.75iter/s]
Best score: 0.3559: : 622iter [00:18, 37.75iter/s]
Best score: 0.3559: : 623iter [00:18, 37.75iter/s]
Best score: 0.3559: : 624iter [00:18, 37.75iter/s]
Best score: 0.3559: : 625iter [00:18, 38.61iter/s]
Best score: 0.3559: : 625iter [00:18, 38.61iter/s]
Best score: 0.3559: : 626iter [00:18, 38.61iter/s]
Best score: 0.3559: : 627iter [00:18, 38.61iter/s]
Best score: 0.3559: : 628iter [00:18, 38.61iter/s]
Best score: 0.3510: : 629iter [00:18, 38.61iter/s]
Best score: 0.3510: : 630iter [00:18, 38.93iter/s]
Best score: 0.3510: : 630iter [00:18, 38.93iter/s]
Best score: 0.3510: : 631iter [00:18, 38.93iter/s]
Best score: 0.3510: : 632iter [00:18, 38.93iter/s]
Best score: 0.3510: : 633iter [00:18, 38.93iter/s]
Best score: 0.3510: : 634iter [00:18, 38.93iter/s]
Best score: 0.3510: : 635iter [00:18, 39.43iter/s]
Best score: 0.3510: : 635iter [00:18, 39.43iter/s]
Best score: 0.3510: : 636iter [00:18, 39.43iter/s]
Best score: 0.3510: : 637iter [00:18, 39.43iter/s]
Best score: 0.3510: : 638iter [00:18, 39.43iter/s]
Best score: 0.3463: : 639iter [00:18, 39.43iter/s]
Best score: 0.3463: : 640iter [00:18, 39.52iter/s]
Best score: 0.3463: : 640iter [00:18, 39.52iter/s]
Best score: 0.3463: : 641iter [00:18, 39.52iter/s]
Best score: 0.3463: : 642iter [00:18, 39.52iter/s]
Best score: 0.3463: : 643iter [00:18, 39.52iter/s]
Best score: 0.3463: : 644iter [00:18, 39.52iter/s]
Best score: 0.3463: : 645iter [00:18, 39.79iter/s]
Best score: 0.3463: : 645iter [00:18, 39.79iter/s]
Best score: 0.3463: : 646iter [00:18, 39.79iter/s]
Best score: 0.3463: : 647iter [00:18, 39.79iter/s]
Best score: 0.3463: : 648iter [00:19, 39.79iter/s]
[ITER 650] Best: 0.3439
[PARAMS] Current best parameters:
  fx: 551.2299
  fy: 550.7723
  px: 995.7673
  py: 741.7513
  k1: -0.07355137
  k2: 0.08206472
  k3: -0.04204906
  k4: 0.00729599
  p1: -0.00218928
  p2: -0.00352950
  p3: -0.02058493
  p4: 0.00877514
  s1: 0.0221
  s2: -0.0092
  s3: 0.0217
  s4: -0.0105
  tilt_x: -0.0109
  tilt_y: 0.0074
  scale: 3.0600
  rotation: -0.4430

Best score: 0.3439: : 649iter [00:19, 39.79iter/s]
Best score: 0.3439: : 650iter [00:19, 39.84iter/s]
Best score: 0.3439: : 650iter [00:19, 39.84iter/s]
Best score: 0.3439: : 651iter [00:19, 39.84iter/s]
Best score: 0.3439: : 652iter [00:19, 39.84iter/s]
Best score: 0.3439: : 653iter [00:19, 39.84iter/s]
Best score: 0.3439: : 654iter [00:19, 33.32iter/s]
Best score: 0.3439: : 654iter [00:19, 33.32iter/s]
Best score: 0.3439: : 655iter [00:19, 33.32iter/s]
Best score: 0.3439: : 656iter [00:19, 33.32iter/s]
Best score: 0.3439: : 657iter [00:19, 33.32iter/s]
Best score: 0.3439: : 658iter [00:19, 33.32iter/s]
Best score: 0.3439: : 659iter [00:19, 35.46iter/s]
Best score: 0.3390: : 659iter [00:19, 35.46iter/s]
Best score: 0.3390: : 660iter [00:19, 35.46iter/s]
Best score: 0.3390: : 661iter [00:19, 35.46iter/s]
Best score: 0.3390: : 662iter [00:19, 35.46iter/s]
Best score: 0.3390: : 663iter [00:19, 36.55iter/s]
Best score: 0.3390: : 663iter [00:19, 36.55iter/s]
Best score: 0.3390: : 664iter [00:19, 36.55iter/s]
Best score: 0.3390: : 665iter [00:19, 36.55iter/s]
Best score: 0.3390: : 666iter [00:19, 36.55iter/s]
Best score: 0.3390: : 667iter [00:19, 36.55iter/s]
Best score: 0.3390: : 668iter [00:19, 37.91iter/s]
Best score: 0.3390: : 668iter [00:19, 37.91iter/s]
Best score: 0.3288: : 669iter [00:19, 37.91iter/s]
Best score: 0.3288: : 670iter [00:19, 37.91iter/s]
Best score: 0.3288: : 671iter [00:19, 37.91iter/s]
Best score: 0.3288: : 672iter [00:19, 37.91iter/s]
Best score: 0.3288: : 673iter [00:19, 38.66iter/s]
Best score: 0.3288: : 673iter [00:19, 38.66iter/s]
Best score: 0.3288: : 674iter [00:19, 38.66iter/s]
Best score: 0.3288: : 675iter [00:19, 38.66iter/s]
Best score: 0.3288: : 676iter [00:19, 38.66iter/s]
Best score: 0.3288: : 677iter [00:19, 38.66iter/s]
Best score: 0.3288: : 678iter [00:19, 39.31iter/s]
Best score: 0.3288: : 678iter [00:19, 39.31iter/s]
Best score: 0.3235: : 679iter [00:19, 39.31iter/s]
Best score: 0.3235: : 680iter [00:19, 39.31iter/s]
Best score: 0.3235: : 681iter [00:19, 39.31iter/s]
Best score: 0.3235: : 682iter [00:19, 39.41iter/s]
Best score: 0.3235: : 682iter [00:19, 39.41iter/s]
Best score: 0.3235: : 683iter [00:20, 39.41iter/s]
Best score: 0.3235: : 684iter [00:20, 39.41iter/s]
Best score: 0.3235: : 685iter [00:20, 39.41iter/s]
Best score: 0.3235: : 686iter [00:20, 32.14iter/s]
Best score: 0.3235: : 686iter [00:20, 32.14iter/s]
Best score: 0.3235: : 687iter [00:20, 32.14iter/s]
Best score: 0.3235: : 688iter [00:20, 32.14iter/s]
Best score: 0.3214: : 689iter [00:20, 32.14iter/s]
Best score: 0.3214: : 690iter [00:20, 33.95iter/s]
Best score: 0.3214: : 690iter [00:20, 33.95iter/s]
Best score: 0.3214: : 691iter [00:20, 33.95iter/s]
Best score: 0.3214: : 692iter [00:20, 33.95iter/s]
Best score: 0.3214: : 693iter [00:20, 33.95iter/s]
Best score: 0.3214: : 694iter [00:20, 33.95iter/s]
Best score: 0.3214: : 695iter [00:20, 35.94iter/s]
Best score: 0.3214: : 695iter [00:20, 35.94iter/s]
Best score: 0.3214: : 696iter [00:20, 35.94iter/s]
Best score: 0.3214: : 697iter [00:20, 35.94iter/s]
Best score: 0.3214: : 698iter [00:20, 35.94iter/s]
[ITER 700] Best: 0.3214
[PARAMS] Current best parameters:
  fx: 559.9531
  fy: 559.5907
  px: 995.7116
  py: 740.3839
  k1: -0.04744871
  k2: 0.03363585
  k3: -0.01759893
  k4: 0.00312993
  p1: -0.00256052
  p2: -0.00725497
  p3: -0.01654664
  p4: 0.00499764
  s1: 0.0202
  s2: -0.0043
  s3: 0.0179
  s4: -0.0070
  tilt_x: -0.0183
  tilt_y: 0.0004
  scale: 3.0183
  rotation: -0.4628

