2026-05-17T22:55:41 - INFO: Slick 0.0.8a11

🟢 Starting Calibration Pipeline

=== Processing MDC103_W050_2026-03-10 ===
2026-05-17T22:55:41 - INFO: Camera ID: 15029790
2026-05-17T22:55:41 - INFO: Wand ID: 
2026-05-17T22:55:41 - INFO: Recorded At: 2026-03-10T16:06:14Z

-------------------------------------------------------------------
                        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 1335 stationary frames, 232 remain


→ 500 (232 images) Phase 1:   0%|          | 0/232 [00:00<?, ?it/s]

→ 500 (232 images) Phase 1:   0%|          | 1/232 [00:00<02:10,  1.77it/s]

→ 500 (232 images) Phase 1:   7%|▋         | 17/232 [00:00<00:06, 33.69it/s]

→ 500 (232 images) Phase 1:  14%|█▍        | 33/232 [00:01<00:05, 37.84it/s]

→ 500 (232 images) Phase 1:  19%|█▉        | 44/232 [00:01<00:03, 49.30it/s]

→ 500 (232 images) Phase 1:  28%|██▊       | 65/232 [00:01<00:03, 54.75it/s]

→ 500 (232 images) Phase 1:  33%|███▎      | 77/232 [00:01<00:02, 64.51it/s]

→ 500 (232 images) Phase 1:  39%|███▉      | 91/232 [00:01<00:01, 77.06it/s]

→ 500 (232 images) Phase 1:  44%|████▍     | 102/232 [00:01<00:02, 62.26it/s]

→ 500 (232 images) Phase 1:  49%|████▊     | 113/232 [00:02<00:01, 70.50it/s]

→ 500 (232 images) Phase 1:  55%|█████▌    | 128/232 [00:02<00:01, 83.19it/s]

→ 500 (232 images) Phase 1:  60%|█████▉    | 139/232 [00:02<00:01, 67.21it/s]

→ 500 (232 images) Phase 1:  64%|██████▍   | 148/232 [00:02<00:01, 68.21it/s]

→ 500 (232 images) Phase 1:  69%|██████▉   | 161/232 [00:02<00:01, 64.31it/s]

→ 500 (232 images) Phase 1:  74%|███████▍  | 172/232 [00:02<00:00, 72.06it/s]

→ 500 (232 images) Phase 1:  78%|███████▊  | 181/232 [00:03<00:00, 74.99it/s]

→ 500 (232 images) Phase 1:  83%|████████▎ | 193/232 [00:03<00:00, 65.76it/s]

→ 500 (232 images) Phase 1:  88%|████████▊ | 204/232 [00:03<00:00, 74.52it/s]

→ 500 (232 images) Phase 1:  94%|█████████▍| 218/232 [00:03<00:00, 88.74it/s]

→ 500 (232 images) Phase 1:  99%|█████████▊| 229/232 [00:03<00:00, 81.92it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/232 [00:00<?, ?it/s]

→ 500 Phase 2: corners:   0%|          | 1/232 [00:00<00:38,  5.98it/s]

→ 500 Phase 2: corners:   6%|▌         | 13/232 [00:00<00:03, 57.24it/s]

→ 500 Phase 2: corners:  11%|█         | 25/232 [00:00<00:02, 78.37it/s]

→ 500 Phase 2: corners:  16%|█▌        | 36/232 [00:00<00:02, 82.81it/s]

→ 500 Phase 2: corners:  24%|██▍       | 56/232 [00:00<00:01, 117.06it/s]

→ 500 Phase 2: corners:  30%|███       | 70/232 [00:00<00:01, 123.30it/s]

→ 500 Phase 2: corners:  36%|███▌      | 83/232 [00:00<00:01, 121.17it/s]

→ 500 Phase 2: corners:  43%|████▎     | 100/232 [00:00<00:00, 135.24it/s]

→ 500 Phase 2: corners:  49%|████▉     | 114/232 [00:01<00:00, 130.96it/s]

→ 500 Phase 2: corners:  56%|█████▌    | 129/232 [00:01<00:00, 134.66it/s]

→ 500 Phase 2: corners:  62%|██████▏   | 143/232 [00:01<00:00, 133.43it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 157/232 [00:01<00:00, 132.27it/s]

→ 500 Phase 2: corners:  74%|███████▎  | 171/232 [00:01<00:00, 128.87it/s]

→ 500 Phase 2: corners:  79%|███████▉  | 184/232 [00:01<00:00, 128.20it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 204/232 [00:01<00:00, 148.50it/s]

                                                                          [PASS2] Frame 0000428: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000429: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000430: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000431: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000430: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 2 frames fixed, 2 untrusted, 232 total


→ 500 Phase 3: finalize:   0%|          | 0/232 [00:00<?, ?it/s]

→ 500 Phase 3: finalize:   2%|▏         | 5/232 [00:00<00:04, 47.27it/s]

→ 500 Phase 3: finalize:   5%|▍         | 11/232 [00:00<00:04, 50.02it/s]

→ 500 Phase 3: finalize:   7%|▋         | 17/232 [00:00<00:04, 51.16it/s]

→ 500 Phase 3: finalize:  10%|▉         | 23/232 [00:00<00:04, 51.78it/s]

→ 500 Phase 3: finalize:  12%|█▎        | 29/232 [00:00<00:03, 52.03it/s]

→ 500 Phase 3: finalize:  15%|█▌        | 35/232 [00:00<00:03, 52.28it/s]

→ 500 Phase 3: finalize:  18%|█▊        | 41/232 [00:00<00:03, 52.47it/s]

→ 500 Phase 3: finalize:  20%|██        | 47/232 [00:00<00:03, 52.55it/s]

→ 500 Phase 3: finalize:  23%|██▎       | 53/232 [00:01<00:03, 52.54it/s]

→ 500 Phase 3: finalize:  25%|██▌       | 59/232 [00:01<00:03, 52.60it/s]

→ 500 Phase 3: finalize:  28%|██▊       | 65/232 [00:01<00:03, 52.26it/s]

→ 500 Phase 3: finalize:  31%|███       | 71/232 [00:01<00:03, 52.21it/s]

→ 500 Phase 3: finalize:  33%|███▎      | 77/232 [00:01<00:02, 52.14it/s]

→ 500 Phase 3: finalize:  36%|███▌      | 83/232 [00:01<00:02, 51.28it/s]

→ 500 Phase 3: finalize:  38%|███▊      | 89/232 [00:01<00:02, 51.43it/s]

→ 500 Phase 3: finalize:  41%|████      | 95/232 [00:01<00:02, 51.81it/s]

→ 500 Phase 3: finalize:  44%|████▎     | 101/232 [00:01<00:02, 52.12it/s]

→ 500 Phase 3: finalize:  46%|████▌     | 107/232 [00:02<00:02, 52.40it/s]

→ 500 Phase 3: finalize:  49%|████▊     | 113/232 [00:02<00:02, 52.49it/s]

→ 500 Phase 3: finalize:  51%|█████▏    | 119/232 [00:02<00:02, 52.63it/s]

→ 500 Phase 3: finalize:  54%|█████▍    | 125/232 [00:02<00:02, 52.67it/s]

→ 500 Phase 3: finalize:  56%|█████▋    | 131/232 [00:02<00:01, 52.76it/s]

→ 500 Phase 3: finalize:  59%|█████▉    | 137/232 [00:02<00:01, 52.41it/s]

→ 500 Phase 3: finalize:  62%|██████▏   | 143/232 [00:02<00:01, 51.87it/s]

→ 500 Phase 3: finalize:  64%|██████▍   | 149/232 [00:02<00:01, 51.72it/s]

→ 500 Phase 3: finalize:  67%|██████▋   | 155/232 [00:02<00:01, 51.71it/s]

→ 500 Phase 3: finalize:  69%|██████▉   | 161/232 [00:03<00:01, 51.33it/s]

→ 500 Phase 3: finalize:  72%|███████▏  | 167/232 [00:03<00:01, 51.56it/s]

→ 500 Phase 3: finalize:  75%|███████▍  | 173/232 [00:03<00:01, 51.39it/s]

→ 500 Phase 3: finalize:  77%|███████▋  | 179/232 [00:03<00:01, 51.68it/s]

→ 500 Phase 3: finalize:  80%|███████▉  | 185/232 [00:03<00:00, 51.93it/s]

→ 500 Phase 3: finalize:  82%|████████▏ | 191/232 [00:03<00:00, 52.17it/s]

→ 500 Phase 3: finalize:  85%|████████▍ | 197/232 [00:03<00:00, 52.51it/s]

→ 500 Phase 3: finalize:  88%|████████▊ | 203/232 [00:03<00:00, 52.67it/s]

→ 500 Phase 3: finalize:  90%|█████████ | 209/232 [00:04<00:00, 52.61it/s]

→ 500 Phase 3: finalize:  93%|█████████▎| 215/232 [00:04<00:00, 52.60it/s]

→ 500 Phase 3: finalize:  95%|█████████▌| 221/232 [00:04<00:00, 52.63it/s]

→ 500 Phase 3: finalize:  98%|█████████▊| 227/232 [00:04<00:00, 52.55it/s]

                                                                          
[TIMING] 500 — 232 frames
  Phase 1  radial detect   (parallel)  :   3.75s  (16.2 ms/frame)
  Phase 2a corner detect   (parallel)  :   1.75s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   0.21s
  Phase 3  finalize        (sequential):   4.45s  (19.2 ms/frame)
    decode + undistort                 :   2.38s  (10.3 ms/frame)
    corner apply + refinement          :   0.92s  (3.9 ms/frame)
    segment                            :   0.93s  (4.0 ms/frame)
    smooth                             :   0.15s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  10.16s


Processing Rigs:  50%|█████     | 1/2 [00:17<00:17, 17.03s/it][INFO] Filtered 1174 stationary frames, 232 remain


→ 1000 (232 images) Phase 1:   0%|          | 0/232 [00:00<?, ?it/s]

→ 1000 (232 images) Phase 1:   0%|          | 1/232 [00:00<02:26,  1.58it/s]

→ 1000 (232 images) Phase 1:   8%|▊         | 19/232 [00:00<00:06, 32.85it/s]

→ 1000 (232 images) Phase 1:  14%|█▍        | 33/232 [00:01<00:05, 37.76it/s]

→ 1000 (232 images) Phase 1:  21%|██        | 49/232 [00:01<00:03, 57.52it/s]

→ 1000 (232 images) Phase 1:  27%|██▋       | 63/232 [00:01<00:02, 72.86it/s]

→ 1000 (232 images) Phase 1:  32%|███▏      | 75/232 [00:01<00:02, 57.74it/s]

→ 1000 (232 images) Phase 1:  40%|███▉      | 92/232 [00:01<00:01, 75.20it/s]

→ 1000 (232 images) Phase 1:  44%|████▍     | 103/232 [00:02<00:02, 58.20it/s]

→ 1000 (232 images) Phase 1:  52%|█████▏    | 121/232 [00:02<00:01, 77.92it/s]

→ 1000 (232 images) Phase 1:  57%|█████▋    | 133/232 [00:02<00:01, 60.29it/s]

→ 1000 (232 images) Phase 1:  66%|██████▌   | 152/232 [00:02<00:00, 80.35it/s]

→ 1000 (232 images) Phase 1:  71%|███████   | 164/232 [00:02<00:01, 62.18it/s]

→ 1000 (232 images) Phase 1:  78%|███████▊  | 181/232 [00:02<00:00, 79.05it/s]

→ 1000 (232 images) Phase 1:  83%|████████▎ | 193/232 [00:03<00:00, 64.40it/s]

→ 1000 (232 images) Phase 1:  90%|████████▉ | 208/232 [00:03<00:00, 78.21it/s]

→ 1000 (232 images) Phase 1:  97%|█████████▋| 224/232 [00:03<00:00, 93.40it/s]

                                                                              

→ 1000 Phase 2: corners:   0%|          | 0/232 [00:00<?, ?it/s]

→ 1000 Phase 2: corners:   0%|          | 1/232 [00:00<01:18,  2.95it/s]

→ 1000 Phase 2: corners:   6%|▌         | 14/232 [00:00<00:05, 39.97it/s]

→ 1000 Phase 2: corners:  12%|█▏        | 27/232 [00:00<00:03, 65.71it/s]

→ 1000 Phase 2: corners:  17%|█▋        | 40/232 [00:00<00:02, 82.46it/s]

→ 1000 Phase 2: corners:  23%|██▎       | 53/232 [00:00<00:01, 95.28it/s]

→ 1000 Phase 2: corners:  28%|██▊       | 66/232 [00:00<00:01, 102.38it/s]

→ 1000 Phase 2: corners:  34%|███▍      | 80/232 [00:00<00:01, 108.90it/s]

→ 1000 Phase 2: corners:  40%|████      | 93/232 [00:01<00:01, 112.55it/s]

→ 1000 Phase 2: corners:  46%|████▌     | 107/232 [00:01<00:01, 120.24it/s]

→ 1000 Phase 2: corners:  52%|█████▏    | 120/232 [00:01<00:01, 109.62it/s]

→ 1000 Phase 2: corners:  57%|█████▋    | 132/232 [00:01<00:00, 110.99it/s]

→ 1000 Phase 2: corners:  62%|██████▎   | 145/232 [00:01<00:00, 113.54it/s]

→ 1000 Phase 2: corners:  69%|██████▉   | 160/232 [00:01<00:00, 123.21it/s]

→ 1000 Phase 2: corners:  76%|███████▋  | 177/232 [00:01<00:00, 135.13it/s]

→ 1000 Phase 2: corners:  84%|████████▎ | 194/232 [00:01<00:00, 139.15it/s]

→ 1000 Phase 2: corners:  90%|█████████ | 209/232 [00:01<00:00, 139.47it/s]

                                                                           [PASS2] Frame 0000155: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000362: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000363: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000364: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000366: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000367: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000368: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000369: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000370: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000371: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000373: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000375: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000376: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000377: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000378: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000383: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000384: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000484: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000485: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000486: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000487: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000488: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000509: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000510: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000511: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000512: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000514: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000619: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000620: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000621: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001056: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001057: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001058: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001059: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001060: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001061: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001062: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001063: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001064: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001198: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001199: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001200: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001202: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001203: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001206: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001207: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001208: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001209: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001212: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001213: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001214: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001217: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001358: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001359: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001360: fixed corners {0, 2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001361: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001363: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001364: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001378: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001379: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001380: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001381: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001382: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001383: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000365: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000369: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000371: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000372: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000373: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000374: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000380: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000383: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000486: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000511: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000620: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001058: fixed corners {2, 3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001061: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001063: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001200: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001201: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001202: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001208: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001214: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001215: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001216: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001356: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001357: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001362: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001363: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001380: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001382: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000379: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000380: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001356: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001362: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 46 frames fixed, 38 untrusted, 232 total


→ 1000 Phase 3: finalize:   0%|          | 0/232 [00:00<?, ?it/s]

→ 1000 Phase 3: finalize:   3%|▎         | 6/232 [00:00<00:03, 58.56it/s]

→ 1000 Phase 3: finalize:   5%|▌         | 12/232 [00:00<00:03, 57.36it/s]

→ 1000 Phase 3: finalize:   8%|▊         | 18/232 [00:00<00:03, 56.33it/s]

→ 1000 Phase 3: finalize:  10%|█         | 24/232 [00:00<00:03, 56.21it/s]

→ 1000 Phase 3: finalize:  13%|█▎        | 30/232 [00:00<00:03, 56.05it/s]

→ 1000 Phase 3: finalize:  16%|█▌        | 36/232 [00:00<00:03, 56.48it/s]

→ 1000 Phase 3: finalize:  18%|█▊        | 42/232 [00:00<00:03, 56.66it/s]

→ 1000 Phase 3: finalize:  21%|██        | 48/232 [00:00<00:03, 56.85it/s]

→ 1000 Phase 3: finalize:  23%|██▎       | 54/232 [00:00<00:03, 57.15it/s]

→ 1000 Phase 3: finalize:  26%|██▌       | 60/232 [00:01<00:03, 57.01it/s]

→ 1000 Phase 3: finalize:  28%|██▊       | 66/232 [00:01<00:03, 55.23it/s]

→ 1000 Phase 3: finalize:  31%|███       | 72/232 [00:01<00:02, 53.74it/s]

→ 1000 Phase 3: finalize:  34%|███▎      | 78/232 [00:01<00:02, 52.61it/s]

→ 1000 Phase 3: finalize:  36%|███▌      | 84/232 [00:01<00:02, 51.27it/s]

→ 1000 Phase 3: finalize:  39%|███▉      | 90/232 [00:01<00:02, 49.70it/s]

→ 1000 Phase 3: finalize:  41%|████      | 95/232 [00:01<00:02, 48.89it/s]

→ 1000 Phase 3: finalize:  43%|████▎     | 100/232 [00:01<00:02, 48.08it/s]

→ 1000 Phase 3: finalize:  45%|████▌     | 105/232 [00:01<00:02, 47.59it/s]

→ 1000 Phase 3: finalize:  47%|████▋     | 110/232 [00:02<00:02, 47.24it/s]

→ 1000 Phase 3: finalize:  50%|████▉     | 115/232 [00:02<00:02, 47.15it/s]

→ 1000 Phase 3: finalize:  52%|█████▏    | 120/232 [00:02<00:02, 47.08it/s]

→ 1000 Phase 3: finalize:  54%|█████▍    | 125/232 [00:02<00:02, 47.09it/s]

→ 1000 Phase 3: finalize:  56%|█████▌    | 130/232 [00:02<00:02, 46.65it/s]

→ 1000 Phase 3: finalize:  58%|█████▊    | 135/232 [00:02<00:02, 46.73it/s]

→ 1000 Phase 3: finalize:  60%|██████    | 140/232 [00:02<00:01, 46.60it/s]

→ 1000 Phase 3: finalize:  62%|██████▎   | 145/232 [00:02<00:01, 46.84it/s]

→ 1000 Phase 3: finalize:  65%|██████▍   | 150/232 [00:02<00:01, 47.26it/s]

→ 1000 Phase 3: finalize:  67%|██████▋   | 155/232 [00:03<00:01, 48.05it/s]

→ 1000 Phase 3: finalize:  69%|██████▉   | 161/232 [00:03<00:01, 49.42it/s]

→ 1000 Phase 3: finalize:  72%|███████▏  | 167/232 [00:03<00:01, 50.28it/s]

→ 1000 Phase 3: finalize:  75%|███████▍  | 173/232 [00:03<00:01, 50.86it/s]

→ 1000 Phase 3: finalize:  77%|███████▋  | 179/232 [00:03<00:01, 51.47it/s]

→ 1000 Phase 3: finalize:  80%|███████▉  | 185/232 [00:03<00:00, 51.92it/s]

→ 1000 Phase 3: finalize:  82%|████████▏ | 191/232 [00:03<00:00, 52.20it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 197/232 [00:03<00:00, 52.47it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 203/232 [00:03<00:00, 52.74it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 209/232 [00:04<00:00, 53.01it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 215/232 [00:04<00:00, 52.96it/s]

→ 1000 Phase 3: finalize:  95%|█████████▌| 221/232 [00:04<00:00, 52.67it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 227/232 [00:04<00:00, 52.45it/s]

                                                                           
[TIMING] 1000 — 232 frames
  Phase 1  radial detect   (parallel)  :   3.69s  (15.9 ms/frame)
  Phase 2a corner detect   (parallel)  :   2.09s  (9.0 ms/frame)
  Phase 2b corner fix      (sequential):   1.61s
  Phase 3  finalize        (sequential):   4.51s  (19.4 ms/frame)
    decode + undistort                 :   2.32s  (10.0 ms/frame)
    corner apply + refinement          :   1.04s  (4.5 ms/frame)
    segment                            :   0.94s  (4.1 ms/frame)
    smooth                             :   0.14s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  11.90s


