2026-05-18T17:58:59 - INFO: Slick 0.0.8a12

🟢 Starting Calibration Pipeline

=== Processing MDC051_W028_2025-08-28 ===
2026-05-18T17:58:59 - INFO: Camera ID: 6396147
2026-05-18T17:58:59 - INFO: Wand ID: 
2026-05-18T17:58:59 - INFO: Recorded At: 2025-08-28T18:06:56Z

-------------------------------------------------------------------
                        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 1231 stationary frames, 224 remain


→ 1000 (224 images) Phase 1:   0%|          | 0/224 [00:00<?, ?it/s]

→ 1000 (224 images) Phase 1:   0%|          | 1/224 [00:00<01:58,  1.89it/s]

→ 1000 (224 images) Phase 1:   8%|▊         | 19/224 [00:00<00:05, 39.29it/s]

→ 1000 (224 images) Phase 1:  15%|█▍        | 33/224 [00:01<00:05, 38.12it/s]

→ 1000 (224 images) Phase 1:  21%|██        | 46/224 [00:01<00:03, 51.81it/s]

→ 1000 (224 images) Phase 1:  29%|██▉       | 65/224 [00:01<00:02, 55.63it/s]

→ 1000 (224 images) Phase 1:  33%|███▎      | 75/224 [00:01<00:02, 62.68it/s]

→ 1000 (224 images) Phase 1:  43%|████▎     | 97/224 [00:01<00:01, 64.35it/s]

→ 1000 (224 images) Phase 1:  48%|████▊     | 107/224 [00:01<00:01, 69.73it/s]

→ 1000 (224 images) Phase 1:  58%|█████▊    | 129/224 [00:02<00:01, 68.95it/s]

→ 1000 (224 images) Phase 1:  62%|██████▏   | 139/224 [00:02<00:01, 73.51it/s]

→ 1000 (224 images) Phase 1:  71%|███████▏  | 160/224 [00:02<00:00, 96.13it/s]

→ 1000 (224 images) Phase 1:  77%|███████▋  | 172/224 [00:02<00:00, 69.01it/s]

→ 1000 (224 images) Phase 1:  86%|████████▌ | 192/224 [00:02<00:00, 89.70it/s]

→ 1000 (224 images) Phase 1:  92%|█████████▏| 205/224 [00:03<00:00, 68.26it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/224 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/224 [00:00<00:32,  6.87it/s]

→ 1000 Phase 2: corners:   5%|▍         | 11/224 [00:00<00:04, 50.00it/s]

→ 1000 Phase 2: corners:  13%|█▎        | 29/224 [00:00<00:02, 97.47it/s]

→ 1000 Phase 2: corners:  19%|█▉        | 42/224 [00:00<00:01, 108.63it/s]

→ 1000 Phase 2: corners:  24%|██▍       | 54/224 [00:00<00:01, 110.16it/s]

→ 1000 Phase 2: corners:  29%|██▉       | 66/224 [00:00<00:01, 108.93it/s]

→ 1000 Phase 2: corners:  35%|███▌      | 79/224 [00:00<00:01, 115.20it/s]

→ 1000 Phase 2: corners:  41%|████      | 91/224 [00:00<00:01, 112.75it/s]

→ 1000 Phase 2: corners:  47%|████▋     | 105/224 [00:01<00:01, 111.68it/s]

→ 1000 Phase 2: corners:  53%|█████▎    | 119/224 [00:01<00:00, 116.61it/s]

→ 1000 Phase 2: corners:  60%|██████    | 135/224 [00:01<00:00, 125.06it/s]

→ 1000 Phase 2: corners:  68%|██████▊   | 153/224 [00:01<00:00, 139.35it/s]

→ 1000 Phase 2: corners:  75%|███████▌  | 168/224 [00:01<00:00, 137.88it/s]

→ 1000 Phase 2: corners:  81%|████████▏ | 182/224 [00:01<00:00, 138.22it/s]

→ 1000 Phase 2: corners:  88%|████████▊ | 198/224 [00:01<00:00, 143.09it/s]

→ 1000 Phase 2: corners:  97%|█████████▋| 218/224 [00:01<00:00, 159.02it/s]

                                                                           [PASS2] Frame 0000368: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000379: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000380: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000381: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000382: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001138: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001139: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001140: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001141: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001142: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001143: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001306: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001307: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001308: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001309: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001310: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001311: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001312: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001313: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001456: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000381: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001140: fixed corners {0, 2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001142: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001308: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001312: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 12 frames fixed, 11 untrusted, 224 total


→ 1000 Phase 3: finalize:   0%|          | 0/224 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   3%|▎         | 6/224 [00:00<00:04, 52.05it/s]

→ 1000 Phase 3: finalize:   5%|▌         | 12/224 [00:00<00:03, 56.31it/s]

→ 1000 Phase 3: finalize:   8%|▊         | 19/224 [00:00<00:03, 58.75it/s]

→ 1000 Phase 3: finalize:  12%|█▏        | 26/224 [00:00<00:03, 60.40it/s]

→ 1000 Phase 3: finalize:  15%|█▍        | 33/224 [00:00<00:03, 61.54it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 40/224 [00:00<00:02, 62.08it/s]

→ 1000 Phase 3: finalize:  21%|██        | 47/224 [00:00<00:02, 61.88it/s]

→ 1000 Phase 3: finalize:  24%|██▍       | 54/224 [00:00<00:02, 61.57it/s]

→ 1000 Phase 3: finalize:  27%|██▋       | 61/224 [00:01<00:02, 58.92it/s]

→ 1000 Phase 3: finalize:  30%|██▉       | 67/224 [00:01<00:02, 56.55it/s]

→ 1000 Phase 3: finalize:  33%|███▎      | 73/224 [00:01<00:02, 54.55it/s]

→ 1000 Phase 3: finalize:  35%|███▌      | 79/224 [00:01<00:02, 53.02it/s]

→ 1000 Phase 3: finalize:  38%|███▊      | 85/224 [00:01<00:02, 51.57it/s]

→ 1000 Phase 3: finalize:  41%|████      | 91/224 [00:01<00:02, 50.49it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 97/224 [00:01<00:02, 49.79it/s]

→ 1000 Phase 3: finalize:  46%|████▌     | 102/224 [00:01<00:02, 49.33it/s]

→ 1000 Phase 3: finalize:  48%|████▊     | 107/224 [00:01<00:02, 48.95it/s]

→ 1000 Phase 3: finalize:  50%|█████     | 112/224 [00:02<00:02, 48.79it/s]

→ 1000 Phase 3: finalize:  52%|█████▏    | 117/224 [00:02<00:02, 48.60it/s]

→ 1000 Phase 3: finalize:  54%|█████▍    | 122/224 [00:02<00:02, 48.43it/s]

→ 1000 Phase 3: finalize:  57%|█████▋    | 127/224 [00:02<00:02, 48.27it/s]

→ 1000 Phase 3: finalize:  59%|█████▉    | 132/224 [00:02<00:01, 48.22it/s]

→ 1000 Phase 3: finalize:  61%|██████    | 137/224 [00:02<00:01, 48.15it/s]

→ 1000 Phase 3: finalize:  63%|██████▎   | 142/224 [00:02<00:01, 48.18it/s]

→ 1000 Phase 3: finalize:  66%|██████▌   | 147/224 [00:02<00:01, 48.38it/s]

→ 1000 Phase 3: finalize:  68%|██████▊   | 153/224 [00:02<00:01, 48.99it/s]

→ 1000 Phase 3: finalize:  71%|███████   | 159/224 [00:03<00:01, 49.67it/s]

→ 1000 Phase 3: finalize:  74%|███████▎  | 165/224 [00:03<00:01, 50.40it/s]

→ 1000 Phase 3: finalize:  76%|███████▋  | 171/224 [00:03<00:01, 50.93it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 177/224 [00:03<00:00, 51.25it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 183/224 [00:03<00:00, 51.57it/s]

→ 1000 Phase 3: finalize:  84%|████████▍ | 189/224 [00:03<00:00, 51.83it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 195/224 [00:03<00:00, 52.04it/s]

→ 1000 Phase 3: finalize:  90%|████████▉ | 201/224 [00:03<00:00, 52.41it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 207/224 [00:03<00:00, 52.77it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 213/224 [00:04<00:00, 53.08it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 219/224 [00:04<00:00, 53.13it/s]

                                                                           
[TIMING] 1000 — 224 frames
  Phase 1  radial detect   (parallel)  :   3.45s  (15.4 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.83s  (8.2 ms/frame)
  Phase 2b corner fix      (sequential):   0.51s
  Phase 3  finalize        (sequential):   4.26s  (19.0 ms/frame)
    decode + undistort                 :   2.12s  (9.4 ms/frame)
    corner apply + refinement          :   1.04s  (4.6 ms/frame)
    segment                            :   0.89s  (4.0 ms/frame)
    smooth                             :   0.14s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.05s


Processing Rigs:  50%|█████     | 1/2 [00:14<00:14, 14.78s/it][INFO] Filtered 988 stationary frames, 197 remain


→ 500 (197 images) Phase 1:   0%|          | 0/197 [00:00<?, ?it/s]

→ 500 (197 images) Phase 1:   1%|          | 1/197 [00:00<01:34,  2.08it/s]

→ 500 (197 images) Phase 1:   2%|▏         | 4/197 [00:00<00:23,  8.26it/s]

→ 500 (197 images) Phase 1:  11%|█         | 22/197 [00:00<00:03, 48.90it/s]

→ 500 (197 images) Phase 1:  16%|█▌        | 31/197 [00:00<00:03, 54.77it/s]

→ 500 (197 images) Phase 1:  20%|█▉        | 39/197 [00:01<00:03, 47.18it/s]

→ 500 (197 images) Phase 1:  28%|██▊       | 55/197 [00:01<00:02, 70.58it/s]

→ 500 (197 images) Phase 1:  33%|███▎      | 65/197 [00:01<00:02, 65.86it/s]

→ 500 (197 images) Phase 1:  38%|███▊      | 74/197 [00:01<00:01, 61.75it/s]

→ 500 (197 images) Phase 1:  46%|████▌     | 90/197 [00:01<00:01, 81.42it/s]

→ 500 (197 images) Phase 1:  51%|█████     | 100/197 [00:01<00:01, 64.15it/s]

→ 500 (197 images) Phase 1:  55%|█████▍    | 108/197 [00:01<00:01, 66.68it/s]

→ 500 (197 images) Phase 1:  62%|██████▏   | 123/197 [00:02<00:00, 84.03it/s]

→ 500 (197 images) Phase 1:  68%|██████▊   | 133/197 [00:02<00:01, 61.47it/s]

→ 500 (197 images) Phase 1:  75%|███████▌  | 148/197 [00:02<00:00, 77.14it/s]

→ 500 (197 images) Phase 1:  80%|████████  | 158/197 [00:02<00:00, 76.97it/s]

→ 500 (197 images) Phase 1:  85%|████████▌ | 168/197 [00:02<00:00, 67.09it/s]

→ 500 (197 images) Phase 1:  92%|█████████▏| 182/197 [00:02<00:00, 81.87it/s]

→ 500 (197 images) Phase 1:  98%|█████████▊| 194/197 [00:03<00:00, 87.31it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/197 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   1%|          | 1/197 [00:00<00:42,  4.61it/s]

→ 500 Phase 2: corners:   7%|▋         | 13/197 [00:00<00:03, 49.53it/s]

→ 500 Phase 2: corners:  12%|█▏        | 23/197 [00:00<00:02, 65.70it/s]

→ 500 Phase 2: corners:  17%|█▋        | 34/197 [00:00<00:02, 80.15it/s]

→ 500 Phase 2: corners:  25%|██▍       | 49/197 [00:00<00:01, 100.48it/s]

→ 500 Phase 2: corners:  32%|███▏      | 63/197 [00:00<00:01, 111.48it/s]

→ 500 Phase 2: corners:  39%|███▉      | 77/197 [00:00<00:01, 119.64it/s]

→ 500 Phase 2: corners:  46%|████▌     | 91/197 [00:00<00:00, 124.87it/s]

→ 500 Phase 2: corners:  53%|█████▎    | 105/197 [00:01<00:00, 129.38it/s]

→ 500 Phase 2: corners:  60%|██████    | 119/197 [00:01<00:00, 121.56it/s]

→ 500 Phase 2: corners:  67%|██████▋   | 132/197 [00:01<00:00, 123.60it/s]

→ 500 Phase 2: corners:  75%|███████▌  | 148/197 [00:01<00:00, 130.57it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 162/197 [00:01<00:00, 128.62it/s]

→ 500 Phase 2: corners:  91%|█████████▏| 180/197 [00:01<00:00, 140.88it/s]

                                                                          [PASS2] Frame 0000331: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000332: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000334: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000333: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 2 untrusted, 197 total


→ 500 Phase 3: finalize:   0%|          | 0/197 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   3%|▎         | 6/197 [00:00<00:03, 59.50it/s]

→ 500 Phase 3: finalize:   7%|▋         | 13/197 [00:00<00:02, 63.50it/s]

→ 500 Phase 3: finalize:  10%|█         | 20/197 [00:00<00:02, 64.92it/s]

→ 500 Phase 3: finalize:  14%|█▎        | 27/197 [00:00<00:02, 65.30it/s]

→ 500 Phase 3: finalize:  17%|█▋        | 34/197 [00:00<00:02, 65.93it/s]

→ 500 Phase 3: finalize:  21%|██        | 41/197 [00:00<00:02, 66.58it/s]

→ 500 Phase 3: finalize:  24%|██▍       | 48/197 [00:00<00:02, 66.69it/s]

→ 500 Phase 3: finalize:  28%|██▊       | 55/197 [00:00<00:02, 64.93it/s]

→ 500 Phase 3: finalize:  31%|███▏      | 62/197 [00:00<00:02, 60.31it/s]

→ 500 Phase 3: finalize:  35%|███▌      | 69/197 [00:01<00:02, 57.44it/s]

→ 500 Phase 3: finalize:  38%|███▊      | 75/197 [00:01<00:02, 55.91it/s]

→ 500 Phase 3: finalize:  41%|████      | 81/197 [00:01<00:02, 54.93it/s]

→ 500 Phase 3: finalize:  44%|████▍     | 87/197 [00:01<00:02, 53.65it/s]

→ 500 Phase 3: finalize:  47%|████▋     | 93/197 [00:01<00:01, 52.68it/s]

→ 500 Phase 3: finalize:  50%|█████     | 99/197 [00:01<00:01, 52.06it/s]

→ 500 Phase 3: finalize:  53%|█████▎    | 105/197 [00:01<00:01, 51.62it/s]

→ 500 Phase 3: finalize:  56%|█████▋    | 111/197 [00:01<00:01, 51.22it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 117/197 [00:02<00:01, 50.98it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 123/197 [00:02<00:01, 50.87it/s]

→ 500 Phase 3: finalize:  65%|██████▌   | 129/197 [00:02<00:01, 50.86it/s]

→ 500 Phase 3: finalize:  69%|██████▊   | 135/197 [00:02<00:01, 50.95it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 141/197 [00:02<00:01, 50.90it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 147/197 [00:02<00:00, 51.31it/s]

→ 500 Phase 3: finalize:  78%|███████▊  | 153/197 [00:02<00:00, 51.78it/s]

→ 500 Phase 3: finalize:  81%|████████  | 159/197 [00:02<00:00, 51.97it/s]

→ 500 Phase 3: finalize:  84%|████████▍ | 165/197 [00:02<00:00, 52.28it/s]

→ 500 Phase 3: finalize:  87%|████████▋ | 171/197 [00:03<00:00, 52.67it/s]

→ 500 Phase 3: finalize:  90%|████████▉ | 177/197 [00:03<00:00, 52.79it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 183/197 [00:03<00:00, 52.51it/s]

→ 500 Phase 3: finalize:  96%|█████████▌| 189/197 [00:03<00:00, 52.55it/s]

→ 500 Phase 3: finalize:  99%|█████████▉| 195/197 [00:03<00:00, 52.15it/s]

                                                                          
[TIMING] 500 — 197 frames
  Phase 1  radial detect   (parallel)  :   3.11s  (15.8 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.75s  (8.9 ms/frame)
  Phase 2b corner fix      (sequential):   0.18s
  Phase 3  finalize        (sequential):   3.59s  (18.2 ms/frame)
    decode + undistort                 :   1.82s  (9.2 ms/frame)
    corner apply + refinement          :   0.77s  (3.9 ms/frame)
    segment                            :   0.79s  (4.0 ms/frame)
    smooth                             :   0.15s  (0.8 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :   8.64s


Processing Rigs: 100%|██████████| 2/2 [00:27<00:00, 13.41s/it]
Processing Rigs: 100%|██████████| 2/2 [00:27<00:00, 13.61s/it]
[💾] Saved RigFrameDataset to /working/MDC051_W028_2025-08-28/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 421 frames from 2 rigs to /working/MDC051_W028_2025-08-28/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC051_W028_2025-08-28/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 224
[📋] 500: 100 frames selected from 197
[💾] Saved RigFrameDataset to /working/MDC051_W028_2025-08-28/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC051_W028_2025-08-28/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<02:03,  3.17s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:01,  3.19s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:56,  3.15s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:52,  3.14s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:15<01:50,  3.14s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:18<01:47,  3.16s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:44,  3.16s/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,  3.19s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:31<01:36,  3.21s/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 [00:38<01:29,  3.21s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:41<01:26,  3.21s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:44<01:23,  3.20s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:47<01:20,  3.21s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:50<01:16,  3.18s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:54<01:12,  3.16s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:57<01:09,  3.14s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:00<01:05,  3.14s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:03<01:02,  3.12s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:06<00:59,  3.12s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:09<00:56,  3.12s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:12<00:53,  3.12s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:15<00:49,  3.12s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:18<00:46,  3.12s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:22<00:43,  3.13s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:25<00:40,  3.12s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:28<00:37,  3.12s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:31<00:34,  3.11s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:34<00:31,  3.14s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:37<00:28,  3.16s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:41<00:25,  3.18s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:44<00:22,  3.20s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:47<00:19,  3.20s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:50<00:16,  3.21s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:53<00:12,  3.21s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [01:56<00:09,  3.17s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:00<00:06,  3.15s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:03<00:03,  3.13s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:06<00:00,  3.12s/it]
                                                                                       