Best score: 0.3214: : 699iter [00:20, 35.94iter/s]
Best score: 0.3214: : 700iter [00:20, 37.05iter/s]
Best score: 0.3214: : 700iter [00:20, 37.05iter/s]
Best score: 0.3214: : 701iter [00:20, 37.05iter/s]
Best score: 0.3214: : 702iter [00:20, 37.05iter/s]
Best score: 0.3214: : 703iter [00:20, 37.05iter/s]
Best score: 0.3214: : 704iter [00:20, 31.38iter/s]
Best score: 0.3214: : 704iter [00:20, 31.38iter/s]
Best score: 0.3214: : 705iter [00:20, 31.38iter/s]
Best score: 0.3214: : 706iter [00:20, 31.38iter/s]
Best score: 0.3214: : 707iter [00:20, 31.38iter/s]
Best score: 0.3214: : 708iter [00:20, 31.38iter/s]
Best score: 0.3214: : 709iter [00:20, 33.84iter/s]
Best score: 0.3135: : 709iter [00:20, 33.84iter/s]
Best score: 0.3135: : 710iter [00:20, 33.84iter/s]
Best score: 0.3135: : 711iter [00:20, 33.84iter/s]
Best score: 0.3135: : 712iter [00:20, 33.84iter/s]
Best score: 0.3135: : 713iter [00:20, 35.19iter/s]
Best score: 0.3135: : 713iter [00:20, 35.19iter/s]
Best score: 0.3135: : 714iter [00:20, 35.19iter/s]
Best score: 0.3135: : 715iter [00:20, 35.19iter/s]
Best score: 0.3135: : 716iter [00:20, 35.19iter/s]
Best score: 0.3135: : 717iter [00:20, 35.19iter/s]
Best score: 0.3135: : 718iter [00:20, 36.69iter/s]
Best score: 0.3135: : 718iter [00:21, 36.69iter/s]
Best score: 0.3127: : 719iter [00:21, 36.69iter/s]
Best score: 0.3127: : 720iter [00:21, 36.69iter/s]
Best score: 0.3127: : 721iter [00:21, 36.69iter/s]
Best score: 0.3127: : 722iter [00:21, 37.43iter/s]
Best score: 0.3127: : 722iter [00:21, 37.43iter/s]
Best score: 0.3127: : 723iter [00:21, 37.43iter/s]
Best score: 0.3127: : 724iter [00:21, 37.43iter/s]
Best score: 0.3127: : 725iter [00:21, 37.43iter/s]
Best score: 0.3127: : 726iter [00:21, 37.43iter/s]
Best score: 0.3127: : 727iter [00:21, 38.59iter/s]
Best score: 0.3127: : 727iter [00:21, 38.59iter/s]
Best score: 0.3127: : 728iter [00:21, 38.59iter/s]
Best score: 0.3099: : 729iter [00:21, 38.59iter/s]
Best score: 0.3099: : 730iter [00:21, 38.59iter/s]
Best score: 0.3099: : 731iter [00:21, 38.88iter/s]
Best score: 0.3099: : 731iter [00:21, 38.88iter/s]
Best score: 0.3099: : 732iter [00:21, 38.88iter/s]
Best score: 0.3099: : 733iter [00:21, 38.88iter/s]
Best score: 0.3099: : 734iter [00:21, 38.88iter/s]
Best score: 0.3099: : 735iter [00:21, 38.88iter/s]
Best score: 0.3099: : 736iter [00:21, 39.53iter/s]
Best score: 0.3099: : 736iter [00:21, 39.53iter/s]
Best score: 0.3099: : 737iter [00:21, 39.53iter/s]
Best score: 0.3099: : 738iter [00:21, 39.53iter/s]
Best score: 0.3026: : 739iter [00:21, 39.53iter/s]
Best score: 0.3026: : 740iter [00:21, 39.56iter/s]
Best score: 0.3026: : 740iter [00:21, 39.56iter/s]
Best score: 0.3026: : 741iter [00:21, 39.56iter/s]
Best score: 0.3026: : 742iter [00:21, 39.56iter/s]
Best score: 0.3026: : 743iter [00:21, 39.56iter/s]
Best score: 0.3026: : 744iter [00:21, 39.56iter/s]
Best score: 0.3026: : 745iter [00:21, 39.93iter/s]
Best score: 0.3026: : 745iter [00:21, 39.93iter/s]
Best score: 0.3026: : 746iter [00:21, 39.93iter/s]
Best score: 0.3026: : 747iter [00:21, 39.93iter/s]
Best score: 0.3026: : 748iter [00:21, 39.93iter/s]
[ITER 750] Best: 0.3026
[PARAMS] Current best parameters:
  fx: 552.7245
  fy: 552.3746
  px: 996.0171
  py: 738.2554
  k1: -0.05925738
  k2: 0.06023650
  k3: -0.03153290
  k4: 0.00557924
  p1: -0.00376504
  p2: -0.00554017
  p3: -0.01739522
  p4: 0.00393157
  s1: 0.0205
  s2: -0.0038
  s3: 0.0165
  s4: -0.0048
  tilt_x: -0.0265
  tilt_y: 0.0019
  scale: 3.0443
  rotation: -0.4579