Processing Rigs: 100%|██████████| 2/2 [00:33<00:00, 16.72s/it]
Processing Rigs: 100%|██████████| 2/2 [00:33<00:00, 16.76s/it]
[💾] Saved RigFrameDataset to /working/MDC103_W050_2026-03-10/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 464 frames from 2 rigs to /working/MDC103_W050_2026-03-10/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC103_W050_2026-03-10/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 500: 100 frames selected from 232
[📋] 1000: 100 frames selected from 232
[💾] Saved RigFrameDataset to /working/MDC103_W050_2026-03-10/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC103_W050_2026-03-10/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<02:07,  3.27s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:06<02:02,  3.23s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:09<01:58,  3.21s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:12<01:55,  3.20s/it]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:16<01:51,  3.19s/it]
Computing max image from bright frames:  15%|█▌        | 6/40 [00:19<01:48,  3.19s/it]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:22<01:45,  3.19s/it]
Computing max image from bright frames:  20%|██        | 8/40 [00:25<01:45,  3.29s/it]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:29<01:41,  3.29s/it]
Computing max image from bright frames:  25%|██▌       | 10/40 [00:32<01:39,  3.31s/it]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:36<01:37,  3.35s/it]
Computing max image from bright frames:  30%|███       | 12/40 [00:39<01:34,  3.39s/it]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:42<01:30,  3.36s/it]
Computing max image from bright frames:  35%|███▌      | 14/40 [00:46<01:27,  3.36s/it]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:49<01:22,  3.30s/it]
Computing max image from bright frames:  40%|████      | 16/40 [00:52<01:18,  3.27s/it]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:55<01:14,  3.25s/it]
Computing max image from bright frames:  45%|████▌     | 18/40 [00:58<01:11,  3.24s/it]
Computing max image from bright frames:  48%|████▊     | 19/40 [01:02<01:07,  3.23s/it]
Computing max image from bright frames:  50%|█████     | 20/40 [01:05<01:04,  3.23s/it]
Computing max image from bright frames:  52%|█████▎    | 21/40 [01:08<01:00,  3.19s/it]
Computing max image from bright frames:  55%|█████▌    | 22/40 [01:11<00:57,  3.18s/it]
Computing max image from bright frames:  57%|█████▊    | 23/40 [01:14<00:53,  3.16s/it]
Computing max image from bright frames:  60%|██████    | 24/40 [01:17<00:50,  3.15s/it]
Computing max image from bright frames:  62%|██████▎   | 25/40 [01:20<00:47,  3.14s/it]
Computing max image from bright frames:  65%|██████▌   | 26/40 [01:24<00:43,  3.14s/it]
Computing max image from bright frames:  68%|██████▊   | 27/40 [01:27<00:40,  3.14s/it]
Computing max image from bright frames:  70%|███████   | 28/40 [01:30<00:37,  3.14s/it]
Computing max image from bright frames:  72%|███████▎  | 29/40 [01:33<00:35,  3.21s/it]
Computing max image from bright frames:  75%|███████▌  | 30/40 [01:37<00:32,  3.25s/it]
Computing max image from bright frames:  78%|███████▊  | 31/40 [01:40<00:29,  3.26s/it]
Computing max image from bright frames:  80%|████████  | 32/40 [01:43<00:26,  3.29s/it]
Computing max image from bright frames:  82%|████████▎ | 33/40 [01:47<00:23,  3.31s/it]
Computing max image from bright frames:  85%|████████▌ | 34/40 [01:50<00:20,  3.34s/it]
Computing max image from bright frames:  88%|████████▊ | 35/40 [01:53<00:16,  3.36s/it]
Computing max image from bright frames:  90%|█████████ | 36/40 [01:57<00:13,  3.29s/it]
Computing max image from bright frames:  92%|█████████▎| 37/40 [02:00<00:09,  3.24s/it]
Computing max image from bright frames:  95%|█████████▌| 38/40 [02:03<00:06,  3.21s/it]
Computing max image from bright frames:  98%|█████████▊| 39/40 [02:06<00:03,  3.19s/it]
Computing max image from bright frames: 100%|██████████| 40/40 [02:09<00:00,  3.10s/it]
                                                                                       
[✅] Casing detection complete: cx=1018.0, cy=744.0

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


  0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   1%|          | 1/100 [00:00<00:28,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   1%|          | 1/100 [00:00<00:28,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   2%|▏         | 2/100 [00:00<00:28,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   3%|▎         | 3/100 [00:00<00:27,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   4%|▍         | 4/100 [00:00<00:27,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   5%|▌         | 5/100 [00:00<00:27,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   6%|▌         | 6/100 [00:00<00:27,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   7%|▋         | 7/100 [00:00<00:26,  3.48it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   8%|▊         | 8/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   8%|▊         | 8/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):   9%|▉         | 9/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  10%|█         | 10/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  11%|█         | 11/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  12%|█▏        | 12/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  13%|█▎        | 13/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  14%|█▍        | 14/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  15%|█▌        | 15/100 [00:00<00:03, 25.06it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  16%|█▌        | 16/100 [00:00<00:02, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 328.283} (0.000):  16%|█▌        | 16/100 [00:00<00:02, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.036 | Best: {'fx': 519.192} (0.036):  17%|█▋        | 17/100 [00:00<00:02, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.065 | Best: {'fx': 533.333} (0.065):  18%|█▊        | 18/100 [00:00<00:02, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.064 | Best: {'fx': 533.333} (0.065):  19%|█▉        | 19/100 [00:00<00:02, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.041 | Best: {'fx': 533.333} (0.065):  20%|██        | 20/100 [00:00<00:02, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.030 | Best: {'fx': 533.333} (0.065):  21%|██        | 21/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.019 | Best: {'fx': 533.333} (0.065):  22%|██▏       | 22/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.065):  23%|██▎       | 23/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.021 | Best: {'fx': 533.333} (0.065):  24%|██▍       | 24/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.017 | Best: {'fx': 533.333} (0.065):  25%|██▌       | 25/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.016 | Best: {'fx': 533.333} (0.065):  26%|██▌       | 26/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  27%|██▋       | 27/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  28%|██▊       | 28/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  29%|██▉       | 29/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.015 | Best: {'fx': 533.333} (0.065):  30%|███       | 30/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  31%|███       | 31/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  32%|███▏      | 32/100 [00:00<00:01, 39.99it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  33%|███▎      | 33/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  33%|███▎      | 33/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.065):  34%|███▍      | 34/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  35%|███▌      | 35/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.014 | Best: {'fx': 533.333} (0.065):  36%|███▌      | 36/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  37%|███▋      | 37/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  38%|███▊      | 38/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.013 | Best: {'fx': 533.333} (0.065):  39%|███▉      | 39/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  40%|████      | 40/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.007 | Best: {'fx': 533.333} (0.065):  41%|████      | 41/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.012 | Best: {'fx': 533.333} (0.065):  42%|████▏     | 42/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  43%|████▎     | 43/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.025 | Best: {'fx': 533.333} (0.065):  44%|████▍     | 44/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  45%|████▌     | 45/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.000 | Best: {'fx': 533.333} (0.065):  46%|████▌     | 46/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  47%|████▋     | 47/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  48%|████▊     | 48/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  49%|████▉     | 49/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.065):  50%|█████     | 50/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.001 | Best: {'fx': 533.333} (0.065):  51%|█████     | 51/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  52%|█████▏    | 52/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.003 | Best: {'fx': 533.333} (0.065):  53%|█████▎    | 53/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.011 | Best: {'fx': 533.333} (0.065):  54%|█████▍    | 54/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  55%|█████▌    | 55/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  56%|█████▌    | 56/100 [00:00<00:00, 76.08it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  57%|█████▋    | 57/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  57%|█████▋    | 57/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  58%|█████▊    | 58/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  59%|█████▉    | 59/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  60%|██████    | 60/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  61%|██████    | 61/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  62%|██████▏   | 62/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  63%|██████▎   | 63/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  64%|██████▍   | 64/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.010 | Best: {'fx': 533.333} (0.065):  65%|██████▌   | 65/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  66%|██████▌   | 66/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  67%|██████▋   | 67/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  68%|██████▊   | 68/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  69%|██████▉   | 69/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  70%|███████   | 70/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  71%|███████   | 71/100 [00:00<00:00, 122.93it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  72%|███████▏  | 72/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  72%|███████▏  | 72/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  73%|███████▎  | 73/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  74%|███████▍  | 74/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  75%|███████▌  | 75/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  76%|███████▌  | 76/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  77%|███████▋  | 77/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  78%|███████▊  | 78/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  79%|███████▉  | 79/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  80%|████████  | 80/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  81%|████████  | 81/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  82%|████████▏ | 82/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  83%|████████▎ | 83/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  84%|████████▍ | 84/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  85%|████████▌ | 85/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  86%|████████▌ | 86/100 [00:00<00:00, 119.70it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  87%|████████▋ | 87/100 [00:00<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  87%|████████▋ | 87/100 [00:00<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  88%|████████▊ | 88/100 [00:00<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  89%|████████▉ | 89/100 [00:00<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  90%|█████████ | 90/100 [00:00<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  91%|█████████ | 91/100 [00:00<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  92%|█████████▏| 92/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  93%|█████████▎| 93/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  94%|█████████▍| 94/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  95%|█████████▌| 95/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  96%|█████████▌| 96/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  97%|█████████▋| 97/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  98%|█████████▊| 98/100 [00:01<00:00, 124.09it/s]
Brute | Start: {'fx': 500.0} (0.004) | Current: 0.009 | Best: {'fx': 533.333} (0.065):  99%|█████████▉| 99/100 [00:01<00:00, 124.09it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.015 | Best: {'fx': 508.333} (0.015):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.015 | Best: {'fx': 508.333} (0.015):   2%|▏         | 1/50 [00:00<00:06,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.017 | Best: {'fx': 509.354} (0.017):   2%|▏         | 1/50 [00:00<00:06,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.022 | Best: {'fx': 510.374} (0.022):   4%|▍         | 2/50 [00:00<00:06,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.022 | Best: {'fx': 511.395} (0.022):   6%|▌         | 3/50 [00:00<00:06,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 512.415} (0.025):   8%|▊         | 4/50 [00:00<00:06,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.028 | Best: {'fx': 514.456} (0.028):  10%|█         | 5/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.026 | Best: {'fx': 514.456} (0.028):  12%|█▏        | 6/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.035 | Best: {'fx': 518.537} (0.035):  14%|█▍        | 7/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 518.537} (0.035):  16%|█▌        | 8/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.031 | Best: {'fx': 518.537} (0.035):  18%|█▊        | 9/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.032 | Best: {'fx': 518.537} (0.035):  20%|██        | 10/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.040 | Best: {'fx': 520.578} (0.040):  22%|██▏       | 11/50 [00:00<00:05,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.037 | Best: {'fx': 520.578} (0.040):  24%|██▍       | 12/50 [00:00<00:04,  7.61it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.037 | Best: {'fx': 520.578} (0.040):  26%|██▌       | 13/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.042 | Best: {'fx': 521.599} (0.042):  26%|██▌       | 13/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.045 | Best: {'fx': 522.619} (0.045):  28%|██▊       | 14/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.051 | Best: {'fx': 523.639} (0.051):  30%|███       | 15/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.063 | Best: {'fx': 525.68} (0.063):  32%|███▏      | 16/50 [00:00<00:00, 63.76it/s] 
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.053 | Best: {'fx': 525.68} (0.063):  34%|███▍      | 17/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.075 | Best: {'fx': 527.721} (0.075):  36%|███▌      | 18/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.066 | Best: {'fx': 527.721} (0.075):  38%|███▊      | 19/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.074 | Best: {'fx': 527.721} (0.075):  40%|████      | 20/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.068 | Best: {'fx': 527.721} (0.075):  42%|████▏     | 21/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.073 | Best: {'fx': 527.721} (0.075):  44%|████▍     | 22/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.064 | Best: {'fx': 527.721} (0.075):  46%|████▌     | 23/50 [00:00<00:00, 63.76it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.064 | Best: {'fx': 527.721} (0.075):  48%|████▊     | 24/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.079 | Best: {'fx': 530.782} (0.079):  48%|████▊     | 24/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.067 | Best: {'fx': 530.782} (0.079):  50%|█████     | 25/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.053 | Best: {'fx': 530.782} (0.079):  52%|█████▏    | 26/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.046 | Best: {'fx': 530.782} (0.079):  54%|█████▍    | 27/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.054 | Best: {'fx': 530.782} (0.079):  56%|█████▌    | 28/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.042 | Best: {'fx': 530.782} (0.079):  58%|█████▊    | 29/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.044 | Best: {'fx': 530.782} (0.079):  60%|██████    | 30/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.039 | Best: {'fx': 530.782} (0.079):  62%|██████▏   | 31/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.049 | Best: {'fx': 530.782} (0.079):  64%|██████▍   | 32/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.038 | Best: {'fx': 530.782} (0.079):  66%|██████▌   | 33/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.035 | Best: {'fx': 530.782} (0.079):  68%|██████▊   | 34/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.035 | Best: {'fx': 530.782} (0.079):  70%|███████   | 35/50 [00:00<00:00, 82.25it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.035 | Best: {'fx': 530.782} (0.079):  72%|███████▏  | 36/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.033 | Best: {'fx': 530.782} (0.079):  72%|███████▏  | 36/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.031 | Best: {'fx': 530.782} (0.079):  74%|███████▍  | 37/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.032 | Best: {'fx': 530.782} (0.079):  76%|███████▌  | 38/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.029 | Best: {'fx': 530.782} (0.079):  78%|███████▊  | 39/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.030 | Best: {'fx': 530.782} (0.079):  80%|████████  | 40/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.028 | Best: {'fx': 530.782} (0.079):  82%|████████▏ | 41/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.028 | Best: {'fx': 530.782} (0.079):  84%|████████▍ | 42/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.024 | Best: {'fx': 530.782} (0.079):  86%|████████▌ | 43/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.026 | Best: {'fx': 530.782} (0.079):  88%|████████▊ | 44/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 530.782} (0.079):  90%|█████████ | 45/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.025 | Best: {'fx': 530.782} (0.079):  92%|█████████▏| 46/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.023 | Best: {'fx': 530.782} (0.079):  94%|█████████▍| 47/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.022 | Best: {'fx': 530.782} (0.079):  96%|█████████▌| 48/50 [00:00<00:00, 94.49it/s]
Brute | Start: {'fx': 533.333} (0.065) | Current: 0.024 | Best: {'fx': 530.782} (0.079):  98%|█████████▊| 49/50 [00:00<00:00, 94.49it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 530.7823129251701
    px: 1018.0
    py: 744.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


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

Initial score: 27.3310: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 27.3310, picklable_objective(x0) = 27.3877