[✅] Casing detection complete: cx=978.0, cy=762.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   1%|          | 1/100 [00:00<00:29,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   1%|          | 1/100 [00:00<00:29,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   3%|▎         | 3/100 [00:00<00:28,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   4%|▍         | 4/100 [00:00<00:28,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   5%|▌         | 5/100 [00:00<00:27,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   6%|▌         | 6/100 [00:00<00:27,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   7%|▋         | 7/100 [00:00<00:27,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   8%|▊         | 8/100 [00:00<00:27,  3.40it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   9%|▉         | 9/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):   9%|▉         | 9/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  10%|█         | 10/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  11%|█         | 11/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  13%|█▎        | 13/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  14%|█▍        | 14/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  15%|█▌        | 15/100 [00:00<00:03, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 342.424} (0.000):  16%|█▌        | 16/100 [00:00<00:02, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.034 | Best: {'fx': 519.192} (0.034):  17%|█▋        | 17/100 [00:00<00:02, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.058 | Best: {'fx': 526.263} (0.058):  18%|█▊        | 18/100 [00:00<00:02, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.045 | Best: {'fx': 526.263} (0.058):  19%|█▉        | 19/100 [00:00<00:02, 28.06it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.045 | Best: {'fx': 526.263} (0.058):  20%|██        | 20/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.067 | Best: {'fx': 533.333} (0.067):  20%|██        | 20/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.032 | Best: {'fx': 533.333} (0.067):  21%|██        | 21/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  22%|██▏       | 22/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  23%|██▎       | 23/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  24%|██▍       | 24/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.026 | Best: {'fx': 533.333} (0.067):  25%|██▌       | 25/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  26%|██▌       | 26/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.022 | Best: {'fx': 533.333} (0.067):  27%|██▋       | 27/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  28%|██▊       | 28/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  29%|██▉       | 29/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.019 | Best: {'fx': 533.333} (0.067):  30%|███       | 30/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.017 | Best: {'fx': 533.333} (0.067):  31%|███       | 31/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.016 | Best: {'fx': 533.333} (0.067):  32%|███▏      | 32/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  33%|███▎      | 33/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  34%|███▍      | 34/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.067):  35%|███▌      | 35/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.001 | Best: {'fx': 533.333} (0.067):  36%|███▌      | 36/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.000 | Best: {'fx': 533.333} (0.067):  37%|███▋      | 37/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.015 | Best: {'fx': 533.333} (0.067):  38%|███▊      | 38/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.002 | Best: {'fx': 533.333} (0.067):  39%|███▉      | 39/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.013 | Best: {'fx': 533.333} (0.067):  40%|████      | 40/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.067):  41%|████      | 41/100 [00:00<00:01, 51.53it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.014 | Best: {'fx': 533.333} (0.067):  42%|████▏     | 42/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.003 | Best: {'fx': 533.333} (0.067):  42%|████▏     | 42/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  43%|████▎     | 43/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  44%|████▍     | 44/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  45%|████▌     | 45/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.012 | Best: {'fx': 533.333} (0.067):  46%|████▌     | 46/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.008 | Best: {'fx': 533.333} (0.067):  47%|████▋     | 47/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  48%|████▊     | 48/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  49%|████▉     | 49/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.018 | Best: {'fx': 533.333} (0.067):  50%|█████     | 50/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  51%|█████     | 51/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  52%|█████▏    | 52/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.011 | Best: {'fx': 533.333} (0.067):  53%|█████▎    | 53/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  54%|█████▍    | 54/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  55%|█████▌    | 55/100 [00:00<00:00, 102.12it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  56%|█████▌    | 56/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  56%|█████▌    | 56/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  57%|█████▋    | 57/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  58%|█████▊    | 58/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  59%|█████▉    | 59/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  60%|██████    | 60/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  61%|██████    | 61/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  62%|██████▏   | 62/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  63%|██████▎   | 63/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  64%|██████▍   | 64/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  65%|██████▌   | 65/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.010 | Best: {'fx': 533.333} (0.067):  66%|██████▌   | 66/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  67%|██████▋   | 67/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  68%|██████▊   | 68/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  69%|██████▉   | 69/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  70%|███████   | 70/100 [00:00<00:00, 113.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  71%|███████   | 71/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  71%|███████   | 71/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  72%|███████▏  | 72/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  73%|███████▎  | 73/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  74%|███████▍  | 74/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  75%|███████▌  | 75/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  76%|███████▌  | 76/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  77%|███████▋  | 77/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  78%|███████▊  | 78/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  79%|███████▉  | 79/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  80%|████████  | 80/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  81%|████████  | 81/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  82%|████████▏ | 82/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  83%|████████▎ | 83/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  84%|████████▍ | 84/100 [00:00<00:00, 122.45it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  85%|████████▌ | 85/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  85%|████████▌ | 85/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  86%|████████▌ | 86/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  87%|████████▋ | 87/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  88%|████████▊ | 88/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  89%|████████▉ | 89/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  90%|█████████ | 90/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  91%|█████████ | 91/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  92%|█████████▏| 92/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  93%|█████████▎| 93/100 [00:00<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  94%|█████████▍| 94/100 [00:01<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  95%|█████████▌| 95/100 [00:01<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  96%|█████████▌| 96/100 [00:01<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  97%|█████████▋| 97/100 [00:01<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  98%|█████████▊| 98/100 [00:01<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067):  99%|█████████▉| 99/100 [00:01<00:00, 123.27it/s]
Brute | Start: {'fx': 500.0} (0.003) | Current: 0.009 | Best: {'fx': 533.333} (0.067): 100%|██████████| 100/100 [00:01<00:00, 117.62it/s]
                                                                                                                                         

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.010 | Best: {'fx': 508.333} (0.010):   2%|▏         | 1/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.012 | Best: {'fx': 509.354} (0.012):   2%|▏         | 1/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.013 | Best: {'fx': 510.374} (0.013):   4%|▍         | 2/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.027 | Best: {'fx': 515.476} (0.027):   6%|▌         | 3/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.016 | Best: {'fx': 515.476} (0.027):   8%|▊         | 4/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.018 | Best: {'fx': 515.476} (0.027):  10%|█         | 5/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.022 | Best: {'fx': 515.476} (0.027):  12%|█▏        | 6/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.029 | Best: {'fx': 516.497} (0.029):  14%|█▍        | 7/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.026 | Best: {'fx': 516.497} (0.029):  16%|█▌        | 8/50 [00:00<00:05,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.031 | Best: {'fx': 517.517} (0.031):  18%|█▊        | 9/50 [00:00<00:04,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.033 | Best: {'fx': 518.537} (0.033):  20%|██        | 10/50 [00:00<00:04,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 519.558} (0.034):  22%|██▏       | 11/50 [00:00<00:04,  8.36it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 519.558} (0.034):  24%|██▍       | 12/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.044 | Best: {'fx': 522.619} (0.044):  24%|██▍       | 12/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.037 | Best: {'fx': 522.619} (0.044):  26%|██▌       | 13/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.054 | Best: {'fx': 525.68} (0.054):  28%|██▊       | 14/50 [00:00<00:00, 60.82it/s] 
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.039 | Best: {'fx': 525.68} (0.054):  30%|███       | 15/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.060 | Best: {'fx': 526.701} (0.060):  32%|███▏      | 16/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.046 | Best: {'fx': 526.701} (0.060):  34%|███▍      | 17/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.049 | Best: {'fx': 526.701} (0.060):  36%|███▌      | 18/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.064 | Best: {'fx': 527.721} (0.064):  38%|███▊      | 19/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.070 | Best: {'fx': 528.741} (0.070):  40%|████      | 20/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.072 | Best: {'fx': 530.782} (0.072):  42%|████▏     | 21/50 [00:00<00:00, 60.82it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.072 | Best: {'fx': 530.782} (0.072):  44%|████▍     | 22/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.068 | Best: {'fx': 530.782} (0.072):  44%|████▍     | 22/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.062 | Best: {'fx': 530.782} (0.072):  46%|████▌     | 23/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.070 | Best: {'fx': 530.782} (0.072):  48%|████▊     | 24/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.055 | Best: {'fx': 530.782} (0.072):  50%|█████     | 25/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.073 | Best: {'fx': 531.803} (0.073):  52%|█████▏    | 26/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.063 | Best: {'fx': 531.803} (0.073):  54%|█████▍    | 27/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.053 | Best: {'fx': 531.803} (0.073):  56%|█████▌    | 28/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.047 | Best: {'fx': 531.803} (0.073):  58%|█████▊    | 29/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.043 | Best: {'fx': 531.803} (0.073):  60%|██████    | 30/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.045 | Best: {'fx': 531.803} (0.073):  62%|██████▏   | 31/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.050 | Best: {'fx': 531.803} (0.073):  64%|██████▍   | 32/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.041 | Best: {'fx': 531.803} (0.073):  66%|██████▌   | 33/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 531.803} (0.073):  68%|██████▊   | 34/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 531.803} (0.073):  70%|███████   | 35/50 [00:00<00:00, 76.79it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.038 | Best: {'fx': 531.803} (0.073):  72%|███████▏  | 36/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 531.803} (0.073):  72%|███████▏  | 36/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.028 | Best: {'fx': 531.803} (0.073):  74%|███████▍  | 37/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.034 | Best: {'fx': 531.803} (0.073):  76%|███████▌  | 38/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.032 | Best: {'fx': 531.803} (0.073):  78%|███████▊  | 39/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.031 | Best: {'fx': 531.803} (0.073):  80%|████████  | 40/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.029 | Best: {'fx': 531.803} (0.073):  82%|████████▏ | 41/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.030 | Best: {'fx': 531.803} (0.073):  84%|████████▍ | 42/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.027 | Best: {'fx': 531.803} (0.073):  86%|████████▌ | 43/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.027 | Best: {'fx': 531.803} (0.073):  88%|████████▊ | 44/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.024 | Best: {'fx': 531.803} (0.073):  90%|█████████ | 45/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.026 | Best: {'fx': 531.803} (0.073):  92%|█████████▏| 46/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.024 | Best: {'fx': 531.803} (0.073):  94%|█████████▍| 47/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.025 | Best: {'fx': 531.803} (0.073):  96%|█████████▌| 48/50 [00:00<00:00, 100.34it/s]
Brute | Start: {'fx': 533.333} (0.067) | Current: 0.023 | Best: {'fx': 531.803} (0.073):  98%|█████████▊| 49/50 [00:00<00:00, 100.34it/s]
                                                                                                                                         
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 978.0
    py: 762.0
    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: 45.5088: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 45.5088, picklable_objective(x0) = 45.5762