Best score: 0.3026: : 749iter [00:21, 39.93iter/s]
Best score: 0.3026: : 750iter [00:21, 39.91iter/s]
Best score: 0.3026: : 750iter [00:21, 39.91iter/s]
Best score: 0.3026: : 751iter [00:21, 39.91iter/s]
Best score: 0.3026: : 752iter [00:21, 39.91iter/s]
Best score: 0.3026: : 753iter [00:21, 39.91iter/s]
Best score: 0.3026: : 754iter [00:21, 33.15iter/s]
Best score: 0.3026: : 754iter [00:21, 33.15iter/s]
Best score: 0.3026: : 755iter [00:22, 33.15iter/s]
Best score: 0.3026: : 756iter [00:22, 33.15iter/s]
Best score: 0.3026: : 757iter [00:22, 33.15iter/s]
Best score: 0.3026: : 758iter [00:22, 33.15iter/s]
Best score: 0.3026: : 759iter [00:22, 35.17iter/s]
Best score: 0.3021: : 759iter [00:22, 35.17iter/s]
Best score: 0.3021: : 760iter [00:22, 35.17iter/s]
Best score: 0.3021: : 761iter [00:22, 35.17iter/s]
Best score: 0.3021: : 762iter [00:22, 35.17iter/s]
Best score: 0.3021: : 763iter [00:22, 36.27iter/s]
Best score: 0.3021: : 763iter [00:22, 36.27iter/s]
Best score: 0.3021: : 764iter [00:22, 36.27iter/s]
Best score: 0.3021: : 765iter [00:22, 36.27iter/s]
Best score: 0.3021: : 766iter [00:22, 36.27iter/s]
Best score: 0.3021: : 767iter [00:22, 36.27iter/s]
Best score: 0.3021: : 768iter [00:22, 37.61iter/s]
Best score: 0.3021: : 768iter [00:22, 37.61iter/s]
Best score: 0.3003: : 769iter [00:22, 37.61iter/s]
Best score: 0.3003: : 770iter [00:22, 37.61iter/s]
Best score: 0.3003: : 771iter [00:22, 37.61iter/s]
Best score: 0.3003: : 772iter [00:22, 38.10iter/s]
Best score: 0.3003: : 772iter [00:22, 38.10iter/s]
Best score: 0.3003: : 773iter [00:22, 38.10iter/s]
Best score: 0.3003: : 774iter [00:22, 38.10iter/s]
Best score: 0.3003: : 775iter [00:22, 38.10iter/s]
Best score: 0.3003: : 776iter [00:22, 38.10iter/s]
Best score: 0.3003: : 777iter [00:22, 38.90iter/s]
Best score: 0.3003: : 777iter [00:22, 38.90iter/s]
Best score: 0.3003: : 778iter [00:22, 38.90iter/s]
Best score: 0.3003: : 779iter [00:22, 38.90iter/s]
Best score: 0.3003: : 780iter [00:22, 38.90iter/s]
Best score: 0.3003: : 781iter [00:22, 39.08iter/s]
Best score: 0.3003: : 781iter [00:22, 39.08iter/s]
Best score: 0.3003: : 782iter [00:22, 39.08iter/s]
Best score: 0.3003: : 783iter [00:22, 39.08iter/s]
Best score: 0.3003: : 784iter [00:22, 39.08iter/s]
Best score: 0.3003: : 785iter [00:22, 39.08iter/s]
Best score: 0.3003: : 786iter [00:22, 39.44iter/s]
Best score: 0.3003: : 786iter [00:22, 39.44iter/s]
Best score: 0.3003: : 787iter [00:22, 39.44iter/s]
Best score: 0.3003: : 788iter [00:22, 39.44iter/s]
Best score: 0.3003: : 789iter [00:22, 39.44iter/s]
Best score: 0.3003: : 790iter [00:22, 39.58iter/s]
Best score: 0.3003: : 790iter [00:22, 39.58iter/s]
Best score: 0.3003: : 791iter [00:22, 39.58iter/s]
Best score: 0.3003: : 792iter [00:22, 39.58iter/s]
Best score: 0.3003: : 793iter [00:22, 39.58iter/s]
Best score: 0.3003: : 794iter [00:22, 39.58iter/s]
Best score: 0.3003: : 795iter [00:22, 40.04iter/s]
Best score: 0.3003: : 795iter [00:22, 40.04iter/s]
Best score: 0.3003: : 796iter [00:23, 40.04iter/s]
Best score: 0.3003: : 797iter [00:23, 40.04iter/s]
Best score: 0.3003: : 798iter [00:23, 40.04iter/s]
[ITER 800] Best: 0.3003
[PARAMS] Current best parameters:
  fx: 554.4837
  fy: 554.1860
  px: 995.7656
  py: 738.3708
  k1: -0.05522561
  k2: 0.05191241
  k3: -0.02703266
  k4: 0.00473999
  p1: -0.00269137
  p2: -0.00614899
  p3: -0.01737860
  p4: 0.00363152
  s1: 0.0207
  s2: -0.0035
  s3: 0.0157
  s4: -0.0047
  tilt_x: -0.0281
  tilt_y: 0.0020
  scale: 3.0365
  rotation: -0.4555

Best score: 0.3003: : 799iter [00:23, 40.04iter/s]
Best score: 0.3003: : 800iter [00:23, 40.06iter/s]
Best score: 0.3003: : 800iter [00:23, 40.06iter/s]
Best score: 0.3003: : 801iter [00:23, 40.06iter/s]
Best score: 0.3003: : 802iter [00:23, 40.06iter/s]
Best score: 0.3003: : 803iter [00:23, 40.06iter/s]
Best score: 0.3003: : 804iter [00:23, 40.06iter/s]
Best score: 0.3003: : 805iter [00:23, 33.73iter/s]
Best score: 0.3003: : 805iter [00:23, 33.73iter/s]
Best score: 0.3003: : 806iter [00:23, 33.73iter/s]
Best score: 0.3003: : 807iter [00:23, 33.73iter/s]
Best score: 0.3003: : 808iter [00:23, 33.73iter/s]
Best score: 0.3003: : 809iter [00:23, 33.73iter/s]
Best score: 0.3003: : 810iter [00:23, 35.51iter/s]
Best score: 0.3003: : 810iter [00:23, 35.51iter/s]
Best score: 0.3003: : 811iter [00:23, 35.51iter/s]
Best score: 0.3003: : 812iter [00:23, 35.51iter/s]
Best score: 0.3003: : 813iter [00:23, 35.51iter/s]
Best score: 0.3003: : 814iter [00:23, 35.51iter/s]
Best score: 0.3003: : 815iter [00:23, 37.03iter/s]
Best score: 0.3003: : 815iter [00:23, 37.03iter/s]
Best score: 0.3003: : 816iter [00:23, 37.03iter/s]
Best score: 0.3003: : 817iter [00:23, 37.03iter/s]
Best score: 0.3003: : 818iter [00:23, 37.03iter/s]
Best score: 0.2974: : 819iter [00:23, 37.03iter/s]
Best score: 0.2974: : 820iter [00:23, 37.89iter/s]
Best score: 0.2974: : 820iter [00:23, 37.89iter/s]
Best score: 0.2974: : 821iter [00:23, 37.89iter/s]
Best score: 0.2974: : 822iter [00:23, 37.89iter/s]
Best score: 0.2974: : 823iter [00:23, 37.89iter/s]
Best score: 0.2974: : 824iter [00:23, 37.89iter/s]
Best score: 0.2974: : 825iter [00:23, 38.67iter/s]
Best score: 0.2974: : 825iter [00:23, 38.67iter/s]
Best score: 0.2974: : 826iter [00:23, 38.67iter/s]
Best score: 0.2974: : 827iter [00:23, 38.67iter/s]
Best score: 0.2974: : 828iter [00:23, 38.67iter/s]
Best score: 0.2973: : 829iter [00:23, 38.67iter/s]
Best score: 0.2973: : 830iter [00:23, 39.05iter/s]
Best score: 0.2973: : 830iter [00:23, 39.05iter/s]
Best score: 0.2973: : 831iter [00:23, 39.05iter/s]
Best score: 0.2973: : 832iter [00:23, 39.05iter/s]
Best score: 0.2973: : 833iter [00:24, 39.05iter/s]
Best score: 0.2973: : 834iter [00:24, 39.05iter/s]
Best score: 0.2973: : 835iter [00:24, 39.58iter/s]
Best score: 0.2973: : 835iter [00:24, 39.58iter/s]
Best score: 0.2973: : 836iter [00:24, 39.58iter/s]
Best score: 0.2973: : 837iter [00:24, 39.58iter/s]
Best score: 0.2973: : 838iter [00:24, 39.58iter/s]
Best score: 0.2971: : 839iter [00:24, 39.58iter/s]
Best score: 0.2971: : 840iter [00:24, 39.62iter/s]
Best score: 0.2971: : 840iter [00:24, 39.62iter/s]
Best score: 0.2971: : 841iter [00:24, 39.62iter/s]
Best score: 0.2971: : 842iter [00:24, 39.62iter/s]
Best score: 0.2971: : 843iter [00:24, 39.62iter/s]
Best score: 0.2971: : 844iter [00:24, 39.62iter/s]
Best score: 0.2971: : 845iter [00:24, 39.97iter/s]
Best score: 0.2971: : 845iter [00:24, 39.97iter/s]
Best score: 0.2971: : 846iter [00:24, 39.97iter/s]
Best score: 0.2971: : 847iter [00:24, 39.97iter/s]
Best score: 0.2971: : 848iter [00:24, 39.97iter/s]
[ITER 850] Best: 0.2956
[PARAMS] Current best parameters:
  fx: 551.5588
  fy: 551.2961
  px: 996.0117
  py: 737.6979
  k1: -0.05646699
  k2: 0.05893018
  k3: -0.03123460
  k4: 0.00552207
  p1: -0.00098561
  p2: -0.00458283
  p3: -0.01937489
  p4: 0.00249325
  s1: 0.0218
  s2: -0.0026
  s3: 0.0147
  s4: -0.0034
  tilt_x: -0.0344
  tilt_y: 0.0038
  scale: 3.0397
  rotation: -0.4534