Best score: 27.3877: : 0iter [00:01, ?iter/s] 
Best score: 27.3877: : 1iter [00:01,  1.87s/iter]
Best score: 27.3877: : 1iter [00:01,  1.87s/iter]
Best score: 27.3877: : 2iter [00:02,  1.87s/iter]
Best score: 27.3877: : 3iter [00:02,  1.83iter/s]
Best score: 27.3877: : 3iter [00:02,  1.83iter/s]
Best score: 27.3877: : 4iter [00:02,  1.83iter/s]
Best score: 27.3877: : 5iter [00:02,  1.83iter/s]
Best score: 27.3877: : 6iter [00:02,  1.83iter/s]
Best score: 27.3877: : 7iter [00:02,  5.18iter/s]
Best score: 27.3877: : 7iter [00:02,  5.18iter/s]
Best score: 27.3877: : 8iter [00:02,  5.18iter/s]
Best score: 27.3877: : 9iter [00:02,  5.18iter/s]
Best score: 27.3877: : 10iter [00:02,  5.18iter/s]
Best score: 27.3877: : 11iter [00:02,  9.04iter/s]
Best score: 27.3877: : 11iter [00:02,  9.04iter/s]
Best score: 27.3877: : 12iter [00:02,  9.04iter/s]
Best score: 27.3877: : 13iter [00:02,  9.04iter/s]
Best score: 27.3877: : 14iter [00:02,  9.04iter/s]
Best score: 27.3877: : 15iter [00:02, 13.24iter/s]
Best score: 27.3877: : 15iter [00:02, 13.24iter/s]
Best score: 27.3877: : 16iter [00:02, 13.24iter/s]
Best score: 27.3877: : 17iter [00:02, 13.24iter/s]
Best score: 27.3877: : 18iter [00:02, 13.24iter/s]
Best score: 27.3877: : 19iter [00:02, 17.56iter/s]
Best score: 27.3877: : 19iter [00:02, 17.56iter/s]
Best score: 27.3877: : 20iter [00:02, 17.56iter/s]
Best score: 27.3877: : 21iter [00:02, 17.56iter/s]
Best score: 27.3877: : 22iter [00:02, 17.56iter/s]
Best score: 27.3877: : 23iter [00:02, 21.71iter/s]
Best score: 27.3877: : 23iter [00:02, 21.71iter/s]
Best score: 27.3877: : 24iter [00:02, 21.71iter/s]
Best score: 27.3877: : 25iter [00:02, 21.71iter/s]
Best score: 27.3877: : 26iter [00:02, 21.71iter/s]
Best score: 27.3877: : 27iter [00:02, 25.55iter/s]
Best score: 27.3877: : 27iter [00:02, 25.55iter/s]
Best score: 27.3877: : 28iter [00:02, 25.55iter/s]
Best score: 27.3877: : 29iter [00:02, 25.55iter/s]
Best score: 27.3877: : 30iter [00:02, 25.55iter/s]
Best score: 27.3877: : 31iter [00:02, 28.69iter/s]
Best score: 27.3877: : 31iter [00:02, 28.69iter/s]
Best score: 27.3877: : 32iter [00:02, 28.69iter/s]
Best score: 27.3877: : 33iter [00:02, 28.69iter/s]
Best score: 27.3877: : 34iter [00:02, 28.69iter/s]
Best score: 27.3877: : 35iter [00:02, 31.42iter/s]
Best score: 27.3877: : 35iter [00:02, 31.42iter/s]
Best score: 27.3877: : 36iter [00:02, 31.42iter/s]
Best score: 27.3877: : 37iter [00:02, 31.42iter/s]
Best score: 27.3877: : 38iter [00:02, 31.42iter/s]
Best score: 27.3877: : 39iter [00:02, 33.55iter/s]
Best score: 27.3877: : 39iter [00:02, 33.55iter/s]
Best score: 27.3877: : 40iter [00:03, 33.55iter/s]
Best score: 27.3877: : 41iter [00:03, 33.55iter/s]
Best score: 27.3877: : 42iter [00:03, 33.55iter/s]
Best score: 27.3877: : 43iter [00:03, 34.97iter/s]
Best score: 27.3877: : 43iter [00:03, 34.97iter/s]
Best score: 27.3877: : 44iter [00:03, 34.97iter/s]
Best score: 27.3877: : 45iter [00:03, 34.97iter/s]
Best score: 27.3877: : 46iter [00:03, 34.97iter/s]
Best score: 27.3877: : 47iter [00:03, 36.21iter/s]
Best score: 27.3877: : 47iter [00:03, 36.21iter/s]
Best score: 27.3877: : 48iter [00:03, 36.21iter/s]
[ITER 50] Best: 27.3877
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 744.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 27.3877: : 49iter [00:03, 36.21iter/s]
Best score: 27.3877: : 50iter [00:03, 36.21iter/s]
Best score: 27.3877: : 51iter [00:03, 36.91iter/s]
Best score: 27.3877: : 51iter [00:03, 36.91iter/s]
Best score: 27.3877: : 52iter [00:03, 36.91iter/s]
Best score: 27.3877: : 53iter [00:03, 36.91iter/s]
Best score: 27.3877: : 54iter [00:03, 36.91iter/s]
Best score: 27.3877: : 55iter [00:03, 37.77iter/s]
Best score: 27.3877: : 55iter [00:03, 37.77iter/s]
Best score: 27.3877: : 56iter [00:03, 37.77iter/s]
Best score: 27.3877: : 57iter [00:03, 37.77iter/s]
Best score: 27.3877: : 58iter [00:03, 37.77iter/s]
Best score: 27.3877: : 59iter [00:03, 38.26iter/s]
Best score: 27.3877: : 59iter [00:03, 38.26iter/s]
Best score: 27.3877: : 60iter [00:03, 38.26iter/s]
Best score: 27.3877: : 61iter [00:03, 38.26iter/s]
Best score: 27.3877: : 62iter [00:03, 38.26iter/s]
Best score: 27.3877: : 63iter [00:03, 38.44iter/s]
Best score: 27.3877: : 63iter [00:03, 38.44iter/s]
Best score: 27.3877: : 64iter [00:03, 38.44iter/s]
Best score: 27.3877: : 65iter [00:03, 38.44iter/s]
Best score: 27.3877: : 66iter [00:03, 38.44iter/s]
Best score: 27.3877: : 67iter [00:03, 38.83iter/s]
Best score: 27.3877: : 67iter [00:03, 38.83iter/s]
Best score: 27.3877: : 68iter [00:03, 38.83iter/s]
Best score: 27.3877: : 69iter [00:03, 38.83iter/s]
Best score: 27.3877: : 70iter [00:03, 38.83iter/s]
Best score: 27.3877: : 71iter [00:03, 38.93iter/s]
Best score: 27.3877: : 71iter [00:03, 38.93iter/s]
Best score: 27.3877: : 72iter [00:03, 38.93iter/s]
Best score: 27.3877: : 73iter [00:03, 38.93iter/s]
Best score: 27.3877: : 74iter [00:03, 38.93iter/s]
Best score: 27.3877: : 75iter [00:03, 39.23iter/s]
Best score: 27.3877: : 75iter [00:03, 39.23iter/s]
Best score: 27.3877: : 76iter [00:03, 39.23iter/s]
Best score: 27.3877: : 77iter [00:03, 39.23iter/s]
Best score: 27.3877: : 78iter [00:03, 39.23iter/s]
Best score: 27.3877: : 79iter [00:03, 39.35iter/s]
Best score: 27.3877: : 79iter [00:03, 39.35iter/s]
Best score: 27.3877: : 80iter [00:04, 39.35iter/s]
Best score: 27.3877: : 81iter [00:04, 39.35iter/s]
Best score: 27.3877: : 82iter [00:04, 39.35iter/s]
Best score: 27.3877: : 83iter [00:04, 39.34iter/s]
Best score: 27.3877: : 83iter [00:04, 39.34iter/s]
Best score: 27.3877: : 84iter [00:04, 39.34iter/s]
Best score: 27.3877: : 85iter [00:04, 39.34iter/s]
Best score: 27.3877: : 86iter [00:04, 39.34iter/s]
Best score: 27.3877: : 87iter [00:04, 39.51iter/s]
Best score: 27.3877: : 87iter [00:04, 39.51iter/s]
Best score: 27.3877: : 88iter [00:04, 39.51iter/s]
Best score: 27.3877: : 89iter [00:04, 39.51iter/s]
Best score: 27.3877: : 90iter [00:04, 39.51iter/s]
Best score: 27.3877: : 91iter [00:04, 39.10iter/s]
Best score: 27.3877: : 91iter [00:04, 39.10iter/s]
Best score: 27.3877: : 92iter [00:04, 39.10iter/s]
Best score: 27.3877: : 93iter [00:04, 39.10iter/s]
Best score: 27.3877: : 94iter [00:04, 39.10iter/s]
Best score: 27.3877: : 95iter [00:04, 39.21iter/s]
Best score: 27.3877: : 95iter [00:04, 39.21iter/s]
Best score: 27.3877: : 96iter [00:04, 39.21iter/s]
Best score: 27.3877: : 97iter [00:04, 39.21iter/s]
Best score: 27.3877: : 98iter [00:04, 39.21iter/s]
Best score: 27.3877: : 99iter [00:04, 39.25iter/s]
[ITER 100] Best: 27.3877
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 744.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 27.3877: : 99iter [00:04, 39.25iter/s]
Best score: 27.3877: : 100iter [00:04, 39.25iter/s]
Best score: 27.3877: : 101iter [00:04, 39.25iter/s]
Best score: 27.3877: : 102iter [00:04, 39.25iter/s]
Best score: 27.3877: : 103iter [00:04, 36.41iter/s]
Best score: 27.3877: : 103iter [00:04, 36.41iter/s]
Best score: 27.3877: : 104iter [00:04, 36.41iter/s]
Best score: 27.3877: : 105iter [00:04, 36.41iter/s]
Best score: 27.3877: : 106iter [00:04, 36.41iter/s]
Best score: 27.3877: : 107iter [00:04, 37.41iter/s]
Best score: 27.3877: : 107iter [00:04, 37.41iter/s]
Best score: 27.3877: : 108iter [00:04, 37.41iter/s]
Best score: 27.3877: : 109iter [00:04, 37.41iter/s]
Best score: 27.3877: : 110iter [00:04, 37.41iter/s]
Best score: 27.3877: : 111iter [00:04, 37.88iter/s]
Best score: 27.3877: : 111iter [00:04, 37.88iter/s]
Best score: 27.3877: : 112iter [00:04, 37.88iter/s]
Best score: 27.3877: : 113iter [00:04, 37.88iter/s]
Best score: 27.3877: : 114iter [00:04, 37.88iter/s]
Best score: 27.3877: : 115iter [00:04, 38.36iter/s]
Best score: 27.3877: : 115iter [00:04, 38.36iter/s]
Best score: 27.3877: : 116iter [00:04, 38.36iter/s]
Best score: 27.3877: : 117iter [00:04, 38.36iter/s]
Best score: 27.3877: : 118iter [00:05, 38.36iter/s]
Best score: 27.3877: : 119iter [00:05, 38.63iter/s]
Best score: 27.3877: : 119iter [00:05, 38.63iter/s]
Best score: 27.3877: : 120iter [00:05, 38.63iter/s]
Best score: 27.3877: : 121iter [00:05, 38.63iter/s]
Best score: 27.3877: : 122iter [00:05, 38.63iter/s]
Best score: 27.3877: : 123iter [00:05, 38.74iter/s]
Best score: 27.3877: : 123iter [00:05, 38.74iter/s]
Best score: 27.3877: : 124iter [00:05, 38.74iter/s]
Best score: 27.3877: : 125iter [00:05, 38.74iter/s]
Best score: 27.3877: : 126iter [00:05, 38.74iter/s]
Best score: 27.3877: : 127iter [00:05, 39.08iter/s]
Best score: 27.3877: : 127iter [00:05, 39.08iter/s]
Best score: 27.3877: : 128iter [00:05, 39.08iter/s]
Best score: 27.3877: : 129iter [00:05, 39.08iter/s]
Best score: 27.3877: : 130iter [00:05, 39.08iter/s]
Best score: 27.3877: : 131iter [00:05, 38.85iter/s]
Best score: 27.3877: : 131iter [00:05, 38.85iter/s]
Best score: 27.3877: : 132iter [00:05, 38.85iter/s]
Best score: 27.3877: : 133iter [00:05, 38.85iter/s]
Best score: 27.3877: : 134iter [00:05, 38.85iter/s]
Best score: 27.3877: : 135iter [00:05, 39.12iter/s]
Best score: 27.3877: : 135iter [00:05, 39.12iter/s]
Best score: 27.3877: : 136iter [00:05, 39.12iter/s]
Best score: 27.3877: : 137iter [00:05, 39.12iter/s]
Best score: 27.3877: : 138iter [00:05, 39.12iter/s]
Best score: 27.3877: : 139iter [00:05, 39.28iter/s]
Best score: 27.3877: : 139iter [00:05, 39.28iter/s]
Best score: 27.3877: : 140iter [00:05, 39.28iter/s]
Best score: 27.3877: : 141iter [00:05, 39.28iter/s]
Best score: 27.3877: : 142iter [00:05, 39.28iter/s]
Best score: 27.3877: : 143iter [00:05, 39.13iter/s]
Best score: 27.3877: : 143iter [00:05, 39.13iter/s]
Best score: 27.3877: : 144iter [00:05, 39.13iter/s]
Best score: 27.3877: : 145iter [00:05, 39.13iter/s]
Best score: 27.3877: : 146iter [00:05, 39.13iter/s]
Best score: 27.3877: : 147iter [00:05, 39.19iter/s]
Best score: 27.3877: : 147iter [00:05, 39.19iter/s]
Best score: 27.3877: : 148iter [00:05, 39.19iter/s]
[ITER 150] Best: 27.3877
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 744.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 27.3877: : 149iter [00:05, 39.19iter/s]
Best score: 27.3877: : 150iter [00:05, 39.19iter/s]
Best score: 27.3877: : 151iter [00:05, 31.39iter/s]
Best score: 27.3877: : 151iter [00:05, 31.39iter/s]
Best score: 27.3877: : 152iter [00:05, 31.39iter/s]
Best score: 27.3877: : 153iter [00:05, 31.39iter/s]
Best score: 27.3877: : 154iter [00:06, 31.39iter/s]
Best score: 27.3877: : 155iter [00:06, 33.51iter/s]
Best score: 27.3877: : 155iter [00:06, 33.51iter/s]
Best score: 27.3877: : 156iter [00:06, 33.51iter/s]
Best score: 27.3877: : 157iter [00:06, 33.51iter/s]
Best score: 27.3877: : 158iter [00:06, 33.51iter/s]
Best score: 27.3877: : 159iter [00:06, 35.16iter/s]
Best score: 27.3877: : 159iter [00:06, 35.16iter/s]
Best score: 27.3877: : 160iter [00:06, 35.16iter/s]
Best score: 27.3877: : 161iter [00:06, 35.16iter/s]
Best score: 27.3877: : 162iter [00:06, 35.16iter/s]
Best score: 27.3877: : 163iter [00:06, 36.17iter/s]
Best score: 27.3877: : 163iter [00:06, 36.17iter/s]
Best score: 27.3877: : 164iter [00:06, 36.17iter/s]
Best score: 27.3877: : 165iter [00:06, 36.17iter/s]
Best score: 27.3877: : 166iter [00:06, 36.17iter/s]
Best score: 27.3877: : 167iter [00:06, 37.06iter/s]
Best score: 27.3877: : 167iter [00:06, 37.06iter/s]
Best score: 27.3877: : 168iter [00:06, 37.06iter/s]
Best score: 27.3877: : 169iter [00:06, 37.06iter/s]
Best score: 27.3877: : 170iter [00:06, 37.06iter/s]
Best score: 27.3877: : 171iter [00:06, 37.74iter/s]
Best score: 27.3877: : 171iter [00:06, 37.74iter/s]
Best score: 27.3877: : 172iter [00:06, 37.74iter/s]
Best score: 27.3877: : 173iter [00:06, 37.74iter/s]
Best score: 27.3877: : 174iter [00:06, 37.74iter/s]
Best score: 27.3877: : 175iter [00:06, 37.74iter/s]
Best score: 27.3877: : 176iter [00:06, 38.52iter/s]
Best score: 27.3877: : 176iter [00:06, 38.52iter/s]
Best score: 27.3877: : 177iter [00:06, 38.52iter/s]
Best score: 27.3877: : 178iter [00:06, 38.52iter/s]
Best score: 27.3877: : 179iter [00:06, 38.52iter/s]
Best score: 27.3877: : 180iter [00:06, 38.52iter/s]
Best score: 27.3877: : 180iter [00:06, 38.52iter/s]
Best score: 27.3877: : 181iter [00:06, 38.52iter/s]
Best score: 27.3877: : 182iter [00:06, 38.52iter/s]
Best score: 27.3877: : 183iter [00:06, 38.52iter/s]
Best score: 27.3877: : 184iter [00:06, 38.88iter/s]
Best score: 27.3877: : 184iter [00:06, 38.88iter/s]
Best score: 27.3877: : 185iter [00:06, 38.88iter/s]
Best score: 27.3877: : 186iter [00:06, 38.88iter/s]
Best score: 27.3877: : 187iter [00:06, 38.88iter/s]
Best score: 27.3877: : 188iter [00:06, 38.88iter/s]
Best score: 27.3877: : 189iter [00:06, 39.34iter/s]
Best score: 27.3877: : 189iter [00:06, 39.34iter/s]
Best score: 27.3877: : 190iter [00:06, 39.34iter/s]
Best score: 27.3877: : 191iter [00:06, 39.34iter/s]
Best score: 27.3877: : 192iter [00:06, 39.34iter/s]
Best score: 27.3877: : 193iter [00:06, 39.32iter/s]
Best score: 27.3877: : 193iter [00:07, 39.32iter/s]
Best score: 27.3877: : 194iter [00:07, 39.32iter/s]
Best score: 27.3877: : 195iter [00:07, 39.32iter/s]
Best score: 27.3877: : 196iter [00:07, 39.32iter/s]
Best score: 27.3877: : 197iter [00:07, 39.43iter/s]
Best score: 27.3877: : 197iter [00:07, 39.43iter/s]
Best score: 27.3877: : 198iter [00:07, 39.43iter/s]
[ITER 200] Best: 27.3877
[PARAMS] Current best parameters:
  fx: 530.7823
  fy: 530.7823
  px: 1018.0000
  py: 744.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 27.3877: : 199iter [00:07, 39.43iter/s]
Best score: 27.3877: : 200iter [00:07, 39.43iter/s]
Best score: 27.3877: : 201iter [00:07, 32.08iter/s]
Best score: 27.3877: : 201iter [00:07, 32.08iter/s]
Best score: 27.3877: : 202iter [00:07, 32.08iter/s]
Best score: 27.3877: : 203iter [00:07, 32.08iter/s]
Best score: 27.3877: : 204iter [00:07, 32.08iter/s]
Best score: 27.3877: : 205iter [00:07, 34.03iter/s]
Best score: 27.3877: : 205iter [00:07, 34.03iter/s]
Best score: 27.3877: : 206iter [00:07, 34.03iter/s]
Best score: 27.3877: : 207iter [00:07, 34.03iter/s]
Best score: 27.3877: : 208iter [00:07, 34.03iter/s]
Best score: 27.3877: : 209iter [00:07, 34.03iter/s]
Best score: 27.3877: : 210iter [00:07, 35.76iter/s]
Best score: 27.3877: : 210iter [00:07, 35.76iter/s]
Best score: 27.3877: : 211iter [00:07, 35.76iter/s]
Best score: 27.3877: : 212iter [00:07, 35.76iter/s]
Best score: 27.3877: : 213iter [00:07, 35.76iter/s]
Best score: 27.3877: : 214iter [00:07, 35.76iter/s]
Best score: 27.3877: : 215iter [00:07, 37.16iter/s]
Best score: 27.3877: : 215iter [00:07, 37.16iter/s]
Best score: 27.3877: : 216iter [00:07, 37.16iter/s]
Best score: 27.3877: : 217iter [00:07, 37.16iter/s]
Best score: 27.3877: : 218iter [00:07, 37.16iter/s]
Best score: 27.3877: : 219iter [00:07, 37.87iter/s]
Best score: 26.7950: : 219iter [00:07, 37.87iter/s]
Best score: 26.7950: : 220iter [00:07, 37.87iter/s]
Best score: 26.7950: : 221iter [00:07, 37.87iter/s]
Best score: 26.7950: : 222iter [00:07, 37.87iter/s]
Best score: 26.7950: : 223iter [00:07, 38.35iter/s]
Best score: 26.7950: : 223iter [00:07, 38.35iter/s]
Best score: 26.7950: : 224iter [00:07, 38.35iter/s]
Best score: 26.7950: : 225iter [00:07, 38.35iter/s]
Best score: 26.7950: : 226iter [00:07, 38.35iter/s]
Best score: 26.7950: : 227iter [00:07, 38.35iter/s]
Best score: 26.7950: : 228iter [00:07, 38.90iter/s]
Best score: 26.7950: : 228iter [00:07, 38.90iter/s]
Best score: 10.5290: : 229iter [00:07, 38.90iter/s]
Best score: 10.5290: : 230iter [00:08, 38.90iter/s]
Best score: 10.5290: : 231iter [00:08, 38.90iter/s]
Best score: 10.5290: : 232iter [00:08, 38.83iter/s]
Best score: 10.5290: : 232iter [00:08, 38.83iter/s]
Best score: 10.5290: : 233iter [00:08, 38.83iter/s]
Best score: 10.5290: : 234iter [00:08, 38.83iter/s]
Best score: 10.5290: : 235iter [00:08, 38.83iter/s]
Best score: 10.5290: : 236iter [00:08, 38.83iter/s]
Best score: 10.5290: : 237iter [00:08, 39.36iter/s]
Best score: 10.5290: : 237iter [00:08, 39.36iter/s]
Best score: 10.5290: : 238iter [00:08, 39.36iter/s]
Best score: 10.5290: : 239iter [00:08, 39.36iter/s]
Best score: 10.5290: : 240iter [00:08, 39.36iter/s]
Best score: 10.5290: : 241iter [00:08, 39.35iter/s]
Best score: 10.5290: : 241iter [00:08, 39.35iter/s]
Best score: 10.5290: : 242iter [00:08, 39.35iter/s]
Best score: 10.5290: : 243iter [00:08, 39.35iter/s]
Best score: 10.5290: : 244iter [00:08, 39.35iter/s]
Best score: 10.5290: : 245iter [00:08, 39.35iter/s]
Best score: 10.5290: : 246iter [00:08, 39.78iter/s]
Best score: 10.5290: : 246iter [00:08, 39.78iter/s]
Best score: 10.5290: : 247iter [00:08, 39.78iter/s]
Best score: 10.5290: : 248iter [00:08, 39.78iter/s]
[ITER 250] Best: 6.0136
[PARAMS] Current best parameters:
  fx: 526.9006
  fy: 527.4181
  px: 1021.0037
  py: 751.1140
  k1: 0.04123780
  k2: -0.01923699
  k3: -0.00789231
  k4: 0.00385599
  p1: -0.00117915
  p2: 0.02074703
  p3: 0.00543146
  p4: -0.00193071
  s1: -0.0095
  s2: 0.0025
  s3: 0.0065
  s4: -0.0017
  tilt_x: 0.0170
  tilt_y: 0.0400
  scale: 2.9025
  rotation: -0.2615