Best score: 45.5762: : 0iter [00:01, ?iter/s] 
Best score: 45.5762: : 1iter [00:01,  1.84s/iter]
Best score: 45.5762: : 1iter [00:01,  1.84s/iter]
Best score: 45.5762: : 2iter [00:02,  1.84s/iter]
Best score: 45.5762: : 3iter [00:02,  1.86iter/s]
Best score: 45.5762: : 3iter [00:02,  1.86iter/s]
Best score: 45.5762: : 4iter [00:02,  1.86iter/s]
Best score: 45.5762: : 5iter [00:02,  1.86iter/s]
Best score: 45.5762: : 6iter [00:02,  1.86iter/s]
Best score: 45.5762: : 7iter [00:02,  5.27iter/s]
Best score: 45.5762: : 7iter [00:02,  5.27iter/s]
Best score: 45.5762: : 8iter [00:02,  5.27iter/s]
Best score: 45.5762: : 9iter [00:02,  5.27iter/s]
Best score: 45.5762: : 10iter [00:02,  5.27iter/s]
Best score: 45.5762: : 11iter [00:02,  9.18iter/s]
Best score: 45.5762: : 11iter [00:02,  9.18iter/s]
Best score: 45.5762: : 12iter [00:02,  9.18iter/s]
Best score: 45.5762: : 13iter [00:02,  9.18iter/s]
Best score: 45.5762: : 14iter [00:02,  9.18iter/s]
Best score: 45.5762: : 15iter [00:02, 13.47iter/s]
Best score: 45.5762: : 15iter [00:02, 13.47iter/s]
Best score: 45.5762: : 16iter [00:02, 13.47iter/s]
Best score: 45.5762: : 17iter [00:02, 13.47iter/s]
Best score: 45.5762: : 18iter [00:02, 13.47iter/s]
Best score: 45.5762: : 19iter [00:02, 17.84iter/s]
Best score: 45.5762: : 19iter [00:02, 17.84iter/s]
Best score: 45.5762: : 20iter [00:02, 17.84iter/s]
Best score: 45.5762: : 21iter [00:02, 17.84iter/s]
Best score: 45.5762: : 22iter [00:02, 17.84iter/s]
Best score: 45.5762: : 23iter [00:02, 21.83iter/s]
Best score: 45.5762: : 23iter [00:02, 21.83iter/s]
Best score: 45.5762: : 24iter [00:02, 21.83iter/s]
Best score: 45.5762: : 25iter [00:02, 21.83iter/s]
Best score: 45.5762: : 26iter [00:02, 21.83iter/s]
Best score: 45.5762: : 27iter [00:02, 21.83iter/s]
Best score: 45.5762: : 28iter [00:02, 26.44iter/s]
Best score: 45.5762: : 28iter [00:02, 26.44iter/s]
Best score: 45.5762: : 29iter [00:02, 26.44iter/s]
Best score: 45.5762: : 30iter [00:02, 26.44iter/s]
Best score: 45.5762: : 31iter [00:02, 26.44iter/s]
Best score: 45.5762: : 32iter [00:02, 29.36iter/s]
Best score: 45.5762: : 32iter [00:02, 29.36iter/s]
Best score: 45.5762: : 33iter [00:02, 29.36iter/s]
Best score: 45.5762: : 34iter [00:02, 29.36iter/s]
Best score: 45.5762: : 35iter [00:02, 29.36iter/s]
Best score: 45.5762: : 36iter [00:02, 31.67iter/s]
Best score: 45.5762: : 36iter [00:02, 31.67iter/s]
Best score: 45.5762: : 37iter [00:02, 31.67iter/s]
Best score: 45.5762: : 38iter [00:02, 31.67iter/s]
Best score: 45.5762: : 39iter [00:02, 31.67iter/s]
Best score: 45.5762: : 40iter [00:02, 33.64iter/s]
Best score: 45.5762: : 40iter [00:02, 33.64iter/s]
Best score: 45.5762: : 41iter [00:02, 33.64iter/s]
Best score: 45.5762: : 42iter [00:03, 33.64iter/s]
Best score: 45.5762: : 43iter [00:03, 33.64iter/s]
Best score: 45.5762: : 44iter [00:03, 35.25iter/s]
Best score: 45.5762: : 44iter [00:03, 35.25iter/s]
Best score: 45.5762: : 45iter [00:03, 35.25iter/s]
Best score: 45.5762: : 46iter [00:03, 35.25iter/s]
Best score: 45.5762: : 47iter [00:03, 35.25iter/s]
Best score: 45.5762: : 48iter [00:03, 36.50iter/s]
Best score: 45.5762: : 48iter [00:03, 36.50iter/s]
[ITER 50] Best: 45.5762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 978.0000
  py: 762.0000
  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: 45.5762: : 49iter [00:03, 36.50iter/s]
Best score: 45.5762: : 50iter [00:03, 36.50iter/s]
Best score: 45.5762: : 51iter [00:03, 36.50iter/s]
Best score: 45.5762: : 52iter [00:03, 37.13iter/s]
Best score: 45.5762: : 52iter [00:03, 37.13iter/s]
Best score: 45.5762: : 53iter [00:03, 37.13iter/s]
Best score: 45.5762: : 54iter [00:03, 37.13iter/s]
Best score: 45.5762: : 55iter [00:03, 37.13iter/s]
Best score: 45.5762: : 56iter [00:03, 37.95iter/s]
Best score: 45.5762: : 56iter [00:03, 37.95iter/s]
Best score: 45.5762: : 57iter [00:03, 37.95iter/s]
Best score: 45.5762: : 58iter [00:03, 37.95iter/s]
Best score: 45.5762: : 59iter [00:03, 37.95iter/s]
Best score: 45.5762: : 60iter [00:03, 38.37iter/s]
Best score: 45.5762: : 60iter [00:03, 38.37iter/s]
Best score: 45.5762: : 61iter [00:03, 38.37iter/s]
Best score: 45.5762: : 62iter [00:03, 38.37iter/s]
Best score: 45.5762: : 63iter [00:03, 38.37iter/s]
Best score: 45.5762: : 64iter [00:03, 38.37iter/s]
Best score: 45.5762: : 65iter [00:03, 38.97iter/s]
Best score: 45.5762: : 65iter [00:03, 38.97iter/s]
Best score: 45.5762: : 66iter [00:03, 38.97iter/s]
Best score: 45.5762: : 67iter [00:03, 38.97iter/s]
Best score: 45.5762: : 68iter [00:03, 38.97iter/s]
Best score: 45.5762: : 69iter [00:03, 38.97iter/s]
Best score: 45.5762: : 70iter [00:03, 39.22iter/s]
Best score: 45.5762: : 70iter [00:03, 39.22iter/s]
Best score: 45.5762: : 71iter [00:03, 39.22iter/s]
Best score: 45.5762: : 72iter [00:03, 39.22iter/s]
Best score: 45.5762: : 73iter [00:03, 39.22iter/s]
Best score: 45.5762: : 74iter [00:03, 39.22iter/s]
Best score: 45.5762: : 75iter [00:03, 39.19iter/s]
Best score: 45.5762: : 75iter [00:03, 39.19iter/s]
Best score: 45.5762: : 76iter [00:03, 39.19iter/s]
Best score: 45.5762: : 77iter [00:03, 39.19iter/s]
Best score: 45.5762: : 78iter [00:03, 39.19iter/s]
Best score: 45.5762: : 79iter [00:03, 38.48iter/s]
Best score: 45.5762: : 79iter [00:03, 38.48iter/s]
Best score: 45.5762: : 80iter [00:03, 38.48iter/s]
Best score: 45.5762: : 81iter [00:04, 38.48iter/s]
Best score: 45.5762: : 82iter [00:04, 38.48iter/s]
Best score: 45.5762: : 83iter [00:04, 38.62iter/s]
Best score: 45.5762: : 83iter [00:04, 38.62iter/s]
Best score: 45.5762: : 84iter [00:04, 38.62iter/s]
Best score: 45.5762: : 85iter [00:04, 38.62iter/s]
Best score: 45.5762: : 86iter [00:04, 38.62iter/s]
Best score: 45.5762: : 87iter [00:04, 38.62iter/s]
Best score: 45.5762: : 88iter [00:04, 39.18iter/s]
Best score: 45.5762: : 88iter [00:04, 39.18iter/s]
Best score: 45.5762: : 89iter [00:04, 39.18iter/s]
Best score: 45.5762: : 90iter [00:04, 39.18iter/s]
Best score: 45.5762: : 91iter [00:04, 39.18iter/s]
Best score: 45.5762: : 92iter [00:04, 39.29iter/s]
Best score: 45.5762: : 92iter [00:04, 39.29iter/s]
Best score: 45.5762: : 93iter [00:04, 39.29iter/s]
Best score: 45.5762: : 94iter [00:04, 39.29iter/s]
Best score: 45.5762: : 95iter [00:04, 39.29iter/s]
Best score: 45.5762: : 96iter [00:04, 39.29iter/s]
Best score: 45.5762: : 97iter [00:04, 39.71iter/s]
Best score: 45.5762: : 97iter [00:04, 39.71iter/s]
Best score: 45.5762: : 98iter [00:04, 39.71iter/s]
[ITER 100] Best: 45.5762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 978.0000
  py: 762.0000
  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: 45.5762: : 99iter [00:04, 39.71iter/s]
Best score: 45.5762: : 100iter [00:04, 39.71iter/s]
Best score: 45.5762: : 101iter [00:04, 37.11iter/s]
Best score: 45.5762: : 101iter [00:04, 37.11iter/s]
Best score: 45.5762: : 102iter [00:04, 37.11iter/s]
Best score: 45.5762: : 103iter [00:04, 37.11iter/s]
Best score: 45.5762: : 104iter [00:04, 37.11iter/s]
Best score: 45.5762: : 105iter [00:04, 37.66iter/s]
Best score: 45.5762: : 105iter [00:04, 37.66iter/s]
Best score: 45.5762: : 106iter [00:04, 37.66iter/s]
Best score: 45.5762: : 107iter [00:04, 37.66iter/s]
Best score: 45.5762: : 108iter [00:04, 37.66iter/s]
Best score: 45.5762: : 109iter [00:04, 38.17iter/s]
Best score: 45.5762: : 109iter [00:04, 38.17iter/s]
Best score: 45.5762: : 110iter [00:04, 38.17iter/s]
Best score: 45.5762: : 111iter [00:04, 38.17iter/s]
Best score: 45.5762: : 112iter [00:04, 38.17iter/s]
Best score: 45.5762: : 113iter [00:04, 38.18iter/s]
Best score: 45.5762: : 113iter [00:04, 38.18iter/s]
Best score: 45.5762: : 114iter [00:04, 38.18iter/s]
Best score: 45.5762: : 115iter [00:04, 38.18iter/s]
Best score: 45.5762: : 116iter [00:04, 38.18iter/s]
Best score: 45.5762: : 117iter [00:04, 38.08iter/s]
Best score: 45.5762: : 117iter [00:04, 38.08iter/s]
Best score: 45.5762: : 118iter [00:04, 38.08iter/s]
Best score: 45.5762: : 119iter [00:05, 38.08iter/s]
Best score: 45.5762: : 120iter [00:05, 38.08iter/s]
Best score: 45.5762: : 121iter [00:05, 38.25iter/s]
Best score: 45.5762: : 121iter [00:05, 38.25iter/s]
Best score: 45.5762: : 122iter [00:05, 38.25iter/s]
Best score: 45.5762: : 123iter [00:05, 38.25iter/s]
Best score: 45.5762: : 124iter [00:05, 38.25iter/s]
Best score: 45.5762: : 125iter [00:05, 38.72iter/s]
Best score: 45.5762: : 125iter [00:05, 38.72iter/s]
Best score: 45.5762: : 126iter [00:05, 38.72iter/s]
Best score: 45.5762: : 127iter [00:05, 38.72iter/s]
Best score: 45.5762: : 128iter [00:05, 38.72iter/s]
Best score: 45.5762: : 129iter [00:05, 38.98iter/s]
Best score: 45.5762: : 129iter [00:05, 38.98iter/s]
Best score: 45.5762: : 130iter [00:05, 38.98iter/s]
Best score: 45.5762: : 131iter [00:05, 38.98iter/s]
Best score: 45.5762: : 132iter [00:05, 38.98iter/s]
Best score: 45.5762: : 133iter [00:05, 39.07iter/s]
Best score: 45.5762: : 133iter [00:05, 39.07iter/s]
Best score: 45.5762: : 134iter [00:05, 39.07iter/s]
Best score: 45.5762: : 135iter [00:05, 39.07iter/s]
Best score: 45.5762: : 136iter [00:05, 39.07iter/s]
Best score: 45.5762: : 137iter [00:05, 39.31iter/s]
Best score: 45.5762: : 137iter [00:05, 39.31iter/s]
Best score: 45.5762: : 138iter [00:05, 39.31iter/s]
Best score: 45.5762: : 139iter [00:05, 39.31iter/s]
Best score: 45.5762: : 140iter [00:05, 39.31iter/s]
Best score: 45.5762: : 141iter [00:05, 39.31iter/s]
Best score: 45.5762: : 141iter [00:05, 39.31iter/s]
Best score: 45.5762: : 142iter [00:05, 39.31iter/s]
Best score: 45.5762: : 143iter [00:05, 39.31iter/s]
Best score: 45.5762: : 144iter [00:05, 39.31iter/s]
Best score: 45.5762: : 145iter [00:05, 39.31iter/s]
Best score: 45.5762: : 146iter [00:05, 39.60iter/s]
Best score: 45.5762: : 146iter [00:05, 39.60iter/s]
Best score: 45.5762: : 147iter [00:05, 39.60iter/s]
Best score: 45.5762: : 148iter [00:05, 39.60iter/s]
[ITER 150] Best: 45.5762
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 978.0000
  py: 762.0000
  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: 45.5762: : 149iter [00:05, 39.60iter/s]
Best score: 45.5762: : 150iter [00:05, 39.41iter/s]
Best score: 45.5762: : 150iter [00:05, 39.41iter/s]
Best score: 45.5762: : 151iter [00:05, 39.41iter/s]
Best score: 45.5762: : 152iter [00:05, 39.41iter/s]
Best score: 45.5762: : 153iter [00:05, 39.41iter/s]
Best score: 45.5762: : 154iter [00:05, 32.24iter/s]
Best score: 45.5762: : 154iter [00:05, 32.24iter/s]
Best score: 45.5762: : 155iter [00:05, 32.24iter/s]
Best score: 45.5762: : 156iter [00:06, 32.24iter/s]
Best score: 45.5762: : 157iter [00:06, 32.24iter/s]
Best score: 45.5762: : 158iter [00:06, 33.84iter/s]
Best score: 45.5762: : 158iter [00:06, 33.84iter/s]
Best score: 45.5762: : 159iter [00:06, 33.84iter/s]
Best score: 45.5762: : 160iter [00:06, 33.84iter/s]
Best score: 45.5762: : 161iter [00:06, 33.84iter/s]
Best score: 45.5762: : 162iter [00:06, 35.15iter/s]
Best score: 45.5762: : 162iter [00:06, 35.15iter/s]
Best score: 45.5762: : 163iter [00:06, 35.15iter/s]
Best score: 45.5762: : 164iter [00:06, 35.15iter/s]
Best score: 45.5762: : 165iter [00:06, 35.15iter/s]
Best score: 45.5762: : 166iter [00:06, 36.35iter/s]
Best score: 45.5762: : 166iter [00:06, 36.35iter/s]
Best score: 45.5762: : 167iter [00:06, 36.35iter/s]
Best score: 45.5762: : 168iter [00:06, 36.35iter/s]
Best score: 45.5762: : 169iter [00:06, 36.35iter/s]
Best score: 45.5762: : 170iter [00:06, 37.20iter/s]
Best score: 45.5762: : 170iter [00:06, 37.20iter/s]
Best score: 45.5762: : 171iter [00:06, 37.20iter/s]
Best score: 45.5762: : 172iter [00:06, 37.20iter/s]
Best score: 45.5762: : 173iter [00:06, 37.20iter/s]
Best score: 45.5762: : 174iter [00:06, 37.20iter/s]
Best score: 45.5762: : 175iter [00:06, 38.29iter/s]
Best score: 45.5762: : 175iter [00:06, 38.29iter/s]
Best score: 45.5762: : 176iter [00:06, 38.29iter/s]
Best score: 45.5762: : 177iter [00:06, 38.29iter/s]
Best score: 45.5762: : 178iter [00:06, 38.29iter/s]
Best score: 45.5762: : 179iter [00:06, 38.72iter/s]
Best score: 45.5762: : 179iter [00:06, 38.72iter/s]
Best score: 45.5762: : 180iter [00:06, 38.72iter/s]
Best score: 45.5762: : 181iter [00:06, 38.72iter/s]
Best score: 45.5762: : 182iter [00:06, 38.72iter/s]
Best score: 45.5762: : 183iter [00:06, 38.84iter/s]
Best score: 45.5762: : 183iter [00:06, 38.84iter/s]
Best score: 45.5762: : 184iter [00:06, 38.84iter/s]
Best score: 45.5762: : 185iter [00:06, 38.84iter/s]
Best score: 45.5762: : 186iter [00:06, 38.84iter/s]
Best score: 45.5762: : 187iter [00:06, 38.84iter/s]
Best score: 45.5762: : 188iter [00:06, 39.31iter/s]
Best score: 45.5762: : 188iter [00:06, 39.31iter/s]
Best score: 45.5762: : 189iter [00:06, 39.31iter/s]
Best score: 45.5762: : 190iter [00:06, 39.31iter/s]
Best score: 45.5762: : 191iter [00:06, 39.31iter/s]
Best score: 45.5762: : 192iter [00:06, 39.48iter/s]
Best score: 45.5762: : 192iter [00:06, 39.48iter/s]
Best score: 45.5762: : 193iter [00:06, 39.48iter/s]
Best score: 45.5762: : 194iter [00:06, 39.48iter/s]
Best score: 45.5762: : 195iter [00:07, 39.48iter/s]
Best score: 45.5762: : 196iter [00:07, 39.48iter/s]
Best score: 45.5762: : 197iter [00:07, 39.86iter/s]
Best score: 45.5762: : 197iter [00:07, 39.86iter/s]
Best score: 45.5762: : 198iter [00:07, 39.86iter/s]
[ITER 200] Best: 35.4498
[PARAMS] Current best parameters:
  fx: 529.2155
  fy: 528.3644
  px: 979.5117
  py: 757.4484
  k1: 0.02063775
  k2: -0.01575595
  k3: 0.00228575
  k4: 0.00070592
  p1: -0.01489741
  p2: -0.00268026
  p3: 0.01085637
  p4: 0.00399921
  s1: -0.0042
  s2: 0.0015
  s3: 0.0121
  s4: -0.0028
  tilt_x: -0.0103
  tilt_y: -0.0090
  scale: 2.9560
  rotation: 0.0513

Best score: 35.4498: : 199iter [00:07, 39.86iter/s]
Best score: 35.4498: : 200iter [00:07, 39.86iter/s]
Best score: 35.4498: : 201iter [00:07, 32.67iter/s]
Best score: 35.4498: : 201iter [00:07, 32.67iter/s]
Best score: 35.4498: : 202iter [00:07, 32.67iter/s]
Best score: 35.4498: : 203iter [00:07, 32.67iter/s]
Best score: 35.4498: : 204iter [00:07, 32.67iter/s]
Best score: 35.4498: : 205iter [00:07, 32.67iter/s]
Best score: 35.4498: : 206iter [00:07, 34.88iter/s]
Best score: 35.4498: : 206iter [00:07, 34.88iter/s]
Best score: 35.4498: : 207iter [00:07, 34.88iter/s]
Best score: 35.4498: : 208iter [00:07, 34.88iter/s]
Best score: 20.6303: : 209iter [00:07, 34.88iter/s]
Best score: 20.6303: : 210iter [00:07, 35.99iter/s]
Best score: 20.6303: : 210iter [00:07, 35.99iter/s]
Best score: 20.6303: : 211iter [00:07, 35.99iter/s]
Best score: 20.6303: : 212iter [00:07, 35.99iter/s]
Best score: 20.6303: : 213iter [00:07, 35.99iter/s]
Best score: 20.6303: : 214iter [00:07, 35.99iter/s]
Best score: 20.6303: : 215iter [00:07, 37.52iter/s]
Best score: 20.6303: : 215iter [00:07, 37.52iter/s]
Best score: 20.6303: : 216iter [00:07, 37.52iter/s]
Best score: 20.6303: : 217iter [00:07, 37.52iter/s]
Best score: 20.6303: : 218iter [00:07, 37.52iter/s]
Best score: 17.1668: : 219iter [00:07, 37.52iter/s]
Best score: 17.1668: : 220iter [00:07, 38.21iter/s]
Best score: 17.1668: : 220iter [00:07, 38.21iter/s]
Best score: 17.1668: : 221iter [00:07, 38.21iter/s]
Best score: 17.1668: : 222iter [00:07, 38.21iter/s]
Best score: 17.1668: : 223iter [00:07, 38.21iter/s]
Best score: 17.1668: : 224iter [00:07, 38.21iter/s]
Best score: 17.1668: : 225iter [00:07, 38.96iter/s]
Best score: 17.1668: : 225iter [00:07, 38.96iter/s]
Best score: 17.1668: : 226iter [00:07, 38.96iter/s]
Best score: 17.1668: : 227iter [00:07, 38.96iter/s]
Best score: 17.1668: : 228iter [00:07, 38.96iter/s]
Best score: 17.1668: : 229iter [00:07, 38.96iter/s]
Best score: 17.1668: : 230iter [00:07, 39.18iter/s]
Best score: 17.1668: : 230iter [00:07, 39.18iter/s]
Best score: 17.1668: : 231iter [00:07, 39.18iter/s]
Best score: 17.1668: : 232iter [00:08, 39.18iter/s]
Best score: 17.1668: : 233iter [00:08, 39.18iter/s]
Best score: 17.1668: : 234iter [00:08, 39.18iter/s]
Best score: 17.1668: : 235iter [00:08, 39.59iter/s]
Best score: 17.1668: : 235iter [00:08, 39.59iter/s]
Best score: 17.1668: : 236iter [00:08, 39.59iter/s]
Best score: 17.1668: : 237iter [00:08, 39.59iter/s]
Best score: 17.1668: : 238iter [00:08, 39.59iter/s]
Best score: 17.1668: : 239iter [00:08, 39.59iter/s]
Best score: 17.1668: : 240iter [00:08, 39.70iter/s]
Best score: 17.1668: : 240iter [00:08, 39.70iter/s]
Best score: 17.1668: : 241iter [00:08, 39.70iter/s]
Best score: 17.1668: : 242iter [00:08, 39.70iter/s]
Best score: 17.1668: : 243iter [00:08, 39.70iter/s]
Best score: 17.1668: : 244iter [00:08, 39.70iter/s]
Best score: 17.1668: : 245iter [00:08, 40.00iter/s]
Best score: 17.1668: : 245iter [00:08, 40.00iter/s]
Best score: 17.1668: : 246iter [00:08, 40.00iter/s]
Best score: 17.1668: : 247iter [00:08, 40.00iter/s]
Best score: 17.1668: : 248iter [00:08, 40.00iter/s]
[ITER 250] Best: 7.7991
[PARAMS] Current best parameters:
  fx: 536.6948
  fy: 535.4995
  px: 980.9766
  py: 764.1923
  k1: 0.02356607
  k2: -0.04270985
  k3: 0.01589381
  k4: -0.00094348
  p1: -0.00501705
  p2: 0.00099778
  p3: -0.00287172
  p4: 0.00799620
  s1: -0.0001
  s2: -0.0033
  s3: 0.0045
  s4: -0.0078
  tilt_x: 0.0012
  tilt_y: -0.0106
  scale: 3.0506
  rotation: -1.0054

Best score: 7.7991: : 249iter [00:08, 40.00iter/s] 
Best score: 7.7991: : 250iter [00:08, 39.99iter/s]
Best score: 7.7991: : 250iter [00:08, 39.99iter/s]
Best score: 7.7991: : 251iter [00:08, 39.99iter/s]
Best score: 7.7991: : 252iter [00:08, 39.99iter/s]
Best score: 7.7991: : 253iter [00:08, 39.99iter/s]
Best score: 7.7991: : 254iter [00:08, 39.99iter/s]
Best score: 7.7991: : 255iter [00:08, 33.85iter/s]
Best score: 7.7991: : 255iter [00:08, 33.85iter/s]
Best score: 7.7991: : 256iter [00:08, 33.85iter/s]
Best score: 7.7991: : 257iter [00:08, 33.85iter/s]
Best score: 7.7991: : 258iter [00:08, 33.85iter/s]
Best score: 7.7991: : 259iter [00:08, 33.85iter/s]
Best score: 7.7991: : 260iter [00:08, 35.42iter/s]
Best score: 7.7991: : 260iter [00:08, 35.42iter/s]
Best score: 7.7991: : 261iter [00:08, 35.42iter/s]
Best score: 7.7991: : 262iter [00:08, 35.42iter/s]
Best score: 7.7991: : 263iter [00:08, 35.42iter/s]
Best score: 7.7991: : 264iter [00:08, 35.42iter/s]
Best score: 7.7991: : 265iter [00:08, 36.84iter/s]
Best score: 7.7991: : 265iter [00:08, 36.84iter/s]
Best score: 7.7991: : 266iter [00:08, 36.84iter/s]
Best score: 7.7991: : 267iter [00:08, 36.84iter/s]
Best score: 7.7991: : 268iter [00:08, 36.84iter/s]
Best score: 4.9232: : 269iter [00:09, 36.84iter/s]
Best score: 4.9232: : 270iter [00:09, 37.71iter/s]
Best score: 4.9232: : 270iter [00:09, 37.71iter/s]
Best score: 4.9232: : 271iter [00:09, 37.71iter/s]
Best score: 4.9232: : 272iter [00:09, 37.71iter/s]
Best score: 4.9232: : 273iter [00:09, 37.71iter/s]
Best score: 4.9232: : 274iter [00:09, 37.71iter/s]
Best score: 4.9232: : 275iter [00:09, 38.63iter/s]
Best score: 4.9232: : 275iter [00:09, 38.63iter/s]
Best score: 4.9232: : 276iter [00:09, 38.63iter/s]
Best score: 4.9232: : 277iter [00:09, 38.63iter/s]
Best score: 4.9232: : 278iter [00:09, 38.63iter/s]
Best score: 4.9232: : 279iter [00:09, 38.63iter/s]
Best score: 4.9232: : 280iter [00:09, 39.04iter/s]
Best score: 4.9232: : 280iter [00:09, 39.04iter/s]
Best score: 4.9232: : 281iter [00:09, 39.04iter/s]
Best score: 4.9232: : 282iter [00:09, 39.04iter/s]
Best score: 4.9232: : 283iter [00:09, 39.04iter/s]
Best score: 4.9232: : 284iter [00:09, 39.04iter/s]
Best score: 4.9232: : 285iter [00:09, 39.39iter/s]
Best score: 4.9232: : 285iter [00:09, 39.39iter/s]
Best score: 4.9232: : 286iter [00:09, 39.39iter/s]
Best score: 4.9232: : 287iter [00:09, 39.39iter/s]
Best score: 4.9232: : 288iter [00:09, 39.39iter/s]
Best score: 4.9232: : 289iter [00:09, 39.50iter/s]
Best score: 4.9232: : 289iter [00:09, 39.50iter/s]
Best score: 4.9232: : 290iter [00:09, 39.50iter/s]
Best score: 4.9232: : 291iter [00:09, 39.50iter/s]
Best score: 4.9232: : 292iter [00:09, 39.50iter/s]
Best score: 4.9232: : 293iter [00:09, 39.48iter/s]
Best score: 4.9232: : 293iter [00:09, 39.48iter/s]
Best score: 4.9232: : 294iter [00:09, 39.48iter/s]
Best score: 4.9232: : 295iter [00:09, 39.48iter/s]
Best score: 4.9232: : 296iter [00:09, 39.48iter/s]
Best score: 4.9232: : 297iter [00:09, 39.48iter/s]
Best score: 4.9232: : 298iter [00:09, 39.84iter/s]
Best score: 4.9232: : 298iter [00:09, 39.84iter/s]
[ITER 300] Best: 4.2917
[PARAMS] Current best parameters:
  fx: 541.6536
  fy: 540.5106
  px: 986.2298
  py: 768.1012
  k1: 0.02027523
  k2: -0.05234833
  k3: 0.03227107
  k4: -0.00715280
  p1: -0.00223211
  p2: 0.01289448
  p3: -0.01090509
  p4: 0.01352944
  s1: -0.0069
  s2: -0.0074
  s3: 0.0108
  s4: -0.0108
  tilt_x: 0.0125
  tilt_y: -0.0144
  scale: 2.9936
  rotation: -1.0173