Best score: 0.2956: : 849iter [00:24, 39.97iter/s]
Best score: 0.2956: : 850iter [00:24, 40.01iter/s]
Best score: 0.2956: : 850iter [00:24, 40.01iter/s]
Best score: 0.2956: : 851iter [00:24, 40.01iter/s]
Best score: 0.2956: : 852iter [00:24, 40.01iter/s]
Best score: 0.2956: : 853iter [00:24, 40.01iter/s]
Best score: 0.2956: : 854iter [00:24, 40.01iter/s]
Best score: 0.2956: : 855iter [00:24, 33.81iter/s]
Best score: 0.2956: : 855iter [00:24, 33.81iter/s]
Best score: 0.2956: : 856iter [00:24, 33.81iter/s]
Best score: 0.2956: : 857iter [00:24, 33.81iter/s]
Best score: 0.2956: : 858iter [00:24, 33.81iter/s]
Best score: 0.2953: : 859iter [00:24, 33.81iter/s]
Best score: 0.2953: : 860iter [00:24, 35.43iter/s]
Best score: 0.2953: : 860iter [00:24, 35.43iter/s]
Best score: 0.2953: : 861iter [00:24, 35.43iter/s]
Best score: 0.2953: : 862iter [00:24, 35.43iter/s]
Best score: 0.2953: : 863iter [00:24, 35.43iter/s]
Best score: 0.2953: : 864iter [00:24, 35.43iter/s]
Best score: 0.2953: : 865iter [00:24, 36.87iter/s]
Best score: 0.2953: : 865iter [00:24, 36.87iter/s]
Best score: 0.2953: : 866iter [00:24, 36.87iter/s]
Best score: 0.2953: : 867iter [00:24, 36.87iter/s]
Best score: 0.2953: : 868iter [00:24, 36.87iter/s]
Best score: 0.2953: : 869iter [00:24, 36.87iter/s]
Best score: 0.2953: : 870iter [00:24, 37.85iter/s]
Best score: 0.2953: : 870iter [00:25, 37.85iter/s]
Best score: 0.2953: : 871iter [00:25, 37.85iter/s]
Best score: 0.2953: : 872iter [00:25, 37.85iter/s]
Best score: 0.2953: : 873iter [00:25, 37.85iter/s]
Best score: 0.2953: : 874iter [00:25, 37.85iter/s]
Best score: 0.2953: : 875iter [00:25, 38.75iter/s]
Best score: 0.2953: : 875iter [00:25, 38.75iter/s]
Best score: 0.2953: : 876iter [00:25, 38.75iter/s]
Best score: 0.2953: : 877iter [00:25, 38.75iter/s]
Best score: 0.2953: : 878iter [00:25, 38.75iter/s]
Best score: 0.2951: : 879iter [00:25, 38.75iter/s]
Best score: 0.2951: : 880iter [00:25, 39.11iter/s]
Best score: 0.2951: : 880iter [00:25, 39.11iter/s]
Best score: 0.2951: : 881iter [00:25, 39.11iter/s]
Best score: 0.2951: : 882iter [00:25, 39.11iter/s]
Best score: 0.2951: : 883iter [00:25, 39.11iter/s]
Best score: 0.2951: : 884iter [00:25, 39.11iter/s]
Best score: 0.2951: : 885iter [00:25, 39.62iter/s]
Best score: 0.2951: : 885iter [00:25, 39.62iter/s]
Best score: 0.2951: : 886iter [00:25, 39.62iter/s]
Best score: 0.2951: : 887iter [00:25, 39.62iter/s]
Best score: 0.2951: : 888iter [00:25, 39.62iter/s]
Best score: 0.2949: : 889iter [00:25, 39.62iter/s]
Best score: 0.2949: : 890iter [00:25, 39.85iter/s]
Best score: 0.2949: : 890iter [00:25, 39.85iter/s]
Best score: 0.2949: : 891iter [00:25, 39.85iter/s]
Best score: 0.2949: : 892iter [00:25, 39.85iter/s]
Best score: 0.2949: : 893iter [00:25, 39.85iter/s]
Best score: 0.2949: : 894iter [00:25, 39.85iter/s]
Best score: 0.2949: : 895iter [00:25, 40.15iter/s]
Best score: 0.2949: : 895iter [00:25, 40.15iter/s]
Best score: 0.2949: : 896iter [00:25, 40.15iter/s]
Best score: 0.2949: : 897iter [00:25, 40.15iter/s]
Best score: 0.2949: : 898iter [00:25, 40.15iter/s]
[ITER 900] Best: 0.2949
[PARAMS] Current best parameters:
  fx: 549.6362
  fy: 549.3719
  px: 996.5371
  py: 737.4960
  k1: -0.06044239
  k2: 0.06688941
  k3: -0.03531371
  k4: 0.00624527
  p1: -0.00087632
  p2: -0.00442835
  p3: -0.01846402
  p4: 0.00234600
  s1: 0.0214
  s2: -0.0026
  s3: 0.0136
  s4: -0.0033
  tilt_x: -0.0356
  tilt_y: 0.0013
  scale: 3.0488
  rotation: -0.4537

Best score: 0.2949: : 899iter [00:25, 40.15iter/s]
Best score: 0.2949: : 900iter [00:25, 40.00iter/s]
Best score: 0.2949: : 900iter [00:25, 40.00iter/s]
Best score: 0.2949: : 901iter [00:25, 40.00iter/s]
Best score: 0.2949: : 902iter [00:25, 40.00iter/s]
Best score: 0.2949: : 903iter [00:25, 40.00iter/s]
Best score: 0.2949: : 904iter [00:25, 40.00iter/s]
Best score: 0.2949: : 905iter [00:25, 33.82iter/s]
Best score: 0.2949: : 905iter [00:25, 33.82iter/s]
Best score: 0.2949: : 906iter [00:25, 33.82iter/s]
Best score: 0.2949: : 907iter [00:25, 33.82iter/s]
Best score: 0.2949: : 908iter [00:26, 33.82iter/s]
Best score: 0.2939: : 909iter [00:26, 33.82iter/s]
Best score: 0.2939: : 910iter [00:26, 35.49iter/s]
Best score: 0.2939: : 910iter [00:26, 35.49iter/s]
Best score: 0.2939: : 911iter [00:26, 35.49iter/s]
Best score: 0.2939: : 912iter [00:26, 35.49iter/s]
Best score: 0.2939: : 913iter [00:26, 35.49iter/s]
Best score: 0.2939: : 914iter [00:26, 35.49iter/s]
Best score: 0.2939: : 915iter [00:26, 36.93iter/s]
Best score: 0.2939: : 915iter [00:26, 36.93iter/s]
Best score: 0.2939: : 916iter [00:26, 36.93iter/s]
Best score: 0.2939: : 917iter [00:26, 36.93iter/s]
Best score: 0.2939: : 918iter [00:26, 36.93iter/s]
Best score: 0.2935: : 919iter [00:26, 36.93iter/s]
Best score: 0.2935: : 920iter [00:26, 37.83iter/s]
Best score: 0.2935: : 920iter [00:26, 37.83iter/s]
Best score: 0.2935: : 921iter [00:26, 37.83iter/s]
Best score: 0.2935: : 922iter [00:26, 37.83iter/s]
Best score: 0.2935: : 923iter [00:26, 37.83iter/s]
Best score: 0.2935: : 924iter [00:26, 37.83iter/s]
Best score: 0.2935: : 925iter [00:26, 38.60iter/s]
Best score: 0.2935: : 925iter [00:26, 38.60iter/s]
Best score: 0.2935: : 926iter [00:26, 38.60iter/s]
Best score: 0.2935: : 927iter [00:26, 38.60iter/s]
Best score: 0.2935: : 928iter [00:26, 38.60iter/s]
Best score: 0.2925: : 929iter [00:26, 38.60iter/s]
Best score: 0.2925: : 930iter [00:26, 38.96iter/s]
Best score: 0.2925: : 930iter [00:26, 38.96iter/s]
Best score: 0.2925: : 931iter [00:26, 38.96iter/s]
Best score: 0.2925: : 932iter [00:26, 38.96iter/s]
Best score: 0.2925: : 933iter [00:26, 38.96iter/s]
Best score: 0.2925: : 934iter [00:26, 38.96iter/s]
Best score: 0.2925: : 935iter [00:26, 39.43iter/s]
Best score: 0.2925: : 935iter [00:26, 39.43iter/s]
Best score: 0.2925: : 936iter [00:26, 39.43iter/s]
Best score: 0.2925: : 937iter [00:26, 39.43iter/s]
Best score: 0.2925: : 938iter [00:26, 39.43iter/s]
Best score: 0.2925: : 939iter [00:26, 39.43iter/s]
Best score: 0.2925: : 940iter [00:26, 39.54iter/s]
Best score: 0.2925: : 940iter [00:26, 39.54iter/s]
Best score: 0.2925: : 941iter [00:26, 39.54iter/s]
Best score: 0.2925: : 942iter [00:26, 39.54iter/s]
Best score: 0.2925: : 943iter [00:26, 39.54iter/s]
Best score: 0.2925: : 944iter [00:26, 39.54iter/s]
Best score: 0.2925: : 945iter [00:26, 39.94iter/s]
Best score: 0.2925: : 945iter [00:26, 39.94iter/s]
Best score: 0.2925: : 946iter [00:26, 39.94iter/s]
Best score: 0.2925: : 947iter [00:26, 39.94iter/s]
Best score: 0.2925: : 948iter [00:27, 39.94iter/s]
[ITER 950] Best: 0.2920
[PARAMS] Current best parameters:
  fx: 550.1509
  fy: 549.9333
  px: 996.5858
  py: 737.3625
  k1: -0.05896554
  k2: 0.06351427
  k3: -0.03327246
  k4: 0.00582599
  p1: 0.00065806
  p2: -0.00360103
  p3: -0.01877997
  p4: 0.00163689
  s1: 0.0207
  s2: -0.0017
  s3: 0.0128
  s4: -0.0028
  tilt_x: -0.0394
  tilt_y: 0.0028
  scale: 3.0484
  rotation: -0.4612