Best score: 6.0136: : 249iter [00:08, 39.78iter/s] 
Best score: 6.0136: : 250iter [00:08, 39.59iter/s]
Best score: 6.0136: : 250iter [00:08, 39.59iter/s]
Best score: 6.0136: : 251iter [00:08, 39.59iter/s]
Best score: 6.0136: : 252iter [00:08, 39.59iter/s]
Best score: 6.0136: : 253iter [00:08, 39.59iter/s]
Best score: 6.0136: : 254iter [00:08, 32.47iter/s]
Best score: 6.0136: : 254iter [00:08, 32.47iter/s]
Best score: 6.0136: : 255iter [00:08, 32.47iter/s]
Best score: 6.0136: : 256iter [00:08, 32.47iter/s]
Best score: 6.0136: : 257iter [00:08, 32.47iter/s]
Best score: 6.0136: : 258iter [00:08, 34.00iter/s]
Best score: 6.0136: : 258iter [00:08, 34.00iter/s]
Best score: 6.0136: : 259iter [00:08, 34.00iter/s]
Best score: 6.0136: : 260iter [00:08, 34.00iter/s]
Best score: 6.0136: : 261iter [00:08, 34.00iter/s]
Best score: 6.0136: : 262iter [00:08, 35.40iter/s]
Best score: 6.0136: : 262iter [00:08, 35.40iter/s]
Best score: 6.0136: : 263iter [00:08, 35.40iter/s]
Best score: 6.0136: : 264iter [00:08, 35.40iter/s]
Best score: 6.0136: : 265iter [00:08, 35.40iter/s]
Best score: 6.0136: : 266iter [00:09, 35.40iter/s]
Best score: 6.0136: : 267iter [00:09, 36.95iter/s]
Best score: 6.0136: : 267iter [00:09, 36.95iter/s]
Best score: 6.0136: : 268iter [00:09, 36.95iter/s]
Best score: 4.9016: : 269iter [00:09, 36.95iter/s]
Best score: 4.9016: : 270iter [00:09, 36.95iter/s]
Best score: 4.9016: : 271iter [00:09, 37.57iter/s]
Best score: 4.9016: : 271iter [00:09, 37.57iter/s]
Best score: 4.9016: : 272iter [00:09, 37.57iter/s]
Best score: 4.9016: : 273iter [00:09, 37.57iter/s]
Best score: 4.9016: : 274iter [00:09, 37.57iter/s]
Best score: 4.9016: : 275iter [00:09, 37.57iter/s]
Best score: 4.9016: : 276iter [00:09, 38.49iter/s]
Best score: 4.9016: : 276iter [00:09, 38.49iter/s]
Best score: 4.9016: : 277iter [00:09, 38.49iter/s]
Best score: 4.9016: : 278iter [00:09, 38.49iter/s]
Best score: 2.9444: : 279iter [00:09, 38.49iter/s]
Best score: 2.9444: : 280iter [00:09, 38.72iter/s]
Best score: 2.9444: : 280iter [00:09, 38.72iter/s]
Best score: 2.9444: : 281iter [00:09, 38.72iter/s]
Best score: 2.9444: : 282iter [00:09, 38.72iter/s]
Best score: 2.9444: : 283iter [00:09, 38.72iter/s]
Best score: 2.9444: : 284iter [00:09, 38.72iter/s]
Best score: 2.9444: : 285iter [00:09, 39.21iter/s]
Best score: 2.9444: : 285iter [00:09, 39.21iter/s]
Best score: 2.9444: : 286iter [00:09, 39.21iter/s]
Best score: 2.9444: : 287iter [00:09, 39.21iter/s]
Best score: 2.9444: : 288iter [00:09, 39.21iter/s]
Best score: 2.9444: : 289iter [00:09, 39.21iter/s]
Best score: 2.9444: : 290iter [00:09, 39.44iter/s]
Best score: 2.9444: : 290iter [00:09, 39.44iter/s]
Best score: 2.9444: : 291iter [00:09, 39.44iter/s]
Best score: 2.9444: : 292iter [00:09, 39.44iter/s]
Best score: 2.9444: : 293iter [00:09, 39.44iter/s]
Best score: 2.9444: : 294iter [00:09, 39.44iter/s]
Best score: 2.9444: : 295iter [00:09, 39.83iter/s]
Best score: 2.9444: : 295iter [00:09, 39.83iter/s]
Best score: 2.9444: : 296iter [00:09, 39.83iter/s]
Best score: 2.9444: : 297iter [00:09, 39.83iter/s]
Best score: 2.9444: : 298iter [00:09, 39.83iter/s]
[ITER 300] Best: 2.6466
[PARAMS] Current best parameters:
  fx: 518.8042
  fy: 519.2880
  px: 1016.8149
  py: 744.4990
  k1: 0.02588990
  k2: -0.01440133
  k3: 0.00079080
  k4: 0.00085645
  p1: -0.00901420
  p2: 0.01271370
  p3: 0.01368525
  p4: -0.00191112
  s1: -0.0128
  s2: -0.0027
  s3: -0.0080
  s4: 0.0033
  tilt_x: -0.0032
  tilt_y: 0.0317
  scale: 3.0130
  rotation: -0.3073

Best score: 2.6466: : 299iter [00:09, 39.83iter/s]
Best score: 2.6466: : 300iter [00:09, 39.88iter/s]
Best score: 2.6466: : 300iter [00:09, 39.88iter/s]
Best score: 2.6466: : 301iter [00:09, 39.88iter/s]
Best score: 2.6466: : 302iter [00:09, 39.88iter/s]
Best score: 2.6466: : 303iter [00:10, 39.88iter/s]
Best score: 2.6466: : 304iter [00:10, 33.24iter/s]
Best score: 2.6466: : 304iter [00:10, 33.24iter/s]
Best score: 2.6466: : 305iter [00:10, 33.24iter/s]
Best score: 2.6466: : 306iter [00:10, 33.24iter/s]
Best score: 2.6466: : 307iter [00:10, 33.24iter/s]
Best score: 2.6466: : 308iter [00:10, 33.24iter/s]
Best score: 2.6466: : 309iter [00:10, 35.28iter/s]
Best score: 1.8798: : 309iter [00:10, 35.28iter/s]
Best score: 1.8798: : 310iter [00:10, 35.28iter/s]
Best score: 1.8798: : 311iter [00:10, 35.28iter/s]
Best score: 1.8798: : 312iter [00:10, 35.28iter/s]
Best score: 1.8798: : 313iter [00:10, 36.30iter/s]
Best score: 1.8798: : 313iter [00:10, 36.30iter/s]
Best score: 1.8798: : 314iter [00:10, 36.30iter/s]
Best score: 1.8798: : 315iter [00:10, 36.30iter/s]
Best score: 1.8798: : 316iter [00:10, 36.30iter/s]
Best score: 1.8798: : 317iter [00:10, 36.30iter/s]
Best score: 1.8798: : 318iter [00:10, 37.59iter/s]
Best score: 1.8798: : 318iter [00:10, 37.59iter/s]
Best score: 1.8798: : 319iter [00:10, 37.59iter/s]
Best score: 1.8798: : 320iter [00:10, 37.59iter/s]
Best score: 1.8798: : 321iter [00:10, 37.59iter/s]
Best score: 1.8798: : 322iter [00:10, 38.08iter/s]
Best score: 1.8798: : 322iter [00:10, 38.08iter/s]
Best score: 1.8798: : 323iter [00:10, 38.08iter/s]
Best score: 1.8798: : 324iter [00:10, 38.08iter/s]
Best score: 1.8798: : 325iter [00:10, 38.08iter/s]
Best score: 1.8798: : 326iter [00:10, 38.08iter/s]
Best score: 1.8798: : 327iter [00:10, 38.92iter/s]
Best score: 1.8798: : 327iter [00:10, 38.92iter/s]
Best score: 1.8798: : 328iter [00:10, 38.92iter/s]
Best score: 1.8798: : 329iter [00:10, 38.92iter/s]
Best score: 1.8798: : 330iter [00:10, 38.92iter/s]
Best score: 1.8798: : 331iter [00:10, 39.10iter/s]
Best score: 1.8798: : 331iter [00:10, 39.10iter/s]
Best score: 1.8798: : 332iter [00:10, 39.10iter/s]
Best score: 1.8798: : 333iter [00:10, 39.10iter/s]
Best score: 1.8798: : 334iter [00:10, 39.10iter/s]
Best score: 1.8798: : 335iter [00:10, 39.10iter/s]
Best score: 1.8798: : 336iter [00:10, 39.47iter/s]
Best score: 1.8798: : 336iter [00:10, 39.47iter/s]
Best score: 1.8798: : 337iter [00:10, 39.47iter/s]
Best score: 1.8798: : 338iter [00:10, 39.47iter/s]
Best score: 1.1706: : 339iter [00:10, 39.47iter/s]
Best score: 1.1706: : 340iter [00:10, 39.42iter/s]
Best score: 1.1706: : 340iter [00:10, 39.42iter/s]
Best score: 1.1706: : 341iter [00:10, 39.42iter/s]
Best score: 1.1706: : 342iter [00:10, 39.42iter/s]
Best score: 1.1706: : 343iter [00:11, 39.42iter/s]
Best score: 1.1706: : 344iter [00:11, 39.42iter/s]
Best score: 1.1706: : 345iter [00:11, 39.69iter/s]
Best score: 1.1706: : 345iter [00:11, 39.69iter/s]
Best score: 1.1706: : 346iter [00:11, 39.69iter/s]
Best score: 1.1706: : 347iter [00:11, 39.69iter/s]
Best score: 1.1706: : 348iter [00:11, 39.69iter/s]
[ITER 350] Best: 1.1573
[PARAMS] Current best parameters:
  fx: 519.5402
  fy: 518.4422
  px: 1015.7969
  py: 740.3957
  k1: 0.02405591
  k2: -0.00946962
  k3: 0.00004974
  k4: 0.00014997
  p1: -0.01552148
  p2: 0.00680365
  p3: 0.01250180
  p4: 0.00215338
  s1: -0.0192
  s2: -0.0027
  s3: -0.0066
  s4: 0.0003
  tilt_x: -0.0082
  tilt_y: 0.0231
  scale: 2.9881
  rotation: -0.2142

Best score: 1.1573: : 349iter [00:11, 39.69iter/s]
Best score: 1.1573: : 350iter [00:11, 39.56iter/s]
Best score: 1.1573: : 350iter [00:11, 39.56iter/s]
Best score: 1.1573: : 351iter [00:11, 39.56iter/s]
Best score: 1.1573: : 352iter [00:11, 39.56iter/s]
Best score: 1.1573: : 353iter [00:11, 39.56iter/s]
Best score: 1.1573: : 354iter [00:11, 32.98iter/s]
Best score: 1.1573: : 354iter [00:11, 32.98iter/s]
Best score: 1.1573: : 355iter [00:11, 32.98iter/s]
Best score: 1.1573: : 356iter [00:11, 32.98iter/s]
Best score: 1.1573: : 357iter [00:11, 32.98iter/s]
Best score: 1.1573: : 358iter [00:11, 32.98iter/s]
Best score: 1.1573: : 359iter [00:11, 29.07iter/s]
Best score: 1.0332: : 359iter [00:11, 29.07iter/s]
Best score: 1.0332: : 360iter [00:11, 29.07iter/s]
Best score: 1.0332: : 361iter [00:11, 29.07iter/s]
Best score: 1.0332: : 362iter [00:11, 29.07iter/s]
Best score: 1.0332: : 363iter [00:11, 31.33iter/s]
Best score: 1.0332: : 363iter [00:11, 31.33iter/s]
Best score: 1.0332: : 364iter [00:11, 31.33iter/s]
Best score: 1.0332: : 365iter [00:11, 31.33iter/s]
Best score: 1.0332: : 366iter [00:11, 31.33iter/s]
Best score: 1.0332: : 367iter [00:11, 31.33iter/s]
Best score: 1.0332: : 368iter [00:11, 33.84iter/s]
Best score: 1.0332: : 368iter [00:11, 33.84iter/s]
Best score: 1.0332: : 369iter [00:11, 33.84iter/s]
Best score: 1.0332: : 370iter [00:11, 33.84iter/s]
Best score: 1.0332: : 371iter [00:11, 33.84iter/s]
Best score: 1.0332: : 372iter [00:11, 35.21iter/s]
Best score: 1.0332: : 372iter [00:11, 35.21iter/s]
Best score: 1.0332: : 373iter [00:11, 35.21iter/s]
Best score: 1.0332: : 374iter [00:11, 35.21iter/s]
Best score: 1.0332: : 375iter [00:11, 35.21iter/s]
Best score: 1.0332: : 376iter [00:11, 35.21iter/s]
Best score: 1.0332: : 377iter [00:11, 36.80iter/s]
Best score: 1.0332: : 377iter [00:12, 36.80iter/s]
Best score: 1.0332: : 378iter [00:12, 36.80iter/s]
Best score: 0.8476: : 379iter [00:12, 36.80iter/s]
Best score: 0.8476: : 380iter [00:12, 36.80iter/s]
Best score: 0.8476: : 381iter [00:12, 36.80iter/s]
Best score: 0.8476: : 382iter [00:12, 37.84iter/s]
Best score: 0.8476: : 382iter [00:12, 37.84iter/s]
Best score: 0.8476: : 383iter [00:12, 37.84iter/s]
Best score: 0.8476: : 384iter [00:12, 37.84iter/s]
Best score: 0.8476: : 385iter [00:12, 37.84iter/s]
Best score: 0.8476: : 386iter [00:12, 37.84iter/s]
Best score: 0.8476: : 387iter [00:12, 38.70iter/s]
Best score: 0.8476: : 387iter [00:12, 38.70iter/s]
Best score: 0.8476: : 388iter [00:12, 38.70iter/s]
Best score: 0.8476: : 389iter [00:12, 38.70iter/s]
Best score: 0.8476: : 390iter [00:12, 38.70iter/s]
Best score: 0.8476: : 391iter [00:12, 38.88iter/s]
Best score: 0.8476: : 391iter [00:12, 38.88iter/s]
Best score: 0.8476: : 392iter [00:12, 38.88iter/s]
Best score: 0.8476: : 393iter [00:12, 38.88iter/s]
Best score: 0.8476: : 394iter [00:12, 38.88iter/s]
Best score: 0.8476: : 395iter [00:12, 38.88iter/s]
Best score: 0.8476: : 396iter [00:12, 39.22iter/s]
Best score: 0.8476: : 396iter [00:12, 39.22iter/s]
Best score: 0.8476: : 397iter [00:12, 39.22iter/s]
Best score: 0.8476: : 398iter [00:12, 39.22iter/s]
[ITER 400] Best: 0.8441
[PARAMS] Current best parameters:
  fx: 521.9382
  fy: 520.9853
  px: 1015.0327
  py: 736.7302
  k1: 0.02824022
  k2: -0.00549624
  k3: -0.01096686
  k4: 0.00403900
  p1: -0.01938230
  p2: 0.00277239
  p3: 0.01577070
  p4: 0.00179838
  s1: -0.0133
  s2: -0.0048
  s3: -0.0057
  s4: 0.0001
  tilt_x: -0.0196
  tilt_y: 0.0200
  scale: 2.9744
  rotation: -0.1768

Best score: 0.8441: : 399iter [00:12, 39.22iter/s]
Best score: 0.8441: : 400iter [00:12, 39.18iter/s]
Best score: 0.8441: : 400iter [00:12, 39.18iter/s]
Best score: 0.8441: : 401iter [00:12, 39.18iter/s]
Best score: 0.8441: : 402iter [00:12, 39.18iter/s]
Best score: 0.8441: : 403iter [00:12, 39.18iter/s]
Best score: 0.8441: : 404iter [00:12, 32.38iter/s]
Best score: 0.8441: : 404iter [00:12, 32.38iter/s]
Best score: 0.8441: : 405iter [00:12, 32.38iter/s]
Best score: 0.8441: : 406iter [00:12, 32.38iter/s]
Best score: 0.8441: : 407iter [00:12, 32.38iter/s]
Best score: 0.8441: : 408iter [00:12, 32.38iter/s]
Best score: 0.8441: : 409iter [00:12, 34.67iter/s]
Best score: 0.7829: : 409iter [00:12, 34.67iter/s]
Best score: 0.7829: : 410iter [00:12, 34.67iter/s]
Best score: 0.7829: : 411iter [00:12, 34.67iter/s]
Best score: 0.7829: : 412iter [00:12, 34.67iter/s]
Best score: 0.7829: : 413iter [00:12, 35.85iter/s]
Best score: 0.7829: : 413iter [00:12, 35.85iter/s]
Best score: 0.7829: : 414iter [00:13, 35.85iter/s]
Best score: 0.7829: : 415iter [00:13, 35.85iter/s]
Best score: 0.7829: : 416iter [00:13, 35.85iter/s]
Best score: 0.7829: : 417iter [00:13, 36.88iter/s]
Best score: 0.7829: : 417iter [00:13, 36.88iter/s]
Best score: 0.7829: : 418iter [00:13, 36.88iter/s]
Best score: 0.6332: : 419iter [00:13, 36.88iter/s]
Best score: 0.6332: : 420iter [00:13, 36.88iter/s]
Best score: 0.6332: : 421iter [00:13, 37.47iter/s]
Best score: 0.6332: : 421iter [00:13, 37.47iter/s]
Best score: 0.6332: : 422iter [00:13, 37.47iter/s]
Best score: 0.6332: : 423iter [00:13, 37.47iter/s]
Best score: 0.6332: : 424iter [00:13, 37.47iter/s]
Best score: 0.6332: : 425iter [00:13, 37.47iter/s]
Best score: 0.6332: : 426iter [00:13, 38.28iter/s]
Best score: 0.6332: : 426iter [00:13, 38.28iter/s]
Best score: 0.6332: : 427iter [00:13, 38.28iter/s]
Best score: 0.6332: : 428iter [00:13, 38.28iter/s]
Best score: 0.6332: : 429iter [00:13, 38.28iter/s]
Best score: 0.6332: : 430iter [00:13, 38.62iter/s]
Best score: 0.6332: : 430iter [00:13, 38.62iter/s]
Best score: 0.6332: : 431iter [00:13, 38.62iter/s]
Best score: 0.6332: : 432iter [00:13, 38.62iter/s]
Best score: 0.6332: : 433iter [00:13, 38.62iter/s]
Best score: 0.6332: : 434iter [00:13, 38.62iter/s]
Best score: 0.6332: : 435iter [00:13, 39.14iter/s]
Best score: 0.6332: : 435iter [00:13, 39.14iter/s]
Best score: 0.6332: : 436iter [00:13, 39.14iter/s]
Best score: 0.6332: : 437iter [00:13, 39.14iter/s]
Best score: 0.6332: : 438iter [00:13, 39.14iter/s]
Best score: 0.5807: : 439iter [00:13, 39.14iter/s]
Best score: 0.5807: : 440iter [00:13, 39.28iter/s]
Best score: 0.5807: : 440iter [00:13, 39.28iter/s]
Best score: 0.5807: : 441iter [00:13, 39.28iter/s]
Best score: 0.5807: : 442iter [00:13, 39.28iter/s]
Best score: 0.5807: : 443iter [00:13, 39.28iter/s]
Best score: 0.5807: : 444iter [00:13, 39.28iter/s]
Best score: 0.5807: : 445iter [00:13, 39.48iter/s]
Best score: 0.5807: : 445iter [00:13, 39.48iter/s]
Best score: 0.5807: : 446iter [00:13, 39.48iter/s]
Best score: 0.5807: : 447iter [00:13, 39.48iter/s]
Best score: 0.5807: : 448iter [00:13, 39.48iter/s]
[ITER 450] Best: 0.4858
[PARAMS] Current best parameters:
  fx: 518.0157
  fy: 517.4583
  px: 1017.3875
  py: 741.0829
  k1: 0.02574827
  k2: -0.00004597
  k3: -0.00835955
  k4: 0.00199630
  p1: -0.01552018
  p2: 0.00774928
  p3: 0.01556738
  p4: -0.00008247
  s1: -0.0218
  s2: 0.0007
  s3: -0.0024
  s4: -0.0001
  tilt_x: -0.0063
  tilt_y: 0.0188
  scale: 2.9602
  rotation: -0.2093