Best score: 4.2917: : 299iter [00:09, 39.84iter/s]
Best score: 4.2917: : 300iter [00:09, 39.84iter/s]
Best score: 4.2917: : 301iter [00:09, 39.84iter/s]
Best score: 4.2917: : 302iter [00:09, 32.84iter/s]
Best score: 4.2917: : 302iter [00:09, 32.84iter/s]
Best score: 4.2917: : 303iter [00:09, 32.84iter/s]
Best score: 4.2917: : 304iter [00:09, 32.84iter/s]
Best score: 4.2917: : 305iter [00:09, 32.84iter/s]
Best score: 4.2917: : 306iter [00:09, 32.84iter/s]
Best score: 4.2917: : 307iter [00:09, 35.18iter/s]
Best score: 4.2917: : 307iter [00:10, 35.18iter/s]
Best score: 4.2917: : 308iter [00:10, 35.18iter/s]
Best score: 3.8380: : 309iter [00:10, 35.18iter/s]
Best score: 3.8380: : 310iter [00:10, 35.18iter/s]
Best score: 3.8380: : 311iter [00:10, 36.31iter/s]
Best score: 3.8380: : 311iter [00:10, 36.31iter/s]
Best score: 3.8380: : 312iter [00:10, 36.31iter/s]
Best score: 3.8380: : 313iter [00:10, 36.31iter/s]
Best score: 3.8380: : 314iter [00:10, 36.31iter/s]
Best score: 3.8380: : 315iter [00:10, 36.31iter/s]
Best score: 3.8380: : 316iter [00:10, 37.63iter/s]
Best score: 3.8380: : 316iter [00:10, 37.63iter/s]
Best score: 3.8380: : 317iter [00:10, 37.63iter/s]
Best score: 3.8380: : 318iter [00:10, 37.63iter/s]
Best score: 3.8380: : 319iter [00:10, 37.63iter/s]
Best score: 3.8380: : 320iter [00:10, 38.16iter/s]
Best score: 3.8380: : 320iter [00:10, 38.16iter/s]
Best score: 3.8380: : 321iter [00:10, 38.16iter/s]
Best score: 3.8380: : 322iter [00:10, 38.16iter/s]
Best score: 3.8380: : 323iter [00:10, 38.16iter/s]
Best score: 3.8380: : 324iter [00:10, 38.16iter/s]
Best score: 3.8380: : 325iter [00:10, 38.82iter/s]
Best score: 3.8380: : 325iter [00:10, 38.82iter/s]
Best score: 3.8380: : 326iter [00:10, 38.82iter/s]
Best score: 3.8380: : 327iter [00:10, 38.82iter/s]
Best score: 3.8380: : 328iter [00:10, 38.82iter/s]
Best score: 2.9687: : 329iter [00:10, 38.82iter/s]
Best score: 2.9687: : 330iter [00:10, 38.97iter/s]
Best score: 2.9687: : 330iter [00:10, 38.97iter/s]
Best score: 2.9687: : 331iter [00:10, 38.97iter/s]
Best score: 2.9687: : 332iter [00:10, 38.97iter/s]
Best score: 2.9687: : 333iter [00:10, 38.97iter/s]
Best score: 2.9687: : 334iter [00:10, 38.97iter/s]
Best score: 2.9687: : 335iter [00:10, 39.48iter/s]
Best score: 2.9687: : 335iter [00:10, 39.48iter/s]
Best score: 2.9687: : 336iter [00:10, 39.48iter/s]
Best score: 2.9687: : 337iter [00:10, 39.48iter/s]
Best score: 2.9687: : 338iter [00:10, 39.48iter/s]
Best score: 2.9687: : 339iter [00:10, 39.48iter/s]
Best score: 2.9687: : 340iter [00:10, 39.42iter/s]
Best score: 2.9687: : 340iter [00:10, 39.42iter/s]
Best score: 2.9687: : 341iter [00:10, 39.42iter/s]
Best score: 2.9687: : 342iter [00:10, 39.42iter/s]
Best score: 2.9687: : 343iter [00:10, 39.42iter/s]
Best score: 2.9687: : 344iter [00:10, 39.42iter/s]
Best score: 2.9687: : 345iter [00:10, 39.78iter/s]
Best score: 2.9687: : 345iter [00:10, 39.78iter/s]
Best score: 2.9687: : 346iter [00:10, 39.78iter/s]
Best score: 2.9687: : 347iter [00:11, 39.78iter/s]
Best score: 2.9687: : 348iter [00:11, 39.78iter/s]
[ITER 350] Best: 2.7554
[PARAMS] Current best parameters:
  fx: 553.1129
  fy: 553.1355
  px: 986.6319
  py: 766.4432
  k1: -0.00287319
  k2: -0.02454735
  k3: 0.01934798
  k4: -0.00598268
  p1: -0.00315176
  p2: 0.01081406
  p3: -0.00290808
  p4: 0.01232296
  s1: -0.0094
  s2: -0.0032
  s3: 0.0019
  s4: -0.0113
  tilt_x: 0.0056
  tilt_y: -0.0151
  scale: 2.9197
  rotation: -0.9119

Best score: 2.7554: : 349iter [00:11, 39.78iter/s]
Best score: 2.7554: : 350iter [00:11, 39.77iter/s]
Best score: 2.7554: : 350iter [00:11, 39.77iter/s]
Best score: 2.7554: : 351iter [00:11, 39.77iter/s]
Best score: 2.7554: : 352iter [00:11, 39.77iter/s]
Best score: 2.7554: : 353iter [00:11, 39.77iter/s]
Best score: 2.7554: : 354iter [00:11, 33.26iter/s]
Best score: 2.7554: : 354iter [00:11, 33.26iter/s]
Best score: 2.7554: : 355iter [00:11, 33.26iter/s]
Best score: 2.7554: : 356iter [00:11, 33.26iter/s]
Best score: 2.7554: : 357iter [00:11, 33.26iter/s]
Best score: 2.7554: : 358iter [00:11, 33.26iter/s]
Best score: 2.7554: : 359iter [00:11, 35.38iter/s]
Best score: 2.0662: : 359iter [00:11, 35.38iter/s]
Best score: 2.0662: : 360iter [00:11, 35.38iter/s]
Best score: 2.0662: : 361iter [00:11, 35.38iter/s]
Best score: 2.0662: : 362iter [00:11, 35.38iter/s]
Best score: 2.0662: : 363iter [00:11, 29.93iter/s]
Best score: 2.0662: : 363iter [00:11, 29.93iter/s]
Best score: 2.0662: : 364iter [00:11, 29.93iter/s]
Best score: 2.0662: : 365iter [00:11, 29.93iter/s]
Best score: 2.0662: : 366iter [00:11, 29.93iter/s]
Best score: 2.0662: : 367iter [00:11, 29.93iter/s]
Best score: 2.0662: : 368iter [00:11, 32.66iter/s]
Best score: 2.0662: : 368iter [00:11, 32.66iter/s]
Best score: 1.7219: : 369iter [00:11, 32.66iter/s]
Best score: 1.7219: : 370iter [00:11, 32.66iter/s]
Best score: 1.7219: : 371iter [00:11, 32.66iter/s]
Best score: 1.7219: : 372iter [00:11, 34.29iter/s]
Best score: 1.7219: : 372iter [00:11, 34.29iter/s]
Best score: 1.7219: : 373iter [00:11, 34.29iter/s]
Best score: 1.7219: : 374iter [00:11, 34.29iter/s]
Best score: 1.7219: : 375iter [00:11, 34.29iter/s]
Best score: 1.7219: : 376iter [00:11, 34.29iter/s]
Best score: 1.7219: : 377iter [00:11, 36.18iter/s]
Best score: 1.7219: : 377iter [00:11, 36.18iter/s]
Best score: 1.7219: : 378iter [00:11, 36.18iter/s]
Best score: 1.7219: : 379iter [00:11, 36.18iter/s]
Best score: 1.7219: : 380iter [00:12, 36.18iter/s]
Best score: 1.7219: : 381iter [00:12, 37.02iter/s]
Best score: 1.7219: : 381iter [00:12, 37.02iter/s]
Best score: 1.7219: : 382iter [00:12, 37.02iter/s]
Best score: 1.7219: : 383iter [00:12, 37.02iter/s]
Best score: 1.7219: : 384iter [00:12, 37.02iter/s]
Best score: 1.7219: : 385iter [00:12, 37.02iter/s]
Best score: 1.7219: : 386iter [00:12, 38.15iter/s]
Best score: 1.7219: : 386iter [00:12, 38.15iter/s]
Best score: 1.7219: : 387iter [00:12, 38.15iter/s]
Best score: 1.7219: : 388iter [00:12, 38.15iter/s]
Best score: 1.0808: : 389iter [00:12, 38.15iter/s]
Best score: 1.0808: : 390iter [00:12, 38.55iter/s]
Best score: 1.0808: : 390iter [00:12, 38.55iter/s]
Best score: 1.0808: : 391iter [00:12, 38.55iter/s]
Best score: 1.0808: : 392iter [00:12, 38.55iter/s]
Best score: 1.0808: : 393iter [00:12, 38.55iter/s]
Best score: 1.0808: : 394iter [00:12, 38.55iter/s]
Best score: 1.0808: : 395iter [00:12, 39.18iter/s]
Best score: 1.0808: : 395iter [00:12, 39.18iter/s]
Best score: 1.0808: : 396iter [00:12, 39.18iter/s]
Best score: 1.0808: : 397iter [00:12, 39.18iter/s]
Best score: 1.0808: : 398iter [00:12, 39.18iter/s]
[ITER 400] Best: 1.0808
[PARAMS] Current best parameters:
  fx: 545.3950
  fy: 545.1141
  px: 980.9655
  py: 766.3011
  k1: 0.01681312
  k2: -0.03449933
  k3: 0.01761482
  k4: -0.00420079
  p1: -0.00074818
  p2: -0.00179109
  p3: 0.00326506
  p4: 0.00759050
  s1: -0.0059
  s2: -0.0017
  s3: -0.0060
  s4: -0.0058
  tilt_x: -0.0013
  tilt_y: -0.0144
  scale: 2.9290
  rotation: -0.7909

Best score: 1.0808: : 399iter [00:12, 39.18iter/s]
Best score: 1.0808: : 400iter [00:12, 39.27iter/s]
Best score: 1.0808: : 400iter [00:12, 39.27iter/s]
Best score: 1.0808: : 401iter [00:12, 39.27iter/s]
Best score: 1.0808: : 402iter [00:12, 39.27iter/s]
Best score: 1.0808: : 403iter [00:12, 39.27iter/s]
Best score: 1.0808: : 404iter [00:12, 32.84iter/s]
Best score: 1.0808: : 404iter [00:12, 32.84iter/s]
Best score: 1.0808: : 405iter [00:12, 32.84iter/s]
Best score: 1.0808: : 406iter [00:12, 32.84iter/s]
Best score: 1.0808: : 407iter [00:12, 32.84iter/s]
Best score: 1.0808: : 408iter [00:12, 32.84iter/s]
Best score: 1.0808: : 409iter [00:12, 35.00iter/s]
Best score: 0.8181: : 409iter [00:12, 35.00iter/s]
Best score: 0.8181: : 410iter [00:12, 35.00iter/s]
Best score: 0.8181: : 411iter [00:12, 35.00iter/s]
Best score: 0.8181: : 412iter [00:12, 35.00iter/s]
Best score: 0.8181: : 413iter [00:12, 36.13iter/s]
Best score: 0.8181: : 413iter [00:12, 36.13iter/s]
Best score: 0.8181: : 414iter [00:12, 36.13iter/s]
Best score: 0.8181: : 415iter [00:12, 36.13iter/s]
Best score: 0.8181: : 416iter [00:12, 36.13iter/s]
Best score: 0.8181: : 417iter [00:13, 36.13iter/s]
Best score: 0.8181: : 418iter [00:13, 37.50iter/s]
Best score: 0.8181: : 418iter [00:13, 37.50iter/s]
Best score: 0.8181: : 419iter [00:13, 37.50iter/s]
Best score: 0.8181: : 420iter [00:13, 37.50iter/s]
Best score: 0.8181: : 421iter [00:13, 37.50iter/s]
Best score: 0.8181: : 422iter [00:13, 38.03iter/s]
Best score: 0.8181: : 422iter [00:13, 38.03iter/s]
Best score: 0.8181: : 423iter [00:13, 38.03iter/s]
Best score: 0.8181: : 424iter [00:13, 38.03iter/s]
Best score: 0.8181: : 425iter [00:13, 38.03iter/s]
Best score: 0.8181: : 426iter [00:13, 38.03iter/s]
Best score: 0.8181: : 427iter [00:13, 38.87iter/s]
Best score: 0.8181: : 427iter [00:13, 38.87iter/s]
Best score: 0.8181: : 428iter [00:13, 38.87iter/s]
Best score: 0.7348: : 429iter [00:13, 38.87iter/s]
Best score: 0.7348: : 430iter [00:13, 38.87iter/s]
Best score: 0.7348: : 431iter [00:13, 38.87iter/s]
Best score: 0.7348: : 432iter [00:13, 39.32iter/s]
Best score: 0.7348: : 432iter [00:13, 39.32iter/s]
Best score: 0.7348: : 433iter [00:13, 39.32iter/s]
Best score: 0.7348: : 434iter [00:13, 39.32iter/s]
Best score: 0.7348: : 435iter [00:13, 39.32iter/s]
Best score: 0.7348: : 436iter [00:13, 39.32iter/s]
Best score: 0.7348: : 437iter [00:13, 39.74iter/s]
Best score: 0.7348: : 437iter [00:13, 39.74iter/s]
Best score: 0.7348: : 438iter [00:13, 39.74iter/s]
Best score: 0.7348: : 439iter [00:13, 39.74iter/s]
Best score: 0.7348: : 440iter [00:13, 39.74iter/s]
Best score: 0.7348: : 441iter [00:13, 39.65iter/s]
Best score: 0.7348: : 441iter [00:13, 39.65iter/s]
Best score: 0.7348: : 442iter [00:13, 39.65iter/s]
Best score: 0.7348: : 443iter [00:13, 39.65iter/s]
Best score: 0.7348: : 444iter [00:13, 39.65iter/s]
Best score: 0.7348: : 445iter [00:13, 39.65iter/s]
Best score: 0.7348: : 446iter [00:13, 39.92iter/s]
Best score: 0.7348: : 446iter [00:13, 39.92iter/s]
Best score: 0.7348: : 447iter [00:13, 39.92iter/s]
Best score: 0.7348: : 448iter [00:13, 39.92iter/s]
[ITER 450] Best: 0.7095
[PARAMS] Current best parameters:
  fx: 543.6049
  fy: 543.5140
  px: 983.9007
  py: 766.3163
  k1: 0.00729980
  k2: -0.02602193
  k3: 0.01525144
  k4: -0.00378465
  p1: -0.00435430
  p2: 0.00191511
  p3: 0.00543152
  p4: 0.00626767
  s1: -0.0026
  s2: -0.0051
  s3: 0.0067
  s4: -0.0096
  tilt_x: 0.0045
  tilt_y: -0.0237
  scale: 2.9689
  rotation: -0.8586

Best score: 0.7095: : 449iter [00:13, 39.92iter/s]
Best score: 0.7095: : 450iter [00:13, 39.90iter/s]
Best score: 0.7095: : 450iter [00:13, 39.90iter/s]
Best score: 0.7095: : 451iter [00:13, 39.90iter/s]
Best score: 0.7095: : 452iter [00:13, 39.90iter/s]
Best score: 0.7095: : 453iter [00:13, 39.90iter/s]
Best score: 0.7095: : 454iter [00:13, 32.82iter/s]
Best score: 0.7095: : 454iter [00:14, 32.82iter/s]
Best score: 0.7095: : 455iter [00:14, 32.82iter/s]
Best score: 0.7095: : 456iter [00:14, 32.82iter/s]
Best score: 0.7095: : 457iter [00:14, 32.82iter/s]
Best score: 0.7095: : 458iter [00:14, 32.82iter/s]
Best score: 0.7095: : 459iter [00:14, 35.05iter/s]
Best score: 0.6143: : 459iter [00:14, 35.05iter/s]
Best score: 0.6143: : 460iter [00:14, 35.05iter/s]
Best score: 0.6143: : 461iter [00:14, 35.05iter/s]
Best score: 0.6143: : 462iter [00:14, 35.05iter/s]
Best score: 0.6143: : 463iter [00:14, 36.24iter/s]
Best score: 0.6143: : 463iter [00:14, 36.24iter/s]
Best score: 0.6143: : 464iter [00:14, 36.24iter/s]
Best score: 0.6143: : 465iter [00:14, 36.24iter/s]
Best score: 0.6143: : 466iter [00:14, 36.24iter/s]
Best score: 0.6143: : 467iter [00:14, 36.24iter/s]
Best score: 0.6143: : 468iter [00:14, 37.62iter/s]
Best score: 0.6143: : 468iter [00:14, 37.62iter/s]
Best score: 0.6143: : 469iter [00:14, 37.62iter/s]
Best score: 0.6143: : 470iter [00:14, 37.62iter/s]
Best score: 0.6143: : 471iter [00:14, 37.62iter/s]
Best score: 0.6143: : 472iter [00:14, 38.13iter/s]
Best score: 0.6143: : 472iter [00:14, 38.13iter/s]
Best score: 0.6143: : 473iter [00:14, 38.13iter/s]
Best score: 0.6143: : 474iter [00:14, 38.13iter/s]
Best score: 0.6143: : 475iter [00:14, 38.13iter/s]
Best score: 0.6143: : 476iter [00:14, 38.13iter/s]
Best score: 0.6143: : 477iter [00:14, 38.79iter/s]
Best score: 0.6143: : 477iter [00:14, 38.79iter/s]
Best score: 0.6143: : 478iter [00:14, 38.79iter/s]
Best score: 0.6143: : 479iter [00:14, 38.79iter/s]
Best score: 0.6143: : 480iter [00:14, 38.79iter/s]
Best score: 0.6143: : 481iter [00:14, 38.91iter/s]
Best score: 0.6143: : 481iter [00:14, 38.91iter/s]
Best score: 0.6143: : 482iter [00:14, 38.91iter/s]
Best score: 0.6143: : 483iter [00:14, 38.91iter/s]
Best score: 0.6143: : 484iter [00:14, 38.91iter/s]
Best score: 0.6143: : 485iter [00:14, 38.91iter/s]
Best score: 0.6143: : 486iter [00:14, 39.52iter/s]
Best score: 0.6143: : 486iter [00:14, 39.52iter/s]
Best score: 0.6143: : 487iter [00:14, 39.52iter/s]
Best score: 0.6143: : 488iter [00:14, 39.52iter/s]
Best score: 0.5954: : 489iter [00:14, 39.52iter/s]
Best score: 0.5954: : 490iter [00:14, 39.52iter/s]
Best score: 0.5954: : 490iter [00:14, 39.52iter/s]
Best score: 0.5954: : 491iter [00:14, 39.52iter/s]
Best score: 0.5954: : 492iter [00:14, 39.52iter/s]
Best score: 0.5954: : 493iter [00:14, 39.52iter/s]
Best score: 0.5954: : 494iter [00:15, 39.52iter/s]
Best score: 0.5954: : 495iter [00:15, 39.84iter/s]
Best score: 0.5954: : 495iter [00:15, 39.84iter/s]
Best score: 0.5954: : 496iter [00:15, 39.84iter/s]
Best score: 0.5954: : 497iter [00:15, 39.84iter/s]
Best score: 0.5954: : 498iter [00:15, 39.84iter/s]
[ITER 500] Best: 0.5520
[PARAMS] Current best parameters:
  fx: 541.6364
  fy: 541.5730
  px: 978.0959
  py: 761.6714
  k1: 0.00892777
  k2: -0.03134027
  k3: 0.02140882
  k4: -0.00559041
  p1: -0.00896999
  p2: -0.00833356
  p3: 0.01962610
  p4: 0.00327739
  s1: -0.0054
  s2: -0.0052
  s3: -0.0073
  s4: -0.0060
  tilt_x: -0.0100
  tilt_y: -0.0111
  scale: 2.9762
  rotation: -0.8535