Best score: 0.2920: : 949iter [00:27, 39.94iter/s]
Best score: 0.2920: : 950iter [00:27, 39.81iter/s]
Best score: 0.2920: : 950iter [00:27, 39.81iter/s]
Best score: 0.2920: : 951iter [00:27, 39.81iter/s]
Best score: 0.2920: : 952iter [00:27, 39.81iter/s]
Best score: 0.2920: : 953iter [00:27, 39.81iter/s]
Best score: 0.2920: : 954iter [00:27, 33.36iter/s]
Best score: 0.2920: : 954iter [00:27, 33.36iter/s]
Best score: 0.2920: : 955iter [00:27, 33.36iter/s]
Best score: 0.2920: : 956iter [00:27, 33.36iter/s]
Best score: 0.2920: : 957iter [00:27, 33.36iter/s]
Best score: 0.2920: : 958iter [00:27, 33.36iter/s]
Best score: 0.2920: : 959iter [00:27, 35.46iter/s]
Best score: 0.2920: : 959iter [00:27, 35.46iter/s]
Best score: 0.2920: : 960iter [00:27, 35.46iter/s]
Best score: 0.2920: : 961iter [00:27, 35.46iter/s]
Best score: 0.2920: : 962iter [00:27, 35.46iter/s]
Best score: 0.2920: : 963iter [00:27, 35.46iter/s]
Best score: 0.2920: : 964iter [00:27, 36.79iter/s]
Best score: 0.2920: : 964iter [00:27, 36.79iter/s]
Best score: 0.2920: : 965iter [00:27, 36.79iter/s]
Best score: 0.2920: : 966iter [00:27, 36.79iter/s]
Best score: 0.2920: : 967iter [00:27, 36.79iter/s]
Best score: 0.2920: : 968iter [00:27, 36.79iter/s]
Best score: 0.2920: : 969iter [00:27, 38.02iter/s]
Best score: 0.2920: : 969iter [00:27, 38.02iter/s]
Best score: 0.2920: : 970iter [00:27, 38.02iter/s]
Best score: 0.2920: : 971iter [00:27, 38.02iter/s]
Best score: 0.2920: : 972iter [00:27, 38.02iter/s]
Best score: 0.2920: : 973iter [00:27, 38.46iter/s]
Best score: 0.2920: : 973iter [00:27, 38.46iter/s]
Best score: 0.2920: : 974iter [00:27, 38.46iter/s]
Best score: 0.2920: : 975iter [00:27, 38.46iter/s]
Best score: 0.2920: : 976iter [00:27, 38.46iter/s]
Best score: 0.2920: : 977iter [00:27, 38.46iter/s]
Best score: 0.2920: : 978iter [00:27, 39.19iter/s]
Best score: 0.2920: : 978iter [00:27, 39.19iter/s]
Best score: 0.2920: : 979iter [00:27, 39.19iter/s]
Best score: 0.2920: : 980iter [00:27, 39.19iter/s]
Best score: 0.2920: : 981iter [00:27, 39.19iter/s]
Best score: 0.2920: : 982iter [00:27, 39.29iter/s]
Best score: 0.2920: : 982iter [00:27, 39.29iter/s]
Best score: 0.2920: : 983iter [00:27, 39.29iter/s]
Best score: 0.2920: : 984iter [00:27, 39.29iter/s]
Best score: 0.2920: : 985iter [00:28, 39.29iter/s]
Best score: 0.2920: : 986iter [00:28, 39.29iter/s]
Best score: 0.2920: : 987iter [00:28, 39.59iter/s]
Best score: 0.2920: : 987iter [00:28, 39.59iter/s]
Best score: 0.2920: : 988iter [00:28, 39.59iter/s]
Best score: 0.2920: : 989iter [00:28, 39.59iter/s]
Best score: 0.2920: : 990iter [00:28, 39.59iter/s]
Best score: 0.2920: : 991iter [00:28, 39.60iter/s]
Best score: 0.2920: : 991iter [00:28, 39.60iter/s]
Best score: 0.2920: : 992iter [00:28, 39.60iter/s]
Best score: 0.2920: : 993iter [00:28, 39.60iter/s]
Best score: 0.2920: : 994iter [00:28, 39.60iter/s]
Best score: 0.2920: : 995iter [00:28, 39.60iter/s]
Best score: 0.2920: : 996iter [00:28, 40.00iter/s]
Best score: 0.2920: : 996iter [00:28, 40.00iter/s]
Best score: 0.2920: : 997iter [00:28, 40.00iter/s]
Best score: 0.2920: : 998iter [00:28, 40.00iter/s]
[ITER 1000] Best: 0.2919
[PARAMS] Current best parameters:
  fx: 549.2280
  fy: 549.0165
  px: 996.6906
  py: 737.1820
  k1: -0.06056495
  k2: 0.06699129
  k3: -0.03506642
  k4: 0.00613662
  p1: 0.00086928
  p2: -0.00323335
  p3: -0.01858588
  p4: 0.00133632
  s1: 0.0204
  s2: -0.0015
  s3: 0.0123
  s4: -0.0026
  tilt_x: -0.0407
  tilt_y: 0.0029
  scale: 3.0522
  rotation: -0.4598