Best score: 0.4858: : 449iter [00:13, 39.48iter/s]
Best score: 0.4858: : 450iter [00:13, 39.42iter/s]
Best score: 0.4858: : 450iter [00:14, 39.42iter/s]
Best score: 0.4858: : 451iter [00:14, 39.42iter/s]
Best score: 0.4858: : 452iter [00:14, 39.42iter/s]
Best score: 0.4858: : 453iter [00:14, 39.42iter/s]
Best score: 0.4858: : 454iter [00:14, 32.99iter/s]
Best score: 0.4858: : 454iter [00:14, 32.99iter/s]
Best score: 0.4858: : 455iter [00:14, 32.99iter/s]
Best score: 0.4858: : 456iter [00:14, 32.99iter/s]
Best score: 0.4858: : 457iter [00:14, 32.99iter/s]
Best score: 0.4858: : 458iter [00:14, 32.99iter/s]
Best score: 0.4858: : 459iter [00:14, 35.05iter/s]
Best score: 0.4858: : 459iter [00:14, 35.05iter/s]
Best score: 0.4858: : 460iter [00:14, 35.05iter/s]
Best score: 0.4858: : 461iter [00:14, 35.05iter/s]
Best score: 0.4858: : 462iter [00:14, 35.05iter/s]
Best score: 0.4858: : 463iter [00:14, 35.99iter/s]
Best score: 0.4858: : 463iter [00:14, 35.99iter/s]
Best score: 0.4858: : 464iter [00:14, 35.99iter/s]
Best score: 0.4858: : 465iter [00:14, 35.99iter/s]
Best score: 0.4858: : 466iter [00:14, 35.99iter/s]
Best score: 0.4858: : 467iter [00:14, 35.99iter/s]
Best score: 0.4858: : 468iter [00:14, 37.39iter/s]
Best score: 0.4858: : 468iter [00:14, 37.39iter/s]
Best score: 0.4697: : 469iter [00:14, 37.39iter/s]
Best score: 0.4697: : 470iter [00:14, 37.39iter/s]
Best score: 0.4697: : 471iter [00:14, 37.39iter/s]
Best score: 0.4697: : 472iter [00:14, 37.96iter/s]
Best score: 0.4697: : 472iter [00:14, 37.96iter/s]
Best score: 0.4697: : 473iter [00:14, 37.96iter/s]
Best score: 0.4697: : 474iter [00:14, 37.96iter/s]
Best score: 0.4697: : 475iter [00:14, 37.96iter/s]
Best score: 0.4697: : 476iter [00:14, 37.96iter/s]
Best score: 0.4697: : 477iter [00:14, 38.68iter/s]
Best score: 0.4697: : 477iter [00:14, 38.68iter/s]
Best score: 0.4697: : 478iter [00:14, 38.68iter/s]
Best score: 0.4536: : 479iter [00:14, 38.68iter/s]
Best score: 0.4536: : 480iter [00:14, 38.68iter/s]
Best score: 0.4536: : 481iter [00:14, 38.90iter/s]
Best score: 0.4536: : 481iter [00:14, 38.90iter/s]
Best score: 0.4536: : 482iter [00:14, 38.90iter/s]
Best score: 0.4536: : 483iter [00:14, 38.90iter/s]
Best score: 0.4536: : 484iter [00:14, 38.90iter/s]
Best score: 0.4536: : 485iter [00:14, 38.90iter/s]
Best score: 0.4536: : 486iter [00:14, 39.38iter/s]
Best score: 0.4536: : 486iter [00:14, 39.38iter/s]
Best score: 0.4536: : 487iter [00:14, 39.38iter/s]
Best score: 0.4536: : 488iter [00:14, 39.38iter/s]
Best score: 0.4536: : 489iter [00:14, 39.38iter/s]
Best score: 0.4536: : 490iter [00:14, 39.33iter/s]
Best score: 0.4536: : 490iter [00:15, 39.33iter/s]
Best score: 0.4536: : 491iter [00:15, 39.33iter/s]
Best score: 0.4536: : 492iter [00:15, 39.33iter/s]
Best score: 0.4536: : 493iter [00:15, 39.33iter/s]
Best score: 0.4536: : 494iter [00:15, 39.33iter/s]
Best score: 0.4536: : 495iter [00:15, 39.65iter/s]
Best score: 0.4536: : 495iter [00:15, 39.65iter/s]
Best score: 0.4536: : 496iter [00:15, 39.65iter/s]
Best score: 0.4536: : 497iter [00:15, 39.65iter/s]
Best score: 0.4536: : 498iter [00:15, 39.65iter/s]
Best score: 0.4536: : 499iter [00:15, 39.72iter/s]
[ITER 500] Best: 0.4536
[PARAMS] Current best parameters:
  fx: 517.3247
  fy: 516.9758
  px: 1017.4775
  py: 743.8175
  k1: 0.02826994
  k2: -0.00285260
  k3: -0.00789499
  k4: 0.00220307
  p1: -0.01100326
  p2: 0.01192343
  p3: 0.01115377
  p4: -0.00027483
  s1: -0.0213
  s2: 0.0007
  s3: 0.0008
  s4: -0.0003
  tilt_x: -0.0020
  tilt_y: 0.0262
  scale: 2.9694
  rotation: -0.1993

Best score: 0.4536: : 499iter [00:15, 39.72iter/s]
Best score: 0.4536: : 500iter [00:15, 39.72iter/s]
Best score: 0.4536: : 501iter [00:15, 39.72iter/s]
Best score: 0.4536: : 502iter [00:15, 39.72iter/s]
Best score: 0.4536: : 503iter [00:15, 32.56iter/s]
Best score: 0.4536: : 503iter [00:15, 32.56iter/s]
Best score: 0.4536: : 504iter [00:15, 32.56iter/s]
Best score: 0.4536: : 505iter [00:15, 32.56iter/s]
Best score: 0.4536: : 506iter [00:15, 32.56iter/s]
Best score: 0.4536: : 507iter [00:15, 32.56iter/s]
Best score: 0.4536: : 508iter [00:15, 34.82iter/s]
Best score: 0.4536: : 508iter [00:15, 34.82iter/s]
Best score: 0.4536: : 509iter [00:15, 34.82iter/s]
Best score: 0.4536: : 510iter [00:15, 34.82iter/s]
Best score: 0.4536: : 511iter [00:15, 34.82iter/s]
Best score: 0.4536: : 512iter [00:15, 35.87iter/s]
Best score: 0.4536: : 512iter [00:15, 35.87iter/s]
Best score: 0.4536: : 513iter [00:15, 35.87iter/s]
Best score: 0.4536: : 514iter [00:15, 35.87iter/s]
Best score: 0.4536: : 515iter [00:15, 35.87iter/s]
Best score: 0.4536: : 516iter [00:15, 35.87iter/s]
Best score: 0.4536: : 517iter [00:15, 37.27iter/s]
Best score: 0.4536: : 517iter [00:15, 37.27iter/s]
Best score: 0.4536: : 518iter [00:15, 37.27iter/s]
Best score: 0.3963: : 519iter [00:15, 37.27iter/s]
Best score: 0.3963: : 520iter [00:15, 37.27iter/s]
Best score: 0.3963: : 521iter [00:15, 37.91iter/s]
Best score: 0.3963: : 521iter [00:15, 37.91iter/s]
Best score: 0.3963: : 522iter [00:15, 37.91iter/s]
Best score: 0.3963: : 523iter [00:15, 37.91iter/s]
Best score: 0.3963: : 524iter [00:15, 37.91iter/s]
Best score: 0.3963: : 525iter [00:15, 38.39iter/s]
Best score: 0.3963: : 525iter [00:15, 38.39iter/s]
Best score: 0.3963: : 526iter [00:15, 38.39iter/s]
Best score: 0.3963: : 527iter [00:16, 38.39iter/s]
Best score: 0.3963: : 528iter [00:16, 38.39iter/s]
Best score: 0.3963: : 529iter [00:16, 38.39iter/s]
Best score: 0.3963: : 530iter [00:16, 38.74iter/s]
Best score: 0.3963: : 530iter [00:16, 38.74iter/s]
Best score: 0.3963: : 531iter [00:16, 38.74iter/s]
Best score: 0.3963: : 532iter [00:16, 38.74iter/s]
Best score: 0.3963: : 533iter [00:16, 38.74iter/s]
Best score: 0.3963: : 534iter [00:16, 38.74iter/s]
Best score: 0.3963: : 535iter [00:16, 39.24iter/s]
Best score: 0.3963: : 535iter [00:16, 39.24iter/s]
Best score: 0.3963: : 536iter [00:16, 39.24iter/s]
Best score: 0.3963: : 537iter [00:16, 39.24iter/s]
Best score: 0.3963: : 538iter [00:16, 39.24iter/s]
Best score: 0.3963: : 539iter [00:16, 39.24iter/s]
Best score: 0.3963: : 540iter [00:16, 39.39iter/s]
Best score: 0.3963: : 540iter [00:16, 39.39iter/s]
Best score: 0.3963: : 541iter [00:16, 39.39iter/s]
Best score: 0.3963: : 542iter [00:16, 39.39iter/s]
Best score: 0.3963: : 543iter [00:16, 39.39iter/s]
Best score: 0.3963: : 544iter [00:16, 39.39iter/s]
Best score: 0.3963: : 545iter [00:16, 39.74iter/s]
Best score: 0.3963: : 545iter [00:16, 39.74iter/s]
Best score: 0.3963: : 546iter [00:16, 39.74iter/s]
Best score: 0.3963: : 547iter [00:16, 39.74iter/s]
Best score: 0.3963: : 548iter [00:16, 39.74iter/s]
[ITER 550] Best: 0.3736
[PARAMS] Current best parameters:
  fx: 516.8082
  fy: 516.4122
  px: 1015.9407
  py: 742.7683
  k1: 0.03066896
  k2: -0.00704897
  k3: -0.00528033
  k4: 0.00170783
  p1: -0.01156943
  p2: 0.01016996
  p3: 0.01428181
  p4: -0.00092141
  s1: -0.0244
  s2: 0.0015
  s3: 0.0009
  s4: -0.0008
  tilt_x: -0.0069
  tilt_y: 0.0313
  scale: 2.9727
  rotation: -0.1789

Best score: 0.3736: : 549iter [00:16, 39.74iter/s]
Best score: 0.3736: : 550iter [00:16, 39.66iter/s]
Best score: 0.3736: : 550iter [00:16, 39.66iter/s]
Best score: 0.3736: : 551iter [00:16, 39.66iter/s]
Best score: 0.3736: : 552iter [00:16, 39.66iter/s]
Best score: 0.3736: : 553iter [00:16, 39.66iter/s]
Best score: 0.3736: : 554iter [00:16, 33.07iter/s]
Best score: 0.3736: : 554iter [00:16, 33.07iter/s]
Best score: 0.3736: : 555iter [00:16, 33.07iter/s]
Best score: 0.3736: : 556iter [00:16, 33.07iter/s]
Best score: 0.3736: : 557iter [00:16, 33.07iter/s]
Best score: 0.3736: : 558iter [00:16, 33.07iter/s]
Best score: 0.3736: : 559iter [00:16, 35.15iter/s]
Best score: 0.3736: : 559iter [00:16, 35.15iter/s]
Best score: 0.3736: : 560iter [00:16, 35.15iter/s]
Best score: 0.3736: : 561iter [00:16, 35.15iter/s]
Best score: 0.3736: : 562iter [00:16, 35.15iter/s]
Best score: 0.3736: : 563iter [00:16, 36.02iter/s]
Best score: 0.3736: : 563iter [00:16, 36.02iter/s]
Best score: 0.3736: : 564iter [00:17, 36.02iter/s]
Best score: 0.3736: : 565iter [00:17, 36.02iter/s]
Best score: 0.3736: : 566iter [00:17, 36.02iter/s]
Best score: 0.3736: : 567iter [00:17, 36.02iter/s]
Best score: 0.3736: : 568iter [00:17, 37.36iter/s]
Best score: 0.3736: : 568iter [00:17, 37.36iter/s]
Best score: 0.3736: : 569iter [00:17, 37.36iter/s]
Best score: 0.3736: : 570iter [00:17, 37.36iter/s]
Best score: 0.3736: : 571iter [00:17, 37.36iter/s]
Best score: 0.3736: : 572iter [00:17, 37.85iter/s]
Best score: 0.3736: : 572iter [00:17, 37.85iter/s]
Best score: 0.3736: : 573iter [00:17, 37.85iter/s]
Best score: 0.3736: : 574iter [00:17, 37.85iter/s]
Best score: 0.3736: : 575iter [00:17, 37.85iter/s]
Best score: 0.3736: : 576iter [00:17, 37.85iter/s]
Best score: 0.3736: : 577iter [00:17, 38.56iter/s]
Best score: 0.3736: : 577iter [00:17, 38.56iter/s]
Best score: 0.3736: : 578iter [00:17, 38.56iter/s]
Best score: 0.3736: : 579iter [00:17, 38.56iter/s]
Best score: 0.3736: : 580iter [00:17, 38.56iter/s]
Best score: 0.3736: : 581iter [00:17, 38.73iter/s]
Best score: 0.3736: : 581iter [00:17, 38.73iter/s]
Best score: 0.3736: : 582iter [00:17, 38.73iter/s]
Best score: 0.3736: : 583iter [00:17, 38.73iter/s]
Best score: 0.3736: : 584iter [00:17, 38.73iter/s]
Best score: 0.3736: : 585iter [00:17, 38.73iter/s]
Best score: 0.3736: : 586iter [00:17, 39.25iter/s]
Best score: 0.3736: : 586iter [00:17, 39.25iter/s]
Best score: 0.3736: : 587iter [00:17, 39.25iter/s]
Best score: 0.3736: : 588iter [00:17, 39.25iter/s]
Best score: 0.3654: : 589iter [00:17, 39.25iter/s]
Best score: 0.3654: : 590iter [00:17, 39.37iter/s]
Best score: 0.3654: : 590iter [00:17, 39.37iter/s]
Best score: 0.3654: : 591iter [00:17, 39.37iter/s]
Best score: 0.3654: : 592iter [00:17, 39.37iter/s]
Best score: 0.3654: : 593iter [00:17, 39.37iter/s]
Best score: 0.3654: : 594iter [00:17, 39.37iter/s]
Best score: 0.3654: : 595iter [00:17, 39.87iter/s]
Best score: 0.3654: : 595iter [00:17, 39.87iter/s]
Best score: 0.3654: : 596iter [00:17, 39.87iter/s]
Best score: 0.3654: : 597iter [00:17, 39.87iter/s]
Best score: 0.3654: : 598iter [00:17, 39.87iter/s]
[ITER 600] Best: 0.3654
[PARAMS] Current best parameters:
  fx: 516.2688
  fy: 516.0004
  px: 1015.7740
  py: 744.1452
  k1: 0.02791436
  k2: -0.00265592
  k3: -0.00754206
  k4: 0.00213451
  p1: -0.00939323
  p2: 0.01152952
  p3: 0.01126116
  p4: -0.00030477
  s1: -0.0247
  s2: 0.0018
  s3: 0.0023
  s4: -0.0013
  tilt_x: -0.0044
  tilt_y: 0.0346
  scale: 2.9786
  rotation: -0.1802

Best score: 0.3654: : 599iter [00:17, 39.87iter/s]
Best score: 0.3654: : 600iter [00:17, 39.75iter/s]
Best score: 0.3654: : 600iter [00:18, 39.75iter/s]
Best score: 0.3654: : 601iter [00:18, 39.75iter/s]
Best score: 0.3654: : 602iter [00:18, 39.75iter/s]
Best score: 0.3654: : 603iter [00:18, 39.75iter/s]
Best score: 0.3654: : 604iter [00:18, 32.98iter/s]
Best score: 0.3654: : 604iter [00:18, 32.98iter/s]
Best score: 0.3654: : 605iter [00:18, 32.98iter/s]
Best score: 0.3654: : 606iter [00:18, 32.98iter/s]
Best score: 0.3654: : 607iter [00:18, 32.98iter/s]
Best score: 0.3654: : 608iter [00:18, 32.98iter/s]
Best score: 0.3654: : 609iter [00:18, 35.07iter/s]
Best score: 0.3469: : 609iter [00:18, 35.07iter/s]
Best score: 0.3469: : 610iter [00:18, 35.07iter/s]
Best score: 0.3469: : 611iter [00:18, 35.07iter/s]
Best score: 0.3469: : 612iter [00:18, 35.07iter/s]
Best score: 0.3469: : 613iter [00:18, 36.08iter/s]
Best score: 0.3469: : 613iter [00:18, 36.08iter/s]
Best score: 0.3469: : 614iter [00:18, 36.08iter/s]
Best score: 0.3469: : 615iter [00:18, 36.08iter/s]
Best score: 0.3469: : 616iter [00:18, 36.08iter/s]
Best score: 0.3469: : 617iter [00:18, 36.08iter/s]
Best score: 0.3469: : 618iter [00:18, 37.27iter/s]
Best score: 0.3469: : 618iter [00:18, 37.27iter/s]
Best score: 0.3469: : 619iter [00:18, 37.27iter/s]
Best score: 0.3469: : 620iter [00:18, 37.27iter/s]
Best score: 0.3469: : 621iter [00:18, 37.27iter/s]
Best score: 0.3469: : 622iter [00:18, 37.77iter/s]
Best score: 0.3469: : 622iter [00:18, 37.77iter/s]
Best score: 0.3469: : 623iter [00:18, 37.77iter/s]
Best score: 0.3469: : 624iter [00:18, 37.77iter/s]
Best score: 0.3469: : 625iter [00:18, 37.77iter/s]
Best score: 0.3469: : 626iter [00:18, 37.77iter/s]
Best score: 0.3469: : 627iter [00:18, 38.56iter/s]
Best score: 0.3469: : 627iter [00:18, 38.56iter/s]
Best score: 0.3469: : 628iter [00:18, 38.56iter/s]
Best score: 0.3469: : 629iter [00:18, 38.56iter/s]
Best score: 0.3469: : 630iter [00:18, 38.56iter/s]
Best score: 0.3469: : 631iter [00:18, 38.79iter/s]
Best score: 0.3469: : 631iter [00:18, 38.79iter/s]
Best score: 0.3469: : 632iter [00:18, 38.79iter/s]
Best score: 0.3469: : 633iter [00:18, 38.79iter/s]
Best score: 0.3469: : 634iter [00:18, 38.79iter/s]
Best score: 0.3469: : 635iter [00:18, 38.79iter/s]
Best score: 0.3469: : 636iter [00:18, 39.28iter/s]
Best score: 0.3469: : 636iter [00:18, 39.28iter/s]
Best score: 0.3469: : 637iter [00:18, 39.28iter/s]
Best score: 0.3469: : 638iter [00:18, 39.28iter/s]
Best score: 0.3469: : 639iter [00:18, 39.28iter/s]
Best score: 0.3469: : 640iter [00:18, 39.35iter/s]
Best score: 0.3469: : 640iter [00:19, 39.35iter/s]
Best score: 0.3469: : 641iter [00:19, 39.35iter/s]
Best score: 0.3469: : 642iter [00:19, 39.35iter/s]
Best score: 0.3469: : 643iter [00:19, 39.35iter/s]
Best score: 0.3469: : 644iter [00:19, 39.35iter/s]
Best score: 0.3469: : 645iter [00:19, 39.77iter/s]
Best score: 0.3469: : 645iter [00:19, 39.77iter/s]
Best score: 0.3469: : 646iter [00:19, 39.77iter/s]
Best score: 0.3469: : 647iter [00:19, 39.77iter/s]
Best score: 0.3469: : 648iter [00:19, 39.77iter/s]
[ITER 650] Best: 0.3469
[PARAMS] Current best parameters:
  fx: 514.4023
  fy: 514.0635
  px: 1014.7815
  py: 742.0700
  k1: 0.02629166
  k2: -0.00036565
  k3: -0.00734982
  k4: 0.00184995
  p1: -0.01289588
  p2: 0.00864994
  p3: 0.01631459
  p4: -0.00039432
  s1: -0.0277
  s2: 0.0018
  s3: 0.0002
  s4: -0.0018
  tilt_x: -0.0073
  tilt_y: 0.0335
  scale: 2.9869
  rotation: -0.1950