Best score: 0.5520: : 499iter [00:15, 39.84iter/s]
Best score: 0.5520: : 500iter [00:15, 39.87iter/s]
Best score: 0.5520: : 500iter [00:15, 39.87iter/s]
Best score: 0.5520: : 501iter [00:15, 39.87iter/s]
Best score: 0.5520: : 502iter [00:15, 39.87iter/s]
Best score: 0.5520: : 503iter [00:15, 39.87iter/s]
Best score: 0.5520: : 504iter [00:15, 33.14iter/s]
Best score: 0.5520: : 504iter [00:15, 33.14iter/s]
Best score: 0.5520: : 505iter [00:15, 33.14iter/s]
Best score: 0.5520: : 506iter [00:15, 33.14iter/s]
Best score: 0.5520: : 507iter [00:15, 33.14iter/s]
Best score: 0.5520: : 508iter [00:15, 33.14iter/s]
Best score: 0.5520: : 509iter [00:15, 35.24iter/s]
Best score: 0.5188: : 509iter [00:15, 35.24iter/s]
Best score: 0.5188: : 510iter [00:15, 35.24iter/s]
Best score: 0.5188: : 511iter [00:15, 35.24iter/s]
Best score: 0.5188: : 512iter [00:15, 35.24iter/s]
Best score: 0.5188: : 513iter [00:15, 36.33iter/s]
Best score: 0.5188: : 513iter [00:15, 36.33iter/s]
Best score: 0.5188: : 514iter [00:15, 36.33iter/s]
Best score: 0.5188: : 515iter [00:15, 36.33iter/s]
Best score: 0.5188: : 516iter [00:15, 36.33iter/s]
Best score: 0.5188: : 517iter [00:15, 36.33iter/s]
Best score: 0.5188: : 518iter [00:15, 37.71iter/s]
Best score: 0.5188: : 518iter [00:15, 37.71iter/s]
Best score: 0.5124: : 519iter [00:15, 37.71iter/s]
Best score: 0.5124: : 520iter [00:15, 37.71iter/s]
Best score: 0.5124: : 521iter [00:15, 37.71iter/s]
Best score: 0.5124: : 522iter [00:15, 38.27iter/s]
Best score: 0.5124: : 522iter [00:15, 38.27iter/s]
Best score: 0.5124: : 523iter [00:15, 38.27iter/s]
Best score: 0.5124: : 524iter [00:15, 38.27iter/s]
Best score: 0.5124: : 525iter [00:15, 38.27iter/s]
Best score: 0.5124: : 526iter [00:15, 38.27iter/s]
Best score: 0.5124: : 527iter [00:15, 38.99iter/s]
Best score: 0.5124: : 527iter [00:15, 38.99iter/s]
Best score: 0.5124: : 528iter [00:15, 38.99iter/s]
Best score: 0.5007: : 529iter [00:15, 38.99iter/s]
Best score: 0.5007: : 530iter [00:15, 38.99iter/s]
Best score: 0.5007: : 531iter [00:15, 39.22iter/s]
Best score: 0.5007: : 531iter [00:16, 39.22iter/s]
Best score: 0.5007: : 532iter [00:16, 39.22iter/s]
Best score: 0.5007: : 533iter [00:16, 39.22iter/s]
Best score: 0.5007: : 534iter [00:16, 39.22iter/s]
Best score: 0.5007: : 535iter [00:16, 39.22iter/s]
Best score: 0.5007: : 536iter [00:16, 39.77iter/s]
Best score: 0.5007: : 536iter [00:16, 39.77iter/s]
Best score: 0.5007: : 537iter [00:16, 39.77iter/s]
Best score: 0.5007: : 538iter [00:16, 39.77iter/s]
Best score: 0.5007: : 539iter [00:16, 39.77iter/s]
Best score: 0.5007: : 540iter [00:16, 39.76iter/s]
Best score: 0.5007: : 540iter [00:16, 39.76iter/s]
Best score: 0.5007: : 541iter [00:16, 39.76iter/s]
Best score: 0.5007: : 542iter [00:16, 39.76iter/s]
Best score: 0.5007: : 543iter [00:16, 39.76iter/s]
Best score: 0.5007: : 544iter [00:16, 39.76iter/s]
Best score: 0.5007: : 545iter [00:16, 40.14iter/s]
Best score: 0.5007: : 545iter [00:16, 40.14iter/s]
Best score: 0.5007: : 546iter [00:16, 40.14iter/s]
Best score: 0.5007: : 547iter [00:16, 40.14iter/s]
Best score: 0.5007: : 548iter [00:16, 40.14iter/s]
[ITER 550] Best: 0.4520
[PARAMS] Current best parameters:
  fx: 540.6303
  fy: 540.5057
  px: 977.3417
  py: 761.7933
  k1: 0.01637044
  k2: -0.03604782
  k3: 0.02059391
  k4: -0.00474855
  p1: -0.00713759
  p2: -0.00726346
  p3: 0.01711828
  p4: 0.00532124
  s1: -0.0095
  s2: -0.0046
  s3: -0.0096
  s4: -0.0067
  tilt_x: -0.0129
  tilt_y: -0.0064
  scale: 2.9664
  rotation: -0.8705

Best score: 0.4520: : 549iter [00:16, 40.14iter/s]
Best score: 0.4520: : 550iter [00:16, 40.13iter/s]
Best score: 0.4520: : 550iter [00:16, 40.13iter/s]
Best score: 0.4520: : 551iter [00:16, 40.13iter/s]
Best score: 0.4520: : 552iter [00:16, 40.13iter/s]
Best score: 0.4520: : 553iter [00:16, 40.13iter/s]
Best score: 0.4520: : 554iter [00:16, 40.13iter/s]
Best score: 0.4520: : 555iter [00:16, 33.74iter/s]
Best score: 0.4520: : 555iter [00:16, 33.74iter/s]
Best score: 0.4520: : 556iter [00:16, 33.74iter/s]
Best score: 0.4520: : 557iter [00:16, 33.74iter/s]
Best score: 0.4520: : 558iter [00:16, 33.74iter/s]
Best score: 0.4520: : 559iter [00:16, 33.74iter/s]
Best score: 0.4520: : 560iter [00:16, 35.46iter/s]
Best score: 0.4520: : 560iter [00:16, 35.46iter/s]
Best score: 0.4520: : 561iter [00:16, 35.46iter/s]
Best score: 0.4520: : 562iter [00:16, 35.46iter/s]
Best score: 0.4520: : 563iter [00:16, 35.46iter/s]
Best score: 0.4520: : 564iter [00:16, 35.46iter/s]
Best score: 0.4520: : 565iter [00:16, 36.96iter/s]
Best score: 0.4520: : 565iter [00:16, 36.96iter/s]
Best score: 0.4520: : 566iter [00:16, 36.96iter/s]
Best score: 0.4520: : 567iter [00:16, 36.96iter/s]
Best score: 0.4520: : 568iter [00:16, 36.96iter/s]
Best score: 0.4520: : 569iter [00:17, 36.96iter/s]
Best score: 0.4520: : 570iter [00:17, 37.85iter/s]
Best score: 0.4520: : 570iter [00:17, 37.85iter/s]
Best score: 0.4520: : 571iter [00:17, 37.85iter/s]
Best score: 0.4520: : 572iter [00:17, 37.85iter/s]
Best score: 0.4520: : 573iter [00:17, 37.85iter/s]
Best score: 0.4520: : 574iter [00:17, 37.85iter/s]
Best score: 0.4520: : 575iter [00:17, 38.73iter/s]
Best score: 0.4520: : 575iter [00:17, 38.73iter/s]
Best score: 0.4520: : 576iter [00:17, 38.73iter/s]
Best score: 0.4520: : 577iter [00:17, 38.73iter/s]
Best score: 0.4520: : 578iter [00:17, 38.73iter/s]
Best score: 0.4232: : 579iter [00:17, 38.73iter/s]
Best score: 0.4232: : 580iter [00:17, 39.06iter/s]
Best score: 0.4232: : 580iter [00:17, 39.06iter/s]
Best score: 0.4232: : 581iter [00:17, 39.06iter/s]
Best score: 0.4232: : 582iter [00:17, 39.06iter/s]
Best score: 0.4232: : 583iter [00:17, 39.06iter/s]
Best score: 0.4232: : 584iter [00:17, 39.06iter/s]
Best score: 0.4232: : 585iter [00:17, 39.56iter/s]
Best score: 0.4232: : 585iter [00:17, 39.56iter/s]
Best score: 0.4232: : 586iter [00:17, 39.56iter/s]
Best score: 0.4232: : 587iter [00:17, 39.56iter/s]
Best score: 0.4232: : 588iter [00:17, 39.56iter/s]
Best score: 0.4232: : 589iter [00:17, 39.56iter/s]
Best score: 0.4151: : 589iter [00:17, 39.56iter/s]
Best score: 0.4151: : 590iter [00:17, 39.56iter/s]
Best score: 0.4151: : 591iter [00:17, 39.56iter/s]
Best score: 0.4151: : 592iter [00:17, 39.56iter/s]
Best score: 0.4151: : 593iter [00:17, 39.56iter/s]
Best score: 0.4151: : 593iter [00:17, 39.56iter/s]
Best score: 0.4151: : 594iter [00:17, 39.56iter/s]
Best score: 0.4151: : 595iter [00:17, 39.56iter/s]
Best score: 0.4151: : 596iter [00:17, 39.56iter/s]
Best score: 0.4151: : 597iter [00:17, 39.56iter/s]
Best score: 0.4151: : 598iter [00:17, 39.91iter/s]
Best score: 0.4151: : 598iter [00:17, 39.91iter/s]
[ITER 600] Best: 0.4052
[PARAMS] Current best parameters:
  fx: 541.3071
  fy: 541.3193
  px: 975.9794
  py: 760.0264
  k1: 0.02254312
  k2: -0.04385609
  k3: 0.02367284
  k4: -0.00516788
  p1: -0.00838153
  p2: -0.00389891
  p3: 0.02150892
  p4: 0.00359172
  s1: -0.0181
  s2: -0.0032
  s3: -0.0146
  s4: -0.0048
  tilt_x: -0.0190
  tilt_y: 0.0071
  scale: 2.9535
  rotation: -0.8829

Best score: 0.4052: : 599iter [00:17, 39.91iter/s]
Best score: 0.4052: : 600iter [00:17, 39.91iter/s]
Best score: 0.4052: : 601iter [00:17, 39.91iter/s]
Best score: 0.4052: : 602iter [00:17, 39.91iter/s]
Best score: 0.4052: : 603iter [00:17, 33.28iter/s]
Best score: 0.4052: : 603iter [00:17, 33.28iter/s]
Best score: 0.4052: : 604iter [00:17, 33.28iter/s]
Best score: 0.4052: : 605iter [00:17, 33.28iter/s]
Best score: 0.4052: : 606iter [00:18, 33.28iter/s]
Best score: 0.4052: : 607iter [00:18, 33.28iter/s]
Best score: 0.4052: : 608iter [00:18, 35.24iter/s]
Best score: 0.4052: : 608iter [00:18, 35.24iter/s]
Best score: 0.3917: : 609iter [00:18, 35.24iter/s]
Best score: 0.3917: : 610iter [00:18, 35.24iter/s]
Best score: 0.3917: : 611iter [00:18, 35.24iter/s]
Best score: 0.3917: : 612iter [00:18, 36.29iter/s]
Best score: 0.3917: : 612iter [00:18, 36.29iter/s]
Best score: 0.3917: : 613iter [00:18, 36.29iter/s]
Best score: 0.3917: : 614iter [00:18, 36.29iter/s]
Best score: 0.3917: : 615iter [00:18, 36.29iter/s]
Best score: 0.3917: : 616iter [00:18, 36.29iter/s]
Best score: 0.3917: : 617iter [00:18, 37.55iter/s]
Best score: 0.3917: : 617iter [00:18, 37.55iter/s]
Best score: 0.3917: : 618iter [00:18, 37.55iter/s]
Best score: 0.3917: : 619iter [00:18, 37.55iter/s]
Best score: 0.3917: : 620iter [00:18, 37.55iter/s]
Best score: 0.3917: : 621iter [00:18, 38.14iter/s]
Best score: 0.3917: : 621iter [00:18, 38.14iter/s]
Best score: 0.3917: : 622iter [00:18, 38.14iter/s]
Best score: 0.3917: : 623iter [00:18, 38.14iter/s]
Best score: 0.3917: : 624iter [00:18, 38.14iter/s]
Best score: 0.3917: : 625iter [00:18, 38.14iter/s]
Best score: 0.3917: : 626iter [00:18, 39.02iter/s]
Best score: 0.3917: : 626iter [00:18, 39.02iter/s]
Best score: 0.3917: : 627iter [00:18, 39.02iter/s]
Best score: 0.3917: : 628iter [00:18, 39.02iter/s]
Best score: 0.3917: : 629iter [00:18, 39.02iter/s]
Best score: 0.3917: : 630iter [00:18, 39.23iter/s]
Best score: 0.3917: : 630iter [00:18, 39.23iter/s]
Best score: 0.3917: : 631iter [00:18, 39.23iter/s]
Best score: 0.3917: : 632iter [00:18, 39.23iter/s]
Best score: 0.3917: : 633iter [00:18, 39.23iter/s]
Best score: 0.3917: : 634iter [00:18, 39.23iter/s]
Best score: 0.3917: : 635iter [00:18, 39.68iter/s]
Best score: 0.3917: : 635iter [00:18, 39.68iter/s]
Best score: 0.3917: : 636iter [00:18, 39.68iter/s]
Best score: 0.3917: : 637iter [00:18, 39.68iter/s]
Best score: 0.3917: : 638iter [00:18, 39.68iter/s]
Best score: 0.3818: : 639iter [00:18, 39.68iter/s]
Best score: 0.3818: : 640iter [00:18, 39.86iter/s]
Best score: 0.3818: : 640iter [00:18, 39.86iter/s]
Best score: 0.3818: : 641iter [00:18, 39.86iter/s]
Best score: 0.3818: : 642iter [00:18, 39.86iter/s]
Best score: 0.3818: : 643iter [00:18, 39.86iter/s]
Best score: 0.3818: : 644iter [00:18, 39.86iter/s]
Best score: 0.3818: : 645iter [00:18, 40.07iter/s]
Best score: 0.3818: : 645iter [00:18, 40.07iter/s]
Best score: 0.3818: : 646iter [00:19, 40.07iter/s]
Best score: 0.3818: : 647iter [00:19, 40.07iter/s]
Best score: 0.3818: : 648iter [00:19, 40.07iter/s]
[ITER 650] Best: 0.3682
[PARAMS] Current best parameters:
  fx: 540.9149
  fy: 541.1454
  px: 975.1688
  py: 757.7213
  k1: 0.02531091
  k2: -0.04921041
  k3: 0.02722630
  k4: -0.00592907
  p1: -0.01001160
  p2: -0.00256470
  p3: 0.02498834
  p4: 0.00189362
  s1: -0.0234
  s2: -0.0018
  s3: -0.0199
  s4: -0.0026
  tilt_x: -0.0271
  tilt_y: 0.0130
  scale: 2.9553
  rotation: -0.8917