Best score: 0.2919: : 999iter [00:28, 40.00iter/s]
Best score: 0.2919: : 1000iter [00:28, 40.00iter/s]
Best score: 0.2919: : 1001iter [00:28, 33.30iter/s]
Best score: 0.2919: : 1001iter [00:28, 33.30iter/s]
Best score: 0.2919: : 1002iter [00:28, 33.30iter/s]
Best score: 0.2919: : 1003iter [00:28, 33.30iter/s]
Best score: 0.2919: : 1004iter [00:28, 33.30iter/s]
Best score: 0.2919: : 1005iter [00:28, 28.75iter/s]
Best score: 0.2919: : 1005iter [00:28, 28.75iter/s]
Best score: 0.2919: : 1006iter [00:28, 28.75iter/s]
Best score: 0.2919: : 1007iter [00:28, 28.75iter/s]
Best score: 0.2919: : 1008iter [00:28, 28.75iter/s]
Best score: 0.2916: : 1009iter [00:28, 28.75iter/s]
Best score: 0.2916: : 1010iter [00:28, 31.56iter/s]
Best score: 0.2916: : 1010iter [00:28, 31.56iter/s]
Best score: 0.2916: : 1011iter [00:28, 31.56iter/s]
Best score: 0.2916: : 1012iter [00:28, 31.56iter/s]
Best score: 0.2916: : 1013iter [00:28, 31.56iter/s]
Best score: 0.2916: : 1014iter [00:28, 31.56iter/s]
Best score: 0.2916: : 1015iter [00:28, 34.06iter/s]
Best score: 0.2916: : 1015iter [00:28, 34.06iter/s]
Best score: 0.2916: : 1016iter [00:28, 34.06iter/s]
Best score: 0.2916: : 1017iter [00:28, 34.06iter/s]
Best score: 0.2916: : 1018iter [00:29, 34.06iter/s]
Best score: 0.2916: : 1019iter [00:29, 34.06iter/s]
Best score: 0.2916: : 1020iter [00:29, 35.70iter/s]
Best score: 0.2916: : 1020iter [00:29, 35.70iter/s]
Best score: 0.2916: : 1021iter [00:29, 35.70iter/s]
Best score: 0.2916: : 1022iter [00:29, 35.70iter/s]
Best score: 0.2916: : 1023iter [00:29, 35.70iter/s]
Best score: 0.2916: : 1024iter [00:29, 35.70iter/s]
Best score: 0.2916: : 1025iter [00:29, 37.08iter/s]
Best score: 0.2916: : 1025iter [00:29, 37.08iter/s]
Best score: 0.2916: : 1026iter [00:29, 37.08iter/s]
Best score: 0.2916: : 1027iter [00:29, 37.08iter/s]
Best score: 0.2916: : 1028iter [00:29, 37.08iter/s]
Best score: 0.2912: : 1029iter [00:29, 37.08iter/s]
Best score: 0.2912: : 1030iter [00:29, 37.96iter/s]
Best score: 0.2912: : 1030iter [00:29, 37.96iter/s]
Best score: 0.2912: : 1031iter [00:29, 37.96iter/s]
Best score: 0.2912: : 1032iter [00:29, 37.96iter/s]
Best score: 0.2912: : 1033iter [00:29, 37.96iter/s]
Best score: 0.2912: : 1034iter [00:29, 37.96iter/s]
Best score: 0.2912: : 1035iter [00:29, 38.73iter/s]
Best score: 0.2912: : 1035iter [00:29, 38.73iter/s]
Best score: 0.2912: : 1036iter [00:29, 38.73iter/s]
Best score: 0.2912: : 1037iter [00:29, 38.73iter/s]
Best score: 0.2912: : 1038iter [00:29, 38.73iter/s]
Best score: 0.2909: : 1039iter [00:29, 38.73iter/s]
Best score: 0.2909: : 1040iter [00:29, 39.27iter/s]
Best score: 0.2909: : 1040iter [00:29, 39.27iter/s]
Best score: 0.2909: : 1041iter [00:29, 39.27iter/s]
Best score: 0.2909: : 1042iter [00:29, 39.27iter/s]
Best score: 0.2909: : 1043iter [00:29, 39.27iter/s]
Best score: 0.2909: : 1044iter [00:29, 39.27iter/s]
Best score: 0.2909: : 1045iter [00:29, 39.77iter/s]
Best score: 0.2909: : 1045iter [00:29, 39.77iter/s]
Best score: 0.2909: : 1046iter [00:29, 39.77iter/s]
Best score: 0.2909: : 1047iter [00:29, 39.77iter/s]
Best score: 0.2909: : 1048iter [00:29, 39.77iter/s]
Best score: 0.2908: : 1049iter [00:29, 39.77iter/s]
Best score: 0.2908: : 1050iter [00:29, 39.67iter/s]
[ITER 1050] Best: 0.2908
[PARAMS] Current best parameters:
  fx: 547.7868
  fy: 547.5565
  px: 996.1988
  py: 737.4004
  k1: -0.06217209
  k2: 0.07195010
  k3: -0.03808688
  k4: 0.00675207
  p1: 0.00174926
  p2: -0.00176956
  p3: -0.02029798
  p4: 0.00094880
  s1: 0.0197
  s2: -0.0009
  s3: 0.0132
  s4: -0.0020
  tilt_x: -0.0413
  tilt_y: 0.0083
  scale: 3.0558
  rotation: -0.4681

Best score: 0.2908: : 1050iter [00:29, 39.67iter/s]
Best score: 0.2908: : 1051iter [00:29, 39.67iter/s]
Best score: 0.2908: : 1052iter [00:29, 39.67iter/s]
Best score: 0.2908: : 1053iter [00:29, 39.67iter/s]
Best score: 0.2908: : 1054iter [00:29, 33.25iter/s]
Best score: 0.2908: : 1054iter [00:29, 33.25iter/s]
Best score: 0.2908: : 1055iter [00:29, 33.25iter/s]
Best score: 0.2908: : 1056iter [00:30, 33.25iter/s]
Best score: 0.2908: : 1057iter [00:30, 33.25iter/s]
Best score: 0.2908: : 1058iter [00:30, 33.25iter/s]
Best score: 0.2908: : 1059iter [00:30, 35.34iter/s]
Best score: 0.2908: : 1059iter [00:30, 35.34iter/s]
Best score: 0.2908: : 1060iter [00:30, 35.34iter/s]
Best score: 0.2908: : 1061iter [00:30, 35.34iter/s]
Best score: 0.2908: : 1062iter [00:30, 35.34iter/s]
Best score: 0.2908: : 1063iter [00:30, 36.33iter/s]
Best score: 0.2908: : 1063iter [00:30, 36.33iter/s]
Best score: 0.2908: : 1064iter [00:30, 36.33iter/s]
Best score: 0.2908: : 1065iter [00:30, 36.33iter/s]
Best score: 0.2908: : 1066iter [00:30, 36.33iter/s]
Best score: 0.2908: : 1067iter [00:30, 36.33iter/s]
Best score: 0.2908: : 1068iter [00:30, 37.58iter/s]
Best score: 0.2908: : 1068iter [00:30, 37.58iter/s]
Best score: 0.2905: : 1069iter [00:30, 37.58iter/s]
Best score: 0.2905: : 1070iter [00:30, 37.58iter/s]
Best score: 0.2905: : 1071iter [00:30, 37.58iter/s]
Best score: 0.2905: : 1072iter [00:30, 38.06iter/s]
Best score: 0.2905: : 1072iter [00:30, 38.06iter/s]
Best score: 0.2905: : 1073iter [00:30, 38.06iter/s]
Best score: 0.2905: : 1074iter [00:30, 38.06iter/s]
Best score: 0.2905: : 1075iter [00:30, 38.06iter/s]
Best score: 0.2905: : 1076iter [00:30, 38.06iter/s]
Best score: 0.2905: : 1077iter [00:30, 38.92iter/s]
Best score: 0.2905: : 1077iter [00:30, 38.92iter/s]
Best score: 0.2905: : 1078iter [00:30, 38.92iter/s]
Best score: 0.2905: : 1079iter [00:30, 38.92iter/s]
Best score: 0.2905: : 1080iter [00:30, 38.92iter/s]
Best score: 0.2905: : 1081iter [00:30, 39.06iter/s]
Best score: 0.2905: : 1081iter [00:30, 39.06iter/s]
Best score: 0.2905: : 1082iter [00:30, 39.06iter/s]
Best score: 0.2905: : 1083iter [00:30, 39.06iter/s]
Best score: 0.2905: : 1084iter [00:30, 39.06iter/s]
Best score: 0.2905: : 1085iter [00:30, 39.06iter/s]
Best score: 0.2905: : 1086iter [00:30, 39.68iter/s]
Best score: 0.2905: : 1086iter [00:30, 39.68iter/s]
Best score: 0.2905: : 1087iter [00:30, 39.68iter/s]
Best score: 0.2905: : 1088iter [00:30, 39.68iter/s]
Best score: 0.2905: : 1089iter [00:30, 39.68iter/s]
Best score: 0.2905: : 1090iter [00:30, 39.71iter/s]
Best score: 0.2905: : 1090iter [00:30, 39.71iter/s]
Best score: 0.2905: : 1091iter [00:30, 39.71iter/s]
Best score: 0.2905: : 1092iter [00:30, 39.71iter/s]
Best score: 0.2905: : 1093iter [00:30, 39.71iter/s]
Best score: 0.2905: : 1094iter [00:30, 39.71iter/s]
Best score: 0.2905: : 1095iter [00:30, 40.14iter/s]
Best score: 0.2905: : 1095iter [00:30, 40.14iter/s]
Best score: 0.2905: : 1096iter [00:31, 40.14iter/s]
Best score: 0.2905: : 1097iter [00:31, 40.14iter/s]
Best score: 0.2905: : 1098iter [00:31, 40.14iter/s]
[ITER 1100] Best: 0.2904
[PARAMS] Current best parameters:
  fx: 548.9504
  fy: 548.7201
  px: 996.5982
  py: 737.2441
  k1: -0.05475133
  k2: 0.05968700
  k3: -0.03161911
  k4: 0.00555687
  p1: 0.00239891
  p2: -0.00031880
  p3: -0.02191060
  p4: 0.00073177
  s1: 0.0202
  s2: -0.0007
  s3: 0.0139
  s4: -0.0018
  tilt_x: -0.0435
  tilt_y: 0.0092
  scale: 3.0454
  rotation: -0.4667