Best score: 0.3469: : 649iter [00:19, 39.77iter/s]
Best score: 0.3469: : 650iter [00:19, 39.71iter/s]
Best score: 0.3469: : 650iter [00:19, 39.71iter/s]
Best score: 0.3469: : 651iter [00:19, 39.71iter/s]
Best score: 0.3469: : 652iter [00:19, 39.71iter/s]
Best score: 0.3469: : 653iter [00:19, 39.71iter/s]
Best score: 0.3469: : 654iter [00:19, 32.94iter/s]
Best score: 0.3469: : 654iter [00:19, 32.94iter/s]
Best score: 0.3469: : 655iter [00:19, 32.94iter/s]
Best score: 0.3469: : 656iter [00:19, 32.94iter/s]
Best score: 0.3469: : 657iter [00:19, 32.94iter/s]
Best score: 0.3469: : 658iter [00:19, 32.94iter/s]
Best score: 0.3469: : 659iter [00:19, 34.98iter/s]
Best score: 0.3435: : 659iter [00:19, 34.98iter/s]
Best score: 0.3435: : 660iter [00:19, 34.98iter/s]
Best score: 0.3435: : 661iter [00:19, 34.98iter/s]
Best score: 0.3435: : 662iter [00:19, 34.98iter/s]
Best score: 0.3435: : 663iter [00:19, 36.04iter/s]
Best score: 0.3435: : 663iter [00:19, 36.04iter/s]
Best score: 0.3435: : 664iter [00:19, 36.04iter/s]
Best score: 0.3435: : 665iter [00:19, 36.04iter/s]
Best score: 0.3435: : 666iter [00:19, 36.04iter/s]
Best score: 0.3435: : 667iter [00:19, 36.04iter/s]
Best score: 0.3435: : 668iter [00:19, 37.42iter/s]
Best score: 0.3435: : 668iter [00:19, 37.42iter/s]
Best score: 0.3370: : 669iter [00:19, 37.42iter/s]
Best score: 0.3370: : 670iter [00:19, 37.42iter/s]
Best score: 0.3370: : 671iter [00:19, 37.42iter/s]
Best score: 0.3370: : 672iter [00:19, 37.87iter/s]
Best score: 0.3370: : 672iter [00:19, 37.87iter/s]
Best score: 0.3370: : 673iter [00:19, 37.87iter/s]
Best score: 0.3370: : 674iter [00:19, 37.87iter/s]
Best score: 0.3370: : 675iter [00:19, 37.87iter/s]
Best score: 0.3370: : 676iter [00:19, 37.87iter/s]
Best score: 0.3370: : 677iter [00:19, 38.61iter/s]
Best score: 0.3370: : 677iter [00:20, 38.61iter/s]
Best score: 0.3370: : 678iter [00:20, 38.61iter/s]
Best score: 0.3287: : 679iter [00:20, 38.61iter/s]
Best score: 0.3287: : 680iter [00:20, 38.61iter/s]
Best score: 0.3287: : 681iter [00:20, 31.20iter/s]
Best score: 0.3287: : 681iter [00:20, 31.20iter/s]
Best score: 0.3287: : 682iter [00:20, 31.20iter/s]
Best score: 0.3287: : 683iter [00:20, 31.20iter/s]
Best score: 0.3287: : 684iter [00:20, 31.20iter/s]
Best score: 0.3287: : 685iter [00:20, 31.20iter/s]
Best score: 0.3287: : 686iter [00:20, 33.72iter/s]
Best score: 0.3287: : 686iter [00:20, 33.72iter/s]
Best score: 0.3287: : 687iter [00:20, 33.72iter/s]
Best score: 0.3287: : 688iter [00:20, 33.72iter/s]
Best score: 0.3287: : 689iter [00:20, 33.72iter/s]
Best score: 0.3287: : 690iter [00:20, 35.03iter/s]
Best score: 0.3287: : 690iter [00:20, 35.03iter/s]
Best score: 0.3287: : 691iter [00:20, 35.03iter/s]
Best score: 0.3287: : 692iter [00:20, 35.03iter/s]
Best score: 0.3287: : 693iter [00:20, 35.03iter/s]
Best score: 0.3287: : 694iter [00:20, 35.03iter/s]
Best score: 0.3287: : 695iter [00:20, 36.56iter/s]
Best score: 0.3287: : 695iter [00:20, 36.56iter/s]
Best score: 0.3287: : 696iter [00:20, 36.56iter/s]
Best score: 0.3287: : 697iter [00:20, 36.56iter/s]
Best score: 0.3287: : 698iter [00:20, 36.56iter/s]
[ITER 700] Best: 0.3267
[PARAMS] Current best parameters:
  fx: 515.5941
  fy: 515.2016
  px: 1014.0644
  py: 742.4939
  k1: 0.02521632
  k2: -0.00412867
  k3: -0.00350657
  k4: 0.00085115
  p1: -0.01160795
  p2: 0.01027275
  p3: 0.01346146
  p4: -0.00040162
  s1: -0.0270
  s2: 0.0018
  s3: 0.0019
  s4: -0.0016
  tilt_x: -0.0082
  tilt_y: 0.0403
  scale: 2.9928
  rotation: -0.2004

Best score: 0.3267: : 699iter [00:20, 36.56iter/s]
Best score: 0.3267: : 700iter [00:20, 37.39iter/s]
Best score: 0.3267: : 700iter [00:20, 37.39iter/s]
Best score: 0.3267: : 701iter [00:20, 37.39iter/s]
Best score: 0.3267: : 702iter [00:20, 37.39iter/s]
Best score: 0.3267: : 703iter [00:20, 37.39iter/s]
Best score: 0.3267: : 704iter [00:20, 31.56iter/s]
Best score: 0.3267: : 704iter [00:20, 31.56iter/s]
Best score: 0.3267: : 705iter [00:20, 31.56iter/s]
Best score: 0.3267: : 706iter [00:20, 31.56iter/s]
Best score: 0.3267: : 707iter [00:20, 31.56iter/s]
Best score: 0.3267: : 708iter [00:20, 31.56iter/s]
Best score: 0.3267: : 709iter [00:20, 34.06iter/s]
Best score: 0.3263: : 709iter [00:20, 34.06iter/s]
Best score: 0.3263: : 710iter [00:21, 34.06iter/s]
Best score: 0.3263: : 711iter [00:21, 34.06iter/s]
Best score: 0.3263: : 712iter [00:21, 34.06iter/s]
Best score: 0.3263: : 713iter [00:21, 35.34iter/s]
Best score: 0.3263: : 713iter [00:21, 35.34iter/s]
Best score: 0.3263: : 714iter [00:21, 35.34iter/s]
Best score: 0.3263: : 715iter [00:21, 35.34iter/s]
Best score: 0.3263: : 716iter [00:21, 35.34iter/s]
Best score: 0.3263: : 717iter [00:21, 35.34iter/s]
Best score: 0.3263: : 718iter [00:21, 36.84iter/s]
Best score: 0.3263: : 718iter [00:21, 36.84iter/s]
Best score: 0.3219: : 719iter [00:21, 36.84iter/s]
Best score: 0.3219: : 720iter [00:21, 36.84iter/s]
Best score: 0.3219: : 721iter [00:21, 36.84iter/s]
Best score: 0.3219: : 722iter [00:21, 37.32iter/s]
Best score: 0.3219: : 722iter [00:21, 37.32iter/s]
Best score: 0.3219: : 723iter [00:21, 37.32iter/s]
Best score: 0.3219: : 724iter [00:21, 37.32iter/s]
Best score: 0.3219: : 725iter [00:21, 37.32iter/s]
Best score: 0.3219: : 726iter [00:21, 37.32iter/s]
Best score: 0.3219: : 727iter [00:21, 38.28iter/s]
Best score: 0.3219: : 727iter [00:21, 38.28iter/s]
Best score: 0.3219: : 728iter [00:21, 38.28iter/s]
Best score: 0.3211: : 729iter [00:21, 38.28iter/s]
Best score: 0.3211: : 730iter [00:21, 38.28iter/s]
Best score: 0.3211: : 731iter [00:21, 38.69iter/s]
Best score: 0.3211: : 731iter [00:21, 38.69iter/s]
Best score: 0.3211: : 732iter [00:21, 38.69iter/s]
Best score: 0.3211: : 733iter [00:21, 38.69iter/s]
Best score: 0.3211: : 734iter [00:21, 38.69iter/s]
Best score: 0.3211: : 735iter [00:21, 38.69iter/s]
Best score: 0.3211: : 736iter [00:21, 39.36iter/s]
Best score: 0.3211: : 736iter [00:21, 39.36iter/s]
Best score: 0.3211: : 737iter [00:21, 39.36iter/s]
Best score: 0.3211: : 738iter [00:21, 39.36iter/s]
Best score: 0.3195: : 739iter [00:21, 39.36iter/s]
Best score: 0.3195: : 740iter [00:21, 39.34iter/s]
Best score: 0.3195: : 740iter [00:21, 39.34iter/s]
Best score: 0.3195: : 741iter [00:21, 39.34iter/s]
Best score: 0.3195: : 742iter [00:21, 39.34iter/s]
Best score: 0.3195: : 743iter [00:21, 39.34iter/s]
Best score: 0.3195: : 744iter [00:21, 39.45iter/s]
Best score: 0.3195: : 744iter [00:21, 39.45iter/s]
Best score: 0.3195: : 745iter [00:21, 39.45iter/s]
Best score: 0.3195: : 746iter [00:21, 39.45iter/s]
Best score: 0.3195: : 747iter [00:21, 39.45iter/s]
Best score: 0.3195: : 748iter [00:21, 39.45iter/s]
Best score: 0.3195: : 749iter [00:21, 39.82iter/s]
[ITER 750] Best: 0.3187
[PARAMS] Current best parameters:
  fx: 514.4763
  fy: 514.1167
  px: 1012.8064
  py: 742.4505
  k1: 0.02148294
  k2: 0.00057556
  k3: -0.00489751
  k4: 0.00093801
  p1: -0.01058557
  p2: 0.01000509
  p3: 0.01389773
  p4: -0.00016070
  s1: -0.0283
  s2: 0.0016
  s3: 0.0005
  s4: -0.0016
  tilt_x: -0.0106
  tilt_y: 0.0456
  scale: 3.0038
  rotation: -0.1981

Best score: 0.3187: : 749iter [00:21, 39.82iter/s]
Best score: 0.3187: : 750iter [00:22, 39.82iter/s]
Best score: 0.3187: : 751iter [00:22, 39.82iter/s]
Best score: 0.3187: : 752iter [00:22, 39.82iter/s]
Best score: 0.3187: : 753iter [00:22, 32.73iter/s]
Best score: 0.3187: : 753iter [00:22, 32.73iter/s]
Best score: 0.3187: : 754iter [00:22, 32.73iter/s]
Best score: 0.3187: : 755iter [00:22, 32.73iter/s]
Best score: 0.3187: : 756iter [00:22, 32.73iter/s]
Best score: 0.3187: : 757iter [00:22, 34.42iter/s]
Best score: 0.3187: : 757iter [00:22, 34.42iter/s]
Best score: 0.3187: : 758iter [00:22, 34.42iter/s]
Best score: 0.3187: : 759iter [00:22, 34.42iter/s]
Best score: 0.3187: : 760iter [00:22, 34.42iter/s]
Best score: 0.3187: : 761iter [00:22, 35.61iter/s]
Best score: 0.3187: : 761iter [00:22, 35.61iter/s]
Best score: 0.3187: : 762iter [00:22, 35.61iter/s]
Best score: 0.3187: : 763iter [00:22, 35.61iter/s]
Best score: 0.3187: : 764iter [00:22, 35.61iter/s]
Best score: 0.3187: : 765iter [00:22, 35.61iter/s]
Best score: 0.3187: : 766iter [00:22, 37.06iter/s]
Best score: 0.3187: : 766iter [00:22, 37.06iter/s]
Best score: 0.3187: : 767iter [00:22, 37.06iter/s]
Best score: 0.3187: : 768iter [00:22, 37.06iter/s]
Best score: 0.3187: : 769iter [00:22, 37.06iter/s]
Best score: 0.3187: : 770iter [00:22, 37.55iter/s]
Best score: 0.3187: : 770iter [00:22, 37.55iter/s]
Best score: 0.3187: : 771iter [00:22, 37.55iter/s]
Best score: 0.3187: : 772iter [00:22, 37.55iter/s]
Best score: 0.3187: : 773iter [00:22, 37.55iter/s]
Best score: 0.3187: : 774iter [00:22, 37.55iter/s]
Best score: 0.3187: : 775iter [00:22, 38.46iter/s]
Best score: 0.3187: : 775iter [00:22, 38.46iter/s]
Best score: 0.3187: : 776iter [00:22, 38.46iter/s]
Best score: 0.3187: : 777iter [00:22, 38.46iter/s]
Best score: 0.3187: : 778iter [00:22, 38.46iter/s]
Best score: 0.3176: : 779iter [00:22, 38.46iter/s]
Best score: 0.3176: : 780iter [00:22, 38.91iter/s]
Best score: 0.3176: : 780iter [00:22, 38.91iter/s]
Best score: 0.3176: : 781iter [00:22, 38.91iter/s]
Best score: 0.3176: : 782iter [00:22, 38.91iter/s]
Best score: 0.3176: : 783iter [00:22, 38.91iter/s]
Best score: 0.3176: : 784iter [00:22, 38.91iter/s]
Best score: 0.3176: : 785iter [00:22, 39.24iter/s]
Best score: 0.3176: : 785iter [00:22, 39.24iter/s]
Best score: 0.3176: : 786iter [00:22, 39.24iter/s]
Best score: 0.3176: : 787iter [00:23, 39.24iter/s]
Best score: 0.3176: : 788iter [00:23, 39.24iter/s]
Best score: 0.3176: : 789iter [00:23, 39.39iter/s]
Best score: 0.3139: : 789iter [00:23, 39.39iter/s]
Best score: 0.3139: : 790iter [00:23, 39.39iter/s]
Best score: 0.3139: : 791iter [00:23, 39.39iter/s]
Best score: 0.3139: : 792iter [00:23, 39.39iter/s]
Best score: 0.3139: : 793iter [00:23, 39.26iter/s]
Best score: 0.3139: : 793iter [00:23, 39.26iter/s]
Best score: 0.3139: : 794iter [00:23, 39.26iter/s]
Best score: 0.3139: : 795iter [00:23, 39.26iter/s]
Best score: 0.3139: : 796iter [00:23, 39.26iter/s]
Best score: 0.3139: : 797iter [00:23, 39.26iter/s]
Best score: 0.3139: : 798iter [00:23, 39.58iter/s]
Best score: 0.3139: : 798iter [00:23, 39.58iter/s]
[ITER 800] Best: 0.3139
[PARAMS] Current best parameters:
  fx: 515.2639
  fy: 514.8893
  px: 1012.6019
  py: 743.4677
  k1: 0.02105522
  k2: 0.00086809
  k3: -0.00544466
  k4: 0.00110926
  p1: -0.01040136
  p2: 0.01147193
  p3: 0.01353599
  p4: -0.00040409
  s1: -0.0302
  s2: 0.0021
  s3: 0.0017
  s4: -0.0016
  tilt_x: -0.0060
  tilt_y: 0.0496
  scale: 3.0021
  rotation: -0.1845

Best score: 0.3139: : 799iter [00:23, 39.58iter/s]
Best score: 0.3139: : 800iter [00:23, 39.58iter/s]
Best score: 0.3139: : 801iter [00:23, 39.58iter/s]
Best score: 0.3139: : 802iter [00:23, 32.62iter/s]
Best score: 0.3139: : 802iter [00:23, 32.62iter/s]
Best score: 0.3139: : 803iter [00:23, 32.62iter/s]
Best score: 0.3139: : 804iter [00:23, 32.62iter/s]
Best score: 0.3139: : 805iter [00:23, 32.62iter/s]
Best score: 0.3139: : 806iter [00:23, 32.62iter/s]
Best score: 0.3139: : 807iter [00:23, 34.79iter/s]
Best score: 0.3139: : 807iter [00:23, 34.79iter/s]
Best score: 0.3139: : 808iter [00:23, 34.79iter/s]
Best score: 0.3138: : 809iter [00:23, 34.79iter/s]
Best score: 0.3138: : 810iter [00:23, 34.79iter/s]
Best score: 0.3138: : 811iter [00:23, 35.95iter/s]
Best score: 0.3138: : 811iter [00:23, 35.95iter/s]
Best score: 0.3138: : 812iter [00:23, 35.95iter/s]
Best score: 0.3138: : 813iter [00:23, 35.95iter/s]
Best score: 0.3138: : 814iter [00:23, 35.95iter/s]
Best score: 0.3138: : 815iter [00:23, 35.95iter/s]
Best score: 0.3138: : 816iter [00:23, 37.42iter/s]
Best score: 0.3138: : 816iter [00:23, 37.42iter/s]
Best score: 0.3138: : 817iter [00:23, 37.42iter/s]
Best score: 0.3138: : 818iter [00:23, 37.42iter/s]
Best score: 0.3134: : 819iter [00:23, 37.42iter/s]
Best score: 0.3134: : 820iter [00:23, 37.86iter/s]
Best score: 0.3134: : 820iter [00:23, 37.86iter/s]
Best score: 0.3134: : 821iter [00:23, 37.86iter/s]
Best score: 0.3134: : 822iter [00:23, 37.86iter/s]
Best score: 0.3134: : 823iter [00:23, 37.86iter/s]
Best score: 0.3134: : 824iter [00:24, 37.86iter/s]
Best score: 0.3134: : 825iter [00:24, 38.73iter/s]
Best score: 0.3134: : 825iter [00:24, 38.73iter/s]
Best score: 0.3134: : 826iter [00:24, 38.73iter/s]
Best score: 0.3134: : 827iter [00:24, 38.73iter/s]
Best score: 0.3134: : 828iter [00:24, 38.73iter/s]
Best score: 0.3134: : 829iter [00:24, 38.73iter/s]
Best score: 0.3134: : 830iter [00:24, 38.95iter/s]
Best score: 0.3134: : 830iter [00:24, 38.95iter/s]
Best score: 0.3134: : 831iter [00:24, 38.95iter/s]
Best score: 0.3134: : 832iter [00:24, 38.95iter/s]
Best score: 0.3134: : 833iter [00:24, 38.95iter/s]
Best score: 0.3134: : 834iter [00:24, 39.17iter/s]
Best score: 0.3134: : 834iter [00:24, 39.17iter/s]
Best score: 0.3134: : 835iter [00:24, 39.17iter/s]
Best score: 0.3134: : 836iter [00:24, 39.17iter/s]
Best score: 0.3134: : 837iter [00:24, 39.17iter/s]
Best score: 0.3134: : 838iter [00:24, 39.17iter/s]
Best score: 0.3134: : 839iter [00:24, 39.49iter/s]
Best score: 0.3122: : 839iter [00:24, 39.49iter/s]
Best score: 0.3122: : 840iter [00:24, 39.49iter/s]
Best score: 0.3122: : 841iter [00:24, 39.49iter/s]
Best score: 0.3122: : 842iter [00:24, 39.49iter/s]
Best score: 0.3122: : 843iter [00:24, 39.30iter/s]
Best score: 0.3122: : 843iter [00:24, 39.30iter/s]
Best score: 0.3122: : 844iter [00:24, 39.30iter/s]
Best score: 0.3122: : 845iter [00:24, 39.30iter/s]
Best score: 0.3122: : 846iter [00:24, 39.30iter/s]
Best score: 0.3122: : 847iter [00:24, 39.30iter/s]
Best score: 0.3122: : 848iter [00:24, 39.73iter/s]
Best score: 0.3122: : 848iter [00:24, 39.73iter/s]
[ITER 850] Best: 0.3110
[PARAMS] Current best parameters:
  fx: 514.0531
  fy: 513.6856
  px: 1012.2887
  py: 743.4700
  k1: 0.01927289
  k2: 0.00453490
  k3: -0.00704094
  k4: 0.00133899
  p1: -0.00971623
  p2: 0.01147801
  p3: 0.01361462
  p4: -0.00052803
  s1: -0.0307
  s2: 0.0024
  s3: 0.0008
  s4: -0.0014
  tilt_x: -0.0072
  tilt_y: 0.0511
  scale: 3.0074
  rotation: -0.1842