Best score: 0.3682: : 649iter [00:19, 40.07iter/s]
Best score: 0.3682: : 650iter [00:19, 40.12iter/s]
Best score: 0.3682: : 650iter [00:19, 40.12iter/s]
Best score: 0.3682: : 651iter [00:19, 40.12iter/s]
Best score: 0.3682: : 652iter [00:19, 40.12iter/s]
Best score: 0.3682: : 653iter [00:19, 40.12iter/s]
Best score: 0.3682: : 654iter [00:19, 40.12iter/s]
Best score: 0.3682: : 655iter [00:19, 33.85iter/s]
Best score: 0.3682: : 655iter [00:19, 33.85iter/s]
Best score: 0.3682: : 656iter [00:19, 33.85iter/s]
Best score: 0.3682: : 657iter [00:19, 33.85iter/s]
Best score: 0.3682: : 658iter [00:19, 33.85iter/s]
Best score: 0.3682: : 659iter [00:19, 33.85iter/s]
Best score: 0.3682: : 660iter [00:19, 35.43iter/s]
Best score: 0.3682: : 660iter [00:19, 35.43iter/s]
Best score: 0.3682: : 661iter [00:19, 35.43iter/s]
Best score: 0.3682: : 662iter [00:19, 35.43iter/s]
Best score: 0.3682: : 663iter [00:19, 35.43iter/s]
Best score: 0.3682: : 664iter [00:19, 35.43iter/s]
Best score: 0.3682: : 665iter [00:19, 36.70iter/s]
Best score: 0.3682: : 665iter [00:19, 36.70iter/s]
Best score: 0.3682: : 666iter [00:19, 36.70iter/s]
Best score: 0.3682: : 667iter [00:19, 36.70iter/s]
Best score: 0.3682: : 668iter [00:19, 36.70iter/s]
Best score: 0.3682: : 669iter [00:19, 36.70iter/s]
Best score: 0.3682: : 670iter [00:19, 37.49iter/s]
Best score: 0.3682: : 670iter [00:19, 37.49iter/s]
Best score: 0.3682: : 671iter [00:19, 37.49iter/s]
Best score: 0.3682: : 672iter [00:19, 37.49iter/s]
Best score: 0.3682: : 673iter [00:19, 37.49iter/s]
Best score: 0.3682: : 674iter [00:19, 37.49iter/s]
Best score: 0.3682: : 675iter [00:19, 38.42iter/s]
Best score: 0.3682: : 675iter [00:19, 38.42iter/s]
Best score: 0.3682: : 676iter [00:19, 38.42iter/s]
Best score: 0.3682: : 677iter [00:19, 38.42iter/s]
Best score: 0.3682: : 678iter [00:19, 38.42iter/s]
Best score: 0.3682: : 679iter [00:19, 38.42iter/s]
Best score: 0.3682: : 680iter [00:19, 38.92iter/s]
Best score: 0.3682: : 680iter [00:19, 38.92iter/s]
Best score: 0.3682: : 681iter [00:19, 38.92iter/s]
Best score: 0.3682: : 682iter [00:19, 38.92iter/s]
Best score: 0.3682: : 683iter [00:20, 38.92iter/s]
Best score: 0.3682: : 684iter [00:20, 32.09iter/s]
Best score: 0.3682: : 684iter [00:20, 32.09iter/s]
Best score: 0.3682: : 685iter [00:20, 32.09iter/s]
Best score: 0.3682: : 686iter [00:20, 32.09iter/s]
Best score: 0.3682: : 687iter [00:20, 32.09iter/s]
Best score: 0.3682: : 688iter [00:20, 32.09iter/s]
Best score: 0.3682: : 689iter [00:20, 34.34iter/s]
Best score: 0.3647: : 689iter [00:20, 34.34iter/s]
Best score: 0.3647: : 690iter [00:20, 34.34iter/s]
Best score: 0.3647: : 691iter [00:20, 34.34iter/s]
Best score: 0.3647: : 692iter [00:20, 34.34iter/s]
Best score: 0.3647: : 693iter [00:20, 35.45iter/s]
Best score: 0.3647: : 693iter [00:20, 35.45iter/s]
Best score: 0.3647: : 694iter [00:20, 35.45iter/s]
Best score: 0.3647: : 695iter [00:20, 35.45iter/s]
Best score: 0.3647: : 696iter [00:20, 35.45iter/s]
Best score: 0.3647: : 697iter [00:20, 36.26iter/s]
Best score: 0.3647: : 697iter [00:20, 36.26iter/s]
Best score: 0.3647: : 698iter [00:20, 36.26iter/s]
[ITER 700] Best: 0.3647
[PARAMS] Current best parameters:
  fx: 540.3973
  fy: 540.6280
  px: 975.3778
  py: 757.9974
  k1: 0.02455029
  k2: -0.04864673
  k3: 0.02755569
  k4: -0.00609783
  p1: -0.00973017
  p2: -0.00120809
  p3: 0.02402614
  p4: 0.00179919
  s1: -0.0229
  s2: -0.0020
  s3: -0.0192
  s4: -0.0025
  tilt_x: -0.0262
  tilt_y: 0.0148
  scale: 2.9590
  rotation: -0.8890

Best score: 0.3647: : 699iter [00:20, 36.26iter/s]
Best score: 0.3647: : 700iter [00:20, 36.26iter/s]
Best score: 0.3647: : 701iter [00:20, 30.27iter/s]
Best score: 0.3647: : 701iter [00:20, 30.27iter/s]
Best score: 0.3647: : 702iter [00:20, 30.27iter/s]
Best score: 0.3647: : 703iter [00:20, 30.27iter/s]
Best score: 0.3647: : 704iter [00:20, 30.27iter/s]
Best score: 0.3647: : 705iter [00:20, 30.27iter/s]
Best score: 0.3647: : 706iter [00:20, 32.97iter/s]
Best score: 0.3647: : 706iter [00:20, 32.97iter/s]
Best score: 0.3647: : 707iter [00:20, 32.97iter/s]
Best score: 0.3647: : 708iter [00:20, 32.97iter/s]
Best score: 0.3635: : 709iter [00:20, 32.97iter/s]
Best score: 0.3635: : 710iter [00:20, 34.54iter/s]
Best score: 0.3635: : 710iter [00:20, 34.54iter/s]
Best score: 0.3635: : 711iter [00:20, 34.54iter/s]
Best score: 0.3635: : 712iter [00:20, 34.54iter/s]
Best score: 0.3635: : 713iter [00:20, 34.54iter/s]
Best score: 0.3635: : 714iter [00:20, 34.54iter/s]
Best score: 0.3635: : 715iter [00:20, 36.34iter/s]
Best score: 0.3635: : 715iter [00:20, 36.34iter/s]
Best score: 0.3635: : 716iter [00:21, 36.34iter/s]
Best score: 0.3635: : 717iter [00:21, 36.34iter/s]
Best score: 0.3635: : 718iter [00:21, 36.34iter/s]
Best score: 0.3586: : 719iter [00:21, 36.34iter/s]
Best score: 0.3586: : 720iter [00:21, 37.43iter/s]
Best score: 0.3586: : 720iter [00:21, 37.43iter/s]
Best score: 0.3586: : 721iter [00:21, 37.43iter/s]
Best score: 0.3586: : 722iter [00:21, 37.43iter/s]
Best score: 0.3586: : 723iter [00:21, 37.43iter/s]
Best score: 0.3586: : 724iter [00:21, 37.43iter/s]
Best score: 0.3586: : 725iter [00:21, 38.52iter/s]
Best score: 0.3586: : 725iter [00:21, 38.52iter/s]
Best score: 0.3586: : 726iter [00:21, 38.52iter/s]
Best score: 0.3586: : 727iter [00:21, 38.52iter/s]
Best score: 0.3586: : 728iter [00:21, 38.52iter/s]
Best score: 0.3586: : 729iter [00:21, 38.52iter/s]
Best score: 0.3586: : 730iter [00:21, 38.98iter/s]
Best score: 0.3586: : 730iter [00:21, 38.98iter/s]
Best score: 0.3586: : 731iter [00:21, 38.98iter/s]
Best score: 0.3586: : 732iter [00:21, 38.98iter/s]
Best score: 0.3586: : 733iter [00:21, 38.98iter/s]
Best score: 0.3586: : 734iter [00:21, 38.98iter/s]
Best score: 0.3586: : 735iter [00:21, 39.44iter/s]
Best score: 0.3586: : 735iter [00:21, 39.44iter/s]
Best score: 0.3586: : 736iter [00:21, 39.44iter/s]
Best score: 0.3586: : 737iter [00:21, 39.44iter/s]
Best score: 0.3586: : 738iter [00:21, 39.44iter/s]
Best score: 0.3571: : 739iter [00:21, 39.44iter/s]
Best score: 0.3571: : 740iter [00:21, 39.42iter/s]
Best score: 0.3571: : 740iter [00:21, 39.42iter/s]
Best score: 0.3571: : 741iter [00:21, 39.42iter/s]
Best score: 0.3571: : 742iter [00:21, 39.42iter/s]
Best score: 0.3571: : 743iter [00:21, 39.42iter/s]
Best score: 0.3571: : 744iter [00:21, 39.42iter/s]
Best score: 0.3571: : 745iter [00:21, 39.73iter/s]
Best score: 0.3571: : 745iter [00:21, 39.73iter/s]
Best score: 0.3571: : 746iter [00:21, 39.73iter/s]
Best score: 0.3571: : 747iter [00:21, 39.73iter/s]
Best score: 0.3571: : 748iter [00:21, 39.73iter/s]
[ITER 750] Best: 0.3527
[PARAMS] Current best parameters:
  fx: 539.3007
  fy: 539.4692
  px: 975.2748
  py: 757.9826
  k1: 0.02525652
  k2: -0.04914551
  k3: 0.02844550
  k4: -0.00639675
  p1: -0.00959825
  p2: -0.00169387
  p3: 0.02373884
  p4: 0.00085376
  s1: -0.0223
  s2: -0.0008
  s3: -0.0179
  s4: -0.0020
  tilt_x: -0.0269
  tilt_y: 0.0147
  scale: 2.9608
  rotation: -0.8959

Best score: 0.3527: : 749iter [00:21, 39.73iter/s]
Best score: 0.3527: : 750iter [00:21, 39.64iter/s]
Best score: 0.3527: : 750iter [00:21, 39.64iter/s]
Best score: 0.3527: : 751iter [00:21, 39.64iter/s]
Best score: 0.3527: : 752iter [00:21, 39.64iter/s]
Best score: 0.3527: : 753iter [00:22, 39.64iter/s]
Best score: 0.3527: : 754iter [00:22, 33.07iter/s]
Best score: 0.3527: : 754iter [00:22, 33.07iter/s]
Best score: 0.3527: : 755iter [00:22, 33.07iter/s]
Best score: 0.3527: : 756iter [00:22, 33.07iter/s]
Best score: 0.3527: : 757iter [00:22, 33.07iter/s]
Best score: 0.3527: : 758iter [00:22, 33.07iter/s]
Best score: 0.3527: : 759iter [00:22, 35.08iter/s]
Best score: 0.3527: : 759iter [00:22, 35.08iter/s]
Best score: 0.3527: : 760iter [00:22, 35.08iter/s]
Best score: 0.3527: : 761iter [00:22, 35.08iter/s]
Best score: 0.3527: : 762iter [00:22, 35.08iter/s]
Best score: 0.3527: : 763iter [00:22, 36.21iter/s]
Best score: 0.3527: : 763iter [00:22, 36.21iter/s]
Best score: 0.3527: : 764iter [00:22, 36.21iter/s]
Best score: 0.3527: : 765iter [00:22, 36.21iter/s]
Best score: 0.3527: : 766iter [00:22, 36.21iter/s]
Best score: 0.3527: : 767iter [00:22, 36.21iter/s]
Best score: 0.3527: : 768iter [00:22, 37.36iter/s]
Best score: 0.3527: : 768iter [00:22, 37.36iter/s]
Best score: 0.3504: : 769iter [00:22, 37.36iter/s]
Best score: 0.3504: : 770iter [00:22, 37.36iter/s]
Best score: 0.3504: : 771iter [00:22, 37.36iter/s]
Best score: 0.3504: : 772iter [00:22, 37.85iter/s]
Best score: 0.3504: : 772iter [00:22, 37.85iter/s]
Best score: 0.3504: : 773iter [00:22, 37.85iter/s]
Best score: 0.3504: : 774iter [00:22, 37.85iter/s]
Best score: 0.3504: : 775iter [00:22, 37.85iter/s]
Best score: 0.3504: : 776iter [00:22, 38.38iter/s]
Best score: 0.3504: : 776iter [00:22, 38.38iter/s]
Best score: 0.3504: : 777iter [00:22, 38.38iter/s]
Best score: 0.3504: : 778iter [00:22, 38.38iter/s]
Best score: 0.3491: : 779iter [00:22, 38.38iter/s]
Best score: 0.3491: : 780iter [00:22, 38.76iter/s]
Best score: 0.3491: : 780iter [00:22, 38.76iter/s]
Best score: 0.3491: : 781iter [00:22, 38.76iter/s]
Best score: 0.3491: : 782iter [00:22, 38.76iter/s]
Best score: 0.3491: : 783iter [00:22, 38.76iter/s]
Best score: 0.3491: : 784iter [00:22, 38.76iter/s]
Best score: 0.3491: : 785iter [00:22, 39.27iter/s]
Best score: 0.3491: : 785iter [00:22, 39.27iter/s]
Best score: 0.3491: : 786iter [00:22, 39.27iter/s]
Best score: 0.3491: : 787iter [00:22, 39.27iter/s]
Best score: 0.3491: : 788iter [00:22, 39.27iter/s]
Best score: 0.3491: : 789iter [00:22, 39.27iter/s]
Best score: 0.3491: : 790iter [00:22, 39.38iter/s]
Best score: 0.3491: : 790iter [00:22, 39.38iter/s]
Best score: 0.3491: : 791iter [00:22, 39.38iter/s]
Best score: 0.3491: : 792iter [00:22, 39.38iter/s]
Best score: 0.3491: : 793iter [00:23, 39.38iter/s]
Best score: 0.3491: : 794iter [00:23, 39.38iter/s]
Best score: 0.3491: : 795iter [00:23, 39.75iter/s]
Best score: 0.3491: : 795iter [00:23, 39.75iter/s]
Best score: 0.3491: : 796iter [00:23, 39.75iter/s]
Best score: 0.3491: : 797iter [00:23, 39.75iter/s]
Best score: 0.3491: : 798iter [00:23, 39.75iter/s]
[ITER 800] Best: 0.3491
[PARAMS] Current best parameters:
  fx: 539.2876
  fy: 539.4605
  px: 974.9424
  py: 757.0273
  k1: 0.02791488
  k2: -0.05085048
  k3: 0.02806355
  k4: -0.00607861
  p1: -0.01114861
  p2: -0.00119237
  p3: 0.02547317
  p4: 0.00044136
  s1: -0.0249
  s2: -0.0004
  s3: -0.0191
  s4: -0.0015
  tilt_x: -0.0287
  tilt_y: 0.0173
  scale: 2.9550
  rotation: -0.9043

Best score: 0.3491: : 799iter [00:23, 39.75iter/s]
Best score: 0.3491: : 800iter [00:23, 39.62iter/s]
Best score: 0.3491: : 800iter [00:23, 39.62iter/s]
Best score: 0.3491: : 801iter [00:23, 39.62iter/s]
Best score: 0.3491: : 802iter [00:23, 39.62iter/s]
Best score: 0.3491: : 803iter [00:23, 39.62iter/s]
Best score: 0.3491: : 804iter [00:23, 33.00iter/s]
Best score: 0.3491: : 804iter [00:23, 33.00iter/s]
Best score: 0.3491: : 805iter [00:23, 33.00iter/s]
Best score: 0.3491: : 806iter [00:23, 33.00iter/s]
Best score: 0.3491: : 807iter [00:23, 33.00iter/s]
Best score: 0.3491: : 808iter [00:23, 33.00iter/s]
Best score: 0.3491: : 809iter [00:23, 35.07iter/s]
Best score: 0.3489: : 809iter [00:23, 35.07iter/s]
Best score: 0.3489: : 810iter [00:23, 35.07iter/s]
Best score: 0.3489: : 811iter [00:23, 35.07iter/s]
Best score: 0.3489: : 812iter [00:23, 35.07iter/s]
Best score: 0.3489: : 813iter [00:23, 36.19iter/s]
Best score: 0.3489: : 813iter [00:23, 36.19iter/s]
Best score: 0.3489: : 814iter [00:23, 36.19iter/s]
Best score: 0.3489: : 815iter [00:23, 36.19iter/s]
Best score: 0.3489: : 816iter [00:23, 36.19iter/s]
Best score: 0.3489: : 817iter [00:23, 36.19iter/s]
Best score: 0.3489: : 818iter [00:23, 37.55iter/s]
Best score: 0.3489: : 818iter [00:23, 37.55iter/s]
Best score: 0.3477: : 819iter [00:23, 37.55iter/s]
Best score: 0.3477: : 820iter [00:23, 37.55iter/s]
Best score: 0.3477: : 821iter [00:23, 37.55iter/s]
Best score: 0.3477: : 822iter [00:23, 38.07iter/s]
Best score: 0.3477: : 822iter [00:23, 38.07iter/s]
Best score: 0.3477: : 823iter [00:23, 38.07iter/s]
Best score: 0.3477: : 824iter [00:23, 38.07iter/s]
Best score: 0.3477: : 825iter [00:23, 38.07iter/s]
Best score: 0.3477: : 826iter [00:23, 38.07iter/s]
Best score: 0.3477: : 827iter [00:23, 38.84iter/s]
Best score: 0.3477: : 827iter [00:23, 38.84iter/s]
Best score: 0.3477: : 828iter [00:23, 38.84iter/s]
Best score: 0.3477: : 829iter [00:23, 38.84iter/s]
Best score: 0.3477: : 830iter [00:24, 38.84iter/s]
Best score: 0.3477: : 831iter [00:24, 38.92iter/s]
Best score: 0.3477: : 831iter [00:24, 38.92iter/s]
Best score: 0.3477: : 832iter [00:24, 38.92iter/s]
Best score: 0.3477: : 833iter [00:24, 38.92iter/s]
Best score: 0.3477: : 834iter [00:24, 38.92iter/s]
Best score: 0.3477: : 835iter [00:24, 38.92iter/s]
Best score: 0.3477: : 836iter [00:24, 39.53iter/s]
Best score: 0.3477: : 836iter [00:24, 39.53iter/s]
Best score: 0.3477: : 837iter [00:24, 39.53iter/s]
Best score: 0.3477: : 838iter [00:24, 39.53iter/s]
Best score: 0.3477: : 839iter [00:24, 39.53iter/s]
Best score: 0.3477: : 840iter [00:24, 39.59iter/s]
Best score: 0.3477: : 840iter [00:24, 39.59iter/s]
Best score: 0.3477: : 841iter [00:24, 39.59iter/s]
Best score: 0.3477: : 842iter [00:24, 39.59iter/s]
Best score: 0.3477: : 843iter [00:24, 39.59iter/s]
Best score: 0.3477: : 844iter [00:24, 39.59iter/s]
Best score: 0.3477: : 845iter [00:24, 39.96iter/s]
Best score: 0.3477: : 845iter [00:24, 39.96iter/s]
Best score: 0.3477: : 846iter [00:24, 39.96iter/s]
Best score: 0.3477: : 847iter [00:24, 39.96iter/s]
Best score: 0.3477: : 848iter [00:24, 39.96iter/s]
[ITER 850] Best: 0.3477
[PARAMS] Current best parameters:
  fx: 539.2209
  fy: 539.4200
  px: 974.7347
  py: 756.6867
  k1: 0.02586682
  k2: -0.04917499
  k3: 0.02804926
  k4: -0.00625275
  p1: -0.01177295
  p2: -0.00142792
  p3: 0.02557209
  p4: 0.00075825
  s1: -0.0245
  s2: -0.0010
  s3: -0.0192
  s4: -0.0016
  tilt_x: -0.0292
  tilt_y: 0.0179
  scale: 2.9592
  rotation: -0.9067