Best score: 0.2904: : 1099iter [00:31, 40.14iter/s]
Best score: 0.2904: : 1100iter [00:31, 40.02iter/s]
Best score: 0.2904: : 1100iter [00:31, 35.29iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.290358 -> 0.290047

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,862 → 25,862 within 3500mm ring
  Ring polish did not improve score: 0.290047
  Success: True
  Tangential distortion: p1=0.002410, p2=-0.000320, p3=-0.021910, p4=0.000728
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2900
[✅] Parameters copied to: /working/MDC015_W038_2025-05-14/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 548.9504272033123
    fy: 548.7200894132769
    px: 996.5982342343597
    py: 737.2441320158695
    k1: -0.05474364382791874
    k2: 0.05969161633167252
    k3: -0.031616744758770375
    k4: 0.005551995758109199
    p1: 0.0024102782869519323
    p2: -0.00031966717969612136
    p3: -0.021910187027996374
    p4: 0.0007281100679816777
    scale: 3.045493653180121
    rotation: -0.46672221993824897
    mmpp: 1.2867647131009086
    nmpp: 1286764.7131009086

[🔄] Converting 484 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/228 [00:00<?, ?frame/s]

   🔄 1000:   8%|▊         | 18/228 [00:00<00:01, 174.51frame/s]

   🔄 1000:  16%|█▌        | 36/228 [00:00<00:01, 177.04frame/s]

   🔄 1000:  24%|██▎       | 54/228 [00:00<00:00, 177.91frame/s]

   🔄 1000:  32%|███▏      | 72/228 [00:00<00:00, 178.10frame/s]

   🔄 1000:  39%|███▉      | 90/228 [00:00<00:00, 177.02frame/s]

   🔄 1000:  47%|████▋     | 108/228 [00:00<00:00, 176.17frame/s]

   🔄 1000:  55%|█████▌    | 126/228 [00:00<00:00, 175.92frame/s]

   🔄 1000:  63%|██████▎   | 144/228 [00:00<00:00, 176.33frame/s]

   🔄 1000:  71%|███████   | 162/228 [00:00<00:00, 176.69frame/s]

   🔄 1000:  79%|███████▉  | 180/228 [00:01<00:00, 177.44frame/s]

   🔄 1000:  87%|████████▋ | 198/228 [00:01<00:00, 178.06frame/s]

   🔄 1000:  95%|█████████▍| 216/228 [00:01<00:00, 178.17frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.33s/rig]

   🔄 500:   0%|          | 0/256 [00:00<?, ?frame/s]

   🔄 500:   7%|▋         | 18/256 [00:00<00:01, 179.25frame/s]

   🔄 500:  14%|█▍        | 37/256 [00:00<00:01, 179.97frame/s]

   🔄 500:  21%|██▏       | 55/256 [00:00<00:01, 179.96frame/s]

   🔄 500:  29%|██▉       | 74/256 [00:00<00:01, 180.06frame/s]

   🔄 500:  36%|███▋      | 93/256 [00:00<00:00, 179.62frame/s]

   🔄 500:  43%|████▎     | 111/256 [00:00<00:00, 179.51frame/s]

   🔄 500:  51%|█████     | 130/256 [00:00<00:00, 179.83frame/s]

   🔄 500:  58%|█████▊    | 149/256 [00:00<00:00, 180.13frame/s]

   🔄 500:  66%|██████▌   | 168/256 [00:00<00:00, 179.89frame/s]

   🔄 500:  73%|███████▎  | 186/256 [00:01<00:00, 179.82frame/s]

   🔄 500:  80%|███████▉  | 204/256 [00:01<00:00, 179.62frame/s]

   🔄 500:  87%|████████▋ | 223/256 [00:01<00:00, 179.85frame/s]

   🔄 500:  94%|█████████▍| 241/256 [00:01<00:00, 179.82frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.49s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.47s/rig]
[✅] Saved PTDs: /working/MDC015_W038_2025-05-14/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 178.25frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 178.26frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 176.45frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 175.76frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 177.08frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.11rig/s]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 177.88frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 177.73frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 177.36frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 177.11frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 177.38frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.01rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.03rig/s]
[✅] Saved PTDs: /working/MDC015_W038_2025-05-14/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 93432/3080448 [00:00<00:03, 934318.66px/s]
📤 Writing LUT:   6%|▌         | 191485/3080448 [00:00<00:03, 961498.28px/s]
📤 Writing LUT:   9%|▉         | 289234/3080448 [00:00<00:02, 968799.79px/s]
📤 Writing LUT:  13%|█▎        | 386114/3080448 [00:00<00:02, 957686.61px/s]
📤 Writing LUT:  16%|█▌        | 481902/3080448 [00:00<00:02, 937498.89px/s]
📤 Writing LUT:  19%|█▊        | 575724/3080448 [00:00<00:02, 926579.19px/s]
📤 Writing LUT:  22%|██▏       | 668435/3080448 [00:00<00:02, 916126.04px/s]
📤 Writing LUT:  25%|██▍       | 760086/3080448 [00:00<00:02, 912041.75px/s]
📤 Writing LUT:  28%|██▊       | 851312/3080448 [00:00<00:02, 904831.65px/s]
📤 Writing LUT:  31%|███       | 941810/3080448 [00:01<00:02, 894353.24px/s]
📤 Writing LUT:  33%|███▎      | 1031264/3080448 [00:01<00:02, 887696.99px/s]
📤 Writing LUT:  36%|███▋      | 1120044/3080448 [00:01<00:02, 884783.98px/s]
📤 Writing LUT:  39%|███▉      | 1210674/3080448 [00:01<00:02, 891196.32px/s]
📤 Writing LUT:  42%|████▏     | 1299807/3080448 [00:01<00:01, 890606.20px/s]
📤 Writing LUT:  45%|████▌     | 1390003/3080448 [00:01<00:01, 893996.17px/s]
📤 Writing LUT:  48%|████▊     | 1481872/3080448 [00:01<00:01, 901383.42px/s]
📤 Writing LUT:  51%|█████     | 1572021/3080448 [00:01<00:01, 900367.15px/s]
📤 Writing LUT:  54%|█████▍    | 1663647/3080448 [00:01<00:01, 905122.73px/s]
📤 Writing LUT:  57%|█████▋    | 1754996/3080448 [00:01<00:01, 907626.25px/s]
📤 Writing LUT:  60%|█████▉    | 1845765/3080448 [00:02<00:01, 903954.97px/s]
📤 Writing LUT:  63%|██████▎   | 1936168/3080448 [00:02<00:01, 897057.48px/s]
📤 Writing LUT:  66%|██████▌   | 2025888/3080448 [00:02<00:01, 889113.05px/s]
📤 Writing LUT:  69%|██████▊   | 2114819/3080448 [00:02<00:01, 884793.75px/s]
📤 Writing LUT:  72%|███████▏  | 2204791/3080448 [00:02<00:00, 889218.92px/s]
📤 Writing LUT:  74%|███████▍  | 2293728/3080448 [00:02<00:00, 885153.17px/s]
📤 Writing LUT:  77%|███████▋  | 2383100/3080448 [00:02<00:00, 887697.85px/s]
📤 Writing LUT:  80%|████████  | 2475013/3080448 [00:02<00:00, 897065.04px/s]
📤 Writing LUT:  83%|████████▎ | 2566308/3080448 [00:02<00:00, 901808.39px/s]
📤 Writing LUT:  86%|████████▌ | 2656565/3080448 [00:02<00:00, 902035.14px/s]
📤 Writing LUT:  89%|████████▉ | 2746777/3080448 [00:03<00:00, 898248.94px/s]
📤 Writing LUT:  92%|█████████▏| 2838682/3080448 [00:03<00:00, 904458.32px/s]
📤 Writing LUT:  95%|█████████▌| 2929591/3080448 [00:03<00:00, 905841.77px/s]
📤 Writing LUT:  98%|█████████▊| 3022221/3080448 [00:03<00:00, 911964.67px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 905331.00px/s]
[✅] LUT written to: /working/MDC015_W038_2025-05-14/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC015_W038_2025-05-14' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC015_W038_2025-05-14/calibration/MDC015_W038_2025-05-14_unknown.lut