Best score: 0.3110: : 849iter [00:24, 39.73iter/s]
Best score: 0.3110: : 850iter [00:24, 39.73iter/s]
Best score: 0.3110: : 851iter [00:24, 39.73iter/s]
Best score: 0.3110: : 852iter [00:24, 32.73iter/s]
Best score: 0.3110: : 852iter [00:24, 32.73iter/s]
Best score: 0.3110: : 853iter [00:24, 32.73iter/s]
Best score: 0.3110: : 854iter [00:24, 32.73iter/s]
Best score: 0.3110: : 855iter [00:24, 32.73iter/s]
Best score: 0.3110: : 856iter [00:24, 32.73iter/s]
Best score: 0.3110: : 857iter [00:24, 35.01iter/s]
Best score: 0.3110: : 857iter [00:24, 35.01iter/s]
Best score: 0.3110: : 858iter [00:24, 35.01iter/s]
Best score: 0.3104: : 859iter [00:24, 35.01iter/s]
Best score: 0.3104: : 860iter [00:25, 35.01iter/s]
Best score: 0.3104: : 861iter [00:25, 36.11iter/s]
Best score: 0.3104: : 861iter [00:25, 36.11iter/s]
Best score: 0.3104: : 862iter [00:25, 36.11iter/s]
Best score: 0.3104: : 863iter [00:25, 36.11iter/s]
Best score: 0.3104: : 864iter [00:25, 36.11iter/s]
Best score: 0.3104: : 865iter [00:25, 36.11iter/s]
Best score: 0.3104: : 866iter [00:25, 37.35iter/s]
Best score: 0.3104: : 866iter [00:25, 37.35iter/s]
Best score: 0.3104: : 867iter [00:25, 37.35iter/s]
Best score: 0.3104: : 868iter [00:25, 37.35iter/s]
Best score: 0.3104: : 869iter [00:25, 37.35iter/s]
Best score: 0.3104: : 870iter [00:25, 37.79iter/s]
Best score: 0.3104: : 870iter [00:25, 37.79iter/s]
Best score: 0.3104: : 871iter [00:25, 37.79iter/s]
Best score: 0.3104: : 872iter [00:25, 37.79iter/s]
Best score: 0.3104: : 873iter [00:25, 37.79iter/s]
Best score: 0.3104: : 874iter [00:25, 37.79iter/s]
Best score: 0.3104: : 875iter [00:25, 38.51iter/s]
Best score: 0.3104: : 875iter [00:25, 38.51iter/s]
Best score: 0.3104: : 876iter [00:25, 38.51iter/s]
Best score: 0.3104: : 877iter [00:25, 38.51iter/s]
Best score: 0.3104: : 878iter [00:25, 38.51iter/s]
Best score: 0.3086: : 879iter [00:25, 38.51iter/s]
Best score: 0.3086: : 880iter [00:25, 38.77iter/s]
Best score: 0.3086: : 880iter [00:25, 38.77iter/s]
Best score: 0.3086: : 881iter [00:25, 38.77iter/s]
Best score: 0.3086: : 882iter [00:25, 38.77iter/s]
Best score: 0.3086: : 883iter [00:25, 38.77iter/s]
Best score: 0.3086: : 884iter [00:25, 38.77iter/s]
Best score: 0.3086: : 885iter [00:25, 39.32iter/s]
Best score: 0.3086: : 885iter [00:25, 39.32iter/s]
Best score: 0.3086: : 886iter [00:25, 39.32iter/s]
Best score: 0.3086: : 887iter [00:25, 39.32iter/s]
Best score: 0.3086: : 888iter [00:25, 39.32iter/s]
Best score: 0.3082: : 889iter [00:25, 39.32iter/s]
Best score: 0.3082: : 890iter [00:25, 39.46iter/s]
Best score: 0.3082: : 890iter [00:25, 39.46iter/s]
Best score: 0.3082: : 891iter [00:25, 39.46iter/s]
Best score: 0.3082: : 892iter [00:25, 39.46iter/s]
Best score: 0.3082: : 893iter [00:25, 39.46iter/s]
Best score: 0.3082: : 894iter [00:25, 39.58iter/s]
Best score: 0.3082: : 894iter [00:25, 39.58iter/s]
Best score: 0.3082: : 895iter [00:25, 39.58iter/s]
Best score: 0.3082: : 896iter [00:25, 39.58iter/s]
Best score: 0.3082: : 897iter [00:25, 39.58iter/s]
Best score: 0.3082: : 898iter [00:25, 39.58iter/s]
Best score: 0.3082: : 899iter [00:25, 39.74iter/s]
[ITER 900] Best: 0.3069
[PARAMS] Current best parameters:
  fx: 512.1810
  fy: 511.8523
  px: 1012.1580
  py: 743.7905
  k1: 0.01437590
  k2: 0.01502794
  k3: -0.01277315
  k4: 0.00237971
  p1: -0.00669687
  p2: 0.01568161
  p3: 0.00953043
  p4: -0.00085234
  s1: -0.0307
  s2: 0.0026
  s3: 0.0020
  s4: -0.0010
  tilt_x: -0.0116
  tilt_y: 0.0600
  scale: 3.0153
  rotation: -0.1909

Best score: 0.3069: : 899iter [00:25, 39.74iter/s]
Best score: 0.3069: : 900iter [00:26, 39.74iter/s]
Best score: 0.3069: : 901iter [00:26, 39.74iter/s]
Best score: 0.3069: : 902iter [00:26, 39.74iter/s]
Best score: 0.3069: : 903iter [00:26, 32.79iter/s]
Best score: 0.3069: : 903iter [00:26, 32.79iter/s]
Best score: 0.3069: : 904iter [00:26, 32.79iter/s]
Best score: 0.3069: : 905iter [00:26, 32.79iter/s]
Best score: 0.3069: : 906iter [00:26, 32.79iter/s]
Best score: 0.3069: : 907iter [00:26, 34.50iter/s]
Best score: 0.3069: : 907iter [00:26, 34.50iter/s]
Best score: 0.3069: : 908iter [00:26, 34.50iter/s]
Best score: 0.3055: : 909iter [00:26, 34.50iter/s]
Best score: 0.3055: : 910iter [00:26, 34.50iter/s]
Best score: 0.3055: : 911iter [00:26, 35.73iter/s]
Best score: 0.3055: : 911iter [00:26, 35.73iter/s]
Best score: 0.3055: : 912iter [00:26, 35.73iter/s]
Best score: 0.3055: : 913iter [00:26, 35.73iter/s]
Best score: 0.3055: : 914iter [00:26, 35.73iter/s]
Best score: 0.3055: : 915iter [00:26, 35.73iter/s]
Best score: 0.3055: : 916iter [00:26, 37.28iter/s]
Best score: 0.3055: : 916iter [00:26, 37.28iter/s]
Best score: 0.3055: : 917iter [00:26, 37.28iter/s]
Best score: 0.3055: : 918iter [00:26, 37.28iter/s]
Best score: 0.3008: : 919iter [00:26, 37.28iter/s]
Best score: 0.3008: : 920iter [00:26, 37.79iter/s]
Best score: 0.3008: : 920iter [00:26, 37.79iter/s]
Best score: 0.3008: : 921iter [00:26, 37.79iter/s]
Best score: 0.3008: : 922iter [00:26, 37.79iter/s]
Best score: 0.3008: : 923iter [00:26, 37.79iter/s]
Best score: 0.3008: : 924iter [00:26, 37.79iter/s]
Best score: 0.3008: : 925iter [00:26, 38.57iter/s]
Best score: 0.3008: : 925iter [00:26, 38.57iter/s]
Best score: 0.3008: : 926iter [00:26, 38.57iter/s]
Best score: 0.3008: : 927iter [00:26, 38.57iter/s]
Best score: 0.3008: : 928iter [00:26, 38.57iter/s]
Best score: 0.3008: : 929iter [00:26, 38.57iter/s]
Best score: 0.3008: : 930iter [00:26, 38.81iter/s]
Best score: 0.3008: : 930iter [00:26, 38.81iter/s]
Best score: 0.3008: : 931iter [00:26, 38.81iter/s]
Best score: 0.3008: : 932iter [00:26, 38.81iter/s]
Best score: 0.3008: : 933iter [00:26, 38.81iter/s]
Best score: 0.3008: : 934iter [00:26, 38.81iter/s]
Best score: 0.3008: : 935iter [00:26, 39.26iter/s]
Best score: 0.3008: : 935iter [00:26, 39.26iter/s]
Best score: 0.3008: : 936iter [00:26, 39.26iter/s]
Best score: 0.3008: : 937iter [00:27, 39.26iter/s]
Best score: 0.3008: : 938iter [00:27, 39.26iter/s]
Best score: 0.3008: : 939iter [00:27, 39.39iter/s]
Best score: 0.2998: : 939iter [00:27, 39.39iter/s]
Best score: 0.2998: : 940iter [00:27, 39.39iter/s]
Best score: 0.2998: : 941iter [00:27, 39.39iter/s]
Best score: 0.2998: : 942iter [00:27, 39.39iter/s]
Best score: 0.2998: : 943iter [00:27, 39.41iter/s]
Best score: 0.2998: : 943iter [00:27, 39.41iter/s]
Best score: 0.2998: : 944iter [00:27, 39.41iter/s]
Best score: 0.2998: : 945iter [00:27, 39.41iter/s]
Best score: 0.2998: : 946iter [00:27, 39.41iter/s]
Best score: 0.2998: : 947iter [00:27, 39.41iter/s]
Best score: 0.2998: : 948iter [00:27, 39.69iter/s]
Best score: 0.2998: : 948iter [00:27, 39.69iter/s]
[ITER 950] Best: 0.2998
[PARAMS] Current best parameters:
  fx: 506.2348
  fy: 505.9141
  px: 1010.9090
  py: 743.7628
  k1: 0.00257413
  k2: 0.03982520
  k3: -0.02553527
  k4: 0.00465685
  p1: -0.00841119
  p2: 0.01449393
  p3: 0.01302441
  p4: -0.00117995
  s1: -0.0342
  s2: 0.0029
  s3: -0.0001
  s4: -0.0005
  tilt_x: -0.0081
  tilt_y: 0.0633
  scale: 3.0406
  rotation: -0.1885

Best score: 0.2998: : 949iter [00:27, 39.69iter/s]
Best score: 0.2998: : 950iter [00:27, 39.69iter/s]
Best score: 0.2998: : 951iter [00:27, 39.69iter/s]
Best score: 0.2998: : 952iter [00:27, 31.95iter/s]
Best score: 0.2998: : 952iter [00:27, 31.95iter/s]
Best score: 0.2998: : 953iter [00:27, 31.95iter/s]
Best score: 0.2998: : 954iter [00:27, 31.95iter/s]
Best score: 0.2998: : 955iter [00:27, 31.95iter/s]
Best score: 0.2998: : 956iter [00:27, 33.73iter/s]
Best score: 0.2998: : 956iter [00:27, 33.73iter/s]
Best score: 0.2998: : 957iter [00:27, 33.73iter/s]
Best score: 0.2998: : 958iter [00:27, 33.73iter/s]
Best score: 0.2998: : 959iter [00:27, 33.73iter/s]
Best score: 0.2998: : 960iter [00:27, 34.96iter/s]
Best score: 0.2998: : 960iter [00:27, 34.96iter/s]
Best score: 0.2998: : 961iter [00:27, 34.96iter/s]
Best score: 0.2998: : 962iter [00:27, 34.96iter/s]
Best score: 0.2998: : 963iter [00:27, 34.96iter/s]
Best score: 0.2998: : 964iter [00:27, 36.17iter/s]
Best score: 0.2998: : 964iter [00:27, 36.17iter/s]
Best score: 0.2998: : 965iter [00:27, 36.17iter/s]
Best score: 0.2998: : 966iter [00:27, 36.17iter/s]
Best score: 0.2998: : 967iter [00:27, 36.17iter/s]
Best score: 0.2998: : 968iter [00:27, 36.17iter/s]
Best score: 0.2998: : 969iter [00:27, 37.46iter/s]
Best score: 0.2998: : 969iter [00:27, 37.46iter/s]
Best score: 0.2998: : 970iter [00:27, 37.46iter/s]
Best score: 0.2998: : 971iter [00:27, 37.46iter/s]
Best score: 0.2998: : 972iter [00:27, 37.46iter/s]
Best score: 0.2998: : 973iter [00:27, 37.94iter/s]
Best score: 0.2998: : 973iter [00:28, 37.94iter/s]
Best score: 0.2998: : 974iter [00:28, 37.94iter/s]
Best score: 0.2998: : 975iter [00:28, 37.94iter/s]
Best score: 0.2998: : 976iter [00:28, 37.94iter/s]
Best score: 0.2998: : 977iter [00:28, 37.94iter/s]
Best score: 0.2998: : 978iter [00:28, 38.77iter/s]
Best score: 0.2998: : 978iter [00:28, 38.77iter/s]
Best score: 0.2994: : 979iter [00:28, 38.77iter/s]
Best score: 0.2994: : 980iter [00:28, 38.77iter/s]
Best score: 0.2994: : 981iter [00:28, 38.77iter/s]
Best score: 0.2994: : 982iter [00:28, 38.93iter/s]
Best score: 0.2994: : 982iter [00:28, 38.93iter/s]
Best score: 0.2994: : 983iter [00:28, 38.93iter/s]
Best score: 0.2994: : 984iter [00:28, 38.93iter/s]
Best score: 0.2994: : 985iter [00:28, 38.93iter/s]
Best score: 0.2994: : 986iter [00:28, 38.93iter/s]
Best score: 0.2994: : 987iter [00:28, 39.35iter/s]
Best score: 0.2994: : 987iter [00:28, 39.35iter/s]
Best score: 0.2994: : 988iter [00:28, 39.35iter/s]
Best score: 0.2988: : 989iter [00:28, 39.35iter/s]
Best score: 0.2988: : 990iter [00:28, 39.35iter/s]
Best score: 0.2988: : 991iter [00:28, 39.22iter/s]
Best score: 0.2988: : 991iter [00:28, 39.22iter/s]
Best score: 0.2988: : 992iter [00:28, 39.22iter/s]
Best score: 0.2988: : 993iter [00:28, 39.22iter/s]
Best score: 0.2988: : 994iter [00:28, 39.22iter/s]
Best score: 0.2988: : 995iter [00:28, 39.22iter/s]
Best score: 0.2988: : 996iter [00:28, 39.49iter/s]
Best score: 0.2988: : 996iter [00:28, 39.49iter/s]
Best score: 0.2988: : 997iter [00:28, 39.49iter/s]
Best score: 0.2988: : 998iter [00:28, 39.49iter/s]
[ITER 1000] Best: 0.2983
[PARAMS] Current best parameters:
  fx: 505.8390
  fy: 505.5402
  px: 1010.8614
  py: 744.1926
  k1: 0.00137449
  k2: 0.04189752
  k3: -0.02650836
  k4: 0.00481582
  p1: -0.00841504
  p2: 0.01461793
  p3: 0.01358708
  p4: -0.00126848
  s1: -0.0351
  s2: 0.0030
  s3: -0.0003
  s4: -0.0004
  tilt_x: -0.0060
  tilt_y: 0.0637
  scale: 3.0425
  rotation: -0.1801

Best score: 0.2983: : 999iter [00:28, 39.49iter/s]
Best score: 0.2983: : 1000iter [00:28, 30.78iter/s]
Best score: 0.2983: : 1000iter [00:28, 30.78iter/s]
Best score: 0.2983: : 1001iter [00:28, 30.78iter/s]
Best score: 0.2983: : 1002iter [00:28, 30.78iter/s]
Best score: 0.2983: : 1003iter [00:28, 30.78iter/s]
Best score: 0.2983: : 1004iter [00:28, 27.77iter/s]
Best score: 0.2983: : 1004iter [00:28, 27.77iter/s]
Best score: 0.2983: : 1005iter [00:29, 27.77iter/s]
Best score: 0.2983: : 1006iter [00:29, 27.77iter/s]
Best score: 0.2983: : 1007iter [00:29, 27.77iter/s]
Best score: 0.2983: : 1008iter [00:29, 27.77iter/s]
Best score: 0.2983: : 1009iter [00:29, 30.97iter/s]
Best score: 0.2975: : 1009iter [00:29, 30.97iter/s]
Best score: 0.2975: : 1010iter [00:29, 30.97iter/s]
Best score: 0.2975: : 1011iter [00:29, 30.97iter/s]
Best score: 0.2975: : 1012iter [00:29, 30.97iter/s]
Best score: 0.2975: : 1013iter [00:29, 32.88iter/s]
Best score: 0.2975: : 1013iter [00:29, 32.88iter/s]
Best score: 0.2975: : 1014iter [00:29, 32.88iter/s]
Best score: 0.2975: : 1015iter [00:29, 32.88iter/s]
Best score: 0.2975: : 1016iter [00:29, 32.88iter/s]
Best score: 0.2975: : 1017iter [00:29, 32.88iter/s]
Best score: 0.2975: : 1018iter [00:29, 34.94iter/s]
Best score: 0.2975: : 1018iter [00:29, 34.94iter/s]
Best score: 0.2975: : 1019iter [00:29, 34.94iter/s]
Best score: 0.2975: : 1020iter [00:29, 34.94iter/s]
Best score: 0.2975: : 1021iter [00:29, 34.94iter/s]
Best score: 0.2975: : 1022iter [00:29, 36.08iter/s]
Best score: 0.2975: : 1022iter [00:29, 36.08iter/s]
Best score: 0.2975: : 1023iter [00:29, 36.08iter/s]
Best score: 0.2975: : 1024iter [00:29, 36.08iter/s]
Best score: 0.2975: : 1025iter [00:29, 36.08iter/s]
Best score: 0.2975: : 1026iter [00:29, 36.08iter/s]
Best score: 0.2975: : 1027iter [00:29, 37.42iter/s]
Best score: 0.2975: : 1027iter [00:29, 37.42iter/s]
Best score: 0.2975: : 1028iter [00:29, 37.42iter/s]
Best score: 0.2975: : 1029iter [00:29, 37.42iter/s]
Best score: 0.2975: : 1030iter [00:29, 37.42iter/s]
Best score: 0.2975: : 1031iter [00:29, 37.88iter/s]
Best score: 0.2975: : 1031iter [00:29, 37.88iter/s]
Best score: 0.2975: : 1032iter [00:29, 37.88iter/s]
Best score: 0.2975: : 1033iter [00:29, 37.88iter/s]
Best score: 0.2975: : 1034iter [00:29, 37.88iter/s]
Best score: 0.2975: : 1035iter [00:29, 38.41iter/s]
Best score: 0.2975: : 1035iter [00:29, 38.41iter/s]
Best score: 0.2975: : 1036iter [00:29, 38.41iter/s]
Best score: 0.2975: : 1037iter [00:29, 38.41iter/s]
Best score: 0.2975: : 1038iter [00:29, 38.41iter/s]
Best score: 0.2971: : 1039iter [00:29, 38.41iter/s]
Best score: 0.2971: : 1040iter [00:29, 38.73iter/s]
Best score: 0.2971: : 1040iter [00:29, 38.73iter/s]
Best score: 0.2971: : 1041iter [00:29, 38.73iter/s]
Best score: 0.2971: : 1042iter [00:29, 38.73iter/s]
Best score: 0.2971: : 1043iter [00:29, 38.73iter/s]
Best score: 0.2971: : 1044iter [00:29, 38.73iter/s]
Best score: 0.2971: : 1045iter [00:29, 39.23iter/s]
Best score: 0.2971: : 1045iter [00:30, 39.23iter/s]
Best score: 0.2971: : 1046iter [00:30, 39.23iter/s]
Best score: 0.2971: : 1047iter [00:30, 39.23iter/s]
Best score: 0.2971: : 1048iter [00:30, 39.23iter/s]
[ITER 1050] Best: 0.2970
[PARAMS] Current best parameters:
  fx: 503.6721
  fy: 503.4751
  px: 1010.8626
  py: 744.3128
  k1: -0.00327727
  k2: 0.05264605
  k3: -0.03262420
  k4: 0.00599835
  p1: -0.00764292
  p2: 0.01605856
  p3: 0.01379803
  p4: -0.00146593
  s1: -0.0365
  s2: 0.0032
  s3: -0.0009
  s4: -0.0003
  tilt_x: -0.0069
  tilt_y: 0.0665
  scale: 3.0493
  rotation: -0.1810