Best score: 0.3477: : 849iter [00:24, 39.96iter/s]
Best score: 0.3477: : 850iter [00:24, 39.65iter/s]
Best score: 0.3477: : 850iter [00:24, 39.65iter/s]
Best score: 0.3477: : 851iter [00:24, 39.65iter/s]
Best score: 0.3477: : 852iter [00:24, 39.65iter/s]
Best score: 0.3477: : 853iter [00:24, 39.65iter/s]
Best score: 0.3477: : 854iter [00:24, 32.94iter/s]
Best score: 0.3477: : 854iter [00:24, 32.94iter/s]
Best score: 0.3477: : 855iter [00:24, 32.94iter/s]
Best score: 0.3477: : 856iter [00:24, 32.94iter/s]
Best score: 0.3477: : 857iter [00:24, 32.94iter/s]
Best score: 0.3477: : 858iter [00:24, 32.94iter/s]
Best score: 0.3477: : 859iter [00:24, 34.94iter/s]
Best score: 0.3477: : 859iter [00:24, 34.94iter/s]
Best score: 0.3477: : 860iter [00:24, 34.94iter/s]
Best score: 0.3477: : 861iter [00:24, 34.94iter/s]
Best score: 0.3477: : 862iter [00:24, 34.94iter/s]
Best score: 0.3477: : 863iter [00:24, 36.01iter/s]
Best score: 0.3477: : 863iter [00:24, 36.01iter/s]
Best score: 0.3477: : 864iter [00:24, 36.01iter/s]
Best score: 0.3477: : 865iter [00:24, 36.01iter/s]
Best score: 0.3477: : 866iter [00:24, 36.01iter/s]
Best score: 0.3477: : 867iter [00:25, 36.01iter/s]
Best score: 0.3477: : 868iter [00:25, 37.50iter/s]
Best score: 0.3477: : 868iter [00:25, 37.50iter/s]
Best score: 0.3476: : 869iter [00:25, 37.50iter/s]
Best score: 0.3476: : 870iter [00:25, 37.50iter/s]
Best score: 0.3476: : 871iter [00:25, 37.50iter/s]
Best score: 0.3476: : 872iter [00:25, 38.04iter/s]
Best score: 0.3476: : 872iter [00:25, 38.04iter/s]
Best score: 0.3476: : 873iter [00:25, 38.04iter/s]
Best score: 0.3476: : 874iter [00:25, 38.04iter/s]
Best score: 0.3476: : 875iter [00:25, 38.04iter/s]
Best score: 0.3476: : 876iter [00:25, 38.04iter/s]
Best score: 0.3476: : 877iter [00:25, 38.82iter/s]
Best score: 0.3476: : 877iter [00:25, 38.82iter/s]
Best score: 0.3476: : 878iter [00:25, 38.82iter/s]
Best score: 0.3472: : 879iter [00:25, 38.82iter/s]
Best score: 0.3472: : 880iter [00:25, 38.82iter/s]
Best score: 0.3472: : 881iter [00:25, 39.07iter/s]
Best score: 0.3472: : 881iter [00:25, 39.07iter/s]
Best score: 0.3472: : 882iter [00:25, 39.07iter/s]
Best score: 0.3472: : 883iter [00:25, 39.07iter/s]
Best score: 0.3472: : 884iter [00:25, 39.07iter/s]
Best score: 0.3472: : 885iter [00:25, 39.07iter/s]
Best score: 0.3472: : 886iter [00:25, 39.54iter/s]
Best score: 0.3472: : 886iter [00:25, 39.54iter/s]
Best score: 0.3472: : 887iter [00:25, 39.54iter/s]
Best score: 0.3472: : 888iter [00:25, 39.54iter/s]
Best score: 0.3472: : 889iter [00:25, 39.54iter/s]
Best score: 0.3472: : 890iter [00:25, 39.51iter/s]
Best score: 0.3472: : 890iter [00:25, 39.51iter/s]
Best score: 0.3472: : 891iter [00:25, 39.51iter/s]
Best score: 0.3472: : 892iter [00:25, 39.51iter/s]
Best score: 0.3472: : 893iter [00:25, 39.51iter/s]
Best score: 0.3472: : 894iter [00:25, 39.51iter/s]
Best score: 0.3472: : 895iter [00:25, 39.73iter/s]
Best score: 0.3472: : 895iter [00:25, 39.73iter/s]
Best score: 0.3472: : 896iter [00:25, 39.73iter/s]
Best score: 0.3472: : 897iter [00:25, 39.73iter/s]
Best score: 0.3472: : 898iter [00:25, 39.73iter/s]
[ITER 900] Best: 0.3472
[PARAMS] Current best parameters:
  fx: 539.2880
  fy: 539.4802
  px: 974.7951
  py: 756.8629
  k1: 0.02680604
  k2: -0.05024550
  k3: 0.02838427
  k4: -0.00626680
  p1: -0.01156177
  p2: -0.00111724
  p3: 0.02585201
  p4: 0.00041917
  s1: -0.0251
  s2: -0.0006
  s3: -0.0193
  s4: -0.0014
  tilt_x: -0.0287
  tilt_y: 0.0182
  scale: 2.9572
  rotation: -0.9081

Best score: 0.3472: : 899iter [00:25, 39.73iter/s]
Best score: 0.3472: : 900iter [00:25, 39.86iter/s]
Best score: 0.3472: : 900iter [00:25, 39.86iter/s]
Best score: 0.3472: : 901iter [00:25, 39.86iter/s]
Best score: 0.3472: : 902iter [00:25, 39.86iter/s]
Best score: 0.3472: : 903iter [00:25, 39.86iter/s]
Best score: 0.3472: : 904iter [00:25, 33.06iter/s]
Best score: 0.3472: : 904iter [00:26, 33.06iter/s]
Best score: 0.3472: : 905iter [00:26, 33.06iter/s]
Best score: 0.3472: : 906iter [00:26, 33.06iter/s]
Best score: 0.3472: : 907iter [00:26, 33.06iter/s]
Best score: 0.3472: : 908iter [00:26, 33.06iter/s]
Best score: 0.3472: : 909iter [00:26, 35.20iter/s]
Best score: 0.3471: : 909iter [00:26, 35.20iter/s]
Best score: 0.3471: : 910iter [00:26, 35.20iter/s]
Best score: 0.3471: : 911iter [00:26, 35.20iter/s]
Best score: 0.3471: : 912iter [00:26, 35.20iter/s]
Best score: 0.3471: : 913iter [00:26, 36.33iter/s]
Best score: 0.3471: : 913iter [00:26, 36.33iter/s]
Best score: 0.3471: : 914iter [00:26, 36.33iter/s]
Best score: 0.3471: : 915iter [00:26, 36.33iter/s]
Best score: 0.3471: : 916iter [00:26, 36.33iter/s]
Best score: 0.3471: : 917iter [00:26, 36.33iter/s]
Best score: 0.3471: : 918iter [00:26, 37.70iter/s]
Best score: 0.3471: : 918iter [00:26, 37.70iter/s]
Best score: 0.3468: : 919iter [00:26, 37.70iter/s]
Best score: 0.3468: : 920iter [00:26, 37.70iter/s]
Best score: 0.3468: : 921iter [00:26, 37.70iter/s]
Best score: 0.3468: : 922iter [00:26, 38.16iter/s]
Best score: 0.3468: : 922iter [00:26, 38.16iter/s]
Best score: 0.3468: : 923iter [00:26, 38.16iter/s]
Best score: 0.3468: : 924iter [00:26, 38.16iter/s]
Best score: 0.3468: : 925iter [00:26, 38.16iter/s]
Best score: 0.3468: : 926iter [00:26, 38.16iter/s]
Best score: 0.3468: : 927iter [00:26, 38.88iter/s]
Best score: 0.3468: : 927iter [00:26, 38.88iter/s]
Best score: 0.3468: : 928iter [00:26, 38.88iter/s]
Best score: 0.3462: : 929iter [00:26, 38.88iter/s]
Best score: 0.3462: : 930iter [00:26, 38.88iter/s]
Best score: 0.3462: : 931iter [00:26, 38.89iter/s]
Best score: 0.3462: : 931iter [00:26, 38.89iter/s]
Best score: 0.3462: : 932iter [00:26, 38.89iter/s]
Best score: 0.3462: : 933iter [00:26, 38.89iter/s]
Best score: 0.3462: : 934iter [00:26, 38.89iter/s]
Best score: 0.3462: : 935iter [00:26, 38.89iter/s]
Best score: 0.3462: : 936iter [00:26, 39.39iter/s]
Best score: 0.3462: : 936iter [00:26, 39.39iter/s]
Best score: 0.3462: : 937iter [00:26, 39.39iter/s]
Best score: 0.3462: : 938iter [00:26, 39.39iter/s]
Best score: 0.3462: : 939iter [00:26, 39.39iter/s]
Best score: 0.3462: : 940iter [00:26, 39.49iter/s]
Best score: 0.3462: : 940iter [00:26, 39.49iter/s]
Best score: 0.3462: : 941iter [00:26, 39.49iter/s]
Best score: 0.3462: : 942iter [00:26, 39.49iter/s]
Best score: 0.3462: : 943iter [00:26, 39.49iter/s]
Best score: 0.3462: : 944iter [00:27, 39.49iter/s]
Best score: 0.3462: : 945iter [00:27, 39.74iter/s]
Best score: 0.3462: : 945iter [00:27, 39.74iter/s]
Best score: 0.3462: : 946iter [00:27, 39.74iter/s]
Best score: 0.3462: : 947iter [00:27, 39.74iter/s]
Best score: 0.3462: : 948iter [00:27, 39.74iter/s]
[ITER 950] Best: 0.3460
[PARAMS] Current best parameters:
  fx: 538.3905
  fy: 538.6265
  px: 974.9148
  py: 757.6787
  k1: 0.02830925
  k2: -0.05093813
  k3: 0.02877926
  k4: -0.00639655
  p1: -0.01041285
  p2: 0.00062078
  p3: 0.02454265
  p4: 0.00017276
  s1: -0.0257
  s2: -0.0003
  s3: -0.0181
  s4: -0.0014
  tilt_x: -0.0268
  tilt_y: 0.0211
  scale: 2.9553
  rotation: -0.9008

Best score: 0.3460: : 949iter [00:27, 39.74iter/s]
Best score: 0.3460: : 950iter [00:27, 39.71iter/s]
Best score: 0.3460: : 950iter [00:27, 39.71iter/s]
Best score: 0.3460: : 951iter [00:27, 39.71iter/s]
Best score: 0.3460: : 952iter [00:27, 39.71iter/s]
Best score: 0.3460: : 953iter [00:27, 39.71iter/s]
Best score: 0.3460: : 954iter [00:27, 33.01iter/s]
Best score: 0.3460: : 954iter [00:27, 33.01iter/s]
Best score: 0.3460: : 955iter [00:27, 33.01iter/s]
Best score: 0.3460: : 956iter [00:27, 33.01iter/s]
Best score: 0.3460: : 957iter [00:27, 33.01iter/s]
Best score: 0.3460: : 958iter [00:27, 33.01iter/s]
Best score: 0.3460: : 959iter [00:27, 35.06iter/s]
Best score: 0.3452: : 959iter [00:27, 35.06iter/s]
Best score: 0.3452: : 960iter [00:27, 35.06iter/s]
Best score: 0.3452: : 961iter [00:27, 35.06iter/s]
Best score: 0.3452: : 962iter [00:27, 35.06iter/s]
Best score: 0.3452: : 963iter [00:27, 36.15iter/s]
Best score: 0.3452: : 963iter [00:27, 36.15iter/s]
Best score: 0.3452: : 964iter [00:27, 36.15iter/s]
Best score: 0.3452: : 965iter [00:27, 36.15iter/s]
Best score: 0.3452: : 966iter [00:27, 36.15iter/s]
Best score: 0.3452: : 967iter [00:27, 36.15iter/s]
Best score: 0.3452: : 968iter [00:27, 37.51iter/s]
Best score: 0.3452: : 968iter [00:27, 37.51iter/s]
Best score: 0.3444: : 969iter [00:27, 37.51iter/s]
Best score: 0.3444: : 970iter [00:27, 37.51iter/s]
Best score: 0.3444: : 971iter [00:27, 37.51iter/s]
Best score: 0.3444: : 972iter [00:27, 38.04iter/s]
Best score: 0.3444: : 972iter [00:27, 38.04iter/s]
Best score: 0.3444: : 973iter [00:27, 38.04iter/s]
Best score: 0.3444: : 974iter [00:27, 38.04iter/s]
Best score: 0.3444: : 975iter [00:27, 38.04iter/s]
Best score: 0.3444: : 976iter [00:27, 38.04iter/s]
Best score: 0.3444: : 977iter [00:27, 38.79iter/s]
Best score: 0.3444: : 977iter [00:27, 38.79iter/s]
Best score: 0.3444: : 978iter [00:27, 38.79iter/s]
Best score: 0.3444: : 979iter [00:27, 38.79iter/s]
Best score: 0.3444: : 980iter [00:27, 38.79iter/s]
Best score: 0.3444: : 981iter [00:27, 39.03iter/s]
Best score: 0.3444: : 981iter [00:28, 39.03iter/s]
Best score: 0.3444: : 982iter [00:28, 39.03iter/s]
Best score: 0.3444: : 983iter [00:28, 39.03iter/s]
Best score: 0.3444: : 984iter [00:28, 39.03iter/s]
Best score: 0.3444: : 985iter [00:28, 39.03iter/s]
Best score: 0.3444: : 986iter [00:28, 39.51iter/s]
Best score: 0.3444: : 986iter [00:28, 39.51iter/s]
Best score: 0.3444: : 987iter [00:28, 39.51iter/s]
Best score: 0.3444: : 988iter [00:28, 39.51iter/s]
Best score: 0.3441: : 989iter [00:28, 39.51iter/s]
Best score: 0.3441: : 990iter [00:28, 39.53iter/s]
Best score: 0.3441: : 990iter [00:28, 39.53iter/s]
Best score: 0.3441: : 991iter [00:28, 39.53iter/s]
Best score: 0.3441: : 992iter [00:28, 39.53iter/s]
Best score: 0.3441: : 993iter [00:28, 39.53iter/s]
Best score: 0.3441: : 994iter [00:28, 39.53iter/s]
Best score: 0.3441: : 995iter [00:28, 39.61iter/s]
Best score: 0.3441: : 995iter [00:28, 39.61iter/s]
Best score: 0.3441: : 996iter [00:28, 39.61iter/s]
Best score: 0.3441: : 997iter [00:28, 39.61iter/s]
Best score: 0.3441: : 998iter [00:28, 39.61iter/s]
[ITER 1000] Best: 0.3440
[PARAMS] Current best parameters:
  fx: 538.2210
  fy: 538.4398
  px: 974.9399
  py: 757.7316
  k1: 0.02668618
  k2: -0.04862175
  k3: 0.02767143
  k4: -0.00620934
  p1: -0.01046426
  p2: 0.00135946
  p3: 0.02410708
  p4: -0.00033444
  s1: -0.0256
  s2: 0.0001
  s3: -0.0175
  s4: -0.0009
  tilt_x: -0.0264
  tilt_y: 0.0225
  scale: 2.9587
  rotation: -0.9083

Best score: 0.3440: : 999iter [00:28, 39.61iter/s]
Best score: 0.3440: : 1000iter [00:28, 39.53iter/s]
Best score: 0.3440: : 1000iter [00:28, 39.53iter/s]
Best score: 0.3440: : 1001iter [00:28, 39.53iter/s]
Best score: 0.3440: : 1002iter [00:28, 39.53iter/s]
Best score: 0.3440: : 1003iter [00:28, 39.53iter/s]
Best score: 0.3440: : 1004iter [00:28, 27.83iter/s]
Best score: 0.3440: : 1004iter [00:28, 27.83iter/s]
Best score: 0.3440: : 1005iter [00:28, 27.83iter/s]
Best score: 0.3440: : 1006iter [00:28, 27.83iter/s]
Best score: 0.3440: : 1007iter [00:28, 27.83iter/s]
Best score: 0.3440: : 1008iter [00:28, 27.83iter/s]
Best score: 0.3440: : 1009iter [00:28, 30.92iter/s]
Best score: 0.3440: : 1009iter [00:28, 30.92iter/s]
Best score: 0.3440: : 1010iter [00:28, 30.92iter/s]
Best score: 0.3440: : 1011iter [00:28, 30.92iter/s]
Best score: 0.3440: : 1012iter [00:28, 30.92iter/s]
Best score: 0.3440: : 1013iter [00:28, 32.84iter/s]
Best score: 0.3440: : 1013iter [00:28, 32.84iter/s]
Best score: 0.3440: : 1014iter [00:29, 32.84iter/s]
Best score: 0.3440: : 1015iter [00:29, 32.84iter/s]
Best score: 0.3440: : 1016iter [00:29, 32.84iter/s]
Best score: 0.3440: : 1017iter [00:29, 32.84iter/s]
Best score: 0.3440: : 1018iter [00:29, 35.06iter/s]
Best score: 0.3440: : 1018iter [00:29, 35.06iter/s]
Best score: 0.3440: : 1019iter [00:29, 35.06iter/s]
Best score: 0.3440: : 1020iter [00:29, 35.06iter/s]
Best score: 0.3440: : 1021iter [00:29, 35.06iter/s]
Best score: 0.3440: : 1022iter [00:29, 36.21iter/s]
Best score: 0.3440: : 1022iter [00:29, 36.21iter/s]
Best score: 0.3440: : 1023iter [00:29, 36.21iter/s]
Best score: 0.3440: : 1024iter [00:29, 36.21iter/s]
Best score: 0.3440: : 1025iter [00:29, 36.21iter/s]
Best score: 0.3440: : 1026iter [00:29, 36.21iter/s]
Best score: 0.3440: : 1027iter [00:29, 37.57iter/s]
Best score: 0.3440: : 1027iter [00:29, 37.57iter/s]
Best score: 0.3440: : 1028iter [00:29, 37.57iter/s]
Best score: 0.3440: : 1029iter [00:29, 37.57iter/s]
Best score: 0.3440: : 1030iter [00:29, 37.57iter/s]
Best score: 0.3440: : 1031iter [00:29, 37.57iter/s]
Best score: 0.3440: : 1032iter [00:29, 38.38iter/s]
Best score: 0.3440: : 1032iter [00:29, 38.38iter/s]
Best score: 0.3440: : 1033iter [00:29, 38.38iter/s]
Best score: 0.3440: : 1034iter [00:29, 38.38iter/s]
Best score: 0.3440: : 1035iter [00:29, 38.38iter/s]
Best score: 0.3440: : 1036iter [00:29, 38.38iter/s]
Best score: 0.3440: : 1037iter [00:29, 39.02iter/s]
Best score: 0.3440: : 1037iter [00:29, 39.02iter/s]
Best score: 0.3440: : 1038iter [00:29, 39.02iter/s]
Best score: 0.3440: : 1039iter [00:29, 39.02iter/s]
Best score: 0.3440: : 1040iter [00:29, 39.02iter/s]
Best score: 0.3440: : 1041iter [00:29, 39.20iter/s]
Best score: 0.3440: : 1041iter [00:29, 39.20iter/s]
Best score: 0.3440: : 1042iter [00:29, 39.20iter/s]
Best score: 0.3440: : 1043iter [00:29, 39.20iter/s]
Best score: 0.3440: : 1044iter [00:29, 39.20iter/s]
Best score: 0.3440: : 1045iter [00:29, 39.20iter/s]
Best score: 0.3440: : 1046iter [00:29, 39.75iter/s]
Best score: 0.3440: : 1046iter [00:29, 39.75iter/s]
Best score: 0.3440: : 1047iter [00:29, 39.75iter/s]
Best score: 0.3440: : 1048iter [00:29, 39.75iter/s]
[ITER 1050] Best: 0.3439
[PARAMS] Current best parameters:
  fx: 537.7207
  fy: 537.9295
  px: 974.7547
  py: 757.9401
  k1: 0.02830544
  k2: -0.04994245
  k3: 0.02816700
  k4: -0.00627185
  p1: -0.01035892
  p2: 0.00132140
  p3: 0.02440346
  p4: -0.00050277
  s1: -0.0261
  s2: 0.0003
  s3: -0.0177
  s4: -0.0007
  tilt_x: -0.0256
  tilt_y: 0.0233
  scale: 2.9573
  rotation: -0.9053