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

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

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

   🔥 1000:   0%|          | 0/228 [00:00<?, ?frame/s]

   🔥 1000:   4%|▎         | 8/228 [00:00<00:02, 78.45frame/s]

   🔥 1000:   7%|▋         | 17/228 [00:00<00:02, 84.20frame/s]

   🔥 1000:  11%|█▏        | 26/228 [00:00<00:02, 85.13frame/s]

   🔥 1000:  15%|█▌        | 35/228 [00:00<00:02, 83.40frame/s]

   🔥 1000:  19%|█▉        | 44/228 [00:00<00:02, 81.32frame/s]

   🔥 1000:  23%|██▎       | 53/228 [00:00<00:02, 80.21frame/s]

   🔥 1000:  27%|██▋       | 62/228 [00:00<00:02, 80.15frame/s]

   🔥 1000:  31%|███       | 71/228 [00:00<00:01, 80.54frame/s]

   🔥 1000:  35%|███▌      | 80/228 [00:00<00:01, 81.13frame/s]

   🔥 1000:  39%|███▉      | 89/228 [00:01<00:01, 81.85frame/s]

   🔥 1000:  43%|████▎     | 98/228 [00:01<00:01, 80.41frame/s]

   🔥 1000:  47%|████▋     | 107/228 [00:01<00:01, 79.76frame/s]

   🔥 1000:  51%|█████     | 116/228 [00:01<00:01, 81.70frame/s]

   🔥 1000:  55%|█████▍    | 125/228 [00:01<00:01, 82.31frame/s]

   🔥 1000:  59%|█████▉    | 134/228 [00:01<00:01, 82.97frame/s]

   🔥 1000:  63%|██████▎   | 143/228 [00:01<00:01, 82.37frame/s]

   🔥 1000:  67%|██████▋   | 152/228 [00:01<00:00, 81.59frame/s]

   🔥 1000:  71%|███████   | 161/228 [00:01<00:00, 80.78frame/s]

   🔥 1000:  75%|███████▍  | 170/228 [00:02<00:00, 80.64frame/s]

   🔥 1000:  79%|███████▊  | 179/228 [00:02<00:00, 80.73frame/s]

   🔥 1000:  82%|████████▏ | 188/228 [00:02<00:00, 80.47frame/s]

   🔥 1000:  86%|████████▋ | 197/228 [00:02<00:00, 82.25frame/s]

   🔥 1000:  90%|█████████ | 206/228 [00:02<00:00, 83.74frame/s]

   🔥 1000:  95%|█████████▍| 216/228 [00:02<00:00, 86.17frame/s]

   🔥 1000:  99%|█████████▉| 226/228 [00:02<00:00, 87.81frame/s]

                                                                [✅] 1000: /working/MDC015_W038_2025-05-14/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC015_W038_2025-05-14/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.72s/rig]

   🔥 500:   0%|          | 0/256 [00:00<?, ?frame/s]

   🔥 500:   3%|▎         | 8/256 [00:00<00:03, 77.67frame/s]

   🔥 500:   7%|▋         | 17/256 [00:00<00:02, 81.56frame/s]

   🔥 500:  10%|█         | 26/256 [00:00<00:02, 83.47frame/s]

   🔥 500:  14%|█▎        | 35/256 [00:00<00:02, 84.63frame/s]

   🔥 500:  17%|█▋        | 44/256 [00:00<00:02, 84.02frame/s]

   🔥 500:  21%|██        | 53/256 [00:00<00:02, 83.43frame/s]

   🔥 500:  24%|██▍       | 62/256 [00:00<00:02, 82.58frame/s]

   🔥 500:  28%|██▊       | 71/256 [00:00<00:02, 83.26frame/s]

   🔥 500:  31%|███▏      | 80/256 [00:00<00:02, 85.10frame/s]

   🔥 500:  35%|███▌      | 90/256 [00:01<00:01, 87.46frame/s]

   🔥 500:  39%|███▉      | 100/256 [00:01<00:01, 88.88frame/s]

   🔥 500:  43%|████▎     | 110/256 [00:01<00:01, 89.77frame/s]

   🔥 500:  47%|████▋     | 120/256 [00:01<00:01, 90.17frame/s]

   🔥 500:  51%|█████     | 130/256 [00:01<00:01, 89.89frame/s]

   🔥 500:  54%|█████▍    | 139/256 [00:01<00:01, 88.53frame/s]

   🔥 500:  58%|█████▊    | 148/256 [00:01<00:01, 86.48frame/s]

   🔥 500:  61%|██████▏   | 157/256 [00:01<00:01, 84.74frame/s]

   🔥 500:  65%|██████▍   | 166/256 [00:01<00:01, 83.25frame/s]

   🔥 500:  68%|██████▊   | 175/256 [00:02<00:00, 81.76frame/s]

   🔥 500:  72%|███████▏  | 184/256 [00:02<00:00, 80.68frame/s]

   🔥 500:  75%|███████▌  | 193/256 [00:02<00:00, 79.90frame/s]

   🔥 500:  79%|███████▊  | 201/256 [00:02<00:00, 79.60frame/s]

   🔥 500:  82%|████████▏ | 210/256 [00:02<00:00, 80.07frame/s]

   🔥 500:  86%|████████▌ | 219/256 [00:02<00:00, 82.41frame/s]

   🔥 500:  89%|████████▉ | 228/256 [00:02<00:00, 84.54frame/s]

   🔥 500:  93%|█████████▎| 237/256 [00:02<00:00, 84.34frame/s]

   🔥 500:  96%|█████████▌| 246/256 [00:02<00:00, 84.07frame/s]

   🔥 500: 100%|█████████▉| 255/256 [00:03<00:00, 83.68frame/s]

                                                               [✅] 500: /working/MDC015_W038_2025-05-14/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC015_W038_2025-05-14/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.81s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.94s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC015_W038_2025-05-14/calibration/debug/5_heatmap
[🏁] Finished processing MDC015_W038_2025-05-14


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