Best score: 0.2970: : 1049iter [00:30, 39.23iter/s]
Best score: 0.2970: : 1050iter [00:30, 39.25iter/s]
Best score: 0.2970: : 1050iter [00:30, 39.25iter/s]
Best score: 0.2970: : 1051iter [00:30, 39.25iter/s]
Best score: 0.2970: : 1052iter [00:30, 39.25iter/s]
Best score: 0.2970: : 1053iter [00:30, 39.25iter/s]
Best score: 0.2970: : 1054iter [00:30, 32.68iter/s]
Best score: 0.2970: : 1054iter [00:30, 32.68iter/s]
Best score: 0.2970: : 1055iter [00:30, 32.68iter/s]
Best score: 0.2970: : 1056iter [00:30, 32.68iter/s]
Best score: 0.2970: : 1057iter [00:30, 32.68iter/s]
Best score: 0.2970: : 1058iter [00:30, 32.68iter/s]
Best score: 0.2970: : 1059iter [00:30, 34.73iter/s]
Best score: 0.2970: : 1059iter [00:30, 34.73iter/s]
Best score: 0.2970: : 1060iter [00:30, 34.73iter/s]
Best score: 0.2970: : 1061iter [00:30, 34.73iter/s]
Best score: 0.2970: : 1062iter [00:30, 34.73iter/s]
Best score: 0.2970: : 1063iter [00:30, 35.74iter/s]
Best score: 0.2970: : 1063iter [00:30, 35.74iter/s]
Best score: 0.2970: : 1064iter [00:30, 35.74iter/s]
Best score: 0.2970: : 1065iter [00:30, 35.74iter/s]
Best score: 0.2970: : 1066iter [00:30, 35.74iter/s]
Best score: 0.2970: : 1067iter [00:30, 35.74iter/s]
Best score: 0.2970: : 1068iter [00:30, 37.02iter/s]
Best score: 0.2970: : 1068iter [00:30, 37.02iter/s]
Best score: 0.2965: : 1069iter [00:30, 37.02iter/s]
Best score: 0.2965: : 1070iter [00:30, 37.02iter/s]
Best score: 0.2965: : 1071iter [00:30, 37.02iter/s]
Best score: 0.2965: : 1072iter [00:30, 37.60iter/s]
Best score: 0.2965: : 1072iter [00:30, 37.60iter/s]
Best score: 0.2965: : 1073iter [00:30, 37.60iter/s]
Best score: 0.2965: : 1074iter [00:30, 37.60iter/s]
Best score: 0.2965: : 1075iter [00:30, 37.60iter/s]
Best score: 0.2965: : 1076iter [00:30, 37.60iter/s]
Best score: 0.2965: : 1077iter [00:30, 38.40iter/s]
Best score: 0.2965: : 1077iter [00:30, 38.40iter/s]
Best score: 0.2965: : 1078iter [00:30, 38.40iter/s]
Best score: 0.2956: : 1079iter [00:30, 38.40iter/s]
Best score: 0.2956: : 1080iter [00:30, 38.40iter/s]
Best score: 0.2956: : 1081iter [00:30, 38.58iter/s]
Best score: 0.2956: : 1081iter [00:30, 38.58iter/s]
Best score: 0.2956: : 1082iter [00:31, 38.58iter/s]
Best score: 0.2956: : 1083iter [00:31, 38.58iter/s]
Best score: 0.2956: : 1084iter [00:31, 38.58iter/s]
Best score: 0.2956: : 1085iter [00:31, 38.58iter/s]
Best score: 0.2956: : 1086iter [00:31, 39.08iter/s]
Best score: 0.2956: : 1086iter [00:31, 39.08iter/s]
Best score: 0.2956: : 1087iter [00:31, 39.08iter/s]
Best score: 0.2956: : 1088iter [00:31, 39.08iter/s]
Best score: 0.2946: : 1089iter [00:31, 39.08iter/s]
Best score: 0.2946: : 1090iter [00:31, 39.03iter/s]
Best score: 0.2946: : 1090iter [00:31, 39.03iter/s]
Best score: 0.2946: : 1091iter [00:31, 39.03iter/s]
Best score: 0.2946: : 1092iter [00:31, 39.03iter/s]
Best score: 0.2946: : 1093iter [00:31, 39.03iter/s]
Best score: 0.2946: : 1094iter [00:31, 39.03iter/s]
Best score: 0.2946: : 1095iter [00:31, 39.41iter/s]
Best score: 0.2946: : 1095iter [00:31, 39.41iter/s]
Best score: 0.2946: : 1096iter [00:31, 39.41iter/s]
Best score: 0.2946: : 1097iter [00:31, 39.41iter/s]
Best score: 0.2946: : 1098iter [00:31, 39.41iter/s]
[ITER 1100] Best: 0.2941
[PARAMS] Current best parameters:
  fx: 501.9382
  fy: 501.8276
  px: 1011.5563
  py: 743.4541
  k1: -0.00430109
  k2: 0.05682816
  k3: -0.03492074
  k4: 0.00641354
  p1: -0.00622848
  p2: 0.01788423
  p3: 0.01197898
  p4: -0.00169808
  s1: -0.0357
  s2: 0.0032
  s3: -0.0014
  s4: -0.0001
  tilt_x: -0.0135
  tilt_y: 0.0668
  scale: 3.0526
  rotation: -0.1897

Best score: 0.2941: : 1099iter [00:31, 39.41iter/s]
Best score: 0.2941: : 1100iter [00:31, 39.52iter/s]
Best score: 0.2941: : 1100iter [00:31, 34.89iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish improved score: 0.294127 -> 0.293676

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 25,821 → 25,821 within 3500mm ring
  Ring polish improved score: 0.293676 -> 0.292310
  Success: True
  Tangential distortion: p1=-0.006233, p2=0.017880, p3=0.011985, p4=-0.001685
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2923
[✅] Parameters copied to: /working/MDC103_W050_2026-03-10/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


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


[DEBUG] Parameters being applied to PTD conversion:
    fx: 501.9381572992004
    fy: 501.8275556223436
    px: 1011.5562933310956
    py: 743.4541389145254
    k1: -0.004301962831124956
    k2: 0.05682695090451594
    k3: -0.03491988221684444
    k4: 0.006414465374991229
    p1: -0.006233350804293362
    p2: 0.017879641493301585
    p3: 0.01198490062235836
    p4: -0.0016849706991184927
    scale: 3.052585214975906
    rotation: -0.18965793239616285
    mmpp: 1.2840477096139897

[🔄] Converting 464 frames to PTD, creating debug images for 200 calibration frames...

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

   🔄 500:   0%|          | 0/232 [00:00<?, ?frame/s]

   🔄 500:   7%|▋         | 17/232 [00:00<00:01, 169.66frame/s]

   🔄 500:  15%|█▌        | 35/232 [00:00<00:01, 171.57frame/s]

   🔄 500:  23%|██▎       | 53/232 [00:00<00:01, 172.76frame/s]

   🔄 500:  31%|███       | 71/232 [00:00<00:00, 173.01frame/s]

   🔄 500:  38%|███▊      | 89/232 [00:00<00:00, 173.26frame/s]

   🔄 500:  46%|████▌     | 107/232 [00:00<00:00, 175.33frame/s]

   🔄 500:  54%|█████▍    | 126/232 [00:00<00:00, 176.94frame/s]

   🔄 500:  62%|██████▏   | 144/232 [00:00<00:00, 176.40frame/s]

   🔄 500:  70%|██████▉   | 162/232 [00:00<00:00, 175.38frame/s]

   🔄 500:  78%|███████▊  | 180/232 [00:01<00:00, 174.54frame/s]

   🔄 500:  85%|████████▌ | 198/232 [00:01<00:00, 174.16frame/s]

   🔄 500:  93%|█████████▎| 216/232 [00:01<00:00, 173.82frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:02<00:02,  2.42s/rig]

   🔄 1000:   0%|          | 0/232 [00:00<?, ?frame/s]

   🔄 1000:   8%|▊         | 19/232 [00:00<00:01, 181.50frame/s]

   🔄 1000:  16%|█▋        | 38/232 [00:00<00:01, 178.50frame/s]

   🔄 1000:  24%|██▍       | 56/232 [00:00<00:00, 177.90frame/s]

   🔄 1000:  32%|███▏      | 74/232 [00:00<00:00, 177.72frame/s]

   🔄 1000:  40%|███▉      | 92/232 [00:00<00:00, 176.72frame/s]

   🔄 1000:  47%|████▋     | 110/232 [00:00<00:00, 175.25frame/s]

   🔄 1000:  55%|█████▌    | 128/232 [00:00<00:00, 174.73frame/s]

   🔄 1000:  63%|██████▎   | 146/232 [00:00<00:00, 174.37frame/s]

   🔄 1000:  71%|███████   | 164/232 [00:00<00:00, 175.15frame/s]

   🔄 1000:  78%|███████▊  | 182/232 [00:01<00:00, 175.95frame/s]

   🔄 1000:  86%|████████▌ | 200/232 [00:01<00:00, 174.85frame/s]

   🔄 1000:  94%|█████████▍| 218/232 [00:01<00:00, 174.93frame/s]

                                                                 
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.46s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:04<00:00,  2.45s/rig]
[✅] Saved PTDs: /working/MDC103_W050_2026-03-10/calibration/debug/4_undistorted/output/ptd

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

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

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 173.78frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 174.07frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 175.76frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 174.18frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 174.54frame/s]

                                                               
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.09rig/s]

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

   🔄 1000:  19%|█▉        | 19/100 [00:00<00:00, 181.20frame/s]

   🔄 1000:  38%|███▊      | 38/100 [00:00<00:00, 177.27frame/s]

   🔄 1000:  56%|█████▌    | 56/100 [00:00<00:00, 174.74frame/s]

   🔄 1000:  74%|███████▍  | 74/100 [00:00<00:00, 174.24frame/s]

   🔄 1000:  92%|█████████▏| 92/100 [00:00<00:00, 174.64frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.01rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.02rig/s]
[✅] Saved PTDs: /working/MDC103_W050_2026-03-10/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 77079/3080448 [00:00<00:03, 770785.22px/s]
📤 Writing LUT:   5%|▌         | 161761/3080448 [00:00<00:03, 815509.62px/s]
📤 Writing LUT:   8%|▊         | 245956/3080448 [00:00<00:03, 827580.48px/s]
📤 Writing LUT:  11%|█         | 331266/3080448 [00:00<00:03, 837652.43px/s]
📤 Writing LUT:  14%|█▎        | 417819/3080448 [00:00<00:03, 847703.34px/s]
📤 Writing LUT:  16%|█▋        | 502882/3080448 [00:00<00:03, 848696.62px/s]
📤 Writing LUT:  19%|█▉        | 590790/3080448 [00:00<00:02, 858628.55px/s]
📤 Writing LUT:  22%|██▏       | 678306/3080448 [00:00<00:02, 863888.99px/s]
📤 Writing LUT:  25%|██▍       | 765422/3080448 [00:00<00:02, 866159.04px/s]
📤 Writing LUT:  28%|██▊       | 852824/3080448 [00:01<00:02, 868584.84px/s]
📤 Writing LUT:  31%|███       | 940706/3080448 [00:01<00:02, 871715.66px/s]
📤 Writing LUT:  33%|███▎      | 1028222/3080448 [00:01<00:02, 872762.45px/s]
📤 Writing LUT:  36%|███▌      | 1115499/3080448 [00:01<00:02, 872325.85px/s]
📤 Writing LUT:  39%|███▉      | 1202757/3080448 [00:01<00:02, 872402.21px/s]
📤 Writing LUT:  42%|████▏     | 1290142/3080448 [00:01<00:02, 872838.14px/s]
📤 Writing LUT:  45%|████▍     | 1378010/3080448 [00:01<00:01, 874595.86px/s]
📤 Writing LUT:  48%|████▊     | 1465999/3080448 [00:01<00:01, 876184.09px/s]
📤 Writing LUT:  50%|█████     | 1553618/3080448 [00:01<00:01, 875575.20px/s]
📤 Writing LUT:  53%|█████▎    | 1641586/3080448 [00:01<00:01, 876804.48px/s]
📤 Writing LUT:  56%|█████▌    | 1729267/3080448 [00:02<00:01, 876311.02px/s]
📤 Writing LUT:  59%|█████▉    | 1817167/3080448 [00:02<00:01, 877117.25px/s]
📤 Writing LUT:  62%|██████▏   | 1905340/3080448 [00:02<00:01, 878500.60px/s]
📤 Writing LUT:  65%|██████▍   | 1993191/3080448 [00:02<00:01, 873037.56px/s]
📤 Writing LUT:  68%|██████▊   | 2081632/3080448 [00:02<00:01, 876432.66px/s]
📤 Writing LUT:  70%|███████   | 2169283/3080448 [00:02<00:01, 875857.74px/s]
📤 Writing LUT:  73%|███████▎  | 2256874/3080448 [00:02<00:00, 875661.86px/s]
📤 Writing LUT:  76%|███████▌  | 2344444/3080448 [00:02<00:00, 875254.31px/s]
📤 Writing LUT:  79%|███████▉  | 2432350/3080448 [00:02<00:00, 876391.77px/s]
📤 Writing LUT:  82%|████████▏ | 2519991/3080448 [00:02<00:00, 873106.07px/s]
📤 Writing LUT:  85%|████████▍ | 2607652/3080448 [00:03<00:00, 874151.34px/s]
📤 Writing LUT:  87%|████████▋ | 2695071/3080448 [00:03<00:00, 874037.81px/s]
📤 Writing LUT:  90%|█████████ | 2782601/3080448 [00:03<00:00, 874414.15px/s]
📤 Writing LUT:  93%|█████████▎| 2870544/3080448 [00:03<00:00, 875915.37px/s]
📤 Writing LUT:  96%|█████████▌| 2958477/3080448 [00:03<00:00, 876935.96px/s]
📤 Writing LUT:  99%|█████████▉| 3046857/3080448 [00:03<00:00, 878990.99px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 869530.03px/s]
[✅] LUT written to: /working/MDC103_W050_2026-03-10/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC103_W050_2026-03-10' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC103_W050_2026-03-10/calibration/MDC103_W050_2026-03-10_unknown.lut

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

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

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

   🔥 500:   0%|          | 0/232 [00:00<?, ?frame/s]

   🔥 500:   3%|▎         | 7/232 [00:00<00:03, 69.98frame/s]

   🔥 500:   6%|▋         | 15/232 [00:00<00:02, 74.25frame/s]

   🔥 500:  10%|▉         | 23/232 [00:00<00:02, 75.54frame/s]

   🔥 500:  13%|█▎        | 31/232 [00:00<00:02, 76.23frame/s]

   🔥 500:  17%|█▋        | 39/232 [00:00<00:02, 77.12frame/s]

   🔥 500:  20%|██        | 47/232 [00:00<00:02, 78.08frame/s]

   🔥 500:  24%|██▍       | 56/232 [00:00<00:02, 79.76frame/s]

   🔥 500:  28%|██▊       | 65/232 [00:00<00:02, 80.99frame/s]

   🔥 500:  32%|███▏      | 74/232 [00:00<00:01, 80.08frame/s]

   🔥 500:  36%|███▌      | 83/232 [00:01<00:01, 79.07frame/s]

   🔥 500:  39%|███▉      | 91/232 [00:01<00:01, 79.27frame/s]

   🔥 500:  43%|████▎     | 99/232 [00:01<00:01, 79.48frame/s]

   🔥 500:  47%|████▋     | 108/232 [00:01<00:01, 80.50frame/s]

   🔥 500:  50%|█████     | 117/232 [00:01<00:01, 81.71frame/s]

   🔥 500:  54%|█████▍    | 126/232 [00:01<00:01, 82.27frame/s]

   🔥 500:  58%|█████▊    | 135/232 [00:01<00:01, 82.43frame/s]

   🔥 500:  62%|██████▏   | 144/232 [00:01<00:01, 80.70frame/s]

   🔥 500:  66%|██████▌   | 153/232 [00:01<00:01, 78.68frame/s]

   🔥 500:  69%|██████▉   | 161/232 [00:02<00:00, 77.26frame/s]

   🔥 500:  73%|███████▎  | 169/232 [00:02<00:00, 76.45frame/s]

   🔥 500:  76%|███████▋  | 177/232 [00:02<00:00, 76.55frame/s]

   🔥 500:  80%|███████▉  | 185/232 [00:02<00:00, 75.98frame/s]

   🔥 500:  83%|████████▎ | 193/232 [00:02<00:00, 76.33frame/s]

   🔥 500:  87%|████████▋ | 201/232 [00:02<00:00, 77.12frame/s]

   🔥 500:  90%|█████████ | 209/232 [00:02<00:00, 77.73frame/s]

   🔥 500:  94%|█████████▎| 217/232 [00:02<00:00, 77.27frame/s]

   🔥 500:  97%|█████████▋| 225/232 [00:02<00:00, 77.45frame/s]

                                                               [✅] 500: /working/MDC103_W050_2026-03-10/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC103_W050_2026-03-10/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:04<00:04,  4.17s/rig]

   🔥 1000:   0%|          | 0/232 [00:00<?, ?frame/s]

   🔥 1000:   4%|▍         | 9/232 [00:00<00:02, 83.36frame/s]

   🔥 1000:   8%|▊         | 18/232 [00:00<00:02, 83.06frame/s]

   🔥 1000:  12%|█▏        | 27/232 [00:00<00:02, 83.78frame/s]

   🔥 1000:  16%|█▌        | 36/232 [00:00<00:02, 84.24frame/s]

   🔥 1000:  19%|█▉        | 45/232 [00:00<00:02, 84.44frame/s]

   🔥 1000:  23%|██▎       | 54/232 [00:00<00:02, 83.61frame/s]

   🔥 1000:  27%|██▋       | 63/232 [00:00<00:02, 82.85frame/s]

   🔥 1000:  31%|███       | 72/232 [00:00<00:01, 81.97frame/s]

   🔥 1000:  35%|███▍      | 81/232 [00:00<00:01, 82.23frame/s]

   🔥 1000:  39%|███▉      | 90/232 [00:01<00:01, 81.57frame/s]

   🔥 1000:  43%|████▎     | 99/232 [00:01<00:01, 81.35frame/s]

   🔥 1000:  47%|████▋     | 108/232 [00:01<00:01, 80.72frame/s]

   🔥 1000:  50%|█████     | 117/232 [00:01<00:01, 80.54frame/s]

   🔥 1000:  54%|█████▍    | 126/232 [00:01<00:01, 81.51frame/s]

   🔥 1000:  58%|█████▊    | 135/232 [00:01<00:01, 83.70frame/s]

   🔥 1000:  62%|██████▏   | 144/232 [00:01<00:01, 84.57frame/s]

   🔥 1000:  66%|██████▌   | 153/232 [00:01<00:00, 83.90frame/s]

   🔥 1000:  70%|██████▉   | 162/232 [00:01<00:00, 83.23frame/s]

   🔥 1000:  74%|███████▎  | 171/232 [00:02<00:00, 83.98frame/s]

   🔥 1000:  78%|███████▊  | 180/232 [00:02<00:00, 84.85frame/s]

   🔥 1000:  81%|████████▏ | 189/232 [00:02<00:00, 85.93frame/s]

   🔥 1000:  85%|████████▌ | 198/232 [00:02<00:00, 85.79frame/s]

   🔥 1000:  89%|████████▉ | 207/232 [00:02<00:00, 86.51frame/s]

   🔥 1000:  93%|█████████▎| 216/232 [00:02<00:00, 87.02frame/s]

   🔥 1000:  97%|█████████▋| 225/232 [00:02<00:00, 86.98frame/s]

                                                                [✅] 1000: /working/MDC103_W050_2026-03-10/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC103_W050_2026-03-10/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:10<00:00,  5.16s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:10<00:00,  5.01s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC103_W050_2026-03-10/calibration/debug/5_heatmap
[🏁] Finished processing MDC103_W050_2026-03-10


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