Best score: 0.3439: : 1049iter [00:29, 39.75iter/s]
Best score: 0.3439: : 1050iter [00:29, 39.75iter/s]
Best score: 0.3439: : 1051iter [00:29, 33.42iter/s]
Best score: 0.3439: : 1051iter [00:30, 33.42iter/s]
Best score: 0.3439: : 1052iter [00:30, 33.42iter/s]
Best score: 0.3439: : 1053iter [00:30, 33.42iter/s]
Best score: 0.3439: : 1054iter [00:30, 33.42iter/s]
Best score: 0.3439: : 1055iter [00:30, 33.42iter/s]
Best score: 0.3439: : 1056iter [00:30, 35.30iter/s]
Best score: 0.3439: : 1056iter [00:30, 35.30iter/s]
Best score: 0.3439: : 1057iter [00:30, 35.30iter/s]
Best score: 0.3439: : 1058iter [00:30, 35.30iter/s]
Best score: 0.3438: : 1059iter [00:30, 35.30iter/s]
Best score: 0.3438: : 1060iter [00:30, 36.28iter/s]
Best score: 0.3438: : 1060iter [00:30, 36.28iter/s]
Best score: 0.3438: : 1061iter [00:30, 36.28iter/s]
Best score: 0.3438: : 1062iter [00:30, 36.28iter/s]
Best score: 0.3438: : 1063iter [00:30, 36.28iter/s]
Best score: 0.3438: : 1064iter [00:30, 36.28iter/s]
Best score: 0.3438: : 1065iter [00:30, 37.56iter/s]
Best score: 0.3438: : 1065iter [00:30, 37.56iter/s]
Best score: 0.3438: : 1066iter [00:30, 37.56iter/s]
Best score: 0.3438: : 1067iter [00:30, 37.56iter/s]
Best score: 0.3438: : 1068iter [00:30, 37.56iter/s]
Best score: 0.3436: : 1069iter [00:30, 37.56iter/s]
Best score: 0.3436: : 1070iter [00:30, 38.10iter/s]
Best score: 0.3436: : 1070iter [00:30, 38.10iter/s]
Best score: 0.3436: : 1071iter [00:30, 38.10iter/s]
Best score: 0.3436: : 1072iter [00:30, 38.10iter/s]
Best score: 0.3436: : 1073iter [00:30, 38.10iter/s]
Best score: 0.3436: : 1074iter [00:30, 38.10iter/s]
Best score: 0.3436: : 1075iter [00:30, 38.74iter/s]
Best score: 0.3436: : 1075iter [00:30, 38.74iter/s]
Best score: 0.3436: : 1076iter [00:30, 38.74iter/s]
Best score: 0.3436: : 1077iter [00:30, 38.74iter/s]
Best score: 0.3436: : 1078iter [00:30, 38.74iter/s]
Best score: 0.3436: : 1079iter [00:30, 38.74iter/s]
Best score: 0.3436: : 1080iter [00:30, 39.06iter/s]
Best score: 0.3436: : 1080iter [00:30, 39.06iter/s]
Best score: 0.3436: : 1081iter [00:30, 39.06iter/s]
Best score: 0.3436: : 1082iter [00:30, 39.06iter/s]
Best score: 0.3436: : 1083iter [00:30, 39.06iter/s]
Best score: 0.3436: : 1084iter [00:30, 39.06iter/s]
Best score: 0.3436: : 1085iter [00:30, 39.67iter/s]
Best score: 0.3436: : 1085iter [00:30, 39.67iter/s]
Best score: 0.3436: : 1086iter [00:30, 39.67iter/s]
Best score: 0.3436: : 1087iter [00:30, 39.67iter/s]
Best score: 0.3436: : 1088iter [00:30, 39.67iter/s]
Best score: 0.3436: : 1089iter [00:30, 39.67iter/s]
Best score: 0.3436: : 1090iter [00:30, 39.81iter/s]
Best score: 0.3436: : 1090iter [00:30, 39.81iter/s]
Best score: 0.3436: : 1091iter [00:30, 39.81iter/s]
Best score: 0.3436: : 1092iter [00:31, 39.81iter/s]
Best score: 0.3436: : 1093iter [00:31, 39.81iter/s]
Best score: 0.3436: : 1094iter [00:31, 39.81iter/s]
Best score: 0.3436: : 1095iter [00:31, 39.98iter/s]
Best score: 0.3436: : 1095iter [00:31, 39.98iter/s]
Best score: 0.3436: : 1096iter [00:31, 39.98iter/s]
Best score: 0.3436: : 1097iter [00:31, 39.98iter/s]
Best score: 0.3436: : 1098iter [00:31, 39.98iter/s]
[ITER 1100] Best: 0.3436
[PARAMS] Current best parameters:
  fx: 538.2168
  fy: 538.4416
  px: 975.0519
  py: 757.7470
  k1: 0.02879470
  k2: -0.05145554
  k3: 0.02896249
  k4: -0.00639931
  p1: -0.01014785
  p2: 0.00183762
  p3: 0.02468474
  p4: -0.00076994
  s1: -0.0267
  s2: 0.0006
  s3: -0.0185
  s4: -0.0004
  tilt_x: -0.0269
  tilt_y: 0.0229
  scale: 2.9564
  rotation: -0.9097

Best score: 0.3436: : 1099iter [00:31, 39.98iter/s]
Best score: 0.3436: : 1100iter [00:31, 39.94iter/s]
Best score: 0.3436: : 1100iter [00:31, 35.17iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.343572 -> 0.343530

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,855 → 25,855 within 3500mm ring
  Ring polish improved score: 0.343530 -> 0.342755
  Success: True
  Tangential distortion: p1=-0.010147, p2=0.001837, p3=0.024684, p4=-0.000771
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.3428
[✅] Parameters copied to: /working/MDC051_W028_2025-08-28/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 538.2168371816808
    fy: 538.4415952381662
    px: 975.0518521440918
    py: 757.7469967162407
    k1: 0.02879542041600111
    k2: -0.05145479163107247
    k3: 0.02896237597857711
    k4: -0.006399421736650392
    p1: -0.010146793296536009
    p2: 0.0018368976739380435
    p3: 0.024684268213425387
    p4: -0.0007714167958306054
    scale: 2.956382074808699
    rotation: -0.9096596708710017
    mmpp: 1.2762454951373325

[🔄] Converting 421 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 1000:   0%|          | 0/224 [00:00<?, ?frame/s]

   🔄 1000:   8%|▊         | 19/224 [00:00<00:01, 180.61frame/s]

   🔄 1000:  17%|█▋        | 38/224 [00:00<00:01, 180.27frame/s]

   🔄 1000:  25%|██▌       | 57/224 [00:00<00:00, 179.91frame/s]

   🔄 1000:  33%|███▎      | 75/224 [00:00<00:00, 179.04frame/s]

   🔄 1000:  42%|████▏     | 93/224 [00:00<00:00, 177.63frame/s]

   🔄 1000:  50%|████▉     | 111/224 [00:00<00:00, 176.66frame/s]

   🔄 1000:  58%|█████▊    | 129/224 [00:00<00:00, 176.38frame/s]

   🔄 1000:  66%|██████▌   | 147/224 [00:00<00:00, 175.91frame/s]

   🔄 1000:  74%|███████▎  | 165/224 [00:00<00:00, 176.61frame/s]

   🔄 1000:  82%|████████▏ | 183/224 [00:01<00:00, 177.53frame/s]

   🔄 1000:  90%|████████▉ | 201/224 [00:01<00:00, 177.99frame/s]

   🔄 1000:  98%|█████████▊| 219/224 [00:01<00:00, 178.05frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.31s/rig]

   🔄 500:   0%|          | 0/197 [00:00<?, ?frame/s]

   🔄 500:   9%|▉         | 18/197 [00:00<00:00, 179.33frame/s]

   🔄 500:  19%|█▉        | 37/197 [00:00<00:00, 179.80frame/s]

   🔄 500:  28%|██▊       | 56/197 [00:00<00:00, 180.32frame/s]

   🔄 500:  38%|███▊      | 75/197 [00:00<00:00, 179.93frame/s]

   🔄 500:  48%|████▊     | 94/197 [00:00<00:00, 180.60frame/s]

   🔄 500:  57%|█████▋    | 113/197 [00:00<00:00, 181.26frame/s]

   🔄 500:  67%|██████▋   | 132/197 [00:00<00:00, 180.89frame/s]

   🔄 500:  77%|███████▋  | 151/197 [00:00<00:00, 180.95frame/s]

   🔄 500:  86%|████████▋ | 170/197 [00:00<00:00, 180.83frame/s]

   🔄 500:  96%|█████████▌| 189/197 [00:01<00:00, 180.54frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.09s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.12s/rig]
[✅] Saved PTDs: /working/MDC051_W028_2025-08-28/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, 177.75frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 178.02frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 175.31frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 174.86frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 176.19frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:01<00:01,  1.00s/rig]

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 179.15frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 178.54frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 178.89frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 178.51frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 178.46frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.02s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:02<00:00,  1.02s/rig]
[✅] Saved PTDs: /working/MDC051_W028_2025-08-28/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%|▎         | 88927/3080448 [00:00<00:03, 889264.49px/s]
📤 Writing LUT:   6%|▌         | 185096/3080448 [00:00<00:03, 931864.22px/s]
📤 Writing LUT:   9%|▉         | 279213/3080448 [00:00<00:02, 936112.81px/s]
📤 Writing LUT:  12%|█▏        | 372825/3080448 [00:00<00:02, 916605.70px/s]
📤 Writing LUT:  15%|█▌        | 464546/3080448 [00:00<00:02, 908955.41px/s]
📤 Writing LUT:  18%|█▊        | 555774/3080448 [00:00<00:02, 910068.88px/s]
📤 Writing LUT:  21%|██        | 646809/3080448 [00:00<00:02, 909726.97px/s]
📤 Writing LUT:  24%|██▍       | 737801/3080448 [00:00<00:02, 905652.00px/s]
📤 Writing LUT:  27%|██▋       | 828380/3080448 [00:00<00:02, 905207.82px/s]
📤 Writing LUT:  30%|██▉       | 921305/3080448 [00:01<00:02, 912584.60px/s]
📤 Writing LUT:  33%|███▎      | 1014860/3080448 [00:01<00:02, 919581.94px/s]
📤 Writing LUT:  36%|███▌      | 1108239/3080448 [00:01<00:02, 923891.42px/s]
📤 Writing LUT:  39%|███▉      | 1200637/3080448 [00:01<00:02, 923328.13px/s]
📤 Writing LUT:  42%|████▏     | 1292976/3080448 [00:01<00:01, 919718.89px/s]
📤 Writing LUT:  45%|████▍     | 1384955/3080448 [00:01<00:01, 919007.05px/s]
📤 Writing LUT:  48%|████▊     | 1479192/3080448 [00:01<00:01, 926015.16px/s]
📤 Writing LUT:  51%|█████     | 1571800/3080448 [00:01<00:01, 925913.68px/s]
📤 Writing LUT:  54%|█████▍    | 1666398/3080448 [00:01<00:01, 931932.46px/s]
📤 Writing LUT:  57%|█████▋    | 1759595/3080448 [00:01<00:01, 930868.80px/s]
📤 Writing LUT:  60%|██████    | 1852685/3080448 [00:02<00:01, 926360.19px/s]
📤 Writing LUT:  63%|██████▎   | 1946690/3080448 [00:02<00:01, 930450.28px/s]
📤 Writing LUT:  66%|██████▌   | 2039742/3080448 [00:02<00:01, 927167.55px/s]
📤 Writing LUT:  69%|██████▉   | 2132465/3080448 [00:02<00:01, 924596.41px/s]
📤 Writing LUT:  72%|███████▏  | 2227865/3080448 [00:02<00:00, 933372.32px/s]
📤 Writing LUT:  75%|███████▌  | 2321211/3080448 [00:02<00:00, 932235.41px/s]
📤 Writing LUT:  78%|███████▊  | 2414861/3080448 [00:02<00:00, 933509.42px/s]
📤 Writing LUT:  81%|████████▏ | 2508366/3080448 [00:02<00:00, 933970.10px/s]
📤 Writing LUT:  84%|████████▍ | 2601767/3080448 [00:02<00:00, 928465.55px/s]
📤 Writing LUT:  87%|████████▋ | 2694623/3080448 [00:02<00:00, 925698.88px/s]
📤 Writing LUT:  90%|█████████ | 2787200/3080448 [00:03<00:00, 924529.10px/s]
📤 Writing LUT:  94%|█████████▎| 2882004/3080448 [00:03<00:00, 931545.53px/s]
📤 Writing LUT:  97%|█████████▋| 2975166/3080448 [00:03<00:00, 931160.55px/s]
📤 Writing LUT: 100%|█████████▉| 3069515/3080448 [00:03<00:00, 934848.81px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 924754.05px/s]
[✅] LUT written to: /working/MDC051_W028_2025-08-28/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC051_W028_2025-08-28' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC051_W028_2025-08-28/calibration/MDC051_W028_2025-08-28_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%|          | 0/224 [00:00<?, ?frame/s]

   🔥 1000:   4%|▍         | 9/224 [00:00<00:02, 85.74frame/s]

   🔥 1000:   8%|▊         | 18/224 [00:00<00:02, 87.72frame/s]

   🔥 1000:  12%|█▏        | 27/224 [00:00<00:02, 87.92frame/s]

   🔥 1000:  16%|█▌        | 36/224 [00:00<00:02, 86.91frame/s]

   🔥 1000:  20%|██        | 45/224 [00:00<00:02, 85.48frame/s]

   🔥 1000:  24%|██▍       | 54/224 [00:00<00:02, 84.48frame/s]

   🔥 1000:  28%|██▊       | 63/224 [00:00<00:01, 84.35frame/s]

   🔥 1000:  32%|███▏      | 72/224 [00:00<00:01, 82.98frame/s]

   🔥 1000:  36%|███▌      | 81/224 [00:00<00:01, 82.62frame/s]

   🔥 1000:  40%|████      | 90/224 [00:01<00:01, 83.14frame/s]

   🔥 1000:  44%|████▍     | 99/224 [00:01<00:01, 82.95frame/s]

   🔥 1000:  48%|████▊     | 108/224 [00:01<00:01, 82.26frame/s]

   🔥 1000:  52%|█████▏    | 117/224 [00:01<00:01, 83.61frame/s]

   🔥 1000:  56%|█████▋    | 126/224 [00:01<00:01, 84.38frame/s]

   🔥 1000:  60%|██████    | 135/224 [00:01<00:01, 83.73frame/s]

   🔥 1000:  64%|██████▍   | 144/224 [00:01<00:00, 82.63frame/s]

   🔥 1000:  68%|██████▊   | 153/224 [00:01<00:00, 81.50frame/s]

   🔥 1000:  72%|███████▏  | 162/224 [00:01<00:00, 81.24frame/s]

   🔥 1000:  76%|███████▋  | 171/224 [00:02<00:00, 81.76frame/s]

   🔥 1000:  80%|████████  | 180/224 [00:02<00:00, 82.74frame/s]

   🔥 1000:  84%|████████▍ | 189/224 [00:02<00:00, 84.78frame/s]

   🔥 1000:  88%|████████▊ | 198/224 [00:02<00:00, 84.55frame/s]

   🔥 1000:  92%|█████████▏| 207/224 [00:02<00:00, 85.43frame/s]

   🔥 1000:  96%|█████████▋| 216/224 [00:02<00:00, 86.37frame/s]

                                                                [✅] 1000: /working/MDC051_W028_2025-08-28/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC051_W028_2025-08-28/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:05<00:05,  5.58s/rig]

   🔥 500:   0%|          | 0/197 [00:00<?, ?frame/s]

   🔥 500:   4%|▍         | 8/197 [00:00<00:02, 75.12frame/s]

   🔥 500:   8%|▊         | 16/197 [00:00<00:02, 76.76frame/s]

   🔥 500:  12%|█▏        | 24/197 [00:00<00:02, 76.94frame/s]

   🔥 500:  16%|█▌        | 32/197 [00:00<00:02, 76.94frame/s]

   🔥 500:  20%|██        | 40/197 [00:00<00:02, 75.62frame/s]

   🔥 500:  24%|██▍       | 48/197 [00:00<00:01, 76.01frame/s]

   🔥 500:  28%|██▊       | 56/197 [00:00<00:01, 76.18frame/s]

   🔥 500:  32%|███▏      | 64/197 [00:00<00:01, 75.59frame/s]

   🔥 500:  37%|███▋      | 72/197 [00:00<00:01, 75.07frame/s]

   🔥 500:  41%|████      | 80/197 [00:01<00:01, 75.04frame/s]

   🔥 500:  45%|████▍     | 88/197 [00:01<00:01, 75.38frame/s]

   🔥 500:  49%|████▊     | 96/197 [00:01<00:01, 75.58frame/s]

   🔥 500:  53%|█████▎    | 104/197 [00:01<00:01, 75.93frame/s]

   🔥 500:  57%|█████▋    | 112/197 [00:01<00:01, 76.16frame/s]

   🔥 500:  61%|██████    | 120/197 [00:01<00:01, 75.99frame/s]

   🔥 500:  65%|██████▍   | 128/197 [00:01<00:00, 75.49frame/s]

   🔥 500:  69%|██████▉   | 136/197 [00:01<00:00, 75.70frame/s]

   🔥 500:  73%|███████▎  | 144/197 [00:01<00:00, 75.50frame/s]

   🔥 500:  77%|███████▋  | 152/197 [00:02<00:00, 75.19frame/s]

   🔥 500:  81%|████████  | 160/197 [00:02<00:00, 75.09frame/s]

   🔥 500:  85%|████████▌ | 168/197 [00:02<00:00, 75.43frame/s]

   🔥 500:  89%|████████▉ | 176/197 [00:02<00:00, 75.55frame/s]

   🔥 500:  93%|█████████▎| 184/197 [00:02<00:00, 76.10frame/s]

   🔥 500:  97%|█████████▋| 192/197 [00:02<00:00, 76.26frame/s]

                                                               [✅] 500: /working/MDC051_W028_2025-08-28/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC051_W028_2025-08-28/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.49s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:09<00:00,  4.66s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC051_W028_2025-08-28/calibration/debug/5_heatmap
[🏁] Finished processing MDC051_W028_2025-08-28


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